slides_clustering_interpret_bank.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playground for Interpretting Clusters\n",
    "\n",
    "- Stephen W. Thomas\n",
    "- Used for MMA 869, MMAI 869, and GMMA 869"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 14:29:40.838751\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print(datetime.datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Default plot settings\n",
    "SMALL_SIZE = 12\n",
    "MEDIUM_SIZE = 14\n",
    "BIGGER_SIZE = 16\n",
    "\n",
    "\n",
    "plt.rc('figure', figsize=[8.0, 5.0])     # controls the size of the figure\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "plt.rc('axes', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Data\n",
    "\n",
    "This data is from the UCI repo: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing\n",
    "\n",
    "The features are described as follows:\n",
    "\n",
    "- age (numeric)\n",
    "- job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "- marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "- education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "- housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "- loan: has personal loan? (categorical: 'no','yes','unknown')\n",
    "\n",
    "\n",
    "(There are others, but we will remove for the purposes of this study.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4521, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4521 entries, 0 to 4520\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   age        4521 non-null   int64 \n",
      " 1   job        4521 non-null   object\n",
      " 2   marital    4521 non-null   object\n",
      " 3   education  4521 non-null   object\n",
      " 4   balance    4521 non-null   int64 \n",
      " 5   housing    4521 non-null   object\n",
      " 6   loan       4521 non-null   object\n",
      "dtypes: int64(2), object(5)\n",
      "memory usage: 247.4+ KB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>1787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>4789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>1350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>1476</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>747</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>307</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>147</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>41</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>221</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>43</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>-88</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age            job  marital  education  balance housing loan\n",
       "0   30     unemployed  married    primary     1787      no   no\n",
       "1   33       services  married  secondary     4789     yes  yes\n",
       "2   35     management   single   tertiary     1350     yes   no\n",
       "3   30     management  married   tertiary     1476     yes  yes\n",
       "4   59    blue-collar  married  secondary        0     yes   no\n",
       "5   35     management   single   tertiary      747      no   no\n",
       "6   36  self-employed  married   tertiary      307     yes   no\n",
       "7   39     technician  married  secondary      147     yes   no\n",
       "8   41   entrepreneur  married   tertiary      221     yes   no\n",
       "9   43       services  married    primary      -88     yes  yes"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/stepthom/sandbox/master/data/bank.csv\")\n",
    "\n",
    "# drop the target feature; not needed for clustering\n",
    "df = df.drop(['default', 'y', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays', 'previous', 'poutcome'], axis=1)\n",
    "\n",
    "df.shape\n",
    "df.info()\n",
    "df.head(n=10)\n",
    "\n",
    "#Small sample for now\n",
    "df = df.iloc[0:1000,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat_col_names: ['job', 'marital', 'education', 'housing', 'loan']\n",
      "num_col_names: ['age', 'balance']\n"
     ]
    }
   ],
   "source": [
    "# Let's figure how many features there are, and which are categorical vs numeric, and save for later\n",
    "\n",
    "n_features = df.shape[1]\n",
    "\n",
    "cat_col_names = list(df.select_dtypes(include=np.object).columns)\n",
    "num_col_names = list(df.select_dtypes(include=np.number).columns)\n",
    "\n",
    "print('cat_col_names: {}'.format(cat_col_names))\n",
    "print('num_col_names: {}'.format(num_col_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>1000.0</td>\n",
       "      <td>41.174</td>\n",
       "      <td>10.463135</td>\n",
       "      <td>19.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1498.592</td>\n",
       "      <td>2778.416508</td>\n",
       "      <td>-1680.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>1526.75</td>\n",
       "      <td>26965.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean          std     min   25%    50%      75%      max\n",
       "age      1000.0    41.174    10.463135    19.0  33.0   40.0    49.00     83.0\n",
       "balance  1000.0  1498.592  2778.416508 -1680.0  81.0  462.0  1526.75  26965.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive stats of the numeric features\n",
    "df.describe(include=[np.number]).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>management</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>secondary</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count unique         top freq\n",
       "job        1000     12  management  221\n",
       "marital    1000      3     married  595\n",
       "education  1000      4   secondary  492\n",
       "housing    1000      2         yes  554\n",
       "loan       1000      2          no  836"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive stats of the categorical features\n",
    "df.describe(include=[np.object]).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.0684742983341025, 0.10385495170152446, 'unemployed', ...,\n",
       "        'primary', 'no', 'no'],\n",
       "       [-0.78160989033318, 1.1848671462591531, 'services', ...,\n",
       "        'secondary', 'yes', 'yes'],\n",
       "       [-0.5903669516658984, -0.05350758294926957, 'management', ...,\n",
       "        'tertiary', 'yes', 'no'],\n",
       "       ...,\n",
       "       [-0.6859884209995392, -0.2972934775913464, 'management', ...,\n",
       "        'tertiary', 'yes', 'yes'],\n",
       "       [-0.11225960499769427, -0.662792270887813, 'services', ...,\n",
       "        'secondary', 'yes', 'no'],\n",
       "       [-2.0246889916705104, -0.43485065891080477, 'student', ...,\n",
       "        'secondary', 'no', 'no']], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X_num = df[num_col_names].to_numpy()\n",
    "X_cat = df[cat_col_names].to_numpy()\n",
    "\n",
    "# Be sure to scale all numeric features\n",
    "scaler = StandardScaler()\n",
    "X_num = scaler.fit_transform(X_num)\n",
    "\n",
    "X = np.concatenate((X_num, X_cat), axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance Calculation\n",
    "\n",
    "Since this dataset has both categorical and numeric features, and no distance method exists in scipy or scikit-learn to handle mixed types, we will define our own called `custom_dist`. It will use Euclidean distance on numeric features, Hamming distance on categorical features, and take the weighted average of the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.spatial import distance\n",
    "\n",
    "# Now, let's define our own function to handle both types of features\n",
    "\n",
    "def custom_dist(x1, x2, cat_cols=None, num_cols=None):\n",
    "   \n",
    "    n = len(x1)\n",
    "    #print(x1)\n",
    "    #print(num_cols)\n",
    "    #print(x1[num_cols])\n",
    "    #print(x2)\n",
    "\n",
    "    dist_num = distance.euclidean(x1[num_cols], x2[num_cols])\n",
    "    #print(dist_num)\n",
    "    dist_cat = distance.hamming(x1[cat_cols], x2[cat_cols])\n",
    "    #print(dist_cat)\n",
    "    \n",
    "    dist = (dist_num*sum(num_cols) + dist_cat*sum(cat_cols)) / n\n",
    "    #print(dist)\n",
    "    return dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we call our custom distance metric on all pairs of rows in our dataset to create our own custom distance matrix.\n",
    "\n",
    "from scipy.spatial.distance import pdist\n",
    "\n",
    "cat_cols = [type(x)==str for x in X[1,:]]\n",
    "num_cols = [not x for x in cat_cols]\n",
    "\n",
    "# Warning: pdist is kind of slow\n",
    "Y = pdist(X, custom_dist, cat_cols=cat_cols, num_cols=num_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform hierarchical clustering, using our custom-built distance matrix.\n",
    "\n",
    "import scipy.cluster\n",
    "\n",
    "aggl = scipy.cluster.hierarchy.linkage(Y, method='ward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8));\n",
    "plt.grid(False)\n",
    "dend = scipy.cluster.hierarchy.dendrogram(aggl); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's find K=5 clusters\n",
    "K=5\n",
    "labels = scipy.cluster.hierarchy.fcluster(aggl, K, criterion=\"maxclust\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Each Cluster, Show Textual Feature Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All Data:\n",
      "Number of Instances: 1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\st50\\AppData\\Roaming\\Python\\Python36\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n"
     ]
    },
    {
     "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>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>1000.00</td>\n",
       "      <td>41.17</td>\n",
       "      <td>10.46</td>\n",
       "      <td>19.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>83.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>1000.00</td>\n",
       "      <td>1498.59</td>\n",
       "      <td>2778.42</td>\n",
       "      <td>-1680.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>462.00</td>\n",
       "      <td>1526.75</td>\n",
       "      <td>26965.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count    mean     std      min   25%    50%     75%      max\n",
       "age     1000.00   41.17   10.46    19.00 33.00  40.00   49.00    83.00\n",
       "balance 1000.00 1498.59 2778.42 -1680.00 81.00 462.00 1526.75 26965.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>1000</td>\n",
       "      <td>12</td>\n",
       "      <td>management</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>secondary</td>\n",
       "      <td>492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count unique         top freq\n",
       "job        1000     12  management  221\n",
       "marital    1000      3     married  595\n",
       "education  1000      4   secondary  492\n",
       "housing    1000      2         yes  554\n",
       "loan       1000      2          no  836"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "management       221\n",
       "blue-collar      206\n",
       "technician       181\n",
       "admin.            96\n",
       "services          76\n",
       "self-employed     53\n",
       "retired           52\n",
       "entrepreneur      40\n",
       "unemployed        25\n",
       "housemaid         24\n",
       "student           21\n",
       "unknown            5\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "married     595\n",
       "single      284\n",
       "divorced    121\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "secondary    492\n",
       "tertiary     313\n",
       "primary      155\n",
       "unknown       40\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "yes    554\n",
       "no     446\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no     836\n",
       "yes    164\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 1:\n",
      "Number of Instances: 345\n"
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    },
    {
     "data": {
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       "      <th>age</th>\n",
       "      <td>345.00</td>\n",
       "      <td>52.50</td>\n",
       "      <td>7.09</td>\n",
       "      <td>37.00</td>\n",
       "      <td>48.00</td>\n",
       "      <td>52.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>83.00</td>\n",
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       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>345.00</td>\n",
       "      <td>1331.73</td>\n",
       "      <td>1928.58</td>\n",
       "      <td>-1680.00</td>\n",
       "      <td>93.00</td>\n",
       "      <td>478.00</td>\n",
       "      <td>1772.00</td>\n",
       "      <td>9676.00</td>\n",
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       "         count    mean     std      min   25%    50%     75%     max\n",
       "age     345.00   52.50    7.09    37.00 48.00  52.00   56.00   83.00\n",
       "balance 345.00 1331.73 1928.58 -1680.00 93.00 478.00 1772.00 9676.00"
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     "execution_count": 14,
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       "      <th>marital</th>\n",
       "      <td>345</td>\n",
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       "      <td>237</td>\n",
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       "      <th>education</th>\n",
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       "      <td>164</td>\n",
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       "      <th>housing</th>\n",
       "      <td>345</td>\n",
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       "      <td>no</td>\n",
       "      <td>192</td>\n",
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       "      <th>loan</th>\n",
       "      <td>345</td>\n",
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       "      <td>no</td>\n",
       "      <td>296</td>\n",
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      "text/plain": [
       "          count unique         top freq\n",
       "job         345     11  management   73\n",
       "marital     345      3     married  237\n",
       "education   345      4   secondary  164\n",
       "housing     345      2          no  192\n",
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     "execution_count": 14,
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    {
     "data": {
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       "management       73\n",
       "blue-collar      59\n",
       "retired          51\n",
       "technician       43\n",
       "admin.           37\n",
       "services         20\n",
       "self-employed    17\n",
       "housemaid        16\n",
       "entrepreneur     16\n",
       "unemployed       10\n",
       "unknown           3\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    {
     "data": {
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       "married     237\n",
       "divorced     80\n",
       "single       28\n",
       "Name: marital, dtype: int64"
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     },
     "execution_count": 14,
     "metadata": {},
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    {
     "data": {
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       "secondary    164\n",
       "tertiary      82\n",
       "primary       73\n",
       "unknown       26\n",
       "Name: education, dtype: int64"
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no     192\n",
       "yes    153\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no     296\n",
       "yes     49\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 2:\n",
      "Number of Instances: 138\n"
     ]
    },
    {
     "data": {
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       "      <th>age</th>\n",
       "      <td>138.00</td>\n",
       "      <td>30.60</td>\n",
       "      <td>5.35</td>\n",
       "      <td>19.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>34.00</td>\n",
       "      <td>45.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>138.00</td>\n",
       "      <td>411.57</td>\n",
       "      <td>612.97</td>\n",
       "      <td>-849.00</td>\n",
       "      <td>22.75</td>\n",
       "      <td>247.00</td>\n",
       "      <td>615.25</td>\n",
       "      <td>4111.00</td>\n",
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      ],
      "text/plain": [
       "         count   mean    std     min   25%    50%    75%     max\n",
       "age     138.00  30.60   5.35   19.00 27.00  30.00  34.00   45.00\n",
       "balance 138.00 411.57 612.97 -849.00 22.75 247.00 615.25 4111.00"
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     "execution_count": 14,
     "metadata": {},
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       "      <td>30</td>\n",
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       "      <th>marital</th>\n",
       "      <td>138</td>\n",
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       "      <td>121</td>\n",
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       "      <td>120</td>\n",
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       "      <th>housing</th>\n",
       "      <td>138</td>\n",
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       "      <td>74</td>\n",
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       "      <th>loan</th>\n",
       "      <td>138</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>105</td>\n",
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      "text/plain": [
       "          count unique         top freq\n",
       "job         138     10  technician   30\n",
       "marital     138      3      single  121\n",
       "education   138      4   secondary  120\n",
       "housing     138      2         yes   74\n",
       "loan        138      2          no  105"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "technician       30\n",
       "blue-collar      29\n",
       "admin.           26\n",
       "services         24\n",
       "student          13\n",
       "self-employed     6\n",
       "management        4\n",
       "unemployed        4\n",
       "retired           1\n",
       "entrepreneur      1\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "single      121\n",
       "married      13\n",
       "divorced      4\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "secondary    120\n",
       "primary        9\n",
       "tertiary       7\n",
       "unknown        2\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "yes    74\n",
       "no     64\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no     105\n",
       "yes     33\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 3:\n",
      "Number of Instances: 410\n"
     ]
    },
    {
     "data": {
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       "      <th>age</th>\n",
       "      <td>410.00</td>\n",
       "      <td>36.33</td>\n",
       "      <td>5.12</td>\n",
       "      <td>26.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>410.00</td>\n",
       "      <td>559.13</td>\n",
       "      <td>716.36</td>\n",
       "      <td>-1212.00</td>\n",
       "      <td>37.50</td>\n",
       "      <td>352.00</td>\n",
       "      <td>939.50</td>\n",
       "      <td>3825.00</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "         count   mean    std      min   25%    50%    75%     max\n",
       "age     410.00  36.33   5.12    26.00 32.00  36.00  40.00   50.00\n",
       "balance 410.00 559.13 716.36 -1212.00 37.50 352.00 939.50 3825.00"
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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       "      <td>410</td>\n",
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       "      <td>115</td>\n",
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       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>410</td>\n",
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       "      <td>300</td>\n",
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       "      <th>education</th>\n",
       "      <td>410</td>\n",
       "      <td>4</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>177</td>\n",
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       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>410</td>\n",
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       "      <td>268</td>\n",
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       "      <th>loan</th>\n",
       "      <td>410</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>335</td>\n",
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      ],
      "text/plain": [
       "          count unique         top freq\n",
       "job         410     11  management  115\n",
       "marital     410      3     married  300\n",
       "education   410      4    tertiary  177\n",
       "housing     410      2         yes  268\n",
       "loan        410      2          no  335"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "management       115\n",
       "blue-collar      101\n",
       "technician        89\n",
       "services          25\n",
       "self-employed     22\n",
       "admin.            21\n",
       "entrepreneur      20\n",
       "unemployed        10\n",
       "housemaid          4\n",
       "unknown            2\n",
       "student            1\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "married     300\n",
       "single       81\n",
       "divorced     29\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "tertiary     177\n",
       "secondary    162\n",
       "primary       63\n",
       "unknown        8\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "yes    268\n",
       "no     142\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no     335\n",
       "yes     75\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 4:\n",
      "Number of Instances: 23\n"
     ]
    },
    {
     "data": {
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       "</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>23.00</td>\n",
       "      <td>44.74</td>\n",
       "      <td>10.51</td>\n",
       "      <td>30.00</td>\n",
       "      <td>37.50</td>\n",
       "      <td>42.00</td>\n",
       "      <td>53.50</td>\n",
       "      <td>67.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>23.00</td>\n",
       "      <td>14201.26</td>\n",
       "      <td>4202.13</td>\n",
       "      <td>10287.00</td>\n",
       "      <td>11286.00</td>\n",
       "      <td>12519.00</td>\n",
       "      <td>15170.50</td>\n",
       "      <td>26965.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count     mean     std      min      25%      50%      75%      max\n",
       "age      23.00    44.74   10.51    30.00    37.50    42.00    53.50    67.00\n",
       "balance  23.00 14201.26 4202.13 10287.00 11286.00 12519.00 15170.50 26965.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>management</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count unique         top freq\n",
       "job          23      6  management    7\n",
       "marital      23      3     married   11\n",
       "education    23      4    tertiary    9\n",
       "housing      23      2          no   18\n",
       "loan         23      1          no   23"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "management       7\n",
       "blue-collar      5\n",
       "housemaid        4\n",
       "self-employed    3\n",
       "technician       2\n",
       "admin.           2\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "married     11\n",
       "single      10\n",
       "divorced     2\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "tertiary     9\n",
       "secondary    8\n",
       "primary      5\n",
       "unknown      1\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no     18\n",
       "yes     5\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no    23\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cluster 5:\n",
      "Number of Instances: 84\n"
     ]
    },
    {
     "data": {
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       "</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>84.00</td>\n",
       "      <td>34.67</td>\n",
       "      <td>4.72</td>\n",
       "      <td>23.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>38.00</td>\n",
       "      <td>47.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>84.00</td>\n",
       "      <td>5077.13</td>\n",
       "      <td>2357.01</td>\n",
       "      <td>1281.00</td>\n",
       "      <td>3387.75</td>\n",
       "      <td>4325.50</td>\n",
       "      <td>6463.00</td>\n",
       "      <td>11555.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count    mean     std     min     25%     50%     75%      max\n",
       "age      84.00   34.67    4.72   23.00   32.00   35.00   38.00    47.00\n",
       "balance  84.00 5077.13 2357.01 1281.00 3387.75 4325.50 6463.00 11555.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>84</td>\n",
       "      <td>9</td>\n",
       "      <td>management</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>single</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>84</td>\n",
       "      <td>4</td>\n",
       "      <td>secondary</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>84</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count unique         top freq\n",
       "job          84      9  management   22\n",
       "marital      84      3      single   44\n",
       "education    84      4   secondary   38\n",
       "housing      84      2         yes   54\n",
       "loan         84      2          no   77"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "management       22\n",
       "technician       17\n",
       "blue-collar      12\n",
       "admin.           10\n",
       "services          7\n",
       "student           7\n",
       "self-employed     5\n",
       "entrepreneur      3\n",
       "unemployed        1\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "single      44\n",
       "married     34\n",
       "divorced     6\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "secondary    38\n",
       "tertiary     38\n",
       "primary       5\n",
       "unknown       3\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "yes    54\n",
       "no     30\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "no     77\n",
       "yes     7\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option(\"display.precision\", 2)\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "print('All Data:')\n",
    "print('Number of Instances: {}'.format(X.shape[0]))\n",
    "df.describe(include=[np.number]).transpose()\n",
    "df.describe(include=[np.object]).transpose()\n",
    "for col in cat_col_names:\n",
    "    df[col].value_counts()\n",
    "\n",
    "\n",
    "for i, label in enumerate(set(labels)):\n",
    "    n = df.iloc[labels==label].shape[0]\n",
    "      \n",
    "    print('\\nCluster {}:'.format(label))\n",
    "    print('Number of Instances: {}'.format(n))\n",
    "\n",
    "    df.iloc[labels==label].describe(include=[np.number]).transpose()\n",
    "    df.iloc[labels==label].describe(include=[np.object]).transpose()\n",
    "    for col in cat_col_names:\n",
    "        df.iloc[labels==label][col].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For Each Cluster, Show Visual Feature Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 35 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "\n",
    "f, axes = plt.subplots(K, n_features, figsize=(24, 12), sharex='col', sharey='col')\n",
    "\n",
    "cols = [x for x in num_col_names]\n",
    "cols += [x for x in cat_col_names]\n",
    "rows = ['Cluster {}'.format(i+1) for i in np.arange(K)]\n",
    "\n",
    "\n",
    "for i, label in enumerate(set(labels)):\n",
    "    n = df.iloc[labels==label].shape[0]\n",
    "  \n",
    "\n",
    "    col_idx = 0\n",
    "    for col in num_col_names:\n",
    "        sns.distplot(df[[col]], hist=False, rug=False, label=\"All\", ax=axes[i-1, col_idx]);\n",
    "        chart = sns.distplot(df.iloc[labels==label][[col]], hist=False, rug=False, label=\"Cluster {}\".format(label), ax=axes[i-1,col_idx]);\n",
    "        chart.set_yticklabels([])\n",
    "        col_idx=col_idx+1\n",
    "    for col in cat_col_names:\n",
    "        \n",
    "        # It's a bit complicated, and it took me a while to figure out, but what we're doing here is this:\n",
    "        # Finding out the (normalized) distribution of each level for \"all data\"\n",
    "        # Finding out the (normalized) distribution fo each level for this cluster\n",
    "        # Plotting both on a bar chart\n",
    "        \n",
    "        all_prop_df = (df[col].value_counts(normalize=True).reset_index())\n",
    "        all_prop_df['Cluster']= 'All'\n",
    "\n",
    "        prop_df = (df.iloc[labels==label][col]\n",
    "           .value_counts(normalize=True)\n",
    "           .reset_index())\n",
    "\n",
    "        prop_df['Cluster']= 'Cluster {}'.format(label)\n",
    "\n",
    "        prop_df = pd.concat([all_prop_df, prop_df])\n",
    "        prop_df = prop_df.reset_index(drop=True)\n",
    "\n",
    "        chart = sns.barplot(x='index', y=col, hue='Cluster', data=prop_df, ci=None, ax=axes[i-1, col_idx])\n",
    "        #chart.set_xticklabels(chart.get_xticklabels(), rotation=55)\n",
    "        \n",
    "        col_idx=col_idx+1\n",
    "\n",
    "for ax1 in axes:\n",
    "    for ax in ax1:\n",
    "        ax.get_legend().remove()\n",
    "        ax.set_ylabel('')\n",
    "        ax.set_xlabel('')\n",
    "        \n",
    "for ax, col in zip(axes[0], cols):\n",
    "    ax.set_title(col, size='large')\n",
    "\n",
    "for ax, row in zip(axes[:,0], rows):\n",
    "    ax.set_ylabel(row, rotation=90, size='large')\n",
    "\n",
    "for j in np.arange(n_features):\n",
    "    ax = axes[K-1,j]\n",
    "    if j >= len(num_col_names): #Tmp hack to avoid rotating numeric lablels\n",
    "        ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
    "    \n",
    "plt.show();\n",
    "    "
   ]
  },
  {
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