KMean Clustering .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "592b45c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c0a1d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</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  subscribed  \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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filePath = 'data/bank-full.csv'\n",
    "data = pd.read_csv(filePath)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e951a6",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f62e2e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'subscribed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd9f103d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4bee6cf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  subscribed      41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2600e114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>40.024060</td>\n",
       "      <td>10.421250</td>\n",
       "      <td>17.000</td>\n",
       "      <td>32.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>47.000</td>\n",
       "      <td>98.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>0.000</td>\n",
       "      <td>102.000</td>\n",
       "      <td>180.000</td>\n",
       "      <td>319.000</td>\n",
       "      <td>4918.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>56.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "      <td>999.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>-3.400</td>\n",
       "      <td>-1.800</td>\n",
       "      <td>1.100</td>\n",
       "      <td>1.400</td>\n",
       "      <td>1.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>92.201</td>\n",
       "      <td>93.075</td>\n",
       "      <td>93.749</td>\n",
       "      <td>93.994</td>\n",
       "      <td>94.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>-50.800</td>\n",
       "      <td>-42.700</td>\n",
       "      <td>-41.800</td>\n",
       "      <td>-36.400</td>\n",
       "      <td>-26.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>0.634</td>\n",
       "      <td>1.344</td>\n",
       "      <td>4.857</td>\n",
       "      <td>4.961</td>\n",
       "      <td>5.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>41188.0</td>\n",
       "      <td>5167.035911</td>\n",
       "      <td>72.251528</td>\n",
       "      <td>4963.600</td>\n",
       "      <td>5099.100</td>\n",
       "      <td>5191.000</td>\n",
       "      <td>5228.100</td>\n",
       "      <td>5228.100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count         mean         std       min       25%  \\\n",
       "age             41188.0    40.024060   10.421250    17.000    32.000   \n",
       "duration        41188.0   258.285010  259.279249     0.000   102.000   \n",
       "campaign        41188.0     2.567593    2.770014     1.000     1.000   \n",
       "pdays           41188.0   962.475454  186.910907     0.000   999.000   \n",
       "previous        41188.0     0.172963    0.494901     0.000     0.000   \n",
       "emp.var.rate    41188.0     0.081886    1.570960    -3.400    -1.800   \n",
       "cons.price.idx  41188.0    93.575664    0.578840    92.201    93.075   \n",
       "cons.conf.idx   41188.0   -40.502600    4.628198   -50.800   -42.700   \n",
       "euribor3m       41188.0     3.621291    1.734447     0.634     1.344   \n",
       "nr.employed     41188.0  5167.035911   72.251528  4963.600  5099.100   \n",
       "\n",
       "                     50%       75%       max  \n",
       "age               38.000    47.000    98.000  \n",
       "duration         180.000   319.000  4918.000  \n",
       "campaign           2.000     3.000    56.000  \n",
       "pdays            999.000   999.000   999.000  \n",
       "previous           0.000     0.000     7.000  \n",
       "emp.var.rate       1.100     1.400     1.400  \n",
       "cons.price.idx    93.749    93.994    94.767  \n",
       "cons.conf.idx    -41.800   -36.400   -26.900  \n",
       "euribor3m          4.857     4.961     5.045  \n",
       "nr.employed     5191.000  5228.100  5228.100  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f20329b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "job               0\n",
       "marital           0\n",
       "education         0\n",
       "default           0\n",
       "housing           0\n",
       "loan              0\n",
       "contact           0\n",
       "month             0\n",
       "day_of_week       0\n",
       "duration          0\n",
       "campaign          0\n",
       "pdays             0\n",
       "previous          0\n",
       "poutcome          0\n",
       "emp.var.rate      0\n",
       "cons.price.idx    0\n",
       "cons.conf.idx     0\n",
       "euribor3m         0\n",
       "nr.employed       0\n",
       "subscribed        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57e2d97",
   "metadata": {},
   "source": [
    "## Continuous Feature Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27d89fb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.histplot(data=data, x='age', bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36ea4f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.histplot(data=data, x='age', hue='loan', bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56beb96e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pdays', ylabel='Count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pday = data[data['pdays']!=999]\n",
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.histplot(data=pday, x='pdays')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "779d9a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.histplot(data=data, x='duration', hue='contact')\n",
    "plt.xlim(0,2000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "32ecf4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.countplot(data=data, x='previous', hue='contact');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b39e5076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.countplot(data=data, x='contact');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "07f51a3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cellular     26144\n",
       "telephone    15044\n",
       "Name: contact, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['contact'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1ab7d0d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35563\n",
       "1     4561\n",
       "2      754\n",
       "3      216\n",
       "4       70\n",
       "5       18\n",
       "6        5\n",
       "7        1\n",
       "Name: previous, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['previous'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1dfd0c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41188"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['previous'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3cfa11",
   "metadata": {},
   "source": [
    "## Categorical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2a1fb13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</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  subscribed  \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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "19866fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UPu0g4IT29onAAUk2SLI9zcWWZ7TLVK5Lskfb/eR5Y6+RJEmSlqQ+ZsBX563AcUleCFwKPAOgqs5PchxwAXATcEhV3dy+5qXAh4GNgM+3H5IkSdKS1WsAr6qvAl9tb/8U2Gs1zzsUOHSe8ZXALtOrUJIkSZosd8KUJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjq0ft8FSFq3Pf8z+/RdwqIc/dQv9F2CJGnGOQMuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1qPMAnmTbJP+e5MIk5yd5RTu+eZKTk3y//bzZ2Gten+TiJBcl2Xts/MFJzm0fOzxJuj4eSZIkaTH6mAG/CfiLqrovsAdwSJKdgdcBp1TVjsAp7X3axw4A7gfsA7wvybL23zoCOBjYsf3Yp8sDkSRJkhar8wBeVVdW1Vnt7euAC4GtgX2BY9qnHQPs197eF/h4Vd1QVZcAFwO7J9kK2KSqTquqAj4y9hpJkiRpSep1DXiS7YBdgdOBu1fVldCEdGDL9mlbA5eNvezydmzr9vbc8fn+n4OTrEyy8pprrpnoMUiSJEmL0VsAT3Jn4FPAK6vqF2t66jxjtYbxWw9WHVlVK6pqxfLlyxdfrCRJkjQhvQTwJHegCd8fq6pPt8NXtctKaD9f3Y5fDmw79vJtgCva8W3mGZckSZKWrD66oAQ4Criwqv5p7KETgYPa2wcBJ4yNH5BkgyTb01xseUa7TOW6JHu0/+bzxl4jSZIkLUnr9/B/PgJ4LnBuknPasTcAbwWOS/JC4FLgGQBVdX6S44ALaDqoHFJVN7eveynwYWAj4PPthyRJkrRkdR7Aq+qbzL9+G2Cv1bzmUODQecZXArtMrjpJkiRputwJU5IkSeqQAVySJEnqUB9rwCVpnfHEz/xj3yUsyr899dV9lyBJg+cMuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktQhA7gkSZLUIQO4JEmS1CEDuCRJktQh+4BLkm6TJx3/sb5LWLST9n9O3yVIkgFckqT57Hf8KX2XsGj/uv9efZcgaQFcgiJJkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yK3oJUlaB738M5f1XcKiHf7UbfsuQZoIA7gkSRqcz3/iJ32XsChPeNYWfZegDrkERZIkSeqQAVySJEnqkAFckiRJ6pBrwCVJkmbMD9/5475LWJTtXvl7fZewpDgDLkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHXIAC5JkiR1yAAuSZIkdcgALkmSJHVo/b4LkCRJkkauetdpfZewaHd/xcMW9XxnwCVJkqQOGcAlSZKkDhnAJUmSpA4ZwCVJkqQOGcAlSZKkDs18AE+yT5KLklyc5HV91yNJkiStyUwH8CTLgPcCTwB2Bp6dZOd+q5IkSZJWb6YDOLA7cHFV/aCqbgQ+Duzbc02SJEnSas16AN8auGzs/uXtmCRJkrQkpar6ruE2S/IMYO+q+l/t/ecCu1fVy+Y872Dg4PbuTsBFHZa5BfCTDv+/rnl8s2vIxwYe36zz+GbXkI8NPL5Z1/Xx3auqls8dnPWt6C8Hth27vw1wxdwnVdWRwJFdFTUuycqqWtHH/90Fj292DfnYwOObdR7f7BrysYHHN+uWyvHN+hKUbwM7Jtk+yR2BA4ATe65JkiRJWq2ZngGvqpuS/DnwRWAZ8KGqOr/nsiRJkqTVmukADlBVnwM+13cda9DL0pcOeXyza8jHBh7frPP4ZteQjw08vlm3JI5vpi/ClCRJkmbNrK8BlyRJkmaKAVySJEnqkAFckiRJ6tDMX4SpbiVZBhxTVQf2Xcu0tMd4d8Z+Pqrq0v4qmpwkjwDOqarrkxwI7Aa8q6p+1HNpE5HkFVX1rrWNzZoku63p8ao6q6tapqH9mXtrVb2671q0OOvA9+bma3q8qv67q1qmKckGVXXD2sY0OV6E2ZEkB7cbAs28JF8EnlxVN/Zdy6QleRnwRuAq4LftcFXVA/qranKSfBd4IPAA4KPAUcDTqurRvRY2IUnOqqrd5oydXVW79lXTJCT59/bmhsAK4DtAaL6Op1fVI/uqbVKSfAXYqwb2RynJ09b0eFV9uqtapmHo35tJLgGK5pjuCfysvb0pcGlVbd9fdZOzmt+dtxqbVUk2AJ4ObMeqk2tv7qsmZ8C7k74LmKAfAt9KciJw/Wiwqv6pt4om5xXATlX1074LmZKbqqqS7Esz831UkoP6Lur2SvJs4E+A7dvvy5G7ADP/tayqxwIk+ThwcFWd297fBfjffdY2QWcDJyT5JKv+XpnpgAo8uf28JfBw4Cvt/ccCXwVm+viG/r05CthJ3g+c2LY+JskTgMf1WdskJPk9YGtgoyS7cktW2QTYuLfCJu8E4FrgTGBJzOobwDtSVR/ou4YJuqL9WI8m4AzJZTQ/pEN1XZLXA88F/rA99X+HnmuahFOBK4EtgMPGxq8DvttLRdNxn1HAAaiq85I8qMd6JmlzmjdLe46NFbMfUJ8PkOQkYOequrK9vxXw3j5rm7Ahf28CPKSqXjK6U1WfT/KWPguakL2BPwW2AcYn0a4D3tBHQVOyTVXt03cR41yCMgVL8VSHFibJUcBOwL8x9i55ILP7o9mOPwG+XVXfSHJP4DFV9ZGeS9MCJDmWZnb4n2nC6YHAnavq2b0WprVKcl5V7TJ2fz3gu+Njs2zo35vt0stvsOrxPaqq9u61sAlJ8vSq+lTfdUxLkiOBd4+/SeybAXwKknyBW0513Dwar6rDVvuiGZJkOfAa4H406/4AqKo9V/uiGZHkjfONV9XfdF3LtCS5F7BjVX05ycbAsqq6ru+6JqFdb/s2mtP9aT+qqjbptbAJSbIh8FLgUe3Q14EjqurX/VU1GUmOpgk2q6iqF/RQzsQleQ+wI3AszXEeAFxcVS/rtbAJGfL3JvzuYsw30hxf0Rzfm4d0ESYDnjhMcgHw+8AlNJNro78NvV3fZQCfgrkzHUOT5EvAJ2jW970EOAi4pqpe22thWqskLwIOBjavqh2S7Ai8v6r26rm0iUhyMc0Fwhf2Xcu0JNkIuGdVXdR3LZOU5OljdzcEngpcUVUv76mkiUvyVMYCalV9ps96tHhJ7lxVv+y7jklbByYO7zXfeJ8dwFwDPh2nJrn/UjrVMWF3ay/ee0VVfQ34WpKv9V3UJAx5dr91CLA7cDpAVX0/yZb9ljRRVw08fD8F+EfgjjQXnD6IZhbuKb0WNgFzT3+3Sxq+3FM503IWcN3o7FOSu8z62ackx1XVM5Ocy/xnMIbSQerhwAeBOwP3TPJA4MVV9Wf9VjYxS26N9IS9kGYJ0alVdf3antwFA/h0PBL407Z90ZI41TFhv2k/X5nkiTQXZG7TYz2T9DGa2f0nMTa732tFk3VDVd2YNBe6J1mfef5ozrCVST4B/CurruGf6Qv5xryR5g3UVwGq6pwk2/VZ0BTtSNP2bRDGzz4BO9B0nng/MOtnn17Rfn5Sr1VM3ztoLlg8EaCqvpPkUWt+yUwZ+sThD4FnA4cnuY4mjH+9qk7oqyAD+HQ8oe8Cpuxvk9wV+Avg3TTtil7Vb0kTM9jZ/dbXkryBpuXU44E/Az7bc02TtAnwK+CPxsZmvpPGmJuq6trRG6ghaf8ojvotF/BjYEjL2gZ59mnU1WUom3mtSVVdNudn7+bVPXcGDXrisKo+BHyobUTwTJoltAfTYyc3A/gEJdmkqn5B075nsKrqpPbmtTS9bIdkyLP7AK+jORV3LvBi4HM0p1UHYdTybcDOS/InwLJ2/f7LaVowzryqGlpL07kGffYpyR40EzL3pVkitQy4figXQAOXtctQKskdaX72hrTcbdATh0k+COxMs8neN4D9aZaE9cYAPln/QnMa7kxumckZKeDefRQ1KUnezRr+YAzkYqkhz+4DbAR8qKr+D/xuC/CNaGaNZ16SPwCOAO5eVbskeQDwlKr6255Lm5SXAX9JM0P1L8AXgUEcW5pk+hxg+6p6S9si8/eq6oyeS5uUoZ99eg9NZ5dP0uyI+TyarhND8RLgXTRLhy4HvkRzVmMQqupHSR5J0yHr6PZ6qDv3XdcE3Y3mTeHPgf8GflJVN/VZkF1QtGBr2zGxqo7pqhbdNkn+A3jc6Cr+JHcGvlRVD++3sslolwu9GvjAaPv5oXQlat8sfbGqZn73vfkkOQL4LbBnVd03yWY035sP6bm0iWj7fr+QZnlUaN48fbAG8kc4ycqqWpHku6NlC0lOHdDvls2H0nJwPm0L3hU0O0H/QZJ7AJ+sqkf0XNpEJbkvzVr+V9G04O3tDLcz4FPSzrxtx6r9NGd6HeqQA3aS11TVP6xuln8gs/sAG4630KqqX7a9wIdi46o6Y846zV5nOSalqm5O8qskd62qIe7W+tCq2i3J2QBV9bP2VP9Q/DFw1Ojs0wD9qv16nZPkH2h2pr1TzzVN0ulJzgE+BHxhKG+cxjwV2JV2WUZVXZFkMMvCkjwJ+EOaNqCbAV+hWYrSGwP4FCT5EPAA4HyaGR0YwIVgST7LmpegzHIrtNFavpW9VjF91yfZrarOAkjyYOB/eq5pkn6SZAfa79Mk+9MEgaH4NXBukpNpdh0EBvMG8TftLP/oa7ecW35/DsEBwLuSfAo4eoDtMp9Lc4r/z2lmF7el2dhlKP4AeBzwAuA9bbelD1fVf/Zb1sTcWFWVZPTzN6Q3T9Cscf868K6quqLvYsAlKFOR5IKq2rnvOiYtyaPX9HjbNURLWJKHAB+nubgUYCvgWVV1Zn9VTU6SewNHAg8Hfkaz69mBVfXDPuualNUtAxvC2akkzwGeBewGHENzkdRfVdUney1sgpJsQtMK7fk0bzSOBo6d9V7g65okj6XZkv5OwHeA11XVaf1Wdfsk+d80rT8fD/w9zRuNf6mqd/da2AQluTswWtJ2RlVd3Ws9BvDJS3IUcFhVXdB3LdPSnmr8g/buRVX1mzU9f1a0M4vPqKqft/c3Az5eVXv3WtgEJbkDsBPNOtTvDeVrN66dvVnPYDNbktyHpi92gFMGOEtMki2AA4FX0px5+33g8FkPOu0p/rcA96I5uz5qYzeILihJ7kbzdXsuTSeNo2h6gj+IZq309v1VNxntxcG/u0ahqk7uuaSJSfIM4O00eyiEZjnKq6vq+N5qMoBPXtuc/7M0fWwH108zyWNoZqh+SHNs2wIHVdXX+6tqMpKcU1UPmjN29uiCvlmVZM+q+kqSp833+KxfnzCSZFOa7gvbser1FzO9RGMd2m1wGXB3Vv3aXdpfRZOT5Mk0s4o7AB8Fjqmqq9trMC6sqnm3yp4VSS4GngacO8D10ST5T5qv29FVdfmcx15bVW/rpzItRJLvAI8fzXq3S9y+XFUP7Ksm14BPx4do3iWfy7DWMI4cBvxRVV0Ev2v9dizw4F6rmoybk9xz9Ec/yb0YRq/eR9NcdPLkeR6b+esTxnwO+A+G97M3+N0Gk7yMZqfPq2g2OBltyDOINxfAM4B3zJ2oqKpfJXlBTzVN0mXAeUMM362d2jXSd0ly5zkXs89s+B7bAGteQzmDQXNGdHzJyU+B9foqBpwBn4okX6mqPfuuY1rG20ytaWwWJdmHZg3xaD37o4CDq+qL/VU1OUmWVdWQdm9bRZKzqmq3vuuYliRvq6rXrm1sFrUzqA+tqp/2Xcu0LLU1qJPUXl/yFprfnTeMxqvqn3oraoKS7EIzA745zZvDa2jO/J7Xa2ETkuTNNGftP0pzfM8B7lJV/9BrYROS5B9p3swf2w49C/hun787DeBTkOR9wKY0y1DGfxENYpax7fJSND+o0Pygrl8D2YWwXaO5B80vodOq6ic9lzQxSS4FvgB8AvjK0GarkrwK+CVwEqv+7A2if+98bzAG9Ob332lOEQ+ibeRcS3EN6iQl+RLNz94qZ5+q6m96K2qCkpwK/GVV/Xt7/zHA3w2oz/npVfXQtY3NsiRPBx5B8/P39ar6TK/1DOzv75KQ5Oh5hquqhnCakSQb0OwA9kjab2TgfVV1wxpfuIQluU9VfS/JvLOno7Z9sy7JRjTLUA6g6TZxEs1Fpt/stbAJSXIIcCjNbmejX25VVbO+C+1LaXZOvDfwf8ceugtwalU9p5fCJqi9eH0n4N8Y5gzqkluDOkmjjXj6rmNaknxn7tdqvrFZ1b7BeC9Nl6yi6dZzyFDeYCxFBnAtWtth4tejpQzthVMbVNXMbmee5MiqOridhZurhrikqO3w8i7gOVW1rO96JiHJ/6VZxjCYsxYASe5Ks3nE3wOvG3vougHN7r9xvvEBzaCeW1X3H7u/HvCd8bFZluStNGfVvtR3LdOQ5DM0m9SMzvweCKyoqv16K2qCkmxH8/fgETQB/FvAKwfUwvVpwNuALWkmDnvv0mMAn6CsZhfFkVnvxDCSgW9nPnRtP/dn0WxM8G3gE1X1qX6rmowkJwIHzPKbwbVJ8khgx6o6ul0udZequqTvuiYlyZ2q6vq1P3O2LMU1qJPUXsx3J+DG9qP3gDNJ7YTF37Dqmd83VdXPei1MC9JeY/LkpdTa1C4okzXaRfERwM4062yhufp9EBudtAa9nXmSh3PrNnYf6a2gCUpyCXAOcBzN+tOhBZ2babbC/ndWXcYwlDe/bwRW0CzVOBq4I82GII/os65JSPIwmt7KdwbumeSBwIur6s/6rWwyqurVc9agHtn3GtRJqqrBbFs+nzZoD+L3yHzapbPztTgdxNJZ4KqlFL7BAD5R1e5Gl+RPgcdWu8FJkvcDQzotN9jtzJN8lKZP7zk0YQ6aX0ozH8DbpUJHV9Wb+65liv61/RiqpwK70pwKp6quSDKU4PNOYG+azU2oqu+0eyoMRnumaRBnm+ZKMuqcsX1VvSXJtsBWVXVGz6XdLkk+y5rPbD+lw3Km6aSx2xvS/K5ZElu2T8jKJJ+g+fuwJJpjGMCn4x40F0eN1mbeuR0bilcCn0yyynbm/ZUzUSuAnYfWHQSgqm5Os4XyYAN4VR3TXmh6z1Gf+oG5se1FXPC76zEGo6oua3Lc78x8y8w19Fke1BIN4H003U/2pGlH+Euai/oesqYXzYC3911AF+YuQ0xyLPDlnsqZhk2AX9Hs9DnS6x4YBvDpeCtw9tgFfY8G3tRfOZNVVd9Os2X0ELczPw/4PeDKvguZklOTvIdmedTvlp8MqMvLk2n+YN4R2D7Jg4A3D2GWqp1hPCnJB4BNk7yIZmfF/9NvZRNzWbv8q5LckeZ0/5I6ZXxbDH1pxpiHVtVuSc6GZslG+3WcaVU12hOC9njuQxPcLqqqG3srbPp2BO7ZdxET9BdzL1hPsn1fxYABfCrai6O+SLMb5oU0fZeHdCqHNnCfN+oe0nc9E7QFcEGSM1j1NNXMB7jW6ELZ8Vnwopm1GoI3AbvT9Fqmqs7p+5fspLQz3/sBrwV+QfMG+K+r6uReC5ucl9B0YdgauJxm2d4hvVY0YW2b00fS/Mx9s6rO7rmkSfpNu8xtdHZmOQPajTbJE4H307QBDc0b/BdX1ef7rWwy5jlT82Oa3zVD8dkkT6iqXwAkuS/wSWCXvgoygE9Bkv9Fs3X0NjRrifcATmM4IWfc0Pq+vqnvAqapqh7bdw1TdlNVXTtnGcOQlhOdBvy8ql7ddyGT1Aa3dw6hn/nqJPlrmgvyR6e8P5zkk1X1tz2WNUmHA58BtkxyKLA/8Ff9ljRRh9Fc23UxQJIdaHrWDyKArwNnav6OJoQ/kWby4iM01yz0xgA+Ha+gWff2H1X12Ha5xiB62c5jMFspw6qnG4cozVbYfwfco6qekGRn4GFVdVTPpU3KeUn+BFiWZEeaZQyn9lzTJD0WeHGSH7HqEqKZ3gmzvT5heZI7Dvi0/rOBXavq1/C7vtlnAYMI4FX1sSRnAnvRzBDvt9S6TtxOV4/Cd+sHDOjvX5JTqmqvtY3Nqqr6tyR3oDmzdhea78/v91mTAXw6fl1Vv05Ckg3aHRZ36ruoSWv79e7Tdx2TtBSb9U/Yh2na1/1le/8/adaDDyWAv4zm2G6g6bf8RZoLwobiCX0XMEU/BL7V9nIff3MxiJ0waY5vQ+DX7f0NWHVX05nWzghfUlXvTbNN++OTXFlVP++1sMk5P8nnaFq4Fs3ZjG+3fzN67aZxeyTZENgY2KLtdT46fbgJA2geMc/+LJvQvHl6WZJeW9QawKfj8iSb0rS7OTnJzxjQGvD2QqkPMsx+vf/AEmvWP2FbVNVxSV4PUFU3JZn5ThMj7QY8f8ktbzAGpap+1HcNU3RF+7EezQzV0NxAE+JOpgkEjwe+meRwGESv+k8BK5L8Ps3fh88C/wL8ca9VTc6GwFU0TRUArgE2B55Mz900bqcX03Q2uwfNfiWhOZ7rgPf0V9bErJxzf8nsyeJOmFPW7jp4V+ALQzm1muR0mvV9J1bVru3YeVXV28UMk5LkW1U185uarE6SrwJPB05uOxbsAbytqh695lfOhtX07L2W5pfwB0an/7V0JdmE5qzTdX3XMklJDlrT46N9JGZVkrPa3ymvAf6nqt6d5OzR3wgtbe01Cu+sql8k+f+B3YC3DKVD1lLkDPiUDXVN8dD69Y5OI7IEm/VP2P9Hs9HJDkm+BSyneTM1FD+gOabx7b6vAv6Apl3fc3uqS2uRZAXN8qi7tPevBV5QVUtmxur2mPWAvQC/SfJs4Hk0s8IAd+ixnolquym9jFvvkjyUDln7V9WbkzyS5uzMYcARwEP7LWsykjyCpsnCvWi+fqPlpffuqyYDuG6LIfbrffLY7SXVrH/CdqBZR7wtzUz4QxnW74Fdq2p898TPJvl6VT0qyfm9VaWF+BDwZ1X1DYA2CBwNzPQFpiNJnkRzPcLcADCU60ueT9NK8tCquqQNrP/cc02T9K8018p8lgG1VxwzmkR7IvD+qjohyZt6rGfSjgJeRbMEZUlMGLoERYuWZAuafr2Po/kj8iXgFVX1014L01ol+W5VPaANN39HM8vxhqoayizHhcDeVXVpe/+eNMu/dvZ0+NI23/KvIS0JS3Ix8DTg3CHutDt0SU4fyu/J+SQ5Cfgvmr/rDwb+Bzijqh7Ya2ETshS/fgZwaUySY2jeTPy8vb8ZcFhVvaDXwiZkFEKT/D1NEPiXIQXTJH/MnM0ygD+j2ZjnRVX1zt6K0xoleQdNN4Zjac46PQv4Gc3FfTO/W2uanZH3qqohzp6S5BLm6bnf5yn+SWrbm+5IM+E0vjxxpr8vR5JsDOxD83fh+0m2Au5fVV/qubSJaNt+LqM5m70kvn4GcC1akqOZ/xftzIfU+cLowALqoGc5AJJsQLNddIDveeHlbGgD6upUVc30RmZJHkKzBOVrrBoABtFmMcndxu5uSNOmb/Oq+uueSpqodtLiuTRv7kdvomb++3JdMfb7ZZRdRkvAevv6DWntp7pz0tjtDYGnMpw2i+sl2ayqfgaQZHOG9XPyTJpZjrdX1c/bWY5B7apIM0u1E8335gPaXq8f6bkmrcU6sEvrocAvab4v79hzLRM3zxLEdyb5JjCIAE7zd+7eQ+lmtg766jxjvc5ADylYqCNV9anx+0mOBb7cUzmTdhhwapLjaX44n0nzh3MQ2j7Znx67fyVwZX8VTVaSNwKPAXYGPkdzwek3abYd1hK2DuzSunlV/dHanzabkuw2dnc9YAXD6uf+HWBTBrT75Trml2O3NwSeRM/NI1yCotut3eXz36rq9/uuZRLaP/x70pyiOqWqLui5JC1QknOBBwJnV9UD21D3wap68lpeqp4l+TztLq3t1259mq/j/XsubSLaNahfGcqa2rnmLCG6iWbnz7dX1UX9VDRZ7R4KDwC+zapLiIbShnCd0i5VPLGq9u6rBmfAtWhJrqOZHR7tmPVj4LW9FjVZmwPXV9XRSZYn2b6qLum7KC3I/1TVb5Pc1G7ocjUwiIvA1gGD3qUVOAR4TZIbgRsZWBvCdWAJ0Rv7LkATtTE9/20wgGvRqmpIpxVX0S5hWEGzhvhomo0k/hkYRCu0dcDKJJvSbLpzJs1pxzN6rUgLdX17IV8BtLu0XttvSZMz5N+bAEnuShNSR334vwa8uaoG8TWsqq8luRewY1V9ue0asqzvurQw7dnR0ZKPZTQbtr25v4pcgqJFmLPG71aG0I4pyTnArsBZo84no97ZvRamRUuyHbBJVX2371q0du3vl3cDuwDn0e7SOpSvX5qtg58DbF9Vb0myLbBVVQ3iDWKST9F83UY7fj4XeGBVPW31r5odSV4EHEyzln+HJDvSbFizV8+laQHaN08jNwFXVdVNfdUDzoBrcQ4buz3+zm20FGUI7ZhurKpKMpqFu1PfBWlxkmzNLbsNkuRRVfX1fqvSmiRZBjy6/diJ5nfKRVX1m14Lm6z30bSv25OmHeEvgfcCD+mzqAnaoaqePnb/b9oJjaE4BNgdOB2g7ZW9Zb8laaGq6kd91zCXAVwLNlrjl2Qjms1NHkkTvL8BHNFjaZN0XJIPAJu2Mx4voFnOoBmQ5G00G7hcwC3bDRdgAF/CqurmJPtW1TuA8/uuZ0oeWlW7JTkboKp+lmRI7Qj/J8kjq+qbAEkeQbPPwFDcUFU3NicyoL1I2CUEus0M4LotjgF+ARze3n82TZu3Z/ZW0eQsB46nOb6daHrYPq7XirQY+wE7VdUNa3uilpxvJXkP8Ang+tHgEJa2tX7TzvSPzq4t55YNXYbgpcAx7VpwaHYxPajHeibta0neAGyU5PE0k1Cf7bkmzTDXgGvRknxn7s6J843NoiRnVdVuc8ZcAz4j2lZ2z6iqX671yVpSVrMT5mB2GkzyHJqzM7vRTGLsD/xVVX2y18ImpG3rtj+wA02/7Gtpvn69Xug2KUnWA14I/BHNEqkv0rQ4NUTpNnEGXLfF2Un2qKr/AEjyUOBbPdd0uyR5Kc2Mxr2TjF/0dRdm/NjWMb8CzklyCqv26n15fyVpgV5YVT8YH0gymBaSVfWxJGcCe9EEuP2qqteNQCbsBODnwFnAf/VbyuRV1W9pliO6JFET4Qy4Fmysjc8daJZnXNrevxdwQVXt0mN5t0t72nQz4O+B1409dF1V/Xc/VWmxksx7yruqjplvXEvHas4+nVlVD+6rpmlJcnBVHdl3HZOU5LxZ/huwNu2a9jdxywXeoz7ug3mTqG45A67FeFLfBUxL26v2Wpr17JpRBu3Zk+Q+wP2AuyYZb1m3Cc2W0UP0EmBQARw4Ncn9q+rcvguZkqOAV9HsLzCkDaLUEwO4FmwptvGRxrW9ef8e2Jmx8OYs1ZK2E82b+02BJ4+NXwe8qI+CJinJBvNcFJxeipmCsTOj6wPPT/IDmuVfoxnioVw/c21Vfb7vIjQcLkGRNBhJvkmzG987aMLc82l+z7mN9BKX5GFVdVrfdUzaaGlNko9W1XPbsW2q6vK+a5uEORuc3MpQJm6SvJVmB8VPs+r1JUPp0qOOGcAlDcZozXCSc6vq/u3YN6rqD/uuTWvWtuV7EbAdY2dnq+oFfdU0CUnOA/6RpqXpq+c+XlWf7rwoLdpYl55RaBrN8A+iS4+65xIUSUPy67Zd2PeT/DlNNwZ3q5sNJ9Bs6vVlhrXG9iU0W9BvyqpLbKAJcwbw2fDVecacwdRtZgCXNCSvBDYGXk6z3fdjgef1WZAWbOOqem3fRUxauzPkN5OsrKqj+q5Ht9n43gIb0ly3MKQ2kuqYS1AkDUaSFcBf0rQKu0M7PKQLwQYryd8Cp1bV5/quZZLmdHa5FZegzKZ246ETq2rvvmvRbDKASxqMJBfRrLM9l7FtvodyIdiQJbmO5uzFjcBvuGWN7Sa9FnY7JTl6DQ/XrK9xX1cl2Qw4o6p27LsWzSaXoEgakmuq6sS+i9BtcleatdLbV9Wbk9wT2Krnmm63qnp+3zXo9htrtwhNN5TlwJv7q0izzhlwSYORZC+azZTmbkXvaf4lLskRNGct9qyq+7YzjF+qqof0XNpEJLk78HfAParqCUl2Bh7muvDZMKfd4k3AVVV1U1/1aPY5Ay5pSJ4P3Idm/fdoCYqdJmbDQ9t+2WcDVNXPktyx76Im6MPA0TTXKAD8J/AJmh0WtcS5jE2TZgCXNCQPHPX/1sz5TZJltKf5277gv13zS2bKFlV1XJLXA1TVTUmG1G5R0iKs13cBkjRB/9Ge2tfsORz4DLBlkkOBb9Is2RiK65PcjVveYOwBXNtvSZL64hpwSYOR5EJgB+ASmjXgo04atiGcAUnuA+xF83U7paoG02c5yW7Au4FdgPNoLuLbv6q+22thknrhEhRJQ7JP3wXotquq7wHf67uOKdkBeAKwLfB04KH4N1haZzkDLknSlCX5blU9IMkjaZbWHAa8oaoe2nNpknrgGnBJkqZvdMHlE4H3V9UJwJC6vEhaBAO4JEnT919JPgA8E/hcu5W5f4OldZRLUCRJmrIkG9Nco3BuVX0/yVbA/avqSz2XJqkHBnBJkiSpQ57+kiRJkjpkAJckSZI6ZACXJP1OklPX8NhjkpzUZT2SNEQGcEnS71TVw/uuQZKGzgAuSfqdJL9M4x+TnJfk3CTPGnvKJkk+k+SCJO9P4t8RSVokt8GVJM31NOBBwAOBLYBvJ/l6+9juwM7Aj4AvtM89vocaJWlmOXMhSZrrkcCxVXVzVV0FfA14SPvYGVX1g6q6GTi2fa4kaREM4JKkubKGx+ZuHuFmEpK0SAZwSdJcXweelWRZkuXAo4Az2sd2T7J9u/b7WcA3+ypSkmaVAVySNK6AzwDfBb4DfAV4TVX9uH38NOCtwHnAJe1zJUmL4Fb0kiQAktwNOKuq7tV3LZI0ZM6AS5JIcg+a2e23912LJA2dM+CSJElSh5wBlyRJkjpkAJckSZI6ZACXJEmSOmQAlyRJkjpkAJckSZI69P8A158DKp1FQqMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(data=data, x='job', order=data['job'].value_counts().index)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c7bd192a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.countplot(data=data, x='education', order=data['education'].value_counts().index)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d28ad6d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi=200)\n",
    "sns.countplot(data=data, x='education', order=data['education'].value_counts().index, hue='default')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e863b795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=data, x='default');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd7dadc",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cdc0beff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</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  subscribed  \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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "02120307",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8fea1401",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
       "0       56       261         1    999         0           1.1          93.994   \n",
       "1       57       149         1    999         0           1.1          93.994   \n",
       "2       37       226         1    999         0           1.1          93.994   \n",
       "3       40       151         1    999         0           1.1          93.994   \n",
       "4       56       307         1    999         0           1.1          93.994   \n",
       "...    ...       ...       ...    ...       ...           ...             ...   \n",
       "41183   73       334         1    999         0          -1.1          94.767   \n",
       "41184   46       383         1    999         0          -1.1          94.767   \n",
       "41185   56       189         2    999         0          -1.1          94.767   \n",
       "41186   44       442         1    999         0          -1.1          94.767   \n",
       "41187   74       239         3    999         1          -1.1          94.767   \n",
       "\n",
       "       cons.conf.idx  euribor3m  nr.employed  ...  day_of_week_fri  \\\n",
       "0              -36.4      4.857       5191.0  ...                0   \n",
       "1              -36.4      4.857       5191.0  ...                0   \n",
       "2              -36.4      4.857       5191.0  ...                0   \n",
       "3              -36.4      4.857       5191.0  ...                0   \n",
       "4              -36.4      4.857       5191.0  ...                0   \n",
       "...              ...        ...          ...  ...              ...   \n",
       "41183          -50.8      1.028       4963.6  ...                1   \n",
       "41184          -50.8      1.028       4963.6  ...                1   \n",
       "41185          -50.8      1.028       4963.6  ...                1   \n",
       "41186          -50.8      1.028       4963.6  ...                1   \n",
       "41187          -50.8      1.028       4963.6  ...                1   \n",
       "\n",
       "       day_of_week_mon  day_of_week_thu  day_of_week_tue  day_of_week_wed  \\\n",
       "0                    1                0                0                0   \n",
       "1                    1                0                0                0   \n",
       "2                    1                0                0                0   \n",
       "3                    1                0                0                0   \n",
       "4                    1                0                0                0   \n",
       "...                ...              ...              ...              ...   \n",
       "41183                0                0                0                0   \n",
       "41184                0                0                0                0   \n",
       "41185                0                0                0                0   \n",
       "41186                0                0                0                0   \n",
       "41187                0                0                0                0   \n",
       "\n",
       "       poutcome_failure  poutcome_nonexistent  poutcome_success  \\\n",
       "0                     0                     1                 0   \n",
       "1                     0                     1                 0   \n",
       "2                     0                     1                 0   \n",
       "3                     0                     1                 0   \n",
       "4                     0                     1                 0   \n",
       "...                 ...                   ...               ...   \n",
       "41183                 0                     1                 0   \n",
       "41184                 0                     1                 0   \n",
       "41185                 0                     1                 0   \n",
       "41186                 0                     1                 0   \n",
       "41187                 1                     0                 0   \n",
       "\n",
       "       subscribed_no  subscribed_yes  \n",
       "0                  1               0  \n",
       "1                  1               0  \n",
       "2                  1               0  \n",
       "3                  1               0  \n",
       "4                  1               0  \n",
       "...              ...             ...  \n",
       "41183              0               1  \n",
       "41184              1               0  \n",
       "41185              1               0  \n",
       "41186              0               1  \n",
       "41187              1               0  \n",
       "\n",
       "[41188 rows x 65 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5b2c286b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "075697f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cda690cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "aac3e2a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0f7f0ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "88ba7906",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_label = model.fit_predict(scaler_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e7924040",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e125a847",
   "metadata": {},
   "outputs": [],
   "source": [
    "X['Cluster'] = cluster_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "958ad288",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
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       "      <th>euribor3m</th>\n",
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       "      <th>...</th>\n",
       "      <th>day_of_week_mon</th>\n",
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       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
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       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>subscribed_no</th>\n",
       "      <th>subscribed_yes</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</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>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>73</td>\n",
       "      <td>334</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>383</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>189</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>442</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>239</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
       "0       56       261         1    999         0           1.1          93.994   \n",
       "1       57       149         1    999         0           1.1          93.994   \n",
       "2       37       226         1    999         0           1.1          93.994   \n",
       "3       40       151         1    999         0           1.1          93.994   \n",
       "4       56       307         1    999         0           1.1          93.994   \n",
       "...    ...       ...       ...    ...       ...           ...             ...   \n",
       "41183   73       334         1    999         0          -1.1          94.767   \n",
       "41184   46       383         1    999         0          -1.1          94.767   \n",
       "41185   56       189         2    999         0          -1.1          94.767   \n",
       "41186   44       442         1    999         0          -1.1          94.767   \n",
       "41187   74       239         3    999         1          -1.1          94.767   \n",
       "\n",
       "       cons.conf.idx  euribor3m  nr.employed  ...  day_of_week_mon  \\\n",
       "0              -36.4      4.857       5191.0  ...                1   \n",
       "1              -36.4      4.857       5191.0  ...                1   \n",
       "2              -36.4      4.857       5191.0  ...                1   \n",
       "3              -36.4      4.857       5191.0  ...                1   \n",
       "4              -36.4      4.857       5191.0  ...                1   \n",
       "...              ...        ...          ...  ...              ...   \n",
       "41183          -50.8      1.028       4963.6  ...                0   \n",
       "41184          -50.8      1.028       4963.6  ...                0   \n",
       "41185          -50.8      1.028       4963.6  ...                0   \n",
       "41186          -50.8      1.028       4963.6  ...                0   \n",
       "41187          -50.8      1.028       4963.6  ...                0   \n",
       "\n",
       "       day_of_week_thu  day_of_week_tue  day_of_week_wed  poutcome_failure  \\\n",
       "0                    0                0                0                 0   \n",
       "1                    0                0                0                 0   \n",
       "2                    0                0                0                 0   \n",
       "3                    0                0                0                 0   \n",
       "4                    0                0                0                 0   \n",
       "...                ...              ...              ...               ...   \n",
       "41183                0                0                0                 0   \n",
       "41184                0                0                0                 0   \n",
       "41185                0                0                0                 0   \n",
       "41186                0                0                0                 0   \n",
       "41187                0                0                0                 1   \n",
       "\n",
       "       poutcome_nonexistent  poutcome_success  subscribed_no  subscribed_yes  \\\n",
       "0                         1                 0              1               0   \n",
       "1                         1                 0              1               0   \n",
       "2                         1                 0              1               0   \n",
       "3                         1                 0              1               0   \n",
       "4                         1                 0              1               0   \n",
       "...                     ...               ...            ...             ...   \n",
       "41183                     1                 0              0               1   \n",
       "41184                     1                 0              1               0   \n",
       "41185                     1                 0              1               0   \n",
       "41186                     1                 0              0               1   \n",
       "41187                     0                 0              1               0   \n",
       "\n",
       "       Cluster  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  \n",
       "...        ...  \n",
       "41183        1  \n",
       "41184        1  \n",
       "41185        1  \n",
       "41186        1  \n",
       "41187        1  \n",
       "\n",
       "[41188 rows x 66 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "cccf24e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "92c4f80a",
   "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>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",
       "      <th>...</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>subscribed_no</th>\n",
       "      <th>subscribed_yes</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>0.004594</td>\n",
       "      <td>-0.034369</td>\n",
       "      <td>0.024365</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.129372</td>\n",
       "      <td>0.010767</td>\n",
       "      <td>-0.017725</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019027</td>\n",
       "      <td>-0.021356</td>\n",
       "      <td>0.019137</td>\n",
       "      <td>-0.023671</td>\n",
       "      <td>-0.002537</td>\n",
       "      <td>-0.016376</td>\n",
       "      <td>0.035626</td>\n",
       "      <td>-0.030399</td>\n",
       "      <td>0.030399</td>\n",
       "      <td>-0.019767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>-0.000866</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.071699</td>\n",
       "      <td>-0.047577</td>\n",
       "      <td>0.020640</td>\n",
       "      <td>-0.027968</td>\n",
       "      <td>0.005312</td>\n",
       "      <td>-0.008173</td>\n",
       "      <td>-0.032897</td>\n",
       "      <td>-0.044703</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023068</td>\n",
       "      <td>0.014666</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>0.014537</td>\n",
       "      <td>-0.013311</td>\n",
       "      <td>-0.011665</td>\n",
       "      <td>0.044876</td>\n",
       "      <td>-0.405274</td>\n",
       "      <td>0.405274</td>\n",
       "      <td>0.039581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>0.004594</td>\n",
       "      <td>-0.071699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>-0.079141</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.127836</td>\n",
       "      <td>-0.013733</td>\n",
       "      <td>0.135133</td>\n",
       "      <td>0.144095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014405</td>\n",
       "      <td>0.005141</td>\n",
       "      <td>-0.025125</td>\n",
       "      <td>-0.021088</td>\n",
       "      <td>-0.068890</td>\n",
       "      <td>0.087645</td>\n",
       "      <td>-0.050893</td>\n",
       "      <td>0.066357</td>\n",
       "      <td>-0.066357</td>\n",
       "      <td>-0.129103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.034369</td>\n",
       "      <td>-0.047577</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.587514</td>\n",
       "      <td>0.271004</td>\n",
       "      <td>0.078889</td>\n",
       "      <td>-0.091342</td>\n",
       "      <td>0.296899</td>\n",
       "      <td>0.372605</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001031</td>\n",
       "      <td>-0.008186</td>\n",
       "      <td>-0.007300</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>0.006354</td>\n",
       "      <td>0.491353</td>\n",
       "      <td>-0.950700</td>\n",
       "      <td>0.324914</td>\n",
       "      <td>-0.324914</td>\n",
       "      <td>-0.267714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.024365</td>\n",
       "      <td>0.020640</td>\n",
       "      <td>-0.079141</td>\n",
       "      <td>-0.587514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420489</td>\n",
       "      <td>-0.203130</td>\n",
       "      <td>-0.050936</td>\n",
       "      <td>-0.454494</td>\n",
       "      <td>-0.501333</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002012</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>-0.003929</td>\n",
       "      <td>0.682608</td>\n",
       "      <td>-0.878776</td>\n",
       "      <td>0.524045</td>\n",
       "      <td>-0.230181</td>\n",
       "      <td>0.230181</td>\n",
       "      <td>0.478493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <td>-0.016376</td>\n",
       "      <td>-0.011665</td>\n",
       "      <td>0.087645</td>\n",
       "      <td>0.491353</td>\n",
       "      <td>-0.878776</td>\n",
       "      <td>0.472501</td>\n",
       "      <td>0.304264</td>\n",
       "      <td>0.099724</td>\n",
       "      <td>0.488406</td>\n",
       "      <td>0.493051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002226</td>\n",
       "      <td>-0.000585</td>\n",
       "      <td>0.001574</td>\n",
       "      <td>0.006545</td>\n",
       "      <td>-0.853119</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.466928</td>\n",
       "      <td>0.193507</td>\n",
       "      <td>-0.193507</td>\n",
       "      <td>-0.544406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome_success</th>\n",
       "      <td>0.035626</td>\n",
       "      <td>0.044876</td>\n",
       "      <td>-0.050893</td>\n",
       "      <td>-0.950700</td>\n",
       "      <td>0.524045</td>\n",
       "      <td>-0.256886</td>\n",
       "      <td>-0.077416</td>\n",
       "      <td>0.084824</td>\n",
       "      <td>-0.281022</td>\n",
       "      <td>-0.350613</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002276</td>\n",
       "      <td>0.008827</td>\n",
       "      <td>0.007258</td>\n",
       "      <td>-0.000390</td>\n",
       "      <td>-0.063006</td>\n",
       "      <td>-0.466928</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.316269</td>\n",
       "      <td>0.316269</td>\n",
       "      <td>0.254406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subscribed_no</th>\n",
       "      <td>-0.030399</td>\n",
       "      <td>-0.405274</td>\n",
       "      <td>0.066357</td>\n",
       "      <td>0.324914</td>\n",
       "      <td>-0.230181</td>\n",
       "      <td>0.298334</td>\n",
       "      <td>0.136211</td>\n",
       "      <td>-0.054878</td>\n",
       "      <td>0.307771</td>\n",
       "      <td>0.354678</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021265</td>\n",
       "      <td>-0.013888</td>\n",
       "      <td>-0.008046</td>\n",
       "      <td>-0.006302</td>\n",
       "      <td>-0.031799</td>\n",
       "      <td>0.193507</td>\n",
       "      <td>-0.316269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.294472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subscribed_yes</th>\n",
       "      <td>0.030399</td>\n",
       "      <td>0.405274</td>\n",
       "      <td>-0.066357</td>\n",
       "      <td>-0.324914</td>\n",
       "      <td>0.230181</td>\n",
       "      <td>-0.298334</td>\n",
       "      <td>-0.136211</td>\n",
       "      <td>0.054878</td>\n",
       "      <td>-0.307771</td>\n",
       "      <td>-0.354678</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.021265</td>\n",
       "      <td>0.013888</td>\n",
       "      <td>0.008046</td>\n",
       "      <td>0.006302</td>\n",
       "      <td>0.031799</td>\n",
       "      <td>-0.193507</td>\n",
       "      <td>0.316269</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.294472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</th>\n",
       "      <td>-0.019767</td>\n",
       "      <td>0.039581</td>\n",
       "      <td>-0.129103</td>\n",
       "      <td>-0.267714</td>\n",
       "      <td>0.478493</td>\n",
       "      <td>-0.932622</td>\n",
       "      <td>-0.679350</td>\n",
       "      <td>-0.330916</td>\n",
       "      <td>-0.959328</td>\n",
       "      <td>-0.886190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024491</td>\n",
       "      <td>0.011232</td>\n",
       "      <td>-0.023368</td>\n",
       "      <td>-0.027712</td>\n",
       "      <td>0.464320</td>\n",
       "      <td>-0.544406</td>\n",
       "      <td>0.254406</td>\n",
       "      <td>-0.294472</td>\n",
       "      <td>0.294472</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age  duration  campaign     pdays  previous  \\\n",
       "age                   1.000000 -0.000866  0.004594 -0.034369  0.024365   \n",
       "duration             -0.000866  1.000000 -0.071699 -0.047577  0.020640   \n",
       "campaign              0.004594 -0.071699  1.000000  0.052584 -0.079141   \n",
       "pdays                -0.034369 -0.047577  0.052584  1.000000 -0.587514   \n",
       "previous              0.024365  0.020640 -0.079141 -0.587514  1.000000   \n",
       "...                        ...       ...       ...       ...       ...   \n",
       "poutcome_nonexistent -0.016376 -0.011665  0.087645  0.491353 -0.878776   \n",
       "poutcome_success      0.035626  0.044876 -0.050893 -0.950700  0.524045   \n",
       "subscribed_no        -0.030399 -0.405274  0.066357  0.324914 -0.230181   \n",
       "subscribed_yes        0.030399  0.405274 -0.066357 -0.324914  0.230181   \n",
       "Cluster              -0.019767  0.039581 -0.129103 -0.267714  0.478493   \n",
       "\n",
       "                      emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "age                      -0.000371        0.000857       0.129372   0.010767   \n",
       "duration                 -0.027968        0.005312      -0.008173  -0.032897   \n",
       "campaign                  0.150754        0.127836      -0.013733   0.135133   \n",
       "pdays                     0.271004        0.078889      -0.091342   0.296899   \n",
       "previous                 -0.420489       -0.203130      -0.050936  -0.454494   \n",
       "...                            ...             ...            ...        ...   \n",
       "poutcome_nonexistent      0.472501        0.304264       0.099724   0.488406   \n",
       "poutcome_success         -0.256886       -0.077416       0.084824  -0.281022   \n",
       "subscribed_no             0.298334        0.136211      -0.054878   0.307771   \n",
       "subscribed_yes           -0.298334       -0.136211       0.054878  -0.307771   \n",
       "Cluster                  -0.932622       -0.679350      -0.330916  -0.959328   \n",
       "\n",
       "                      nr.employed  ...  day_of_week_mon  day_of_week_thu  \\\n",
       "age                     -0.017725  ...         0.019027        -0.021356   \n",
       "duration                -0.044703  ...        -0.023068         0.014666   \n",
       "campaign                 0.144095  ...         0.014405         0.005141   \n",
       "pdays                    0.372605  ...         0.001031        -0.008186   \n",
       "previous                -0.501333  ...        -0.002012         0.001512   \n",
       "...                           ...  ...              ...              ...   \n",
       "poutcome_nonexistent     0.493051  ...         0.002226        -0.000585   \n",
       "poutcome_success        -0.350613  ...        -0.002276         0.008827   \n",
       "subscribed_no            0.354678  ...         0.021265        -0.013888   \n",
       "subscribed_yes          -0.354678  ...        -0.021265         0.013888   \n",
       "Cluster                 -0.886190  ...         0.024491         0.011232   \n",
       "\n",
       "                      day_of_week_tue  day_of_week_wed  poutcome_failure  \\\n",
       "age                          0.019137        -0.023671         -0.002537   \n",
       "duration                     0.004140         0.014537         -0.013311   \n",
       "campaign                    -0.025125        -0.021088         -0.068890   \n",
       "pdays                       -0.007300         0.001021          0.006354   \n",
       "previous                     0.000090        -0.003929          0.682608   \n",
       "...                               ...              ...               ...   \n",
       "poutcome_nonexistent         0.001574         0.006545         -0.853119   \n",
       "poutcome_success             0.007258        -0.000390         -0.063006   \n",
       "subscribed_no               -0.008046        -0.006302         -0.031799   \n",
       "subscribed_yes               0.008046         0.006302          0.031799   \n",
       "Cluster                     -0.023368        -0.027712          0.464320   \n",
       "\n",
       "                      poutcome_nonexistent  poutcome_success  subscribed_no  \\\n",
       "age                              -0.016376          0.035626      -0.030399   \n",
       "duration                         -0.011665          0.044876      -0.405274   \n",
       "campaign                          0.087645         -0.050893       0.066357   \n",
       "pdays                             0.491353         -0.950700       0.324914   \n",
       "previous                         -0.878776          0.524045      -0.230181   \n",
       "...                                    ...               ...            ...   \n",
       "poutcome_nonexistent              1.000000         -0.466928       0.193507   \n",
       "poutcome_success                 -0.466928          1.000000      -0.316269   \n",
       "subscribed_no                     0.193507         -0.316269       1.000000   \n",
       "subscribed_yes                   -0.193507          0.316269      -1.000000   \n",
       "Cluster                          -0.544406          0.254406      -0.294472   \n",
       "\n",
       "                      subscribed_yes   Cluster  \n",
       "age                         0.030399 -0.019767  \n",
       "duration                    0.405274  0.039581  \n",
       "campaign                   -0.066357 -0.129103  \n",
       "pdays                      -0.324914 -0.267714  \n",
       "previous                    0.230181  0.478493  \n",
       "...                              ...       ...  \n",
       "poutcome_nonexistent       -0.193507 -0.544406  \n",
       "poutcome_success            0.316269  0.254406  \n",
       "subscribed_no              -1.000000 -0.294472  \n",
       "subscribed_yes              1.000000  0.294472  \n",
       "Cluster                     0.294472  1.000000  \n",
       "\n",
       "[66 rows x 66 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8d11e2da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                    -0.019767\n",
       "duration                0.039581\n",
       "campaign               -0.129103\n",
       "pdays                  -0.267714\n",
       "previous                0.478493\n",
       "                          ...   \n",
       "poutcome_nonexistent   -0.544406\n",
       "poutcome_success        0.254406\n",
       "subscribed_no          -0.294472\n",
       "subscribed_yes          0.294472\n",
       "Cluster                 1.000000\n",
       "Name: Cluster, Length: 66, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr()['Cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9c619fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "X.corr()['Cluster'].iloc[:-1].sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31270bf2",
   "metadata": {},
   "source": [
    "## Choosing the right K value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7338daa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ssd = []\n",
    "\n",
    "for k in range(2,10):\n",
    "    model = KMeans(n_clusters=k) \n",
    "    model.fit(scaler_X)\n",
    "    \n",
    "    ssd.append(model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "76b10d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sum of squared Distance')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(2,10),ssd,'o--')\n",
    "plt.xlabel(\"K Value\")\n",
    "plt.ylabel(\"Sum of squared Distance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "11ec91ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2469792.367302056,\n",
       " 2370786.6626720186,\n",
       " 2271503.2235369184,\n",
       " 2200694.2311942847,\n",
       " 2169756.9060791107,\n",
       " 2111319.78465061,\n",
       " 2029347.6979756632,\n",
       " 2025223.474258906]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416fc1aa",
   "metadata": {},
   "source": [
    "THE END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68bee587",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}