MachineLearning_Bank Customer Churn Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fdf10e12",
   "metadata": {},
   "source": [
    "# Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d3041547",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.ensemble import GradientBoostingClassifier \n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9c516e",
   "metadata": {},
   "source": [
    "# Data cleaning and Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "78f85604",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>CustomerId</th>\n",
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       "      <th>CreditScore</th>\n",
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       "      <th>HasCrCard</th>\n",
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       "      <th>...</th>\n",
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       "      <td>9997</td>\n",
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       "      <td>516</td>\n",
       "      <td>France</td>\n",
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       "      <td>10</td>\n",
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       "      <td>9998</td>\n",
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      "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": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Churn_Modelling.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "7a52311a",
   "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": 105,
   "id": "48a6913f",
   "metadata": {},
   "outputs": [
    {
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      "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  "
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     "execution_count": 105,
     "metadata": {},
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   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "49510d15",
   "metadata": {},
   "outputs": [
    {
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       "      <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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "3f0746cc",
   "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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "02bcb5b5",
   "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>count</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Smith</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5014</td>\n",
       "      <td>5457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>7.193619e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.556570e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>1.562853e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>652.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.581569e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RowNumber    CustomerId Surname   CreditScore Geography Gender  \\\n",
       "count   10000.00000  1.000000e+04   10000  10000.000000     10000  10000   \n",
       "unique          NaN           NaN    2932           NaN         3      2   \n",
       "top             NaN           NaN   Smith           NaN    France   Male   \n",
       "freq            NaN           NaN      32           NaN      5014   5457   \n",
       "mean     5000.50000  1.569094e+07     NaN    650.528800       NaN    NaN   \n",
       "std      2886.89568  7.193619e+04     NaN     96.653299       NaN    NaN   \n",
       "min         1.00000  1.556570e+07     NaN    350.000000       NaN    NaN   \n",
       "25%      2500.75000  1.562853e+07     NaN    584.000000       NaN    NaN   \n",
       "50%      5000.50000  1.569074e+07     NaN    652.000000       NaN    NaN   \n",
       "75%      7500.25000  1.575323e+07     NaN    718.000000       NaN    NaN   \n",
       "max     10000.00000  1.581569e+07     NaN    850.000000       NaN    NaN   \n",
       "\n",
       "                 Age        Tenure        Balance  NumOfProducts    HasCrCard  \\\n",
       "count   10000.000000  10000.000000   10000.000000   10000.000000  10000.00000   \n",
       "unique           NaN           NaN            NaN            NaN          NaN   \n",
       "top              NaN           NaN            NaN            NaN          NaN   \n",
       "freq             NaN           NaN            NaN            NaN          NaN   \n",
       "mean       38.921800      5.012800   76485.889288       1.530200      0.70550   \n",
       "std        10.487806      2.892174   62397.405202       0.581654      0.45584   \n",
       "min        18.000000      0.000000       0.000000       1.000000      0.00000   \n",
       "25%        32.000000      3.000000       0.000000       1.000000      0.00000   \n",
       "50%        37.000000      5.000000   97198.540000       1.000000      1.00000   \n",
       "75%        44.000000      7.000000  127644.240000       2.000000      1.00000   \n",
       "max        92.000000     10.000000  250898.090000       4.000000      1.00000   \n",
       "\n",
       "        IsActiveMember  EstimatedSalary        Exited  \n",
       "count     10000.000000     10000.000000  10000.000000  \n",
       "unique             NaN              NaN           NaN  \n",
       "top                NaN              NaN           NaN  \n",
       "freq               NaN              NaN           NaN  \n",
       "mean          0.515100    100090.239881      0.203700  \n",
       "std           0.499797     57510.492818      0.402769  \n",
       "min           0.000000        11.580000      0.000000  \n",
       "25%           0.000000     51002.110000      0.000000  \n",
       "50%           1.000000    100193.915000      0.000000  \n",
       "75%           1.000000    149388.247500      0.000000  \n",
       "max           1.000000    199992.480000      1.000000  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get overall static about the dataset\n",
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "989d4e81",
   "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": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "acb63c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['RowNumber', 'CustomerId', 'Surname'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "daef9c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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",
       "   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  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "cba7863a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['France', 'Spain', 'Germany'], dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Geography'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f6ad57a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "21396d8f",
   "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>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</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>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</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>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</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>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</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>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619   42       2       0.00              1          1   \n",
       "1          608   41       1   83807.86              1          0   \n",
       "2          502   42       8  159660.80              3          1   \n",
       "3          699   39       1       0.00              2          0   \n",
       "4          850   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_Germany  \\\n",
       "0               1        101348.88       1                  0   \n",
       "1               1        112542.58       0                  0   \n",
       "2               0        113931.57       1                  0   \n",
       "3               0         93826.63       0                  0   \n",
       "4               1         79084.10       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  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ee39f872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7963\n",
       "1    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Exited'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5011b6fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       0\n",
       "2       1\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "9995    0\n",
       "9996    0\n",
       "9997    1\n",
       "9998    1\n",
       "9999    0\n",
       "Name: Exited, Length: 10000, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop('Exited' , axis=1)\n",
    "Y= data['Exited']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6b544b",
   "metadata": {},
   "source": [
    "# Spiliting data  and Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f7eb0d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Spiliting data \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "77c2285f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature scaling\n",
    "#creat object\n",
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0f0ae361",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "38402962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.058568  ,  1.71508648,  0.68472287, ..., -0.57831252,\n",
       "        -0.57773517,  0.90750738],\n",
       "       [ 0.91362605, -0.65993547, -0.6962018 , ...,  1.72916886,\n",
       "        -0.57773517,  0.90750738],\n",
       "       [ 1.07927399, -0.18493108, -1.73189531, ...,  1.72916886,\n",
       "        -0.57773517, -1.10191942],\n",
       "       ...,\n",
       "       [ 0.16821031, -0.18493108,  1.3751852 , ..., -0.57831252,\n",
       "        -0.57773517, -1.10191942],\n",
       "       [ 0.37527024, -0.37493284,  1.02995403, ..., -0.57831252,\n",
       "         1.73089688,  0.90750738],\n",
       "       [ 1.56586482,  1.14508121,  0.68472287, ..., -0.57831252,\n",
       "         1.73089688,  0.90750738]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8ab5b6",
   "metadata": {},
   "source": [
    "# Creation Model Machine Learning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "2ef2d54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use Model Logistic Regression\n",
    "#creat object \n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c564762a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" 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-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" 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": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c47c348b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use SVM \n",
    "#creat object \n",
    "svm_model = SVC()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f5d37ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" 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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" 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": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model on your training data\n",
    "svm_model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d17de50e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use KNN\n",
    "#creat object \n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8ce1e95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {color: black;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=4)</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-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7ba9990a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use DecisionTreeClassifier\n",
    "tree_classifier = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4c04cbf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {color: black;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 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-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 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-14 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-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 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-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 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-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 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-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" 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-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" 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": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "717a68a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use GradientBoosting\n",
    "gb_classifier = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "c89a201f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-15 {color: black;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 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-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 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-15 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-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 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-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 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-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 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-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" 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-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" 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": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5caaf909",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with the model Logistic Regression\n",
    "y_pred1 = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "e7366d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with the model SVM\n",
    "y_pred2 = svm_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c9337c10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with the model KNeighborsClassifier\n",
    "y_pred3 = knn_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ea7502a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with the model DecisionTreeClassifier\n",
    "y_pred4 = tree_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "e97ab4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with the model GradientBoostingClassifier\n",
    "y_pred5 = gb_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a64453",
   "metadata": {},
   "source": [
    "# Evaluation of performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b8e4bce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluation of performance model Logistic Regression \n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy1 = accuracy_score(Y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "be96d2d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.809"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "79e1572b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5939849624060151"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#precision score model Logistic Regression \n",
    "precision_score(Y_test,y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "7963ace8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluation of performance model SVM\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy2 = accuracy_score(Y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "18e09358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8625"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "e9cf6f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8367346938775511"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#precision score model SVM\n",
    "precision_score(Y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "664baaf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluation of performance model KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy3 = accuracy_score(Y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "31f20ee4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8285"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "8a10868f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7105263157894737"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#precision score model KNeighborsClassifier\n",
    "precision_score(Y_test,y_pred3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "29ceeb1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluation of performance model DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy4 = accuracy_score(Y_test,y_pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "0bb14527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7865"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c2a68273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4771689497716895"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#precision score model DecisionTreeClassifier\n",
    "precision_score(Y_test,y_pred4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7a0dfa5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluation of performance model GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy5 = accuracy_score(Y_test,y_pred5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "91f2129f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8685"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d8fc61b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7926829268292683"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#precision score model GradientBoostingClassifier\n",
    "precision_score(Y_test,y_pred5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff642963",
   "metadata": {},
   "source": [
    "# Choose best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "c485ed83",
   "metadata": {},
   "outputs": [],
   "source": [
    "finall_data = pd.DataFrame({'Models':['model', 'svm_model', 'knn_classifier', 'tree_classifier', 'gb_classifier'], \n",
    "                           'ACC' : [accuracy_score(Y_test,y_pred1),\n",
    "                                   accuracy_score(Y_test,y_pred2),\n",
    "                                   accuracy_score(Y_test,y_pred3),\n",
    "                                   accuracy_score(Y_test,y_pred4),\n",
    "                                   accuracy_score(Y_test,y_pred5)]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "a4431aa5",
   "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>Models</th>\n",
       "      <th>ACC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>model</td>\n",
       "      <td>0.8090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>svm_model</td>\n",
       "      <td>0.8625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>knn_classifier</td>\n",
       "      <td>0.8285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tree_classifier</td>\n",
       "      <td>0.7865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gb_classifier</td>\n",
       "      <td>0.8685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Models     ACC\n",
       "0            model  0.8090\n",
       "1        svm_model  0.8625\n",
       "2   knn_classifier  0.8285\n",
       "3  tree_classifier  0.7865\n",
       "4    gb_classifier  0.8685"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finall_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "263ded0b",
   "metadata": {},
   "source": [
    "# Data visualisation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "b2ab1e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Models', y='ACC', data=finall_data)\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Precision (ACC)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5a018e",
   "metadata": {},
   "source": [
    "# Save best_model and best_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "352493df",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = None\n",
    "best_accuracy = 0.0\n",
    "\n",
    "accuracy1 = accuracy_score(Y_test, y_pred1)\n",
    "accuracy2 = accuracy_score(Y_test, y_pred2)\n",
    "accuracy3 = accuracy_score(Y_test, y_pred3)\n",
    "accuracy4 = accuracy_score(Y_test, y_pred4)\n",
    "accuracy5 = accuracy_score(Y_test, y_pred5)\n",
    "\n",
    "if accuracy1 > best_accuracy:\n",
    "    best_model = model\n",
    "    best_accuracy = accuracy1\n",
    "\n",
    "if accuracy2 > best_accuracy:\n",
    "    best_model = svm_model\n",
    "    best_accuracy = accuracy2\n",
    "\n",
    "if accuracy3 > best_accuracy:\n",
    "    best_model = knn_classifier\n",
    "    best_accuracy = accuracy3\n",
    "    \n",
    "if accuracy4 > best_accuracy:\n",
    "    best_model = tree_classifier\n",
    "    best_accuracy = accuracy4 \n",
    "    \n",
    "if accuracy5 > best_accuracy:\n",
    "    best_model = gb_classifier\n",
    "    best_accuracy = accuracy5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3ee77065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model : GradientBoostingClassifier()\n",
      "best_accuracy : 0.8685\n"
     ]
    }
   ],
   "source": [
    "print(\"Best model :\", best_model)\n",
    "print(\"best_accuracy :\", best_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2bb1d78d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['best_model_gb.pkl']"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model_gb = GradientBoostingClassifier()\n",
    "joblib.dump(best_model_gb, 'best_model_gb.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f661e1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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