Random Forest Classification Implementation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Importing important libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>ProdTaken</th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeofContact</th>\n",
       "      <th>CityTier</th>\n",
       "      <th>DurationOfPitch</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Gender</th>\n",
       "      <th>NumberOfPersonVisiting</th>\n",
       "      <th>NumberOfFollowups</th>\n",
       "      <th>ProductPitched</th>\n",
       "      <th>PreferredPropertyStar</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>NumberOfTrips</th>\n",
       "      <th>Passport</th>\n",
       "      <th>PitchSatisfactionScore</th>\n",
       "      <th>OwnCar</th>\n",
       "      <th>NumberOfChildrenVisiting</th>\n",
       "      <th>Designation</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200002</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Free Lancer</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Single</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200003</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200004</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Small Business</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>18468.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  ProdTaken   Age    TypeofContact  CityTier  DurationOfPitch  \\\n",
       "0      200000          1  41.0     Self Enquiry         3              6.0   \n",
       "1      200001          0  49.0  Company Invited         1             14.0   \n",
       "2      200002          1  37.0     Self Enquiry         1              8.0   \n",
       "3      200003          0  33.0  Company Invited         1              9.0   \n",
       "4      200004          0   NaN     Self Enquiry         1              8.0   \n",
       "\n",
       "       Occupation  Gender  NumberOfPersonVisiting  NumberOfFollowups  \\\n",
       "0        Salaried  Female                       3                3.0   \n",
       "1        Salaried    Male                       3                4.0   \n",
       "2     Free Lancer    Male                       3                4.0   \n",
       "3        Salaried  Female                       2                3.0   \n",
       "4  Small Business    Male                       2                3.0   \n",
       "\n",
       "  ProductPitched  PreferredPropertyStar MaritalStatus  NumberOfTrips  \\\n",
       "0         Deluxe                    3.0        Single            1.0   \n",
       "1         Deluxe                    4.0      Divorced            2.0   \n",
       "2          Basic                    3.0        Single            7.0   \n",
       "3          Basic                    3.0      Divorced            2.0   \n",
       "4          Basic                    4.0      Divorced            1.0   \n",
       "\n",
       "   Passport  PitchSatisfactionScore  OwnCar  NumberOfChildrenVisiting  \\\n",
       "0         1                       2       1                       0.0   \n",
       "1         0                       3       1                       2.0   \n",
       "2         1                       3       0                       0.0   \n",
       "3         1                       5       1                       1.0   \n",
       "4         0                       5       1                       0.0   \n",
       "\n",
       "  Designation  MonthlyIncome  \n",
       "0     Manager        20993.0  \n",
       "1     Manager        20130.0  \n",
       "2   Executive        17090.0  \n",
       "3   Executive        17909.0  \n",
       "4   Executive        18468.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Travel.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "## Handling Misssing Values \n",
    "1. Handling Missing Values \n",
    "2. Handling Duplicates\n",
    "3. Check Data Type\n",
    "4. Understand the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                    0\n",
       "ProdTaken                     0\n",
       "Age                         226\n",
       "TypeofContact                25\n",
       "CityTier                      0\n",
       "DurationOfPitch             251\n",
       "Occupation                    0\n",
       "Gender                        0\n",
       "NumberOfPersonVisiting        0\n",
       "NumberOfFollowups            45\n",
       "ProductPitched                0\n",
       "PreferredPropertyStar        26\n",
       "MaritalStatus                 0\n",
       "NumberOfTrips               140\n",
       "Passport                      0\n",
       "PitchSatisfactionScore        0\n",
       "OwnCar                        0\n",
       "NumberOfChildrenVisiting     66\n",
       "Designation                   0\n",
       "MonthlyIncome               233\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Male       2916\n",
       "Female     1817\n",
       "Fe Male     155\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MaritalStatus\n",
       "Married      2340\n",
       "Divorced      950\n",
       "Single        916\n",
       "Unmarried     682\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MaritalStatus'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TypeofContact\n",
       "Self Enquiry       3444\n",
       "Company Invited    1419\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TypeofContact'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = df['Gender'].replace('Fe Male', 'Female')\n",
    "df['MaritalStatus'] = df['MaritalStatus'].replace('Single', 'Unmarried')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CustomerID</th>\n",
       "      <th>ProdTaken</th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeofContact</th>\n",
       "      <th>CityTier</th>\n",
       "      <th>DurationOfPitch</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Gender</th>\n",
       "      <th>NumberOfPersonVisiting</th>\n",
       "      <th>NumberOfFollowups</th>\n",
       "      <th>ProductPitched</th>\n",
       "      <th>PreferredPropertyStar</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>NumberOfTrips</th>\n",
       "      <th>Passport</th>\n",
       "      <th>PitchSatisfactionScore</th>\n",
       "      <th>OwnCar</th>\n",
       "      <th>NumberOfChildrenVisiting</th>\n",
       "      <th>Designation</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200002</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Free Lancer</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200003</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200004</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Small Business</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>18468.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  ProdTaken   Age    TypeofContact  CityTier  DurationOfPitch  \\\n",
       "0      200000          1  41.0     Self Enquiry         3              6.0   \n",
       "1      200001          0  49.0  Company Invited         1             14.0   \n",
       "2      200002          1  37.0     Self Enquiry         1              8.0   \n",
       "3      200003          0  33.0  Company Invited         1              9.0   \n",
       "4      200004          0   NaN     Self Enquiry         1              8.0   \n",
       "\n",
       "       Occupation  Gender  NumberOfPersonVisiting  NumberOfFollowups  \\\n",
       "0        Salaried  Female                       3                3.0   \n",
       "1        Salaried    Male                       3                4.0   \n",
       "2     Free Lancer    Male                       3                4.0   \n",
       "3        Salaried  Female                       2                3.0   \n",
       "4  Small Business    Male                       2                3.0   \n",
       "\n",
       "  ProductPitched  PreferredPropertyStar MaritalStatus  NumberOfTrips  \\\n",
       "0         Deluxe                    3.0     Unmarried            1.0   \n",
       "1         Deluxe                    4.0      Divorced            2.0   \n",
       "2          Basic                    3.0     Unmarried            7.0   \n",
       "3          Basic                    3.0      Divorced            2.0   \n",
       "4          Basic                    4.0      Divorced            1.0   \n",
       "\n",
       "   Passport  PitchSatisfactionScore  OwnCar  NumberOfChildrenVisiting  \\\n",
       "0         1                       2       1                       0.0   \n",
       "1         0                       3       1                       2.0   \n",
       "2         1                       3       0                       0.0   \n",
       "3         1                       5       1                       1.0   \n",
       "4         0                       5       1                       0.0   \n",
       "\n",
       "  Designation  MonthlyIncome  \n",
       "0     Manager        20993.0  \n",
       "1     Manager        20130.0  \n",
       "2   Executive        17090.0  \n",
       "3   Executive        17909.0  \n",
       "4   Executive        18468.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age 4.62357 % missing values\n",
      "TypeofContact 0.51146 % missing values\n",
      "DurationOfPitch 5.13502 % missing values\n",
      "NumberOfFollowups 0.92062 % missing values\n",
      "PreferredPropertyStar 0.53191 % missing values\n",
      "NumberOfTrips 2.86416 % missing values\n",
      "NumberOfChildrenVisiting 1.35025 % missing values\n",
      "MonthlyIncome 4.76678 % missing values\n"
     ]
    }
   ],
   "source": [
    "## Check Missing Values\n",
    "## these are the features with nan values\n",
    "\n",
    "features_with_na = [features for features in df.columns if df[features].isnull().sum()>=1]\n",
    "for features in features_with_na:\n",
    "    print(features,np.round(df[features].isnull().mean()*100,5), '% missing values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DurationOfPitch</th>\n",
       "      <th>NumberOfFollowups</th>\n",
       "      <th>PreferredPropertyStar</th>\n",
       "      <th>NumberOfTrips</th>\n",
       "      <th>NumberOfChildrenVisiting</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4662.000000</td>\n",
       "      <td>4637.000000</td>\n",
       "      <td>4843.000000</td>\n",
       "      <td>4862.000000</td>\n",
       "      <td>4748.000000</td>\n",
       "      <td>4822.000000</td>\n",
       "      <td>4655.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.622265</td>\n",
       "      <td>15.490835</td>\n",
       "      <td>3.708445</td>\n",
       "      <td>3.581037</td>\n",
       "      <td>3.236521</td>\n",
       "      <td>1.187267</td>\n",
       "      <td>23619.853491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.316387</td>\n",
       "      <td>8.519643</td>\n",
       "      <td>1.002509</td>\n",
       "      <td>0.798009</td>\n",
       "      <td>1.849019</td>\n",
       "      <td>0.857861</td>\n",
       "      <td>5380.698361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22347.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25571.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>98678.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  DurationOfPitch  NumberOfFollowups  PreferredPropertyStar  \\\n",
       "count  4662.000000      4637.000000        4843.000000            4862.000000   \n",
       "mean     37.622265        15.490835           3.708445               3.581037   \n",
       "std       9.316387         8.519643           1.002509               0.798009   \n",
       "min      18.000000         5.000000           1.000000               3.000000   \n",
       "25%      31.000000         9.000000           3.000000               3.000000   \n",
       "50%      36.000000        13.000000           4.000000               3.000000   \n",
       "75%      44.000000        20.000000           4.000000               4.000000   \n",
       "max      61.000000       127.000000           6.000000               5.000000   \n",
       "\n",
       "       NumberOfTrips  NumberOfChildrenVisiting  MonthlyIncome  \n",
       "count    4748.000000               4822.000000    4655.000000  \n",
       "mean        3.236521                  1.187267   23619.853491  \n",
       "std         1.849019                  0.857861    5380.698361  \n",
       "min         1.000000                  0.000000    1000.000000  \n",
       "25%         2.000000                  1.000000   20346.000000  \n",
       "50%         3.000000                  1.000000   22347.000000  \n",
       "75%         4.000000                  2.000000   25571.000000  \n",
       "max        22.000000                  3.000000   98678.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[features_with_na].select_dtypes(exclude='object').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Null Values\n",
    "\n",
    "1. Impute Median values for Age column \n",
    "2. Impute Mode for Type of Contract\n",
    "3. Impute Median for Duration of Pitch\n",
    "4. Impute Mode for NumberofFollowup as it is Discrete feature\n",
    "5. Impute Mode for PreferredPropertyStar\n",
    "6. Impute Median for NumberofTrips\n",
    "7. Impute Mode for NumberOfChildrenVisiting \n",
    "8. Impute Median for MonthlyIncome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age\n",
    "df.Age.fillna(df.Age.median(), inplace=True)\n",
    "\n",
    "#TypeofContract\n",
    "df.TypeofContact.fillna(df.TypeofContact.mode()[0], inplace=True)\n",
    "\n",
    "#DurationOfPitch\n",
    "df.DurationOfPitch.fillna(df.DurationOfPitch.median(), inplace=True)\n",
    "\n",
    "#NumberofFollowups\n",
    "df.NumberOfFollowups.fillna(df.NumberOfFollowups.mode()[0], inplace=True)\n",
    "\n",
    "#PreferredPropertyStar\n",
    "df.PreferredPropertyStar.fillna(df.PreferredPropertyStar.mode()[0], inplace=True)\n",
    "\n",
    "#NumberOfTrips\n",
    "df.NumberOfTrips.fillna(df.NumberOfTrips.median(), inplace=True)\n",
    "\n",
    "#NumberOfChildrenVisiting\n",
    "df.NumberOfChildrenVisiting.fillna(df.NumberOfChildrenVisiting.mode()[0], inplace=True)\n",
    "\n",
    "#MonthlyIncome\n",
    "df.MonthlyIncome.fillna(df.MonthlyIncome.median(),inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerID                  0\n",
       "ProdTaken                   0\n",
       "Age                         0\n",
       "TypeofContact               0\n",
       "CityTier                    0\n",
       "DurationOfPitch             0\n",
       "Occupation                  0\n",
       "Gender                      0\n",
       "NumberOfPersonVisiting      0\n",
       "NumberOfFollowups           0\n",
       "ProductPitched              0\n",
       "PreferredPropertyStar       0\n",
       "MaritalStatus               0\n",
       "NumberOfTrips               0\n",
       "Passport                    0\n",
       "PitchSatisfactionScore      0\n",
       "OwnCar                      0\n",
       "NumberOfChildrenVisiting    0\n",
       "Designation                 0\n",
       "MonthlyIncome               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>CustomerID</th>\n",
       "      <th>ProdTaken</th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeofContact</th>\n",
       "      <th>CityTier</th>\n",
       "      <th>DurationOfPitch</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Gender</th>\n",
       "      <th>NumberOfPersonVisiting</th>\n",
       "      <th>NumberOfFollowups</th>\n",
       "      <th>ProductPitched</th>\n",
       "      <th>PreferredPropertyStar</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>NumberOfTrips</th>\n",
       "      <th>Passport</th>\n",
       "      <th>PitchSatisfactionScore</th>\n",
       "      <th>OwnCar</th>\n",
       "      <th>NumberOfChildrenVisiting</th>\n",
       "      <th>Designation</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200000</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200001</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200002</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Free Lancer</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200003</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200004</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Small Business</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>18468.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  ProdTaken   Age    TypeofContact  CityTier  DurationOfPitch  \\\n",
       "0      200000          1  41.0     Self Enquiry         3              6.0   \n",
       "1      200001          0  49.0  Company Invited         1             14.0   \n",
       "2      200002          1  37.0     Self Enquiry         1              8.0   \n",
       "3      200003          0  33.0  Company Invited         1              9.0   \n",
       "4      200004          0  36.0     Self Enquiry         1              8.0   \n",
       "\n",
       "       Occupation  Gender  NumberOfPersonVisiting  NumberOfFollowups  \\\n",
       "0        Salaried  Female                       3                3.0   \n",
       "1        Salaried    Male                       3                4.0   \n",
       "2     Free Lancer    Male                       3                4.0   \n",
       "3        Salaried  Female                       2                3.0   \n",
       "4  Small Business    Male                       2                3.0   \n",
       "\n",
       "  ProductPitched  PreferredPropertyStar MaritalStatus  NumberOfTrips  \\\n",
       "0         Deluxe                    3.0     Unmarried            1.0   \n",
       "1         Deluxe                    4.0      Divorced            2.0   \n",
       "2          Basic                    3.0     Unmarried            7.0   \n",
       "3          Basic                    3.0      Divorced            2.0   \n",
       "4          Basic                    4.0      Divorced            1.0   \n",
       "\n",
       "   Passport  PitchSatisfactionScore  OwnCar  NumberOfChildrenVisiting  \\\n",
       "0         1                       2       1                       0.0   \n",
       "1         0                       3       1                       2.0   \n",
       "2         1                       3       0                       0.0   \n",
       "3         1                       5       1                       1.0   \n",
       "4         0                       5       1                       0.0   \n",
       "\n",
       "  Designation  MonthlyIncome  \n",
       "0     Manager        20993.0  \n",
       "1     Manager        20130.0  \n",
       "2   Executive        17090.0  \n",
       "3   Executive        17909.0  \n",
       "4   Executive        18468.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('CustomerID', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Create new column for features \n",
    "df['TotalVisiting'] = df['NumberOfChildrenVisiting'] + df['NumberOfPersonVisiting']\n",
    "df.drop(columns=['NumberOfChildrenVisiting', 'NumberOfPersonVisiting'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of numeriacal Features :  12\n"
     ]
    }
   ],
   "source": [
    "## get all the numeric features\n",
    "num_features = [feature for feature in df.columns if df[feature].dtype != 'O']\n",
    "print(\"Num of numeriacal Features : \", len(num_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num of catgorical Features :  6\n"
     ]
    }
   ],
   "source": [
    "##Categorical Features \n",
    "cat_features = [feature for feature in df.columns if df[feature].dtype == 'O']\n",
    "print(\"Num of catgorical Features : \", len(cat_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of discreate features are 9\n"
     ]
    }
   ],
   "source": [
    "## Discrete Features\n",
    "discrete_features= [features for features in num_features if len(df[features].unique())<=25]\n",
    "print(\"Number of discreate features are\",len(discrete_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Contunious features features are 3\n"
     ]
    }
   ],
   "source": [
    "## Contunious_features\n",
    "Contunious_features= [features for features in num_features if features not in discrete_features]\n",
    "print(\"Number of Contunious features features are\",len(Contunious_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split And Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x = df.drop(['ProdTaken'], axis=1)\n",
    "y = df['ProdTaken']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProdTaken\n",
       "0    3968\n",
       "1     920\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>TypeofContact</th>\n",
       "      <th>CityTier</th>\n",
       "      <th>DurationOfPitch</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Gender</th>\n",
       "      <th>NumberOfFollowups</th>\n",
       "      <th>ProductPitched</th>\n",
       "      <th>PreferredPropertyStar</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>NumberOfTrips</th>\n",
       "      <th>Passport</th>\n",
       "      <th>PitchSatisfactionScore</th>\n",
       "      <th>OwnCar</th>\n",
       "      <th>Designation</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>TotalVisiting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20993.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Deluxe</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Manager</td>\n",
       "      <td>20130.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Free Lancer</td>\n",
       "      <td>Male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17090.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>Company Invited</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Female</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Executive</td>\n",
       "      <td>17909.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.0</td>\n",
       "      <td>Self Enquiry</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Small Business</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Basic</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Executive</td>\n",
       "      <td>18468.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age    TypeofContact  CityTier  DurationOfPitch      Occupation  Gender  \\\n",
       "0  41.0     Self Enquiry         3              6.0        Salaried  Female   \n",
       "1  49.0  Company Invited         1             14.0        Salaried    Male   \n",
       "2  37.0     Self Enquiry         1              8.0     Free Lancer    Male   \n",
       "3  33.0  Company Invited         1              9.0        Salaried  Female   \n",
       "4  36.0     Self Enquiry         1              8.0  Small Business    Male   \n",
       "\n",
       "   NumberOfFollowups ProductPitched  PreferredPropertyStar MaritalStatus  \\\n",
       "0                3.0         Deluxe                    3.0     Unmarried   \n",
       "1                4.0         Deluxe                    4.0      Divorced   \n",
       "2                4.0          Basic                    3.0     Unmarried   \n",
       "3                3.0          Basic                    3.0      Divorced   \n",
       "4                3.0          Basic                    4.0      Divorced   \n",
       "\n",
       "   NumberOfTrips  Passport  PitchSatisfactionScore  OwnCar Designation  \\\n",
       "0            1.0         1                       2       1     Manager   \n",
       "1            2.0         0                       3       1     Manager   \n",
       "2            7.0         1                       3       0   Executive   \n",
       "3            2.0         1                       5       1   Executive   \n",
       "4            1.0         0                       5       1   Executive   \n",
       "\n",
       "   MonthlyIncome  TotalVisiting  \n",
       "0        20993.0            3.0  \n",
       "1        20130.0            5.0  \n",
       "2        17090.0            3.0  \n",
       "3        17909.0            3.0  \n",
       "4        18468.0            2.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3910, 17), (978, 17))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate dataset into train and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state= 40)\n",
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4888 entries, 0 to 4887\n",
      "Data columns (total 18 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   ProdTaken               4888 non-null   int64  \n",
      " 1   Age                     4888 non-null   float64\n",
      " 2   TypeofContact           4888 non-null   object \n",
      " 3   CityTier                4888 non-null   int64  \n",
      " 4   DurationOfPitch         4888 non-null   float64\n",
      " 5   Occupation              4888 non-null   object \n",
      " 6   Gender                  4888 non-null   object \n",
      " 7   NumberOfFollowups       4888 non-null   float64\n",
      " 8   ProductPitched          4888 non-null   object \n",
      " 9   PreferredPropertyStar   4888 non-null   float64\n",
      " 10  MaritalStatus           4888 non-null   object \n",
      " 11  NumberOfTrips           4888 non-null   float64\n",
      " 12  Passport                4888 non-null   int64  \n",
      " 13  PitchSatisfactionScore  4888 non-null   int64  \n",
      " 14  OwnCar                  4888 non-null   int64  \n",
      " 15  Designation             4888 non-null   object \n",
      " 16  MonthlyIncome           4888 non-null   float64\n",
      " 17  TotalVisiting           4888 non-null   float64\n",
      "dtypes: float64(7), int64(5), object(6)\n",
      "memory usage: 687.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Column Tranformer with 3 type of transformers\n",
    "cat_features = x.select_dtypes(include=\"object\").columns\n",
    "num_features = x.select_dtypes(exclude =\"object\").columns\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "numeric_transformer = StandardScaler()\n",
    "oh_transformer = OneHotEncoder(drop='first')\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    [\n",
    "        (\"OneHotEncoder\", oh_transformer, cat_features),\n",
    "        (\"StandardScaler\", numeric_transformer, num_features),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;OneHotEncoder&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
       "                                 Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;StandardScaler&#x27;, StandardScaler(),\n",
       "                                 Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;OneHotEncoder&#x27;, OneHotEncoder(drop=&#x27;first&#x27;),\n",
       "                                 Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
       "      dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;StandardScaler&#x27;, StandardScaler(),\n",
       "                                 Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">OneHotEncoder</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;TypeofContact&#x27;, &#x27;Occupation&#x27;, &#x27;Gender&#x27;, &#x27;ProductPitched&#x27;,\n",
       "       &#x27;MaritalStatus&#x27;, &#x27;Designation&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(drop=&#x27;first&#x27;)</pre></div> </div></div></div></div></div><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 \">StandardScaler</label><div class=\"sk-toggleable__content \"><pre>Index([&#x27;Age&#x27;, &#x27;CityTier&#x27;, &#x27;DurationOfPitch&#x27;, &#x27;NumberOfFollowups&#x27;,\n",
       "       &#x27;PreferredPropertyStar&#x27;, &#x27;NumberOfTrips&#x27;, &#x27;Passport&#x27;,\n",
       "       &#x27;PitchSatisfactionScore&#x27;, &#x27;OwnCar&#x27;, &#x27;MonthlyIncome&#x27;, &#x27;TotalVisiting&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</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 \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ColumnTransformer(transformers=[('OneHotEncoder', OneHotEncoder(drop='first'),\n",
       "                                 Index(['TypeofContact', 'Occupation', 'Gender', 'ProductPitched',\n",
       "       'MaritalStatus', 'Designation'],\n",
       "      dtype='object')),\n",
       "                                ('StandardScaler', StandardScaler(),\n",
       "                                 Index(['Age', 'CityTier', 'DurationOfPitch', 'NumberOfFollowups',\n",
       "       'PreferredPropertyStar', 'NumberOfTrips', 'Passport',\n",
       "       'PitchSatisfactionScore', 'OwnCar', 'MonthlyIncome', 'TotalVisiting'],\n",
       "      dtype='object'))])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.760080</td>\n",
       "      <td>0.298888</td>\n",
       "      <td>-0.725667</td>\n",
       "      <td>-0.121745</td>\n",
       "      <td>1.57339</td>\n",
       "      <td>0.681916</td>\n",
       "      <td>-1.284154</td>\n",
       "      <td>2.437806</td>\n",
       "      <td>0.645722</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>3910 rows × 26 columns</p>\n",
       "</div>"
      ],
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       "       0    1    2    3    4    5    6    7    8    9   ...        16  \\\n",
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       "3909  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  ... -0.711858   \n",
       "\n",
       "            17        18        19        20       21        22        23  \\\n",
       "0    -0.404857  0.298888 -0.725667 -0.677196  1.57339  0.681916 -1.284154   \n",
       "1     0.897627  0.298888 -0.725667 -1.232647  1.57339  1.413008  0.778723   \n",
       "2    -1.115303 -0.710311 -0.725667  0.989158  1.57339 -0.780268 -1.284154   \n",
       "3    -0.523265  0.298888 -0.725667 -0.677196  1.57339  0.681916 -1.284154   \n",
       "4    -1.115303 -0.710311 -0.725667  0.433707 -0.63557 -0.049176  0.778723   \n",
       "...        ...       ...       ...       ...      ...       ...       ...   \n",
       "3905 -0.523265  0.298888 -0.725667 -0.677196 -0.63557 -0.049176  0.778723   \n",
       "3906  2.318518  0.298888 -0.725667  1.544609 -0.63557  1.413008  0.778723   \n",
       "3907  1.844888  1.308087  1.781941 -0.121745 -0.63557  1.413008 -1.284154   \n",
       "3908  2.200111  1.308087  1.781941 -0.121745 -0.63557  0.681916  0.778723   \n",
       "3909 -0.760080  0.298888 -0.725667 -0.121745  1.57339  0.681916 -1.284154   \n",
       "\n",
       "            24        25  \n",
       "0    -0.006331  0.645722  \n",
       "1    -1.142555 -0.765160  \n",
       "2    -0.125914 -0.765160  \n",
       "3    -0.153654 -0.765160  \n",
       "4    -1.054086 -0.765160  \n",
       "...        ...       ...  \n",
       "3905  0.035091 -0.059719  \n",
       "3906 -0.438741  0.645722  \n",
       "3907  0.366849  2.056604  \n",
       "3908 -0.430307  0.645722  \n",
       "3909  2.437806  0.645722  \n",
       "\n",
       "[3910 rows x 26 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Applying Transformation in training(fit_transform)\n",
    "X_train=preprocessor.fit_transform(X_train)\n",
    "pd.DataFrame(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## applying transformation on test(transform)\n",
    "X_test=preprocessor.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.778723</td>\n",
       "      <td>0.066393</td>\n",
       "      <td>0.645722</td>\n",
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       "      <td>-0.710311</td>\n",
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       "      <td>0.778723</td>\n",
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       "      <td>0.778723</td>\n",
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       "      <td>0.645722</td>\n",
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       "</table>\n",
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       "975  0.066393  0.645722  \n",
       "976 -1.206283 -1.470601  \n",
       "977  2.252434  0.645722  \n",
       "\n",
       "[978 rows x 26 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, ConfusionMatrixDisplay,\\\n",
    "    precision_score, recall_score, f1_score, roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 0.8411764705882353\n",
      "- F1 score: {:.4f} 0.8139852683181467\n",
      "- Precision: {:.4f} 0.6900958466453674\n",
      "- Recall: {:.4f} 0.2918918918918919\n",
      "- roc_auc_score: {:.4f} 0.6306462614033593\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.8496932515337423\n",
      "- F1 score: {:.4f} 0.8241295070987011\n",
      "- Precision: {:.4f} 0.7142857142857143\n",
      "- Recall: {:.4f} 0.3055555555555556\n",
      "- roc_auc_score: {:.4f} 0.6389933166248956\n",
      "Decision Tree\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 1.0\n",
      "- F1 score: {:.4f} 1.0\n",
      "- Precision: {:.4f} 1.0\n",
      "- Recall: {:.4f} 1.0\n",
      "- roc_auc_score: {:.4f} 1.0\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.8967280163599182\n",
      "- F1 score: {:.4f} 0.8974854277080809\n",
      "- Precision: {:.4f} 0.7112299465240641\n",
      "- Recall: {:.4f} 0.7388888888888889\n",
      "- roc_auc_score: {:.4f} 0.835609857978279\n",
      "Random Forest\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 1.0\n",
      "- F1 score: {:.4f} 1.0\n",
      "- Precision: {:.4f} 1.0\n",
      "- Recall: {:.4f} 1.0\n",
      "- roc_auc_score: {:.4f} 1.0\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.9212678936605317\n",
      "- F1 score: {:.4f} 0.9138521044797266\n",
      "- Precision: {:.4f} 0.9557522123893806\n",
      "- Recall: {:.4f} 0.6\n",
      "- roc_auc_score: {:.4f} 0.7968671679197995\n",
      "GradientBoost\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 0.8872122762148338\n",
      "- F1 score: {:.4f} 0.8737646613319032\n",
      "- Precision: {:.4f} 0.8655256723716381\n",
      "- Recall: {:.4f} 0.4783783783783784\n",
      "- roc_auc_score: {:.4f} 0.7305141103248358\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.8609406952965235\n",
      "- F1 score: {:.4f} 0.8408618379911565\n",
      "- Precision: {:.4f} 0.75\n",
      "- Recall: {:.4f} 0.36666666666666664\n",
      "- roc_auc_score: {:.4f} 0.669548872180451\n",
      "AdaBoost\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 0.8475703324808184\n",
      "- F1 score: {:.4f} 0.8260433751515414\n",
      "- Precision: {:.4f} 0.6967213114754098\n",
      "- Recall: {:.4f} 0.34459459459459457\n",
      "- roc_auc_score: {:.4f} 0.6547894108619661\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.843558282208589\n",
      "- F1 score: {:.4f} 0.825256587181769\n",
      "- Precision: {:.4f} 0.6336633663366337\n",
      "- Recall: {:.4f} 0.35555555555555557\n",
      "- roc_auc_score: {:.4f} 0.6545948203842941\n"
     ]
    }
   ],
   "source": [
    "models={\n",
    "    \"Logistic Regression\":LogisticRegression(),\n",
    "    \"Decision Tree\":DecisionTreeClassifier(),\n",
    "    \"Random Forest\":RandomForestClassifier(),\n",
    "    \"GradientBoost\": GradientBoostingClassifier(),\n",
    "    \"AdaBoost\": AdaBoostClassifier()\n",
    "}\n",
    "\n",
    "for i in range(len(list(models))):\n",
    "    model = list(models.values())[i]\n",
    "    model.fit(X_train,y_train)  #Train model\n",
    "\n",
    "    # Make Predictions\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    # training set performance\n",
    "    model_train_accuracy = accuracy_score(y_train,y_train_pred)\n",
    "    model_train_f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
    "    model_train_precision = precision_score(y_train, y_train_pred)\n",
    "    model_train_recall = recall_score(y_train, y_train_pred)\n",
    "    model_train_rocauc_score =  roc_auc_score(y_train, y_train_pred)\n",
    "\n",
    "        # Testing set performance\n",
    "    model_test_accuracy = accuracy_score(y_test,y_test_pred)\n",
    "    model_test_f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "    model_test_precision = precision_score(y_test, y_test_pred)\n",
    "    model_test_recall = recall_score(y_test, y_test_pred)\n",
    "    model_test_rocauc_score =  roc_auc_score(y_test,  y_test_pred)\n",
    "\n",
    "    print(list(models.keys())[i])\n",
    "\n",
    "    print('model performance for training data set')\n",
    "    print(\"- Accuracy: {:.4f}\", format(model_train_accuracy))\n",
    "    print(\"- F1 score: {:.4f}\", format(model_train_f1))\n",
    "    print(\"- Precision: {:.4f}\", format(model_train_precision))\n",
    "    print(\"- Recall: {:.4f}\", format(model_train_recall))\n",
    "    print(\"- roc_auc_score: {:.4f}\", format( model_train_rocauc_score))\n",
    "\n",
    "    print(\"-------------------------------------------------------------\")\n",
    "\n",
    "    print('model performance for test dataset')\n",
    "    print(\"- Accuracy: {:.4f}\", format(model_test_accuracy))\n",
    "    print(\"- F1 score: {:.4f}\", format(model_test_f1))\n",
    "    print(\"- Precision: {:.4f}\", format(model_test_precision))\n",
    "    print(\"- Recall: {:.4f}\", format(model_test_recall))\n",
    "    print(\"- roc_auc_score: {:.4f}\", format( model_test_rocauc_score))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyperparameter Training\n",
    "\n",
    "rf_params = {\"max_depth\": [5, 8, None, 10],\n",
    "             \"max_features\": [5, 7, \"auto\", 8],\n",
    "             \"min_samples_split\": [2, 8, 15, 20],\n",
    "             \"n_estimators\": [100, 200, 500, 1000]}\n",
    "adaboost_params ={\n",
    "    \"n_estimators\":[50,60,70,80,90],\n",
    "    \"algorithm\": ['SAMME', 'SAMME.R']\n",
    "}\n",
    "gradboost_params = {\n",
    "    \n",
    "    \"loss\": ['log_loss','deviance', 'exponential'],\n",
    "    \"criterion\": ['friedman_mse','squared_error','mse'],\n",
    "    \"min_samples_split\": [2, 8, 15, 20],\n",
    "    \"n_estimators\":[100,200,500],\n",
    "    \"max_depth\": [5, 8, 15, None, 10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': ['log_loss', 'deviance', 'exponential'],\n",
       " 'criterion': ['friedman_mse', 'squared_error', 'mse'],\n",
       " 'min_samples_split': [2, 8, 15, 20],\n",
       " 'n_estimators': [100, 200, 500],\n",
       " 'max_depth': [5, 8, 15, None, 10]}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradboost_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': [5, 8, None, 10],\n",
       " 'max_features': [5, 7, 'auto', 8],\n",
       " 'min_samples_split': [2, 8, 15, 20],\n",
       " 'n_estimators': [100, 200, 500, 1000]}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': [50, 60, 70, 80, 90], 'algorithm': ['SAMME', 'SAMME.R']}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adaboost_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models list for Hyperparameter Tuning\n",
    "randomcv_models = [\n",
    "    (\"RF\", RandomForestClassifier(), rf_params),\n",
    "    (\"AB\", AdaBoostClassifier(), adaboost_params),\n",
    "    (\"GB\",GradientBoostingClassifier(), gradboost_params)\n",
    "\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('RF',\n",
       "  RandomForestClassifier(),\n",
       "  {'max_depth': [5, 8, None, 10],\n",
       "   'max_features': [5, 7, 'auto', 8],\n",
       "   'min_samples_split': [2, 8, 15, 20],\n",
       "   'n_estimators': [100, 200, 500, 1000]}),\n",
       " ('AB',\n",
       "  AdaBoostClassifier(),\n",
       "  {'n_estimators': [50, 60, 70, 80, 90], 'algorithm': ['SAMME', 'SAMME.R']}),\n",
       " ('GB',\n",
       "  GradientBoostingClassifier(),\n",
       "  {'loss': ['log_loss', 'deviance', 'exponential'],\n",
       "   'criterion': ['friedman_mse', 'squared_error', 'mse'],\n",
       "   'min_samples_split': [2, 8, 15, 20],\n",
       "   'n_estimators': [100, 200, 500],\n",
       "   'max_depth': [5, 8, 15, None, 10]})]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomcv_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
      "----------------Best Params for RF----------------\n",
      "{'n_estimators': 500, 'min_samples_split': 2, 'max_features': 7, 'max_depth': None}\n",
      "----------------Best Params for AB----------------\n",
      "{'n_estimators': 70, 'algorithm': 'SAMME.R'}\n",
      "----------------Best Params for GB----------------\n",
      "{'n_estimators': 500, 'min_samples_split': 15, 'max_depth': 15, 'loss': 'log_loss', 'criterion': 'squared_error'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "model_param = {}\n",
    "for name, model, params in randomcv_models:\n",
    "    random = RandomizedSearchCV(estimator=model, \n",
    "                                param_distributions=params,\n",
    "                                n_iter=100,\n",
    "                                cv=3,\n",
    "                                verbose=2,\n",
    "                                n_jobs=-1)\n",
    "    random.fit(X_train, y_train)\n",
    "    model_param[name] = random.best_params_\n",
    "\n",
    "for model_name in model_param:\n",
    "    print(f\"----------------Best Params for {model_name}----------------\")\n",
    "    print(model_param[model_name])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 1.0\n",
      "- F1 score: {:.4f} 1.0\n",
      "- Precision: {:.4f} 1.0\n",
      "- Recall: {:.4f} 1.0\n",
      "- roc_auc_score: {:.4f} 1.0\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.9335378323108384\n",
      "- F1 score: {:.4f} 0.9288689399616511\n",
      "- Precision: {:.4f} 0.952755905511811\n",
      "- Recall: {:.4f} 0.6722222222222223\n",
      "- roc_auc_score: {:.4f} 0.8323517126148705\n",
      "AdaBoostClassifier\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 0.8429667519181586\n",
      "- F1 score: {:.4f} 0.8063886113451593\n",
      "- Precision: {:.4f} 0.7863636363636364\n",
      "- Recall: {:.4f} 0.23378378378378378\n",
      "- roc_auc_score: {:.4f} 0.6094786426805354\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.8466257668711656\n",
      "- F1 score: {:.4f} 0.8100093406245507\n",
      "- Precision: {:.4f} 0.7884615384615384\n",
      "- Recall: {:.4f} 0.22777777777777777\n",
      "- roc_auc_score: {:.4f} 0.6069966583124479\n",
      "GradientBoostClassifier\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 0.8872122762148338\n",
      "- F1 score: {:.4f} 0.8737646613319032\n",
      "- Precision: {:.4f} 0.8655256723716381\n",
      "- Recall: {:.4f} 0.4783783783783784\n",
      "- roc_auc_score: {:.4f} 0.7305141103248358\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.8619631901840491\n",
      "- F1 score: {:.4f} 0.8423379410505807\n",
      "- Precision: {:.4f} 0.7528089887640449\n",
      "- Recall: {:.4f} 0.37222222222222223\n",
      "- roc_auc_score: {:.4f} 0.6723266499582289\n"
     ]
    }
   ],
   "source": [
    "models={\n",
    "    \"Random Forest\": RandomForestClassifier(n_estimators=200, min_samples_split=2, max_features=8, max_depth=None), # type: ignore\n",
    "    \"AdaBoostClassifier\": AdaBoostClassifier(n_estimators= 50, algorithm= 'SAMME'),          \n",
    "    \"GradientBoostClassifier\": GradientBoostingClassifier()\n",
    "}\n",
    "\n",
    "for i in range(len(list(models))):\n",
    "    model = list(models.values())[i]\n",
    "    model.fit(X_train,y_train)  #Train model\n",
    "\n",
    "    # Make Predictions\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    # training set performance\n",
    "    model_train_accuracy = accuracy_score(y_train,y_train_pred)\n",
    "    model_train_f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
    "    model_train_precision = precision_score(y_train, y_train_pred)\n",
    "    model_train_recall = recall_score(y_train, y_train_pred)\n",
    "    model_train_rocauc_score =  roc_auc_score(y_train, y_train_pred)\n",
    "\n",
    "        # Testing set performance\n",
    "    model_test_accuracy = accuracy_score(y_test,y_test_pred)\n",
    "    model_test_f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "    model_test_precision = precision_score(y_test, y_test_pred)\n",
    "    model_test_recall = recall_score(y_test, y_test_pred)\n",
    "    model_test_rocauc_score =  roc_auc_score(y_test,  y_test_pred)\n",
    "\n",
    "    print(list(models.keys())[i])\n",
    "\n",
    "    print('model performance for training data set')\n",
    "    print(\"- Accuracy: {:.4f}\", format(model_train_accuracy))\n",
    "    print(\"- F1 score: {:.4f}\", format(model_train_f1))\n",
    "    print(\"- Precision: {:.4f}\", format(model_train_precision))\n",
    "    print(\"- Recall: {:.4f}\", format(model_train_recall))\n",
    "    print(\"- roc_auc_score: {:.4f}\", format( model_train_rocauc_score))\n",
    "\n",
    "    print(\"-------------------------------------------------------------\")\n",
    "\n",
    "    print('model performance for test dataset')\n",
    "    print(\"- Accuracy: {:.4f}\", format(model_test_accuracy))\n",
    "    print(\"- F1 score: {:.4f}\", format(model_test_f1))\n",
    "    print(\"- Precision: {:.4f}\", format(model_test_precision))\n",
    "    print(\"- Recall: {:.4f}\", format(model_test_recall))\n",
    "    print(\"- roc_auc_score: {:.4f}\", format( model_test_rocauc_score))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 1.0\n",
      "- F1 score: {:.4f} 1.0\n",
      "- Precision: {:.4f} 1.0\n",
      "- Recall: {:.4f} 1.0\n",
      "- roc_auc_score: {:.4f} 1.0\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.87\n",
      "- F1 score: {:.4f} 0.870013000144446\n",
      "- Precision: {:.4f} 0.8972602739726028\n",
      "- Recall: {:.4f} 0.8451612903225807\n",
      "- roc_auc_score: {:.4f} 0.8708565072302559\n",
      "AdaBoostClassifier\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 0.9314285714285714\n",
      "- F1 score: {:.4f} 0.9314252118406809\n",
      "- Precision: {:.4f} 0.9329446064139941\n",
      "- Recall: {:.4f} 0.927536231884058\n",
      "- roc_auc_score: {:.4f} 0.931373749744846\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.8466666666666667\n",
      "- F1 score: {:.4f} 0.846707571917656\n",
      "- Precision: {:.4f} 0.8609271523178808\n",
      "- Recall: {:.4f} 0.8387096774193549\n",
      "- roc_auc_score: {:.4f} 0.8469410456062292\n",
      "GradientBoostClassifier\n",
      "model performance for training data set\n",
      "- Accuracy: {:.4f} 1.0\n",
      "- F1 score: {:.4f} 1.0\n",
      "- Precision: {:.4f} 1.0\n",
      "- Recall: {:.4f} 1.0\n",
      "- roc_auc_score: {:.4f} 1.0\n",
      "-------------------------------------------------------------\n",
      "model performance for test dataset\n",
      "- Accuracy: {:.4f} 0.88\n",
      "- F1 score: {:.4f} 0.8800213342815236\n",
      "- Precision: {:.4f} 0.9047619047619048\n",
      "- Recall: {:.4f} 0.8580645161290322\n",
      "- roc_auc_score: {:.4f} 0.8807563959955506\n"
     ]
    }
   ],
   "source": [
    "## Retrained Model Using Hyperparameter Tuning\n",
    "models={\n",
    "    \"Random Forest\": RandomForestClassifier(n_estimators=200, min_samples_split=2, max_features=8, max_depth=None), # type: ignore\n",
    "    \"AdaBoostClassifier\": AdaBoostClassifier(n_estimators= 50, algorithm= 'SAMME'),          \n",
    "    \"GradientBoostClassifier\": GradientBoostingClassifier(n_estimators = 500, min_samples_split= 15, max_depth=15, loss= 'log_loss', criterion= 'squared_error')\n",
    "}\n",
    "\n",
    "for i in range(len(list(models))):\n",
    "    model = list(models.values())[i]\n",
    "    model.fit(X_train,y_train)  #Train model\n",
    "\n",
    "    # Make Predictions\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    # training set performance\n",
    "    model_train_accuracy = accuracy_score(y_train,y_train_pred)\n",
    "    model_train_f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
    "    model_train_precision = precision_score(y_train, y_train_pred)\n",
    "    model_train_recall = recall_score(y_train, y_train_pred)\n",
    "    model_train_rocauc_score =  roc_auc_score(y_train, y_train_pred)\n",
    "\n",
    "        # Testing set performance\n",
    "    model_test_accuracy = accuracy_score(y_test,y_test_pred)\n",
    "    model_test_f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
    "    model_test_precision = precision_score(y_test, y_test_pred)\n",
    "    model_test_recall = recall_score(y_test, y_test_pred)\n",
    "    model_test_rocauc_score =  roc_auc_score(y_test,  y_test_pred)\n",
    "\n",
    "    print(list(models.keys())[i])\n",
    "\n",
    "    print('model performance for training data set')\n",
    "    print(\"- Accuracy: {:.4f}\", format(model_train_accuracy))\n",
    "    print(\"- F1 score: {:.4f}\", format(model_train_f1))\n",
    "    print(\"- Precision: {:.4f}\", format(model_train_precision))\n",
    "    print(\"- Recall: {:.4f}\", format(model_train_recall))\n",
    "    print(\"- roc_auc_score: {:.4f}\", format( model_train_rocauc_score))\n",
    "\n",
    "    print(\"-------------------------------------------------------------\")\n",
    "\n",
    "    print('model performance for test dataset')\n",
    "    print(\"- Accuracy: {:.4f}\", format(model_test_accuracy))\n",
    "    print(\"- F1 score: {:.4f}\", format(model_test_f1))\n",
    "    print(\"- Precision: {:.4f}\", format(model_test_precision))\n",
    "    print(\"- Recall: {:.4f}\", format(model_test_recall))\n",
    "    print(\"- roc_auc_score: {:.4f}\", format( model_test_rocauc_score))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Sample dataset creation\n",
    "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# List of models you want to compare\n",
    "auc_models = [\n",
    "    {\n",
    "        'label': 'Random Forest Classifier',\n",
    "        'model': RandomForestClassifier(n_estimators=200, min_samples_split=2, max_features=8, max_depth=None),\n",
    "        'auc': 0.8323  # This will be updated with the actual computed AUC\n",
    "    }\n",
    "]\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# Loop through all models\n",
    "for algo in auc_models:\n",
    "    model = algo['model']  # Select the model\n",
    "    model.fit(X_train, y_train)  # Train the model\n",
    "    \n",
    "    # Compute False Positive Rate and True Positive Rate\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
    "    \n",
    "    # Calculate Area Under the Curve to display on the plot\n",
    "    auc_score = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])\n",
    "    algo['auc'] = auc_score  # Update the AUC in the dictionary\n",
    "    \n",
    "    # Plot the ROC curve\n",
    "    plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % (algo['label'], auc_score))\n",
    "\n",
    "# Custom settings for the plot\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')  # Diagonal line representing random performance\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('1 - Specificity (False Positive Rate)')\n",
    "plt.ylabel('Sensitivity (True Positive Rate)')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig(\"auc.png\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MTNjb26Nz58749ddfERsbm6/+o0ePNP/v27cvLly4gODg4Hz18r6d9+/fH/fv38eKFSvy1cnIyNCMfipM/fr10bhxY2zatAmbNm1CtWrVtJISmUyGvn37YuvWrQX+oX4+Xl21bdsWXl5eWLNmDXbu3Jnv+a+++go3btzA559/nu+bekhIiFYfn1OnTuHkyZOaDyFdzvPLyGSyfC00P//8M1QqlVZZ3pxDLyZIr8PDwwNVq1bFihUrkJubqylfv3695vbZyzg7O2PUqFHYs2cPfv7553zPq9Vq/PTTT7h3755OceW1GL14m3DNmjV630f37t1hYWGBuXPnIjMzU+u557c1MzMr8Fbl674/CqJSqfIdy97eHk5OTsjKynplTC+ys7NDx44dsXr1asTExGg9p6/WQSrf2CJE5d5nn32Gfv36Ye3atRg9ejSWLFmC9u3bo3Hjxhg1ahRq1aqFhw8f4vjx47h37x4uXLig2W7Lli3o168fhg8fjpYtW+LJkyfYvn07li1bhqZNm2Lw4MHYvHkzRo8ejYMHD6Jdu3ZQqVS4fv06Nm/ejN27d2tu1RVmwIABmDFjBoyNjTFixIh8kx/OmzcPBw8eRJs2bTBq1Cg0aNAAT548wdmzZ7Fv3z48efKk2Odm3bp16NatG/z8/DBo0CB06NABWVlZCAoKQnh4OAYMGIDPPvss33a1a9dG+/btMWbMGGRlZWHhwoWoWrUqPv/8c02dop7nl+nduzd+//13WFlZoUGDBjh+/Dj27duHqlWratVr1qwZZDIZvv/+eyQlJUGpVKJr166wt7cv9rlRKBSYNWsWxo8fj65du6J///6Ijo7G2rVr4e7uXqQWh59++gmRkZGYMGECgoKC0Lt3b9jY2CAmJgZ//fUXrl+/rtUCWBTdu3eHQqGAr68vPvzwQ6SmpmLFihWwt7cvMOl8nX1YWlpiwYIFGDlyJFq1aoVBgwbBxsYGFy5cQHp6On777TcAQMuWLbFp0yZMnjwZrVq1grm5OXx9ffXy/nhRSkoKatSogXfeeQdNmzaFubk59u3bh3///Ver5bCwmAqyePFitG/fHi1atMAHH3wANzc3REdHIzQ0FOfPn9cpPqqADDJWjUhHhU2oKIQQKpVKuLu7C3d3d83w7MjISDFkyBDh6Ogo5HK5qF69uujdu7fYsmWL1raPHz8W48aNE9WrV9dMBhcYGKg1lD07O1t8//33omHDhkKpVAobGxvRsmVL8fXXX4ukpCRNvReHz+e5efOmZtK3I0eOFPj6Hj58KMaOHSucnZ2FXC4Xjo6Oolu3bmL58uWaOnnDwv/66y+dzl1KSoqYNWuWaNiwoTAxMREWFhaiXbt2Yu3atfmGDz8/oeJPP/0knJ2dhVKpFB06dBAXLlzIt++inOeXXbvExEQxbNgwYWtrK8zNzYW3t7e4fv16gedyxYoVolatWkImkxVpQsUXz1NhE+0tXrxYuLi4CKVSKVq3bi2OHj0qWrZsKXr06FGEsytEbm6uWLlypejQoYOwsrIScrlcuLi4iGHDhmkNrc8bPv/8ZJ3Pn5/nJ5Hcvn27aNKkiTA2Nhaurq7i+++/F6tXr85XL29CxYIUdR95ddu2bStMTEyEpaWlaN26tfjzzz81z6empopBgwYJa2vrfBMqFvX9gf+fULEgeG74fFZWlvjss89E06ZNhYWFhTAzMxNNmzbNNxlkYTEVdp0vX74s+vTpI6ytrYWxsbF44403xPTp0wuMhyoXiRBsGySiZ6Kjo+Hm5oYff/wRn376qaHDMQi1Wg07OzsEBAQUeMuHiCoW9hEiokorMzMzXz+RdevW4cmTJ+jcubNhgiKiUsU+QkRUaZ04cQIff/wx+vXrh6pVq+Ls2bNYtWoVGjVqhH79+hk6PCIqBUyEiKjScnV1hbOzMxYvXownT56gSpUqGDJkCObNm2fQVe2JqPSwjxARERFVWuwjRERERJUWEyEiIiKqtCpdHyG1Wo0HDx7AwsKCKw8TERGVE0IIpKSkwMnJKd/EtK+j0iVCDx48eOVCmURERFQ23b17FzVq1NDb/ipdImRhYQHg2Ym0tLQ0cDRERERUFMnJyXB2dtZ8jutLpUuE8m6HWVpaMhEiIiIqZ/TdrYWdpYmIiKjSYiJERERElRYTISIiIqq0mAgRERFRpcVEiIiIiCotJkJERERUaTERIiIiokqLiRARERFVWkyEiIiIqNJiIkRERESVlkEToX/++Qe+vr5wcnKCRCJBSEjIK7cJDw9HixYtoFQqUbt2baxdu7bE4yQiIqKKyaCJUFpaGpo2bYolS5YUqX5UVBR69eqFLl264Pz585g0aRJGjhyJ3bt3l3CkREREVBEZdNHVnj17omfPnkWuv2zZMri5ueGnn34CANSvXx9HjhzBggUL4O3tXVJhEhERUQVVrlafP378OLy8vLTKvL29MWnSJMMERERERAAAIQQyclQltv+UjKwS2W+5SoTi4uLg4OCgVebg4IDk5GRkZGTAxMQk3zZZWVnIyvrfyUtOTi7xOImIiCoTIQTeWXYcZ+4kltQR0B3nSmTPFX7U2Ny5c2FlZaX5cXZ2NnRIREREFYIQAunZuXicll2CSRAASHApx7FE9lyuWoQcHR3x8OFDrbKHDx/C0tKywNYgAJg6dSomT56seZycnMxkiIiI6DUV1gp0epoXTBWy197/w7hYpKelw83dHcCzz+9q8z947f2+qFwlQp6enti1a5dW2d69e+Hp6VnoNkqlEkqlsqRDIyIiqvCe7weUnq3KlwR5uNigqpkCEonktY5x7NgxHDhwAAqFAmPGjIGlpSVyFSWTshg0EUpNTcWtW7c0j6OionD+/HlUqVIFNWvWxNSpU3H//n2sW7cOADB69Gj88ssv+PzzzzF8+HAcOHAAmzdvRmhoqKFeAhERUaXwsn5Aea1AJnLZayVBSUlJCAkJQXR0NADA1dUVRkYlm6oYNBE6ffo0unTponmcdwsrMDAQa9euRWxsLGJiYjTPu7m5ITQ0FB9//DEWLVqEGjVqYOXKlRw6T0REVMIycvK3AAH6aQUCgCtXrmDnzp3IzMyEXC5Hjx490Lx589fe76tIhBCiRI9QxiQnJ8PKygpJSUmwtLQ0dDhERFSAkh6KTbpLz1bB49t9ALT7Ab1uK5AQAtu3b8f58+cBAE5OTggICEDVqlW16pXU53e56iNEREQVX8kPxabXZaqQwVRPfXYkEgmMjIwgkUjQvn17dOrUCTLZ63e2LiomQkREpFev25pTUCdcKjs8XGxgIn+9REWtViMrK0sz4rt79+5o0qSJQUZ1MxEiIiK90Xdrjr6GYpP+vO6tsMTERAQHB0MqlWLIkCGQSqWQy+UGm9qGiRARUQVXmv1t9Nmao69OuFQ2CCFw8eJF7Nq1C9nZ2VAqlUhISIC9vb1B42IiRERUgRmyv83rtua8bssDlR2ZmZkIDQ3F5cuXAQDOzs4ICAiAtbW1YQMDEyEiojJHny04hupvw9YcyhMdHY3g4GAkJydDIpGgc+fOaN++PaTSsrHKFxMhIqIypCRbcEqzvw1bcwh49vscFhaG5ORk2NjYICAgADVq1DB0WFqYCBERlSGFTVr3uthCQ4YgkUjg7++Pf//9F97e3lAoFIYOKR8mQkREZZQ+W3DYQkOlQQiBs2fPIjs7W7MOqKOjI3x9fQ0cWeGYCBERlVH6nLSOqKSlp6djx44duH79OqRSKdzd3Q0+Iqwo+A4jIiKi1xIZGYmQkBCkpqZCKpWiW7dusLOzM3RYRcJEiIiIiIolNzcX+/btw8mTJwEAtra26Nu3LxwdHQ0cWdExESIinXAxzJKVns1zS+WDWq3GmjVr8ODBAwBAq1at8NZbb0Eulxs4Mt0wESKiIuNimESURyqVonHjxnj69Cn8/PxQt25dQ4dULEyEiCoRLoZZfuhjYUsifUtNTUV6erqmE3SbNm3QpEkTmJqaGjiy4mMiRFRJcDHM8oXD3amsiYiIwPbt22FsbIwPP/wQCsWzeanKcxIEMBEiqjT0OVEfJ+cjqjxycnKwZ88enD59GgBgYWGB9PT0Mjk5YnEwESKqAIpyy+v5TrhcDJOIiiI2NhZBQUFISEgAAHh6eqJr164wMqo46UPFeSVElVRxbnlxoj4iehkhBI4dO4YDBw5ArVbD3Nwcffr0Qa1atQwdmt7xLyGRAeljKLquHZjZCZeIiiI6OhpqtRr16tWDr69vue8LVBgmQkQGUhJD0Ytyy4u3tYioMGq1GlKpFBKJBH5+frh16xaaNm1aof9mMBEiKmV5rUD6HorODsxEVFxZWVkICwsDAPj5+QEAzM3N0axZMwNGVTqYCBGVosJagfQxFJ0tPURUHPfu3UNQUBASExMhkUjg6elZLhZL1RcmQkSlqKAh7GzJISJDUKvVOHz4MA4dOgQhBKysrNCnT59KlQQBTISIDCavFYgtOURU2hITExEcHIy7d+8CABo1aoRevXrB2NjYwJGVPiZCRAbCIexEZAhqtRp//PEHnjx5AqVSCR8fHzRp0sTQYRkM/woTFVNxhr5zZXEiMjSpVIoePXrgyJEj6NOnD6ytrQ0dkkExESIqBq7CTkTlyZ07d5CZmYk33ngDAFCnTh3Url2bt+XBRIioWF533S5OakhEpUGlUiE8PBxHjhyBsbExRo8eDSsrKwBgEvT/mAgRvabiDH1nB2kiKmkJCQkICgpCbGwsAKBevXqVsjP0qzARInqFgvoCPd/Xh52eiagsEULg7Nmz2L17N3JycmBsbAxfX180aNDA0KGVSfzrTfQS7AtEROWJWq3GX3/9hevXrwMA3Nzc4O/vD0tLSwNHVnYxESJ6iVf1BWJfHyIqS6RSKSwtLSGVStGtWzd4enryNvwrMBEiKqKC+gKxrw8RGVpubi6ysrJgZmYGAPDy8kKLFi3g4OBg4MjKByZCREXEvkBEVNbEx8cjKCgIxsbGGDJkCKRSKeRyOZMgHfCvOpWq4kxCaEicAJGIyiIhBE6dOoW9e/dCpVLB1NQUiYmJqFq1qqFDK3eYCFGpYcdjIqLXl5qaim3btuHWrVsAgNq1a8PPzw/m5uYGjqx8YiJEelGUlp707NebhNCQ2CmaiMqCiIgIbN++Henp6TAyMsJbb72FVq1asa/ia2AiRK+tOC09xZmE0JDYKZqIDE2tVuPAgQNIT0+Hg4MDAgICYG9vb+iwyj0mQlRkhbX66NrS4+Fig6pmCiYWREQ6kEqlCAgIwMWLF9GlSxcYGfEjXB94FqlIitrqU5SWHrauEBG9mhACx44dgxAC7du3BwA4ODjgrbfeMnBkFQsTISqSoiwyypYeIiL9SE5ORkhICKKioiCRSFCvXj3Y2toaOqwKiYkQ6aywVh+29BARvb4rV65g586dyMzMhFwuR48ePTgsvgQxESKdcWJBIiL9y8rKQlhYGM6fPw8AcHJyQkBAAJOgEsZPMyIiIgNTq9VYvXo14uPjAQAdOnRAp06dIJOVn9G15RUTISIiIgOTSqVo0aIFjh8/jj59+sDFxcXQIVUaTISIiIgMIDExEVlZWXB0dAQAtG7dGs2aNYNSqTRwZJULEyEiIqJSJITApUuXEBoaCjMzM3z44YdQKpWQSCRMggyAiVAFpe/FTbn4KBHR68vMzERoaCguX74M4Nm8QNnZ2UyADIiJUAXExU2JiMqeO3fuIDg4GElJSZBIJOjcuTPat28PqVRq6NAqNSZCFVBRJj8sLi4+SkSkG7VajYMHD+LIkSMAABsbGwQEBKBGjRoGjowAJkIVnr4XN+WkiUREupFIJHj48CEAoFmzZujRowdvhZUhTIQqOE5+SERU+oQQUKlUMDIygkQigZ+fH2JiYlC/fn1Dh0Yv4I1JIiIiPUpPT8fmzZuxc+dOTZmZmRmToDKKTQVERER6EhkZiZCQEKSmpkIqlaJDhw5cIqOMYyJERET0mnJzc7F//36cOHECAGBra8t1wsoJJkJERESvIT4+HkFBQZoO0R4eHujevTvkcrmBI6OieK1EKCsriz3fiYio0lKr1fjzzz/x9OlTmJqaws/PD3Xr1jV0WKQDnRKhv//+Gxs3bsThw4dx9+5dqNVqmJmZoXnz5ujevTuGDRsGJyenkoq1QtPnTNCcBZqIqHRIpVL06tULp06dwttvvw1zc3NDh0Q6kgghxKsqBQcH44svvkBKSgp8fHzQunVrODk5wcTEBE+ePMHly5dx+PBhHD9+HEOHDsXs2bNhZ2dXGvHrLDk5GVZWVkhKSoKlpaWhwwFQsjNBX/3Gm8PniYj06MaNG1CpVFqjwIQQnGOthJXU53eRPiF/+OEHLFiwAD179ixwKvD+/fsDAO7fv4+ff/4Zf/zxBz7++OMiBbBkyRL8+OOPiIuLQ9OmTfHzzz+jdevWhdZfuHAhli5dipiYGNja2uKdd97B3LlzYWxsXKTjlUUlNRM0Z4EmItKfnJwc7NmzB6dPn4ZSqYSTkxOsrKwAgElQOVakROj48eNF2ln16tUxb968Ih9806ZNmDx5MpYtW4Y2bdpg4cKF8Pb2RkREBOzt7fPV37BhA6ZMmYLVq1ejbdu2uHHjBoYOHQqJRIL58+cX+bhlmT5nguYs0ERE+hEbG4ugoCAkJCQAAJo3bw4zMzMDR0X6UOx7JtnZ2YiKioK7uzuMjIq3m/nz52PUqFEYNmwYAGDZsmUIDQ3F6tWrMWXKlHz1jx07hnbt2mHQoEEAAFdXVwwcOBAnT54s7ssoczgTNBFR2SGEwLFjx3DgwAGo1WqYm5vD398f7u7uhg6N9ETnmaXT09MxYsQImJqaomHDhoiJiQEAjB8/XqfWoOzsbJw5cwZeXl7/C0YqhZeXV6EtUG3btsWZM2dw6tQpAMDt27exa9cu+Pj4FHqcrKwsJCcna/0QERG9ikqlwu+//459+/ZBrVajXr16GDNmDJOgCkbnRGjq1Km4cOECwsPDtfrleHl5YdOmTUXeT0JCAlQqFRwcHLTKHRwcEBcXV+A2gwYNwjfffIP27dtDLpfD3d0dnTt3xpdfflnocebOnQsrKyvNj7Ozc5Fj1DchBNKzcwv44SgvIqKyRiaTwd7eHnK5HL6+vujfvz9MTU0NHRbpmc73YEJCQrBp0ya8+eabWv1PGjZsiMjISL0G96Lw8HDMmTMH//3vf9GmTRvcunULEydOxOzZszF9+vQCt5k6dSomT56seZycnGyQZKgkR4YREZF+ZGVlITs7GxYWFgCefclv3bo1qlSpYuDIqKTonAg9evSowI7MaWlpOnXMtbW1hUwm08zEmefhw4dwdHQscJvp06dj8ODBGDlyJACgcePGSEtLwwcffICvvvqqwBFtSqWyTEz6WJSRYRzlRURkOPfu3UNQUBDMzc0xdOhQSKVSGBkZMQmq4HROhDw8PBAaGorx48cD+N+QwZUrV8LT07PI+1EoFGjZsiX2798Pf39/AM9m6Ny/fz/GjRtX4Dbp6en5kh2Z7FniUITpkMqMwkaGcZQXEVHpU6vVOHz4MA4dOgQhBNRqNZKSkmBjY2Po0KgU6JwIzZkzBz179sTVq1eRm5uLRYsW4erVqzh27BgOHTqk074mT56MwMBAeHh4oHXr1li4cCHS0tI0o8iGDBmC6tWrY+7cuQAAX19fzJ8/H82bN9fcGps+fTp8fX01CVF5wJFhRERlQ2JiIoKDg3H37l0AQKNGjdCrV69yPTcd6UbnT+P27dvj/PnzmDdvHho3bow9e/agRYsWOH78OBo3bqzTvgYMGIBHjx5hxowZiIuLQ7NmzRAWFqbpQB0TE6PVAjRt2jRIJBJMmzYN9+/fh52dHXx9ffHdd9/p+jKIiKgSE0Lg0qVLCA0NRXZ2NhQKBXr16oUmTZoYOjQqZUVaYqMiMdQSG+nZuWgwYzcALntBRGRoKpUKK1aswMOHD+Hs7Iw+ffrwVlgZZ9AlNp4nk8kQGxubr8P048ePYW9vD5WKQ8GJiKhsk8lk6Nu3L65du4b27dsXONiGKgedE6HCGpCysrKgUCheOyAiIiJ9U6lUCA8Ph1wuR8eOHQEAdnZ2ZXaBcCo9RU6EFi9eDODZKLGVK1fC3Nxc85xKpcI///yDevXq6T/Cck4IgYwcFSdNJCIykMePHyMoKAgPHjyARCJBo0aNOCSeNIqcCC1YsADAsw/2ZcuWaY3SUigUcHV1xbJly/QfYTnGSRSJiAxHCIGzZ89i9+7dyMnJgbGxMXx9fZkEkZYiJ0JRUVEAgC5duiAoKIidyoqgoEkUOWkiEVHJS09Px44dO3D9+nUAgJubG/z9/Ut1kAyVDzr3ETp48GBJxFHh5U2iyEkTiYhKlkqlwsqVK5GYmAipVIpu3brB09OTf3upQMUaw33v3j1s374dMTExyM7O1npu/vz5egmsouEkikREpUMmk8HT0xOnTp1CQEAAqlWrZuiQqAzT+ZN5//79ePvtt1GrVi1cv34djRo1QnR0NIQQaNGiRUnESERE9FLx8fHIyclB9erVATxbDqpZs2aQy+UGjozKOp0nTpg6dSo+/fRTXLp0CcbGxti6dSvu3r2LTp06oV+/fiURIxERUYGEEDh58iSWL1+OLVu2ICsrC8CzEc5MgqgodE6Erl27hiFDhgAAjIyMkJGRAXNzc3zzzTf4/vvv9R4gERFRQVJTU7FhwwaEhYVBpVLB1taWk/qSznS+NWZmZqbpF1StWjVERkaiYcOGAICEhAT9RkdERFSAGzduYNu2bUhPT4eRkRHeeusttGrVih2iSWc6J0Jvvvkmjhw5gvr168PHxweffPIJLl26hKCgILz55pslESMRERGAZyPCwsLCcPr0aQCAg4MDAgIC8i37RFRUOidC8+fPR2pqKgDg66+/RmpqKjZt2oQ6depwxBgREZUoqVSKlJQUAICnpye6du0KIyOOyKXi0/m3p1atWpr/m5mZcTZpIiIqUUII5ObmQi6XQyKRwNfXF61bt9b6PCIqLr0ttxsUFIQmTZroa3dERERISkrCunXrsGPHDk2ZmZkZkyDSG51ahH799Vfs3bsXCoUCEydORJs2bXDgwAF88sknuHHjhmY0GRER0eu6cuUKdu7ciczMTMjlciQmJnJ5J9K7IidC8+bNw4wZM9CkSRNcv34d27Ztw1dffYWff/4ZEydOxIcffshfUPxvtXkAXHGeiKgYsrKy8Pfff+PChQsAACcnJwQEBPAzhkpEkROhNWvWYMWKFQgMDMThw4fRqVMnHDt2DLdu3YKZmVlJxlhucLV5IqLXc+/ePQQFBSExMRESiQTt27dHp06dIJNxsWoqGUVOhGJiYtC1a1cAQIcOHSCXy/H1118zCXpOQavNA1xxnoioKFQqFf766y8kJyfDysoKffr0gYuLi6HDogquyIlQVlYWjI2NNY8VCgWqVKlSIkFVBHmrzQPgivNEREUgk8nw9ttv48KFC/Dx8dH6zCEqKTp1lp4+fTpMTU0BANnZ2fj2229hZWWlVYdzCT3D1eaJiF5OCIGLFy9CJpOhUaNGAAB3d3e4u7sbODKqTIr8Sd2xY0dERERoHrdt2xa3b9/WqlNZWj2e7xD9PHaOJiIqmszMTISGhuLy5ctQKBRwdnbO98WaqDQUOREKDw8vwTDKD3aIJiJ6PdHR0QgODkZycjIkEgnatWsHCwsLQ4dFlRTv3eiosA7Rz2PnaCKi/FQqFcLDw3HkyBEAgI2NDQICAlCjRg0DR0aVGROh1/B8h+jnsXM0EZG23NxcrFmzBg8ePAAANGvWDD179oRCoTBwZFTZMRF6DewQTURUNEZGRnBxccGTJ0/g6+uLBg0aGDokIgBMhIiIqISkp6cjJydH0wm6a9euePPNN2FpaWngyIj+R2+LrhIREeWJjIzE0qVLsWXLFqjVagDPWoWYBFFZU6xE6PDhw3j//ffh6emJ+/fvAwB+//13TQc4IiKqnHJzcxEWFoY//vgDqampyMzMRGpqqqHDIiqUzonQ1q1b4e3tDRMTE5w7dw5ZWVkAgKSkJMyZM0fvARIRUfkQHx+PFStW4OTJkwAADw8PfPDBB2wFojJN50To22+/xbJly7BixQrI5XJNebt27XD27Fm9BkdERGWfEAInT57E8uXLER8fD1NTUwwcOBC9evXS+pwgKot07iwdERGBjh075iu3srLC06dP9RETERGVI2q1GufPn4dKpULt2rXh5+cHc3NzQ4dFVCQ6J0KOjo64desWXF1dtcqPHDmCWrVq6SsuIiIq44QQkEgkkMlk6Nu3L27fvo1WrVpxHjUqV3S+NTZq1ChMnDgRJ0+ehEQiwYMHD7B+/Xp8+umnGDNmTEnESEREZUhOTg527typtfSSra0tWrduzSSIyh2dW4SmTJkCtVqNbt26IT09HR07doRSqcSnn36K8ePHl0SMRERURsTGxiIoKAgJCQmQSqVo3rw5rK2tDR0WUbHpnAhJJBJ89dVX+Oyzz3Dr1i2kpqaiQYMGvB9MRFSBCSFw7NgxHDhwAGq1Gubm5vD392cSROWezonQH3/8gYCAAJiamnKKdCKiSiApKQkhISGIjo4GANSrVw++vr4wNTU1bGBEeqBzH6GPP/4Y9vb2GDRoEHbt2gWVSlUScRERURmQm5uL1atXIzo6GnK5HL6+vujfvz+TIKowdE6EYmNjsXHjRkgkEvTv3x/VqlXD2LFjcezYsZKIj4iIDMjIyAgdO3aEk5MTPvzwQ7Ro0YIdoqlC0fnWmJGREXr37o3evXsjPT0dwcHB2LBhA7p06YIaNWogMjKyJOIkIqJScu/ePQgh4OzsDABo0aIFmjVrBplMZuDIiPTvtVafNzU1hbe3NxITE3Hnzh1cu3ZNX3EREVEpU6vVOHz4MA4dOgRLS0uMHj0axsbGmrmCiCqiYiVCeS1B69evx/79++Hs7IyBAwdiy5Yt+o6PiIhKQWJiIoKDg3H37l0A0LQGEVV0OidC7777Lnbu3AlTU1P0798f06dPh6enZ0nERkREJUwIgYsXL2LXrl3Izs6GUqmEj48PmjRpYujQiEqFzomQTCbD5s2b4e3tXamaSoUQyMhRIT2bo+SIqGLIzc3Ftm3bcPnyZQDPWoECAgI4NxBVKjonQuvXry+JOMo0IQTeWXYcZ+4kGjoUIiK9kclkyM3NhUQiQefOndG+fXtIpToPJiYq14qUCC1evBgffPABjI2NsXjx4pfWnTBhgl4CK0syclT5kiAPFxuYyCtPixgRVQwqlQq5ublQKpWQSCTw9fVF+/btUb16dUOHRmQQEiGEeFUlNzc3nD59GlWrVoWbm1vhO5NIcPv2bb0GqG/JycmwsrJCUlISLC0tX1r3+dthHt/uAwCcnuYFU4UMJnIZ59IgonLl8ePHCAoKgo2NDfr27cu/YVSu6PL5rYsitQhFRUUV+P+KrLDbYaYKGUwVrzXrABFRqRJC4OzZs9i9ezdycnLw5MkTzYcKUWWn883gb775Bunp6fnKMzIy8M033+glqLKAt8OIqCJIT0/H5s2bsXPnTuTk5MDNzQ1jxoxhEkT0/4p0a+x5MpkMsbGxsLe31yp//Pgx7O3ty/zaY0VtWkvPzkWDGbsB8HYYEZVPkZGRCAkJQWpqKqRSKbp16wZPT0/+HaNyyaC3xp4nhCjwTXThwgVUqVJFL0GVNbwdRkTlTW5uLrZv347U1FTY2toiICAA1apVM3RYRGVOkT/dbWxsIJFIIJFIULduXa1kSKVSITU1FaNHjy6RIImISDdGRkbw9/fH1atX0b17d8jlckOHRFQmFTkRWrhwIYQQGD58OL7++mut+8sKhQKurq7lfobpvFFiADhxIhGVK0IInDp1CiYmJppZod3c3F460peIdEiEAgMDATx7Y7Vt27bCfbvgpIlEVF6lpqZi27ZtuHXrluaLqT77UBBVZEVKhJKTkzVvqubNmyMjIwMZGRkF1i2vb76CRokBHClGRGVbREQEtm/fjvT0dBgZGaFbt26wsLAwdFhE5UaREiEbGxvNSDFra+sCO0vndaIu66PGiiJvlBgAjhQjojIpJycHe/bswenTpwEADg4OCAgIyDeil4herkiJ0IEDBzQjwg4ePFiiAZUFHCVGRGVZTk4OVqxYgUePHgEAPD090bVrVxgZ8e8Wka6K9K7p1KlTgf8nIqLSJ5fLUadOHWRkZMDf3x/u7u6GDomo3NJ5ZumwsDAcOXJE83jJkiVo1qwZBg0ahMREdjQmIioJycnJWn9ju3btijFjxjAJInpNOidCn332GZKTkwEAly5dwuTJk+Hj44OoqChMnjxZ5wCWLFkCV1dXGBsbo02bNjh16tRL6z99+hRjx45FtWrVoFQqUbduXezatUvn4xIRlRdXrlzB0qVLsXXrVk0/TJlMBlNTUwNHRlT+6XxDOSoqCg0aNAAAbN26Fb6+vpgzZw7Onj0LHx8fnfa1adMmTJ48GcuWLUObNm2wcOFCeHt7IyIiosAOf9nZ2Xjrrbdgb2+PLVu2oHr16rhz5w6sra11fRlERGVeVlYWwsLCcP78eQD/P9dZRgbMzc0NGxhRBaJzIqRQKDSLru7btw9DhgwBAFSpUkXTUlRU8+fPx6hRozBs2DAAwLJlyxAaGorVq1djypQp+eqvXr0aT548wbFjxzTzGLm6uur6EoiIyrx79+4hKChIczusQ4cO6NSpE2QyTudBpE863xpr3749Jk+ejNmzZ+PUqVPo1asXAODGjRuoUaNGkfeTnZ2NM2fOwMvL63/BSKXw8vLC8ePHC9xm+/bt8PT0xNixY+Hg4IBGjRphzpw5FWLIPhERAKjVahw6dAirV69GYmIirKysMHToUHTt2pVJEFEJ0DkR+uWXX2BkZIQtW7Zg6dKlqF69OgDg77//Ro8ePYq8n4SEBKhUKjg4OGiVOzg4IC4ursBtbt++jS1btkClUmHXrl2YPn06fvrpJ3z77beFHicrKwvJyclaP0REZZUQAhERERBCoFGjRhg9ejRcXFwMHRZRhaXzrbGaNWti586d+coXLFigl4BeRq1Ww97eHsuXL4dMJkPLli1x//59/Pjjj5g5c2aB28ydOxdff/11icdGRFRcQggAgEQigUwmQ0BAAB48eKBZM4yISk6xZt9SqVQICQnBtWvXAAANGzbE22+/rVOzra2tLWQyGR4+fKhV/vDhQzg6Oha4TbVq1SCXy7WOU79+fcTFxSE7OxsKhSLfNlOnTtUazZacnAxnZ+cix0lEVJIyMzMRGhoKGxsbdO3aFcCzv4+2trYGjoyoctD51titW7dQv359DBkyBEFBQQgKCsL777+Phg0bIjIyssj7USgUaNmyJfbv368pU6vV2L9/f6Gr2Ldr1w63bt2CWq3WlN24cQPVqlUrMAkCAKVSCUtLS62f5wkhkJ6dy9XmiajU3blzB8uWLcPly5dx7Ngx3ronMgCdW4QmTJgAd3d3nDhxQrPsxuPHj/H+++9jwoQJCA0NLfK+Jk+ejMDAQHh4eKB169ZYuHAh0tLSNKPIhgwZgurVq2Pu3LkAgDFjxuCXX37BxIkTMX78eNy8eRNz5szBhAkTdH0ZALjiPBEZhkqlQnh4uGZyWhsbGwQEBJTbRauJyjOdE6FDhw5pJUEAULVqVcybNw/t2rXTaV8DBgzAo0ePMGPGDMTFxaFZs2YICwvTdKCOiYmBVPq/RitnZ2fs3r0bH3/8MZo0aYLq1atj4sSJ+OKLL3R9GQAKXnGeq80TUUl6/PgxgoKC8ODBAwBAs2bN0KNHDyiVSgNHRlQ56ZwIKZVKpKSk5CtPTU0t9PbUy4wbNw7jxo0r8Lnw8PB8ZZ6enjhx4oTOx3mVvBXnudo8EZWUnJwcrFmzBmlpaTA2Noavr69mgloiMgyd+wj17t0bH3zwAU6ePAkhBIQQOHHiBEaPHo233367JGIsFXkrzjMJIqKSIpfL0bVrV7i5uWHMmDFMgojKAJ1bhBYvXozAwEB4enpqZnfOzc3F22+/jUWLFuk9QCKi8iwyMhJyuRw1a9YEADRv3hzNmzfnly6iMkLnRMja2hrbtm3DrVu3NMPn69evj9q1a+s9OCKi8io3Nxf79+/HiRMnYGlpidGjR8PExIQJEFEZU+RESK1W48cff8T27duRnZ2Nbt26YebMmTAxMSnJ+IiIyp34+HgEBQVp5kmrW7cujIyKNW0bEZWwIr8zv/vuO8yaNQteXl4wMTHBokWLEB8fj9WrV5dkfERE5YYQAqdOncLevXuhUqlgamoKPz8/1K1b19ChEVEhipwIrVu3Dv/973/x4YcfAni28nyvXr2wcuVKrSHuRESVUU5ODjZv3oxbt24BAGrXrg0/Pz+Ym5sbODIiepkiJ0IxMTHw8fHRPPby8oJEIsGDBw90WnWeiKgiMjIygkKhgEwmQ/fu3dGqVSv2ByIqB4qcCOXm5sLY2FirTC6XIycnR+9BERGVBzk5OVCpVDA2NoZEIkHv3r3RqVMn2NvbGzo0IiqiIidCQggMHTpUa/bTzMxMjB49GmZmZpqyoKAg/UZIRFQGxcbGIigoCPb29njnnXcgkUhgYmLCASRE5UyRE6HAwMB8Ze+//75egyEiKuuEEDh27BgOHDgAtVqNzMxMpKamwsLCwtChEVExFDkRWrNmTUnGQURU5iUnJyMkJARRUVEAgHr16sHX1xempqYGjoyIiosTWxARFcHVq1exY8cOZGZmQi6Xo0ePHpwhmqgCYCJERPQKOTk52L17NzIzM+Hk5ISAgABUrVrV0GERkR4wESIiegW5XA5/f3/cvn0bnTt3hkwmM3RIRKQnTISIiF6gVqtx+PBhWFlZoVmzZgAANzc3uLm5GTYwItI7JkJERM9JTExEcHAw7t69C7lcDnd3d44II6rAirU2xu+//4527drByckJd+7cAQAsXLgQ27Zt02twRESlRQiBixcvYtmyZbh79y6USiV69+7NJIiogtM5EVq6dCkmT54MHx8fPH36FCqVCgBgbW2NhQsX6ju+EpOenYv0bJWhwyCiMiAzMxNBQUEIDg5GdnY2nJ2dMXr0aDRp0sTQoRFRCdP51tjPP/+MFStWwN/fH/PmzdOUe3h44NNPP9VrcCWp9Xf7IVVy7g+iyi4nJwe//vornj59ColEgs6dO6N9+/ZcTJqoktA5EYqKikLz5s3zlSuVSqSlpeklqNLm4WIDEzlHgRBVRnK5HA0bNsTVq1cREBDARaSJKhmdEyE3NzecP38eLi4uWuVhYWGoX7++3gIraYc+6wwHWxsAgIlcxknRiCqRx48fQyKRoEqVKgCALl26oEOHDlprKRJR5aBzIjR58mSMHTsWmZmZEELg1KlT+PPPPzF37lysXLmyJGIsESYKGUwVHDRHVJkIIXD27Fns3r0bdnZ2GD58OGQymeaHiCofnTOBkSNHwsTEBNOmTUN6ejoGDRoEJycnLFq0CO+++25JxEhE9NrS09OxY8cOXL9+HcCz2/lZWVlcJ4yokpMIIURxN05PT0dqairs7e31GVOJSk5OhpWVFWIfPYajbRVDh0NEpSAyMhIhISFITU2FVCpFt27d4OnpyVviROVI3ud3UlISLC0t9bbf17o3ZGpqym9TRFRm5ebmYv/+/Thx4gQAwNbWFn379oWjo6OBIyOisqJYnaVf9i3q9u3brxUQEZG+SCQSxMTEAABatWqFt956C3K53MBREVFZonMiNGnSJK3HOTk5OHfuHMLCwvDZZ5/pKy4iomIRQkAIAalUCplMhoCAADx+/Bh169Y1dGhEVAbpnAhNnDixwPIlS5bg9OnTrx0QEVFxpaamYtu2bXBwcICXlxcAoGrVqqhataqBIyOiskpvU6f27NkTW7du1dfuiIh0EhERgaVLl+LWrVs4deoUUlNTDR0SEZUDeptIZ8uWLZrJyYiISktOTg52796NM2fOAAAcHBwQEBAAc3NzA0dGROWBzolQ8+bNtTpLCyEQFxeHR48e4b///a9egyMiepnY2Fhs3boVjx8/BgB4enqia9euMDLiZKlEVDQ6/7Xw9/fXeiyVSmFnZ4fOnTujXr16+oqLiOilsrOz8fvvvyMjIwMWFhbw9/dHrVq1DB0WEZUzOiVCubm5cHNzg7e3NxwcHEoqJiKiV1IoFOjevTsiIiLg6+vLOc2IqFh0nlna1NQU165dy7foannBmaWJyq8rV67AzMwMrq6uAJ7dmgfAGaKJKoGSmlla51FjrVu3xrlz5/QWABHRq2RlZWHbtm3YsmULgoKCkJGRAeBZAsQkiIheh859hD766CN88sknuHfvHlq2bAkzMzOt55s0aaK34IiI7t27h6CgICQmJgIAmjVrBoVCYeCoiKiiKPKtseHDh2PhwoWwtrbOvxOJBEIISCQSqFQqfceoV7w1RlQ+qNVqHD58GIcOHYIQAlZWVujTp0+5vS1PRK+npG6NFTkRkslkiI2N1TRJF6as/5FiIkRU9mVnZ+OPP/7A3bt3AQCNGzeGj48PjI2NDRwZERmKwVefz8uXynqiQ0Tln1wuh6WlJZRKJXx8fHjLnYhKjE59hFJSUl75jUyfWRoRVR6ZmZkQQsDExAQSiQS9evVCZmYmbGxsDB0aEVVgOiVCL1u9ubz0ESKisic6OhrBwcFwcnJC//79IZFIYGJiAhMTE0OHRkQVnE6JENcTIyJ9UqlUCA8Px5EjRwA864uYnp6ebzQqEVFJ0SkRateuHezt7UsqFiKqRBISEhAUFITY2FgAz4bF9+jRA0ql0sCREVFlwpUJiahUCSFw9uxZ7N69Gzk5OTA2Noavry8aNGhg6NCIqBIqciLk4uICmUxWkrEQUSWQk5ODw4cPIycnB25ubvD39+cgCyIymCInQlFRUSUZBxFVEgqFAn369MH9+/fh6enJJTKIyKCKtNZYjx49cOLEiVfWS0lJwffff48lS5a8dmBEVDHk5uZi9+7dOHPmjKbMxcUFbdu2ZRJERAZXpBahfv36oW/fvrCysoKvry88PDzg5OQEY2NjJCYm4urVqzhy5Ah27dqFXr164ccffyzpuImoHIiPj8fWrVsRHx8PuVyOevXqcUQYEZUpRV5iIysrC3/99Rc2bdqEI0eOICkp6dkOJBI0aNAA3t7eGDFiBOrXr1+iAb8uLrFBVPKEEDh16hT27t0LlUoFU1NT+Pn5vXQuMiKilzH4WmMvSkpKQkZGBqpWrQq5XK63gEoaEyGikpWamopt27bh1q1bAIDatWvDz88P5ubmBo6MiMozg6819iIrKytYWVnpLRAiKv+ysrLw66+/IjU1FUZGRnjrrbfQqlUr9gUiojKL8wgRkd4olUo0b94cN27cQEBAACdgJaIyj4kQEb2W2NhYyOVy2NraAgA6deqEjh07wsiIf16IqOwr0vB5IqIXCSFw9OhRrFy5EkFBQZoFl2UyGZMgIio3+NeKiHSWnJyMkJAQzUSrVlZWyMnJ4ezzRFTu6JwIBQYGYsSIEejYsWNJxENEZdyVK1ewc+dOZGZmQi6Xo0ePHmjevDk7RBNRuaRzIpSUlAQvLy+4uLhg2LBhCAwMRPXq1UsiNiIqQ3JycrBr1y6cP38eAODk5ISAgABUrVrVsIEREb0GnfsIhYSE4P79+xgzZgw2bdoEV1dX9OzZE1u2bEFOTk5JxEhEZYBMJkNCQgIAoEOHDhg+fDiTICIq94o9oWKes2fPYs2aNVi5ciXMzc3x/vvv46OPPkKdOnX0FaNecUJFoqJTq9UQQmj6/jx58gQpKSlwcXExcGREVNmU1ISKrzVqLDY2Fnv37sXevXshk8ng4+ODS5cuoUGDBliwYIG+YiQiA0hMTMTatWtx4MABTVmVKlWYBBFRhaJzIpSTk4OtW7eid+/ecHFxwV9//YVJkybhwYMH+O2337Bv3z5s3rwZ33zzTZH3uWTJEri6usLY2Bht2rTBqVOnirTdxo0bIZFI4O/vr+vLIKJCCCFw4cIFLFu2DHfv3sXZs2eRnp5u6LCIiEqEzp2lq1WrBrVajYEDB+LUqVNo1qxZvjpdunSBtbV1kfa3adMmTJ48GcuWLUObNm2wcOFCeHt7IyIi4qWz0kZHR+PTTz9Fhw4ddH0JRFSIzMxMhIaG4vLlywAAZ2dnBAQEwNTU1MCRERGVDJ37CP3+++/o168fjI2N9RJAmzZt0KpVK/zyyy8AnvVJcHZ2xvjx4zFlypQCt1GpVOjYsSOGDx+Ow4cP4+nTpwgJCSnS8dhHiKhg0dHRCA4ORnJyMiQSCTp37oz27dtDKuW8q0RkeGWmj9DBgwcLHB2WlpaG4cOH67Sv7OxsnDlzBl5eXv8LSCqFl5cXjh8/Xuh233zzDezt7TFixIhXHiMrKwvJyclaP0SkLTMzExs3bkRycjJsbGwwfPhwdOzYkUkQEVV4Ov+V++2335CRkZGvPCMjA+vWrdNpXwkJCVCpVHBwcNAqd3BwQFxcXIHbHDlyBKtWrcKKFSuKdIy5c+fCyspK8+Ps7KxTjESVgbGxMXr27IlmzZrhww8/RI0aNQwdEhFRqShyH6Hk5GQIISCEQEpKitatMZVKhV27dpX4StMpKSkYPHgwVqxYoVng8VWmTp2KyZMnax4nJyczGaJKTwiBs2fPwsbGBrVq1QIANG3aFE2bNjVwZEREpavIiZC1tTUkEgkkEgnq1q2b73mJRIKvv/5ap4Pb2tpCJpPh4cOHWuUPHz6Eo6NjvvqRkZGIjo6Gr6+vpkytVgMAjIyMEBERAXd3d61tlEollEqlTnERVWTp6enYsWMHrl+/DnNzc3z00UcwMTExdFhERAZR5ETo4MGDEEKga9eu2Lp1K6pU+V9HY4VCARcXFzg5Oel0cIVCgZYtW2L//v2aIfBqtRr79+/HuHHj8tWvV68eLl26pFU2bdo0pKSkYNGiRWzpIXqFyMhIhISEIDU1FVKpFJ6ennob+EBEVB4VORHq1KkTACAqKgo1a9bU2wKLkydPRmBgIDw8PNC6dWssXLgQaWlpGDZsGABgyJAhqF69OubOnQtjY2M0atRIa/u8YfovlhPR/+Tm5mLfvn04efIkgGetsQEBAahWrZqBIyMiMqwiJUIXL15Eo0aNIJVKkZSUlK9V5nlNmjTRKYABAwbg0aNHmDFjBuLi4tCsWTOEhYVpOlDHxMRw5ArRa8jMzMSaNWsQHx8PAPDw8ED37t0hl8sNHBkRkeEVaR4hqVSKuLg42NvbQyqVQiKRoKDNJBIJVCpViQSqL5xHiCobIQSCgoJw+/Zt+Pn5FdjHj4iorCupeYSK1CIUFRUFOzs7zf+JqGzL6wNkamoKiUSCXr16ITc3F+bm5oYOjYioTClSIvT8IosODg7sXElUhkVERGD79u1wdnbGgAEDIJFI+J4lIiqEzp1v7O3tERgYiL1792qGrhOR4eXk5CA0NBQbN25Eeno6nj59iszMTEOHRURUphVrZun09HT4+fmhevXqmDRpEk6fPl0SsRFREcXGxmL58uWa9+Kbb76JkSNHcn4gIqJX0HnR1TwpKSnYsmUL/vzzTxw4cAC1atXC+++/jxkzZug7Rr1iZ2mqSIQQOHbsGA4cOAC1Wg1zc3P4+/vnm1iUiKi8K6nO0sVOhJ539epVvPfee7h48SJHjRGVoqysLCxduhRJSUmoV68efH19YWpqauiwiIj0zqCjxgqSmZmJ7du3Y8OGDZp5fz777DO9BUZEhRNCQCKRQKlUIiAgAAkJCWjevLneJjolIqosdE6Edu/ejQ0bNiAkJARGRkZ45513sGfPHnTs2LEk4iOi52RlZSEsLAzVq1eHh4cHAKBmzZqoWbOmgSMjIiqfdE6E+vTpg969e2PdunXw8fHh7LREpeTevXsICgpCYmIirl69ioYNG7IzNBHRa9I5EXr48CEsLCxKIhYiKoBarcbhw4dx6NAhCCFgZWWFPn36MAkiItKDIiVCycnJmo5JQggkJycXWlefHZiIKrvExEQEBwfj7t27AJ4tLtyrVy9OkEhEpCdFSoRsbGwQGxsLe3t7WFtbF9ghM6/zZlkfNUZUXmRmZmL58uXIzMyEQqFAr169dF7UmIiIXq5IidCBAwdQpcqzoeYHDx4s0YCI6BljY2O0adMGt2/fRp8+fWBjY2PokIiIKhyd5xGKiYmBs7NzvlYhIQTu3r1b5kevcB4hKsvu3LkDU1NTzSLHecvYSKU6TwJPRFShlNQ8Qjr/dXVzc8OjR4/ylT958gRubm56CYqoslGpVNi/fz/Wrl2LoKAg5ObmAniWADEJIiIqOTqPGsvrC/Si1NRUduAkKobHjx8jKCgIDx48AAA4OjpyQWMiolJS5ERo8uTJAACJRILp06drTeOvUqlw8uRJNGvWTO8BElVUQgicPXsWu3fvRk5ODoyNjeHr64sGDRoYOjQiokqjyInQuXPnADz7433p0iUoFArNcwqFAk2bNsWnn36q/wiJKqCsrCyEhITg+vXrAJ7dcvb39+f0E0REpazIiVDeaLFhw4Zh0aJF/INN9BrkcjnS0tIglUrRrVs3eHp6cp0wIiID0Mvq8+UJR42RoeR1gDYyevb9IzExEZmZmahWrZohwyIiKhcMuvp8QEAA1q5dC0tLSwQEBLy0blBQkF4CI6pI4uPjERQUBDc3N3h7ewMA5wUiIioDipQIWVlZaZrtraysSjQgoopECIFTp05h3759yM3NRWpqKjp27Mh1woiIygjeGiMqIampqdi2bRtu3boFAKhduzb8/Pxgbm5u4MiIiMofg94ae15GRgaEEJrh83fu3EFwcDAaNGiA7t276y0wovLsxo0b2LZtG9LT0yGTydC9e3e0atWKHaKJiMoYnRMhPz8/BAQEYPTo0Xj69Clat24NhUKBhIQEzJ8/H2PGjCmJOInKjYyMDAQFBSErKwsODg4ICAiAvb29ocMiIqIC6Dx3/9mzZ9GhQwcAwJYtW+Do6Ig7d+5g3bp1WLx4sd4DJCpvTExM0KtXL7z55psYOXIkkyAiojJM5xah9PR0WFhYAAD27NmDgIAASKVSvPnmm7hz547eAyQq64QQOHbsGBwcHFC7dm0AQOPGjdG4cWMDR0ZERK+ic4tQ7dq1ERISgrt372L37t2afkHx8fGcZJEqneTkZPz+++/Yt28ftm3bhszMTEOHREREOtA5EZoxYwY+/fRTuLq6ok2bNvD09ATwrHWoefPmeg+QqKy6cuUKli5diqioKMjlcnTt2hVKpdLQYRERkQ6KNXw+Li4OsbGxaNq0KaTSZ7nUqVOnYGlpiXr16uk9SH3i8Hl6XVlZWQgLC8P58+cBAE5OTggICEDVqlUNGxgRUQVWZobPA4CjoyMcHR21ylq3bq2XgIjKsoyMDKxYsQKJiYkAgA4dOqBTp06QyWQGjoyIiIpD50QoLS0N8+bNw/79+xEfHw+1Wq31/O3bt/UWHFFZY2JiAmdnZ6jVavTp0wcuLi6GDomIiF6DzonQyJEjcejQIQwePBjVqlXjBHFU4SUmJkKhUMDMzAwA4OPjAyEEjI2NDRwZERG9Lp0Tob///huhoaFo165dScRDVGYIIXDx4kXs2rULrq6uePfddyGRSNghmoioAtE5EbKxsUGVKuxkTBVbZmYmQkNDcfnyZc3jrKwstgIREVUwOg+fnz17NmbMmIH09PSSiIfI4O7cuYNly5bh8uXLkEgk6NKlCwIDA5kEERFVQDq3CP3000+IjIyEg4MDXF1dIZfLtZ4/e/as3oIjKk0qlQrh4eE4cuQIgGetnwEBAahRo4aBIyMiopKicyLk7+9fAmEQGV5ubq7mVljz5s3Ro0cPKBQKA0dFREQlqVgTKpZnnFCRnpf36583+vHevXtITk5GgwYNDBkWERG9oKQmVNS5jxAAPH36FCtXrsTUqVPx5MkTAM9uid2/f19vgRGVtPT0dGzatAmnT5/WlNWoUYNJEBFRJaLzrbGLFy/Cy8sLVlZWiI6OxqhRo1ClShUEBQUhJiYG69atK4k4ifQqMjISISEhSE1NRXR0NBo3bszO0ERElZDOLUKTJ0/G0KFDcfPmTa0PDh8fH/zzzz96DY5I33JzcxEWFoY//vgDqampsLW1xdChQ5kEERFVUjq3CP3777/49ddf85VXr14dcXFxegmKqCTEx8dj69atiI+PBwB4eHige/fu+UY+EhFR5aFzIqRUKpGcnJyv/MaNG7Czs9NLUET6lp6ejlWrViE7Oxumpqbw8/ND3bp1DR0WEREZmM63xt5++2188803yMnJAfBstE1MTAy++OIL9O3bV+8BEumDqakp2rZti9q1a2PMmDFMgoiICEAxhs8nJSXhnXfewenTp5GSkgInJyfExcXB09MTu3bt0ixMWVZx+HzlERERARsbG9jb2wMA1Go1JBIJFwomIiqHSmr4vM63xqysrLB3714cPXoUFy5cQGpqKlq0aAEvLy+9BUX0OnJycrB7926cOXMGDg4OGDlyJIyMjCCVFmu2CCIiqsB0ToTytGvXjivQU5kTGxuLrVu34vHjxwAANzc3A0dERERlWZG/Ih8/fhw7d+7UKlu3bh3c3Nxgb2+PDz74AFlZWXoPkKgohBA4evQoVq5cicePH8Pc3ByDBw+Gt7c3jIyKne8TEVEFV+RE6JtvvsGVK1c0jy9duoQRI0bAy8sLU6ZMwY4dOzB37twSCZLoZTIyMrBu3Trs27cParUa9erVw5gxY1CrVi1Dh0ZERGVckb8qnz9/HrNnz9Y83rhxI9q0aYMVK1YAAJydnTFz5kzMmjVL70ESvYxSqYRarYZcLkePHj3QvHlzdogmIqIiKXIilJiYCAcHB83jQ4cOoWfPnprHrVq1wt27d/UbHVEhsrKyIJPJNJ2gAwICkJubi6pVqxo6NCIiKkeKfGvMwcEBUVFRAIDs7GycPXsWb775pub5lJQUztBLpeLevXv49ddfsW/fPk2ZlZUVkyAiItJZkRMhHx8fTJkyBYcPH8bUqVNhamqKDh06aJ6/ePEi3N3dSyRIIuDZPECHDh3C6tWrkZiYiOvXr7ODPhERvZYi3xqbPXs2AgIC0KlTJ5ibm+O3336DQqHQPL969Wp07969RIIkSkxMRHBwsOb2a+PGjeHj4wOlUmngyIiIqDwr1szS5ubmkMlkWuVPnjyBubm5VnJUFnFm6fJFCIGLFy9i165dyM7OhlKphI+PD5o0aWLo0IiIqBSVqZmlC1KlCpMK0r+MjAz8/fffyM7OhrOzMwICAmBtbW3osIiIqILgTHNUppmamqJ379548uQJ2rdvz2UyiIhIr5gIUZmiUqkQHh6OmjVrok6dOgCARo0aGTgqIiKqqMrE1+slS5bA1dUVxsbGaNOmDU6dOlVo3RUrVqBDhw6wsbGBjY0NvLy8Xlqfyo+EhASsWrUKR44cwbZt2zgijIiISpzBE6FNmzZh8uTJmDlzJs6ePYumTZvC29sb8fHxBdYPDw/HwIEDcfDgQRw/fhzOzs7o3r077t+/X8qRk74IIXDmzBksX74csbGxMDY25ogwIiIqFTqPGtO3Nm3aoFWrVvjll18APJsrxtnZGePHj8eUKVNeub1KpYKNjQ1++eUXDBky5JX1OWqsbElPT8eOHTtw/fp1AM9Wi/f399friAAiIir/ysyoMX3Kzs7GmTNnMHXqVE2ZVCqFl5cXjh8/XqR9pKenIycnp9BRa1lZWVq3WJKTk18vaNKbtLQ0LFu2DKmpqZBKpejWrRs8PT25ThgREZUag94aS0hIgEql0lrDDHi2nEdcXFyR9vHFF1/AyckJXl5eBT4/d+5cWFlZaX6cnZ1fO27SDzMzM7i7u8PW1hYjR45E27ZtmQQREVGpKtejxubNm4eNGzciPDwcxsbGBdaZOnUqJk+erHmcnJzMZMiA4uPjYWpqCnNzcwDPlm6RSCRcp46IiAzCoImQra0tZDIZHj58qFX+8OFDODo6vnTb//znP5g3bx727dv30lmGlUolO92WAUIInDp1Cnv37kWtWrUwcOBASCSSMj8TORERVWwGvTWmUCjQsmVL7N+/X1OmVquxf/9+eHp6FrrdDz/8gNmzZyMsLAweHh6lESq9htTUVGzYsAFhYWFQqVQAgJycHANHRUREVAZujU2ePBmBgYHw8PBA69atsXDhQqSlpWHYsGEAgCFDhqB69eqYO3cuAOD777/HjBkzsGHDBri6umr6Epmbm2tut1DZERERge3btyM9PR1GRkZ466230KpVK/YFIiKiMsHgidCAAQPw6NEjzJgxA3FxcWjWrBnCwsI0HahjYmK0llVYunQpsrOz8c4772jtZ+bMmZg1a1Zphk4vkZOTg927d+PMmTMAnnWADwgIgL29vYEjIyIi+h+DzyNU2jiPUOnIysrCr7/+isTERHh6eqJr164wMjJ43k1EROVUhZxHiCqWvJxaIpFAqVSib9++yMrKQq1atQwcGRERUcGYCJFeJCcnIyQkBG+88QbatGkDAKhevbqBoyIiIno5JkL02q5cuYKdO3ciMzMTcXFxaN68OYfFExFRucBEiIotKysLYWFhOH/+PADAyckJAQEBTIKIiKjcYCJExXLv3j0EBQUhMTERANChQwd06tQJMpnMwJEREREVHRMh0llqaip+++035ObmwsrKCn369IGLi4uhwyIiItIZEyHSmbm5OTp27Ij4+Hj06tWr0HXeiIiIyjomQvRKQghcvHgRjo6Omoku27dvz9mhiYio3GMiRC+VmZmJ0NBQXL58GXZ2dhg1ahTkcjmTICIiqhCYCFGhoqOjERwcjOTkZEgkEjRq1IidoYmIqEJhIkT5qFQqhIeH48iRIwAAGxsbBAQEoEaNGgaOjIiISL+YCJGWtLQ0bNiwAQ8ePAAANGvWDD169IBSqTRwZERERPrHRIi0mJiYQC6Xw9jYGL6+vmjQoIGhQyIiIioxTIQI6enpkMvlkMvlkEqlCAgIAAC9ru5LRERUFkkNHQAZVmRkJJYuXYq9e/dqyiwtLZkEERFRpcAWoUoqNzcX+/fvx4kTJwAAUVFRyM7O5jphRERUqTARqoTi4+MRFBSEhw8fAgA8PDzQvXt3yOVyA0dGRERUupgIVSJCCJw6dQp79+6FSqWCqakp/Pz8ULduXUOHRkREZBBMhCqRtLQ0hIeHQ6VSoXbt2vDz84O5ubmhwyIiIjIYJkKViLm5OXx9fZGamopWrVpxmQwiIqr0mAhVYDk5OdizZw/q1Kmjuf3FeYGIiIj+h8PnK6jY2FgsX74cp0+fxvbt25GdnW3okIiIiMoctghVMEIIHDt2DAcOHIBarYa5uTn8/f05LJ6IiKgATIQqkOTkZISEhCAqKgoAUK9ePfj6+sLU1NTAkREREZVNTIQqiJSUFCxduhSZmZmQy+Xo0aMHmjdvzg7RREREL8FEqIKwsLBAvXr1EB8fj4CAAFStWtXQIREREZV5TITKsXv37sHKygoWFhYAAB8fH0ilUshkMgNHRkREVD5w1Fg5pFarcejQIaxevRrbtm2DEAIAIJfLmQQRERHpgC1C5UxiYiKCg4Nx9+5dAICJiQlyc3O5ThgREVExMBEqJ4QQuHTpEkJDQ5GdnQ2lUgkfHx80adLE0KERERGVW0yEyoGsrCzs3LkTly9fBgA4OzujT58+sLGxMXBkRERE5RsToXJAIpHgwYMHkEgk6NSpEzp06ACplN27KhshBHJzc6FSqQwdChFRiTBEX1cmQmWUSqWCVCqFRCKBQqHAO++8A5VKhRo1ahg6NDKA7OxsxMbGIj093dChEBGVGIlEgho1asDc3LzUjslEqAx6/PgxgoKC0LhxY7z55psAgGrVqhk4KjIUtVqNqKgoyGQyODk5QaFQcKJMIqpwhBB49OgR7t27hzp16pRayxAToTJECIGzZ89i9+7dyMnJQXJyMlq2bMkRYZVcdnY21Go1nJ2duVwKEVVodnZ2iI6ORk5ODhOhyiY9PR07duzA9evXAQBubm7w9/dnEkQa7BdGRBWdIVq7mQiVAZGRkQgJCUFqaiqkUim6desGT09P3v4gIiIqYUyEDCwlJQV//vknVCoVbG1tERAQwP5AREREpYRt7QZmYWGBzp07w8PDAx988AGTICIAs2bNQrNmzQwdhsENHToU/v7+pXIsV1dXLFy4UPM4Li4Ob731FszMzGBtbQ3g2W2LkJCQUonHkCIiIuDo6IiUlBRDh1KhZGdnw9XVFadPnzZ0KFqYCJUyIQROnTqFuLg4TVm7du3Qq1cv9geiCuv48eOQyWTo1atXiR3D1dUVEokEEolEM8JuxIgRSExMLLFjvig8PBwSiQRPnz59ZV0hBJYvX442bdrA3Nwc1tbW8PDwwMKFCw0yTcK///6LDz74QPN4wYIFiI2Nxfnz53Hjxg0AQGxsLHr27Kn3Y69du1Zz7aRSKapVq4YBAwYgJiYmX90rV66gf//+sLOzg1KpRN26dTFjxowCz9m5c+fQr18/ODg4wNjYGHXq1MGoUaM0r6cwU6dOxfjx4zULWldES5YsgaurK4yNjdGmTRucOnXqpfVzcnLwzTffwN3dHcbGxmjatCnCwsK06sydOxetWrWChYUF7O3t4e/vj4iICM3zCoUCn376Kb744osSeU3FxUSoFKWmpmLDhg34+++/sXXrVuTm5gIwTOcwotK0atUqjB8/Hv/88w8ePHhQYsf55ptvEBsbi5iYGKxfvx7//PMPJkyYUGLHex2DBw/GpEmT4Ofnh4MHD+L8+fOYPn06tm3bhj179pR6PHZ2dlqjEiMjI9GyZUvUqVMH9vb2AABHR0colcpiHyM7O7vQ5ywtLREbG4v79+9j69atiIiIQL9+/bTqnDhxAm3atEF2djZCQ0Nx48YNfPfdd1i7di3eeustrf3v3LkTb775JrKysrB+/Xpcu3YNf/zxB6ysrDB9+vRC44iJicHOnTsxdOjQYr/OV71WQ9u0aRMmT56MmTNn4uzZs2jatCm8vb0RHx9f6DbTpk3Dr7/+ip9//hlXr17F6NGj0adPH5w7d05T59ChQxg7dixOnDiBvXv3IicnB927d0daWpqmznvvvYcjR47gypUrJfoadSIqmaSkJAFAxD56XKrHjYiIED/88IOYNWuWmD17tjh58qRQq9WlGgOVTxkZGeLq1asiIyNDU6ZWq0VaVo5BfnT9vU1JSRHm5ubi+vXrYsCAAeK7777LV2fu3LnC3t5emJubi+HDh4svvvhCNG3aVPP8qVOnhJeXl6hataqwtLQUHTt2FGfOnNHah4uLi1iwYIFW2ezZs0WDBg20yrZs2SIaNGggFAqFcHFxEf/5z3+0nn/y5IkYPHiwsLa2FiYmJqJHjx7ixo0bmuejo6NF7969hbW1tTA1NRUNGjQQoaGhIioqSgDQ+gkMDCzwnGzatEkAECEhIfmeU6vV4unTp0IIIQIDA4Wfn5/mub///lu0a9dOWFlZiSpVqohevXqJW7duaZ7PysoSY8eOFY6OjkKpVIqaNWuKOXPmaPY7c+ZM4ezsLBQKhahWrZoYP358gefPxcWlwNcBQAQHB2u2iYmJEf369RNWVlbCxsZGvP322yIqKkrzfF783377rahWrZpwdXUt8HysWbNGWFlZaZUtXrxYABBJSUma+Bs0aCA8PDyESqXSqnv+/HkhkUjEvHnzhBBCpKWlCVtbW+Hv71/g8RITEwssF0KIH3/8UXh4eGiVJSQkiHfffVc4OTkJExMT0ahRI7FhwwatOp06dRJjx44VEydOFFWrVhWdO3cWQghx6dIl0aNHD2FmZibs7e3F+++/Lx49eqTZ7lXXtCS0bt1ajB07VvNYpVIJJycnMXfu3EK3qVatmvjll1+0ygICAsR7771X6Dbx8fECgDh06JBWeZcuXcS0adMK3Kagv3d58j6/834n9IWdpUtYTk4O9uzZo7kn6uDggICAAM03LKLiyMhRocGM3QY59tVvvGGqKPqfjs2bN6NevXp444038P7772PSpEmYOnWqpiV08+bNmDVrFpYsWYL27dvj999/x+LFi1GrVi3NPlJSUhAYGIiff/4ZQgj89NNP8PHxwc2bNwu9fXH//n3s2LEDbdq00ZSdOXMG/fv3x6xZszBgwAAcO3YMH330EapWrappARg6dChu3ryJ7du3w9LSEl988QV8fHxw9epVyOVyjB07FtnZ2fjnn39gZmaGq1evwtzcHM7Ozti6dSv69u2LiIgIWFpawsTEpMDY1q9fjzfeeAN+fn75npNIJLCysipwu7S0NEyePBlNmjRBamoqZsyYgT59+uD8+fOQSqVYvHgxtm/fjs2bN6NmzZq4e/cu7t69CwDYunUrFixYgI0bN6Jhw4aIi4vDhQsXCjzOv//+iyFDhsDS0hKLFi0q8HXk5OTA29sbnp6eOHz4MIyMjPDtt9+iR48euHjxIhQKBQBg//79sLS0xN69ews8VkHi4+MRHBwMmUymmUvm/PnzuHr1KjZs2JBvKommTZvCy8sLf/75J7744gvs3r0bCQkJ+Pzzzwvcf16fp4IcPnwYHh4eWmWZmZlo2bIlvvjiC1haWiI0NBSDBw+Gu7s7Wrduran322+/YcyYMTh69CgA4OnTp+jatStGjhyJBQsWICMjA1988QX69++PAwcOAHj1NS3InDlzMGfOnJeew6tXr6JmzZr5yrOzs3HmzBlMnTpVUyaVSuHl5YXjx48Xur+srCwYGxtrlZmYmODIkSOFbpOUlAQAqFKlilZ569atcfjw4ZfGX5qYCJWglJQUrFu3DgkJCQCAN998E926dYOREU87VR6rVq3C+++/DwDo0aMHkpKScOjQIXTu3BkAsHDhQowYMQIjRowAAHz77bfYt28fMjMzNfvo2rWr1j6XL18Oa2trHDp0CL1799aUf/HFF5g2bRpUKhUyMzPRpk0bzJ8/X/P8/Pnz0a1bN82tkbp16+Lq1av48ccftRKgo0ePom3btgCeJS3Ozs4ICQlBv379EBMTg759+6Jx48YAoJWw5f3Bt7e3f+mH7c2bN/HGG2/odB4BoG/fvlqPV69eDTs7O1y9ehWNGjVCTEwM6tSpg/bt20MikcDFxUVTNyYmBo6OjvDy8oJcLkfNmjW1PsSfl9f/xsTEBI6OjgXW2bRpE9RqNVauXKlJatesWQNra2uEh4eje/fuAAAzMzOsXLlSkxgVJikpCebm5hBCaPr7TJgwAWZmZgCg6ddTv379ArevX7++5kP55s2bAIB69eq99JgFuXPnTr5EqHr16vj00081j8ePH4/du3dj8+bNWuewTp06+OGHHzSPv/32WzRv3lwraVm9ejWcnZ1x48YN1K1b95XXtCCjR49G//79X/o6nJycCixPSEiASqWCg4ODVrmDg4NmHruCeHt7Y/78+ejYsSPc3d2xf/9+BAUFFbr2oVqtxqRJk9CuXbt8r8PJyQl37tx5afyliZ/IJcjc3Bzm5ubIzMyEv78/3N3dDR0SVRAmchmufuNtsGMXVUREBE6dOoXg4GAAgJGREQYMGIBVq1ZpEqFr165h9OjRWtt5enri4MGDmscPHz7EtGnTEB4ejvj4eKhUKqSnp+frTPvZZ59h6NChEELg7t27+PLLL9GrVy/8888/kMlkuHbtWr5WmHbt2mHhwoVQqVS4du0ajIyMtFqRqlatijfeeAPXrl0D8OzDecyYMdizZw+8vLzQt29fNGnSpMjnBHjWUbo4bt68iRkzZuDkyZNISEiAWq0G8CzJadSoEYYOHYq33noLb7zxBnr06IHevXtrEpJ+/fph4cKFqFWrFnr06AEfHx/4+voW+4vZhQsXcOvWrXwtcpmZmYiMjNQ8bty48SuTIODZCNqzZ88iJycHf//9N9avX4/vvvsuX72inLvinl8AyMjIyNfyoVKpMGfOHGzevBn3799HdnY2srKy8s303rJlS63HFy5cwMGDBwtcNysyMhJ169Z95TUtSJUqVfK1spS0RYsWYdSoUahXrx4kEgnc3d0xbNgwrF69usD6Y8eOxeXLlwtsMTIxMSlT6yYyEdKz5ORkmJiYQC6XQyKRICAgADKZjEsjkF5JJBKdbk8ZyqpVq5Cbm6v17VQIAaVSiV9++aXQW0AvCgwMxOPHj7Fo0SK4uLhAqVTC09MzX4dUW1tb1K5dG8Czb+cLFy7UJFVeXl56eU0jR46Et7c3QkNDsWfPHsydOxc//fQTxo8fX+R91K1b96Xfvgvj6+sLFxcXrFixAk5OTlCr1WjUqJHmPLRo0QJRUVH4+++/sW/fPvTv3x9eXl7YsmULnJ2dERERgX379mHv3r346KOP8OOPP+LQoUPFGrGampqKli1bYv369fmes7Oz0/w/r0XnVaRSqeba1a9fH5GRkRgzZgx+//13AM/OGfAscW7evHm+7a9du6apk/fv9evX4enpqcOrevY79OJIwx9//BGLFi3CwoUL0bhxY5iZmWHSpEn5fv9efK2pqanw9fXF999/n+84eVOlvOqaFuR1bo3Z2tpCJpPh4cOHWuUPHz4stPUPeHZNQ0JCkJmZicePH8PJyQlTpkzRahHNM27cOOzcuRP//PNPgQuFP3nyROt3xNA4akyPrly5gqVLl2qN+LCwsGASRJVSbm4u1q1bh59++gnnz5/X/Fy4cAFOTk74888/ATz70Dt58qTWtidOnNB6fPToUUyYMAE+Pj5o2LAhlEql5pbzy+T1L8nIyNAcK6//xvP7rlu3LmQyGerXr4/c3FyteB4/foyIiAg0aNBAU+bs7IzRo0cjKCgIn3zyCVasWAEAmpaPwm4X5Bk0aBBu3LiBbdu25XtOCKHpW/G8vDimTZuGbt26oX79+gVODWBpaYkBAwZgxYoV2LRpE7Zu3YonT54AePZN3NfXF4sXL0Z4eDiOHz+OS5cuvTTWwrRo0QI3b96Evb09ateurfVT1AT3ZaZMmYJNmzbh7NmzAIBmzZqhXr16WLBggabVJM+FCxewb98+DBw4EADQvXt32Nraat2met7Lpjdo3rw5rl69qlV29OhR+Pn54f3330fTpk1Rq1atVw7BB56doytXrsDV1TXfOTIzMyvyNX3R6NGjtd5TBf0UdmtMoVCgZcuW2L9/v6ZMrVZj//79RUoajY2NUb16deTm5mLr1q1aLaxCCIwbNw7BwcE4cOAA3NzcCtzH5cuXC0xmDYWJkB5kZWVh27Zt2LJlCzIzMxEbG4ucnBxDh0VkUDt37kRiYiJGjBiBRo0aaf307dsXq1atAgBMnDgRq1evxpo1a3Djxg3MnDkz39DaOnXq4Pfff8e1a9dw8uRJvPfeewV24E1JSUFcXBxiY2Nx6tQpfPbZZ7Czs9P09/nkk0+wf/9+zJ49Gzdu3MBvv/2GX375RdP/o06dOvDz88OoUaNw5MgRXLhwAe+//z6qV6+u+YM/adIk7N69G1FRUTh79iwOHjyo6bfi4uICiUSCnTt34tGjR0hNTS3w3PTv3x8DBgzAwIEDMWfOHJw+fRp37tzBzp074eXlpXVbMI+NjQ2qVq2K5cuX49atWzhw4AAmT56sVWf+/Pn4888/cf36ddy4cQN//fUXHB0dYW1tjbVr12LVqlW4fPkybt++jT/++AMmJiZa/Yh08d5778HW1hZ+fn44fPgwoqKiEB4ejgkTJuDevXvF2ufznJ2d0adPH8yYMQPAs1bQVatW4erVq+jbty9OnTqFmJgY/PXXX/D19YWnpycmTZoE4H/9kkJDQ/H2229j3759iI6OxunTp/H555/nuxX7PG9vbxw/flwrma1Tpw727t2LY8eO4dq1a/jwww/ztagUZOzYsXjy5AkGDhyIf//9F5GRkdi9ezeGDRsGlUpVpGtakCpVquRLrF78edktz8mTJ2PFihX47bffcO3aNYwZMwZpaWkYNmyYps6QIUO0OlSfPHkSQUFBuH37Ng4fPowePXpArVZrdUgfO3Ys/vjjD2zYsAEWFhaIi4tDXFyc5otInsOHD2tu2ZYJeh2DVg7oe/j83bt3xaJFi8SsWbPErFmzxP79+0Vubq5e9k0kxMuHk5ZlvXv3Fj4+PgU+d/LkSQFAXLhwQQghxHfffSdsbW2Fubm5CAwMFJ9//rnW8PmzZ88KDw8PYWxsLOrUqSP++uuvfMPlXxzybWdnJ3x8fMS5c+e0jp03fF4ul4uaNWuKH3/8Uev5vOHzVlZWwsTERHh7e2sNnx83bpxwd3cXSqVS2NnZicGDB4uEhATN8998841wdHQUEomk0OHzQjwbsrx06VLRqlUrYWpqKiwtLUXLli3FokWLRHp6uhAi//D5vXv3ivr16wulUimaNGkiwsPDtYa0L1++XDRr1kyYmZkJS0tL0a1bN3H27FkhhBDBwcGiTZs2wtLSUpiZmYk333xT7Nu3T+v8PX8+/fz88sWPF4bPx8bGiiFDhghbW1uhVCpFrVq1xKhRozTDm1+MvzAFDZ8XQojjx48LAOLkyZOasosXL4q+ffuKKlWqCLlcLtzd3cW0adNEWlpavu3//fdfERAQIOzs7IRSqRS1a9cWH3zwgbh582ahseTk5AgnJycRFhamKXv8+LHw8/MT5ubmwt7eXkybNk0MGTJE67V16tRJTJw4Md/+bty4Ifr06aOZjqFevXpi0qRJmmkoXnVNS8rPP/8satasKRQKhWjdurU4ceKE1vOdOnXSuv7h4eGaOKtWrSoGDx4s7t+/r7UNXpg+Iu9nzZo1mjrHjh0T1tbWmt/xFxli+Lzk/4OvNJKTk2FlZYXYR4/haFv8zmZqtRqHDx/GoUOHIISAlZUV+vTpU+xvV0SFyczMRFRUFNzc3PJ14iQi/VuyZAm2b9+O3bsNM0VFRTZgwAA0bdoUX375ZYHPv+zvXd7nd1JSEiwtLfUWU9nvbVlGpaWl4eTJkxBCoFGjRujVqxc/pIiIKoAPP/wQT58+RUpKSoVeZqO0ZWdno3Hjxvj4448NHYoWtgi9huvXryM7O1vnobNEumCLEBFVFmwRKsMyMzMRGhqKhg0baibpKs5kXURERFR2MBEqgujoaAQHByM5ORnR0dGv7JFPRERE5QM/zV9CpVLh4MGDmnlHbGxsEBAQwCSIDKKS3cUmokrIEH/n+IleiISEBAQFBSE2NhbAs8m8evbsWaSp4on0KW/W3/T09EIX8SQiqgjyZtTOmwy1NDARKkBSUhKWL1+OnJwcGBsbw9fXV2tWWaLSJJPJYG1tjfj4eACAqampZpFLIqKKQq1W49GjRzA1NS3VOy9MhApgZWWFxo0bIzExEf7+/nrtnU5UHHlrAOUlQ0REFZFUKkXNmjVL9cseE6H/FxkZCXt7e82cET179oRMJuM3byoTJBIJqlWrBnt7ey7fQkQVlkKhgFRauqt/lYlEaMmSJfjxxx8RFxeHpk2b4ueff0br1q0Lrf/XX39h+vTpiI6ORp06dfD999/Dx8enWMfOzc3Fvn37cPLkSdSqVQvvv/8+JBIJO0RTmSSTyUr13jkRUUVn8EVXN23ahMmTJ2PmzJk4e/YsmjZtCm9v70JvARw7dgwDBw7EiBEjcO7cOfj7+8Pf3x+XL1/W+djx8fFYsWKFZqXpKlWqvHLVaCIiIqo4DD6zdJs2bdCqVSv88ssvAJ51lnJ2dsb48eMxZcqUfPUHDBiAtLQ07Ny5U1P25ptvolmzZli2bNkrj5c3M2XYnr349+QJqFQqmJqaws/PD3Xr1tXfCyMiIiK9KamZpQ3aIpSdnY0zZ87Ay8tLUyaVSuHl5YXjx48XuM3x48e16gOAt7d3ofULE35gP1QqFWrXro0xY8YwCSIiIqqEDNoRJiEhASqVCg4ODlrlDg4OuH79eoHbxMXFFVg/Li6uwPpZWVnIysrSPE5KSgLwrG9Qhw4d0LJlS6jVaiQnJ7/OSyEiIqISlPc5re8bWRW+R/DcuXPx9ddf5yv/z3/+g//85z8GiIiIiIiK6/Hjx7CystLb/gyaCNna2kImk+Hhw4da5Q8fPtTMm/IiR0dHnepPnToVkydP1jx++vQpXFxcEBMTo9cTSbpLTk6Gs7Mz7t69y7maygBej7KD16Ls4LUoO5KSklCzZk1UqVJFr/s1aCKkUCjQsmVL7N+/H/7+/gCedZbev38/xo0bV+A2np6e2L9/PyZNmqQp27t3Lzw9PQusr1QqoVQq85VbWVnxl7qMsLS05LUoQ3g9yg5ei7KD16Ls0Pc8Qwa/NTZ58mQEBgbCw8MDrVu3xsKFC5GWloZhw4YBAIYMGYLq1atj7ty5AICJEyeiU6dO+Omnn9CrVy9s3LgRp0+fxvLlyw35MoiIiKgcMngiNGDAADx69AgzZsxAXFwcmjVrhrCwME2H6JiYGK3sr23bttiwYQOmTZuGL7/8EnXq1EFISAgaNWpkqJdARERE5ZTBEyEAGDduXKG3wsLDw/OV9evXD/369SvWsZRKJWbOnFng7TIqXbwWZQuvR9nBa1F28FqUHSV1LQw+oSIRERGRoRh8iQ0iIiIiQ2EiRERERJUWEyEiIiKqtJgIERERUaVVIROhJUuWwNXVFcbGxmjTpg1OnTr10vp//fUX6tWrB2NjYzRu3Bi7du0qpUgrPl2uxYoVK9ChQwfY2NjAxsYGXl5er7x2pBtd3xt5Nm7cCIlEopn4lF6frtfi6dOnGDt2LKpVqwalUom6devyb5We6HotFi5ciDfeeAMmJiZwdnbGxx9/jMzMzFKKtuL6559/4OvrCycnJ0gkEoSEhLxym/DwcLRo0QJKpRK1a9fG2rVrdT+wqGA2btwoFAqFWL16tbhy5YoYNWqUsLa2Fg8fPiyw/tGjR4VMJhM//PCDuHr1qpg2bZqQy+Xi0qVLpRx5xaPrtRg0aJBYsmSJOHfunLh27ZoYOnSosLKyEvfu3SvlyCsmXa9HnqioKFG9enXRoUMH4efnVzrBVnC6XousrCzh4eEhfHx8xJEjR0RUVJQIDw8X58+fL+XIKx5dr8X69euFUqkU69evF1FRUWL37t2iWrVq4uOPPy7lyCueXbt2ia+++koEBQUJACI4OPil9W/fvi1MTU3F5MmTxdWrV8XPP/8sZDKZCAsL0+m4FS4Rat26tRg7dqzmsUqlEk5OTmLu3LkF1u/fv7/o1auXVlmbNm3Ehx9+WKJxVga6XosX5ebmCgsLC/Hbb7+VVIiVSnGuR25urmjbtq1YuXKlCAwMZCKkJ7pei6VLl4patWqJ7Ozs0gqx0tD1WowdO1Z07dpVq2zy5MmiXbt2JRpnZVOUROjzzz8XDRs21CobMGCA8Pb21ulYFerWWHZ2Ns6cOQMvLy9NmVQqhZeXF44fP17gNsePH9eqDwDe3t6F1qeiKc61eFF6ejpycnL0vsBeZVTc6/HNN9/A3t4eI0aMKI0wK4XiXIvt27fD09MTY8eOhYODAxo1aoQ5c+ZApVKVVtgVUnGuRdu2bXHmzBnN7bPbt29j165d8PHxKZWY6X/09fldJmaW1peEhASoVCrN8hx5HBwccP369QK3iYuLK7B+XFxcicVZGRTnWrzoiy++gJOTU75fdNJdca7HkSNHsGrVKpw/f74UIqw8inMtbt++jQMHDuC9997Drl27cOvWLXz00UfIycnBzJkzSyPsCqk412LQoEFISEhA+/btIYRAbm4uRo8ejS+//LI0QqbnFPb5nZycjIyMDJiYmBRpPxWqRYgqjnnz5mHjxo0IDg6GsbGxocOpdFJSUjB48GCsWLECtra2hg6n0lOr1bC3t8fy5cvRsmVLDBgwAF999RWWLVtm6NAqnfDwcMyZMwf//e9/cfbsWQQFBSE0NBSzZ882dGhUTBWqRcjW1hYymQwPHz7UKn/48CEcHR0L3MbR0VGn+lQ0xbkWef7zn/9g3rx52LdvH5o0aVKSYVYaul6PyMhIREdHw9fXV1OmVqsBAEZGRoiIiIC7u3vJBl1BFee9Ua1aNcjlcshkMk1Z/fr1ERcXh+zsbCgUihKNuaIqzrWYPn06Bg8ejJEjRwIAGjdujLS0NHzwwQf46quvtBYJp5JV2Oe3paVlkVuDgArWIqRQKNCyZUvs379fU6ZWq7F//354enoWuI2np6dWfQDYu3dvofWpaIpzLQDghx9+wOzZsxEWFgYPD4/SCLVS0PV61KtXD5cuXcL58+c1P2+//Ta6dOmC8+fPw9nZuTTDr1CK895o164dbt26pUlGAeDGjRuoVq0ak6DXUJxrkZ6eni/ZyUtQBZfuLFV6+/zWrR932bdx40ahVCrF2rVrxdWrV8UHH3wgrK2tRVxcnBBCiMGDB4spU6Zo6h89elQYGRmJ//znP+LatWti5syZHD6vJ7pei3nz5gmFQiG2bNkiYmNjNT8pKSmGegkViq7X40UcNaY/ul6LmJgYYWFhIcaNGyciIiLEzp07hb29vfj2228N9RIqDF2vxcyZM4WFhYX4888/xe3bt8WePXuEu7u76N+/v6FeQoWRkpIizp07J86dOycAiPnz54tz586JO3fuCCGEmDJlihg8eLCmft7w+c8++0xcu3ZNLFmyhMPn8/z888+iZs2aQqFQiNatW4sTJ05onuvUqZMIDAzUqr9582ZRt25doVAoRMOGDUVoaGgpR1xx6XItXFxcBIB8PzNnziz9wCsoXd8bz2MipF+6Xotjx46JNm3aCKVSKWrVqiW+++47kZubW8pRV0y6XIucnBwxa9Ys4e7uLoyNjYWzs7P46KOPRGJiYukHXsEcPHiwwM+AvPMfGBgoOnXqlG+bZs2aCYVCIWrVqiXWrFmj83ElQrAtj4iIiCqnCtVHiIiIiEgXTISIiIio0mIiRERERJUWEyEiIiKqtJgIERERUaXFRIiIiIgqLSZCREREVGkxESKqoGbNmoVmzZrlK3NwcIBEIkFISAiGDh0Kf3//Iu0vOjoaEomkxFajX7VqFbp3766XfYWHh0MikeDp06d62V9pc3V1xcKFC19ap6DrW9EsW7ZMa707ohLxujNBEpVXhw4dEr179xbVqlUTAERwcLDe9h0UFCTatGkjLC0thbm5uWjQoIGYOHGi3vZfFCkpKSIhIUHz+OrVq5rXGRsbKzIzM8XTp0+LPCNubm6uiI2NFTk5OUKI/80Cq48ZdTMyMkS1atXEkSNHNGUzZ84scJbZvXv3vnJ/+oztZfvP+7G3txcBAQEiMjJSL/uPj48XaWlpmscF/X6+eH1Lwpo1azSvUSKRCEdHR9G/f3/NkgdFNXPmTNG0aVOdj5+VlSWcnJzEP//8o/O2REXFFiGqtNLS0tC0aVMsWbJEr/vdv38/BgwYgL59++LUqVM4c+YMvvvuO+Tk5Oj1OK9ibm6OqlWrah5HRkYCAPz8/ODo6AilUgkrKytYW1sXaX8ymQyOjo4wMjLSe6xbtmyBpaUl2rVrp1XesGFDxMbGav107NhR78cvroiICDx48AB//fUXrly5Al9fX6hUqtfer52dHUxNTV9a58XrW1IsLS0RGxuL+/fvY+vWrYiIiEC/fv1K/LjAs0VRBw0ahMWLF5fK8aiSMnQmRlQWQI8tQhMnThSdO3d+aZ28b8jLli0TNWrUECYmJqJfv37i6dOnWvVWrFgh6tWrJ5RKpXjjjTfEkiVLtJ6/e/euePfdd4WNjY0wNTUVLVu21KyT9Py38IJaV4TIv36YSqUS33//vXB3dxcKhUI4OztrFvaMiooSAMS5c+c0/8cL6wH99ttvokqVKiIzM1MrTj8/P/H+++8Xej569eolPv300wLPUUHWrVsnWrZsKczNzYWDg4MYOHCgePjwoeb5F1uEoqOjRe/evYW1tbUwNTUVDRo00FpT8NKlS6JHjx7CzMxM2Nvbi/fff188evSo0HgLanFav369ACCuX78uhBDiv//9r6hVq5aQy+Wibt26Yt26dZq6arVazJw5Uzg7OwuFQiGqVasmxo8fr3nexcVFLFiwQPP/58+zi4tLvvOze/duoVQq87WATZgwQXTp0kXz+PDhw6J9+/bC2NhY1KhRQ4wfP16kpqYW+jrXrFkjrKystMoWL14sAIikpCRN2eeffy7q1KkjTExMhJubm5g2bZrIzs7W7OPF35W89aASExPFiBEjhK2trbCwsBBdunQR58+f1zreoUOHhEKhEOnp6YXGSfQ62CJEpGeOjo64cuUKLl++/NJ6t27dwubNm7Fjxw6EhYXh3Llz+OijjzTPr1+/HjNmzMB3332Ha9euYc6cOZg+fTp+++03AEBqaio6deqE+/fvY/v27bhw4QI+//xzqNXqfMf69NNPsWbNGgDQtKwUZOrUqZg3bx6mT5+Oq1evYsOGDXBwcMhXz9nZGVu3bgXwrFUkNjYWixYtQr9+/aBSqbB9+3ZN3fj4eISGhmL48OGFnosjR47Aw8PjpefreTk5OZg9ezYuXLiAkJAQREdHY+jQoYXWHzt2LLKysvDPP//g0qVL+P7772Fubg4AePr0Kbp27YrmzZvj9OnTCAsLw8OHD9G/f/8ixwMAJiYmAIDs7GwEBwdj4sSJ+OSTT3D58mV8+OGHGDZsGA4ePAgA2Lp1KxYsWIBff/0VN2/eREhICBo3blzgfv/9918AwJo1axAbG6t5/Lxu3brB2tpac00AQKVSYdOmTXjvvfcAPGsR7NGjB/r27YuLFy9i06ZNOHLkCMaNG1fk1xgfH4/g4GDIZDLIZDJNuYWFBdauXYurV69i0aJFWLFiBRYsWAAAGDBgAD755BOt1r0BAwYAAPr164f4+Hj8/fffOHPmDFq0aIFu3brhyZMnmn17eHggNzcXJ0+eLHKcRDoxdCZGVBZAjy1CqampwsfHR/PtfcCAAWLVqlVarSQzZ84UMplM3Lt3T1P2999/C6lUKmJjY4UQQri7u4sNGzZo7Xv27NnC09NTCCHEr7/+KiwsLMTjx48LjOPFFpXg4GDx4lv++Rah5ORkoVQqxYoVKwrc3/MtQkIU3g9nzJgxomfPnprHP/30k6hVq5ZQq9UF7jcxMVEAyNcPZObMmUIqlQozMzPNT6tWrQrcx7///isAiJSUlAJja9y4sZg1a1aB286ePVt0795dq+zu3bsCgIiIiChwmxf3/+DBA9G2bVtRvXp1kZWVJdq2bStGjRqltU2/fv2Ej4+P5pzUrVtX02ryoudbhIQo+Pfzxes7ceJE0bVrV83jF1uJRowYIT744AOtfRw+fFhIpVKRkZFRYBx5rTlmZmbC1NRU06IzYcKEAuvn+fHHH0XLli0LjTXv2JaWlvlaD93d3cWvv/6qVWZjYyPWrl370mMSFRdbhIiKKCYmBubm5pqfOXPmFFjPzMwMoaGhuHXrFqZNmwZzc3N88sknaN26NdLT0zX1atasierVq2see3p6Qq1WIyIiAmlpaYiMjMSIESO0jvntt99q+vqcP38ezZs3R5UqVfTy+q5du4asrCx069bttfYzatQo7NmzB/fv3wcArF27FkOHDoVEIimwfkZGBgDA2Ng433NvvPEGzp8/r/nJa/E4c+YMfH19UbNmTVhYWKBTp04Anl2jgkyYMAHffvst2rVrh5kzZ+LixYua5y5cuICDBw9qned69eoB+F+/qsLUqFEDZmZmcHJyQlpaGrZu3QqFQoFr167l6+/Url07XLt2DcCzlpCMjAzUqlULo0aNQnBwMHJzc196rFd57733EB4ejgcPHgB41qLYq1cvTR+wCxcuYO3atVqv09vbG2q1GlFRUYXu18LCAufPn8fp06fx008/oUWLFvjuu++06mzatAnt2rWDo6MjzM3NMW3atEKvRZ4LFy4gNTUVVatW1YopKioq33k3MTHReu8Q6ZP+ez0SVVBOTk5aQ8dflYC4u7vD3d0dI0eOxFdffYW6deti06ZNGDZs2CuPlZqaCgBYsWIF2rRpo/Vc3i2JvFsx+qKv/TVv3hxNmzbFunXr0L17d1y5cgWhoaGF1q9atSokEgkSExPzPadQKFC7dm2tsrS0NHh7e8Pb2xvr16+HnZ0dYmJi4O3tjezs7AKPMXLkSHh7eyM0NBR79uzB3Llz8dNPP2H8+PFITU2Fr68vvv/++3zbVatW7aWv9fDhw7C0tIS9vT0sLCxeWvd5zs7OiIiIwL59+7B371589NFH+PHHH3Ho0CHI5fIi7+d5rVq1gru7OzZu3IgxY8YgODgYa9eu1TyfmpqKDz/8EBMmTMi3bc2aNQvdr1Qq1VyD+vXrIzIyEmPGjMHvv/8OADh+/Djee+89fP311/D29oaVlRU2btyIn3766aXxpqamolq1aggPD8/33Isd+J88eQI7O7uX7o+ouJgIERWRkZFRvg/lonJ1dYWpqSnS0tI0ZTExMXjw4AGcnJwAACdOnIBUKsUbb7wBBwcHODk54fbt25o+Hi9q0qQJVq5ciSdPnuilVahOnTowMTHB/v37MXLkyFfWVygUAFDgKKmRI0di4cKFuH//Pry8vODs7PzS/TRo0ABXr14t0jxC169fx+PHjzFv3jzNfk+fPv3K7ZydnTF69GiMHj0aU6dOxYoVKzB+/Hi0aNECW7duhaurq84j4tzc3AocdVe/fn0cPXoUgYGBmrKjR4+iQYMGmscmJibw9fWFr68vxo4di3r16uHSpUto0aJFvv3J5fIijUZ77733sH79etSoUQNSqRS9evXSPNeiRQtcvXq12L/DeaZMmQJ3d3d8/PHHaNGiBY4dOwYXFxd89dVXmjp37tzR2kahUOSLv0WLFoiLi4ORkRFcXV0LPV5kZCQyMzPRvHnz14qbqDC8NUaVVmpqquaWCwBERUXh/Pnzr2zSf5VZs2bh888/R3h4OKKionDu3DkMHz4cOTk5eOuttzT1jI2NERgYiAsXLuDw4cOYMGEC+vfvD0dHRwDA119/jblz52Lx4sW4ceMGLl26hDVr1mD+/PkAgIEDB8LR0RH+/v44evQobt++ja1bt+L48ePFitvY2BhffPEFPv/8c6xbtw6RkZE4ceIEVq1aVWB9FxcXSCQS7Ny5E48ePdK0YgHAoEGDcO/ePaxYseKlnaTzeHt748iRI0WKs2bNmlAoFPj5559x+/ZtbN++HbNnz37pNpMmTcLu3bsRFRWFs2fP4uDBg6hfvz6AZx2pnzx5goEDB+Lff/9FZGQkdu/ejWHDhhV7KPxnn32GtWvXYunSpbh58ybmz5+PoKAg/F97d+ySWh/GAfwJMjspZUQUQnnIoCCIiohcGsqiMTlGg/gXpNBQRAQRkUU0BA1tLVHR1CnHKAkEo4ZoyMUoxKkhEppq+r6DdLjerNfLveF73/P9rD9/x0fxwMP5Pc/j9PS0iOSOC7e3t+X29lYeHh5kd3dXFEURl8tV8HqqqsrZ2Zk8Pj4WfHL2LhAIyPX1tUQiEfH7/WK1Wo212dlZSSQSEgqF5ObmRu7u7uT4+PiXiqVFcgmlz+eThYUFEckl0JlMRg4ODuT+/l42NzdF1/UP8b/fX09PT/L29iZer1c8Ho+MjY3JycmJpNNpSSQSMj8/n5fYxuNxaWlpEbfb/UtxEhWt1EVKRKXy81A8+aEN/HfEYjFomma0Rjc0NGB0dBTxeNx4zXvx6NbWFpxOJyorK+H3+/H8/Jx3rb29PXR1daGiogK1tbUYGBjA4eGhsZ5Op6FpGqqrq1FVVYXe3l5cXl7mvce7fyuWBnLt88vLy3C5XLBYLGhubsbKygqAj8XSALC0tITGxkaUlZV9+N6CwWDBVvpCkskkFEXJGx/wVfv8/v4+VFWF1WqFx+NBNBr9spA7FArB7XbDarWivr4ewWAwbxhhKpWCz+eDw+GAoihob2/H1NTUpwXexQxs/Kp9Xtd1Y+CmzWZDf38/Tk9PjfWfi6Wj0ShaW1tRXl5esH3+R319fRARxGKxD2tXV1cYHh6G3W6HzWZDZ2cnIpHIp5+hUPs8AFxcXEBEjN/azMwM6urqYLfbMTExgY2Njbx9r6+v0DQNDocjr33+5eUF4XAYTqcTFosFTU1NCAQCyGQyxt6RkRGsrq5+GiPR7yoDgJJkYEQmtri4KEdHR9/2dxX/BUNDQ9LR0VH0MLzx8XHp6emRubm5b46M/hbJZFIGBwcllUpJTU1NqcOh/ykejRHRH5XNZkXXdTk/P5fJycmi962vrxuzfYhEcjOvdnZ2mATRt2KxNBH9Ud3d3ZLNZmVtbU3a2tqK3qeqqoTD4W+MjP42Xq+31CGQCfBojIiIiEyLR2NERERkWkyEiIiIyLSYCBEREZFpMREiIiIi02IiRERERKbFRIiIiIhMi4kQERERmRYTISIiIjItJkJERERkWv8ACMQIwul1wdgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Adaboost AUC-ROC Curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Sample dataset creation\n",
    "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# List of models you want to compare\n",
    "auc_models = [\n",
    "    {\n",
    "        'label': 'AdaBoost Classifier',\n",
    "        'model': AdaBoostClassifier(n_estimators= 50, algorithm= 'SAMME'),\n",
    "        'auc': 0.6069  # This will be updated with the actual computed AUC\n",
    "    }\n",
    "]\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# Loop through all models\n",
    "for algo in auc_models:\n",
    "    model = algo['model']  # Select the model\n",
    "    model.fit(X_train, y_train)  # Train the model\n",
    "    \n",
    "    # Compute False Positive Rate and True Positive Rate\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
    "    \n",
    "    # Calculate Area Under the Curve to display on the plot\n",
    "    auc_score = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])\n",
    "    algo['auc'] = auc_score  # Update the AUC in the dictionary\n",
    "    \n",
    "    # Plot the ROC curve\n",
    "    plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % (algo['label'], auc_score))\n",
    "\n",
    "# Custom settings for the plot\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')  # Diagonal line representing random performance\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('1 - Specificity (False Positive Rate)')\n",
    "plt.ylabel('Sensitivity (True Positive Rate)')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig(\"auc.png\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Adaboost AUC-ROC Curve\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "# Sample dataset creation\n",
    "X, y = make_classification(n_samples=1000, n_features=20, random_state=42)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# List of models you want to compare\n",
    "auc_models = [\n",
    "    {\n",
    "        'label': 'GradientBoost Classifier',\n",
    "        'model': GradientBoostingClassifier(n_estimators = 500, min_samples_split= 15, max_depth=15, loss= 'log_loss', criterion= 'squared_error'),\n",
    "        'auc': 0.8807  # This will be updated with the actual computed AUC\n",
    "    }\n",
    "]\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# Loop through all models\n",
    "for algo in auc_models:\n",
    "    model = algo['model']  # Select the model\n",
    "    model.fit(X_train, y_train)  # Train the model\n",
    "    \n",
    "    # Compute False Positive Rate and True Positive Rate\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
    "    \n",
    "    # Calculate Area Under the Curve to display on the plot\n",
    "    auc_score = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])\n",
    "    algo['auc'] = auc_score  # Update the AUC in the dictionary\n",
    "    \n",
    "    # Plot the ROC curve\n",
    "    plt.plot(fpr, tpr, label='%s ROC (area = %0.2f)' % (algo['label'], auc_score))\n",
    "\n",
    "# Custom settings for the plot\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')  # Diagonal line representing random performance\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('1 - Specificity (False Positive Rate)')\n",
    "plt.ylabel('Sensitivity (True Positive Rate)')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig(\"auc.png\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}