Bank_Customer_Churn_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "af1d49ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc6fe061",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Churn_Modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b00cb2a3",
   "metadata": {},
   "outputs": [
    {
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       "<p>10000 rows × 14 columns</p>\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",
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       "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e82edb73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07d984cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c7ed180",
   "metadata": {},
   "outputs": [
    {
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       "<p>10000 rows × 11 columns</p>\n",
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      ],
      "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "597d6a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74e45827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   CreditScore      10000 non-null  int64  \n",
      " 1   Geography        10000 non-null  object \n",
      " 2   Gender           10000 non-null  object \n",
      " 3   Age              10000 non-null  int64  \n",
      " 4   Tenure           10000 non-null  int64  \n",
      " 5   Balance          10000 non-null  float64\n",
      " 6   NumOfProducts    10000 non-null  int64  \n",
      " 7   HasCrCard        10000 non-null  int64  \n",
      " 8   IsActiveMember   10000 non-null  int64  \n",
      " 9   EstimatedSalary  10000 non-null  float64\n",
      " 10  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 859.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "10d49160",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</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>mean</th>\n",
       "      <td>650.528800</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>96.653299</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>350.000000</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>584.000000</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>652.000000</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>718.000000</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>850.000000</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": [
       "        CreditScore           Age        Tenure        Balance  NumOfProducts  \\\n",
       "count  10000.000000  10000.000000  10000.000000   10000.000000   10000.000000   \n",
       "mean     650.528800     38.921800      5.012800   76485.889288       1.530200   \n",
       "std       96.653299     10.487806      2.892174   62397.405202       0.581654   \n",
       "min      350.000000     18.000000      0.000000       0.000000       1.000000   \n",
       "25%      584.000000     32.000000      3.000000       0.000000       1.000000   \n",
       "50%      652.000000     37.000000      5.000000   97198.540000       1.000000   \n",
       "75%      718.000000     44.000000      7.000000  127644.240000       2.000000   \n",
       "max      850.000000     92.000000     10.000000  250898.090000       4.000000   \n",
       "\n",
       "         HasCrCard  IsActiveMember  EstimatedSalary        Exited  \n",
       "count  10000.00000    10000.000000     10000.000000  10000.000000  \n",
       "mean       0.70550        0.515100    100090.239881      0.203700  \n",
       "std        0.45584        0.499797     57510.492818      0.402769  \n",
       "min        0.00000        0.000000        11.580000      0.000000  \n",
       "25%        0.00000        0.000000     51002.110000      0.000000  \n",
       "50%        1.00000        1.000000    100193.915000      0.000000  \n",
       "75%        1.00000        1.000000    149388.247500      0.000000  \n",
       "max        1.00000        1.000000    199992.480000      1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d40cc92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79042437",
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_enc = OneHotEncoder(sparse_output=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c0a9ede9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Geography'] = onehot_enc.fit_transform(pd.DataFrame(df['Geography']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79f4dee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gender'] = onehot_enc.fit_transform(pd.DataFrame(df['Gender']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "085d2764",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
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       "      <td>41</td>\n",
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       "      <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>1.0</td>\n",
       "      <td>1.0</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>699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</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",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <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>1.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>0.0</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",
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       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <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        1.0     1.0   42       2       0.00              1   \n",
       "1             608        0.0     1.0   41       1   83807.86              1   \n",
       "2             502        1.0     1.0   42       8  159660.80              3   \n",
       "3             699        1.0     1.0   39       1       0.00              2   \n",
       "4             850        0.0     1.0   43       2  125510.82              1   \n",
       "...           ...        ...     ...  ...     ...        ...            ...   \n",
       "9995          771        1.0     0.0   39       5       0.00              2   \n",
       "9996          516        1.0     0.0   35      10   57369.61              1   \n",
       "9997          709        1.0     1.0   36       7       0.00              1   \n",
       "9998          772        0.0     0.0   42       3   75075.31              2   \n",
       "9999          792        1.0     1.0   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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6eab0f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Exited', axis=1)\n",
    "y = df['Exited'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "78f21e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Geography</th>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
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       "      <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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0             619        1.0     1.0   42       2       0.00              1   \n",
       "1             608        0.0     1.0   41       1   83807.86              1   \n",
       "2             502        1.0     1.0   42       8  159660.80              3   \n",
       "3             699        1.0     1.0   39       1       0.00              2   \n",
       "4             850        0.0     1.0   43       2  125510.82              1   \n",
       "...           ...        ...     ...  ...     ...        ...            ...   \n",
       "9995          771        1.0     0.0   39       5       0.00              2   \n",
       "9996          516        1.0     0.0   35      10   57369.61              1   \n",
       "9997          709        1.0     1.0   36       7       0.00              1   \n",
       "9998          772        0.0     0.0   42       3   75075.31              2   \n",
       "9999          792        1.0     1.0   28       4  130142.79              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0             1               1        101348.88  \n",
       "1             0               1        112542.58  \n",
       "2             1               0        113931.57  \n",
       "3             0               0         93826.63  \n",
       "4             1               1         79084.10  \n",
       "...         ...             ...              ...  \n",
       "9995          1               0         96270.64  \n",
       "9996          1               1        101699.77  \n",
       "9997          0               1         42085.58  \n",
       "9998          1               0         92888.52  \n",
       "9999          1               0         38190.78  \n",
       "\n",
       "[10000 rows x 10 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "76d50696",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "61627ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "950dab35",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e3699f75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8000, 10), (2000, 10), (8000,), (2000,))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b2a1f504",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train.columns), len(X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2d83dbec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_features = X_train.select_dtypes(include=[np.number]).columns\n",
    "# cat_features = X_train.select_dtypes(exclude=[np.number]).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "80aee0dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "894ba244",
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_pipeline = Pipeline([\n",
    "#     ('impute', SimpleImputer(strategy='median')),\n",
    "#     ('scale', StandardScaler())\n",
    "# ])\n",
    "\n",
    "# cat_pipeline = Pipeline([\n",
    "#     ('impute', SimpleImputer(strategy='most_frequent')),\n",
    "#     ('onehot', OneHotEncoder(sparse_output=False, handle_unknown='ignore'))\n",
    "# ])\n",
    "\n",
    "# transformer = ColumnTransformer([\n",
    "#     ('num', num_pipeline, num_features),\n",
    "#     ('object', cat_pipeline, cat_features)\n",
    "# ], remainder='passthrough')\n",
    "\n",
    "# estimator = RandomForestClassifier()\n",
    "# full_pipeline = Pipeline([\n",
    "#     ('preprocessing', transformer),\n",
    "#     ('estimator', estimator)\n",
    "# ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5fef82d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#full_pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "26610ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#full_pipeline.score(X_train, y_train), full_pipeline.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8e25daf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "46de31d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression(max_iter=1000, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9fd1b710",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</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(max_iter=1000, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "baee087a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = log_reg.predict(X_test)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ebfe38c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.78725, 0.8005)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_train, y_train), log_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ce5cf92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07b6120a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8005"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "99e3dd9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8005"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, preds, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "520c812e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7409119561237059"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(y_test, preds, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8e65ecdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.737248367147662"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, preds, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1f2339a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.81      0.98      0.89      1607\\n           1       0.45      0.07      0.12       393\\n\\n    accuracy                           0.80      2000\\n   macro avg       0.63      0.53      0.51      2000\\nweighted avg       0.74      0.80      0.74      2000\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9b2ba3c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1573,   34],\n",
       "       [ 365,   28]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9abb88c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f9b135a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "277d7f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_resampled, y_resampled = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c7980a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exited\n",
       "0    6356\n",
       "1    6356\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_resampled.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e9cf2b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_reg = LogisticRegression(max_iter=1000, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4045105c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "da22e790",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_balanced = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "13674994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, ..., 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4690ddfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.632"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, preds_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b0a61c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.89      0.62      0.73      1607\\n           1       0.30      0.68      0.42       393\\n\\n    accuracy                           0.63      2000\\n   macro avg       0.60      0.65      0.58      2000\\nweighted avg       0.77      0.63      0.67      2000\\n'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test, preds_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b8b617c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[997, 610],\n",
       "       [126, 267]], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, preds_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1babb5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9765bb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "edfe27ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ab16f3a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_preds = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "910bcfac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6bf37d80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.832"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, rfc_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6d1587df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.90      0.90      1607\n",
      "           1       0.58      0.54      0.56       393\n",
      "\n",
      "    accuracy                           0.83      2000\n",
      "   macro avg       0.73      0.72      0.73      2000\n",
      "weighted avg       0.83      0.83      0.83      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, rfc_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "48f47f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1450  157]\n",
      " [ 179  214]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, rfc_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e71644b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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