Churn_Model_building.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc2453dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c8cb06a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Churn</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>City_Tier</th>\n",
       "      <th>CC_Contacted_LY</th>\n",
       "      <th>Payment</th>\n",
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       "      <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",
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       "      <td>Regular +</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
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       "      <td>15.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Regular +</td>\n",
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       "      <td>Single</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>129.600000</td>\n",
       "      <td>Mobile</td>\n",
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       "  </tbody>\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  159.930000       Mobile  \n",
       "1                      0.0                   0.0  120.900000       Mobile  \n",
       "2                      0.0                   3.0  196.227799       Mobile  \n",
       "3                      0.0                   3.0  134.070000       Mobile  \n",
       "4                      1.0                   3.0  129.600000       Mobile  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn = pd.read_csv('churn2_for tableau.csv')\n",
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2accaed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "churn.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f56f7afd",
   "metadata": {},
   "outputs": [
    {
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       "      <td>8.0</td>\n",
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       "      <td>23.0</td>\n",
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       "      <td>Male</td>\n",
       "      <td>2.0</td>\n",
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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  159.930000       Mobile  \n",
       "1                      0.0                   0.0  120.900000       Mobile  \n",
       "2                      0.0                   3.0  196.227799       Mobile  \n",
       "3                      0.0                   3.0  134.070000       Mobile  \n",
       "4                      1.0                   3.0  129.600000       Mobile  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f57fbdf3",
   "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": 6,
   "id": "a78ccd3f",
   "metadata": {
    "scrolled": false
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   "outputs": [
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       "      <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>120.900000</td>\n",
       "      <td>Mobile</td>\n",
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       "    <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>196.227799</td>\n",
       "      <td>Mobile</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>134.070000</td>\n",
       "      <td>Mobile</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>129.600000</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",
       "      <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>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.0</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>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>153.710000</td>\n",
       "      <td>Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11256</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</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>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>226.910000</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11257</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</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>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>191.420000</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11258</th>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</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>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>179.900000</td>\n",
       "      <td>Computer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11259</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</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>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>175.040000</td>\n",
       "      <td>Mobile</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11260 rows × 18 columns</p>\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",
       "11255      0    10.0        1.0             34.0  Credit Card    Male   \n",
       "11256      0    13.0        1.0             19.0  Credit Card    Male   \n",
       "11257      0     1.0        1.0             14.0   Debit Card    Male   \n",
       "11258      0    23.0        3.0             11.0  Credit Card    Male   \n",
       "11259      0     8.0        1.0             22.0  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",
       "11255            3.0                   2           Super             1.0   \n",
       "11256            3.0                   5             HNI             5.0   \n",
       "11257            3.0                   2           Super             4.0   \n",
       "11258            4.0                   5           Super             4.0   \n",
       "11259            3.0                   2           Super             3.0   \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",
       "11255        Married            9.0          0.0            19.0   \n",
       "11256        Married            7.0          0.0            16.0   \n",
       "11257        Married            7.0          1.0            22.0   \n",
       "11258        Married            7.0          0.0            16.0   \n",
       "11259        Married            5.0          0.0            13.0   \n",
       "\n",
       "       coupon_used_for_payment  Day_Since_CC_connect    cashback Login_device  \n",
       "0                          1.0                   5.0  159.930000       Mobile  \n",
       "1                          0.0                   0.0  120.900000       Mobile  \n",
       "2                          0.0                   3.0  196.227799       Mobile  \n",
       "3                          0.0                   3.0  134.070000       Mobile  \n",
       "4                          1.0                   3.0  129.600000       Mobile  \n",
       "...                        ...                   ...         ...          ...  \n",
       "11255                      1.0                   4.0  153.710000     Computer  \n",
       "11256                      1.0                   8.0  226.910000       Mobile  \n",
       "11257                      1.0                   4.0  191.420000       Mobile  \n",
       "11258                      2.0                   9.0  179.900000     Computer  \n",
       "11259                      2.0                   3.0  175.040000       Mobile  \n",
       "\n",
       "[11260 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = churn.copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c6194e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4203d7fc",
   "metadata": {},
   "source": [
    "### Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d109e9d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d6176eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15f6c02e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04309b29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbd53e41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd497dab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e23c8dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "59e3e20e",
   "metadata": {},
   "source": [
    "#### Data encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "831ebca5",
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfe = pd.get_dummies(df, drop_first =True)\n",
    "dfe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a7fd694",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6822bef4",
   "metadata": {},
   "source": [
    "#### Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b8c8b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a3576f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfe.drop('Churn', axis=1)\n",
    "y = dfe[['Churn']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d66d8147",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.30, random_state=1, stratify=df['Churn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7f1df73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7882, 24)\n",
      "(3378, 24)\n",
      "(7882, 1)\n",
      "(3378, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "59502e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.0\n",
      "30.0\n",
      "70.0\n",
      "30.0\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape[0]/11260*100)\n",
    "print(X_test.shape[0]/11260*100)\n",
    "print(y_train.shape[0]/11260*100)\n",
    "print(y_test.shape[0]/11260*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "700913da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3634a396",
   "metadata": {},
   "source": [
    "## 1) CART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "681dabe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4791a561",
   "metadata": {},
   "outputs": [],
   "source": [
    "cart = DecisionTreeClassifier(criterion = 'gini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76bf3ef4",
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64ade67",
   "metadata": {},
   "source": [
    "##### Tree visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f9415b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0b5c6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_char_label = ['0', '1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "300caaa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_tree_file = open('d:\\churn_tree.dot', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97712eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dot_data = tree.export_graphviz(cart, out_file=churn_tree_file, feature_names=list(X_train), class_names=list(train_char_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "174bca67",
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_tree_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eff71fde",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "434ed931",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "75ea9978",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6f70ca23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d7c2dbe3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9476021314387212"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cart.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67ff6175",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "11045444",
   "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": null,
   "id": "1ebb9856",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5975f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b9b02f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [   0, 1327]], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_cart_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3015edb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2721,   88],\n",
       "       [  89,  480]], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_cart_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "520ac523",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a368943",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      6555\n",
      "           1       1.00      1.00      1.00      1327\n",
      "\n",
      "    accuracy                           1.00      7882\n",
      "   macro avg       1.00      1.00      1.00      7882\n",
      "weighted avg       1.00      1.00      1.00      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_cart_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba980ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.97      0.97      2809\n",
      "           1       0.85      0.84      0.84       569\n",
      "\n",
      "    accuracy                           0.95      3378\n",
      "   macro avg       0.91      0.91      0.91      3378\n",
      "weighted avg       0.95      0.95      0.95      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_cart_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ce9ba3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26260044",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2fbb3e1a",
   "metadata": {},
   "source": [
    "## 2) RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "107fbad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b04ee58b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "82890a43",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\AppData\\Local\\Temp\\ipykernel_7124\\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-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>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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "378ede91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.oob_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7148aea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd4e8af1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "90d5b3f4",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e945bebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6c3656b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9692125518058022"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "310392e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "23e1a8aa",
   "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": null,
   "id": "3a249f28",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e9aba878",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7748a474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [   0, 1327]], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_rf_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c09028bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2799,   10],\n",
       "       [  94,  475]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_rf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aec1559",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9cbd1024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      6555\n",
      "           1       1.00      1.00      1.00      1327\n",
      "\n",
      "    accuracy                           1.00      7882\n",
      "   macro avg       1.00      1.00      1.00      7882\n",
      "weighted avg       1.00      1.00      1.00      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_rf_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "915eeba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98      2809\n",
      "           1       0.98      0.83      0.90       569\n",
      "\n",
      "    accuracy                           0.97      3378\n",
      "   macro avg       0.97      0.92      0.94      3378\n",
      "weighted avg       0.97      0.97      0.97      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_rf_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c7aa78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dfa875f9",
   "metadata": {},
   "source": [
    "## 3) Logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2c32cb79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a84eb1d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lor = LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a62a2ad3",
   "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-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>LogisticRegression(max_iter=1000)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a93ba680",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "544a8c7f",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "22c7ba98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8908906368941893"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lor.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "92da45c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8801065719360568"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lor.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b909fe84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b8bfca1f",
   "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": null,
   "id": "0d1d9fe4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a3e9389c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d48d2872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6359,  196],\n",
       "       [ 664,  663]], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_lor_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3dffa5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2731,   78],\n",
       "       [ 297,  272]], dtype=int64)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_lor_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a5c99d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cfbf590e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.97      0.94      6555\n",
      "           1       0.77      0.50      0.61      1327\n",
      "\n",
      "    accuracy                           0.89      7882\n",
      "   macro avg       0.84      0.73      0.77      7882\n",
      "weighted avg       0.88      0.89      0.88      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_lor_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a2ebcd2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.97      0.94      2809\n",
      "           1       0.78      0.48      0.59       569\n",
      "\n",
      "    accuracy                           0.89      3378\n",
      "   macro avg       0.84      0.73      0.76      3378\n",
      "weighted avg       0.88      0.89      0.88      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lor_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3083c280",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "75c9c00b",
   "metadata": {},
   "source": [
    "## 4) LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d2273840",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "051e85eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "lda = LinearDiscriminantAnalysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "48d9ed82",
   "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": [
       "<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>LinearDiscriminantAnalysis()</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\">LinearDiscriminantAnalysis</label><div class=\"sk-toggleable__content\"><pre>LinearDiscriminantAnalysis()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearDiscriminantAnalysis()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7ccf381",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c565d7f6",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7d865faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8845470692717584"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "88e46a66",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8854351687388987"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dc9a8b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cd47232a",
   "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": null,
   "id": "0c62452e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4590693e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9a4d0110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6357,  198],\n",
       "       [ 712,  615]], dtype=int64)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_lda_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4731a880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2735,   74],\n",
       "       [ 313,  256]], dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_lda_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "701597c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "799111bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.97      0.93      6555\n",
      "           1       0.76      0.46      0.57      1327\n",
      "\n",
      "    accuracy                           0.88      7882\n",
      "   macro avg       0.83      0.72      0.75      7882\n",
      "weighted avg       0.88      0.88      0.87      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_lda_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4d9549ca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.97      0.93      2809\n",
      "           1       0.78      0.45      0.57       569\n",
      "\n",
      "    accuracy                           0.89      3378\n",
      "   macro avg       0.84      0.71      0.75      3378\n",
      "weighted avg       0.88      0.89      0.87      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lda_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc1aa81c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ef39617d",
   "metadata": {},
   "source": [
    "## 5) KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "da0103a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "039be31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a6e910fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:207: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b3a2d57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "79229461",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b6d0bbc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9199441766049226"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ae83e40b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8715216104203671"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6268b63c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f268744a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_knn_train = knn.predict(X_train)\n",
    "y_pred_knn_test = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a23eb39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1951f273",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7c9d02e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6381,  174],\n",
       "       [ 457,  870]], dtype=int64)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_knn_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f45787bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2676,  133],\n",
       "       [ 301,  268]], dtype=int64)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b6922f9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "598fa3a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.97      0.95      6555\n",
      "           1       0.83      0.66      0.73      1327\n",
      "\n",
      "    accuracy                           0.92      7882\n",
      "   macro avg       0.88      0.81      0.84      7882\n",
      "weighted avg       0.92      0.92      0.92      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_knn_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c30736ed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.95      0.92      2809\n",
      "           1       0.67      0.47      0.55       569\n",
      "\n",
      "    accuracy                           0.87      3378\n",
      "   macro avg       0.78      0.71      0.74      3378\n",
      "weighted avg       0.86      0.87      0.86      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_knn_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b824616b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "78334834",
   "metadata": {},
   "source": [
    "## 6) ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fece027c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ce1fe986",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "473d4b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "91815d5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X1_train = sc.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4d85a244",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "X1_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "195ca5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann = MLPClassifier(hidden_layer_sizes=100,\n",
    "                    max_iter=5000,\n",
    "                    solver='sgd', \n",
    "                    verbose=True,  \n",
    "                    random_state=21,\n",
    "                    tol=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "484b1fd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1118: DataConversionWarning: A column-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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.70513628\n",
      "Iteration 2, loss = 0.50796925\n",
      "Iteration 3, loss = 0.45071022\n",
      "Iteration 4, loss = 0.42575216\n",
      "Iteration 5, loss = 0.40989829\n",
      "Iteration 6, loss = 0.39779315\n",
      "Iteration 7, loss = 0.38777547\n",
      "Iteration 8, loss = 0.37924123\n",
      "Iteration 9, loss = 0.37188797\n",
      "Iteration 10, loss = 0.36543868\n",
      "Iteration 11, loss = 0.35968923\n",
      "Iteration 12, loss = 0.35461801\n",
      "Iteration 13, loss = 0.35003536\n",
      "Iteration 14, loss = 0.34590404\n",
      "Iteration 15, loss = 0.34215816\n",
      "Iteration 16, loss = 0.33873000\n",
      "Iteration 17, loss = 0.33558041\n",
      "Iteration 18, loss = 0.33272582\n",
      "Training loss did not improve more than tol=0.010000 for 10 consecutive epochs. Stopping.\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>MLPClassifier(hidden_layer_sizes=100, max_iter=5000, random_state=21,\n",
       "              solver=&#x27;sgd&#x27;, tol=0.01, verbose=True)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=100, max_iter=5000, random_state=21,\n",
       "              solver=&#x27;sgd&#x27;, tol=0.01, verbose=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=100, max_iter=5000, random_state=21,\n",
       "              solver='sgd', tol=0.01, verbose=True)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.fit(X1_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d07fe2ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a9bbfd4e",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "455fe21b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86513575234712"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.score(X1_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d0da6df2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8315571343990527"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.score(X1_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c38daf8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "230f82f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_ann_train = ann.predict(X1_train)\n",
    "y_pred_ann_test = ann.predict(X1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee81580b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "177a3642",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "99f479fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6473,   82],\n",
       "       [ 981,  346]], dtype=int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_ann_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "033ae9ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2809,    0],\n",
       "       [ 569,    0]], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_ann_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b632d29",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "438c0a25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.99      0.92      6555\n",
      "           1       0.81      0.26      0.39      1327\n",
      "\n",
      "    accuracy                           0.87      7882\n",
      "   macro avg       0.84      0.62      0.66      7882\n",
      "weighted avg       0.86      0.87      0.83      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_ann_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "40959cb1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      1.00      0.91      2809\n",
      "           1       0.00      0.00      0.00       569\n",
      "\n",
      "    accuracy                           0.83      3378\n",
      "   macro avg       0.42      0.50      0.45      3378\n",
      "weighted avg       0.69      0.83      0.76      3378\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1334: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_ann_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c1361e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3d383029",
   "metadata": {},
   "source": [
    "## Ensemble modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a933ed9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c676f0df",
   "metadata": {},
   "source": [
    "## 1) Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f54842ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "85ab5277",
   "metadata": {},
   "outputs": [],
   "source": [
    "bag = BaggingClassifier(base_estimator = rf, random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3ecbe52b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_bagging.py:760: DataConversionWarning: A column-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": [
       "<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>BaggingClassifier(base_estimator=RandomForestClassifier(), random_state=1)</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(base_estimator=RandomForestClassifier(), random_state=1)</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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_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-5\" type=\"checkbox\" ><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></div></div></div></div></div>"
      ],
      "text/plain": [
       "BaggingClassifier(base_estimator=RandomForestClassifier(), random_state=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0331006",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e1f7e22e",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7800b54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9954326313118498"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5a8376ff",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9603315571343991"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d268093",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c762553",
   "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": "c5cfbe2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dbeb9998",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d459a13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [  36, 1291]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_bag_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e7542e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2803,    6],\n",
       "       [ 128,  441]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_bag_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccc71ce3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8714e971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      1.00      6555\n",
      "           1       1.00      0.97      0.99      1327\n",
      "\n",
      "    accuracy                           1.00      7882\n",
      "   macro avg       1.00      0.99      0.99      7882\n",
      "weighted avg       1.00      1.00      1.00      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_bag_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75819763",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98      2809\n",
      "           1       0.99      0.78      0.87       569\n",
      "\n",
      "    accuracy                           0.96      3378\n",
      "   macro avg       0.97      0.89      0.92      3378\n",
      "weighted avg       0.96      0.96      0.96      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_bag_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89c1d5aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a9f20ce5",
   "metadata": {},
   "source": [
    "## 2) Adaboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f444eafc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "84b7b068",
   "metadata": {},
   "outputs": [],
   "source": [
    "aboost = AdaBoostClassifier(base_estimator = rf, random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ba7d9be",
   "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": [
       "<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>AdaBoostClassifier(base_estimator=RandomForestClassifier(), random_state=1)</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\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(base_estimator=RandomForestClassifier(), random_state=1)</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\">base_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": [
       "AdaBoostClassifier(base_estimator=RandomForestClassifier(), random_state=1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aboost.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49f8bf6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "73309b75",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cc378887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aboost.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "84b271e3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9706927175843695"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aboost.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c017885",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "42fbae1e",
   "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": "f7b458be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4e63c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "38d5f435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6555,    0],\n",
       "       [   0, 1327]], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_aboost_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1631b500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2801,    8],\n",
       "       [  91,  478]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_aboost_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7748f25d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5996fdc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      6555\n",
      "           1       1.00      1.00      1.00      1327\n",
      "\n",
      "    accuracy                           1.00      7882\n",
      "   macro avg       1.00      1.00      1.00      7882\n",
      "weighted avg       1.00      1.00      1.00      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_aboost_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d79ea79f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.98      2809\n",
      "           1       0.98      0.84      0.91       569\n",
      "\n",
      "    accuracy                           0.97      3378\n",
      "   macro avg       0.98      0.92      0.94      3378\n",
      "weighted avg       0.97      0.97      0.97      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_aboost_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "279c6bf2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "93c8ce1b",
   "metadata": {},
   "source": [
    "## 3) Gradientboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a5ba8452",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b9f30075",
   "metadata": {},
   "outputs": [],
   "source": [
    "gboost = GradientBoostingClassifier(random_state= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "de438f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nandha Keshore\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_gb.py:570: DataConversionWarning: A column-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": [
       "<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>GradientBoostingClassifier(random_state=1)</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\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(random_state=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(random_state=1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gboost.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d908811",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0251e2d6",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8b29fd3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.922481603653895"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gboost.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "977a34b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9103019538188277"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gboost.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f590a6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eeb9dadd",
   "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": "e06bf31d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "45d62b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "871e4153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6406,  149],\n",
       "       [ 462,  865]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_gboost_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a7692f5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2738,   71],\n",
       "       [ 232,  337]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_gboost_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7399edd0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6df99af1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.98      0.95      6555\n",
      "           1       0.85      0.65      0.74      1327\n",
      "\n",
      "    accuracy                           0.92      7882\n",
      "   macro avg       0.89      0.81      0.85      7882\n",
      "weighted avg       0.92      0.92      0.92      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_gboost_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cbdbd658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.95      2809\n",
      "           1       0.83      0.59      0.69       569\n",
      "\n",
      "    accuracy                           0.91      3378\n",
      "   macro avg       0.87      0.78      0.82      3378\n",
      "weighted avg       0.91      0.91      0.90      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_gboost_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8bbfc48",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ba4707d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "10758445",
   "metadata": {},
   "source": [
    "## 4) Xboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7877857e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboostNote: you may need to restart the kernel to use updated packages.\n",
      "  Downloading xgboost-1.7.4-py3-none-win_amd64.whl (89.1 MB)\n",
      "Requirement already satisfied: scipy in c:\\users\\nandha keshore\\anaconda3\\lib\\site-packages (from xgboost) (1.7.3)\n",
      "Requirement already satisfied: numpy in c:\\users\\nandha keshore\\anaconda3\\lib\\site-packages (from xgboost) (1.21.5)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.7.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6a517707",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f424e6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "xboost = xgb.XGBClassifier(random_state=1,learning_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "17d41ab3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=1, ...)</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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=1, ...)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xboost.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c48b2d3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f2377e7a",
   "metadata": {},
   "source": [
    "##### Model_evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "870e2de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9218472468916519"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xboost.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "2253f30b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9088217880402605"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xboost.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eeed62b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a2b693e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_xboost_train = xboost.predict(X_train)\n",
    "y_pred_xboost_test = xboost.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd1da7e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dad5e2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2c0a41e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6366,  189],\n",
       "       [ 427,  900]], dtype=int64)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train, y_pred_xboost_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "833d0de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2722,   87],\n",
       "       [ 221,  348]], dtype=int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_xboost_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c91ac0ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "11e1fb35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.97      0.95      6555\n",
      "           1       0.83      0.68      0.75      1327\n",
      "\n",
      "    accuracy                           0.92      7882\n",
      "   macro avg       0.88      0.82      0.85      7882\n",
      "weighted avg       0.92      0.92      0.92      7882\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_xboost_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1408432d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.97      0.95      2809\n",
      "           1       0.80      0.61      0.69       569\n",
      "\n",
      "    accuracy                           0.91      3378\n",
      "   macro avg       0.86      0.79      0.82      3378\n",
      "weighted avg       0.90      0.91      0.90      3378\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_xboost_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33852a30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4246a67c",
   "metadata": {},
   "source": [
    "## Model tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "426bb0e8",
   "metadata": {},
   "source": [
    "### 1) CART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "740fa279",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DecisionTreeClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m cart_reg \u001b[38;5;241m=\u001b[39m \u001b[43mDecisionTreeClassifier\u001b[49m(criterion \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgini\u001b[39m\u001b[38;5;124m'\u001b[39m, max_depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, min_samples_leaf \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20\u001b[39m, min_samples_split\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m50\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'DecisionTreeClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "cart_reg = DecisionTreeClassifier(criterion = 'gini', max_depth=10, min_samples_leaf = 20, min_samples_split= 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "86b8f4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "##### Regularised Model evaluation"
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