AspireNex Task3 CUSTOMER CHURN  PREDICTION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35087959",
   "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": "code",
   "execution_count": 2,
   "id": "5de234e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  </thead>\n",
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       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
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       "      <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",
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       "    <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",
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       "      <td>113931.57</td>\n",
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       "    <tr>\n",
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       "      <td>4</td>\n",
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       "      <td>Boni</td>\n",
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       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>92888.52</td>\n",
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       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
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       "      <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(\"Churn_Modelling.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0aad89da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
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       "      <th>CreditScore</th>\n",
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       "      <td>Female</td>\n",
       "      <td>42</td>\n",
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       "      <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",
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       "      <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",
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       "    <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": "f03e267b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28623082",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "551537cc",
   "metadata": {},
   "source": [
    "# Dropping of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d7e7dfc",
   "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": "84882000",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
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       "      <th>Tenure</th>\n",
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       "      <th>Exited</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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": "1942ba2d",
   "metadata": {},
   "source": [
    "# changing categorical values to numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2be7fa63",
   "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": 10,
     "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": "54cc48d2",
   "metadata": {},
   "source": [
    "# Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce64424e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exited\n",
       "0    7963\n",
       "1    2037\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Exited'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9659816d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Exited', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5872f434",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Exited',axis=1)\n",
    "y = data['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e56dc4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in c:\\users\\chatt\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in c:\\users\\chatt\\anaconda3\\lib\\site-packages (from imblearn) (0.10.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\chatt\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.24.3)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\chatt\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.11.1)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\chatt\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.3.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\chatt\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\chatt\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1a0ae597",
   "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": 17,
   "id": "3724dbed",
   "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": 18,
   "id": "8e8338f9",
   "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": 19,
   "id": "462e76ac",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0952d94e",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb3fb244",
   "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": 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",
    "LR = LogisticRegression()\n",
    "LR.fit(X_train_scaled, y_train_classified)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5f773f2a",
   "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": "markdown",
   "id": "f5d2e5ab",
   "metadata": {},
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "26cc64dc",
   "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": 24,
     "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": 25,
   "id": "da850aae",
   "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": "59f80ff0",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b9cdec12",
   "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": 27,
     "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": 28,
   "id": "928f971d",
   "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",
    "accuracy3 = rf.score(X_test_scaled, y_test_classified)\n",
    "print(\"Model Accuracy:\", accuracy3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3469130b",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "407e1caa",
   "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>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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "id": "00f37773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.799\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": "06577797",
   "metadata": {},
   "source": [
    "## KNeighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2db15198",
   "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>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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" 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": 33,
     "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": 34,
   "id": "0605b6b2",
   "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": "5a7f7721",
   "metadata": {},
   "source": [
    "## Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2ec32a57",
   "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>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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" 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": 36,
     "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": 37,
   "id": "36ebcdef",
   "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": 38,
   "id": "8b7954b8",
   "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>0.867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dt</td>\n",
       "      <td>0.799</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  0.867\n",
       "3    dt  0.799\n",
       "4    rf  0.840\n",
       "5   GBC  0.867"
      ]
     },
     "execution_count": 38,
     "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": "d404e677",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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