Exploratory+data+analysis_7_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6dc39ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85157ca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>...</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>horsepower-binned</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>11.190476</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.822681</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>0.848630</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "      <td>13.055556</td>\n",
       "      <td>Medium</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make aspiration num-of-doors  \\\n",
       "0          3                122  alfa-romero        std          two   \n",
       "1          3                122  alfa-romero        std          two   \n",
       "2          1                122  alfa-romero        std          two   \n",
       "3          2                164         audi        std         four   \n",
       "4          2                164         audi        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base    length  ...  \\\n",
       "0  convertible          rwd           front        88.6  0.811148  ...   \n",
       "1  convertible          rwd           front        88.6  0.811148  ...   \n",
       "2    hatchback          rwd           front        94.5  0.822681  ...   \n",
       "3        sedan          fwd           front        99.8  0.848630  ...   \n",
       "4        sedan          4wd           front        99.4  0.848630  ...   \n",
       "\n",
       "   compression-ratio  horsepower  peak-rpm city-mpg highway-mpg    price  \\\n",
       "0                9.0       111.0    5000.0       21          27  13495.0   \n",
       "1                9.0       111.0    5000.0       21          27  16500.0   \n",
       "2                9.0       154.0    5000.0       19          26  16500.0   \n",
       "3               10.0       102.0    5500.0       24          30  13950.0   \n",
       "4                8.0       115.0    5500.0       18          22  17450.0   \n",
       "\n",
       "  city-L/100km  horsepower-binned  diesel  gas  \n",
       "0    11.190476             Medium       0    1  \n",
       "1    11.190476             Medium       0    1  \n",
       "2    12.368421             Medium       0    1  \n",
       "3     9.791667             Medium       0    1  \n",
       "4    13.055556             Medium       0    1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/automobileEDA.csv'\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81a05c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\mark\\anaconda3\\lib\\site-packages (0.11.2)Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: matplotlib>=2.2 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from seaborn) (3.5.2)\n",
      "Requirement already satisfied: pandas>=0.23 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from seaborn) (1.4.4)\n",
      "Requirement already satisfied: scipy>=1.0 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from seaborn) (1.9.1)\n",
      "Requirement already satisfied: numpy>=1.15 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from seaborn) (1.21.5)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (9.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.4.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from matplotlib>=2.2->seaborn) (4.25.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\mark\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f70df089",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0891c7e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling              int64\n",
      "normalized-losses      int64\n",
      "make                  object\n",
      "aspiration            object\n",
      "num-of-doors          object\n",
      "body-style            object\n",
      "drive-wheels          object\n",
      "engine-location       object\n",
      "wheel-base           float64\n",
      "length               float64\n",
      "width                float64\n",
      "height               float64\n",
      "curb-weight            int64\n",
      "engine-type           object\n",
      "num-of-cylinders      object\n",
      "engine-size            int64\n",
      "fuel-system           object\n",
      "bore                 float64\n",
      "stroke               float64\n",
      "compression-ratio    float64\n",
      "horsepower           float64\n",
      "peak-rpm             float64\n",
      "city-mpg               int64\n",
      "highway-mpg            int64\n",
      "price                float64\n",
      "city-L/100km         float64\n",
      "horsepower-binned     object\n",
      "diesel                 int64\n",
      "gas                    int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.00000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840796</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>98.797015</td>\n",
       "      <td>0.837102</td>\n",
       "      <td>0.915126</td>\n",
       "      <td>53.766667</td>\n",
       "      <td>2555.666667</td>\n",
       "      <td>126.875622</td>\n",
       "      <td>3.330692</td>\n",
       "      <td>3.256904</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>103.405534</td>\n",
       "      <td>5117.665368</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "      <td>13207.129353</td>\n",
       "      <td>9.944145</td>\n",
       "      <td>0.099502</td>\n",
       "      <td>0.900498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</td>\n",
       "      <td>31.99625</td>\n",
       "      <td>6.066366</td>\n",
       "      <td>0.059213</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>2.447822</td>\n",
       "      <td>517.296727</td>\n",
       "      <td>41.546834</td>\n",
       "      <td>0.268072</td>\n",
       "      <td>0.319256</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>37.365700</td>\n",
       "      <td>478.113805</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</td>\n",
       "      <td>7947.066342</td>\n",
       "      <td>2.534599</td>\n",
       "      <td>0.300083</td>\n",
       "      <td>0.300083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>0.678039</td>\n",
       "      <td>0.837500</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "      <td>4.795918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>0.801538</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2169.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.832292</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>137.00000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>0.881788</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2926.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.00000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "      <td>18.076923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  normalized-losses  wheel-base      length       width  \\\n",
       "count  201.000000          201.00000  201.000000  201.000000  201.000000   \n",
       "mean     0.840796          122.00000   98.797015    0.837102    0.915126   \n",
       "std      1.254802           31.99625    6.066366    0.059213    0.029187   \n",
       "min     -2.000000           65.00000   86.600000    0.678039    0.837500   \n",
       "25%      0.000000          101.00000   94.500000    0.801538    0.890278   \n",
       "50%      1.000000          122.00000   97.000000    0.832292    0.909722   \n",
       "75%      2.000000          137.00000  102.400000    0.881788    0.925000   \n",
       "max      3.000000          256.00000  120.900000    1.000000    1.000000   \n",
       "\n",
       "           height  curb-weight  engine-size        bore      stroke  \\\n",
       "count  201.000000   201.000000   201.000000  201.000000  197.000000   \n",
       "mean    53.766667  2555.666667   126.875622    3.330692    3.256904   \n",
       "std      2.447822   517.296727    41.546834    0.268072    0.319256   \n",
       "min     47.800000  1488.000000    61.000000    2.540000    2.070000   \n",
       "25%     52.000000  2169.000000    98.000000    3.150000    3.110000   \n",
       "50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n",
       "75%     55.500000  2926.000000   141.000000    3.580000    3.410000   \n",
       "max     59.800000  4066.000000   326.000000    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         201.000000  201.000000   201.000000  201.000000   201.000000   \n",
       "mean           10.164279  103.405534  5117.665368   25.179104    30.686567   \n",
       "std             4.004965   37.365700   478.113805    6.423220     6.815150   \n",
       "min             7.000000   48.000000  4150.000000   13.000000    16.000000   \n",
       "25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n",
       "50%             9.000000   95.000000  5125.369458   24.000000    30.000000   \n",
       "75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \n",
       "max            23.000000  262.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "              price  city-L/100km      diesel         gas  \n",
       "count    201.000000    201.000000  201.000000  201.000000  \n",
       "mean   13207.129353      9.944145    0.099502    0.900498  \n",
       "std     7947.066342      2.534599    0.300083    0.300083  \n",
       "min     5118.000000      4.795918    0.000000    0.000000  \n",
       "25%     7775.000000      7.833333    0.000000    1.000000  \n",
       "50%    10295.000000      9.791667    0.000000    1.000000  \n",
       "75%    16500.000000     12.368421    0.000000    1.000000  \n",
       "max    45400.000000     18.076923    1.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.dtypes)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ae43cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.466264</td>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.365404</td>\n",
       "      <td>-0.242423</td>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.233118</td>\n",
       "      <td>-0.110581</td>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.008245</td>\n",
       "      <td>-0.182196</td>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.279740</td>\n",
       "      <td>-0.035527</td>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>-0.196735</td>\n",
       "      <td>0.196735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
       "      <td>0.466264</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.101546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>-0.307237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>-0.365404</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>-0.211187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.242423</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>-0.244356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>-0.281578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curb-weight</th>\n",
       "      <td>-0.233118</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>-0.221046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>-0.110581</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>-0.070779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.054458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.008245</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>-0.241303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>-0.182196</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.985231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>0.169053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>0.279740</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.475812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>-0.035527</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>-0.265676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>-0.198690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.110326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-L/100km</th>\n",
       "      <td>0.066171</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>0.241282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diesel</th>\n",
       "      <td>-0.196735</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>0.196735</td>\n",
       "      <td>0.101546</td>\n",
       "      <td>-0.307237</td>\n",
       "      <td>-0.211187</td>\n",
       "      <td>-0.244356</td>\n",
       "      <td>-0.281578</td>\n",
       "      <td>-0.221046</td>\n",
       "      <td>-0.070779</td>\n",
       "      <td>-0.054458</td>\n",
       "      <td>-0.241303</td>\n",
       "      <td>-0.985231</td>\n",
       "      <td>0.169053</td>\n",
       "      <td>0.475812</td>\n",
       "      <td>-0.265676</td>\n",
       "      <td>-0.198690</td>\n",
       "      <td>-0.110326</td>\n",
       "      <td>0.241282</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   symboling  normalized-losses  wheel-base    length  \\\n",
       "symboling           1.000000           0.466264   -0.535987 -0.365404   \n",
       "normalized-losses   0.466264           1.000000   -0.056661  0.019424   \n",
       "wheel-base         -0.535987          -0.056661    1.000000  0.876024   \n",
       "length             -0.365404           0.019424    0.876024  1.000000   \n",
       "width              -0.242423           0.086802    0.814507  0.857170   \n",
       "height             -0.550160          -0.373737    0.590742  0.492063   \n",
       "curb-weight        -0.233118           0.099404    0.782097  0.880665   \n",
       "engine-size        -0.110581           0.112360    0.572027  0.685025   \n",
       "bore               -0.140019          -0.029862    0.493244  0.608971   \n",
       "stroke             -0.008245           0.055563    0.158502  0.124139   \n",
       "compression-ratio  -0.182196          -0.114713    0.250313  0.159733   \n",
       "horsepower          0.075819           0.217299    0.371147  0.579821   \n",
       "peak-rpm            0.279740           0.239543   -0.360305 -0.285970   \n",
       "city-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \n",
       "highway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \n",
       "price              -0.082391           0.133999    0.584642  0.690628   \n",
       "city-L/100km        0.066171           0.238567    0.476153  0.657373   \n",
       "diesel             -0.196735          -0.101546    0.307237  0.211187   \n",
       "gas                 0.196735           0.101546   -0.307237 -0.211187   \n",
       "\n",
       "                      width    height  curb-weight  engine-size      bore  \\\n",
       "symboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \n",
       "normalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \n",
       "wheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \n",
       "length             0.857170  0.492063     0.880665     0.685025  0.608971   \n",
       "width              1.000000  0.306002     0.866201     0.729436  0.544885   \n",
       "height             0.306002  1.000000     0.307581     0.074694  0.180449   \n",
       "curb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \n",
       "engine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \n",
       "bore               0.544885  0.180449     0.644060     0.572609  1.000000   \n",
       "stroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \n",
       "compression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \n",
       "horsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \n",
       "peak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \n",
       "city-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \n",
       "highway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \n",
       "price              0.751265  0.135486     0.834415     0.872335  0.543155   \n",
       "city-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \n",
       "diesel             0.244356  0.281578     0.221046     0.070779  0.054458   \n",
       "gas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n",
       "\n",
       "                     stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "symboling         -0.008245          -0.182196    0.075819  0.279740   \n",
       "normalized-losses  0.055563          -0.114713    0.217299  0.239543   \n",
       "wheel-base         0.158502           0.250313    0.371147 -0.360305   \n",
       "length             0.124139           0.159733    0.579821 -0.285970   \n",
       "width              0.188829           0.189867    0.615077 -0.245800   \n",
       "height            -0.062704           0.259737   -0.087027 -0.309974   \n",
       "curb-weight        0.167562           0.156433    0.757976 -0.279361   \n",
       "engine-size        0.209523           0.028889    0.822676 -0.256733   \n",
       "bore              -0.055390           0.001263    0.566936 -0.267392   \n",
       "stroke             1.000000           0.187923    0.098462 -0.065713   \n",
       "compression-ratio  0.187923           1.000000   -0.214514 -0.435780   \n",
       "horsepower         0.098462          -0.214514    1.000000  0.107885   \n",
       "peak-rpm          -0.065713          -0.435780    0.107885  1.000000   \n",
       "city-mpg          -0.034696           0.331425   -0.822214 -0.115413   \n",
       "highway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \n",
       "price              0.082310           0.071107    0.809575 -0.101616   \n",
       "city-L/100km       0.037300          -0.299372    0.889488  0.115830   \n",
       "diesel             0.241303           0.985231   -0.169053 -0.475812   \n",
       "gas               -0.241303          -0.985231    0.169053  0.475812   \n",
       "\n",
       "                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\n",
       "symboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \n",
       "normalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \n",
       "wheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \n",
       "length            -0.665192    -0.698142  0.690628      0.657373  0.211187   \n",
       "width             -0.633531    -0.680635  0.751265      0.673363  0.244356   \n",
       "height            -0.049800    -0.104812  0.135486      0.003811  0.281578   \n",
       "curb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \n",
       "engine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \n",
       "bore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \n",
       "stroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \n",
       "compression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \n",
       "horsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \n",
       "peak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \n",
       "city-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \n",
       "highway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \n",
       "price             -0.686571    -0.704692  1.000000      0.789898  0.110326   \n",
       "city-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \n",
       "diesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \n",
       "gas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n",
       "\n",
       "                        gas  \n",
       "symboling          0.196735  \n",
       "normalized-losses  0.101546  \n",
       "wheel-base        -0.307237  \n",
       "length            -0.211187  \n",
       "width             -0.244356  \n",
       "height            -0.281578  \n",
       "curb-weight       -0.221046  \n",
       "engine-size       -0.070779  \n",
       "bore              -0.054458  \n",
       "stroke            -0.241303  \n",
       "compression-ratio -0.985231  \n",
       "horsepower         0.169053  \n",
       "peak-rpm           0.475812  \n",
       "city-mpg          -0.265676  \n",
       "highway-mpg       -0.198690  \n",
       "price             -0.110326  \n",
       "city-L/100km       0.241282  \n",
       "diesel            -1.000000  \n",
       "gas                1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be3b1548",
   "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>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       bore    stroke  compression-ratio  horsepower\n",
       "bore               1.000000 -0.055390           0.001263    0.566936\n",
       "stroke            -0.055390  1.000000           0.187923    0.098462\n",
       "compression-ratio  0.001263  0.187923           1.000000   -0.214514\n",
       "horsepower         0.566936  0.098462          -0.214514    1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['bore', 'stroke', 'compression-ratio', 'horsepower']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ceda826",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Continuous Numerical Variables\n",
    "\n",
    "#Continuous numerical variables are variables that may contain any value within some range. \n",
    "#Continuous numerical variables can have the type \"int64\" or \"float64\". \n",
    "#A great way to visualize these variables is by using scatterplots with fitted lines.\n",
    "\n",
    "#In order to start understanding the (linear) relationship between an individual variable and the price. \n",
    "#We can do this by using \"regplot\", which plots the scatterplot plus the fitted regression line for the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4f678342",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Positive linear relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7491f0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 53727.417725796135)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Engine size as potential predictor variable of price\n",
    "sns.regplot(x=\"engine-size\", y=\"price\", data=df)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aa0c9f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "#As the engine-size goes up, the price goes up: this indicates a positive direct correlation between these two variables. \n",
    "#Engine size seems like a pretty good predictor of price since the regression line is almost a perfect diagonal line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e6a602a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can examine the correlation between 'engine-size' and 'price' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "396b8c5d",
   "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>engine-size</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.872335</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             engine-size     price\n",
       "engine-size     1.000000  0.872335\n",
       "price           0.872335  1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"engine-size\", \"price\"]].corr()    #0.87"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2dc6fc38",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Highway mpg is a potential predictor variable of price "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7b00b3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='highway-mpg', ylabel='price'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5UfbVwKGClRBChg8qskhK+rIND+kZFayEEDJ8cKzrx2kyKPx+PzweD3w+H9zu7Jm1oADNgbH9aBOe21KNQw3tUHQDFoHHpDIX7lpMOVmEEJLJ+nL9psZ30ieUfTWQGBgYzP8x9Bg4RgghJCvRciFJG8NgqKr1YevhM6iq9VH2U6doGOnBhnbk2y0YmW9Dvt2Cgw0BPLKhCtuPNg31IRJCCBkANJNF0oK2jUmuaxhpNCvLygsoc/No8Eewbms15o8tpGVYQgjJcjSTRQZcdKbmQL0fDllEiUuGQxZj28YM55mavoSREkIIyW5UZJEBRdvG9C6VMFKVwkgJISQnUJFFBhTN1PQuPoyUgSGk6GgPqwgpOhgYhZESQkgOoZ4sMqBo25jeRcNI99a2QdOZWWwxgOMAi8BDFDjMGJlHYaSEEJIDaCaLDCjaNqZ3PM/higlF6IjoCCo6ACBajwYVHR0RHVdMKKKmd0IIyQFUZJEBRdvG9M4wGLYdaYLdIsBuMTfZjtaj5mMith1pGrY9a4QQkkuoyCIDiraN6V20Z63UbcWYIgdGFTgwMt+GUQUOjClyoMQtD+ueNUIIySXUk0UGXHSfw2hOls9gkHgOk8tdQ5qTlQlbAsX3rHEcB5tFACDEnh/uPWuEEJJLqMgiabFwfBHmjy0c8qImKlPCUeN71qy80O354d6zRgghuYSWC0naRPc5XDSxGNNHeoa0wMqUcFTqWSOEkOGDiiyS0zItHJV61gghZPigIovktEwMR432rE0udyEY0dAYiCAY0TC53IU1N00f1ns7EkJILqGeLJLTMjUcNdN61gghhAy8rJnJWrduHWbMmAG32w23240FCxbg3XffjT3PGMNjjz0Gr9cLm82GxYsXY//+/QmvEYlEcO+996KoqAgOhwPLly9HbW1twpjW1lasWrUKHo8HHo8Hq1atQltbW8KYkydPYtmyZXA4HCgqKsJ9990HRaG7wTJRJoejZkrPGiGEkPTImiJr5MiRePzxx7Fr1y7s2rULV155JW688cZYIfXkk0/i6aefxrPPPotPPvkEZWVluOaaa9De3h57jdWrV2PDhg1Yv349PvzwQwQCASxduhS6rsfGrFy5EpWVlXjvvffw3nvvobKyEqtWrYo9r+s6brjhBnR0dODDDz/E+vXr8frrr+OBBx4YvB8GSRk1mhNCCBkqHOt65ckiBQUFeOqpp/CNb3wDXq8Xq1evxkMPPQTAnLUqLS3FE088gTvuuAM+nw/FxcV45ZVX8KUvfQkAUFdXh4qKCrzzzju49tprceDAAUyZMgU7duzAvHnzAAA7duzAggULcPDgQUyaNAnvvvsuli5dipqaGni9XgDA+vXrceutt6KxsRFud2oXa7/fD4/HA5/Pl/LXkP6J3l0YiOjIs0uQBR4R3UBbUIVTFqgPihBCSMr6cv3OmpmseLquY/369ejo6MCCBQtw/PhxNDQ0YMmSJbExsixj0aJF2L59OwBg9+7dUFU1YYzX68W0adNiYz766CN4PJ5YgQUA8+fPh8fjSRgzbdq0WIEFANdeey0ikQh2797d4zFHIhH4/f6EX2RwUKM5IYSQoZBVje9VVVVYsGABwuEwnE4nNmzYgClTpsQKoNLS0oTxpaWlOHHiBACgoaEBFosF+fn53cY0NDTExpSUlHT7viUlJQljun6f/Px8WCyW2Jhk1q5dix/+8Id9PGMyUKjRnBBCyGDLqiJr0qRJqKysRFtbG15//XXccsst2Lp1a+z5rrfoM8a6PdZV1zHJxvdnTFcPP/ww7r///tjv/X4/Kioqej02MrCijeaEEELIYMiq5UKLxYLx48dj7ty5WLt2LWbOnIn//M//RFlZGQB0m0lqbGyMzTqVlZVBURS0trb2Oub06dPdvu+ZM2cSxnT9Pq2trVBVtdsMVzxZlmN3RkZ/EUIIISR3ZVWR1RVjDJFIBGPGjEFZWRk2b94ce05RFGzduhULFy4EAMyZMweSJCWMqa+vx759+2JjFixYAJ/Ph48//jg2ZufOnfD5fAlj9u3bh/r6+tiYTZs2QZZlzJkzJ63nSwghhJDskTXLhY888giuv/56VFRUoL29HevXr8eWLVvw3nvvgeM4rF69GmvWrMGECRMwYcIErFmzBna7HStXrgQAeDwe3HbbbXjggQdQWFiIgoICPPjgg5g+fTquvvpqAMDkyZNx3XXX4fbbb8fzzz8PAPjmN7+JpUuXYtKkSQCAJUuWYMqUKVi1ahWeeuoptLS04MEHH8Ttt99Os1OEEEIIicmaIuv06dNYtWoV6uvr4fF4MGPGDLz33nu45pprAADf+973EAqFcNddd6G1tRXz5s3Dpk2b4HK5Yq/xzDPPQBRF3HzzzQiFQrjqqqvw8ssvQxCE2JhXX30V9913X+wuxOXLl+PZZ5+NPS8IAt5++23cdddduPTSS2Gz2bBy5Ur85Cc/GaSfBMkVhsGoEZ8QQnJYVudkZTPKyRreth9twnNbjuJgQztUjUESOVxY5sJdi8dTpAQhhGSwvly/s2Ymi5Bcsf1oE77zh0q0dCiIfcRRgJ3HW3CksRLP3DyLCi1CCMkBWd34Tki2MQyGte8ewJn2CAzGIPAcRJ6DwHMwGMOZ9gjWvnsAhkETzIQQku2oyCJkEFWd8uFQQwAcAInnwXMcOI4Dz3GQeB4cgEMNAVSd8g31oRJCCDlPVGTlIEUz0OgPI6Lp5x5MBtWemjZohgFB4LqF13IcB0HgoBkG9tS0Dc0BEkIIGTDUk5WDGBgCEQ2BiAanLCLPboFFpHo6E3DRVUAGINmNhKzLOEIIIVmLiqwcR8VWZpl1QR4kgYemG+B5Bi6u0mJg0A0GSeAx64K8oTtIQgghA4KuuMNEIKKhtjWIM+0RaLox1IczbE0f4cHEUicYAFUzYDAGBgaDMaiaAQZgYqkT00fQHouEEJLtqMgaZtrDKmpaQ2gKULE1FHiew8PXT0axSwbPc9ANA6pmQDcM8DyHEpeMh6+fTKGkhBCSA6jIGoYYY/CHzGKrpUOhuIBBtnB8EZ65eRbmjSlEvl2G0yoh3y5j3phCPE0ZWYQQkjOoJ2sYY4yhLajAH1KRZ5fgsUnd7ngj6bFwfBHmjy2kbXUIISSHUZFFYDCGlg4F/pAGj12C2ypSsTUIeJ7D9JHUe0UIIbmKlgtJjGYYaA5EUNsagj+sDvXhEEIIIVmNiizSjaobaGqPoKYliEBEG+rDIYQQQrISLReSHqm6mRzfKvDIs0twWaWhPiRCCCEka9BMFjknVTdwpnNmq52WEQkhhJCU0EwWSVm02GoLqjSzNQAMg9HdhYQQksOoyCJ9RsXW+dt+tAnrtlajujEAVWeQBA7jSpy4c9E4yskihJAcQcuFpN9oGbF/th9twiMbqnCg3g+HLKLEJcMhizhQ345HNlRh+9GmoT5EQgghA4CKLHLe4ostuhuxd4bBsG5rNQIRDWVuK6ySAJ7nYJUElLllBCI61m2tphR+QgjJAVRk5ZiTzUE88e5B1LWFBv17R+9GrG0NIqhQsZXM/jo/qhsDyLdbugW+chyHPLuE6sYA9tf5h+gICSGEDBTqycox/7PzBH7zt8/x0t8+x7yxBbhxlhcXjy4AP4gJ7opmoMEXhlUSUOCwwCoJg/a9M11LUIGqM1iE5J9vZIGHz2BoCSqDfGSEEEIGGhVZOSSk6Pj9JzUAAAZgx7EW7DjWghF5Niyf5cX1U8vgtA7eWx5WddS1hWC3iMh3SJBFKrYK7BZIAgdFNyDzPMKKAc0wIPI8rBYeEd2AxHMosFuG+lAJIYScJyqycgjHAd+5egJ++9EJHG/qiD1+qi2EdVuq8dKHx3H1lFLcOMuLccXOQTuuoKIhqGhwyiLy7BZYxOG7Sj3V68a4Eif21rZB0xkU3QBj5ntnEXiIAocZI/Mw1ese6kMlhBBynqjIyiFWScCtl47Bly+pwFuf1mPjnlP46Fgzoj3UYc3A/+6tx//urceMkR6smOXFZeOLIPawdDXQAhENgYgGl1VCvl0atO+bSXiewxUTivBRdTN0g0EUOAg8YDAgqOgQOp+nvCxCCMl+VGTlII7jMGdUPuaMykeDL4w3P63DO1X18IfPNqPvrfVhb60PhU4Lls0ox9IZXhQ4BmeJqj2sIhDR4LaaM1vCMCooDINh25Em2C0CdMOcydINcybLbhEg8Dy2HWnCbZeNpUKLEEKyHMcYo3vFh4Df74fH44HP54PbPbBLQxFNx6nWxLsLFc3AB4casWHPKRw+Hej2NSLP4fIJRbhp9ghM9bq73fmWLjzHwWOT4LGZgaa5noBeVevDHa/sgkMWIYs8wmpcT5bEI6wZCEY0PL9qLqaP9Az14RJCCOmiL9dvmskaJiwij2unlmHJlFIcbGjHhj2nsPXwGai6WWNrBsMHh87gg0NnML7YiRWzvbjywpK03xloMIbWoIIthxrx+101ONHUAc1Aziagx99dyHEcbBYBwNmfMd1dSAghuWP4NcUMcxzHYXK5G4/8w2Ss/+Z83HbZaBQ75YQxR88E8JNNh/GlX+3Aui3Vac/c2nOyFT/ZdAiHGtohSwIKHRbYLUJOJqDH312YDN1dSAghuYOKrGEs327BV+eNwmu3z8Njy6dgVkVewvPtYQ1/3F2LVb/+GI9sqMLHx1tgDPDqssEYXvu4BkFFR5HTAovAw2AMAs+j2GnJuQT06N2FrUEVXVfqGWNoC6oYV+KkuwsJISQH0HIh6byjrRhXTCjG8aYOvFFZh02fNSCsmrMtXTO3bpzlxXUDlLl19HQHapo74LZK4HC2/4oxBp0BDlnAkdPt2F/nz4keJZ7ncOeicXhkQxUa/BHk2SXIgpmP1RZU4ZQF3LloXM71ohFCyHBEM1kkwZgiB1ZfPQF/uGMB7vnCeIzMtyU8f6othOe2VOPm5z/C05sPo/pM9yb6vvCFFagGgyQkLyoknkNEN3CksT1ntupZOL4Ia26ajsnlLgQjGhoDEQQjGiaXu7Dmpuk51YNGCCHDGc1kkaScsogvXjQCK2Z78fcTrXijsu4cmVsjcNn4wj5nX3msFkg8B1VnkMXuhZaiM0gcB7skxrbqybdbOhvGs9fC8UWYP7Yw5++mJISQ4YyKLNIrnuMwd3QB5o4uSEvm1vhSByoKHTh2JoAipyVxyRAM7WEVY4udGF/qAGBu1VPvC+VMsUUIISR3UU7WEBnsnKwBfX1VxweHzmBjZc+ZW1dMLMaKWd6UMrf2nGzF05sPI6jocFklWAQOim4WWHaLgPuvmYjZF+Qn/VqrJCDPLsFuya7PC9uPNmHd1mpUNwag6mzAIysMg9EsGSGEpEFfrt9UZA2RbC6yohhjOFDfjo2ViZlb8caXOLFi1rkzt/acbMVrH9egprkDKjOXCCsKHVh5SUWPBVY8WRKQZ5PgkDO/2Np+tAmPbKhCIKIh327eUanoBlo7G9/Pty8r3QUcIYQMZ1RkZYFcKLLitXQoeKeqHm99Wo8zgUi3511WEddPK8PymV5482xJXsGMczh6ugO+sAKP1YLxpQ7wfUyet4g88uwWODO02DIMhlte+hgH6v0oc1sTZvkYY2jwRzC53IXffv2Sfs08pbuAI4SQ4Y6KrCyQa0VWlG4w/K26CRv31KGypq3b8xyAeWMLsGLWCMwdnd/nIipVksAjzy7BZZXS8vr9Fb+tTrKZvZCq93tbnfgCrtQtI6Ky2JY9ssThtF85rwKOEEIIbatDhtBQZm7FU3UDZ9ojaAuq8NgluGRx0PZj7E38tjrJnM+2Ovvr/KhuDEAWBZxoDiGi6WDM3HxaFgV4bBKqGwM5kzlGCCGZjoqsHCSLArx5NgQVHR0RDWoPW7ikWzRz618uH4NN+09jY+Up1MbNsEUzt37z4XFcPaUUN87yYlyxc0CPQdUNNLVH4AuqGTGzFb+tjpXvPpN1PtvqtAQVdCjmTBiDWfByPMCYeVdmRNVhl0XaF5EQQgYJFVk5yioJsEoCChwWhFWz2Aoq+pAUXIOVudWb+JmtAoclpQb5dNyhF91W50B9O8rcfLeerLagisnlrn5tq5NnkxBWdRiMQRL5WBwGxwGcAKiagbCqI8+WWUuohBCSq6jIGgaiBVchMKQFV7ozt1Kh6gZO+8OQJQH5vUQ/pOsOvXRvqxPrsGQA4l+CdXmeEEJI2lHj+xBJZ+N7qqIFV0dEh2YMzZJiNHNrw55TONJ4/plbfZWs2BqMO/QSijiDQeLPv4jbevgM7nttD0KqBp2ZPzsOZn2lGQwCB9gsIv7rK7OxaGLxeR0/IYQMV9T4TlISm+FyDl3BJUsCrptWhmunlsYyt7YcOgOtcy1RMxjeP9iI9w82YnyxEytmnztzqy8iqo4Gnx4rtqyigHVbqxGIaAkRC1ZeQJmbR4M/gnVbqzF/bOF5LR2mY1udArsFDlmA0yrAF1IR0YxY47tN4uG2SQBD0n4vTTPw1t56nGoLYkSeHctmlEMUaWtTQgg5HzSTNUQyYSarJ0M9w5VK5tZ1U8tw46yeM7f66/iZDnx/YxVcVmnAIxbS7WyEQztKXRZEtLgIB5HD6fbkEQ4vbKvGL7ZUoz2kwoC5a7zLJuHuxeNw+xXjhux8CCEkE/Xl+k0fVUk35uyWjAsK7fDm2eC2SRAGMVepwGHB1+aPwmu3z8Njy6ZgVkVewvPtYQ1/3F2LVb/+GI9sqMLHx1tgDNBnhaaOCCKaATAz86vrZxBZ4KH2M2Ih3aL9Xk5ZwOl2BeAAh0UEOOB0u5K03+uFbdV44r1D8AVV8DwHi8CB5zn4giqeeO8QXthWPYRnRAgh2Y2WC0mvYkuKDgtCqo5AREMwog9YUdMbobMf64qJg5e55bFaIPFmxALHARzHQeABvvPfzydiYTAsHF+ENTdNj/V7+Tr7vSaXu7r1e2magV9sqYZuMFhEDjxnfubiOYDnDCgawy+2VOPrC8fQ0iEhhPQDLRcOkUxeLjwXxhg6FB2BsIaQqneb7UmnQETDpv0N2FhZl5C5FWUVeVzTmbk1th+ZWwZjeOj1Khw7E0CR0xIXg8CB54DG9gimeN0Zn5qeSvzEhr+fwoN/rATPcxD57kWUZhgwDIaf/NMs3HTRiME6dEIIyWjU+E7SiuM4OGURTlmEbjB0KBoCYQ1hVU/79zYzt0ZixewR+PuJVmysrMNH1c3RhAKEOxu43+pn5hbPcVh5SQWe3nwYTQEFLqsEi8BB0Qy0h1XYLQJWzRuVvhMcIDzPnbNn7FRbEAYAsYdakecAvXMcIYSQvqMii5wXgefgtkpwWyWouoGOiIZARIOipbdhvq+ZW8tneHHDjPKUMrdmX5CP+6+ZiNc+rkFNcwfaGYPEcRhb7MTKSyowvtSJky1BuG0SPIPcrzaQRuTZwQMwmFlQdWUwM2prRJ59sA+NEEJyAi0XDpFsXi5MhaIZCES0Qd3WJ5q5tbHyFA6fPv/MLYMxHD3dAV9YgcdqwfhSR7cNrXmOg8sqIs9uybpiS9MMzF3zZ/iCakJPFgAYzOzJ8tgl7HrkaurJIoSQTjl5d+HatWtx8cUXw+VyoaSkBCtWrMChQ4cSxjDG8Nhjj8Hr9cJms2Hx4sXYv39/wphIJIJ7770XRUVFcDgcWL58OWpraxPGtLa2YtWqVfB4PPB4PFi1ahXa2toSxpw8eRLLli2Dw+FAUVER7rvvPihK5t1xNlQsIo8ChwUVBeYdinl2C6QB3CYnmWjm1rqvXoRfrJyNqyeXQBLOFj7RzK371lfijv/5O96tqkfkPJc4DcbgC6k42RJEcyAC3ciezyyiyOPuxeMg8ByUzrgHgxnQDLPAEngOdy8eRwUWIYT0U9bMZF133XX48pe/jIsvvhiapuH73/8+qqqq8Nlnn8HhcAAAnnjiCfz4xz/Gyy+/jIkTJ+JHP/oRtm3bhkOHDsHlcgEA7rzzTrz11lt4+eWXUVhYiAceeAAtLS3YvXs3BMHMRbr++utRW1uLX/3qVwCAb37zmxg9ejTeeustAICu65g1axaKi4vx05/+FM3NzbjlllvwxS9+ET//+c9TOp9cn8nqSUTT0REZvI2rWzoUvF1Vj7c+rUNToHsR7LaKuG5aGZbPTMzc2nOyNbZcGE1kryh0YOUlFZh9QX6P34/jOLitIjw2aUD3Xkyn+Jys6G48lJNFCCHJ9eX6nTVFVldnzpxBSUkJtm7diiuuuAKMMXi9XqxevRoPPfQQAHPWqrS0FE888QTuuOMO+Hw+FBcX45VXXsGXvvQlAEBdXR0qKirwzjvv4Nprr8WBAwcwZcoU7NixA/PmzQMA7NixAwsWLMDBgwcxadIkvPvuu1i6dClqamrg9XoBAOvXr8ett96KxsbGpD/0SCSCSORssKbf70dFRcWwK7LiRTQdwYgZC5Hugks3GP52tAkbK+tQWdPW7XkOwLyxBbhp9gjwHPCzPx9BUNHhtkqQBA6qzuDvbHy//5qJvRZagFlsuawi8rKk2KLEd0IISU1OLhd25fP5AAAFBQUAgOPHj6OhoQFLliyJjZFlGYsWLcL27dsBALt374aqqgljvF4vpk2bFhvz0UcfwePxxAosAJg/fz48Hk/CmGnTpsUKLAC49tprEYlEsHv37qTHu3bt2tjyo8fjQUVFxUD8GLKaLArI71xSHJlvR34alxSjmVtP3zwTv75lLpbP9MIqnf1e0cyth16vwv+3cT9aOhTk2yXIIg+e4yCLPIqcFgQVHa99XHPOnDDGGPwhFTWtIZxpjwz6Ztx9JYo8brpoBO65cgJuumgEFViEEDIA+v036SuvvIJLL70UXq8XJ06cAAD87Gc/wxtvvDFgB9cTxhjuv/9+XHbZZZg2bRoAoKGhAQBQWlqaMLa0tDT2XENDAywWC/Lz83sdU1JS0u17lpSUJIzp+n3y8/NhsVhiY7p6+OGH4fP5Yr9qamr6eto5zSLysYJrRL7Zw5Usu2kgjClyYPXVE/CHOxbgni+Mw8j8xK15IpqBkGrgeFMQp/1hRDSzb4sDB5dVQk1zB46e7kjpezHG0B5WUZuk2DIMhqpaH7YePoOqWh+MLOrnIoQQcm79inBYt24d/u3f/g2rV6/Gj3/8Y+i6eRHKy8vDz372M9x4440DepBd3XPPPdi7dy8+/PDDbs91vWOMMXbOu8i6jkk2vj9j4smyDFmWez0OYpJFAbIooMBhQbgzZb4jop13U3myuwWjmVu7T7TijS6ZWwyAL6zBF9Zgk3jk2SywW3i0MwZfuG83OUSLrfawCqdVxME6P3711+M41NAORTdgEXhMKnPhrsWJqeyEEEKyV7+KrJ///Od44YUXsGLFCjz++OOxx+fOnYsHH3xwwA4umXvvvRdvvvkmtm3bhpEjR8YeLysrA2DOMpWXl8ceb2xsjM06lZWVQVEUtLa2JsxmNTY2YuHChbExp0+f7vZ9z5w5k/A6O3fuTHi+tbUVqqp2m+Ei52egtvU5VyP7xaMLcPHoAmw/2ow173yGsGYgvqYLqQZCahgCx5nLjKz/cQ1/PXwGa945AF9IRfQ0OA7YeVzBkcZ2PHPzLCq0CCEkB/RrPeb48eOYPXt2t8dlWUZHR2rLKH3FGMM999yDP/3pT3j//fcxZsyYhOfHjBmDsrIybN68OfaYoijYunVrrICaM2cOJElKGFNfX499+/bFxixYsAA+nw8ff/xxbMzOnTvh8/kSxuzbtw/19fWxMZs2bYIsy5gzZ87AnzwBx3GwW0SUuKwYVWhHidsKhyyec5YSMAuspzcfxrEzAdgsIgodFtgsIo6dCeDpzYex52RrbOz8cQWY7PXAbRVR6rJA7tKbpHduKfT9jVX40dsHsO+Ur0/bChmM4fmt1WjpUGEYZjK7KHDgOQ6GwXCmPYK17x7otnRIS4uEEJJ9+jWTNWbMGFRWVmLUqMTtRd59911MmTJlQA6sq7vvvhuvvfYa3njjDbhcrljvk8fjgc1mA8dxWL16NdasWYMJEyZgwoQJWLNmDex2O1auXBkbe9ttt+GBBx5AYWEhCgoK8OCDD2L69Om4+uqrAQCTJ0/Gddddh9tvvx3PP/88ADPCYenSpZg0aRIAYMmSJZgyZQpWrVqFp556Ci0tLXjwwQdx++23D9s7BQdT/LY+hsEQUMzlxJDSPfPKYAyvfVyDoKKj0GmBojKEVB0Cx6PQaUFzQMFrH9dgZkUeeI5L2FYnqOgocclgjKElqCIY9/rRzK33DzZifLETK2Z7ceWFJbBKQq/Hfvh0AMebg+AAiCIHxgDDAMABosBB1RgOnw6g6pQPMyvyAADbjzbFNnxWdQZJ4DCuxNltw2dCBkIq+14SQlLTryLru9/9Lu6++26Ew2EwxvDxxx/jd7/7HdauXYsXX3xxoI8RgNkHBgCLFy9OePyll17CrbfeCgD43ve+h1AohLvuugutra2YN28eNm3aFMvIAoBnnnkGoiji5ptvRigUwlVXXYWXX345lpEFAK+++iruu+++2F2Iy5cvx7PPPht7XhAEvP3227jrrrtw6aWXwmazYeXKlfjJT36SlnMnPePjtvXRdAMdER0BRYuFjB493YGa5g5IAo+61jAUXQdj5vKcRRDgtIqxRvaJZeaG0l231VEZg9MiYHyJEyPyrPjk89aEzK2jZwL4yabDeH7bMVw3tQw3zkrM3Ip3oM4PTWfgOUDVGOLnoziY29somoFPjrdgZkUeth9twiMbqhCIaMi3W2AReCi6gQP17XhkQxXW3DQ9aaFFF0rSH1TQEzKw+p2T9cILL+BHP/pR7C65ESNG4LHHHsNtt902oAeYq4ZrGOlgie6j+JeDjfjBG/sRVjQwmEUZx6FzBomBA2C1iHh06WRcPLog4TV62lbnbObWKVTW+Lp9bw7ANK8HX5lXgUvGFCRsxfOnPafw7PtHz3n891w5Hl+5uALf/b97cfh0O0rdMiKqmcou8jxkicNpv4LJ5S789uuXJBRQdKEk/dFTQd8aVOGUhR4LekKGm75cv/u9QfTtt9+O22+/HU1NTTAMI2nsASFDRRJ45NktGF/shKLpMBggiRzMEsicyeIEQNMYIpoOl1Xq9ho8x8Vmt+JFM7eumFiM400deKOyDu/ta4DSGc/AAFTV+VC1wYcipwU3z63AdVPL4LSKuLDM1e31krmwzIWqWj+Onm6HwHP4vCkIRTdis3CyKMBjk1DdGMD+Oj+mj/QA6PlCea6Zr+GGZvoSGQbDuq3VCEQ0lLmtsV5HKy+gzM2jwR/Buq3VmD+2cFj/nAjpq34VWcePH4emaZgwYQKKis7+hX3kyBFIkoTRo0cP1PERMgA4cBwDh7MXBxb7v7h/9sOYIgcWTSzCJ583oy2oQtUZtLim9KaAgue2VOM3Hx7H1VNKMWOkByIHaL18T5EzCzxfWEFQNWKzcALPwYwO4xBWdURUHXZZREvQXLqkC2VqaKavu/11flQ3BpBvt3S7mYTjOOTZuxf0hJBz69fdhbfeemss/Tzezp07Y/1RhGSCtpAKmyRA4DmoRmIPlGYwCLwZE9EeVvv1+tHG+rBqYGS+DWMK7RiRZ4XDktgAH9YM/O/eeqx55yB6y36PLl+2h1W4rBIinbNwgsB1XvzMC6DAm987rOrIs5mzcH25UA62TLk7MjrTd6DeD4csosQlwyGLsZm+7UebhuS4hlpLUIGqM1h62HFBFnioBosV9ISQ1PRrJmvPnj249NJLuz0+f/583HPPPed9UIQMlAK7BQ5ZgNMqoC2kIqIasdBYm8VccgMDJpS4kGe39Hnj6mhjvdsqmTNlHOCwiHBYRKi6gaYOBYGwllDc9VZfcABEnoPHaoEBljjbFlc3MXa2rywqlQulbwgulJkyc0QzfT0rsFsgCRwU3YCV736HbEQ3IPEcCuyWITg6QrJXv2ayOI5De3t7t8d9Pl8s/Z2QTDDV68a4EicCEQ3MYACis1kMzGDoiGgYV+LErIo8FHRu6+PNs8Fjk1La1scXVsxwU6H7RVkSeJS6ZHjsIv5pzkhMKOne3xXFARA4wICZxTWu2IH2sApZFCDw5qybwRgYM/8ZnYWTJQHVZwIIKXrChTKZobhQZtLMUSbP9A216J+T1qDaLfeNMYa2oIpxJU5M9dJNOoT0Rb+KrMsvvxxr165NKKh0XcfatWtx2WWXDdjBEXK+eJ7DFROK0BHREVINcBwHqXPpLaSakQ9XTChKmLmwSgIKnTIuKDQLLpdVgtDDzIbHaoHEc1D15NNTis5g4XlcdWEpfvm1i/C9ayclLcgYgOhLaJ2N6h6ruY1PvsMCq8iDMQbdMAsta+dej3aJh10SUe8LId8uYXSRI2MulF1njqySAJ7nYJUElLllBCI61m2tHrSlQ1oS6xnPc7hz0Tg4ZQEN/ghCqg7DMDPlGvwROGUBdy4aN+xm+Ag5X/1aLnzyySdxxRVXYNKkSbj88ssBAH/961/h9/vx/vvvD+gBEnI+DINh25Em2C0CdINB0Q3ohnmHnt0iQOB5bDvShNsuG5v0AhLd1oexzm19who6FD1WxIwvdaCi0IFjZwIoclq6NNeb+xWOLXZifKkDHMeh0GmBLAhQda3HYw6pBh76014snVGOErcVp9pC8ObZoGgMOjMgcDwsIofmDiX22gCg6Ab+z0Uj8Mzmw6j3hZHvsEAWeER0A22dt+EP1IUylbvzMq2ZOn6mT+Z5hBUjFolhtfDDfkls4fgirLlpemxp19e5/dTkctewvimAkPPRryJrypQp2Lt3L5599ll8+umnsNls+Od//mfcc889KCgoOPcLEDJIohf6UrcVssgjrMZdWCUeYc1I6UIf3dbHbjFT5jsUDR0RHUFFiyXENwUUyCIfy+GKaAbsFgErL6mIZWW5rBJCas8FVlRI1fHH3bXgAFhEHqoeRr7dApskQNEZmjuUbq8NmEGq3+kMUq1tCaKNmTM357pQ9iXSINUeq0zrEYsuie2tbYOms4RIDIvAQxQ4zBiZN6yXxBaOL8L8sYUUb0HIAOl3TpbX68WaNWsG8lgIGXDxF/posztwtrG3Pxd6nufgskpwWSXoBkOhU8bxpg68/LfP0Ro8e5ei0yJg2VxzA+oogzH0sLKYFINZrEU0IKSEYJF4OCQBY4udsc2tu5p9QT5mVuTFglRLXFZcMroADmvyP+59aUzvSw5XpjVTR5eOP6puhm4wiALXeZcmEFR0M/+sy9LxcMTzHMU0EDJAUu7J2rt3LwzDiP17b78IyRTpbgYXeA77T/nw6s6TCHTZOzGg6Hjt45MJG1AfbOh+w0gyl08owsj8xK15dAaEFAO+sIYylwy3rXuAajIR1UC9P4S6tlC3/R3jG9MFnoPNwkPgORyo93drTI/vsSp1y2AM6FA0MAaUui3deqwyrZk6funY3hmxEf3PwnxMxLYjTbT5NiFkwKQ8kzVr1iw0NDSgpKQEs2bNAsdx3f7iBMxlFbrDkGSK6IX+QH07ytx8Qm9Q9EI/udzV7wu9YTB8f2NVwgxWPH9YwzN/PoyXv34JeI4Dx87uUWgwJN270GDArJF5+MGyKdh9ohX//dGJhDveVJ3h7X0NeHtfA2aM9GDFrBG4bHwhxM5luT0nW8/uu9jZV1NR6IjNfMmSgHy7BKsoYN3WarQGFWg6gy+kJiyfqbqREGkQXXqVRQEnmkOIaHqvCfTRZupHNlShwR9Bnl1KW49YKgZq6ZgQQlKVcpF1/PhxFBcXx/6dkGyQ7gv9p7Vt+Lwp2OuY2tYwalqCGFvsxGSvG6LAQdcZJJEDM1MlAM4sVjTNXMaa7HWD5ziIPIe2oAJ351JfIKIl5GztrfVhb60PhU4Lls0ox6hCO17863EEFd3cOFsw73w8diaApzcfxv3XTMTsC/LR4NNx/EwH9p3yoSNifigSeA4cf7afLKIBn9X5Y0VHS1BBh6IjGDmbQB8dnyyBHsisZup0LB0TQkhvUi6yRo0aBQBQVRWPPfYYHn30UYwdOzZtB0bIQEnnhf7dvfUp7crz0dFmLJ5UgkKHBeOKHTjUEICmMTPJvbNQ0TQzw2tMoR0TS52xNPmgoqPULYMDB4MxtIc1M1hVO7sE2hxQ8PL2E2aYqcCh1C3D0hlVIYscipwWNAUUvPZxDWZW5IHnOJxpj6A9rMIw0FnwMTBmbqAtChxUzYA/rKI5EAEA5NkkhFUdBmOQRD52J2V0H0hVMxIS6ON//pnQTJ1pPWKEkNzX58Z3SZKwYcMGPProo+k4HkLSIl0X+tq2UMrjOI6D0yrh35ZOxerf70FLhwLDYLElN543C5k7Fo0Dz3E43BBITJOHuaehxybBbRXhj2hoC6rQDDM/CzAnxVSdobY1DFnkkWeT4LKK4DmzWb+muQNHT3dgYpkTvs4Ci+MARTsb0mp+n86lS4MlLIXGOgS6JNBHK80kHQTm62VAM3W6l44JIaSrfoWR3nTTTdi4ceMAHwoh6RW90C+aWBzrGepNKvvtjejSnN6T+HELxxfhZ1+ajflji1DgkOG0isizWzD7gnw8/A9TYncM9pYmz3EcXLIIuyzg4esn4euXjobblviZKaIZON0ewbGmDpwJRMCBQWUMvrC5HOa2SQBnNtR3PTODAZoBgOOQ5zBnprruAxmfQK8arLNx3ty+KBNR4CYhZLD1K8Jh/Pjx+I//+A9s374dc+bMgcPhSHj+vvvuG5CDI2SopBprcN20Mrzw13P3KF43rSzh9z3NrAGIZXDl2c6mycti9wu/ojNIHIeReQ5ceWEp5l5QgIc37O3spzq7lGgwoDWoojWowiJwqG0JYc4oBo9NAg/0vmE1Y7Hlv/h9IH2dy5XRWTibxJtFG0NGL7dlUo8YIST39avIevHFF5GXl4fdu3dj9+7dCc9xHEdFFslqfcmCig8C7U3K4+IyuAodJXi5xImDDQEUOaVe0+QBYFK5ExNKXTh2JoBStwxfSIU/rCUs4Sk6w88/OIo/7TmFBeMKz3k8HMfhtC+Mlg4FF5a5YsttowrsiGgsdneeLHI43a5kxXJbpvSIEUJyX7+KrPi7C6MxDl23zSAkG3Xdby/637WVF1Dm5tHgjyTEGsRnYPVmz8nWhODQVGbKJJHHvVdOwCMbqtDSocJtkyByHCK6gfaw2i3xnee4WPp8e1iDx2ZBgd2CtpAKX0hNuCvxVFsI/3d37TmPWxQ4+MIq2oIK/CEVX73kAqx99wBOtyvIs0twWEREdAOn25WsWm7LhB4xQkju61dPFgD8+te/xrRp02C1WmG1WjFt2jS8+OKLA3lshAy6vuy3BwD7TvmTvUw38ePiA0AdsogSlwyHLMZmyuIDQKPLW1O8bkRUHW1hFaqmY2KpCw8umdQt8X32Bfm4/5qJGFvsRFjR0BZWIfEcZlXk4ZtXjMHCcYVIpQTiOn9pBoPLai4XGoxhYpkL9101EeOKHegIq2gMRBCMaJhc7kqY4SOEENLPmaxHH30UzzzzDO69914sWLAAAPDRRx/hO9/5Dj7//HP86Ec/GtCDJGSw9HW/PZvcPQogmei4vs6UAb0vb4UUHe0RFcGIGa0AdN9Wx2O1YHypw5zxuhho8IXx5qd1eOvTOnQoyYODWbd/OWv2BXmYWeFBdWMHIrqBijybGQuRBTNYhBAymPpVZK1btw4vvPACvvKVr8QeW758OWbMmIF7772XiiyStfqapXRRRT5e3XHynK97UYU549SXmbL45ayelrdsFgE2iwDDYW5aHYhoCCk6eI7DxDJn0mMp81jxzSvGYtoIN3741mdQe9lM0WAMVafaMKnUmXC8PMdhQunZ12/qiCDPZoFF7PfkOCGE5Jx+/Y2o6zrmzp3b7fE5c+ZA07TzPihC0iGVSIa+7rd340wvrFLvf4ysEo8bZ3oBpDZTpvYjdTzaMF/useGCAjsKHOcueAoc8jlfV2fAui3HcMf//B3vVtUjoiaf+QqENdS2BtHoDyOi0bZahBAC9HMm62tf+xrWrVuHp59+OuHxX/3qV/jqV786IAdGyEBKNZKhr9vw8DyHco8Vx3vZWqfcY42NH4zUcVHgkWe3IM9uQUTTEQibkRCakSSsgZmftAQeAMeBGQwGgK7159HGAJ7adBjPbzuG66aVYflML7x53TPCAhFzNs1mMfcytFv69VcMIYTkhH7/DfjrX/8amzZtwvz58wEAO3bsQE1NDf75n/8Z999/f2xc10KMkMHWl0gGoG9ZSvvr/PCH1NjGzl3xHOAPqbHlv8FOHZdFAbJTQKETCCk6AhENHRGtc3seFbIoIKJp0BkgcGbRyAFgOoNZF3LQ42b0/GENf9hViz/uqsW8sQW4afYIzBmV3y2iIqToCCk6LCIPj02CUxbpDmRCyLDTryJr3759uOiiiwAA1dXVAIDi4mIUFxdj3759sXH0lyoZav1pNAdSz1Jq6ojAH9bAcYCFAxjHxQI6OcagwyxMmjrM/f/SvWF1b6L9W0VOC4KKjjK3DXYLD7tsQSCsQdENGHHhok6rCDDgW4vGo7K2DZs+a0BYNWfDGIAdx1qw41gLRubbsHymF9dNLTO/Jo6iGTjTHkFzQEF9WxhhXUeRQ6ZcKkLIsNCvIuuDDz4Y6OMgJC3622gOpJal1NahwjAYeJ6DwHfvgWKGAcNgaOs4u9VMdKbsuS1HcbChPbZ8eWGZC3ctHp/2GASO4+CQRVw+oQgTy1z4rL4dI/NtCKsMOjMgcDwsIofmDgVji524YlIRFl9YjH+5fAw27W/Axso61Lae3bOxtjWE57ZU4zcfHsc1U0px4ywvxhafbYrfc7IVr31cg5rmDmgGIAlm0/xgnCsxP2hQ8CohQ4MaJkhO62skQ7xULk75dgk8z5kbPfOsWyp7tADLt0tJvjvXOT76dYN74eN5DnctHm+GnQY1eGwi7LyEkKajuUPpFnbqlEV88aKRWDF7BP5+ohUbK+vwUXVzLOUhrBl4a2893tpbjxkjPVgxawScsoD//MsRBBUdbqsESTC3Cdpf58f3Xt+LH6+YhkWTSgb1vIeTVHsRCSHpQUUWyWnxjeYyzyOsGLGtYKwWvsdG81QvToVOGW6rBF9IhaYzCLy53MYYoBsMHMfBbZVQ6JQTXjvaI2aTBNgsPBgDDjb4k/aI9Ueqsxc9zapNLnfh1gVjMNnrhqonNszzHIe5owswd3QB6n0hvPVpPd6pqoc/fPbO4r21Puyt9UESOAg8h3K3DKmz0JVFDkVOC5oCCv7r/aOYUOJCgdMCq5Ra5hhJTV97EQkhA4+KLJLToo3me2vboOkMin52U2OLwEMUOMwYmZfQaN6Xi9NUrxtTvG7srW2Dqhmxvia+8/UlkccUrzv2+tEesdagAk1n8IXUhONRdSNpj1hf9G/2InFWjefMnqyKAnuvdyiWe2z45hVjccuCUfjg0Bls2HMKRxoDsedVnUHVGY43h+CSReTZJVhFs+HfZZVQ09yBvbU+TCxzQpYE5NkkOGT6a+l89bcXkRAysCg5kOQ0nudwxYQidER0BDvTzaMrh0FFR0dExxUTimIXmq4XJ6skgOc5WCUBZW4ZgYiOdVurYxlb0UZ2qyRANRgYM1+HMQ6qwWCVEhvZ99f58VmdHx0RHRHNAM9xEAUOPMchohnoiOj4rM4f27anr/qyZU/8+IMNfuTZJYzMsyHPLuFgw9nxsiig0CnjgkI7yj02OK1it7sJZUnAddPK8MuvXYRnvzIbV08ugdBlTHtEQ01rCCdbQ/CFVIg8oDIGX9hcqo2oOk77w6hpCaI93D2nbCClkpmWzfq6PRQhJD3oIyPJaYbBsO1IE+wWAbphzmTphjlzZLcIEHge24404bbLxoLnufNqlDcx89Y7jiFZj1VzIAJ/ZwEhiXysh4vjAE4AVM2AP6yiORDp17n2ZfYifnypS4YvrMEfViEJPEpdFpxuV7rNdkTvUGROhg5FR0dEQ1DREzaKn9I5u7dkchl++L/7EVaNhBiIiGbgdHsEfACwijxULbHAUXXzjsS2oAq3VYLLKg7obMtw6FM6n15EQsjAoZksktOiRVOp24oxRQ6MKnBgZL4NowocGFPkQIlbTvhE39dE9mihElZ1SEJnIdD5D0ngEFYTZ75ag2fvRuS6FGEcuFjx0xpU0Vd9nb2IjmcMOHg6gNrWEE77I6htDeHg6QAYYz3OdnAcB6csotRtRUW+DQUOC8Qud1deNDoPF5a74bKas4C2Lsn4BgOCqoFH39iHRzZU4ZPPW2L7LwJmsdXcEcHJliBaOhRoepIw1T7q60xftorvRUxmIEJvCSHnRkUWyWnxRRPHcbBZBLisEmwWARzHdSua+npxii7/BcIaQooBnZnFg86AkGIgENYSlv/yHJ13IzKWdNseg5kFWJ4j2d2IqZ9rMl3PtSWowBdW0dyhmE36QOyXbjA0d5jPn2u2I5owf0GhHWUeKxydwaM8x2HlJRVwyCIimoFil1mQObv0XEUztx56vQq3vvQJXv97LQJxTfQGY2gLKqhpDaGxvf/b9vR1KTib9XV7KEJIelCRRXJaX4umvl6cmgMR+EIq9M5VwoTxMIstX+js8l+RQ4a7s6dJNVis2DIYg2ow8BwHt1VEUQr7Cp7vuXpkMdanxkX/r/NXdB4sqOjw9KER3W4xZ7cuKLCj0CHjkjGFuP+aiRhb7ERY0RBQNNglHrMq8rBilhcj8xO35qltDeEXH1Tj5uc/wjObD+N4U0fsOcYYAmENp1pDaPCFEVL6VmwNpz6laK+gUxbQ4I8gpOowDIaQqqPBH0lr6C0h5CzqySI5ra/b2PQ1kb25Q4F2jpkPrXNWKHo8U7wefFrjg24YCXc7WkUeAs9jitfTrxmGvp5rdVMHonVk1w4yFvs6c9ysUfl9OhaB5+CxS/DYJRS7ZFw2vhiVNW1oCynwWC0YX+oAz5kzen8/0YoNe+qw41jyzK0JJU585ZIKXDa+CGLnLF1Q0RBUNFhEcxat6+xYMsOtT6kv20MRQtKDiiyS0/qzjU1fLk6+UGoX5Oi4+ONpD2vId1ggcOb+gCFFh8sq9nuGoa/nWu8LJXx9spv5uCTj+soqCbB6BFzjKkVA0dAe1hBRzVmoZJlbb35aF5thA4AjjQH8+/8egMcm4YuzR+CGGeUocJizcYpmoNEfRqvAw22T4Lb2vEfiYGzOnWlS3R6KEJIeVGSRnNefT/SpXpzqfeGUjiF+XNfjiRjmxX2K133eMwx9OdcReXYInDlr1dPm1lznuIHA82Ywq9sqIaLpaA9rCIS1WLN7uceGi0fnY9vhRgCAphtQ9LMH5gupeGn753hlxwksmliMFbO9mFLuBsdxZpN8IIK2oAKX1Sy2xC4zVoO9OXemSGV7KEJIelCRRYaFdH2i51Lske46Lp0zDKm+9rIZ5Xj0jSoEIsl7mwwGOGUBy2aUn/cxdSWLAmSngAK7Be0RDe1hFWFVx2sf1yCkGhiRZwWYuWzYFlTRHjnbCK8ZDH852Ii/HGzE+BInbprlxZUXlkCWzJiOtqACX0iFQxbgsUmQRXPWaig35yaEDE9UZJFhoy+f6LcfbTq71YzGIInJN3Auz7P18ipnJRuXzhmGVF6b5zkUu2QEIsEexxS75KRFx0BtOszzHDw2CR6bhN2ft6K2JQi3VTLjLTiY2w55BBQbBpo7VLSH1YRZt6ONATy16TCe33YM108rw/JZXpR7bLEm+UBYg80iwG01k+SpT4kQMpioyCKki+1Hm/CdP1SipUM526ekADuPt+BIYyWeuXlW7GJ80ah8CDzQW4STwJvjMs3+Oj/8IQ08gGSHzwPwh7Ruwav9CfNMpSgLKBoMBjgsAtDZFB+NUxB5HsVOCziOYekML/ad8qGyxhf7Wn9Yw+931eIPu2oxb2wBbpo9AnNG5YPnOIQUHSFFhyTwcFslzB9bSH1KhJBBQUUWIXEMg2Htuwdwpj0CjjMv7uaOfoBmmEnka989gDfuvgw8z2FquRs2SehxyQ0wZ2Omlmden080fZ7jAFngYDDE7nTkOUDTWbf0+f5sOpxqURZtTDe3I+IhgIPBm4WWzhgUjcHC87hsXDG+cekYHG/qwBuVddj0WQPCqlkmRjO3dhxrwch8G26c5cW1U8vglMVYuGlLUIFTFs39EkXalJoQkj6Uk0VInKpTPhxqCIADIPE8eI6LBWtKnQXXoYYAqk6ZsygHGtohizyEHmZBBJ6DLPI40NA+eCeRovj0eZ7jIfI8JMH8J8/x3dLnE7bhcctgDOhQNDAGlLotScM84xPWBZ6DzWL+rA7U+7slrCfLKDP3duQh8RwCEQ2jihwYX+oAAIwpcmD11RPwhzsW4K5F42J3HEYlZG79+WzmFmMM7WEVp1pDqGsLIRDR0rpPIiFk+KIii5A4e2raoBkGBMHsCTIYg94ZGgoOEAQOmmFgT00bADN7ied4jMyzwSELEHkOAgeIPAeHLGBkng08z2dk9lJf0+ejYZ6yKOBEcwifN3egpsX854nmEGSRTwjzjBZlrUEFIUVHgz+MurYwGvxmkGhrUEm62XayAM3T7QrcVhHfuXoiRubb4bJKsU2q39lbj1d2nkBrR/KfcVg18Nan9bjtt7vwnd9XYuvhM7EtesKqjkZ/OLZ1jzoAW/cQQkgULRcSEid6F6BhMGiMgbGzQZ3RZbT4cdElLovIY0yhA2HVgGYYEHkeVolHWDMg6UZGZi9F0+f9YQ2qwSDyiFsa7Z4+3xJU0KHoCHQ2n8fKMgZ0RDSEFA1OqxQrKKNbDnV0LqUKPAeON5ckI5qBiIbYlkPRnq9UG9OtkoBChwW/+OAIXvjrMegM6KyLY0n7HACLyCOinS2cPq314dNaH4qcFiyb6cUN083MrehdiW1BBXaLCJdVhKMPSfckPQbqBgtChgr9LUJInFkX5EHgOaid+UzR4gqdexIazNz4edYFeQC6Zi/JsFkEAGafT6ZnL/U1fT7PJiGoaIhGV8W24mFnC5ugoiHPZs58RXu+GGOQRD62ITbHAZwAqJrRrecLSD2CwjAYfv23z2PHo3dZ8WMw36t7vjARb35ahyONgdhzTQEFL/3tc/z39hO4aFQ+Vs2/AFO9ZuZWNE1e5Hm4rGbB1TVzi6Rff26wICTT0N8chMSZWu6GLHb5Y9Hl4i2LfKyRPZv3iIsee4FDglUSUOq2YkSeDaWdmycXOKSEYzcYgxZfyXBd/gmzWT4aLhrf88Uh8fw5cN16vroe2/SRHiyaWIzpIz1Jf35v7a2HP3T2a5P9hDsiOiwij19+7SI8+5XZuHpyCYS4EFKdMXzyeQvuW1+JW37zCd7d1xBLo9cMA63Rjan9YYTV/m1MTfouvpfPIYsocclwyGLsBov4Xj5CMhkVWYTE6drIzuJ+Ackb2aNLXJPLXQhGNDQGIghGNEwudyW92y6TRI99itcNw2AIdhaJU7zubse+Ny4yATCX/aK/4kXH9bXnK55hMFTV+rD18BlU1foSmumjatqCCZlZyVrXGYAGfxgcZybqXz+tDMUuC6wSj651W21bCE/9v0P40q924Pmt1bHthBhjCEQ01LWFUNMShC+kJj0eMjDib7Ao6yz4eZ6DVRJQ5paT3mBBSKai5UJC4sQ3sjcFIohoBhhj4DizuCpyygiqerdG9mzeIy7VY2ecudQncIjFPXTtVzOYOQ7oe89XVKpBsEnDvZKwiQIkgUdEMxPlAxHNPGaYnzLji2ig98yt6PY9rR0KHLIIt02kGIgBFr3BIt9u6bYPJcdxyLNLsRssaLsgkumoyCIkTkIje1HfGtmzeY+4VI59dkUeRJ6HbhgQOYDxXKyHi2MMOjNzxWZX5AHo3vMVVg0wMHDgzBysLj1fQN+CYKd4XSmd20Wj8lBRYMcnx1tQ3RhAqHPzaZ7nwHFmsajrrFux1VvmltEZA9EeViFLAtxWEU65582pSepaggpUncHSQx+cLPDwGSwj79glpCtaLiQkTnxWEwDYLAJcVqmzoR1oC6oYV+LMyEb2dJs+woNJZU4YDFAMQNUZNINB1RkUw5zFmlTmxPQRZrEW7fkSeKBD0aEzBoOZfVAdig6BR2LPV1wQrMGYWbzBLOIMxmJBsNFlovZwaj1S0XEdES2W6yUKZvYZB/Ofomh2jQmcuafjyPzEbZB6ytwCgIiq40x7BCeag52zn9S7dT6iH3SUHuI0Irq5oXom3rFLSFdZVWRt27YNy5Ytg9frBcdx2LhxY8LzjDE89thj8Hq9sNlsWLx4Mfbv358wJhKJ4N5770VRUREcDgeWL1+O2trahDGtra1YtWoVPB4PPB4PVq1ahba2toQxJ0+exLJly+BwOFBUVIT77rsPikKfrLJdNjeypxvPc1g+09utlyn2PAfz+bgB++t88IW6N7YDgC+kYn/d2T6vaBAsABiG2USvGmazvdF5vY0PgnXbU5uIj46LNuILghm+Gl0yBKKN+Oas1rQRHrz89YvxxP+ZjgVjCxMa6nvL3DIYgz90NuS0PaxSyGk/JAuljYresTtcP+iQ7JNVRVZHRwdmzpyJZ599NunzTz75JJ5++mk8++yz+OSTT1BWVoZrrrkG7e1nm5RXr16NDRs2YP369fjwww8RCASwdOlS6PrZT58rV65EZWUl3nvvPbz33nuorKzEqlWrYs/ruo4bbrgBHR0d+PDDD7F+/Xq8/vrreOCBB9J38mTQZHMjezoZBsO2I01wWkXYLTyEzj4sgQPsFh5Oq4htR5piM02aZuAXW6rRU3+ywYBfbKmG1pljtaemDapuJGRwRQsc1jlejQuCbWqPdH3JpKLjujbic3Fp/uicZeN5wG0zg04vHl2AH980Df9928VYMqUUdkti79WntT788K3P8JUXd+KVj06gJS4MNdw5u3WyJYhmmt3qk/gPOvW+MFqDCvwhFa1BBfW+8LD+oEOyT1b1ZF1//fW4/vrrkz7HGMPPfvYzfP/738cXv/hFAMBvf/tblJaW4rXXXsMdd9wBn8+HX//613jllVdw9dVXAwD+53/+BxUVFfjzn/+Ma6+9FgcOHMB7772HHTt2YN68eQCAF154AQsWLMChQ4cwadIkbNq0CZ999hlqamrg9XoBAD/96U9x66234sc//jHc7u6fsCKRCCKRsxcFv98/oD8bMrCyuZE9XaINyU5ZQltIQayhqbNYccqJDcm9RSxEiyh/SMVbe+tx00UjwBjrVlzFfy1D9G5Gc9SO6uaUjntHdTO+dPEFvTfiM/POUZcsYITHBo7jwBjDnpOteO3jGtQ0d8AicBBkARpDrK8LAJoDCl7a/jle2XECiyYWY8VsL6aUm5lbusHgC6nwhVRYRB4uqwSnLPa4DRMxLRxfhK/OuwC/2FKN+rYQDJgzAi6bhK/Ou2DYftAh2SerZrJ6c/z4cTQ0NGDJkiWxx2RZxqJFi7B9+3YAwO7du6GqasIYr9eLadOmxcZ89NFH8Hg8sQILAObPnw+Px5MwZtq0abECCwCuvfZaRCIR7N69O+nxrV27Nrb86PF4UFFRMXAnT9Iilaym85FKTEEmiSa+N/hCCEb0zu2GAN1gCEbMxzuUs3dexkcsxM9Ixf/eYOY4AHBbz0Y5sCS/oqLj/CEtpeOOjos24tskEVaRN3O/OrdMsoo8bJKIqSPycNmEIlTk23D4dDue3nwY1Y3t4HkOdouZAm/hORQ5LZh9QR7EuP8mNIPhLwcbce/vKvGt//l7QuYWACiaeWfiyZYgGju3FiLJbT/ahFd3noTIc/Dm2VCRb4M3zwaR5/DqzpOUk0WyRlbNZPWmoaEBAFBaWprweGlpKU6cOBEbY7FYkJ+f321M9OsbGhpQUlLS7fVLSkoSxnT9Pvn5+bBYLLExXT388MO4//77Y7/3+/1UaA1j2Zhm3dfE9/iIha537SXoHFfgtMRiIHrCc+Y4AJhe4cFfDjWe87inVyQ24j+yoQrtYTV2lyDPmftRuqxx4asG8OrOk/CHVOgG69xE2py8kwQemmFuXv272+fhnX0NeOvTOjQFzi4XHmkM4Kn/dwjPb63G9dPKsHyWF+Ues5k+mrsViGiQBB5OmVLl43XNyYq/Y9PDGBr8EazbWo35YwuH9cwyyQ4596e66y3U0d6L3nQdk2x8f8bEk2UZbrc74RcZnrI1zbqvie/TRqT233h0XIHdkpDGnozAnb2r7K4rxkE4xzVW4MxxUdFlKN0AzgQiaAooOBOIQDeQsAxl7rvoQ1gzEO7sGYsGr4ZVAxFNQ3VjO5oDKlbNH4XX/mUeHls2BbMqEmMwoplbX3vxY3x/wz588nlL7OcDAKpupsqfbAmi3kfN8kDfcrIIyXQ5U2SVlZUBQLeZpMbGxtisU1lZGRRFQWtra69jTp8+3e31z5w5kzCm6/dpbW2FqqrdZrgIiZfNadZ9TXxP9Rb7+HEcx4FH8p4sHokfbiwWAV++uPfZ4C9fXAFLXMP69qNNePHD4whE1Fg8BAcgEFHx4ofHYwVuU0cE/rDWWTQCmmFOuOksGqYKBCIaWkPm7JUo8LhiYjGevnkWfn3LXCyf6YVVOvvXKwPw0bFmPPR6FW596RO8/vdaBCKJy50h5WwURGP78F1OTCUnS6WcLJIlcqbIGjNmDMrKyrB58+bYY4qiYOvWrVi4cCEAYM6cOZAkKWFMfX099u3bFxuzYMEC+Hw+fPzxx7ExO3fuhM/nSxizb98+1NfXx8Zs2rQJsixjzpw5aT1Pkt2y+VN6NPFd5M1lu/iJLL7zcY47m/heHZcl1ZvouLaQCpskQBS4zmU5DhLPQer8vShwsFkEtMU1098wI7GYiWeVeNww42zfZHwOF4NZGFkEHqLAgwEJOVxtHSo0nSXdqidKM8xNrru+j2OKHFh99QT84Y4FuPsL4/qUuQWYM4aBsIZ6n7mNT2uHArWHzKhcRDlZJJdkVU9WIBDA0aNHY78/fvw4KisrUVBQgAsuuACrV6/GmjVrMGHCBEyYMAFr1qyB3W7HypUrAQAejwe33XYbHnjgARQWFqKgoAAPPvggpk+fHrvbcPLkybjuuutw++234/nnnwcAfPOb38TSpUsxadIkAMCSJUswZcoUrFq1Ck899RRaWlrw4IMP4vbbb6dlQNKrbE6zjk98l3iz4or2KaGziTw+8f1UazCl142OK7Bb4JAFOK0CfCHV3NIIZhFntwhw2ySAnZ35ihZNEbWHi7FqYO27B/DG3ZeB57lYDhcHQOL5WHEU/b2qG7EcLpdV6LXAivLmWXFBgR2+kAp/SE1YCnTKIv7PRSNx0+wR2H2iFRv31GHHsebY60Yzt976tB4zR3qwYvYIXDquMKE3K7qc2BpUYJUEOK0inBYxp3uRojlZB+rbUebmE4rYaE7W5HIX5WSRrJBVRdauXbvwhS98Ifb7aCP5Lbfcgpdffhnf+973EAqFcNddd6G1tRXz5s3Dpk2b4HKd3X7jmWeegSiKuPnmmxEKhXDVVVfh5ZdfhiCcXVJ49dVXcd9998XuQly+fHlCNpcgCHj77bdx11134dJLL4XNZsPKlSvxk5/8JN0/ApLl4j+lW/nue95l8qf0aOL7/jo/NMYgchx4LhqBYM76xCe+n/anlmMVHRd/cR1VYEdEY7EtjWSRw+l2JeHiWnXKh8/q/T0WQwzAZ/V+VJ3yYWZFHvbUtJmvJ3BJZxEFgYOmmzlc/mDyANWuPqtrx9VTylDgsCDPJqE9rMEXUqEZZwu/aObWxaMLUO8L4c3KOry7rwH+8Nnlwk9rffi01ocipwXLZnpxw/RyFDgS/xsIq7qZv8UiqG0NIaLpKHNbMW3EwN/5OpTib1Bo8EeQZ5cgCzwiuoG2oEo5WSSrcGy4d1kOEb/fD4/HA5/PR7Nfw4hhMNzy0sedn9Llbp/SG/wRTC534bdfvyQjLyLxewsaBmJ7EfI8UOCwJOwt+LPNh/Czvxw9xysCq68aj9XXTIq9/iMbqhCI6EkvrvFhsL/+8Bj+438PnPP1H106GbddNha//dvn+OH/7ofIcxD47jOJumFAMxh+sHQq2sIKntl85Jyv/Z1rJuDbV01MeIwxBn9Ygy+YWGzFi6g63j90Bhv3nMKRxkC350We65a5BcDM7dpZg2NNgdiM6IRSJ+5ePA5XTOp+V3Q2S7gD12CQ+My/A7crw2CUtZeD+nL9zqqZLEKyXbZ/Sl84vgj/ctkYPPvBUbSHtM5ZJAa7RcS/XDYm4eLHdWtfTy5+XDRtP3px9XVeXCeXu7pdXPefSq1vLTpu1gV5ZvyCboDnWaxpn+vsNdMNBkngMeuCPHyQQjQE0L3pHzBnxTw2KRZ8mqzYkiUB108rw3VTS7G/zo//2XESu0+0Qu98wWjm1l8ONmJ8iRM3zfKi0GnBU//vEHwhNXbsIQ7YfaIF3/lDO36wbCoWX1iSM8uJ2R4InI0xLWTgUZFFyCDrSyGRaeJDIkfk28Bz5jY1QUXHqztPYqrXEzt+py21v166jkv14mrroeG9q+i46SM8mFjqxL5TfoST9HHxACaWmsudH3+eWpq8Q+6+5BsVX2y1R8xiq2sDe2VNWyxR3mkVoOkMim5AjYvKONoYwFObDscyxDgAQufNBwzmPo8tHSqeff8IpnjdaOZ5OCxC5/ZH2f1XfDQQONucnZHVYjdzMAYcqPfjkQ1VPW7PpWkG3tpbj1NtQYzIs2PZjHKIYs7cnzYsZfefQEKyVDZ+So+Pnyjv3HomymPrHhIpcqldHFId11WJy9qncdENrvef6rmPK7rB9f5Tvh5GJEplHMdxcFsluK0SAhENbUEFimZgz8lWPL35MIKKbj4vcFB1Bl9IAc9xyHdYUH3m7J2H0VSPaIRE7PU7/3m8OYjDpwO4sMwVCzuN9rNtO9yE0+1hunAPguifk9agAk1nsZlHjgMsgnmDRbIw1Re2VeMXW6rRHlJj2wj98H/34+7F43B7XNYbyS5UZBEyRLLtU3pf4iemj/RgeophpF3HpbrMMjXF14+OMwyGNz+tMyMmklRZjAPe/LQOt102Fo0pNu2nOi7KKYtwyiLaQyrWf1KDoKKjyGmJLZnKIodil4ymgII8uwUv/POFePPTOrxb1QCth+y06KOqznCgzo8Ly87e6PPazhN4dedJM7EeZkH22Jv7cM+V4+nCnSZmkK0fHREz50zgOXC8ubwb0QxENOCzOn/szwlgFlhPvHcIusEgChzEzllLX1DFE+8dAgB6v7IUfZwhhKSkryGRfc3JAvqWht8e1s/Z9cV1jgMQi3DgYRYzsmjmZMmiOdvDA7EIB48ttbs7Ux3X1efNQZxqDaHAYQHfZSaPAweXVUJNcwd0HfjO1RPx9YWjUnrdM+3h2L//4ZMa/GrbMbRHtFghxgD4whrWvnMQz31w7sZ+0nfNgQj8ncn9osCB5zjz5hCO61w2ZPCHVTQHzAJd0wz8Yks1dIPBInIQeR48x0PkeVhEc5PxX2yphqYNn6y0XEJFFiEkJX0Nify0NrUlt+i4+OXIUrcMxoAOxdwzsNRt6ZaGn2+XIJxjeVXgOeTbzb0UoxEOgsCB53jwHAeBNy9+PMebEQ6GGeFw5eTU7tRLdVxX0YLVKgqwiDwkITEPyiJwUBmDL2wWrPkOOaXXXb+rFt/5fSU+ONiI324/jp4uywaAZ98/imONAbR0mMuXZGC0BlUYBgPPc91u/jDvxOVgGAytnTEhb+2tR3tI7SzIEi/JPMdDFDi0h1S8tbcegynbNrDPVLRcSAhJSV9DIh1Sz03h8aLjosuRsijgRLOZAxXtZZFFAR5b4nJkvsOCc2x1CI4D8jvzprj46ZxkXxfd+JoBN80agYf/tBe91R4ib47rj655aTzPwdJ58dUMBkUzIHEcPFbz2DvCqeV2AWczt84lqBrY9NlpLJlairagAlkSYsuZ5ypeSc/yHJJZSDHWbT9bxsy9PXmeQ57DLP5PtQVhABB7+JHzHKB3jhssdGfkwKGZLEJISqLxE05ZQIM/jLagAl9IQVtQQYM/3C1+4rrpZSkt51033dx3tCWooEPR0egPI6zqseUVnuMQVs3HOxQ9MQ3/XB+u456PRjjoBkPXDXMYWEKEA5B8E/iEYz9XhdeLaMHaGkzcEJrv3EaoI6JhdJED40sdAIDGQN96v1L1Wf3ZGIyIqqM5EMHJliAa/WEEFW3Yb1bdH0UOGW6rCJ7joBosVmwZjEE1GHiOg9sqoqhzdnJEnh08zt7Y0FX0jtIRefZBOf5s3cA+U1GRRQhJ2cLxRfjqvAugGQx1bSHUtoZQ1xaCZjB8dd4FCZ9yZ47Mw+ii3i8Mo4vsmDkyDwCQZ5MQVnUYPfSyGIwhrOrIs5kzAK0dCoxzVFkGGFo7zKIsGuHAYO45aDCz2DIYg9q5hU80wuGNT+sSYhSSUXWGNz6t6/0H1oPEgjWCkKrDMBhCqo4GfwQuq4jVV0/EyHw7bBYBLR2pbbM0d1Qerp5cglQnomxJ7jJkjCEQ0dDgC8c2q86WgisTlrimet2Y4vXAJomwijyMzi2nDMZgFXnYJBFTvJ7YjO+yGeVw2aTOzcgTp04NZkDTGVw2CctmlKf92LN5A/tMRUUWISRl0ZwsgQO8eTZU5NvhzbNB4Di8uvNkwqdcnufw4xXTkW+Xus1ocTB7qn68YnrCbewsfkkvHuvyPMzeF7CzmVFdN6wWePPror0vPM/h4esno9glg+c56IYBVTOgGwZ4nkOJS8bD108Gz3OoPNmW0s8j1XHJRPPSJpe7EIxoaAxEEIxomFzuiuUoWSUB5R5b8tTTJGSBwyP/MBk/unFqSuMnl7t6fT66WXWDL4yTLUGcaY8gpOgpvfZg2360Cbe89DHueGUXHvzDp7jjlV245aWPB33mJVpAFzgkWCUBpW4rRuTZUNpZtBQ4pIQZX1HkcfficRB4DkrnVlIGM6AZBhSNQeA53L143KDEbmTzBvZdZULBDVBPFiEkRX3NyQLMQuIXKy/CLz44iv11fii6AYvAY6rXjbu/MD5h5qstpMImCQip5rKKyJsFk5kLZV5sbBYBbSGzaIrvfUm6YXWX3pfo8Txz8yw8t6UahxraY8czqcyFuxaf7Tex9RIyGi/VcT1JNS+tLaT18AqJouOsltQuyD/83wOY99lp3DR7BOaMygffyxKobjC0h1W0h1UIPAdHZ/+WNcXeu3Tqb/hnunQNHI50bqo+xetO2tcUjWeI5mTpMP/b99ilQc3JyuYN7ONlUk8ZFVmEkJT0NScrKtVCosBugUMW4LQK8IVURDQjVjTZJB5umwQwxO5ejPa++MMaNGY2Dp/dsBrdel/6cjzXTi3Fr7YdO+fP5NqppX39MXaTSl6aM8Vizm4xx/3mr5+nNJ4B2HGsBTuOtWBkvg03zvLi2qllcMq9Xxp0g8EfUuEPqRB5Hg5ZgGOICq7+hn+mW18Dh2+/Yhy+vnDMkCa+Z/MG9lHxBXe+3QKLwEPRjVhP2WAX3FRkEUJScj6fclMpJOLvXhxVYEekc+kkmlp+ul1JuHsx2vvyaY0PumFA0c8WZVaRh8DzCb0vfTkeM6uo52ZkwCzoxCQbTaeDN98O4Nxb/ZS4zXT7lo6+N8rXtobwiw+q8esPj+OayaW4cZYXY4ud5/w6zTDgCxnwdRZcTqs5w2UZpOKgP+Gfg6WvgcOiyOOmi/p3x+pA6OsdxJmma09Z9PitvIAyN590tj3dqCeLEJKSvuZk9VV8M/jpdgXgAIdFBDjgdLvS7e7Fvva+9EVbSIVLlnpsIOc5wGWVYkuX6ZZqHtf8cYUAAF5I7ZyLnBLmjy1I6JkLq+b+ef/y37vxnd9XYuvhM9B6eM+70gwDbUEFta1B1LYGzc2xU/za/upr+Cfp2bluyMj0DewzsaeMZrIIISkZjE+5fd08u6+9L6mKX7ps7YggojEYzCyuZJFDnkNOWLpMtzKXDXaJRzDJxtZRdonHlDIPHLKY8gybUxax5qbpqPeF8GZlHd7d1wB/+Gz/VzRzq8hpwbIZXtwwoxwFjtTOWdEMNGsRNHcANou5nCjzPN7Z1zCgy2F9Df8kvcvmDewzsaeMiixCSEqin3If2VCFBn8EeXYJssAjohtoC6oD9im3r70s6dhsO76gHFPkPOfSZbpN9boxZ3QBPj7WgkiSmSFZ4DFndAFmX5BnnneKN1JpnTEV5R4b7lg0DrcuHI2/HGzE7z+pQU1rKDauKaDgpe2f45UdJ7BoYjFWzPZiSrk75aywkKLjt3/7vNs+ij98az/u/kLPjd2Gwc75vvY1/JOcWzZuYA9kZk8ZFVmEkJRl6qfcgd5sO76gPN2uIM8uwWEREdGNpEuX6RY9nsOn23HGHwHD2eB6DkB+l6XRGSM9+Lwl1MsrmiaWJvZcfVbvxweHziCsaHDJAhTdTJ+P1myawfCXg434y8FGTChxYsUsL668sATyORreo/soxpeHDOay7Np3DoIxhm8uGp/wNaneIRZ/A0Syu1J7ugGC9C7bNrAHMrOnjIosQkifpPtTbqbcfj1YBWUqszXxhM54giguycTVLZeOwZt7G875vW+6aGTs3/ecbMXTmw8jqOhwWyV47BxUnaEtpMRSx+OXEo80BvDUpsN4ftsxXD+tDMtnec1Mry50neG/P/q8130Uf/bnI1g6w4s8hwU2ScBH1c0p3yF2PjdAkOyRyp+TwZpt7wuOZUOMbw7y+/3weDzw+Xxwu+kPPyFAz7dft3b+BTnYt18DfS+C+iLVgtIwGG556WMcqPej1CX3uHz5269fEutBWvj4X9Dg77nZu8ghYf0dC8Bz5lLbQ69X4diZAIqcloTeJgaGpoCCMUUOLJ1Rjjcq65LujcgBmD+2ECtmexMytzbtP43H3zt4zp/Fv153IZZMLQUH4F//VIWjje3d8tgYM/PY4s81+nN8ZEMV2sMabBYBAsdBZwwhRYfLKg7Jfzdk4PT1g1fC+M4PRwP5Qa0v12+aySKEZIRMvP0aSN+ySV/yfOLvmuJ5HjYLAJxdouuaUcbzHJ6+eRa++couBCLdE9odFh5rvjgjtpfj0dMdqGnugNsqJW0ed1kl1LYE4fXY8cyXZuHYmQDeqKzD5s9OI9y5izYD8NGxZnx0rDkhcyt+f8TefFbvx5KppTjUEMDxMwE4ZQmqzsBzZk8Vz3E95rGl6wYIMvT6k3uVST1lVGQRQjJCf8NOs1FfC8r+3DW1cHwRfrVqLp59/wj2nvJD1Q1IAo8ZI9y458oJWDi+yIw3CGloj7RCNRjcPUQ/WAQO7YzBFzZff2yxE9+5ZiJuv3ws/t9nDXijsg61cY3y8ZlbI5IsISYT3UfRF1Zix8IYg87M8FOO4yDwHCw8l/QOsUy6sJKBcT4fvDKlp4yKLEJIRsjE26/Tpa8FZX/vmjpX4cFxHDx2CZNKXZBFHqrOIIvdixJFZ5A4Dh5r4us7rSL+z0UjcdPsEdh9ohUb9pzCzmMtsR6xsGqguqkjpZ/J5ZOKAQAeqwUSz3U7FsYYNJ0hrBngYfZbRaMbojLlwkoGRi588KIiixCSETLx9ut06WtBeT53TaVSeEwb4cGEUhc+q/PDZpFhMHZ2U26YexaOLXZifKkj6dfzHIeLRxfg4tEFPWZu9WZkvhUXlpmbVY8vdaCi0NFjf1j0WAqdFpxoCcLRmcFltwgpR0qQ7JALH7wo8Z0QkhGihURr0EzvjhctJMaVOHPiLrG+puenO4k7+vouq4imgNK5PAeENQNNAQV2i4CVl1T0uoF0VDRz6/ffnI/vXjsJE0p635rHbhGw+qoJsdfmOQ4rL6mA3SKgKaAgrBkwGEt6LIwxBCIaTvvDONEcxJn2CIKK1u2/H5Kd0r3LxGCgIosQkhGyfUuPvuhPQRlt7p5c7kIwoqExEEEwomFyuWtA7p6Lf/2QoqMlqELVdEwsdeH+ayZh9gX5fXo9WRJw/bQy/PJrF+HZr8zGRRfkIdk7F1R0PL/tON7d14CIajbpz74gH/dfMxFji50IKxqagwrCioaxxU7cf83EpMdiMHOWq8EXxskWs+AKKd2b/kn2yIUPXhThMEQowoGQ5NJ9+3WmOHvXlJ40z6enwimdkRI9vb7BGNpCKtrD5zdL1ByI4JWPTmLLkUb4Q92XEt1WMSFzy2DmnY++sAKP1YLxpY6UZtPiCTwHu0WEQxZgk2hJMdv0989JOvXl+k1F1hChIouQnqW7kMgU2VZQqrqB1qCCQIq9Vj3RdAPbq5uxsfIUKmtSz9w6XzzHwS4LcMoiFVxZJNP+nFCRlQWoyCKEANlZUCqagbaggkDk/IotADje1IE3Kuuw6bMGhJNsgD0y34YVs7xYMrUMTnng7tWKFlwOCzXNZ4NM+nNCRVYWoCKLEJLtIpqOtqCKjgEotgIRDZv2N2Bjl8ytKKvE45oppVgxawTGFCW/y7G/eI6DQxbNGS5L7/swEkJFVhagIosQkisGstgyGMPuE63YuKcOO441d9uXEQBmVXhw46wRuHRcIcQebu/vL5Hn4bSaBZdFpHvDSHdUZGUBKrIIIbkmoulo7VARVM6/2AKAel8Ib31aj3eq6pNmbhU5LVg204sbppejwDHwt/FbRB4uWYJDFga8mCPZi4qsLEBFFiEkV4VVHa1BZcAiFCKqjvcPncHGPadwpDHQ7XmR57B4UjFunOXFlHJ3WvqrbBazYd5hETO+Z46kFxVZWYCKLEJIrgspZrEVVgem2GKM4UB9OzZWnsKWQ2egGd0vXxNKnFgxewSunFQMWRr4/iqO4+CwCHBa6Q7F4YqKrCxARRYhZLgIKhpag2osbHQgtHQoeLuqHm99WoemQPdtVdxWEf8wvRzLZ3pR5rEO2PeNJ/BnG+ataSjoSGaiIisLUJFFCBlu0lFsDVXmVleSwMMpi3BaRUjUv5XTqMjKAlRkEUKGq46IhtagAkVLviddfx1v6sDGylPY/NnpHjO3bpzlxbUDnLnVlSyZ/VtOWYRA/Vs5h4qsLEBFFiFkuEvHzBYwtJlb8TiOg00y+7ccFHiaM6jIygJUZBFCiCldxdZQZ27FiybMu2SJAk+zHBVZWYCKLEIISZSuYgsY+syteCLPwyGbM1yySAVXtqEiKwtQkUUIIcmls9iKZm5t2HMKR4cocyseBZ5mHyqysgAVWYQQ0ruBztmKxxjDZ/V+bNxTh62HhyZzqyubRTAjISjwNKNRkZUFqMgihJDUpLPYAjIjcyteNPDUIYuwU8N8xqEiKwtQkUUIIX2T7mJL0w38rboZG/ecwqe1yTO3FowrxI2z0pu5FY8CTzMPFVlZgIosQgjpn3QXW0Bc5tb+0wgnyfMamW/DilleLElz5lY8CjzNDFRkZQEqsggh5PwMRrEVCGv4f5814I1eMreWTCnDjbO8ac3c6v59hdgMFwWeDi4qsrIAFVmEEDIwQoqOtpCCkJK+YivVzK0Vs0bg0vFFg1b4xAee2iWBGuYHARVZWYCKLEIIGVhh1ZzZSmexBZiZW29W1uHdfQ1JM7eKnTKWziwflMyteNHAU6cswm4ZnCXM4YiKrCxARRYhhKTHYBVbqWZurZg1ApPLXYN6l6DAc3DKIhzUMD/gqMjKAlRkEUJIeg1WscUYw/46P96o7Dlza2KpEzfOGrzMrXjUMD+wqMgaRM899xyeeuop1NfXY+rUqfjZz36Gyy+//JxfR0UWIYQMjrCqo6UjvQ3yUZmWudWVNbZhNTXM9xcVWYPk97//PVatWoXnnnsOl156KZ5//nm8+OKL+Oyzz3DBBRf0+rVUZBFCyOAKKTpagkpatuvpStMNfHi0GW9UZk7mVsL35zjYOwNPHRR42idUZA2SefPm4aKLLsK6detij02ePBkrVqzA2rVre/1aKrIIIWRoBBUNLR0KlCT5V+mQiZlb8eIb5m0SFVznQkXWIFAUBXa7HX/84x9x0003xR7/9re/jcrKSmzdujVhfCQSQSQSif3e7/ejoqKCiixCCBkigYiG1g4Fqj44xVYgrOG9/Q1489PMytyKRwnz59aXIovu8eynpqYm6LqO0tLShMdLS0vR0NDQbfzatWvxwx/+cLAOjxBCyDk4O5fK2iMafEE17cWW0yriH+eMxBcvGoHdJ1qxYc8p7DzWEsvcCqsG3vy0Dm9+WjckmVsAoBsM/pAKf0iFyPNwyALdoXgeqMg6T12nVRljSadaH374Ydx///2x30dnsgghhAwdjuPgtkpwWyW0h1W0DUKxxXMcLh5dgItHF6CuLYS3Pq3DO/sa0B6XuVVZ40NljW/IMrcAQDMM+EIGfCEVksCb/VuyAFmkgitVVGT1U1FREQRB6DZr1djY2G12CwBkWYYsy4N1eIQQQvrIZZXgskroiGhoDQ5Oz5Y3z4Y7Fo3DLQtH44ODjdiwpw5Hz5zN3DoTiOClv32OVz46MWSZWwCg6gbaggragmcjIRyyCItIkRC9oZ6s8zBv3jzMmTMHzz33XOyxKVOm4MYbb6TGd0IIyXKD3bMFZH7mVlcW8WzBNVwyuKjxfZBEIxx++ctfYsGCBfjVr36FF154Afv378eoUaN6/VoqsgghJDsM1jJiVy0dCt7eW48399ahOQMzt7qSJQFOi7mkKOZwwUVF1iB67rnn8OSTT6K+vh7Tpk3DM888gyuuuOKcX0dFFiGEZA/GGNojGto6VGjG4BZbmZ65lYxVEmJ3KeZa6CkVWVmAiixCCMk+jDH4Qip8IRV6kqW8dDt2JoA3Kuuw+bPkmVsV+TbcOISZW8nYOkNPnRYRfA4UXFRkZQEqsgghJHsZBkNbZ9SBMQSX0WzI3OqK4zjYJMGMhcjigouKrCxARRYhhGQ/3TBntoaq2DIYS5q5FW9WRR5WzPIOeuZWbziOg6NzhsueZdv6UJGVBajIIoSQ3KEbDG1BBf6whqG6rNa1hfDmp3V4t0vmVlSxU8aymeW4YUY58u2Dm7nVG547mzJvs2R+BhcVWVmAiixCCMk9Qz2zBQBhVU+auRUlCRwWTRy6zK3eRFPmnVYxY0NPqcjKAlRkEUJI7sqEYiuaubWxsg7besncWjFrBL6QAZlbXUVDT53WzMrgoiIrC1CRRQghuc+IFlvhobkbMaqlQ8H/7q3DW3vrsyJzqytZEmI9XENdcFGRlQWoyCKEkOHDMBj84aGLfohKJXNr/thCrJidOZlbXUVDT+2yMCQFFxVZWYCKLEIIGX4Mg6E9rKEtpAxpsQWYmVsbK+vw5x4yt0bm27AiwzK3uhqKlHkqsrIAFVmEEDJ8McbgD2nwhQY/Qb6rVDK3rplSihWzRmRM5lYyg1VwUZGVBajIIoQQwhiDP6zBFxz6Yiu1zC0Pbpw1ApeOK8zo/QmtkgC3TUrLDBwVWVmAiixCCCFR0b0RfUOwEXUy58rcKnJasGymFzdML0eBI3Myt+LZLALKPbYBf10qsrIAFVmEEEK6YowhENHQliHFVljV8f7BRmzsIXNL5DksnlSMG2d5MaXcnVGZW1RkDWNUZBFCCOlNIKKhtUPJiGIrPnNr6+EzSZv2J5Q4sWL2CFyZIZlbVGQNY1RkEUIISYU5s6VASXIH4FDIlswtKrKGMSqyCCGE9EUmzWwBZzO3Nlaewt4MzNyiImsYoyKLEEJIX0Ub5Ns6hv5uxHjVZwJ4s7IOmzMoc4uKrGGMiixCCCH9lUnRD/GimVtvVNbhVNvQZm5RkTWMUZFFCCHkfGVSqGk8gzHs+rwVGyuHLnOLiqxhjIosQgghAyVTiy1g6DK3qMgaxqjIIoQQMtAydRkRMDO3PjjYiA3nyNxaMWsEJpe7zjtzi4qsYYyKLEIIIemSaQny8QYrc4uKrGGMiixCCCGDoT2sZkyCfFctHQre3luPN/fWDXjmFhVZwxgVWYQQQgZTpoWaxotmbm3YcwpVp5Jnbi0YV4gbZ6WeuUVF1jBGRRYhhJCh0BHR0BZSEVH1oT6UpI6dCeCNAcjcoiJrGKMiixBCyFAKKuZG1OEMLbZSydxaMqUMN87yJs3coiJrGKMiixBCSCYIKTraQgpCSmYWW6lmbq2YNQKXji+CwJtLiVRkDWNUZBFCCMkkYVVHW1BFUOmeZZUpzpW5VeyUsXRmOW6YXo4R+TYqsoYrKrIIIYRkomwotlLJ3LpqcgnuWDQOsyvyzjtzKx4VWVmAiixCCCGZLBuKrfjMrW2Hz0BLkrn1jUvH4N+WTRmw79mX6/fgbIVNCCGEkKxilQSUeQSEVR2twczs2eI4DtNGeDBthActi8clzdy6YmLRkB0fFVmEEEII6ZFVMhvIw6qOlg4lY+9GLHBYsGrBKHzlkgp8eLQZb35ah7aggismFA/ZMVGRRQghhJBzskoCvHk2hBQdLUElY3O2RIHH4knFuH56GRyyCJ4fuH6sPh/LkH1nQgghhGQdm0XACIsNQUVDazBzQ00BwG2VhvT7U5FFCCGEkD6zW0TYLSI6IhpaM3S7nqFGRRYhhBBC+s0hi3DIVGwlQ0UWIYQQQs4bFVvdUZFFCCGEkAETLbYCEQ2tHQpUffgWW1RkEUIIIWTAOWURTllEe1hFW1AdlsUWFVmEEEIISRuXVTKLrYiGtg4VmjF8ii0qsgghhBCSVhzHwW2V4JJF+MMafMHhUWxRkUUIIYSQQcFxHDw2CW6rCH9IQ1tIgZ5kv8FcQUUWIYQQQgYVx3Hw2CW4rCJ8IRW+kAqD5V6xRUUWIYQQQoYEz3PId1jgtknwhVT4c6zY4of6AAghhBAyvAk8hwKHBRUFdnhsEjhu6PYbHEhUZBFCCCEkIwg8h0KnjIp8G9w5UGxRkUUIIYSQjCIKPIqcMkbm2+C0Zm9nExVZhBBCCMlIksCjxGXFyHw7nHL2FVtUZBFCCCEko1lEHiVus9hyZFGxlT1HSgghhJBhzSLyKHVbEdF0tHaoCCraUB9Sr6jIIoQQQkhWkUUBZR4BYVVHa1BBSNGH+pCSyprlwh//+MdYuHAh7HY78vLyko45efIkli1bBofDgaKiItx3331QFCVhTFVVFRYtWgSbzYYRI0bg3//938G6ZHJs3boVc+bMgdVqxdixY/HLX/6y2/d6/fXXMWXKFMiyjClTpmDDhg0Ddq6EEEIIOTerJKDcY4M3zwarJAz14XSTNUWWoij4p3/6J9x5551Jn9d1HTfccAM6Ojrw4YcfYv369Xj99dfxwAMPxMb4/X5cc8018Hq9+OSTT/Dzn/8cP/nJT/D000/Hxhw/fhz/8A//gMsvvxx79uzBI488gvvuuw+vv/56bMxHH32EL33pS1i1ahU+/fRTrFq1CjfffDN27tyZvh8AIYQQQpKySgK8eTaUe2yQM6nYYlnmpZdeYh6Pp9vj77zzDuN5np06dSr22O9+9zsmyzLz+XyMMcaee+455vF4WDgcjo1Zu3Yt83q9zDAMxhhj3/ve99iFF16Y8Np33HEHmz9/fuz3N998M7vuuusSxlx77bXsy1/+csrn4fP5GIDYsRFCCCFkYHREVNYciKTltfty/c6amaxz+eijjzBt2jR4vd7YY9deey0ikQh2794dG7No0SLIspwwpq6uDp9//nlszJIlSxJe+9prr8WuXbugqmqvY7Zv397j8UUiEfj9/oRfhBBCCBl4douIAodlqA8je5YLz6WhoQGlpaUJj+Xn58NisaChoaHHMdHfn2uMpmloamrqdUz0NZJZu3YtPB5P7FdFRUU/zpIQQggh2WJIi6zHHnsMHMf1+mvXrl0pv16y+H3GWMLjXcewzqb3gRjTW/z/ww8/DJ/PF/tVU1NzrtMhhBBCSBYb0giHe+65B1/+8pd7HTN69OiUXqusrKxb43lraytUVY3NOpWVlXWbbWpsbASAc44RRRGFhYW9juk6uxVPluWEZUpCCCGE5LYhLbKKiopQVFQ0IK+1YMEC/PjHP0Z9fT3Ky8sBAJs2bYIsy5gzZ05szCOPPAJFUWCxWGJjvF5vrJhbsGAB3nrrrYTX3rRpE+bOnQtJkmJjNm/ejO985zsJYxYuXDgg50IIIYSQ7Jc1PVknT55EZWUlTp48CV3XUVlZicrKSgQCAQDAkiVLMGXKFKxatQp79uzBX/7yFzz44IO4/fbb4Xa7AQArV66ELMu49dZbsW/fPmzYsAFr1qzB/fffH1vq+9a3voUTJ07g/vvvx4EDB/Cb3/wGv/71r/Hggw/GjuXb3/42Nm3ahCeeeAIHDx7EE088gT//+c9YvXr1oP9cCCGEEJKh0nJ/YxrccsstDEC3Xx988EFszIkTJ9gNN9zAbDYbKygoYPfcc09CXANjjO3du5ddfvnlTJZlVlZWxh577LFYfEPUli1b2OzZs5nFYmGjR49m69at63Y8f/zjH9mkSZOYJEnswgsvZK+//nqfzociHAghhJDs05frN8dYl7hzMij8fj88Hg98Pl9spo0QQgghma0v1++sWS4khBBCCMkmVGQRQgghhKQBFVmEEEIIIWlARRYhhBBCSBpQkUUIIYQQkgZUZBFCCCGEpAEVWYQQQgghaUBFFiGEEEJIGgzp3oXDWTQD1u/3D/GREEIIISRV0et2KlnuVGQNkfb2dgBARUXFEB8JIYQQQvqqvb0dHo+n1zG0rc4QMQwDdXV1cLlcsc2pe+L3+1FRUYGampqc34JnOJ0rMLzOl841dw2n86VzzV2pni9jDO3t7fB6veD53ruuaCZriPA8j5EjR/bpa9xu97D4Dx0YXucKDK/zpXPNXcPpfOlcc1cq53uuGawoanwnhBBCCEkDKrIIIYQQQtKAiqwsIMsyfvCDH0CW5aE+lLQbTucKDK/zpXPNXcPpfOlcc1c6zpca3wkhhBBC0oBmsgghhBBC0oCKLEIIIYSQNKAiixBCCCEkDajIIoQQQghJAyqyMsi2bduwbNkyeL1ecByHjRs3Jjx/6623guO4hF/z588fmoM9D2vXrsXFF18Ml8uFkpISrFixAocOHUoYwxjDY489Bq/XC5vNhsWLF2P//v1DdMTnJ5XzzZX3dt26dZgxY0YszG/BggV49913Y8/n0vt6rnPNlfc0mbVr14LjOKxevTr2WC69t10lO99ceX8fe+yxbudRVlYWez7X3tdzne9Av69UZGWQjo4OzJw5E88++2yPY6677jrU19fHfr3zzjuDeIQDY+vWrbj77ruxY8cObN68GZqmYcmSJejo6IiNefLJJ/H000/j2WefxSeffIKysjJcc801sT0fs0kq5wvkxns7cuRIPP7449i1axd27dqFK6+8EjfeeGPsL+Vcel/Pda5AbrynXX3yySf41a9+hRkzZiQ8nkvvbbyezhfInfd36tSpCedRVVUVey4X39fezhcY4PeVkYwEgG3YsCHhsVtuuYXdeOONQ3I86dTY2MgAsK1btzLGGDMMg5WVlbHHH388NiYcDjOPx8N++ctfDtVhDpiu58tY7r63jDGWn5/PXnzxxZx/Xxk7e66M5eZ72t7eziZMmMA2b97MFi1axL797W8zxnL3z2xP58tY7ry/P/jBD9jMmTOTPpeL72tv58vYwL+vNJOVZbZs2YKSkhJMnDgRt99+OxobG4f6kM6bz+cDABQUFAAAjh8/joaGBixZsiQ2RpZlLFq0CNu3bx+SYxxIXc83KtfeW13XsX79enR0dGDBggU5/b52PdeoXHtP7777btxwww24+uqrEx7P1fe2p/ONypX398iRI/B6vRgzZgy+/OUv49ixYwBy933t6XyjBvJ9pQ2is8j111+Pf/qnf8KoUaNw/PhxPProo7jyyiuxe/furE3kZYzh/vvvx2WXXYZp06YBABoaGgAApaWlCWNLS0tx4sSJQT/GgZTsfIHcem+rqqqwYMEChMNhOJ1ObNiwAVOmTIn9pZxL72tP5wrk1nsKAOvXr8fu3buxa9eubs/l4p/Z3s4XyJ33d968efjv//5vTJw4EadPn8aPfvQjLFy4EPv378/J97W38y0sLBz493XA5sTIgEKS5cKu6urqmCRJ7PXXXx+cg0qDu+66i40aNYrV1NTEHvvb3/7GALC6urqEsf/yL//Crr322sE+xAGV7HyTyeb3NhKJsCNHjrBPPvmE/eu//isrKipi+/fvz8n3tadzTSab39OTJ0+ykpISVllZGXssfvks197bc51vMtn8/sYLBAKstLSU/fSnP8259zWZ+PNN5nzfV1ouzGLl5eUYNWoUjhw5MtSH0i/33nsv3nzzTXzwwQcYOXJk7PHonR7RT1FRjY2N3T5RZZOezjeZbH5vLRYLxo8fj7lz52Lt2rWYOXMm/vM//zMn39eezjWZbH5Pd+/ejcbGRsyZMweiKEIURWzduhX/9V//BVEUY+9frry35zpfXde7fU02v7/xHA4Hpk+fjiNHjuTkn9mu4s83mfN9X6nIymLNzc2oqalBeXn5UB9KnzDGcM899+BPf/oT3n//fYwZMybh+TFjxqCsrAybN2+OPaYoCrZu3YqFCxcO9uGet3OdbzLZ+t4mwxhDJBLJufc1mei5JpPN7+lVV12FqqoqVFZWxn7NnTsXX/3qV1FZWYmxY8fm1Ht7rvMVBKHb12Tz+xsvEongwIEDKC8vHxZ/ZuPPN5nzfl/7O8VGBl57ezvbs2cP27NnDwPAnn76abZnzx524sQJ1t7ezh544AG2fft2dvz4cfbBBx+wBQsWsBEjRjC/3z/Uh94nd955J/N4PGzLli2svr4+9isYDMbGPP7448zj8bA//elPrKqqin3lK19h5eXlWXeujJ37fHPpvX344YfZtm3b2PHjx9nevXvZI488wnieZ5s2bWKM5db72tu55tJ72pOuy2e59N4mE3++ufT+PvDAA2zLli3s2LFjbMeOHWzp0qXM5XKxzz//nDGWe+9rb+ebjveViqwM8sEHHzAA3X7dcsstLBgMsiVLlrDi4mImSRK74IIL2C233MJOnjw51IfdZ8nOEQB76aWXYmMMw2A/+MEPWFlZGZNlmV1xxRWsqqpq6A76PJzrfHPpvf3GN77BRo0axSwWCysuLmZXXXVVrMBiLLfe197ONZfe0550LbJy6b1NJv58c+n9/dKXvsTKy8uZJEnM6/WyL37xiwl9hbn2vvZ2vul4XznGGOvfHBghhBBCCOkJ9WQRQgghhKQBFVmEEEIIIWlARRYhhBBCSBpQkUUIIYQQkgZUZBFCCCGEpAEVWYQQQgghaUBFFiGEEEJIGlCRRQghhBCSBlRkEUIy2uLFi7F69eoen+c4Dhs3bkz59bZs2QKO49DW1nbex0YIIb0Rh/oACCHkfNTX1yM/P3+oD4MQQrqhIosQktXKysqG+hAIISQpWi4khGQ8wzDwve99DwUFBSgrK8Njjz0We67rcuH27dsxa9YsWK1WzJ07Fxs3bgTHcaisrEx4zd27d2Pu3Lmw2+1YuHAhDh06BADw+XwQBAG7d+8GADDGUFBQgIsvvjj2tb/73e9QXl4e+/1DDz2EiRMnwm63Y+zYsXj00UehqioA4PPPPwfP89i1a1fC9//5z3+OUaNGoaftY2+99VasWLECa9asQWlpKfLy8vDDH/4Qmqbhu9/9LgoKCjBy5Ej85je/iX3N559/Do7jsH79eixcuBBWqxVTp07Fli1bEl77zTffxIQJE2Cz2fCFL3wBv/3tb2kJlZA0oCKLEJLxfvvb38LhcGDnzp148skn8e///u/YvHlzt3Ht7e1YtmwZpk+fjr///e/4j//4Dzz00ENJX/P73/8+fvrTn2LXrl0QRRHf+MY3AAAejwezZs2KFSZ79+6N/dPv9wMw+7oWLVoUey2Xy4WXX34Zn332Gf7zP/8TL7zwAp555hkAwOjRo3H11VfjpZdeSvj+L730Em699VZwHNfjeb///vuoq6vDtm3b8PTTT+Oxxx7D0qVLkZ+fj507d+Jb3/oWvvWtb6Gmpibh67773e/igQcewJ49e7Bw4UIsX74czc3NAMxC7B//8R+xYsUKVFZW4o477sD3v//9Ho+BEHIeGCGEZLBFixaxyy67LOGxiy++mD300EOMMcYAsA0bNjDGGFu3bh0rLCxkoVAoNvaFF15gANiePXsYY4x98MEHDAD785//HBvz9ttvMwCxr7v//vvZ0qVLGWOM/exnP2P/+I//yC666CL29ttvM8YYmzhxIlu3bl2Px/zkk0+yOXPmxH7/+9//nuXn57NwOMwYY6yyspJxHMeOHz/e42vccsstbNSoUUzX9dhjkyZNYpdffnns95qmMYfDwX73u98xxhg7fvw4A8Aef/zx2BhVVdnIkSPZE088wRhj7KGHHmLTpk1L+F7f//73GQDW2tra4/EQQvqOZrIIIRlvxowZCb8vLy9HY2Njt3GHDh3CjBkzYLVaY49dcskl53zN6NJf9DUXL16Mv/71rzAMA1u3bsXixYuxePFibN26FQ0NDTh8+HDCTNb//b//F5dddhnKysrgdDrx6KOP4uTJk7HnV6xYAVEUsWHDBgDAb37zG3zhC1/A6NGjcfLkSTidztivNWvWxL5u6tSp4Pmzf02XlpZi+vTpsd8LgoDCwsJuP4sFCxbE/l0URcydOxcHDvz/7dxPKPt/HAfw5+eAXJRImaJpB1vic1FO2xydSBHKQYTyucjhk5ZdWBEnf8aNZIxEspSTtKZd+EwOW6Rw2onExYdtv8O3fbLvfPebn+8nv9bzUTuoz969P6/Ts9frNWGtRh9Hn5lqRETfw5BFRP97eXl5KX8LgoB4PJ72XCKRSBu/Jf6w8/TxzOR3kmdarVY8Pz/j/Pwcfr8fdrsdNpsNJycnOD4+RllZGcxmMwAgGAyis7MTzc3N8Pl8UBQFDocDqqpq5+fn56OnpwcrKytQVRUbGxvaeNJgMCAUCmmfoaGhjO+dbS1+l3zHr9SIiL6Hvy4kopxRU1MDj8eD19dXFBQUAEDawnk2kntZCwsLEAQBFosFBoMBiqLA5/OldLECgQCqqqpS9pru7u7Szuzv70dtbS3cbjfe3t7Q1tYG4FenyWQyffmOmQSDQVitVgDA+/s7zs7OIEkSgF81Ojw8THn+v9SIiP4dO1lElDO6u7sRj8cxMDCAcDiMo6MjzM7OAkDGBfPP2O12rK+vw2azQRAEFBcXw2KxYGtrC3a7XXvOZDLh/v4eXq8XNzc3mJub08aCH5nNZjQ2NkKWZXR1daGwsPBb75rJ4uIi9vb2EIlEMDw8jMfHR61zNjg4iEgkAlmWcXV1he3tbayurgL4eo2IKDOGLCLKGUVFRTg4OEAoFIIoinA4HHA6nQCQsqeVjaamJsRisZRAZbPZEIvFUjpZLS0tGBkZgSRJEEURp6enGB8f//TMvr4+qKqqBR69TE1NYXp6GvX19fD7/djf30dpaSkAwGg0YmdnB7u7u6irq8PS0pLWhUt2/4jo7xASHMYTUQ7zeDzo7e3F09OTrt2jbLhcLni9XlxeXupy/u3tLYxGIxRFgSiKX7rX8vJy2r+CIKLv4U4WEeWUtbU1VFdXo6KiAhcXF5BlGR0dHT8asF5eXhAOhzE/P4+JiYkfu0eS2+1GQ0MDSkpKEAgEMDMzo+1sEdHfw5BFRDklGo3C6XQiGo2ivLwc7e3tcLlcP3onSZKwubmJ1tZW3UeF2bi+vsbk5CQeHh5QWVmJ0dFRjI2N/fS1iHIOx4VEREREOuDiOxEREZEOGLKIiIiIdMCQRURERKQDhiwiIiIiHTBkEREREemAIYuIiIhIBwxZRERERDpgyCIiIiLSwT9BgVpf9bqS0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"highway-mpg\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "20c0320d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#As the highway-mpg goes up, the price goes down: this indicates an inverse/negative relationship between these two variables. \n",
    "#Highway mpg could potentially be a predictor of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed06db0b",
   "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>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             highway-mpg     price\n",
       "highway-mpg     1.000000 -0.704692\n",
       "price          -0.704692  1.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df[['highway-mpg', 'price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d6c2e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correlation is -0.7046"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6666355e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Weak Linear Relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce24ea65",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's see if \"Peak-rpm\" as a predictor variable of \"price\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c8c7a7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='peak-rpm', ylabel='price'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"peak-rpm\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec5aca1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Peak rpm does not seem like a good predictor of the price at all since the regression line is close to horizontal. \n",
    "#Also, the data points are very scattered and far from the fitted line, showing lots of variability. \n",
    "#Therefore it's it is not a reliable variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "04164491",
   "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>peak-rpm</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.101616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.101616</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          peak-rpm     price\n",
       "peak-rpm  1.000000 -0.101616\n",
       "price    -0.101616  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can examine the correlation between 'peak-rpm' and 'price' and see it's approximately -0.101616 \n",
    "df[['peak-rpm','price']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9009fca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Categorical variables\n",
    "\n",
    "#These are variables that describe a 'characteristic' of a data unit, and are selected from a small group of categories. \n",
    "#The categorical variables can have the type \"object\" or \"int64\". \n",
    "#A good way to visualize categorical variables is by using boxplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f12c3996",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's look at the relationship between \"body-style\" and \"price\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1dca9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='body-style', ylabel='price'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"body-style\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ad3ab109",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We see that the distributions of price between the different body-style categories have a significant overlap, \n",
    "#and so body-style would not be a good predictor of price. \n",
    "#Let's examine engine \"engine-location\" and \"price\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7942e9b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='engine-location', ylabel='price'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"engine-location\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c475113f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we see that the distribution of price between these two engine-location categories, front and rear, \n",
    "#are distinct enough to take engine-location as a potential good predictor of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a050f07e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's examine \"drive-wheels\" and \"price\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "63af0338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='drive-wheels', ylabel='price'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C7G4A/sMYY/sVuI0xmj9/vl544QXNmTNHxhg1Nzfb1k9ISAi7BgEAkiSH4QI4F0xTU5NcLpfcbrciIiLsbuecNTc3Ky0tze42/MqGDRsUGhpqdxsAgO+Qr7/f7J4DAADwAbvnYAkJCdGGDRts7aGlpUVTp06VJL311lsKCQmxtR+7Px8A4D8ITbA4HA6/2hUVEhLiV/0AALo3ds8BAAD4gNAEAADgA1tD0/PPP68hQ4YoIiJCERERSklJ0TvvvGPNz8rKksPh8HoMHz7caxkej0fz5s1TdHS0wsLCNGXKFNXU1HjVNDQ0KDMzUy6XSy6XS5mZmWpsbPSq2bdvnyZPnqywsDBFR0dr/vz5am1t/c7WHQAAXFxsDU19+vTR448/ru3bt2v79u269dZbNXXqVO3atcuqGT9+vGpra63H+vXrvZaRk5OjtWvXqqCgQCUlJTp8+LAmTZqk9vZ2qyY9PV2VlZUqLCxUYWGhKisrlZmZac1vb2/XxIkTdeTIEZWUlKigoEBvvvmmcnNzv/svAQAAXByMn4mMjDQvvfSSMcaYGTNmmKlTp3Za29jYaHr06GEKCgqsaV988YUJCAgwhYWFxhhjPvroIyPJlJWVWTWlpaVGkvn444+NMcasX7/eBAQEmC+++MKqWbNmjXE6ncbtdvvcu9vtNpLO6T3w9s0335hRo0aZUaNGmW+++cbudgAA3YCvv99+c0xTe3u7CgoKdOTIEaWkpFjTN23apJiYGPXv31/Z2dmqr6+35lVUVKitrU2pqanWtISEBCUlJWnLli2SpNLSUrlcLg0bNsyqGT58uFwul1dNUlKSEhISrJq0tDR5PB5VVFR02rPH41FTU5PXAwAAXJpsD007d+7UZZddJqfTqQceeEBr167VoEGDJEkTJkxQfn6+3n//fS1btkzl5eW69dZb5fF4JEl1dXUKDg5WZGSk1zJjY2NVV1dn1cTExHT43JiYGK+a2NhYr/mRkZEKDg62ak4nLy/POk7K5XKpb9++5/9FAAAAv2b7dZoGDBigyspKNTY26s0339SMGTNUXFysQYMG6e6777bqkpKSNHToUCUmJmrdunW64447Ol2mMcbrfmGnu3fY+dScavHixVq4cKH1uqmpieAEAMAlyvaRpuDgYP3gBz/Q0KFDlZeXp2uuuUbPPvvsaWvj4+OVmJioTz/9VJIUFxen1tZWNTQ0eNXV19dbI0dxcXHav39/h2UdOHDAq+bUEaWGhga1tbV1GIE6mdPptM78O/EAAACXJttD06mMMdbut1MdPHhQ1dXVio+PlyQlJyerR48e2rhxo1VTW1urqqoqjRgxQpKUkpIit9utbdu2WTVbt26V2+32qqmqqlJtba1VU1RUJKfTqeTk5Au+jgAA4OJj6+65hx9+WBMmTFDfvn116NAhFRQUaNOmTSosLNThw4e1ZMkS3XnnnYqPj9fnn3+uhx9+WNHR0br99tslSS6XSzNnzlRubq6ioqLUq1cvLVq0SIMHD9a4ceMkSQMHDtT48eOVnZ2tlStXSpJmzZqlSZMmacCAAZKk1NRUDRo0SJmZmXryySf19ddfa9GiRcrOzmb0CAAASLI5NO3fv1+ZmZmqra2Vy+XSkCFDVFhYqNtuu03Nzc3auXOnXn31VTU2Nio+Pl633HKL3njjDYWHh1vLWL58uYKCgjR9+nQ1Nzdr7NixWrVqlQIDA62a/Px8zZ8/3zrLbsqUKVqxYoU1PzAwUOvWrdPs2bM1cuRIhYaGKj09XU899VTXfRkAAMCvOYwxxu4mLhVNTU1yuVxyu92MUJ2n5uZmpaWlSZI2bNjADXsBAN85X3+//e6YJgAAAH9EaAIAAPABoQkAAMAHhCYAAAAfEJoAAAB8QGgCAADwAaEJAADAB4QmAAAAHxCaAAAAfEBoAgAA8AGhCQAAwAeEJgAAAB8QmgAAAHxAaAIAAPABoQkAAMAHhCYAAAAfEJoAAAB8QGgCAADwAaEJAADAB4QmAAAAHxCaAAAAfEBoAgAA8AGhCQAAwAeEJgAAAB8QmgAAAHxAaAIAAPABoQkAAMAHhCYAAAAfEJoAAAB8QGgCAADwAaEJAADAB7aGpueff15DhgxRRESEIiIilJKSonfeeceab4zRkiVLlJCQoNDQUI0ZM0a7du3yWobH49G8efMUHR2tsLAwTZkyRTU1NV41DQ0NyszMlMvlksvlUmZmphobG71q9u3bp8mTJyssLEzR0dGaP3++Wltbv7N1BwAAFxdbQ1OfPn30+OOPa/v27dq+fbtuvfVWTZ061QpGTzzxhJ5++mmtWLFC5eXliouL02233aZDhw5Zy8jJydHatWtVUFCgkpISHT58WJMmTVJ7e7tVk56ersrKShUWFqqwsFCVlZXKzMy05re3t2vixIk6cuSISkpKVFBQoDfffFO5ubld92UAAAD/ZvxMZGSkeemll8yxY8dMXFycefzxx615LS0txuVymd/85jfGGGMaGxtNjx49TEFBgVXzxRdfmICAAFNYWGiMMeajjz4ykkxZWZlVU1paaiSZjz/+2BhjzPr1601AQID54osvrJo1a9YYp9Np3G53p722tLQYt9ttPaqrq42kM74HZ/bNN9+YUaNGmVGjRplvvvnG7nYAAN2A2+326ffbb45pam9vV0FBgY4cOaKUlBTt2bNHdXV1Sk1NtWqcTqdGjx6tLVu2SJIqKirU1tbmVZOQkKCkpCSrprS0VC6XS8OGDbNqhg8fLpfL5VWTlJSkhIQEqyYtLU0ej0cVFRWd9pyXl2ft8nO5XOrbt++F+TIAAIDfsT007dy5U5dddpmcTqceeOABrV27VoMGDVJdXZ0kKTY21qs+NjbWmldXV6fg4GBFRkaesSYmJqbD58bExHjVnPo5kZGRCg4OtmpOZ/HixXK73dajurr6HNceAABcLILsbmDAgAGqrKxUY2Oj3nzzTc2YMUPFxcXWfIfD4VVvjOkw7VSn1pyu/nxqTuV0OuV0Os/YCwAAuDTYPtIUHBysH/zgBxo6dKjy8vJ0zTXX6Nlnn1VcXJwkdRjpqa+vt0aF4uLi1NraqoaGhjPW7N+/v8PnHjhwwKvm1M9paGhQW1tbhxEoAADQPdkemk5ljJHH49HVV1+tuLg4bdy40ZrX2tqq4uJijRgxQpKUnJysHj16eNXU1taqqqrKqklJSZHb7da2bdusmq1bt8rtdnvVVFVVqba21qopKiqS0+lUcnLyd7q+AADg4mDr7rmHH35YEyZMUN++fXXo0CEVFBRo06ZNKiwslMPhUE5OjpYuXap+/fqpX79+Wrp0qXr27Kn09HRJksvl0syZM5Wbm6uoqCj16tVLixYt0uDBgzVu3DhJ0sCBAzV+/HhlZ2dr5cqVkqRZs2Zp0qRJGjBggCQpNTVVgwYNUmZmpp588kl9/fXXWrRokbKzsxUREWHPlwMAAPyKraFp//79yszMVG1trVwul4YMGaLCwkLddtttkqSHHnpIzc3Nmj17thoaGjRs2DAVFRUpPDzcWsby5csVFBSk6dOnq7m5WWPHjtWqVasUGBho1eTn52v+/PnWWXZTpkzRihUrrPmBgYFat26dZs+erZEjRyo0NFTp6el66qmnuuibAAAA/s5hjDF2N3GpaGpqksvlktvtZoTqPDU3NystLU2StGHDBoWGhtrcEQDgUufr77ffHdMEAADgjwhNAAAAPiA0AQAA+IDQBAAA4ANCEwAAgA8ITQAAAD4gNAEAAPiA0AQAAOADQhMAAIAPCE0AAAA+IDQBAAD4gNAEAADgA0ITAACADwhNAAAAPiA0AQAA+IDQBAAA4ANCEwAAgA8ITQAAAD4gNAEAAPjgvEPT6tWrNXLkSCUkJGjv3r2SpGeeeUZvvfXWBWsOAADAX5xXaHr++ee1cOFC/c//+T/V2Nio9vZ2SdLll1+uZ5555kL2BwAA4BfOKzQ999xzevHFF/Xzn/9cgYGB1vShQ4dq586dF6w5AAAAf3FeoWnPnj267rrrOkx3Op06cuTIt24KAADA35xXaLr66qtVWVnZYfo777yjQYMGfdueAAAA/E7Q+bzppz/9qebMmaOWlhYZY7Rt2zatWbNGeXl5eumlly50jwAAALY7r9D04x//WEePHtVDDz2kb775Runp6briiiv07LPP6kc/+tGF7hEAAMB25xWaJCk7O1vZ2dn66quvdOzYMcXExFzIvgAAAPzKeYWmPXv26OjRo+rXr5+io6Ot6Z9++ql69Oihq6666kL1BwAA4BfO60DwrKwsbdmypcP0rVu3Kisr69v2BAAA4HfOKzT95S9/0ciRIztMHz58+GnPqgMAALjYnVdocjgcOnToUIfpbrfbujq4L/Ly8nTDDTcoPDxcMTExmjZtmj755BOvmqysLDkcDq/H8OHDvWo8Ho/mzZun6OhohYWFacqUKaqpqfGqaWhoUGZmplwul1wulzIzM9XY2OhVs2/fPk2ePFlhYWGKjo7W/Pnz1dra6vP6AACAS9d5haZRo0YpLy/PKyC1t7crLy9PN910k8/LKS4u1pw5c1RWVqaNGzfq6NGjSk1N7XCBzPHjx6u2ttZ6rF+/3mt+Tk6O1q5dq4KCApWUlOjw4cOaNGmSV3/p6emqrKxUYWGhCgsLVVlZqczMTK/+J06cqCNHjqikpEQFBQV68803lZube65fDwAAuBSZ87Br1y4TFRVlvv/975usrCyTlZVlvv/975vevXubnTt3ns8ijTHG1NfXG0mmuLjYmjZjxgwzderUTt/T2NhoevToYQoKCqxpX3zxhQkICDCFhYXGGGM++ugjI8mUlZVZNaWlpUaS+fjjj40xxqxfv94EBASYL774wqpZs2aNcTqdxu12+9S/2+02knyuR0fffPONGTVqlBk1apT55ptv7G4HANAN+Pr7fV4jTYMGDdKOHTs0ffp01dfX69ChQ7rvvvv08ccfKykp6bwDnNvtliT16tXLa/qmTZsUExOj/v37Kzs7W/X19da8iooKtbW1KTU11ZqWkJCgpKQk62D10tJSuVwuDRs2zKoZPny4XC6XV01SUpISEhKsmrS0NHk8HlVUVJy2X4/Ho6amJq8HAAC4NJ33dZoSEhK0dOnSC9aIMUYLFy7UTTfd5BW8JkyYoLvuukuJiYnas2ePfvnLX+rWW29VRUWFnE6n6urqFBwcrMjISK/lxcbGqq6uTpJUV1d32utIxcTEeNXExsZ6zY+MjFRwcLBVc6q8vDw98sgj32q9AQDAxcHn0LRjxw4lJSUpICBAO3bsOGPtkCFDzrmRuXPnaseOHSopKfGafvfdd1vPk5KSNHToUCUmJmrdunW64447Ol2eMUYOh8N6ffLzb1NzssWLF2vhwoXW66amJvXt27fTngAAwMXL59B07bXXWiM21157rRwOh4wxHeocDsc5nUEnSfPmzdMf/vAHffjhh+rTp88Za+Pj45WYmKhPP/1UkhQXF6fW1lY1NDR4jTbV19drxIgRVs3+/fs7LOvAgQPW6FJcXJy2bt3qNb+hoUFtbW0dRqBOcDqdcjqdvq8oAAC4aPl8TNOePXvUu3dv6/k//vEP7dmzp8PjH//4h88fbozR3Llz9V//9V96//33dfXVV5/1PQcPHlR1dbXi4+MlScnJyerRo4c2btxo1dTW1qqqqsoKTSkpKXK73dq2bZtVs3XrVrndbq+aqqoq1dbWWjVFRUVyOp1KTk72eZ0AAMAl6lyPMG9tbTVZWVnms88+O4/j07395Cc/MS6Xy2zatMnU1tZajxNnTR06dMjk5uaaLVu2mD179pgPPvjApKSkmCuuuMI0NTVZy3nggQdMnz59zLvvvmv+/Oc/m1tvvdVcc8015ujRo1bN+PHjzZAhQ0xpaakpLS01gwcPNpMmTbLmHz161CQlJZmxY8eaP//5z+bdd981ffr0MXPnzvV5fTh77tvj7Dn4q5KSEvO//tf/MiUlJXa3AuAC8/X3+7wuOeByuS5IaJJ02sdvf/tbY8zxH9DU1FTTu3dv06NHD3PllVeaGTNmmH379nktp7m52cydO9f06tXLhIaGmkmTJnWoOXjwoMnIyDDh4eEmPDzcZGRkmIaGBq+avXv3mokTJ5rQ0FDTq1cvM3fuXNPS0uLz+hCavj1CE/xRc3Ozuf32282oUaPM7bffbpqbm+1uCcAF5Ovvt8OY0xyYdBY//vGPNXjwYK+DoHH8QHCXyyW3262IiAi727koNTc3Ky0tTZK0YcMGhYaG2twRIL300ktavXq1dWLIfffdp5kzZ9rdFoALxNff7/O65MAPfvAD/Z//83+0ZcsWJScnKywszGv+/Pnzz2exAOB3ampqlJ+fb534YoxRfn6+0tLSznriCoBLy3mFppdeekmXX365KioqOlz40eFwEJoAXBKMMVq+fHmn05966qlOL0kC4NJzXqFpz5491vMT//riLw4Al5q9e/eqvLy8w/T29naVl5dr7969uuqqq7q+MQC2OK/bqEjSyy+/rKSkJIWEhCgkJERJSUl66aWXLmRvAGCrxMRE3XDDDQoMDPSaHhgYqBtvvFGJiYk2dQbADucVmn75y19qwYIFmjx5sv7zP/9T//mf/6nJkyfrwQcf1C9+8YsL3SMA2MLhcOjBBx/sdDoj7ED3cl67555//nm9+OKLuueee6xpU6ZM0ZAhQzRv3jw9+uijF6xBALBTnz59lJGR4XX2XEZGhq644gq7WwPQxc5rpKm9vV1Dhw7tMD05OVlHjx791k0BgD+59957FRUVJUmKjo5WRkaGzR0BsMN5haZ7771Xzz//fIfpL7zwAn+ZALjkhISEKDc3V7GxsVq4cKFCQkLsbgmADc5r95x0/EDwoqIiDR8+XJJUVlam6upq3XfffV4XvXz66ae/fZcAYLORI0dq5MiRdrcBwEbnFZqqqqp0/fXXS5I+++wzSVLv3r3Vu3dvVVVVWXUcJAkAAC4V5xWaPvjggwvdBwAAgF877+s0AQAAdCfnfUwTLixjjFpaWuxuw3Ynfwd8H8eFhISwqxsA/AChyU+0tLQoLS3N7jb8ytSpU+1uwS9s2LBBoaGhdrcBAN0eu+cAAAB8wEiTHzpyfYYU0E3/1xgjHft/F0gNCJK6626pY0cV9ud8u7sAAJykm/4y+7mAICmwh91d2CjY7gYAAOiA3XMAAAA+IDQBAAD4gNAEAADgA0ITAACADwhNAAAAPiA0AQAA+IDQBAAA4ANCEwAAgA8ITQAAAD4gNAEAAPiA0AQAAOADQhMAAIAPCE0AAAA+IDQBAAD4wNbQlJeXpxtuuEHh4eGKiYnRtGnT9Mknn3jVGGO0ZMkSJSQkKDQ0VGPGjNGuXbu8ajwej+bNm6fo6GiFhYVpypQpqqmp8appaGhQZmamXC6XXC6XMjMz1djY6FWzb98+TZ48WWFhYYqOjtb8+fPV2tr6naw7AAC4uNgamoqLizVnzhyVlZVp48aNOnr0qFJTU3XkyBGr5oknntDTTz+tFStWqLy8XHFxcbrtttt06NAhqyYnJ0dr165VQUGBSkpKdPjwYU2aNEnt7e1WTXp6uiorK1VYWKjCwkJVVlYqMzPTmt/e3q6JEyfqyJEjKikpUUFBgd58803l5uZ2zZcBAAD8msMYY+xu4oQDBw4oJiZGxcXFuvnmm2WMUUJCgnJycvSzn/1M0vFRpdjYWP3rv/6r7r//frndbvXu3VurV6/W3XffLUn68ssv1bdvX61fv15paWnavXu3Bg0apLKyMg0bNkySVFZWppSUFH388ccaMGCA3nnnHU2aNEnV1dVKSEiQJBUUFCgrK0v19fWKiIg4a/9NTU1yuVxyu90+1Z+sublZaWlpkqQjQ2dIgT3O6f24xLS3KWz7K5KkDRs2KDQ01OaGAODS5evvt18d0+R2uyVJvXr1kiTt2bNHdXV1Sk1NtWqcTqdGjx6tLVu2SJIqKirU1tbmVZOQkKCkpCSrprS0VC6XywpMkjR8+HC5XC6vmqSkJCswSVJaWpo8Ho8qKipO26/H41FTU5PXAwAAXJr8JjQZY7Rw4ULddNNNSkpKkiTV1dVJkmJjY71qY2NjrXl1dXUKDg5WZGTkGWtiYmI6fGZMTIxXzamfExkZqeDgYKvmVHl5edYxUi6XS3379j3X1QYAABcJvwlNc+fO1Y4dO7RmzZoO8xwOh9drY0yHaac6teZ09edTc7LFixfL7XZbj+rq6jP2BAAALl5+EZrmzZunP/zhD/rggw/Up08fa3pcXJwkdRjpqa+vt0aF4uLi1NraqoaGhjPW7N+/v8PnHjhwwKvm1M9paGhQW1tbhxGoE5xOpyIiIrweAADg0mRraDLGaO7cufqv//ovvf/++7r66qu95l999dWKi4vTxo0brWmtra0qLi7WiBEjJEnJycnq0aOHV01tba2qqqqsmpSUFLndbm3bts2q2bp1q9xut1dNVVWVamtrrZqioiI5nU4lJydf+JUHAAAXFVtD05w5c/Taa6/p9ddfV3h4uOrq6lRXV6fm5mZJx3eX5eTkaOnSpVq7dq2qqqqUlZWlnj17Kj09XZLkcrk0c+ZM5ebm6r333tNf/vIX3XvvvRo8eLDGjRsnSRo4cKDGjx+v7OxslZWVqaysTNnZ2Zo0aZIGDBggSUpNTdWgQYOUmZmpv/zlL3rvvfe0aNEiZWdnM4IEQJs3b9Zdd92lzZs3290KAJsE2fnhzz//vCRpzJgxXtN/+9vfKisrS5L00EMPqbm5WbNnz1ZDQ4OGDRumoqIihYeHW/XLly9XUFCQpk+frubmZo0dO1arVq1SYGCgVZOfn6/58+dbZ9lNmTJFK1assOYHBgZq3bp1mj17tkaOHKnQ0FClp6frqaee+o7WHsDFoqWlRcuWLdNXX32lZcuWKTk5WSEhIXa3BaCL+dV1mi52XKcJFwzXafIrL730kl599VXr9YwZMzRz5kwbOwJwIV2U12kCAH9TU1Oj1157zWvaa6+91uFWTQAufYQmAOiEMUbLly/XqQPyx44dO+10AJc2QhMAdGLv3r0qLy/vEI6MMSovL9fevXtt6gyAHQhNANCJK6+8stPjGyIiInTllVd2cUcA7ERoAoBO7Nu3r9N7SjY1NWnfvn1d3BEAOxGaAKATiYmJuuGGG04778Ybb1RiYmIXdwTAToQmAOiEw+HQPffcc9p599xzz1nvgQng0kJoAoBOGGO0Zs2aDuHI4XDo9ddf5+w5oJshNAFAJzh7DsDJCE0A0IkTxzSdbqSJY5pgN+6H2PUITQDQiRPHNJ1upIljmmCnE/dD3L9/v5YtW6aWlha7W+oWCE0A0AmOaYK/eu2113Tw4EFJ0sGDB5Wfn29zR90DoQkAOsExTfBHNTU1ys/Pt7ZLY4zy8/O5H2IXIDQBQCdOHNMUGBjoNT0wMJBjmmCLE/dD7Gw6o5/fLUITAHTC4XDowQcf7HQ6xzShq50Y/Wxvb/ea3t7ezuhnFyA0AcAZ9OnTRxkZGVZAcjgcysjI0BVXXGFzZ+iOGP20F6EJAM7i3nvvVVRUlCQpOjpaGRkZNneE7orRT3sF2d0AjvPaD93eZl8j8A8nbQMco2C/kJAQ5ebm6plnnlFOTo5CQkLsbgnd2InRz9WrV8sYw+hnFyI0+QmPx2M9D/vL6zZ2An/j8XjUs2dPu9vo9kaOHKmRI0fa3QYg6fjo5/r16/XVV18x+tmF2D0HAMBF5sToZ2xsrBYuXMjoZxdhpMlPOJ1O6/mR69KlwB42dgPbtbdZI44nbxsAcAKjn12P0OQnvA7eC+xBaIKFAzsBwD+wew4AAMAHhCYAAAAfEJoAAAB8QGgCAB9s3rxZd911lzZv3mx3KwBsQmgCgLNoaWnRsmXLtH//fi1btkwtLS12twTABoQmADiL1157TQcPHpQkHTx4UPn5+TZ3BMAOhCYAOIOamhrl5+dbt7Mxxig/P181NTU2dwagqxGaAKATxhgtX7680+ncFxDoXri4JQC/Zoyx7Riiffv2qby8vMP09vZ2lZeX629/+5uuvPLKLu8rJCSEi54CNrA1NH344Yd68sknVVFRodraWq1du1bTpk2z5mdlZemVV17xes+wYcNUVlZmvfZ4PFq0aJHWrFmj5uZmjR07Vv/+7/+uPn36WDUNDQ2aP3++/vCHP0iSpkyZoueee06XX365VbNv3z7NmTNH77//vkJDQ5Wenq6nnnpKwcHB383KA/BJS0uL0tLS7G7jtLKzs2353A0bNig0NNSWzwa6M1t3zx05ckTXXHONVqxY0WnN+PHjVVtbaz3Wr1/vNT8nJ0dr165VQUGBSkpKdPjwYU2aNEnt7e1WTXp6uiorK1VYWKjCwkJVVlYqMzPTmt/e3q6JEyfqyJEjKikpUUFBgd58803l5uZe+JUGAAAXJVtHmiZMmKAJEyacscbpdCouLu6089xut15++WWtXr1a48aNk3T8LJe+ffvq3XffVVpamnbv3q3CwkKVlZVp2LBhkqQXX3xRKSkp+uSTTzRgwAAVFRXpo48+UnV1tRISEiRJy5YtU1ZWlh577DFFRESc9vM9Ho88Ho/1uqmp6Zy/AwBnFhISog0bNtjaw8svv6z/+I//kHT8XoD33HOPZsyYYVs/3NEesIffH9O0adMmxcTE6PLLL9fo0aP12GOPKSYmRpJUUVGhtrY2paamWvUJCQlKSkrSli1blJaWptLSUrlcLiswSdLw4cPlcrm0ZcsWDRgwQKWlpUpKSrICkySlpaXJ4/GooqJCt9xyy2l7y8vL0yOPPPIdrTkA6XhIsXtXVEZGhhWaoqKilJWVRXABuiG/PntuwoQJys/P1/vvv69ly5apvLxct956qzW6U1dXp+DgYEVGRnq9LzY2VnV1dVbNiZB1spiYGK+a2NhYr/mRkZEKDg62ak5n8eLFcrvd1qO6uvpbrS8A/3RyQJo3bx6BCeim/Hqk6e6777aeJyUlaejQoUpMTNS6det0xx13dPo+Y4zXmSWnO8vkfGpO5XQ65XQ6z7oeAC4dw4cPt7sFADbx65GmU8XHxysxMVGffvqpJCkuLk6tra1qaGjwqquvr7dGjuLi4rR///4Oyzpw4IBXzakjSg0NDWpra+swAgUAALqniyo0HTx4UNXV1YqPj5ckJScnq0ePHtq4caNVU1tbq6qqKo0YMUKSlJKSIrfbrW3btlk1W7duldvt9qqpqqpSbW2tVVNUVCSn06nk5OSuWDUAAODnbN09d/jwYf3973+3Xu/Zs0eVlZXq1auXevXqpSVLlujOO+9UfHy8Pv/8cz388MOKjo7W7bffLklyuVyaOXOmcnNzFRUVpV69emnRokUaPHiwdTbdwIEDNX78eGVnZ2vlypWSpFmzZmnSpEkaMGCAJCk1NVWDBg1SZmamnnzySX399ddatGiRsrOzOz1zDgAAdC+2hqbt27d7nZm2cOFCSdKMGTP0/PPPa+fOnXr11VfV2Nio+Ph43XLLLXrjjTcUHh5uvWf58uUKCgrS9OnTrYtbrlq1SoGBgVZNfn6+5s+fb51lN2XKFK9rQwUGBmrdunWaPXu2Ro4c6XVxSwAAAElyGG6edME0NTXJ5XLJ7Xaf8whVc3OzddXjI0NnSIE9vosWcbFob1PY9uNXw+fqz/Y7+c8n/z+AS4+vv98X1TFNAAAAdiE0AQAA+IDQBAAA4AO/vrhlt3XsqN0d2MeY/17/gCDpDBcXvaR1520AAPwUockPhf053+4WAADAKdg9BwAA4ANGmvxESEiINmzYYHcbtmtpadHUqVMlSW+99RY3RpX4DgDATxCa/ITD4eDaL6cICQnhOwEA+A12zwEAAPiA0AQAAOADQhMAAIAPCE0AAAA+IDQBAAD4gNAEAADgA0ITAACADwhNAAAAPiA0AQAA+IDQBAAA4ANuowIAwDkwxqilpcX2HjwejyTJ6XTK4XDY2k9ISIjtPXQFQhMAAOegpaVFaWlpdrfhVzZs2NAt7hXK7jkAAAAfMNIEALho+MuusbfeesvWHlpaWnT33XdLkt544w2FhITY2o8xRs3Nzbb20BW7CAlNAICLBrvGOjoRnrq7rthFyO45AAAAHzDSBAC4KLVPbu++v2JGUvv/ex4o6dI/ce30jkqBbwd22cd1180NAHCxC1L3/hXrYXcD3Q+75wAAAHxAaAIAAPABoQkAAMAH3XlvMICz8Idr4viDk78Dvo/justtM4CTEZoAdIpr4nQ0depUu1vwC93lthnAyWzdPffhhx9q8uTJSkhIkMPh0O9//3uv+cYYLVmyRAkJCQoNDdWYMWO0a9curxqPx6N58+YpOjpaYWFhmjJlimpqarxqGhoalJmZKZfLJZfLpczMTDU2NnrV7Nu3T5MnT1ZYWJiio6M1f/58tba2fherDQAALkK2jjQdOXJE11xzjX784x/rzjvv7DD/iSee0NNPP61Vq1apf//+evTRR3Xbbbfpk08+UXh4uCQpJydHb7/9tgoKChQVFaXc3FxNmjRJFRUVCgw8fu2G9PR01dTUqLCwUJI0a9YsZWZm6u2335Yktbe3a+LEierdu7dKSkp08OBBzZgxQ8YYPffcc130bQD+7dc3N8oZaOxuwxbGSK3Hjj8PDpC6614pT7tDcz683O42ANvYGpomTJigCRMmnHaeMUbPPPOMfv7zn+uOO+6QJL3yyiuKjY3V66+/rvvvv19ut1svv/yyVq9erXHjxkmSXnvtNfXt21fvvvuu0tLStHv3bhUWFqqsrEzDhg2TJL344otKSUnRJ598ogEDBqioqEgfffSRqqurlZCQIElatmyZsrKy9NhjjykiIuK0PXo8Hnk8Hut1U1PTBftuAH/jDDQK6bpryPkddkRJx6+oCHRffnv23J49e1RXV6fU1FRrmtPp1OjRo7VlyxZJUkVFhdra2rxqEhISlJSUZNWUlpbK5XJZgUmShg8fLpfL5VWTlJRkBSZJSktLk8fjUUVFRac95uXlWbv8XC6X+vbte2FWHgAA+B2/DU11dXWSpNjYWK/psbGx1ry6ujoFBwcrMjLyjDUxMTEdlh8TE+NVc+rnREZGKjg42Ko5ncWLF8vtdluP6urqc1xLAABwsfD7s+dOPaXVGHPW01xPrTld/fnUnMrpdMrpdJ6xFwAAcGnw25GmuLg4Seow0lNfX2+NCsXFxam1tVUNDQ1nrNm/f3+H5R84cMCr5tTPaWhoUFtbW4cRKAAA0D35bWi6+uqrFRcXp40bN1rTWltbVVxcrBEjRkiSkpOT1aNHD6+a2tpaVVVVWTUpKSlyu93atm2bVbN161a53W6vmqqqKtXW1lo1RUVFcjqdSk5O/k7XEwAAXBxs3T13+PBh/f3vf7de79mzR5WVlerVq5euvPJK5eTkaOnSperXr5/69eunpUuXqmfPnkpPT5ckuVwuzZw5U7m5uYqKilKvXr20aNEiDR482DqbbuDAgRo/fryys7O1cuVKSccvOTBp0iQNGDBAkpSamqpBgwYpMzNTTz75pL7++mstWrRI2dnZnZ45BwAAuhdbQ9P27dt1yy23WK8XLlwoSZoxY4ZWrVqlhx56SM3NzZo9e7YaGho0bNgwFRUVWddokqTly5crKChI06dPV3Nzs8aOHatVq1ZZ12iSpPz8fM2fP986y27KlClasWKFNT8wMFDr1q3T7NmzNXLkSIWGhio9PV1PPfXUd/0VAADOgTEnXfbgqH19wE+ctA14bRvfEYfpik/pJpqamuRyueR2uxmhOk/Nzc3WbTu4TYP9Tv7/8dItDd36Ok2QWtql/++D42cr2/Xns6GhgVvZ4LTeeuutDmfT+8rX32+/PaYJAADAn/j9JQcAADjh5Mu8tE9u51esuzsqBb59fAi8Ky4BxOYGALhoeF07L0j8isFytms4XgjsngMAAPABoQkAAMAHDGwC6NTJJ9d62m1sBH7h5G2AE6/RHRGaAHTK4/FYz+d8eH6n8uLS5PF41LNnT7vbALoUoQkAcHHqzhe3NJJOjPwFSvruj4H2T128DRCaAHTq5FN4f31zg5xc3LJb87T/94hjV5zefTYnTjUHugqhCUCnTj6F1xkorggOS1ec3g34G0ITAOCiERISog0bNtjagzHG63g/O7S0tOjuu++WJL3xxhsKCQmxtR+n02l7kO6K74DQBAC4aDgcDtvvSdnc3OxX9787EZ7s1F3uFcp1mgAAAHzASBMAAOfA33YRdpddY/6A0AQAwDnwh12EkrhOlg3YPQcAAOADRpoA+MTT7tDxK+p1P8ZIrceOPw8OkLrr2fbHtwGg+yI0wWKMUUtLi609nPz5dvciHd9Pb/exAv5izoeX290CANiK0ARLS0uL0tLS7G7D4g+n9HaX02gBAGdHaALQKX84S8gftLS0WCH+rbfe6jZnCp0J3wG6I0ITLP7wA8lptP7FX84S8ichISF8J0A3RWiCxV9+IDmNFgDgj7jkAAAAgA8ITQAAAD4gNAEAAPiA0AQAAOADQhMAAIAPCE0AAAA+IDQBAAD4gNAEAADgA78OTUuWLJHD4fB6xMXFWfONMVqyZIkSEhIUGhqqMWPGaNeuXV7L8Hg8mjdvnqKjoxUWFqYpU6aopqbGq6ahoUGZmZlyuVxyuVzKzMxUY2NjV6wiAAC4SPj9FcF/+MMf6t1337VeBwYGWs+feOIJPf3001q1apX69++vRx99VLfddps++eQThYeHS5JycnL09ttvq6CgQFFRUcrNzdWkSZNUUVFhLSs9PV01NTUqLCyUJM2aNUuZmZl6++23u3BNAZyOMUYtLS229nDy59vdi3T8Vi5232II6I4cxhhjdxOdWbJkiX7/+9+rsrKywzxjjBISEpSTk6Of/exnko6PKsXGxupf//Vfdf/998vtdqt3795avXq17r77bknSl19+qb59+2r9+vVKS0vT7t27NWjQIJWVlWnYsGGSpLKyMqWkpOjjjz/WgAEDfO63qalJLpdLbrdbERER3/4LAKDm5malpaXZ3YZf2bBhg1/c8gi4VPj6++3Xu+ck6dNPP1VCQoKuvvpq/ehHP9I//vEPSdKePXtUV1en1NRUq9bpdGr06NHasmWLJKmiokJtbW1eNQkJCUpKSrJqSktL5XK5rMAkScOHD5fL5bJqOuPxeNTU1OT1AAAAlya/3j03bNgwvfrqq+rfv7/279+vRx99VCNGjNCuXbtUV1cnSYqNjfV6T2xsrPbu3StJqqurU3BwsCIjIzvUnHh/XV2dYmJiOnx2TEyMVdOZvLw8PfLII+e9fgDOLiQkRBs2bLC1B2OMPB6PpOP/OLN711hISIitnw90V34dmiZMmGA9Hzx4sFJSUvT9739fr7zyioYPHy5JHf7yMsac9S+0U2tOV+/LchYvXqyFCxdar5uamtS3b98zvgfAuXE4HH6xK6pnz552twDAZn6/e+5kYWFhGjx4sD799FPrLLpTR4Pq6+ut0ae4uDi1traqoaHhjDX79+/v8FkHDhzoMIp1KqfTqYiICK8HAAC4NF1Uocnj8Wj37t2Kj4/X1Vdfrbi4OG3cuNGa39raquLiYo0YMUKSlJycrB49enjV1NbWqqqqyqpJSUmR2+3Wtm3brJqtW7fK7XZbNQAAAH69e27RokWaPHmyrrzyStXX1+vRRx9VU1OTZsyYIYfDoZycHC1dulT9+vVTv379tHTpUvXs2VPp6emSJJfLpZkzZyo3N1dRUVHq1auXFi1apMGDB2vcuHGSpIEDB2r8+PHKzs7WypUrJR2/5MCkSZPO6cw5AABwafPr0FRTU6N77rlHX331lXr37q3hw4errKxMiYmJkqSHHnpIzc3Nmj17thoaGjRs2DAVFRVZ12iSpOXLlysoKEjTp09Xc3Ozxo4dq1WrVnld7yk/P1/z58+3zrKbMmWKVqxY0bUrCwAA/JpfX6fpYsN1mgAAuPhcMtdpAgAA8AeEJgAAAB8QmgAAAHxAaAIAAPABoQkAAMAHhCYAAAAfEJoAAAB8QGgCAADwgV9fEfxic+I6oU1NTTZ3AgAAfHXid/ts1/smNF1Ahw4dkiT17dvX5k4AAMC5OnTokFwuV6fzuY3KBXTs2DF9+eWXCg8Pl8PhsLudi1ZTU5P69u2r6upqbkcDv8F2CX/DNnnhGGN06NAhJSQkKCCg8yOXGGm6gAICAtSnTx+727hkRERE8BcB/A7bJfwN2+SFcaYRphM4EBwAAMAHhCYAAAAfEJrgd5xOp371q1/J6XTa3QpgYbuEv2Gb7HocCA4AAOADRpoAAAB8QGgCAADwAaEJAADAB4QmXNSysrI0bdo0u9vARcwYo1mzZqlXr15yOByqrKz8Vstjm4S/WrJkia699lq727ioEZoAdGuFhYVatWqV/vjHP6q2tlZJSUl2twQoLy9PDodDOTk5dreCk3BFcNimtbVVwcHBdreBbu6zzz5TfHy8RowYYXcrgCSpvLxcL7zwgoYMGWJ3KzgFI03oMmPGjNHcuXO1cOFCRUdHKyUlRZMnT7bmP/PMM3I4HFq3bp01bcCAAVq5cqUkqb29XQsXLtTll1+uqKgoPfTQQ2e9IzVwJllZWZo3b5727dsnh8OhqKgotknY6vDhw8rIyNCLL76oyMhIa/pzzz2nwYMHW69///vfy+Fw6Ne//rU1LS0tTYsXL7ZeP/7444qNjVV4eLhmzpyplpaWrlmJSxihCV3qlVdeUVBQkDZv3qysrCz96U9/0rFjxyRJxcXFio6OVnFxsSSprq5Of/vb3zR69GhJ0rJly/R//+//1csvv6ySkhJ9/fXXWrt2rW3rgovfs88+q3/5l39Rnz59VFtbq2eeeYZtEraaM2eOJk6cqHHjxnlNHzNmjHbt2qWvvvpKUsdt8+jRo9qyZYu1bf7Hf/yHfvWrX+mxxx7T9u3bFR8fr3//93/v2pW5FBmgi4wePdpce+211uvGxkYTEBBgtm/fbo4dO2aioqJMXl6eueGGG4wxxrz++usmNjbWqo+PjzePP/649bqtrc306dPHTJ06tcvWAZee5cuXm8TERGMM2yTstWbNGvPDH/7QNDc3G2OO/525YMECY4wxx44dM9HR0eZ3v/udMcaYa6+91uTl5ZmYmBhjjDFbtmwxQUFB5tChQ8YYY1JSUswDDzzgtfxhw4aZa665pmtW5hLFSBO61NChQ63nLpdL1157rTZt2qSdO3cqICBA999/v/7617/q0KFD2rRpk/WvJrfbrdraWqWkpFjvDwoK8loe8G2xTcIu1dXVWrBggfLz8xUSEtJhvsPh0M0336xNmzapsbFRu3bt0gMPPKD29nbt3r1bmzZt0vXXX6/LLrtMkrR7926vbVNSh9c4d4QmdKmwsDCv12PGjNGmTZtUXFys0aNHKzIyUj/84Q+1efNmbdq0SWPGjLGnUXRbbJOwQ0VFherr65WcnKygoCAFBQWpuLhY//Zv/6agoCC1t7db2+af/vQnXXPNNbr88st18803q7i4mG2zixCaYKsxY8boT3/6k95//33rD/zo0aNVUFDgdeyIy+VSfHy8ysrKrPcePXpUFRUVdrSNSxjbJOwwduxY7dy5U5WVldZj6NChysjIUGVlpQIDA63jmn73u995bZvvvvuu1/FMkjRw4ECvbVNSh9c4d1xyALa6+eabdejQIb399tt69NFHJR3/0brzzjvVu3dvDRo0yKpdsGCBHn/8cfXr108DBw7U008/rcbGRps6x6WKbRJ2CA8P73CNsLCwMEVFRVnTk5KSFBUVpfz8fL311luSjm+bubm5kqSbbrrJeu+CBQs0Y8YMDR06VDfddJPy8/O1a9cufe973+uiNbo0MdIEW7lcLl133XXq1auX9WM0atQoHTt2zOtfTZKUm5ur++67T1lZWUpJSVF4eLhuv/12O9rGJYxtEv7K4XBY2+CoUaMkSUOGDLG22YiICKv27rvv1j//8z/rZz/7mZKTk7V371795Cc/saXvS4nDGC4qAgAAcDaMNAEAAPiA0AQAAOADQhMAAIAPCE0AAAA+IDQBAAD4gNAEAADgA0ITAACADwhNAAAAPiA0AfA7Y8aMUU5OTqfzP//8czkcDlVWVnZZT2ezadMmORyO7/w2KkuWLNG11177nX4GgNPj3nMALjp9+/ZVbW2toqOj7W4FQDdCaAJwUWltbVVwcLDi4uLsbgVAN8PuOQC2OnLkiO677z5ddtllio+P17Jly7zmX3XVVXr00UeVlZUll8ul7Oxsr91zx44dU58+ffSb3/zG631//vOf5XA49I9//EOS5Ha7NWvWLMXExCgiIkK33nqr/vrXv3ba186dOxUQEKCvvvpKktTQ0KCAgADdddddVk1eXp5SUlK83ldRUaGhQ4eqZ8+eGjFihD755BOv+W+//baSk5MVEhKi733ve3rkkUd09OhRa/659rlp0ybdeOONCgsL0+WXX66RI0dq7969ndYDOH+EJgC2+ulPf6oPPvhAa9euVVFRkTZt2qSKigqvmieffFJJSUmqqKjQL3/5S695AQEB+tGPfqT8/Hyv6a+//rpSUlL0ve99T8YYTZw4UXV1dVq/fr0qKip0/fXXa+zYsfr6669P21dSUpKioqJUXFwsSfrwww8VFRWlDz/80KrZtGmTddf5E37+859r2bJl2r59u4KCgvRP//RP1rwNGzbo3nvv1fz58/XRRx9p5cqVWrVqlR577DFJOuc+jx49qmnTpmn06NHasWOHSktLNWvWLDkcjrN97QDOhwEAmxw6dMgEBwebgoICa9rBgwdNaGioWbBggTHGmMTERDNt2jSv9+3Zs8dIMn/5y1+MMcb8+c9/Ng6Hw3z++efGGGPa29vNFVdcYX79618bY4x57733TEREhGlpafFazve//32zcuXKTvu74447zNy5c40xxuTk5Jjc3FwTHR1tdu3aZdra2sxll11m3nnnHWOMMR988IGRZN59913r/evWrTOSTHNzszHGmFGjRpmlS5d6fcbq1atNfHy8z33+6le/Mtdcc431XUkymzZt6nQdAFw4jDQBsM1nn32m1tZWr11cvXr10oABA7zqhg4desblXHfddfof/+N/aM2aNZKk4uJi1dfXa/r06ZKO7zI7fPiwoqKidNlll1mPPXv26LPPPtO+ffu8pi9dulTS8bP4Nm3aZC3zlltu0c0336zi4mKVl5erublZI0eO9OplyJAh1vP4+HhJUn19vdXHv/zLv3h9VnZ2tmpra/XNN9+ctc9T9erVS1lZWUpLS9PkyZP17LPPqra29qzfO4Dzw4HgAGxjjPGpLiws7Kw1GRkZev311/W///f/1uuvv660tDTr7Lpjx44pPj7eCkAnu/zyy3X55Zd7Xb6gV69eko6HpgULFujvf/+7qqqqNGrUKH322WcqLi5WY2OjkpOTFR4e7rW8Hj16WM9P7CY7duyY9d9HHnlEd9xxR4c+QkJCztrn6fz2t7/V/PnzVVhYqDfeeEO/+MUvtHHjRg0fPrzT7wrA+SE0AbDND37wA/Xo0UNlZWW68sorJR0/4Ppvf/tbh2OFziY9PV2/+MUvVFFRod/97nd6/vnnrXnXX3+96urqFBQUpKuuuqrTXk514rimRx99VNdcc40iIiI0evRo5eXlqaGh4Zx7vP766/XJJ5+c9rN87fN0rrvuOl133XVavHixUlJS9PrrrxOagO8Au+cA2Oayyy7TzJkz9dOf/lTvvfeeqqqqlJWVpYCAc/+r6eqrr9aIESM0c+ZMHT16VFOnTrXmjRs3TikpKZo2bZo2bNigzz//XFu2bNEvfvELbd++vdNlOhwO3XzzzXrttdc0ZswYScd3v7W2tuq9996zpvnqn//5n/Xqq69qyZIl2rVrl3bv3m2NDp1Pn3v27NHixYtVWlqqvXv3qqioSH/72980cODAc+oLgG8ITQBs9eSTT+rmm2/WlClTNG7cON10001KTk4+r2VlZGTor3/9q+644w6FhoZa0x0Oh9avX6+bb75Z//RP/6T+/fvrRz/6kT7//HPFxsaecZm33HKL2tvbrYDkcDg0atQoSdJNN910Tv2lpaXpj3/8ozZu3KgbbrhBw4cP19NPP63ExMTz6rNnz576+OOPdeedd6p///6aNWuW5s6dq/vvv/+c+gLgG4fx9aACAACAboyRJgAAAB8QmgAAAHxAaAIAAPABoQkAAMAHhCYAAAAfEJoAAAB8QGgCAADwAaEJAADAB4QmAAAAHxCaAAAAfEBoAgAA8MH/Dxe+1Uld56QpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drive-wheels\n",
    "sns.boxplot(x=\"drive-wheels\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b64df30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we see that the distribution of price between the different drive-wheels categories differs; \n",
    "#as such drive-wheels could potentially be a predictor of price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2b8f421b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Descriptive Statistical Analysis\n",
    "\n",
    "#Let's first take a look at the variables by utilizing a description method.\n",
    "\n",
    "#The describe function automatically computes basic statistics for all continuous variables. Any NaN values are automatically skipped in these statistics.\n",
    "\n",
    "#This will show:\n",
    "\n",
    "#    the count of that variable\n",
    " #   the mean\n",
    "  #  the standard deviation (std)\n",
    "   # the minimum value\n",
    "    #the IQR (Interquartile Range: 25%, 50% and 75%)\n",
    "    #the maximum value\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "57a3a037",
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.00000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.840796</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>98.797015</td>\n",
       "      <td>0.837102</td>\n",
       "      <td>0.915126</td>\n",
       "      <td>53.766667</td>\n",
       "      <td>2555.666667</td>\n",
       "      <td>126.875622</td>\n",
       "      <td>3.330692</td>\n",
       "      <td>3.256904</td>\n",
       "      <td>10.164279</td>\n",
       "      <td>103.405534</td>\n",
       "      <td>5117.665368</td>\n",
       "      <td>25.179104</td>\n",
       "      <td>30.686567</td>\n",
       "      <td>13207.129353</td>\n",
       "      <td>9.944145</td>\n",
       "      <td>0.099502</td>\n",
       "      <td>0.900498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.254802</td>\n",
       "      <td>31.99625</td>\n",
       "      <td>6.066366</td>\n",
       "      <td>0.059213</td>\n",
       "      <td>0.029187</td>\n",
       "      <td>2.447822</td>\n",
       "      <td>517.296727</td>\n",
       "      <td>41.546834</td>\n",
       "      <td>0.268072</td>\n",
       "      <td>0.319256</td>\n",
       "      <td>4.004965</td>\n",
       "      <td>37.365700</td>\n",
       "      <td>478.113805</td>\n",
       "      <td>6.423220</td>\n",
       "      <td>6.815150</td>\n",
       "      <td>7947.066342</td>\n",
       "      <td>2.534599</td>\n",
       "      <td>0.300083</td>\n",
       "      <td>0.300083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>0.678039</td>\n",
       "      <td>0.837500</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "      <td>4.795918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>0.801538</td>\n",
       "      <td>0.890278</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2169.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7775.000000</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.00000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.832292</td>\n",
       "      <td>0.909722</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10295.000000</td>\n",
       "      <td>9.791667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>137.00000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>0.881788</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2926.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>12.368421</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.00000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "      <td>18.076923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  normalized-losses  wheel-base      length       width  \\\n",
       "count  201.000000          201.00000  201.000000  201.000000  201.000000   \n",
       "mean     0.840796          122.00000   98.797015    0.837102    0.915126   \n",
       "std      1.254802           31.99625    6.066366    0.059213    0.029187   \n",
       "min     -2.000000           65.00000   86.600000    0.678039    0.837500   \n",
       "25%      0.000000          101.00000   94.500000    0.801538    0.890278   \n",
       "50%      1.000000          122.00000   97.000000    0.832292    0.909722   \n",
       "75%      2.000000          137.00000  102.400000    0.881788    0.925000   \n",
       "max      3.000000          256.00000  120.900000    1.000000    1.000000   \n",
       "\n",
       "           height  curb-weight  engine-size        bore      stroke  \\\n",
       "count  201.000000   201.000000   201.000000  201.000000  197.000000   \n",
       "mean    53.766667  2555.666667   126.875622    3.330692    3.256904   \n",
       "std      2.447822   517.296727    41.546834    0.268072    0.319256   \n",
       "min     47.800000  1488.000000    61.000000    2.540000    2.070000   \n",
       "25%     52.000000  2169.000000    98.000000    3.150000    3.110000   \n",
       "50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n",
       "75%     55.500000  2926.000000   141.000000    3.580000    3.410000   \n",
       "max     59.800000  4066.000000   326.000000    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         201.000000  201.000000   201.000000  201.000000   201.000000   \n",
       "mean           10.164279  103.405534  5117.665368   25.179104    30.686567   \n",
       "std             4.004965   37.365700   478.113805    6.423220     6.815150   \n",
       "min             7.000000   48.000000  4150.000000   13.000000    16.000000   \n",
       "25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n",
       "50%             9.000000   95.000000  5125.369458   24.000000    30.000000   \n",
       "75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \n",
       "max            23.000000  262.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "              price  city-L/100km      diesel         gas  \n",
       "count    201.000000    201.000000  201.000000  201.000000  \n",
       "mean   13207.129353      9.944145    0.099502    0.900498  \n",
       "std     7947.066342      2.534599    0.300083    0.300083  \n",
       "min     5118.000000      4.795918    0.000000    0.000000  \n",
       "25%     7775.000000      7.833333    0.000000    1.000000  \n",
       "50%    10295.000000      9.791667    0.000000    1.000000  \n",
       "75%    16500.000000     12.368421    0.000000    1.000000  \n",
       "max    45400.000000     18.076923    1.000000    1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "680056cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Value Counts\n",
    "\n",
    "#Value-counts is a good way of understanding how many units of each characteristic/variable we have. \n",
    "#We can apply the \"value_counts\" method on the column 'drive-wheels'. \n",
    "#Don’t forget the method \"value_counts\" only works on Pandas series, not Pandas Dataframes. \n",
    "#As a result, we only include one bracket \"df['drive-wheels']\" not two brackets \"df[['drive-wheels']]\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "208dc6bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    118\n",
       "rwd     75\n",
       "4wd      8\n",
       "Name: drive-wheels, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4fb0b374",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can convert the series to a Dataframe as follows :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ddbf8048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     drive-wheels\n",
       "fwd           118\n",
       "rwd            75\n",
       "4wd             8"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ec95c4a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's repeat the above steps but save the results to the dataframe \"drive_wheels_counts\" and rename the column 'drive-wheels' to 'value_counts'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6f22922b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     value_counts\n",
       "fwd           118\n",
       "rwd            75\n",
       "4wd             8"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_wheels_counts = df['drive-wheels'].value_counts().to_frame()\n",
    "drive_wheels_counts.rename(columns={'drive-wheels': 'value_counts'}, inplace=True)\n",
    "drive_wheels_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b3690bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now let's rename the index to 'drive-wheels':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5f77c71b",
   "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>value_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              value_counts\n",
       "drive-wheels              \n",
       "fwd                    118\n",
       "rwd                     75\n",
       "4wd                      8"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_wheels_counts.index.name = 'drive-wheels'\n",
    "drive_wheels_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bec6058a",
   "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>value_counts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>front</th>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rear</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 value_counts\n",
       "engine-location              \n",
       "front                     198\n",
       "rear                        3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# engine-location as variable\n",
    "engine_loc_counts = df['engine-location'].value_counts().to_frame()\n",
    "engine_loc_counts.rename(columns={'engine-location': 'value_counts'}, inplace=True)\n",
    "engine_loc_counts.index.name = 'engine-location'\n",
    "engine_loc_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a943b774",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Examining the value counts of the engine location would not be a good predictor variable for the price. \n",
    "#This is because we only have three cars with a rear engine and 198 with an engine in the front, this result is skewed. \n",
    "#Thus, we are not able to draw any conclusions about the engine location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a615dad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Basics of Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b4f9a136",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The \"groupby\" method groups data by different categories. \n",
    "#The data is grouped based on one or several variables and analysis is performed on the individual groups.\n",
    "\n",
    "#For example, let's group by the variable \"drive-wheels\". We see that there are 3 different categories of drive wheels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a97787f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rwd', 'fwd', '4wd'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['drive-wheels'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e50f66e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If we want to know, on average, which type of drive wheel is most valuable, we can group \"drive-wheels\" and then average them.\n",
    "\n",
    "#We can select the columns 'drive-wheels', 'body-style' and 'price', then assign it to the variable \"df_group_one\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "81c4c65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_group_one = df[['drive-wheels','body-style','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dbbc9036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4wd</td>\n",
       "      <td>10241.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fwd</td>\n",
       "      <td>9244.779661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>19757.613333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drive-wheels         price\n",
       "0          4wd  10241.000000\n",
       "1          fwd   9244.779661\n",
       "2          rwd  19757.613333"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping results\n",
    "df_group_one = df_group_one.groupby(['drive-wheels'],as_index=False).mean()\n",
    "df_group_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "47604423",
   "metadata": {},
   "outputs": [],
   "source": [
    "#From our data, it seems rear-wheel drive vehicles are, on average, \n",
    "#the most expensive, while 4-wheel and front-wheel are approximately the same in price.\n",
    "\n",
    "#You can also group with multiple variables. \n",
    "#For example, let's group by both 'drive-wheels' and 'body-style'. \n",
    "#This groups the dataframe by the unique combinations 'drive-wheels' and 'body-style'.\n",
    "#We can store the results in the variable 'grouped_test1'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aaedc6f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4wd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>7603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>12647.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4wd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>11595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>8249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>8396.387755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9811.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>23949.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>24202.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>14337.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>21711.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drive-wheels   body-style         price\n",
       "0           4wd    hatchback   7603.000000\n",
       "1           4wd        sedan  12647.333333\n",
       "2           4wd        wagon   9095.750000\n",
       "3           fwd  convertible  11595.000000\n",
       "4           fwd      hardtop   8249.000000\n",
       "5           fwd    hatchback   8396.387755\n",
       "6           fwd        sedan   9811.800000\n",
       "7           fwd        wagon   9997.333333\n",
       "8           rwd  convertible  23949.600000\n",
       "9           rwd      hardtop  24202.714286\n",
       "10          rwd    hatchback  14337.777778\n",
       "11          rwd        sedan  21711.833333\n",
       "12          rwd        wagon  16994.222222"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This grouped data is much easier to visualize when it is made into a pivot table. \n",
    "#A pivot table is like an Excel spreadsheet, with one variable along the column and another along the row. \n",
    "#We can convert the dataframe to a pivot table using the method \"pivot \" to create a pivot table from the groups.\n",
    "\n",
    "# grouping results\n",
    "df_gptest = df[['drive-wheels','body-style','price']]\n",
    "grouped_test1 = df_gptest.groupby(['drive-wheels','body-style'],as_index=False).mean()\n",
    "grouped_test1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0c7dfb3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</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>4wd</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7603.000000</td>\n",
       "      <td>12647.333333</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9811.800000</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14337.777778</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  NaN           NaN   7603.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9811.800000   \n",
       "rwd              23949.6  24202.714286  14337.777778  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pivot = grouped_test1.pivot(index='drive-wheels',columns='body-style')\n",
    "grouped_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2b595fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</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>4wd</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>12647.333333</td>\n",
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       "      <th>fwd</th>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9811.800000</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14337.777778</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  0.0      0.000000   7603.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9811.800000   \n",
       "rwd              23949.6  24202.714286  14337.777778  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Often, we won't have data for some of the pivot cells. \n",
    "#We can fill these missing cells with the value 0, but any other value could potentially be used as well. \n",
    "grouped_pivot = grouped_pivot.fillna(0) #fill missing values with 0\n",
    "grouped_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fba99961",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Correlation and Causation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f5654b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Correlation: a measure of the extent of interdependence between variables.\n",
    "\n",
    "#Causation: the relationship between cause and effect between two variables.\n",
    "\n",
    "#It is important to know the difference between these two and that correlation does not imply causation. \n",
    "#Determining correlation is much simpler the determining causation as causation may require independent experimentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "15020e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pearson Correlation\n",
    "\n",
    "#The Pearson Correlation measures the linear dependence between two variables X and Y.\n",
    "\n",
    "#The resulting coefficient is a value between -1 and 1 inclusive, where:\n",
    "\n",
    "#     1: Total positive linear correlation.\n",
    "#     0: No linear correlation, the two variables most likely do not affect each other.\n",
    "#    -1: Total negative linear correlation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8b972e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pearson Correlation is the default method of the function \"corr\". \n",
    "#Like before we can calculate the Pearson Correlation of the of the 'int64' or 'float64' variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "866dae08",
   "metadata": {},
   "outputs": [
    {
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       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
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       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "      <th>city-L/100km</th>\n",
       "      <th>diesel</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>-0.535987</td>\n",
       "      <td>-0.365404</td>\n",
       "      <td>-0.242423</td>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.233118</td>\n",
       "      <td>-0.110581</td>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.008245</td>\n",
       "      <td>-0.182196</td>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.279740</td>\n",
       "      <td>-0.035527</td>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.066171</td>\n",
       "      <td>-0.196735</td>\n",
       "      <td>0.196735</td>\n",
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       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
       "      <td>0.466264</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.101546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>-0.535987</td>\n",
       "      <td>-0.056661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>-0.307237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>-0.365404</td>\n",
       "      <td>0.019424</td>\n",
       "      <td>0.876024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>-0.211187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.242423</td>\n",
       "      <td>0.086802</td>\n",
       "      <td>0.814507</td>\n",
       "      <td>0.857170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>-0.244356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.550160</td>\n",
       "      <td>-0.373737</td>\n",
       "      <td>0.590742</td>\n",
       "      <td>0.492063</td>\n",
       "      <td>0.306002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>-0.281578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curb-weight</th>\n",
       "      <td>-0.233118</td>\n",
       "      <td>0.099404</td>\n",
       "      <td>0.782097</td>\n",
       "      <td>0.880665</td>\n",
       "      <td>0.866201</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>-0.221046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>-0.110581</td>\n",
       "      <td>0.112360</td>\n",
       "      <td>0.572027</td>\n",
       "      <td>0.685025</td>\n",
       "      <td>0.729436</td>\n",
       "      <td>0.074694</td>\n",
       "      <td>0.849072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>-0.070779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>-0.140019</td>\n",
       "      <td>-0.029862</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.544885</td>\n",
       "      <td>0.180449</td>\n",
       "      <td>0.644060</td>\n",
       "      <td>0.572609</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.054458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.008245</td>\n",
       "      <td>0.055563</td>\n",
       "      <td>0.158502</td>\n",
       "      <td>0.124139</td>\n",
       "      <td>0.188829</td>\n",
       "      <td>-0.062704</td>\n",
       "      <td>0.167562</td>\n",
       "      <td>0.209523</td>\n",
       "      <td>-0.055390</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>-0.241303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>-0.182196</td>\n",
       "      <td>-0.114713</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>0.159733</td>\n",
       "      <td>0.189867</td>\n",
       "      <td>0.259737</td>\n",
       "      <td>0.156433</td>\n",
       "      <td>0.028889</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.187923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.985231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.075819</td>\n",
       "      <td>0.217299</td>\n",
       "      <td>0.371147</td>\n",
       "      <td>0.579821</td>\n",
       "      <td>0.615077</td>\n",
       "      <td>-0.087027</td>\n",
       "      <td>0.757976</td>\n",
       "      <td>0.822676</td>\n",
       "      <td>0.566936</td>\n",
       "      <td>0.098462</td>\n",
       "      <td>-0.214514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>0.169053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>0.279740</td>\n",
       "      <td>0.239543</td>\n",
       "      <td>-0.360305</td>\n",
       "      <td>-0.285970</td>\n",
       "      <td>-0.245800</td>\n",
       "      <td>-0.309974</td>\n",
       "      <td>-0.279361</td>\n",
       "      <td>-0.256733</td>\n",
       "      <td>-0.267392</td>\n",
       "      <td>-0.065713</td>\n",
       "      <td>-0.435780</td>\n",
       "      <td>0.107885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.475812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>-0.035527</td>\n",
       "      <td>-0.225016</td>\n",
       "      <td>-0.470606</td>\n",
       "      <td>-0.665192</td>\n",
       "      <td>-0.633531</td>\n",
       "      <td>-0.049800</td>\n",
       "      <td>-0.749543</td>\n",
       "      <td>-0.650546</td>\n",
       "      <td>-0.582027</td>\n",
       "      <td>-0.034696</td>\n",
       "      <td>0.331425</td>\n",
       "      <td>-0.822214</td>\n",
       "      <td>-0.115413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>-0.265676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>0.036233</td>\n",
       "      <td>-0.181877</td>\n",
       "      <td>-0.543304</td>\n",
       "      <td>-0.698142</td>\n",
       "      <td>-0.680635</td>\n",
       "      <td>-0.104812</td>\n",
       "      <td>-0.794889</td>\n",
       "      <td>-0.679571</td>\n",
       "      <td>-0.591309</td>\n",
       "      <td>-0.035201</td>\n",
       "      <td>0.268465</td>\n",
       "      <td>-0.804575</td>\n",
       "      <td>-0.058598</td>\n",
       "      <td>0.972044</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>-0.198690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.082391</td>\n",
       "      <td>0.133999</td>\n",
       "      <td>0.584642</td>\n",
       "      <td>0.690628</td>\n",
       "      <td>0.751265</td>\n",
       "      <td>0.135486</td>\n",
       "      <td>0.834415</td>\n",
       "      <td>0.872335</td>\n",
       "      <td>0.543155</td>\n",
       "      <td>0.082310</td>\n",
       "      <td>0.071107</td>\n",
       "      <td>0.809575</td>\n",
       "      <td>-0.101616</td>\n",
       "      <td>-0.686571</td>\n",
       "      <td>-0.704692</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.110326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-L/100km</th>\n",
       "      <td>0.066171</td>\n",
       "      <td>0.238567</td>\n",
       "      <td>0.476153</td>\n",
       "      <td>0.657373</td>\n",
       "      <td>0.673363</td>\n",
       "      <td>0.003811</td>\n",
       "      <td>0.785353</td>\n",
       "      <td>0.745059</td>\n",
       "      <td>0.554610</td>\n",
       "      <td>0.037300</td>\n",
       "      <td>-0.299372</td>\n",
       "      <td>0.889488</td>\n",
       "      <td>0.115830</td>\n",
       "      <td>-0.949713</td>\n",
       "      <td>-0.930028</td>\n",
       "      <td>0.789898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>0.241282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diesel</th>\n",
       "      <td>-0.196735</td>\n",
       "      <td>-0.101546</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>0.211187</td>\n",
       "      <td>0.244356</td>\n",
       "      <td>0.281578</td>\n",
       "      <td>0.221046</td>\n",
       "      <td>0.070779</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>0.241303</td>\n",
       "      <td>0.985231</td>\n",
       "      <td>-0.169053</td>\n",
       "      <td>-0.475812</td>\n",
       "      <td>0.265676</td>\n",
       "      <td>0.198690</td>\n",
       "      <td>0.110326</td>\n",
       "      <td>-0.241282</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas</th>\n",
       "      <td>0.196735</td>\n",
       "      <td>0.101546</td>\n",
       "      <td>-0.307237</td>\n",
       "      <td>-0.211187</td>\n",
       "      <td>-0.244356</td>\n",
       "      <td>-0.281578</td>\n",
       "      <td>-0.221046</td>\n",
       "      <td>-0.070779</td>\n",
       "      <td>-0.054458</td>\n",
       "      <td>-0.241303</td>\n",
       "      <td>-0.985231</td>\n",
       "      <td>0.169053</td>\n",
       "      <td>0.475812</td>\n",
       "      <td>-0.265676</td>\n",
       "      <td>-0.198690</td>\n",
       "      <td>-0.110326</td>\n",
       "      <td>0.241282</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   symboling  normalized-losses  wheel-base    length  \\\n",
       "symboling           1.000000           0.466264   -0.535987 -0.365404   \n",
       "normalized-losses   0.466264           1.000000   -0.056661  0.019424   \n",
       "wheel-base         -0.535987          -0.056661    1.000000  0.876024   \n",
       "length             -0.365404           0.019424    0.876024  1.000000   \n",
       "width              -0.242423           0.086802    0.814507  0.857170   \n",
       "height             -0.550160          -0.373737    0.590742  0.492063   \n",
       "curb-weight        -0.233118           0.099404    0.782097  0.880665   \n",
       "engine-size        -0.110581           0.112360    0.572027  0.685025   \n",
       "bore               -0.140019          -0.029862    0.493244  0.608971   \n",
       "stroke             -0.008245           0.055563    0.158502  0.124139   \n",
       "compression-ratio  -0.182196          -0.114713    0.250313  0.159733   \n",
       "horsepower          0.075819           0.217299    0.371147  0.579821   \n",
       "peak-rpm            0.279740           0.239543   -0.360305 -0.285970   \n",
       "city-mpg           -0.035527          -0.225016   -0.470606 -0.665192   \n",
       "highway-mpg         0.036233          -0.181877   -0.543304 -0.698142   \n",
       "price              -0.082391           0.133999    0.584642  0.690628   \n",
       "city-L/100km        0.066171           0.238567    0.476153  0.657373   \n",
       "diesel             -0.196735          -0.101546    0.307237  0.211187   \n",
       "gas                 0.196735           0.101546   -0.307237 -0.211187   \n",
       "\n",
       "                      width    height  curb-weight  engine-size      bore  \\\n",
       "symboling         -0.242423 -0.550160    -0.233118    -0.110581 -0.140019   \n",
       "normalized-losses  0.086802 -0.373737     0.099404     0.112360 -0.029862   \n",
       "wheel-base         0.814507  0.590742     0.782097     0.572027  0.493244   \n",
       "length             0.857170  0.492063     0.880665     0.685025  0.608971   \n",
       "width              1.000000  0.306002     0.866201     0.729436  0.544885   \n",
       "height             0.306002  1.000000     0.307581     0.074694  0.180449   \n",
       "curb-weight        0.866201  0.307581     1.000000     0.849072  0.644060   \n",
       "engine-size        0.729436  0.074694     0.849072     1.000000  0.572609   \n",
       "bore               0.544885  0.180449     0.644060     0.572609  1.000000   \n",
       "stroke             0.188829 -0.062704     0.167562     0.209523 -0.055390   \n",
       "compression-ratio  0.189867  0.259737     0.156433     0.028889  0.001263   \n",
       "horsepower         0.615077 -0.087027     0.757976     0.822676  0.566936   \n",
       "peak-rpm          -0.245800 -0.309974    -0.279361    -0.256733 -0.267392   \n",
       "city-mpg          -0.633531 -0.049800    -0.749543    -0.650546 -0.582027   \n",
       "highway-mpg       -0.680635 -0.104812    -0.794889    -0.679571 -0.591309   \n",
       "price              0.751265  0.135486     0.834415     0.872335  0.543155   \n",
       "city-L/100km       0.673363  0.003811     0.785353     0.745059  0.554610   \n",
       "diesel             0.244356  0.281578     0.221046     0.070779  0.054458   \n",
       "gas               -0.244356 -0.281578    -0.221046    -0.070779 -0.054458   \n",
       "\n",
       "                     stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "symboling         -0.008245          -0.182196    0.075819  0.279740   \n",
       "normalized-losses  0.055563          -0.114713    0.217299  0.239543   \n",
       "wheel-base         0.158502           0.250313    0.371147 -0.360305   \n",
       "length             0.124139           0.159733    0.579821 -0.285970   \n",
       "width              0.188829           0.189867    0.615077 -0.245800   \n",
       "height            -0.062704           0.259737   -0.087027 -0.309974   \n",
       "curb-weight        0.167562           0.156433    0.757976 -0.279361   \n",
       "engine-size        0.209523           0.028889    0.822676 -0.256733   \n",
       "bore              -0.055390           0.001263    0.566936 -0.267392   \n",
       "stroke             1.000000           0.187923    0.098462 -0.065713   \n",
       "compression-ratio  0.187923           1.000000   -0.214514 -0.435780   \n",
       "horsepower         0.098462          -0.214514    1.000000  0.107885   \n",
       "peak-rpm          -0.065713          -0.435780    0.107885  1.000000   \n",
       "city-mpg          -0.034696           0.331425   -0.822214 -0.115413   \n",
       "highway-mpg       -0.035201           0.268465   -0.804575 -0.058598   \n",
       "price              0.082310           0.071107    0.809575 -0.101616   \n",
       "city-L/100km       0.037300          -0.299372    0.889488  0.115830   \n",
       "diesel             0.241303           0.985231   -0.169053 -0.475812   \n",
       "gas               -0.241303          -0.985231    0.169053  0.475812   \n",
       "\n",
       "                   city-mpg  highway-mpg     price  city-L/100km    diesel  \\\n",
       "symboling         -0.035527     0.036233 -0.082391      0.066171 -0.196735   \n",
       "normalized-losses -0.225016    -0.181877  0.133999      0.238567 -0.101546   \n",
       "wheel-base        -0.470606    -0.543304  0.584642      0.476153  0.307237   \n",
       "length            -0.665192    -0.698142  0.690628      0.657373  0.211187   \n",
       "width             -0.633531    -0.680635  0.751265      0.673363  0.244356   \n",
       "height            -0.049800    -0.104812  0.135486      0.003811  0.281578   \n",
       "curb-weight       -0.749543    -0.794889  0.834415      0.785353  0.221046   \n",
       "engine-size       -0.650546    -0.679571  0.872335      0.745059  0.070779   \n",
       "bore              -0.582027    -0.591309  0.543155      0.554610  0.054458   \n",
       "stroke            -0.034696    -0.035201  0.082310      0.037300  0.241303   \n",
       "compression-ratio  0.331425     0.268465  0.071107     -0.299372  0.985231   \n",
       "horsepower        -0.822214    -0.804575  0.809575      0.889488 -0.169053   \n",
       "peak-rpm          -0.115413    -0.058598 -0.101616      0.115830 -0.475812   \n",
       "city-mpg           1.000000     0.972044 -0.686571     -0.949713  0.265676   \n",
       "highway-mpg        0.972044     1.000000 -0.704692     -0.930028  0.198690   \n",
       "price             -0.686571    -0.704692  1.000000      0.789898  0.110326   \n",
       "city-L/100km      -0.949713    -0.930028  0.789898      1.000000 -0.241282   \n",
       "diesel             0.265676     0.198690  0.110326     -0.241282  1.000000   \n",
       "gas               -0.265676    -0.198690 -0.110326      0.241282 -1.000000   \n",
       "\n",
       "                        gas  \n",
       "symboling          0.196735  \n",
       "normalized-losses  0.101546  \n",
       "wheel-base        -0.307237  \n",
       "length            -0.211187  \n",
       "width             -0.244356  \n",
       "height            -0.281578  \n",
       "curb-weight       -0.221046  \n",
       "engine-size       -0.070779  \n",
       "bore              -0.054458  \n",
       "stroke            -0.241303  \n",
       "compression-ratio -0.985231  \n",
       "horsepower         0.169053  \n",
       "peak-rpm           0.475812  \n",
       "city-mpg          -0.265676  \n",
       "highway-mpg       -0.198690  \n",
       "price             -0.110326  \n",
       "city-L/100km       0.241282  \n",
       "diesel            -1.000000  \n",
       "gas                1.000000  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2845e9e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#P-value:\n",
    "\n",
    "#What is this P-value? The P-value is the probability value that the correlation between these two variables is statistically significant. Normally, we choose a significance level of 0.05, which means that we are 95% confident that the correlation between the variables is significant.\n",
    "\n",
    "#By convention, when the\n",
    "\n",
    " #  p-value is < 0.001: we say there is strong evidence that the correlation is significant.\n",
    "#   the p-value is < 0.05: there is moderate evidence that the correlation is significant.\n",
    "#   the p-value is < 0.1: there is weak evidence that the correlation is significant.\n",
    "#   the p-value is > 0.1: there is no evidence that the correlation is significant.\n",
    "    \n",
    " # We can obtain this information using \"stats\" module in the \"scipy\" library.\n",
    "   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4f518c59",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "38e6900d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.5846418222655081  with a P-value of P = 8.076488270732989e-20\n"
     ]
    }
   ],
   "source": [
    "#Let's calculate the  Pearson Correlation Coefficient and P-value of 'wheel-base' and 'price'. \n",
    "pearson_coef, p_value = stats.pearsonr(df['wheel-base'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P =\", p_value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "91935568",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion:\n",
    "\n",
    "#Since the p-value is < 0.001, the correlation between wheel-base and price is statistically significant, \n",
    "#although the linear relationship isn't extremely strong (~0.585)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a6f957e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's calculate the  Pearson Correlation Coefficient and P-value of 'horsepower' and 'price'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4cc9c5f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.809574567003656  with a P-value of P =  6.369057428259557e-48\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['horsepower'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f07566bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion:\n",
    "\n",
    "#Since the p-value is < 0.001, the correlation between horsepower and price is statistically significant, \n",
    "#and the linear relationship is quite strong (~0.809, close to 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0b39476b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's calculate the Pearson Correlation Coefficient and P-value of 'length' and 'price'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "484abcc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Pearson Correlation Coefficient is 0.690628380448364  with a P-value of P =  8.016477466158986e-30\n"
     ]
    }
   ],
   "source": [
    "pearson_coef, p_value = stats.pearsonr(df['length'], df['price'])\n",
    "print(\"The Pearson Correlation Coefficient is\", pearson_coef, \" with a P-value of P = \", p_value) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e275cfe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion:¶\n",
    "\n",
    "#Since the p-value is < 0.001, the correlation between length and price is statistically significant, \n",
    "#and the linear relationship is moderately strong (~0.691)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6cc10732",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ANOVA\n",
    "\n",
    "#ANOVA: Analysis of Variance\n",
    "\n",
    "#The Analysis of Variance (ANOVA) is a statistical method used to test whether there are significant differences \n",
    "#between the means of two or more groups. \n",
    "\n",
    "#ANOVA returns two parameters:\n",
    "\n",
    "#F-test score: ANOVA assumes the means of all groups are the same, calculates how much the actual means deviate from the \n",
    "#assumption, and reports it as the F-test score. A larger score means there is a larger difference between the means.\n",
    "\n",
    "#P-value: P-value tells how statistically significant is our calculated score value.\n",
    "\n",
    "#If our price variable is strongly correlated with the variable we are analyzing, \n",
    "#expect ANOVA to return a sizeable F-test score and a small p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d00728fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since ANOVA analyzes the difference between different groups of the same variable, the groupby function will come in handy. \n",
    "#Because the ANOVA algorithm averages the data automatically, we do not need to take the average before hand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "90abce89",
   "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>drive-wheels</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rwd</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rwd</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rwd</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4wd</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fwd</td>\n",
       "      <td>15250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fwd</td>\n",
       "      <td>17710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fwd</td>\n",
       "      <td>18920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>fwd</td>\n",
       "      <td>23875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rwd</td>\n",
       "      <td>16430.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rwd</td>\n",
       "      <td>16925.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>4wd</td>\n",
       "      <td>7603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>4wd</td>\n",
       "      <td>9233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>4wd</td>\n",
       "      <td>11259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>4wd</td>\n",
       "      <td>8013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    drive-wheels    price\n",
       "0            rwd  13495.0\n",
       "1            rwd  16500.0\n",
       "2            rwd  16500.0\n",
       "3            fwd  13950.0\n",
       "4            4wd  17450.0\n",
       "5            fwd  15250.0\n",
       "6            fwd  17710.0\n",
       "7            fwd  18920.0\n",
       "8            fwd  23875.0\n",
       "9            rwd  16430.0\n",
       "10           rwd  16925.0\n",
       "136          4wd   7603.0\n",
       "140          4wd   9233.0\n",
       "141          4wd  11259.0\n",
       "144          4wd   8013.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_test2=df_gptest[['drive-wheels', 'price']].groupby(['drive-wheels'])\n",
    "grouped_test2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e5e8b7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can use the function 'f_oneway' in the module 'stats' to obtain the F-test score and P-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "728a4ef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 67.95406500780399 , P = 3.3945443577151245e-23\n"
     ]
    }
   ],
   "source": [
    "f_val, p_val = stats.f_oneway(grouped_test2.get_group('fwd')['price'], grouped_test2.get_group('rwd')['price'], grouped_test2.get_group('4wd')['price'])  \n",
    " \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "38ecaa11",
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is a great result, with a large F test score \n",
    "#showing a strong correlation and a P value of almost 0 implying almost certain statistical significance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "71a79f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conclusion: Important Variables\n",
    "\n",
    "#We now have a better idea of what our data looks like and which variables are important to take into account when predicting the car price. We have narrowed it down to the following variables:\n",
    "\n",
    "#Continuous numerical variables:\n",
    "\n",
    "#    Length\n",
    "#    Width\n",
    "#    Curb-weight\n",
    "#    Engine-size\n",
    "#    Horsepower\n",
    "#    City-mpg\n",
    "#    Highway-mpg\n",
    "#    Wheel-base\n",
    "#    Bore\n",
    "\n",
    "#Categorical variables:\n",
    "\n",
    "#    Drive-wheels\n",
    "\n",
    "#As we now move into building machine learning models to automate our analysis, feeding the model with variables \n",
    "#that meaningfully affect our target variable will improve our model's prediction performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4cdaf645",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Explore your target variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61fa340c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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