Bank_Marketing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading libraries\n",
    "import pandas as pd \n",
    "import numpy as np                     \n",
    "import seaborn as sns                   \n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load datas#Load dataset\n",
    "bank = pd.read_csv(\"/Dataset/Project/bank/bank-full.csv\", delimiter=';')\n",
    "#bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size = 768587\n",
      "Shape =(45211, 17)\n",
      "Shape[0] x Shape[1] = 768587\n"
     ]
    }
   ],
   "source": [
    "# size of dataframe\n",
    "size = bank.size \n",
    "  \n",
    "# shape of dataframe \n",
    "shape = bank.shape \n",
    "  \n",
    "# dimensions of data frames and series.\n",
    "df_ndim = bank.ndim \n",
    "\n",
    "# printing size and shape \n",
    "print(\"Size = {}\\nShape ={}\\nShape[0] x Shape[1] = {}\". \n",
    "format(size, shape, shape[0]*shape[1])) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age           int64\n",
      "job          object\n",
      "marital      object\n",
      "education    object\n",
      "default      object\n",
      "balance       int64\n",
      "housing      object\n",
      "loan         object\n",
      "contact      object\n",
      "day           int64\n",
      "month        object\n",
      "duration      int64\n",
      "campaign      int64\n",
      "pdays         int64\n",
      "previous      int64\n",
      "poutcome     object\n",
      "y            object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(bank.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
      "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
      "       'previous', 'poutcome', 'y'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(bank.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Imputaion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "bank.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        45211 non-null  int64 \n",
      " 1   job        45211 non-null  object\n",
      " 2   marital    45211 non-null  object\n",
      " 3   education  45211 non-null  object\n",
      " 4   default    45211 non-null  object\n",
      " 5   balance    45211 non-null  int64 \n",
      " 6   housing    45211 non-null  object\n",
      " 7   loan       45211 non-null  object\n",
      " 8   contact    45211 non-null  object\n",
      " 9   day        45211 non-null  int64 \n",
      " 10  month      45211 non-null  object\n",
      " 11  duration   45211 non-null  int64 \n",
      " 12  campaign   45211 non-null  int64 \n",
      " 13  pdays      45211 non-null  int64 \n",
      " 14  previous   45211 non-null  int64 \n",
      " 15  poutcome   45211 non-null  object\n",
      " 16  y          45211 non-null  object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "bank.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation: No null values found in bank-full.cvs dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading training and test data\n",
    "bank_train=pd.read_csv(\"/Users/shanpriyahome/Personal/Family Folders/Priya/Python/Dataset/Project/bank/bank-full.csv\", delimiter=';')\n",
    "bank_test=pd.read_csv(\"/Users/shanpriyahome/Personal/Family Folders/Priya/Python/Dataset/Project/bank/bank.csv\", delimiter=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   age           job  marital  education default  balance housing loan  \\\n",
      "0   58    management  married   tertiary      no     2143     yes   no   \n",
      "1   44    technician   single  secondary      no       29     yes   no   \n",
      "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
      "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
      "4   33       unknown   single    unknown      no        1      no   no   \n",
      "\n",
      "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
      "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
      "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
      "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
      "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
      "4  unknown    5   may       198         1     -1         0  unknown  no  \n"
     ]
    }
   ],
   "source": [
    "print(bank_train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
      "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
      "       'previous', 'poutcome', 'y'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(bank_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
      "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
      "       'previous', 'poutcome', 'y'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(bank_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((45211, 17), (4521, 17))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_train.shape, bank_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     0.883015\n",
       "yes    0.116985\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_train['y'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4f9a8da00>"
      ]
     },
     "execution_count": 16,
     "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": [
    "bank_train.groupby('y').size().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#catergorical Variables \n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(221)\n",
    "bank_train['loan'].value_counts(normalize=True).plot.bar(figsize=(20,20), title= 'Personal loan')\n",
    "\n",
    "plt.subplot(222) \n",
    "bank_train['housing'].value_counts(normalize=True).plot.bar(figsize=(20,20),title= 'Housing Loan') \n",
    "\n",
    "plt.subplot(223) \n",
    "bank_train['default'].value_counts(normalize=True).plot.bar(figsize=(20,20),title= 'Credit_History') \n",
    "\n",
    "plt.subplot(224) \n",
    "bank_train['marital'].value_counts(normalize=True).plot.bar(figsize=(20,20),title= 'Marital status')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "Around 88% of the people in the dataset are not having personal loan.\n",
    "Around 55% of the people in the dataset are having housing loan.\n",
    "Most of the people need to improve thier credit score.\n",
    "Around 5% of people in the dataset are having good credit score. \n",
    "Around 60% of the people in the dataset are married.\n",
    "Around 40% of people in the dataset are either single or divorced.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Independent Variable \n",
    "\n",
    "plt.figure(1) \n",
    "\n",
    "plt.subplot(131) \n",
    "bank_train['education'].value_counts(normalize=True).plot.bar(figsize=(24,6), title= 'education') \n",
    "\n",
    "plt.subplot(132) \n",
    "bank_train['job'].value_counts(normalize=True).plot.bar(title= 'job') \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "Most people in this dataset have completed secondary education(Around 55%)\n",
    "Most people in this dataset have blue-collar(around 25%) and management(around 23%) job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'y')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Independent Variable (Numerical)\n",
    "bank_train.boxplot('age','y',figsize=(15,5))\n",
    "plt.ylabel('Age')\n",
    "plt.xlabel('y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'balance')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bank_train.boxplot('balance',figsize=(15,5))\n",
    "plt.ylabel('balance')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age              0\n",
       "job            288\n",
       "marital          0\n",
       "education     1857\n",
       "default          0\n",
       "balance          0\n",
       "housing          0\n",
       "loan             0\n",
       "contact      13020\n",
       "day              0\n",
       "month            0\n",
       "duration         0\n",
       "campaign         0\n",
       "pdays            0\n",
       "previous         0\n",
       "poutcome     36959\n",
       "y                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking number of unknowns in each column\n",
    "(bank_train=='unknown').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'unknown'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_train.job.mode()[0]\n",
    "bank_train.education.mode()[0]\n",
    "bank_train.contact.mode()[0]\n",
    "bank_train.poutcome.mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in bank_train.job:\n",
    "    bank_train['job'].fillna(bank_train['job'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age              0\n",
      "job            288\n",
      "marital          0\n",
      "education     1857\n",
      "default          0\n",
      "balance          0\n",
      "housing          0\n",
      "loan             0\n",
      "contact      13020\n",
      "day              0\n",
      "month            0\n",
      "duration         0\n",
      "campaign         0\n",
      "pdays            0\n",
      "previous         0\n",
      "poutcome     36959\n",
      "y                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print((bank_train=='unknown').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}