bank_churn_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bank customer churn prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beta Bank customers are leaving and the bankers figured it’s cheaper to save the existing customers rather than attract the new ones.\n",
    "\n",
    "The goal is to predict whether a customer will leave the bank soon. The Bank provided us with the historical data on clients’ past behavior and termination of contracts with the bank.\n",
    "It is necessary to build a model with the maximum possible F1 score. The bank's requirement for F1 score is at least 0.59 (59%).\n",
    "\n",
    "Data source: [https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling](https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature variables**\n",
    "<br>RowNumber - row index in data\n",
    "<br>CustomerId — unique customer ID\n",
    "<br>Surname - client's surname\n",
    "<br>CreditScore - credit rating\n",
    "<br>Geography - country of residence\n",
    "<br>Gender - client's gender\n",
    "<br>Age - client's age\n",
    "<br>Tenure — how many years a person has been a bank client\n",
    "<br>Balance — account balance\n",
    "<br>NumOfProducts — number of bank products used by the client\n",
    "<br>HasCrCard - the presence of a credit card\n",
    "<br>IsActiveMember - cis the client active member\n",
    "<br>EstimatedSalary - estimated salary\n",
    "\n",
    "**Target variable**\n",
    "<br>Exited - did the client leave the bank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Studying general information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils import shuffle\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "# opening the data file\n",
    "df = pd.read_csv('Churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "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>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0     2.0       0.00              1          1               1   \n",
       "1     1.0   83807.86              1          0               1   \n",
       "2     8.0  159660.80              3          1               0   \n",
       "3     1.0       0.00              2          0               0   \n",
       "4     2.0  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing first 5 lines of data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "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           9091 non-null   float64\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(3), int64(8), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# looking at the general information\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "909"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding the number of the missing values in 'Tenure'\n",
    "df['Tenure'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RowNumber</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>5.000500e+03</td>\n",
       "      <td>2886.895680</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2500.75</td>\n",
       "      <td>5.000500e+03</td>\n",
       "      <td>7.500250e+03</td>\n",
       "      <td>10000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustomerId</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>71936.186123</td>\n",
       "      <td>15565701.00</td>\n",
       "      <td>15628528.25</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>15815690.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>6.505288e+02</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>350.00</td>\n",
       "      <td>584.00</td>\n",
       "      <td>6.520000e+02</td>\n",
       "      <td>7.180000e+02</td>\n",
       "      <td>850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>3.892180e+01</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>18.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>9091.0</td>\n",
       "      <td>4.997690e+00</td>\n",
       "      <td>2.894723</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>7.648589e+04</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.719854e+04</td>\n",
       "      <td>1.276442e+05</td>\n",
       "      <td>250898.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumOfProducts</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.530200e+00</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HasCrCard</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>7.055000e-01</td>\n",
       "      <td>0.455840</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IsActiveMember</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>5.151000e-01</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.000902e+05</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>11.58</td>\n",
       "      <td>51002.11</td>\n",
       "      <td>1.001939e+05</td>\n",
       "      <td>1.493882e+05</td>\n",
       "      <td>199992.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exited</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>2.037000e-01</td>\n",
       "      <td>0.402769</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count          mean           std          min  \\\n",
       "RowNumber        10000.0  5.000500e+03   2886.895680         1.00   \n",
       "CustomerId       10000.0  1.569094e+07  71936.186123  15565701.00   \n",
       "CreditScore      10000.0  6.505288e+02     96.653299       350.00   \n",
       "Age              10000.0  3.892180e+01     10.487806        18.00   \n",
       "Tenure            9091.0  4.997690e+00      2.894723         0.00   \n",
       "Balance          10000.0  7.648589e+04  62397.405202         0.00   \n",
       "NumOfProducts    10000.0  1.530200e+00      0.581654         1.00   \n",
       "HasCrCard        10000.0  7.055000e-01      0.455840         0.00   \n",
       "IsActiveMember   10000.0  5.151000e-01      0.499797         0.00   \n",
       "EstimatedSalary  10000.0  1.000902e+05  57510.492818        11.58   \n",
       "Exited           10000.0  2.037000e-01      0.402769         0.00   \n",
       "\n",
       "                         25%           50%           75%          max  \n",
       "RowNumber            2500.75  5.000500e+03  7.500250e+03     10000.00  \n",
       "CustomerId       15628528.25  1.569074e+07  1.575323e+07  15815690.00  \n",
       "CreditScore           584.00  6.520000e+02  7.180000e+02       850.00  \n",
       "Age                    32.00  3.700000e+01  4.400000e+01        92.00  \n",
       "Tenure                  2.00  5.000000e+00  7.000000e+00        10.00  \n",
       "Balance                 0.00  9.719854e+04  1.276442e+05    250898.09  \n",
       "NumOfProducts           1.00  1.000000e+00  2.000000e+00         4.00  \n",
       "HasCrCard               0.00  1.000000e+00  1.000000e+00         1.00  \n",
       "IsActiveMember          0.00  1.000000e+00  1.000000e+00         1.00  \n",
       "EstimatedSalary     51002.11  1.001939e+05  1.493882e+05    199992.48  \n",
       "Exited                  0.00  0.000000e+00  0.000000e+00         1.00  "
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the numeric values\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Surname</th>\n",
       "      <td>10000</td>\n",
       "      <td>2932</td>\n",
       "      <td>Smith</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>France</td>\n",
       "      <td>5014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>10000</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>5457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           count unique     top  freq\n",
       "Surname    10000   2932   Smith    32\n",
       "Geography  10000      3  France  5014\n",
       "Gender     10000      2    Male  5457"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the quantitative values\n",
    "df.describe(include='object').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Surname</th>\n",
       "      <td>10000</td>\n",
       "      <td>2932</td>\n",
       "      <td>Smith</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Geography</th>\n",
       "      <td>10000</td>\n",
       "      <td>3</td>\n",
       "      <td>France</td>\n",
       "      <td>5014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>10000</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>5457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count unique     top  freq\n",
       "Surname    10000   2932   Smith    32\n",
       "Geography  10000      3  France  5014\n",
       "Gender     10000      2    Male  5457"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking values for the columns with object data type\n",
    "df.describe(include='object').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num. duplicates: 0\n"
     ]
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "print('Num. duplicates:', df.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable 'Exited' must be checked for the balance of classes. Most often the threshold for the class balance level is 25-30%. We set it as 30%, if the value found is bellow 30% - the data requires class balancing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes in target variables: 0    7963\n",
      "1    2037\n",
      "Name: Exited, dtype: int64\n",
      "Class balance level: 25.58 %\n"
     ]
    }
   ],
   "source": [
    "#checking the imbalance of classes.\n",
    "print('Classes in target variables:', df['Exited'].value_counts())\n",
    "\n",
    "print('Class balance level:',round((2037/7963)*100,2),'%') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary:** Data contains 15 columns and 10000 rows. Findings and next steps:\n",
    "\n",
    "* Found 909 NaN values in 'Tenure' columns. It's only 9% from all the data. We will drop it.\n",
    "* Change float type to int in 'Tenure' column (it is supposed to be numerical).\n",
    "* We don't need 'RowNumber', 'CustomerId' and 'Surname' columns for building a prediction model. Drop them.\n",
    "* Process data in 'Geography', 'Gender' with One-Hot Encoding (OHE).\n",
    "* Data has the class imbalance in target variable. We will use either downsampling or upsanpling techniques to process it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping missing values\n",
    "df.dropna(subset=['Tenure'], inplace=True) \n",
    "df.drop(columns=['RowNumber', 'CustomerId', 'Surname'], inplace=True) # удаление ненужных столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing datatype in 'Tenure'\n",
    "df['Tenure'] = df['Tenure'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transforming data with OHE\n",
    "data = pd.get_dummies(df, columns=(['Geography', 'Gender']), drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary:** \n",
    "* dropped the unnecessary columns and missing values\n",
    "* changed data type in 'Tenure' to the appropriate data type('int')\n",
    "* transformed data in columns with categorical data with OHE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting features and target variables\n",
    "features = data.drop(['Exited'], axis=1)\n",
    "target = data['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "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>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CreditScore</td>\n",
       "      <td>21.208943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age</td>\n",
       "      <td>12.213651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tenure</td>\n",
       "      <td>3.851954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Balance</td>\n",
       "      <td>3.189289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NumOfProducts</td>\n",
       "      <td>7.827226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HasCrCard</td>\n",
       "      <td>3.287674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsActiveMember</td>\n",
       "      <td>2.078944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>EstimatedSalary</td>\n",
       "      <td>3.892154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Geography_Germany</td>\n",
       "      <td>1.792308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Geography_Spain</td>\n",
       "      <td>1.484706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gender_Male</td>\n",
       "      <td>2.174860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              feature        VIF\n",
       "0         CreditScore  21.208943\n",
       "1                 Age  12.213651\n",
       "2              Tenure   3.851954\n",
       "3             Balance   3.189289\n",
       "4       NumOfProducts   7.827226\n",
       "5           HasCrCard   3.287674\n",
       "6      IsActiveMember   2.078944\n",
       "7     EstimatedSalary   3.892154\n",
       "8   Geography_Germany   1.792308\n",
       "9     Geography_Spain   1.484706\n",
       "10        Gender_Male   2.174860"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for multicollinearity between the variables in 'features'\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"feature\"] = features.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(features.values, i) for i in range(len(features.columns))]\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No multicollinearity found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data in train, valid, and test sets of data\n",
    "features_train, features_test, target_train, target_test = train_test_split(features, target,test_size=0.2,random_state=12345)\n",
    "features_train,features_valid, target_train, target_valid = train_test_split(features_train,target_train,test_size = 0.25,random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5454 entries, 3706 to 208\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   CreditScore        5454 non-null   int64  \n",
      " 1   Age                5454 non-null   int64  \n",
      " 2   Tenure             5454 non-null   int32  \n",
      " 3   Balance            5454 non-null   float64\n",
      " 4   NumOfProducts      5454 non-null   int64  \n",
      " 5   HasCrCard          5454 non-null   int64  \n",
      " 6   IsActiveMember     5454 non-null   int64  \n",
      " 7   EstimatedSalary    5454 non-null   float64\n",
      " 8   Geography_Germany  5454 non-null   uint8  \n",
      " 9   Geography_Spain    5454 non-null   uint8  \n",
      " 10  Gender_Male        5454 non-null   uint8  \n",
      "dtypes: float64(2), int32(1), int64(5), uint8(3)\n",
      "memory usage: 378.2 KB\n"
     ]
    }
   ],
   "source": [
    "# checking\n",
    "features_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary:** \n",
    "* data was split into 3 sets: train, valid, and test. \n",
    "* data in features was additionally checked for multicollinearity (none found)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balancing classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try both methods of upsampling and downsampling the classes and determine the most effective one with F1 score from logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeat = ((target_train == 0).sum() / (target_train == 1).sum()).round().astype(int)\n",
    "repeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing the function for upsampling\n",
    "def upsample(features, target, repeat):\n",
    "    features_zeros = features[target == 0]\n",
    "    features_ones = features[target == 1]\n",
    "    target_zeros = target[target == 0]\n",
    "    target_ones = target[target == 1]\n",
    "\n",
    "    features_upsampled = pd.concat([features_zeros] + [features_ones] * repeat)\n",
    "    target_upsampled = pd.concat([target_zeros] + [target_ones] * repeat)\n",
    "    \n",
    "    features_upsampled, target_upsampled = shuffle(\n",
    "        features_upsampled, target_upsampled, random_state=12345)\n",
    "    \n",
    "    return features_upsampled, target_upsampled\n",
    "\n",
    "features_upsampled, target_upsampled = upsample(features_train, target_train, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.4493392070484581\n"
     ]
    }
   ],
   "source": [
    "#chiecking\n",
    "model = LogisticRegression(random_state=12345,solver='liblinear')\n",
    "model.fit(features_upsampled, target_upsampled)\n",
    "predicted_valid = model.predict(features_valid)\n",
    "print(\"F1:\", f1_score(target_valid, predicted_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "# writing the function for downsampling\n",
    "def downsample(features, target, fraction):\n",
    "    features_zeros = features[target == 0]\n",
    "    features_ones = features[target == 1]\n",
    "    target_zeros = target[target == 0]\n",
    "    target_ones = target[target == 1]\n",
    "\n",
    "    features_downsampled = pd.concat(\n",
    "        [features_zeros.sample(frac=fraction, random_state=12345)] + [features_ones])\n",
    "    target_downsampled = pd.concat(\n",
    "        [target_zeros.sample(frac=fraction, random_state=12345)] + [target_ones])\n",
    "    \n",
    "    features_downsampled, target_downsampled = shuffle(\n",
    "        features_downsampled, target_downsampled, random_state=12345)\n",
    "    \n",
    "    return features_downsampled, target_downsampled\n",
    "\n",
    "\n",
    "features_downsampled, target_downsampled = downsample(features_train, target_train, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.3584710743801653\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1553 entries, 5658 to 1410\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   CreditScore        1553 non-null   int64  \n",
      " 1   Age                1553 non-null   int64  \n",
      " 2   Tenure             1553 non-null   int32  \n",
      " 3   Balance            1553 non-null   float64\n",
      " 4   NumOfProducts      1553 non-null   int64  \n",
      " 5   HasCrCard          1553 non-null   int64  \n",
      " 6   IsActiveMember     1553 non-null   int64  \n",
      " 7   EstimatedSalary    1553 non-null   float64\n",
      " 8   Geography_Germany  1553 non-null   uint8  \n",
      " 9   Geography_Spain    1553 non-null   uint8  \n",
      " 10  Gender_Male        1553 non-null   uint8  \n",
      "dtypes: float64(2), int32(1), int64(5), uint8(3)\n",
      "memory usage: 107.7 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(random_state=12345,solver='liblinear')\n",
    "model.fit(features_downsampled, target_downsampled)\n",
    "predicted_valid = model.predict(features_valid)\n",
    "\n",
    "\n",
    "print(\"F1:\", f1_score(target_valid, predicted_valid))\n",
    "print(features_downsampled.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary** Logistic regression for upsampling showed the result of F1_score equalling to 0.44, while Downsampling showed only 0.35. We will use upsampling in the building of models later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Сhoosing the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will build and compare the four following models:\n",
    "* Decision Tree Classifier\n",
    "* Random Forest Classifier\n",
    "* Logistic Regression Classifier\n",
    "* Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "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>max_depth</th>\n",
       "      <th>f1_train</th>\n",
       "      <th>f1_valid</th>\n",
       "      <th>AUC_ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  f1_train  f1_valid  AUC_ROC\n",
       "6          7      0.82      0.56     0.82\n",
       "5          6      0.79      0.55     0.81\n",
       "8          9      0.86      0.54     0.78\n",
       "7          8      0.84      0.54     0.80\n",
       "4          5      0.75      0.54     0.81"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dtc = pd.DataFrame() # create dataframe \n",
    "for depth in range(1, 10):\n",
    "    # defining the model\n",
    "    model = DecisionTreeClassifier(max_depth=depth, random_state=12345)\n",
    "\n",
    "    # fitting the model\n",
    "    model.fit(features_upsampled, target_upsampled)\n",
    "\n",
    "    # predicting values\n",
    "    predicted_train = model.predict(features_upsampled)\n",
    "    predicted_valid = model.predict(features_valid)\n",
    "\n",
    "    # calculating f1_score\n",
    "    f1_train = f1_score(target_upsampled, predicted_train)\n",
    "    f1_valid = f1_score(target_valid, predicted_valid)\n",
    "\n",
    "    # predicting probabilities\n",
    "    probabilities_valid = model.predict_proba(features_valid)\n",
    "    probabilities_one_valid = probabilities_valid[:, 1]\n",
    "\n",
    "    # calculating auc_roc score\n",
    "    auc_roc = roc_auc_score(target_valid, probabilities_one_valid) \n",
    "\n",
    "    # creating the temporary dataframe with metrics\n",
    "    df_decissiontree_temp = pd.DataFrame({'max_depth': [depth],'f1_train': [f1_train],'f1_valid': [f1_valid],'AUC_ROC': [auc_roc]})\n",
    "\n",
    "    # appending the new values to df_dtc  \n",
    "    df_dtc = df_dtc.append(df_decissiontree_temp)\n",
    "\n",
    "\n",
    "dtc = df_dtc.reset_index(drop=True)\n",
    "round(dtc.sort_values(by='f1_valid', ascending=False).head(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary:** The best result with max_depth=7, f1_valid_up=0.56, and AUC ROC = 0.82, which is a good result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "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>est</th>\n",
       "      <th>f1_train</th>\n",
       "      <th>f1_valid</th>\n",
       "      <th>AUC_ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    est  f1_train  f1_valid  AUC_ROC\n",
       "9    10      0.78      0.60     0.85\n",
       "7     8      0.76      0.59     0.84\n",
       "13   14      0.77      0.59     0.85\n",
       "28   29      0.78      0.59     0.85\n",
       "11   12      0.78      0.59     0.85"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rfc = pd.DataFrame() # create dataframe \n",
    "for est in range(1, 50):\n",
    "    # defining the model\n",
    "    model = RandomForestClassifier(max_depth=5, n_estimators=est, random_state=12345)\n",
    "\n",
    "    # fitting the model\n",
    "    model.fit(features_upsampled, target_upsampled)\n",
    "\n",
    "    # predicting values\n",
    "    predicted_train = model.predict(features_upsampled)\n",
    "    predicted_valid = model.predict(features_valid)\n",
    "\n",
    "    # calculating f1_score\n",
    "    f1_train = f1_score(target_upsampled, predicted_train)\n",
    "    f1_valid = f1_score(target_valid, predicted_valid)\n",
    "\n",
    "    # predicting probabilities\n",
    "    probabilities_valid = model.predict_proba(features_valid)\n",
    "    probabilities_one_valid = probabilities_valid[:, 1]\n",
    "\n",
    "    # calculating auc_roc score\n",
    "    auc_roc = roc_auc_score(target_valid, probabilities_one_valid) \n",
    "\n",
    "    # creating the temporary dataframe with metrics\n",
    "    df_rf_temp = pd.DataFrame({'est': [est],'f1_train': [f1_train],'f1_valid': [f1_valid],'AUC_ROC': [auc_roc]})\n",
    "\n",
    "    # appending the new values to df_dtc  \n",
    "    df_rfc = df_rfc.append(df_rf_temp)\n",
    "\n",
    "\n",
    "rfc = df_rfc.reset_index(drop=True)\n",
    "round(rfc.sort_values(by='f1_valid', ascending=False).head(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary:** The best result with n_estimators=10. We have f1_valid_up=0.60, and AUC ROC = 0.85. It's higher than for DecisionTreeClassifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>f1_train</th>\n",
       "      <th>f1_valid</th>\n",
       "      <th>AUC_ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.67</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   f1_train  f1_valid  AUC_ROC\n",
       "0      0.67      0.45     0.71"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg = pd.DataFrame() # create dataframe \n",
    "\n",
    "model = LogisticRegression(solver='liblinear', class_weight='balanced', random_state=12345)\n",
    "\n",
    "# fitting the model\n",
    "model.fit(features_upsampled, target_upsampled)\n",
    "\n",
    "# predicting values\n",
    "predicted_train = model.predict(features_upsampled)\n",
    "predicted_valid = model.predict(features_valid)\n",
    "\n",
    "# calculating f1_score\n",
    "f1_train = f1_score(target_upsampled, predicted_train)\n",
    "f1_valid = f1_score(target_valid, predicted_valid)\n",
    "\n",
    "# predicting probabilities\n",
    "probabilities_valid = model.predict_proba(features_valid)\n",
    "probabilities_one_valid = probabilities_valid[:, 1]\n",
    "\n",
    "# calculating auc_roc score\n",
    "auc_roc = roc_auc_score(target_valid, probabilities_one_valid) \n",
    "\n",
    "# creating the temporary dataframe with metrics\n",
    "df_lr_temp = pd.DataFrame({'f1_train': [f1_train],'f1_valid': [f1_valid],'AUC_ROC': [auc_roc]})\n",
    "\n",
    "# appending the new values to df_dtc  \n",
    "log_reg = log_reg.append(df_lr_temp)\n",
    "\n",
    "\n",
    "lr_reg = log_reg.reset_index(drop=True)\n",
    "round(lr_reg.sort_values(by='f1_valid', ascending=False).head(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary** Logistic regression showed the worst result with f1 score = 0.45 on valid set, and AUC ROC = 0.71"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>est</th>\n",
       "      <th>f1_train</th>\n",
       "      <th>f1_valid</th>\n",
       "      <th>AUC_ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    est  f1_train  f1_valid  AUC_ROC\n",
       "35   36      0.98       0.6     0.83\n",
       "38   39      0.99       0.6     0.83\n",
       "40   41      0.99       0.6     0.83\n",
       "39   40      0.99       0.6     0.83\n",
       "41   42      0.99       0.6     0.83"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gb = pd.DataFrame() # create dataframe \n",
    "for est in range(1, 50):\n",
    "    # defining the model\n",
    "    model = GradientBoostingClassifier(max_depth=9, n_estimators=est, random_state=12345)\n",
    "\n",
    "    # fitting the model\n",
    "    model.fit(features_upsampled, target_upsampled)\n",
    "\n",
    "    # predicting values\n",
    "    predicted_train = model.predict(features_upsampled)\n",
    "    predicted_valid = model.predict(features_valid)\n",
    "\n",
    "    # calculating f1_score\n",
    "    f1_train = f1_score(target_upsampled, predicted_train)\n",
    "    f1_valid = f1_score(target_valid, predicted_valid)\n",
    "\n",
    "    # predicting probabilities\n",
    "    probabilities_valid = model.predict_proba(features_valid)\n",
    "    probabilities_one_valid = probabilities_valid[:, 1]\n",
    "\n",
    "    # calculating auc_roc score\n",
    "    auc_roc = roc_auc_score(target_valid, probabilities_one_valid) \n",
    "\n",
    "    # creating the temporary dataframe with metrics\n",
    "    df_gb_temp = pd.DataFrame({'est': [est],'f1_train': [f1_train],'f1_valid': [f1_valid],'AUC_ROC': [auc_roc]})\n",
    "\n",
    "    # appending the new values to df_dtc  \n",
    "    df_gb = df_gb.append(df_gb_temp)\n",
    "\n",
    "\n",
    "gb = df_gb.reset_index(drop=True)\n",
    "round(gb.sort_values(by='f1_valid', ascending=False).head(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary** Gradient boosting has result with f1 = 0.60 and auc roc 0.85"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyzed the 4 models and compared their f1 score and AUC ROC score on the valid sets\n",
    "\n",
    "|model|f1_valid|AUC ROC|\n",
    "|---|---|---|\n",
    "|DecisionTree|0.56|0.82|\n",
    "|RandomForest|0.60|0.86|\n",
    "|LogisticRegression|0.45|0.71|\n",
    "|GradientBoosting|0.60|0.83|\n",
    "\n",
    "Thus 'Random Forest Classifier' with max_depth=5 and n_estimators=10 is the best model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score: 0.6078212290502794\n",
      "auc_roc: 0.8510784038874871\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=12345, max_depth=5, n_estimators=10)\n",
    "model.fit(features_upsampled, target_upsampled) # fit the model\n",
    "predicted_test = model.predict(features_test)\n",
    "\n",
    "# predict probabilities\n",
    "probabilities_test = model.predict_proba(features_test)\n",
    "probabilities_one_test = probabilities_test[:, 1]\n",
    "\n",
    "# calculate auc_roc\n",
    "auc_roc = roc_auc_score(target_test, probabilities_one_test) \n",
    "\n",
    "f1_test = f1_score(target_test, predicted_test) # count the test score\n",
    "print('f1_score:', f1_test)\n",
    "print('auc_roc:', auc_roc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Summary:** These are good f1  and auc_roc score. The model is not overfitting.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overall conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Opened and analyzed the data; checked for duplicates and imbalance of classes in the target var.\n",
    "2. Balanced out the classes in the target var, deleted the unnecessary columns and missing values\n",
    "3. Analyzed a total of 4 models \"Decision Tree\", \"Random Forest\", \"Gradient Boosting\", and \"Logistic Regression\". The best result of the f1 score (0.60), AUC-ROC (0.85) was shown by the Random Forest model.\n",
    "5. The f1 score of the measure is significantly lower than the auc_roc value.\n",
    "\n",
    "According to the task, it was necessary to build a classification model with an the best possible value of the F1-score, with the threshold being equal to 0.59.\n",
    "'Random Forest Classifier' with max_depth=5 and n_estimators=10 is the best model, with f1 score equal to 0.50, and AUC ROC equal to 0.85."
   ]
  }
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