SVM_BANK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e34f461",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import svm \n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "110ca633",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(r'C:\\Users\\Bushra Ansari\\Desktop\\LMS_DATASET\\SVM\\Predicting Term Deposit Subscription by a client')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e6481fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bank-additional-full.csv', 'Problem Statement_SVM.pdf']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1a1bc104",
   "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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857         5191  no  \n",
       "1          93.994          -36.4      4.857         5191  no  \n",
       "2          93.994          -36.4      4.857         5191  no  \n",
       "3          93.994          -36.4      4.857         5191  no  \n",
       "4          93.994          -36.4      4.857         5191  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank = pd.read_csv('bank-additional-full.csv')\n",
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c20dc49b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ab16e71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  int64  \n",
      " 20  y               41188 non-null  object \n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "bank.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bea6d71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bank.duplicated().sum())\n",
    "bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37d804cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41176, 21)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank = bank.drop_duplicates()\n",
    "bank.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a169f05",
   "metadata": {},
   "source": [
    "#### Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "215cf577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     36537\n",
       "yes     4639\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['y'].value_counts(dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "730df348",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank['y'].replace({'no' : '0', 'yes' : '1'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64d461a2",
   "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"
    }
   ],
   "source": [
    "sns.countplot(x = bank['y'], data = bank, palette = 'Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8b9de9",
   "metadata": {},
   "source": [
    "From above countplot, we can see that data is imbalanced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5343ea47",
   "metadata": {},
   "source": [
    "#### Categorical Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98ce6212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact',\n",
       "       'month', 'day_of_week', 'poutcome', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.dtypes[bank.dtypes == 'object'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "521a9793",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "admin.           10419\n",
       "blue-collar       9253\n",
       "technician        6739\n",
       "services          3967\n",
       "management        2924\n",
       "retired           1718\n",
       "entrepreneur      1456\n",
       "self-employed     1421\n",
       "housemaid         1060\n",
       "unemployed        1014\n",
       "student            875\n",
       "unknown            330\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['job'].value_counts(dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "512f5cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank['job'] = bank['job'].replace(['management', 'admin.'], 'White-collar')\n",
    "\n",
    "bank['job'] = bank['job'].replace(['services', 'housemaid'], 'Pink-collar')\n",
    "\n",
    "bank['job'] = bank['job'].replace(['retired', 'student', 'unemployed', 'unknown'], 'Others')\n",
    "\n",
    "bank['job'] = bank['job'].replace(['self-employed', 'entrepreneur'], 'Self-depend')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "50fa85d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Pink-collar', 'White-collar', 'blue-collar', 'technician',\n",
       "       'Others', 'Self-depend'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['job'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aca18f10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank['marital'].value_counts(dropna=True).plot(kind = 'pie', autopct = '%0.2f%%', figsize = [5,5], \n",
    "             explode = [0,0.02,0.05,0.07],colors = ['#AB82FF', '#FFA54F','#CAFF70', '#CDCD00'], shadow = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "507d7db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['basic.4y', 'high.school', 'basic.6y', 'basic.9y',\n",
       "       'professional.course', 'unknown', 'university.degree',\n",
       "       'illiterate'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f40618d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank['education'] = bank['education'].replace(['basic.4y', 'basic.6y', 'basic.9y'], 'primary')\n",
    "\n",
    "bank['education'] = bank['education'].replace([ 'high.school'], 'secondary')\n",
    "\n",
    "bank['education'] = bank['education'].replace(['university.degree', 'professional.course'], 'teritary')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1acd9d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['primary', 'secondary', 'teritary', 'unknown', 'illiterate'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63f85bfe",
   "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"
    }
   ],
   "source": [
    "sns.countplot(x = bank['default'], data = bank, palette = 'Dark2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0eb0c46",
   "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"
    }
   ],
   "source": [
    "sns.countplot(x = bank['housing'], data = bank, palette = 'tab20c')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "896fc1df",
   "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"
    }
   ],
   "source": [
    "sns.countplot(x = bank['loan'], data = bank, palette = 'Set3')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "71745367",
   "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"
    }
   ],
   "source": [
    "sns.countplot(x = bank['housing'], data = bank, hue = 'loan', palette = 'Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9d5de7df",
   "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"
    }
   ],
   "source": [
    "sns.countplot(x = bank['contact'], data = bank, palette = 'Set1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01bde4e6",
   "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>poutcome</th>\n",
       "      <th>failure</th>\n",
       "      <th>nonexistent</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cellular</th>\n",
       "      <td>3952</td>\n",
       "      <td>20913</td>\n",
       "      <td>1270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>telephone</th>\n",
       "      <td>300</td>\n",
       "      <td>14638</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "poutcome   failure  nonexistent  success\n",
       "contact                                 \n",
       "cellular      3952        20913     1270\n",
       "telephone      300        14638      103"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chi square contact & poutcome to check whether keep contact or not\n",
    "ContactPoutcome = pd.crosstab(bank.contact, bank.poutcome, rownames=['contact'], colnames=['poutcome'])\n",
    "ContactPoutcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d1a5dc6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p value is 0.0\n",
      "Dependent (reject Ho)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "stat, p, dof, expected = chi2_contingency(ContactPoutcome)\n",
    "alpha = 0.05\n",
    "print(\"p value is \" + str(p))\n",
    "if p <= alpha:\n",
    "    print('Dependent (reject Ho)')\n",
    "else:\n",
    "    print('Independent (Ho holds true)')\n",
    "    \n",
    "# Since dependent, we'll keep the variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1fbfbb3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['may', 'jun', 'jul', 'aug', 'oct', 'nov', 'dec', 'mar', 'apr',\n",
       "       'sep'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7b893a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank['month'] = bank['month'].replace(['mar'], 'Quarter1')\n",
    "\n",
    "bank['month'] = bank['month'].replace(['apr', 'may', 'jun'], 'Quarter2')\n",
    "\n",
    "bank['month'] = bank['month'].replace(['jul', 'aug', 'sep'], 'Quarter3')\n",
    "\n",
    "bank['month'] = bank['month'].replace(['oct', 'nov', 'dec'], 'Quarter4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "621271ab",
   "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"
    }
   ],
   "source": [
    "bank['month'].value_counts().plot(kind = 'barh', color = '#CAFF70', edgecolor = 'r')\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c663df0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank['poutcome'].value_counts().plot(kind = 'pie', autopct = '%0.2f%%', figsize = [6,6],\n",
    "                                    colors = ['#AB82FF', '#EE2C2C', '#FFC125'])\n",
    "plt.title('poutcome')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "be73d7f1",
   "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"
    }
   ],
   "source": [
    "sns.countplot(x = bank['poutcome'], data = bank, hue = 'y', palette = 'Accent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f38d92af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mon', 'tue', 'wed', 'thu', 'fri'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['day_of_week'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ed829097",
   "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"
    }
   ],
   "source": [
    "bank['day_of_week'].value_counts().plot(kind = 'bar', edgecolor = 'y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b52cff",
   "metadata": {},
   "source": [
    "As people are buying term deposits on any day of the week as counts are almost equal for all weekdays. Therefore, we can drop 'day_of_week' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "79ffe650",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank.drop(['day_of_week'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "936dc04d",
   "metadata": {},
   "source": [
    "#### Numerical Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "aeddf1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
       "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.dtypes[bank.dtypes != 'object'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "aa41d8a3",
   "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"
    }
   ],
   "source": [
    "bank['age'].plot(kind = 'hist', y = 'Frequency', color = '#CD5C5C', edgecolor = 'y' )\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9fd19297",
   "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"
    }
   ],
   "source": [
    "sns.boxplot(x = 'y', y = 'age', data = bank)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d85f412",
   "metadata": {},
   "source": [
    "Boxplot for both the classes overlap quite a lot which means that age is not necessarily a good indicator for which customer will subscribe and which customer will not to term deposit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b265d2a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bushra Ansari\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.distplot(bank['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d649147c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(bank['duration'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "701f836f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.366111111111111"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['duration'].max()/60/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "fe4a313c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67239.24947519663"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['duration'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f5862921",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bushra Ansari\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\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": [
    "sns.distplot(bank['campaign'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7bacfd20",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bushra Ansari\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEICAYAAABF82P+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcHElEQVR4nO3de3RV533m8e+jGyAEyAhxMdgIDL6QOLYpARxPmkxuNY4buto1U7txSTxNGa/aM0k6M11OMtOVznR10sysTOqOl6nr0NhxEje3JtRD4jhtLvUVcEywMWDLBoMAg8RdXCQEv/njbDkn0pZ0JJ19jiSez1pn6Zx3v3uf32uwHvbt3YoIzMzMeqoodwFmZjYyOSDMzCyVA8LMzFI5IMzMLJUDwszMUjkgzMwsVaYBIelGSTskNUu6O2W5JN2TLN8iaXHesl2SXpC0WdKmLOs0M7PeqrLasKRK4F7g/UALsFHSuoh4Ka/bCmBh8loG3Jf87PavI6Kt0O+cNm1aNDU1Dbd0M7MLxnPPPdcWEY1pyzILCGAp0BwRrwFIegRYCeQHxErgocjdrfeMpHpJsyJi/1C+sKmpiU2bvLNhZlYoSa/3tSzLQ0yzgT15n1uStkL7BPBDSc9JWp1ZlWZmlirLPQiltPWc16O/PjdExD5J04HHJW2PiJ/1+pJceKwGuPTSS4dTr5mZ5clyD6IFuCTv8xxgX6F9IqL750HgH8gdsuolIu6PiCURsaSxMfUwmpmZDUGWAbERWChpnqQa4BZgXY8+64BVydVMy4FjEbFf0kRJkwAkTQQ+ALyYYa1mZtZDZoeYIqJL0l3AY0AlsDYitkq6I1m+BlgP3AQ0A6eA25PVZwD/IKm7xq9FxA+yqtXMzHrTWJrue8mSJeGrmMzMCifpuYhYkrbMd1KbmVkqB4SZmaVyQJiZWaos74MwM7PE157d3avt95aN7Hu3vAdhZmapHBBmZpbKAWFmZqkcEGZmlsoBYWZmqRwQZmaWygFhZmapHBBmZpbKAWFmZqkcEGZmlsoBYWZmqRwQZmaWygFhZmapHBBmZpbKAWFmZqkcEGZmlsoBYWZmqRwQZmaWygFhZmapHBBmZpbKAWFmZqkcEGZmlsoBYWZmqRwQZmaWygFhZmapHBBmZpbKAWFmZqkcEGZmlsoBYWZmqRwQZmaWKtOAkHSjpB2SmiXdnbJcku5Jlm+RtLjH8kpJz0t6NMs6zcyst8wCQlIlcC+wAlgE3CppUY9uK4CFyWs1cF+P5R8HtmVVo5mZ9S3LPYilQHNEvBYRncAjwMoefVYCD0XOM0C9pFkAkuYAHwQeyLBGMzPrQ5YBMRvYk/e5JWkrtM8XgT8BzmdUn5mZ9SPLgFBKWxTSR9LNwMGIeG7AL5FWS9okaVNra+tQ6jQzsxRZBkQLcEne5znAvgL73AB8SNIucoem3iPp4bQviYj7I2JJRCxpbGwsVu1mZhe8LANiI7BQ0jxJNcAtwLoefdYBq5KrmZYDxyJif0R8KiLmRERTst4/R8RtGdZqZmY9VGW14YjoknQX8BhQCayNiK2S7kiWrwHWAzcBzcAp4Pas6jEzs8HJLCAAImI9uRDIb1uT9z6AOwfYxk+An2RQnpmZ9cN3UpuZWSoHhJmZpXJAmJlZKgeEmZmlckCYmVkqB4SZmaVyQJiZWSoHhJmZpXJAmJlZKgeEmZmlckCYmVkqB4SZmaVyQJiZWSoHhJmZpXJAmJlZKgeEmZmlckCYmVkqB4SZmaVyQJiZWSoHhJmZpXJAmJlZKgeEmZmlckCYmVkqB4SZmaVyQJiZWSoHhJmZpXJAmJlZKgeEmZmlckCYmVkqB4SZmaVyQJiZWSoHhJmZpXJAmJlZKgeEmZmlyjQgJN0oaYekZkl3pyyXpHuS5VskLU7ax0vaIOkXkrZK+rMs6zQzs94yCwhJlcC9wApgEXCrpEU9uq0AFiav1cB9SXsH8J6IuAa4FrhR0vKsajUzs96y3INYCjRHxGsR0Qk8Aqzs0Wcl8FDkPAPUS5qVfG5P+lQnr8iwVjMz6yHLgJgN7Mn73JK0FdRHUqWkzcBB4PGIeDa7Us3MrKcsA0IpbT33AvrsExHnIuJaYA6wVNJbU79EWi1pk6RNra2tw6nXzMzyZBkQLcAleZ/nAPsG2ycijgI/AW5M+5KIuD8ilkTEksbGxmGWbGZm3bIMiI3AQknzJNUAtwDrevRZB6xKrmZaDhyLiP2SGiXVA0iaALwP2J5hrWZm1kNVIZ0kfRtYC3w/Is4Xsk5EdEm6C3gMqATWRsRWSXcky9cA64GbgGbgFHB7svos4MHkSqgK4BsR8WjhwzIzs+EqKCDIXX56O3CPpG8CX46IAf9FHxHryYVAftuavPcB3Jmy3hbgugJrMzOzDBR0iCkifhQRHwYWA7uAxyU9Jel2SdVZFmhmZuVR8DkISQ3AR4GPAc8Df0UuMB7PpDIzMyurQs9BfAe4EvgK8JsRsT9Z9PeSNmVVnJmZlU+h5yAeSM4nvEnSuIjoiIglGdRlZmZlVughpj9PaXu6mIWYmdnI0u8ehKSZ5Ka+mCDpOn555/NkoDbj2szMrIwGOsT0G+ROTM8BvpDXfgL4dEY1mZnZCNBvQETEg+RuWPudiPh2iWoyM7MRYKBDTLdFxMNAk6Q/7rk8Ir6QspqZmY0BAx1impj8rMu6EDMzG1kGOsT0N8lPP/LTzOwCU9BlrpI+L2mypGpJ/ySpTdJtWRdnZmblU+h9EB+IiOPAzeSe4XA58F8yq8rMzMqu0IDonpDvJuDrEXE4o3rMzGyEKHSqjX+UtB04DfyRpEbgTHZlmZlZuRU63ffdwPXAkog4C5wEVmZZmJmZlVehexAAV5G7HyJ/nYeKXI+ZmY0QhU73/RXgMmAzcC5pDhwQZmZjVqF7EEuARckjQs3M7AJQ6FVMLwIzsyzEzMxGlkL3IKYBL0naAHR0N0bEhzKpyszMyq7QgPhslkWYmdnIU1BARMRPJc0FFkbEjyTVApXZlmZmZuVU6FxMfwh8C/ibpGk28N2MajIzsxGg0JPUdwI3AMcBIuIVYHpWRZmZWfkVGhAdEdHZ/SG5Wc6XvJqZjWGFBsRPJX0amCDp/cA3gX/MriwzMyu3QgPibqAVeAH498B64L9mVZSZmZVfoVcxnZf0XeC7EdGabUlmZjYS9LsHoZzPSmoDtgM7JLVK+tPSlGdmZuUy0CGmT5C7euntEdEQEVOBZcANkj6ZdXFmZlY+AwXEKuDWiNjZ3RARrwG3JcvMzGyMGiggqiOirWdjch6iOqW/mZmNEQMFROcQl5mZ2Sg3UEBcI+l4yusEcPVAG5d0o6Qdkpol3Z2yXJLuSZZvkbQ4ab9E0o8lbZO0VdLHhzY8MzMbqn4vc42IIU/IJ6kSuBd4P9ACbJS0LiJeyuu2AliYvJYB9yU/u4D/FBE/lzQJeE7S4z3WNTOzDBV6o9xQLAWaI+K1ZJqOR4CVPfqsBB6KnGeAekmzImJ/RPwcICJOANvITRBoZmYlkmVAzAb25H1uofcv+QH7SGoCrgOeLX6JZmbWlywDQiltPSf467ePpDrg28AnIuJ46pdIqyVtkrSptdU3eZuZFUuWAdECXJL3eQ6wr9A+kqrJhcNXI+I7fX1JRNwfEUsiYkljY2NRCjczs2wDYiOwUNI8STXALcC6Hn3WAauSq5mWA8ciYr8kAV8CtkXEFzKs0czM+lDoM6kHLSK6JN0FPEbu8aRrI2KrpDuS5WvIzQp7E9AMnAJuT1a/Afh94AVJm5O2T0fE+qzqNTOzX5VZQAAkv9DX92hbk/c+yD2trud6T5B+fsLMzEoky0NMZmY2ijkgzMwslQPCzMxSOSDMzCyVA8LMzFI5IMzMLJUDwsysxCKC9S/s5xsb9wzcuYwyvQ/CzMx623noJE80t/FEcxtb9x3jsx96C7kJJEYW70GYmZXYk82HqK2p5Nall/Lg06+z48CJcpeUygFhZlZCh9o72L7/OMvmTeU/vGcBAE+80lbmqtI5IMzMSuj5PUcBWDa/gYvrJzC/cSJPNDsgzMwueHuPnGb65HFMHl8NwDsXTOPZ1w7T2XW+zJX15oAwMyuRiKDl6Glm19e+2XbDgmmcPnuO53cfKWNl6RwQZmYlcuz0WU52dDHnoglvti2/rIHKCo3Iw0wOCDOzEmk5chqA2fW/DIjJ46u5fMYkXth7rFxl9ckBYWZWInuPnqZCMHPK+F9pv3xGHa8caC9TVX1zQJiZlcjeo6eZOXk81ZW/+qt34fQ69h49TXtHV5kqS+eAMDMrgYhg75HTzM47/9Bt4YxJADQfHFl7EQ4IM7MSaO/o4vTZc8yYPL7XssuTgHh5hN1R7YAwMyuBQ+2dAEyrG9dr2aVTa6mpquAVB4SZ2YXn0MlcQDRMrOm1rLJCXNZYx8sj7ES1A8LMrAQOneygQlBf2zsgoPtKJu9BmJldcA61d1JfW0NlRfq03pfPmMS+Y2c4ceZsiSvrmwPCzKwEDp/sTD281O2yxjoAdradLFVJA3JAmJllLCI4dLKDhrq+A6JpWm5+ptcPnSpVWQNyQJiZZezIqbOcOXuehom9r2DqdunU7oDwHoSZ2QWj+7BRf4eYamuqmDF5HDvbvAdhZnbB6N4rmNrPISaAuQ0TvQdhZnYh2XXoFAKm9nGJa7d5DRPZ5XMQZmYXjtcPnaS+tpqqyv5/5c6dVktbe8eImbTPAWFmlrFdh071e4K6W1PDRGDknKh2QJiZZez1QycHPP8AMLdhZF3q6oAwM8vQ0VOdHD11tt8rmLrNTfYgdnkPwsxs7OveGyjkEFPduCqm1Y3j9RFyqWumASHpRkk7JDVLujtluSTdkyzfImlx3rK1kg5KejHLGs3MstS9N9DfXdT5mhpqx/4ehKRK4F5gBbAIuFXSoh7dVgALk9dq4L68ZV8GbsyqPjOzUujeg5hawCEm6L4XYuzvQSwFmiPitYjoBB4BVvbosxJ4KHKeAeolzQKIiJ8BhzOsz8wsc7vaTjJrSu/nUPelqaGWN46f4XTnuYwrG1iWATEb2JP3uSVpG2wfM7NRa9ehk29enVSIudNyJ6p3Hy7/XkSWAZE26XkMoU//XyKtlrRJ0qbW1tbBrGpmlrnXD51iXvJLvxBNSZiMhPMQWQZEC3BJ3uc5wL4h9OlXRNwfEUsiYkljY+OQCjUzy8LxM2c5dLLzzctXCzF36si5WS7LgNgILJQ0T1INcAuwrkefdcCq5Gqm5cCxiNifYU1mZiWzOznZ3DSIQ0xTaqu5qLZ6RMzJlFlAREQXcBfwGLAN+EZEbJV0h6Q7km7rgdeAZuBvgT/qXl/S14GngSsktUj6g6xqNTPLQvdhosHsQXT3Hwl7EFVZbjwi1pMLgfy2NXnvA7izj3VvzbI2M7OsdV+uOrehlud3Hy14vaaGWjbuOpJRVYXzndRmZhnZ1XaS6ZPGUVszuH+Lz22YyL5jp+noKu+lrg4IM7OMvH7o1JsztA7GvGkTiYA9h09nUFXhHBBmZhnZOch7ILr9clbX8p6HcECYmWXgZEcXrSc6aBrEPRDdmt6c1bW8VzI5IMzMMpB/gnqw6murmTy+ynsQZmZjUfcv96Gcg5BE07SJ7GxzQJiZjTm7hrEHkVuv/LO6OiDMzDKwq+0k0+pqmDS+ekjrNzXU0nLkFJ1d54tcWeEcEGZmGWhubeeyxrohrz+3YSLnA/YeLd+lrg4IM7MiiwheOXCCBdOHHhAjYVZXB4SZWZG1tndw/EwXC4cREN3zN71exhPVDggzsyJrPtAOwILpk4a8jWl1NUysqSzrvRAOCDOzImtuzQXEwhlD34OQxNyG8l7q6oAwMyuy5oPtTBpXxfRJ44a1nctn1PHygRNFqmrwHBBmZkX2yoF2FsyoQ0p7qnLhrpo1mf3HznD0VGeRKhscB4SZWZE1t7azYBiXuHa7atZkALbtL89ehAPCzKyIjp06S+uJjmGdf+h25azcSe5t+48Pe1tD4YAwMyuirfuOAXDlzMnD3tb0SeOZVlfjgDAzGwt+0ZILiLfNmVKU7V01azLb3/AhJjOzUW9Ly1HmNtRSX1tTlO1dOXMSOw6coOtc6edkckCYmRXRlpZjvG1OfdG2d9WsyXR2nS/L/RAOCDOzImlr72Dv0dNcU6TDSwBXz85t6/k9R4u2zUI5IMzMimRLy1GAou5BLJhex9SJNWzYebho2yyUA8LMrEh+secYFYK3XDz8K5i6SeLtTRc5IMzMRrONuw5z+YxJTBxXVdTtLp3XwO7Dp9h/rLTPhnBAmJkVQXtHFxt3HeZdlzcWfdvL5k0FKPlehAPCzKwInmpu4+y54F1XFD8grpo1mUnjqnjWAWFmNvr8eEcrdeOqWDJ3atG3XVkhls6bys9ebiUiir79vjggzMyGKSL46Y6D3LCggZqqbH6tfvBts2g5cpqf7z6SyfbTOCDMzIbpxb3H2XfsDO++Ynpm3/GBt8xkXFUF39u8L7Pv6Km4p9qNrz27u1fb7y27tAyVmFmpPPj0LiZUV3LTW2dl9h1146p436IZPLplP//t5kVUV2b/73vvQZiZDUNbewfrNu/jd35tNlNqqzP9rpXXXMzhk5386KUDmX5PNweEmdkwfPWZ3XSeO89H3zEv8+96z5XTuaxxIv/rhzs4W4LJ+xwQZmZD9GprO/f9tJn3XTWdBdOH/4CggVRVVvCpFVfxWutJHtnQ+3B20b8vy41LuhH4K6ASeCAiPtdjuZLlNwGngI9GxM8LWXekOHc+2LrvGBt2HubV1pNs3n2ErvNBTVUFUyZUM7t+AsvnT6WpYSIVFcN7Pq2ZjRwdXef45N9vZnx1JX/+W1eX7Hvfe9V0rp/fwOe+v52r59Rz7SX1mX1XZgEhqRK4F3g/0AJslLQuIl7K67YCWJi8lgH3AcsKXLcszp8Ptr1xnKdfPcTTrx5iw87DnOjoAuCi2mrGV1dSVSGOnj7LywdO8NS54JvPtVBfW83yeQ28Y0ED77isgcsah/9AczMrjwPHz3DHw8+xpeUY9314MTOnjC/Zd0vii7dcy79Z8zQfWbuBv771On49g7u3Ids9iKVAc0S8BiDpEWAlkP9LfiXwUOTu/HhGUr2kWUBTAesW3bnzQWfXeTrPnefM2XO0tXfQ1t7JG8dOs+ONdrbtP85L+49z7PRZAJoaarn5mlksn9/A9fMbmD55/K9cxXTufNB6ooPZF41n064jPPXqIX6w9Q0Apk8axzWX1LNgeh0Lp9dxcf0ELqqt4aLaamrHVVFVIaoqRGWFHCQ2JP3dUNXXor7W6Hdbg/6Owdc1lP59fU//66Q723WetvYOtr9xgn95pZXvbd5HZYW478OLWXF1dlcu9WXG5PF89WPLWLV2A6vWbuB9V83gr2+9jgk1lUX9niwDYjawJ+9zC7m9hIH6zC5w3aK59r//kOOnz3K+n78446sruGLmZG66eiZvb5rK8vkNXFw/od/tVlaImVPG87tvv5TfffulRAR7Dp/myVfbePrVQ2zbf5yf7DjI2XP9/19RmQRFhUCUPixGSj4V8xdOn2uU6BdOX79w+/ubUMIbaK0fE6or+e3Fs/nDd85nfmP25x36csnUWn7wiXey9oldbGk5yvjq4p9SzjIg0n6t9Pwr3lefQtbNbUBaDaxOPrZL2lFwhYO0A1g3cLdpQFt+w4ezKafUeo1rDBiLYwKPK3OfS17D9eEijmnN7w951bl9LcgyIFqAS/I+zwF63gLYV5+aAtYFICLuB+4fbrHFImlTRCwpdx3FNhbHNRbHBB7XaDLSx5TlZa4bgYWS5kmqAW6h9z/A1wGrlLMcOBYR+wtc18zMMpTZHkREdEm6C3iM3KWqayNiq6Q7kuVrgPXkLnFtJneZ6+39rZtVrWZm1lum90FExHpyIZDftibvfQB3FrruKDFiDncV2Vgc11gcE3hco8mIHpNKObe4mZmNHp5qw8zMUjkgikTSjZJ2SGqWdHe56xkMSZdI+rGkbZK2Svp40j5V0uOSXkl+XpS3zqeSse6Q9Bvlq75/kiolPS/p0eTzWBhTvaRvSdqe/JldP0bG9cnk79+Lkr4uafxoHJektZIOSnoxr23Q45D0a5JeSJbdo3LcMRsRfg3zRe5E+qvAfHKX6P4CWFTuugZR/yxgcfJ+EvAysAj4PHB30n438JfJ+0XJGMcB85KxV5Z7HH2M7Y+BrwGPJp/HwpgeBD6WvK8B6kf7uMjdHLsTmJB8/gbw0dE4LuDXgcXAi3ltgx4HsAG4ntx9Yd8HVpR6LN6DKI43pxWJiE6ge2qQUSEi9kcySWJEnAC2kfsfdiW5X0YkP38reb8SeCQiOiJiJ7mr0JaWtOgCSJoDfBB4IK95tI9pMrlfQF8CiIjOiDjKKB9XogqYIKkKqCV379OoG1dE/Aw43KN5UONIphyaHBFPRy4tHspbp2QcEMXR15Qho46kJuA64FlgRuTuSyH52f08xdEy3i8CfwLkT5w/2sc0H2gF/i45dPaApImM8nFFxF7gfwO7gf3k7on6IaN8XHkGO47Zyfue7SXlgCiOgqcGGckk1QHfBj4REcf765rSNqLGK+lm4GBEPFfoKiltI2pMiSpyhy/ui4jrgJPkDln0ZVSMKzkmv5LcYZaLgYmSbutvlZS2ETeuAgx7uqEsOSCKo5BpRUY0SdXkwuGrEfGdpPlAsqtL8vNg0j4axnsD8CFJu8gd8nuPpIcZ3WOCXJ0tEfFs8vlb5AJjtI/rfcDOiGiNiLPAd4B3MPrH1W2w42hJ3vdsLykHRHGM6qlBkqsjvgRsi4gv5C1aB3wkef8R4Ht57bdIGidpHrnneWwoVb2FiIhPRcSciGgi9+fxzxFxG6N4TAAR8QawR9IVSdN7yU2DP6rHRe7Q0nJJtcnfx/eSOxc22sfVbVDjSA5DnZC0PPnvsSpvndIp9xn/sfIiN2XIy+SuQvhMuesZZO3/itzu6xZgc/K6CWgA/gl4Jfk5NW+dzyRj3UEZrq4Y5PjezS+vYhr1YwKuBTYlf17fBS4aI+P6M2A78CLwFXJX9oy6cQFfJ3ce5Sy5PYE/GMo4gCXJf4tXgf9LcmNzKV++k9rMzFL5EJOZmaVyQJiZWSoHhJmZpXJAmJlZKgeEmZmlckCYFZmkd3fPHms2mjkgzMwslQPCbBAkNSXPYXhQ0pbkuQy1yj0PZLukJ4Dfzuu/VNJTycR6T3XfAS3pXyRdm9fvSUlvk/QuSZuT1/OSJpV+lGY5DgizwbsCuD8i3gYcJ/fMib8FfhN4JzAzr+924NcjN7HenwJ/kbQ/QO55B0i6HBgXEVuA/wzcGRHXJts6nfVgzPrigDAbvD0R8WTy/mFyUyLsjIhXIjc1wcN5facA30yeLvZ/gLck7d8Ebk4mSfx3wJeT9ieBL0j6j0B9RHRlOxSzvjkgzAav5/w0U1Lauv0P4McR8VZyexjjASLiFPA4uSmu/y25p94REZ8DPgZMAJ6RdGXRqzcrkAPCbPAulXR98v5W4EfAPEmX5bV1mwLsTd5/tMd2HgDuATZGxGEASZdFxAsR8ZfkJuRzQFjZOCDMBm8b8BFJW4Cp5A4drQb+X3KS+vW8vp8H/qekJ8k9u/xNkXuY0XHg7/KaPyHpRUm/IHf+4fvZDcOsf57N1WwQkkeyPpocMhruti4GfgJcGRHnB+huVnLegzArA0mryD33+zMOBxupvAdhZmapvAdhZmapHBBmZpbKAWFmZqkcEGZmlsoBYWZmqRwQZmaW6v8DdiWABsMeQh4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(bank['pdays'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5648dbf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([999,   6,   4,   3,   5,   1,   0,  10,   7,   8,   9,  11,   2,\n",
       "        12,  13,  14,  15,  16,  21,  17,  18,  22,  25,  26,  19,  27,\n",
       "        20], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['pdays'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "88d20c47",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank['recent_pdays'] = 1/ bank['pdays'] # Values divided by 1 so that recently called ones have big values and other has\n",
    "# small values for easy comparison\n",
    "\n",
    "bank.drop(columns = ['pdays'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "01a229e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001001    39661\n",
       "0.333333      439\n",
       "0.166667      412\n",
       "0.250000      118\n",
       "0.111111       64\n",
       "0.500000       61\n",
       "0.142857       60\n",
       "0.083333       58\n",
       "0.100000       52\n",
       "0.200000       46\n",
       "0.076923       36\n",
       "0.090909       28\n",
       "1.000000       26\n",
       "0.066667       24\n",
       "0.071429       20\n",
       "0.125000       18\n",
       "inf            15\n",
       "0.062500       11\n",
       "0.058824        8\n",
       "0.055556        7\n",
       "0.045455        3\n",
       "0.052632        3\n",
       "0.047619        2\n",
       "0.040000        1\n",
       "0.038462        1\n",
       "0.037037        1\n",
       "0.050000        1\n",
       "Name: recent_pdays, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['recent_pdays'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b483e79b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['previous'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "15e075e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35551\n",
       "1     4561\n",
       "2      754\n",
       "3      216\n",
       "4       70\n",
       "5       18\n",
       "6        5\n",
       "7        1\n",
       "Name: previous, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['previous'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7e2d0fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.4    16228\n",
       "-1.8     9182\n",
       " 1.1     7762\n",
       "-0.1     3682\n",
       "-2.9     1662\n",
       "-3.4     1070\n",
       "-1.7      773\n",
       "-1.1      635\n",
       "-3.0      172\n",
       "-0.2       10\n",
       "Name: emp.var.rate, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['emp.var.rate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "83bb7d75",
   "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"
    }
   ],
   "source": [
    "sns.boxplot(x = 'y', y = 'emp.var.rate', data = bank)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e9c62f40",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bushra Ansari\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\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": [
    "sns.distplot(bank['emp.var.rate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b712e3e9",
   "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"
    }
   ],
   "source": [
    "sns.boxplot(x = 'y', y = 'euribor3m', data = bank)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67eefe68",
   "metadata": {},
   "source": [
    "#### Checking Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "45cb0456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = bank.corr()\n",
    "plt.figure(figsize = [10,7])\n",
    "sns.heatmap(corr, annot = True, cmap = 'Spectral', fmt = '0.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d2445ad",
   "metadata": {},
   "source": [
    "The emp.var.rate, cons.price.idx, euribor3m and nr.employed features have very high correlation. With euribor3m and nr.employed having the highest correlation of 0.95."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08708f5",
   "metadata": {},
   "source": [
    "#### Checking missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "22de3a04",
   "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>Missing</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Missing, Percentage]\n",
       "Index: []"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def missingValues(data):\n",
    "    m=data.isnull().sum()\n",
    "    md=(data.isnull().sum()/ data.shape[0]*100)\n",
    "    df=pd.concat([m, md], axis=1, keys=[\"Missing\", \"Percentage\"])\n",
    "    fg=df.sort_values(by =\"Percentage\", ascending=False)\n",
    "    fg1=fg[fg[\"Percentage\"]>0]\n",
    "    return fg1\n",
    "\n",
    "missingValues(bank)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6318b3c5",
   "metadata": {},
   "source": [
    "No missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15917af0",
   "metadata": {},
   "source": [
    "#### Checking Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "52c19db1",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>1%</th>\n",
       "      <th>2%</th>\n",
       "      <th>3%</th>\n",
       "      <th>4%</th>\n",
       "      <th>5%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>95%</th>\n",
       "      <th>96%</th>\n",
       "      <th>97%</th>\n",
       "      <th>98%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>40.023800</td>\n",
       "      <td>10.420680</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>98.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>258.315815</td>\n",
       "      <td>259.305321</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>551.000000</td>\n",
       "      <td>753.000000</td>\n",
       "      <td>821.000000</td>\n",
       "      <td>911.000000</td>\n",
       "      <td>1052.500000</td>\n",
       "      <td>1271.250000</td>\n",
       "      <td>4918.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>2.567879</td>\n",
       "      <td>2.770318</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>56.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>0.173013</td>\n",
       "      <td>0.494964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>0.081922</td>\n",
       "      <td>1.570883</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>-2.900000</td>\n",
       "      <td>-2.900000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>93.575720</td>\n",
       "      <td>0.578839</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>92.379000</td>\n",
       "      <td>92.431000</td>\n",
       "      <td>92.469000</td>\n",
       "      <td>92.713000</td>\n",
       "      <td>92.893000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>94.465000</td>\n",
       "      <td>94.465000</td>\n",
       "      <td>94.465000</td>\n",
       "      <td>94.465000</td>\n",
       "      <td>94.465000</td>\n",
       "      <td>94.465000</td>\n",
       "      <td>94.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>-40.502863</td>\n",
       "      <td>4.627860</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>-49.500000</td>\n",
       "      <td>-47.100000</td>\n",
       "      <td>-47.100000</td>\n",
       "      <td>-47.100000</td>\n",
       "      <td>-47.100000</td>\n",
       "      <td>-46.200000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>-36.100000</td>\n",
       "      <td>-33.600000</td>\n",
       "      <td>-31.400000</td>\n",
       "      <td>-31.400000</td>\n",
       "      <td>-30.100000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>-26.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>3.621293</td>\n",
       "      <td>1.734437</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>0.658000</td>\n",
       "      <td>0.714000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.797000</td>\n",
       "      <td>1.046000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>4.964000</td>\n",
       "      <td>4.966000</td>\n",
       "      <td>4.967000</td>\n",
       "      <td>4.967000</td>\n",
       "      <td>4.968000</td>\n",
       "      <td>4.968000</td>\n",
       "      <td>5.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>5167.017972</td>\n",
       "      <td>72.177913</td>\n",
       "      <td>4964.000000</td>\n",
       "      <td>4964.000000</td>\n",
       "      <td>4992.000000</td>\n",
       "      <td>4992.000000</td>\n",
       "      <td>5009.000000</td>\n",
       "      <td>5018.000000</td>\n",
       "      <td>5076.000000</td>\n",
       "      <td>5099.000000</td>\n",
       "      <td>5191.000000</td>\n",
       "      <td>5228.000000</td>\n",
       "      <td>5228.000000</td>\n",
       "      <td>5228.000000</td>\n",
       "      <td>5228.000000</td>\n",
       "      <td>5228.000000</td>\n",
       "      <td>5228.000000</td>\n",
       "      <td>5228.000000</td>\n",
       "      <td>5228.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recent_pdays</th>\n",
       "      <td>41176.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.001001</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count         mean         std          min           1%  \\\n",
       "age             41176.0    40.023800   10.420680    17.000000    23.000000   \n",
       "duration        41176.0   258.315815  259.305321     0.000000    11.000000   \n",
       "campaign        41176.0     2.567879    2.770318     1.000000     1.000000   \n",
       "previous        41176.0     0.173013    0.494964     0.000000     0.000000   \n",
       "emp.var.rate    41176.0     0.081922    1.570883    -3.400000    -3.400000   \n",
       "cons.price.idx  41176.0    93.575720    0.578839    92.201000    92.201000   \n",
       "cons.conf.idx   41176.0   -40.502863    4.627860   -50.800000   -49.500000   \n",
       "euribor3m       41176.0     3.621293    1.734437     0.634000     0.658000   \n",
       "nr.employed     41176.0  5167.017972   72.177913  4964.000000  4964.000000   \n",
       "recent_pdays    41176.0          inf         NaN     0.001001     0.001001   \n",
       "\n",
       "                         2%           3%           4%           5%  \\\n",
       "age               24.000000    25.000000    25.000000    26.000000   \n",
       "duration          17.000000    23.000000    29.000000    36.000000   \n",
       "campaign           1.000000     1.000000     1.000000     1.000000   \n",
       "previous           0.000000     0.000000     0.000000     0.000000   \n",
       "emp.var.rate      -3.400000    -3.000000    -2.900000    -2.900000   \n",
       "cons.price.idx    92.379000    92.431000    92.469000    92.713000   \n",
       "cons.conf.idx    -47.100000   -47.100000   -47.100000   -47.100000   \n",
       "euribor3m          0.714000     0.720000     0.740000     0.797000   \n",
       "nr.employed     4992.000000  4992.000000  5009.000000  5018.000000   \n",
       "recent_pdays       0.001001     0.001001     0.001001     0.001001   \n",
       "\n",
       "                        10%          25%          50%          75%  \\\n",
       "age               28.000000    32.000000    38.000000    47.000000   \n",
       "duration          59.000000   102.000000   180.000000   319.000000   \n",
       "campaign           1.000000     1.000000     2.000000     3.000000   \n",
       "previous           0.000000     0.000000     0.000000     0.000000   \n",
       "emp.var.rate      -1.800000    -1.800000     1.100000     1.400000   \n",
       "cons.price.idx    92.893000    93.075000    93.749000    93.994000   \n",
       "cons.conf.idx    -46.200000   -42.700000   -41.800000   -36.400000   \n",
       "euribor3m          1.046000     1.344000     4.857000     4.961000   \n",
       "nr.employed     5076.000000  5099.000000  5191.000000  5228.000000   \n",
       "recent_pdays       0.001001     0.001001     0.001001     0.001001   \n",
       "\n",
       "                        90%          95%          96%          97%  \\\n",
       "age               55.000000    58.000000    59.000000    59.000000   \n",
       "duration         551.000000   753.000000   821.000000   911.000000   \n",
       "campaign           5.000000     7.000000     8.000000     9.000000   \n",
       "previous           1.000000     1.000000     1.000000     1.000000   \n",
       "emp.var.rate       1.400000     1.400000     1.400000     1.400000   \n",
       "cons.price.idx    94.465000    94.465000    94.465000    94.465000   \n",
       "cons.conf.idx    -36.100000   -33.600000   -31.400000   -31.400000   \n",
       "euribor3m          4.964000     4.966000     4.967000     4.967000   \n",
       "nr.employed     5228.000000  5228.000000  5228.000000  5228.000000   \n",
       "recent_pdays       0.001001     0.001001     0.001001     0.111111   \n",
       "\n",
       "                        98%          99%       max  \n",
       "age               62.000000    71.000000    98.000  \n",
       "duration        1052.500000  1271.250000  4918.000  \n",
       "campaign          11.000000    14.000000    56.000  \n",
       "previous           2.000000     2.000000     7.000  \n",
       "emp.var.rate       1.400000     1.400000     1.400  \n",
       "cons.price.idx    94.465000    94.465000    94.767  \n",
       "cons.conf.idx    -30.100000   -26.900000   -26.900  \n",
       "euribor3m          4.968000     4.968000     5.045  \n",
       "nr.employed     5228.000000  5228.000000  5228.000  \n",
       "recent_pdays       0.166667     0.333333       inf  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe(percentiles = [0.01,0.02,0.03,0.04,0.05,0.1,0.25,0.50,0.75,0.9,0.95,0.96,0.97,0.98,0.99]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2185bba3",
   "metadata": {},
   "source": [
    "#### Outliers Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2d3b0e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outliers(x):\n",
    "    x=x.clip(upper=x.quantile(.99))\n",
    "    x=x.clip(lower=x.quantile(.01))\n",
    "    return x "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b80750e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_var = bank.dtypes[bank.dtypes != \"object\"].index\n",
    "data_num = bank[numerical_var]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b8de8c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_num1 = data_num.apply(lambda x : outliers(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e3b46bf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_var = bank.dtypes[bank.dtypes == \"object\"].index\n",
    "data_cat = bank[categorical_var]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0c6ce0fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank1 = pd.concat([data_cat, data_num1], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef99ff10",
   "metadata": {},
   "source": [
    "#### Creating Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3e8ca6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41176, 20)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f8a78c02",
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_bank = pd.get_dummies(bank1, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d7fc90c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41176, 35)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1ea5a0cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'duration', 'campaign', 'previous', 'emp.var.rate',\n",
       "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed',\n",
       "       'recent_pdays', 'job_Pink-collar', 'job_Self-depend',\n",
       "       'job_White-collar', 'job_blue-collar', 'job_technician',\n",
       "       'marital_married', 'marital_single', 'marital_unknown',\n",
       "       'education_primary', 'education_secondary', 'education_teritary',\n",
       "       'education_unknown', 'default_unknown', 'default_yes',\n",
       "       'housing_unknown', 'housing_yes', 'loan_unknown', 'loan_yes',\n",
       "       'contact_telephone', 'month_Quarter2', 'month_Quarter3',\n",
       "       'month_Quarter4', 'poutcome_nonexistent', 'poutcome_success', 'y_1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e826db6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.887337\n",
       "1    0.112663\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank['y'].value_counts(normalize=True)\n",
    "#Imbalanced data problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5c2c9676",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = cleaned_bank.drop(columns = ['y_1'])\n",
    "y = cleaned_bank['y_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3b4e9775",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d635b77",
   "metadata": {},
   "source": [
    "#### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "2e3fe369",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "sc_fit = sc.fit(x_train)\n",
    "\n",
    "x_train_sc = sc_fit.transform(x_train)\n",
    "x_test_sc = sc_fit.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9c1b8fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_sc_df = pd.DataFrame(x_train_sc, columns=x_train.columns)\n",
    "x_test_sc_df = pd.DataFrame(x_test_sc, columns=x_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e8a6e7fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bushra Ansari\\anaconda3\\lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:193: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  vif = 1. / (1. - r_squared_i)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data['Var'] = x_train_sc_df.columns\n",
    "vif_data['VIF'] = [variance_inflation_factor(x_train_sc_df.values, i) for i in range(len(x_train_sc_df.columns))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "314a8709",
   "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>Var</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>housing_unknown</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>loan_unknown</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>education_teritary</td>\n",
       "      <td>576.238703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>education_primary</td>\n",
       "      <td>498.932665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>education_secondary</td>\n",
       "      <td>419.931599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>euribor3m</td>\n",
       "      <td>129.870843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>education_unknown</td>\n",
       "      <td>94.465111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>emp.var.rate</td>\n",
       "      <td>63.838491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nr.employed</td>\n",
       "      <td>41.323692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>month_Quarter2</td>\n",
       "      <td>20.674980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>month_Quarter3</td>\n",
       "      <td>19.530615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>previous</td>\n",
       "      <td>10.995304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>month_Quarter4</td>\n",
       "      <td>10.378662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>poutcome_nonexistent</td>\n",
       "      <td>10.135024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cons.price.idx</td>\n",
       "      <td>9.618097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>poutcome_success</td>\n",
       "      <td>5.827111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>recent_pdays</td>\n",
       "      <td>5.782328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cons.conf.idx</td>\n",
       "      <td>3.787314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>job_White-collar</td>\n",
       "      <td>3.404962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>contact_telephone</td>\n",
       "      <td>3.297844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>job_blue-collar</td>\n",
       "      <td>3.183498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>marital_single</td>\n",
       "      <td>2.876904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>job_technician</td>\n",
       "      <td>2.663165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>marital_married</td>\n",
       "      <td>2.579060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>job_Pink-collar</td>\n",
       "      <td>2.223338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>job_Self-depend</td>\n",
       "      <td>1.741412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>1.361938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>default_unknown</td>\n",
       "      <td>1.142598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>campaign</td>\n",
       "      <td>1.048820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>housing_yes</td>\n",
       "      <td>1.040936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>marital_unknown</td>\n",
       "      <td>1.019166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>duration</td>\n",
       "      <td>1.015416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>loan_yes</td>\n",
       "      <td>1.007758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>default_yes</td>\n",
       "      <td>1.000817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Var         VIF\n",
       "24       housing_unknown         inf\n",
       "26          loan_unknown         inf\n",
       "20    education_teritary  576.238703\n",
       "18     education_primary  498.932665\n",
       "19   education_secondary  419.931599\n",
       "7              euribor3m  129.870843\n",
       "21     education_unknown   94.465111\n",
       "4           emp.var.rate   63.838491\n",
       "8            nr.employed   41.323692\n",
       "29        month_Quarter2   20.674980\n",
       "30        month_Quarter3   19.530615\n",
       "3               previous   10.995304\n",
       "31        month_Quarter4   10.378662\n",
       "32  poutcome_nonexistent   10.135024\n",
       "5         cons.price.idx    9.618097\n",
       "33      poutcome_success    5.827111\n",
       "9           recent_pdays    5.782328\n",
       "6          cons.conf.idx    3.787314\n",
       "12      job_White-collar    3.404962\n",
       "28     contact_telephone    3.297844\n",
       "13       job_blue-collar    3.183498\n",
       "16        marital_single    2.876904\n",
       "14        job_technician    2.663165\n",
       "15       marital_married    2.579060\n",
       "10       job_Pink-collar    2.223338\n",
       "11       job_Self-depend    1.741412\n",
       "0                    age    1.361938\n",
       "22       default_unknown    1.142598\n",
       "2               campaign    1.048820\n",
       "25           housing_yes    1.040936\n",
       "17       marital_unknown    1.019166\n",
       "1               duration    1.015416\n",
       "27              loan_yes    1.007758\n",
       "23           default_yes    1.000817"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_data.sort_values(by = ['VIF'], ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c93d9a",
   "metadata": {},
   "source": [
    "#### Imbalanced Data Treatment with RandomUnderSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "80975029",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in c:\\users\\bushra ansari\\anaconda3\\lib\\site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in c:\\users\\bushra ansari\\anaconda3\\lib\\site-packages (from imblearn) (0.9.0)\n",
      "Requirement already satisfied: numpy>=1.14.6 in c:\\users\\bushra ansari\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.20.3)\n",
      "Requirement already satisfied: scikit-learn>=1.0.1 in c:\\users\\bushra ansari\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.0.2)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\bushra ansari\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.7.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\bushra ansari\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (2.2.0)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\bushra ansari\\anaconda3\\lib\\site-packages (from imbalanced-learn->imblearn) (1.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4eb9aae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "undersample = RandomUnderSampler(replacement=False, sampling_strategy=.6)\n",
    "\n",
    "x_under, y_under = undersample.fit_resample(x_train_sc_df, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9f37d3c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before sampling class distribution:- Counter({0: 29268, 1: 3672})\n",
      "New class distribution:- Counter({0: 6120, 1: 3672})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "print('Before sampling class distribution:-',Counter(y_train))\n",
    "\n",
    "print('New class distribution:-',Counter(y_under))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "92045033",
   "metadata": {},
   "outputs": [],
   "source": [
    "def new_ratio(y):\n",
    "    zero=len(y)-y.sum()\n",
    "    one=y.sum()\n",
    "    rro=round(((one/len(y))*100),2)\n",
    "    rrz=round(((zero/len(y))*100),2)\n",
    "    print(f\"Class ratio 1 ({one}) :0 ({zero}) is {rro}% : {rrz}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "00ef2021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class ratio 1 (3672) :0 (6120) is 37.5% : 62.5%\n"
     ]
    }
   ],
   "source": [
    "new_ratio(y_under)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a5ab90",
   "metadata": {},
   "source": [
    "### Support Vector Macine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cced0063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=SVC(), n_jobs=-1,\n",
       "                   param_distributions={'C': [0.1, 1, 10], 'degree': [1, 2, 3],\n",
       "                                        'gamma': [0.1, 1, 0.001],\n",
       "                                        'kernel': ['linear', 'poly', 'rbf']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using GridSearchCV for hyperparameter tuning\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "params1 = {\"kernel\":['linear', 'poly', 'rbf'],\n",
    "       \"degree\":[1,2,3],\n",
    "       \"gamma\":[0.1,1,.001],\n",
    "       \"C\":[0.1,1,10]}\n",
    "\n",
    "sv1 = svm.SVC()\n",
    "svm_class1 = RandomizedSearchCV(sv1, params1, cv=3, n_jobs=-1, verbose=True)\n",
    "svm_class1.fit(x_under, y_under)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2db27a43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best roc auc score :  0.8585580065359477\n",
      "SVC(C=1, degree=1, gamma=0.1, kernel='poly')\n"
     ]
    }
   ],
   "source": [
    "print(\"Best roc auc score : \", svm_class1.best_score_)\n",
    "print(svm_class1.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0326821f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score : 0.8896037581699346\n",
      "Test score : 0.8725109276347741\n",
      "Time Taken 14.493157863616943 sec\n"
     ]
    }
   ],
   "source": [
    "# Model with optimal hyperparameters\n",
    "import time\n",
    "\n",
    "st = time.time()\n",
    "\n",
    "sv3 = svm.SVC(kernel=\"rbf\", degree=2, gamma=0.01, C=10, probability=True)\n",
    "\n",
    "sv3.fit(x_under, y_under)\n",
    "\n",
    "print(\"Train score :\", sv3.score(x_under, y_under))\n",
    "print(\"Test score :\", sv3.score(x_test_sc_df, y_test))\n",
    "\n",
    "et = time.time()\n",
    "print(\"Time Taken\", et-st, \"sec\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494d2958",
   "metadata": {},
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8770d6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_roc( actual, probs ):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,\n",
    "                                              drop_intermediate = False )\n",
    "    auc_score = metrics.roc_auc_score( actual, probs )\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic example')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    return None\n",
    "# Creating function to display ROC-AUC score, f1 score and classification report\n",
    "def display_scores(y_test, y_pred):\n",
    "    '''\n",
    "    Display ROC-AUC score, f1 score and classification report of a model.\n",
    "    '''\n",
    "    print(f\"F1 Score: {round(metrics.f1_score(y_test, y_pred)*100,2)}%\") \n",
    "    print(f\"Classification Report: \\n {metrics.classification_report(y_test, y_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ebe2e9c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 85.89%\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.89      0.91      6120\n",
      "           1       0.82      0.90      0.86      3672\n",
      "\n",
      "    accuracy                           0.89      9792\n",
      "   macro avg       0.88      0.89      0.88      9792\n",
      "weighted avg       0.89      0.89      0.89      9792\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Predictions on the train set\n",
    "y_train_pred1 = sv3.predict(x_under)\n",
    "display_scores(y_under, y_train_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "02a9b3ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicted probability\n",
    "y_train_pred_proba1 = sv3.predict_proba(x_under)[:,1]\n",
    "# Plot the ROC curve\n",
    "draw_roc(y_under, y_train_pred_proba1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dc95e01c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 61.82%\n",
      "Classification Report: \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.87      0.92      7269\n",
      "           1       0.48      0.88      0.62       967\n",
      "\n",
      "    accuracy                           0.87      8236\n",
      "   macro avg       0.73      0.88      0.77      8236\n",
      "weighted avg       0.92      0.87      0.89      8236\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluating model on the test data\n",
    "y_test_pred1 = sv3.predict(x_test_sc_df)\n",
    "display_scores(y_test, y_test_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ed393b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicted probability\n",
    "y_test_pred_proba1 = sv3.predict_proba(x_test_sc_df)[:,1]\n",
    "# Plot the ROC curve\n",
    "draw_roc(y_test, y_test_pred_proba1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0548f096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>ROC Score</th>\n",
       "      <th>Precision Score</th>\n",
       "      <th>Recall Score</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Accuracy Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machine</td>\n",
       "      <td>0.875327</td>\n",
       "      <td>0.476725</td>\n",
       "      <td>0.879007</td>\n",
       "      <td>0.618182</td>\n",
       "      <td>0.872511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  ROC Score  Precision Score  Recall Score  \\\n",
       "0   Support Vector Machine     0.875327         0.476725      0.879007   \n",
       "\n",
       "   F1 Score  Accuracy Score  \n",
       "0  0.618182        0.872511  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp2 = pd.Series({'Model': \" Support Vector Machine  \",\n",
    "                 'ROC Score' : metrics.roc_auc_score(y_test, y_test_pred1),\n",
    "                 'Precision Score': metrics.precision_score(y_test, y_test_pred1),\n",
    "                 'Recall Score': metrics.recall_score(y_test, y_test_pred1),\n",
    "                 'F1 Score' : metrics.f1_score(y_test, y_test_pred1),\n",
    "                 'Accuracy Score': metrics.accuracy_score(y_test, y_test_pred1)})\n",
    "\n",
    "model_svm_imb_report = model_report.append(tmp2, ignore_index = True)\n",
    "model_svm_imb_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4109d017",
   "metadata": {},
   "source": [
    "The performance metric used for this case study is AUC ROC score also known as AUROC (Area Under the Receiver Operating Characteristics).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a977c2c",
   "metadata": {},
   "source": [
    "The reason we are choosing AUC over accuracy is because,\n",
    "as we will see in Exploratory data analysis, the dataset we are working with is an imbalanced dataset with the class “no” being the majority class. If we use accuracy as our metric, any random model can give us a very good accuracy. But at the end, it will be a random model. AUC gets over this problem by looking into both the True positive rate (TPR) and False positive rate (FPR). Only if both the TPR and FPR are well above the random line in the ROC curve, we will get a good AUC. Accuracy does not guarantee that."
   ]
  }
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