Cluster KMeans Model for Credit (US Survey Data 2019) two features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cluster KMeans Model for Credit (US Survey Data 2019)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading in Data to Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>YY1</th>\n",
       "      <th>Y1</th>\n",
       "      <th>WGT</th>\n",
       "      <th>HHSEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>AGECL</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EDCL</th>\n",
       "      <th>MARRIED</th>\n",
       "      <th>KIDS</th>\n",
       "      <th>...</th>\n",
       "      <th>NWCAT</th>\n",
       "      <th>INCCAT</th>\n",
       "      <th>ASSETCAT</th>\n",
       "      <th>NINCCAT</th>\n",
       "      <th>NINC2CAT</th>\n",
       "      <th>NWPCTLECAT</th>\n",
       "      <th>INCPCTLECAT</th>\n",
       "      <th>NINCPCTLECAT</th>\n",
       "      <th>INCQRTCAT</th>\n",
       "      <th>NINCQRTCAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>3790.476607</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>3798.868505</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>3799.468393</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>3788.076005</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>3793.066589</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 351 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YY1  Y1          WGT HHSEX  AGE  AGECL  EDUC                        EDCL  \\\n",
       "0    2  21  3790.476607  male   50  45-54     8  high school diploma or GED   \n",
       "1    2  22  3798.868505  male   50  45-54     8  high school diploma or GED   \n",
       "2    2  23  3799.468393  male   50  45-54     8  high school diploma or GED   \n",
       "3    2  24  3788.076005  male   50  45-54     8  high school diploma or GED   \n",
       "4    2  25  3793.066589  male   50  45-54     8  high school diploma or GED   \n",
       "\n",
       "   MARRIED  KIDS  ...  NWCAT   INCCAT  ASSETCAT  NINCCAT  NINC2CAT  \\\n",
       "0        1     3  ...      1  20-39.9         1        2         1   \n",
       "1        1     3  ...      1  20-39.9         1        2         1   \n",
       "2        1     3  ...      1  20-39.9         1        2         1   \n",
       "3        1     3  ...      1  20-39.9         1        2         1   \n",
       "4        1     3  ...      1  20-39.9         1        2         1   \n",
       "\n",
       "   NWPCTLECAT  INCPCTLECAT  NINCPCTLECAT  INCQRTCAT  NINCQRTCAT  \n",
       "0           1            4             4          2           2  \n",
       "1           1            4             3          2           2  \n",
       "2           1            4             4          2           2  \n",
       "3           1            4             4          2           2  \n",
       "4           1            4             4          2           2  \n",
       "\n",
       "[5 rows x 351 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credit = pd.read_csv('C:/Users/Admin 21/Downloads/Customer Segmentation/Data/df_credit.csv').drop(columns=\"Unnamed: 0\")\n",
    "df_credit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4623, 351)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credit.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have **```4623```** households in our data. These are households who have been turned debt or feared being turned down debt in the past five years."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## House Value vs Debt \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "`HOUSES` - Total value of primary residence of household, 2019 dollars\n",
    "\n",
    "`DEBT`- Total value of debt held by household, 2019 dollars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    4.623000e+03\n",
      "mean     1.907981e+05\n",
      "std      1.365502e+06\n",
      "min      0.000000e+00\n",
      "25%      0.000000e+00\n",
      "50%      0.000000e+00\n",
      "75%      1.250000e+05\n",
      "max      3.709000e+07\n",
      "Name: HOUSES, dtype: float64\n",
      "count    4.623000e+03\n",
      "mean     1.269438e+05\n",
      "std      6.963218e+05\n",
      "min      0.000000e+00\n",
      "25%      1.000000e+03\n",
      "50%      1.860000e+04\n",
      "75%      9.920000e+04\n",
      "max      1.903220e+07\n",
      "Name: DEBT, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df_credit[\"HOUSES\"].describe())\n",
    "print(df_credit[\"DEBT\"].describe())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can note the values involved are in scientific notation due to their large values. We will normalize the data in million USD for easy visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Low Credit Score: Home Value against Household Debt')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xva6urvD19dW4Xl1MTU0xdOhQHDt2TKOpfN26dWjQoAHCwsIAAHK5HCYmD/87U6lUuHfvHmxtbdG8eXPpc/e0DLl3ulhZWUn/zs/PR3p6Op577jkAkGJUqVTYvXs3IiIiNFqtfHx8EB4eXuY5MzMzkZ6ejtDQUFy9elWvR6ejRo3S6B9U8n/F1atXAQAKhQIA8Mcff+hsmXka48aN0/l5ffXVVzXqPcnvXXkcHByQk5ODXbt2lVpn06ZNCAkJQb169TTud/fu3aFSqTQe7wDAkCFDUK9ePWn78Z9lSkoK4uLiMHLkSNSvX1+q17ZtW7zwwgv4/fffS41l9+7dKCwsxFtvvaXxGLSkVb0i2NraAoA06nLTpk1o2bIlWrRooXH9JS3pj3/eg4KCEBAQIG03btwY/fv3xx9//AGVSlVhcVYkPgKrQe7evYvc3Fw0b95ca1/Lli2hVqtx48YNtG7dWvrlOnToEBo1aoTY2FjMmzcPzs7O0vDSQ4cOwd7eHv7+/qW+p729PQCUOdRYF29vb63YMzIysGrVKqxatUrnMSX9OK5fvw4fHx+t/gy6rrsiNGvWDD/88ANUKhUuXryI7du3Y+HChRg3bhy8vb3RvXt3JCYmAkCpQ8lL4i4tzhYtWuDw4cMaZWZmZmjUqJFGWXx8PDIzM3X2PwKermO2jY0NunfvrlWu6xn79evXERgYqFXesmVLaf+jP4vGjRtr1Cv5YvTw8NAqV6vVyMzMhKOjI+Lj4yGEgK+vr86Y9enkPmzYMCxevBjr1q3D+++/j5s3b+LQoUOYOHEiTE1NATzsm7NkyRL897//RVJSksZ/uBU1Quxp7939+/cxZ84c/PTTT1p1S5KVO3fuIC8vDz4+PlrH6yo7cuQIZs2ahWPHjmklKJmZmdJ9Ks3j97XkC7zksam3tzcmT56MRYsW4ccff0RISAj69euHV155pdxzl8fX11fn5/Xx3yNDf+/08eabb2Ljxo0IDw9Hw4YN0aNHDwwePBi9evWS6sTHx+Ps2bOlPqJ5/B6W97Ms6zpatmyJP/74o9SBEiXHPv575OzsrJF0PY3s7GwA//tjOD4+HpcuXdL7+nX9jjdr1gy5ubm4e/duuX1RqwMToFrK3d0d3t7eOHjwILy8vCCEQFBQEJydnTFp0iRcv34dhw4dQqdOnaS/jHWxt7eHu7s7zp8/b9D7P/qXJwCpU+Arr7yCESNG6DymrL46VcHU1BR+fn7w8/NDUFAQunXrhh9//FHnf8IV4dFWiRJqtRouLi748ccfdR5Trc/Dy1CSaOhbLv6/I75arYZMJsOOHTt01i35q7MsAQEBaNGiBdavX4/3338f69evhxBCY/TXxx9/jBkzZmD06NGYO3cu6tevDxMTE7z99tuldlgtIZPJNAYOlHj8r9anvXeDBw/G0aNHMXXqVLRr1w62trZQq9Xo1atXuTHqkpiYiLCwMLRo0QKLFi2Ch4cHLCws8Pvvv2Px4sV6nbO8+wcAn3/+OUaOHIlt27bhzz//xMSJExEVFYXjx49rJfjVTd976eLigri4OPzxxx/YsWMHduzYgdWrV2P48OFYs2YNgIf3+4UXXsC7776r872aNWumsa3Pz7ImO3/+PExNTaU/btVqNfz8/EodHv/4Hz+1EROgGsTZ2RnW1tYa896U+Oeff2BiYqLxoQsJCcHBgwfh7e2Ndu3awc7ODv7+/lAoFNi5cydOnz6NOXPmlPu+ffr0wapVq3Ds2DEEBQU9cex2dnZQqVTlJhSenp44f/48hBAarUC6rvtxj7caPalnn30WwMNmaeB/nTbPnz+v8y9tANJIssuXL2t1qL58+bLOkWaPa9q0KXbv3o3g4GCtJLIqeXp6lvo5K9lfEZo2bQohBLy9vbW+MAwxbNgwzJgxA2fPnsW6devg6+uLDh06SPs3b96Mbt264dtvv9U4LiMjQ+psWZp69epJjykeVfJX96PX8qT37sGDB9izZw/mzJmj0UE8Pj5eo56LiwssLS21RmIB0Cr79ddfUVBQgF9++UWj9aG8R3FPouQPhw8//BBHjx5FcHAwvvrqK8ybNw9Axf1e6mLI752+9xJ4ODVA37590bdvX6jVarz55ptYuXIlZsyYAR8fHzRt2hTZ2dkV9gfSo9fxuH/++QdOTk6lTpNRcmx8fLxG94i7d+/qNQKxPMnJyThw4ACCgoKkFqCmTZvizJkzCAsL0+v+Pv5ZBoArV67A2tpa+uOgMj8nT4J9gGoQU1NT9OjRA9u2bdN4bJGWloZ169ahc+fO0iMr4GECdO3aNWzYsEF6JGZiYoJOnTph0aJFKCoqKncEGAC8++67sLGxwdixY5GWlqa1PzExUWt4qK7YBw0ahC1btuhsTbp796707969e+P27dvYvHmzVJabm1vqo7NHlYyk0ncirUOHDqGoqEirvOR5e0lzdI8ePWBnZ4eoqCjk5+dr1C35C+7ZZ5+Fi4sLvvrqK41hoDt27MClS5fw4osvlhvP4MGDoVKpMHfuXK19xcXFGtdVmcPge/fujRMnTuDYsWNSWU5ODlatWgUvLy+N/l1PY+DAgTA1NcWcOXO0/hIWQug9g3NJa8/MmTMRFxenNfePqamp1vk3bdqkNUxal6ZNm+Kff/7R+IyeOXMGR44c0ahnyL17XEnrwOMxPj6SyNTUFN27d0dMTAxu374tlSckJGDHjh3lnjMzMxOrV68uNQ5DKZVKqT9XCT8/P5iYmGj8DtjY2FTa5HaG/N7pey8f/9yZmJhILdQl7zF48GAcO3YMf/zxh1ZMGRkZWj+X8ri5uaFdu3ZYs2aNxs/q/Pnz+PPPP8ucf6d79+4wNzfH0qVLNe7345+fJ3H//n1ERkZCpVLhgw8+kMoHDx6MW7duafSVLJGXl4ecnByNsmPHjmn0t7tx4wa2bduGHj16SJ/VkgSPEyEase+++06aN+dRkyZNwrx587Br1y507twZb775JszMzLBy5UoUFBRg4cKFGvVLkpvLly/j448/lsq7dOmCHTt2SHNRlKdp06ZYt24dhgwZgpYtW2L48OFo06YNCgsLcfToUWzatEmvjrgLFizAvn37EBgYiNdeew2tWrXC/fv3cfr0aezevRv3798HALz22mtYtmwZhg8fjlOnTsHNzQ0//PCDlNyUxcrKCq1atcKGDRvQrFkz1K9fH23atCm1784nn3yCU6dOYeDAgdJ/cKdPn8b333+P+vXrS50I7e3tsXjxYowdOxYdOnTAyy+/jHr16uHMmTPIzc3FmjVrYG5ujk8++QSjRo1CaGgoIiMjpeG4Xl5e+M9//lNu/KGhoXj99dcRFRWFuLg49OjRA+bm5oiPj8emTZuwZMkSqXO4ocPgDTFt2jSsX78e4eHhmDhxIurXr481a9YgKSkJW7ZsKfOxqSGaNm2KefPmYfr06bh27RoiIiJgZ2eHpKQkbN26FePGjcOUKVPKPY+3tzc6deqEbdu2AYBWAtSnTx989NFHGDVqFDp16oRz587hxx9/1PhruTSjR4/GokWL0LNnT4wZMwZ37tzBV199hdatW0uDBADD7t3j7O3t0aVLFyxcuBBFRUVo2LAh/vzzTyQlJWnVnT17Nv78808EBwfjjTfegEqlwrJly9CmTRvExcVJ9Xr06CG1Yrz++uvIzs7G119/DRcXF6ll82nt3bsXEyZMwEsvvYRmzZqhuLgYP/zwg/QHT4mAgADs3r0bixYtkh7P6+pj9iQM+b3T916OHTsW9+/fx/PPP49GjRrh+vXrWLp0Kdq1ayf1g5s6dSp++eUX9OnTByNHjkRAQABycnJw7tw5bN68GdeuXSu3dfFxn376KcLDwxEUFIQxY8ZIw+AVCkWZ85k5OztjypQpiIqKQp8+fdC7d2/ExsZix44dBsVw5coVrF27FkIIKJVKnDlzBps2bUJ2djYWLVqk0Qfq1VdfxcaNG/Hvf/8b+/btQ3BwMFQqFf755x9s3LgRf/zxh9SSDjzsP9mzZ0+NYfAANJ5ClHSS/uCDDzB06FCYm5ujb9++1TdBbBWOODN6JUMbS3vduHFDCCHE6dOnRc+ePYWtra2wtrYW3bp1E0ePHtV5ThcXFwFApKWlSWWHDx8WAERISIhB8V25ckW89tprwsvLS1hYWAg7OzsRHBwsli5dKvLz86V6KGM4ZVpamhg/frzw8PAQ5ubmwtXVVYSFhYlVq1Zp1Lt+/bro16+fsLa2Fk5OTmLSpEnScOKyhsELIcTRo0dFQECAsLCwKHdI/JEjR8T48eNFmzZthEKhEObm5qJx48Zi5MiRGlMNlPjll19Ep06dhJWVlbC3txcdO3YU69ev16izYcMG0b59eyGXy0X9+vXFsGHDxM2bNzXqjBgxQtjY2JQa16pVq0RAQICwsrISdnZ2ws/PT7z77rvi9u3bUh1Dh8G3bt1a576SYbqPDoMXQojExETxr3/9Szg4OAhLS0vRsWNHsX37do06JcOlHx8GXdqUDrNmzdIYwlxiy5YtonPnzsLGxkbY2NiIFi1aiPHjx4vLly+Xe20lli9fLgCIjh07au3Lz88X77zzjnBzcxNWVlYiODhYHDt2TGtYtK5h8EIIsXbtWtGkSRNhYWEh2rVrJ/744w+dnz0h9Lt3uty8eVMMGDBAODg4CIVCIV566SVx+/ZtnZ/hPXv2iPbt2wsLCwvRtGlT8c0334h33nlHWFpaatT75ZdfRNu2bYWlpaXw8vISn3zyifjuu++0hhqXNgz+8fv6+M/n6tWrYvTo0aJp06bC0tJS1K9fX3Tr1k3s3r1b47h//vlHdOnSRZpyoqxh2aV9HkuU9hnS5/dOCP3u5ebNm0WPHj2Ei4uLsLCwEI0bNxavv/66SElJ0ThXVlaWmD59uvDx8REWFhbCyclJdOrUSXz22WfStBBlXY+ue7t7924RHBws/R/Tt29fcfHiRY06jw+DF0IIlUol5syZI33Gu3btKs6fPy88PT31HgZf8jIxMREODg6iffv2YtKkSVpTmpQoLCwUn3zyiWjdurWQy+WiXr16IiAgQMyZM0dkZmZqnHv8+PFi7dq1wtfXV8jlctG+fXuN/8tLzJ07VzRs2FCYmJhU+5B4mRC1pIcWEZERi4iIwIULF3T2tSAiw7EPEBFRDZOXl6exHR8fj99//13nMg9E9GTYAkREVMO4ublJ64Zdv34dK1asQEFBAWJjY0udU4mIDMNO0ERENUyvXr2wfv16pKamQi6XIygoCB9//DGTH6IKxBYgIiIiMjrsA0RERERGhwkQERERGR32AdJBrVbj9u3bsLOzq3FTdxMREZFuQghkZWXB3d293AldmQDpcPv27Tqx0BsREZExunHjRrmL9TIB0qFkMbgbN25orL1FRERENZdSqYSHh4f0PV4WJkA6lDz2sre3ZwJERERUy+jTfYWdoImIiMjoMAEiIiIio8MEiIiIiIwOEyAiIiIyOkyAiIiIyOgwASIiIiKjwwSIiIiIjA4TICIiIjI6TICIiIjI6DABIiIiIqPDpTCIiIhqsczcQqRnF0KZXwR7K3M42VhAYW1R3WHVeEyAiIiIaqnbGXl4b8tZHIpPl8q6+DphwaC2cHewqsbIaj4+AiMiIqqFMnMLtZIfADgYn45pW84iM7ewmiKrHZgAERER1ULp2YVayU+Jg/HpSM9mAlQWJkBERES1kDK/qMz9WeXsN3ZMgIiIiGohe0vzMvfblbPf2DEBIiIiqoWcbC3QxddJ574uvk5wsuVIsLIwASIiIqqFFNYWWDCorVYS1MXXCZ8Masuh8OXgMHgiIqJayt3BCksj2yM9uxBZ+UWwszSHky3nAdIHEyAiIqJaTGHNhOdJ8BEYERERGR0mQERERGR0mAARERGR0WECREREREaHnaCJiIiqQWWs4l6VK8M/yXvVpJXrmQARERFVscpYxb0qV4Z/kveqaSvX8xEYERFRFaqMVdyrcmX4J3mvmrhyPRMgIiKiKlQZq7hX5crwT/JeNXHleiZAREREVagyVnGvypXhn+S9auLK9UyAiIiIqlBlrOJelSvDP8l71cSV65kAERERVaHKWMW9KleGf5L3qokr1zMBIiIiqkKVsYp7Va4M/yTvVRNXrpcJIUSVv2sNp1QqoVAokJmZCXt7++oOh4iI6qCSOXEqchX3yjhnRb5XZcdnyPc35wEiIiKqBpWxintVrgz/JO9Vk1au5yMwIiIiMjpMgIiIiMjoMAEiIiIio1OtCdDBgwfRt29fuLu7QyaTISYmRmO/TCbT+fr0009LPefs2bO16rdo0aKSr4SIiIhqk2pNgHJycuDv74/ly5fr3J+SkqLx+u677yCTyTBo0KAyz9u6dWuN4w4fPlwZ4RMREVEtVa2jwMLDwxEeHl7qfldXV43tbdu2oVu3bmjSpEmZ5zUzM9M6loiIiKhErekDlJaWht9++w1jxowpt258fDzc3d3RpEkTDBs2DMnJyWXWLygogFKp1HgRERFR3VVrEqA1a9bAzs4OAwcOLLNeYGAgoqOjsXPnTqxYsQJJSUkICQlBVlZWqcdERUVBoVBILw8Pj4oOn4iIiGqQGjMTtEwmw9atWxEREaFzf4sWLfDCCy9g6dKlBp03IyMDnp6eWLRoUamtRwUFBSgoKJC2lUolPDw8OBM0ERFRLVLnZoI+dOgQLl++jA0bNhh8rIODA5o1a4aEhIRS68jlcsjl8qcJkYiIiGqRWvEI7Ntvv0VAQAD8/f0NPjY7OxuJiYlwc3OrhMiIiIioNqrWBCg7OxtxcXGIi4sDACQlJSEuLk6j07JSqcSmTZswduxYnecICwvDsmXLpO0pU6bgwIEDuHbtGo4ePYoBAwbA1NQUkZGRlXotREREVHtU6yOwkydPolu3btL25MmTAQAjRoxAdHQ0AOCnn36CEKLUBCYxMRHp6enS9s2bNxEZGYl79+7B2dkZnTt3xvHjx+Hs7Fx5F0JERES1So3pBF2TGNKJioiIiGoGQ76/a0UfICIiIqKKxASIiIiIjA4TICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjpMgIiIiMjoMAEiIiIio8MEiIiIiIwOEyAiIiIyOkyAiIiIyOgwASIiIiKjwwSIiIiIjA4TICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjpMgIiIiMjoMAEiIiIio8MEiIiIiIwOEyAiIiIyOkyAiIiIyOgwASIiIiKjwwSIiIiIjA4TICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjpMgIiIiMjoMAEiIiIio1OtCdDBgwfRt29fuLu7QyaTISYmRmP/yJEjIZPJNF69evUq97zLly+Hl5cXLC0tERgYiBMnTlTSFRAREVFtVK0JUE5ODvz9/bF8+fJS6/Tq1QspKSnSa/369WWec8OGDZg8eTJmzZqF06dPw9/fHz179sSdO3cqOnwiIiKqpcyq883Dw8MRHh5eZh25XA5XV1e9z7lo0SK89tprGDVqFADgq6++wm+//YbvvvsO06ZNe6p4iYiIqG6o8X2A9u/fDxcXFzRv3hxvvPEG7t27V2rdwsJCnDp1Ct27d5fKTExM0L17dxw7dqzU4woKCqBUKjVeREREVHfV6ASoV69e+P7777Fnzx588sknOHDgAMLDw6FSqXTWT09Ph0qlQoMGDTTKGzRogNTU1FLfJyoqCgqFQnp5eHhU6HUQERFRzVKtj8DKM3ToUOnffn5+aNu2LZo2bYr9+/cjLCyswt5n+vTpmDx5srStVCqZBBEREdVhNboF6HFNmjSBk5MTEhISdO53cnKCqakp0tLSNMrT0tLK7Eckl8thb2+v8SIiIqK6q1YlQDdv3sS9e/fg5uamc7+FhQUCAgKwZ88eqUytVmPPnj0ICgqqqjCJiIiohqvWBCg7OxtxcXGIi4sDACQlJSEuLg7JycnIzs7G1KlTcfz4cVy7dg179uxB//794ePjg549e0rnCAsLw7Jly6TtyZMn4+uvv8aaNWtw6dIlvPHGG8jJyZFGhRERERFVax+gkydPolu3btJ2ST+cESNGYMWKFTh79izWrFmDjIwMuLu7o0ePHpg7dy7kcrl0TGJiItLT06XtIUOG4O7du5g5cyZSU1PRrl077Ny5U6tjNBERERkvmRBCVHcQNY1SqYRCoUBmZib7AxEREdUShnx/16o+QEREREQVgQkQERERGR0mQERERGR0mAARERGR0WECREREREaHCRAREREZHSZAREREZHSYABEREZHRYQJERERERocJEBERERkdJkBERERkdJgAERERkdFhAkRERERGhwkQERERGR0mQERERGR0mAARERGR0WECREREREaHCRAREREZHSZAREREZHSYABEREZHRMdOn0pdffmnwiUeNGgU7OzuDjyMiIiKqbDIhhCivkomJCRo1agRTU1O9Tnrjxg1cuXIFTZo0eeoAq4NSqYRCoUBmZibs7e2rOxwiIiLSgyHf33q1AAHAyZMn4eLiolddtvwQERFRTaZXH6BZs2bB1tZW75O+//77qF+//hMHRURERFSZ9HoEZmz4CIyIiKj2MeT7u8JGgZ09exYWFhYVdToiIiKiSlNhCZAQAiqVqqJOR0RERFRpOA8QERERGR0mQERERGR09B4Gr1Qqy9yflZX11MEQERERVQW9EyAHBwfIZLJS9wshytxPREREVFPonQDt27evMuMgIiIiqjJ6J0ChoaGVGQcRERFRldG7E7RSqdTrZYiDBw+ib9++cHd3h0wmQ0xMjLSvqKgI7733Hvz8/GBjYwN3d3cMHz4ct2/fLvOcs2fPhkwm03i1aNHCoLiIiIiobqvwPkCGzAWUk5MDf39/jB49GgMHDtTYl5ubi9OnT2PGjBnw9/fHgwcPMGnSJPTr1w8nT54s87ytW7fG7t27pW0zM70vk4iIiIxAtfYBCg8PR3h4uM59CoUCu3bt0ihbtmwZOnbsiOTkZDRu3LjU85qZmcHV1bVCYyUiIqK6o1b1AcrMzIRMJoODg0OZ9eLj4+Hu7g5LS0sEBQUhKiqqzISpoKAABQUF0rahj/KIiIiodqk1EyHm5+fjvffeQ2RkZJkLnAUGBiI6Oho7d+7EihUrkJSUhJCQkDLnKYqKioJCoZBeHh4elXEJREREVEPovRq8qampXid80vXAZDIZtm7dioiICK19RUVFGDRoEG7evIn9+/cbtEJ7RkYGPD09sWjRIowZM0ZnHV0tQB4eHlwNnoiIqBYxZDV4vR+BCSHg6emJESNGoH379k8dpL6KioowePBgXL9+HXv37jU4IXFwcECzZs2QkJBQah25XA65XP60oRIREVEtoXcCdOLECXz77bdYsmQJvL29MXr0aAwbNgz16tWrtOBKkp/4+Hjs27cPjo6OBp8jOzsbiYmJePXVVyshQiIiIqqN9O4D9Oyzz2LFihVISUnB5MmTsXXrVjRq1AhDhw7VGq2lr+zsbMTFxSEuLg4AkJSUhLi4OCQnJ6OoqAj/+te/cPLkSfz4449QqVRITU1FamoqCgsLpXOEhYVh2bJl0vaUKVNw4MABXLt2DUePHsWAAQNgamqKyMjIJ4qRiIiI6h6DO0FbWlrilVdewZ49e3D+/HncuXMHvXr1wv379w1+85MnT6J9+/bSI7XJkyejffv2mDlzJm7duoVffvkFN2/eRLt27eDm5ia9jh49Kp0jMTER6enp0vbNmzcRGRmJ5s2bY/DgwXB0dMTx48fh7OxscHxERERUNz3RDIE3b95EdHQ0oqOjkZubi6lTpz5RZ+GuXbuirD7Y+vTPvnbtmsb2Tz/9ZHAcREREZFz0ToAKCwuxdetWfPvttzh06BDCw8PxxRdfIDw8XO8RYkREREQ1gd4JkJubG+zs7DBixAj897//hYuLC4CHy1k8isPGiYiIqKbTex4gE5P/dRfStSbYk6wFVlMZMo8AERER1QyVMg9QZawFRkRERFQdatVaYEREREQVocLWAisuLkZycnJFnY6IiIio0lRYAnThwgV4e3tX1OmIiIiIKk2tWQ2eiIiIqKLo3QfomWeeKXN/Xl7eUwdDREREVBX0ToAuXryIoUOHlvqYKyUlBVeuXKmwwIiIiIgqi94JUJs2bRAYGIg33nhD5/64uDh8/fXXFRYYERERUWXRuw9QcHAwLl++XOp+Ozs7dOnSpUKCIiIiIqpMes8EbUw4EzQREVHtY8j3N0eBERERkdF56gTo3r172LdvH9LS0ioiHiIiIqJKZ1ACtHLlSqxcuVLajouLg4+PD8LCwtCkSRP88ccfFR4gERERUUUzKAH6+uuv4eTkJG3PmjUL/fr1g1KpxDvvvIMPPvigwgMkIiIiqmh6JUAHDx7EgQMHcPXqVWRmZkrb+/btQ1BQEE6fPo2AgABcunQJBw8exMGDBys7biIiIqInptc8QElJSQAAtVqNlJQUmJqaIj4+HqamprC2tkZSUhKKi4uhUqlw7do1CCE4JJ6IiIhqLIOGwXfu3Blt2rTBrFmz8N577yEvLw+bNm0CAFy5cgW9evXC1atXKy3YqsJh8ERERLWPId/fes8EDQBz585FRESE1Bdo79690r7169fj+eeff7KIiYiIiKqQQQlQt27dkJycjISEBDRv3hy2trbSvn79+sHNza3CAyQiIiKqaAYlQACgUCgQEBCgVd6+ffsKCYiIiIiosuk1CuyXX35BUVGR3if9/fffkZeX98RBEREREVUmvRKgAQMGICMjQ++TDh06FCkpKU8aExEREVGl0usRmBACI0eOhFwu1+uk+fn5TxUUERERUWXSKwEaMWKEQScdNmwYh48TERFRjaVXArR69erKjoOIiIioyjz1avBEREREtQ0TICIiIjI6TICIiIjI6DABIiIiIqPzVAkQh7sTERFRbWRwAqRWqzF37lw0bNgQtra20urvM2bMwLfffmvQuQ4ePIi+ffvC3d0dMpkMMTExGvuFEJg5cybc3NxgZWWF7t27Iz4+vtzzLl++HF5eXrC0tERgYCBOnDhhUFxERERUtxmcAM2bNw/R0dFYuHAhLCwspPI2bdrgm2++MehcOTk58Pf3x/Lly3XuX7hwIb788kt89dVX+Ouvv2BjY4OePXuW2fK0YcMGTJ48GbNmzcLp06fh7++Pnj174s6dOwbFRkRERHWXTAghDDnAx8cHK1euRFhYGOzs7HDmzBk0adIE//zzD4KCgvDgwYMnC0Qmw9atWxEREQHgYeuPu7s73nnnHUyZMgUAkJmZiQYNGiA6OhpDhw7VeZ7AwEB06NABy5YtA/CwxcrDwwNvvfUWpk2bplcsSqUSCoUCmZmZnNCRiIioljDk+9vgFqBbt27Bx8dHq1ytVhu0YGp5kpKSkJqaiu7du0tlCoUCgYGBOHbsmM5jCgsLcerUKY1jTExM0L1791KPAYCCggIolUqNFxEREdVdBidArVq1wqFDh7TKN2/ejPbt21dIUACQmpoKAGjQoIFGeYMGDaR9j0tPT4dKpTLoGACIioqCQqGQXh4eHk8ZPREREdVkei2F8aiZM2dixIgRuHXrFtRqNX7++WdcvnwZ33//PbZv314ZMVa66dOnY/LkydK2UqlkEkRERFSHGdwC1L9/f/z666/YvXs3bGxsMHPmTFy6dAm//vorXnjhhQoLzNXVFQCQlpamUZ6Wlibte5yTkxNMTU0NOgYA5HI57O3tNV5ERERUdz3RPEAhISHYtWsX7ty5g9zcXBw+fBg9evSo0MC8vb3h6uqKPXv2SGVKpRJ//fUXgoKCdB5jYWGBgIAAjWPUajX27NlT6jFERERkfAx+BFaRsrOzkZCQIG0nJSUhLi4O9evXR+PGjfH2229j3rx58PX1hbe3N2bMmAF3d3dppBgAhIWFYcCAAZgwYQIAYPLkyRgxYgSeffZZdOzYEV988QVycnIwatSoqr48IiIiqqEMToBMTEwgk8lK3a9SqfQ+18mTJ9GtWzdpu6QfzogRIxAdHY13330XOTk5GDduHDIyMtC5c2fs3LkTlpaW0jGJiYlIT0+XtocMGYK7d+9i5syZSE1NRbt27bBz506tjtFERERkvAyeB2jbtm0a20VFRYiNjcWaNWswZ84cjBkzpkIDrA6cB4iIiKj2MeT72+AEqDTr1q3Dhg0btBKk2ogJEBERUe1TqRMhlua5557T6HxMREREVFNVSAKUl5eHL7/8Eg0bNqyI0xERERFVKoM7QderV0+jE7QQAllZWbC2tsbatWsrNDgiIiKiymBwArR48WKNBMjExATOzs4IDAxEvXr1KjQ4IiIiospgcAI0cuTISgiDiIiIqOrolQCdPXtW7xO2bdv2iYMhIiIiqgp6JUDt2rWDTCZDeSPmZTKZQRMhEhEREVUHvRKgpKSkyo6DiIiIqMrolQB5enpWdhxEREREVeaJF0O9ePEikpOTUVhYqFHer1+/pw6KiIiIqDIZnABdvXoVAwYMwLlz5zT6BZUMjWcfICIiIqrpDJ4JetKkSfD29sadO3dgbW2NCxcu4ODBg3j22Wexf//+SgiRiIiIqGIZ3AJ07Ngx7N27F05OTjAxMYGJiQk6d+6MqKgoTJw4EbGxsZURJxEREVGFMbgFSKVSwc7ODgDg5OSE27dvA3jYUfry5csVGx0RERFRJTC4BahNmzY4c+YMvL29ERgYiIULF8LCwgKrVq1CkyZNKiNGIiIiogplcAL04YcfIicnBwDw0UcfoU+fPggJCYGjoyM2bNhQ4QESERERVTS9E6Bnn30WY8eOxcsvvwx7e3sAgI+PD/755x/cv39fa5V4IiIioppK7z5A/v7+ePfdd+Hm5obhw4drjPiqX78+kx8iIiKqNfROgL799lukpqZi+fLlSE5ORlhYGHx8fPDxxx/j1q1blRkjERERUYUyaBSYtbU1Ro4cif379+PKlSsYOnQoVq5cCS8vL7z44ov4+eefKytOIiIiogojE+Ut8V4OIQS2bNmC119/HRkZGXViJmilUgmFQoHMzEypvxMRERHVbIZ8fz/xWmAAsH//fqxevRpbtmyBmZkZXnvttac5HREREVGVMDgBunnzJqKjoxEdHY2rV68iJCQE//3vf/HSSy/BysqqMmIkIiIiqlB6J0AbN27Ed999hz179sDFxQUjRozA6NGj4ePjU5nxEREREVU4vROgV155BS+++CK2bt2K3r17w8TE4FU0iIiIiGoEvROgmzdvwsXFpTJjISIiIqoSejfjMPkhIiKiuoLPsYiIiMjoMAEiIiIio8MEiIiIiIzOEyVAGRkZ+OabbzB9+nTcv38fAHD69GmuCUZERES1gsETIZ49exbdu3eHQqHAtWvX8Nprr6F+/fr4+eefkZycjO+//74y4iQiIiKqMAa3AE2ePBkjR45EfHw8LC0tpfLevXvj4MGDFRocAHh5eUEmk2m9xo8fr7N+dHS0Vt1H4yQiIiIyuAXo77//xsqVK7XKGzZsiNTU1AoJ6vH3e3SB1fPnz+OFF17ASy+9VOox9vb2uHz5srQtk8kqPC4iIiKqvQxOgORyOZRKpVb5lStX4OzsXCFBPerxcy5YsABNmzZFaGhoqcfIZDK4urpWeCxERERUNxj8CKxfv3746KOPUFRUBOBhspGcnIz33nsPgwYNqvAAH1VYWIi1a9di9OjRZbbqZGdnw9PTEx4eHujfvz8uXLhQqXERERFR7WJwAvT5558jOzsbLi4uyMvLQ2hoKHx8fGBnZ4f58+dXRoySmJgYZGRkYOTIkaXWad68Ob777jts27YNa9euhVqtRqdOnXDz5s1SjykoKIBSqdR4ERERUd0lE0KIJznwyJEjOHPmDLKzs/HMM8+ge/fuFR2blp49e8LCwgK//vqr3scUFRWhZcuWiIyMxNy5c3XWmT17NubMmaNVnpmZCXt7+yeOl4iIiKqOUqmEQqHQ6/v7iROgR2VkZMDBweFpT1Om69evo0mTJvj555/Rv39/g4596aWXYGZmhvXr1+vcX1BQgIKCAmlbqVTCw8ODCRAREVEtYkgCZPAjsE8++QQbNmyQtgcPHgxHR0c0bNgQZ86cMTxaPa1evRouLi548cUXDTpOpVLh3LlzcHNzK7WOXC6Hvb29xouIiIjqLoMToK+++goeHh4AgF27dmHXrl3YsWMHwsPDMXXq1AoPEADUajVWr16NESNGwMxMc+Da8OHDMX36dGn7o48+wp9//omrV6/i9OnTeOWVV3D9+nWMHTu2UmIjIiKi2sfgYfCpqalSArR9+3YMHjwYPXr0gJeXFwIDAys8QADYvXs3kpOTMXr0aK19ycnJMDH5Xx734MEDvPbaa0hNTUW9evUQEBCAo0ePolWrVpUSGxEREdU+BidA9erVw40bN+Dh4YGdO3di3rx5AAAhhMaEhRWpR48eKK2r0v79+zW2Fy9ejMWLF1dKHERERFQ3GJwADRw4EC+//DJ8fX1x7949hIeHAwBiY2Ph4+NT4QESERERVTSDE6DFixfDy8sLN27cwMKFC2FrawsASElJwZtvvlnhARIRERFVtAoZBl/XGDKMjoiIiGoGQ76/DW4B+v7778vcP3z4cENPSURERFSlDG4BqlevnsZ2UVERcnNzYWFhAWtra9y/f79CA6wObAEiIiKqfSp1IsQHDx5ovLKzs3H58mV07ty51JmWiYiIiGoSgxMgXXx9fbFgwQJMmjSpIk5HREREVKkqJAECADMzM9y+fbuiTkdERERUaQzuBP3LL79obAshkJKSgmXLliE4OLjCAiMiIiKqLAYnQBERERrbMpkMzs7OeP755/H5559XVFxERERElcbgBEitVldGHERERERV5qn6AAkhSl2ji4iIiKimeqIE6Pvvv4efnx+srKxgZWWFtm3b4ocffqjo2IjIAJm5hUi8k43Y5AdIvJuNzNzC6g6JiKjGMvgR2KJFizBjxgxMmDBB6vR8+PBh/Pvf/0Z6ejr+85//VHiQRFS22xl5eG/LWRyKT5fKuvg6YcGgtnB3sKrGyIiIaiaDZ4L29vbGnDlztJa8WLNmDWbPno2kpKQKDbA6cCZoqk0ycwsxYX2sRvJToouvE5ZGtofC2qIaIiMiqlqVOhN0SkoKOnXqpFXeqVMnpKSkGHo6InpK6dmFOpMfADgYn470bD4KIyJ6nMEJkI+PDzZu3KhVvmHDBvj6+lZIUESkP2V+UZn7s8rZT0RkjAzuAzRnzhwMGTIEBw8elPoAHTlyBHv27NGZGBFR5bKVl/1rbFPOfiIiY2RwC9CgQYPw119/wcnJCTExMYiJiYGTkxNOnDiBAQMGVEaMRFQGC1MTBPs46twX7OMIC9MKW/GGiKjOeKI/DQMCArB27dqKjoWInkBGXiFGBXsDAI4k3JPKg30cMSrYG5l5hQBstI7LzC1EenYhlPlFsLcyh5ONBTtLE5HR0DsBUiqVetXjqCmiqmUrN0fk139hdGdvjA72RkGxGnIzE8TeyMDE9bH4dUJnrWM4bJ6IjJ3eCZCDgwNkMlmp+4UQkMlkUKlUFRIYEenHydYCM/q0goudHAXFaliam+J08gN8dzgJz3rWg5OtZqtOZm6hVvIDPBwxNm3LWQ6bJyKjoHcCtG/fPunfQgj07t0b33zzDRo2bFgpgRGRfnIKVfj9bAoOJfwvoQn2ccR3IzvAq761VjKjz7B5JkBEVNfpnQCFhoZqbJuamuK5555DkyZNKjwoItKP1JqToJnQHEm4B1OZDEsj22sdw2HzRERPuRgqEVWvJ5kE0d7SvMxz2pWzn4ioLmACRFSLPUlrjpOtBbr4Oums38XXCU62FlxYlYjqvKeaIa2sTtFEVPmepDVHYW2BBYPaYtqWszj42CiwTwa1RW6hCu8+xQgxDq8notpA7wRo4MCBGtv5+fn497//DRsbzflFfv7554qJjIjKVdKac7CUhVAfHwFWwt3BCksj2yM9uxBZ+UWwszSX6upaWFXfEWIcXk9EtYXeCZBCodDYfuWVVyo8GCIyTHmtOWUlKwpr7ZaZxDvZTzxCjMPriag20TsBWr16dWXGQURPqLTWnCdJNp5mhBiH1xNRbcJVEonqAF2tOU/iaUaIcXg9EdUmHAVGRBJ9RoiVprxV6Tm8nohqEiZARCQp6VP0eBJUXp+i2xl5OHn9Qamr0peXPBERVbUanQDNnj0bMplM49WiRYsyj9m0aRNatGgBS0tL+Pn54ffff6+iaInqhpI+RXsmhyLmzU7YMzkUSyPbw9rCVOfcQCWdn+duv4hRwd5aSZA+HbKJiKpaje8D1Lp1a+zevVvaNjMrPeSjR48iMjISUVFR6NOnD9atW4eIiAicPn0abdq0qYpwieqEx/sUlTW8Pb9IJZVPXB+rtSq9j7Mt3DgEnohqGJkQQlR3EKWZPXs2YmJiEBcXp1f9IUOGICcnB9u3b5fKnnvuObRr1w5fffWV3u+rVCqhUCiQmZkJe3t7Q8MmqlMycwt1zg0EPEyCPuzTCj0WHyz1+F/Gd4Know0nRySiSmfI93eNbwGKj4+Hu7s7LC0tERQUhKioKDRu3Fhn3WPHjmHy5MkaZT179kRMTEwVREpUN5U3vL1IpcZ3IzvgdPIDfHc4CbmFKmm/tYUp7K0stBIoTo5IRNWtRvcBCgwMRHR0NHbu3IkVK1YgKSkJISEhyMrK0lk/NTUVDRo00Chr0KABUlNTy3yfgoICKJVKjRcRPVTe8Pbr93IxOvpvxCY/wJf/31eoxIw+rTAj5nypkyNyjTEiqi41OgEKDw/HSy+9hLZt26Jnz574/fffkZGRgY0bN1bo+0RFRUGhUEgvDw+PCj0/UW1W3txAcrOH/40cSbiH1UeSMLqzN4CHrTzPNHbAoQTDVqsnIqoKNToBepyDgwOaNWuGhIQEnftdXV2RlpamUZaWlgZXV9cyzzt9+nRkZmZKrxs3blRYzES1XVlzAwX7OCL2Roa0fSThHnq3cZVGjj36OEwXTo5IRNWlViVA2dnZSExMhJubm879QUFB2LNnj0bZrl27EBQUVOZ55XI57O3tNV5E9FBpcwMF+zhiVLA3vjucpFFeWKxGUxdbKKwtnmpmaSKiylSjO0FPmTIFffv2haenJ27fvo1Zs2bB1NQUkZGRAIDhw4ejYcOGiIqKAgBMmjQJoaGh+Pzzz/Hiiy/ip59+wsmTJ7Fq1arqvAyiWu/R9cYe5BYiM68IsTcyMHF9rFYrz6NJzZOuVk9EVNlqdAJ08+ZNREZG4t69e3B2dkbnzp1x/PhxODs7AwCSk5NhYvK/RqxOnTph3bp1+PDDD/H+++/D19cXMTExnAOoDsvMLeTw6ipSMjdQZm4h3lofq1dS8zSr1RMRVaYaPQ9QdeE8QLVDWZPzcXh15bqdkVdqUqNr0sOSRPVpV6snIiqLId/fTIB0YAJU85U3Od/SyPb8gq1kTGqIqKapUxMhEulS3uR86dmF/DKuZI8vl0FEVJvUqlFgRCXKm5yPw6uJiKgsbAGiWsne0hzWFqYY3dkbzzSuBzMTGerbWKBIpYYyrxj12DJBRERlYAsQ1UpOthaIHtUB7gpLCCGQXVCMO1kFuJSihFoIzPvtIm5n5FV3mEREVEOxBYhqLbUa+O1cCo4k3JPKgn0cMaGbD9o0UmDalrPsDE1ERDoxAaIaRd95fTJyi7B0X7xG8gNA2n6vVwss3hXPztBERKQTEyCqMQyZ1yensFgr+SnxaDk7QxMRkS7sA0Q1QmZuoVbyAzwc0j5ty1lk5mquGp5TziKbJcszcK0pIiLShQkQ1Qj6zOvzKAershMbMxMZ15oiIqJSMQGiGsHQeX1c7OQIeWx18hIhPk5ISs/hWlNERFQqJkBUI9iX86jq8UdZCmsLfDKoLbo8lgSF+DphbkQb9GjVQOeaVERERAA7QVMN4WRrgS6+TnqtMF7C3cEKSyPbcz0qIiIyGFuAqEZQWFtggY4WnZIVxktLahTWFmjqYot2jeuhqYstkx8iItILW4CoxmCLDhERVRUmQFSjcIVxIiKqCnwERkREREaHCRAREREZHSZAREREZHSYABEREZHRYQJERERERocJEBERERkdJkBERERkdJgAERERkdFhAkRERERGhwkQERERGR0uhWEEbj7IRVZ+MZR5RVBYmcPW0gyN6llXd1hERETVhglQHXf9Xg7e33oORxLuSWWdfRwxf4AfPB1tqjEyIiKi6sNHYHXYzQe5WskPABxOuIcPtp7DzQe51RQZERFR9WICVIdl5RdrJT8lDifcQ1Z+cRVHREREVDMwAarDlHlFZe7Pyi97PxERUV3FBKgOs7cyL3O/nWXZ+4mIiOoqJkB1mJ2lGTr7OOrc19nHEXaW7ANPRETGqUYnQFFRUejQoQPs7Ozg4uKCiIgIXL58ucxjoqOjIZPJNF6WlpZVFHHN0qieNeYP8NNKgkpGgXEoPBERGasa3QRw4MABjB8/Hh06dEBxcTHef/999OjRAxcvXoSNTelDuO3t7TUSJZlMVhXh1kiejjZYMKgtsvKLkZVfBDtLc9hxHiAiIjJyNToB2rlzp8Z2dHQ0XFxccOrUKXTp0qXU42QyGVxdXSs7vFqDyQ4REZGmGv0I7HGZmZkAgPr165dZLzs7G56envDw8ED//v1x4cKFqgiPiIiIaolakwCp1Wq8/fbbCA4ORps2bUqt17x5c3z33XfYtm0b1q5dC7VajU6dOuHmzZulHlNQUAClUqnxIiIiorpLJoQQ1R2EPt544w3s2LEDhw8fRqNGjfQ+rqioCC1btkRkZCTmzp2rs87s2bMxZ84crfLMzEzY29s/ccxERERUdZRKJRQKhV7f37WiBWjChAnYvn079u3bZ1DyAwDm5uZo3749EhISSq0zffp0ZGZmSq8bN248bchERERUg9XoTtBCCLz11lvYunUr9u/fD29vb4PPoVKpcO7cOfTu3bvUOnK5HHK5/GlCJSIiolqkRidA48ePx7p167Bt2zbY2dkhNTUVAKBQKGBlZQUAGD58OBo2bIioqCgAwEcffYTnnnsOPj4+yMjIwKefforr169j7Nix1XYdREREVLPU6ARoxYoVAICuXbtqlK9evRojR44EACQnJ8PE5H9P8h48eIDXXnsNqampqFevHgICAnD06FG0atWqqsImIiKiGq7WdIKuSoZ0oiIiIqKawZDv7xrdAkS1X2ZuIe7nFEImA/KL1VDmFcNWbgorc1PYyM3gYm+cy5QQEVH1YgJEleZ2Rh7m/3YRb3dvho9+vYBDCfekfcE+jnirmy8KilTwcCx9WRMiIqLKwASoFsvMLUR6diGU+UWwtzKHk40FFNYW1R0WgIexvbflLEZ28tJKfgDgyP9v9/Fzw/PmpmjAliAiIqpCTIBqqdsZeXhvy1kcik+Xyrr4OmHBoLawsTCt9sQoPbsQh+LT8W6v5lrJT4kjCfcwOtgbD3IKmQAREVGVYgJUC5W0rjya/ADAwfh0vLflLHr7uWH6z+ek8pLEyN3BqspiVOYXAQCy81Vl1isoVkOZX1wVIREREUlqxUzQpKmkdUWXQ/HpcLHTnNTxYHw6pm05i8zcwqoIDwBgb2kOALC1NC2zntzMBPaWzMOJiKhqMQGqhUpaV0pTUKzWKjsYn4707KpLgJxsLdDF1wl3lAUI8XHUWSfYxxF3lPmoZ1Mz+i0REZHxYAJUC9nKy24xkZvpvq1Z5SROFUlhbYEFg9pi6+mbmNm3NUJ8nDT2l4wC6+zjxP4/RERU5fjsoRayMDVBiI+jzs7FwT6OiL2RofM4u/9/LFVV3B2sMH+AH+7nFGJuRGvkF6uRlVcMa7kprDkPEBERVSMmQLVQRl4hZvRpjbnbNYeXh/g44s1uvhiz5m+tY7r4OsHJtuofNSmsa87QfCIiohJMgGohW7k5Eu5mY2qvFhiZVYCCYjXkZiY4dysT+UUqtG/sIM2zAzxMfj4Z1JaJCBER0f9jAlRNbj7IRVZ+MZR5RVBYmcPW0gyN6lmXWv4oJ1sLpCnN8OfFNMQmP9BIdqwtTDG6szfGd/WBpbkpFFbmcLJlKwwREdGjmABVg+v3cvD+1nMaicurgR4Y26WpVnlnH0fMH+AHz0eWi1BYW8DT0RrfHr6KUcHeAP43s3JuoQpxyQ8w9FkPNKqvmTgRERHRQ1wNXofKXA3+5oNcvLflrEaSAwD7p4Tig5jzWuXAwyQoaoAfZCYyjdagm/dzMfvXC2jhZo/2Hg4oKFbDwcocno7WaFiPyQ8RERkXrgZfg2XlFyM2OQMTnveRkhZLc1OoBRCbnKHzmMMJ95BdqML83y5qtAY1qm+Nz1/yR3p2IbLyi2BnycddRERE+uA8QFUsO78Iy19+Bu4KzeHff129j+UvPwNrC90zJ2flF+Gt532RW6jCzQe5UrnC2gJNXWzRrnE9NHWxZfJDRESkB7YAVTEnWzlMZAWwsrCBjdwM2fkq2FmawdrCBOYmMrwe2gSLd8VrHWdnaY7wJYcA6O4XRERERPpjAlTFTGQPW21m/XJBq7Pz7H5t0KNlA60EqLOPI2wtTLFh3HOwtzKHlbkJvjmYiNe7+miNECMiIqLyMQGqQrce5AIyGWb9ot3Z+XDCPcz+5TzmR/hplJe09oxfdxpnbymlsnkRfsgv5irqRERET4IJUBVJvpeDwwnpaN+4ns6RXsDDJCi3SIUdk0KkTs3WFqZ465Hkp6TehzHnEDXAT+d5iIiIqGzsBF0Fbj/Ixa2MPPx2LgXKvLIXJM3KL4K1hSmW7Hn4GCz00/0ayU+JkpFhREREZDgmQFUgv1iNZfsScCThHuytyl6Q1M7SHF8fTMT8iDYAgA3jnsOOSSHYPyUUbRtqzmlQlau7ExER1SV8BFYF8gpV0mOvYpUanX0ccbiUCQ/rWZhibEhTfBCjPSP0l5HPYOL6/z0Oq+rV3YHSl/AgIiKqTZgAVYHcQpW0RpcytwjzIvzwYcw5jSSopGNzEaCV/AD/6/fzZWR7dP3sgDQy7Hp6NsxMTapk5mddS3hwSD4REdVGTICqgMLaDKteDcCN+w8nMJTLgA9ebAkBGbLyimBnZY5ilRqvfvsXVg1/tsxO0nlFainpWHkwEX38G8LcVAaVWqBxJSYhNx/kaiU/JTF9sPUcFgxqy5YgIiKqNdgHqAo4WFnAzESG7edS4OxggcwiFX47l4qUjDzcyylEamY+/ryYhns5hXp1kp4X0QZFahXW/nUDS/fG405WAWQALqUo8dfVe/gnRakxW3RFyMovLjMxy8rnkHwiIqo92AJUBXIKirH97G18HNEGpjIZ8iEQm/wAy/YmSHWCfRzxZWR7vTpJd/3sADaOew4AkJVXhJau9phWyY+m9EnMiIiIagu2AFWBYqjwWkhT3MspQKFaYI6OiRCPJNzD6iNJsLEwRWcfR53n6ezjCCvzh7fM7v8TpS8j25faZ+iDrecqrCVIn8SMiIiotmALUBWwNDHDncxcuCisocwvRmZeEfZPCUVekRrKvCJpeYuJ62NxLzsP8wf44YOt2p2k5w/wQ8qDXIS1cJYSobwidZU8mrKzNCtz9JqdJT9KRERUe/BbqwoItQouCmvcysyDwtIEK15+BsXif/tlAIQA/hv5DPLUBbAG8PEAP+QUqqQZoW0tTKFSq7E17hZm922N8etOA6i6R1ON6lmXmZixAzQREdUmTICqgJmJKQrVarjaW0IGIE+l1rkY6rwIP9iaypGrVkHITDHvt4s666Qpc3H2lhLBPo5V+mjK09EGCwa1RVZ+sZSY2XEeICIiqoWYAFURmcwERxPT4e1sg6V7E0qd5ydqgB/yVYCl+cMOzqXV+W1iZ1ibm8LMRFalj6aY7BARUV3ATtBVQA3gw5hzcLG3hI3crMw+O9mFKqQpC/DRrxfwZeQzWstflNR58cvD6Pb5AYxfdxrzIvy0Ok7z0RQREVHp2AJUBbKLVHC2NYfCyhzZ+WUvYJqVV4SvD12Fv4eDxszPj9cpcfaWEhPXn8bSl59B7iN9hvhoioiIqHS1ogVo+fLl8PLygqWlJQIDA3HixIky62/atAktWrSApaUl/Pz88Pvvv1dRpLqZyIoxvXsLNLCTw8HaXFrg9NDUrlg+pI1GXTsrc8zt3xohTepLMz8/zu6xfj9nbymxZNc/sLcwhZu9JUxkQEpGPi7ezkTS3WzcepCLK3cqb5JEIiKi2qbGtwBt2LABkydPxldffYXAwEB88cUX6NmzJy5fvgwXFxet+kePHkVkZCSioqLQp08frFu3DhERETh9+jTatGmj4x0qXz0zOXIE8IGOyQrnRfhh+ZA2GL/hPDr7OMLMVIYPY85jXoQf2ja01+oH1NnHEXJTzbx1YDtXvN29BfJVasz+5QIOPfIewT6OmNDNB24KS0zZdAY3HuRx/S4iIjJ6MiGEKL9a9QkMDESHDh2wbNkyAIBarYaHhwfeeustTJs2Tav+kCFDkJOTg+3bt0tlzz33HNq1a4evvvpKr/dUKpVQKBTIzMyEvb19+QeU48a9HK2Zmkt09nFE1AA/TN96DvMH+OG1NSdx5U72/ydHbZBXpEb4kkNS3fkD/GAqBDo/8ljs0NSuuJWZh+V7EzSSnxLBPo7o4+eG1g0V6LfsiHQurt9FRER1iSHf3zW6BaiwsBCnTp3C9OnTpTITExN0794dx44d03nMsWPHMHnyZI2ynj17IiYmptT3KSgoQEFBgbStVCqfLvDHZBeqyu34PKdfGyn5KSnPK1LD1sIUG8c9Bzsrc8jNTGAqBLIeeyyWXaiCjdxMZ/IDPJxlenSwN8weaTni+l1ERGTManQfoPT0dKhUKjRo0ECjvEGDBkhNTdV5TGpqqkH1ASAqKgoKhUJ6eXh4PH3wjyh3ssK8IoQtOiAlP1J5fhHGrzsNOytzzP/tIkxNZHhzfazWYzFlXlG5nasLitVax3H9LiIiMlY1ugWoqkyfPl2j1UipVFZoElTuZIWl7LezNIe91cNZoKMG+GH8utM4e0sJOytz7JgUIo34AoBitXZn6UfJzUy03ofrdxERkbGq0S1ATk5OMDU1RVpamkZ5WloaXF1ddR7j6upqUH0AkMvlsLe313hVpJJ1tHTp7OMIWwtTneXWFqaYP8APqw4mQiUEzt5SSpMbtnSzR0dvR7R0s4edpRlyCooR4uuk8z2CfRxxR5mPYpVa4/xcv4uIiIxVjU6ALCwsEBAQgD179khlarUae/bsQVBQkM5jgoKCNOoDwK5du0qtXxVK1tEqbbLC+8pcneVyAL+fuYmxXZpi4vrYUic3bFTPGg0VVpjdt5VWEvRwFJgvgpo6YfyPpzXOzw7QRERkrGr8KLANGzZgxIgRWLlyJTp27IgvvvgCGzduxD///IMGDRpg+PDhaNiwIaKiogA8HAYfGhqKBQsW4MUXX8RPP/2Ejz/+2KBh8BU9CqzEzQe5Guto2VqYwlKdj3wTS2Q/tvCpOYBstQpyE1Nk5uXD1LT8yQ1vP8iFSi2QW6SCMq8Y1nJTWJmbQm5mgpyiYmTkcJJEIiKqu+rMKDDg4bD2u3fvYubMmUhNTUW7du2wc+dOqaNzcnIyTEz+15DVqVMnrFu3Dh9++CHef/99+Pr6IiYmptrmAHqU7qSj/Ll4PPSoAwDuTGqIiIj0UuNbgKpDZbUAERERUeUx5Pu7RvcBIiIiIqoMTICIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjpMgIiIiMjoMAEiIiIio8MEiIiIiIwOEyAiIiIyOjV+KYzqUDI5tlKprOZIiIiISF8l39v6LHLBBEiHrKwsAICHh0c1R0JERESGysrKgkKhKLMO1wLTQa1W4/bt27Czs4NMJqvQcyuVSnh4eODGjRtGsc6YsV0vwGvmNdddxnbNxna9QO2/ZiEEsrKy4O7urrFQui5sAdLBxMQEjRo1qtT3sLe3r5UfridlbNcL8JqNBa+57jO26wVq9zWX1/JTgp2giYiIyOgwASIiIiKjwwSoisnlcsyaNQtyuby6Q6kSxna9AK/ZWPCa6z5ju17AuK6ZnaCJiIjI6LAFiIiIiIwOEyAiIiIyOkyAiIiIyOgwASIiIiKjwwSoCi1fvhxeXl6wtLREYGAgTpw4Ud0hVZrZs2dDJpNpvFq0aFHdYVWogwcPom/fvnB3d4dMJkNMTIzGfiEEZs6cCTc3N1hZWaF79+6Ij4+vnmArSHnXPHLkSK373qtXr+oJtgJERUWhQ4cOsLOzg4uLCyIiInD58mWNOvn5+Rg/fjwcHR1ha2uLQYMGIS0trZoifnr6XHPXrl217vO///3vaor46a1YsQJt27aVJv8LCgrCjh07pP117R6Xd7117f6WhglQFdmwYQMmT56MWbNm4fTp0/D390fPnj1x586d6g6t0rRu3RopKSnS6/Dhw9UdUoXKycmBv78/li9frnP/woUL8eWXX+Krr77CX3/9BRsbG/Ts2RP5+flVHGnFKe+aAaBXr14a9339+vVVGGHFOnDgAMaPH4/jx49j165dKCoqQo8ePZCTkyPV+c9//oNff/0VmzZtwoEDB3D79m0MHDiwGqN+OvpcMwC89tprGvd54cKF1RTx02vUqBEWLFiAU6dO4eTJk3j++efRv39/XLhwAUDdu8flXS9Qt+5vqQRViY4dO4rx48dL2yqVSri7u4uoqKhqjKryzJo1S/j7+1d3GFUGgNi6dau0rVarhaurq/j000+lsoyMDCGXy8X69eurIcKK9/g1CyHEiBEjRP/+/aslnqpw584dAUAcOHBACPHwnpqbm4tNmzZJdS5duiQAiGPHjlVXmBXq8WsWQojQ0FAxadKk6guqCtSrV0988803RnGPhfjf9QphHPdXCCHYAlQFCgsLcerUKXTv3l0qMzExQffu3XHs2LFqjKxyxcfHw93dHU2aNMGwYcOQnJxc3SFVmaSkJKSmpmrcc4VCgcDAwDp9zwFg//79cHFxQfPmzfHGG2/g3r171R1ShcnMzAQA1K9fHwBw6tQpFBUVadznFi1aoHHjxnXmPj9+zSV+/PFHODk5oU2bNpg+fTpyc3OrI7wKp1Kp8NNPPyEnJwdBQUF1/h4/fr0l6ur9fRQXQ60C6enpUKlUaNCggUZ5gwYN8M8//1RTVJUrMDAQ0dHRaN68OVJSUjBnzhyEhITg/PnzsLOzq+7wKl1qaioA6LznJfvqol69emHgwIHw9vZGYmIi3n//fYSHh+PYsWMwNTWt7vCeilqtxttvv43g4GC0adMGwMP7bGFhAQcHB426deU+67pmAHj55Zfh6ekJd3d3nD17Fu+99x4uX76Mn3/+uRqjfTrnzp1DUFAQ8vPzYWtri61bt6JVq1aIi4urk/e4tOsF6ub91YUJEFWK8PBw6d9t27ZFYGAgPD09sXHjRowZM6YaI6PKNHToUOnffn5+aNu2LZo2bYr9+/cjLCysGiN7euPHj8f58+frXF+2spR2zePGjZP+7efnBzc3N4SFhSExMRFNmzat6jArRPPmzREXF4fMzExs3rwZI0aMwIEDB6o7rEpT2vW2atWqTt5fXfgIrAo4OTnB1NRUa9RAWloaXF1dqymqquXg4IBmzZohISGhukOpEiX31ZjvOQA0adIETk5Otf6+T5gwAdu3b8e+ffvQqFEjqdzV1RWFhYXIyMjQqF8X7nNp16xLYGAgANTq+2xhYQEfHx8EBAQgKioK/v7+WLJkSZ29x6Vdry514f7qwgSoClhYWCAgIAB79uyRytRqNfbs2aPxzLUuy87ORmJiItzc3Ko7lCrh7e0NV1dXjXuuVCrx119/Gc09B4CbN2/i3r17tfa+CyEwYcIEbN26FXv37oW3t7fG/oCAAJibm2vc58uXLyM5ObnW3ufyrlmXuLg4AKi191kXtVqNgoKCOnmPdSm5Xl3q4v0FwFFgVeWnn34ScrlcREdHi4sXL4px48YJBwcHkZqaWt2hVYp33nlH7N+/XyQlJYkjR46I7t27CycnJ3Hnzp3qDq3CZGVlidjYWBEbGysAiEWLFonY2Fhx/fp1IYQQCxYsEA4ODmLbtm3i7Nmzon///sLb21vk5eVVc+RPrqxrzsrKElOmTBHHjh0TSUlJYvfu3eKZZ54Rvr6+Ij8/v7pDfyJvvPGGUCgUYv/+/SIlJUV65ebmSnX+/e9/i8aNG4u9e/eKkydPiqCgIBEUFFSNUT+d8q45ISFBfPTRR+LkyZMiKSlJbNu2TTRp0kR06dKlmiN/ctOmTRMHDhwQSUlJ4uzZs2LatGlCJpOJP//8UwhR9+5xWddbF+9vaZgAVaGlS5eKxo0bCwsLC9GxY0dx/Pjx6g6p0gwZMkS4ubkJCwsL0bBhQzFkyBCRkJBQ3WFVqH379gkAWq8RI0YIIR4OhZ8xY4Zo0KCBkMvlIiwsTFy+fLl6g35KZV1zbm6u6NGjh3B2dhbm5ubC09NTvPbaa7U6ydd1rQDE6tWrpTp5eXnizTffFPXq1RPW1tZiwIABIiUlpfqCfkrlXXNycrLo0qWLqF+/vpDL5cLHx0dMnTpVZGZmVm/gT2H06NHC09NTWFhYCGdnZxEWFiYlP0LUvXtc1vXWxftbGpkQQlRdexMRERFR9WMfICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMDhMgIiIiMjpMgIiIiMjoMAEiIiIio8MEiIgq3OzZs9GuXbunOsf+/fshk8m01mB6VHR0tNYq3ZXJy8sLX3zxxVOf59q1a5DJZJDJZKX+nEaOHPnU71NZSmKvyp89UUVjAkRUS4wcORIRERFa5fokCsas5MtaJpPBxsYGvr6+GDlyJE6dOlVp7xcTE6NX3d27d2usMaWPgwcPom/fvnB3dy/1vbp27QqZTIYFCxZo7XvxxRchk8kwe/Zsjfpvv/12qduPS0lJqZBEkKg6MQEiojpv9erVSElJwYULF7B8+XJkZ2cjMDAQ33//fbXG5ejoCEdHR2k7Ly8PEydORJMmTbBu3Tp4eXmhb9++SE1Nlerk5OTA398fy5cvL/PcHh4eiI6O1ii7desW9uzZ89SLWrq6ukKhUDzVOYiqGxMgojpoy5YtaN26NeRyOby8vPD5559r7NfVcuDg4CB9YRYWFmLChAlwc3ODpaUlPD09ERUVJdXNyMjA2LFj4ezsDHt7ezz//PM4c+aMVhw//PADvLy8oFAoMHToUGRlZUn7CgoKMHHiRLi4uMDS0hKdO3fG33//XeZ1RUdHo3HjxrC2tsaAAQNw7949vX4eDg4OcHV1hZeXF3r06IHNmzdj2LBhmDBhAh48eCDVO3z4MEJCQmBlZQUPDw9MnDgROTk5GufKyspCZGQkbGxs0LBhQ41ExMvLCwAwYMAAyGQyaVtfH3/8MTZs2IClS5eiT58+WLt2LTp27IjCwkKpTnh4OObNm4cBAwaUea4+ffogPT0dR44ckcrWrFmDHj16wMXFxaC4iOoiJkBEdcypU6cwePBgDB06FOfOncPs2bMxY8YMrdaAsnz55Zf45ZdfsHHjRly+fBk//vijxpf5Sy+9hDt37mDHjh04deoUnnnmGYSFheH+/ftSncTERMTExGD79u3Yvn07Dhw4oPFI5t1338WWLVuwZs0anD59Gj4+PujZs6fGOR71119/YcyYMZgwYQLi4uLQrVs3zJs3z+CfT4n//Oc/yMrKwq5du6R4e/XqhUGDBuHs2bPYsGEDDh8+jAkTJmgc9+mnn8Lf3x+xsbGYNm0aJk2aJJ2jJIEraXEqL6F7XGxsLPr164cXX3wR9vb26Ny5M2bMmIHGjRsbfH0WFhYYNmwYVq9eLZVFR0dj9OjRBp+LqC4yq+4AiEh/27dvh62trUaZSqXS2F60aBHCwsIwY8YMAECzZs1w8eJFfPrpp3p3rE1OToavry86d+4MmUwGT09Pad/hw4dx4sQJ3LlzB3K5HADw2WefISYmBps3b8a4ceMAAGq1GtHR0bCzswMAvPrqq9izZw/mz5+PnJwcrFixAtHR0QgPDwcAfP3119i1axe+/fZbTJ06VSumJUuWoFevXnj33Xel6zp69Ch27typ1zU9rkWLFgAedkgGgKioKAwbNkzq++Lr64svv/wSoaGhWLFiBSwtLQEAwcHBmDZtmhTDkSNHsHjxYrzwwgtwdnYG8L8WJ0MFBwdL9+/RVp8nNXr0aISEhGDJkiU4deoUMjMz0adPH43+P0TGii1ARLVIt27dEBcXp/H65ptvNOpcunQJwcHBGmXBwcGIj4/XSpZKM3LkSMTFxaF58+aYOHEi/vzzT2nfmTNnkJ2dDUdHR9ja2kqvpKQkJCYmSvW8vLyk5AcA3NzccOfOHQAPW1uKioo04jQ3N0fHjh1x6dIlnTFdunQJgYGBGmVBQUF6XY8uQggADx8HllxXdHS0xjX17NkTarUaSUlJpb5nUFBQqTEbaurUqXj33Xcxf/58/PTTT2jRogU+/vhjFBUVPdH5/P394evri82bN+O7777Dq6++CjMz/t1LBLAFiKhWsbGxgY+Pj0bZzZs3DT6PTCaTEoASj37JPvPMM0hKSsKOHTuwe/duDB48GN27d8fmzZuRnZ0NNzc37N+/X+u8jw6LNjc313pPtVptcKyVpSRp8fb2BgBkZ2fj9ddfx8SJE7XqPskjqCdhZmaGqVOnYurUqRg8eDAGDBiASZMmITs7Gx9//PETnXP06NFYvnw5Ll68iBMnTlRwxES1FxMgojqmZcuWGh1fAeDIkSNo1qwZTE1NAQDOzs5ISUmR9sfHxyM3N1fjGHt7ewwZMgRDhgzBv/71L/Tq1Qv379/HM888g9TUVJiZmRncybdE06ZNYWFhgSNHjkiP14qKivD333+XOvy6ZcuW+OuvvzTKjh8//kTvDwBffPEF7O3t0b17dwAPk76LFy9qJZiPe/w9jx8/jpYtW0rb5ubmere0lcXa2hqRkZE4efIkDh069MTnefnllzFlyhT4+/ujVatWTx0XUV3BBIiojnnnnXfQoUMHzJ07F0OGDMGxY8ewbNky/Pe//5XqPP/881i2bBmCgoKgUqnw3nvvabTYLFq0CG5ubmjfvj1MTEywadMmuLq6wsHBAd27d0dQUBAiIiKwcOFCNGvWDLdv38Zvv/2GAQMG4Nlnny03RhsbG7zxxhuYOnUq6tevj8aNG2PhwoXIzc3FmDFjdB4zceJEBAcH47PPPkP//v3xxx9/6N3/JyMjA6mpqSgoKMCVK1ewcuVKxMTE4Pvvv5dard577z0899xzmDBhAsaOHQsbGxtcvHgRu3btwrJly6RzHTlyBAsXLkRERAR27dqFTZs24bfffpP2e3l5Yc+ePQgODoZcLke9evX0ihEAZs2ahcDAQAQGBkIIgZMnT2Lbtm3o06ePVCc7OxsJCQnSdlJSEuLi4qSf4+Pq1auHlJQUrRa58ty9exdxcXEaZW5ubmjQoIFB5yGqsQQR1QojRowQ/fv31yrft2+fACAePHgglW3evFm0atVKmJubi8aNG4tPP/1U45hbt26JHj16CBsbG+Hr6yt+//13oVAoxOrVq4UQQqxatUq0a9dO2NjYCHt7exEWFiZOnz4tHa9UKsVbb70l3N3dhbm5ufDw8BDDhg0TycnJQgghZs2aJfz9/TXec/HixcLT01PazsvLE2+99ZZwcnIScrlcBAcHixMnTpR5Xd9++61o1KiRsLKyEn379hWfffaZUCgUZf7cAEgvS0tL0bRpUzFixAhx6tQprbonTpwQL7zwgrC1tRU2Njaibdu2Yv78+dJ+T09PMWfOHPHSSy8Ja2tr4erqKpYsWaJxjl9++UX4+PgIMzMzjet9VFJSkgAgYmNjNcq///570alTJ+Hg4CBkMplo0KCBGDNmjMjKytL6uTz+GjFihFQnNDRUTJo0qdSfib+/v5g1a1ap9UNDQ3W+x9y5c6U6q1evLvdnT1STyYR4rCMAERFVqmvXrsHb2xuxsbFlLoVhyNQFVS06Ohpvv/02ZyCnWouPwIiIqkmnTp3Qrl07HD16tLpDMYitrS2Ki4ulqQGIaiO2ABERVbHi4mJp/iG5XA4PD4/qDchAJX2QTE1NpVF0RLUNEyAiIiIyOpwIkYiIiIwOEyAiIiIyOkyAiIiIyOgwASIiIiKjwwSIiIiIjA4TICIiIjI6TICIiIjI6DABIiIiIqPDBIiIiIiMzv8BGyre6Zof9n4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df_credit[\"HOUSES\"]/1e6, y=df_credit[\"DEBT\"]/1e6)\n",
    "plt.xlabel(\"Household Debt [$1MIL]\")\n",
    "plt.ylabel(\"House Value [$1MIL]\")\n",
    "plt.title(\"Low Credit Score: Home Value against Household Debt\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph tends to show a linear relationship between Household Debt and House Value. The higher the house value the greater the household debt. We can also see the dataset has cluster groups."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UNSUPERVISED K-MEANS MODEL"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting our Dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use vertical split to create our feature matrix. Our features include:\n",
    "\n",
    "- `Home Value`\n",
    "- `Household Debt`\n",
    "\n",
    "The households are those that were turned down credit or fear being turned down credit past five months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4623, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df_credit[[\"DEBT\",\"HOUSES\"]]\n",
    "X.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Centroids\n",
    "\n",
    "We can use the Cluster Widget to preview the best number of clusters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will use 3 clusters and random state of 42 for reproducibility\n",
    "model= KMeans(n_clusters=3, random_state=42)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Fitting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit our feature matrix to the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=3, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=3, random_state=42)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Cluster Label From k-means Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4623"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels=model.labels_\n",
    "len(labels)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract labels to divide the data points into three clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[200:252]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Low Credit Score Households: Home Value against Household Debt')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df_credit[\"DEBT\"]/1e6,\n",
    "                y=df_credit[\"HOUSES\"]/1e6,\n",
    "                hue=labels,\n",
    "                palette=\"deep\"\n",
    ")\n",
    "plt.xlabel(\"Household Debt [$1MIL]\")\n",
    "plt.ylabel(\"House Value [$1MIL]\")\n",
    "plt.title(\"Low Credit Score Households: Home Value against Household Debt\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the three clusters in our data clearly. We normalize our feature matrix to base 6 (`1e6`) for easy comprehension and uniformity in our xlim and ylim."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Centroids of our Clusters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``Centroids`` - These are the centre of the cluster datasets. They represent the mean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   91017.57766674,   116150.29328699],\n",
       "       [18384100.        , 34484000.        ],\n",
       "       [ 5065800.        , 11666666.66666667]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = model.cluster_centers_\n",
    "centroids"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is are the coordinates for our centroids in the plot."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f6ceaf6f70>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df_credit[\"DEBT\"]/1e6,\n",
    "                y=df_credit[\"HOUSES\"]/1e6,\n",
    "                hue=labels,\n",
    "                palette=\"pastel\"\n",
    "                )\n",
    "plt.xlabel(\"Household Debt [$1MIL]\")\n",
    "plt.ylabel(\"House Value [$1MIL]\")\n",
    "plt.title(\"Low Credit Score Households: Home Value against Household Debt\")\n",
    "\n",
    "#plotting centroids\n",
    "\n",
    "plt.scatter(x=centroids[:,0]/1e6,\n",
    "            y=centroids[:,1]/1e6,\n",
    "            color=\"black\",\n",
    "            marker=\"*\",\n",
    "            s=150)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having plotted the centroids we can clearly see the centroid of our clusters.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL EVALUATION"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use inertia and silhouette score to evaluate our model performance."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### INERTIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "939554010797046.9"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inertia = model.inertia_\n",
    "inertia"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model perfomed poorly with it being far away from `0`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SILHOUETTE SCORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9768842462944348"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silh = silhouette_score(X, labels)\n",
    "silh\n",
    "# or alternatively silhouette_score(X, model.labels_)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see our model `silhouette_score`  was good, with it nearer to one."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the Best Number of Clusters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = range(2,13,1)\n",
    "inertias = []\n",
    "silhouette_scores = []\n",
    "# Loop to give the different inertia and silhouette scores as a list\n",
    "for k in clusters:\n",
    "    #instatiate our model\n",
    "    \n",
    "    model=KMeans(n_clusters=k, random_state=42)\n",
    "    model.fit(X)\n",
    "    \n",
    "    # model metrics\n",
    "    inertias.append(model.inertia_)\n",
    "    silhouette_scores.append(silhouette_score(X, model.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3018038313336920.0,\n",
       " 939554010797046.9,\n",
       " 546098841715654.56,\n",
       " 309310386410920.3,\n",
       " 235243397481789.06,\n",
       " 182225729179699.88,\n",
       " 150670779013795.2,\n",
       " 114321995931020.75,\n",
       " 100340259483917.19,\n",
       " 86229997033602.47,\n",
       " 74757234072100.47]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inertias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9855099957519555,\n",
       " 0.9768842462944348,\n",
       " 0.9490311483406091,\n",
       " 0.839330043242819,\n",
       " 0.7287406719898627,\n",
       " 0.726989114305748,\n",
       " 0.7263840026889208,\n",
       " 0.7335125606476427,\n",
       " 0.692157992955073,\n",
       " 0.6949309528556856,\n",
       " 0.6951831031001252]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_scores"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inertia and Silhouette Score Elbow Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6cf8e2c40>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clusters, inertias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6cf571730>]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(clusters, silhouette_scores)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots we can pick `4` as the best number of clusters to use. The line starts to normalize near four in both plots."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best K-means Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=4, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=4, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMeans(n_clusters=4, random_state=42)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = KMeans(n_clusters=4, random_state=42)\n",
    "best_model.fit(X)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Cluster Points from Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Low Credit Score Households: Home Value against Household Debt')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df_credit[\"DEBT\"]/1e6,\n",
    "                y=df_credit[\"HOUSES\"]/1e6,\n",
    "                hue=best_model.labels_,\n",
    "                palette=\"deep\"\n",
    ")\n",
    "plt.xlabel(\"Household Debt [$1MIL]\")\n",
    "plt.ylabel(\"House Value [$1MIL]\")\n",
    "plt.title(\"Low Credit Score Households: Home Value against Household Debt\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model Centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids_best = best_model.cluster_centers_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Plotting Centroids "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f6ce2cdac0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=df_credit[\"DEBT\"]/1e6,\n",
    "                y=df_credit[\"HOUSES\"]/1e6,\n",
    "                hue=best_model.labels_,\n",
    "                palette=\"deep\"\n",
    ")\n",
    "plt.xlabel(\"Household Debt [$1MIL]\")\n",
    "plt.ylabel(\"House Value [$1MIL]\")\n",
    "plt.title(\"Low Credit Score Households: Home Value against Household Debt\")\n",
    "\n",
    "plt.scatter(x=centroids_best[:,0]/1e6,\n",
    "            y=centroids_best[:,1]/1e6,\n",
    "            color=\"black\",\n",
    "            marker=\"*\",\n",
    "            s=150)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Centroids Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>DEBT</th>\n",
       "      <th>HOUSES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.488629e+04</td>\n",
       "      <td>1.031872e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.838410e+07</td>\n",
       "      <td>3.448400e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.472800e+06</td>\n",
       "      <td>1.407400e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.420929e+06</td>\n",
       "      <td>4.551429e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DEBT        HOUSES\n",
       "0  8.488629e+04  1.031872e+05\n",
       "1  1.838410e+07  3.448400e+07\n",
       "2  5.472800e+06  1.407400e+07\n",
       "3  2.420929e+06  4.551429e+06"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cent= X.groupby(best_model.labels_).mean()\n",
    "df_cent"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The values are similar to the centroids from the model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Home Value against House Debt by Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_cent/1e6).plot(kind=\"bar\",\n",
    "                xlabel=\"Clusters\",\n",
    "                ylabel=\"Value [$MIL]\",\n",
    "                )\n",
    "plt.title(\"Mean Home Value against House Debt by Cluster\")\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can note that the value of debt in all clusters is directly proportional with the mean value of houses. Cluster 1 has the largest mean value of homes and debt respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportion of Debt to Home Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Proportion of Debt to Home Value'}, xlabel='Cluster', ylabel='Proportion Debt / Home'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df_cent[\"DEBT\"]/df_cent[\"HOUSES\"]).plot(kind=\"bar\",\n",
    "                                    xlabel = \"Cluster\",\n",
    "                                    ylabel = \"Proportion Debt / Home\",\n",
    "                                    title = \"Proportion of Debt to Home Value\"\n",
    "                                    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can note that in Clusters `0,1,3` the proportion of debt to the home value is above half and cluster two seems two would be good target for the bank credit."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "orig_nbformat": 4,
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   "interpreter": {
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 },
 "nbformat": 4,
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}