Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98ce1ef8",
   "metadata": {},
   "source": [
    "## Output variable - y\n",
    "**y - Whether the client has subscribed a term deposit or not. \n",
    "Binomial (\"yes\" or \"no\")**\n",
    "\n",
    "### Attribute information For bank dataset\n",
    "\n",
    "### Input variables:\n",
    "**Bank client data:**\n",
    "*   1 - age : (numeric)\n",
    "*   2 - job : type of job (categorical: \"admin.\", \"unknown\", \"unemployed\", \"management\", \"housemaid\", \"entrepreneur\", \"student\", \"blue-collar\", \"self-employed\", \"retired\", \"technician\", \"services\")\n",
    "*   3 - marital : marital status (categorical: \"married\", \"divorced\", \"single\"; note: \"divorced\" means divorced or widowed)\n",
    "*   4 - education (categorical: \"unknown\", \"secondary\",\"primary\", \"tertiary\")\n",
    "*   5 - default: has credit in default? (binary: \"yes\", \"no\")\n",
    "*   6 - balance: average yearly balance, in euros (numeric) \n",
    "*   7 - housing: has housing loan? (binary: \"yes\", \"no\")\n",
    "*   8 - loan: has personal loan? (binary: \"yes\", \"no\")\n",
    "  \n",
    "**Related with the last contact of the current campaign:**\n",
    "*   9 - contact: contact communication type (categorical: \"unknown\",\"telephone\",\"cellular\") \n",
    "*  10 - day: last contact day of the month (numeric)\n",
    "*  11 - month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "*  12 - duration: last contact duration, in seconds (numeric)\n",
    "   \n",
    "**Other attributes:**\n",
    "*  13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "*  14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)\n",
    "*  15 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "*  16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\", \"other\", \"failure\", \"success\")\n",
    "\n",
    "### Output variable (desired target):\n",
    "*  17 - y - has the client subscribed a term deposit? (binary: \"yes\", \"no\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed86445d",
   "metadata": {},
   "source": [
    "## 1. Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "668c8b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve, classification_report\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5775e701",
   "metadata": {},
   "source": [
    "## 2. Importing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "beea4f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data = pd.read_csv(r'D:\\Assignment ExcelR\\Logistic Regression\\bank-full.csv', delimiter=';')\n",
    "bank_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d26084d",
   "metadata": {},
   "source": [
    "## 3. Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73097ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45211, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16d336f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
       "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f2cf7a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   default    45211 non-null  object\n",
      " 5   balance    45211 non-null  int64 \n",
      " 6   housing    45211 non-null  object\n",
      " 7   loan       45211 non-null  object\n",
      " 8   contact    45211 non-null  object\n",
      " 9   day        45211 non-null  int64 \n",
      " 10  month      45211 non-null  object\n",
      " 11  duration   45211 non-null  int64 \n",
      " 12  campaign   45211 non-null  int64 \n",
      " 13  pdays      45211 non-null  int64 \n",
      " 14  previous   45211 non-null  int64 \n",
      " 15  poutcome   45211 non-null  object\n",
      " 16  y          45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "bank_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52808a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d67cae50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         1.127581e+02\n",
       "balance     9.270599e+06\n",
       "day         6.926361e+01\n",
       "duration    6.632057e+04\n",
       "campaign    9.597733e+00\n",
       "pdays       1.002577e+04\n",
       "previous    5.305841e+00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "466dc4f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0.684818\n",
       "balance      8.360308\n",
       "day          0.093079\n",
       "duration     3.144318\n",
       "campaign     4.898650\n",
       "pdays        2.615715\n",
       "previous    41.846454\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f89bb24f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age            0.319570\n",
       "balance      140.751547\n",
       "day           -1.059897\n",
       "duration      18.153915\n",
       "campaign      39.249651\n",
       "pdays          6.935195\n",
       "previous    4506.860660\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.kurt()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72cd055b",
   "metadata": {},
   "source": [
    "### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43466dad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0023e586",
   "metadata": {},
   "source": [
    "### Duplicated Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2288dcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 17)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data[bank_data.duplicated()].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e677338",
   "metadata": {},
   "source": [
    "### Let's find how many discrete and continuous feature are their in our dataset by seperating them in variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f74906f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Variables Count: 10\n"
     ]
    }
   ],
   "source": [
    "discrete_feature = [feature for feature in bank_data.columns if len(bank_data[feature].unique())<20 and feature]\n",
    "print('Discrete Variables Count: {}'.format(len(discrete_feature)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "778aba69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous Feature Count 7\n"
     ]
    }
   ],
   "source": [
    "continuous_feature = [feature for feature in bank_data.columns if bank_data[feature].dtype!='O' and feature not in discrete_feature]\n",
    "print('Continuous Feature Count {}'.format(len(continuous_feature)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcc3a1c",
   "metadata": {},
   "source": [
    "## 4. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fb6421e3",
   "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>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097783</td>\n",
       "      <td>-0.009120</td>\n",
       "      <td>-0.004648</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>-0.023758</td>\n",
       "      <td>0.001288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.097783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004503</td>\n",
       "      <td>0.021560</td>\n",
       "      <td>-0.014578</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>0.016674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>-0.009120</td>\n",
       "      <td>0.004503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030206</td>\n",
       "      <td>0.162490</td>\n",
       "      <td>-0.093044</td>\n",
       "      <td>-0.051710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>-0.004648</td>\n",
       "      <td>0.021560</td>\n",
       "      <td>-0.030206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084570</td>\n",
       "      <td>-0.001565</td>\n",
       "      <td>0.001203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>0.004760</td>\n",
       "      <td>-0.014578</td>\n",
       "      <td>0.162490</td>\n",
       "      <td>-0.084570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.088628</td>\n",
       "      <td>-0.032855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.023758</td>\n",
       "      <td>0.003435</td>\n",
       "      <td>-0.093044</td>\n",
       "      <td>-0.001565</td>\n",
       "      <td>-0.088628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.454820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.001288</td>\n",
       "      <td>0.016674</td>\n",
       "      <td>-0.051710</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>-0.032855</td>\n",
       "      <td>0.454820</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age   balance       day  duration  campaign     pdays  previous\n",
       "age       1.000000  0.097783 -0.009120 -0.004648  0.004760 -0.023758  0.001288\n",
       "balance   0.097783  1.000000  0.004503  0.021560 -0.014578  0.003435  0.016674\n",
       "day      -0.009120  0.004503  1.000000 -0.030206  0.162490 -0.093044 -0.051710\n",
       "duration -0.004648  0.021560 -0.030206  1.000000 -0.084570 -0.001565  0.001203\n",
       "campaign  0.004760 -0.014578  0.162490 -0.084570  1.000000 -0.088628 -0.032855\n",
       "pdays    -0.023758  0.003435 -0.093044 -0.001565 -0.088628  1.000000  0.454820\n",
       "previous  0.001288  0.016674 -0.051710  0.001203 -0.032855  0.454820  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9e20ab09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize= (9,6))\n",
    "sns.heatmap(bank_data.corr(), annot=True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a4f648",
   "metadata": {},
   "source": [
    "### Lets analyze the continuous values by creating histograms to understand the distribution of the numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b11fc66e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    bank_data1 = bank_data.copy()\n",
    "    bank_data1[feature].hist(bins=15)\n",
    "    plt.ylabel('Count')\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0126d73",
   "metadata": {},
   "source": [
    "### Log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "765a3e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ql6uqieBPcD5SZYCJ1fVA1VVwO1920iSZsGsXLNIsgJ4C/AQsKSq9kOvUIB/0K22DPh232b7urFl3eOJ45KkWZLe/6wP8QWSRcCXgRur6q4kz1fVqX3Ln6uqxUl+F3igqu7oxm8FvgB8C/itqrqoG78Q+PdV9S8mea1N9E5XsWTJktVbt26dUuYDBw6waNGiKW07F8YtL4xf5kHy7nrqhRl9zVXLTpnytkfKO9MZZ8qShfDMS3Od4tiMauYj/b0Z9N/cunXrdlbVmonjx00/2pElOR74I+BTVXVXN/xMkqVVtb87xfRsN74POLNv8+XA09348knGD1NVm4HNAGvWrKm1a9dOKfeOHTuY6rZzYdzywvhlHiTvVdffPaOvuffyo7/e0Rwp70xnnCnXrTrIR3YN9cfRjBvVzEf6ezPdf3PDfDdUgFuB3VX1232LtgNXdo+vBD7XN74xyYlJzqJ3Ifvh7lTVi0ku6PZ5Rd82kqRZMMxafDvwfmBXkq92Y78B3ARsS/IBeqeYLgOoqseSbAO+Tu+dVNdU1avddlcDtwELgXu6L0nSLBlaWVTVV4AjfR7inUfY5kbgxknGHwHOnbl00uhbMY1TRtetOjiyp5w0nvwEtySpybKQJDVZFpKkJstCktRkWUiSmiwLSVKTZSFJarIsJElNo3djE2mWHMuH3vyQm+Y7jywkSU2WhSSpydNQAqZ3H6LJ7L3p4hndn6S55ZGFJKnJspAkNVkWkqQmy0KS1OQFbg1F64L5sX5uwQvm0tzyyEKS1GRZSJKaLAtJUpNlIUlqsiwkSU2WhSSpybKQJDVZFpKkJstCktRkWUiSmiwLSVKTZSFJarIsJElNloUkqcmykCQ1WRaSpCbLQpLU5G/Km8Sup144pt/i1uJveZM07jyykCQ1WRaSpCbLQpLUZFlIkprGpiySrE/yeJI9Sa6f6zySNJ+MRVkkWQD8LvDPgbOBn09y9tymkqT5YyzKAjgf2FNVf1NVrwBbgQ1znEmS5o1U1VxnaEryHmB9Vf3r7vn7gZ+uql+asN4mYFP39I3A41N8ydOB70xx27kwbnlh/DKbd7jGLS+MX+ZB8/54VZ0xcXBcPpSXScYOa7mq2gxsnvaLJY9U1Zrp7me2jFteGL/M5h2uccsL45d5unnH5TTUPuDMvufLgafnKIskzTvjUhZ/AaxMclaSE4CNwPY5ziRJ88ZYnIaqqoNJfgn4U2AB8AdV9dgQX3Lap7Jm2bjlhfHLbN7hGre8MH6Zp5V3LC5wS5Lm1richpIkzSHLQpLUNK/LonULkfTc3C3/6yTnzUXOvjytvGuTvJDkq93Xf5yLnH15/iDJs0m+doTloza/rbyjNr9nJrk/ye4kjyW5dpJ1RmaOB8w7anN8UpKHk/xVl/k/TbLOKM3xIHmnNsdVNS+/6F0o/z/ATwAnAH8FnD1hnZ8B7qH3OY8LgIdGPO9a4PNzPbd9ed4BnAd87QjLR2Z+B8w7avO7FDive/x64H+P+N/hQfKO2hwHWNQ9Ph54CLhghOd4kLxTmuP5fGQxyC1ENgC3V8+DwKlJls520M7Y3fKkqv4c+LujrDJK8ztI3pFSVfur6tHu8YvAbmDZhNVGZo4HzDtSunk70D09vvua+K6gUZrjQfJOyXwui2XAt/ue7+Pwv7iDrDNbBs3y1u4Q9J4k58xOtCkbpfkd1EjOb5IVwFvo/Z9kv5Gc46PkhRGb4yQLknwVeBa4t6pGeo4HyAtTmOP5XBaD3EJkoNuMzJJBsjxK774uPwl8DPjssENN0yjN7yBGcn6TLAL+CPiVqvrexMWTbDKnc9zIO3JzXFWvVtWb6d054vwk505YZaTmeIC8U5rj+VwWg9xCZJRuM9LMUlXfO3QIWlVfAI5PcvrsRTxmozS/TaM4v0mOp/eD91NVddckq4zUHLfyjuIcH1JVzwM7gPUTFo3UHB9ypLxTneP5XBaD3EJkO3BF926HC4AXqmr/bAftNPMm+bEk6R6fT++/73dnPengRml+m0ZtfrsstwK7q+q3j7DayMzxIHlHcI7PSHJq93ghcBHwjQmrjdIcN/NOdY7H4nYfw1BHuIVIkn/TLf894Av03umwB/h74BdHPO97gKuTHAReAjZW9/aHuZDk0/TeeXF6kn3Ah+ldcBu5+YWB8o7U/AJvB94P7OrOUQP8BvAGGMk5HiTvqM3xUmBLer+A7TXAtqr6/Kj+nGCwvFOaY2/3IUlqms+noSRJA7IsJElNloUkqcmykCQ1WRaSpCbLQpLUZFlIkpr+P5vv0SiIJy1bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    bank_data2 = bank_data.copy()\n",
    "    if 0 in bank_data2[feature].unique():\n",
    "        pass\n",
    "    else:\n",
    "        bank_data2[feature] = np.log(bank_data2[feature])\n",
    "        bank_data2[feature].hist(bins=15)\n",
    "        plt.ylabel('Count')\n",
    "        plt.title(feature)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dd6bda1",
   "metadata": {},
   "source": [
    "### Outliers Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "213995b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outlier = bank_data.copy() \n",
    "fig, axes = plt.subplots(7,1,figsize=(10,8), sharex=False, sharey=False)\n",
    "sns.boxplot(x='age',data=outlier,palette='crest',ax=axes[0])\n",
    "sns.boxplot(x='balance',data=outlier,palette='crest',ax=axes[1])\n",
    "sns.boxplot(x='day',data=outlier,palette='crest',ax=axes[2])\n",
    "sns.boxplot(x='duration',data=outlier,palette='crest',ax=axes[3])\n",
    "sns.boxplot(x='campaign',data=outlier,palette='crest',ax=axes[4])\n",
    "sns.boxplot(x='pdays',data=outlier,palette='crest',ax=axes[5])\n",
    "sns.boxplot(x='previous',data=outlier,palette='crest',ax=axes[6])\n",
    "plt.tight_layout(pad=2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb3880c",
   "metadata": {},
   "source": [
    "#### There are lot of outliers present in the dataframe but we can't drop them because they are present in a very large quantity and can be important for model building\n",
    "### After Log-Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66e4d3c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IchxwPnDPlDG/kSTd8jldPT8HbgXOSPKKJPOBy4D1fdUqSdpXnzOIU4Bru/MQc4CvVtV1SS4HqKo1wFuAP0iyG3gSuKyqCtid5ApgIzAX+GJ3bkKSNCS9BURVbQFe3WhfM7D8GeAz02x/A3BDX/VJkvbPO6klSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1NRbQCRZkORHSW5PcmeSjzTGvC3Jlu71/SRnD/Tdn+QnSTYnua2vOiVJbcf0+N47gfOqakeSecB3k9xYVbcMjPkZ8IaqeizJRcBa4LUD/Suq6tEea5QkTaO3gKiqAnZ0q/O6V00Z8/2B1VuARX3VI0k6MH3OIEgyF9gEnA5cXVU/3M/wdwM3DqwXcFOSAj5XVWun2ccqYBXA2NgYExMTh6J06ZDzs6nZpteAqKpngGVJTgTWJXlVVd0xdVySFUwGxO8MNJ9bVQ8leRnwzST3VNV3GvtYy+ShKcbHx2v58uU9/CbSQdpwPX42NdsM5SqmqvolMAFcOLUvyVnAF4BLqurnA9s81P18BFgHnDOMWiVJk/q8iunkbuZAkuOA84F7pox5OfB14B1Vde9A+8Ikx+9dBt4E7DPzkCT1p89DTKcA13bnIeYAX62q65JcDlBVa4APAS8FrkkCsLuqxoExJg9J7a3xy1W1ocdaJUlT9HkV0xbg1Y32NQPL7wHe0xhzH3D21HZJ0vB4J7UkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmnoLiCQLkvwoye1J7kzykcaYJPlUkq1JtiR5zUDfhUl+2vVd1VedkqS2PmcQO4HzqupsYBlwYZLXTRlzEXBG91oFfBYgyVzg6q7/TOCtSc7ssVZJ0hS9BURN2tGtzuteNWXYJcBfdGNvAU5McgpwDrC1qu6rqqeBr3RjJUlDckyfb97NBDYBpwNXV9UPpww5FXhwYH1b19Zqf+00+1jF5OyDsbExJiYmDknt0qHmZ1OzTa8BUVXPAMuSnAisS/KqqrpjYEham+2nvbWPtcBagPHx8Vq+fPlB1Sz1YsP1+NnUbDOUq5iq6pfABHDhlK5twOKB9UXAQ/tplyQNSZ9XMZ3czRxIchxwPnDPlGHrgXd2VzO9Dni8qh4GbgXOSPKKJPOBy7qxkqQh6fMQ0ynAtd15iDnAV6vquiSXA1TVGuAG4GJgK/Ar4F1d3+4kVwAbgbnAF6vqzh5rlSRN0VtAVNUW4NWN9jUDywW8d5rtb2AyQCRJI9DrSWrpSJS0rqGYwXYfO/BtJv8PJY2Gj9qQDlBVHfDr5ptvfkHbSaNkQEiSmgwISVKT5yCkHrXOV3joSLOFMwipJ3vDYd68eXzyk59k3rx5z2mXDncGhNSjefPm8fTTT3PWWWfx9NNPPxsS0mxgQEg9uvnmm/e7Lh3ODAipRytWrNjvunQ4MyCkHu3atYv58+ezZcsW5s+fz65du0ZdkjRjXsUk9aSqSMKuXbtYvXr1c9ql2cAZhNSj1p3U0mxhQEiSmgwISVKTASFJajIgJElNBoQkqSlH0lUVSf4R+PtR1yE1nAQ8OuoipIbTqurkVscRFRDS4SrJbVU1Puo6pAPhISZJUpMBIUlqMiCk4Vg76gKkA+U5CElSkzMISVKTASFJajIgJElNBoQkqcmAkA6BJN9IsinJnUlWdW3vTnJvkokkn0/yma795CR/leTW7nXuaKuX2ryKSToEkrykqn6R5DjgVuAC4HvAa4DtwLeA26vqiiRfBq6pqu8meTmwsaqWjqx4aRp+5ah0aLwvyaXd8mLgHcC3q+oXAEn+EvjnXf/5wJlJ9m774iTHV9X2YRYsPR8DQjpISZYz+Uf/9VX1qyQTwE+B6WYFc7qxTw6lQOkF8hyEdPBOAB7rwuGVwOuAFwFvSPLrSY4B/u3A+JuAK/auJFk2zGKlmTIgpIO3ATgmyRbgvwC3AP8A/DHwQ+CvgbuAx7vx7wPGk2xJchdw+fBLlp6fJ6mlniT5tara0c0g1gFfrKp1o65LmilnEFJ/PpxkM3AH8DPgGyOtRjpAziAkSU3OICRJTQaEJKnJgJAkNRkQkqQmA0KS1PT/AMQKvSTf53L0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mh4aq6lCSdwB/BpwAfLSq9ia5amjfBlwGXJ3kEPAEcHn5OEZJmqim7yMYDvfsXLZs25Lp64HrW9YgSTo67yyWpM4ZBJLUOYNAkjpnEEhS5wwCSeqcQSBJnTMIJKlzBoEkdc4gkKTOGQSS1DmDQJI6ZxBIUucMAknqnEEgSZ0zCCSpcwaBJHXOIJCkzhkEktQ5g0CSOmcQSFLnmgZBkguSfDXJviTXjmlPko8M7fcnObdlPVIrSUjCm9/85u9PS+tFsyBIcgJwA3Ah8CrgbUletazbhcDW4TMP3NiqHqmVI/2nbxhovWg5IjgP2FdVX6uqJ4FPApcu63MpcHMtuhs4JclpDWuSmqkqdu3aRVVNuxTpuJzYcN2bgYeWzO8Hzl9Bn83AgaWdksyzOGJgZmaG0Wi02rVKz9poNGJhYeFp+6f7qtaDlkEwbly8/E+llfShqrYD2wFmZ2drbm7uWRcnrba5uTlGoxFL90/3Va0HLYNgP3DGkvnTgYefQR9pXfCcgNarlucI7gG2JnlZkucDlwO3LetzG3DFcPXQ64CDVXVg+YqktexI5wQ8V6D1olkQVNUh4B3AnwEPAJ+qqr1Jrkpy1dBtJ/A1YB/we8AvtqpHaqmqnnay2BDQetLy0BBVtZPF/+yXLtu2ZLqAa1rWIEk6Ou8slqTOGQSS1DmDQJI6ZxBIUuey3q5uSPK3wDemXYd0BJuAb027CGmMs6rqpeMa1l0QSGtZki9W1ey065COh4eGJKlzBoEkdc4gkFbX9mkXIB0vzxFIUuccEUhS5wwCSeqcQSA9Q0k+mORd065DerYMAknqnEEgHYck70ny1ST/A3jFsOw/JLknyX1JPp3kB5JsTPL1JBuGPi9K8uBT89JaYhBIK5TkR1l80945wFuB1w5Nt1TVa6vqh1l8CdO/r6rHgBFw8dDncuDTVfW9yVYtHZtBIK3cG4HPVNV3qupR/unVq2cnuTPJl4CfB149LP994Mph+krgDydarbRCBoF0fMbdePMx4B1V9RrgOuAkgKr638CWJD8OnFBVX55YldJxMAiklbsD+JkkJyfZCPzksHwjcGA4/v/zy75zM/AJHA1oDfPOYuk4JHkPcAWLj0LfD3wFeBz49WHZl4CNVfULQ/9Tga8Dp1XVI1MoWTomg0BqKMllwKVV9fZp1yIdyYnTLkB6rkryX4ELgYumXYt0NI4IJKlzniyWpM4ZBJLUOYNAkjpnEEhS5wwCSerc/wdznguT/QuikQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continuous_feature:\n",
    "    bank_data3 = bank_data.copy()\n",
    "    bank_data3[feature] = np.log(bank_data3[feature])\n",
    "    bank_data3.boxplot(column=feature)\n",
    "    plt.ylabel(feature)\n",
    "    plt.title(feature)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fa90142",
   "metadata": {},
   "source": [
    "## 5. Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "46fafe46",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_data[['job','marital','education','default','housing','loan','contact','poutcome','month','y']] = bank_data[\n",
    "    ['job','marital','education','default','housing','loan','contact','poutcome','month','y']].astype('category')\n",
    "bank_data_new = bank_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea022fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype   \n",
      "---  ------     --------------  -----   \n",
      " 0   age        45211 non-null  int64   \n",
      " 1   job        45211 non-null  category\n",
      " 2   marital    45211 non-null  category\n",
      " 3   education  45211 non-null  category\n",
      " 4   default    45211 non-null  category\n",
      " 5   balance    45211 non-null  int64   \n",
      " 6   housing    45211 non-null  category\n",
      " 7   loan       45211 non-null  category\n",
      " 8   contact    45211 non-null  category\n",
      " 9   day        45211 non-null  int64   \n",
      " 10  month      45211 non-null  category\n",
      " 11  duration   45211 non-null  int64   \n",
      " 12  campaign   45211 non-null  int64   \n",
      " 13  pdays      45211 non-null  int64   \n",
      " 14  previous   45211 non-null  int64   \n",
      " 15  poutcome   45211 non-null  category\n",
      " 16  y          45211 non-null  category\n",
      "dtypes: category(10), int64(7)\n",
      "memory usage: 2.8 MB\n"
     ]
    }
   ],
   "source": [
    "bank_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3739b54e",
   "metadata": {},
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62b58162",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_data_new['month'] = bank_data_new['month'].cat.codes\n",
    "bank_data_new['job'] = bank_data_new['job'].cat.codes\n",
    "bank_data_new['marital'] = bank_data_new['marital'].cat.codes\n",
    "bank_data_new['education'] = bank_data_new['education'].cat.codes\n",
    "bank_data_new['default'] = bank_data_new['default'].cat.codes\n",
    "bank_data_new['housing'] = bank_data_new['housing'].cat.codes\n",
    "bank_data_new['loan'] = bank_data_new['loan'].cat.codes\n",
    "bank_data_new['contact'] = bank_data_new['contact'].cat.codes\n",
    "bank_data_new['poutcome'] = bank_data_new['poutcome'].cat.codes\n",
    "bank_data_new['y'] = bank_data_new['y'].cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821a8405",
   "metadata": {},
   "source": [
    "## 6. Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "784b8bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = bank_data_new.drop('y', axis=1)\n",
    "y1 = bank_data_new[['y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a88c1be",
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1729</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5715</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>668</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>508</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2971</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0       58    4        1          2        0     2143        1     0        2   \n",
       "1       44    9        2          1        0       29        1     0        2   \n",
       "2       33    2        1          1        0        2        1     1        2   \n",
       "3       47    1        1          3        0     1506        1     0        2   \n",
       "4       33   11        2          3        0        1        0     0        2   \n",
       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
       "45206   51    9        1          2        0      825        0     0        0   \n",
       "45207   71    5        0          0        0     1729        0     0        0   \n",
       "45208   72    5        1          1        0     5715        0     0        0   \n",
       "45209   57    1        1          1        0      668        0     0        1   \n",
       "45210   37    2        1          1        0     2971        0     0        0   \n",
       "\n",
       "       day  month  duration  campaign  pdays  previous  poutcome  \n",
       "0        5      8       261         1     -1         0         3  \n",
       "1        5      8       151         1     -1         0         3  \n",
       "2        5      8        76         1     -1         0         3  \n",
       "3        5      8        92         1     -1         0         3  \n",
       "4        5      8       198         1     -1         0         3  \n",
       "...    ...    ...       ...       ...    ...       ...       ...  \n",
       "45206   17      9       977         3     -1         0         3  \n",
       "45207   17      9       456         2     -1         0         3  \n",
       "45208   17      9      1127         5    184         3         2  \n",
       "45209   17      9       508         4     -1         0         3  \n",
       "45210   17      9       361         2    188        11         1  \n",
       "\n",
       "[45211 rows x 16 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "36a8d7f0",
   "metadata": {
    "scrolled": false
   },
   "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>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       y\n",
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "...   ..\n",
       "45206  1\n",
       "45207  1\n",
       "45208  1\n",
       "45209  0\n",
       "45210  0\n",
       "\n",
       "[45211 rows x 1 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3bf1e49c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train :  (36168, 16)\n",
      "Shape of X_test  :  (9043, 16)\n",
      "Shape of y_train :  (36168, 1)\n",
      "Shape of y_test  :  (9043, 1)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x1,y1,test_size=0.20,random_state=12)\n",
    "print(\"Shape of X_train : \",x_train.shape)\n",
    "print(\"Shape of X_test  : \",x_test.shape)\n",
    "print(\"Shape of y_train : \",y_train.shape)\n",
    "print(\"Shape of y_test  : \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b71d8f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model = LogisticRegression()\n",
    "logistic_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9324949c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.56272589e-02,  1.39543529e-02, -1.09578144e-01,\n",
       "        -1.57397743e-02, -5.33858503e-03,  3.04918356e-05,\n",
       "        -2.19114435e-01, -6.58876024e-02, -2.24076246e-01,\n",
       "        -1.79125869e-02, -5.54945378e-02,  3.45844766e-03,\n",
       "        -3.08452326e-01, -3.68682383e-04,  1.22766131e-01,\n",
       "        -2.09630796e-01]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "25fdfb8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.08435757])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e62c5b9",
   "metadata": {},
   "source": [
    "#### MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4b9272d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "scalar = MinMaxScaler(feature_range= (0,1))\n",
    "scalar.fit(bank_data_new)\n",
    "scaled_x = scalar.transform(bank_data_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dfd4f583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.51948052, 0.36363636, 0.5       , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.33766234, 0.81818182, 1.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.19480519, 0.18181818, 0.5       , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.7012987 , 0.45454545, 0.5       , ..., 0.01090909, 0.66666667,\n",
       "        1.        ],\n",
       "       [0.50649351, 0.09090909, 0.5       , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.24675325, 0.18181818, 0.5       , ..., 0.04      , 0.33333333,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f0b9b9e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier1 = LogisticRegression()\n",
    "classifier1.fit(scaled_x,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1d1f9fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0870051 ,  0.04775924,  0.12651324,  0.16252677, -0.0576052 ,\n",
       "         0.03974224, -0.34339486, -0.23072377, -0.46707242, -0.10404694,\n",
       "         0.124258  ,  0.90757706, -0.1296082 ,  0.21393142,  0.01923177,\n",
       "        -0.1480058 , 13.73203525]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c01c8635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.99760781e-01, 2.39218924e-04],\n",
       "       [9.99762234e-01, 2.37766449e-04],\n",
       "       [9.99832546e-01, 1.67453699e-04],\n",
       "       ...,\n",
       "       [1.67888481e-03, 9.98321115e-01],\n",
       "       [9.99598875e-01, 4.01125023e-04],\n",
       "       [9.99437111e-01, 5.62889153e-04]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proba1 = classifier1.predict_proba(scaled_x)\n",
    "proba1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "24d87268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0], dtype=int8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1 = classifier1.predict(scaled_x)\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a436fc91",
   "metadata": {},
   "source": [
    "## 7. Model Testing || 8. Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b3ea3b",
   "metadata": {},
   "source": [
    "### Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9d1a7a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train1 = logistic_model.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d8339468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[31344   585]\n",
      " [ 3502   737]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_train, y_pred_train1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "31f27146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94     31929\n",
      "           1       0.56      0.17      0.27      4239\n",
      "\n",
      "    accuracy                           0.89     36168\n",
      "   macro avg       0.73      0.58      0.60     36168\n",
      "weighted avg       0.86      0.89      0.86     36168\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train,y_pred_train1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cabe86ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8869995576199956"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train,y_pred_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "79b4c2df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score : 81.67%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_train,logistic_model.predict_proba (x_train)[:,1])\n",
    "\n",
    "auc = roc_auc_score(y_train,logistic_model.predict_proba (x_train)[:,1])\n",
    "print('AUC score : {:.2f}%'.format(auc*100))\n",
    "\n",
    "plt.plot(fpr, tpr, color='red', label='logit model ( area  = %0.2f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4712699b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94     31929\n",
      "           1       0.56      0.17      0.27      4239\n",
      "\n",
      "    accuracy                           0.89     36168\n",
      "   macro avg       0.73      0.58      0.60     36168\n",
      "weighted avg       0.86      0.89      0.86     36168\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report1 = classification_report(y_train,y_pred_train1)\n",
    "print(classification_report1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe80e7a9",
   "metadata": {},
   "source": [
    "### Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "221ba689",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test1 = logistic_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cb73c3cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7849  144]\n",
      " [ 852  198]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred_test1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1ae33018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94      7993\n",
      "           1       0.58      0.19      0.28      1050\n",
      "\n",
      "    accuracy                           0.89      9043\n",
      "   macro avg       0.74      0.59      0.61      9043\n",
      "weighted avg       0.86      0.89      0.86      9043\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred_test1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "792fe1a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8898595598805706"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "00e8e536",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score : 83.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test,logistic_model.predict_proba (x_test)[:,1])\n",
    "\n",
    "auc = roc_auc_score(y_test,logistic_model.predict_proba (x_test)[:,1])\n",
    "print('AUC score : {:.2f}%'.format(auc*100))\n",
    "\n",
    "plt.plot(fpr, tpr, color='red', label='logit model ( area  = %0.2f)'%auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fe27619d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94      7993\n",
      "           1       0.58      0.19      0.28      1050\n",
      "\n",
      "    accuracy                           0.89      9043\n",
      "   macro avg       0.74      0.59      0.61      9043\n",
      "weighted avg       0.86      0.89      0.86      9043\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report2 = classification_report(y_test,y_pred_test1)\n",
    "print(classification_report2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274af933",
   "metadata": {},
   "source": [
    "### Compare the train-set and test-set accuracy\n",
    "\n",
    "#### Check for overfitting and underfitting (the scores on training and test set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2685d123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score : 88.70%\n",
      "Test set score     : 88.99%\n"
     ]
    }
   ],
   "source": [
    "print('Training set score : {:.2f}%'.format(logistic_model.score(x_train, y_train)*100))\n",
    "print('Test set score     : {:.2f}%'.format(logistic_model.score(x_test, y_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37ff0f34",
   "metadata": {},
   "source": [
    "## 9. Model Deployement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "172966f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b86cebb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dump(logistic_model,open('Bank_Prediction.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "de34869f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pickle import load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "63c18874",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_logistic_model = load(open('Bank_Prediction.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d6d942cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = loaded_logistic_model.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0093f330",
   "metadata": {},
   "source": [
    "# THE END  !!!"
   ]
  }
 ],
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