Codway intern Task-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e0eb6b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99dad90c",
   "metadata": {},
   "source": [
    "# Bank Customer Churn Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c003c9e",
   "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>Obijiaku</td>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>Johnstone</td>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>Liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
       "0             1    15634602   Hargrave          619    France  Female   42   \n",
       "1             2    15647311       Hill          608     Spain  Female   41   \n",
       "2             3    15619304       Onio          502    France  Female   42   \n",
       "3             4    15701354       Boni          699    France  Female   39   \n",
       "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
       "...         ...         ...        ...          ...       ...     ...  ...   \n",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0          2       0.00              1          1               1   \n",
       "1          1   83807.86              1          0               1   \n",
       "2          8  159660.80              3          1               0   \n",
       "3          1       0.00              2          0               0   \n",
       "4          2  125510.82              1          1               1   \n",
       "...      ...        ...            ...        ...             ...   \n",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       4  130142.79              1          1               0   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "0           101348.88       1  \n",
       "1           112542.58       0  \n",
       "2           113931.57       1  \n",
       "3            93826.63       0  \n",
       "4            79084.10       0  \n",
       "...               ...     ...  \n",
       "9995         96270.64       0  \n",
       "9996        101699.77       0  \n",
       "9997         42085.58       1  \n",
       "9998         92888.52       1  \n",
       "9999         38190.78       0  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:\\\\Users\\\\abhin\\\\Downloads\\\\Churn_Modelling.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a7b843a",
   "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "acdec945",
   "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>Obijiaku</td>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>Johnstone</td>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>Liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       4  130142.79              1          1               0   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "9995         96270.64       0  \n",
       "9996        101699.77       0  \n",
       "9997         42085.58       1  \n",
       "9998         92888.52       1  \n",
       "9999         38190.78       0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09ce3dd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of       RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
       "0             1    15634602   Hargrave          619    France  Female   42   \n",
       "1             2    15647311       Hill          608     Spain  Female   41   \n",
       "2             3    15619304       Onio          502    France  Female   42   \n",
       "3             4    15701354       Boni          699    France  Female   39   \n",
       "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
       "...         ...         ...        ...          ...       ...     ...  ...   \n",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0          2       0.00              1          1               1   \n",
       "1          1   83807.86              1          0               1   \n",
       "2          8  159660.80              3          1               0   \n",
       "3          1       0.00              2          0               0   \n",
       "4          2  125510.82              1          1               1   \n",
       "...      ...        ...            ...        ...             ...   \n",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       4  130142.79              1          1               0   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "0           101348.88       1  \n",
       "1           112542.58       0  \n",
       "2           113931.57       1  \n",
       "3            93826.63       0  \n",
       "4            79084.10       0  \n",
       "...               ...     ...  \n",
       "9995         96270.64       0  \n",
       "9996        101699.77       0  \n",
       "9997         42085.58       1  \n",
       "9998         92888.52       1  \n",
       "9999         38190.78       0  \n",
       "\n",
       "[10000 rows x 14 columns]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2cc7e660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7b18b7",
   "metadata": {},
   "source": [
    "# Dropping of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "beb27f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n",
       "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "605715c9",
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0             619    France  Female   42       2       0.00              1   \n",
       "1             608     Spain  Female   41       1   83807.86              1   \n",
       "2             502    France  Female   42       8  159660.80              3   \n",
       "3             699    France  Female   39       1       0.00              2   \n",
       "4             850     Spain  Female   43       2  125510.82              1   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9995          771    France    Male   39       5       0.00              2   \n",
       "9996          516    France    Male   35      10   57369.61              1   \n",
       "9997          709    France  Female   36       7       0.00              1   \n",
       "9998          772   Germany    Male   42       3   75075.31              2   \n",
       "9999          792    France  Female   28       4  130142.79              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0             1               1        101348.88       1  \n",
       "1             0               1        112542.58       0  \n",
       "2             1               0        113931.57       1  \n",
       "3             0               0         93826.63       0  \n",
       "4             1               1         79084.10       0  \n",
       "...         ...             ...              ...     ...  \n",
       "9995          1               0         96270.64       0  \n",
       "9996          1               1        101699.77       0  \n",
       "9997          0               1         42085.58       1  \n",
       "9998          1               0         92888.52       1  \n",
       "9999          1               0         38190.78       0  \n",
       "\n",
       "[10000 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['RowNumber', 'CustomerId', 'Surname'],axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418015bf",
   "metadata": {},
   "source": [
    "# Changing categorical values to numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d50c295",
   "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>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore  Age  Tenure  Balance  NumOfProducts  HasCrCard  \\\n",
       "0             619   42       2        0              1          1   \n",
       "1             608   41       1    83807              1          0   \n",
       "2             502   42       8   159660              3          1   \n",
       "3             699   39       1        0              2          0   \n",
       "4             850   43       2   125510              1          1   \n",
       "...           ...  ...     ...      ...            ...        ...   \n",
       "9995          771   39       5        0              2          1   \n",
       "9996          516   35      10    57369              1          1   \n",
       "9997          709   36       7        0              1          0   \n",
       "9998          772   42       3    75075              2          1   \n",
       "9999          792   28       4   130142              1          1   \n",
       "\n",
       "      IsActiveMember  EstimatedSalary  Exited  Geography_Germany  \\\n",
       "0                  1           101348       1                  0   \n",
       "1                  1           112542       0                  0   \n",
       "2                  0           113931       1                  0   \n",
       "3                  0            93826       0                  0   \n",
       "4                  1            79084       0                  0   \n",
       "...              ...              ...     ...                ...   \n",
       "9995               0            96270       0                  0   \n",
       "9996               1           101699       0                  0   \n",
       "9997               1            42085       1                  0   \n",
       "9998               0            92888       1                  1   \n",
       "9999               0            38190       0                  0   \n",
       "\n",
       "      Geography_Spain  Gender_Male  \n",
       "0                   0            0  \n",
       "1                   1            0  \n",
       "2                   0            0  \n",
       "3                   0            0  \n",
       "4                   1            0  \n",
       "...               ...          ...  \n",
       "9995                0            1  \n",
       "9996                0            1  \n",
       "9997                0            0  \n",
       "9998                0            1  \n",
       "9999                0            0  \n",
       "\n",
       "[10000 rows x 12 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data,drop_first = True)\n",
    "data.head()\n",
    "data = data.astype(int)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf894014",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5e95dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exited\n",
       "0    7963\n",
       "1    2037\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Exited'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fcd3acba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAINCAYAAAAwWcmFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0IklEQVR4nO3df3RU9Z3/8deYkDFgciVAZpg1alxzaGii1eCGUJUoEKCNWdcewcYdcUHAorAjIMhaW+pqsuAKtObIAqWg/Nh4TtdY12JKsDWKIYCpqYKA2EaBkiFYJ5OAaYJhvn/45W6HIGJIMhM+z8c5c87Ove+593P9g32ee25uHaFQKCQAAADAEBdFegEAAABATyKAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYJTYSC+gtzh58qQOHz6shIQEORyOSC8HAAAApwmFQmpubpbH49FFF335fV4C+BwdPnxYKSkpkV4GAAAAvsLBgwd12WWXfel+AvgcJSQkSPriP2hiYmKEVwMAAIDTNTU1KSUlxe62L0MAn6NTjz0kJiYSwAAAAFHsqx5X5Y/gAAAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARoloAH/++ef64Q9/qNTUVMXHx+uqq67S448/rpMnT9ozoVBICxculMfjUXx8vHJzc7V79+6w47S2tmrmzJkaOHCg+vXrp4KCAh06dChsJhAIyOv1yrIsWZYlr9erxsbGnrhMAAAARJGIBvCiRYv0X//1XyopKdGePXu0ePFiPfXUU3rmmWfsmcWLF2vJkiUqKSnRzp075Xa7NWbMGDU3N9szPp9PZWVlKi0t1datW3Xs2DHl5+ervb3dniksLFRtba3Ky8tVXl6u2tpaeb3eHr1eAAAARJ4jFAqFInXy/Px8uVwurV692t72ve99T3379tW6desUCoXk8Xjk8/k0f/58SV/c7XW5XFq0aJGmT5+uYDCoQYMGad26dZo4caIk6fDhw0pJSdGmTZs0duxY7dmzR0OHDlV1dbWys7MlSdXV1crJydHevXs1ZMiQr1xrU1OTLMtSMBhUYmJiN/zXAAAAwPk4116L6B3gG2+8Ua+99po++OADSdIf/vAHbd26Vd/5znckSXV1dfL7/crLy7N/43Q6NXLkSFVVVUmSampqdOLEibAZj8ejjIwMe2bbtm2yLMuOX0kaPny4LMuyZwAAAGCG2EiefP78+QoGg/rGN76hmJgYtbe368knn9T3v/99SZLf75ckuVyusN+5XC59/PHH9kxcXJz69+/fYebU7/1+v5KTkzucPzk52Z45XWtrq1pbW+3vTU1NnbxKAAAARJOI3gF+4YUXtH79em3cuFG///3v9dxzz+k///M/9dxzz4XNORyOsO+hUKjDttOdPnOm+bMdp7i42P6DOcuylJKScq6XBQAAgCgW0QB++OGH9cgjj+iuu+5SZmamvF6vHnroIRUXF0uS3G63JHW4S9vQ0GDfFXa73Wpra1MgEDjrzJEjRzqc/+jRox3uLp+yYMECBYNB+3Pw4MHzu1gAAABEhYg+AvHZZ5/poovCGzwmJsZ+DVpqaqrcbrcqKip03XXXSZLa2tpUWVmpRYsWSZKysrLUp08fVVRUaMKECZKk+vp67dq1S4sXL5Yk5eTkKBgMaseOHfqHf/gHSdL27dsVDAY1YsSIM67N6XTK6XR2/UV3UtbDz0d6CQC6Sc1T90R6CQBglIgG8G233aYnn3xSl19+ub75zW/qnXfe0ZIlSzR58mRJXzy24PP5VFRUpLS0NKWlpamoqEh9+/ZVYWGhJMmyLE2ZMkVz5szRgAEDlJSUpLlz5yozM1OjR4+WJKWnp2vcuHGaOnWqVqxYIUmaNm2a8vPzz+kNEAAAALhwRDSAn3nmGT322GOaMWOGGhoa5PF4NH36dP3oRz+yZ+bNm6eWlhbNmDFDgUBA2dnZ2rx5sxISEuyZpUuXKjY2VhMmTFBLS4tGjRqltWvXKiYmxp7ZsGGDZs2aZb8toqCgQCUlJT13sQAAAIgKEX0PcG8S6fcA8wgEcOHiEQgA6Bq94j3AAAAAQE8jgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGiWgAX3nllXI4HB0+DzzwgCQpFApp4cKF8ng8io+PV25urnbv3h12jNbWVs2cOVMDBw5Uv379VFBQoEOHDoXNBAIBeb1eWZYly7Lk9XrV2NjYU5cJAACAKBLRAN65c6fq6+vtT0VFhSTpzjvvlCQtXrxYS5YsUUlJiXbu3Cm3260xY8aoubnZPobP51NZWZlKS0u1detWHTt2TPn5+Wpvb7dnCgsLVVtbq/LycpWXl6u2tlZer7dnLxYAAABRwREKhUKRXsQpPp9Pr7zyivbv3y9J8ng88vl8mj9/vqQv7va6XC4tWrRI06dPVzAY1KBBg7Ru3TpNnDhRknT48GGlpKRo06ZNGjt2rPbs2aOhQ4equrpa2dnZkqTq6mrl5ORo7969GjJkyDmtrampSZZlKRgMKjExsRuu/uyyHn6+x88JoGfUPHVPpJcAABeEc+21qHkGuK2tTevXr9fkyZPlcDhUV1cnv9+vvLw8e8bpdGrkyJGqqqqSJNXU1OjEiRNhMx6PRxkZGfbMtm3bZFmWHb+SNHz4cFmWZc+cSWtrq5qamsI+AAAA6P2iJoBfeuklNTY26t5775Uk+f1+SZLL5Qqbc7lc9j6/36+4uDj179//rDPJyckdzpecnGzPnElxcbH9zLBlWUpJSen0tQEAACB6RE0Ar169WuPHj5fH4wnb7nA4wr6HQqEO2053+syZ5r/qOAsWLFAwGLQ/Bw8ePJfLAAAAQJSLigD++OOPtWXLFt133332NrfbLUkd7tI2NDTYd4Xdbrfa2toUCATOOnPkyJEO5zx69GiHu8t/y+l0KjExMewDAACA3i8qAnjNmjVKTk7Wd7/7XXtbamqq3G63/WYI6YvnhCsrKzVixAhJUlZWlvr06RM2U19fr127dtkzOTk5CgaD2rFjhz2zfft2BYNBewYAAADmiI30Ak6ePKk1a9Zo0qRJio39v+U4HA75fD4VFRUpLS1NaWlpKioqUt++fVVYWChJsixLU6ZM0Zw5czRgwAAlJSVp7ty5yszM1OjRoyVJ6enpGjdunKZOnaoVK1ZIkqZNm6b8/PxzfgMEAAAALhwRD+AtW7bowIEDmjx5cod98+bNU0tLi2bMmKFAIKDs7Gxt3rxZCQkJ9szSpUsVGxurCRMmqKWlRaNGjdLatWsVExNjz2zYsEGzZs2y3xZRUFCgkpKS7r84AAAARJ2oeg9wNOM9wAC6C+8BBoCu0eveAwwAAAD0BAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARol4AP/5z3/WP//zP2vAgAHq27evvvWtb6mmpsbeHwqFtHDhQnk8HsXHxys3N1e7d+8OO0Zra6tmzpypgQMHql+/fiooKNChQ4fCZgKBgLxeryzLkmVZ8nq9amxs7IlLBAAAQBSJaAAHAgF9+9vfVp8+ffTqq6/q/fff19NPP61LL73Unlm8eLGWLFmikpIS7dy5U263W2PGjFFzc7M94/P5VFZWptLSUm3dulXHjh1Tfn6+2tvb7ZnCwkLV1taqvLxc5eXlqq2tldfr7cnLBQAAQBRwhEKhUKRO/sgjj+itt97Sm2++ecb9oVBIHo9HPp9P8+fPl/TF3V6Xy6VFixZp+vTpCgaDGjRokNatW6eJEydKkg4fPqyUlBRt2rRJY8eO1Z49ezR06FBVV1crOztbklRdXa2cnBzt3btXQ4YM+cq1NjU1ybIsBYNBJSYmdtF/gXOX9fDzPX5OAD2j5ql7Ir0EALggnGuvRfQO8Msvv6xhw4bpzjvvVHJysq677jqtWrXK3l9XVye/36+8vDx7m9Pp1MiRI1VVVSVJqqmp0YkTJ8JmPB6PMjIy7Jlt27bJsiw7fiVp+PDhsizLnjlda2urmpqawj4AAADo/SIawH/605+0fPlypaWl6Te/+Y3uv/9+zZo1S88//8XdTr/fL0lyuVxhv3O5XPY+v9+vuLg49e/f/6wzycnJHc6fnJxsz5yuuLjYfl7YsiylpKSc38UCAAAgKkQ0gE+ePKnrr79eRUVFuu666zR9+nRNnTpVy5cvD5tzOBxh30OhUIdtpzt95kzzZzvOggULFAwG7c/BgwfP9bIAAAAQxSIawIMHD9bQoUPDtqWnp+vAgQOSJLfbLUkd7tI2NDTYd4Xdbrfa2toUCATOOnPkyJEO5z969GiHu8unOJ1OJSYmhn0AAADQ+0U0gL/97W9r3759Yds++OADXXHFFZKk1NRUud1uVVRU2Pvb2tpUWVmpESNGSJKysrLUp0+fsJn6+nrt2rXLnsnJyVEwGNSOHTvsme3btysYDNozAAAAMENsJE/+0EMPacSIESoqKtKECRO0Y8cOrVy5UitXrpT0xWMLPp9PRUVFSktLU1pamoqKitS3b18VFhZKkizL0pQpUzRnzhwNGDBASUlJmjt3rjIzMzV69GhJX9xVHjdunKZOnaoVK1ZIkqZNm6b8/PxzegMEAAAALhwRDeAbbrhBZWVlWrBggR5//HGlpqZq2bJluvvuu+2ZefPmqaWlRTNmzFAgEFB2drY2b96shIQEe2bp0qWKjY3VhAkT1NLSolGjRmnt2rWKiYmxZzZs2KBZs2bZb4soKChQSUlJz10sAAAAokJE3wPcm/AeYADdhfcAA0DX6BXvAQYAAAB6GgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAo0Q0gBcuXCiHwxH2cbvd9v5QKKSFCxfK4/EoPj5eubm52r17d9gxWltbNXPmTA0cOFD9+vVTQUGBDh06FDYTCATk9XplWZYsy5LX61VjY2NPXCIAAACiTMTvAH/zm99UfX29/XnvvffsfYsXL9aSJUtUUlKinTt3yu12a8yYMWpubrZnfD6fysrKVFpaqq1bt+rYsWPKz89Xe3u7PVNYWKja2lqVl5ervLxctbW18nq9PXqdAAAAiA6xEV9AbGzYXd9TQqGQli1bpkcffVR33HGHJOm5556Ty+XSxo0bNX36dAWDQa1evVrr1q3T6NGjJUnr169XSkqKtmzZorFjx2rPnj0qLy9XdXW1srOzJUmrVq1STk6O9u3bpyFDhvTcxQIAACDiIn4HeP/+/fJ4PEpNTdVdd92lP/3pT5Kkuro6+f1+5eXl2bNOp1MjR45UVVWVJKmmpkYnTpwIm/F4PMrIyLBntm3bJsuy7PiVpOHDh8uyLHvmTFpbW9XU1BT2AQAAQO8X0QDOzs7W888/r9/85jdatWqV/H6/RowYob/85S/y+/2SJJfLFfYbl8tl7/P7/YqLi1P//v3POpOcnNzh3MnJyfbMmRQXF9vPDFuWpZSUlPO6VgAAAESHiAbw+PHj9b3vfU+ZmZkaPXq0fv3rX0v64lGHUxwOR9hvQqFQh22nO33mTPNfdZwFCxYoGAzan4MHD57TNQEAACC6RfwRiL/Vr18/ZWZmav/+/fZzwaffpW1oaLDvCrvdbrW1tSkQCJx15siRIx3OdfTo0Q53l/+W0+lUYmJi2AcAAAC9X1QFcGtrq/bs2aPBgwcrNTVVbrdbFRUV9v62tjZVVlZqxIgRkqSsrCz16dMnbKa+vl67du2yZ3JychQMBrVjxw57Zvv27QoGg/YMAAAAzBHRt0DMnTtXt912my6//HI1NDToiSeeUFNTkyZNmiSHwyGfz6eioiKlpaUpLS1NRUVF6tu3rwoLCyVJlmVpypQpmjNnjgYMGKCkpCTNnTvXfqRCktLT0zVu3DhNnTpVK1askCRNmzZN+fn5vAECAADAQBEN4EOHDun73/++PvnkEw0aNEjDhw9XdXW1rrjiCknSvHnz1NLSohkzZigQCCg7O1ubN29WQkKCfYylS5cqNjZWEyZMUEtLi0aNGqW1a9cqJibGntmwYYNmzZplvy2ioKBAJSUlPXuxAAAAiAqOUCgUivQieoOmpiZZlqVgMBiR54GzHn6+x88JoGfUPHVPpJcAABeEc+21qHoGGAAAAOhuBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjNKpAL711lvV2NjYYXtTU5NuvfXW810TAAAA0G06FcCvv/662traOmz/61//qjfffPO8FwUAAAB0l9ivM/zuu+/a//f7778vv99vf29vb1d5ebn+7u/+rutWBwAAAHSxrxXA3/rWt+RwOORwOM74qEN8fLyeeeaZLlscAAAA0NW+VgDX1dUpFArpqquu0o4dOzRo0CB7X1xcnJKTkxUTE9PliwQAAAC6ytcK4CuuuEKSdPLkyW5ZDAAAANDdvlYA/60PPvhAr7/+uhoaGjoE8Y9+9KPzXhgAAADQHToVwKtWrdIPfvADDRw4UG63Ww6Hw97ncDgIYAAAAEStTgXwE088oSeffFLz58/v6vUAAAAA3apT7wEOBAK68847u3otAAAAQLfrVADfeeed2rx5c1evBQAAAOh2nXoE4uqrr9Zjjz2m6upqZWZmqk+fPmH7Z82a1SWLAwAAALpapwJ45cqVuuSSS1RZWanKysqwfQ6HgwAGAABA1OpUANfV1XX1OgAAAIAe0alngAEAAIDeqlN3gCdPnnzW/b/4xS86tRgAAACgu3UqgAOBQNj3EydOaNeuXWpsbNStt97aJQsDAAAAukOnArisrKzDtpMnT2rGjBm66qqrzntRAAAAQHfpsmeAL7roIj300ENaunRpVx0SAAAA6HJd+kdwf/zjH/X555935SEBAACALtWpRyBmz54d9j0UCqm+vl6//vWvNWnSpC5ZGAAAANAdOhXA77zzTtj3iy66SIMGDdLTTz/9lW+IAAAAACKpU49A/O53vwv7vPbaayotLdW0adMUG9upplZxcbEcDod8Pp+9LRQKaeHChfJ4PIqPj1dubq52794d9rvW1lbNnDlTAwcOVL9+/VRQUKBDhw6FzQQCAXm9XlmWJcuy5PV61djY2Kl1AgAAoHc7r2eAjx49qq1bt+qtt97S0aNHO32cnTt3auXKlbrmmmvCti9evFhLlixRSUmJdu7cKbfbrTFjxqi5udme8fl8KisrU2lpqbZu3apjx44pPz9f7e3t9kxhYaFqa2tVXl6u8vJy1dbWyuv1dnq9AAAA6L06FcDHjx/X5MmTNXjwYN1888266aab5PF4NGXKFH322Wdf61jHjh3T3XffrVWrVql///729lAopGXLlunRRx/VHXfcoYyMDD333HP67LPPtHHjRklSMBjU6tWr9fTTT2v06NG67rrrtH79er333nvasmWLJGnPnj0qLy/Xz3/+c+Xk5CgnJ0erVq3SK6+8on379nXm8gEAANCLdSqAZ8+ercrKSv3v//6vGhsb1djYqF/96leqrKzUnDlzvtaxHnjgAX33u9/V6NGjw7bX1dXJ7/crLy/P3uZ0OjVy5EhVVVVJkmpqanTixImwGY/Ho4yMDHtm27ZtsixL2dnZ9szw4cNlWZY9AwAAAHN06oHd//mf/9Evf/lL5ebm2tu+853vKD4+XhMmTNDy5cvP6TilpaX6/e9/r507d3bY5/f7JUkulytsu8vl0scff2zPxMXFhd05PjVz6vd+v1/Jyckdjp+cnGzPnElra6taW1vt701NTed0TQAAAIhunboD/Nlnn3UIU+mLqDzXRyAOHjyof/3Xf9X69et18cUXf+mcw+EI+x4KhTpsO93pM2ea/6rjFBcX2380Z1mWUlJSznpOAAAA9A6dCuCcnBz9+Mc/1l//+ld7W0tLi37yk58oJyfnnI5RU1OjhoYGZWVlKTY2VrGxsaqsrNTPfvYzxcbG2oF9+l3ahoYGe5/b7VZbW5sCgcBZZ44cOdLh/EePHj1jxJ+yYMECBYNB+3Pw4MFzui4AAABEt04F8LJly1RVVaXLLrtMo0aN0ujRo5WSkqK33npLP/3pT8/pGKNGjdJ7772n2tpa+zNs2DDdfffdqq2t1VVXXSW3262Kigr7N21tbaqsrNSIESMkSVlZWerTp0/YTH19vXbt2mXP5OTkKBgMaseOHfbM9u3bFQwG7ZkzcTqdSkxMDPsAAACg9+vUM8CZmZnav3+/1q9fr7179yoUCumuu+7S3Xffrfj4+HM6RkJCgjIyMsK29evXTwMGDLC3+3w+FRUVKS0tTWlpaSoqKlLfvn1VWFgoSbIsS1OmTNGcOXM0YMAAJSUlae7cucrMzLT/qC49PV3jxo3T1KlTtWLFCknStGnTlJ+fryFDhnTm8gEAANCLdSqAi4uL5XK5NHXq1LDtv/jFL3T06FHNnz+/SxY3b948tbS0aMaMGQoEAsrOztbmzZuVkJBgzyxdulSxsbGaMGGCWlpaNGrUKK1du1YxMTH2zIYNGzRr1iz7bREFBQUqKSnpkjUCAACgd3GEQqHQ1/3RlVdeqY0bN3Z4hGD79u266667VFdX12ULjBZNTU2yLEvBYDAij0NkPfx8j58TQM+oeeqeSC8BAC4I59prnXoG2O/3a/DgwR22Dxo0SPX19Z05JAAAANAjOhXAp/7g7XRvvfWWPB7PeS8KAAAA6C6degb4vvvuk8/n04kTJ3TrrbdKkl577TXNmzfva/8vwQEAAAA9qVMBPG/ePH366aeaMWOG2traJEkXX3yx5s+frwULFnTpAgEAAICu1KkAdjgcWrRokR577DHt2bNH8fHxSktLk9Pp7Or1AQAAAF2qUwF8yiWXXKIbbrihq9YCAAAAdLtO/REcAAAA0FsRwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjRDSAly9frmuuuUaJiYlKTExUTk6OXn31VXt/KBTSwoUL5fF4FB8fr9zcXO3evTvsGK2trZo5c6YGDhyofv36qaCgQIcOHQqbCQQC8nq9sixLlmXJ6/WqsbGxJy4RAAAAUSaiAXzZZZfpP/7jP/T222/r7bff1q233qp//Md/tCN38eLFWrJkiUpKSrRz50653W6NGTNGzc3N9jF8Pp/KyspUWlqqrVu36tixY8rPz1d7e7s9U1hYqNraWpWXl6u8vFy1tbXyer09fr0AAACIPEcoFApFehF/KykpSU899ZQmT54sj8cjn8+n+fPnS/ribq/L5dKiRYs0ffp0BYNBDRo0SOvWrdPEiRMlSYcPH1ZKSoo2bdqksWPHas+ePRo6dKiqq6uVnZ0tSaqurlZOTo727t2rIUOGnNO6mpqaZFmWgsGgEhMTu+fizyLr4ed7/JwAekbNU/dEegkAcEE4116LmmeA29vbVVpaquPHjysnJ0d1dXXy+/3Ky8uzZ5xOp0aOHKmqqipJUk1NjU6cOBE24/F4lJGRYc9s27ZNlmXZ8StJw4cPl2VZ9syZtLa2qqmpKewDAACA3i/iAfzee+/pkksukdPp1P3336+ysjINHTpUfr9fkuRyucLmXS6Xvc/v9ysuLk79+/c/60xycnKH8yYnJ9szZ1JcXGw/M2xZllJSUs7rOgEAABAdIh7AQ4YMUW1traqrq/WDH/xAkyZN0vvvv2/vdzgcYfOhUKjDttOdPnOm+a86zoIFCxQMBu3PwYMHz/WSAAAAEMUiHsBxcXG6+uqrNWzYMBUXF+vaa6/VT3/6U7ndbknqcJe2oaHBvivsdrvV1tamQCBw1pkjR450OO/Ro0c73F3+W06n0347xakPAAAAer+IB/DpQqGQWltblZqaKrfbrYqKCntfW1ubKisrNWLECElSVlaW+vTpEzZTX1+vXbt22TM5OTkKBoPasWOHPbN9+3YFg0F7BgAAAOaIjeTJ/+3f/k3jx49XSkqKmpubVVpaqtdff13l5eVyOBzy+XwqKipSWlqa0tLSVFRUpL59+6qwsFCSZFmWpkyZojlz5mjAgAFKSkrS3LlzlZmZqdGjR0uS0tPTNW7cOE2dOlUrVqyQJE2bNk35+fnn/AYIAAAAXDgiGsBHjhyR1+tVfX29LMvSNddco/Lyco0ZM0aSNG/ePLW0tGjGjBkKBALKzs7W5s2blZCQYB9j6dKlio2N1YQJE9TS0qJRo0Zp7dq1iomJsWc2bNigWbNm2W+LKCgoUElJSc9eLAAAAKJC1L0HOFrxHmAA3YX3AANA1+h17wEGAAAAegIBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKNENICLi4t1ww03KCEhQcnJybr99tu1b9++sJlQKKSFCxfK4/EoPj5eubm52r17d9hMa2urZs6cqYEDB6pfv34qKCjQoUOHwmYCgYC8Xq8sy5JlWfJ6vWpsbOzuSwQAAECUiWgAV1ZW6oEHHlB1dbUqKir0+eefKy8vT8ePH7dnFi9erCVLlqikpEQ7d+6U2+3WmDFj1NzcbM/4fD6VlZWptLRUW7du1bFjx5Sfn6/29nZ7prCwULW1tSovL1d5eblqa2vl9Xp79HoBAAAQeY5QKBSK9CJOOXr0qJKTk1VZWambb75ZoVBIHo9HPp9P8+fPl/TF3V6Xy6VFixZp+vTpCgaDGjRokNatW6eJEydKkg4fPqyUlBRt2rRJY8eO1Z49ezR06FBVV1crOztbklRdXa2cnBzt3btXQ4YM+cq1NTU1ybIsBYNBJSYmdt9/hC+R9fDzPX5OAD2j5ql7Ir2EiDjweGaklwCgm1z+o/cict5z7bWoegY4GAxKkpKSkiRJdXV18vv9ysvLs2ecTqdGjhypqqoqSVJNTY1OnDgRNuPxeJSRkWHPbNu2TZZl2fErScOHD5dlWfbM6VpbW9XU1BT2AQAAQO8XNQEcCoU0e/Zs3XjjjcrIyJAk+f1+SZLL5Qqbdblc9j6/36+4uDj179//rDPJyckdzpmcnGzPnK64uNh+XtiyLKWkpJzfBQIAACAqRE0AP/jgg3r33Xf13//93x32ORyOsO+hUKjDttOdPnOm+bMdZ8GCBQoGg/bn4MGD53IZAAAAiHJREcAzZ87Uyy+/rN/97ne67LLL7O1ut1uSOtylbWhosO8Ku91utbW1KRAInHXmyJEjHc579OjRDneXT3E6nUpMTAz7AAAAoPeLaACHQiE9+OCDevHFF/Xb3/5WqampYftTU1PldrtVUVFhb2tra1NlZaVGjBghScrKylKfPn3CZurr67Vr1y57JicnR8FgUDt27LBntm/frmAwaM8AAADADLGRPPkDDzygjRs36le/+pUSEhLsO72WZSk+Pl4Oh0M+n09FRUVKS0tTWlqaioqK1LdvXxUWFtqzU6ZM0Zw5czRgwAAlJSVp7ty5yszM1OjRoyVJ6enpGjdunKZOnaoVK1ZIkqZNm6b8/PxzegMEAAAALhwRDeDly5dLknJzc8O2r1mzRvfee68kad68eWppadGMGTMUCASUnZ2tzZs3KyEhwZ5funSpYmNjNWHCBLW0tGjUqFFau3atYmJi7JkNGzZo1qxZ9tsiCgoKVFJS0r0XCAAAgKgTVe8Bjma8BxhAd+E9wAAuNLwHGAAAAIgiBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMEtEAfuONN3TbbbfJ4/HI4XDopZdeCtsfCoW0cOFCeTwexcfHKzc3V7t37w6baW1t1cyZMzVw4ED169dPBQUFOnToUNhMIBCQ1+uVZVmyLEter1eNjY3dfHUAAACIRhEN4OPHj+vaa69VSUnJGfcvXrxYS5YsUUlJiXbu3Cm3260xY8aoubnZnvH5fCorK1Npaam2bt2qY8eOKT8/X+3t7fZMYWGhamtrVV5ervLyctXW1srr9Xb79QEAACD6xEby5OPHj9f48ePPuC8UCmnZsmV69NFHdccdd0iSnnvuOblcLm3cuFHTp09XMBjU6tWrtW7dOo0ePVqStH79eqWkpGjLli0aO3as9uzZo/LyclVXVys7O1uStGrVKuXk5Gjfvn0aMmRIz1wsAAAAokLUPgNcV1cnv9+vvLw8e5vT6dTIkSNVVVUlSaqpqdGJEyfCZjwejzIyMuyZbdu2ybIsO34lafjw4bIsy545k9bWVjU1NYV9AAAA0PtFbQD7/X5JksvlCtvucrnsfX6/X3Fxcerfv/9ZZ5KTkzscPzk52Z45k+LiYvuZYcuylJKScl7XAwAAgOgQtQF8isPhCPseCoU6bDvd6TNnmv+q4yxYsEDBYND+HDx48GuuHAAAANEoagPY7XZLUoe7tA0NDfZdYbfbrba2NgUCgbPOHDlypMPxjx492uHu8t9yOp1KTEwM+wAAAKD3i9oATk1NldvtVkVFhb2tra1NlZWVGjFihCQpKytLffr0CZupr6/Xrl277JmcnBwFg0Ht2LHDntm+fbuCwaA9AwAAAHNE9C0Qx44d04cffmh/r6urU21trZKSknT55ZfL5/OpqKhIaWlpSktLU1FRkfr27avCwkJJkmVZmjJliubMmaMBAwYoKSlJc+fOVWZmpv1WiPT0dI0bN05Tp07VihUrJEnTpk1Tfn4+b4AAAAAwUEQD+O2339Ytt9xif589e7YkadKkSVq7dq3mzZunlpYWzZgxQ4FAQNnZ2dq8ebMSEhLs3yxdulSxsbGaMGGCWlpaNGrUKK1du1YxMTH2zIYNGzRr1iz7bREFBQVf+u5hAAAAXNgcoVAoFOlF9AZNTU2yLEvBYDAizwNnPfx8j58TQM+oeeqeSC8hIg48nhnpJQDoJpf/6L2InPdcey1qnwEGAAAAugMBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADAKAQwAAACjEMAAAAAwCgEMAAAAoxDAAAAAMAoBDAAAAKMQwAAAADCKUQH87LPPKjU1VRdffLGysrL05ptvRnpJAAAA6GHGBPALL7wgn8+nRx99VO+8845uuukmjR8/XgcOHIj00gAAANCDjAngJUuWaMqUKbrvvvuUnp6uZcuWKSUlRcuXL4/00gAAANCDYiO9gJ7Q1tammpoaPfLII2Hb8/LyVFVVdcbftLa2qrW11f4eDAYlSU1NTd230LNob22JyHkBdL9I/bsSac1/bY/0EgB0k0j9u3bqvKFQ6KxzRgTwJ598ovb2drlcrrDtLpdLfr//jL8pLi7WT37ykw7bU1JSumWNAMxlPXN/pJcAAF2r2Iro6Zubm2VZX74GIwL4FIfDEfY9FAp12HbKggULNHv2bPv7yZMn9emnn2rAgAFf+hugKzQ1NSklJUUHDx5UYmJipJcDAOeNf9fQU0KhkJqbm+XxeM46Z0QADxw4UDExMR3u9jY0NHS4K3yK0+mU0+kM23bppZd21xKBDhITE/l/FAAuKPy7hp5wtju/pxjxR3BxcXHKyspSRUVF2PaKigqNGDEiQqsCAABAJBhxB1iSZs+eLa/Xq2HDhiknJ0crV67UgQMHdP/9PHsHAABgEmMCeOLEifrLX/6ixx9/XPX19crIyNCmTZt0xRVXRHppQBin06kf//jHHR7BAYDein/XEG0coa96TwQAAABwATHiGWAAAADgFAIYAAAARiGAAQAAYBQCGAAAAEYhgIEo8uyzzyo1NVUXX3yxsrKy9Oabb0Z6SQDQaW+88YZuu+02eTweORwOvfTSS5FeEiCJAAaixgsvvCCfz6dHH31U77zzjm666SaNHz9eBw4ciPTSAKBTjh8/rmuvvVYlJSWRXgoQhtegAVEiOztb119/vZYvX25vS09P1+23367i4uIIrgwAzp/D4VBZWZluv/32SC8F4A4wEA3a2tpUU1OjvLy8sO15eXmqqqqK0KoAALgwEcBAFPjkk0/U3t4ul8sVtt3lcsnv90doVQAAXJgIYCCKOByOsO+hUKjDNgAAcH4IYCAKDBw4UDExMR3u9jY0NHS4KwwAAM4PAQxEgbi4OGVlZamioiJse0VFhUaMGBGhVQEAcGGKjfQCAHxh9uzZ8nq9GjZsmHJycrRy5UodOHBA999/f6SXBgCdcuzYMX344Yf297q6OtXW1iopKUmXX355BFcG0/EaNCCKPPvss1q8eLHq6+uVkZGhpUuX6uabb470sgCgU15//XXdcsstHbZPmjRJa9eu7fkFAf8fAQwAAACj8AwwAAAAjEIAAwAAwCgEMAAAAIxCAAMAAMAoBDAAAACMQgADAADAKAQwAAAAjEIAA4AhcnNz5fP5uuXYV155pZYtW9YtxwaArkYAA0Avce+998rhcHT4jBs37px+/+KLL+rf//3f7e9EKwBTxUZ6AQCAczdu3DitWbMmbJvT6Tyn3yYlJXXHkgCg1+EOMAD0Ik6nU263O+zTv39/vf7664qLi9Obb75pzz799NMaOHCg6uvrJYU/ApGbm6uPP/5YDz30kH0n+ZSqqirdfPPNio+PV0pKimbNmqXjx4/b+xsaGnTbbbcpPj5eqamp2rBhQ89cPAB0EQIYAC4Ap+LW6/UqGAzqD3/4gx599FGtWrVKgwcP7jD/4osv6rLLLtPjjz+u+vp6O5Lfe+89jR07VnfccYfeffddvfDCC9q6dasefPBB+7f33nuvPvroI/32t7/VL3/5Sz377LNqaGjosWsFgPPFIxAA0Iu88soruuSSS8K2zZ8/X4899pieeOIJbdmyRdOmTdPu3bvl9Xr1T//0T2c8TlJSkmJiYpSQkCC3221vf+qpp1RYWGjfKU5LS9PPfvYzjRw5UsuXL9eBAwf06quvqrq6WtnZ2ZKk1atXKz09vXsuGAC6AQEMAL3ILbfcouXLl4dtO/Vsb1xcnNavX69rrrlGV1xxRaf+wK2mpkYffvhh2GMNoVBIJ0+eVF1dnT744APFxsZq2LBh9v5vfOMbuvTSSzt1PQAQCQQwAPQi/fr109VXX/2l+6uqqiRJn376qT799FP169fvax3/5MmTmj59umbNmtVh3+WXX659+/ZJUtgzwwDQ2/AMMABcIP74xz/qoYce0qpVqzR8+HDdc889Onny5JfOx8XFqb29PWzb9ddfr927d+vqq6/u8ImLi1N6ero+//xzvf322/Zv9u3bp8bGxu66LADocgQwAPQira2t8vv9YZ9PPvlE7e3t8nq9ysvL07/8y79ozZo12rVrl55++ukvPdaVV16pN954Q3/+85/1ySefSPrieeJt27bpgQceUG1trfbv36+XX35ZM2fOlCQNGTJE48aN09SpU7V9+3bV1NTovvvuU3x8fI9cPwB0BQIYAHqR8vJyDR48OOxz44036sknn9RHH32klStXSpLcbrd+/vOf64c//KFqa2vPeKzHH39cH330kf7+7/9egwYNkiRdc801qqys1P79+3XTTTfpuuuu02OPPRb2Jok1a9YoJSVFI0eO1B133KFp06YpOTm5268dALqKIxQKhSK9CAAAAKCncAcYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABgFAIYAAAARiGAAQAAYBQCGAAAAEYhgAEAAGAUAhgAAABGIYABAABglP8HIiIrvIfanhMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(8,6))\n",
    "sns.countplot(x='Exited',data = data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "79b9b744",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Exited',axis=1)\n",
    "y = data['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56ec9c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression , LogisticRegression\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ed638792",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Shape:  (9000, 11)\n",
      "Testing Shape:  (1000, 11)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "print('Training Shape: ', X_train.shape)\n",
    "print('Testing Shape: ', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e5e80e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b266ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.47944328,  0.19687202, -0.00234647, ..., -0.5761528 ,\n",
       "        -0.57700814,  0.91105005],\n",
       "       [ 1.04580863,  1.33803657,  1.03625698, ..., -0.5761528 ,\n",
       "        -0.57700814, -1.09763453],\n",
       "       [-0.85297437, -0.08841912,  1.03625698, ..., -0.5761528 ,\n",
       "         1.73307782,  0.91105005],\n",
       "       ...,\n",
       "       [ 0.86941896, -0.08841912, -1.38715108, ..., -0.5761528 ,\n",
       "        -0.57700814, -1.09763453],\n",
       "       [ 0.16386025,  0.38706611,  1.03625698, ..., -0.5761528 ,\n",
       "        -0.57700814,  0.91105005],\n",
       "       [ 0.47513615,  1.14784248, -1.38715108, ...,  1.73565068,\n",
       "        -0.57700814,  0.91105005]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab8596f",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0a26c9d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "y_train_classified = [1 if value > threshold else 0 for value in y_train]\n",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "69219960",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.809\n"
     ]
    }
   ],
   "source": [
    "y_test_classified = [1 if value > threshold else 0 for value in y_test]\n",
    "accuracy1 = LR.score(X_test_scaled, y_test_classified)\n",
    "print(\"Model Accuracy:\", accuracy1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5029e93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "threshold = 0.5\n",
    "y_train_classified = [1 if value > threshold else 0 for value in y_train]\n",
    "svm = svm.SVC()\n",
    "svm.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dca07d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.865\n"
     ]
    }
   ],
   "source": [
    "y_test_classified = [1 if value > threshold else 0 for value in y_test]\n",
    "accuracy2 = svm.score(X_test_scaled, y_test_classified)\n",
    "print(\"Model Accuracy:\", accuracy2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c876d8f",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "25efa618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "y_train_classified = [1 if value > threshold else 0 for value in y_train]\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "306d1864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "y_train_classified = [1 if value > threshold else 0 for value in y_train]\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9530c55d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "test_classified = [1 if value > threshold else 0 for value in y_test]\n",
    "accuracy3 = rf.score(X_test_scaled, y_test_classified)\n",
    "print(\"Model Accuracy:\", accuracy3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb908fec",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4bab47b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "y_train_classified = [1 if value > threshold else 0 for value in y_train]\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dd163386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.809\n"
     ]
    }
   ],
   "source": [
    "y_test_classified = [1 if value > threshold else 0 for value in y_test]\n",
    "accuracy4 = dt.score(X_test_scaled, y_test_classified)\n",
    "print(\"Model Accuracy:\", accuracy4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b515d06",
   "metadata": {},
   "source": [
    "# KNeighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ee5b86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "y_train_classified = [1 if value > threshold else 0 for value in y_train]\n",
    "KNN = KNeighborsClassifier()\n",
    "KNN.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7be6375e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.84\n"
     ]
    }
   ],
   "source": [
    "y_test_classified = [1 if value > threshold else 0 for value in y_test]\n",
    "accuracy5 = KNN.score(X_test_scaled, y_test_classified)\n",
    "print(\"Model Accuracy:\", accuracy5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b3d77b",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9539c8fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "threshold = 0.5\n",
    "y_train_classified = [1 if value > threshold else 0 for value in y_train]\n",
    "GBC = GradientBoostingClassifier()\n",
    "GBC.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7df6a13e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.867\n"
     ]
    }
   ],
   "source": [
    "y_test_classified = [1 if value > threshold else 0 for value in y_test]\n",
    "accuracy6 = GBC.score(X_test_scaled, y_test_classified)\n",
    "print(\"Model Accuracy:\", accuracy6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bac32e94",
   "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>Model</th>\n",
       "      <th>ACC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LR</td>\n",
       "      <td>0.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dt</td>\n",
       "      <td>0.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rf</td>\n",
       "      <td>0.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GBC</td>\n",
       "      <td>0.867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Model    ACC\n",
       "0    LR  0.809\n",
       "1   svm  0.865\n",
       "2   KNN  0.860\n",
       "3    dt  0.809\n",
       "4    rf  0.840\n",
       "5   GBC  0.867"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "performance_summary = pd.DataFrame({\n",
    "    'Model':['LR','svm','KNN','dt','rf','GBC'],\n",
    "    'ACC':[accuracy1,\n",
    "           accuracy2,\n",
    "           accuracy3,\n",
    "           accuracy4,\n",
    "           accuracy5,\n",
    "           accuracy6\n",
    "          ]\n",
    "})\n",
    "performance_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22cef46f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}