├── K Means Clustering
    └── init
├── Executing K Means in Python
    ├── init
    └── Hopkins+Statistic.ipynb
├── Other Forms of Clustering
    ├── init
    ├── K-Prototype+clustering (1).ipynb
    └── K-Mode+Bank+Marketing.ipynb
└── README.md


/K Means Clustering/init:
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/Executing K Means in Python/init:
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/Other Forms of Clustering/init:
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/README.md:
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1 | # Unsupervised-Learning-Clustering


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/Executing K Means in Python/Hopkins+Statistic.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "## Hopkins Statistics:\n",
  8 |     "The Hopkins statistic, is a statistic which gives a value which indicates the cluster tendency, in other words: how well the data can be clustered.\n",
  9 |     "\n",
 10 |     "- If the value is between {0.01, ...,0.3}, the data is regularly spaced.\n",
 11 |     "\n",
 12 |     "- If the value is around 0.5, it is random.\n",
 13 |     "\n",
 14 |     "- If the value is between {0.7, ..., 0.99}, it has a high tendency to cluster."
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "markdown",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     "Some usefull links to understand Hopkins Statistics:\n",
 22 |     "- [WikiPedia](https://en.wikipedia.org/wiki/Hopkins_statistic)\n",
 23 |     "- [Article](http://www.sthda.com/english/articles/29-cluster-validation-essentials/95-assessing-clustering-tendency-essentials/)"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 2,
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "from sklearn.neighbors import NearestNeighbors\n",
 33 |     "from random import sample\n",
 34 |     "from numpy.random import uniform\n",
 35 |     "import numpy as np\n",
 36 |     "from math import isnan\n",
 37 |     " \n",
 38 |     "def hopkins(X):\n",
 39 |     "    d = X.shape[1]\n",
 40 |     "    #d = len(vars) # columns\n",
 41 |     "    n = len(X) # rows\n",
 42 |     "    m = int(0.1 * n) \n",
 43 |     "    nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
 44 |     " \n",
 45 |     "    rand_X = sample(range(0, n, 1), m)\n",
 46 |     " \n",
 47 |     "    ujd = []\n",
 48 |     "    wjd = []\n",
 49 |     "    for j in range(0, m):\n",
 50 |     "        u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
 51 |     "        ujd.append(u_dist[0][1])\n",
 52 |     "        w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
 53 |     "        wjd.append(w_dist[0][1])\n",
 54 |     " \n",
 55 |     "    H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
 56 |     "    if isnan(H):\n",
 57 |     "        print(ujd, wjd)\n",
 58 |     "        H = 0\n",
 59 |     " \n",
 60 |     "    return H"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "code",
 65 |    "execution_count": null,
 66 |    "metadata": {},
 67 |    "outputs": [],
 68 |    "source": [
 69 |     "#First convert the numpy array that you have to a dataframe\n",
 70 |     "rfm_df_scaled = pd.DataFrame(rfm_df_scaled)\n",
 71 |     "rfm_df_scaled.columns = ['amount', 'frequency', 'recency']"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "code",
 76 |    "execution_count": null,
 77 |    "metadata": {},
 78 |    "outputs": [],
 79 |    "source": [
 80 |     "#Use the Hopkins Statistic function by passing the above dataframe as a paramter\n",
 81 |     "hopkins(rfm_df_scaled)"
 82 |    ]
 83 |   }
 84 |  ],
 85 |  "metadata": {
 86 |   "kernelspec": {
 87 |    "display_name": "Python 3",
 88 |    "language": "python",
 89 |    "name": "python3"
 90 |   },
 91 |   "language_info": {
 92 |    "codemirror_mode": {
 93 |     "name": "ipython",
 94 |     "version": 3
 95 |    },
 96 |    "file_extension": ".py",
 97 |    "mimetype": "text/x-python",
 98 |    "name": "python",
 99 |    "nbconvert_exporter": "python",
100 |    "pygments_lexer": "ipython3",
101 |    "version": "3.6.5"
102 |   }
103 |  },
104 |  "nbformat": 4,
105 |  "nbformat_minor": 2
106 | }
107 | 


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/Other Forms of Clustering/K-Prototype+clustering (1).ipynb:
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  1 | {
  2 |   "metadata": {
  3 |     "kernelspec": {
  4 |       "name": "python",
  5 |       "display_name": "Pyolite",
  6 |       "language": "python"
  7 |     },
  8 |     "language_info": {
  9 |       "codemirror_mode": {
 10 |         "name": "python",
 11 |         "version": 3
 12 |       },
 13 |       "file_extension": ".py",
 14 |       "mimetype": "text/x-python",
 15 |       "name": "python",
 16 |       "nbconvert_exporter": "python",
 17 |       "pygments_lexer": "ipython3",
 18 |       "version": "3.8"
 19 |     }
 20 |   },
 21 |   "nbformat_minor": 4,
 22 |   "nbformat": 4,
 23 |   "cells": [
 24 |     {
 25 |       "cell_type": "markdown",
 26 |       "source": "# K-Prototype Clustering",
 27 |       "metadata": {}
 28 |     },
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "source": "**About Blood Transfusion dataset**<br><br>\nTo demonstrate the RFMTC marketing model (a modified version of RFM), this study adopted the donor database of Blood Transfusion Service Center in Hsin-Chu City in Taiwan. The center passes their blood transfusion service bus to one university in Hsin-Chu City to gather blood donated about every three months. To build a FRMTC model, we selected 748 donors at random from the donor database. These 748 donor data, each one included R (Recency - months since last donation), F (Frequency - total number of donation), M (Monetary - total blood donated in c.c.), T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood).",
 32 |       "metadata": {}
 33 |     },
 34 |     {
 35 |       "cell_type": "markdown",
 36 |       "source": "**Attribute Information:**\n\n- R (Recency - months since last donation), \n- F (Frequency - total number of donation), \n- M (Monetary - total blood donated in c.c.), \n- T (Time - months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood). \n\n- Variable\tData Type\tMeasurement\tDescription\tmin\tmax\tmean\tstd \n- Recency quantitative\tMonths\tInput\t0.03\t74.4\t9.74\t8.07 \n- Frequency quantitative\tTimes\tInput\t1\t50\t5.51\t5.84 \n- Monetary\tquantitative\tc.c. blood\tInput\t250\t12500\t1378.68\t1459.83 \n- Time quantitative\tMonths\tInput\t2.27\t98.3\t34.42\t24.32 \n- Whether he/she donated blood in March 2007\tbinary\t1=yes 0=no\tOutput\t0\t1\t1 (24%) 0 (76%) ",
 37 |       "metadata": {}
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "source": "pip install kmodes",
 42 |       "metadata": {},
 43 |       "execution_count": null,
 44 |       "outputs": []
 45 |     },
 46 |     {
 47 |       "cell_type": "code",
 48 |       "source": "# Importing Libraries\nimport numpy as np\nimport pandas as pd\nfrom kmodes.kprototypes import KPrototypes\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport seaborn as sns",
 49 |       "metadata": {},
 50 |       "execution_count": 1,
 51 |       "outputs": []
 52 |     },
 53 |     {
 54 |       "cell_type": "code",
 55 |       "source": "help(KPrototypes)",
 56 |       "metadata": {},
 57 |       "execution_count": 2,
 58 |       "outputs": [
 59 |         {
 60 |           "name": "stdout",
 61 |           "output_type": "stream",
 62 |           "text": "Help on class KPrototypes in module kmodes.kprototypes:\n\n\n\nclass KPrototypes(kmodes.kmodes.KModes)\n\n |  k-protoypes clustering algorithm for mixed numerical/categorical data.\n\n |  \n\n |  Parameters\n\n |  -----------\n\n |  n_clusters : int, optional, default: 8\n\n |      The number of clusters to form as well as the number of\n\n |      centroids to generate.\n\n |  \n\n |  max_iter : int, default: 300\n\n |      Maximum number of iterations of the k-modes algorithm for a\n\n |      single run.\n\n |  \n\n |  num_dissim : func, default: euclidian_dissim\n\n |      Dissimilarity function used by the algorithm for numerical variables.\n\n |      Defaults to the Euclidian dissimilarity function.\n\n |  \n\n |  cat_dissim : func, default: matching_dissim\n\n |      Dissimilarity function used by the kmodes algorithm for categorical variables.\n\n |      Defaults to the matching dissimilarity function.\n\n |  \n\n |  n_init : int, default: 10\n\n |      Number of time the k-modes algorithm will be run with different\n\n |      centroid seeds. The final results will be the best output of\n\n |      n_init consecutive runs in terms of cost.\n\n |  \n\n |  init : {'Huang', 'Cao', 'random' or a list of ndarrays}, default: 'Cao'\n\n |      Method for initialization:\n\n |      'Huang': Method in Huang [1997, 1998]\n\n |      'Cao': Method in Cao et al. [2009]\n\n |      'random': choose 'n_clusters' observations (rows) at random from\n\n |      data for the initial centroids.\n\n |      If a list of ndarrays is passed, it should be of length 2, with\n\n |      shapes (n_clusters, n_features) for numerical and categorical\n\n |      data respectively. These are the initial centroids.\n\n |  \n\n |  gamma : float, default: None\n\n |      Weighing factor that determines relative importance of numerical vs.\n\n |      categorical attributes (see discussion in Huang [1997]). By default,\n\n |      automatically calculated from data.\n\n |  \n\n |  verbose : integer, optional\n\n |      Verbosity mode.\n\n |  \n\n |  Attributes\n\n |  ----------\n\n |  cluster_centroids_ : array, [n_clusters, n_features]\n\n |      Categories of cluster centroids\n\n |  \n\n |  labels_ :\n\n |      Labels of each point\n\n |  \n\n |  cost_ : float\n\n |      Clustering cost, defined as the sum distance of all points to\n\n |      their respective cluster centroids.\n\n |  \n\n |  n_iter_ : int\n\n |      The number of iterations the algorithm ran for.\n\n |  \n\n |  gamma : float\n\n |      The (potentially calculated) weighing factor.\n\n |  \n\n |  Notes\n\n |  -----\n\n |  See:\n\n |  Huang, Z.: Extensions to the k-modes algorithm for clustering large\n\n |  data sets with categorical values, Data Mining and Knowledge\n\n |  Discovery 2(3), 1998.\n\n |  \n\n |  Method resolution order:\n\n |      KPrototypes\n\n |      kmodes.kmodes.KModes\n\n |      sklearn.base.BaseEstimator\n\n |      sklearn.base.ClusterMixin\n\n |      builtins.object\n\n |  \n\n |  Methods defined here:\n\n |  \n\n |  __init__(self, n_clusters=8, max_iter=100, num_dissim=<function euclidean_dissim at 0x000002215DD8AB70>, cat_dissim=<function matching_dissim at 0x000002215DD8AA60>, init='Huang', n_init=10, gamma=None, verbose=0)\n\n |      Initialize self.  See help(type(self)) for accurate signature.\n\n |  \n\n |  fit(self, X, y=None, categorical=None)\n\n |      Compute k-prototypes clustering.\n\n |      \n\n |      Parameters\n\n |      ----------\n\n |      X : array-like, shape=[n_samples, n_features]\n\n |      categorical : Index of columns that contain categorical data\n\n |  \n\n |  predict(self, X, categorical=None)\n\n |      Predict the closest cluster each sample in X belongs to.\n\n |      \n\n |      Parameters\n\n |      ----------\n\n |      X : array-like, shape = [n_samples, n_features]\n\n |          New data to predict.\n\n |      categorical : Index of columns that contain categorical data\n\n |      \n\n |      Returns\n\n |      -------\n\n |      labels : array, shape [n_samples,]\n\n |          Index of the cluster each sample belongs to.\n\n |  \n\n |  ----------------------------------------------------------------------\n\n |  Data descriptors defined here:\n\n |  \n\n |  cluster_centroids_\n\n |  \n\n |  ----------------------------------------------------------------------\n\n |  Methods inherited from kmodes.kmodes.KModes:\n\n |  \n\n |  fit_predict(self, X, y=None, **kwargs)\n\n |      Compute cluster centroids and predict cluster index for each sample.\n\n |      \n\n |      Convenience method; equivalent to calling fit(X) followed by\n\n |      predict(X).\n\n |  \n\n |  ----------------------------------------------------------------------\n\n |  Methods inherited from sklearn.base.BaseEstimator:\n\n |  \n\n |  __getstate__(self)\n\n |  \n\n |  __repr__(self)\n\n |      Return repr(self).\n\n |  \n\n |  __setstate__(self, state)\n\n |  \n\n |  get_params(self, deep=True)\n\n |      Get parameters for this estimator.\n\n |      \n\n |      Parameters\n\n |      ----------\n\n |      deep : boolean, optional\n\n |          If True, will return the parameters for this estimator and\n\n |          contained subobjects that are estimators.\n\n |      \n\n |      Returns\n\n |      -------\n\n |      params : mapping of string to any\n\n |          Parameter names mapped to their values.\n\n |  \n\n |  set_params(self, **params)\n\n |      Set the parameters of this estimator.\n\n |      \n\n |      The method works on simple estimators as well as on nested objects\n\n |      (such as pipelines). The latter have parameters of the form\n\n |      ``<component>__<parameter>`` so that it's possible to update each\n\n |      component of a nested object.\n\n |      \n\n |      Returns\n\n |      -------\n\n |      self\n\n |  \n\n |  ----------------------------------------------------------------------\n\n |  Data descriptors inherited from sklearn.base.BaseEstimator:\n\n |  \n\n |  __dict__\n\n |      dictionary for instance variables (if defined)\n\n |  \n\n |  __weakref__\n\n |      list of weak references to the object (if defined)\n\n\n"
 63 |         }
 64 |       ]
 65 |     },
 66 |     {
 67 |       "cell_type": "code",
 68 |       "source": "# Reading Dataset\nblood = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/blood-transfusion/transfusion.data', sep=\",\",engine = 'python')",
 69 |       "metadata": {},
 70 |       "execution_count": 3,
 71 |       "outputs": []
 72 |     },
 73 |     {
 74 |       "cell_type": "code",
 75 |       "source": "#Sanity Check\nblood.head()",
 76 |       "metadata": {},
 77 |       "execution_count": 4,
 78 |       "outputs": [
 79 |         {
 80 |           "execution_count": 4,
 81 |           "output_type": "execute_result",
 82 |           "data": {
 83 |             "text/html": [
 84 |               "<div>\n",
 85 |               "<style scoped>\n",
 86 |               "    .dataframe tbody tr th:only-of-type {\n",
 87 |               "        vertical-align: middle;\n",
 88 |               "    }\n",
 89 |               "\n",
 90 |               "    .dataframe tbody tr th {\n",
 91 |               "        vertical-align: top;\n",
 92 |               "    }\n",
 93 |               "\n",
 94 |               "    .dataframe thead th {\n",
 95 |               "        text-align: right;\n",
 96 |               "    }\n",
 97 |               "</style>\n",
 98 |               "<table border=\"1\" class=\"dataframe\">\n",
 99 |               "  <thead>\n",
100 |               "    <tr style=\"text-align: right;\">\n",
101 |               "      <th></th>\n",
102 |               "      <th>Recency (months)</th>\n",
103 |               "      <th>Frequency (times)</th>\n",
104 |               "      <th>Monetary (c.c. blood)</th>\n",
105 |               "      <th>Time (months)</th>\n",
106 |               "      <th>whether he/she donated blood in March 2007</th>\n",
107 |               "    </tr>\n",
108 |               "  </thead>\n",
109 |               "  <tbody>\n",
110 |               "    <tr>\n",
111 |               "      <th>0</th>\n",
112 |               "      <td>2</td>\n",
113 |               "      <td>50</td>\n",
114 |               "      <td>12500</td>\n",
115 |               "      <td>98</td>\n",
116 |               "      <td>1</td>\n",
117 |               "    </tr>\n",
118 |               "    <tr>\n",
119 |               "      <th>1</th>\n",
120 |               "      <td>0</td>\n",
121 |               "      <td>13</td>\n",
122 |               "      <td>3250</td>\n",
123 |               "      <td>28</td>\n",
124 |               "      <td>1</td>\n",
125 |               "    </tr>\n",
126 |               "    <tr>\n",
127 |               "      <th>2</th>\n",
128 |               "      <td>1</td>\n",
129 |               "      <td>16</td>\n",
130 |               "      <td>4000</td>\n",
131 |               "      <td>35</td>\n",
132 |               "      <td>1</td>\n",
133 |               "    </tr>\n",
134 |               "    <tr>\n",
135 |               "      <th>3</th>\n",
136 |               "      <td>2</td>\n",
137 |               "      <td>20</td>\n",
138 |               "      <td>5000</td>\n",
139 |               "      <td>45</td>\n",
140 |               "      <td>1</td>\n",
141 |               "    </tr>\n",
142 |               "    <tr>\n",
143 |               "      <th>4</th>\n",
144 |               "      <td>1</td>\n",
145 |               "      <td>24</td>\n",
146 |               "      <td>6000</td>\n",
147 |               "      <td>77</td>\n",
148 |               "      <td>0</td>\n",
149 |               "    </tr>\n",
150 |               "  </tbody>\n",
151 |               "</table>\n",
152 |               "</div>"
153 |             ],
154 |             "text/plain": [
155 |               "   Recency (months)  Frequency (times)  Monetary (c.c. blood)  Time (months)  \\\n",
156 |               "0                 2                 50                  12500             98   \n",
157 |               "1                 0                 13                   3250             28   \n",
158 |               "2                 1                 16                   4000             35   \n",
159 |               "3                 2                 20                   5000             45   \n",
160 |               "4                 1                 24                   6000             77   \n",
161 |               "\n",
162 |               "   whether he/she donated blood in March 2007  \n",
163 |               "0                                           1  \n",
164 |               "1                                           1  \n",
165 |               "2                                           1  \n",
166 |               "3                                           1  \n",
167 |               "4                                           0  "
168 |             ]
169 |           },
170 |           "metadata": {}
171 |         }
172 |       ]
173 |     },
174 |     {
175 |       "cell_type": "code",
176 |       "source": "# standardizing data\ncolumns_to_normalize     = ['Recency (months)','Frequency (times)','Monetary (c.c. blood)','Time (months)']\nblood[columns_to_normalize] = blood[columns_to_normalize].apply(lambda x: (x - x.mean()) / np.std(x))",
177 |       "metadata": {},
178 |       "execution_count": 5,
179 |       "outputs": []
180 |     },
181 |     {
182 |       "cell_type": "code",
183 |       "source": "# Re-check after standardizing data\nblood.head()",
184 |       "metadata": {},
185 |       "execution_count": 6,
186 |       "outputs": [
187 |         {
188 |           "execution_count": 6,
189 |           "output_type": "execute_result",
190 |           "data": {
191 |             "text/html": [
192 |               "<div>\n",
193 |               "<style scoped>\n",
194 |               "    .dataframe tbody tr th:only-of-type {\n",
195 |               "        vertical-align: middle;\n",
196 |               "    }\n",
197 |               "\n",
198 |               "    .dataframe tbody tr th {\n",
199 |               "        vertical-align: top;\n",
200 |               "    }\n",
201 |               "\n",
202 |               "    .dataframe thead th {\n",
203 |               "        text-align: right;\n",
204 |               "    }\n",
205 |               "</style>\n",
206 |               "<table border=\"1\" class=\"dataframe\">\n",
207 |               "  <thead>\n",
208 |               "    <tr style=\"text-align: right;\">\n",
209 |               "      <th></th>\n",
210 |               "      <th>Recency (months)</th>\n",
211 |               "      <th>Frequency (times)</th>\n",
212 |               "      <th>Monetary (c.c. blood)</th>\n",
213 |               "      <th>Time (months)</th>\n",
214 |               "      <th>whether he/she donated blood in March 2007</th>\n",
215 |               "    </tr>\n",
216 |               "  </thead>\n",
217 |               "  <tbody>\n",
218 |               "    <tr>\n",
219 |               "      <th>0</th>\n",
220 |               "      <td>-0.927899</td>\n",
221 |               "      <td>7.623346</td>\n",
222 |               "      <td>7.623346</td>\n",
223 |               "      <td>2.615633</td>\n",
224 |               "      <td>1</td>\n",
225 |               "    </tr>\n",
226 |               "    <tr>\n",
227 |               "      <th>1</th>\n",
228 |               "      <td>-1.175118</td>\n",
229 |               "      <td>1.282738</td>\n",
230 |               "      <td>1.282738</td>\n",
231 |               "      <td>-0.257881</td>\n",
232 |               "      <td>1</td>\n",
233 |               "    </tr>\n",
234 |               "    <tr>\n",
235 |               "      <th>2</th>\n",
236 |               "      <td>-1.051508</td>\n",
237 |               "      <td>1.796842</td>\n",
238 |               "      <td>1.796842</td>\n",
239 |               "      <td>0.029471</td>\n",
240 |               "      <td>1</td>\n",
241 |               "    </tr>\n",
242 |               "    <tr>\n",
243 |               "      <th>3</th>\n",
244 |               "      <td>-0.927899</td>\n",
245 |               "      <td>2.482313</td>\n",
246 |               "      <td>2.482313</td>\n",
247 |               "      <td>0.439973</td>\n",
248 |               "      <td>1</td>\n",
249 |               "    </tr>\n",
250 |               "    <tr>\n",
251 |               "      <th>4</th>\n",
252 |               "      <td>-1.051508</td>\n",
253 |               "      <td>3.167784</td>\n",
254 |               "      <td>3.167784</td>\n",
255 |               "      <td>1.753579</td>\n",
256 |               "      <td>0</td>\n",
257 |               "    </tr>\n",
258 |               "  </tbody>\n",
259 |               "</table>\n",
260 |               "</div>"
261 |             ],
262 |             "text/plain": [
263 |               "   Recency (months)  Frequency (times)  Monetary (c.c. blood)  Time (months)  \\\n",
264 |               "0         -0.927899           7.623346               7.623346       2.615633   \n",
265 |               "1         -1.175118           1.282738               1.282738      -0.257881   \n",
266 |               "2         -1.051508           1.796842               1.796842       0.029471   \n",
267 |               "3         -0.927899           2.482313               2.482313       0.439973   \n",
268 |               "4         -1.051508           3.167784               3.167784       1.753579   \n",
269 |               "\n",
270 |               "   whether he/she donated blood in March 2007  \n",
271 |               "0                                           1  \n",
272 |               "1                                           1  \n",
273 |               "2                                           1  \n",
274 |               "3                                           1  \n",
275 |               "4                                           0  "
276 |             ]
277 |           },
278 |           "metadata": {}
279 |         }
280 |       ]
281 |     },
282 |     {
283 |       "cell_type": "code",
284 |       "source": "# Converting the dataset into matrix\nblood_matrix = blood.values",
285 |       "metadata": {
286 |         "trusted": true
287 |       },
288 |       "execution_count": 1,
289 |       "outputs": [
290 |         {
291 |           "ename": "<class 'NameError'>",
292 |           "evalue": "name 'blood' is not defined",
293 |           "traceback": [
294 |             "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
295 |             "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
296 |             "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Converting the dataset into matrix\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m blood_matrix \u001b[38;5;241m=\u001b[39m \u001b[43mblood\u001b[49m\u001b[38;5;241m.\u001b[39mvalues\n",
297 |             "\u001b[0;31mNameError\u001b[0m: name 'blood' is not defined"
298 |           ],
299 |           "output_type": "error"
300 |         }
301 |       ]
302 |     },
303 |     {
304 |       "cell_type": "code",
305 |       "source": "# Martix for analysis\nblood_matrix",
306 |       "metadata": {},
307 |       "execution_count": 8,
308 |       "outputs": [
309 |         {
310 |           "execution_count": 8,
311 |           "output_type": "execute_result",
312 |           "data": {
313 |             "text/plain": [
314 |               "array([[-0.92789873,  7.62334626,  7.62334626,  2.61563344,  1.        ],\n",
315 |               "       [-1.17511806,  1.28273826,  1.28273826, -0.2578809 ,  1.        ],\n",
316 |               "       [-1.0515084 ,  1.79684161,  1.79684161,  0.02947053,  1.        ],\n",
317 |               "       ...,\n",
318 |               "       [ 1.66790417, -0.43093957, -0.43093957,  1.13782607,  0.        ],\n",
319 |               "       [ 3.64565877, -0.77367514, -0.77367514,  0.19367135,  0.        ],\n",
320 |               "       [ 7.72477762, -0.77367514, -0.77367514,  1.54832812,  0.        ]])"
321 |             ]
322 |           },
323 |           "metadata": {}
324 |         }
325 |       ]
326 |     },
327 |     {
328 |       "cell_type": "code",
329 |       "source": "# Running K-Prototype clustering\nkproto = KPrototypes(n_clusters=5, init='Cao')\nclusters = kproto.fit_predict(blood_matrix, categorical=[4])",
330 |       "metadata": {},
331 |       "execution_count": 9,
332 |       "outputs": []
333 |     },
334 |     {
335 |       "cell_type": "code",
336 |       "source": "kproto.cluster_centroids_",
337 |       "metadata": {},
338 |       "execution_count": 12,
339 |       "outputs": [
340 |         {
341 |           "execution_count": 12,
342 |           "output_type": "execute_result",
343 |           "data": {
344 |             "text/plain": [
345 |               "[array([[ 1.20155863, -0.0434147 , -0.0434147 ,  1.16721428],\n",
346 |               "        [-0.5217527 ,  4.8324995 ,  4.8324995 ,  2.12009883],\n",
347 |               "        [ 0.77649881, -0.51171646, -0.51171646, -0.41737993],\n",
348 |               "        [-0.77483521, -0.39947752, -0.39947752, -0.73541024],\n",
349 |               "        [-0.46834379,  0.94841338,  0.94841338,  0.92401242]]), array([[0.],\n",
350 |               "        [0.],\n",
351 |               "        [0.],\n",
352 |               "        [0.],\n",
353 |               "        [0.]])]"
354 |             ]
355 |           },
356 |           "metadata": {}
357 |         }
358 |       ]
359 |     },
360 |     {
361 |       "cell_type": "code",
362 |       "source": "# Checking the cost of the clusters created.\nkproto.cost_",
363 |       "metadata": {},
364 |       "execution_count": 9,
365 |       "outputs": [
366 |         {
367 |           "execution_count": 9,
368 |           "output_type": "execute_result",
369 |           "data": {
370 |             "text/plain": [
371 |               "915.5424569187537"
372 |             ]
373 |           },
374 |           "metadata": {}
375 |         }
376 |       ]
377 |     },
378 |     {
379 |       "cell_type": "code",
380 |       "source": "# Adding the predicted clusters to the main dataset\nblood['cluster_id'] = clusters",
381 |       "metadata": {},
382 |       "execution_count": 10,
383 |       "outputs": []
384 |     },
385 |     {
386 |       "cell_type": "code",
387 |       "source": "# Re-check\nblood.head()",
388 |       "metadata": {},
389 |       "execution_count": 11,
390 |       "outputs": [
391 |         {
392 |           "execution_count": 11,
393 |           "output_type": "execute_result",
394 |           "data": {
395 |             "text/html": [
396 |               "<div>\n",
397 |               "<style scoped>\n",
398 |               "    .dataframe tbody tr th:only-of-type {\n",
399 |               "        vertical-align: middle;\n",
400 |               "    }\n",
401 |               "\n",
402 |               "    .dataframe tbody tr th {\n",
403 |               "        vertical-align: top;\n",
404 |               "    }\n",
405 |               "\n",
406 |               "    .dataframe thead th {\n",
407 |               "        text-align: right;\n",
408 |               "    }\n",
409 |               "</style>\n",
410 |               "<table border=\"1\" class=\"dataframe\">\n",
411 |               "  <thead>\n",
412 |               "    <tr style=\"text-align: right;\">\n",
413 |               "      <th></th>\n",
414 |               "      <th>Recency (months)</th>\n",
415 |               "      <th>Frequency (times)</th>\n",
416 |               "      <th>Monetary (c.c. blood)</th>\n",
417 |               "      <th>Time (months)</th>\n",
418 |               "      <th>whether he/she donated blood in March 2007</th>\n",
419 |               "      <th>cluster_id</th>\n",
420 |               "    </tr>\n",
421 |               "  </thead>\n",
422 |               "  <tbody>\n",
423 |               "    <tr>\n",
424 |               "      <th>0</th>\n",
425 |               "      <td>-0.927899</td>\n",
426 |               "      <td>7.623346</td>\n",
427 |               "      <td>7.623346</td>\n",
428 |               "      <td>2.615633</td>\n",
429 |               "      <td>1</td>\n",
430 |               "      <td>4</td>\n",
431 |               "    </tr>\n",
432 |               "    <tr>\n",
433 |               "      <th>1</th>\n",
434 |               "      <td>-1.175118</td>\n",
435 |               "      <td>1.282738</td>\n",
436 |               "      <td>1.282738</td>\n",
437 |               "      <td>-0.257881</td>\n",
438 |               "      <td>1</td>\n",
439 |               "      <td>0</td>\n",
440 |               "    </tr>\n",
441 |               "    <tr>\n",
442 |               "      <th>2</th>\n",
443 |               "      <td>-1.051508</td>\n",
444 |               "      <td>1.796842</td>\n",
445 |               "      <td>1.796842</td>\n",
446 |               "      <td>0.029471</td>\n",
447 |               "      <td>1</td>\n",
448 |               "      <td>3</td>\n",
449 |               "    </tr>\n",
450 |               "    <tr>\n",
451 |               "      <th>3</th>\n",
452 |               "      <td>-0.927899</td>\n",
453 |               "      <td>2.482313</td>\n",
454 |               "      <td>2.482313</td>\n",
455 |               "      <td>0.439973</td>\n",
456 |               "      <td>1</td>\n",
457 |               "      <td>3</td>\n",
458 |               "    </tr>\n",
459 |               "    <tr>\n",
460 |               "      <th>4</th>\n",
461 |               "      <td>-1.051508</td>\n",
462 |               "      <td>3.167784</td>\n",
463 |               "      <td>3.167784</td>\n",
464 |               "      <td>1.753579</td>\n",
465 |               "      <td>0</td>\n",
466 |               "      <td>3</td>\n",
467 |               "    </tr>\n",
468 |               "  </tbody>\n",
469 |               "</table>\n",
470 |               "</div>"
471 |             ],
472 |             "text/plain": [
473 |               "   Recency (months)  Frequency (times)  Monetary (c.c. blood)  Time (months)  \\\n",
474 |               "0         -0.927899           7.623346               7.623346       2.615633   \n",
475 |               "1         -1.175118           1.282738               1.282738      -0.257881   \n",
476 |               "2         -1.051508           1.796842               1.796842       0.029471   \n",
477 |               "3         -0.927899           2.482313               2.482313       0.439973   \n",
478 |               "4         -1.051508           3.167784               3.167784       1.753579   \n",
479 |               "\n",
480 |               "   whether he/she donated blood in March 2007  cluster_id  \n",
481 |               "0                                           1           4  \n",
482 |               "1                                           1           0  \n",
483 |               "2                                           1           3  \n",
484 |               "3                                           1           3  \n",
485 |               "4                                           0           3  "
486 |             ]
487 |           },
488 |           "metadata": {}
489 |         }
490 |       ]
491 |     },
492 |     {
493 |       "cell_type": "code",
494 |       "source": "# Checking the clusters created\nblooddf = pd.DataFrame(blood['cluster_id'].value_counts())\nblooddf",
495 |       "metadata": {},
496 |       "execution_count": 14,
497 |       "outputs": [
498 |         {
499 |           "execution_count": 14,
500 |           "output_type": "execute_result",
501 |           "data": {
502 |             "text/html": [
503 |               "<div>\n",
504 |               "<style scoped>\n",
505 |               "    .dataframe tbody tr th:only-of-type {\n",
506 |               "        vertical-align: middle;\n",
507 |               "    }\n",
508 |               "\n",
509 |               "    .dataframe tbody tr th {\n",
510 |               "        vertical-align: top;\n",
511 |               "    }\n",
512 |               "\n",
513 |               "    .dataframe thead th {\n",
514 |               "        text-align: right;\n",
515 |               "    }\n",
516 |               "</style>\n",
517 |               "<table border=\"1\" class=\"dataframe\">\n",
518 |               "  <thead>\n",
519 |               "    <tr style=\"text-align: right;\">\n",
520 |               "      <th></th>\n",
521 |               "      <th>cluster_id</th>\n",
522 |               "    </tr>\n",
523 |               "  </thead>\n",
524 |               "  <tbody>\n",
525 |               "    <tr>\n",
526 |               "      <th>1</th>\n",
527 |               "      <td>267</td>\n",
528 |               "    </tr>\n",
529 |               "    <tr>\n",
530 |               "      <th>0</th>\n",
531 |               "      <td>213</td>\n",
532 |               "    </tr>\n",
533 |               "    <tr>\n",
534 |               "      <th>2</th>\n",
535 |               "      <td>180</td>\n",
536 |               "    </tr>\n",
537 |               "    <tr>\n",
538 |               "      <th>3</th>\n",
539 |               "      <td>80</td>\n",
540 |               "    </tr>\n",
541 |               "    <tr>\n",
542 |               "      <th>4</th>\n",
543 |               "      <td>8</td>\n",
544 |               "    </tr>\n",
545 |               "  </tbody>\n",
546 |               "</table>\n",
547 |               "</div>"
548 |             ],
549 |             "text/plain": [
550 |               "   cluster_id\n",
551 |               "1         267\n",
552 |               "0         213\n",
553 |               "2         180\n",
554 |               "3          80\n",
555 |               "4           8"
556 |             ]
557 |           },
558 |           "metadata": {}
559 |         }
560 |       ]
561 |     },
562 |     {
563 |       "cell_type": "code",
564 |       "source": "sns.barplot(x=blooddf.index, y=blooddf['cluster_id'])",
565 |       "metadata": {},
566 |       "execution_count": 16,
567 |       "outputs": [
568 |         {
569 |           "execution_count": 16,
570 |           "output_type": "execute_result",
571 |           "data": {
572 |             "text/plain": [
573 |               "<matplotlib.axes._subplots.AxesSubplot at 0x16353c16240>"
574 |             ]
575 |           },
576 |           "metadata": {}
577 |         },
578 |         {
579 |           "output_type": "display_data",
580 |           "data": {
581 |             "image/png": 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\n",
582 |             "text/plain": [
583 |               "<matplotlib.figure.Figure at 0x16352ba1b38>"
584 |             ]
585 |           },
586 |           "metadata": {}
587 |         }
588 |       ]
589 |     },
590 |     {
591 |       "cell_type": "code",
592 |       "source": "#Choosing optimal K\ncost = []\nfor num_clusters in list(range(1,14)):\n    kproto = KPrototypes(n_clusters=num_clusters, init='Cao')\n    kproto.fit_predict(blood_matrix, categorical=[4])\n    cost.append(kproto.cost_)\n    \nplt.plot(cost)",
593 |       "metadata": {},
594 |       "execution_count": 24,
595 |       "outputs": [
596 |         {
597 |           "execution_count": 24,
598 |           "output_type": "execute_result",
599 |           "data": {
600 |             "text/plain": [
601 |               "[<matplotlib.lines.Line2D at 0x23e612a6e10>]"
602 |             ]
603 |           },
604 |           "metadata": {}
605 |         },
606 |         {
607 |           "output_type": "display_data",
608 |           "data": {
609 |             "image/png": 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\n",
610 |             "text/plain": [
611 |               "<matplotlib.figure.Figure at 0x23e611d5390>"
612 |             ]
613 |           },
614 |           "metadata": {}
615 |         }
616 |       ]
617 |     }
618 |   ]
619 | }


--------------------------------------------------------------------------------
/Other Forms of Clustering/K-Mode+Bank+Marketing.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# K-Mode Clustering on Bank Marketing Dataset"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "markdown",
  12 |    "metadata": {},
  13 |    "source": [
  14 |     "The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. "
  15 |    ]
  16 |   },
  17 |   {
  18 |    "cell_type": "markdown",
  19 |    "metadata": {},
  20 |    "source": [
  21 |     "**Attribute Information(Categorical):**\n",
  22 |     "\n",
  23 |     "- age (numeric)\n",
  24 |     "- job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
  25 |     "- marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
  26 |     "- education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
  27 |     "- default: has credit in default? (categorical: 'no','yes','unknown')\n",
  28 |     "- housing: has housing loan? (categorical: 'no','yes','unknown')\n",
  29 |     "- loan: has personal loan? (categorical: 'no','yes','unknown')\n",
  30 |     "- contact: contact communication type (categorical: 'cellular','telephone') \n",
  31 |     "- month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
  32 |     "- day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
  33 |     "- poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
  34 |     "- UCI Repository: <https://archive.ics.uci.edu/ml/datasets/bank+marketing>"
  35 |    ]
  36 |   },
  37 |   {
  38 |    "cell_type": "code",
  39 |    "execution_count": 24,
  40 |    "metadata": {},
  41 |    "outputs": [],
  42 |    "source": [
  43 |     "# Importing Libraries\n",
  44 |     "import pandas as pd\n",
  45 |     "import numpy as np\n",
  46 |     "%matplotlib inline\n",
  47 |     "import matplotlib.pyplot as plt\n",
  48 |     "import seaborn as sns\n",
  49 |     "from kmodes.kmodes import KModes\n",
  50 |     "import warnings\n",
  51 |     "warnings.filterwarnings(\"ignore\") "
  52 |    ]
  53 |   },
  54 |   {
  55 |    "cell_type": "code",
  56 |    "execution_count": null,
  57 |    "metadata": {},
  58 |    "outputs": [],
  59 |    "source": [
  60 |     "help(KModes)"
  61 |    ]
  62 |   },
  63 |   {
  64 |    "cell_type": "code",
  65 |    "execution_count": 25,
  66 |    "metadata": {},
  67 |    "outputs": [],
  68 |    "source": [
  69 |     "bank = pd.read_csv('bankmarketing.csv')"
  70 |    ]
  71 |   },
  72 |   {
  73 |    "cell_type": "code",
  74 |    "execution_count": 26,
  75 |    "metadata": {},
  76 |    "outputs": [
  77 |     {
  78 |      "data": {
  79 |       "text/html": [
  80 |        "<div>\n",
  81 |        "<style scoped>\n",
  82 |        "    .dataframe tbody tr th:only-of-type {\n",
  83 |        "        vertical-align: middle;\n",
  84 |        "    }\n",
  85 |        "\n",
  86 |        "    .dataframe tbody tr th {\n",
  87 |        "        vertical-align: top;\n",
  88 |        "    }\n",
  89 |        "\n",
  90 |        "    .dataframe thead th {\n",
  91 |        "        text-align: right;\n",
  92 |        "    }\n",
  93 |        "</style>\n",
  94 |        "<table border=\"1\" class=\"dataframe\">\n",
  95 |        "  <thead>\n",
  96 |        "    <tr style=\"text-align: right;\">\n",
  97 |        "      <th></th>\n",
  98 |        "      <th>age</th>\n",
  99 |        "      <th>job</th>\n",
 100 |        "      <th>marital</th>\n",
 101 |        "      <th>education</th>\n",
 102 |        "      <th>default</th>\n",
 103 |        "      <th>housing</th>\n",
 104 |        "      <th>loan</th>\n",
 105 |        "      <th>contact</th>\n",
 106 |        "      <th>month</th>\n",
 107 |        "      <th>day_of_week</th>\n",
 108 |        "      <th>...</th>\n",
 109 |        "      <th>campaign</th>\n",
 110 |        "      <th>pdays</th>\n",
 111 |        "      <th>previous</th>\n",
 112 |        "      <th>poutcome</th>\n",
 113 |        "      <th>emp.var.rate</th>\n",
 114 |        "      <th>cons.price.idx</th>\n",
 115 |        "      <th>cons.conf.idx</th>\n",
 116 |        "      <th>euribor3m</th>\n",
 117 |        "      <th>nr.employed</th>\n",
 118 |        "      <th>y</th>\n",
 119 |        "    </tr>\n",
 120 |        "  </thead>\n",
 121 |        "  <tbody>\n",
 122 |        "    <tr>\n",
 123 |        "      <th>0</th>\n",
 124 |        "      <td>56</td>\n",
 125 |        "      <td>housemaid</td>\n",
 126 |        "      <td>married</td>\n",
 127 |        "      <td>basic.4y</td>\n",
 128 |        "      <td>no</td>\n",
 129 |        "      <td>no</td>\n",
 130 |        "      <td>no</td>\n",
 131 |        "      <td>telephone</td>\n",
 132 |        "      <td>may</td>\n",
 133 |        "      <td>mon</td>\n",
 134 |        "      <td>...</td>\n",
 135 |        "      <td>1</td>\n",
 136 |        "      <td>999</td>\n",
 137 |        "      <td>0</td>\n",
 138 |        "      <td>nonexistent</td>\n",
 139 |        "      <td>1.1</td>\n",
 140 |        "      <td>93.994</td>\n",
 141 |        "      <td>-36.4</td>\n",
 142 |        "      <td>4.857</td>\n",
 143 |        "      <td>5191.0</td>\n",
 144 |        "      <td>no</td>\n",
 145 |        "    </tr>\n",
 146 |        "    <tr>\n",
 147 |        "      <th>1</th>\n",
 148 |        "      <td>57</td>\n",
 149 |        "      <td>services</td>\n",
 150 |        "      <td>married</td>\n",
 151 |        "      <td>high.school</td>\n",
 152 |        "      <td>unknown</td>\n",
 153 |        "      <td>no</td>\n",
 154 |        "      <td>no</td>\n",
 155 |        "      <td>telephone</td>\n",
 156 |        "      <td>may</td>\n",
 157 |        "      <td>mon</td>\n",
 158 |        "      <td>...</td>\n",
 159 |        "      <td>1</td>\n",
 160 |        "      <td>999</td>\n",
 161 |        "      <td>0</td>\n",
 162 |        "      <td>nonexistent</td>\n",
 163 |        "      <td>1.1</td>\n",
 164 |        "      <td>93.994</td>\n",
 165 |        "      <td>-36.4</td>\n",
 166 |        "      <td>4.857</td>\n",
 167 |        "      <td>5191.0</td>\n",
 168 |        "      <td>no</td>\n",
 169 |        "    </tr>\n",
 170 |        "    <tr>\n",
 171 |        "      <th>2</th>\n",
 172 |        "      <td>37</td>\n",
 173 |        "      <td>services</td>\n",
 174 |        "      <td>married</td>\n",
 175 |        "      <td>high.school</td>\n",
 176 |        "      <td>no</td>\n",
 177 |        "      <td>yes</td>\n",
 178 |        "      <td>no</td>\n",
 179 |        "      <td>telephone</td>\n",
 180 |        "      <td>may</td>\n",
 181 |        "      <td>mon</td>\n",
 182 |        "      <td>...</td>\n",
 183 |        "      <td>1</td>\n",
 184 |        "      <td>999</td>\n",
 185 |        "      <td>0</td>\n",
 186 |        "      <td>nonexistent</td>\n",
 187 |        "      <td>1.1</td>\n",
 188 |        "      <td>93.994</td>\n",
 189 |        "      <td>-36.4</td>\n",
 190 |        "      <td>4.857</td>\n",
 191 |        "      <td>5191.0</td>\n",
 192 |        "      <td>no</td>\n",
 193 |        "    </tr>\n",
 194 |        "    <tr>\n",
 195 |        "      <th>3</th>\n",
 196 |        "      <td>40</td>\n",
 197 |        "      <td>admin.</td>\n",
 198 |        "      <td>married</td>\n",
 199 |        "      <td>basic.6y</td>\n",
 200 |        "      <td>no</td>\n",
 201 |        "      <td>no</td>\n",
 202 |        "      <td>no</td>\n",
 203 |        "      <td>telephone</td>\n",
 204 |        "      <td>may</td>\n",
 205 |        "      <td>mon</td>\n",
 206 |        "      <td>...</td>\n",
 207 |        "      <td>1</td>\n",
 208 |        "      <td>999</td>\n",
 209 |        "      <td>0</td>\n",
 210 |        "      <td>nonexistent</td>\n",
 211 |        "      <td>1.1</td>\n",
 212 |        "      <td>93.994</td>\n",
 213 |        "      <td>-36.4</td>\n",
 214 |        "      <td>4.857</td>\n",
 215 |        "      <td>5191.0</td>\n",
 216 |        "      <td>no</td>\n",
 217 |        "    </tr>\n",
 218 |        "    <tr>\n",
 219 |        "      <th>4</th>\n",
 220 |        "      <td>56</td>\n",
 221 |        "      <td>services</td>\n",
 222 |        "      <td>married</td>\n",
 223 |        "      <td>high.school</td>\n",
 224 |        "      <td>no</td>\n",
 225 |        "      <td>no</td>\n",
 226 |        "      <td>yes</td>\n",
 227 |        "      <td>telephone</td>\n",
 228 |        "      <td>may</td>\n",
 229 |        "      <td>mon</td>\n",
 230 |        "      <td>...</td>\n",
 231 |        "      <td>1</td>\n",
 232 |        "      <td>999</td>\n",
 233 |        "      <td>0</td>\n",
 234 |        "      <td>nonexistent</td>\n",
 235 |        "      <td>1.1</td>\n",
 236 |        "      <td>93.994</td>\n",
 237 |        "      <td>-36.4</td>\n",
 238 |        "      <td>4.857</td>\n",
 239 |        "      <td>5191.0</td>\n",
 240 |        "      <td>no</td>\n",
 241 |        "    </tr>\n",
 242 |        "  </tbody>\n",
 243 |        "</table>\n",
 244 |        "<p>5 rows × 21 columns</p>\n",
 245 |        "</div>"
 246 |       ],
 247 |       "text/plain": [
 248 |        "   age        job  marital    education  default housing loan    contact  \\\n",
 249 |        "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
 250 |        "1   57   services  married  high.school  unknown      no   no  telephone   \n",
 251 |        "2   37   services  married  high.school       no     yes   no  telephone   \n",
 252 |        "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
 253 |        "4   56   services  married  high.school       no      no  yes  telephone   \n",
 254 |        "\n",
 255 |        "  month day_of_week ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
 256 |        "0   may         mon ...         1    999         0  nonexistent          1.1   \n",
 257 |        "1   may         mon ...         1    999         0  nonexistent          1.1   \n",
 258 |        "2   may         mon ...         1    999         0  nonexistent          1.1   \n",
 259 |        "3   may         mon ...         1    999         0  nonexistent          1.1   \n",
 260 |        "4   may         mon ...         1    999         0  nonexistent          1.1   \n",
 261 |        "\n",
 262 |        "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
 263 |        "0          93.994          -36.4      4.857       5191.0  no  \n",
 264 |        "1          93.994          -36.4      4.857       5191.0  no  \n",
 265 |        "2          93.994          -36.4      4.857       5191.0  no  \n",
 266 |        "3          93.994          -36.4      4.857       5191.0  no  \n",
 267 |        "4          93.994          -36.4      4.857       5191.0  no  \n",
 268 |        "\n",
 269 |        "[5 rows x 21 columns]"
 270 |       ]
 271 |      },
 272 |      "execution_count": 26,
 273 |      "metadata": {},
 274 |      "output_type": "execute_result"
 275 |     }
 276 |    ],
 277 |    "source": [
 278 |     "bank.head()"
 279 |    ]
 280 |   },
 281 |   {
 282 |    "cell_type": "code",
 283 |    "execution_count": 27,
 284 |    "metadata": {},
 285 |    "outputs": [
 286 |     {
 287 |      "data": {
 288 |       "text/plain": [
 289 |        "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
 290 |        "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
 291 |        "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
 292 |        "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
 293 |        "      dtype='object')"
 294 |       ]
 295 |      },
 296 |      "execution_count": 27,
 297 |      "metadata": {},
 298 |      "output_type": "execute_result"
 299 |     }
 300 |    ],
 301 |    "source": [
 302 |     "bank.columns"
 303 |    ]
 304 |   },
 305 |   {
 306 |    "cell_type": "code",
 307 |    "execution_count": 28,
 308 |    "metadata": {},
 309 |    "outputs": [],
 310 |    "source": [
 311 |     "bank_cust = bank[['age','job', 'marital', 'education', 'default', 'housing', 'loan','contact','month','day_of_week','poutcome']]"
 312 |    ]
 313 |   },
 314 |   {
 315 |    "cell_type": "code",
 316 |    "execution_count": 29,
 317 |    "metadata": {},
 318 |    "outputs": [
 319 |     {
 320 |      "data": {
 321 |       "text/html": [
 322 |        "<div>\n",
 323 |        "<style scoped>\n",
 324 |        "    .dataframe tbody tr th:only-of-type {\n",
 325 |        "        vertical-align: middle;\n",
 326 |        "    }\n",
 327 |        "\n",
 328 |        "    .dataframe tbody tr th {\n",
 329 |        "        vertical-align: top;\n",
 330 |        "    }\n",
 331 |        "\n",
 332 |        "    .dataframe thead th {\n",
 333 |        "        text-align: right;\n",
 334 |        "    }\n",
 335 |        "</style>\n",
 336 |        "<table border=\"1\" class=\"dataframe\">\n",
 337 |        "  <thead>\n",
 338 |        "    <tr style=\"text-align: right;\">\n",
 339 |        "      <th></th>\n",
 340 |        "      <th>age</th>\n",
 341 |        "      <th>job</th>\n",
 342 |        "      <th>marital</th>\n",
 343 |        "      <th>education</th>\n",
 344 |        "      <th>default</th>\n",
 345 |        "      <th>housing</th>\n",
 346 |        "      <th>loan</th>\n",
 347 |        "      <th>contact</th>\n",
 348 |        "      <th>month</th>\n",
 349 |        "      <th>day_of_week</th>\n",
 350 |        "      <th>poutcome</th>\n",
 351 |        "    </tr>\n",
 352 |        "  </thead>\n",
 353 |        "  <tbody>\n",
 354 |        "    <tr>\n",
 355 |        "      <th>0</th>\n",
 356 |        "      <td>56</td>\n",
 357 |        "      <td>housemaid</td>\n",
 358 |        "      <td>married</td>\n",
 359 |        "      <td>basic.4y</td>\n",
 360 |        "      <td>no</td>\n",
 361 |        "      <td>no</td>\n",
 362 |        "      <td>no</td>\n",
 363 |        "      <td>telephone</td>\n",
 364 |        "      <td>may</td>\n",
 365 |        "      <td>mon</td>\n",
 366 |        "      <td>nonexistent</td>\n",
 367 |        "    </tr>\n",
 368 |        "    <tr>\n",
 369 |        "      <th>1</th>\n",
 370 |        "      <td>57</td>\n",
 371 |        "      <td>services</td>\n",
 372 |        "      <td>married</td>\n",
 373 |        "      <td>high.school</td>\n",
 374 |        "      <td>unknown</td>\n",
 375 |        "      <td>no</td>\n",
 376 |        "      <td>no</td>\n",
 377 |        "      <td>telephone</td>\n",
 378 |        "      <td>may</td>\n",
 379 |        "      <td>mon</td>\n",
 380 |        "      <td>nonexistent</td>\n",
 381 |        "    </tr>\n",
 382 |        "    <tr>\n",
 383 |        "      <th>2</th>\n",
 384 |        "      <td>37</td>\n",
 385 |        "      <td>services</td>\n",
 386 |        "      <td>married</td>\n",
 387 |        "      <td>high.school</td>\n",
 388 |        "      <td>no</td>\n",
 389 |        "      <td>yes</td>\n",
 390 |        "      <td>no</td>\n",
 391 |        "      <td>telephone</td>\n",
 392 |        "      <td>may</td>\n",
 393 |        "      <td>mon</td>\n",
 394 |        "      <td>nonexistent</td>\n",
 395 |        "    </tr>\n",
 396 |        "    <tr>\n",
 397 |        "      <th>3</th>\n",
 398 |        "      <td>40</td>\n",
 399 |        "      <td>admin.</td>\n",
 400 |        "      <td>married</td>\n",
 401 |        "      <td>basic.6y</td>\n",
 402 |        "      <td>no</td>\n",
 403 |        "      <td>no</td>\n",
 404 |        "      <td>no</td>\n",
 405 |        "      <td>telephone</td>\n",
 406 |        "      <td>may</td>\n",
 407 |        "      <td>mon</td>\n",
 408 |        "      <td>nonexistent</td>\n",
 409 |        "    </tr>\n",
 410 |        "    <tr>\n",
 411 |        "      <th>4</th>\n",
 412 |        "      <td>56</td>\n",
 413 |        "      <td>services</td>\n",
 414 |        "      <td>married</td>\n",
 415 |        "      <td>high.school</td>\n",
 416 |        "      <td>no</td>\n",
 417 |        "      <td>no</td>\n",
 418 |        "      <td>yes</td>\n",
 419 |        "      <td>telephone</td>\n",
 420 |        "      <td>may</td>\n",
 421 |        "      <td>mon</td>\n",
 422 |        "      <td>nonexistent</td>\n",
 423 |        "    </tr>\n",
 424 |        "  </tbody>\n",
 425 |        "</table>\n",
 426 |        "</div>"
 427 |       ],
 428 |       "text/plain": [
 429 |        "   age        job  marital    education  default housing loan    contact  \\\n",
 430 |        "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
 431 |        "1   57   services  married  high.school  unknown      no   no  telephone   \n",
 432 |        "2   37   services  married  high.school       no     yes   no  telephone   \n",
 433 |        "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
 434 |        "4   56   services  married  high.school       no      no  yes  telephone   \n",
 435 |        "\n",
 436 |        "  month day_of_week     poutcome  \n",
 437 |        "0   may         mon  nonexistent  \n",
 438 |        "1   may         mon  nonexistent  \n",
 439 |        "2   may         mon  nonexistent  \n",
 440 |        "3   may         mon  nonexistent  \n",
 441 |        "4   may         mon  nonexistent  "
 442 |       ]
 443 |      },
 444 |      "execution_count": 29,
 445 |      "metadata": {},
 446 |      "output_type": "execute_result"
 447 |     }
 448 |    ],
 449 |    "source": [
 450 |     "bank_cust.head()"
 451 |    ]
 452 |   },
 453 |   {
 454 |    "cell_type": "code",
 455 |    "execution_count": 30,
 456 |    "metadata": {},
 457 |    "outputs": [],
 458 |    "source": [
 459 |     "bank_cust['age_bin'] = pd.cut(bank_cust['age'], [0, 20, 30, 40, 50, 60, 70, 80, 90, 100], \n",
 460 |     "                              labels=['0-20', '20-30', '30-40', '40-50','50-60','60-70','70-80', '80-90','90-100'])"
 461 |    ]
 462 |   },
 463 |   {
 464 |    "cell_type": "code",
 465 |    "execution_count": 31,
 466 |    "metadata": {},
 467 |    "outputs": [
 468 |     {
 469 |      "data": {
 470 |       "text/html": [
 471 |        "<div>\n",
 472 |        "<style scoped>\n",
 473 |        "    .dataframe tbody tr th:only-of-type {\n",
 474 |        "        vertical-align: middle;\n",
 475 |        "    }\n",
 476 |        "\n",
 477 |        "    .dataframe tbody tr th {\n",
 478 |        "        vertical-align: top;\n",
 479 |        "    }\n",
 480 |        "\n",
 481 |        "    .dataframe thead th {\n",
 482 |        "        text-align: right;\n",
 483 |        "    }\n",
 484 |        "</style>\n",
 485 |        "<table border=\"1\" class=\"dataframe\">\n",
 486 |        "  <thead>\n",
 487 |        "    <tr style=\"text-align: right;\">\n",
 488 |        "      <th></th>\n",
 489 |        "      <th>age</th>\n",
 490 |        "      <th>job</th>\n",
 491 |        "      <th>marital</th>\n",
 492 |        "      <th>education</th>\n",
 493 |        "      <th>default</th>\n",
 494 |        "      <th>housing</th>\n",
 495 |        "      <th>loan</th>\n",
 496 |        "      <th>contact</th>\n",
 497 |        "      <th>month</th>\n",
 498 |        "      <th>day_of_week</th>\n",
 499 |        "      <th>poutcome</th>\n",
 500 |        "      <th>age_bin</th>\n",
 501 |        "    </tr>\n",
 502 |        "  </thead>\n",
 503 |        "  <tbody>\n",
 504 |        "    <tr>\n",
 505 |        "      <th>0</th>\n",
 506 |        "      <td>56</td>\n",
 507 |        "      <td>housemaid</td>\n",
 508 |        "      <td>married</td>\n",
 509 |        "      <td>basic.4y</td>\n",
 510 |        "      <td>no</td>\n",
 511 |        "      <td>no</td>\n",
 512 |        "      <td>no</td>\n",
 513 |        "      <td>telephone</td>\n",
 514 |        "      <td>may</td>\n",
 515 |        "      <td>mon</td>\n",
 516 |        "      <td>nonexistent</td>\n",
 517 |        "      <td>50-60</td>\n",
 518 |        "    </tr>\n",
 519 |        "    <tr>\n",
 520 |        "      <th>1</th>\n",
 521 |        "      <td>57</td>\n",
 522 |        "      <td>services</td>\n",
 523 |        "      <td>married</td>\n",
 524 |        "      <td>high.school</td>\n",
 525 |        "      <td>unknown</td>\n",
 526 |        "      <td>no</td>\n",
 527 |        "      <td>no</td>\n",
 528 |        "      <td>telephone</td>\n",
 529 |        "      <td>may</td>\n",
 530 |        "      <td>mon</td>\n",
 531 |        "      <td>nonexistent</td>\n",
 532 |        "      <td>50-60</td>\n",
 533 |        "    </tr>\n",
 534 |        "    <tr>\n",
 535 |        "      <th>2</th>\n",
 536 |        "      <td>37</td>\n",
 537 |        "      <td>services</td>\n",
 538 |        "      <td>married</td>\n",
 539 |        "      <td>high.school</td>\n",
 540 |        "      <td>no</td>\n",
 541 |        "      <td>yes</td>\n",
 542 |        "      <td>no</td>\n",
 543 |        "      <td>telephone</td>\n",
 544 |        "      <td>may</td>\n",
 545 |        "      <td>mon</td>\n",
 546 |        "      <td>nonexistent</td>\n",
 547 |        "      <td>30-40</td>\n",
 548 |        "    </tr>\n",
 549 |        "    <tr>\n",
 550 |        "      <th>3</th>\n",
 551 |        "      <td>40</td>\n",
 552 |        "      <td>admin.</td>\n",
 553 |        "      <td>married</td>\n",
 554 |        "      <td>basic.6y</td>\n",
 555 |        "      <td>no</td>\n",
 556 |        "      <td>no</td>\n",
 557 |        "      <td>no</td>\n",
 558 |        "      <td>telephone</td>\n",
 559 |        "      <td>may</td>\n",
 560 |        "      <td>mon</td>\n",
 561 |        "      <td>nonexistent</td>\n",
 562 |        "      <td>30-40</td>\n",
 563 |        "    </tr>\n",
 564 |        "    <tr>\n",
 565 |        "      <th>4</th>\n",
 566 |        "      <td>56</td>\n",
 567 |        "      <td>services</td>\n",
 568 |        "      <td>married</td>\n",
 569 |        "      <td>high.school</td>\n",
 570 |        "      <td>no</td>\n",
 571 |        "      <td>no</td>\n",
 572 |        "      <td>yes</td>\n",
 573 |        "      <td>telephone</td>\n",
 574 |        "      <td>may</td>\n",
 575 |        "      <td>mon</td>\n",
 576 |        "      <td>nonexistent</td>\n",
 577 |        "      <td>50-60</td>\n",
 578 |        "    </tr>\n",
 579 |        "  </tbody>\n",
 580 |        "</table>\n",
 581 |        "</div>"
 582 |       ],
 583 |       "text/plain": [
 584 |        "   age        job  marital    education  default housing loan    contact  \\\n",
 585 |        "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
 586 |        "1   57   services  married  high.school  unknown      no   no  telephone   \n",
 587 |        "2   37   services  married  high.school       no     yes   no  telephone   \n",
 588 |        "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
 589 |        "4   56   services  married  high.school       no      no  yes  telephone   \n",
 590 |        "\n",
 591 |        "  month day_of_week     poutcome age_bin  \n",
 592 |        "0   may         mon  nonexistent   50-60  \n",
 593 |        "1   may         mon  nonexistent   50-60  \n",
 594 |        "2   may         mon  nonexistent   30-40  \n",
 595 |        "3   may         mon  nonexistent   30-40  \n",
 596 |        "4   may         mon  nonexistent   50-60  "
 597 |       ]
 598 |      },
 599 |      "execution_count": 31,
 600 |      "metadata": {},
 601 |      "output_type": "execute_result"
 602 |     }
 603 |    ],
 604 |    "source": [
 605 |     "bank_cust.head()"
 606 |    ]
 607 |   },
 608 |   {
 609 |    "cell_type": "code",
 610 |    "execution_count": 32,
 611 |    "metadata": {},
 612 |    "outputs": [],
 613 |    "source": [
 614 |     "bank_cust  = bank_cust.drop('age',axis = 1)"
 615 |    ]
 616 |   },
 617 |   {
 618 |    "cell_type": "code",
 619 |    "execution_count": 33,
 620 |    "metadata": {},
 621 |    "outputs": [
 622 |     {
 623 |      "data": {
 624 |       "text/html": [
 625 |        "<div>\n",
 626 |        "<style scoped>\n",
 627 |        "    .dataframe tbody tr th:only-of-type {\n",
 628 |        "        vertical-align: middle;\n",
 629 |        "    }\n",
 630 |        "\n",
 631 |        "    .dataframe tbody tr th {\n",
 632 |        "        vertical-align: top;\n",
 633 |        "    }\n",
 634 |        "\n",
 635 |        "    .dataframe thead th {\n",
 636 |        "        text-align: right;\n",
 637 |        "    }\n",
 638 |        "</style>\n",
 639 |        "<table border=\"1\" class=\"dataframe\">\n",
 640 |        "  <thead>\n",
 641 |        "    <tr style=\"text-align: right;\">\n",
 642 |        "      <th></th>\n",
 643 |        "      <th>job</th>\n",
 644 |        "      <th>marital</th>\n",
 645 |        "      <th>education</th>\n",
 646 |        "      <th>default</th>\n",
 647 |        "      <th>housing</th>\n",
 648 |        "      <th>loan</th>\n",
 649 |        "      <th>contact</th>\n",
 650 |        "      <th>month</th>\n",
 651 |        "      <th>day_of_week</th>\n",
 652 |        "      <th>poutcome</th>\n",
 653 |        "      <th>age_bin</th>\n",
 654 |        "    </tr>\n",
 655 |        "  </thead>\n",
 656 |        "  <tbody>\n",
 657 |        "    <tr>\n",
 658 |        "      <th>0</th>\n",
 659 |        "      <td>housemaid</td>\n",
 660 |        "      <td>married</td>\n",
 661 |        "      <td>basic.4y</td>\n",
 662 |        "      <td>no</td>\n",
 663 |        "      <td>no</td>\n",
 664 |        "      <td>no</td>\n",
 665 |        "      <td>telephone</td>\n",
 666 |        "      <td>may</td>\n",
 667 |        "      <td>mon</td>\n",
 668 |        "      <td>nonexistent</td>\n",
 669 |        "      <td>50-60</td>\n",
 670 |        "    </tr>\n",
 671 |        "    <tr>\n",
 672 |        "      <th>1</th>\n",
 673 |        "      <td>services</td>\n",
 674 |        "      <td>married</td>\n",
 675 |        "      <td>high.school</td>\n",
 676 |        "      <td>unknown</td>\n",
 677 |        "      <td>no</td>\n",
 678 |        "      <td>no</td>\n",
 679 |        "      <td>telephone</td>\n",
 680 |        "      <td>may</td>\n",
 681 |        "      <td>mon</td>\n",
 682 |        "      <td>nonexistent</td>\n",
 683 |        "      <td>50-60</td>\n",
 684 |        "    </tr>\n",
 685 |        "    <tr>\n",
 686 |        "      <th>2</th>\n",
 687 |        "      <td>services</td>\n",
 688 |        "      <td>married</td>\n",
 689 |        "      <td>high.school</td>\n",
 690 |        "      <td>no</td>\n",
 691 |        "      <td>yes</td>\n",
 692 |        "      <td>no</td>\n",
 693 |        "      <td>telephone</td>\n",
 694 |        "      <td>may</td>\n",
 695 |        "      <td>mon</td>\n",
 696 |        "      <td>nonexistent</td>\n",
 697 |        "      <td>30-40</td>\n",
 698 |        "    </tr>\n",
 699 |        "    <tr>\n",
 700 |        "      <th>3</th>\n",
 701 |        "      <td>admin.</td>\n",
 702 |        "      <td>married</td>\n",
 703 |        "      <td>basic.6y</td>\n",
 704 |        "      <td>no</td>\n",
 705 |        "      <td>no</td>\n",
 706 |        "      <td>no</td>\n",
 707 |        "      <td>telephone</td>\n",
 708 |        "      <td>may</td>\n",
 709 |        "      <td>mon</td>\n",
 710 |        "      <td>nonexistent</td>\n",
 711 |        "      <td>30-40</td>\n",
 712 |        "    </tr>\n",
 713 |        "    <tr>\n",
 714 |        "      <th>4</th>\n",
 715 |        "      <td>services</td>\n",
 716 |        "      <td>married</td>\n",
 717 |        "      <td>high.school</td>\n",
 718 |        "      <td>no</td>\n",
 719 |        "      <td>no</td>\n",
 720 |        "      <td>yes</td>\n",
 721 |        "      <td>telephone</td>\n",
 722 |        "      <td>may</td>\n",
 723 |        "      <td>mon</td>\n",
 724 |        "      <td>nonexistent</td>\n",
 725 |        "      <td>50-60</td>\n",
 726 |        "    </tr>\n",
 727 |        "  </tbody>\n",
 728 |        "</table>\n",
 729 |        "</div>"
 730 |       ],
 731 |       "text/plain": [
 732 |        "         job  marital    education  default housing loan    contact month  \\\n",
 733 |        "0  housemaid  married     basic.4y       no      no   no  telephone   may   \n",
 734 |        "1   services  married  high.school  unknown      no   no  telephone   may   \n",
 735 |        "2   services  married  high.school       no     yes   no  telephone   may   \n",
 736 |        "3     admin.  married     basic.6y       no      no   no  telephone   may   \n",
 737 |        "4   services  married  high.school       no      no  yes  telephone   may   \n",
 738 |        "\n",
 739 |        "  day_of_week     poutcome age_bin  \n",
 740 |        "0         mon  nonexistent   50-60  \n",
 741 |        "1         mon  nonexistent   50-60  \n",
 742 |        "2         mon  nonexistent   30-40  \n",
 743 |        "3         mon  nonexistent   30-40  \n",
 744 |        "4         mon  nonexistent   50-60  "
 745 |       ]
 746 |      },
 747 |      "execution_count": 33,
 748 |      "metadata": {},
 749 |      "output_type": "execute_result"
 750 |     }
 751 |    ],
 752 |    "source": [
 753 |     "bank_cust.head()"
 754 |    ]
 755 |   },
 756 |   {
 757 |    "cell_type": "code",
 758 |    "execution_count": 34,
 759 |    "metadata": {},
 760 |    "outputs": [
 761 |     {
 762 |      "name": "stdout",
 763 |      "output_type": "stream",
 764 |      "text": [
 765 |       "<class 'pandas.core.frame.DataFrame'>\n",
 766 |       "RangeIndex: 41188 entries, 0 to 41187\n",
 767 |       "Data columns (total 11 columns):\n",
 768 |       "job            41188 non-null object\n",
 769 |       "marital        41188 non-null object\n",
 770 |       "education      41188 non-null object\n",
 771 |       "default        41188 non-null object\n",
 772 |       "housing        41188 non-null object\n",
 773 |       "loan           41188 non-null object\n",
 774 |       "contact        41188 non-null object\n",
 775 |       "month          41188 non-null object\n",
 776 |       "day_of_week    41188 non-null object\n",
 777 |       "poutcome       41188 non-null object\n",
 778 |       "age_bin        41188 non-null category\n",
 779 |       "dtypes: category(1), object(10)\n",
 780 |       "memory usage: 3.2+ MB\n"
 781 |      ]
 782 |     }
 783 |    ],
 784 |    "source": [
 785 |     "bank_cust.info()"
 786 |    ]
 787 |   },
 788 |   {
 789 |    "cell_type": "code",
 790 |    "execution_count": 35,
 791 |    "metadata": {},
 792 |    "outputs": [
 793 |     {
 794 |      "data": {
 795 |       "text/html": [
 796 |        "<div>\n",
 797 |        "<style scoped>\n",
 798 |        "    .dataframe tbody tr th:only-of-type {\n",
 799 |        "        vertical-align: middle;\n",
 800 |        "    }\n",
 801 |        "\n",
 802 |        "    .dataframe tbody tr th {\n",
 803 |        "        vertical-align: top;\n",
 804 |        "    }\n",
 805 |        "\n",
 806 |        "    .dataframe thead th {\n",
 807 |        "        text-align: right;\n",
 808 |        "    }\n",
 809 |        "</style>\n",
 810 |        "<table border=\"1\" class=\"dataframe\">\n",
 811 |        "  <thead>\n",
 812 |        "    <tr style=\"text-align: right;\">\n",
 813 |        "      <th></th>\n",
 814 |        "      <th>job</th>\n",
 815 |        "      <th>marital</th>\n",
 816 |        "      <th>education</th>\n",
 817 |        "      <th>default</th>\n",
 818 |        "      <th>housing</th>\n",
 819 |        "      <th>loan</th>\n",
 820 |        "      <th>contact</th>\n",
 821 |        "      <th>month</th>\n",
 822 |        "      <th>day_of_week</th>\n",
 823 |        "      <th>poutcome</th>\n",
 824 |        "      <th>age_bin</th>\n",
 825 |        "    </tr>\n",
 826 |        "  </thead>\n",
 827 |        "  <tbody>\n",
 828 |        "    <tr>\n",
 829 |        "      <th>0</th>\n",
 830 |        "      <td>3</td>\n",
 831 |        "      <td>1</td>\n",
 832 |        "      <td>0</td>\n",
 833 |        "      <td>0</td>\n",
 834 |        "      <td>0</td>\n",
 835 |        "      <td>0</td>\n",
 836 |        "      <td>1</td>\n",
 837 |        "      <td>6</td>\n",
 838 |        "      <td>1</td>\n",
 839 |        "      <td>1</td>\n",
 840 |        "      <td>4</td>\n",
 841 |        "    </tr>\n",
 842 |        "    <tr>\n",
 843 |        "      <th>1</th>\n",
 844 |        "      <td>7</td>\n",
 845 |        "      <td>1</td>\n",
 846 |        "      <td>3</td>\n",
 847 |        "      <td>1</td>\n",
 848 |        "      <td>0</td>\n",
 849 |        "      <td>0</td>\n",
 850 |        "      <td>1</td>\n",
 851 |        "      <td>6</td>\n",
 852 |        "      <td>1</td>\n",
 853 |        "      <td>1</td>\n",
 854 |        "      <td>4</td>\n",
 855 |        "    </tr>\n",
 856 |        "    <tr>\n",
 857 |        "      <th>2</th>\n",
 858 |        "      <td>7</td>\n",
 859 |        "      <td>1</td>\n",
 860 |        "      <td>3</td>\n",
 861 |        "      <td>0</td>\n",
 862 |        "      <td>2</td>\n",
 863 |        "      <td>0</td>\n",
 864 |        "      <td>1</td>\n",
 865 |        "      <td>6</td>\n",
 866 |        "      <td>1</td>\n",
 867 |        "      <td>1</td>\n",
 868 |        "      <td>2</td>\n",
 869 |        "    </tr>\n",
 870 |        "    <tr>\n",
 871 |        "      <th>3</th>\n",
 872 |        "      <td>0</td>\n",
 873 |        "      <td>1</td>\n",
 874 |        "      <td>1</td>\n",
 875 |        "      <td>0</td>\n",
 876 |        "      <td>0</td>\n",
 877 |        "      <td>0</td>\n",
 878 |        "      <td>1</td>\n",
 879 |        "      <td>6</td>\n",
 880 |        "      <td>1</td>\n",
 881 |        "      <td>1</td>\n",
 882 |        "      <td>2</td>\n",
 883 |        "    </tr>\n",
 884 |        "    <tr>\n",
 885 |        "      <th>4</th>\n",
 886 |        "      <td>7</td>\n",
 887 |        "      <td>1</td>\n",
 888 |        "      <td>3</td>\n",
 889 |        "      <td>0</td>\n",
 890 |        "      <td>0</td>\n",
 891 |        "      <td>2</td>\n",
 892 |        "      <td>1</td>\n",
 893 |        "      <td>6</td>\n",
 894 |        "      <td>1</td>\n",
 895 |        "      <td>1</td>\n",
 896 |        "      <td>4</td>\n",
 897 |        "    </tr>\n",
 898 |        "  </tbody>\n",
 899 |        "</table>\n",
 900 |        "</div>"
 901 |       ],
 902 |       "text/plain": [
 903 |        "   job  marital  education  default  housing  loan  contact  month  \\\n",
 904 |        "0    3        1          0        0        0     0        1      6   \n",
 905 |        "1    7        1          3        1        0     0        1      6   \n",
 906 |        "2    7        1          3        0        2     0        1      6   \n",
 907 |        "3    0        1          1        0        0     0        1      6   \n",
 908 |        "4    7        1          3        0        0     2        1      6   \n",
 909 |        "\n",
 910 |        "   day_of_week  poutcome  age_bin  \n",
 911 |        "0            1         1        4  \n",
 912 |        "1            1         1        4  \n",
 913 |        "2            1         1        2  \n",
 914 |        "3            1         1        2  \n",
 915 |        "4            1         1        4  "
 916 |       ]
 917 |      },
 918 |      "execution_count": 35,
 919 |      "metadata": {},
 920 |      "output_type": "execute_result"
 921 |     }
 922 |    ],
 923 |    "source": [
 924 |     "from sklearn import preprocessing\n",
 925 |     "le = preprocessing.LabelEncoder()\n",
 926 |     "bank_cust = bank_cust.apply(le.fit_transform)\n",
 927 |     "bank_cust.head()"
 928 |    ]
 929 |   },
 930 |   {
 931 |    "cell_type": "code",
 932 |    "execution_count": 38,
 933 |    "metadata": {},
 934 |    "outputs": [],
 935 |    "source": [
 936 |     "# Checking the count per category\n",
 937 |     "job_df = pd.DataFrame(bank_cust['job'].value_counts())"
 938 |    ]
 939 |   },
 940 |   {
 941 |    "cell_type": "code",
 942 |    "execution_count": 39,
 943 |    "metadata": {},
 944 |    "outputs": [
 945 |     {
 946 |      "data": {
 947 |       "text/plain": [
 948 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1a72f14d400>"
 949 |       ]
 950 |      },
 951 |      "execution_count": 39,
 952 |      "metadata": {},
 953 |      "output_type": "execute_result"
 954 |     },
 955 |     {
 956 |      "data": {
 957 |       "image/png": 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\n",
 958 |       "text/plain": [
 959 |        "<matplotlib.figure.Figure at 0x1a72f14d780>"
 960 |       ]
 961 |      },
 962 |      "metadata": {},
 963 |      "output_type": "display_data"
 964 |     }
 965 |    ],
 966 |    "source": [
 967 |     "sns.barplot(x=job_df.index, y=job_df['job'])"
 968 |    ]
 969 |   },
 970 |   {
 971 |    "cell_type": "code",
 972 |    "execution_count": 40,
 973 |    "metadata": {},
 974 |    "outputs": [],
 975 |    "source": [
 976 |     "# Checking the count per category\n",
 977 |     "age_df = pd.DataFrame(bank_cust['age_bin'].value_counts())"
 978 |    ]
 979 |   },
 980 |   {
 981 |    "cell_type": "code",
 982 |    "execution_count": 41,
 983 |    "metadata": {
 984 |     "scrolled": true
 985 |    },
 986 |    "outputs": [
 987 |     {
 988 |      "data": {
 989 |       "text/plain": [
 990 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1a72f140da0>"
 991 |       ]
 992 |      },
 993 |      "execution_count": 41,
 994 |      "metadata": {},
 995 |      "output_type": "execute_result"
 996 |     },
 997 |     {
 998 |      "data": {
 999 |       "image/png": 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\n",
1000 |       "text/plain": [
1001 |        "<matplotlib.figure.Figure at 0x1a72f1554e0>"
1002 |       ]
1003 |      },
1004 |      "metadata": {},
1005 |      "output_type": "display_data"
1006 |     }
1007 |    ],
1008 |    "source": [
1009 |     "sns.barplot(x=age_df.index, y=age_df['age_bin'])"
1010 |    ]
1011 |   },
1012 |   {
1013 |    "cell_type": "markdown",
1014 |    "metadata": {},
1015 |    "source": [
1016 |     "## Using K-Mode with \"Cao\" initialization"
1017 |    ]
1018 |   },
1019 |   {
1020 |    "cell_type": "code",
1021 |    "execution_count": 42,
1022 |    "metadata": {
1023 |     "scrolled": true
1024 |    },
1025 |    "outputs": [
1026 |     {
1027 |      "name": "stdout",
1028 |      "output_type": "stream",
1029 |      "text": [
1030 |       "Init: initializing centroids\n",
1031 |       "Init: initializing clusters\n",
1032 |       "Starting iterations...\n",
1033 |       "Run 1, iteration: 1/100, moves: 5322, cost: 192203.0\n",
1034 |       "Run 1, iteration: 2/100, moves: 1160, cost: 192203.0\n"
1035 |      ]
1036 |     }
1037 |    ],
1038 |    "source": [
1039 |     "km_cao = KModes(n_clusters=2, init = \"Cao\", n_init = 1, verbose=1)\n",
1040 |     "fitClusters_cao = km_cao.fit_predict(bank_cust)"
1041 |    ]
1042 |   },
1043 |   {
1044 |    "cell_type": "code",
1045 |    "execution_count": 43,
1046 |    "metadata": {},
1047 |    "outputs": [
1048 |     {
1049 |      "data": {
1050 |       "text/plain": [
1051 |        "array([1, 1, 0, ..., 0, 1, 0], dtype=uint8)"
1052 |       ]
1053 |      },
1054 |      "execution_count": 43,
1055 |      "metadata": {},
1056 |      "output_type": "execute_result"
1057 |     }
1058 |    ],
1059 |    "source": [
1060 |     "# Predicted Clusters\n",
1061 |     "fitClusters_cao"
1062 |    ]
1063 |   },
1064 |   {
1065 |    "cell_type": "code",
1066 |    "execution_count": 44,
1067 |    "metadata": {},
1068 |    "outputs": [],
1069 |    "source": [
1070 |     "clusterCentroidsDf = pd.DataFrame(km_cao.cluster_centroids_)\n",
1071 |     "clusterCentroidsDf.columns = bank_cust.columns"
1072 |    ]
1073 |   },
1074 |   {
1075 |    "cell_type": "code",
1076 |    "execution_count": 45,
1077 |    "metadata": {},
1078 |    "outputs": [
1079 |     {
1080 |      "data": {
1081 |       "text/html": [
1082 |        "<div>\n",
1083 |        "<style scoped>\n",
1084 |        "    .dataframe tbody tr th:only-of-type {\n",
1085 |        "        vertical-align: middle;\n",
1086 |        "    }\n",
1087 |        "\n",
1088 |        "    .dataframe tbody tr th {\n",
1089 |        "        vertical-align: top;\n",
1090 |        "    }\n",
1091 |        "\n",
1092 |        "    .dataframe thead th {\n",
1093 |        "        text-align: right;\n",
1094 |        "    }\n",
1095 |        "</style>\n",
1096 |        "<table border=\"1\" class=\"dataframe\">\n",
1097 |        "  <thead>\n",
1098 |        "    <tr style=\"text-align: right;\">\n",
1099 |        "      <th></th>\n",
1100 |        "      <th>job</th>\n",
1101 |        "      <th>marital</th>\n",
1102 |        "      <th>education</th>\n",
1103 |        "      <th>default</th>\n",
1104 |        "      <th>housing</th>\n",
1105 |        "      <th>loan</th>\n",
1106 |        "      <th>contact</th>\n",
1107 |        "      <th>month</th>\n",
1108 |        "      <th>day_of_week</th>\n",
1109 |        "      <th>poutcome</th>\n",
1110 |        "      <th>age_bin</th>\n",
1111 |        "    </tr>\n",
1112 |        "  </thead>\n",
1113 |        "  <tbody>\n",
1114 |        "    <tr>\n",
1115 |        "      <th>0</th>\n",
1116 |        "      <td>0</td>\n",
1117 |        "      <td>1</td>\n",
1118 |        "      <td>6</td>\n",
1119 |        "      <td>0</td>\n",
1120 |        "      <td>2</td>\n",
1121 |        "      <td>0</td>\n",
1122 |        "      <td>0</td>\n",
1123 |        "      <td>6</td>\n",
1124 |        "      <td>2</td>\n",
1125 |        "      <td>1</td>\n",
1126 |        "      <td>2</td>\n",
1127 |        "    </tr>\n",
1128 |        "    <tr>\n",
1129 |        "      <th>1</th>\n",
1130 |        "      <td>1</td>\n",
1131 |        "      <td>1</td>\n",
1132 |        "      <td>3</td>\n",
1133 |        "      <td>0</td>\n",
1134 |        "      <td>0</td>\n",
1135 |        "      <td>0</td>\n",
1136 |        "      <td>1</td>\n",
1137 |        "      <td>6</td>\n",
1138 |        "      <td>0</td>\n",
1139 |        "      <td>1</td>\n",
1140 |        "      <td>3</td>\n",
1141 |        "    </tr>\n",
1142 |        "  </tbody>\n",
1143 |        "</table>\n",
1144 |        "</div>"
1145 |       ],
1146 |       "text/plain": [
1147 |        "   job  marital  education  default  housing  loan  contact  month  \\\n",
1148 |        "0    0        1          6        0        2     0        0      6   \n",
1149 |        "1    1        1          3        0        0     0        1      6   \n",
1150 |        "\n",
1151 |        "   day_of_week  poutcome  age_bin  \n",
1152 |        "0            2         1        2  \n",
1153 |        "1            0         1        3  "
1154 |       ]
1155 |      },
1156 |      "execution_count": 45,
1157 |      "metadata": {},
1158 |      "output_type": "execute_result"
1159 |     }
1160 |    ],
1161 |    "source": [
1162 |     "# Mode of the clusters\n",
1163 |     "clusterCentroidsDf"
1164 |    ]
1165 |   },
1166 |   {
1167 |    "cell_type": "markdown",
1168 |    "metadata": {},
1169 |    "source": [
1170 |     "## Using K-Mode with \"Huang\" initialization"
1171 |    ]
1172 |   },
1173 |   {
1174 |    "cell_type": "code",
1175 |    "execution_count": 46,
1176 |    "metadata": {},
1177 |    "outputs": [
1178 |     {
1179 |      "name": "stdout",
1180 |      "output_type": "stream",
1181 |      "text": [
1182 |       "Init: initializing centroids\n",
1183 |       "Init: initializing clusters\n",
1184 |       "Starting iterations...\n",
1185 |       "Run 1, iteration: 1/100, moves: 8403, cost: 195645.0\n",
1186 |       "Run 1, iteration: 2/100, moves: 0, cost: 195645.0\n"
1187 |      ]
1188 |     }
1189 |    ],
1190 |    "source": [
1191 |     "km_huang = KModes(n_clusters=2, init = \"Huang\", n_init = 1, verbose=1)\n",
1192 |     "fitClusters_huang = km_huang.fit_predict(bank_cust)"
1193 |    ]
1194 |   },
1195 |   {
1196 |    "cell_type": "code",
1197 |    "execution_count": 47,
1198 |    "metadata": {},
1199 |    "outputs": [
1200 |     {
1201 |      "data": {
1202 |       "text/plain": [
1203 |        "array([0, 0, 1, ..., 0, 0, 1], dtype=uint8)"
1204 |       ]
1205 |      },
1206 |      "execution_count": 47,
1207 |      "metadata": {},
1208 |      "output_type": "execute_result"
1209 |     }
1210 |    ],
1211 |    "source": [
1212 |     "# Predicted clusters\n",
1213 |     "fitClusters_huang"
1214 |    ]
1215 |   },
1216 |   {
1217 |    "cell_type": "markdown",
1218 |    "metadata": {},
1219 |    "source": [
1220 |     "## Choosing K by comparing Cost against each K"
1221 |    ]
1222 |   },
1223 |   {
1224 |    "cell_type": "code",
1225 |    "execution_count": 68,
1226 |    "metadata": {},
1227 |    "outputs": [
1228 |     {
1229 |      "name": "stdout",
1230 |      "output_type": "stream",
1231 |      "text": [
1232 |       "Init: initializing centroids\n",
1233 |       "Init: initializing clusters\n",
1234 |       "Starting iterations...\n",
1235 |       "Run 1, iteration: 1/100, moves: 0, cost: 258139.0\n",
1236 |       "Init: initializing centroids\n",
1237 |       "Init: initializing clusters\n",
1238 |       "Starting iterations...\n",
1239 |       "Run 1, iteration: 1/100, moves: 5319, cost: 233390.0\n",
1240 |       "Run 1, iteration: 2/100, moves: 1165, cost: 233389.0\n",
1241 |       "Run 1, iteration: 3/100, moves: 0, cost: 233389.0\n",
1242 |       "Init: initializing centroids\n",
1243 |       "Init: initializing clusters\n",
1244 |       "Starting iterations...\n",
1245 |       "Run 1, iteration: 1/100, moves: 4991, cost: 226325.0\n",
1246 |       "Run 1, iteration: 2/100, moves: 1369, cost: 226323.0\n",
1247 |       "Run 1, iteration: 3/100, moves: 0, cost: 226323.0\n",
1248 |       "Init: initializing centroids\n",
1249 |       "Init: initializing clusters\n",
1250 |       "Starting iterations...\n",
1251 |       "Run 1, iteration: 1/100, moves: 0, cost: 223701.0\n"
1252 |      ]
1253 |     }
1254 |    ],
1255 |    "source": [
1256 |     "cost = []\n",
1257 |     "for num_clusters in list(range(1,5)):\n",
1258 |     "    kmode = KModes(n_clusters=num_clusters, init = \"Cao\", n_init = 1, verbose=1)\n",
1259 |     "    kmode.fit_predict(bank_cust)\n",
1260 |     "    cost.append(kmode.cost_)"
1261 |    ]
1262 |   },
1263 |   {
1264 |    "cell_type": "code",
1265 |    "execution_count": 69,
1266 |    "metadata": {},
1267 |    "outputs": [
1268 |     {
1269 |      "data": {
1270 |       "text/plain": [
1271 |        "[<matplotlib.lines.Line2D at 0x1a7313562b0>]"
1272 |       ]
1273 |      },
1274 |      "execution_count": 69,
1275 |      "metadata": {},
1276 |      "output_type": "execute_result"
1277 |     },
1278 |     {
1279 |      "data": {
1280 |       "image/png": 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\n",
1281 |       "text/plain": [
1282 |        "<matplotlib.figure.Figure at 0x1a7311b8cf8>"
1283 |       ]
1284 |      },
1285 |      "metadata": {},
1286 |      "output_type": "display_data"
1287 |     }
1288 |    ],
1289 |    "source": [
1290 |     "y = np.array([i for i in range(1,5,1)])\n",
1291 |     "plt.plot(y,cost)"
1292 |    ]
1293 |   },
1294 |   {
1295 |    "cell_type": "code",
1296 |    "execution_count": 50,
1297 |    "metadata": {},
1298 |    "outputs": [],
1299 |    "source": [
1300 |     "## Choosing K=2"
1301 |    ]
1302 |   },
1303 |   {
1304 |    "cell_type": "code",
1305 |    "execution_count": 51,
1306 |    "metadata": {},
1307 |    "outputs": [
1308 |     {
1309 |      "name": "stdout",
1310 |      "output_type": "stream",
1311 |      "text": [
1312 |       "Init: initializing centroids\n",
1313 |       "Init: initializing clusters\n",
1314 |       "Starting iterations...\n",
1315 |       "Run 1, iteration: 1/100, moves: 5322, cost: 192203.0\n",
1316 |       "Run 1, iteration: 2/100, moves: 1160, cost: 192203.0\n"
1317 |      ]
1318 |     }
1319 |    ],
1320 |    "source": [
1321 |     "km_cao = KModes(n_clusters=2, init = \"Cao\", n_init = 1, verbose=1)\n",
1322 |     "fitClusters_cao = km_cao.fit_predict(bank_cust)"
1323 |    ]
1324 |   },
1325 |   {
1326 |    "cell_type": "code",
1327 |    "execution_count": 52,
1328 |    "metadata": {},
1329 |    "outputs": [
1330 |     {
1331 |      "data": {
1332 |       "text/plain": [
1333 |        "array([1, 1, 0, ..., 0, 1, 0], dtype=uint8)"
1334 |       ]
1335 |      },
1336 |      "execution_count": 52,
1337 |      "metadata": {},
1338 |      "output_type": "execute_result"
1339 |     }
1340 |    ],
1341 |    "source": [
1342 |     "fitClusters_cao"
1343 |    ]
1344 |   },
1345 |   {
1346 |    "cell_type": "markdown",
1347 |    "metadata": {},
1348 |    "source": [
1349 |     "### Combining the predicted clusters with the original DF."
1350 |    ]
1351 |   },
1352 |   {
1353 |    "cell_type": "code",
1354 |    "execution_count": 53,
1355 |    "metadata": {},
1356 |    "outputs": [],
1357 |    "source": [
1358 |     "bank_cust = bank_cust.reset_index()\n",
1359 |     "clustersDf = pd.DataFrame(fitClusters_cao)\n",
1360 |     "clustersDf.columns = ['cluster_predicted']\n",
1361 |     "combinedDf = pd.concat([bank_cust, clustersDf], axis = 1).reset_index()\n",
1362 |     "combinedDf = combinedDf.drop(['index', 'level_0'], axis = 1)"
1363 |    ]
1364 |   },
1365 |   {
1366 |    "cell_type": "code",
1367 |    "execution_count": 54,
1368 |    "metadata": {},
1369 |    "outputs": [
1370 |     {
1371 |      "data": {
1372 |       "text/html": [
1373 |        "<div>\n",
1374 |        "<style scoped>\n",
1375 |        "    .dataframe tbody tr th:only-of-type {\n",
1376 |        "        vertical-align: middle;\n",
1377 |        "    }\n",
1378 |        "\n",
1379 |        "    .dataframe tbody tr th {\n",
1380 |        "        vertical-align: top;\n",
1381 |        "    }\n",
1382 |        "\n",
1383 |        "    .dataframe thead th {\n",
1384 |        "        text-align: right;\n",
1385 |        "    }\n",
1386 |        "</style>\n",
1387 |        "<table border=\"1\" class=\"dataframe\">\n",
1388 |        "  <thead>\n",
1389 |        "    <tr style=\"text-align: right;\">\n",
1390 |        "      <th></th>\n",
1391 |        "      <th>job</th>\n",
1392 |        "      <th>marital</th>\n",
1393 |        "      <th>education</th>\n",
1394 |        "      <th>default</th>\n",
1395 |        "      <th>housing</th>\n",
1396 |        "      <th>loan</th>\n",
1397 |        "      <th>contact</th>\n",
1398 |        "      <th>month</th>\n",
1399 |        "      <th>day_of_week</th>\n",
1400 |        "      <th>poutcome</th>\n",
1401 |        "      <th>age_bin</th>\n",
1402 |        "      <th>cluster_predicted</th>\n",
1403 |        "    </tr>\n",
1404 |        "  </thead>\n",
1405 |        "  <tbody>\n",
1406 |        "    <tr>\n",
1407 |        "      <th>0</th>\n",
1408 |        "      <td>3</td>\n",
1409 |        "      <td>1</td>\n",
1410 |        "      <td>0</td>\n",
1411 |        "      <td>0</td>\n",
1412 |        "      <td>0</td>\n",
1413 |        "      <td>0</td>\n",
1414 |        "      <td>1</td>\n",
1415 |        "      <td>6</td>\n",
1416 |        "      <td>1</td>\n",
1417 |        "      <td>1</td>\n",
1418 |        "      <td>4</td>\n",
1419 |        "      <td>1</td>\n",
1420 |        "    </tr>\n",
1421 |        "    <tr>\n",
1422 |        "      <th>1</th>\n",
1423 |        "      <td>7</td>\n",
1424 |        "      <td>1</td>\n",
1425 |        "      <td>3</td>\n",
1426 |        "      <td>1</td>\n",
1427 |        "      <td>0</td>\n",
1428 |        "      <td>0</td>\n",
1429 |        "      <td>1</td>\n",
1430 |        "      <td>6</td>\n",
1431 |        "      <td>1</td>\n",
1432 |        "      <td>1</td>\n",
1433 |        "      <td>4</td>\n",
1434 |        "      <td>1</td>\n",
1435 |        "    </tr>\n",
1436 |        "    <tr>\n",
1437 |        "      <th>2</th>\n",
1438 |        "      <td>7</td>\n",
1439 |        "      <td>1</td>\n",
1440 |        "      <td>3</td>\n",
1441 |        "      <td>0</td>\n",
1442 |        "      <td>2</td>\n",
1443 |        "      <td>0</td>\n",
1444 |        "      <td>1</td>\n",
1445 |        "      <td>6</td>\n",
1446 |        "      <td>1</td>\n",
1447 |        "      <td>1</td>\n",
1448 |        "      <td>2</td>\n",
1449 |        "      <td>0</td>\n",
1450 |        "    </tr>\n",
1451 |        "    <tr>\n",
1452 |        "      <th>3</th>\n",
1453 |        "      <td>0</td>\n",
1454 |        "      <td>1</td>\n",
1455 |        "      <td>1</td>\n",
1456 |        "      <td>0</td>\n",
1457 |        "      <td>0</td>\n",
1458 |        "      <td>0</td>\n",
1459 |        "      <td>1</td>\n",
1460 |        "      <td>6</td>\n",
1461 |        "      <td>1</td>\n",
1462 |        "      <td>1</td>\n",
1463 |        "      <td>2</td>\n",
1464 |        "      <td>0</td>\n",
1465 |        "    </tr>\n",
1466 |        "    <tr>\n",
1467 |        "      <th>4</th>\n",
1468 |        "      <td>7</td>\n",
1469 |        "      <td>1</td>\n",
1470 |        "      <td>3</td>\n",
1471 |        "      <td>0</td>\n",
1472 |        "      <td>0</td>\n",
1473 |        "      <td>2</td>\n",
1474 |        "      <td>1</td>\n",
1475 |        "      <td>6</td>\n",
1476 |        "      <td>1</td>\n",
1477 |        "      <td>1</td>\n",
1478 |        "      <td>4</td>\n",
1479 |        "      <td>1</td>\n",
1480 |        "    </tr>\n",
1481 |        "  </tbody>\n",
1482 |        "</table>\n",
1483 |        "</div>"
1484 |       ],
1485 |       "text/plain": [
1486 |        "   job  marital  education  default  housing  loan  contact  month  \\\n",
1487 |        "0    3        1          0        0        0     0        1      6   \n",
1488 |        "1    7        1          3        1        0     0        1      6   \n",
1489 |        "2    7        1          3        0        2     0        1      6   \n",
1490 |        "3    0        1          1        0        0     0        1      6   \n",
1491 |        "4    7        1          3        0        0     2        1      6   \n",
1492 |        "\n",
1493 |        "   day_of_week  poutcome  age_bin  cluster_predicted  \n",
1494 |        "0            1         1        4                  1  \n",
1495 |        "1            1         1        4                  1  \n",
1496 |        "2            1         1        2                  0  \n",
1497 |        "3            1         1        2                  0  \n",
1498 |        "4            1         1        4                  1  "
1499 |       ]
1500 |      },
1501 |      "execution_count": 54,
1502 |      "metadata": {},
1503 |      "output_type": "execute_result"
1504 |     }
1505 |    ],
1506 |    "source": [
1507 |     "combinedDf.head()"
1508 |    ]
1509 |   },
1510 |   {
1511 |    "cell_type": "code",
1512 |    "execution_count": 55,
1513 |    "metadata": {},
1514 |    "outputs": [],
1515 |    "source": [
1516 |     "# Data for Cluster1\n",
1517 |     "cluster1 = combinedDf[combinedDf.cluster_predicted==1]"
1518 |    ]
1519 |   },
1520 |   {
1521 |    "cell_type": "code",
1522 |    "execution_count": 56,
1523 |    "metadata": {},
1524 |    "outputs": [],
1525 |    "source": [
1526 |     "# Data for Cluster0\n",
1527 |     "cluster0 = combinedDf[combinedDf.cluster_predicted==0]"
1528 |    ]
1529 |   },
1530 |   {
1531 |    "cell_type": "code",
1532 |    "execution_count": 57,
1533 |    "metadata": {},
1534 |    "outputs": [
1535 |     {
1536 |      "name": "stdout",
1537 |      "output_type": "stream",
1538 |      "text": [
1539 |       "<class 'pandas.core.frame.DataFrame'>\n",
1540 |       "Int64Index: 12895 entries, 0 to 41186\n",
1541 |       "Data columns (total 12 columns):\n",
1542 |       "job                  12895 non-null int64\n",
1543 |       "marital              12895 non-null int64\n",
1544 |       "education            12895 non-null int64\n",
1545 |       "default              12895 non-null int64\n",
1546 |       "housing              12895 non-null int64\n",
1547 |       "loan                 12895 non-null int64\n",
1548 |       "contact              12895 non-null int64\n",
1549 |       "month                12895 non-null int64\n",
1550 |       "day_of_week          12895 non-null int64\n",
1551 |       "poutcome             12895 non-null int64\n",
1552 |       "age_bin              12895 non-null int64\n",
1553 |       "cluster_predicted    12895 non-null uint8\n",
1554 |       "dtypes: int64(11), uint8(1)\n",
1555 |       "memory usage: 1.2 MB\n"
1556 |      ]
1557 |     }
1558 |    ],
1559 |    "source": [
1560 |     "cluster1.info()"
1561 |    ]
1562 |   },
1563 |   {
1564 |    "cell_type": "code",
1565 |    "execution_count": 58,
1566 |    "metadata": {},
1567 |    "outputs": [
1568 |     {
1569 |      "name": "stdout",
1570 |      "output_type": "stream",
1571 |      "text": [
1572 |       "<class 'pandas.core.frame.DataFrame'>\n",
1573 |       "Int64Index: 28293 entries, 2 to 41187\n",
1574 |       "Data columns (total 12 columns):\n",
1575 |       "job                  28293 non-null int64\n",
1576 |       "marital              28293 non-null int64\n",
1577 |       "education            28293 non-null int64\n",
1578 |       "default              28293 non-null int64\n",
1579 |       "housing              28293 non-null int64\n",
1580 |       "loan                 28293 non-null int64\n",
1581 |       "contact              28293 non-null int64\n",
1582 |       "month                28293 non-null int64\n",
1583 |       "day_of_week          28293 non-null int64\n",
1584 |       "poutcome             28293 non-null int64\n",
1585 |       "age_bin              28293 non-null int64\n",
1586 |       "cluster_predicted    28293 non-null uint8\n",
1587 |       "dtypes: int64(11), uint8(1)\n",
1588 |       "memory usage: 2.6 MB\n"
1589 |      ]
1590 |     }
1591 |    ],
1592 |    "source": [
1593 |     "cluster0.info()"
1594 |    ]
1595 |   },
1596 |   {
1597 |    "cell_type": "code",
1598 |    "execution_count": 79,
1599 |    "metadata": {},
1600 |    "outputs": [],
1601 |    "source": [
1602 |     "# Checking the count per category for JOB\n",
1603 |     "job1_df = pd.DataFrame(cluster1['job'].value_counts())\n",
1604 |     "job0_df = pd.DataFrame(cluster0['job'].value_counts())"
1605 |    ]
1606 |   },
1607 |   {
1608 |    "cell_type": "code",
1609 |    "execution_count": 80,
1610 |    "metadata": {},
1611 |    "outputs": [
1612 |     {
1613 |      "data": {
1614 |       "image/png": 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\n",
1615 |       "text/plain": [
1616 |        "<matplotlib.figure.Figure at 0x1a73134bb70>"
1617 |       ]
1618 |      },
1619 |      "metadata": {},
1620 |      "output_type": "display_data"
1621 |     }
1622 |    ],
1623 |    "source": [
1624 |     "fig, ax =plt.subplots(1,2,figsize=(20,5))\n",
1625 |     "sns.barplot(x=job1_df.index, y=job1_df['job'], ax=ax[0])\n",
1626 |     "sns.barplot(x=job0_df.index, y=job0_df['job'], ax=ax[1])\n",
1627 |     "fig.show()"
1628 |    ]
1629 |   },
1630 |   {
1631 |    "cell_type": "code",
1632 |    "execution_count": 81,
1633 |    "metadata": {},
1634 |    "outputs": [],
1635 |    "source": [
1636 |     "age1_df = pd.DataFrame(cluster1['age_bin'].value_counts())\n",
1637 |     "age0_df = pd.DataFrame(cluster0['age_bin'].value_counts())"
1638 |    ]
1639 |   },
1640 |   {
1641 |    "cell_type": "code",
1642 |    "execution_count": 83,
1643 |    "metadata": {},
1644 |    "outputs": [
1645 |     {
1646 |      "data": {
1647 |       "image/png": 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VTBuT3D2MX0pybJKl4RG8xyfZPRXfY/qa7+nui5NcnCSbN29+UEMKAFg8h7LC6ZwkT+/ub69UMgAAC2rsOuvOJKdU1aOT/PckpyXZkeSDSV6c5IokW5JcPYy/Zjj+6HD+A93dVXVNkj+qqt9J8iNJNiX52Eg5AgBz7FAaTnckeVgSDScAgHGNWmd1941VdVWSTyR5IMknM1mB9H8nuaKq3jjELhkuuSTJH1bVzkxWNp073OfWqroyk5VXDyQ5v7u/GwCAAziUhtM3k9xcVddnqhjq7l8cPSsAgMUyep3V3RckuWCv8B1Z5i1z3f2tJC/Zx30uTHLh4eYBACymQ2k4XTP8AAAwLnUWADBXDrrh1N2XrmQiAACLSp0FAMybAzacqurK7v7HVXVLlnkNbnf/2IpkBgAw59RZAMC8OpgVTq8ePl+4kokAACwgdRYAMJd+6EADuvue4fMvMtnE8llJfizJt4cYAACHQZ0FAMyrAzac9qiqX0jysST/a5IXJ7mhqv7ZAa55ZFV9rKo+VVW3VtVvDfETqurGqrq9qt5dVQ8f4o8YjncO54+futfrhvjnq+qMQ58qAMCR6XDqLACAI9mhvKXuV5M8u7v/Kkmq6olJ/t8k2/ZzzbeTPK+7v1FVD0vyZ1X1p0l+OclbuvuKqvr3Sc5LctHweV93P62qzk3y5iT/pKpOTHJukmck+ZEk/7Wq/sfu/u4hzRYA4Mh0OHUWAMAR66BXOCVZSvL1qeOvJ7lrfxf0xDeGw4cNP53keUmuGuKXJjln+H72cJzh/GlVVUP8iu7+dnd/McnOJCcdQu4AAEeyQ66zAACOZAfzlrpfHr5+OcmNVXV1Jk2jszNZ+n2g649KclOSpyX5gyRfSPK17n5gGLKUZMPwfUOG4qq7H6iq+5M8cYjfMHXb6Wumf9fWJFuT5LjjjjtQagAAM/VQ6ywAgCPVwTxS99jh8wvDzx5XH8wvGB57+/GqOjrJe5P86HLDhs/ax7l9xff+XRcnuThJNm/e/KDzAABHmIdUZwEAHKkO2HDq7t86mBtV1e9196v2c5+vVdWHkpyS5OiqWjesctqY5O5h2FKSY5MsVdW6JI9Psnsqvsf0NQAAa9JYdRYAwJHmUPZwOpBT9w5U1fphZVOq6lFJfibJbUk+mMkbWJJkS77/V7xrhuMM5z/Q3T3Ezx3eYndCkk2xzBwAWBwPqrMAAI5kh/KWusPx1CSXDvs4/VCSK7v7T6rqs0muqKo3JvlkkkuG8Zck+cOq2pnJyqZzk6S7b62qK5N8NskDSc73hjoAAACAI9OKNpy6+9NJnr1M/I4s85a57v5Wkpfs414XJrlw7BwBAAAAGNeYDaflNvaGI8apvzc/TyP8+av+fNYpALC61FkAwJpyyHs4VdVj9nHqrQ8xFwCAhabOAgDmxUE3nKrqJ4e9l24bjp9VVW/fc7673zl+egAA80+dBQDMm0NZ4fSWJGck+ask6e5PJXnuSiQFALBg1FkAwFw5pEfquvuuvULeFAcAMAJ1FgAwTw5l0/C7quonk3RVPTzJL2ZY9g0AwEOizgIA5sqhrHD635Ocn2RDkqUkPz4cAwDw0KizAIC5ctArnLr7q0l+bgVzAQBYSOosAGDeHHTDqaretkz4/iQ7uvvq8VICAFgs6iwAYN4cyiN1j8xkefftw8+PJXlCkvOq6ndXIDcAgEWhzgIA5sqhbBr+tCTP6+4HkqSqLkry/iTPT3LLCuQGALAo1FkAwFw5lBVOG5I8Zur4MUl+pLu/m+Tbo2YFALBY1FkAwFw5lBVO/2eSm6vqQ0kqyXOT/JuqekyS/7oCuQEALAp1FgAwVw7lLXWXVNWfJnlZks9lssx7qbv/JsmvrlB+AABzT50FAMybQ3lL3S8keXWSjUluTnJKko8med7KpAYAsBjUWQDAvDmUPZxeneR/TvIX3f0Pkzw7ya4VyQoAYLGoswCAuXIoDadvdfe3kqSqHtHdn0vy9JVJCwBgoYxeZ1XV0VV1VVV9rqpuq6p/UFVPqKrtVXX78HnMMLaq6m1VtbOqPl1Vz5m6z5Zh/O1VteUhzRIAWBiH0nBaqqqjk/xfSbZX1dVJ7l6ZtAAAFspK1FlvTfK+7v6fkjwryW1JXpvk+u7elOT64ThJzkqyafjZmuSiJKmqJyS5IMnJSU5KcsGeJhUAwP4cyqbh/8vw9Ter6oNJHp/kfSuSFQDAAhm7zqqqx2XyprufH+7/nSTfqaqzk/z0MOzSJB9K8pokZye5rLs7yQ3D6qinDmO3d/fu4b7bk5yZ5PLDzQ0AWAwH3XCa1t0fHjsRAABGq7P+XiZ7QP3HqnpWkpsy2SfqKd19z/B77qmqJw/jNyS5a+r6pSG2r/gPqKqtmayMynHHHTdC+gDAWncoj9QBALA2rEvynCQXdfezk/xNvv/43HJqmVjvJ/6Dge6Lu3tzd29ev3794eQLAMwZDScAgPmzlGSpu28cjq/KpAH1leFRuQyf906NP3bq+o2Z7CG1rzgAwH5pOAEAzJnu/sskd1XVnjfdnZbks0muSbLnTXNbklw9fL8mycuHt9WdkuT+4dG765KcXlXHDJuFnz7EAAD267D2cAIA4Ij3qiTvqqqHJ7kjySsy+WPjlVV1XpI7k7xkGHttkhck2Znkm8PYdPfuqnpDko8P416/ZwNxAID90XACAJhD3X1zks3LnDptmbGd5Px93Gdbkm3jZgcAzDuP1AEAAAAwKg0nAAAAAEal4QQAAADAqOzhtGDufP3fn3UKoznuX98y6xQAAACAZVjhBAAAAMCoNJwAAAAAGJWGEwAAAACj0nACAAAAYFQaTgAAAACMSsMJAAAAgFFpOAEAAAAwKg0nAAAAAEa1og2nqjq2qj5YVbdV1a1V9eoh/oSq2l5Vtw+fxwzxqqq3VdXOqvp0VT1n6l5bhvG3V9WWlcwbAAAAgMO30iucHkjyK939o0lOSXJ+VZ2Y5LVJru/uTUmuH46T5Kwkm4afrUkuSiYNqiQXJDk5yUlJLtjTpAIAAADgyLJuJW/e3fckuWf4/vWqui3JhiRnJ/npYdilST6U5DVD/LLu7iQ3VNXRVfXUYez27t6dJFW1PcmZSS5fyfyB+fD7v/JfZp3CaF757/7RrFMAYBl3vv7vzzqFQ3bcv75l1ikAMMdWbQ+nqjo+ybOT3JjkKUMzak9T6snDsA1J7pq6bGmI7SsOAAAAwBFmVRpOVfXDSf44yS9191/vb+gysd5PfO/fs7WqdlTVjl27dh1esgAAAAA8JCvecKqqh2XSbHpXd79nCH9leFQuw+e9Q3wpybFTl29Mcvd+4j+guy/u7s3dvXn9+vXjTgQAAACAg7LSb6mrJJckua27f2fq1DVJ9rxpbkuSq6fiLx/eVndKkvuHR+6uS3J6VR0zbBZ++hADAAAA4AizopuGJzk1ycuS3FJVNw+xX0/ypiRXVtV5Se5M8pLh3LVJXpBkZ5JvJnlFknT37qp6Q5KPD+Nev2cDcQAAgIfqw8/9qVmncMh+6iMfnnUKAPu00m+p+7Msv/9Skpy2zPhOcv4+7rUtybbxsgMAAABgJazaW+oAAAAAWAwaTgAAAACMSsMJAAAAgFFpOAEAAAAwKg0nAAAAAEal4QQAAADAqDScAAAAABjVulknAAAAwMr6/V/5L7NO4ZC98t/9o1mnADwEVjgBAMyhqjqqqj5ZVX8yHJ9QVTdW1e1V9e6qevgQf8RwvHM4f/zUPV43xD9fVWfMZiYAwFqk4QQAMJ9eneS2qeM3J3lLd29Kcl+S84b4eUnu6+6nJXnLMC5VdWKSc5M8I8mZSd5eVUetUu4AwBqn4QQAMGeqamOSn03yjuG4kjwvyVXDkEuTnDN8P3s4znD+tGH82Umu6O5vd/cXk+xMctLqzAAAWOs0nAAA5s/vJvm1JH87HD8xyde6+4HheCnJhuH7hiR3Jclw/v5h/Pfiy1zzA6pqa1XtqKodu3btGnMeAMAapeEEADBHquqFSe7t7pumw8sM7QOc2981Pxjsvri7N3f35vXr1x9SvgDAfPKWOgCA+XJqkhdV1QuSPDLJ4zJZ8XR0Va0bVjFtTHL3MH4pybFJlqpqXZLHJ9k9Fd9j+hoAgP2ywgkAYI509+u6e2N3H5/Jpt8f6O6fS/LBJC8ehm1JcvXw/ZrhOMP5D3R3D/Fzh7fYnZBkU5KPrdI0AIA1zgonAIDF8JokV1TVG5N8MsklQ/ySJH9YVTszWdl0bpJ0961VdWWSzyZ5IMn53f3d1U8bAFiLNJwAAOZUd38oyYeG73dkmbfMdfe3krxkH9dfmOTClcsQAJhXHqkDAAAAYFQaTgAAAACMSsMJAAAAgFFpOAEAAAAwKg0nAAAAAEal4QQAAADAqDScAAAAABiVhhMAAAAAo9JwAgAAAGBUGk4AAAAAjErDCQAAAIBRaTgBAAAAMCoNJwAAAABGpeEEAAAAwKg0nAAAAAAYlYYTAAAAAKPScAIAAABgVBpOAAAAAIxKwwkAAACAUWk4AQAAADAqDScAAAAARrWiDaeq2lZV91bVZ6ZiT6iq7VV1+/B5zBCvqnpbVe2sqk9X1XOmrtkyjL+9qrasZM4AAAAAPDQrvcLpnUnO3Cv22iTXd/emJNcPx0lyVpJNw8/WJBclkwZVkguSnJzkpCQX7GlSAQAAAHDkWdGGU3d/JMnuvcJnJ7l0+H5pknOm4pf1xA1Jjq6qpyY5I8n27t7d3fcl2Z4HN7EAAAAAOELMYg+np3T3PUkyfD55iG9IctfUuKUhtq/4g1TV1qraUVU7du3aNXriAAAAABzYkbRpeC0T6/3EHxzsvri7N3f35vXr14+aHAAAAAAHZxYNp68Mj8pl+Lx3iC8lOXZq3MYkd+8nDgAAAMARaBYNp2uS7HnT3JYkV0/FXz68re6UJPcPj9xdl+T0qjpm2Cz89CEGAAAAwBFo3UrevKouT/LTSZ5UVUuZvG3uTUmurKrzktyZ5CXD8GuTvCDJziTfTPKKJOnu3VX1hiQfH8a9vrv33ogcAAAAgCPEijacuvul+zh12jJjO8n5+7jPtiTbRkwNAGBuVdWxSS5L8neS/G2Si7v7rVX1hCTvTnJ8ki8l+cfdfV9VVZK3ZvLHv28m+fnu/sRwry1J/tVw6zd296UBADiAI2nTcAAAxvFAkl/p7h9NckqS86vqxCSvTXJ9d29Kcv1wnCRnJdk0/GxNclGSDA2qC5KcnOSkJBcMWxwAAOyXhhMAwJzp7nv2rFDq7q8nuS3JhiRnJ9mzQunSJOcM389OcllP3JDk6OHlLmck2d7du7v7viTbk5y5ilMBANYoDScAgDlWVccneXaSG5M8ZXgpS4bPJw/DNiS5a+qypSG2r/jev2NrVe2oqh27du0aewoAwBqk4QQAMKeq6oeT/HGSX+ruv97f0GVivZ/4Dwa6L+7uzd29ef369YeXLAAwVzScAADmUFU9LJNm07u6+z1D+CvDo3IZPu8d4ktJjp26fGOSu/cTBwDYLw0nAIA5M7x17pIkt3X370yduibJluH7liRXT8VfXhOnJLl/eOTuuiSnV9Uxw2bhpw8xAID9WjfrBAAAGN2pSV6W5JaqunmI/XqSNyW5sqrOS3JnkpcM565N8oIkO5N8M8krkqS7d1fVG5J8fBj3+u7evTpTAADWMg0nAIA5091/luX3X0qS05YZ30nO38e9tiXZNl52AMAi8EgdAAAAAKPScAIAAABgVBpOAAAAAIxKwwkAAACAUWk4AQAAADAqDScAAAAARqXhBAAAAMCoNJwAAAAAGJWGEwAAAACj0nACAAAAYFQaTgAAAACMSsMJAAAAgFFpOAEAAAAwKg0nAAAAAEal4QQAAADAqDScAAAAABiVhhMAAAAAo9JwAgAAAGBUGk4AAAAAjErDCQAAAIBRaTgBAAAAMCoNJwAAAABGpeEEAAAAwKg0nAAAAAAYlYYTAAAAAKNaN+sEAAAA4KG48J++eNYpHJbf+E9XzToFWDFWOAEAAAAwKg0nAAAAAEblkToAAAA4wt124QdmncIh+9HfeN6sU2CG1lTDqarOTPLWJEcleUd3v2nGKQEc8dbqngZ7s8cBzIb6CwA4HGvmkbqqOirJHyQx/ZwdAAAG8ElEQVQ5K8mJSV5aVSfONisAgPml/gIADtdaWuF0UpKd3X1HklTVFUnOTvLZQ73RT/zqZSOnNhs3/fbLZ50CwBFtLS493xdL0pmRha6/1FoAq+c3f/M3Z53CIVuLOa+mtdRw2pDkrqnjpSQnzygXADiizVMBNE9zWYPUXwDAYanunnUOB6WqXpLkjO7+heH4ZUlO6u5XTY3ZmmTrcPj0JJ9f9US/70lJvjrD3z9Lizz3ZLHnv8hzTxZ7/ua+uGY9/7/b3etn+Pvn2sHUX0N8ljXYrP87uBrmfY7mt/bN+xzNb+2b9zmu9vwOqv5aSyuclpIcO3W8Mcnd0wO6++IkF69mUvtSVTu6e/Os85iFRZ57stjzX+S5J4s9f3NfzLkn5r8ADlh/JbOtwRbhv4PzPkfzW/vmfY7mt/bN+xyP1PmtmU3Dk3w8yaaqOqGqHp7k3CTXzDgnAIB5pv4CAA7Lmlnh1N0PVNUrk1yXyWt5t3X3rTNOCwBgbqm/AIDDtWYaTknS3dcmuXbWeRykI+LRvhlZ5Lkniz3/RZ57stjzN/fFtejzn3troP5ahP8OzvsczW/tm/c5mt/aN+9zPCLnt2Y2DQcAAABgbVhLezgBAAAAsAZoOAEAAAAwKg2nkVXVmVX1+araWVWvnXU+q6mqtlXVvVX1mVnnstqq6tiq+mBV3VZVt1bVq2ed02qqqkdW1ceq6lPD/H9r1jmttqo6qqo+WVV/MutcVltVfamqbqmqm6tqx6zzWU1VdXRVXVVVnxv+9/8PZp3Taqiqpw//ee/5+euq+qVZ58Ximee6a97rqkWonRalPpr3Gmje65x5rmUWpV6pqn85/DPmM1V1eVU9ctY57WEPpxFV1VFJ/luS5ydZyuRVwi/t7s/ONLFVUlXPTfKNJJd19zNnnc9qqqqnJnlqd3+iqh6b5KYk5yzQf/aV5DHd/Y2qeliSP0vy6u6+YcaprZqq+uUkm5M8rrtfOOt8VlNVfSnJ5u7+6qxzWW1VdWmS/6e73zG8Mv7R3f21Wee1moZ/9305ycnd/RezzofFMe9117zXVYtQOy1KfTTvNdC81zmLUsvMa71SVRsy+WfLid3936vqyiTXdvc7Z5vZhBVO4zopyc7uvqO7v5PkiiRnzzinVdPdH0mye9Z5zEJ339Pdnxi+fz3JbUk2zDar1dMT3xgOHzb8LEw3u6o2JvnZJO+YdS6snqp6XJLnJrkkSbr7O/NYoB2E05J8YZ6KN9aMua675r2uWoTaaRHqIzXQ2rZgtcw81yvrkjyqqtYleXSSu2ecz/doOI1rQ5K7po6XMmf/4uTAqur4JM9OcuNsM1ldw3Lqm5Pcm2R7dy/S/H83ya8l+dtZJzIjneT9VXVTVW2ddTKr6O8l2ZXkPw6PEryjqh4z66Rm4Nwkl886CRaSumtOzHPttAD10SLUQPNc5yxSLTOX9Up3fznJv01yZ5J7ktzf3e+fbVbfp+E0rlomNld/xWD/quqHk/xxkl/q7r+edT6rqbu/290/nmRjkpOqau6W/y+nql6Y5N7uvmnWuczQqd39nCRnJTl/eAxkEaxL8pwkF3X3s5P8TZK52kPmQIal9y9K8p9nnQsLSd01B+a9dprn+miBaqB5rnMWopaZ53qlqo7JZHXvCUl+JMljquqfzjar79NwGtdSkmOnjjfmCFrOxsoans3/4yTv6u73zDqfWRmW4X4oyZkzTmW1nJrkRcPz/VckeV5V/afZprS6uvvu4fPeJO/N5DGXRbCUZGnqr9VXZVK0LZKzknyiu78y60RYSOquNW6Raqc5rY8Wogaa8zpnUWqZea5XfibJF7t7V3f/f0nek+QnZ5zT92g4jevjSTZV1QlDF/XcJNfMOCdWwbAp5CVJbuvu35l1PqutqtZX1dHD90dl8g++z802q9XR3a/r7o3dfXwm/5v/QHcfMX9VWGlV9Zhhs9cMS7BPTzKXb1TaW3f/ZZK7qurpQ+i0JHOz2e1BemnmcHk6a4a6aw1bhNpp3uujRaiB5r3OWaBaZp7rlTuTnFJVjx7+uXpaJnviHRHWzTqBedLdD1TVK5Ncl+SoJNu6+9YZp7VqquryJD+d5ElVtZTkgu6+ZLZZrZpTk7wsyS3Dc/pJ8uvdfe0Mc1pNT01y6fD2hx9KcmV3z+WrcXmQpyR57+Tfb1mX5I+6+32zTWlVvSrJu4b/s3tHklfMOJ9VU1WPzuTtYP/brHNhMc173bUAddUi1E7qo7VvEeqcua5l5r1e6e4bq+qqJJ9I8kCSTya5eLZZfV91e9QdAAAAgPF4pA4AAACAUWk4AQAAADAqDScAAAAARqXhBAAAAMCoNJwAAAAAGJWGEwAAAACj0nACAAAAYFT/P1VW1AyVKj3LAAAAAElFTkSuQmCC\n",
1648 |       "text/plain": [
1649 |        "<matplotlib.figure.Figure at 0x1a72ecc3cf8>"
1650 |       ]
1651 |      },
1652 |      "metadata": {},
1653 |      "output_type": "display_data"
1654 |     }
1655 |    ],
1656 |    "source": [
1657 |     "fig, ax =plt.subplots(1,2,figsize=(20,5))\n",
1658 |     "sns.barplot(x=age1_df.index, y=age1_df['age_bin'], ax=ax[0])\n",
1659 |     "sns.barplot(x=age0_df.index, y=age0_df['age_bin'], ax=ax[1])\n",
1660 |     "fig.show()"
1661 |    ]
1662 |   },
1663 |   {
1664 |    "cell_type": "code",
1665 |    "execution_count": 63,
1666 |    "metadata": {},
1667 |    "outputs": [
1668 |     {
1669 |      "name": "stdout",
1670 |      "output_type": "stream",
1671 |      "text": [
1672 |       "1    8636\n",
1673 |       "2    2732\n",
1674 |       "0    1501\n",
1675 |       "3      26\n",
1676 |       "Name: marital, dtype: int64\n",
1677 |       "1    16292\n",
1678 |       "2     8836\n",
1679 |       "0     3111\n",
1680 |       "3       54\n",
1681 |       "Name: marital, dtype: int64\n"
1682 |      ]
1683 |     }
1684 |    ],
1685 |    "source": [
1686 |     "print(cluster1['marital'].value_counts())\n",
1687 |     "print(cluster0['marital'].value_counts())"
1688 |    ]
1689 |   },
1690 |   {
1691 |    "cell_type": "code",
1692 |    "execution_count": 65,
1693 |    "metadata": {},
1694 |    "outputs": [
1695 |     {
1696 |      "name": "stdout",
1697 |      "output_type": "stream",
1698 |      "text": [
1699 |       "3    4186\n",
1700 |       "2    2572\n",
1701 |       "0    1981\n",
1702 |       "5    1459\n",
1703 |       "1    1033\n",
1704 |       "6     977\n",
1705 |       "7     680\n",
1706 |       "4       7\n",
1707 |       "Name: education, dtype: int64\n",
1708 |       "6    11191\n",
1709 |       "3     5329\n",
1710 |       "5     3784\n",
1711 |       "2     3473\n",
1712 |       "0     2195\n",
1713 |       "1     1259\n",
1714 |       "7     1051\n",
1715 |       "4       11\n",
1716 |       "Name: education, dtype: int64\n"
1717 |      ]
1718 |     }
1719 |    ],
1720 |    "source": [
1721 |     "print(cluster1['education'].value_counts())\n",
1722 |     "print(cluster0['education'].value_counts())"
1723 |    ]
1724 |   }
1725 |  ],
1726 |  "metadata": {
1727 |   "kernelspec": {
1728 |    "display_name": "Python 3",
1729 |    "language": "python",
1730 |    "name": "python3"
1731 |   },
1732 |   "language_info": {
1733 |    "codemirror_mode": {
1734 |     "name": "ipython",
1735 |     "version": 3
1736 |    },
1737 |    "file_extension": ".py",
1738 |    "mimetype": "text/x-python",
1739 |    "name": "python",
1740 |    "nbconvert_exporter": "python",
1741 |    "pygments_lexer": "ipython3",
1742 |    "version": "3.6.4"
1743 |   }
1744 |  },
1745 |  "nbformat": 4,
1746 |  "nbformat_minor": 2
1747 | }
1748 | 


--------------------------------------------------------------------------------