telemarketing_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Portages Bank Telemarketing Analysis\n",
    "<p>Part 1</p>\n",
    "\n",
    "### Main objective:\n",
    "<p>Predict customers' response to its telemarketing campaign and establish\n",
    "a target customer profile for future marketing plans.</p>\n",
    "\n",
    "<p>We'll do this by analyzing customer features, such as demographics and transaction history, the bank\n",
    "will be able to predict customer saving behaviours and identify which type of customers\n",
    "is more likely to make term deposits.</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Loading the data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "\n",
    "file = 'bank-full.csv'\n",
    "dataset1 = pd.read_csv(file, sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "(45211, 17)"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<p>There are 45,211 observation in this dataset. Each represent an existing customer that the\n",
    "bank reached via a phone call. For each observation the dataset records 16 input variables that are\n",
    "both qualitative and quantitative attributes</p>\n",
    "<p>There is a single binary output variable the denotes 'yes' or 'no' showing the outcome of the \n",
    "phone calls.</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "   age           job  marital  education default  balance housing loan  \\\n0   58    management  married   tertiary      no     2143     yes   no   \n1   44    technician   single  secondary      no       29     yes   no   \n2   33  entrepreneur  married  secondary      no        2     yes  yes   \n3   47   blue-collar  married    unknown      no     1506     yes   no   \n4   33       unknown   single    unknown      no        1      no   no   \n\n   contact  day month  duration  campaign  pdays  previous poutcome   y  \n0  unknown    5   may       261         1     -1         0  unknown  no  \n1  unknown    5   may       151         1     -1         0  unknown  no  \n2  unknown    5   may        76         1     -1         0  unknown  no  \n3  unknown    5   may        92         1     -1         0  unknown  no  \n4  unknown    5   may       198         1     -1         0  unknown  no  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>day</th>\n      <th>month</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>poutcome</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>management</td>\n      <td>married</td>\n      <td>tertiary</td>\n      <td>no</td>\n      <td>2143</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>261</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>technician</td>\n      <td>single</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>29</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>151</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>entrepreneur</td>\n      <td>married</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>2</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>76</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>47</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>1506</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>92</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>33</td>\n      <td>unknown</td>\n      <td>single</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>1</td>\n      <td>no</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>198</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n       'previous', 'poutcome', 'y'],\n      dtype='object')"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cleaning the data\n",
    "\n",
    "<p>There is no missing values in the dataset however, they are values like 'unknown', 'others', which\n",
    "are like missing values. So, we removed the values from the dataset</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "#Delete the rows with columns 'poutcome' contains 'other'\n",
    "condition = dataset1.poutcome == 'other'\n",
    "dataset2 = dataset1.drop(dataset1[condition].index, axis=0, inplace=False)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "#Replace 'unknown' in job and education with 'other'\n",
    "dataset2[['job', 'education']] = dataset2[['job', 'education']].replace(['unknown'], 'other')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Drop outliers in the column 'balance'\n",
    "\n",
    "<p>To capture general trends in the dataset, outliners in the column \"balance\" are dropped.\n",
    "Outliers are defined as values which are more than 3 standard deviations away from the mean.</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "\n",
    "dataset2[['balance']].mean()\n",
    "dataset2[['balance']].mean()\n",
    "\n",
    "dataset2['balance_outliers'] = dataset2['balance']\n",
    "dataset2['balance_outliers'] = zscore(dataset2['balance_outliers'])\n",
    "\n",
    "condition1 = (dataset2['balance_outliers']>3) | (dataset2['balance_outliers']<-3)\n",
    "dataset3 = dataset2.drop(dataset2[condition1].index, axis=0, inplace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "dataset4 = dataset3.drop('balance_outliers', axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create and transforming data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "0    37785\n1     4870\nName: response_binary, dtype: int64"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Change column name: 'y' to 'response'\n",
    "dataset4.rename(index=str, columns={'y': 'response'}, inplace=True)\n",
    "\n",
    "def convert(dataset4, new_column, old_column):\n",
    "    dataset4[new_column] = dataset4[old_column].apply(lambda x: 0 if x == 'no' else 1)\n",
    "    return dataset4[new_column].value_counts()\n",
    "\n",
    "convert(dataset4, \"response_binary\", \"response\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "#Drop column \"contact\" which is useless\n",
    "dataset5 = dataset4.drop('contact', axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "#Change the unit of 'duration' from seconds to minutes\n",
    "dataset5['duration'] = dataset5['duration'].apply(lambda n: n/60).round(2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "#Change 'month' from words to numbers for easier analysis\n",
    "first  = [dataset5]\n",
    "for column in first:\n",
    "    column.loc[column['month'] == 'jan', 'month_int'] = 1\n",
    "    column.loc[column['month'] == 'feb', 'month_int'] = 2\n",
    "    column.loc[column['month'] == 'mar', 'month_int'] = 3\n",
    "    column.loc[column['month'] == 'apr', 'month_int'] = 4\n",
    "    column.loc[column['month'] == 'may', 'month_int'] = 5\n",
    "    column.loc[column['month'] == 'jun', 'month_int'] = 6\n",
    "    column.loc[column['month'] == 'jul', 'month_int'] = 7\n",
    "    column.loc[column['month'] == 'aug', 'month_int'] = 8\n",
    "    column.loc[column['month'] == 'sep', 'month_int'] = 9\n",
    "    column.loc[column['month'] == 'oct', 'month_int'] = 10\n",
    "    column.loc[column['month'] == 'nov', 'month_int'] = 11\n",
    "    column.loc[column['month'] == 'dec', 'month_int'] = 12"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Filtering"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "#Drop rows that 'duration' < 5s\n",
    "condition2 = (dataset5['duration']<5/60)\n",
    "dataset6 = dataset5.drop(dataset5[condition2].index, axis=0, inplace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "#Drop customer values with 'other' education\n",
    "condition3 = (dataset6['education'] == 'other')\n",
    "dataset7 = dataset6.drop(dataset6[condition3].index, axis=0, inplace=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "   age           job  marital  education default  balance housing loan  day  \\\n0   58    management  married   tertiary      no     2143     yes   no    5   \n1   44    technician   single  secondary      no       29     yes   no    5   \n2   33  entrepreneur  married  secondary      no        2     yes  yes    5   \n5   35    management  married   tertiary      no      231     yes   no    5   \n6   28    management   single   tertiary      no      447     yes  yes    5   \n\n  month  duration  campaign  pdays  previous poutcome response  \\\n0   may      4.35         1     -1         0  unknown       no   \n1   may      2.52         1     -1         0  unknown       no   \n2   may      1.27         1     -1         0  unknown       no   \n5   may      2.32         1     -1         0  unknown       no   \n6   may      3.62         1     -1         0  unknown       no   \n\n   response_binary  month_int  \n0                0        5.0  \n1                0        5.0  \n2                0        5.0  \n5                0        5.0  \n6                0        5.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>day</th>\n      <th>month</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>poutcome</th>\n      <th>response</th>\n      <th>response_binary</th>\n      <th>month_int</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>58</td>\n      <td>management</td>\n      <td>married</td>\n      <td>tertiary</td>\n      <td>no</td>\n      <td>2143</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>5</td>\n      <td>may</td>\n      <td>4.35</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>technician</td>\n      <td>single</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>29</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>5</td>\n      <td>may</td>\n      <td>2.52</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>entrepreneur</td>\n      <td>married</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>2</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>5</td>\n      <td>may</td>\n      <td>1.27</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>35</td>\n      <td>management</td>\n      <td>married</td>\n      <td>tertiary</td>\n      <td>no</td>\n      <td>231</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>5</td>\n      <td>may</td>\n      <td>2.32</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>0</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>28</td>\n      <td>management</td>\n      <td>single</td>\n      <td>tertiary</td>\n      <td>no</td>\n      <td>447</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>5</td>\n      <td>may</td>\n      <td>3.62</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0</td>\n      <td>unknown</td>\n      <td>no</td>\n      <td>0</td>\n      <td>5.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset7.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Part 3: EDA"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 720x432 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the distribution of 'age' and 'balance'\n",
    "dist_age_balance = plt.figure(figsize=(10,6))\n",
    "\n",
    "age = dist_age_balance.add_subplot(1, 2, 1)\n",
    "bal = dist_age_balance.add_subplot(1, 2, 2)\n",
    "\n",
    "age.hist(dataset7['age'])\n",
    "age.set_title('The Distribution of Age')\n",
    "\n",
    "bal.hist(dataset7['balance'], color='skyblue')\n",
    "bal.set_title('The Distribution of Balance')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<p><b>The distribution of age:</b> The Majority of the customers called are in the age range of 30 to 40s</p>\n",
    "\n",
    "<p><b>The distribution of balance:</b> Even after dropping outliers in balance, the range is pretty massive.\n",
    "The distribution of balance has a huge standard deviation relative to the mean, this could suggest\n",
    "a large variability in customers' balance levels.</p>\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 504x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the relationship between 'age' and 'balance'\n",
    "scatter_age_balance = dataset7.plot.scatter('age', 'balance', figsize=(7,5))\n",
    "\n",
    "plt.title('The Relationship between Age and Balance')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "<p>Based on this scatter plot, there is no clear relationship between client's age and balance\n",
    "level. We can also see that customers over the age of 60, the client tend to have a significantly\n",
    "lower balance.</p>"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 576x576 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the distribution of 'duration' & 'campaign'\n",
    "dist_dur_cam = dataset7[['duration','campaign']].plot(kind = 'box',\n",
    "                                                      figsize = (8,8),\n",
    "                                                      subplots = True, layout = (1,2),\n",
    "                                                      sharex = False, sharey = False,\n",
    "                                                      title='The Distribution of Duration and Campaign')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
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}