MVP_PCA_byStatesYes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler, MinMaxScaler, RobustScaler\n",
    "\n",
    "from scipy.cluster import hierarchy\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto=pd.read_csv('./auto_insurance/train.csv').drop('id', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Age</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1-2 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&gt; 2 Years</td>\n",
       "      <td>Yes</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 1 Year</td>\n",
       "      <td>No</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Driving_License  Region_Code  Previously_Insured Vehicle_Age  \\\n",
       "0    Male   44                1         28.0                   0   > 2 Years   \n",
       "1    Male   76                1          3.0                   0    1-2 Year   \n",
       "2    Male   47                1         28.0                   0   > 2 Years   \n",
       "3    Male   21                1         11.0                   1    < 1 Year   \n",
       "4  Female   29                1         41.0                   1    < 1 Year   \n",
       "\n",
       "  Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  Response  \n",
       "0            Yes         40454.0                  26.0      217         1  \n",
       "1             No         33536.0                  26.0      183         0  \n",
       "2            Yes         38294.0                  26.0       27         1  \n",
       "3             No         28619.0                 152.0      203         0  \n",
       "4             No         27496.0                 152.0       39         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381109, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert categorical to numerical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "auto['Gender'] = auto['Gender'].map( {'Female': 0, 'Male': 1} ).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "auto['Vehicle_Damage'] = auto['Vehicle_Damage'].map( {'Yes': 1, 'No': 0} ).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assigned dummies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto=pd.get_dummies(auto, columns=['Vehicle_Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33536.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>183</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28619.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27496.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0       1   44                1         28.0                   0   \n",
       "1       1   76                1          3.0                   0   \n",
       "2       1   47                1         28.0                   0   \n",
       "3       1   21                1         11.0                   1   \n",
       "4       0   29                1         41.0                   1   \n",
       "\n",
       "   Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  Response  \\\n",
       "0               1         40454.0                  26.0      217         1   \n",
       "1               0         33536.0                  26.0      183         0   \n",
       "2               1         38294.0                  26.0       27         1   \n",
       "3               0         28619.0                 152.0      203         0   \n",
       "4               0         27496.0                 152.0       39         0   \n",
       "\n",
       "   Vehicle_Age_1-2 Year  Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years  \n",
       "0                     0                     0                      1  \n",
       "1                     1                     0                      0  \n",
       "2                     0                     0                      1  \n",
       "3                     0                     1                      0  \n",
       "4                     0                     1                      0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#auto.Region_Code.value_counts().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46710, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#auto=auto[(auto['Region_Code']==28.0)|(auto['Region_Code']==8.0)|(auto['Region_Code']==46.0)|(auto['Region_Code']==41.0)|(auto['Region_Code']==15.0)|(auto['Region_Code']==30.0)|(auto['Region_Code']==29.0)|(auto['Region_Code']==50.0)|(auto['Region_Code']==3.0)|(auto['Region_Code']==11.0)]\n",
    "auto=auto[auto['Response']==1]\n",
    "auto.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rename regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40454.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>38294.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32031.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>47576.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2630.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>147</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Driving_License  Region_Code  Previously_Insured  \\\n",
       "0        1   44                1         28.0                   0   \n",
       "2        1   47                1         28.0                   0   \n",
       "7        0   56                1         28.0                   0   \n",
       "10       0   47                1         35.0                   0   \n",
       "15       1   37                1          6.0                   0   \n",
       "\n",
       "    Vehicle_Damage  Annual_Premium  Policy_Sales_Channel  Vintage  Response  \\\n",
       "0                1         40454.0                  26.0      217         1   \n",
       "2                1         38294.0                  26.0       27         1   \n",
       "7                1         32031.0                  26.0       72         1   \n",
       "10               1         47576.0                 124.0       46         1   \n",
       "15               1          2630.0                 156.0      147         1   \n",
       "\n",
       "    Vehicle_Age_1-2 Year  Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years  \n",
       "0                      0                     0                      1  \n",
       "2                      0                     0                      1  \n",
       "7                      1                     0                      0  \n",
       "10                     1                     0                      0  \n",
       "15                     1                     0                      0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped by regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoAll=auto.groupby('Region_Code',as_index=False).agg('mean')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region_Code</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.695402</td>\n",
       "      <td>49.270115</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5002.798851</td>\n",
       "      <td>59.793103</td>\n",
       "      <td>159.310345</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.729885</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.252874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.623853</td>\n",
       "      <td>44.779817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>3131.376147</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>157.889908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788991</td>\n",
       "      <td>0.110092</td>\n",
       "      <td>0.100917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.651568</td>\n",
       "      <td>37.498258</td>\n",
       "      <td>0.996516</td>\n",
       "      <td>0.020906</td>\n",
       "      <td>0.972125</td>\n",
       "      <td>25570.641115</td>\n",
       "      <td>117.616725</td>\n",
       "      <td>146.425087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.630662</td>\n",
       "      <td>0.310105</td>\n",
       "      <td>0.059233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region_Code    Gender        Age  Driving_License  Previously_Insured  \\\n",
       "0          0.0  0.695402  49.270115         1.000000            0.005747   \n",
       "1          1.0  0.623853  44.779817         1.000000            0.009174   \n",
       "2          2.0  0.651568  37.498258         0.996516            0.020906   \n",
       "\n",
       "   Vehicle_Damage  Annual_Premium  Policy_Sales_Channel     Vintage  Response  \\\n",
       "0        1.000000     5002.798851             59.793103  159.310345       1.0   \n",
       "1        0.990826     3131.376147             94.000000  157.889908       1.0   \n",
       "2        0.972125    25570.641115            117.616725  146.425087       1.0   \n",
       "\n",
       "   Vehicle_Age_1-2 Year  Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years  \n",
       "0              0.729885              0.017241               0.252874  \n",
       "1              0.788991              0.110092               0.100917  \n",
       "2              0.630662              0.310105               0.059233  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoAll.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#auto=auto.set_index('Region_Code')\n",
    "autoAll.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['Gender', 'Age', 'Driving_License', 'Previously_Insured', 'Vehicle_Damage', 'Annual_Premium',\n",
    "     'Policy_Sales_Channel', 'Vintage', 'Response', 'Vehicle_Age_1-2 Year','Vehicle_Age_< 1 Year','Vehicle_Age_> 2 Years']\n",
    "autoX=autoAll[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.695402</td>\n",
       "      <td>49.270115</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5002.798851</td>\n",
       "      <td>59.793103</td>\n",
       "      <td>159.310345</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.729885</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.252874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.623853</td>\n",
       "      <td>44.779817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>3131.376147</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>157.889908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788991</td>\n",
       "      <td>0.110092</td>\n",
       "      <td>0.100917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.651568</td>\n",
       "      <td>37.498258</td>\n",
       "      <td>0.996516</td>\n",
       "      <td>0.020906</td>\n",
       "      <td>0.972125</td>\n",
       "      <td>25570.641115</td>\n",
       "      <td>117.616725</td>\n",
       "      <td>146.425087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.630662</td>\n",
       "      <td>0.310105</td>\n",
       "      <td>0.059233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.609653</td>\n",
       "      <td>40.329382</td>\n",
       "      <td>0.998307</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.971211</td>\n",
       "      <td>23189.732430</td>\n",
       "      <td>110.138865</td>\n",
       "      <td>154.568163</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.741744</td>\n",
       "      <td>0.210838</td>\n",
       "      <td>0.047417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.652632</td>\n",
       "      <td>40.421053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>0.975439</td>\n",
       "      <td>23914.459649</td>\n",
       "      <td>113.491228</td>\n",
       "      <td>156.185965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.792982</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender        Age  Driving_License  Previously_Insured  Vehicle_Damage  \\\n",
       "0  0.695402  49.270115         1.000000            0.005747        1.000000   \n",
       "1  0.623853  44.779817         1.000000            0.009174        0.990826   \n",
       "2  0.651568  37.498258         0.996516            0.020906        0.972125   \n",
       "3  0.609653  40.329382         0.998307            0.006774        0.971211   \n",
       "4  0.652632  40.421053         1.000000            0.003509        0.975439   \n",
       "\n",
       "   Annual_Premium  Policy_Sales_Channel     Vintage  Response  \\\n",
       "0     5002.798851             59.793103  159.310345       1.0   \n",
       "1     3131.376147             94.000000  157.889908       1.0   \n",
       "2    25570.641115            117.616725  146.425087       1.0   \n",
       "3    23189.732430            110.138865  154.568163       1.0   \n",
       "4    23914.459649            113.491228  156.185965       1.0   \n",
       "\n",
       "   Vehicle_Age_1-2 Year  Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years  \n",
       "0              0.729885              0.017241               0.252874  \n",
       "1              0.788991              0.110092               0.100917  \n",
       "2              0.630662              0.310105               0.059233  \n",
       "3              0.741744              0.210838               0.047417  \n",
       "4              0.792982              0.154386               0.052632  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoX.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Region=autoAll['Region_Code']\n",
    "df_Region.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "autoX.hist(figsize=(10, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 12)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoX.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hopkins statistic \n",
    "\n",
    "- I started by checking **Hopkins statistics** in order to access the clusterability of the dataset. A score between 0 and 1, a score around 0.5 express no clusterability, and score tending to zero express high cluster tendency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15628761039823225"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyclustertend import hopkins\n",
    "hopkins(autoX, 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers detection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "attributes = ['Age','Annual_Premium','Vintage']\n",
    "plt.rcParams['figure.figsize'] = [12,8]\n",
    "sns.boxplot(data = autoX[attributes], orient=\"v\", palette=\"Set2\" ,whis=1.5,saturation=1, width=0.7)\n",
    "plt.title(\"Outliers Variable Distribution\", fontsize = 14, fontweight = 'bold')\n",
    "plt.ylabel(\"Range\", fontweight = 'bold')\n",
    "plt.xlabel(\"Attributes\", fontweight = 'bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Removing (statistical) outliers for Annual_Premium\n",
    "# cols = [ 'Annual_Premium'] # one or more\n",
    "\n",
    "# Q1 = autoX[cols].quantile(0.25)\n",
    "# Q3 = autoX[cols].quantile(0.75)\n",
    "# IQR = Q3 - Q1\n",
    "\n",
    "# autoX = autoX[~((autoX[cols] < (Q1 - 1.5 * IQR)) |(autoX[cols] > (Q3 + 1.5 * IQR))).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# attributes = ['Age','Annual_Premium','Vintage']\n",
    "# plt.rcParams['figure.figsize'] = [12,8]\n",
    "# sns.boxplot(data = autoX[attributes], orient=\"v\", palette=\"Set2\" ,whis=1.5,saturation=1, width=0.7)\n",
    "# plt.title(\"Outliers Variable Distribution\", fontsize = 14, fontweight = 'bold')\n",
    "# plt.ylabel(\"Range\", fontweight = 'bold')\n",
    "# plt.xlabel(\"Attributes\", fontweight = 'bold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling with StandardScaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=autoX.drop('Response', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region_Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.695402</td>\n",
       "      <td>49.270115</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5002.798851</td>\n",
       "      <td>59.793103</td>\n",
       "      <td>159.310345</td>\n",
       "      <td>0.729885</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.252874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.623853</td>\n",
       "      <td>44.779817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>3131.376147</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>157.889908</td>\n",
       "      <td>0.788991</td>\n",
       "      <td>0.110092</td>\n",
       "      <td>0.100917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.651568</td>\n",
       "      <td>37.498258</td>\n",
       "      <td>0.996516</td>\n",
       "      <td>0.020906</td>\n",
       "      <td>0.972125</td>\n",
       "      <td>25570.641115</td>\n",
       "      <td>117.616725</td>\n",
       "      <td>146.425087</td>\n",
       "      <td>0.630662</td>\n",
       "      <td>0.310105</td>\n",
       "      <td>0.059233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.609653</td>\n",
       "      <td>40.329382</td>\n",
       "      <td>0.998307</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.971211</td>\n",
       "      <td>23189.732430</td>\n",
       "      <td>110.138865</td>\n",
       "      <td>154.568163</td>\n",
       "      <td>0.741744</td>\n",
       "      <td>0.210838</td>\n",
       "      <td>0.047417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.652632</td>\n",
       "      <td>40.421053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>0.975439</td>\n",
       "      <td>23914.459649</td>\n",
       "      <td>113.491228</td>\n",
       "      <td>156.185965</td>\n",
       "      <td>0.792982</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>0.614865</td>\n",
       "      <td>37.263514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.959459</td>\n",
       "      <td>23969.162162</td>\n",
       "      <td>111.378378</td>\n",
       "      <td>164.412162</td>\n",
       "      <td>0.506757</td>\n",
       "      <td>0.412162</td>\n",
       "      <td>0.081081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>0.669704</td>\n",
       "      <td>37.403189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004556</td>\n",
       "      <td>0.952164</td>\n",
       "      <td>23539.958998</td>\n",
       "      <td>117.353075</td>\n",
       "      <td>150.794989</td>\n",
       "      <td>0.608200</td>\n",
       "      <td>0.366743</td>\n",
       "      <td>0.025057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0.587963</td>\n",
       "      <td>44.078704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.983796</td>\n",
       "      <td>25426.125000</td>\n",
       "      <td>96.194444</td>\n",
       "      <td>150.268519</td>\n",
       "      <td>0.831019</td>\n",
       "      <td>0.106481</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0.653669</td>\n",
       "      <td>44.752840</td>\n",
       "      <td>0.998158</td>\n",
       "      <td>0.003377</td>\n",
       "      <td>0.983727</td>\n",
       "      <td>34686.067240</td>\n",
       "      <td>87.630949</td>\n",
       "      <td>152.875959</td>\n",
       "      <td>0.790298</td>\n",
       "      <td>0.112680</td>\n",
       "      <td>0.097022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0.644269</td>\n",
       "      <td>39.826087</td>\n",
       "      <td>0.996047</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.984190</td>\n",
       "      <td>28765.581028</td>\n",
       "      <td>108.798419</td>\n",
       "      <td>147.557312</td>\n",
       "      <td>0.656126</td>\n",
       "      <td>0.276680</td>\n",
       "      <td>0.067194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.604563</td>\n",
       "      <td>36.878327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.961977</td>\n",
       "      <td>23569.676806</td>\n",
       "      <td>118.996198</td>\n",
       "      <td>154.509506</td>\n",
       "      <td>0.589354</td>\n",
       "      <td>0.357414</td>\n",
       "      <td>0.053232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0.631124</td>\n",
       "      <td>41.170989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008646</td>\n",
       "      <td>0.955812</td>\n",
       "      <td>25689.235351</td>\n",
       "      <td>103.401537</td>\n",
       "      <td>156.075889</td>\n",
       "      <td>0.752161</td>\n",
       "      <td>0.204611</td>\n",
       "      <td>0.043228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0.602564</td>\n",
       "      <td>40.032051</td>\n",
       "      <td>0.996795</td>\n",
       "      <td>0.003205</td>\n",
       "      <td>0.964744</td>\n",
       "      <td>21878.336538</td>\n",
       "      <td>110.458333</td>\n",
       "      <td>153.858974</td>\n",
       "      <td>0.717949</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.032051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0.631868</td>\n",
       "      <td>37.472527</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005495</td>\n",
       "      <td>0.978022</td>\n",
       "      <td>24143.667582</td>\n",
       "      <td>108.843407</td>\n",
       "      <td>153.804945</td>\n",
       "      <td>0.620879</td>\n",
       "      <td>0.343407</td>\n",
       "      <td>0.035714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Gender        Age  Driving_License  Previously_Insured  \\\n",
       "Region_Code                                                             \n",
       "0.0          0.695402  49.270115         1.000000            0.005747   \n",
       "1.0          0.623853  44.779817         1.000000            0.009174   \n",
       "2.0          0.651568  37.498258         0.996516            0.020906   \n",
       "3.0          0.609653  40.329382         0.998307            0.006774   \n",
       "4.0          0.652632  40.421053         1.000000            0.003509   \n",
       "5.0          0.614865  37.263514         1.000000            0.000000   \n",
       "6.0          0.669704  37.403189         1.000000            0.004556   \n",
       "7.0          0.587963  44.078704         1.000000            0.000000   \n",
       "8.0          0.653669  44.752840         0.998158            0.003377   \n",
       "9.0          0.644269  39.826087         0.996047            0.000000   \n",
       "10.0         0.604563  36.878327         1.000000            0.007605   \n",
       "11.0         0.631124  41.170989         1.000000            0.008646   \n",
       "12.0         0.602564  40.032051         0.996795            0.003205   \n",
       "13.0         0.631868  37.472527         1.000000            0.005495   \n",
       "\n",
       "             Vehicle_Damage  Annual_Premium  Policy_Sales_Channel     Vintage  \\\n",
       "Region_Code                                                                     \n",
       "0.0                1.000000     5002.798851             59.793103  159.310345   \n",
       "1.0                0.990826     3131.376147             94.000000  157.889908   \n",
       "2.0                0.972125    25570.641115            117.616725  146.425087   \n",
       "3.0                0.971211    23189.732430            110.138865  154.568163   \n",
       "4.0                0.975439    23914.459649            113.491228  156.185965   \n",
       "5.0                0.959459    23969.162162            111.378378  164.412162   \n",
       "6.0                0.952164    23539.958998            117.353075  150.794989   \n",
       "7.0                0.983796    25426.125000             96.194444  150.268519   \n",
       "8.0                0.983727    34686.067240             87.630949  152.875959   \n",
       "9.0                0.984190    28765.581028            108.798419  147.557312   \n",
       "10.0               0.961977    23569.676806            118.996198  154.509506   \n",
       "11.0               0.955812    25689.235351            103.401537  156.075889   \n",
       "12.0               0.964744    21878.336538            110.458333  153.858974   \n",
       "13.0               0.978022    24143.667582            108.843407  153.804945   \n",
       "\n",
       "             Vehicle_Age_1-2 Year  Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years  \n",
       "Region_Code                                                                     \n",
       "0.0                      0.729885              0.017241               0.252874  \n",
       "1.0                      0.788991              0.110092               0.100917  \n",
       "2.0                      0.630662              0.310105               0.059233  \n",
       "3.0                      0.741744              0.210838               0.047417  \n",
       "4.0                      0.792982              0.154386               0.052632  \n",
       "5.0                      0.506757              0.412162               0.081081  \n",
       "6.0                      0.608200              0.366743               0.025057  \n",
       "7.0                      0.831019              0.106481               0.062500  \n",
       "8.0                      0.790298              0.112680               0.097022  \n",
       "9.0                      0.656126              0.276680               0.067194  \n",
       "10.0                     0.589354              0.357414               0.053232  \n",
       "11.0                     0.752161              0.204611               0.043228  \n",
       "12.0                     0.717949              0.250000               0.032051  \n",
       "13.0                     0.620879              0.343407               0.035714  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set Region_Code as an index\n",
    "X1=auto.groupby('Region_Code',as_index=True).agg('mean').drop('Response',axis=1)\n",
    "X1.head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# auto['Region_Code'] = auto['Region_Code'].replace(to_replace=[8,15,28,46,41,0,1,2,3,4,5,6,7,9,10,11,12,13,14,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,34,35,36,37,38,39,40,42,43,44,45,47,48,49,50,51,52],\n",
    "#                                                                                 value=['Alabama','Iowa','Nevada','Virginia','SDakota','NDakota','Alaska','Arizona','Arkanzas','California','Colorado','Connecticut','Delaware','Florida',\n",
    "#                                                                                 'Georgia', 'Hawaii', 'Idaho','Illinois','Indiana','Kansas','Kentucky', 'Luisiana','Maine','Maryland','Massachusetts','Michigan',\n",
    "#                                                                                     'Minnesota','Mississippi', 'Missouri','Monatana','Nebraska','New Hampshire','New Jersey','New Mexico','New York','North Carolina','North Dakota','Ohio',\n",
    "#                                                                                      'Oklahoma','Oregon','Pennsylvania', 'Rhode Island','South Carolina','Tennessee','Texas','Utah','Vermont','Washington','West Virginia','Wisconsin','Wyoming',\n",
    "#                                                                                       'Puerto Rico', 'D.C.'],inplace=True)\n",
    "\n",
    "\n",
    "X1.rename({8:'NDakota', 15: 'Iowa', 0:'Alabama', 28:'Nevada', 46:'Virginia',41:'SDakota',1:'Alaska',2:'Arizona',3:'Arkanzas',4:'California',5:'Colorado',6:'Connecticut',7:'Delaware', 9:'Florida',\n",
    "         10:'Georgia',11: 'Hawaii', 12:'Idaho',13:'Illinois',14:'Indiana',16:'Kansas',17:'Kentucky',18:'Luisiana',19:'Maine',20:'Maryland',21:'Massachusetts',22:'Michigan',23:'Minnesota',24:'Mississippi',25:'Missouri',26:'Monatana',27:'Nebraska',\n",
    "         29:'New Hampshire',30:'New Jersey',31:'New Mexico',32:'New York',33:'North Carolina',34:'North Dakota',35:'Ohio',36:'Oklahoma',37:'Oregon',38:'Pennsylvania', 39:'Rhode Island',40:'South Carolina',\n",
    "         42:'Tennessee',43:'Texas',44:'Utah',45:'Vermont',47:'Washington',48:'West Virginia',49:'Wisconsin',50:'Wyoming', 51:'Puerto Rico', 52:'D.C.'}, axis='index',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 11)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler()\n",
    "scaled_X=scaler.fit_transform(X)\n",
    "scaled_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = pd.DataFrame(scale(X1), index=X1.index, columns=X1.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_auto_1=PCA().fit(X1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scree plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.082471200097813, 46.649424001956255)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "num_components = pca_auto_1.n_components_\n",
    "idx = np.arange(num_components)\n",
    "vals = pca_auto_1.explained_variance_ratio_*100\n",
    "\n",
    "ax.plot(idx, vals, color='blue')\n",
    "ax.scatter(idx, vals, color='blue', s=50)\n",
    "\n",
    "for i in range(9):\n",
    "    ax.annotate(r\"{:2.2f}%\".format(vals[i]), (idx[i]+0.1, vals[i]+0.5))\n",
    "\n",
    "ax.set_title('Scree Plot')\n",
    "ax.set_xlabel('Principal Components')\n",
    "ax.set_ylabel('% Variance Explained')\n",
    "\n",
    "ax.set_xticklabels(idx+1, fontsize=12)\n",
    "ax.set_xticks(np.arange(9))\n",
    "ax.set_xlim(left=0-0.5, right=8+0.5)\n",
    "ax.set_ylim(top=max(vals)+5)\n",
    "#plt.savefig('./images/PCA__scree-1',dsi=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates  PCA models with only three the first  principal components.\n",
    "pca = PCA(n_components=3) #pca object\n",
    "X_pca = pca.fit_transform(X1) # from 11 features to 3 PCA features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.944859</td>\n",
       "      <td>2.149066</td>\n",
       "      <td>0.667665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.426677</td>\n",
       "      <td>0.654637</td>\n",
       "      <td>1.651000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.966457</td>\n",
       "      <td>-1.282431</td>\n",
       "      <td>2.699027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.437731</td>\n",
       "      <td>-0.845176</td>\n",
       "      <td>0.456842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.258976</td>\n",
       "      <td>-0.039151</td>\n",
       "      <td>0.049686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3\n",
       "0  5.944859  2.149066  0.667665\n",
       "1  2.426677  0.654637  1.651000\n",
       "2 -1.966457 -1.282431  2.699027\n",
       "3 -0.437731 -0.845176  0.456842\n",
       "4  0.258976 -0.039151  0.049686"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changes PC arrays into dataframes.\n",
    "df = pd.DataFrame(X_pca, columns=['PC1', 'PC2','PC3'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.41649424 0.15029618 0.11323267]\n",
      "The 2 principal components explain 68.0% of the variance in the original data.\n"
     ]
    }
   ],
   "source": [
    "evr = pca.explained_variance_ratio_\n",
    "print(evr)\n",
    "print(\"The 2 principal components explain {0:0.1f}%\"\n",
    "      \" of the variance in the original data.\".format(evr.sum()*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loadings. Dataset X1 with Region_Code set as index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>0.201001</td>\n",
       "      <td>0.295969</td>\n",
       "      <td>0.307419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.451641</td>\n",
       "      <td>-0.041041</td>\n",
       "      <td>-0.051156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Driving_License</th>\n",
       "      <td>-0.004458</td>\n",
       "      <td>0.498702</td>\n",
       "      <td>-0.292266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Previously_Insured</th>\n",
       "      <td>-0.124227</td>\n",
       "      <td>0.163750</td>\n",
       "      <td>0.676103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <td>0.293807</td>\n",
       "      <td>-0.051734</td>\n",
       "      <td>0.288999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual_Premium</th>\n",
       "      <td>-0.135221</td>\n",
       "      <td>-0.113513</td>\n",
       "      <td>-0.462526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <td>-0.434813</td>\n",
       "      <td>-0.050098</td>\n",
       "      <td>0.130784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vintage</th>\n",
       "      <td>0.027721</td>\n",
       "      <td>0.677597</td>\n",
       "      <td>-0.025422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <td>0.363223</td>\n",
       "      <td>-0.271390</td>\n",
       "      <td>0.078425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <td>-0.442721</td>\n",
       "      <td>0.116930</td>\n",
       "      <td>0.015365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "      <td>0.343215</td>\n",
       "      <td>0.265830</td>\n",
       "      <td>-0.196860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             V1        V2        V3\n",
       "Gender                 0.201001  0.295969  0.307419\n",
       "Age                    0.451641 -0.041041 -0.051156\n",
       "Driving_License       -0.004458  0.498702 -0.292266\n",
       "Previously_Insured    -0.124227  0.163750  0.676103\n",
       "Vehicle_Damage         0.293807 -0.051734  0.288999\n",
       "Annual_Premium        -0.135221 -0.113513 -0.462526\n",
       "Policy_Sales_Channel  -0.434813 -0.050098  0.130784\n",
       "Vintage                0.027721  0.677597 -0.025422\n",
       "Vehicle_Age_1-2 Year   0.363223 -0.271390  0.078425\n",
       "Vehicle_Age_< 1 Year  -0.442721  0.116930  0.015365\n",
       "Vehicle_Age_> 2 Years  0.343215  0.265830 -0.196860"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The loading vectors\n",
    "pca_loadings = pd.DataFrame(PCA(n_components=3).fit(X1).components_.T, index=X1.columns, columns=['V1','V2','V3'])\n",
    "pca_loadings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>PC11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region_Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>5.944859</td>\n",
       "      <td>2.149066</td>\n",
       "      <td>0.667665</td>\n",
       "      <td>0.480157</td>\n",
       "      <td>2.449510</td>\n",
       "      <td>-0.491196</td>\n",
       "      <td>0.379679</td>\n",
       "      <td>-1.233022</td>\n",
       "      <td>-0.000143</td>\n",
       "      <td>-0.213182</td>\n",
       "      <td>9.412952e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>2.426677</td>\n",
       "      <td>0.654637</td>\n",
       "      <td>1.651000</td>\n",
       "      <td>2.057563</td>\n",
       "      <td>0.927912</td>\n",
       "      <td>0.013292</td>\n",
       "      <td>-0.033593</td>\n",
       "      <td>0.447202</td>\n",
       "      <td>0.031784</td>\n",
       "      <td>-0.014854</td>\n",
       "      <td>-1.677675e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>-1.966457</td>\n",
       "      <td>-1.282431</td>\n",
       "      <td>2.699027</td>\n",
       "      <td>-1.577830</td>\n",
       "      <td>0.772888</td>\n",
       "      <td>0.636117</td>\n",
       "      <td>0.107811</td>\n",
       "      <td>-1.194941</td>\n",
       "      <td>-0.480546</td>\n",
       "      <td>-0.222620</td>\n",
       "      <td>-1.361071e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkanzas</th>\n",
       "      <td>-0.437731</td>\n",
       "      <td>-0.845176</td>\n",
       "      <td>0.456842</td>\n",
       "      <td>0.043685</td>\n",
       "      <td>-0.010041</td>\n",
       "      <td>0.382767</td>\n",
       "      <td>0.263023</td>\n",
       "      <td>0.505121</td>\n",
       "      <td>-0.082681</td>\n",
       "      <td>-0.197209</td>\n",
       "      <td>4.794442e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>0.258976</td>\n",
       "      <td>-0.039151</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>0.403252</td>\n",
       "      <td>-1.182512</td>\n",
       "      <td>-0.251483</td>\n",
       "      <td>-0.211605</td>\n",
       "      <td>0.462449</td>\n",
       "      <td>-0.654841</td>\n",
       "      <td>-0.288052</td>\n",
       "      <td>6.074246e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>-2.377306</td>\n",
       "      <td>1.868110</td>\n",
       "      <td>-1.262124</td>\n",
       "      <td>-0.148719</td>\n",
       "      <td>1.094756</td>\n",
       "      <td>-0.822079</td>\n",
       "      <td>0.688381</td>\n",
       "      <td>0.322876</td>\n",
       "      <td>0.266460</td>\n",
       "      <td>0.030365</td>\n",
       "      <td>2.398097e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>-2.444933</td>\n",
       "      <td>0.228766</td>\n",
       "      <td>-0.033252</td>\n",
       "      <td>0.261632</td>\n",
       "      <td>-0.740379</td>\n",
       "      <td>-0.978536</td>\n",
       "      <td>0.507808</td>\n",
       "      <td>-1.167669</td>\n",
       "      <td>0.064987</td>\n",
       "      <td>0.149327</td>\n",
       "      <td>3.409650e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>1.610085</td>\n",
       "      <td>-1.334880</td>\n",
       "      <td>-0.956418</td>\n",
       "      <td>0.891854</td>\n",
       "      <td>-0.310534</td>\n",
       "      <td>0.176177</td>\n",
       "      <td>-0.939655</td>\n",
       "      <td>0.451970</td>\n",
       "      <td>0.074991</td>\n",
       "      <td>0.013540</td>\n",
       "      <td>-3.159739e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NDakota</th>\n",
       "      <td>2.116237</td>\n",
       "      <td>-1.040709</td>\n",
       "      <td>-0.564769</td>\n",
       "      <td>-1.607407</td>\n",
       "      <td>-0.272159</td>\n",
       "      <td>0.346931</td>\n",
       "      <td>-0.308346</td>\n",
       "      <td>-0.127150</td>\n",
       "      <td>0.207891</td>\n",
       "      <td>-0.017889</td>\n",
       "      <td>5.595221e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>-0.481938</td>\n",
       "      <td>-2.135555</td>\n",
       "      <td>0.179633</td>\n",
       "      <td>-1.808140</td>\n",
       "      <td>0.872140</td>\n",
       "      <td>-1.294243</td>\n",
       "      <td>0.632643</td>\n",
       "      <td>0.177305</td>\n",
       "      <td>-0.212047</td>\n",
       "      <td>0.009672</td>\n",
       "      <td>-1.279506e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>-2.543029</td>\n",
       "      <td>0.464526</td>\n",
       "      <td>-0.061891</td>\n",
       "      <td>0.404634</td>\n",
       "      <td>0.488246</td>\n",
       "      <td>-0.133074</td>\n",
       "      <td>-0.177287</td>\n",
       "      <td>-0.350182</td>\n",
       "      <td>-0.230612</td>\n",
       "      <td>-0.077948</td>\n",
       "      <td>1.007155e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>-0.362439</td>\n",
       "      <td>0.139562</td>\n",
       "      <td>-0.073798</td>\n",
       "      <td>0.413548</td>\n",
       "      <td>-0.963986</td>\n",
       "      <td>0.953723</td>\n",
       "      <td>0.152831</td>\n",
       "      <td>-0.391091</td>\n",
       "      <td>0.334716</td>\n",
       "      <td>-0.115548</td>\n",
       "      <td>-2.642531e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>-0.903014</td>\n",
       "      <td>-1.593744</td>\n",
       "      <td>0.356136</td>\n",
       "      <td>-0.236813</td>\n",
       "      <td>0.328557</td>\n",
       "      <td>0.054097</td>\n",
       "      <td>1.285366</td>\n",
       "      <td>0.793720</td>\n",
       "      <td>0.267675</td>\n",
       "      <td>-0.129209</td>\n",
       "      <td>2.148377e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>-1.624483</td>\n",
       "      <td>0.266348</td>\n",
       "      <td>0.138745</td>\n",
       "      <td>0.065630</td>\n",
       "      <td>0.170153</td>\n",
       "      <td>-0.935532</td>\n",
       "      <td>-0.738379</td>\n",
       "      <td>-0.133513</td>\n",
       "      <td>0.373075</td>\n",
       "      <td>-0.143987</td>\n",
       "      <td>-4.517460e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>-1.056751</td>\n",
       "      <td>0.205506</td>\n",
       "      <td>0.210202</td>\n",
       "      <td>0.698958</td>\n",
       "      <td>-1.200277</td>\n",
       "      <td>0.948774</td>\n",
       "      <td>0.440564</td>\n",
       "      <td>-0.306125</td>\n",
       "      <td>0.038075</td>\n",
       "      <td>-0.162110</td>\n",
       "      <td>-2.956599e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>-1.186948</td>\n",
       "      <td>0.009010</td>\n",
       "      <td>-1.152853</td>\n",
       "      <td>0.269687</td>\n",
       "      <td>-0.319492</td>\n",
       "      <td>-0.080437</td>\n",
       "      <td>0.083904</td>\n",
       "      <td>0.045567</td>\n",
       "      <td>-0.064270</td>\n",
       "      <td>-0.046010</td>\n",
       "      <td>2.447984e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>-2.415857</td>\n",
       "      <td>0.303358</td>\n",
       "      <td>-0.936246</td>\n",
       "      <td>0.579056</td>\n",
       "      <td>0.917079</td>\n",
       "      <td>-1.145800</td>\n",
       "      <td>0.185421</td>\n",
       "      <td>-0.359268</td>\n",
       "      <td>-0.261643</td>\n",
       "      <td>0.020860</td>\n",
       "      <td>1.266115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>0.234258</td>\n",
       "      <td>-0.311287</td>\n",
       "      <td>-0.841974</td>\n",
       "      <td>0.587638</td>\n",
       "      <td>-1.686561</td>\n",
       "      <td>1.522471</td>\n",
       "      <td>0.634334</td>\n",
       "      <td>-0.224460</td>\n",
       "      <td>-0.154066</td>\n",
       "      <td>0.085502</td>\n",
       "      <td>2.068709e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Luisiana</th>\n",
       "      <td>-0.553445</td>\n",
       "      <td>0.188281</td>\n",
       "      <td>-0.195088</td>\n",
       "      <td>0.453525</td>\n",
       "      <td>-1.338348</td>\n",
       "      <td>0.124321</td>\n",
       "      <td>0.470835</td>\n",
       "      <td>-0.079703</td>\n",
       "      <td>-0.018498</td>\n",
       "      <td>0.087187</td>\n",
       "      <td>5.006385e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>1.583227</td>\n",
       "      <td>-0.085579</td>\n",
       "      <td>-0.260726</td>\n",
       "      <td>0.088408</td>\n",
       "      <td>0.007524</td>\n",
       "      <td>1.043898</td>\n",
       "      <td>-1.140222</td>\n",
       "      <td>0.288353</td>\n",
       "      <td>0.693253</td>\n",
       "      <td>-0.253506</td>\n",
       "      <td>1.498831e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>3.215540</td>\n",
       "      <td>0.924554</td>\n",
       "      <td>1.253895</td>\n",
       "      <td>1.831153</td>\n",
       "      <td>0.554628</td>\n",
       "      <td>-0.565238</td>\n",
       "      <td>0.478630</td>\n",
       "      <td>0.505608</td>\n",
       "      <td>-0.376577</td>\n",
       "      <td>0.125137</td>\n",
       "      <td>-1.779767e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>-2.583821</td>\n",
       "      <td>-0.346730</td>\n",
       "      <td>-0.872646</td>\n",
       "      <td>1.037709</td>\n",
       "      <td>0.446610</td>\n",
       "      <td>-0.034041</td>\n",
       "      <td>0.074180</td>\n",
       "      <td>-0.307798</td>\n",
       "      <td>0.238026</td>\n",
       "      <td>0.066829</td>\n",
       "      <td>-3.939769e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>1.500132</td>\n",
       "      <td>1.585026</td>\n",
       "      <td>0.645014</td>\n",
       "      <td>-0.947633</td>\n",
       "      <td>0.455191</td>\n",
       "      <td>0.367323</td>\n",
       "      <td>-0.777480</td>\n",
       "      <td>-0.470822</td>\n",
       "      <td>-0.215647</td>\n",
       "      <td>0.067252</td>\n",
       "      <td>-3.280409e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>-0.238943</td>\n",
       "      <td>-0.212496</td>\n",
       "      <td>0.774139</td>\n",
       "      <td>0.185620</td>\n",
       "      <td>-1.651922</td>\n",
       "      <td>-0.862000</td>\n",
       "      <td>0.021440</td>\n",
       "      <td>-1.195183</td>\n",
       "      <td>-0.063240</td>\n",
       "      <td>-0.052235</td>\n",
       "      <td>1.324892e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>0.507521</td>\n",
       "      <td>-1.998699</td>\n",
       "      <td>0.320511</td>\n",
       "      <td>-0.666144</td>\n",
       "      <td>0.086239</td>\n",
       "      <td>-0.030953</td>\n",
       "      <td>0.312178</td>\n",
       "      <td>0.801021</td>\n",
       "      <td>-0.490362</td>\n",
       "      <td>0.177846</td>\n",
       "      <td>1.324555e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>-0.721771</td>\n",
       "      <td>0.537238</td>\n",
       "      <td>1.909144</td>\n",
       "      <td>-0.185568</td>\n",
       "      <td>0.388686</td>\n",
       "      <td>0.405365</td>\n",
       "      <td>-1.715081</td>\n",
       "      <td>-0.468023</td>\n",
       "      <td>0.337449</td>\n",
       "      <td>0.364076</td>\n",
       "      <td>-5.037176e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monatana</th>\n",
       "      <td>0.562821</td>\n",
       "      <td>-0.837896</td>\n",
       "      <td>-1.110637</td>\n",
       "      <td>-0.015602</td>\n",
       "      <td>0.623574</td>\n",
       "      <td>-0.660353</td>\n",
       "      <td>-1.295564</td>\n",
       "      <td>-0.221974</td>\n",
       "      <td>-0.619866</td>\n",
       "      <td>0.040004</td>\n",
       "      <td>1.492224e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>-3.232748</td>\n",
       "      <td>-2.642679</td>\n",
       "      <td>1.087886</td>\n",
       "      <td>-0.907874</td>\n",
       "      <td>1.524723</td>\n",
       "      <td>0.224154</td>\n",
       "      <td>1.626716</td>\n",
       "      <td>-0.585605</td>\n",
       "      <td>0.330149</td>\n",
       "      <td>0.211672</td>\n",
       "      <td>-3.487115e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>2.703940</td>\n",
       "      <td>-0.631358</td>\n",
       "      <td>-2.038196</td>\n",
       "      <td>-1.423719</td>\n",
       "      <td>1.032451</td>\n",
       "      <td>1.170193</td>\n",
       "      <td>-1.184539</td>\n",
       "      <td>-0.052547</td>\n",
       "      <td>0.236697</td>\n",
       "      <td>-0.125581</td>\n",
       "      <td>1.813776e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>0.844522</td>\n",
       "      <td>-1.573795</td>\n",
       "      <td>0.342241</td>\n",
       "      <td>-0.936132</td>\n",
       "      <td>-0.497711</td>\n",
       "      <td>0.360772</td>\n",
       "      <td>0.748403</td>\n",
       "      <td>0.503978</td>\n",
       "      <td>0.308559</td>\n",
       "      <td>-0.245338</td>\n",
       "      <td>1.976648e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>-2.369020</td>\n",
       "      <td>0.498782</td>\n",
       "      <td>-0.520685</td>\n",
       "      <td>0.502376</td>\n",
       "      <td>-0.590233</td>\n",
       "      <td>-0.616969</td>\n",
       "      <td>0.739878</td>\n",
       "      <td>-0.236687</td>\n",
       "      <td>-0.082742</td>\n",
       "      <td>0.015256</td>\n",
       "      <td>-2.329988e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>1.946842</td>\n",
       "      <td>-0.655172</td>\n",
       "      <td>1.313030</td>\n",
       "      <td>2.701540</td>\n",
       "      <td>-0.003117</td>\n",
       "      <td>-0.456170</td>\n",
       "      <td>0.297777</td>\n",
       "      <td>-0.081626</td>\n",
       "      <td>-0.016992</td>\n",
       "      <td>0.086431</td>\n",
       "      <td>2.873038e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>-3.520410</td>\n",
       "      <td>0.170159</td>\n",
       "      <td>-1.204183</td>\n",
       "      <td>0.694428</td>\n",
       "      <td>-0.112503</td>\n",
       "      <td>-0.640930</td>\n",
       "      <td>0.663864</td>\n",
       "      <td>-0.122289</td>\n",
       "      <td>0.346517</td>\n",
       "      <td>0.006966</td>\n",
       "      <td>2.531903e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>0.953875</td>\n",
       "      <td>-0.572928</td>\n",
       "      <td>-0.142312</td>\n",
       "      <td>-0.305854</td>\n",
       "      <td>-0.111420</td>\n",
       "      <td>0.064005</td>\n",
       "      <td>-0.131218</td>\n",
       "      <td>0.978021</td>\n",
       "      <td>-0.326838</td>\n",
       "      <td>0.151389</td>\n",
       "      <td>-1.071160e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>-2.279135</td>\n",
       "      <td>0.847716</td>\n",
       "      <td>-1.418160</td>\n",
       "      <td>0.533624</td>\n",
       "      <td>0.425378</td>\n",
       "      <td>0.131337</td>\n",
       "      <td>0.287149</td>\n",
       "      <td>1.388953</td>\n",
       "      <td>-0.044319</td>\n",
       "      <td>0.042796</td>\n",
       "      <td>-7.058627e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.950734</td>\n",
       "      <td>-0.897655</td>\n",
       "      <td>-0.663341</td>\n",
       "      <td>0.619595</td>\n",
       "      <td>-0.662881</td>\n",
       "      <td>0.104543</td>\n",
       "      <td>-0.383089</td>\n",
       "      <td>-0.396135</td>\n",
       "      <td>0.251803</td>\n",
       "      <td>0.079562</td>\n",
       "      <td>2.828909e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>-1.263857</td>\n",
       "      <td>0.365674</td>\n",
       "      <td>-0.083875</td>\n",
       "      <td>0.157875</td>\n",
       "      <td>-0.795444</td>\n",
       "      <td>0.291380</td>\n",
       "      <td>0.041240</td>\n",
       "      <td>-0.138278</td>\n",
       "      <td>-0.270256</td>\n",
       "      <td>-0.076144</td>\n",
       "      <td>1.608264e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-4.112229</td>\n",
       "      <td>0.673669</td>\n",
       "      <td>0.412876</td>\n",
       "      <td>-0.017936</td>\n",
       "      <td>1.409695</td>\n",
       "      <td>0.739412</td>\n",
       "      <td>-0.956958</td>\n",
       "      <td>-0.788813</td>\n",
       "      <td>0.008385</td>\n",
       "      <td>0.309320</td>\n",
       "      <td>3.313659e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>1.401188</td>\n",
       "      <td>-0.555299</td>\n",
       "      <td>0.030415</td>\n",
       "      <td>0.732696</td>\n",
       "      <td>-1.785636</td>\n",
       "      <td>1.215390</td>\n",
       "      <td>0.349161</td>\n",
       "      <td>-0.673718</td>\n",
       "      <td>-0.148769</td>\n",
       "      <td>0.358773</td>\n",
       "      <td>-3.455704e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>0.566632</td>\n",
       "      <td>-0.010083</td>\n",
       "      <td>-0.473339</td>\n",
       "      <td>0.397027</td>\n",
       "      <td>-0.480888</td>\n",
       "      <td>0.356849</td>\n",
       "      <td>-0.465706</td>\n",
       "      <td>0.774477</td>\n",
       "      <td>-0.235637</td>\n",
       "      <td>-0.072728</td>\n",
       "      <td>2.361275e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>0.229629</td>\n",
       "      <td>-0.681650</td>\n",
       "      <td>-0.875068</td>\n",
       "      <td>0.406445</td>\n",
       "      <td>0.946006</td>\n",
       "      <td>-0.753958</td>\n",
       "      <td>-1.370353</td>\n",
       "      <td>0.432025</td>\n",
       "      <td>-0.093973</td>\n",
       "      <td>0.084416</td>\n",
       "      <td>-1.457566e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SDakota</th>\n",
       "      <td>0.253851</td>\n",
       "      <td>-0.323152</td>\n",
       "      <td>-0.698866</td>\n",
       "      <td>-0.527721</td>\n",
       "      <td>-0.474040</td>\n",
       "      <td>0.198142</td>\n",
       "      <td>0.039015</td>\n",
       "      <td>0.197245</td>\n",
       "      <td>0.308069</td>\n",
       "      <td>-0.093852</td>\n",
       "      <td>-1.621066e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>1.392561</td>\n",
       "      <td>2.384189</td>\n",
       "      <td>-0.168259</td>\n",
       "      <td>-1.453889</td>\n",
       "      <td>-1.020601</td>\n",
       "      <td>-1.773843</td>\n",
       "      <td>0.758315</td>\n",
       "      <td>-0.416946</td>\n",
       "      <td>0.020878</td>\n",
       "      <td>-0.591828</td>\n",
       "      <td>-5.951577e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>2.038090</td>\n",
       "      <td>-1.753989</td>\n",
       "      <td>1.578272</td>\n",
       "      <td>-1.638405</td>\n",
       "      <td>0.134943</td>\n",
       "      <td>0.252395</td>\n",
       "      <td>0.616925</td>\n",
       "      <td>0.703904</td>\n",
       "      <td>-0.142975</td>\n",
       "      <td>0.136523</td>\n",
       "      <td>-3.645051e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-2.891833</td>\n",
       "      <td>3.690480</td>\n",
       "      <td>3.437154</td>\n",
       "      <td>-0.742666</td>\n",
       "      <td>-0.397895</td>\n",
       "      <td>1.891468</td>\n",
       "      <td>-0.402352</td>\n",
       "      <td>0.857697</td>\n",
       "      <td>0.003941</td>\n",
       "      <td>-0.128943</td>\n",
       "      <td>1.073379e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>-0.102562</td>\n",
       "      <td>0.162467</td>\n",
       "      <td>0.497747</td>\n",
       "      <td>0.041830</td>\n",
       "      <td>-0.358797</td>\n",
       "      <td>-0.181978</td>\n",
       "      <td>-0.860472</td>\n",
       "      <td>-0.030130</td>\n",
       "      <td>-0.011008</td>\n",
       "      <td>-0.119413</td>\n",
       "      <td>-3.865319e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>0.925377</td>\n",
       "      <td>-1.427920</td>\n",
       "      <td>-0.306336</td>\n",
       "      <td>0.142141</td>\n",
       "      <td>0.056335</td>\n",
       "      <td>0.106731</td>\n",
       "      <td>-0.603838</td>\n",
       "      <td>-0.039352</td>\n",
       "      <td>0.024034</td>\n",
       "      <td>-0.083494</td>\n",
       "      <td>-2.781204e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>-0.290909</td>\n",
       "      <td>-0.544550</td>\n",
       "      <td>-0.632507</td>\n",
       "      <td>-0.179588</td>\n",
       "      <td>-0.108887</td>\n",
       "      <td>-0.903373</td>\n",
       "      <td>-1.120439</td>\n",
       "      <td>-0.168809</td>\n",
       "      <td>-0.112127</td>\n",
       "      <td>-0.144971</td>\n",
       "      <td>-1.510156e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>3.973896</td>\n",
       "      <td>-0.572463</td>\n",
       "      <td>1.388552</td>\n",
       "      <td>1.263478</td>\n",
       "      <td>0.784547</td>\n",
       "      <td>-0.602448</td>\n",
       "      <td>1.343965</td>\n",
       "      <td>0.516752</td>\n",
       "      <td>0.533748</td>\n",
       "      <td>0.045967</td>\n",
       "      <td>-5.061035e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>-3.953306</td>\n",
       "      <td>1.938385</td>\n",
       "      <td>-0.131678</td>\n",
       "      <td>-0.263322</td>\n",
       "      <td>0.799864</td>\n",
       "      <td>-0.230420</td>\n",
       "      <td>-0.381002</td>\n",
       "      <td>1.543470</td>\n",
       "      <td>0.028925</td>\n",
       "      <td>0.029241</td>\n",
       "      <td>2.195361e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>1.650135</td>\n",
       "      <td>-0.709606</td>\n",
       "      <td>-0.693328</td>\n",
       "      <td>-0.151661</td>\n",
       "      <td>-0.194512</td>\n",
       "      <td>-0.077508</td>\n",
       "      <td>-1.062054</td>\n",
       "      <td>-0.594425</td>\n",
       "      <td>0.177382</td>\n",
       "      <td>0.062078</td>\n",
       "      <td>-3.265804e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puerto Rico</th>\n",
       "      <td>3.474307</td>\n",
       "      <td>2.788980</td>\n",
       "      <td>0.193039</td>\n",
       "      <td>-2.488075</td>\n",
       "      <td>-1.857543</td>\n",
       "      <td>-1.876528</td>\n",
       "      <td>0.108185</td>\n",
       "      <td>0.424594</td>\n",
       "      <td>0.251957</td>\n",
       "      <td>0.628232</td>\n",
       "      <td>2.227459e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D.C.</th>\n",
       "      <td>2.648968</td>\n",
       "      <td>2.572144</td>\n",
       "      <td>-3.220299</td>\n",
       "      <td>-0.716067</td>\n",
       "      <td>1.430683</td>\n",
       "      <td>2.317063</td>\n",
       "      <td>1.919612</td>\n",
       "      <td>-0.566024</td>\n",
       "      <td>-0.348750</td>\n",
       "      <td>0.130428</td>\n",
       "      <td>-4.610423e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     PC1       PC2       PC3       PC4       PC5       PC6  \\\n",
       "Region_Code                                                                  \n",
       "Alabama         5.944859  2.149066  0.667665  0.480157  2.449510 -0.491196   \n",
       "Alaska          2.426677  0.654637  1.651000  2.057563  0.927912  0.013292   \n",
       "Arizona        -1.966457 -1.282431  2.699027 -1.577830  0.772888  0.636117   \n",
       "Arkanzas       -0.437731 -0.845176  0.456842  0.043685 -0.010041  0.382767   \n",
       "California      0.258976 -0.039151  0.049686  0.403252 -1.182512 -0.251483   \n",
       "Colorado       -2.377306  1.868110 -1.262124 -0.148719  1.094756 -0.822079   \n",
       "Connecticut    -2.444933  0.228766 -0.033252  0.261632 -0.740379 -0.978536   \n",
       "Delaware        1.610085 -1.334880 -0.956418  0.891854 -0.310534  0.176177   \n",
       "NDakota         2.116237 -1.040709 -0.564769 -1.607407 -0.272159  0.346931   \n",
       "Florida        -0.481938 -2.135555  0.179633 -1.808140  0.872140 -1.294243   \n",
       "Georgia        -2.543029  0.464526 -0.061891  0.404634  0.488246 -0.133074   \n",
       "Hawaii         -0.362439  0.139562 -0.073798  0.413548 -0.963986  0.953723   \n",
       "Idaho          -0.903014 -1.593744  0.356136 -0.236813  0.328557  0.054097   \n",
       "Illinois       -1.624483  0.266348  0.138745  0.065630  0.170153 -0.935532   \n",
       "Indiana        -1.056751  0.205506  0.210202  0.698958 -1.200277  0.948774   \n",
       "Iowa           -1.186948  0.009010 -1.152853  0.269687 -0.319492 -0.080437   \n",
       "Kansas         -2.415857  0.303358 -0.936246  0.579056  0.917079 -1.145800   \n",
       "Kentucky        0.234258 -0.311287 -0.841974  0.587638 -1.686561  1.522471   \n",
       "Luisiana       -0.553445  0.188281 -0.195088  0.453525 -1.338348  0.124321   \n",
       "Maine           1.583227 -0.085579 -0.260726  0.088408  0.007524  1.043898   \n",
       "Maryland        3.215540  0.924554  1.253895  1.831153  0.554628 -0.565238   \n",
       "Massachusetts  -2.583821 -0.346730 -0.872646  1.037709  0.446610 -0.034041   \n",
       "Michigan        1.500132  1.585026  0.645014 -0.947633  0.455191  0.367323   \n",
       "Minnesota      -0.238943 -0.212496  0.774139  0.185620 -1.651922 -0.862000   \n",
       "Mississippi     0.507521 -1.998699  0.320511 -0.666144  0.086239 -0.030953   \n",
       "Missouri       -0.721771  0.537238  1.909144 -0.185568  0.388686  0.405365   \n",
       "Monatana        0.562821 -0.837896 -1.110637 -0.015602  0.623574 -0.660353   \n",
       "Nebraska       -3.232748 -2.642679  1.087886 -0.907874  1.524723  0.224154   \n",
       "Nevada          2.703940 -0.631358 -2.038196 -1.423719  1.032451  1.170193   \n",
       "New Hampshire   0.844522 -1.573795  0.342241 -0.936132 -0.497711  0.360772   \n",
       "New Jersey     -2.369020  0.498782 -0.520685  0.502376 -0.590233 -0.616969   \n",
       "New Mexico      1.946842 -0.655172  1.313030  2.701540 -0.003117 -0.456170   \n",
       "New York       -3.520410  0.170159 -1.204183  0.694428 -0.112503 -0.640930   \n",
       "North Carolina  0.953875 -0.572928 -0.142312 -0.305854 -0.111420  0.064005   \n",
       "North Dakota   -2.279135  0.847716 -1.418160  0.533624  0.425378  0.131337   \n",
       "Ohio            0.950734 -0.897655 -0.663341  0.619595 -0.662881  0.104543   \n",
       "Oklahoma       -1.263857  0.365674 -0.083875  0.157875 -0.795444  0.291380   \n",
       "Oregon         -4.112229  0.673669  0.412876 -0.017936  1.409695  0.739412   \n",
       "Pennsylvania    1.401188 -0.555299  0.030415  0.732696 -1.785636  1.215390   \n",
       "Rhode Island    0.566632 -0.010083 -0.473339  0.397027 -0.480888  0.356849   \n",
       "South Carolina  0.229629 -0.681650 -0.875068  0.406445  0.946006 -0.753958   \n",
       "SDakota         0.253851 -0.323152 -0.698866 -0.527721 -0.474040  0.198142   \n",
       "Tennessee       1.392561  2.384189 -0.168259 -1.453889 -1.020601 -1.773843   \n",
       "Texas           2.038090 -1.753989  1.578272 -1.638405  0.134943  0.252395   \n",
       "Utah           -2.891833  3.690480  3.437154 -0.742666 -0.397895  1.891468   \n",
       "Vermont        -0.102562  0.162467  0.497747  0.041830 -0.358797 -0.181978   \n",
       "Virginia        0.925377 -1.427920 -0.306336  0.142141  0.056335  0.106731   \n",
       "Washington     -0.290909 -0.544550 -0.632507 -0.179588 -0.108887 -0.903373   \n",
       "West Virginia   3.973896 -0.572463  1.388552  1.263478  0.784547 -0.602448   \n",
       "Wisconsin      -3.953306  1.938385 -0.131678 -0.263322  0.799864 -0.230420   \n",
       "Wyoming         1.650135 -0.709606 -0.693328 -0.151661 -0.194512 -0.077508   \n",
       "Puerto Rico     3.474307  2.788980  0.193039 -2.488075 -1.857543 -1.876528   \n",
       "D.C.            2.648968  2.572144 -3.220299 -0.716067  1.430683  2.317063   \n",
       "\n",
       "                     PC7       PC8       PC9      PC10          PC11  \n",
       "Region_Code                                                           \n",
       "Alabama         0.379679 -1.233022 -0.000143 -0.213182  9.412952e-16  \n",
       "Alaska         -0.033593  0.447202  0.031784 -0.014854 -1.677675e-16  \n",
       "Arizona         0.107811 -1.194941 -0.480546 -0.222620 -1.361071e-16  \n",
       "Arkanzas        0.263023  0.505121 -0.082681 -0.197209  4.794442e-16  \n",
       "California     -0.211605  0.462449 -0.654841 -0.288052  6.074246e-17  \n",
       "Colorado        0.688381  0.322876  0.266460  0.030365  2.398097e-16  \n",
       "Connecticut     0.507808 -1.167669  0.064987  0.149327  3.409650e-17  \n",
       "Delaware       -0.939655  0.451970  0.074991  0.013540 -3.159739e-16  \n",
       "NDakota        -0.308346 -0.127150  0.207891 -0.017889  5.595221e-16  \n",
       "Florida         0.632643  0.177305 -0.212047  0.009672 -1.279506e-16  \n",
       "Georgia        -0.177287 -0.350182 -0.230612 -0.077948  1.007155e-16  \n",
       "Hawaii          0.152831 -0.391091  0.334716 -0.115548 -2.642531e-17  \n",
       "Idaho           1.285366  0.793720  0.267675 -0.129209  2.148377e-16  \n",
       "Illinois       -0.738379 -0.133513  0.373075 -0.143987 -4.517460e-17  \n",
       "Indiana         0.440564 -0.306125  0.038075 -0.162110 -2.956599e-16  \n",
       "Iowa            0.083904  0.045567 -0.064270 -0.046010  2.447984e-16  \n",
       "Kansas          0.185421 -0.359268 -0.261643  0.020860  1.266115e-17  \n",
       "Kentucky        0.634334 -0.224460 -0.154066  0.085502  2.068709e-16  \n",
       "Luisiana        0.470835 -0.079703 -0.018498  0.087187  5.006385e-16  \n",
       "Maine          -1.140222  0.288353  0.693253 -0.253506  1.498831e-16  \n",
       "Maryland        0.478630  0.505608 -0.376577  0.125137 -1.779767e-16  \n",
       "Massachusetts   0.074180 -0.307798  0.238026  0.066829 -3.939769e-16  \n",
       "Michigan       -0.777480 -0.470822 -0.215647  0.067252 -3.280409e-16  \n",
       "Minnesota       0.021440 -1.195183 -0.063240 -0.052235  1.324892e-16  \n",
       "Mississippi     0.312178  0.801021 -0.490362  0.177846  1.324555e-16  \n",
       "Missouri       -1.715081 -0.468023  0.337449  0.364076 -5.037176e-16  \n",
       "Monatana       -1.295564 -0.221974 -0.619866  0.040004  1.492224e-16  \n",
       "Nebraska        1.626716 -0.585605  0.330149  0.211672 -3.487115e-17  \n",
       "Nevada         -1.184539 -0.052547  0.236697 -0.125581  1.813776e-17  \n",
       "New Hampshire   0.748403  0.503978  0.308559 -0.245338  1.976648e-16  \n",
       "New Jersey      0.739878 -0.236687 -0.082742  0.015256 -2.329988e-16  \n",
       "New Mexico      0.297777 -0.081626 -0.016992  0.086431  2.873038e-16  \n",
       "New York        0.663864 -0.122289  0.346517  0.006966  2.531903e-16  \n",
       "North Carolina -0.131218  0.978021 -0.326838  0.151389 -1.071160e-16  \n",
       "North Dakota    0.287149  1.388953 -0.044319  0.042796 -7.058627e-17  \n",
       "Ohio           -0.383089 -0.396135  0.251803  0.079562  2.828909e-16  \n",
       "Oklahoma        0.041240 -0.138278 -0.270256 -0.076144  1.608264e-16  \n",
       "Oregon         -0.956958 -0.788813  0.008385  0.309320  3.313659e-16  \n",
       "Pennsylvania    0.349161 -0.673718 -0.148769  0.358773 -3.455704e-17  \n",
       "Rhode Island   -0.465706  0.774477 -0.235637 -0.072728  2.361275e-16  \n",
       "South Carolina -1.370353  0.432025 -0.093973  0.084416 -1.457566e-16  \n",
       "SDakota         0.039015  0.197245  0.308069 -0.093852 -1.621066e-16  \n",
       "Tennessee       0.758315 -0.416946  0.020878 -0.591828 -5.951577e-16  \n",
       "Texas           0.616925  0.703904 -0.142975  0.136523 -3.645051e-18  \n",
       "Utah           -0.402352  0.857697  0.003941 -0.128943  1.073379e-16  \n",
       "Vermont        -0.860472 -0.030130 -0.011008 -0.119413 -3.865319e-17  \n",
       "Virginia       -0.603838 -0.039352  0.024034 -0.083494 -2.781204e-16  \n",
       "Washington     -1.120439 -0.168809 -0.112127 -0.144971 -1.510156e-16  \n",
       "West Virginia   1.343965  0.516752  0.533748  0.045967 -5.061035e-16  \n",
       "Wisconsin      -0.381002  1.543470  0.028925  0.029241  2.195361e-16  \n",
       "Wyoming        -1.062054 -0.594425  0.177382  0.062078 -3.265804e-17  \n",
       "Puerto Rico     0.108185  0.424594  0.251957  0.628232  2.227459e-16  \n",
       "D.C.            1.919612 -0.566024 -0.348750  0.130428 -4.610423e-16  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the PCA model and transform X1 to get the principal components\n",
    "pca = PCA()\n",
    "df_plot = pd.DataFrame(pca.fit_transform(X1), columns=['PC1', 'PC2', 'PC3','PC4','PC5','PC6','PC7','PC8','PC9','PC10','PC11'], index=X1.index)\n",
    "df_plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bi plot displays both PC scores and vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.FancyArrow at 0x7f999dcc5b10>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , ax1 = plt.subplots(figsize=(14,10))\n",
    "\n",
    "ax1.set_xlim(-4.5,4.5)\n",
    "ax1.set_ylim(-4.5,4.5)\n",
    "\n",
    "# Plot Principal Components 1 and 2\n",
    "for i in df_plot.index:\n",
    "    ax1.annotate(i, (df_plot.PC1.loc[i], -df_plot.PC2.loc[i]), ha='center')\n",
    "\n",
    "# Plot reference lines\n",
    "ax1.hlines(0,-4.5,4.5, linestyles='dotted', colors='grey')\n",
    "ax1.vlines(0,-4.5,4.5, linestyles='dotted', colors='grey')\n",
    "\n",
    "ax1.set_xlabel('First Principal Component')\n",
    "ax1.set_ylabel('Second Principal Component')\n",
    "    \n",
    "# Plot Principal Component loading vectors, using a second y-axis.\n",
    "ax2 = ax1.twinx().twiny() \n",
    "\n",
    "ax2.set_ylim(-1,1)\n",
    "ax2.set_xlim(-1,1)\n",
    "ax2.tick_params(axis='y', colors='orange')\n",
    "ax2.set_xlabel('Principal Component loading vectors', color='orange')\n",
    "\n",
    "# Plot labels for vectors. Variable 'a' is a small offset parameter to separate arrow tip and text.\n",
    "a = 1.07  \n",
    "for i in pca_loadings[['V1', 'V2']].index:\n",
    "    ax2.annotate(i, (pca_loadings.V1.loc[i]*a, -pca_loadings.V2.loc[i]*a), color='orange')\n",
    "\n",
    "# Plot vectors\n",
    "ax2.arrow(0,0,pca_loadings.V1[0], -pca_loadings.V2[0])\n",
    "ax2.arrow(0,0,pca_loadings.V1[1], -pca_loadings.V2[1])\n",
    "ax2.arrow(0,0,pca_loadings.V1[2], -pca_loadings.V2[2])\n",
    "ax2.arrow(0,0,pca_loadings.V1[3], -pca_loadings.V2[3])\n",
    "ax2.arrow(0,0,pca_loadings.V1[4], -pca_loadings.V2[4])\n",
    "ax2.arrow(0,0,pca_loadings.V1[5], -pca_loadings.V2[5])\n",
    "ax2.arrow(0,0,pca_loadings.V1[6], -pca_loadings.V2[6])\n",
    "ax2.arrow(0,0,pca_loadings.V1[7], -pca_loadings.V2[7])\n",
    "ax2.arrow(0,0,pca_loadings.V1[8], -pca_loadings.V2[8])\n",
    "ax2.arrow(0,0,pca_loadings.V1[9], -pca_loadings.V2[9])\n",
    "ax2.arrow(0,0,pca_loadings.V1[10], -pca_loadings.V2[10]);\n",
    "#plt.savefig('./images/Bi_plot',dsi=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Silhouette_scores \n",
    "**is a method of validation and interpretation of consistency within clusters of data.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score, silhouette_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=[]\n",
    "K=range(2,5)\n",
    "for k in K:\n",
    "    kmeans=KMeans(n_clusters=k).fit(X_pca)\n",
    "    score = silhouette_score(df, kmeans.labels_)\n",
    "    sc.append(score)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "ax.plot(range(2, 5), sc, color='blue')\n",
    "\n",
    "ax.set_title('Silhouette Coefficients for K_Means')\n",
    "ax.set_xlabel('Number of Clusters')\n",
    "ax.set_ylabel('Silhouette Coefficient')\n",
    "# plt.savefig('../imgs/sil-coef-1.png')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import pairwise_distances\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3655643820656864"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_model_3 = KMeans(n_clusters=3, random_state=1).fit(X_pca)# score for 3 clasters\n",
    "labels = kmeans_model_3.labels_\n",
    "metrics.silhouette_score(X_pca, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "**Clustering K=3** \n",
    "\n",
    "- The score is bounded between -1 for incorrect clustering and +1 for highly dense clustering. Scores around zero indicate overlapping clusters.\n",
    "- The score is higher when clusters are dense and well separated, which relates to a standard concept of a cluster.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, n_init=20)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km3 = KMeans(n_clusters=3, n_init=20)\n",
    "km3.fit(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 2, 0,\n",
       "       2, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 2, 1,\n",
       "       0, 1, 1, 1, 2, 0, 1, 2, 2], dtype=int32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km3.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    27\n",
       "0    18\n",
       "2     8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(km3.labels_).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.36354981,  0.43310479,  0.12319121],\n",
       "       [ 0.6654428 , -0.75118587, -0.17140925],\n",
       "       [ 3.07211761,  1.56076655,  0.301326  ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km3.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 4)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_km=pd.concat([df.reset_index().drop('index', axis=1), pd.Series(km3.labels_).reset_index().drop('index',axis=1)], axis=1)\n",
    "df_km.columns=['PC1','PC2','PC3','ClusterID']\n",
    "df_km.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def without_hue(plot, feature):\n",
    "    total = len(feature)\n",
    "    for p in ax.patches:\n",
    "        percentage = '{:.1f}%'.format(100 * p.get_height()/total)\n",
    "        x = p.get_x() + p.get_width() / 2 - 0.05\n",
    "        y = p.get_y() + p.get_height()\n",
    "        ax.annotate(percentage, (x, y), size = 12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=df_km.ClusterID.value_counts().plot(kind='bar');\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "without_hue(ax, df_km.ClusterID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAJ8ozwAAAIBPlGcAAADAJ8ozAAAA4BPlGQAAAGUnl/eUy3lBxxiAfZ4BAABQNvKep67urJ7d/LpS6ZxWLpqicY0Jxcvk4CXKMwAAAMpGT29O3/vvzcpkC7POL+08og9dvUBTm+sCTlbAsg0AAACUjV17O/qK8xue3fS6srl8QIn6ozwDAACgbMSiA+tpLBqWmQWQZiDKMwAAAMrG9Mn1aqyL9T2OREJadc5URcLlUVtZ8wwAAICyUZuM6oarW7V7X4dSmZzmzhin2kT5VNbySQIAAACoUKAXzJ0QdIxBlcf8NwAAAFABKM8AAACAT4GVZzObYWaPmNkWM9tsZp8OKgsAAADgR5BrnnOSPuOcW29m9ZLWmdka59yLAWYCAAAATiqwmWfn3D7n3Prj/9wpaYuk6UHlAQAAAE6nLNY8m9lsScskPT3Ix241s7Vmtra9vX20owEAAAB9Ai/PZlYn6SeSbnfOdZz4cefcXc65lc65lc3NzaMf8DQ85+lIukePtG3TI23bdCTdo7zzTv+FAAAAqDiB7vNsZlEVivMPnHP3BZlluI5lUvq79Q+oJ5eRJP3Xa8/rb5ZfrfHx2oCTAQAAoNiC3G3DJH1H0hbn3D8HlWOknnx9R19xlqSeXFZPvL49wEQAAAAolSCXbVwg6SOSLjOzDcf/XB1gnmHJeLmBY/l8AEkAAABQaoEt23DOPS7Jgnr+YnnH1Hn6VdtW5Y6vc45YSBdNnRdwqrEjm8+rO5dRzuUVDYXVEE2o8KYGAABA8QW65rkaNEaT+tsV12jNni2SpCtaFqgpVhNwqrEh6+W17dh+fXvrE+rNZzUhXqtPLb5EU2oag44GAACqFOV5hKLhsCYl63XDGSslSeFQ4BuYjBnd2Yzu2vK40seXzhxKd+s7257UpxZdqvpYIuB0AACgGlGei4TSPPrSXravOL/hta4j8uQCSgQAAKodjQ8VKxGOqiYS6zd2ZkOzwsZfawAAUBq0DFSsukhMn158qSYmCntqz66foJvnv1110XjAyQAAQLVi2QYqVjgU1qy68fqLJe9U3jlFQyHVRVnrDAAASofyjIpmZmqIJYOOAQAAxgiWbQAAAAA+UZ4BAAAAnyjPAAAAgE+UZwAAAMAnyjMAAADgE+UZAAAA8InyDAAAAPhEeQYAAAB8ojwDAAAAPlGeAQAAAJ84nhsAUHLdvVntPdCpTNbTrKkNqklGFQpZ0LEAYMgozwCAkuruzepHD2zRsa6MJCkWDesj1y1UY1084GQAMHQs2wAAlNRr+zr6irMkZbJ5rd30uvKeF2AqABgeyjMAoKRS6dygY84FEAYARojyDAAoqTNmjlMk3P/HzYpFUwaMAUAlYM0zAKCkahMRfeQPFur3L+xTNpvXuYunaFxjIuhYADAslGcAQEmFwyGNa0zoivNmSc4pGg0HHQkAho3yDAAYFdEIyzQAVD6+kwEAAAA+UZ4BAAAAnyjPAAAAgE+UZwAAAMAnyjMAAADgE+UZAAAA8InyDAAAAPhEeQYAAAB8ojwDAAAAPlGeAQAAAJ8ozwAAAIBPlGcAAADAJ8ozAAAA4BPlGQAAAPCJ8gwAAAD4RHkGAAAAfKI8AwAAAD5RngEAAACfKM8AAACAT5RnAAAAwCfKMwAAAOAT5RkAAADwifIMAAAA+ER5BgAAAHyiPAMAAAA+UZ4BAAAAnyjPAAAAgE+UZwAAAMCnQMuzmX3XzA6Y2aYgcwAAAAB+BD3zfLek1QFnAAAAAHwJtDw7534r6XCQGQAAAAC/gp55BgAAACpG2ZdnM7vVzNaa2dr29vag4wAAAGAMK/vy7Jy7yzm30jm3srm5Oeg4AABUnZ7erI50pHSsK63eVDboOEBZiwQdAAAABKe7N6v71ryk9iO9kqQzZjbpyvNmqSYZDTgZUJ6C3qruHklPSZpvZnvM7GNB5gEAYCzxPKdNL7f3FWdJ2v7aUR043BNgKqC8BTrz7Jz7UJDPDwDAWJb3PO0/NLAoHzjco9nTGwNIBJS/sl/zDAAASiMaCat1zvgB43NnNAWQBqgMlGdUpc5sSjs6DurJ17ervbdT6Tw3wADAYFqm1OuCZdOVTETUUBvTNRfNVX0N652Bk+GGQVSd7mxaP96+Xs+075QkmUyfWHSxFo2bKjMLNhwAlJlkIqoViyZr8byJkknJeEShEN8rgZNh5hlVpzef7SvOkuTk9OMd69WZTQUXCgDKWCQcUm1NVLXJKMUZOA3KM6pOzvMGjPXk0nIBZAEAANWF8oyqk4xENSlZ32/soqlnqiYSCygRAACoFqx5RtVpjCX1mbMv10N7tmhP9xGtmjRbSye0KBoKBx0NAABUOMozqlJTvEbXz1mqjJdXMhzlRkEAAFAUlGdUrUgorAizzQAAoIhY8wwAAAD4RHkGAAAAfKI8AwAAAD6x5vkUOjMpbT26X9s72rWieaam1TSqNhoPOhYAAAACQnk+ia5sWne/9HttOrJXkvTIvpf0gTnLdcm0M7kJDQDQT09vVse60upN5zRpfI1qEpzUB1QryvNJpPPZvuL8hl/s3qRzJ81SYywZUCoAQLnp6c3qvx/drrYDXZKkeDSsG69tVVNDIuBkAEqBNc9DwgHPAID+Dnek+oqzJKWzeT25Ya+y2XyAqQCUCuX5JOLhiBY2Te03trplkWo54hkA8BbdPdkBY109WeU9JlyAasSyjZOoiyZ08/zztfnIXr18rF1va56llrpxrHcGAPQzbVKdIpGQcjmvb2zJgmYl4vyIBarRaf/PNrMGSc3Oue0njJ/jnHu+ZMnKQEMsofMnz9X5k+cGHQUAUKZqEhHdeE2rfrduj3rTOS1bMEmzpjYEHQtAiZyyPJvZByXdKemAmUUl3eSce/b4h++WtLy08QAAKG/hcEgTmpJ61zvmyPOcEvGIzNhpA6hWp1vzfIekFc65pZJulvQ9M3vf8Y/xnQHAAJ2ZlI6ke3Qs0yvPeaf/AqBKxGMRJRNRijNQ5U63bCPsnNsnSc65Z8zsUkk/N7MWsfUEgBO093bp37b8Vnu6j2pcvEZ/suACzaobz70CAICqcbqZ504zO+ONB8eL9CWS3i1pUQlzAUOS8/LKe8xyBqkrm9K/v/Sk9nQflSQdSffoG5sfU1c2E3AyAACK53Qzz/+XTlie4ZzrNLPVkj5YslSAT+l8Vgd6u7Rmz1bVRKO6YvoCjYvVKBxiF8bRlndO2zsO9hvryWWU9rKSOFgIAFAdTleeuyVNlvTKCePnSfp9SRIBQ7C/t1P/8NyDfWuIntr/qr644hqNi9cEmmssCsk0q268dnUd7htLhCOKh6IBpgIAoLhONz13p6TOQcZ7j38MKInOTEqbDu/VL157Qbu7jqgnN/Ct/0w+p4d2v9hv8X0qn9XmE45Vx+iojyX0sflvV3OiTpJUG4nrtoUXcbAQAKCqnG7mefZgezk759aa2eySJMKY15VN6/uvPKMNh/ZIkv5r1wv647PO06rm2f2WY5iZYuGBf4VjIQ4mCMqkZL0+v+RKZby8oqGwaiMxbhYEAFSV0808J07xMRYxoiRS+WxfcX7Dz3ZuVFcu3W8sGgpr9YyFir6lnI2L12h+0+RRyYmBzEwNsaQmJurUGEtSnAEAVed0U3TPmtmfOOe+9dZBM/uYpHWli4WxzLmBuyBmvfygnzshXqu/W3Gt1h/arZpwVIvGT1NjjN/rAABAaZyuPN8u6X4zu1FvluWVkmKS3lvKYBi7EuGoZtdP0M7OQ31jV0xfMOja2UgorPGJWl0xfcFoRgQAAGPUKcuzc26/pLcfPxxl8fHhXzjnflPyZBiz6mMJfXzhRXr6wE7t6DyoVZNma17DJJYAAACAwJ2yPJtZQtJtkuZJekHSd5xzudEIhrGtIZbU5dMX6BLn9VvTjJPLO09dmbRe7+1QXTSuxlhCddFT3bYAAACG6nTLNv5DUlbS7yS9S1KrCks5gJILmSlkFGe/DvZ26csbHlJvPitJWjJ+uj5y1irVU6ABACia05Xnhc65syXJzL4j6ZnSRwIwVKlcVve9uqGvOEvSxsNtujbdQ3kGAKCITrdVXd9PYpZrAOUr6+V1ONM9YPxYpjeANAAAVK/TleclZtZx/E+npHPe+Gcz6xiNgMBgjmV6tf7ga3p6/6s6mu6R57ygIwWqNhrThZPP6DcWC4U1o3ZcQIkAAKhOp9ttgwWnKDvHMr36xw0P61C6MNNaE4npb5a9S+MTtQEnC07IQlrRPFOec/rt66+oKZbU++cuV100HnQ0AACqCucYo+JsOry3rzhLUk8uo9/s3ab3zVmqkJ3uzZTy0J1NK53PycwUD0VUEx24h/VQ1UUTumjamVrRPEuRUEg1g+yLDQAARobyjIrTlU0PGOvMpuWcJBv9PEPVmUnpe688rY2H2mSSzps8V9fPXqr62Mhv7AtbSA1FuA4AABhcZUzTAW+xYuJMRd4yw2wqnEAYDlXGX+fnD7dp46E2SZKT9NT+HdrZdTjYUAAAwBdmnlExnHM6mOrW+oO79GdnX65H972krJfX6hmL1JyoCzqeL57naevR/QPGXz62X2ePnxZAIgAAMBSUZ1SMjmxK/3vjw+rIptQUS2rFxJla0DRFM2qbKubo7lAopOUTZ+iZ9p39xs8ePz2QPF3ZtDqzKR1J92hqTaPqInFFw5Xx3xIAgCBQnlExjmV61ZFNSZKOZnr1673btO7ga7pj2Wo1xpIBp/PvzMZJunL6Aj2y9yWFLaTVMxZqak3jqOfozmb089de0CN7X5IkRSykz5xzheY2TCz5czvnpJ6Owp9YQoolZcnKePcAADC2UZ4rSDqfVU8uK895ioUiRbnBrJIkw9EBY02xGoUq4S7Bt6iLxvUHs87RldNbJRW22gtitjeVz/QVZ0nKOU8/fOVZfXrxpaX/u9VxSN6Pvix1H5Uk2eILpXd8oKQF2uVzkpxskL9HAAD4RXmuED3ZjH73+iv6r13PK+c8zaobr/+58CI1xWuCjjZqkpGY3j55rp7cv0NSYab0hnkrKvKXiHg4ong42P/9UvmBh4YezfTIc66kz+syKXmP/6SvOEuS2/S4bPmVUgnKs8vnpc7DcmsfkrIpaeVVUmOzrAL/3gAAgkd5rhCd2ZTu27mh7/GursP6xWub9IG5yxULuISNlrpoXNfPWaYrpi/Q4XS3WmrHqS7CISDDVReNqzGW7HeE96pJc0q/P3QuKx1+fcCw6zgkm9gy6Je4TEpK9xbKbywp1dTL/K5z7zkm73t/Kx3f4tBt/b1Cf/S3UvOMYf8rAADGrrHRuqrAvp6Bp6Hv6DyodD43ZsqzVCh8ddG4ptc2BR2l4jVEE/rcOVfqJ68+p9d7O7Ri4kxdMvXM0i8hidfIWlfJtb/25lg4IjtJmXWZlNyLT8o9co/kPClRp9AHPic1D160B3z9K8/1FefCgJNb+6D0zptYwgEAGLKx07oqXEtdk0yFfYHfsHjcVCUj/PDH8JiZmpN1uums85TxcqqJxBUZhb2yLRyWFl4gpXvlNj8u1TYqdOmNUrJ+8C9I975ZnCUp1SVvzd0KvefTspqTfM1bDXZEeTSpijhRBwBQdijPFaIuEtP/WHCB7tm+Vt3ZjJZNaNHl01srZos2lK9EJKqERveXMKupl1ZdK1tyqRQKn7oEZ3rfLM5vOLRXcnl/zzV7sVzdOKnrSGEgGpetvEo2ht6xAQAUDz89KkQiEtOyCS06s2GSnKRYOFz6talACVkkKtX5WH6TqC38SXW/+bVzl0g+17tbXZNCH/5ruV2bpUxKNm+ZFMDWgACA6kB5riDhUFiN8crZzxgoimSdQh/4vLyH/106vE82d4nskhtkQ/h/weqaZIsuKGFIAMBYQXkGUNYsFJaaWxR67+2FpRqR+JCKMwAAxUR5BlARfN0cCABAiVGegSrmOU+d2bRynqdoKKT6aEJm7DIBAMBwBVqezWy1pK9LCkv6tnPuK0HmAapJ3vP0Wvdh/duLv9PRTK8mJev18YUXaQo3y5Ull0kV9qMOR2WJsXNyKABUmtJv6noSZhaW9E1J75K0UNKHzGxhUHmAatOVTeubmx/T0eMnCB7o7dT/u+VxdWRSASfDiVzXUblff1/e974o74XOsXoAABpYSURBVBf/Jne0PehIAICTCKw8S3qbpFecczuccxlJP5L07gDzAFUl4+XU+daT9STt7Tmm/Il7JmPYXLpXrutI4c8wfylxqR55a/5DbstTUk+HtGuzvJ98Ta77WJHTAgCKIchlG9Ml7X7L4z2SVp34SWZ2q6RbJWnmzJmjkwyoArFwRPXReL8CPa2mUREL8nfm6uF6OuUev69wSqKZbPmV0rmrZSc7KfFkchnp1Rf6jx072P9IcQBA2Qjyp+hgdy25AQPO3eWcW+mcW9nc3DwKsYDqUBeJ6+OLLlZTrLCt26REvf609ULVxxIBJ6sO7rUX5Tb9tnD6oZeXW/ugXPueoV/ITGqY0H8sHJEio3vqIwDAnyBnnvdImvGWxy2S9gaUBag64VBIs+rG645lq4/vthFWfdTfqXw4NeflpR0bB35g5yZpZuvQLlZTr9Dqj8m775+lXLYwi33ZjVK8+m8adKkeKZuSPK9wbDrbEQKoAEGW52clnWlmcyS1SbpB0ocDzANUnZCF1BjjQJFis1BYbs450tan+39g1qKhX8tCclPmKHTLl6WuY1JtgxRLyqr8Fx3X2yn3xE/lnn9MkpOmnqHQdR+X1bIbDIDyFtiyDedcTtInJD0kaYukHzvnNgeVBwCGwmYtki18e2HZRSgsW3aFrHnG6b9wsGtForK6cbIps2X148fGCYpHDsg9/6j6Vuvt2y73/GOFWX0AKGOB7vPsnHtA0gNBZgCA4bCaeunSD8kueF/hDo5YUsZ6ct/c/p0Dx/Ztl2Uz0lj45QFAxeKEQQAYJovXjIm1yaVgM1sH3CFu81ZI/AICoMyxZxUAYPTVNcmuullK1knhSGHZy7xlHB8PoOwx8wwAGHUWr5Faz5fNPrswEEtU/U2SAKoD5RkAEAgLhSV21wBQYSjPACqe6z4mpbqlSKwwg5msCzoSAKBKUZ4BVDTXeVje//ePUsdBSZItvEC6+ANDPyYbAAAfuGEQQMVy2YzcU//dV5wlyb34hNRxOMBUAIBqRnkGULlyGblDbQOG3dH9AYQBAIwFlGcAlSteI1uwqv+YhWRT5waTBwBQ9VjzDKBiWSgkLXib1NMh98JvpWS9Qpd9WGK9MwCgRCjPACqaJeulVdfKll5amHWuaSj5czrPk3o7pXxOikSlZD2HewDAGMGyDQAVzyJRWW3TKBXnvNT+WuFGxe5jcrtelA7tlUt1l/y5AQDBY+YZZSXvPHVn0zKZ6mOJoOMAA/V0ynvqvxQ6+yJ5P/5HKZ+Tk2QrrirMgCdqgk4IACghyjPKRlc2rbXtu/Srtq2KhyO6fs4yza2fqEQkGnQ04E35rGzecnmP31dYtnGcW/ewbNkVEuUZAKoayzZQNl4+dkD3bF+r9lSX9nQf1b9uelQd2VTQsYD+IjFZXZPUdeJe0k7KpQOJBAAYPZRnlIV0Pqsn9+/oN+bktOnw3oASASeRrJNrbJaddW7/8bpxUpxZZwCodizbQFmIWFhTaxr0/OH+B15MGYUbwIChsFBYamqWzn+3VFMv9/J6acJ0hS56v8TfVwCoepRnlIVwKKTLps3XuoO7dTDVJUlqbZqiltpxAScDBjILSXVN0qrrZEsvLyzliCeDjgUAGAWUZ5SNpniNPr/kSnVkUoqEQqqLxNlxA2XNIhEp0hh0DADAKKI8o6w0xpJqjDGDBwAAyhM3DAIAAAA+UZ4BAAAAnyjPAAAAgE+UZwAAAMAnyjMAAADgE7ttAKg4Lt0jdR6We3md1DxDNm2ejANKyobr6ZSck5J1shBzNACqC+UZQEVxzpPb9aLcz/+fN8dmtCp0zZ/KauoDTFbeXLpXkkp6mIvLpKT9O+X99j+lbFq24irpzGWyRF3JnhNjm3NOqXROoZApHqPSYHTwNw1AZenplHv8vv5ju7dI6R6J8jyAy6Skw/vkPflTyTmFzr9OmtgiK8UBRN0d8u79WmHWWZJbc7dU2yibe07xnwtjXm86p1f3HNXGre2qSUZ04fIWNdXHFQ7zbgdKi/IMoPLkcwPHnDf6OSpB11F59/x9X6H1dr2o0Ef+VmqeUfSncjs29j1P39gLj8nNmC+Lxov+fBi7nHPa2XZMDz6+s2/stX2duvk9i1VXGwsuGMYEfj0DUFmSdbJzV/cfm9giJWqDyVPm3OYnTii0Tm7jo3InlNyiGD9lwJCNnyqFmadBcaUyeW3c2t5vLJvztO9gd0CJMJbwHQ1ARbFQWJr/NqlpkrT5CWnSLNnC87lh8GTqmwYZGyczK/pT2eRZci1nSXteKgw0TJAtu7zwmuG0XKpHyqYkmRRLlHR9eqULh0y1NQMrTG1NNIA0GGsozwAqjiXrZLMXy81YIIVCMuNNtJOxeSvk1j4sdRwsDNQ1yRZdUJrnqmlQ6Nr/KfV0SLlsoaTXNpbkuU7HOSc5VzG7fbieTnmP/FDa9qxkJltyqXTeH3AT7EnEomFduLxFu/Z2KpPNS5JaptSpqZ7lQSg9yjOAimUsBzgtq2tS6EN3yLXvljxPNnlWSQut1dQHeuOmc57UdVRu46NSb5e07PLCDHgpbpAsIvfqRmnbM8cfOLkNv5bmLZPNbA02WBlrqIvppvcs0oHDPUrGo2qsj6kmwcwzSo+fPAAwRM55Uk9n4UEsUfY3w1ltY2AzwKOuu0Pe9/6XlOqSJLlNv1Xow38tTZ4dbK5TcPmctHPzwA+8tkWiPJ9UOBRSXU1MdTXcIIjRVRnvZwFAmXCZlNyOF+T98O/lfecv5B77ceFQEJQFt3trX3EuDDh5Tz8gl00HF+o0LByR5i4ZOD578eiHAXBalGcAGIreLrmf/d9S5yEpl5V7/lG5Tb+T8/JBJ6t4LtUt13FI7sh+ue5jw7vIIDcnWjgsqfg3SBaTzVokO/viQv5wRLbqWmnCtKBjARgEyzYAYAjc/p2STtjL+JX1ssXv4JCWEXC9XXKP3yf3wmOFgXFTFHr/Z2X144Z0HZt+plzdOKnrSGEgFJatulYWLe+39q2mXrroA7Lz/0CF3TaSslh5LwcCxirKMwAMgY2fohN3SLZJs6QyX/dc9joOvVmcJenI63LPPiBd9EFZxP9NYFbXpNCH/1rulfVSb5es9TypbpDt+sqQxZMS29MBZY/yDABDUdskW3GV3LqHJTlp/FTZeeU/s1nu3OF9A8cOvCbLZqQhlGepUKBt6WXFigYA/VCeAWAILFknnXetbPkVhWPCowlZLQe0jJRNO0POrN9piDb/XGZiAZQdbhgEgCGyeI2sfrysaRLFuViS9bJ3f0pqbC6s913xTtn8VRVzyAmAsYOZZwBA4CyWkOYslk2+Q5IrFGiWwgAoQ5RnAEBZMAtJzOQDKHOUZwBlyaW6C/vdjoFdLFwmJWV6pUxaiicKSxgG2a8YABA8yjOAsuJ6u+R2bpJ7/lGpbpxCb3+P1Dixasuky6Tktj0j9+vvS15eStYr9IHPSROnBx0NADAI7sQAUDacc3KvviD3y29JbS9L256R98MvSdV8/HWm983iLEm9nfIe/neO/AaAMkV5BlA+Ul1yG37dfyzdK9e+O5g8oyHd+2ZxfsOhvQPHAABlgWUbAMpHKCwlBx5xbcm6AMKMkkSNlKiVUt1vjs05p2gnFrpcVuo5Jrfjeam2UTZtnqy2sSjXBoCxiPIMoGxYvEahiz4gb/dWKZcpDLacJTVMDDZYKSUKa5y9B78rHd4rzTlHoUtuKBzVXAxHD8j7wd8VDnSR5CZMU+j9n6VAA8AwUZ4BlJemSQrd/A9y+7bL6pqkpkmymoGz0dXCwmGpeYZC1/+Z5HlSNF604uwyKXlP3N9XnCVJh/bKHWyjPAPAMFGeAZQVC0ek+nGy+pVBRxlVVlOC/Y2dJ2VTA8cHGwMA+MINgwBQpSxeIzv3Xf0HE7WyKXODCQQAVYCZZwCoYjZljuyDfyFv/RpZbZPs3NVSKWa5AWCMCKQ8m9kHJH1RUquktznn1gaRAwCqncVrpJazFJo8S7KwLMKcCQCMRFDLNjZJep+k3wb0/AAwplg0TnEGgCII5Dupc26LJJlZEE8PAAAADEvZT0OY2a2SbpWkmTNnBpwGAFAtXE+HXNvL0t7tsjNXSOOnyBK1QccCUOZKVp7N7FeSpgzyoS84537m9zrOubsk3SVJK1eudEWKBwAYw1xvl7yH75Z2bCw8XveQ7LI/ks5+R2G7RAA4iZJ9h3DOXVGqawMAMCLZdF9xfoN76qeyM5dLHCAD4BTY5xkAMPa4Qd7IHGwMAE4QSHk2s/ea2R5J50v6hZk9FEQOAMAYFUtILfP7Ddm575LirHkGcGpB7bZxv6T7g3huAAAsWafQtbfJvfKc1Pay1HqebPJstvMDcFp8lwAAjElW0yA752K5xe+QhVjFCMAfvlsAAMY0ijOAoeA7BgAAAOAT5RkAAADwifIMAAAA+ER5BgAAAHyiPAMAAAA+UZ4BAAAAnyjPAAAAgE8ckgKcRkcmpbbuI2pPdam1aaoaYgnFw/yvAwDAWEQDAE6hM5PSt7c+oW3H9kuSQjJ95pwrNK+xOeBkAAAgCCzbAE6hI5vqK86S5MnpJ68+p65sOsBUAAAgKMw8A6eQyecGjPXmM/KcCyANUJ1c9zEpm5bCUSmekMWSQUcCgJOiPAOnMD5Rq6ZYUkczvX1jl09boLpoLMBUQPVwHQfl/efXpGPtUigse/t7pCWXyOI1QUcDgEFRnoFTaIwl9ZdL36k1e7Zqf2+nLpxyhs5snKSQseIJGCmX7pX36I8LxVmSvLzc4z+RzT9XojwDKFOUZ+A0xsVr9b45S5XzPCUi0aDjANUjl5HaXxsw7DoOybgpF0CZYvoM8CESClOcgWKLJWVzl/QfC4Vl4yYHkwcAfGDmGQAQCIvGpLddLfV2yr20Vqofp9CVN0uJ2qCjAcBJUZ4BoEq53k6p66jcsYOyybOkZL2szN5BsdpG6fKPyC7+w8JATYPMLNhQAHAKlGcAqEKut0vu0R/LbXmy8DgcUeiDfyFNnRtwsoEsnpTibE8HoDKw5hkAqlG6p684S5LyOXmP/LAwGw0AGDbKMwBUo7fsTd6n+5jkeaOfBQCqCOUZAKpRbZNU29hvyBa/Q4pzMx4AjARrngGgGtU0KHTDX8k9cb/ckddlrecX/kT4tg8AI8F3UQCoQmYmNTZLV3xUlstKiRpZKBx0LACoeJRnAKhiFktIsUTQMQCgarDmGQAAAPCJmWcAqAIu3VvYYaOno3CzYKK27A5EAYBqQHkGcFqut0vKZaVQSKqplxlvWpUTl0nLbX1a7jffl5yTIjGFrv9zuWnzOK0PAIqMn4AATsl1HJb303+V963Pyrvn76V9O+TyuaBj4a0yPXKP3lMozpKUy8h76LuFWWgAQFFRngGclEt1y3v4bmnf9sJAxyF5990p9XYFmgsnyGWlE3+hOdYuyQUSBwCqGeUZwMnlc1Lbtv5jmV4pkwomDwYXjUsNE/qPzV0iRWLB5AGAKkZ5BnByobA0aVb/sUiMrc/KTU2DQu//rDRrkVTTIFt4vkJXfFQWrwk6GQBUHW4YBHBSlqxTaPXH5N33z9Kxg1IsKVv9MYlSVlbMTGqapNA1f1pYwhFLymLxoGMBQFWiPAM4taZJCt1wh5TNSJEoW6CVMUvUBh0BAKoe5RnAKZmZVNsYdAwAAMoCa54BAAAAnyjPAAAAgE8s2wAAjAmclAmgGCjPAICq5zoPy3vgLqntZalhgkLv+hO5ybO5+RXAkPFrNwCgqrlUj7xffa9QnKU3T8pMdQcbDEBFojwDAKpbPiPt3tp/LJuS0j3B5AFQ0SjPAIDqFopIk084KTMc4bAfAMNCeQYAVDVL1il01S1SY3NhIJaQvetWKZ4MNhiAisQNgwCA6tfYrNANf8VJmQBGjPIMAKh6nJQJoFhYtgEAAAD4RHkGAAAAfKI8AwAAAD5RngEAAACfKM8AAACAT5RnAAAAwCfKMwAAAOAT5RkAAuLSvXLp3qBjAACGIJBDUszsq5L+QFJG0nZJNzvnjgaRBQBGm8ukpMP75D35U0lS6PzrpAnTZbFEwMkAAKcT1MzzGkmLnXPnSHpJ0l8FlAMARl/nYXn3/L20c5O0c5O8e74sdR4KOhUAwIdAyrNz7mHnXO74w99LagkiBwAEwb3wO8m5t47IPf9YYHkAAP6Vw5rnWyT98mQfNLNbzWytma1tb28fxVgAUCL14waO1Y0f/RwAgCErWXk2s1+Z2aZB/rz7LZ/zBUk5ST842XWcc3c551Y651Y2NzeXKi4AjBqb/zap/i1luW6crPW84AIBAHwr2Q2DzrkrTvVxM/tjSddKuty5fu9fAkBVs7omhT7813IHdklOssmzZLWNQccCAPgQ1G4bqyX9haSLnXM9QWQAgCBZbaNszjlBxwAADFFQa56/Iale0hoz22Bm/xZQDgAAAMC3QGaenXPzgnheAOXLpbqlTEry8lIsIatpCDoSAAADBFKeAeCtXE+n3G//U+7FJwoDk2Yq9N7bWQcMACg75bBVHYCx7sjrbxZnSTrwmtyG38h5+eAyAQAwCMozgMC59t0Dx/bvlLKZ0Q8DAMApUJ4BBM5mtg4cO+tcKZYIIA0AACdHeQYQvNom2TV/KtWNK9wseO67ZGcskZkFnQwAgH64YRBA4CyelM5cIWuZXxiIJWXRWLChAAAYBOUZQFmwUFhidw0AQJlj2QYAAADgE+UZAAAA8InyDAAAAPhEeQYAAAB8ojwDAAAAPlGeAQAAAJ8ozwAAAIBPlGcAAADAJ8ozAAAA4BPlGQAAAPCJ8gwAAAD4RHkGAAAAfKI8AwAAAD5RngEAAACfKM8AAACAT5RnAAAAwCfKMwAAAOAT5RkAAADwifIMAAAA+ER5BgAAAHyiPAMAAAA+UZ4BAAAAnyjPAAAAgE+UZwAAAMCnSNABMLq6s2nt7+3Q+oN7dEbDBM1rmKT6WCLoWAAAABWB8jyG5DxPz7Tv0o+2r5UkrWmTzhk/XX981nmqi8YDTgcAAFD+WLYxhnTn0vrvXS/0G3v+cJvS+WxAiQAAACoL5XmMcfIGGQMAAIAflOcxpDYS0zunL+w3Nr9xsuJhVu8AAAD4QWsaQyKhsN4xdZ5m1I3TM+07Na9hkpZOaFF9lBsGAQAA/KA8jzF10bgWj5+mheOmKGS88QAAADAUtKcxiuIMAAAwdDQoAAAAwCfKMwAAAOAT5RkAAADwifIMAAAA+ER5BgAAAHyiPAMAAAA+UZ4BAAAAnyjPAAAAgE+UZwAAAMAnyjMAAADgE+UZAAAA8InyDAAAAPhEeQYAAAB8ojwDAAAAPlGeAQAAAJ/MORd0Bt/MrF3SrqBznGCipINBh0BJ8RpXP17j6sdrXP14javfaL/Gs5xzzScOVlR5LkdmttY5tzLoHCgdXuPqx2tc/XiNqx+vcfUrl9eYZRsAAACAT5RnAAAAwCfK88jdFXQAlByvcfXjNa5+vMbVj9e4+pXFa8yaZwAAAMAnZp4BAAAAnyjPAAAAgE+U5yIys8+amTOziUFnQXGZ2VfNbKuZPW9m95tZU9CZUBxmttrMtpnZK2b2l0HnQXGZ2Qwze8TMtpjZZjP7dNCZUBpmFjaz58zs50FnQfGZWZOZ3Xv8Z/EWMzs/qCyU5yIxsxmSrpT0WtBZUBJrJC12zp0j6SVJfxVwHhSBmYUlfVPSuyQtlPQhM1sYbCoUWU7SZ5xzrZLOk/RxXuOq9WlJW4IOgZL5uqQHnXMLJC1RgK815bl4/kXS5yVxB2YVcs497JzLHX/4e0ktQeZB0bxN0ivOuR3OuYykH0l6d8CZUETOuX3OufXH/7lThR+404NNhWIzsxZJ10j6dtBZUHxm1iDpIknfkSTnXMY5dzSoPJTnIjCz6yS1Oec2Bp0Fo+IWSb8MOgSKYrqk3W95vEcUq6plZrMlLZP0dLBJUAJ3qjCB5QUdBCUxV1K7pH8/vjTn22ZWG1SYSFBPXGnM7FeSpgzyoS9IukPSO0c3EYrtVK+xc+5nxz/nCyq8DfyD0cyGkrFBxnj3qAqZWZ2kn0i63TnXEXQeFI+ZXSvpgHNunZldEnQelERE0nJJn3TOPW1mX5f0l5L+Jqgw8ME5d8Vg42Z2tqQ5kjaamVR4O3+9mb3NOff6KEbECJ3sNX6Dmf2xpGslXe7YIL1a7JE04y2PWyTtDSgLSsTMoioU5x845+4LOg+K7gJJ15nZ1ZISkhrM7PvOuT8KOBeKZ4+kPc65N941uleF8hwIDkkpMjPbKWmlc+5g0FlQPGa2WtI/S7rYOdcedB4Uh5lFVLgB9HJJbZKelfRh59zmQIOhaKwwq/Efkg47524POg9K6/jM82edc9cGnQXFZWa/k/Q/nHPbzOyLkmqdc58LIgszz4A/35AUl7Tm+DsMv3fO3RZsJIyUcy5nZp+Q9JCksKTvUpyrzgWSPiLpBTPbcHzsDufcAwFmAjB0n5T0AzOLSdoh6eaggjDzDAAAAPjEbhsAAACAT5RnAAAAwCfKMwAAAOAT5RkAAADwifIMAAAA+ER5BoAKZmZ5M9tgZpvM7D/NrOb4+BQz+5GZbTezF83sATM76/jHHjSzo2b282DTA0DloTwDQGXrdc4tdc4tlpSRdNvxg0Hul/Soc+4M59xCSXdImnz8a76qwt7HAIAhojwDQPX4naR5ki6VlHXO/dsbH3DObXDO/e74P/9a+v/bu2OTiIIwCqP3B0HsxcDE2EAWMRRsYfuwESuwh002dSPZBkRswMBwDHyCmQMGwxvOCSe60eNjgjf5GDMRYN3EM8AElqfGb5O8JDlPchi7CGBO4hlg3c6WZ6efk7wmeRy8B2BqJ6MHAPAvn621i98HVXVMcj9oD8DU3DwDzGeX5LSqtj8HVXVZVVcDNwFMQTwDTKa11pLcJdksv6o7JnlI8p4kVbVP8pTkuqrequpm2FiAlanvbywAAPAXN88AANBJPAMAQCfxDAAAncQzAAB0Es8AANBJPAMAQCfxDAAAnb4AbFQKfCl9h8MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='PC1', y='PC2', hue='ClusterID', data=df_km, palette='Set2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53 entries, 0 to 52\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   PC1        53 non-null     float64\n",
      " 1   PC2        53 non-null     float64\n",
      " 2   PC3        53 non-null     float64\n",
      " 3   ClusterID  53 non-null     int32  \n",
      "dtypes: float64(3), int32(1)\n",
      "memory usage: 1.6 KB\n"
     ]
    }
   ],
   "source": [
    "df_km.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 5)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the dataset\n",
    "df_temp = pd.concat([df_km,df_Region], axis = 1)\n",
    "df_temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>ClusterID</th>\n",
       "      <th>Region_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.944859</td>\n",
       "      <td>2.149066</td>\n",
       "      <td>0.667665</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.426677</td>\n",
       "      <td>0.654637</td>\n",
       "      <td>1.651000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.966457</td>\n",
       "      <td>-1.282431</td>\n",
       "      <td>2.699027</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.437731</td>\n",
       "      <td>-0.845176</td>\n",
       "      <td>0.456842</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.258976</td>\n",
       "      <td>-0.039151</td>\n",
       "      <td>0.049686</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1       PC2       PC3  ClusterID  Region_Code\n",
       "0  5.944859  2.149066  0.667665          2          0.0\n",
       "1  2.426677  0.654637  1.651000          2          1.0\n",
       "2 -1.966457 -1.282431  2.699027          0          2.0\n",
       "3 -0.437731 -0.845176  0.456842          1          3.0\n",
       "4  0.258976 -0.039151  0.049686          1          4.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 13)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoAll.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 14)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the Cluster Profiling dataset\n",
    "df_ClusterProfiling = pd.merge(autoAll, df_temp[['ClusterID', 'Region_Code']], on = 'Region_Code')\n",
    "df_ClusterProfiling.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename Regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_ClusterProfiling['Region_Code'] = df_ClusterProfiling['Region_Code'].replace(to_replace=[8,15,28,46,41,0,1,2,3,4,5,6,7,9,10,11,12,13,14,16,17,18,19,20,21,22,23,24,25,26,27,29,30,31,32,33,34,35,36,37,38,39,40,42,43,44,45,47,48,49,50],\n",
    "                                                                                #value=['Columbia','Iowa','Nevada','Virginia','SDakota','Alabama','Alaska','Arizona','Arkanzas','California','Colorado','Connecticut','Delaware','Florida',\n",
    "                                                                                #'Georgia', 'Hawaii', 'Idaho','Illinois','Indiana','Kansas','Kentucky', 'Luisiana','Maine','Maryland','Massachusetts','Michigan',\n",
    "                                                                                    #'Minnesota','Mississippi', 'Missouri','Monatana','Nebraska','New Hampshire','New Jersey','New Mexico','New York','North Carolina','North Dakota','Ohio',\n",
    "                                                                                     #'Oklahoma','Oregon','Pennsylvania', 'Rhode Island','South Carolina','Tennessee','Texas','Utah','Vermont','Washington','West Virginia','Wisconsin','Wyoming'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 14)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ClusterProfiling.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Clusters as Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Clustering the numerical columns by Cluster ID\n",
    "# df_ClusterProfiling.set_index('ClusterID')\n",
    "# df_ClusterProfiling.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "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>Region_Code</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Driving_License</th>\n",
       "      <th>Previously_Insured</th>\n",
       "      <th>Vehicle_Damage</th>\n",
       "      <th>Annual_Premium</th>\n",
       "      <th>Policy_Sales_Channel</th>\n",
       "      <th>Vintage</th>\n",
       "      <th>Response</th>\n",
       "      <th>Vehicle_Age_1-2 Year</th>\n",
       "      <th>Vehicle_Age_&lt; 1 Year</th>\n",
       "      <th>Vehicle_Age_&gt; 2 Years</th>\n",
       "      <th>ClusterID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.695402</td>\n",
       "      <td>49.270115</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5002.798851</td>\n",
       "      <td>59.793103</td>\n",
       "      <td>159.310345</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.729885</td>\n",
       "      <td>0.017241</td>\n",
       "      <td>0.252874</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.623853</td>\n",
       "      <td>44.779817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009174</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>3131.376147</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>157.889908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.788991</td>\n",
       "      <td>0.110092</td>\n",
       "      <td>0.100917</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.651568</td>\n",
       "      <td>37.498258</td>\n",
       "      <td>0.996516</td>\n",
       "      <td>0.020906</td>\n",
       "      <td>0.972125</td>\n",
       "      <td>25570.641115</td>\n",
       "      <td>117.616725</td>\n",
       "      <td>146.425087</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.630662</td>\n",
       "      <td>0.310105</td>\n",
       "      <td>0.059233</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.609653</td>\n",
       "      <td>40.329382</td>\n",
       "      <td>0.998307</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.971211</td>\n",
       "      <td>23189.732430</td>\n",
       "      <td>110.138865</td>\n",
       "      <td>154.568163</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.741744</td>\n",
       "      <td>0.210838</td>\n",
       "      <td>0.047417</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.652632</td>\n",
       "      <td>40.421053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003509</td>\n",
       "      <td>0.975439</td>\n",
       "      <td>23914.459649</td>\n",
       "      <td>113.491228</td>\n",
       "      <td>156.185965</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.792982</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region_Code    Gender        Age  Driving_License  Previously_Insured  \\\n",
       "0          0.0  0.695402  49.270115         1.000000            0.005747   \n",
       "1          1.0  0.623853  44.779817         1.000000            0.009174   \n",
       "2          2.0  0.651568  37.498258         0.996516            0.020906   \n",
       "3          3.0  0.609653  40.329382         0.998307            0.006774   \n",
       "4          4.0  0.652632  40.421053         1.000000            0.003509   \n",
       "\n",
       "   Vehicle_Damage  Annual_Premium  Policy_Sales_Channel     Vintage  Response  \\\n",
       "0        1.000000     5002.798851             59.793103  159.310345       1.0   \n",
       "1        0.990826     3131.376147             94.000000  157.889908       1.0   \n",
       "2        0.972125    25570.641115            117.616725  146.425087       1.0   \n",
       "3        0.971211    23189.732430            110.138865  154.568163       1.0   \n",
       "4        0.975439    23914.459649            113.491228  156.185965       1.0   \n",
       "\n",
       "   Vehicle_Age_1-2 Year  Vehicle_Age_< 1 Year  Vehicle_Age_> 2 Years  \\\n",
       "0              0.729885              0.017241               0.252874   \n",
       "1              0.788991              0.110092               0.100917   \n",
       "2              0.630662              0.310105               0.059233   \n",
       "3              0.741744              0.210838               0.047417   \n",
       "4              0.792982              0.154386               0.052632   \n",
       "\n",
       "   ClusterID  \n",
       "0          2  \n",
       "1          2  \n",
       "2          0  \n",
       "3          1  \n",
       "4          1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ClusterProfiling.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ploting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the box plots to observe distribution\n",
    "plt.figure(figsize = (14,12))\n",
    "var = [ 'Gender', 'Age', 'Previously_Insured', 'Vintage','Vehicle_Damage','Vehicle_Age_< 1 Year']\n",
    "for i in enumerate(var):\n",
    "    plt.subplot(3,3,i[0]+1)\n",
    "    sns.boxplot(x= 'ClusterID', y = i[1], data = df_ClusterProfiling)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the box plots to observe distribution\n",
    "plt.figure(figsize = (14,12))\n",
    "var = [ 'Vehicle_Age_1-2 Year', 'Vehicle_Age_> 2 Years', 'Driving_License']\n",
    "for i in enumerate(var):\n",
    "    plt.subplot(3,3,i[0]+1)\n",
    "    sns.boxplot(x= 'ClusterID', y = i[1], data = df_ClusterProfiling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2      2.0\n",
       "5      5.0\n",
       "6      6.0\n",
       "10    10.0\n",
       "13    13.0\n",
       "Name: Region_Code, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ClusterProfiling[df_ClusterProfiling['ClusterID'] ==0]['Region_Code'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      3.0\n",
       "4      4.0\n",
       "7      7.0\n",
       "8      8.0\n",
       "9      9.0\n",
       "11    11.0\n",
       "12    12.0\n",
       "17    17.0\n",
       "18    18.0\n",
       "19    19.0\n",
       "Name: Region_Code, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ClusterProfiling[df_ClusterProfiling['ClusterID'] ==1]['Region_Code'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.0\n",
       "1      1.0\n",
       "20    20.0\n",
       "22    22.0\n",
       "42    42.0\n",
       "Name: Region_Code, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ClusterProfiling[df_ClusterProfiling['ClusterID'] ==2]['Region_Code'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarcical clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "- Ward minimizes the sum of squared differences within all clusters. It is a variance-minimizing approach and in this sense is similar to the k-means objective function but tackled with an agglomerative hierarchical approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3) = plt.subplots(3,1, figsize=(20,20))\n",
    "\n",
    "for linkage, cluster, ax in zip([hierarchy.complete(df), hierarchy.average(df),hierarchy.ward(df)], ['c1','c2','c3'],\n",
    "                                [ax1,ax2,ax3]):\n",
    "    cluster = hierarchy.dendrogram(linkage,ax=ax, color_threshold=0)\n",
    "\n",
    "ax1.set_title('Complete Linkage')\n",
    "ax2.set_title('Average Linkage')\n",
    "ax3.set_title('Ward');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,15))\n",
    "cut = hierarchy.dendrogram(hierarchy.ward(df),\n",
    "                            orientation='right', color_threshold=40, leaf_font_size=10)\n",
    "plt.vlines(10,0,plt.gca().yaxis.get_data_interval()[1], colors='r', linestyles='dashed');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Keep the 'specie' column appart + make it numeric for coloring\n",
    "#dfR=df_km.copy()\n",
    "df_km['ClusterID']=pd.Categorical(df_km['ClusterID'])\n",
    "my_color=df_km['ClusterID'].cat.codes\n",
    "df_km = df_km.drop('ClusterID', axis=1)\n",
    "# Plot initialisation\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(df_km['PC1'], df_km['PC2'], df_km['PC3'], c=my_color, cmap=\"Set2_r\", s=60)\n",
    "\n",
    "# make simple, bare axis lines through space:\n",
    "xAxisLine = ((min(df_km['PC1']), max(df_km['PC1'])), (0, 0), (0,0))\n",
    "ax.plot(xAxisLine[0], xAxisLine[1], xAxisLine[2], 'r')\n",
    "yAxisLine = ((0, 0), (min(df_km['PC2']), max(df_km['PC2'])), (0,0))\n",
    "ax.plot(yAxisLine[0], yAxisLine[1], yAxisLine[2], 'r')\n",
    "zAxisLine = ((0, 0), (0,0), (min(df_km['PC3']), max(df_km['PC3'])))\n",
    "ax.plot(zAxisLine[0], zAxisLine[1], zAxisLine[2], 'r')\n",
    "\n",
    "# label the axes\n",
    "ax.set_xlabel(\"PC1\")\n",
    "ax.set_ylabel(\"PC2\")\n",
    "ax.set_zlabel(\"PC3\")\n",
    "ax.set_title(\"PCA Health/Auto insurance data\");\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(pca.components_,\n",
    "                 cmap='YlGnBu',\n",
    "                 yticklabels=[ \"PCA\"+str(x) for x in range(1,pca.n_components_+1)],\n",
    "                 xticklabels=list(X.columns),\n",
    "                 cbar_kws={\"orientation\": \"vertical\"})\n",
    "ax.set_aspect(\"equal\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}