Practice+Question+K-Prototype.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from kmodes.kprototypes import KPrototypes\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank = pd.read_csv('bankmarketing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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>5191.0</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>5191.0</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>5191.0</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>5191.0</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>5191.0</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       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      duration      campaign         pdays      previous  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
       "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
       "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
       "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
       "count  41188.000000    41188.000000   41188.000000  41188.000000  41188.000000  \n",
       "mean       0.081886       93.575664     -40.502600      3.621291   5167.035911  \n",
       "std        1.570960        0.578840       4.628198      1.734447     72.251528  \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \n",
       "max        1.400000       94.767000     -26.900000      5.045000   5228.100000  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_cat = bank[['job', 'marital', 'education', 'default', 'housing', 'loan','contact','month','day_of_week','poutcome']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>marital</th>\n",
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       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
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       "    <tr>\n",
       "      <th>1</th>\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",
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       "      <td>may</td>\n",
       "      <td>mon</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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>nonexistent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>nonexistent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>nonexistent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         job  marital    education  default housing loan    contact month  \\\n",
       "0  housemaid  married     basic.4y       no      no   no  telephone   may   \n",
       "1   services  married  high.school  unknown      no   no  telephone   may   \n",
       "2   services  married  high.school       no     yes   no  telephone   may   \n",
       "3     admin.  married     basic.6y       no      no   no  telephone   may   \n",
       "4   services  married  high.school       no      no  yes  telephone   may   \n",
       "\n",
       "  day_of_week     poutcome  \n",
       "0         mon  nonexistent  \n",
       "1         mon  nonexistent  \n",
       "2         mon  nonexistent  \n",
       "3         mon  nonexistent  \n",
       "4         mon  nonexistent  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_num = bank[['age','duration','euribor3m']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>euribor3m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>261</td>\n",
       "      <td>4.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>149</td>\n",
       "      <td>4.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>226</td>\n",
       "      <td>4.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>151</td>\n",
       "      <td>4.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>307</td>\n",
       "      <td>4.857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  duration  euribor3m\n",
       "0   56       261      4.857\n",
       "1   57       149      4.857\n",
       "2   37       226      4.857\n",
       "3   40       151      4.857\n",
       "4   56       307      4.857"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_num.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>housing</th>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   job  marital  education  default  housing  loan  contact  month  \\\n",
       "0    3        1          0        0        0     0        1      6   \n",
       "1    7        1          3        1        0     0        1      6   \n",
       "2    7        1          3        0        2     0        1      6   \n",
       "3    0        1          1        0        0     0        1      6   \n",
       "4    7        1          3        0        0     2        1      6   \n",
       "\n",
       "   day_of_week  poutcome  \n",
       "0            1         1  \n",
       "1            1         1  \n",
       "2            1         1  \n",
       "3            1         1  \n",
       "4            1         1  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "bank_cat = bank_cat.apply(le.fit_transform)\n",
    "bank_cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_cust = pd.concat([bank_cat, bank_num], ignore_index=True,axis = 1)\n",
    "bank_cust.columns = ['job', 'marital', 'education', 'default', 'housing', 'loan','contact','month','day_of_week','poutcome','age','duration','euribor3m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4.857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   job  marital  education  default  housing  loan  contact  month  \\\n",
       "0    3        1          0        0        0     0        1      6   \n",
       "1    7        1          3        1        0     0        1      6   \n",
       "2    7        1          3        0        2     0        1      6   \n",
       "3    0        1          1        0        0     0        1      6   \n",
       "4    7        1          3        0        0     2        1      6   \n",
       "\n",
       "   day_of_week  poutcome  age  duration  euribor3m  \n",
       "0            1         1   56       261      4.857  \n",
       "1            1         1   57       149      4.857  \n",
       "2            1         1   37       226      4.857  \n",
       "3            1         1   40       151      4.857  \n",
       "4            1         1   56       307      4.857  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_cust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 13 columns):\n",
      "job            41188 non-null int64\n",
      "marital        41188 non-null int64\n",
      "education      41188 non-null int64\n",
      "default        41188 non-null int64\n",
      "housing        41188 non-null int64\n",
      "loan           41188 non-null int64\n",
      "contact        41188 non-null int64\n",
      "month          41188 non-null int64\n",
      "day_of_week    41188 non-null int64\n",
      "poutcome       41188 non-null int64\n",
      "age            41188 non-null int64\n",
      "duration       41188 non-null int64\n",
      "euribor3m      41188 non-null float64\n",
      "dtypes: float64(1), int64(12)\n",
      "memory usage: 4.1 MB\n"
     ]
    }
   ],
   "source": [
    "bank_cust.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_cust = bank_cust.apply(lambda x: (x - x.mean()) / np.std(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.421501</td>\n",
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       "      <td>-0.283741</td>\n",
       "      <td>-0.349730</td>\n",
       "      <td>-0.513600</td>\n",
       "      <td>0.942127</td>\n",
       "      <td>-0.452491</td>\n",
       "      <td>1.31827</td>\n",
       "      <td>0.762558</td>\n",
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       "      <td>0.192622</td>\n",
       "      <td>-0.290186</td>\n",
       "      <td>-0.124520</td>\n",
       "      <td>0.71246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.036184</td>\n",
       "      <td>-0.283741</td>\n",
       "      <td>-1.285860</td>\n",
       "      <td>-0.513600</td>\n",
       "      <td>-1.087707</td>\n",
       "      <td>-0.452491</td>\n",
       "      <td>1.31827</td>\n",
       "      <td>0.762558</td>\n",
       "      <td>-0.718834</td>\n",
       "      <td>0.192622</td>\n",
       "      <td>-0.002309</td>\n",
       "      <td>-0.413787</td>\n",
       "      <td>0.71246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.911227</td>\n",
       "      <td>-0.283741</td>\n",
       "      <td>-0.349730</td>\n",
       "      <td>-0.513600</td>\n",
       "      <td>-1.087707</td>\n",
       "      <td>2.311440</td>\n",
       "      <td>1.31827</td>\n",
       "      <td>0.762558</td>\n",
       "      <td>-0.718834</td>\n",
       "      <td>0.192622</td>\n",
       "      <td>1.533034</td>\n",
       "      <td>0.187888</td>\n",
       "      <td>0.71246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        job   marital  education   default   housing      loan  contact  \\\n",
       "0 -0.201579 -0.283741  -1.753925 -0.513600 -1.087707 -0.452491  1.31827   \n",
       "1  0.911227 -0.283741  -0.349730  1.945327 -1.087707 -0.452491  1.31827   \n",
       "2  0.911227 -0.283741  -0.349730 -0.513600  0.942127 -0.452491  1.31827   \n",
       "3 -1.036184 -0.283741  -1.285860 -0.513600 -1.087707 -0.452491  1.31827   \n",
       "4  0.911227 -0.283741  -0.349730 -0.513600 -1.087707  2.311440  1.31827   \n",
       "\n",
       "      month  day_of_week  poutcome       age  duration  euribor3m  \n",
       "0  0.762558    -0.718834  0.192622  1.533034  0.010471    0.71246  \n",
       "1  0.762558    -0.718834  0.192622  1.628993 -0.421501    0.71246  \n",
       "2  0.762558    -0.718834  0.192622 -0.290186 -0.124520    0.71246  \n",
       "3  0.762558    -0.718834  0.192622 -0.002309 -0.413787    0.71246  \n",
       "4  0.762558    -0.718834  0.192622  1.533034  0.187888    0.71246  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_cust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "      <td>4.118800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.320301e-15</td>\n",
       "      <td>-1.254184e-14</td>\n",
       "      <td>1.276469e-14</td>\n",
       "      <td>-3.089842e-15</td>\n",
       "      <td>-1.290978e-15</td>\n",
       "      <td>-1.687192e-15</td>\n",
       "      <td>1.333122e-13</td>\n",
       "      <td>-6.127413e-17</td>\n",
       "      <td>5.006085e-17</td>\n",
       "      <td>-1.428795e-14</td>\n",
       "      <td>-3.653590e-16</td>\n",
       "      <td>-3.170967e-16</td>\n",
       "      <td>3.930748e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "      <td>1.000012e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.036184e+00</td>\n",
       "      <td>-1.926061e+00</td>\n",
       "      <td>-1.753925e+00</td>\n",
       "      <td>-5.135997e-01</td>\n",
       "      <td>-1.087707e+00</td>\n",
       "      <td>-4.524906e-01</td>\n",
       "      <td>-7.585700e-01</td>\n",
       "      <td>-1.823653e+00</td>\n",
       "      <td>-1.434368e+00</td>\n",
       "      <td>-2.563098e+00</td>\n",
       "      <td>-2.209365e+00</td>\n",
       "      <td>-9.961775e-01</td>\n",
       "      <td>-1.722351e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.036184e+00</td>\n",
       "      <td>-2.837415e-01</td>\n",
       "      <td>-8.177951e-01</td>\n",
       "      <td>-5.135997e-01</td>\n",
       "      <td>-1.087707e+00</td>\n",
       "      <td>-4.524906e-01</td>\n",
       "      <td>-7.585700e-01</td>\n",
       "      <td>-5.305474e-01</td>\n",
       "      <td>-7.188345e-01</td>\n",
       "      <td>1.926221e-01</td>\n",
       "      <td>-7.699804e-01</td>\n",
       "      <td>-6.027745e-01</td>\n",
       "      <td>-1.312994e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-4.797808e-01</td>\n",
       "      <td>-2.837415e-01</td>\n",
       "      <td>-3.497303e-01</td>\n",
       "      <td>-5.135997e-01</td>\n",
       "      <td>9.421274e-01</td>\n",
       "      <td>-4.524906e-01</td>\n",
       "      <td>-7.585700e-01</td>\n",
       "      <td>-9.951230e-02</td>\n",
       "      <td>-3.300753e-03</td>\n",
       "      <td>1.926221e-01</td>\n",
       "      <td>-1.942267e-01</td>\n",
       "      <td>-3.019369e-01</td>\n",
       "      <td>7.124599e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.112268e-01</td>\n",
       "      <td>1.358578e+00</td>\n",
       "      <td>1.054464e+00</td>\n",
       "      <td>-5.135997e-01</td>\n",
       "      <td>9.421274e-01</td>\n",
       "      <td>-4.524906e-01</td>\n",
       "      <td>1.318270e+00</td>\n",
       "      <td>7.625579e-01</td>\n",
       "      <td>7.122329e-01</td>\n",
       "      <td>1.926221e-01</td>\n",
       "      <td>6.694038e-01</td>\n",
       "      <td>2.341712e-01</td>\n",
       "      <td>7.724221e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.024033e+00</td>\n",
       "      <td>3.000897e+00</td>\n",
       "      <td>1.522529e+00</td>\n",
       "      <td>4.404253e+00</td>\n",
       "      <td>9.421274e-01</td>\n",
       "      <td>2.311440e+00</td>\n",
       "      <td>1.318270e+00</td>\n",
       "      <td>2.055663e+00</td>\n",
       "      <td>1.427767e+00</td>\n",
       "      <td>2.948342e+00</td>\n",
       "      <td>5.563310e+00</td>\n",
       "      <td>1.797202e+01</td>\n",
       "      <td>8.208531e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                job       marital     education       default       housing  \\\n",
       "count  4.118800e+04  4.118800e+04  4.118800e+04  4.118800e+04  4.118800e+04   \n",
       "mean  -3.320301e-15 -1.254184e-14  1.276469e-14 -3.089842e-15 -1.290978e-15   \n",
       "std    1.000012e+00  1.000012e+00  1.000012e+00  1.000012e+00  1.000012e+00   \n",
       "min   -1.036184e+00 -1.926061e+00 -1.753925e+00 -5.135997e-01 -1.087707e+00   \n",
       "25%   -1.036184e+00 -2.837415e-01 -8.177951e-01 -5.135997e-01 -1.087707e+00   \n",
       "50%   -4.797808e-01 -2.837415e-01 -3.497303e-01 -5.135997e-01  9.421274e-01   \n",
       "75%    9.112268e-01  1.358578e+00  1.054464e+00 -5.135997e-01  9.421274e-01   \n",
       "max    2.024033e+00  3.000897e+00  1.522529e+00  4.404253e+00  9.421274e-01   \n",
       "\n",
       "               loan       contact         month   day_of_week      poutcome  \\\n",
       "count  4.118800e+04  4.118800e+04  4.118800e+04  4.118800e+04  4.118800e+04   \n",
       "mean  -1.687192e-15  1.333122e-13 -6.127413e-17  5.006085e-17 -1.428795e-14   \n",
       "std    1.000012e+00  1.000012e+00  1.000012e+00  1.000012e+00  1.000012e+00   \n",
       "min   -4.524906e-01 -7.585700e-01 -1.823653e+00 -1.434368e+00 -2.563098e+00   \n",
       "25%   -4.524906e-01 -7.585700e-01 -5.305474e-01 -7.188345e-01  1.926221e-01   \n",
       "50%   -4.524906e-01 -7.585700e-01 -9.951230e-02 -3.300753e-03  1.926221e-01   \n",
       "75%   -4.524906e-01  1.318270e+00  7.625579e-01  7.122329e-01  1.926221e-01   \n",
       "max    2.311440e+00  1.318270e+00  2.055663e+00  1.427767e+00  2.948342e+00   \n",
       "\n",
       "                age      duration     euribor3m  \n",
       "count  4.118800e+04  4.118800e+04  4.118800e+04  \n",
       "mean  -3.653590e-16 -3.170967e-16  3.930748e-14  \n",
       "std    1.000012e+00  1.000012e+00  1.000012e+00  \n",
       "min   -2.209365e+00 -9.961775e-01 -1.722351e+00  \n",
       "25%   -7.699804e-01 -6.027745e-01 -1.312994e+00  \n",
       "50%   -1.942267e-01 -3.019369e-01  7.124599e-01  \n",
       "75%    6.694038e-01  2.341712e-01  7.724221e-01  \n",
       "max    5.563310e+00  1.797202e+01  8.208531e-01  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_cust.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the dataset into matrix\n",
    "bank_matrix = bank_cust.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.20157925, -0.2837415 , -1.75392459, ...,  1.53303429,\n",
       "         0.01047142,  0.71245988],\n",
       "       [ 0.91122681, -0.2837415 , -0.34973033, ...,  1.62899323,\n",
       "        -0.42150051,  0.71245988],\n",
       "       [ 0.91122681, -0.2837415 , -0.34973033, ..., -0.29018564,\n",
       "        -0.12451981,  0.71245988],\n",
       "       ...,\n",
       "       [ 0.35482378, -0.2837415 ,  1.05446393, ...,  1.53303429,\n",
       "        -0.26722482, -1.49518647],\n",
       "       [ 1.46762984, -0.2837415 ,  0.58639918, ...,  0.38152696,\n",
       "         0.70856893, -1.49518647],\n",
       "       [ 0.35482378, -0.2837415 ,  0.58639918, ...,  3.26029527,\n",
       "        -0.07438021, -1.49518647]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "kproto = KPrototypes(n_clusters=5, init='Cao')\n",
    "clusters = kproto.fit_predict(bank_matrix, categorical=[0,1,2,3,4,5,6,7,8,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129099.42128920963"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kproto.cost_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22c9eef1080>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22c9cde5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Choosing optimal K\n",
    "cost = []\n",
    "for num_clusters in list(range(1,8)):\n",
    "    kproto = KPrototypes(n_clusters=num_clusters, init='Cao')\n",
    "    kproto.fit_predict(bank_matrix, categorical=[0,1,2,3,4,5,6,7,8,9])\n",
    "    cost.append(kproto.cost_)\n",
    "    \n",
    "plt.plot(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "kproto = KPrototypes(n_clusters=4, init='Cao')\n",
    "clusters = kproto.fit_predict(bank_matrix, categorical=[0,1,2,3,4,5,6,7,8,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank['cluster_id'] = clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>cluster_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_id\n",
       "2       14846\n",
       "3       12806\n",
       "1        9557\n",
       "0        3979"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the clusters created\n",
    "bankcluster = pd.DataFrame(bank['cluster_id'].value_counts())\n",
    "bankcluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c9dba0278>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22c9f2b4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=bankcluster.index, y=bankcluster['cluster_id'])"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}