MMA831Project_Clustering_Kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples\n",
    "import sklearn.metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "import itertools\n",
    "\n",
    "import scipy\n",
    "from kmodes.kmodes import KModes\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['customer_ID',\n",
       " 'shopping_pt',\n",
       " 'record_type',\n",
       " 'day',\n",
       " 'time',\n",
       " 'state',\n",
       " 'location',\n",
       " 'group_size',\n",
       " 'homeowner',\n",
       " 'car_age',\n",
       " 'car_value',\n",
       " 'risk_factor',\n",
       " 'age_oldest',\n",
       " 'age_youngest',\n",
       " 'married_couple',\n",
       " 'C_previous',\n",
       " 'duration_previous',\n",
       " 'A',\n",
       " 'B',\n",
       " 'C',\n",
       " 'D',\n",
       " 'E',\n",
       " 'F',\n",
       " 'G',\n",
       " 'cost',\n",
       " 'plan',\n",
       " 'hour',\n",
       " 'timeofday',\n",
       " 'weekend',\n",
       " 'family',\n",
       " 'agediff',\n",
       " 'individual',\n",
       " 'stategroup',\n",
       " 'changed',\n",
       " 'changelog']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(97009, 35)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 97009 entries, 0 to 97008\n",
      "Data columns (total 35 columns):\n",
      "customer_ID          97009 non-null int64\n",
      "shopping_pt          97009 non-null int64\n",
      "record_type          97009 non-null int64\n",
      "day                  97009 non-null int64\n",
      "time                 97009 non-null object\n",
      "state                97009 non-null object\n",
      "location             97009 non-null int64\n",
      "group_size           97009 non-null int64\n",
      "homeowner            97009 non-null int64\n",
      "car_age              97009 non-null int64\n",
      "car_value            97009 non-null object\n",
      "risk_factor          97009 non-null int64\n",
      "age_oldest           97009 non-null int64\n",
      "age_youngest         97009 non-null int64\n",
      "married_couple       97009 non-null int64\n",
      "C_previous           97009 non-null object\n",
      "duration_previous    97009 non-null int64\n",
      "A                    97009 non-null int64\n",
      "B                    97009 non-null int64\n",
      "C                    97009 non-null int64\n",
      "D                    97009 non-null int64\n",
      "E                    97009 non-null int64\n",
      "F                    97009 non-null int64\n",
      "G                    97009 non-null int64\n",
      "cost                 97009 non-null int64\n",
      "plan                 97009 non-null int64\n",
      "hour                 97009 non-null int64\n",
      "timeofday            97009 non-null object\n",
      "weekend              97009 non-null object\n",
      "family               97009 non-null object\n",
      "agediff              97009 non-null int64\n",
      "individual           97009 non-null object\n",
      "stategroup           97009 non-null object\n",
      "changed              97009 non-null object\n",
      "changelog            97009 non-null bool\n",
      "dtypes: bool(1), int64(24), object(10)\n",
      "memory usage: 25.3+ MB\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customer_ID</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>1.007656e+07</td>\n",
       "      <td>44050.904207</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>10038531.0</td>\n",
       "      <td>10076385.0</td>\n",
       "      <td>10114706.0</td>\n",
       "      <td>10152724.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shopping_pt</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>6.857601e+00</td>\n",
       "      <td>1.998357</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>record_type</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>2.082683e+00</td>\n",
       "      <td>1.468060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>1.227287e+04</td>\n",
       "      <td>1564.614140</td>\n",
       "      <td>10001.0</td>\n",
       "      <td>10937.0</td>\n",
       "      <td>12031.0</td>\n",
       "      <td>13429.0</td>\n",
       "      <td>16580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_size</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>1.235617e+00</td>\n",
       "      <td>0.461796</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>homeowner</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>5.454855e-01</td>\n",
       "      <td>0.497929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_age</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>8.149326e+00</td>\n",
       "      <td>5.629674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk_factor</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>2.637374e+00</td>\n",
       "      <td>0.936293</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_oldest</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>4.518162e+01</td>\n",
       "      <td>17.389647</td>\n",
       "      <td>18.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_youngest</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>4.268254e+01</td>\n",
       "      <td>17.488953</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married_couple</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>2.116917e-01</td>\n",
       "      <td>0.408510</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration_previous</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>6.040254e+00</td>\n",
       "      <td>4.712596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>9.450670e-01</td>\n",
       "      <td>0.618705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>4.760898e-01</td>\n",
       "      <td>0.499431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>2.291622e+00</td>\n",
       "      <td>1.001741</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>2.516251e+00</td>\n",
       "      <td>0.714176</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>4.626066e-01</td>\n",
       "      <td>0.498602</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>1.166861e+00</td>\n",
       "      <td>0.948329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>2.278263e+00</td>\n",
       "      <td>0.888322</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cost</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>6.344685e+02</td>\n",
       "      <td>43.065970</td>\n",
       "      <td>263.0</td>\n",
       "      <td>605.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plan</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>1.018169e+06</td>\n",
       "      <td>628954.726425</td>\n",
       "      <td>11001.0</td>\n",
       "      <td>1012021.0</td>\n",
       "      <td>1043123.0</td>\n",
       "      <td>1133121.0</td>\n",
       "      <td>2143134.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>1.305985e+01</td>\n",
       "      <td>2.798442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agediff</th>\n",
       "      <td>97009.0</td>\n",
       "      <td>2.499077e+00</td>\n",
       "      <td>7.378392</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count          mean            std         min  \\\n",
       "customer_ID        97009.0  1.007656e+07   44050.904207  10000000.0   \n",
       "shopping_pt        97009.0  6.857601e+00       1.998357         3.0   \n",
       "record_type        97009.0  1.000000e+00       0.000000         1.0   \n",
       "day                97009.0  2.082683e+00       1.468060         0.0   \n",
       "location           97009.0  1.227287e+04    1564.614140     10001.0   \n",
       "group_size         97009.0  1.235617e+00       0.461796         1.0   \n",
       "homeowner          97009.0  5.454855e-01       0.497929         0.0   \n",
       "car_age            97009.0  8.149326e+00       5.629674         0.0   \n",
       "risk_factor        97009.0  2.637374e+00       0.936293         1.0   \n",
       "age_oldest         97009.0  4.518162e+01      17.389647        18.0   \n",
       "age_youngest       97009.0  4.268254e+01      17.488953        16.0   \n",
       "married_couple     97009.0  2.116917e-01       0.408510         0.0   \n",
       "duration_previous  97009.0  6.040254e+00       4.712596         0.0   \n",
       "A                  97009.0  9.450670e-01       0.618705         0.0   \n",
       "B                  97009.0  4.760898e-01       0.499431         0.0   \n",
       "C                  97009.0  2.291622e+00       1.001741         1.0   \n",
       "D                  97009.0  2.516251e+00       0.714176         1.0   \n",
       "E                  97009.0  4.626066e-01       0.498602         0.0   \n",
       "F                  97009.0  1.166861e+00       0.948329         0.0   \n",
       "G                  97009.0  2.278263e+00       0.888322         1.0   \n",
       "cost               97009.0  6.344685e+02      43.065970       263.0   \n",
       "plan               97009.0  1.018169e+06  628954.726425     11001.0   \n",
       "hour               97009.0  1.305985e+01       2.798442         0.0   \n",
       "agediff            97009.0  2.499077e+00       7.378392         0.0   \n",
       "\n",
       "                          25%         50%         75%         max  \n",
       "customer_ID        10038531.0  10076385.0  10114706.0  10152724.0  \n",
       "shopping_pt               5.0         7.0         8.0        13.0  \n",
       "record_type               1.0         1.0         1.0         1.0  \n",
       "day                       1.0         2.0         3.0         6.0  \n",
       "location              10937.0     12031.0     13429.0     16580.0  \n",
       "group_size                1.0         1.0         1.0         4.0  \n",
       "homeowner                 0.0         1.0         1.0         1.0  \n",
       "car_age                   3.0         8.0        12.0        30.0  \n",
       "risk_factor               2.0         3.0         3.0         4.0  \n",
       "age_oldest               29.0        44.0        60.0        75.0  \n",
       "age_youngest             26.0        40.0        57.0        75.0  \n",
       "married_couple            0.0         0.0         0.0         1.0  \n",
       "duration_previous         2.0         5.0         9.0        15.0  \n",
       "A                         1.0         1.0         1.0         2.0  \n",
       "B                         0.0         0.0         1.0         1.0  \n",
       "C                         1.0         2.0         3.0         4.0  \n",
       "D                         2.0         3.0         3.0         3.0  \n",
       "E                         0.0         0.0         1.0         1.0  \n",
       "F                         0.0         1.0         2.0         3.0  \n",
       "G                         2.0         2.0         3.0         4.0  \n",
       "cost                    605.0       634.0       663.0       839.0  \n",
       "plan                1012021.0   1043123.0   1133121.0   2143134.0  \n",
       "hour                     11.0        13.0        15.0        23.0  \n",
       "agediff                   0.0         0.0         0.0        59.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
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       "      <th>timeofday</th>\n",
       "      <th>weekend</th>\n",
       "      <th>family</th>\n",
       "      <th>agediff</th>\n",
       "      <th>individual</th>\n",
       "      <th>stategroup</th>\n",
       "      <th>changed</th>\n",
       "      <th>changelog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12:07</td>\n",
       "      <td>IN</td>\n",
       "      <td>10001</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1022121</td>\n",
       "      <td>12</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000005</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>09:09</td>\n",
       "      <td>NY</td>\n",
       "      <td>10006</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>32002</td>\n",
       "      <td>9</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000007</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>14:26</td>\n",
       "      <td>PA</td>\n",
       "      <td>10008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>12001</td>\n",
       "      <td>14</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>09:31</td>\n",
       "      <td>WV</td>\n",
       "      <td>10014</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1132113</td>\n",
       "      <td>9</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>g3</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000014</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17:50</td>\n",
       "      <td>MO</td>\n",
       "      <td>10015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1111022</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10000016</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13:15</td>\n",
       "      <td>OH</td>\n",
       "      <td>10018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2033033</td>\n",
       "      <td>13</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000019</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11:34</td>\n",
       "      <td>OK</td>\n",
       "      <td>10021</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2123123</td>\n",
       "      <td>11</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10000023</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14:32</td>\n",
       "      <td>PA</td>\n",
       "      <td>10025</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1113112</td>\n",
       "      <td>14</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10000025</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15:26</td>\n",
       "      <td>OH</td>\n",
       "      <td>10027</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>11003</td>\n",
       "      <td>15</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10000026</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15:24</td>\n",
       "      <td>OH</td>\n",
       "      <td>10028</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1133123</td>\n",
       "      <td>15</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_ID  shopping_pt  record_type  day   time state  location  \\\n",
       "0     10000000            9            1    0  12:07    IN     10001   \n",
       "1     10000005            6            1    3  09:09    NY     10006   \n",
       "2     10000007            8            1    4  14:26    PA     10008   \n",
       "3     10000013            4            1    4  09:31    WV     10014   \n",
       "4     10000014            6            1    1  17:50    MO     10015   \n",
       "5     10000016           10            1    3  13:15    OH     10018   \n",
       "6     10000019            8            1    1  11:34    OK     10021   \n",
       "7     10000023            7            1    1  14:32    PA     10025   \n",
       "8     10000025            7            1    0  15:26    OH     10027   \n",
       "9     10000026            4            1    3  15:24    OH     10028   \n",
       "\n",
       "   group_size  homeowner  car_age  ...     plan  hour  timeofday  weekend  \\\n",
       "0           2          0        2  ...  1022121    12        day       No   \n",
       "1           1          0       10  ...    32002     9        day       No   \n",
       "2           1          0       11  ...    12001    14        day       No   \n",
       "3           2          1        3  ...  1132113     9        day       No   \n",
       "4           1          0        5  ...  1111022    17    evening       No   \n",
       "5           1          0        6  ...  2033033    13        day       No   \n",
       "6           1          0        7  ...  2123123    11        day       No   \n",
       "7           1          1        2  ...  1113112    14        day       No   \n",
       "8           2          1       15  ...    11003    15        day       No   \n",
       "9           2          1        5  ...  1133123    15        day       No   \n",
       "\n",
       "   family agediff  individual  stategroup  changed  changelog  \n",
       "0      No       4          No          g4       No      False  \n",
       "1      No       0         Yes          g4       No      False  \n",
       "2      No       0         Yes          g4      Yes       True  \n",
       "3      No       2          No          g3       No      False  \n",
       "4      No       4          No          g4       No      False  \n",
       "5      No       0         Yes          g4      Yes       True  \n",
       "6      No       0         Yes          g3      Yes       True  \n",
       "7      No       0         Yes          g4       No      False  \n",
       "8      No      35          No          g4       No      False  \n",
       "9      No       2          No          g4       No      False  \n",
       "\n",
       "[10 rows x 35 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('/System/Volumes/Data/Users/Michael/Library/CloudStorage/iCloud Drive/MMA/MMA 831/allstate-purchase-prediction-challenge/trainsub.csv')\n",
    "\n",
    "list(df)\n",
    "df.shape\n",
    "df.info()\n",
    "df.describe().transpose()\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_ID</th>\n",
       "      <th>shopping_pt</th>\n",
       "      <th>record_type</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>state</th>\n",
       "      <th>location</th>\n",
       "      <th>group_size</th>\n",
       "      <th>homeowner</th>\n",
       "      <th>car_age</th>\n",
       "      <th>...</th>\n",
       "      <th>plan</th>\n",
       "      <th>hour</th>\n",
       "      <th>timeofday</th>\n",
       "      <th>weekend</th>\n",
       "      <th>family</th>\n",
       "      <th>agediff</th>\n",
       "      <th>individual</th>\n",
       "      <th>stategroup</th>\n",
       "      <th>changed</th>\n",
       "      <th>changelog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12:07</td>\n",
       "      <td>IN</td>\n",
       "      <td>10001</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1022121</td>\n",
       "      <td>12</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000005</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>09:09</td>\n",
       "      <td>NY</td>\n",
       "      <td>10006</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>32002</td>\n",
       "      <td>9</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000007</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>14:26</td>\n",
       "      <td>PA</td>\n",
       "      <td>10008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>12001</td>\n",
       "      <td>14</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>09:31</td>\n",
       "      <td>WV</td>\n",
       "      <td>10014</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1132113</td>\n",
       "      <td>9</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>g3</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000014</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17:50</td>\n",
       "      <td>MO</td>\n",
       "      <td>10015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1111022</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10000016</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13:15</td>\n",
       "      <td>OH</td>\n",
       "      <td>10018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2033033</td>\n",
       "      <td>13</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10000019</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11:34</td>\n",
       "      <td>OK</td>\n",
       "      <td>10021</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2123123</td>\n",
       "      <td>11</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10000023</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14:32</td>\n",
       "      <td>PA</td>\n",
       "      <td>10025</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1113112</td>\n",
       "      <td>14</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10000025</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15:26</td>\n",
       "      <td>OH</td>\n",
       "      <td>10027</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>11003</td>\n",
       "      <td>15</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10000026</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>15:24</td>\n",
       "      <td>OH</td>\n",
       "      <td>10028</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1133123</td>\n",
       "      <td>15</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_ID  shopping_pt  record_type  day   time state  location  \\\n",
       "0     10000000            9            1    0  12:07    IN     10001   \n",
       "1     10000005            6            1    3  09:09    NY     10006   \n",
       "2     10000007            8            1    4  14:26    PA     10008   \n",
       "3     10000013            4            1    4  09:31    WV     10014   \n",
       "4     10000014            6            1    1  17:50    MO     10015   \n",
       "5     10000016           10            1    3  13:15    OH     10018   \n",
       "6     10000019            8            1    1  11:34    OK     10021   \n",
       "7     10000023            7            1    1  14:32    PA     10025   \n",
       "8     10000025            7            1    0  15:26    OH     10027   \n",
       "9     10000026            4            1    3  15:24    OH     10028   \n",
       "\n",
       "   group_size  homeowner  car_age  ...     plan  hour  timeofday  weekend  \\\n",
       "0           2          0        2  ...  1022121    12        day       No   \n",
       "1           1          0       10  ...    32002     9        day       No   \n",
       "2           1          0       11  ...    12001    14        day       No   \n",
       "3           2          1        3  ...  1132113     9        day       No   \n",
       "4           1          0        5  ...  1111022    17    evening       No   \n",
       "5           1          0        6  ...  2033033    13        day       No   \n",
       "6           1          0        7  ...  2123123    11        day       No   \n",
       "7           1          1        2  ...  1113112    14        day       No   \n",
       "8           2          1       15  ...    11003    15        day       No   \n",
       "9           2          1        5  ...  1133123    15        day       No   \n",
       "\n",
       "   family agediff  individual  stategroup  changed  changelog  \n",
       "0      No       4          No          g4       No      False  \n",
       "1      No       0         Yes          g4       No      False  \n",
       "2      No       0         Yes          g4      Yes       True  \n",
       "3      No       2          No          g3       No      False  \n",
       "4      No       4          No          g4       No      False  \n",
       "5      No       0         Yes          g4      Yes       True  \n",
       "6      No       0         Yes          g3      Yes       True  \n",
       "7      No       0         Yes          g4       No      False  \n",
       "8      No      35          No          g4       No      False  \n",
       "9      No       2          No          g4       No      False  \n",
       "\n",
       "[10 rows x 35 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 97009 entries, 0 to 97008\n",
      "Data columns (total 35 columns):\n",
      "customer_ID          97009 non-null int64\n",
      "shopping_pt          97009 non-null int64\n",
      "record_type          97009 non-null int64\n",
      "day                  97009 non-null int64\n",
      "time                 97009 non-null object\n",
      "state                97009 non-null object\n",
      "location             97009 non-null int64\n",
      "group_size           97009 non-null int64\n",
      "homeowner            97009 non-null int64\n",
      "car_age              97009 non-null int64\n",
      "car_value            97009 non-null object\n",
      "risk_factor          97009 non-null int64\n",
      "age_oldest           97009 non-null int64\n",
      "age_youngest         97009 non-null int64\n",
      "married_couple       97009 non-null int64\n",
      "C_previous           97009 non-null object\n",
      "duration_previous    97009 non-null int64\n",
      "A                    97009 non-null int64\n",
      "B                    97009 non-null int64\n",
      "C                    97009 non-null int64\n",
      "D                    97009 non-null int64\n",
      "E                    97009 non-null int64\n",
      "F                    97009 non-null int64\n",
      "G                    97009 non-null int64\n",
      "cost                 97009 non-null int64\n",
      "plan                 97009 non-null int64\n",
      "hour                 97009 non-null int64\n",
      "timeofday            97009 non-null object\n",
      "weekend              97009 non-null object\n",
      "family               97009 non-null object\n",
      "agediff              97009 non-null int64\n",
      "individual           97009 non-null object\n",
      "stategroup           97009 non-null object\n",
      "changed              97009 non-null object\n",
      "changelog            97009 non-null bool\n",
      "dtypes: bool(1), int64(24), object(10)\n",
      "memory usage: 25.3+ MB\n"
     ]
    }
   ],
   "source": [
    "X = df.copy()\n",
    "X.head(10)\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state', 'car_value', 'C_previous', 'timeofday', 'weekend', 'family',\n",
       "       'individual', 'stategroup', 'changed', 'changelog'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['state'] = X['state'].astype('category')\n",
    "X['car_value'] = X['car_value'].astype('category')\n",
    "X['C_previous'] = X['C_previous'].astype('category')\n",
    "X['timeofday'] = X['timeofday'].astype('category')\n",
    "X['weekend'] = X['weekend'].astype('category')\n",
    "X['family'] = X['family'].astype('category')\n",
    "X['individual'] = X['individual'].astype('category')\n",
    "X['stategroup'] = X['stategroup'].astype('category')\n",
    "X['changed'] = X['changed'].astype('category')\n",
    "X['changelog'] = X['changelog'].astype('category')\n",
    "\n",
    "cat_columns = X.select_dtypes(['category']).columns\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[cat_columns] = X[cat_columns].apply(lambda x: x.cat.codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>customer_ID</th>\n",
       "      <th>shopping_pt</th>\n",
       "      <th>record_type</th>\n",
       "      <th>day</th>\n",
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       "      <td>10000000</td>\n",
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       "      <td>6</td>\n",
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       "      <td>3</td>\n",
       "      <td>09:09</td>\n",
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       "      <td>10006</td>\n",
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       "      <td>10</td>\n",
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       "      <td>32002</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>14:26</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>09:31</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_ID  shopping_pt  record_type  day   time  state  location  \\\n",
       "0     10000000            9            1    0  12:07     10     10001   \n",
       "1     10000005            6            1    3  09:09     23     10006   \n",
       "2     10000007            8            1    4  14:26     27     10008   \n",
       "3     10000013            4            1    4  09:31     34     10014   \n",
       "4     10000014            6            1    1  17:50     15     10015   \n",
       "\n",
       "   group_size  homeowner  car_age  ...     plan  hour  timeofday  weekend  \\\n",
       "0           2          0        2  ...  1022121    12          0        0   \n",
       "1           1          0       10  ...    32002     9          0        0   \n",
       "2           1          0       11  ...    12001    14          0        0   \n",
       "3           2          1        3  ...  1132113     9          0        0   \n",
       "4           1          0        5  ...  1111022    17          1        0   \n",
       "\n",
       "   family  agediff  individual  stategroup  changed  changelog  \n",
       "0       0        4           0           3        0          0  \n",
       "1       0        0           1           3        0          0  \n",
       "2       0        0           1           3        1          1  \n",
       "3       0        2           0           2        0          0  \n",
       "4       0        4           0           3        0          0  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "features = [e for e in list(X) if e not in ('time','customer_ID','record_type')]\n",
    "X[features] = scaler.fit_transform(X[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-1.216463</td>\n",
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       "      <td>0.147632</td>\n",
       "      <td>1.407987</td>\n",
       "      <td>1.660615</td>\n",
       "      <td>-0.127509</td>\n",
       "      <td>-0.101385</td>\n",
       "      <td>0.203422</td>\n",
       "      <td>-1.674641</td>\n",
       "      <td>0.241593</td>\n",
       "      <td>-0.673580</td>\n",
       "      <td>-0.673580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   shopping_pt       day     state  location  group_size  homeowner   car_age  \\\n",
       "0     1.072086 -1.418670 -0.686552 -1.452038    1.655247  -1.095513 -1.092312   \n",
       "1    -0.429155  0.624853  0.600367 -1.448842   -0.510222  -1.095513  0.328737   \n",
       "2     0.571672  1.306027  0.996343 -1.447564   -0.510222  -1.095513  0.506368   \n",
       "3    -1.429982  1.306027  1.689299 -1.443729    1.655247   0.912814 -0.914680   \n",
       "4    -0.429155 -0.737496 -0.191583 -1.443090   -0.510222  -1.095513 -0.559418   \n",
       "\n",
       "   car_value  risk_factor  age_oldest  ...      plan      hour  timeofday  \\\n",
       "0   1.270775     0.387302    0.047061  ...  0.006284 -0.378731  -0.492279   \n",
       "1  -0.387383     1.455349   -0.988043  ... -1.567953 -1.450761  -0.492279   \n",
       "2  -2.045542     0.387302   -0.125456  ... -1.599754  0.335956  -0.492279   \n",
       "3  -1.216463     0.387302    0.967154  ...  0.181166 -1.450761  -0.492279   \n",
       "4  -1.216463     0.387302   -0.758019  ...  0.147632  1.407987   1.660615   \n",
       "\n",
       "    weekend    family   agediff  individual  stategroup   changed  changelog  \n",
       "0 -0.127509 -0.101385  0.203422   -1.674641    0.241593 -0.673580  -0.673580  \n",
       "1 -0.127509 -0.101385 -0.338704    0.597143    0.241593 -0.673580  -0.673580  \n",
       "2 -0.127509 -0.101385 -0.338704    0.597143    0.241593  1.484604   1.484604  \n",
       "3 -0.127509 -0.101385 -0.067641   -1.674641   -1.711954 -0.673580  -0.673580  \n",
       "4 -0.127509 -0.101385  0.203422   -1.674641    0.241593 -0.673580  -0.673580  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[features].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>state</th>\n",
       "      <th>location</th>\n",
       "      <th>group_size</th>\n",
       "      <th>homeowner</th>\n",
       "      <th>car_age</th>\n",
       "      <th>car_value</th>\n",
       "      <th>risk_factor</th>\n",
       "      <th>age_oldest</th>\n",
       "      <th>age_youngest</th>\n",
       "      <th>...</th>\n",
       "      <th>plan</th>\n",
       "      <th>hour</th>\n",
       "      <th>timeofday</th>\n",
       "      <th>weekend</th>\n",
       "      <th>family</th>\n",
       "      <th>agediff</th>\n",
       "      <th>individual</th>\n",
       "      <th>stategroup</th>\n",
       "      <th>changed</th>\n",
       "      <th>changelog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.418670</td>\n",
       "      <td>-0.686552</td>\n",
       "      <td>-1.452038</td>\n",
       "      <td>1.655247</td>\n",
       "      <td>-1.095513</td>\n",
       "      <td>-1.092312</td>\n",
       "      <td>1.270775</td>\n",
       "      <td>0.387302</td>\n",
       "      <td>0.047061</td>\n",
       "      <td>-0.039027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006284</td>\n",
       "      <td>-0.378731</td>\n",
       "      <td>-0.492279</td>\n",
       "      <td>-0.127509</td>\n",
       "      <td>-0.101385</td>\n",
       "      <td>0.203422</td>\n",
       "      <td>-1.674641</td>\n",
       "      <td>0.241593</td>\n",
       "      <td>-0.673580</td>\n",
       "      <td>-0.673580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.624853</td>\n",
       "      <td>0.600367</td>\n",
       "      <td>-1.448842</td>\n",
       "      <td>-0.510222</td>\n",
       "      <td>-1.095513</td>\n",
       "      <td>0.328737</td>\n",
       "      <td>-0.387383</td>\n",
       "      <td>1.455349</td>\n",
       "      <td>-0.988043</td>\n",
       "      <td>-0.839537</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.567953</td>\n",
       "      <td>-1.450761</td>\n",
       "      <td>-0.492279</td>\n",
       "      <td>-0.127509</td>\n",
       "      <td>-0.101385</td>\n",
       "      <td>-0.338704</td>\n",
       "      <td>0.597143</td>\n",
       "      <td>0.241593</td>\n",
       "      <td>-0.673580</td>\n",
       "      <td>-0.673580</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.306027</td>\n",
       "      <td>0.996343</td>\n",
       "      <td>-1.447564</td>\n",
       "      <td>-0.510222</td>\n",
       "      <td>-1.095513</td>\n",
       "      <td>0.506368</td>\n",
       "      <td>-2.045542</td>\n",
       "      <td>0.387302</td>\n",
       "      <td>-0.125456</td>\n",
       "      <td>0.018152</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.599754</td>\n",
       "      <td>0.335956</td>\n",
       "      <td>-0.492279</td>\n",
       "      <td>-0.127509</td>\n",
       "      <td>-0.101385</td>\n",
       "      <td>-0.338704</td>\n",
       "      <td>0.597143</td>\n",
       "      <td>0.241593</td>\n",
       "      <td>1.484604</td>\n",
       "      <td>1.484604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.306027</td>\n",
       "      <td>1.689299</td>\n",
       "      <td>-1.443729</td>\n",
       "      <td>1.655247</td>\n",
       "      <td>0.912814</td>\n",
       "      <td>-0.914680</td>\n",
       "      <td>-1.216463</td>\n",
       "      <td>0.387302</td>\n",
       "      <td>0.967154</td>\n",
       "      <td>0.990199</td>\n",
       "      <td>...</td>\n",
       "      <td>0.181166</td>\n",
       "      <td>-1.450761</td>\n",
       "      <td>-0.492279</td>\n",
       "      <td>-0.127509</td>\n",
       "      <td>-0.101385</td>\n",
       "      <td>-0.067641</td>\n",
       "      <td>-1.674641</td>\n",
       "      <td>-1.711954</td>\n",
       "      <td>-0.673580</td>\n",
       "      <td>-0.673580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.737496</td>\n",
       "      <td>-0.191583</td>\n",
       "      <td>-1.443090</td>\n",
       "      <td>-0.510222</td>\n",
       "      <td>-1.095513</td>\n",
       "      <td>-0.559418</td>\n",
       "      <td>-1.216463</td>\n",
       "      <td>0.387302</td>\n",
       "      <td>-0.758019</td>\n",
       "      <td>-0.839537</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147632</td>\n",
       "      <td>1.407987</td>\n",
       "      <td>1.660615</td>\n",
       "      <td>-0.127509</td>\n",
       "      <td>-0.101385</td>\n",
       "      <td>0.203422</td>\n",
       "      <td>-1.674641</td>\n",
       "      <td>0.241593</td>\n",
       "      <td>-0.673580</td>\n",
       "      <td>-0.673580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        day     state  location  group_size  homeowner   car_age  car_value  \\\n",
       "0 -1.418670 -0.686552 -1.452038    1.655247  -1.095513 -1.092312   1.270775   \n",
       "1  0.624853  0.600367 -1.448842   -0.510222  -1.095513  0.328737  -0.387383   \n",
       "2  1.306027  0.996343 -1.447564   -0.510222  -1.095513  0.506368  -2.045542   \n",
       "3  1.306027  1.689299 -1.443729    1.655247   0.912814 -0.914680  -1.216463   \n",
       "4 -0.737496 -0.191583 -1.443090   -0.510222  -1.095513 -0.559418  -1.216463   \n",
       "\n",
       "   risk_factor  age_oldest  age_youngest  ...      plan      hour  timeofday  \\\n",
       "0     0.387302    0.047061     -0.039027  ...  0.006284 -0.378731  -0.492279   \n",
       "1     1.455349   -0.988043     -0.839537  ... -1.567953 -1.450761  -0.492279   \n",
       "2     0.387302   -0.125456      0.018152  ... -1.599754  0.335956  -0.492279   \n",
       "3     0.387302    0.967154      0.990199  ...  0.181166 -1.450761  -0.492279   \n",
       "4     0.387302   -0.758019     -0.839537  ...  0.147632  1.407987   1.660615   \n",
       "\n",
       "    weekend    family   agediff  individual  stategroup   changed  changelog  \n",
       "0 -0.127509 -0.101385  0.203422   -1.674641    0.241593 -0.673580  -0.673580  \n",
       "1 -0.127509 -0.101385 -0.338704    0.597143    0.241593 -0.673580  -0.673580  \n",
       "2 -0.127509 -0.101385 -0.338704    0.597143    0.241593  1.484604   1.484604  \n",
       "3 -0.127509 -0.101385 -0.067641   -1.674641   -1.711954 -0.673580  -0.673580  \n",
       "4 -0.127509 -0.101385  0.203422   -1.674641    0.241593 -0.673580  -0.673580  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 97009 entries, 0 to 97008\n",
      "Data columns (total 31 columns):\n",
      "day                  97009 non-null float64\n",
      "state                97009 non-null float64\n",
      "location             97009 non-null float64\n",
      "group_size           97009 non-null float64\n",
      "homeowner            97009 non-null float64\n",
      "car_age              97009 non-null float64\n",
      "car_value            97009 non-null float64\n",
      "risk_factor          97009 non-null float64\n",
      "age_oldest           97009 non-null float64\n",
      "age_youngest         97009 non-null float64\n",
      "married_couple       97009 non-null float64\n",
      "C_previous           97009 non-null float64\n",
      "duration_previous    97009 non-null float64\n",
      "A                    97009 non-null float64\n",
      "B                    97009 non-null float64\n",
      "C                    97009 non-null float64\n",
      "D                    97009 non-null float64\n",
      "E                    97009 non-null float64\n",
      "F                    97009 non-null float64\n",
      "G                    97009 non-null float64\n",
      "cost                 97009 non-null float64\n",
      "plan                 97009 non-null float64\n",
      "hour                 97009 non-null float64\n",
      "timeofday            97009 non-null float64\n",
      "weekend              97009 non-null float64\n",
      "family               97009 non-null float64\n",
      "agediff              97009 non-null float64\n",
      "individual           97009 non-null float64\n",
      "stategroup           97009 non-null float64\n",
      "changed              97009 non-null float64\n",
      "changelog            97009 non-null float64\n",
      "dtypes: float64(31)\n",
      "memory usage: 22.9 MB\n"
     ]
    }
   ],
   "source": [
    "X[features].iloc[:,1:].head()\n",
    "X[features].iloc[:,1:].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elbow Method (Manual)\n",
    "\n",
    "**Find the best K**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=X[features]\n",
    "inertias = {}\n",
    "silhouettes = {}\n",
    "for k in range(2, 11):\n",
    "    kmeans = KMeans(init='k-means++', n_init=10, n_clusters=k, max_iter=1000, random_state=42).fit(X)\n",
    "    inertias[k] = kmeans.inertia_ # Inertia: Sum of distances of samples to their closest cluster center\n",
    "    silhouettes[k] = silhouette_score(X, kmeans.labels_, metric='euclidean')\n",
    "    \n",
    "\n",
    "plt.figure();\n",
    "plt.grid(True);\n",
    "plt.plot(list(inertias.keys()), list(inertias.values()));\n",
    "plt.title('K-Means, Elbow Method')\n",
    "plt.xlabel(\"Number of clusters, K\");\n",
    "plt.ylabel(\"Inertia\");\n",
    "\n",
    "\n",
    "plt.figure();\n",
    "plt.grid(True);\n",
    "plt.plot(list(silhouettes.keys()), list(silhouettes.values()));\n",
    "plt.title('K-Means, Elbow Method')\n",
    "plt.xlabel(\"Number of clusters, K\");\n",
    "plt.ylabel(\"Silhouette\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering with Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=2, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=42, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means = KMeans(init='k-means++', n_clusters=2, n_init=10, random_state=42)\n",
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Group']=k_means.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_ID</th>\n",
       "      <th>shopping_pt</th>\n",
       "      <th>record_type</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>state</th>\n",
       "      <th>location</th>\n",
       "      <th>group_size</th>\n",
       "      <th>homeowner</th>\n",
       "      <th>car_age</th>\n",
       "      <th>...</th>\n",
       "      <th>hour</th>\n",
       "      <th>timeofday</th>\n",
       "      <th>weekend</th>\n",
       "      <th>family</th>\n",
       "      <th>agediff</th>\n",
       "      <th>individual</th>\n",
       "      <th>stategroup</th>\n",
       "      <th>changed</th>\n",
       "      <th>changelog</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12:07</td>\n",
       "      <td>IN</td>\n",
       "      <td>10001</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000005</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>09:09</td>\n",
       "      <td>NY</td>\n",
       "      <td>10006</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000007</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>14:26</td>\n",
       "      <td>PA</td>\n",
       "      <td>10008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>g4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000013</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>09:31</td>\n",
       "      <td>WV</td>\n",
       "      <td>10014</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>day</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>g3</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000014</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17:50</td>\n",
       "      <td>MO</td>\n",
       "      <td>10015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>g4</td>\n",
       "      <td>No</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_ID  shopping_pt  record_type  day   time state  location  \\\n",
       "0     10000000            9            1    0  12:07    IN     10001   \n",
       "1     10000005            6            1    3  09:09    NY     10006   \n",
       "2     10000007            8            1    4  14:26    PA     10008   \n",
       "3     10000013            4            1    4  09:31    WV     10014   \n",
       "4     10000014            6            1    1  17:50    MO     10015   \n",
       "\n",
       "   group_size  homeowner  car_age  ... hour  timeofday  weekend  family  \\\n",
       "0           2          0        2  ...   12        day       No      No   \n",
       "1           1          0       10  ...    9        day       No      No   \n",
       "2           1          0       11  ...   14        day       No      No   \n",
       "3           2          1        3  ...    9        day       No      No   \n",
       "4           1          0        5  ...   17    evening       No      No   \n",
       "\n",
       "   agediff individual  stategroup  changed  changelog  Group  \n",
       "0        4         No          g4       No      False      1  \n",
       "1        0        Yes          g4       No      False      0  \n",
       "2        0        Yes          g4      Yes       True      0  \n",
       "3        2         No          g3       No      False      1  \n",
       "4        4         No          g4       No      False      1  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('/System/Volumes/Data/Users/Michael/Library/CloudStorage/iCloud Drive/MMA/MMA 831/allstate-purchase-prediction-challenge/clustering.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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