Churn_Model_building_smote_scaling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4cbe12d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32bc3548",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Complain_ly</th>\n",
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      "text/plain": [
       "   Unnamed: 0  Churn  Tenure  City_Tier  CC_Contacted_LY      Payment  Gender  \\\n",
       "0           0      1     4.0        3.0              6.0   Debit Card  Female   \n",
       "1           1      1     0.0        1.0              8.0          UPI    Male   \n",
       "2           2      1     0.0        1.0             30.0   Debit Card    Male   \n",
       "3           3      1     0.0        3.0             15.0   Debit Card    Male   \n",
       "4           4      1     0.0        1.0             12.0  Credit Card    Male   \n",
       "\n",
       "   Service_Score  Account_user_count account_segment  CC_Agent_Score  \\\n",
       "0            3.0                   3           Super             2.0   \n",
       "1            3.0                   4       Regular +             3.0   \n",
       "2            2.0                   4       Regular +             3.0   \n",
       "3            2.0                   4           Super             5.0   \n",
       "4            2.0                   3       Regular +             5.0   \n",
       "\n",
       "  Marital_Status  rev_per_month  Complain_ly  rev_growth_yoy  \\\n",
       "0         Single            9.0          1.0            11.0   \n",
       "1         Single            7.0          1.0            15.0   \n",
       "2         Single            6.0          1.0            14.0   \n",
       "3         Single            8.0          0.0            23.0   \n",
       "4         Single            3.0          0.0            11.0   \n",
       "\n",
       "   coupon_used_for_payment  Day_Since_CC_connect  cashback Login_device  \n",
       "0                      1.0                   5.0    159.93       Mobile  \n",
       "1                      0.0                   0.0    120.90       Mobile  \n",
       "2                      0.0                   3.0    165.24       Mobile  \n",
       "3                      0.0                   3.0    134.07       Mobile  \n",
       "4                      1.0                   3.0    129.60       Mobile  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn = pd.read_csv('churn2.csv')\n",
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5488982e",
   "metadata": {},
   "outputs": [],
   "source": [
    "churn.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "753d5af5",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Churn  Tenure  City_Tier  CC_Contacted_LY      Payment  Gender  \\\n",
       "0      1     4.0        3.0              6.0   Debit Card  Female   \n",
       "1      1     0.0        1.0              8.0          UPI    Male   \n",
       "2      1     0.0        1.0             30.0   Debit Card    Male   \n",
       "3      1     0.0        3.0             15.0   Debit Card    Male   \n",
       "4      1     0.0        1.0             12.0  Credit Card    Male   \n",
       "\n",
       "   Service_Score  Account_user_count account_segment  CC_Agent_Score  \\\n",
       "0            3.0                   3           Super             2.0   \n",
       "1            3.0                   4       Regular +             3.0   \n",
       "2            2.0                   4       Regular +             3.0   \n",
       "3            2.0                   4           Super             5.0   \n",
       "4            2.0                   3       Regular +             5.0   \n",
       "\n",
       "  Marital_Status  rev_per_month  Complain_ly  rev_growth_yoy  \\\n",
       "0         Single            9.0          1.0            11.0   \n",
       "1         Single            7.0          1.0            15.0   \n",
       "2         Single            6.0          1.0            14.0   \n",
       "3         Single            8.0          0.0            23.0   \n",
       "4         Single            3.0          0.0            11.0   \n",
       "\n",
       "   coupon_used_for_payment  Day_Since_CC_connect  cashback Login_device  \n",
       "0                      1.0                   5.0    159.93       Mobile  \n",
       "1                      0.0                   0.0    120.90       Mobile  \n",
       "2                      0.0                   3.0    165.24       Mobile  \n",
       "3                      0.0                   3.0    134.07       Mobile  \n",
       "4                      1.0                   3.0    129.60       Mobile  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c8d890f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11260 entries, 0 to 11259\n",
      "Data columns (total 18 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Churn                    11260 non-null  int64  \n",
      " 1   Tenure                   11260 non-null  float64\n",
      " 2   City_Tier                11260 non-null  float64\n",
      " 3   CC_Contacted_LY          11260 non-null  float64\n",
      " 4   Payment                  11260 non-null  object \n",
      " 5   Gender                   11260 non-null  object \n",
      " 6   Service_Score            11260 non-null  float64\n",
      " 7   Account_user_count       11260 non-null  int64  \n",
      " 8   account_segment          11260 non-null  object \n",
      " 9   CC_Agent_Score           11260 non-null  float64\n",
      " 10  Marital_Status           11260 non-null  object \n",
      " 11  rev_per_month            11260 non-null  float64\n",
      " 12  Complain_ly              11260 non-null  float64\n",
      " 13  rev_growth_yoy           11260 non-null  float64\n",
      " 14  coupon_used_for_payment  11260 non-null  float64\n",
      " 15  Day_Since_CC_connect     11260 non-null  float64\n",
      " 16  cashback                 11260 non-null  float64\n",
      " 17  Login_device             11260 non-null  object \n",
      "dtypes: float64(11), int64(2), object(5)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "churn.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d44d99fb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = churn.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e32391a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 =churn.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a7e0faea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 =churn.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "219f79d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0    11.440624\n",
       "1     3.458333\n",
       "Name: Tenure, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Churn')['Tenure'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "25e3f1dc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>180.184004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.458333</td>\n",
       "      <td>1.817511</td>\n",
       "      <td>19.229958</td>\n",
       "      <td>2.917722</td>\n",
       "      <td>3.938291</td>\n",
       "      <td>3.386603</td>\n",
       "      <td>5.379219</td>\n",
       "      <td>0.521097</td>\n",
       "      <td>16.077532</td>\n",
       "      <td>1.371308</td>\n",
       "      <td>3.404280</td>\n",
       "      <td>162.960206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Tenure  City_Tier  CC_Contacted_LY  Service_Score  \\\n",
       "Churn                                                         \n",
       "0      11.440624   1.612986        17.506728       2.900470   \n",
       "1       3.458333   1.817511        19.229958       2.917722   \n",
       "\n",
       "       Account_user_count  CC_Agent_Score  rev_per_month  Complain_ly  \\\n",
       "Churn                                                                   \n",
       "0                3.657732        3.000854       5.055852     0.226719   \n",
       "1                3.938291        3.386603       5.379219     0.521097   \n",
       "\n",
       "       rev_growth_yoy  coupon_used_for_payment  Day_Since_CC_connect  \\\n",
       "Churn                                                                  \n",
       "0           16.216147                 1.422683              4.839756   \n",
       "1           16.077532                 1.371308              3.404280   \n",
       "\n",
       "         cashback  \n",
       "Churn              \n",
       "0      180.184004  \n",
       "1      162.960206  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Churn').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce02d921",
   "metadata": {},
   "source": [
    "### Data scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "87b025b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "concat1 = df1[['Tenure', 'CC_Contacted_LY', 'rev_per_month', 'rev_growth_yoy', 'Day_Since_CC_connect', 'cashback']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d33e07e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tenure</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <th>Day_Since_CC_connect</th>\n",
       "      <th>cashback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>159.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>165.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>134.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>129.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11255</th>\n",
       "      <td>10.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>153.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11256</th>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>226.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11257</th>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>191.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11258</th>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>179.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11259</th>\n",
       "      <td>8.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>175.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11260 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tenure  CC_Contacted_LY  rev_per_month  rev_growth_yoy  \\\n",
       "0         4.0              6.0            9.0            11.0   \n",
       "1         0.0              8.0            7.0            15.0   \n",
       "2         0.0             30.0            6.0            14.0   \n",
       "3         0.0             15.0            8.0            23.0   \n",
       "4         0.0             12.0            3.0            11.0   \n",
       "...       ...              ...            ...             ...   \n",
       "11255    10.0             34.0            9.0            19.0   \n",
       "11256    13.0             19.0            7.0            16.0   \n",
       "11257     1.0             14.0            7.0            22.0   \n",
       "11258    23.0             11.0            7.0            16.0   \n",
       "11259     8.0             22.0            5.0            13.0   \n",
       "\n",
       "       Day_Since_CC_connect  cashback  \n",
       "0                       5.0    159.93  \n",
       "1                       0.0    120.90  \n",
       "2                       3.0    165.24  \n",
       "3                       3.0    134.07  \n",
       "4                       3.0    129.60  \n",
       "...                     ...       ...  \n",
       "11255                   4.0    153.71  \n",
       "11256                   8.0    226.91  \n",
       "11257                   4.0    191.42  \n",
       "11258                   9.0    179.90  \n",
       "11259                   3.0    175.04  \n",
       "\n",
       "[11260 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cf1e94c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(['Tenure', 'CC_Contacted_LY', 'rev_per_month', 'rev_growth_yoy', 'Day_Since_CC_connect', 'cashback'], inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a45d4f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = ['Tenure', 'CC_Contacted_LY', 'rev_per_month', 'rev_growth_yoy', 'Day_Since_CC_connect', 'cashback']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd4da7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f03aea6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tenure</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <th>Day_Since_CC_connect</th>\n",
       "      <th>cashback</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.679037</td>\n",
       "      <td>-1.376583</td>\n",
       "      <td>1.324592</td>\n",
       "      <td>-1.382086</td>\n",
       "      <td>0.115434</td>\n",
       "      <td>-0.398283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-1.143202</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>-0.317470</td>\n",
       "      <td>-1.320473</td>\n",
       "      <td>-1.294053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.124559</td>\n",
       "      <td>1.423984</td>\n",
       "      <td>0.302976</td>\n",
       "      <td>-0.583624</td>\n",
       "      <td>-0.458929</td>\n",
       "      <td>-0.276415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-0.326370</td>\n",
       "      <td>0.984054</td>\n",
       "      <td>1.811761</td>\n",
       "      <td>-0.458929</td>\n",
       "      <td>-0.991791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-0.676441</td>\n",
       "      <td>-0.718639</td>\n",
       "      <td>-1.382086</td>\n",
       "      <td>-0.458929</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
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       "      <td>0.323390</td>\n",
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       "    <tr>\n",
       "      <th>11257</th>\n",
       "      <td>-1.013179</td>\n",
       "      <td>-0.443060</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>1.545607</td>\n",
       "      <td>-0.171748</td>\n",
       "      <td>0.324437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11258</th>\n",
       "      <td>1.437197</td>\n",
       "      <td>-0.793131</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>-0.051316</td>\n",
       "      <td>1.264159</td>\n",
       "      <td>0.060044</td>\n",
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       "    <tr>\n",
       "      <th>11259</th>\n",
       "      <td>-0.233514</td>\n",
       "      <td>0.490462</td>\n",
       "      <td>-0.037562</td>\n",
       "      <td>-0.849778</td>\n",
       "      <td>-0.458929</td>\n",
       "      <td>-0.051497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11260 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Tenure  CC_Contacted_LY  rev_per_month  rev_growth_yoy  \\\n",
       "0     -0.679037        -1.376583       1.324592       -1.382086   \n",
       "1     -1.124559        -1.143202       0.643515       -0.317470   \n",
       "2     -1.124559         1.423984       0.302976       -0.583624   \n",
       "3     -1.124559        -0.326370       0.984054        1.811761   \n",
       "4     -1.124559        -0.676441      -0.718639       -1.382086   \n",
       "...         ...              ...            ...             ...   \n",
       "11255 -0.010752         1.890745       1.324592        0.747146   \n",
       "11256  0.323390         0.140391       0.643515       -0.051316   \n",
       "11257 -1.013179        -0.443060       0.643515        1.545607   \n",
       "11258  1.437197        -0.793131       0.643515       -0.051316   \n",
       "11259 -0.233514         0.490462      -0.037562       -0.849778   \n",
       "\n",
       "       Day_Since_CC_connect  cashback  \n",
       "0                  0.115434 -0.398283  \n",
       "1                 -1.320473 -1.294053  \n",
       "2                 -0.458929 -0.276415  \n",
       "3                 -0.458929 -0.991791  \n",
       "4                 -0.458929 -1.094381  \n",
       "...                     ...       ...  \n",
       "11255             -0.171748 -0.541037  \n",
       "11256              0.976978  1.138961  \n",
       "11257             -0.171748  0.324437  \n",
       "11258              1.264159  0.060044  \n",
       "11259             -0.458929 -0.051497  \n",
       "\n",
       "[11260 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs1 = concat1.apply(zscore)\n",
    "dfs1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4e6417ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
       "      <th>account_segment</th>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Complain_ly</th>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <th>Login_device</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Super</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>UPI</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Super</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11255</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Super</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11256</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>HNI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11257</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Super</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11258</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Super</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11259</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Super</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11260 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Churn  City_Tier      Payment  Gender  Service_Score  \\\n",
       "0          1        3.0   Debit Card  Female            3.0   \n",
       "1          1        1.0          UPI    Male            3.0   \n",
       "2          1        1.0   Debit Card    Male            2.0   \n",
       "3          1        3.0   Debit Card    Male            2.0   \n",
       "4          1        1.0  Credit Card    Male            2.0   \n",
       "...      ...        ...          ...     ...            ...   \n",
       "11255      0        1.0  Credit Card    Male            3.0   \n",
       "11256      0        1.0  Credit Card    Male            3.0   \n",
       "11257      0        1.0   Debit Card    Male            3.0   \n",
       "11258      0        3.0  Credit Card    Male            4.0   \n",
       "11259      0        1.0  Credit Card    Male            3.0   \n",
       "\n",
       "       Account_user_count account_segment  CC_Agent_Score Marital_Status  \\\n",
       "0                       3           Super             2.0         Single   \n",
       "1                       4       Regular +             3.0         Single   \n",
       "2                       4       Regular +             3.0         Single   \n",
       "3                       4           Super             5.0         Single   \n",
       "4                       3       Regular +             5.0         Single   \n",
       "...                   ...             ...             ...            ...   \n",
       "11255                   2           Super             1.0        Married   \n",
       "11256                   5             HNI             5.0        Married   \n",
       "11257                   2           Super             4.0        Married   \n",
       "11258                   5           Super             4.0        Married   \n",
       "11259                   2           Super             3.0        Married   \n",
       "\n",
       "       Complain_ly  coupon_used_for_payment Login_device  \n",
       "0              1.0                      1.0       Mobile  \n",
       "1              1.0                      0.0       Mobile  \n",
       "2              1.0                      0.0       Mobile  \n",
       "3              0.0                      0.0       Mobile  \n",
       "4              0.0                      1.0       Mobile  \n",
       "...            ...                      ...          ...  \n",
       "11255          0.0                      1.0     Computer  \n",
       "11256          0.0                      1.0       Mobile  \n",
       "11257          1.0                      1.0       Mobile  \n",
       "11258          0.0                      2.0     Computer  \n",
       "11259          0.0                      2.0       Mobile  \n",
       "\n",
       "[11260 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4a49956",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
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       "      <th>Login_device</th>\n",
       "      <th>Tenure</th>\n",
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       "      <th>rev_growth_yoy</th>\n",
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       "      <td>0.115434</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>UPI</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-1.143202</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>-0.317470</td>\n",
       "      <td>-1.320473</td>\n",
       "      <td>-1.294053</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Regular +</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.423984</td>\n",
       "      <td>0.302976</td>\n",
       "      <td>-0.583624</td>\n",
       "      <td>-0.458929</td>\n",
       "      <td>-0.276415</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Super</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-0.326370</td>\n",
       "      <td>0.984054</td>\n",
       "      <td>1.811761</td>\n",
       "      <td>-0.458929</td>\n",
       "      <td>-0.991791</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-0.676441</td>\n",
       "      <td>-0.718639</td>\n",
       "      <td>-1.382086</td>\n",
       "      <td>-0.458929</td>\n",
       "      <td>-1.094381</td>\n",
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       "      <th>11255</th>\n",
       "      <td>0</td>\n",
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       "      <td>Credit Card</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Super</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Computer</td>\n",
       "      <td>-0.010752</td>\n",
       "      <td>1.890745</td>\n",
       "      <td>1.324592</td>\n",
       "      <td>0.747146</td>\n",
       "      <td>-0.171748</td>\n",
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       "      <th>11256</th>\n",
       "      <td>0</td>\n",
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       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>HNI</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>0.323390</td>\n",
       "      <td>0.140391</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>-0.051316</td>\n",
       "      <td>0.976978</td>\n",
       "      <td>1.138961</td>\n",
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       "    <tr>\n",
       "      <th>11257</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Super</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>-1.013179</td>\n",
       "      <td>-0.443060</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>1.545607</td>\n",
       "      <td>-0.171748</td>\n",
       "      <td>0.324437</td>\n",
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       "      <th>11258</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Super</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Computer</td>\n",
       "      <td>1.437197</td>\n",
       "      <td>-0.793131</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>-0.051316</td>\n",
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       "      <th>11259</th>\n",
       "      <td>0</td>\n",
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       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Super</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Married</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>-0.233514</td>\n",
       "      <td>0.490462</td>\n",
       "      <td>-0.037562</td>\n",
       "      <td>-0.849778</td>\n",
       "      <td>-0.458929</td>\n",
       "      <td>-0.051497</td>\n",
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       "<p>11260 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "       Churn  City_Tier      Payment  Gender  Service_Score  \\\n",
       "0          1        3.0   Debit Card  Female            3.0   \n",
       "1          1        1.0          UPI    Male            3.0   \n",
       "2          1        1.0   Debit Card    Male            2.0   \n",
       "3          1        3.0   Debit Card    Male            2.0   \n",
       "4          1        1.0  Credit Card    Male            2.0   \n",
       "...      ...        ...          ...     ...            ...   \n",
       "11255      0        1.0  Credit Card    Male            3.0   \n",
       "11256      0        1.0  Credit Card    Male            3.0   \n",
       "11257      0        1.0   Debit Card    Male            3.0   \n",
       "11258      0        3.0  Credit Card    Male            4.0   \n",
       "11259      0        1.0  Credit Card    Male            3.0   \n",
       "\n",
       "       Account_user_count account_segment  CC_Agent_Score Marital_Status  \\\n",
       "0                       3           Super             2.0         Single   \n",
       "1                       4       Regular +             3.0         Single   \n",
       "2                       4       Regular +             3.0         Single   \n",
       "3                       4           Super             5.0         Single   \n",
       "4                       3       Regular +             5.0         Single   \n",
       "...                   ...             ...             ...            ...   \n",
       "11255                   2           Super             1.0        Married   \n",
       "11256                   5             HNI             5.0        Married   \n",
       "11257                   2           Super             4.0        Married   \n",
       "11258                   5           Super             4.0        Married   \n",
       "11259                   2           Super             3.0        Married   \n",
       "\n",
       "       Complain_ly  coupon_used_for_payment Login_device    Tenure  \\\n",
       "0              1.0                      1.0       Mobile -0.679037   \n",
       "1              1.0                      0.0       Mobile -1.124559   \n",
       "2              1.0                      0.0       Mobile -1.124559   \n",
       "3              0.0                      0.0       Mobile -1.124559   \n",
       "4              0.0                      1.0       Mobile -1.124559   \n",
       "...            ...                      ...          ...       ...   \n",
       "11255          0.0                      1.0     Computer -0.010752   \n",
       "11256          0.0                      1.0       Mobile  0.323390   \n",
       "11257          1.0                      1.0       Mobile -1.013179   \n",
       "11258          0.0                      2.0     Computer  1.437197   \n",
       "11259          0.0                      2.0       Mobile -0.233514   \n",
       "\n",
       "       CC_Contacted_LY  rev_per_month  rev_growth_yoy  Day_Since_CC_connect  \\\n",
       "0            -1.376583       1.324592       -1.382086              0.115434   \n",
       "1            -1.143202       0.643515       -0.317470             -1.320473   \n",
       "2             1.423984       0.302976       -0.583624             -0.458929   \n",
       "3            -0.326370       0.984054        1.811761             -0.458929   \n",
       "4            -0.676441      -0.718639       -1.382086             -0.458929   \n",
       "...                ...            ...             ...                   ...   \n",
       "11255         1.890745       1.324592        0.747146             -0.171748   \n",
       "11256         0.140391       0.643515       -0.051316              0.976978   \n",
       "11257        -0.443060       0.643515        1.545607             -0.171748   \n",
       "11258        -0.793131       0.643515       -0.051316              1.264159   \n",
       "11259         0.490462      -0.037562       -0.849778             -0.458929   \n",
       "\n",
       "       cashback  \n",
       "0     -0.398283  \n",
       "1     -1.294053  \n",
       "2     -0.276415  \n",
       "3     -0.991791  \n",
       "4     -1.094381  \n",
       "...         ...  \n",
       "11255 -0.541037  \n",
       "11256  1.138961  \n",
       "11257  0.324437  \n",
       "11258  0.060044  \n",
       "11259 -0.051497  \n",
       "\n",
       "[11260 rows x 18 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scaled = pd.concat([df1, dfs1], axis=1)\n",
    "df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a889ec1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42144b59",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4dd8668c",
   "metadata": {},
   "source": [
    "### Data encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85aca489",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>Complain_ly</th>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <th>...</th>\n",
       "      <th>Payment_E wallet</th>\n",
       "      <th>Payment_UPI</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>account_segment_Regular</th>\n",
       "      <th>account_segment_Regular +</th>\n",
       "      <th>account_segment_Super</th>\n",
       "      <th>account_segment_Super +</th>\n",
       "      <th>Marital_Status_Married</th>\n",
       "      <th>Marital_Status_Single</th>\n",
       "      <th>Login_device_Mobile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Churn  Tenure  City_Tier  CC_Contacted_LY  Service_Score  \\\n",
       "0      1     4.0        3.0              6.0            3.0   \n",
       "1      1     0.0        1.0              8.0            3.0   \n",
       "2      1     0.0        1.0             30.0            2.0   \n",
       "3      1     0.0        3.0             15.0            2.0   \n",
       "4      1     0.0        1.0             12.0            2.0   \n",
       "\n",
       "   Account_user_count  CC_Agent_Score  rev_per_month  Complain_ly  \\\n",
       "0                   3             2.0            9.0          1.0   \n",
       "1                   4             3.0            7.0          1.0   \n",
       "2                   4             3.0            6.0          1.0   \n",
       "3                   4             5.0            8.0          0.0   \n",
       "4                   3             5.0            3.0          0.0   \n",
       "\n",
       "   rev_growth_yoy  ...  Payment_E wallet  Payment_UPI  Gender_Male  \\\n",
       "0            11.0  ...                 0            0            0   \n",
       "1            15.0  ...                 0            1            1   \n",
       "2            14.0  ...                 0            0            1   \n",
       "3            23.0  ...                 0            0            1   \n",
       "4            11.0  ...                 0            0            1   \n",
       "\n",
       "   account_segment_Regular  account_segment_Regular +  account_segment_Super  \\\n",
       "0                        0                          0                      1   \n",
       "1                        0                          1                      0   \n",
       "2                        0                          1                      0   \n",
       "3                        0                          0                      1   \n",
       "4                        0                          1                      0   \n",
       "\n",
       "   account_segment_Super +  Marital_Status_Married  Marital_Status_Single  \\\n",
       "0                        0                       0                      1   \n",
       "1                        0                       0                      1   \n",
       "2                        0                       0                      1   \n",
       "3                        0                       0                      1   \n",
       "4                        0                       0                      1   \n",
       "\n",
       "   Login_device_Mobile  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfe = pd.get_dummies(df, drop_first =True)\n",
    "dfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5d09b013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <th>Complain_ly</th>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>...</th>\n",
       "      <th>Payment_E wallet</th>\n",
       "      <th>Payment_UPI</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>account_segment_Regular</th>\n",
       "      <th>account_segment_Regular +</th>\n",
       "      <th>account_segment_Super</th>\n",
       "      <th>account_segment_Super +</th>\n",
       "      <th>Marital_Status_Married</th>\n",
       "      <th>Marital_Status_Single</th>\n",
       "      <th>Login_device_Mobile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>3.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-1.143202</td>\n",
       "      <td>0.643515</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.124559</td>\n",
       "      <td>1.423984</td>\n",
       "      <td>0.302976</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-0.326370</td>\n",
       "      <td>0.984054</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.124559</td>\n",
       "      <td>-0.676441</td>\n",
       "      <td>-0.718639</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Churn  City_Tier  Service_Score  Account_user_count  CC_Agent_Score  \\\n",
       "0      1        3.0            3.0                   3             2.0   \n",
       "1      1        1.0            3.0                   4             3.0   \n",
       "2      1        1.0            2.0                   4             3.0   \n",
       "3      1        3.0            2.0                   4             5.0   \n",
       "4      1        1.0            2.0                   3             5.0   \n",
       "\n",
       "   Complain_ly  coupon_used_for_payment    Tenure  CC_Contacted_LY  \\\n",
       "0          1.0                      1.0 -0.679037        -1.376583   \n",
       "1          1.0                      0.0 -1.124559        -1.143202   \n",
       "2          1.0                      0.0 -1.124559         1.423984   \n",
       "3          0.0                      0.0 -1.124559        -0.326370   \n",
       "4          0.0                      1.0 -1.124559        -0.676441   \n",
       "\n",
       "   rev_per_month  ...  Payment_E wallet  Payment_UPI  Gender_Male  \\\n",
       "0       1.324592  ...                 0            0            0   \n",
       "1       0.643515  ...                 0            1            1   \n",
       "2       0.302976  ...                 0            0            1   \n",
       "3       0.984054  ...                 0            0            1   \n",
       "4      -0.718639  ...                 0            0            1   \n",
       "\n",
       "   account_segment_Regular  account_segment_Regular +  account_segment_Super  \\\n",
       "0                        0                          0                      1   \n",
       "1                        0                          1                      0   \n",
       "2                        0                          1                      0   \n",
       "3                        0                          0                      1   \n",
       "4                        0                          1                      0   \n",
       "\n",
       "   account_segment_Super +  Marital_Status_Married  Marital_Status_Single  \\\n",
       "0                        0                       0                      1   \n",
       "1                        0                       0                      1   \n",
       "2                        0                       0                      1   \n",
       "3                        0                       0                      1   \n",
       "4                        0                       0                      1   \n",
       "\n",
       "   Login_device_Mobile  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = pd.get_dummies(df_scaled, drop_first =True)\n",
    "dfs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3169f2c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3680a804",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bffb7702",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a8279b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "wss_1 =[] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fbe5555a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,11):\n",
    "    KM = KMeans(n_clusters=i)\n",
    "    KM.fit(dfs)\n",
    "    wss_1.append(KM.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42dd2314",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[154014.75017761986,\n",
       " 138600.57491694653,\n",
       " 127352.18001754541,\n",
       " 121483.81048359559,\n",
       " 117578.44838400539,\n",
       " 114391.24672052139,\n",
       " 111349.571242252,\n",
       " 108945.97155315714,\n",
       " 106382.94764494187,\n",
       " 104621.47073146458]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wss_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dbbdc350",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "046e169f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c5d3380a00>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,11), wss_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1a6a557b",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_means = KMeans(n_clusters = 3)\n",
    "k_means.fit(dfs)\n",
    "labels = k_means.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2256443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Churn</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>Payment</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
       "      <th>account_segment</th>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>Complain_ly</th>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <th>Day_Since_CC_connect</th>\n",
       "      <th>cashback</th>\n",
       "      <th>Login_device</th>\n",
       "      <th>Clus_kmeans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Super</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>159.93</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>UPI</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120.90</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>165.24</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Debit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Super</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>134.07</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Credit Card</td>\n",
       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Regular +</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>129.60</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Churn  Tenure  City_Tier  CC_Contacted_LY      Payment  Gender  \\\n",
       "0      1     4.0        3.0              6.0   Debit Card  Female   \n",
       "1      1     0.0        1.0              8.0          UPI    Male   \n",
       "2      1     0.0        1.0             30.0   Debit Card    Male   \n",
       "3      1     0.0        3.0             15.0   Debit Card    Male   \n",
       "4      1     0.0        1.0             12.0  Credit Card    Male   \n",
       "\n",
       "   Service_Score  Account_user_count account_segment  CC_Agent_Score  \\\n",
       "0            3.0                   3           Super             2.0   \n",
       "1            3.0                   4       Regular +             3.0   \n",
       "2            2.0                   4       Regular +             3.0   \n",
       "3            2.0                   4           Super             5.0   \n",
       "4            2.0                   3       Regular +             5.0   \n",
       "\n",
       "  Marital_Status  rev_per_month  Complain_ly  rev_growth_yoy  \\\n",
       "0         Single            9.0          1.0            11.0   \n",
       "1         Single            7.0          1.0            15.0   \n",
       "2         Single            6.0          1.0            14.0   \n",
       "3         Single            8.0          0.0            23.0   \n",
       "4         Single            3.0          0.0            11.0   \n",
       "\n",
       "   coupon_used_for_payment  Day_Since_CC_connect  cashback Login_device  \\\n",
       "0                      1.0                   5.0    159.93       Mobile   \n",
       "1                      0.0                   0.0    120.90       Mobile   \n",
       "2                      0.0                   3.0    165.24       Mobile   \n",
       "3                      0.0                   3.0    134.07       Mobile   \n",
       "4                      1.0                   3.0    129.60       Mobile   \n",
       "\n",
       "   Clus_kmeans  \n",
       "0            0  \n",
       "1            2  \n",
       "2            2  \n",
       "3            2  \n",
       "4            2  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[\"Clus_kmeans\"] = labels\n",
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cedd0b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    4809\n",
       "1    3294\n",
       "0    3157\n",
       "Name: Clus_kmeans, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Clus_kmeans'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "11a6850d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clus_kmeans\n",
       "0     9.490339\n",
       "1    16.430783\n",
       "2     6.155750\n",
       "Name: Tenure, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Clus_kmeans')['Tenure'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "25d76eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clus_kmeans\n",
       "0    172.169373\n",
       "1    216.618722\n",
       "2    153.698228\n",
       "Name: cashback, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Clus_kmeans')['cashback'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c9afaf85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clus_kmeans  account_segment\n",
       "0            Super              1278\n",
       "             Regular +          1205\n",
       "             HNI                 432\n",
       "             Super +             166\n",
       "             Regular              76\n",
       "1            Super              1110\n",
       "             HNI                 880\n",
       "             Super +             636\n",
       "             Regular             434\n",
       "             Regular +           234\n",
       "2            Regular +          2685\n",
       "             Super              1771\n",
       "             HNI                 327\n",
       "             Super +              16\n",
       "             Regular              10\n",
       "Name: account_segment, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Clus_kmeans')['account_segment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1eaf4cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clus_kmeans\n",
       "0    4.842889\n",
       "1    6.033090\n",
       "2    4.653774\n",
       "Name: rev_per_month, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby('Clus_kmeans')['rev_per_month'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dc970dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ee796fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fc005f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "27668e42",
   "metadata": {},
   "source": [
    "### Data splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8cbd1ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfe.drop('Churn', axis=1)\n",
    "y = dfe[['Churn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "31055584",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "93b64ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1_train, X_test, y1_train, y_test = train_test_split(X, y, test_size= 0.30, random_state=1, stratify=dfe['Churn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ec0f35c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X1_train is (7882, 24)\n",
      "Shape of X_test is (3378, 24)\n",
      "Shape of y1_train is (7882, 1)\n",
      "Shape of y_test is (3378, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of X1_train is', X1_train.shape)\n",
    "print('Shape of X_test is', X_test.shape)\n",
    "print('Shape of y1_train is', y1_train.shape)\n",
    "print('Shape of y_test is', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7668b4bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split percentage of X1_train is 70.0\n",
      "Split percentage of X_test is 30.0\n",
      "Split percentage of y1_train is 70.0\n",
      "Split percentage of y_test is 30.0\n"
     ]
    }
   ],
   "source": [
    "print('Split percentage of X1_train is', X1_train.shape[0]/11260*100)\n",
    "print('Split percentage of X_test is', X_test.shape[0]/11260*100)\n",
    "print('Split percentage of y1_train is', y1_train.shape[0]/11260*100)\n",
    "print('Split percentage of y_test is', y_test.shape[0]/11260*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "666b1c70",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0        0.83\n",
       "1        0.17\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(y1_train.value_counts(normalize=True),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "418959dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "402e0b94",
   "metadata": {},
   "source": [
    "## Smote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "86359150",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "08ca03e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "480035a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = smote.fit_resample(X1_train, y1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a5800566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn\n",
       "0        0.5\n",
       "1        0.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "151a95de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train is (13110, 24)\n",
      "Shape of X_test is (3378, 24)\n",
      "Shape of y_train is (13110, 1)\n",
      "Shape of y_test is (3378, 1)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of X_train is', X_train.shape)\n",
    "print('Shape of X_test is', X_test.shape)\n",
    "print('Shape of y_train is', y_train.shape)\n",
    "print('Shape of y_test is', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "770d2f84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "46d1cce1",
   "metadata": {},
   "source": [
    "### Scaling for KNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d365e7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tenure</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>Complain_ly</th>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <th>...</th>\n",
       "      <th>Payment_E wallet</th>\n",
       "      <th>Payment_UPI</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>account_segment_Regular</th>\n",
       "      <th>account_segment_Regular +</th>\n",
       "      <th>account_segment_Super</th>\n",
       "      <th>account_segment_Super +</th>\n",
       "      <th>Marital_Status_Married</th>\n",
       "      <th>Marital_Status_Single</th>\n",
       "      <th>Login_device_Mobile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.079109</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.745637</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.441199</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.558963</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.626034</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.066090</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-0.756489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.288212</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.549205</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.877818</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.071779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.829715</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.194540</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.977559</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.756489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.100467</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.743558</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.281793</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.756489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.288212</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Tenure  City_Tier  CC_Contacted_LY  Service_Score  Account_user_count  \\\n",
       "0  0.079109        1.0        -0.745637            3.0                   3   \n",
       "1 -0.626034        3.0         0.066090            2.0                   3   \n",
       "2  0.549205        3.0         0.877818            3.0                   3   \n",
       "3  2.194540        3.0        -0.977559            3.0                   4   \n",
       "4 -0.743558        3.0        -0.281793            3.0                   4   \n",
       "\n",
       "   CC_Agent_Score  rev_per_month  Complain_ly  rev_growth_yoy  \\\n",
       "0             3.0      -1.441199          1.0       -0.558963   \n",
       "1             4.0      -0.756489          0.0       -0.288212   \n",
       "2             5.0      -0.071779          0.0       -0.829715   \n",
       "3             5.0      -0.756489          0.0       -1.100467   \n",
       "4             1.0      -0.756489          0.0       -0.288212   \n",
       "\n",
       "   coupon_used_for_payment  ...  Payment_E wallet  Payment_UPI  Gender_Male  \\\n",
       "0                      1.0  ...                 0            0            1   \n",
       "1                      2.0  ...                 1            0            1   \n",
       "2                      3.0  ...                 1            0            1   \n",
       "3                      2.0  ...                 1            0            1   \n",
       "4                      2.0  ...                 1            0            1   \n",
       "\n",
       "   account_segment_Regular  account_segment_Regular +  account_segment_Super  \\\n",
       "0                        0                          1                      0   \n",
       "1                        0                          1                      0   \n",
       "2                        0                          0                      0   \n",
       "3                        0                          0                      0   \n",
       "4                        0                          0                      1   \n",
       "\n",
       "   account_segment_Super +  Marital_Status_Married  Marital_Status_Single  \\\n",
       "0                        0                       0                      0   \n",
       "1                        0                       1                      0   \n",
       "2                        1                       0                      1   \n",
       "3                        1                       0                      0   \n",
       "4                        0                       0                      1   \n",
       "\n",
       "   Login_device_Mobile  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_s = X_train.copy()\n",
    "X_train_s[num] = X_train_s[num].apply(zscore)\n",
    "X_train_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ac32118e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tenure</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>Complain_ly</th>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <th>...</th>\n",
       "      <th>Payment_E wallet</th>\n",
       "      <th>Payment_UPI</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>account_segment_Regular</th>\n",
       "      <th>account_segment_Regular +</th>\n",
       "      <th>account_segment_Super</th>\n",
       "      <th>account_segment_Super +</th>\n",
       "      <th>Marital_Status_Married</th>\n",
       "      <th>Marital_Status_Single</th>\n",
       "      <th>Login_device_Mobile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6888</th>\n",
       "      <td>-0.124840</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.261623</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.073557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.074920</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>1.095895</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.132506</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.073557</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.394129</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2347</th>\n",
       "      <td>-0.235816</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.726332</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.729717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.394129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1794</th>\n",
       "      <td>0.208088</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.435441</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.073557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.394129</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3125</th>\n",
       "      <td>-1.012647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.493977</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.333319</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.860429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Tenure  City_Tier  CC_Contacted_LY  Service_Score  Account_user_count  \\\n",
       "6888 -0.124840        1.0         0.261623            3.0                   1   \n",
       "467   1.095895        1.0        -1.132506            2.0                   3   \n",
       "2347 -0.235816        1.0         0.726332            2.0                   3   \n",
       "1794  0.208088        1.0        -0.435441            2.0                   3   \n",
       "3125 -1.012647        1.0         0.493977            3.0                   4   \n",
       "\n",
       "      CC_Agent_Score  rev_per_month  Complain_ly  rev_growth_yoy  \\\n",
       "6888             4.0      -1.073557          0.0        2.074920   \n",
       "467              2.0      -1.073557          1.0       -1.394129   \n",
       "2347             1.0      -0.729717          0.0       -1.394129   \n",
       "1794             3.0      -1.073557          0.0       -1.394129   \n",
       "3125             3.0       1.333319          1.0       -0.860429   \n",
       "\n",
       "      coupon_used_for_payment  ...  Payment_E wallet  Payment_UPI  \\\n",
       "6888                      1.0  ...                 0            0   \n",
       "467                       3.0  ...                 0            1   \n",
       "2347                      2.0  ...                 0            0   \n",
       "1794                      1.0  ...                 0            0   \n",
       "3125                      1.0  ...                 0            0   \n",
       "\n",
       "      Gender_Male  account_segment_Regular  account_segment_Regular +  \\\n",
       "6888            1                        0                          1   \n",
       "467             0                        0                          0   \n",
       "2347            1                        0                          0   \n",
       "1794            0                        0                          1   \n",
       "3125            1                        0                          0   \n",
       "\n",
       "      account_segment_Super  account_segment_Super +  Marital_Status_Married  \\\n",
       "6888                      0                        0                       1   \n",
       "467                       0                        1                       0   \n",
       "2347                      0                        0                       0   \n",
       "1794                      0                        0                       1   \n",
       "3125                      0                        0                       0   \n",
       "\n",
       "      Marital_Status_Single  Login_device_Mobile  \n",
       "6888                      0                    0  \n",
       "467                       0                    1  \n",
       "2347                      1                    0  \n",
       "1794                      0                    0  \n",
       "3125                      0                    1  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_s = X_test.copy()\n",
    "X_test_s[num] = X_test_s[num].apply(zscore)\n",
    "X_test_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69150c21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0af95eb3",
   "metadata": {},
   "source": [
    "## 1) CART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0a8e37ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "eb75b6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "cart = DecisionTreeClassifier(criterion = 'gini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "61bf56c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2542a5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "23ecd32e",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c0a8a7ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart_train_score = cart.score(X_train, y_train)\n",
    "cart_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d65fce5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9262877442273535"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart_test_score = cart.score(X_test, y_test)\n",
    "cart_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "76177993",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_cart_train = cart.predict(X_train)\n",
    "y_pred_cart_test = cart.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d5e575fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5a641563",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [   0, 6555]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_cart_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bd5ccdfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2663,  146],\n",
       "       [ 103,  466]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_cart_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "949c0720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score  support\n",
       "0                   1.0     1.0       1.0   6555.0\n",
       "1                   1.0     1.0       1.0   6555.0\n",
       "accuracy            1.0     1.0       1.0      1.0\n",
       "macro avg           1.0     1.0       1.0  13110.0\n",
       "weighted avg        1.0     1.0       1.0  13110.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cart_train=pd.DataFrame(classification_report(y_train, y_pred_cart_train, output_dict=True)).T\n",
    "df_cart_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7ede3a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.962762</td>\n",
       "      <td>0.948024</td>\n",
       "      <td>0.955336</td>\n",
       "      <td>2809.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.761438</td>\n",
       "      <td>0.818981</td>\n",
       "      <td>0.789162</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.926288</td>\n",
       "      <td>0.926288</td>\n",
       "      <td>0.926288</td>\n",
       "      <td>0.926288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.862100</td>\n",
       "      <td>0.883502</td>\n",
       "      <td>0.872249</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.928850</td>\n",
       "      <td>0.926288</td>\n",
       "      <td>0.927345</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score      support\n",
       "0              0.962762  0.948024  0.955336  2809.000000\n",
       "1              0.761438  0.818981  0.789162   569.000000\n",
       "accuracy       0.926288  0.926288  0.926288     0.926288\n",
       "macro avg      0.862100  0.883502  0.872249  3378.000000\n",
       "weighted avg   0.928850  0.926288  0.927345  3378.000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cart_test=pd.DataFrame(classification_report(y_test, y_pred_cart_test, output_dict=True)).T\n",
    "df_cart_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5eb2234c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1e190ef4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_cart_train: 1.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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s21tKg7gYEuOiuXtwB6LN6JadFuyyRH5EFxaJHIVzjrfnr/9RM63uOY0U5lIvaYYucgQbdx3g3jcX8fH3mzk5O41LemQFuySRo1Kgi1Rj2pJN3P76fDxex33nd+JX/XLVTEvqPQW6SDVaZyRRkNuIP1/YhZz0BsEuR8QvWkMXASo8XsbOWMEdr88HfM20Xry2l8JcQopm6BLxvtu4m5ETCyks3sWgTk3VTEtClgJdIlZphYenPl3B058WkdYglqeu6M6Qrs3UTEtClgJdItbekgpembWGC7u14L7zO9EoKS7YJYmcEAW6RJT9ZRW8+vVarj21NemVzbQyU9QVUcKDAl0ixhdFWxk1qZB12w/QqXlD+rXNUJhLWFGgS9jbdaCc/33vO16fu47WGUm8PqwPvduomZaEHwW6hL0bX57LnNU7GD4gjxFnt9MZLBK2FOgSlrbsKSUpPpoGcTGMHNyBmKgoumalBrsskVqlC4skrDjnmPRNMYMenc6j03zNtE7JaaQwl4igGbqEjfU7D3DPmwv5bOkWuuekcWnP7GCXJFKnFOgSFj5c/AO3vz4fBzxwQSeu6qtmWhJ5FOgS0pxzmBl5TZLp0yadBy7sTHZj9V+RyKQ1dAlJFR4v//xsBbdXNtPKy0zmuV/1VJhLRNMMXULOkg27uXviAhat3825ndVMS+QgBbqEjJJyD09+UsSY6StIaxDHP6/sznldmwe7LJF6Q4EuIWNfaQWvzl7L0JNbct/5HUlroGZaIlX5tYZuZoPNbKmZFZnZqKMc19PMPGZ2SeBKlEi2r7SCsTNW4PE60pPjmXb76TzyX90U5iLVqHGGbmbRwFPAIKAYmGNmk51zS6o57q/A1NooVCLPjGVb+P2khWzYdYAuLVPpl5dBerKaaYkciT8z9F5AkXNupXOuDBgPDK3muFuAicDmANYnEWjn/jLu+s8Crn5+NvGxUfznxr70y8sIdlki9Z4/a+gtgXVVtouB3lUPMLOWwMXAQKDnkZ7IzIYBwwBycnKOtVaJEMNense8NTu46cw8bhmoZloi/vIn0Ku73M4dtv0YMNI55zna7bucc2OBsQAFBQWHP4dEsM17SkiOj6FBXAx/GNKR2Gijcwv1XxE5Fv4EejFQtSlGFrDhsGMKgPGVYZ4BDDGzCufcW4EoUsKXc44J84p56L3v+GWPLO49vxMnZ6cFuyyRkORPoM8B2plZa2A9cBlwRdUDnHOtD35vZi8C7yrMpSbrtu/nD28uZObyrfTMbcTlvbUMJ3Iiagx051yFmd2M7+yVaOB559xiMxteuX9MLdcoYeiDRT9wxxvzMeDPQzvz371bEaVmWiInxK8Li5xzU4Aphz1WbZA753514mVJuDrYTCu/aTKnts3gjxd0IquR+q+IBIKac0mdKPd4eerTIm4bPx+ANpnJPHN1gcJcJIAU6FLrFq3fxdAnv+DhqUvxOEdphSfYJYmEJfVykVpTUu7h8Y+XM3bGShonxfGvq3pwbudmwS5LJGwp0KXW7C/z8Macdfyie0vuGdKJ1AaxwS5JJKwp0CWg9pZW8MqsNdzQvw2Nk+KYdscAGiepkZZIXVCgS8B8tnQz97y5iA27DtAtK42+eekKc5E6pECXE7ZjXxkPvreESd+sp22TZCYM70ePVo2CXZZIxFGgywm78ZV5fLNmB7cObMtNA9sSH6NmWiLBoECX47J5dwlJ8TEkxcdwz5COxEZH0alFw2CXJRLRdB66HBPnHG/MWcdZo6czetoyALplpynMReoBzdDFb2u3+ZppfV60lV6tG3OlmmmJ1CsKdPHLB4s2cvvrC4iOMh66qAtX9MpRMy2RekaBLkd1sJlW+2YNGZCfyf0XdKJFWmKwyxKRamgNXapVVuHlHx8v59bx83HO0TojiTFX9VCYi9RjCnT5icLinVz45Oc8UvlLzzKPN8gViYg/tOQih5SUe3h02jKembmSzJR4nrm6gEGdmga7LBHxkwJdDtlf5mHCvGIu7ZnNqPM6kpqoZloioUSBHuH2lJTz8qw13Hh6Ho2T4vjojgE0Uv8VkZCkQI9gn3y/iXveXMSm3SWckt2IvnnpCnOREKZAj0Db9pby53eX8Pb8DeQ3TebpK/txSo6aaYmEOgV6BPrNK9/w7bodjDi7Hb89oy1xMTrZSSQcKNAjxA+7SkhJ8DXTuu/8TsTFRNG+WUqwyxKRANLULMw553ht9loGVWmm1TUrVWEuEoY0Qw9ja7btY9TEhXy1cht926Rzdd9WwS5JRGqRAj1MTVm4kTvemE9sVBT/9/OuXNYzGzM10xIJZwr0MHOwmVbH5g0Z2KEJ953fieap6r8iEgm0hh4myiq8PPbRMm5+7dtDzbSevrKHwlwkgijQw8D8dTu54B+f89hHy4mJMjXTEolQWnIJYQfKPIyetpTnPl9Fk5QEnrumgLM6qpmWSKRSoIewknIPb367gct75TDqvA6kJKiZlkgk82vJxcwGm9lSMysys1HV7L/SzAorv740s26BL1UAdpeU8+Qny6nweGmUFMfHdwzgfy7uqjAXkZpn6GYWDTwFDAKKgTlmNtk5t6TKYauAAc65HWZ2HjAW6F0bBUeyj5Zs4p63FrJlTyk9WjWmb146qQ0U5CLi48+SSy+gyDm3EsDMxgNDgUOB7pz7ssrxs4CsQBYZ6bbtLeWBd5bwzoINdGiWwjNXF3BSVlqwyxKResafQG8JrKuyXczRZ9/XA+9Xt8PMhgHDAHJycvwsUQ4207pjUD7DB+SpmZaIVMufQK/u8kJX7YFmZ+IL9NOq2++cG4tvOYaCgoJqn0N8Nu46QMOEWJLiY7j/Al8zrfym6r8iIkfmz1SvGMiusp0FbDj8IDM7CXgWGOqc2xaY8iKP1+v499drGDR6Bo986Gum1aVlqsJcRGrkzwx9DtDOzFoD64HLgCuqHmBmOcAk4Crn3LKAVxkhVm3dx6iJhXy9ajuntk3nV/1yg12SiISQGgPdOVdhZjcDU4Fo4Hnn3GIzG165fwxwP5AOPF3ZAKrCOVdQe2WHn/cKfc204mKi+NsvTuKXBVlqpiUix8SvC4ucc1OAKYc9NqbK978Gfh3Y0iLDwWZanVs0ZFCnptx3fieaNkwIdlkiEoJ0ukSQlFZ4GP3hUm569Rucc+RmJPHkFd0V5iJy3BToQfDN2h2c/8TnPPFJEQkx0WqmJSIBoV4udWh/WQV/n7qMF75cRfOGCbxwbU/ObN8k2GWJSJhQoNeh0nIv7xRu4Ko+rbh7cAeS4/XPLyKBo0SpZbsOlPPSl6v57Rl5NEqK46M7BpCaqP4rIhJ4CvRaNHXxD9z31iK27Sujd+vG9G6TrjAXkVqjQK8FW/aU8sDkxby3cCMdmzfkuWt60jUrNdhliUiYU6DXgt/+ex4L1u3irnPyuXFAHrHROplIRGqfAj1A1u88QGpiLMnxMfzxgs7Ex0TRTv1XRKQOaep4grxex7ivVnPO6OmMrtJMS2EuInVNM/QTsGLLXkZNLGTO6h30b5fBtafmBrskEYlgCvTj9G7hBu54YwEJMVE8fMlJXNJDzbREJLgU6MfoYDOtri1TGdy5Gfee35EmKeq/IiLBpzV0P5WUe3h46vf85hVfM61W6Uk8cfkpCnMRqTcU6H6Yt2Y7P3tiJk99uoKk+Bg10xKReklLLkexr7SCh6cu5aWvVtMiNZGXruvFgPzMYJclIlItBfpRlHu8TFm4kav7tOJ3aqYlIvWcEuowO/eX8cIXq7llYFvSGsTx0Z0DaJig/isiUv8p0Kt4f+FG7nt7MTv2l9EvL53ebdIV5iISMhTowObdJdz/9mI+WPwDnVs05KXretK5hZppiUhoUaADN736DQuKdzFycAdu6N+aGDXTEpEQFLGBXrxjP2kN4kiOj+GBCzuTEBtNXmZysMsSETluETcV9XodL36xinMencEjHy4FoHOLVIW5iIS8iJqhF232NdOau2YHA/Izuf601sEuSUQkYCIm0Ccv2MBdbyygQXw0o/+rGxef0lLNtEQkrIR9oHu9jqgoo1tWKkO6NuOen3UiMyU+2GWJiARc2K6hl5R7+Mv73zP8lXmHmmk9dtkpCnMRCVthGeizV21nyOMzGTN9BY0axFHuccEuSUSk1oXVksve0gr++v73vDxrDdmNE3nl+t6c1i4j2GWJiNSJsAr0Co+XD5f8wHWntuauc/NpEBdWwxMROaqQT7wd+8p44YtV3HpWO9IaxPHxnWeoK6KIRCS/1tDNbLCZLTWzIjMbVc1+M7MnKvcXmln3wJf6Y8453ivcyKBHp/P0Zyv4Zu1OAIW5iESsGtPPzKKBp4BBQDEwx8wmO+eWVDnsPKBd5Vdv4J+VfwZe6W4q9u/k78+MY8zKDLq2TGXcdb3p1KJhrbyciEio8Gc62wsocs6tBDCz8cBQoGqgDwXGOeccMMvM0sysuXNuY0CrXTcbNi0i2nm5bddddOj3L87/WT810xIRwb8ll5bAuirbxZWPHesxmNkwM5trZnO3bNlyrLXC6pngHAYkRHm4qNEqhbmISCV/0rC66+MPP7Hbn2Nwzo11zhU45woyM4/j3py5/SEmASwai47zbYuICODfkksxkF1lOwvYcBzHnLjsXnDNZN9MPbe/b1tERAD/An0O0M7MWgPrgcuAKw47ZjJwc+X6em9gV8DXzw/K7qUgFxGpRo2B7pyrMLObgalANPC8c26xmQ2v3D8GmAIMAYqA/cC1tVeyiIhUx6+Ttp1zU/CFdtXHxlT53gE3BbY0ERE5FjpFREQkTCjQRUTChAJdRCRMKNBFRMKE+X6fGYQXNtsCrDnOv54BbA1gOaFAY44MGnNkOJExt3LOVXtlZtAC/USY2VznXEGw66hLGnNk0JgjQ22NWUsuIiJhQoEuIhImQjXQxwa7gCDQmCODxhwZamXMIbmGLiIiPxWqM3QRETmMAl1EJEzU60Cvjzenrm1+jPnKyrEWmtmXZtYtGHUGUk1jrnJcTzPzmNkldVlfbfBnzGZ2hpnNN7PFZja9rmsMND9+tlPN7B0zW1A55pDu2mpmz5vZZjNbdIT9gc8v51y9/MLXqncF0AaIAxYAnQ47ZgjwPr47JvUBvg523XUw5n5Ao8rvz4uEMVc57hN8XT8vCXbddfA+p+G7b29O5XaTYNddB2P+A/DXyu8zge1AXLBrP4Exnw50BxYdYX/A86s+z9AP3ZzaOVcGHLw5dVWHbk7tnJsFpJlZ87ouNIBqHLNz7kvn3I7KzVn47g4Vyvx5nwFuASYCm+uyuFriz5ivACY559YCOOdCfdz+jNkBKWZmQDK+QK+o2zIDxzk3A98YjiTg+VWfAz1gN6cOIcc6nuvxfcKHshrHbGYtgYuBMYQHf97nfKCRmX1mZvPM7Oo6q652+DPmJ4GO+G5fuRC4zTnnrZvygiLg+eXXDS6CJGA3pw4hfo/HzM7EF+in1WpFtc+fMT8GjHTOeXyTt5Dnz5hjgB7AWUAi8JWZzXLOLavt4mqJP2M+F5gPDATygGlmNtM5t7uWawuWgOdXfQ70+nNz6rrj13jM7CTgWeA859y2Oqqttvgz5gJgfGWYZwBDzKzCOfdWnVQYeP7+bG91zu0D9pnZDKAbEKqB7s+YrwX+4nwLzEVmtgroAMyumxLrXMDzqz4vuRy6ObWZxeG7OfXkw46ZDFxd+dviPtTmzanrRo1jNrMcYBJwVQjP1qqqcczOudbOuVznXC4wAfhtCIc5+Pez/TbQ38xizKwBvpuvf1fHdQaSP2Nei+9/JJhZU6A9sLJOq6xbAc+vejtDdxF4c2o/x3w/kA48XTljrXAh3KnOzzGHFX/G7Jz7zsw+AAoBL/Csc67a099CgZ/v84PAi2a2EN9yxEjnXMi21TWz14AzgAwzKwb+CMRC7eWXLv0XEQkT9XnJRUREjoECXUQkTCjQRUTChAJdRCRMKNBFRMKEAl1EJEwo0EVEwsT/AyB3wB+EzjAjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_cart_train = cart.predict_proba(X_train)\n",
    "probs_cart_train = probs_cart_train[:, 1]\n",
    "\n",
    "auc_cart_train = roc_auc_score(y_train, probs_cart_train)\n",
    "print('AUC_cart_train: %.3f' % auc_cart_train)\n",
    "\n",
    "fpr_cart_train, tpr_cart_train, thresholds_cart_train = roc_curve(y_train, probs_cart_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_cart_train, tpr_cart_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fe988565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_cart_test: 0.884\n"
     ]
    },
    {
     "data": {
      "image/png": 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+9dZ63llXxaCeGVx+Vi+rS1KnKHoC3VmlSxaVOoGPN+7l7jmrONrk5qeX9Gf6+X21mVYEip5Ad1RDZr7VVSgVlvKzkhncK4NfTxtCv25pVpejTlP0/AjWEbpSrTwewwufb2fG3DUAlHRP56+3jtEwj3DRMUJ3u6B2v24qUgrYss/J/a+vYcWOQ5xfmqvNtGwkOgK9di9gdISuolqT28PsxVv53YebSY6P5bf/NoxvDc/TZlo2Eh2B3nJSkY7QVRSrOdrE7MVbmTSwGw9dMZhu6dpMy26iI9Bb+rjoCF1FmfomN39bUcH1o3uTk5bIu3ePp2em9l+xq+gIdB2hqyi0fPtB7n99DVv311KUk8Z5JTka5jYXHYHurAYE0rpZXYlSQedscPG/737Ni1/sIL9LMi/dos20okV0BLqjClKyITbe6kqUCrrpL67gi60HuOncPtx7cX9StZlW1IiO77SzWptyKVs7XNdIYlwsyQmx/OTiUkAY0buL1WWpEIuOjUWOKm2bq2xrwdo9TJq5iCeam2mN6N1VwzxKRc8IvdtAq6tQKqD2HqnnwbfW8d76aobmZTLt7DyrS1IWs3+gezzeQNcRurKRj76u5u45q2hweZhx6QC+d14RcdpMK+rZP9CPHgSPS+fQla0Udk1hWEEW/3HFYPrmav8V5WX/H+kOPXpORT63x/Dsp9u47/XVAPTrls5Lt4zWMFdt2H+E3nKWqI7QVYTaXO3g/rlr+HLnYS7sr8201InZP9BbzhLVEbqKMI0uD/+3aAt/+Kic1MRYnrjmbKad3UubaakT8mvKRUSmiMhGESkXkRknuOYCEVklIutFZFFgyzwDOkJXEepIfRN//mwbFw/uzsJ7JnDlOdoZUZ1cpyN0EYkFngImA5XAchGZb4zZ4HNNFvA0MMUYs1NEwmePvaMaEjMhXntYqPBX3+Tm1eUV3DDG20zrvbvPp3uGdkVU/vFnymUUUG6M2QogInOAacAGn2uuA+YZY3YCGGP2BrrQ06YnFakI8c+tB5gxby3b9tfSr1sa5/bL0TBXp8SfKZc8oMLncWXz53yVAl1E5BMRWSkiN3b0QiIyXURWiMiKffv2nV7Fp8qha9BVeHPUN/GLN9dyzeyluDwe/vK90ZzbT5tpqVPnzwi9o0k708HrjAAuApKBL0RkqTFmU5svMmY2MBugrKys/WsEh2MPFIwKyVspdTqmv7iSpdsOcMt5Rfzk4lJSEuy/VkEFhz//ciqBAp/H+cDuDq7Zb4ypBWpFZDEwDNiElYzRXaIqLB2sbSQ53ttM695L+iMCwwu1/4o6M/5MuSwHSkSkSEQSgGuB+e2ueQsYLyJxIpICjAa+Cmypp6G+Blz1usJFhQ1jDPNX72bSzEU83tpMq4uGuQqITkfoxhiXiNwJvAfEAs8aY9aLyO3Nz88yxnwlIu8CawAP8IwxZl0wC/dLy9FzelKRCgNVNfX84s11fPBVNcPyM/nmcG2mpQLLr8k6Y8wCYEG7z81q9/hR4NHAlRYALdv+dZWLstiHX3mbaTV5PDwwdSA3n1dEbIyuKVeBZe/fvugIXYWJ3tmpDO/dhf+4YjB9clKtLkfZlL2bc+kIXVnE7TE8s2QrP3mtpZlWGi/cPErDXAWV/UfoccmQmGF1JSqKbKp2cN/ra1hVcZiJA7ppMy0VMvYOdEfzLlHtf6FCoNHl4Y+fbOHJjzeTnhTP7649myuGaTMtFTr2DnRntc6fq5A5Ut/E859vY+rQnvzyskFkpyVaXZKKMvYOdEcVdB9kdRXKxo42unll2U6+M65PazOtbtp/RVnE3oHurIbiiVZXoWzq8y37mTF3LTsP1tG/Rzrn9svRMFeWsm+gN9ZBwxFd4aIC7kh9E/+94GteWbaT3tkpvHLrGMYWZ1tdllI2DvSWgy10Dl0F2PQXV7Bs20FuO78vd08qJTlBV7Co8GDfQG85ek5H6CoADjgbSEmIIzkhlvumDCBWhGEFWVaXpVQb9t1YpCN0FQDGGN5atatNM63hhV00zFVYioIRuga6Oj17ao7yizfW8eHXezm7IIurR+RbXZJSJ2XfQHdWQUwcJHe1uhIVgRZuqObHr67C7TE8eNkgvjuujzbTUmHPvoHecvRcjH1nlVTwFOWkUtanC7++YgiF2SlWl6OUX+ybds4qPalI+c3l9jB78RbueXUV4G2m9fxNozTMVUSx9wi9S2+rq1AR4Ks9R7h/7hrWVNYweVB3baalIpZ9A91ZpYdDq5NqcLl56uMtPP1xOVkp8Tx13XCmDu2hzbRUxLJnoLsaoe6ArnBRJ+Wsd/Hy0h1cMawXD142iC6pCVaXpNQZsWeg1+71/qlz6KqdukYXf/3nTm46t4js5mZauenaFVHZgz0DXdegqw58Vr6fGfPWUHHwKIN6ZjCuX46GubIVewZ66y5RHaErqDnaxH+9/RWvrqigKCeVV6ePYXRfbaal7Meegd56lqiO0BXc9tIKlm8/xO0Tirl7UomuYFG2Zc9Ad1YDAqndrK5EWWSfo4HUxFhSEuK4f8oA4mJiGJqfaXVZSgWVPTcWOaogNQdi7fnzSp2YMYZ5X1Yy+fFFPL7Q20zrnMIuGuYqKtgz8fQs0ai06/BRHnhjLZ9s3MfwwiyuGVlgdUlKhZQ9A91RpfPnUeb99VX8+NVVGOChywdxw1htpqWijz0D3VkNPYZYXYUKAWMMIkJxtzTG9M3moSsGU9BV+6+o6GS/OXSPG5x7dcrF5lxuD3/8ZAs/bm6mVZybxp+/O1LDXEU1+43Q6w6AceuUi41t2H2E++auZt2uI1wyWJtpKdXCfoHu0E1FdlXf5ObJj8qZtWgLWSkJ/PH64Vw6tKfVZSkVNuwX6E7d9m9XtQ0u/rpsJ9POzuPBywaSlaLNtJTy5dccuohMEZGNIlIuIjNOct1IEXGLyNWBK/EU6QjdVmobXMxevAW3x5CdlsjCH5/PY/8+TMNcqQ50OkIXkVjgKWAyUAksF5H5xpgNHVz3P8B7wSjUb9rHxTYWb9rHz+atZXfNUYbkZTKuOIfsNG2mpdSJ+DNCHwWUG2O2GmMagTnAtA6uuwuYC+wNYH2nzlENSVkQn2RpGer0Ha5r5N6/rebGZ5eRGB/D324by7jiHKvLUirs+TOHngdU+DyuBEb7XiAiecBVwERg5IleSESmA9MBCgsLT7VW/zh1U1Gkm/7SSlbuOMQdFxZz10RtpqWUv/wJ9I6225l2j58A7jfGuE92fJcxZjYwG6CsrKz9awSGo1qnWyLQXkc9aYlxpCTE8fOpA4mPFQb30v4rSp0Kf6ZcKgHfphj5wO5215QBc0RkO3A18LSIXBmIAk+ZjtAjijGGv62oYPLMxcx839tM6+yCLA1zpU6DPyP05UCJiBQBu4Brget8LzDGFLX8XUSeB/5hjHkzcGX6yRgdoUeQioN1/PyNtSzZvJ+Rfbrw7dFBmoZTKkp0GujGGJeI3Il39Uos8KwxZr2I3N78/Kwg1+i/+sPgbtARegR4d10V97y2CgF+PW0w/290b2K0mZZSZ8SvjUXGmAXAgnaf6zDIjTHfPfOyTlPLWaI6Qg9bLc20SruncW6/HH51+SDyu2j/FaUCwV7NuZx69Fy4anJ7eOrjcn40ZxUAfXPT+NONZRrmSgWQvQK9dYSugR5O1u2qYdqTn/HoextxG0ODy211SUrZkr16ubSO0HXKJRzUN7n53Yebmb14K11TE/i/G0ZwyWD9YatUsNgr0B3VEJ8KielWV6KAukY3ry2v4FvD83hg6iAyU+KtLkkpW7NXoDurdHRuMWeDi5eX7uDW8X3pmprAwnsm0DVVG2kpFQr2CnSHHg5tpU827uWBN9axu+Yow/KzGFucrWGuVAjZK9CdVdBjqNVVRJ1DtY08/PYG5n25i37d0nj99nGM6N3F6rKUijr2CnRHNfSbbHUVUee2l1fy5Y5D/HBiP+6Y2I/EOG2mpZQV7BPoDU5odOgceojsPVJPamIcqYlxPDB1IPGxMQzqlWF1WUpFNfusQ3fqGvRQMMbw2vIKLpq5iJkLvc20hhVkaZgrFQbsM0J36Br0YNt5wNtM69Py/Ywq6sr12kxLqbBin0BvPXpOR+jB8O66Pfz41dXExgiPXDmE60YVajMtpcKMfQK9Zdu/9nEJqJZmWv17ZDChNJdfXj6IXlnJVpellOqAjebQqyA2AZJ1uVwgNLo8/OHDzfxwziqMMRTlpDLrhhEa5kqFMfsEesvBFic5Ak/5Z03lYa548lMea/6lZ6PbY3FFSil/2GfKxVmlfdDPUH2Tm8cXbuJPS7aSm57In24sY/Ig/d9UqUhhn0B3VEN2sdVVRLS6Rjevr6zkmpEFzLh0IJnJ2kxLqUhin0B3VkHvcVZXEXEc9U28tHQHt51fTNfUBD64ZwJdtP+KUhHJHoHuaoCjh3SFyyn66OtqHnhjHdVH6jmnoAtji7M1zJWKYPYIdKeeJXoqDjgb+PU/NvDWqt2Udk/j6evHcU6hrg5SKtLZI9B1Dfop+f7LX/KvikPcPamEH1zQj4Q4+yx2Uiqa2SPQW3eJ6gj9RKpq6klP8jbTevCyQSTExdC/h57spJSd2GNo1trHRUfo7RljeGXZTib7NNMamp+pYa6UDdlkhF4NEgOpuVZXElZ2HKhlxty1fLH1AGP7ZnPj2N5Wl6SUCiJ7BLqjyhvmMXqwQosFa/dwz2uriI+J4b+/OZRrRxYguotWKVuzR6A7q3X+vFlLM62BPTOYOKAbD142iJ6Z2n9FqWhgnzn0KJ8/b3R5eOKDTdz5yr9am2k9ff0IDXOloog9Aj3KR+irKg5z+R8+5YkPNhMXI9pMS6koFflTLh431O6LyhH60UY3Mxdu5M+fbqNbehJ//k4ZFw2M3h9sSkW7yA/02n1gPFE5Qq9vcvPGv3bz7VGFzLh0AOlJ2kxLqWjm15SLiEwRkY0iUi4iMzp4/noRWdP88bmIDAt8qScQZWvQj9Q38eRHm3G5PXRJTeDDeybwn1cN1TBXSnU+QheRWOApYDJQCSwXkfnGmA0+l20DJhhjDonIpcBsYHQwCj5Oax8X+wf6BxuqeeDNtexzNDCid1fGFmeTmaJBrpTy8mfKZRRQbozZCiAic4BpQGugG2M+97l+KZAfyCJPqnWEbt8plwPOBh76+wb+vno3A3qk86cbyzgrP8vqspRSYcafQM8DKnweV3Ly0fctwDsdPSEi04HpAIWFhX6W2Iko6LTY0kzrnsml3D6hWJtpKaU65E+gd7S90HR4ociFeAP9vI6eN8bMxjsdQ1lZWYevccocVd6DoeMSA/Jy4WJPzVEykuJJTYzjl5d7m2mVdtf+K0qpE/NnqFcJFPg8zgd2t79IRM4CngGmGWMOBKY8PzirbTV/7vEY/vLPHUyeuZjH3vc20xqSl6lhrpTqlD8j9OVAiYgUAbuAa4HrfC8QkUJgHnCDMWZTwKs8GUeVbebPt+2vZcbcNfxz20HO7ZfNd8f1sbokpVQE6TTQjTEuEbkTeA+IBZ41xqwXkdubn58F/BLIBp5ubgDlMsaUBa9sH85qyO4XkrcKprfXeJtpJcTF8L/fOot/K8vXZlpKqVPi18YiY8wCYEG7z83y+fv3gO8FtjQ/GOMN9Ageobc00xrcK4PJg7rz4GWD6J6RZHVZSqkIFNnLJY4eAncjpPe0upJT1uByM/P9jdzx1y8xxtAnJ5UnrxuuYa6UOm2RHeiOyDx67sudh7js95/y+4/KSYqL1WZaSqmAiOxeLs7I2vZf1+jit+9t4rnPt9EzI4nnbhrJhf27WV2WUsomIjvQHZG1qaihycPf1+zmhjG9uW/KANISI/t/fqVUeInsRImAEXrN0SZe+Hw7P7igmC6pCXxwzwQyk7X/ilIq8CI70B3VkJAOCalWV9Kh99ZX8eCb6zhQ28jooq6M7putYa6UCprIDnRneG4q2udo4KH563l77R4G9szgz98ZydD8TKvLUkrZXGQHuiM8t/3/4C8rWV1Rw70Xl3LbhGLiYyN7MZFSKjJEdqA7q6DXOVZXAcCuw0fJTI4nLTGOX10+mMS4GEq0/4pSKoQie+gYBiN0j8fw4hfbuXjmImb6NNPSMFdKhVrkjtAbHNBUa+kc+pZ9TmbMXcPy7YcYX5LDTef2sawWpZSK3EB3WHv03D/W7Oae11aTFBfDo1efxdUjtJmWUspakRvoTmuOnmtppjU0L5Mpg3vwi8sG0i1d+68opawXuXPorX1cQjNCr29y8+h7X/P9l73NtHpnp/L7b5+jYa6UChuRG+gtZ4mGYIS+csdBvvH7JTz18RZSE+O0mZZSKixF7pSLowpiEyEpK2hvUdvg4tH3NvLCF9vplZnMCzePYkJpbtDeTymlzkTkBnrLwRZB/EVkk9vDgrV7uHFMb36qzbSUUmEuchPKURWU+fPDdY0899l27prYj6yUBD74yQQykrT/ilIq/EVuoDurIackoC/5zto9PPjWeg7VNTKuOJvRfbM1zJVSESNyA91RBX3GB+Sl9h6p55dvrefd9VUM7pXBCzePZHAvbaallIoskRnoTfVQfzhgK1zu+OuXrK6s4f4pA7h1fBFx2kxLKRWBIjPQnWe+S7TyUB1ZKQmkJcbx0BWDSYqPpTg3LUAFKqVU6EXmUNRx+icVeTyG5z/bxsWPL+ax9zcCMLhXpoa5UiriRegIvWWX6KlNuZTv9TbTWrHjEBNKc7nlvKIgFKeUUtaIzEBvacx1CiP0+at3c+9rq0lJjGXmvw/jqnPytJmWUspWIjPQnVUgsZCS0+mlHo8hJkYYlp/J1KE9eOAbg8hNTwxBkUopFVoROodeDWndIObE5dc3ufnNO19z+8srW5tpPXHtORrmSinbisxAd1addP582baDTP3dEmYt2kKXlASa3CaExSmllDUic8rFUQ2Zecd92tng4n/e+ZqXlu6goGsyL98ymvNKOp+WUUopO4jMQHdWQd7w4z7tcnt4f0MVN59bxL2XlJKSEJm3p5RSpyPyEs/tgtr9rStcDtU28txn2/jhRSVkpSTw4U8u0K6ISqmo5NccuohMEZGNIlIuIjM6eF5E5PfNz68RkeOHz4FSuxcwmLTuvL1mD5MfX8TTn2zhy52HATTMlVJRq9P0E5FY4ClgMlAJLBeR+caYDT6XXQqUNH+MBv7Y/GfglX8IwNzPN3Dvnh4MzcvkxZtHM6hXRlDeTimlIoU/w9lRQLkxZiuAiMwBpgG+gT4NeNEYY4ClIpIlIj2NMXsCWm3FMnj7HgAuP/gCcePO5bJvjNNmWkophX9TLnlAhc/jyubPneo1iMh0EVkhIiv27dt3qrXC9iXgcQGQEOPhyi7bNMyVUqqZP2nY0f749gu7/bkGY8xsY0yZMaYsN/c0zubsM957jqjEIrEJAeuHrpRSduDPlEslUODzOB/YfRrXnLmCUfCd+d6Rep/x3sdKKaUA/wJ9OVAiIkXALuBa4Lp218wH7myeXx8N1AR8/rxFwSgNcqWU6kCngW6McYnIncB7QCzwrDFmvYjc3vz8LGABMBUoB+qAm4JXslJKqY74tWjbGLMAb2j7fm6Wz98NcEdgS1NKKXUqdImIUkrZhAa6UkrZhAa6UkrZhAa6UkrZhHh/n2nBG4vsA3ac5pfnAPsDWE4k0HuODnrP0eFM7rm3MabDnZmWBfqZEJEVxpgyq+sIJb3n6KD3HB2Cdc865aKUUjahga6UUjYRqYE+2+oCLKD3HB30nqNDUO45IufQlVJKHS9SR+hKKaXa0UBXSimbCOtAD6vDqUPEj3u+vvle14jI5yIyzIo6A6mze/a5bqSIuEXk6lDWFwz+3LOIXCAiq0RkvYgsCnWNgebHv+1MEfm7iKxuvueI7toqIs+KyF4RWXeC5wOfX8aYsPzA26p3C9AXSABWA4PaXTMVeAfviUljgH9aXXcI7nkc0KX575dGwz37XPcR3q6fV1tddwi+z1l4z+0tbH7czeq6Q3DPPwf+p/nvucBBIMHq2s/gns8HhgPrTvB8wPMrnEforYdTG2MagZbDqX21Hk5tjFkKZIlIz1AXGkCd3rMx5nNjzKHmh0vxng4Vyfz5PgPcBcwF9oayuCDx556vA+YZY3YCGGMi/b79uWcDpIuIAGl4A90V2jIDxxizGO89nEjA8yucAz1gh1NHkFO9n1vw/oSPZJ3es4jkAVcBs7AHf77PpUAXEflERFaKyI0hqy44/LnnJ4GBeI+vXAv8yBjjCU15lgh4fvl1wIVFAnY4dQTx+35E5EK8gX5eUCsKPn/u+QngfmOM2zt4i3j+3HMcMAK4CEgGvhCRpcaYTcEuLkj8uedLgFXARKAYWCgiS4wxR4Jcm1UCnl/hHOjhczh16Ph1PyJyFvAMcKkx5kCIagsWf+65DJjTHOY5wFQRcRlj3gxJhYHn77/t/caYWqBWRBYDw4BIDXR/7vkm4DfGO8FcLiLbgAHAstCUGHIBz69wnnJpPZxaRBLwHk49v90184Ebm39bPIZgHk4dGp3es4gUAvOAGyJ4tOar03s2xhQZY/oYY/oArwM/iOAwB//+bb8FjBeROBFJwXv4+lchrjOQ/LnnnXj/iwQR6Q70B7aGtMrQCnh+he0I3UTh4dR+3vMvgWzg6eYRq8tEcKc6P+/ZVvy5Z2PMVyLyLrAG8ADPGGM6XP4WCfz8Pj8MPC8ia/FOR9xvjInYtroi8gpwAZAjIpXAr4B4CF5+6dZ/pZSyiXCeclFKKXUKNNCVUsomNNCVUsomNNCVUsomNNCVUsomNNCVUsomNNCVUsom/j+HFsk5nhIXQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_cart_test = cart.predict_proba(X_test)\n",
    "probs_cart_test = probs_cart_test[:, 1]\n",
    "\n",
    "auc_cart_test = roc_auc_score(y_test, probs_cart_test)\n",
    "print('AUC_cart_test: %.3f' % auc_cart_test)\n",
    "\n",
    "fpr_cart_test, tpr_cart_test, thresholds_cart_test = roc_curve(y_test, probs_cart_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_cart_test, tpr_cart_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8762f0f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>38.654735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complain_ly</th>\n",
       "      <td>14.273146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <td>6.265917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <td>4.897206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cashback</th>\n",
       "      <td>4.252205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev_per_month</th>\n",
       "      <td>3.777421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Tier</th>\n",
       "      <td>3.672103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_Since_CC_connect</th>\n",
       "      <td>3.611889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status_Married</th>\n",
       "      <td>3.381555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <td>2.813215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_Credit Card</th>\n",
       "      <td>1.910121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Login_device_Mobile</th>\n",
       "      <td>1.677737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Super</th>\n",
       "      <td>1.570458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Regular +</th>\n",
       "      <td>1.480290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>1.280292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Service_Score</th>\n",
       "      <td>1.278012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <td>1.201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Regular</th>\n",
       "      <td>0.976483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account_user_count</th>\n",
       "      <td>0.926429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_Debit Card</th>\n",
       "      <td>0.601078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status_Single</th>\n",
       "      <td>0.431638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_UPI</th>\n",
       "      <td>0.395058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_E wallet</th>\n",
       "      <td>0.376616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Super +</th>\n",
       "      <td>0.294784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "Tenure                     38.654735\n",
       "Complain_ly                14.273146\n",
       "CC_Agent_Score              6.265917\n",
       "CC_Contacted_LY             4.897206\n",
       "cashback                    4.252205\n",
       "rev_per_month               3.777421\n",
       "City_Tier                   3.672103\n",
       "Day_Since_CC_connect        3.611889\n",
       "Marital_Status_Married      3.381555\n",
       "rev_growth_yoy              2.813215\n",
       "Payment_Credit Card         1.910121\n",
       "Login_device_Mobile         1.677737\n",
       "account_segment_Super       1.570458\n",
       "account_segment_Regular +   1.480290\n",
       "Gender_Male                 1.280292\n",
       "Service_Score               1.278012\n",
       "coupon_used_for_payment     1.201612\n",
       "account_segment_Regular     0.976483\n",
       "Account_user_count          0.926429\n",
       "Payment_Debit Card          0.601078\n",
       "Marital_Status_Single       0.431638\n",
       "Payment_UPI                 0.395058\n",
       "Payment_E wallet            0.376616\n",
       "account_segment_Super +     0.294784"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi_cart = pd.DataFrame(cart.feature_importances_*100, index=X_train.columns).sort_values(by=0, ascending=False)\n",
    "fi_cart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47eeefd7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2de6875b",
   "metadata": {},
   "source": [
    "### CART tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2b06909e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3d4ea956",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_cart = {\n",
    "    'min_samples_leaf': [1, 2],\n",
    "    'min_samples_split': [2, 3], \n",
    "    'max_leaf_nodes': [3000, 5000],\n",
    "    'class_weight' : ['balanced', 'None']\n",
    "}\n",
    "\n",
    "cartg = DecisionTreeClassifier()\n",
    "grid_search_cart = GridSearchCV(estimator = cartg, param_grid = param_grid_cart, cv = 3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1b84be9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n",
      "24 fits failed out of a total of 48.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "24 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 969, in fit\n",
      "    super().fit(\n",
      "  File \"C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 227, in fit\n",
      "    expanded_class_weight = compute_sample_weight(\n",
      "  File \"C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\utils\\class_weight.py\", line 127, in compute_sample_weight\n",
      "    raise ValueError(\n",
      "ValueError: The only valid preset for class_weight is \"balanced\". Given \"None\".\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.9317315  0.92967201 0.92562929 0.92616323 0.93203661 0.93043478\n",
      " 0.92509535 0.92433257        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;class_weight&#x27;: [&#x27;balanced&#x27;, &#x27;None&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [3000, 5000],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;class_weight&#x27;: [&#x27;balanced&#x27;, &#x27;None&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [3000, 5000],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'class_weight': ['balanced', 'None'],\n",
       "                         'max_leaf_nodes': [3000, 5000],\n",
       "                         'min_samples_leaf': [1, 2],\n",
       "                         'min_samples_split': [2, 3]})"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_cart.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9e510ccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'class_weight': 'balanced', 'max_leaf_nodes': 5000, 'min_samples_leaf': 1, 'min_samples_split': 2}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search_cart.best_params_)\n",
    "best_grid_cart = grid_search_cart.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0a7b05d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "27531f06",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b7948c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cartg_train_score = best_grid_cart.score(X_train, y_train)\n",
    "cartg_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1af4a75b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9253996447602132"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cartg_test_score = best_grid_cart.score(X_test, y_test)\n",
    "cartg_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6c6bbba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_cartg_train = best_grid_cart.predict(X_train)\n",
    "y_pred_cartg_test = best_grid_cart.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f76c2438",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a669fa66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [   0, 6555]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_cartg_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "00859014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2656,  153],\n",
       "       [  99,  470]], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_cartg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "54910054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score  support\n",
       "0                   1.0     1.0       1.0   6555.0\n",
       "1                   1.0     1.0       1.0   6555.0\n",
       "accuracy            1.0     1.0       1.0      1.0\n",
       "macro avg           1.0     1.0       1.0  13110.0\n",
       "weighted avg        1.0     1.0       1.0  13110.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cartg_train=pd.DataFrame(classification_report(y_train, y_pred_cartg_train, output_dict=True)).T\n",
    "df_cartg_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "07d6271e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.964065</td>\n",
       "      <td>0.945532</td>\n",
       "      <td>0.954709</td>\n",
       "      <td>2809.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.754414</td>\n",
       "      <td>0.826011</td>\n",
       "      <td>0.788591</td>\n",
       "      <td>569.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.925400</td>\n",
       "      <td>0.925400</td>\n",
       "      <td>0.925400</td>\n",
       "      <td>0.9254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.859240</td>\n",
       "      <td>0.885771</td>\n",
       "      <td>0.871650</td>\n",
       "      <td>3378.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.928751</td>\n",
       "      <td>0.925400</td>\n",
       "      <td>0.926727</td>\n",
       "      <td>3378.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "              precision    recall  f1-score    support\n",
       "0              0.964065  0.945532  0.954709  2809.0000\n",
       "1              0.754414  0.826011  0.788591   569.0000\n",
       "accuracy       0.925400  0.925400  0.925400     0.9254\n",
       "macro avg      0.859240  0.885771  0.871650  3378.0000\n",
       "weighted avg   0.928751  0.925400  0.926727  3378.0000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cartg_test=pd.DataFrame(classification_report(y_test, y_pred_cartg_test, output_dict=True)).T\n",
    "df_cartg_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "51742f4b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_cartg_train: 1.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_cartg_train = best_grid_cart.predict_proba(X_train)\n",
    "probs_cartg_train = probs_cartg_train[:, 1]\n",
    "\n",
    "auc_cartg_train = roc_auc_score(y_train, probs_cart_train)\n",
    "print('AUC_cartg_train: %.3f' % auc_cartg_train)\n",
    "\n",
    "fpr_cartg_train, tpr_cartg_train, thresholds_cartg_train = roc_curve(y_train, probs_cartg_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_cartg_train, tpr_cartg_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "aed973dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_cartg_test: 0.886\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_cartg_test = best_grid_cart.predict_proba(X_test)\n",
    "probs_cartg_test = probs_cartg_test[:, 1]\n",
    "\n",
    "auc_cartg_test = roc_auc_score(y_test, probs_cartg_test)\n",
    "print('AUC_cartg_test: %.3f' % auc_cartg_test)\n",
    "\n",
    "fpr_cartg_test, tpr_cartg_test, thresholds_cartg_test = roc_curve(y_test, probs_cartg_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_cartg_test, tpr_cartg_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3005021",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c8ae723c",
   "metadata": {},
   "source": [
    "## 2) RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "69a3627d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3ecf35d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2792faca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\AppData\\Local\\Temp\\ipykernel_1348\\2725846524.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  rf.fit(X_train, y_train)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3cae2e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa84ca8e",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e3151e51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_train_score = rf.score(X_train, y_train)\n",
    "rf_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0e66d606",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9721728833629366"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_test_score = rf.score(X_test, y_test)\n",
    "rf_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b1b906cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rf_train = rf.predict(X_train)\n",
    "y_pred_rf_test = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4f5c1594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [   0, 6555]], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_rf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "db9c8bc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2781,   28],\n",
       "       [  66,  503]], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_rf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "30bed231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score  support\n",
       "0                   1.0     1.0       1.0   6555.0\n",
       "1                   1.0     1.0       1.0   6555.0\n",
       "accuracy            1.0     1.0       1.0      1.0\n",
       "macro avg           1.0     1.0       1.0  13110.0\n",
       "weighted avg        1.0     1.0       1.0  13110.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rf_train=pd.DataFrame(classification_report(y_train, y_pred_rf_train, output_dict=True)).T\n",
    "df_rf_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "58e02434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.976818</td>\n",
       "      <td>0.990032</td>\n",
       "      <td>0.983380</td>\n",
       "      <td>2809.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.947269</td>\n",
       "      <td>0.884007</td>\n",
       "      <td>0.914545</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.972173</td>\n",
       "      <td>0.972173</td>\n",
       "      <td>0.972173</td>\n",
       "      <td>0.972173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.962044</td>\n",
       "      <td>0.937020</td>\n",
       "      <td>0.948963</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.971840</td>\n",
       "      <td>0.972173</td>\n",
       "      <td>0.971786</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score      support\n",
       "0              0.976818  0.990032  0.983380  2809.000000\n",
       "1              0.947269  0.884007  0.914545   569.000000\n",
       "accuracy       0.972173  0.972173  0.972173     0.972173\n",
       "macro avg      0.962044  0.937020  0.948963  3378.000000\n",
       "weighted avg   0.971840  0.972173  0.971786  3378.000000"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rf_test=pd.DataFrame(classification_report(y_test, y_pred_rf_test, output_dict=True)).T\n",
    "df_rf_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0ced3e78",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_rf_train: 1.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_rf_train = rf.predict_proba(X_train)\n",
    "probs_rf_train = probs_rf_train[:, 1]\n",
    "\n",
    "auc_rf_train = roc_auc_score(y_train, probs_rf_train)\n",
    "print('AUC_rf_train: %.3f' % auc_rf_train)\n",
    "\n",
    "fpr_rf_train, tpr_rf_train, thresholds_rf_train = roc_curve(y_train, probs_rf_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_rf_train, tpr_rf_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9708352a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_rf_test: 0.992\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_rf_test = rf.predict_proba(X_test)\n",
    "probs_rf_test = probs_rf_test[:, 1]\n",
    "\n",
    "auc_rf_test = roc_auc_score(y_test, probs_rf_test)\n",
    "print('AUC_rf_test: %.3f' % auc_rf_test)\n",
    "\n",
    "fpr_rf_test, tpr_rf_test, thresholds_rf_test = roc_curve(y_test, probs_rf_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_rf_test, tpr_rf_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aeb27e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "92ee9e60",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>23.310731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complain_ly</th>\n",
       "      <td>13.734121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cashback</th>\n",
       "      <td>6.104147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <td>6.083613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_Since_CC_connect</th>\n",
       "      <td>5.595076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status_Married</th>\n",
       "      <td>5.382031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <td>4.752811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <td>4.360608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rev_per_month</th>\n",
       "      <td>4.206233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Tier</th>\n",
       "      <td>3.519288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Super</th>\n",
       "      <td>3.231164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <td>2.669865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_Credit Card</th>\n",
       "      <td>2.565527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Service_Score</th>\n",
       "      <td>2.243850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_Debit Card</th>\n",
       "      <td>2.109858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Account_user_count</th>\n",
       "      <td>2.065264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Regular +</th>\n",
       "      <td>1.808702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Login_device_Mobile</th>\n",
       "      <td>1.711405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>1.349961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status_Single</th>\n",
       "      <td>1.120311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_UPI</th>\n",
       "      <td>0.699314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Super +</th>\n",
       "      <td>0.574427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payment_E wallet</th>\n",
       "      <td>0.529565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>account_segment_Regular</th>\n",
       "      <td>0.272127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0\n",
       "Tenure                     23.310731\n",
       "Complain_ly                13.734121\n",
       "cashback                    6.104147\n",
       "CC_Agent_Score              6.083613\n",
       "Day_Since_CC_connect        5.595076\n",
       "Marital_Status_Married      5.382031\n",
       "CC_Contacted_LY             4.752811\n",
       "rev_growth_yoy              4.360608\n",
       "rev_per_month               4.206233\n",
       "City_Tier                   3.519288\n",
       "account_segment_Super       3.231164\n",
       "coupon_used_for_payment     2.669865\n",
       "Payment_Credit Card         2.565527\n",
       "Service_Score               2.243850\n",
       "Payment_Debit Card          2.109858\n",
       "Account_user_count          2.065264\n",
       "account_segment_Regular +   1.808702\n",
       "Login_device_Mobile         1.711405\n",
       "Gender_Male                 1.349961\n",
       "Marital_Status_Single       1.120311\n",
       "Payment_UPI                 0.699314\n",
       "account_segment_Super +     0.574427\n",
       "Payment_E wallet            0.529565\n",
       "account_segment_Regular     0.272127"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fi_rf = pd.DataFrame(rf.feature_importances_*100, index=X_train.columns).sort_values(by=0, ascending=False)\n",
    "fi_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4092eec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f587de68",
   "metadata": {},
   "source": [
    "### RF tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "68313ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_rf = {\n",
    "    'n_estimators': [100, 300],\n",
    "    'criterion': ['gini', 'entropy', 'log_loss'],\n",
    "    'max_features': ['sqrt', 'log2'],\n",
    "    'min_samples_leaf': [1, 2],\n",
    "    'min_samples_split': [2, 3], \n",
    "    'class_weight' : ['balanced', 'None']\n",
    "}\n",
    "\n",
    "rfg = RandomForestClassifier()\n",
    "grid_search_rf = GridSearchCV(estimator = rfg, param_grid = param_grid_rf, cv = 3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "12fb1893",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:686: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n",
      "144 fits failed out of a total of 288.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "144 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 371, in fit\n",
      "    y, expanded_class_weight = self._validate_y_class_weight(y)\n",
      "  File \"C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 782, in _validate_y_class_weight\n",
      "    raise ValueError(\n",
      "ValueError: Valid presets for class_weight include \"balanced\" and \"balanced_subsample\".Given \"None\".\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [0.96971777 0.96926011 0.96796339 0.96803966 0.96315789 0.96300534\n",
      " 0.96285278 0.96399695 0.96872616 0.96979405 0.96773455 0.96811594\n",
      " 0.96300534 0.96346301 0.96262395 0.96315789 0.96948894 0.9706331\n",
      " 0.96880244 0.96811594 0.96453089 0.964836   0.96392067 0.96346301\n",
      " 0.96987033 0.97078566 0.968955   0.96956522 0.96559878 0.96437834\n",
      " 0.96437834 0.96407323 0.9696415  0.96987033 0.96849733 0.96918383\n",
      " 0.96392067 0.96514111 0.96460717 0.96437834 0.96971777 0.96994661\n",
      " 0.96803966 0.96834477 0.96346301 0.96514111 0.96437834 0.96445461\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:910: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;class_weight&#x27;: [&#x27;balanced&#x27;, &#x27;None&#x27;],\n",
       "                         &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3],\n",
       "                         &#x27;n_estimators&#x27;: [100, 300]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
       "             param_grid={&#x27;class_weight&#x27;: [&#x27;balanced&#x27;, &#x27;None&#x27;],\n",
       "                         &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;, &#x27;log_loss&#x27;],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3],\n",
       "                         &#x27;n_estimators&#x27;: [100, 300]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
       "             param_grid={'class_weight': ['balanced', 'None'],\n",
       "                         'criterion': ['gini', 'entropy', 'log_loss'],\n",
       "                         'max_features': ['sqrt', 'log2'],\n",
       "                         'min_samples_leaf': [1, 2],\n",
       "                         'min_samples_split': [2, 3],\n",
       "                         'n_estimators': [100, 300]})"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "4f2c2cd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'class_weight': 'balanced', 'criterion': 'entropy', 'max_features': 'log2', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search_rf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "19212899",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_grid_rf = grid_search_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "240502d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b6304f68",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c11d92de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfg_train_score = best_grid_rf.score(X_train, y_train)\n",
    "rfg_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4b1d926b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736530491415039"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfg_test_score = best_grid_rf.score(X_test, y_test)\n",
    "rfg_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "851f1a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_rfg_train = best_grid_rf.predict(X_train)\n",
    "y_pred_rfg_test = best_grid_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "49c8bae0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [   0, 6555]], dtype=int64)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_rfg_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3a836197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2784,   25],\n",
       "       [  64,  505]], dtype=int64)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_rfg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "bc8c5f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13110.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision  recall  f1-score  support\n",
       "0                   1.0     1.0       1.0   6555.0\n",
       "1                   1.0     1.0       1.0   6555.0\n",
       "accuracy            1.0     1.0       1.0      1.0\n",
       "macro avg           1.0     1.0       1.0  13110.0\n",
       "weighted avg        1.0     1.0       1.0  13110.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rfg_train=pd.DataFrame(classification_report(y_train, y_pred_rfg_train, output_dict=True)).T\n",
    "df_rfg_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "59fd87fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.977528</td>\n",
       "      <td>0.991100</td>\n",
       "      <td>0.984267</td>\n",
       "      <td>2809.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.952830</td>\n",
       "      <td>0.887522</td>\n",
       "      <td>0.919017</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.973653</td>\n",
       "      <td>0.973653</td>\n",
       "      <td>0.973653</td>\n",
       "      <td>0.973653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.965179</td>\n",
       "      <td>0.939311</td>\n",
       "      <td>0.951642</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.973368</td>\n",
       "      <td>0.973653</td>\n",
       "      <td>0.973276</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score      support\n",
       "0              0.977528  0.991100  0.984267  2809.000000\n",
       "1              0.952830  0.887522  0.919017   569.000000\n",
       "accuracy       0.973653  0.973653  0.973653     0.973653\n",
       "macro avg      0.965179  0.939311  0.951642  3378.000000\n",
       "weighted avg   0.973368  0.973653  0.973276  3378.000000"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rfg_test=pd.DataFrame(classification_report(y_test, y_pred_rfg_test, output_dict=True)).T\n",
    "df_rfg_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d4d0ac35",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_rfg_train: 1.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_rfg_train = best_grid_rf.predict_proba(X_train)\n",
    "probs_rfg_train = probs_rfg_train[:, 1]\n",
    "\n",
    "auc_rfg_train = roc_auc_score(y_train, probs_rfg_train)\n",
    "print('AUC_rfg_train: %.3f' % auc_rfg_train)\n",
    "\n",
    "fpr_rfg_train, tpr_rfg_train, thresholds_rfg_train = roc_curve(y_train, probs_rfg_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_rfg_train, tpr_rfg_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7790e3fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_rfg_test: 0.992\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_rfg_test = best_grid_rf.predict_proba(X_test)\n",
    "probs_rfg_test = probs_rfg_test[:, 1]\n",
    "\n",
    "auc_rfg_test = roc_auc_score(y_test, probs_rfg_test)\n",
    "print('AUC_rfg_test: %.3f' % auc_rfg_test)\n",
    "\n",
    "fpr_rfg_test, tpr_rfg_test, thresholds_rfg_test = roc_curve(y_test, probs_rfg_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_rfg_test, tpr_rfg_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e94c1141",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eb2847ce",
   "metadata": {},
   "source": [
    "## 3) Logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c3613e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b56e2d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lor = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b5e4c637",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "069f68b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "94a41cc1",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8d2a0e16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8479023646071701"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lor_train_score = lor.score(X_train, y_train)\n",
    "lor_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6f7dbb10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8140911782119598"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lor_test_score = lor.score(X_test, y_test)\n",
    "lor_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1f3b0343",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lor_train = lor.predict(X_train)\n",
    "y_pred_lor_test = lor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "30373114",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "be4d92df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5533, 1022],\n",
       "       [ 972, 5583]], dtype=int64)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_lor_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d99158b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2335,  474],\n",
       "       [ 154,  415]], dtype=int64)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_lor_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0b4b3c60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.850576</td>\n",
       "      <td>0.844088</td>\n",
       "      <td>0.847320</td>\n",
       "      <td>6555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.851716</td>\n",
       "      <td>0.848480</td>\n",
       "      <td>6555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.847902</td>\n",
       "      <td>0.847902</td>\n",
       "      <td>0.847902</td>\n",
       "      <td>0.847902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.847923</td>\n",
       "      <td>0.847902</td>\n",
       "      <td>0.847900</td>\n",
       "      <td>13110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.847923</td>\n",
       "      <td>0.847902</td>\n",
       "      <td>0.847900</td>\n",
       "      <td>13110.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score       support\n",
       "0              0.850576  0.844088  0.847320   6555.000000\n",
       "1              0.845269  0.851716  0.848480   6555.000000\n",
       "accuracy       0.847902  0.847902  0.847902      0.847902\n",
       "macro avg      0.847923  0.847902  0.847900  13110.000000\n",
       "weighted avg   0.847923  0.847902  0.847900  13110.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lor_train=pd.DataFrame(classification_report(y_train, y_pred_lor_train, output_dict=True)).T\n",
    "df_lor_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cd95f2b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.938128</td>\n",
       "      <td>0.831257</td>\n",
       "      <td>0.881465</td>\n",
       "      <td>2809.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.466817</td>\n",
       "      <td>0.729350</td>\n",
       "      <td>0.569273</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.814091</td>\n",
       "      <td>0.814091</td>\n",
       "      <td>0.814091</td>\n",
       "      <td>0.814091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.702472</td>\n",
       "      <td>0.780303</td>\n",
       "      <td>0.725369</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.858739</td>\n",
       "      <td>0.814091</td>\n",
       "      <td>0.828878</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score      support\n",
       "0              0.938128  0.831257  0.881465  2809.000000\n",
       "1              0.466817  0.729350  0.569273   569.000000\n",
       "accuracy       0.814091  0.814091  0.814091     0.814091\n",
       "macro avg      0.702472  0.780303  0.725369  3378.000000\n",
       "weighted avg   0.858739  0.814091  0.828878  3378.000000"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lor_test=pd.DataFrame(classification_report(y_test, y_pred_lor_test, output_dict=True)).T\n",
    "df_lor_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "25343373",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_lor_train: 0.918\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_lor_train = lor.predict_proba(X_train)\n",
    "probs_lor_train = probs_lor_train[:, 1]\n",
    "\n",
    "auc_lor_train = roc_auc_score(y_train, probs_lor_train)\n",
    "print('AUC_lor_train: %.3f' % auc_lor_train)\n",
    "\n",
    "fpr_lor_train, tpr_lor_train, thresholds_lor_train = roc_curve(y_train, probs_lor_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_lor_train, tpr_lor_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "17ad6523",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_lor_test: 0.852\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_lor_test = lor.predict_proba(X_test)\n",
    "probs_lor_test = probs_lor_test[:, 1]\n",
    "\n",
    "auc_lor_test = roc_auc_score(y_test, probs_lor_test)\n",
    "print('AUC_lor_test: %.3f' % auc_lor_test)\n",
    "\n",
    "fpr_lor_test, tpr_lor_test, thresholds_lor_test = roc_curve(y_test, probs_lor_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_lor_test, tpr_lor_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "1d137477",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.43256587, 0.64737974, 0.67025823, ..., 0.67675113, 0.04069901,\n",
       "       0.95878676])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs_lor_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6681e53",
   "metadata": {},
   "source": [
    "### Model tuning by cut-off method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1f24a583",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics,model_selection\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cb44d399",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 \n",
      "\n",
      "Accuracy Score 0.7117\n",
      "Recall 0.964 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 \n",
      "\n",
      "Accuracy Score 0.7789\n",
      "Recall 0.9396 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3 \n",
      "\n",
      "Accuracy Score 0.8128\n",
      "Recall 0.9175 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4 \n",
      "\n",
      "Accuracy Score 0.8355\n",
      "Recall 0.89 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 \n",
      "\n",
      "Accuracy Score 0.8479\n",
      "Recall 0.8517 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6 \n",
      "\n",
      "Accuracy Score 0.8439\n",
      "Recall 0.798 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7 \n",
      "\n",
      "Accuracy Score 0.8284\n",
      "Recall 0.7256 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8 \n",
      "\n",
      "Accuracy Score 0.7936\n",
      "Recall 0.622 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9 \n",
      "\n",
      "Accuracy Score 0.7098\n",
      "Recall 0.4323 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j in np.arange(0.1,1,0.1):\n",
    "    custom_prob_lor = j\n",
    "    custom_cutoff_data_lor=[]\n",
    "    for i in range(0,len(y_train)):\n",
    "        if np.array(probs_lor_train)[i] > custom_prob_lor:\n",
    "            a=1\n",
    "        else:\n",
    "            a=0\n",
    "        custom_cutoff_data_lor.append(a)\n",
    "    print(round(j,3),'\\n')\n",
    "    print('Accuracy Score',round(metrics.accuracy_score(y_train,custom_cutoff_data_lor),4))\n",
    "    print('Recall',round(metrics.recall_score(y_train,custom_cutoff_data_lor),4),'\\n')\n",
    "    plt.figure(figsize=(6,4))\n",
    "    print('Confusion Matrix')\n",
    "    sns.heatmap(metrics.confusion_matrix(y_train,custom_cutoff_data_lor),annot=True,fmt='.4g'),'\\n\\n'\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c8ff85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f82f1357",
   "metadata": {},
   "source": [
    "### Statsmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "69bed9de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tenure</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>Service_Score</th>\n",
       "      <th>Account_user_count</th>\n",
       "      <th>CC_Agent_Score</th>\n",
       "      <th>rev_per_month</th>\n",
       "      <th>Complain_ly</th>\n",
       "      <th>rev_growth_yoy</th>\n",
       "      <th>coupon_used_for_payment</th>\n",
       "      <th>...</th>\n",
       "      <th>Payment_UPI</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>account_segment_Regular</th>\n",
       "      <th>account_segment_Regular +</th>\n",
       "      <th>account_segment_Super</th>\n",
       "      <th>account_segment_Super +</th>\n",
       "      <th>Marital_Status_Married</th>\n",
       "      <th>Marital_Status_Single</th>\n",
       "      <th>Login_device_Mobile</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tenure  City_Tier  CC_Contacted_LY  Service_Score  Account_user_count  \\\n",
       "0     8.0        1.0             12.0            3.0                   3   \n",
       "1     2.0        3.0             19.0            2.0                   3   \n",
       "2    12.0        3.0             26.0            3.0                   3   \n",
       "3    26.0        3.0             10.0            3.0                   4   \n",
       "4     1.0        3.0             16.0            3.0                   4   \n",
       "\n",
       "   CC_Agent_Score  rev_per_month  Complain_ly  rev_growth_yoy  \\\n",
       "0             3.0            1.0          1.0            14.0   \n",
       "1             4.0            3.0          0.0            15.0   \n",
       "2             5.0            5.0          0.0            13.0   \n",
       "3             5.0            3.0          0.0            12.0   \n",
       "4             1.0            3.0          0.0            15.0   \n",
       "\n",
       "   coupon_used_for_payment  ...  Payment_UPI  Gender_Male  \\\n",
       "0                      1.0  ...            0            1   \n",
       "1                      2.0  ...            0            1   \n",
       "2                      3.0  ...            0            1   \n",
       "3                      2.0  ...            0            1   \n",
       "4                      2.0  ...            0            1   \n",
       "\n",
       "   account_segment_Regular  account_segment_Regular +  account_segment_Super  \\\n",
       "0                        0                          1                      0   \n",
       "1                        0                          1                      0   \n",
       "2                        0                          0                      0   \n",
       "3                        0                          0                      0   \n",
       "4                        0                          0                      1   \n",
       "\n",
       "   account_segment_Super +  Marital_Status_Married  Marital_Status_Single  \\\n",
       "0                        0                       0                      0   \n",
       "1                        0                       1                      0   \n",
       "2                        1                       0                      1   \n",
       "3                        1                       0                      0   \n",
       "4                        0                       0                      1   \n",
       "\n",
       "   Login_device_Mobile  Churn  \n",
       "0                    1      0  \n",
       "1                    1      0  \n",
       "2                    1      0  \n",
       "3                    1      0  \n",
       "4                    1      0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = pd.concat([X_train, y_train], axis=1)\n",
    "data_test=pd.concat([X_test,y_test],axis=1)\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2e869310",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Tenure', 'City_Tier', 'CC_Contacted_LY', 'Service_Score',\n",
       "       'Account_user_count', 'CC_Agent_Score', 'rev_per_month', 'Complain_ly',\n",
       "       'rev_growth_yoy', 'coupon_used_for_payment', 'Day_Since_CC_connect',\n",
       "       'cashback', 'Payment_Credit Card', 'Payment_Debit Card',\n",
       "       'Payment_E wallet', 'Payment_UPI', 'Gender_Male',\n",
       "       'account_segment_Regular', 'account_segment_Regular +',\n",
       "       'account_segment_Super', 'account_segment_Super +',\n",
       "       'Marital_Status_Married', 'Marital_Status_Single',\n",
       "       'Login_device_Mobile', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e3808cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.rename(columns = {\"account_segment_Regular +\": \"account_segment_Regular_plus\", \"account_segment_Super +\": \"account_segment_Super_plus\"}, \n",
    "                      inplace = True) \n",
    "\n",
    "data_test.rename(columns = {\"account_segment_Regular +\": \"account_segment_Regular_plus\", \"account_segment_Super +\": \"account_segment_Super_plus\"}, \n",
    "                      inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f417e6d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.rename(columns = {\"Payment_Credit Card\": \"Payment_Credit_Card\", \"Payment_Debit Card\": \"Payment_Debit_Card\", \"Payment_E wallet\": \"Payment_E_wallet\"}, \n",
    "                      inplace = True) \n",
    "\n",
    "data_test.rename(columns = {\"Payment_Credit Card\": \"Payment_Credit_Card\", \"Payment_Debit Card\": \"Payment_Debit_Card\", \"Payment_E wallet\": \"Payment_E_wallet\"}, \n",
    "                      inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "69544df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "expr = 'Churn ~ Tenure + City_Tier + CC_Contacted_LY + Service_Score + Account_user_count + CC_Agent_Score + rev_per_month + Complain_ly + rev_growth_yoy + coupon_used_for_payment + Day_Since_CC_connect + cashback + Payment_Credit_Card + Payment_Debit_Card + Payment_E_wallet + Payment_UPI + Gender_Male + account_segment_Regular + account_segment_Regular_plus + account_segment_Super + account_segment_Super_plus + Marital_Status_Married + Marital_Status_Single + Login_device_Mobile'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f0e986d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                       0.786024\n",
       "Tenure                         -0.019031\n",
       "City_Tier                       0.051699\n",
       "CC_Contacted_LY                 0.004386\n",
       "Service_Score                   0.002501\n",
       "Account_user_count              0.033832\n",
       "CC_Agent_Score                  0.029427\n",
       "rev_per_month                   0.017629\n",
       "Complain_ly                     0.215888\n",
       "rev_growth_yoy                 -0.003671\n",
       "coupon_used_for_payment         0.016666\n",
       "Day_Since_CC_connect           -0.009912\n",
       "cashback                       -0.001177\n",
       "Payment_Credit_Card            -0.294027\n",
       "Payment_Debit_Card             -0.260163\n",
       "Payment_E_wallet               -0.205826\n",
       "Payment_UPI                    -0.275297\n",
       "Gender_Male                    -0.004039\n",
       "account_segment_Regular         0.103544\n",
       "account_segment_Regular_plus   -0.078688\n",
       "account_segment_Super          -0.255078\n",
       "account_segment_Super_plus     -0.024911\n",
       "Marital_Status_Married         -0.197377\n",
       "Marital_Status_Single          -0.045726\n",
       "Login_device_Mobile            -0.079083\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "lm1 = smf.ols(formula= expr, data = data_train).fit()\n",
    "lm1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "482109c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Churn   R-squared:                       0.529\n",
      "Model:                            OLS   Adj. R-squared:                  0.528\n",
      "Method:                 Least Squares   F-statistic:                     613.2\n",
      "Date:                Fri, 17 Mar 2023   Prob (F-statistic):               0.00\n",
      "Time:                        06:49:56   Log-Likelihood:                -4575.0\n",
      "No. Observations:               13110   AIC:                             9200.\n",
      "Df Residuals:                   13085   BIC:                             9387.\n",
      "Df Model:                          24                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================================\n",
      "                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------\n",
      "Intercept                        0.7860      0.033     24.133      0.000       0.722       0.850\n",
      "Tenure                          -0.0190      0.000    -45.010      0.000      -0.020      -0.018\n",
      "City_Tier                        0.0517      0.004     12.941      0.000       0.044       0.060\n",
      "CC_Contacted_LY                  0.0044      0.000     12.302      0.000       0.004       0.005\n",
      "Service_Score                    0.0025      0.005      0.500      0.617      -0.007       0.012\n",
      "Account_user_count               0.0338      0.003     10.021      0.000       0.027       0.040\n",
      "CC_Agent_Score                   0.0294      0.002     12.566      0.000       0.025       0.034\n",
      "rev_per_month                    0.0176      0.001     16.439      0.000       0.016       0.020\n",
      "Complain_ly                      0.2159      0.007     32.161      0.000       0.203       0.229\n",
      "rev_growth_yoy                  -0.0037      0.001     -4.437      0.000      -0.005      -0.002\n",
      "coupon_used_for_payment          0.0167      0.004      4.521      0.000       0.009       0.024\n",
      "Day_Since_CC_connect            -0.0099      0.001     -9.377      0.000      -0.012      -0.008\n",
      "cashback                        -0.0012      0.000     -8.278      0.000      -0.001      -0.001\n",
      "Payment_Credit_Card             -0.2940      0.009    -32.342      0.000      -0.312      -0.276\n",
      "Payment_Debit_Card              -0.2602      0.008    -31.673      0.000      -0.276      -0.244\n",
      "Payment_E_wallet                -0.2058      0.013    -16.145      0.000      -0.231      -0.181\n",
      "Payment_UPI                     -0.2753      0.014    -19.158      0.000      -0.303      -0.247\n",
      "Gender_Male                     -0.0040      0.006     -0.657      0.511      -0.016       0.008\n",
      "account_segment_Regular          0.1035      0.020      5.058      0.000       0.063       0.144\n",
      "account_segment_Regular_plus    -0.0787      0.011     -6.845      0.000      -0.101      -0.056\n",
      "account_segment_Super           -0.2551      0.010    -25.677      0.000      -0.275      -0.236\n",
      "account_segment_Super_plus      -0.0249      0.018     -1.410      0.158      -0.060       0.010\n",
      "Marital_Status_Married          -0.1974      0.008    -25.072      0.000      -0.213      -0.182\n",
      "Marital_Status_Single           -0.0457      0.008     -5.694      0.000      -0.061      -0.030\n",
      "Login_device_Mobile             -0.0791      0.006    -12.225      0.000      -0.092      -0.066\n",
      "==============================================================================\n",
      "Omnibus:                       75.774   Durbin-Watson:                   1.545\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               68.532\n",
      "Skew:                           0.136   Prob(JB):                     1.31e-15\n",
      "Kurtosis:                       2.773   Cond. No.                     2.05e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.05e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(lm1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "beb182be",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train.drop(['Service_Score', 'Gender_Male', 'account_segment_Super_plus'], axis=1, inplace=True)\n",
    "data_test.drop(['Service_Score', 'Gender_Male', 'account_segment_Super_plus'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c842990d",
   "metadata": {},
   "outputs": [],
   "source": [
    "expr1 = 'Churn ~ Tenure + City_Tier + CC_Contacted_LY + Account_user_count + CC_Agent_Score + rev_per_month + Complain_ly + rev_growth_yoy + coupon_used_for_payment + Day_Since_CC_connect + cashback + Payment_Credit_Card + Payment_Debit_Card + Payment_E_wallet + Payment_UPI + account_segment_Regular + account_segment_Regular_plus + account_segment_Super + Marital_Status_Married + Marital_Status_Single + Login_device_Mobile'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5d5a3fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept                       0.798361\n",
       "Tenure                         -0.019153\n",
       "City_Tier                       0.051604\n",
       "CC_Contacted_LY                 0.004402\n",
       "Account_user_count              0.034878\n",
       "CC_Agent_Score                  0.029438\n",
       "rev_per_month                   0.017696\n",
       "Complain_ly                     0.215800\n",
       "rev_growth_yoy                 -0.003635\n",
       "coupon_used_for_payment         0.017391\n",
       "Day_Since_CC_connect           -0.009907\n",
       "cashback                       -0.001253\n",
       "Payment_Credit_Card            -0.295287\n",
       "Payment_Debit_Card             -0.261415\n",
       "Payment_E_wallet               -0.206592\n",
       "Payment_UPI                    -0.277229\n",
       "account_segment_Regular         0.113704\n",
       "account_segment_Regular_plus   -0.079194\n",
       "account_segment_Super          -0.252898\n",
       "Marital_Status_Married         -0.198002\n",
       "Marital_Status_Single          -0.046439\n",
       "Login_device_Mobile            -0.078814\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "lm2 = smf.ols(formula= expr1, data = data_train).fit()\n",
    "lm2.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "53c49f78",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Churn   R-squared:                       0.529\n",
      "Model:                            OLS   Adj. R-squared:                  0.528\n",
      "Method:                 Least Squares   F-statistic:                     700.7\n",
      "Date:                Fri, 17 Mar 2023   Prob (F-statistic):               0.00\n",
      "Time:                        06:56:43   Log-Likelihood:                -4576.5\n",
      "No. Observations:               13110   AIC:                             9197.\n",
      "Df Residuals:                   13088   BIC:                             9361.\n",
      "Df Model:                          21                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================================\n",
      "                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------------\n",
      "Intercept                        0.7984      0.031     25.445      0.000       0.737       0.860\n",
      "Tenure                          -0.0192      0.000    -46.167      0.000      -0.020      -0.018\n",
      "City_Tier                        0.0516      0.004     12.927      0.000       0.044       0.059\n",
      "CC_Contacted_LY                  0.0044      0.000     12.364      0.000       0.004       0.005\n",
      "Account_user_count               0.0349      0.003     10.739      0.000       0.029       0.041\n",
      "CC_Agent_Score                   0.0294      0.002     12.610      0.000       0.025       0.034\n",
      "rev_per_month                    0.0177      0.001     16.562      0.000       0.016       0.020\n",
      "Complain_ly                      0.2158      0.007     32.163      0.000       0.203       0.229\n",
      "rev_growth_yoy                  -0.0036      0.001     -4.409      0.000      -0.005      -0.002\n",
      "coupon_used_for_payment          0.0174      0.004      4.830      0.000       0.010       0.024\n",
      "Day_Since_CC_connect            -0.0099      0.001     -9.373      0.000      -0.012      -0.008\n",
      "cashback                        -0.0013      0.000     -9.965      0.000      -0.002      -0.001\n",
      "Payment_Credit_Card             -0.2953      0.009    -32.612      0.000      -0.313      -0.278\n",
      "Payment_Debit_Card              -0.2614      0.008    -31.982      0.000      -0.277      -0.245\n",
      "Payment_E_wallet                -0.2066      0.013    -16.215      0.000      -0.232      -0.182\n",
      "Payment_UPI                     -0.2772      0.014    -19.381      0.000      -0.305      -0.249\n",
      "account_segment_Regular          0.1137      0.019      6.035      0.000       0.077       0.151\n",
      "account_segment_Regular_plus    -0.0792      0.011     -7.019      0.000      -0.101      -0.057\n",
      "account_segment_Super           -0.2529      0.010    -25.875      0.000      -0.272      -0.234\n",
      "Marital_Status_Married          -0.1980      0.008    -25.221      0.000      -0.213      -0.183\n",
      "Marital_Status_Single           -0.0464      0.008     -5.794      0.000      -0.062      -0.031\n",
      "Login_device_Mobile             -0.0788      0.006    -12.202      0.000      -0.091      -0.066\n",
      "==============================================================================\n",
      "Omnibus:                       74.834   Durbin-Watson:                   1.544\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               68.378\n",
      "Skew:                           0.138   Prob(JB):                     1.42e-15\n",
      "Kurtosis:                       2.780   Cond. No.                     1.95e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.95e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(lm2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb087f9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b343e550",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lm2.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfe23673",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3053a8e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0c862ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cc6c1d0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## let's check the vif value\n",
    "\n",
    "def vif_cal(input_data):\n",
    "    x_vars=input_data\n",
    "    xvar_names=input_data.columns\n",
    "    for i in range(0,xvar_names.shape[0]):\n",
    "        y=x_vars[xvar_names[i]] \n",
    "        x=x_vars[xvar_names.drop(xvar_names[i])]\n",
    "        rsq=smf.ols(formula=\"y~x\", data=x_vars).fit().rsquared  \n",
    "        vif=round(1/(1-rsq),2)\n",
    "        print (xvar_names[i], \" VIF = \" , vif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c5663d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tenure  VIF =  1.39\n",
      "City_Tier  VIF =  1.43\n",
      "CC_Contacted_LY  VIF =  1.05\n",
      "Account_user_count  VIF =  1.1\n",
      "CC_Agent_Score  VIF =  1.03\n",
      "rev_per_month  VIF =  1.08\n",
      "Complain_ly  VIF =  1.05\n",
      "rev_growth_yoy  VIF =  1.03\n",
      "coupon_used_for_payment  VIF =  1.29\n",
      "Day_Since_CC_connect  VIF =  1.38\n",
      "cashback  VIF =  3.06\n",
      "Payment_Credit_Card  VIF =  1.6\n",
      "Payment_Debit_Card  VIF =  1.65\n",
      "Payment_E_wallet  VIF =  1.54\n",
      "Payment_UPI  VIF =  1.18\n",
      "account_segment_Regular  VIF =  1.32\n",
      "account_segment_Regular_plus  VIF =  3.49\n",
      "account_segment_Super  VIF =  2.11\n",
      "Marital_Status_Married  VIF =  1.65\n",
      "Marital_Status_Single  VIF =  1.54\n",
      "Login_device_Mobile  VIF =  1.05\n"
     ]
    }
   ],
   "source": [
    "vif_cal(input_data= data_train.drop('Churn',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "356e7a47",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c80f7e13",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9272300",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f04d07fb",
   "metadata": {},
   "source": [
    "## 4) LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a6b40041",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2f97cdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LinearDiscriminantAnalysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "67b5230a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "01788a14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "84ec22a8",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "6bda600a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8574370709382151"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_train_score = lda.score(X_train, y_train)\n",
    "lda_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fb1cbaa0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8342214328004737"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_test_score = lda.score(X_test, y_test)\n",
    "lda_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "773fbe50",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lda_train = lda.predict(X_train)\n",
    "y_pred_lda_test = lda.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7940eb6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "642f072f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5626,  929],\n",
       "       [ 940, 5615]], dtype=int64)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_lda_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "97376ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2426,  383],\n",
       "       [ 177,  392]], dtype=int64)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_lda_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3203ae5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.856838</td>\n",
       "      <td>0.858276</td>\n",
       "      <td>0.857557</td>\n",
       "      <td>6555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.858038</td>\n",
       "      <td>0.856598</td>\n",
       "      <td>0.857317</td>\n",
       "      <td>6555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.857437</td>\n",
       "      <td>0.857437</td>\n",
       "      <td>0.857437</td>\n",
       "      <td>0.857437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.857438</td>\n",
       "      <td>0.857437</td>\n",
       "      <td>0.857437</td>\n",
       "      <td>13110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.857438</td>\n",
       "      <td>0.857437</td>\n",
       "      <td>0.857437</td>\n",
       "      <td>13110.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score       support\n",
       "0              0.856838  0.858276  0.857557   6555.000000\n",
       "1              0.858038  0.856598  0.857317   6555.000000\n",
       "accuracy       0.857437  0.857437  0.857437      0.857437\n",
       "macro avg      0.857438  0.857437  0.857437  13110.000000\n",
       "weighted avg   0.857438  0.857437  0.857437  13110.000000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lda_train=pd.DataFrame(classification_report(y_train, y_pred_lda_train, output_dict=True)).T\n",
    "df_lda_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b3f4ed12",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.932002</td>\n",
       "      <td>0.863653</td>\n",
       "      <td>0.896526</td>\n",
       "      <td>2809.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.505806</td>\n",
       "      <td>0.688928</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.834221</td>\n",
       "      <td>0.834221</td>\n",
       "      <td>0.834221</td>\n",
       "      <td>0.834221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.718904</td>\n",
       "      <td>0.776290</td>\n",
       "      <td>0.739930</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.860212</td>\n",
       "      <td>0.834221</td>\n",
       "      <td>0.843771</td>\n",
       "      <td>3378.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score      support\n",
       "0              0.932002  0.863653  0.896526  2809.000000\n",
       "1              0.505806  0.688928  0.583333   569.000000\n",
       "accuracy       0.834221  0.834221  0.834221     0.834221\n",
       "macro avg      0.718904  0.776290  0.739930  3378.000000\n",
       "weighted avg   0.860212  0.834221  0.843771  3378.000000"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lda_test=pd.DataFrame(classification_report(y_test, y_pred_lda_test, output_dict=True)).T\n",
    "df_lda_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8b505b2f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_lda_train: 0.930\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_lda_train = lda.predict_proba(X_train)\n",
    "probs_lda_train = probs_lda_train[:, 1]\n",
    "\n",
    "auc_lda_train = roc_auc_score(y_train, probs_lda_train)\n",
    "print('AUC_lda_train: %.3f' % auc_lda_train)\n",
    "\n",
    "fpr_lda_train, tpr_lda_train, thresholds_lda_train = roc_curve(y_train, probs_lda_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_lda_train, tpr_lda_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1ada60be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC_lda_test: 0.854\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "probs_lda_test = lda.predict_proba(X_test)\n",
    "probs_lda_test = probs_lda_test[:, 1]\n",
    "\n",
    "auc_lda_test = roc_auc_score(y_test, probs_lda_test)\n",
    "print('AUC_lda_test: %.3f' % auc_lda_test)\n",
    "\n",
    "fpr_lda_test, tpr_lda_test, thresholds_lda_test = roc_curve(y_test, probs_lda_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_lda_test, tpr_lda_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b20eaf92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d12e03a2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7638b3d0",
   "metadata": {},
   "source": [
    "### Model tuning by cut-off method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "cbefbf48",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics,model_selection\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "53d67751",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1 \n",
      "\n",
      "Accuracy Score 0.7413\n",
      "Recall 0.9719 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 \n",
      "\n",
      "Accuracy Score 0.804\n",
      "Recall 0.9474 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3 \n",
      "\n",
      "Accuracy Score 0.8295\n",
      "Recall 0.9117 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4 \n",
      "\n",
      "Accuracy Score 0.849\n",
      "Recall 0.8877 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5 \n",
      "\n",
      "Accuracy Score 0.8574\n",
      "Recall 0.8566 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6 \n",
      "\n",
      "Accuracy Score 0.8548\n",
      "Recall 0.8029 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7 \n",
      "\n",
      "Accuracy Score 0.8376\n",
      "Recall 0.7381 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8 \n",
      "\n",
      "Accuracy Score 0.8118\n",
      "Recall 0.6595 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9 \n",
      "\n",
      "Accuracy Score 0.7574\n",
      "Recall 0.5292 \n",
      "\n",
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in np.arange(0.1,1,0.1):\n",
    "    custom_prob_lda = k\n",
    "    custom_cutoff_data_lda=[]\n",
    "    for i in range(0,len(y_train)):\n",
    "        if np.array(probs_lda_train)[i] > custom_prob_lda:\n",
    "            a=1\n",
    "        else:\n",
    "            a=0\n",
    "        custom_cutoff_data_lda.append(a)\n",
    "    print(round(k,3),'\\n')\n",
    "    print('Accuracy Score',round(metrics.accuracy_score(y_train,custom_cutoff_data_lda),4))\n",
    "    print('Recall',round(metrics.recall_score(y_train,custom_cutoff_data_lda),4),'\\n')\n",
    "    plt.figure(figsize=(6,4))\n",
    "    print('Confusion Matrix')\n",
    "    sns.heatmap(metrics.confusion_matrix(y_train,custom_cutoff_data_lda),annot=True,fmt='.4g'),'\\n\\n'\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "521dbbd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45db234b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95b3909e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "da45c1b6",
   "metadata": {},
   "source": [
    "## 5) KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6174d33f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5ca982c",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3db671e",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn.fit(X_train_s, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d05f97e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ec52125e",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b533204b",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_train_score= knn.score(X_train_s, y_train)\n",
    "knn_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "953ef74f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "knn_test_score = knn.score(X_test_s, y_test)\n",
    "knn_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "274a4476",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn_train = knn.predict(X_train_s)\n",
    "y_pred_knn_test = knn.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce208460",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_train, y_pred_knn_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "842187b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_test, y_pred_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87be77f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_knn_train=pd.DataFrame(classification_report(y_train, y_pred_knn_train, output_dict=True)).T\n",
    "df_knn_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1dce307",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_knn_test=pd.DataFrame(classification_report(y_test, y_pred_knn_test, output_dict=True)).T\n",
    "df_knn_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08d3752",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "probs_knn_train = knn.predict_proba(X_train_s)\n",
    "probs_knn_train = probs_knn_train[:, 1]\n",
    "\n",
    "auc_knn_train = roc_auc_score(y_train, probs_knn_train)\n",
    "print('AUC_knn_train: %.3f' % auc_knn_train)\n",
    "\n",
    "fpr_knn_train, tpr_knn_train, thresholds_knn_train = roc_curve(y_train, probs_knn_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_knn_train, tpr_knn_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67bbe8ec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "probs_knn_test = knn.predict_proba(X_test_s)\n",
    "probs_knn_test = probs_knn_test[:, 1]\n",
    "\n",
    "auc_knn_test = roc_auc_score(y_test, probs_knn_test)\n",
    "print('AUC_knn_test: %.3f' % auc_knn_test)\n",
    "\n",
    "fpr_knn_test, tpr_knn_test, thresholds_knn_test = roc_curve(y_test, probs_knn_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_knn_test, tpr_knn_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c872183c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "241b905c",
   "metadata": {},
   "source": [
    "### KNN Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ff48b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid_knn = {\n",
    "    'n_neighbors': [2,3],\n",
    "    'weights': ['uniform', 'distance'],\n",
    "    'algorithm': ['auto', 'ball_tree'],\n",
    "    'leaf_size': [10, 20],\n",
    "    'p': [1, 2], \n",
    "}\n",
    "\n",
    "knng = KNeighborsClassifier()\n",
    "grid_search_knn = GridSearchCV(estimator = knng, param_grid = param_grid_knn, cv = 3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a929a17",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "grid_search_knn.fit(X_train_s, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9203952d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(grid_search_knn.best_params_)\n",
    "best_grid_knn = grid_search_knn.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ecc4de6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "48552465",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b5fad53",
   "metadata": {},
   "outputs": [],
   "source": [
    "knng_train_score = best_grid_knn.score(X_train_s, y_train)\n",
    "knng_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2e97806",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "knng_test_score = best_grid_knn.score(X_test_s, y_test)\n",
    "knng_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f158128",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knng_train = best_grid_knn.predict(X_train_s)\n",
    "y_pred_knng_test = best_grid_knn.predict(X_test_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e54f20cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "confusion_matrix(y_train, y_pred_knng_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af7dfb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_test, y_pred_knng_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eccb8a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_knng_train=pd.DataFrame(classification_report(y_train, y_pred_knng_train, output_dict=True)).T\n",
    "df_knng_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4691bdcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_knng_test=pd.DataFrame(classification_report(y_test, y_pred_knng_test, output_dict=True)).T\n",
    "df_knng_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2790f50",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "probs_knng_train = best_grid_knn.predict_proba(X_train_s)\n",
    "probs_knng_train = probs_knng_train[:, 1]\n",
    "\n",
    "auc_knng_train = roc_auc_score(y_train, probs_knng_train)\n",
    "print('AUC_knng_train: %.3f' % auc_knng_train)\n",
    "\n",
    "fpr_knng_train, tpr_knng_train, thresholds_knng_train = roc_curve(y_train, probs_knng_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_knng_train, tpr_knng_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dc5c37f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "probs_knng_test = best_grid_knn.predict_proba(X_test_s)\n",
    "probs_knng_test = probs_knng_test[:, 1]\n",
    "\n",
    "auc_knng_test = roc_auc_score(y_test, probs_knng_test)\n",
    "print('AUC_knng_test: %.3f' % auc_knng_test)\n",
    "\n",
    "fpr_knng_test, tpr_knng_test, thresholds_knng_test = roc_curve(y_test, probs_knng_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_knng_test, tpr_knng_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbdb5134",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa7766c6",
   "metadata": {},
   "source": [
    "### Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "179ba5d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "index=['Accuracy', 'AUC', 'Precision','Recall','F1 Score']\n",
    "data_models = pd.DataFrame(\n",
    "{'CART_Train':[cart_train_score, auc_cart_train, df_cart_train.iloc[1,0], df_cart_train.iloc[1,1], df_cart_train.iloc[1,2]],\n",
    "'CART_Test':[cart_test_score, auc_cart_test, df_cart_test.iloc[1,0], df_cart_test.iloc[1,1], df_cart_test.iloc[1,2]],\n",
    " 'RF_Train':[rf_train_score, auc_rf_train, df_rf_train.iloc[1,0], df_rf_train.iloc[1,1], df_rf_train.iloc[1,2]],\n",
    "'RF_Test':[rf_test_score, auc_rf_test, df_rf_test.iloc[1,0], df_rf_test.iloc[1,1], df_rf_test.iloc[1,2]],\n",
    " 'LOR_Train':[lor_train_score, auc_lor_train, df_lor_train.iloc[1,0], df_lor_train.iloc[1,1], df_lor_train.iloc[1,2]],\n",
    "'LOR_Test':[lor_test_score, auc_lor_test, df_lor_test.iloc[1,0], df_lor_test.iloc[1,1], df_lor_test.iloc[1,2]],\n",
    " 'LDA_Train':[lda_train_score, auc_lda_train, df_lda_train.iloc[1,0], df_lda_train.iloc[1,1], df_lda_train.iloc[1,2]],\n",
    "'LDA_Test':[lda_test_score, auc_lda_test, df_lda_test.iloc[1,0], df_lda_test.iloc[1,1], df_lda_test.iloc[1,2]],\n",
    "'KNN_Train':[knn_train_score, auc_knn_train, df_knn_train.iloc[1,0], df_knn_train.iloc[1,1], df_knn_train.iloc[1,2]],\n",
    "'KNN_Test':[knn_test_score, auc_knn_test, df_knn_test.iloc[1,0], df_knn_test.iloc[1,1], df_knn_test.iloc[1,2]]\n",
    "},\n",
    "index=index).T\n",
    "round(data_models,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7727aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "index=['Accuracy', 'AUC', 'Precision','Recall','F1 Score']\n",
    "data_models_tuned = pd.DataFrame(\n",
    "{'CARTg_Train':[cartg_train_score, auc_cartg_train, df_cartg_train.iloc[1,0], df_cartg_train.iloc[1,1], df_cartg_train.iloc[1,2]],\n",
    "'CARTg_Test':[cartg_test_score, auc_cartg_test, df_cartg_test.iloc[1,0], df_cartg_test.iloc[1,1], df_cartg_test.iloc[1,2]],\n",
    " 'RFg_Train':[rfg_train_score, auc_rfg_train, df_rfg_train.iloc[1,0], df_rfg_train.iloc[1,1], df_rfg_train.iloc[1,2]],\n",
    "'RFg_Test':[rfg_test_score, auc_rfg_test, df_rfg_test.iloc[1,0], df_rfg_test.iloc[1,1], df_rfg_test.iloc[1,2]],\n",
    " 'KNNg_Train':[knng_train_score, auc_knng_train, df_knng_train.iloc[1,0], df_knng_train.iloc[1,1], df_knng_train.iloc[1,2]],\n",
    "'KNNg_Test':[knng_test_score, auc_knng_test, df_knng_test.iloc[1,0], df_knng_test.iloc[1,1], df_knng_test.iloc[1,2]]\n",
    "},\n",
    "index=index).T\n",
    "round(data_models_tuned,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d743b644",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6c09313b",
   "metadata": {},
   "source": [
    "## Ensemble modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "026c70b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e422f282",
   "metadata": {},
   "source": [
    "## 1) Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85e63b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e04786a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bag = BaggingClassifier(base_estimator = rf, random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5964f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "bag.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fa334cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "da86d403",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f1374ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "bag_train_score = bag.score(X_train, y_train)\n",
    "bag_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4257a3d8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bag_test_score = bag.score(X_test, y_test)\n",
    "bag_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "436616a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_bag_train = bag.predict(X_train)\n",
    "y_pred_bag_test = bag.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c805dcb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdf34945",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_train, y_pred_bag_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f21f6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_test, y_pred_bag_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fae320d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_bag_train=pd.DataFrame(classification_report(y_train, y_pred_bag_train, output_dict=True)).T\n",
    "df_bag_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9361ebd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bag_test=pd.DataFrame(classification_report(y_test, y_pred_bag_test, output_dict=True)).T\n",
    "df_bag_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e037966",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "probs_bag_train = bag.predict_proba(X_train)\n",
    "probs_bag_train = probs_bag_train[:, 1]\n",
    "\n",
    "auc_bag_train = roc_auc_score(y_train, probs_bag_train)\n",
    "print('AUC_bag_train: %.3f' % auc_bag_train)\n",
    "\n",
    "fpr_bag_train, tpr_bag_train, thresholds_bag_train = roc_curve(y_train, probs_bag_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_bag_train, tpr_bag_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ade208d",
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_bag_test = bag.predict_proba(X_test)\n",
    "probs_bag_test = probs_bag_test[:, 1]\n",
    "\n",
    "auc_bag_test = roc_auc_score(y_test, probs_bag_test)\n",
    "print('AUC_bag_test: %.3f' % auc_bag_test)\n",
    "\n",
    "fpr_bag_test, tpr_bag_test, thresholds_bag_test = roc_curve(y_test, probs_bag_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_bag_test, tpr_bag_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbbdeb4b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "47074b64",
   "metadata": {},
   "source": [
    "## 2) Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b12f84d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "850dcea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "aboost = AdaBoostClassifier(base_estimator = rf, random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c43b2480",
   "metadata": {},
   "outputs": [],
   "source": [
    "aboost.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f57aeda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e635e40d",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3297e686",
   "metadata": {},
   "outputs": [],
   "source": [
    "aboost_train_score = aboost.score(X_train, y_train)\n",
    "aboost_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2eec4a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "aboost_test_score = aboost.score(X_test, y_test)\n",
    "aboost_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1eb2a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_aboost_train = aboost.predict(X_train)\n",
    "y_pred_aboost_test = aboost.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f294c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce9de101",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_train, y_pred_aboost_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7614e8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_test, y_pred_aboost_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e997bba3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_aboost_train=pd.DataFrame(classification_report(y_train, y_pred_aboost_train, output_dict=True)).T\n",
    "df_aboost_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa1fa542",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aboost_test=pd.DataFrame(classification_report(y_test, y_pred_aboost_test, output_dict=True)).T\n",
    "df_aboost_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03b1bfb8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "probs_aboost_train = aboost.predict_proba(X_train)\n",
    "probs_aboost_train = probs_aboost_train[:, 1]\n",
    "\n",
    "auc_aboost_train = roc_auc_score(y_train, probs_aboost_train)\n",
    "print('AUC_bag_train: %.3f' % auc_aboost_train)\n",
    "\n",
    "fpr_aboost_train, tpr_aboost_train, thresholds_aboost_train = roc_curve(y_train, probs_aboost_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_aboost_train, tpr_aboost_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e389f053",
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_aboost_test = aboost.predict_proba(X_test)\n",
    "probs_aboost_test = probs_aboost_test[:, 1]\n",
    "\n",
    "auc_aboost_test = roc_auc_score(y_test, probs_aboost_test)\n",
    "print('AUC_bag_test: %.3f' % auc_aboost_test)\n",
    "\n",
    "fpr_aboost_test, tpr_aboost_test, thresholds_aboost_test = roc_curve(y_test, probs_aboost_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_aboost_test, tpr_aboost_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "394111e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "892fd220",
   "metadata": {},
   "source": [
    "## 3) Gradientboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb485ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96ecaf17",
   "metadata": {},
   "outputs": [],
   "source": [
    "gboost = GradientBoostingClassifier(random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6427f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "gboost.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39c8cfd7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "34c797c8",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e0725be",
   "metadata": {},
   "outputs": [],
   "source": [
    "gboost_train_score = gboost.score(X_train, y_train)\n",
    "gboost_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "359b60cd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "gboost_test_score = gboost.score(X_test, y_test)\n",
    "gboost_test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09234096",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "308dab6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_gboost_train = gboost.predict(X_train)\n",
    "y_pred_gboost_test = gboost.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6b896fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3c300ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc0d995f",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_train, y_pred_gboost_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "815fa18a",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_test, y_pred_gboost_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63e86e0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_gboost_train=pd.DataFrame(classification_report(y_train, y_pred_gboost_train, output_dict=True)).T\n",
    "df_gboost_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d774f121",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gboost_test=pd.DataFrame(classification_report(y_test, y_pred_gboost_test, output_dict=True)).T\n",
    "df_gboost_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd735aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "probs_gboost_train = gboost.predict_proba(X_train)\n",
    "probs_gboost_train = probs_gboost_train[:, 1]\n",
    "\n",
    "auc_gboost_train = roc_auc_score(y_train, probs_gboost_train)\n",
    "print('AUC_gboost_train: %.3f' % auc_gboost_train)\n",
    "\n",
    "fpr_gboost_train, tpr_gboost_train, thresholds_gboost_train = roc_curve(y_train, probs_gboost_train)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_gboost_train, tpr_gboost_train, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45ea2b16",
   "metadata": {},
   "outputs": [],
   "source": [
    "probs_gboost_test = gboost.predict_proba(X_test)\n",
    "probs_gboost_test = probs_gboost_test[:, 1]\n",
    "\n",
    "auc_gboost_test = roc_auc_score(y_test, probs_gboost_test)\n",
    "print('AUC_gboost_test: %.3f' % auc_gboost_test)\n",
    "\n",
    "fpr_gboost_test, tpr_gboost_test, thresholds_gboost_test = roc_curve(y_test, probs_gboost_test)\n",
    "plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "plt.plot(fpr_gboost_test, tpr_gboost_test, marker='.')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f62f9828",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd45505",
   "metadata": {},
   "outputs": [],
   "source": [
    "index=['Accuracy', 'AUC', 'Precision','Recall','F1 Score']\n",
    "data_models_tuned = pd.DataFrame(\n",
    "{'Bagging_Train':[bag_train_score, auc_bag_train, df_bag_train.iloc[1,0], df_bag_train.iloc[1,1], df_bag_train.iloc[1,2]],\n",
    "'Bagging_Test':[bag_test_score, auc_bag_test, df_bag_test.iloc[1,0], df_bag_test.iloc[1,1], df_bag_test.iloc[1,2]],\n",
    " 'Adaboost_Train':[aboost_train_score, auc_aboost_train, df_aboost_train.iloc[1,0], df_aboost_train.iloc[1,1], df_aboost_train.iloc[1,2]],\n",
    "'Adaboost_Test':[aboost_test_score, auc_aboost_test, df_aboost_test.iloc[1,0], df_aboost_test.iloc[1,1], df_aboost_test.iloc[1,2]],\n",
    " 'Gboost_Train':[gboost_train_score, auc_gboost_train, df_gboost_train.iloc[1,0], df_gboost_train.iloc[1,1], df_gboost_train.iloc[1,2]],\n",
    "'Gboost_Test':[gboost_test_score, auc_gboost_test, df_gboost_test.iloc[1,0], df_gboost_test.iloc[1,1], df_gboost_test.iloc[1,2]]\n",
    "},\n",
    "index=index).T\n",
    "round(data_models_tuned,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92a5fcb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96ef8a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "748ca5d9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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