Bank Marketing Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import preprocessing\n",
    "from sklearn import model_selection\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading all the four Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>campaign</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>4</td>\n",
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       "      <td>2</td>\n",
       "      <td>2143</td>\n",
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       "      <td>3</td>\n",
       "      <td>no</td>\n",
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       "      <th>1</th>\n",
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       "      <td>29</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>447</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
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       "      <td>3</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>593</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   age  job  marital  education  balance  housing  loan  day  campaign  \\\n",
       "0   58    4        1          2     2143        1     0    5         1   \n",
       "1   44    9        2          1       29        1     0    5         1   \n",
       "2   33    2        1          1        2        1     1    5         1   \n",
       "3   47    1        1          3     1506        1     0    5         1   \n",
       "4   33   11        2          3        1        0     0    5         1   \n",
       "5   35    4        1          2      231        1     0    5         1   \n",
       "6   28    4        2          2      447        1     1    5         1   \n",
       "7   42    2        0          2        2        1     0    5         1   \n",
       "8   58    5        1          0      121        1     0    5         1   \n",
       "9   43    9        2          1      593        1     0    5         1   \n",
       "\n",
       "   previous  poutcome   y  \n",
       "0         0         3  no  \n",
       "1         0         3  no  \n",
       "2         0         3  no  \n",
       "3         0         3  no  \n",
       "4         0         3  no  \n",
       "5         0         3  no  \n",
       "6         0         3  no  \n",
       "7         0         3  no  \n",
       "8         0         3  no  \n",
       "9         0         3  no  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bank_full = pd.read_csv('bank-full.csv', sep =';')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   age  job  marital  education  balance  housing  loan  day  campaign  \\\n",
       "0   58    4        1          2     2143        1     0    5         1   \n",
       "1   44    9        2          1       29        1     0    5         1   \n",
       "2   33    2        1          1        2        1     1    5         1   \n",
       "3   47    1        1          3     1506        1     0    5         1   \n",
       "4   33   11        2          3        1        0     0    5         1   \n",
       "5   35    4        1          2      231        1     0    5         1   \n",
       "6   28    4        2          2      447        1     1    5         1   \n",
       "7   42    2        0          2        2        1     0    5         1   \n",
       "8   58    5        1          0      121        1     0    5         1   \n",
       "9   43    9        2          1      593        1     0    5         1   \n",
       "\n",
       "   previous  poutcome   y  \n",
       "0         0         3  no  \n",
       "1         0         3  no  \n",
       "2         0         3  no  \n",
       "3         0         3  no  \n",
       "4         0         3  no  \n",
       "5         0         3  no  \n",
       "6         0         3  no  \n",
       "7         0         3  no  \n",
       "8         0         3  no  \n",
       "9         0         3  no  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bank= pd.read_csv('bank.csv', sep =';')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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       "      <td>3</td>\n",
       "      <td>1506</td>\n",
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       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>231</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>447</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>593</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  balance  housing  loan  day  campaign  \\\n",
       "0   58    4        1          2     2143        1     0    5         1   \n",
       "1   44    9        2          1       29        1     0    5         1   \n",
       "2   33    2        1          1        2        1     1    5         1   \n",
       "3   47    1        1          3     1506        1     0    5         1   \n",
       "4   33   11        2          3        1        0     0    5         1   \n",
       "5   35    4        1          2      231        1     0    5         1   \n",
       "6   28    4        2          2      447        1     1    5         1   \n",
       "7   42    2        0          2        2        1     0    5         1   \n",
       "8   58    5        1          0      121        1     0    5         1   \n",
       "9   43    9        2          1      593        1     0    5         1   \n",
       "\n",
       "   previous  poutcome   y  \n",
       "0         0         3  no  \n",
       "1         0         3  no  \n",
       "2         0         3  no  \n",
       "3         0         3  no  \n",
       "4         0         3  no  \n",
       "5         0         3  no  \n",
       "6         0         3  no  \n",
       "7         0         3  no  \n",
       "8         0         3  no  \n",
       "9         0         3  no  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_add = pd.read_csv('bank-additional.csv', sep =';')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>balance</th>\n",
       "      <th>housing</th>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>33</td>\n",
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       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>33</td>\n",
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       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>447</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <th>8</th>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
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       "      <td>no</td>\n",
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       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>593</td>\n",
       "      <td>1</td>\n",
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       "      <td>no</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  job  marital  education  balance  housing  loan  day  campaign  \\\n",
       "0   58    4        1          2     2143        1     0    5         1   \n",
       "1   44    9        2          1       29        1     0    5         1   \n",
       "2   33    2        1          1        2        1     1    5         1   \n",
       "3   47    1        1          3     1506        1     0    5         1   \n",
       "4   33   11        2          3        1        0     0    5         1   \n",
       "5   35    4        1          2      231        1     0    5         1   \n",
       "6   28    4        2          2      447        1     1    5         1   \n",
       "7   42    2        0          2        2        1     0    5         1   \n",
       "8   58    5        1          0      121        1     0    5         1   \n",
       "9   43    9        2          1      593        1     0    5         1   \n",
       "\n",
       "   previous  poutcome   y  \n",
       "0         0         3  no  \n",
       "1         0         3  no  \n",
       "2         0         3  no  \n",
       "3         0         3  no  \n",
       "4         0         3  no  \n",
       "5         0         3  no  \n",
       "6         0         3  no  \n",
       "7         0         3  no  \n",
       "8         0         3  no  \n",
       "9         0         3  no  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_add_full = pd.read_csv('bank-additional-full.csv', sep =';')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meaning of Input variables:\n",
    "\n",
    "1 - age (numeric)\n",
    "\n",
    "2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "\n",
    "3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "\n",
    "4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "\n",
    "5 - default: has credit in default? (categorical: 'no','yes','unknown')\n",
    "\n",
    "6 - housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "7 - loan: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "8 - contact: contact communication type (categorical: 'cellular','telephone')\n",
    "\n",
    "9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "\n",
    "10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
    "\n",
    "11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no')\n",
    "\n",
    "12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "\n",
    "13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "\n",
    "14 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "\n",
    "15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "\n",
    "16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
    "\n",
    "17 - cons.price.idx: consumer price index - monthly indicator (numeric)\n",
    "\n",
    "18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)\n",
    "19 - euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
    "20 - nr.employed: number of employees - quarterly indicator (numeric)\n",
    "\n",
    "Output variable (desired target):\n",
    "21 - y - has the client subscribed a term deposit? (binary: 'yes','no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4521, 17)\n"
     ]
    }
   ],
   "source": [
    "print(df_bank.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4119, 21)\n"
     ]
    }
   ],
   "source": [
    "print(df_add.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 21)\n"
     ]
    }
   ],
   "source": [
    "print(df_add_full.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing Bank Full File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45211, 17)\n"
     ]
    }
   ],
   "source": [
    "print(df_bank_full.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
       "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bank_full.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
      "age          45211 non-null int64\n",
      "job          45211 non-null object\n",
      "marital      45211 non-null object\n",
      "education    45211 non-null object\n",
      "default      45211 non-null object\n",
      "balance      45211 non-null int64\n",
      "housing      45211 non-null object\n",
      "loan         45211 non-null object\n",
      "contact      45211 non-null object\n",
      "day          45211 non-null int64\n",
      "month        45211 non-null object\n",
      "duration     45211 non-null int64\n",
      "campaign     45211 non-null int64\n",
      "pdays        45211 non-null int64\n",
      "previous     45211 non-null int64\n",
      "poutcome     45211 non-null object\n",
      "y            45211 non-null object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_bank_full.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
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       "  </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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bank_full.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bank_full.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
       "0  unknown    5   may       261         1     -1         0  unknown  no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown  no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown  no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown  no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown  no  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bank_full.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing some important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2213c2e6940>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "df_bank_full.groupby(['duration']).count()[['job','education']].plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_col=['job', 'marital', 'education','default','balance','housing', 'loan',\n",
    "             'contact', 'month','month','previous','poutcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}