Python Class Work 2.ipynb

{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "# import pandas library\n",
        "import pandas as pd\n",
        "import matplotlib.pylab as plt\n",
        "import numpy as np"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "# Read the online file by the URL provides above, and assign it to variable \"df\"\n",
        "other_path = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/auto.csv\"\n",
        "df = pd.read_csv(other_path, header=None)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "# create headers list\n",
        "headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
        "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
        "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
        "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n",
        "print(\"headers\\n\", headers)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "headers\n ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.columns = headers"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.dropna(subset=[\"price\"], axis=0)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "     symboling normalized-losses         make fuel-type aspiration  \\\n0            3                 ?  alfa-romero       gas        std   \n1            3                 ?  alfa-romero       gas        std   \n2            1                 ?  alfa-romero       gas        std   \n3            2               164         audi       gas        std   \n4            2               164         audi       gas        std   \n..         ...               ...          ...       ...        ...   \n200         -1                95        volvo       gas        std   \n201         -1                95        volvo       gas      turbo   \n202         -1                95        volvo       gas        std   \n203         -1                95        volvo    diesel      turbo   \n204         -1                95        volvo       gas      turbo   \n\n    num-of-doors   body-style drive-wheels engine-location  wheel-base  ...  \\\n0            two  convertible          rwd           front        88.6  ...   \n1            two  convertible          rwd           front        88.6  ...   \n2            two    hatchback          rwd           front        94.5  ...   \n3           four        sedan          fwd           front        99.8  ...   \n4           four        sedan          4wd           front        99.4  ...   \n..           ...          ...          ...             ...         ...  ...   \n200         four        sedan          rwd           front       109.1  ...   \n201         four        sedan          rwd           front       109.1  ...   \n202         four        sedan          rwd           front       109.1  ...   \n203         four        sedan          rwd           front       109.1  ...   \n204         four        sedan          rwd           front       109.1  ...   \n\n     engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n0            130         mpfi  3.47    2.68               9.0        111   \n1            130         mpfi  3.47    2.68               9.0        111   \n2            152         mpfi  2.68    3.47               9.0        154   \n3            109         mpfi  3.19    3.40              10.0        102   \n4            136         mpfi  3.19    3.40               8.0        115   \n..           ...          ...   ...     ...               ...        ...   \n200          141         mpfi  3.78    3.15               9.5        114   \n201          141         mpfi  3.78    3.15               8.7        160   \n202          173         mpfi  3.58    2.87               8.8        134   \n203          145          idi  3.01    3.40              23.0        106   \n204          141         mpfi  3.78    3.15               9.5        114   \n\n     peak-rpm city-mpg highway-mpg  price  \n0        5000       21          27  13495  \n1        5000       21          27  16500  \n2        5000       19          26  16500  \n3        5500       24          30  13950  \n4        5500       18          22  17450  \n..        ...      ...         ...    ...  \n200      5400       23          28  16845  \n201      5300       19          25  19045  \n202      5500       18          23  21485  \n203      4800       26          27  22470  \n204      5400       19          25  22625  \n\n[205 rows x 26 columns]",
            "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>make</th>\n      <th>fuel-type</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>...</th>\n      <th>engine-size</th>\n      <th>fuel-system</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>?</td>\n      <td>alfa-romero</td>\n      <td>gas</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>...</td>\n      <td>130</td>\n      <td>mpfi</td>\n      <td>3.47</td>\n      <td>2.68</td>\n      <td>9.0</td>\n      <td>111</td>\n      <td>5000</td>\n      <td>21</td>\n      <td>27</td>\n      <td>13495</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>?</td>\n      <td>alfa-romero</td>\n      <td>gas</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>...</td>\n      <td>130</td>\n      <td>mpfi</td>\n      <td>3.47</td>\n      <td>2.68</td>\n      <td>9.0</td>\n      <td>111</td>\n      <td>5000</td>\n      <td>21</td>\n      <td>27</td>\n      <td>16500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>?</td>\n      <td>alfa-romero</td>\n      <td>gas</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>...</td>\n      <td>152</td>\n      <td>mpfi</td>\n      <td>2.68</td>\n      <td>3.47</td>\n      <td>9.0</td>\n      <td>154</td>\n      <td>5000</td>\n      <td>19</td>\n      <td>26</td>\n      <td>16500</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>gas</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>...</td>\n      <td>109</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>10.0</td>\n      <td>102</td>\n      <td>5500</td>\n      <td>24</td>\n      <td>30</td>\n      <td>13950</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>gas</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>...</td>\n      <td>136</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.0</td>\n      <td>115</td>\n      <td>5500</td>\n      <td>18</td>\n      <td>22</td>\n      <td>17450</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>200</th>\n      <td>-1</td>\n      <td>95</td>\n      <td>volvo</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>109.1</td>\n      <td>...</td>\n      <td>141</td>\n      <td>mpfi</td>\n      <td>3.78</td>\n      <td>3.15</td>\n      <td>9.5</td>\n      <td>114</td>\n      <td>5400</td>\n      <td>23</td>\n      <td>28</td>\n      <td>16845</td>\n    </tr>\n    <tr>\n      <th>201</th>\n      <td>-1</td>\n      <td>95</td>\n      <td>volvo</td>\n      <td>gas</td>\n      <td>turbo</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>109.1</td>\n      <td>...</td>\n      <td>141</td>\n      <td>mpfi</td>\n      <td>3.78</td>\n      <td>3.15</td>\n      <td>8.7</td>\n      <td>160</td>\n      <td>5300</td>\n      <td>19</td>\n      <td>25</td>\n      <td>19045</td>\n    </tr>\n    <tr>\n      <th>202</th>\n      <td>-1</td>\n      <td>95</td>\n      <td>volvo</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>109.1</td>\n      <td>...</td>\n      <td>173</td>\n      <td>mpfi</td>\n      <td>3.58</td>\n      <td>2.87</td>\n      <td>8.8</td>\n      <td>134</td>\n      <td>5500</td>\n      <td>18</td>\n      <td>23</td>\n      <td>21485</td>\n    </tr>\n    <tr>\n      <th>203</th>\n      <td>-1</td>\n      <td>95</td>\n      <td>volvo</td>\n      <td>diesel</td>\n      <td>turbo</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>109.1</td>\n      <td>...</td>\n      <td>145</td>\n      <td>idi</td>\n      <td>3.01</td>\n      <td>3.40</td>\n      <td>23.0</td>\n      <td>106</td>\n      <td>4800</td>\n      <td>26</td>\n      <td>27</td>\n      <td>22470</td>\n    </tr>\n    <tr>\n      <th>204</th>\n      <td>-1</td>\n      <td>95</td>\n      <td>volvo</td>\n      <td>gas</td>\n      <td>turbo</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>rwd</td>\n      <td>front</td>\n      <td>109.1</td>\n      <td>...</td>\n      <td>141</td>\n      <td>mpfi</td>\n      <td>3.78</td>\n      <td>3.15</td>\n      <td>9.5</td>\n      <td>114</td>\n      <td>5400</td>\n      <td>19</td>\n      <td>25</td>\n      <td>22625</td>\n    </tr>\n  </tbody>\n</table>\n<p>205 rows × 26 columns</p>\n</div>"
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index(['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration',\n       'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',\n       'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',\n       'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',\n       'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',\n       'highway-mpg', 'price'],\n      dtype='object')"
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "symboling              int64\nnormalized-losses     object\nmake                  object\nfuel-type             object\naspiration            object\nnum-of-doors          object\nbody-style            object\ndrive-wheels          object\nengine-location       object\nwheel-base           float64\nlength               float64\nwidth                float64\nheight               float64\ncurb-weight            int64\nengine-type           object\nnum-of-cylinders      object\nengine-size            int64\nfuel-system           object\nbore                  object\nstroke                object\ncompression-ratio    float64\nhorsepower            object\npeak-rpm              object\ncity-mpg               int64\nhighway-mpg            int64\nprice                 object\ndtype: object"
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "        symboling  wheel-base      length       width      height  \\\ncount  205.000000  205.000000  205.000000  205.000000  205.000000   \nmean     0.834146   98.756585  174.049268   65.907805   53.724878   \nstd      1.245307    6.021776   12.337289    2.145204    2.443522   \nmin     -2.000000   86.600000  141.100000   60.300000   47.800000   \n25%      0.000000   94.500000  166.300000   64.100000   52.000000   \n50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n75%      2.000000  102.400000  183.100000   66.900000   55.500000   \nmax      3.000000  120.900000  208.100000   72.300000   59.800000   \n\n       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \ncount   205.000000   205.000000         205.000000  205.000000   205.000000  \nmean   2555.565854   126.907317          10.142537   25.219512    30.751220  \nstd     520.680204    41.642693           3.972040    6.542142     6.886443  \nmin    1488.000000    61.000000           7.000000   13.000000    16.000000  \n25%    2145.000000    97.000000           8.600000   19.000000    25.000000  \n50%    2414.000000   120.000000           9.000000   24.000000    30.000000  \n75%    2935.000000   141.000000           9.400000   30.000000    34.000000  \nmax    4066.000000   326.000000          23.000000   49.000000    54.000000  ",
            "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>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>compression-ratio</th>\n      <th>city-mpg</th>\n      <th>highway-mpg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.834146</td>\n      <td>98.756585</td>\n      <td>174.049268</td>\n      <td>65.907805</td>\n      <td>53.724878</td>\n      <td>2555.565854</td>\n      <td>126.907317</td>\n      <td>10.142537</td>\n      <td>25.219512</td>\n      <td>30.751220</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.245307</td>\n      <td>6.021776</td>\n      <td>12.337289</td>\n      <td>2.145204</td>\n      <td>2.443522</td>\n      <td>520.680204</td>\n      <td>41.642693</td>\n      <td>3.972040</td>\n      <td>6.542142</td>\n      <td>6.886443</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-2.000000</td>\n      <td>86.600000</td>\n      <td>141.100000</td>\n      <td>60.300000</td>\n      <td>47.800000</td>\n      <td>1488.000000</td>\n      <td>61.000000</td>\n      <td>7.000000</td>\n      <td>13.000000</td>\n      <td>16.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>94.500000</td>\n      <td>166.300000</td>\n      <td>64.100000</td>\n      <td>52.000000</td>\n      <td>2145.000000</td>\n      <td>97.000000</td>\n      <td>8.600000</td>\n      <td>19.000000</td>\n      <td>25.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>97.000000</td>\n      <td>173.200000</td>\n      <td>65.500000</td>\n      <td>54.100000</td>\n      <td>2414.000000</td>\n      <td>120.000000</td>\n      <td>9.000000</td>\n      <td>24.000000</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>102.400000</td>\n      <td>183.100000</td>\n      <td>66.900000</td>\n      <td>55.500000</td>\n      <td>2935.000000</td>\n      <td>141.000000</td>\n      <td>9.400000</td>\n      <td>30.000000</td>\n      <td>34.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>120.900000</td>\n      <td>208.100000</td>\n      <td>72.300000</td>\n      <td>59.800000</td>\n      <td>4066.000000</td>\n      <td>326.000000</td>\n      <td>23.000000</td>\n      <td>49.000000</td>\n      <td>54.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.describe(include=\"all\")"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "         symboling normalized-losses    make fuel-type aspiration  \\\ncount   205.000000               205     205       205        205   \nunique         NaN                52      22         2          2   \ntop            NaN                 ?  toyota       gas        std   \nfreq           NaN                41      32       185        168   \nmean      0.834146               NaN     NaN       NaN        NaN   \nstd       1.245307               NaN     NaN       NaN        NaN   \nmin      -2.000000               NaN     NaN       NaN        NaN   \n25%       0.000000               NaN     NaN       NaN        NaN   \n50%       1.000000               NaN     NaN       NaN        NaN   \n75%       2.000000               NaN     NaN       NaN        NaN   \nmax       3.000000               NaN     NaN       NaN        NaN   \n\n       num-of-doors body-style drive-wheels engine-location  wheel-base  ...  \\\ncount           205        205          205             205  205.000000  ...   \nunique            3          5            3               2         NaN  ...   \ntop            four      sedan          fwd           front         NaN  ...   \nfreq            114         96          120             202         NaN  ...   \nmean            NaN        NaN          NaN             NaN   98.756585  ...   \nstd             NaN        NaN          NaN             NaN    6.021776  ...   \nmin             NaN        NaN          NaN             NaN   86.600000  ...   \n25%             NaN        NaN          NaN             NaN   94.500000  ...   \n50%             NaN        NaN          NaN             NaN   97.000000  ...   \n75%             NaN        NaN          NaN             NaN  102.400000  ...   \nmax             NaN        NaN          NaN             NaN  120.900000  ...   \n\n        engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\ncount    205.000000          205   205     205        205.000000        205   \nunique          NaN            8    39      37               NaN         60   \ntop             NaN         mpfi  3.62    3.40               NaN         68   \nfreq            NaN           94    23      20               NaN         19   \nmean     126.907317          NaN   NaN     NaN         10.142537        NaN   \nstd       41.642693          NaN   NaN     NaN          3.972040        NaN   \nmin       61.000000          NaN   NaN     NaN          7.000000        NaN   \n25%       97.000000          NaN   NaN     NaN          8.600000        NaN   \n50%      120.000000          NaN   NaN     NaN          9.000000        NaN   \n75%      141.000000          NaN   NaN     NaN          9.400000        NaN   \nmax      326.000000          NaN   NaN     NaN         23.000000        NaN   \n\n        peak-rpm    city-mpg highway-mpg price  \ncount        205  205.000000  205.000000   205  \nunique        24         NaN         NaN   187  \ntop         5500         NaN         NaN     ?  \nfreq          37         NaN         NaN     4  \nmean         NaN   25.219512   30.751220   NaN  \nstd          NaN    6.542142    6.886443   NaN  \nmin          NaN   13.000000   16.000000   NaN  \n25%          NaN   19.000000   25.000000   NaN  \n50%          NaN   24.000000   30.000000   NaN  \n75%          NaN   30.000000   34.000000   NaN  \nmax          NaN   49.000000   54.000000   NaN  \n\n[11 rows x 26 columns]",
            "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>make</th>\n      <th>fuel-type</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>...</th>\n      <th>engine-size</th>\n      <th>fuel-system</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>205.000000</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205.000000</td>\n      <td>...</td>\n      <td>205.000000</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205.000000</td>\n      <td>205</td>\n      <td>205</td>\n      <td>205.000000</td>\n      <td>205.000000</td>\n      <td>205</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>52</td>\n      <td>22</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>5</td>\n      <td>3</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>8</td>\n      <td>39</td>\n      <td>37</td>\n      <td>NaN</td>\n      <td>60</td>\n      <td>24</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>187</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>?</td>\n      <td>toyota</td>\n      <td>gas</td>\n      <td>std</td>\n      <td>four</td>\n      <td>sedan</td>\n      <td>fwd</td>\n      <td>front</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>mpfi</td>\n      <td>3.62</td>\n      <td>3.40</td>\n      <td>NaN</td>\n      <td>68</td>\n      <td>5500</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>?</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>41</td>\n      <td>32</td>\n      <td>185</td>\n      <td>168</td>\n      <td>114</td>\n      <td>96</td>\n      <td>120</td>\n      <td>202</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>94</td>\n      <td>23</td>\n      <td>20</td>\n      <td>NaN</td>\n      <td>19</td>\n      <td>37</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.834146</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>98.756585</td>\n      <td>...</td>\n      <td>126.907317</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>10.142537</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>25.219512</td>\n      <td>30.751220</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.245307</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.021776</td>\n      <td>...</td>\n      <td>41.642693</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.972040</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.542142</td>\n      <td>6.886443</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-2.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>86.600000</td>\n      <td>...</td>\n      <td>61.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>13.000000</td>\n      <td>16.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>94.500000</td>\n      <td>...</td>\n      <td>97.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.600000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>19.000000</td>\n      <td>25.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>97.000000</td>\n      <td>...</td>\n      <td>120.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>24.000000</td>\n      <td>30.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>102.400000</td>\n      <td>...</td>\n      <td>141.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.400000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>30.000000</td>\n      <td>34.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>120.900000</td>\n      <td>...</td>\n      <td>326.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>23.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>49.000000</td>\n      <td>54.000000</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>11 rows × 26 columns</p>\n</div>"
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df[['length','compression-ratio']].describe()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "           length  compression-ratio\ncount  205.000000         205.000000\nmean   174.049268          10.142537\nstd     12.337289           3.972040\nmin    141.100000           7.000000\n25%    166.300000           8.600000\n50%    173.200000           9.000000\n75%    183.100000           9.400000\nmax    208.100000          23.000000",
            "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>length</th>\n      <th>compression-ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>205.000000</td>\n      <td>205.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>174.049268</td>\n      <td>10.142537</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>12.337289</td>\n      <td>3.972040</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>141.100000</td>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>166.300000</td>\n      <td>8.600000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>173.200000</td>\n      <td>9.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>183.100000</td>\n      <td>9.400000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>208.100000</td>\n      <td>23.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 205 entries, 0 to 204\nData columns (total 26 columns):\n #   Column             Non-Null Count  Dtype  \n---  ------             --------------  -----  \n 0   symboling          205 non-null    int64  \n 1   normalized-losses  205 non-null    object \n 2   make               205 non-null    object \n 3   fuel-type          205 non-null    object \n 4   aspiration         205 non-null    object \n 5   num-of-doors       205 non-null    object \n 6   body-style         205 non-null    object \n 7   drive-wheels       205 non-null    object \n 8   engine-location    205 non-null    object \n 9   wheel-base         205 non-null    float64\n 10  length             205 non-null    float64\n 11  width              205 non-null    float64\n 12  height             205 non-null    float64\n 13  curb-weight        205 non-null    int64  \n 14  engine-type        205 non-null    object \n 15  num-of-cylinders   205 non-null    object \n 16  engine-size        205 non-null    int64  \n 17  fuel-system        205 non-null    object \n 18  bore               205 non-null    object \n 19  stroke             205 non-null    object \n 20  compression-ratio  205 non-null    float64\n 21  horsepower         205 non-null    object \n 22  peak-rpm           205 non-null    object \n 23  city-mpg           205 non-null    int64  \n 24  highway-mpg        205 non-null    int64  \n 25  price              205 non-null    object \ndtypes: float64(5), int64(5), object(16)\nmemory usage: 41.8+ KB\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df['city-kmpg']= 235/df['city-mpg']\n",
        "df.rename(columns={'city-kmpg':'city-l/100km'}, inplace=True)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n0          3                 ?  alfa-romero       gas        std          two   \n1          3                 ?  alfa-romero       gas        std          two   \n2          1                 ?  alfa-romero       gas        std          two   \n3          2               164         audi       gas        std         four   \n4          2               164         audi       gas        std         four   \n\n    body-style drive-wheels engine-location  wheel-base  ...  fuel-system  \\\n0  convertible          rwd           front        88.6  ...         mpfi   \n1  convertible          rwd           front        88.6  ...         mpfi   \n2    hatchback          rwd           front        94.5  ...         mpfi   \n3        sedan          fwd           front        99.8  ...         mpfi   \n4        sedan          4wd           front        99.4  ...         mpfi   \n\n   bore  stroke  compression-ratio horsepower peak-rpm  city-mpg highway-mpg  \\\n0  3.47    2.68                9.0        111     5000        21          27   \n1  3.47    2.68                9.0        111     5000        21          27   \n2  2.68    3.47                9.0        154     5000        19          26   \n3  3.19    3.40               10.0        102     5500        24          30   \n4  3.19    3.40                8.0        115     5500        18          22   \n\n   price city-l/100km  \n0  13495    11.190476  \n1  16500    11.190476  \n2  16500    12.368421  \n3  13950     9.791667  \n4  17450    13.055556  \n\n[5 rows x 27 columns]",
            "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>make</th>\n      <th>fuel-type</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>...</th>\n      <th>fuel-system</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>?</td>\n      <td>alfa-romero</td>\n      <td>gas</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>...</td>\n      <td>mpfi</td>\n      <td>3.47</td>\n      <td>2.68</td>\n      <td>9.0</td>\n      <td>111</td>\n      <td>5000</td>\n      <td>21</td>\n      <td>27</td>\n      <td>13495</td>\n      <td>11.190476</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>?</td>\n      <td>alfa-romero</td>\n      <td>gas</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>...</td>\n      <td>mpfi</td>\n      <td>3.47</td>\n      <td>2.68</td>\n      <td>9.0</td>\n      <td>111</td>\n      <td>5000</td>\n      <td>21</td>\n      <td>27</td>\n      <td>16500</td>\n      <td>11.190476</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>?</td>\n      <td>alfa-romero</td>\n      <td>gas</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>...</td>\n      <td>mpfi</td>\n      <td>2.68</td>\n      <td>3.47</td>\n      <td>9.0</td>\n      <td>154</td>\n      <td>5000</td>\n      <td>19</td>\n      <td>26</td>\n      <td>16500</td>\n      <td>12.368421</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>gas</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>...</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>10.0</td>\n      <td>102</td>\n      <td>5500</td>\n      <td>24</td>\n      <td>30</td>\n      <td>13950</td>\n      <td>9.791667</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>164</td>\n      <td>audi</td>\n      <td>gas</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>...</td>\n      <td>mpfi</td>\n      <td>3.19</td>\n      <td>3.40</td>\n      <td>8.0</td>\n      <td>115</td>\n      <td>5500</td>\n      <td>18</td>\n      <td>22</td>\n      <td>17450</td>\n      <td>13.055556</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>"
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "symboling              int64\nnormalized-losses     object\nmake                  object\nfuel-type             object\naspiration            object\nnum-of-doors          object\nbody-style            object\ndrive-wheels          object\nengine-location       object\nwheel-base           float64\nlength               float64\nwidth                float64\nheight               float64\ncurb-weight            int64\nengine-type           object\nnum-of-cylinders      object\nengine-size            int64\nfuel-system           object\nbore                  object\nstroke                object\ncompression-ratio    float64\nhorsepower            object\npeak-rpm              object\ncity-mpg               int64\nhighway-mpg            int64\nprice                 object\ncity-l/100km         float64\ndtype: object"
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "testDf = df.dropna(axis=0)\n",
        "\n",
        "indexNames = testDf[ testDf['price'] == \"?\" ].index\n",
        "testDf.drop(indexNames , inplace=True)\n",
        "\n",
        "testDf['price'] = testDf['price'].astype('int')\n",
        "\n",
        "#print(testDf['price'].describe())\n",
        "#print(testDf.info())\n",
        "#print(testDf['price'].min())\n",
        "#print(testDf['price'].max())\n",
        "\n",
        "#testDf.describe(include=\"all\")\n",
        "\n",
        "testDf['scalePrice'] = testDf['price']/testDf['price'].max()\n",
        "testDf['zScorePrice'] = (testDf['price']-testDf['price'].mean())/testDf['price'].std()\n",
        "\n",
        "testDf['zScorePrice'].std()\n"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "1.0000000000000004"
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "bins = np.linspace(min(testDf['price']),max(testDf['price']), 4)\n",
        "groups = ['Low','Medium','High']\n",
        "\n",
        "#testDf['price']\n",
        "testDf['priceBinned'] = pd.cut(testDf['price'], bins, labels=groups, include_lowest=True)\n",
        "testDf['priceBinned'].describe()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "count     201\nunique      3\ntop       Low\nfreq      171\nName: priceBinned, dtype: object"
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "#aveStroke = df['stroke'].astype('float').mean(axis=0)\n",
        "#aveStroke"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "testDf['drive-wheels'].value_counts()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "fwd    118\nrwd     75\n4wd      8\nName: drive-wheels, dtype: int64"
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=\"drive-wheels\", y=\"price\", data=testDf)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x204478dd948>"
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "y=testDf['engine-size']\n",
        "x=testDf['scalePrice']\n",
        "plt.scatter(x,y)\n",
        "plt.title(\"scatter plot\")\n",
        "plt.ylabel(\"engine size\")\n",
        "plt.xlabel(\"scalePrice\")"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0.5, 0, 'scalePrice')"
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "dfGroupBy = testDf[['drive-wheels','body-style','price']]\n",
        "#dfGroupBy.describe()\n",
        "dfGroupByResult = dfGroupBy.groupby(['drive-wheels','body-style'], as_index=False).mean()\n",
        "dfGroupByResult"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "   drive-wheels   body-style         price\n0           4wd    hatchback   7603.000000\n1           4wd        sedan  12647.333333\n2           4wd        wagon   9095.750000\n3           fwd  convertible  11595.000000\n4           fwd      hardtop   8249.000000\n5           fwd    hatchback   8396.387755\n6           fwd        sedan   9811.800000\n7           fwd        wagon   9997.333333\n8           rwd  convertible  23949.600000\n9           rwd      hardtop  24202.714286\n10          rwd    hatchback  14337.777778\n11          rwd        sedan  21711.833333\n12          rwd        wagon  16994.222222",
            "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>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>"
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "dfPivot = dfGroupByResult.pivot(index=\"drive-wheels\", columns=\"body-style\")\n",
        "dfPivot"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   price                                            \\\nbody-style   convertible       hardtop     hatchback         sedan   \ndrive-wheels                                                         \n4wd                  NaN           NaN   7603.000000  12647.333333   \nfwd              11595.0   8249.000000   8396.387755   9811.800000   \nrwd              23949.6  24202.714286  14337.777778  21711.833333   \n\n                            \nbody-style           wagon  \ndrive-wheels                \n4wd            9095.750000  \nfwd            9997.333333  \nrwd           16994.222222  ",
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <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>"
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "plt.pcolor(dfPivot, cmap='RdBu')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "dfAnova = testDf[[\"make\",\"price\"]]\n",
        "groupedAnova = dfAnova.groupby(['make'])\n",
        "groupedAnovaResults1 = stats.f_oneway(groupedAnova.get_group(\"honda\")[\"price\"],groupedAnova.get_group(\"subaru\")[\"price\"])\n",
        "print(groupedAnovaResults1)\n",
        "groupedAnovaResults2 = stats.f_oneway(groupedAnova.get_group(\"jaguar\")[\"price\"],groupedAnova.get_group(\"subaru\")[\"price\"])\n",
        "print(groupedAnovaResults2)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "F_onewayResult(statistic=0.19744030127462606, pvalue=0.6609478240622193)\nF_onewayResult(statistic=425.4959652050705, pvalue=2.5711916905902728e-11)\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "sns.regplot(x=\"wheel-base\", y=\"price\", data=testDf)\n",
        "plt.ylim(0,)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0, 47602.159882719316)"
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "stats.pearsonr(testDf['curb-weight'], testDf['price'])"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.8344145257702846, 2.1895772388936914e-53)"
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "testDf.corr()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "                   symboling  wheel-base    length     width    height  \\\nsymboling           1.000000   -0.535987 -0.365404 -0.242423 -0.550160   \nwheel-base         -0.535987    1.000000  0.876024  0.814507  0.590742   \nlength             -0.365404    0.876024  1.000000  0.857170  0.492063   \nwidth              -0.242423    0.814507  0.857170  1.000000  0.306002   \nheight             -0.550160    0.590742  0.492063  0.306002  1.000000   \ncurb-weight        -0.233118    0.782097  0.880665  0.866201  0.307581   \nengine-size        -0.110581    0.572027  0.685025  0.729436  0.074694   \ncompression-ratio  -0.182196    0.250313  0.159733  0.189867  0.259737   \ncity-mpg           -0.035527   -0.470606 -0.665192 -0.633531 -0.049800   \nhighway-mpg         0.036233   -0.543304 -0.698142 -0.680635 -0.104812   \nprice              -0.082391    0.584642  0.690628  0.751265  0.135486   \ncity-l/100km        0.066171    0.476153  0.657373  0.673363  0.003811   \nscalePrice         -0.082391    0.584642  0.690628  0.751265  0.135486   \nzScorePrice        -0.082391    0.584642  0.690628  0.751265  0.135486   \n\n                   curb-weight  engine-size  compression-ratio  city-mpg  \\\nsymboling            -0.233118    -0.110581          -0.182196 -0.035527   \nwheel-base            0.782097     0.572027           0.250313 -0.470606   \nlength                0.880665     0.685025           0.159733 -0.665192   \nwidth                 0.866201     0.729436           0.189867 -0.633531   \nheight                0.307581     0.074694           0.259737 -0.049800   \ncurb-weight           1.000000     0.849072           0.156433 -0.749543   \nengine-size           0.849072     1.000000           0.028889 -0.650546   \ncompression-ratio     0.156433     0.028889           1.000000  0.331425   \ncity-mpg             -0.749543    -0.650546           0.331425  1.000000   \nhighway-mpg          -0.794889    -0.679571           0.268465  0.972044   \nprice                 0.834415     0.872335           0.071107 -0.686571   \ncity-l/100km          0.785353     0.745059          -0.299372 -0.949713   \nscalePrice            0.834415     0.872335           0.071107 -0.686571   \nzScorePrice           0.834415     0.872335           0.071107 -0.686571   \n\n                   highway-mpg     price  city-l/100km  scalePrice  \\\nsymboling             0.036233 -0.082391      0.066171   -0.082391   \nwheel-base           -0.543304  0.584642      0.476153    0.584642   \nlength               -0.698142  0.690628      0.657373    0.690628   \nwidth                -0.680635  0.751265      0.673363    0.751265   \nheight               -0.104812  0.135486      0.003811    0.135486   \ncurb-weight          -0.794889  0.834415      0.785353    0.834415   \nengine-size          -0.679571  0.872335      0.745059    0.872335   \ncompression-ratio     0.268465  0.071107     -0.299372    0.071107   \ncity-mpg              0.972044 -0.686571     -0.949713   -0.686571   \nhighway-mpg           1.000000 -0.704692     -0.930028   -0.704692   \nprice                -0.704692  1.000000      0.789898    1.000000   \ncity-l/100km         -0.930028  0.789898      1.000000    0.789898   \nscalePrice           -0.704692  1.000000      0.789898    1.000000   \nzScorePrice          -0.704692  1.000000      0.789898    1.000000   \n\n                   zScorePrice  \nsymboling            -0.082391  \nwheel-base            0.584642  \nlength                0.690628  \nwidth                 0.751265  \nheight                0.135486  \ncurb-weight           0.834415  \nengine-size           0.872335  \ncompression-ratio     0.071107  \ncity-mpg             -0.686571  \nhighway-mpg          -0.704692  \nprice                 1.000000  \ncity-l/100km          0.789898  \nscalePrice            1.000000  \nzScorePrice           1.000000  ",
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<td>-0.110581</td>\n      <td>-0.182196</td>\n      <td>-0.035527</td>\n      <td>0.036233</td>\n      <td>-0.082391</td>\n      <td>0.066171</td>\n      <td>-0.082391</td>\n      <td>-0.082391</td>\n    </tr>\n    <tr>\n      <th>wheel-base</th>\n      <td>-0.535987</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>0.250313</td>\n      <td>-0.470606</td>\n      <td>-0.543304</td>\n      <td>0.584642</td>\n      <td>0.476153</td>\n      <td>0.584642</td>\n      <td>0.584642</td>\n    </tr>\n    <tr>\n      <th>length</th>\n      <td>-0.365404</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>0.159733</td>\n      <td>-0.665192</td>\n      <td>-0.698142</td>\n      <td>0.690628</td>\n      <td>0.657373</td>\n      <td>0.690628</td>\n      <td>0.690628</td>\n   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<td>0.849072</td>\n      <td>0.156433</td>\n      <td>-0.749543</td>\n      <td>-0.794889</td>\n      <td>0.834415</td>\n      <td>0.785353</td>\n      <td>0.834415</td>\n      <td>0.834415</td>\n    </tr>\n    <tr>\n      <th>engine-size</th>\n      <td>-0.110581</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>0.028889</td>\n      <td>-0.650546</td>\n      <td>-0.679571</td>\n      <td>0.872335</td>\n      <td>0.745059</td>\n      <td>0.872335</td>\n      <td>0.872335</td>\n    </tr>\n    <tr>\n      <th>compression-ratio</th>\n      <td>-0.182196</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>1.000000</td>\n      <td>0.331425</td>\n      <td>0.268465</td>\n      <td>0.071107</td>\n      <td>-0.299372</td>\n      <td>0.071107</td>\n      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<td>0.135486</td>\n      <td>0.834415</td>\n      <td>0.872335</td>\n      <td>0.071107</td>\n      <td>-0.686571</td>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n      <td>0.789898</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>city-l/100km</th>\n      <td>0.066171</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>-0.299372</td>\n      <td>-0.949713</td>\n      <td>-0.930028</td>\n      <td>0.789898</td>\n      <td>1.000000</td>\n      <td>0.789898</td>\n      <td>0.789898</td>\n    </tr>\n    <tr>\n      <th>scalePrice</th>\n      <td>-0.082391</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>0.071107</td>\n      <td>-0.686571</td>\n      <td>-0.704692</td>\n      <td>1.000000</td>\n      <td>0.789898</td>\n 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          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "sns.heatmap(testDf.corr())"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x20447a5d908>"
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "stats.pearsonr(testDf[\"engine-size\"],testDf[\"price\"])"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0.8723351674455185, 9.265491622198389e-64)"
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lm=LinearRegression()\n",
        "x=testDf[['highway-mpg']]\n",
        "y=testDf['price']\n",
        "lm.fit(x,y)\n",
        "yHat = lm.predict(x)\n",
        "print(x.count())\n",
        "#print(yHat)\n",
        "print(lm.intercept_)\n",
        "print(lm.coef_)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "highway-mpg    201\ndtype: int64\n38423.305858157386\n[-821.73337832]\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "z=testDf[['engine-size','curb-weight','highway-mpg']]\n",
        "y=testDf['price']\n",
        "lmr = LinearRegression()\n",
        "lmr.fit(z,y)\n",
        "yHat = lmr.predict(z)\n",
        "print(lmr.intercept_)\n",
        "print(lmr.coef_)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "-7203.194379014687\n[ 111.9726602     3.89932261 -122.58282166]\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "z.corr()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "             engine-size  curb-weight  highway-mpg\nengine-size     1.000000     0.849072    -0.679571\ncurb-weight     0.849072     1.000000    -0.794889\nhighway-mpg    -0.679571    -0.794889     1.000000",
            "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>curb-weight</th>\n      <th>highway-mpg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>engine-size</th>\n      <td>1.000000</td>\n      <td>0.849072</td>\n      <td>-0.679571</td>\n    </tr>\n    <tr>\n      <th>curb-weight</th>\n      <td>0.849072</td>\n      <td>1.000000</td>\n      <td>-0.794889</td>\n    </tr>\n    <tr>\n      <th>highway-mpg</th>\n      <td>-0.679571</td>\n      <td>-0.794889</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "sns.residplot(x,y)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x20447b29d48>"
          },
          "metadata": {},
          "execution_count": 107
        },
        {
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "axis1 = sns.distplot(y, hist=False, color=\"r\", label=\"Actual Value\")\n",
        "sns.distplot(yHat, hist=False, label=\"Fitted Values\", ax=axis1)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x20447ba89c8>"
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
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1H88oc/HFcOIE2Ocdq+anOXeHn49q8/fhStLoDhwu932yXeawjjGmGMgGgqq411l5EJBlx3D4XiLSCwgDVjlqrIjcKyIxIhKTmprqwsdTdbZ/PxQX18+TBlhPG6rZ8fPzIz09XRNHE2GMIT09HT8/vxrd58qEXUeTjyv+rTur46zcUbKqqn55U4GlxhiHJ64bY+YD88EaCHdUR9Wzus6cKjNokLVmY/16uPXWurdLNSkhISEkJyejv8w1HX5+foSEhNToHleSRjLQo9z3IcARJ3WSRcQLCAQyqrnXUXka0E5EvOynDUfvNRV40IV2q8ayaxd4ekL//nWL4+UFI0fqYHgz5e3tTVhYmLuboerIle6pTUC4PavJB+uH9ooKdVYAM+zXNwGrjPUMugKYas+uCgPCgY3OYtr3rLZjYMc8s3OXiPQH2gP6U6UpiYuDsDDw9a17rIsvhm3brN1ylVJNTrVJw/6N/yFgJbAXWGKM2S0iz4nIRLvaAiBIROKBmcBs+97dwBJgD/A18KAxpsRZTDvWLGCmHSvIjl1mGtbAunY7NSVxcRAeXj+xLrrIGh/ZvLl+4iml6pUu7lN1Y4x1St/dd8Pf/173eMeOQdeu8Je/wMyZdY+nlKqxOi/uU8qp48etPafq60mjSxfo2RM2bqyfeEqpeqVJQ9VNXJz1tb6SBsCFF8KGDfUXTylVbzRpqLqJj7e+9u1bfzGjoyEhwVrop5RqUjRpqLqJi7OmyoaG1l/MCy+0vm7aVH8xlVL1QpOGqpv4eCth1PFgl3MMH26t+9AuKqWaHE0aqm7qc7ptmdatrdXhOhiuVJOjx72q2jPGetIYPdrF6obdR06ycvcxPD2EqyI6E9E1wPExmdHRsHSp9R41PXNcKdVgNGmo2jt+3Dqe1YUnjaT0XO55fxNxJ3Lw9BBKjeG1b+MI79SGN28fQZ/gNufecOGF8M9/Wkmpvp9klFK1pt1TqvbKZk5V80P9aHYet769ntScAl762WA2PX0lm56+kjlTBpOZW8jPXv+RjYcyzr0pOtr6quMaSjUpmjRU7ZWt0ahium16TgHT395AVm4RH9wdzdTonnRo7UPHNr7cMrIn//7VJQS19uG2tzfw04H0szdGRFhjGzquoVSToklD1V58fLXTbf/85T6SM/J4586RDAmpfKh9zyB//vWrUYR0aMWji7aSnlNgXfD0hKgofdJQqonRpKFqLz7eOhfcyXTbvUdP8u+tycwY1YvosA5Ow7Rv7cM/pg0nK6+IX3+6ndJSez+0Cy+0drwtKGiI1iulakGThqq9hARrS3Qn5ny9j7a+Xjw4tvrV4hHdAnjmugGsiU3lnXX2ca/R0dZRstu311ODlVJ1pUlD1V5iovWk4cCPB9JYE5vKg2P70s7fx6Vwt13Uiysu6MRr38Zx4lT+2ZXhOq6hVJOhSUPVTn6+NeXWSdL427dxdA30Y8aoUJdDigjPXB9BQXEJf/3vfuje3domXcc1lGoyNGmo2jl82Pras2elS4npp9lwKIPbLuqFn7dnjcKGdWzNjItDWRxzmN1HT1pPG/qkoVSToUlD1U5iovXVwZPGv7akIAI3Duteq9APjwunvb8Pz3++BzMyGvbvh8zMurRWKVVPXEoaIjJBRGJFJF5EZju47isii+3rG0QktNy1J+3yWBEZX11M+9zwDSISZ8f0KXft5yKyR0R2i8gntf3Qqh4kJVlfKySN0lLDvzYnc2nfjnRr16pWoQNbefPIFX1ZfzCD9f3sw8N0x1ulmoRqk4aIeALzgGuACGCaiERUqHYPkGmM6QvMBebY90YAU4GBwATgdRHxrCbmHGCuMSYcyLRjIyLhwJPAJcaYgcBjtf7Uqu4SE609obqf+zSx/lA6KVl53DQipE7hp0b3JLitL/9I87feR8c1lGoSXHnSiAbijTEHjTGFwCJgUoU6k4D37ddLgXFi7UI3CVhkjCkwxhwC4u14DmPa91xhx8COOdl+/UtgnjEmE8AYoyf0uFNSEnTrBj7nzoxaujmZtr5ejB/YpU7h/bw9uXd0b9YlZLF51Hgd11CqiXAlaXQHDpf7Ptkuc1jHGFMMZANBVdzrrDwIyLJjVHyvfkA/EVknIutFZIKjxorIvSISIyIxqampLnw8VSuJiZUGwfOLSvhq5zGuH9q1xgPgjky/qCft/b35v5FTrKRhTJ1jKqXqxpWk4Whf6or/ep3Vqa9ysHbkDQfGANOAt0Wk0r4Uxpj5xpgoY0xUcHCwg3CqXjhYo/HTwXTyikq4ZlDXenkLfx8vfjG6N2t8u7KLNmcH35VSbuNK0kgGepT7PgQ44qyOiHgBgUBGFfc6K08D2tkxKr5XMvCZMabI7uqKxUoiqrGVllpTbiskje9iU2nl7VnlliE1ddtFvfD3Et6NmqhdVEo1Aa4kjU1AuD2ryQdrYHtFhTorgBn265uAVcYYY5dPtWdXhWH9kN/oLKZ9z2o7BnbMz+zXy4GxACLSEau76mBNP7CqB8eOQVFRpe6p1bEnuLhPUL10TZUJbOXNlOEh/GfA5aRu2FJvcZVStVNt0rDHFx4CVgJ7gSXGmN0i8pyITLSrLQCCRCQemAnMtu/dDSwB9gBfAw8aY0qcxbRjzQJm2rGC7NjYddNFZA9WYvmNMabcXtqq0ThYo3Eo7TSJ6bmM6V//XYJ3ju5NoZc3nxyt99BKqRpy6eQ+Y8yXwJcVyn5f7nU+cLOTe/8E/MmVmHb5QazZVRXLDVZCmulKm1UDcrBGY02sNZltTL9O9f52fYLbcHlpGh8FDeJXeQX4tPKt9/dQSrlGV4Srmit70ijXPbUmNpXewa3pGeTfIG95V19/Ulu354uVmxskvlLKNZo0VM0lJUG7dhAQAFhTbdcfTG+Qp4wyl40bTu/0w7y75ThGp94q5TaaNFTNVVijsf5gOgXFpQ0ynlHGo3dv7ty/hh2FPmxJymqw91FKVU2Thqq5Cms0Nh7KwMtDGBlaf1NtKxFhSlAJbQtzebfskCalVKPTpKFqLinpnKQRk5DJoO6BtPKpv6m2jrS+9GJu2baSr3Ye5Wh2XoO+l1LKMU0aqmays60/dvdUQXEJ25KzGBnavuHf+7LLmLHlc4wxfPiTrg5Xyh00aaiaqTDddlfKSQqLSxnRqwG7pspERtKj5DRXFh9n4cYk8otKGv49lVLn0KShaqbCwr6YhAwAohrjScPLCy6+mLtjVpCZW8TyrSkN/55KqXNo0lA1U2GNxqaETHp3bE3HNo204G70aC78/j9EdGrNO+sO6fRbpRqZJg1VM0lJ1hkanTtTWmrYnJjBiF6N8JRRZvRoxBjuCsxh//Ec1sXrTjJKNSZNGqpmEhOhRw/w8OBgWg6ZuUUNO9W2ogsvBG9vboj9gY5tfHhHp98q1ag0aaiaKbdGY1NCJtBI4xllWrWCkSPxW7Oa6Rf2YtW+ExxKO914769UC6dJQ9VMuTUaMQmZdGjtQ1jH1o3bhiuvhJgYpvcPwMfTg/f0aUOpRqNJQ7musBCOHj0zCL4zJYuhIYFYR7s3oquuAmPotHEt1w/tyqebk8nOK2rcNijVQmnSUK5LTrbO6e7Vi9zCYuJP5DA4pNKJuw3vwguhbVv45hvuviSM3MISlmw6XP19Sqk606ShXFdujcaeIycpNTCke2Djt8PbG8aMgW+/ZVD3QKLDOvDejwkUl5Q2fluUamE0aSjXlVujsSM5G4DBIW5IGmB1UR08CAcPcvclYaRk5fHfPcfd0xalWhCXkoaITBCRWBGJF5HZDq77ishi+/oGEQktd+1JuzxWRMZXF9M+N3yDiMTZMX3s8jtFJFVEttl/flGXD65qoWwLkR492JWSTae2vnQO8HNPW666yvr6zTdcFdGZ0CB/Xl8Tr4v9lGpg1SYNEfEE5gHXABHANBGJqFDtHiDTGNMXmAvMse+NAKYCA4EJwOsi4llNzDnAXGNMOJBpxy6z2BgTaf95u1afWNVeYiJ07Qq+vuxIyWawO7qmyvTvDyEh8M03eHoID4zpy66Uk6yJTXVfm5RqAVx50ogG4o0xB40xhcAiYFKFOpOA9+3XS4FxYk2pmQQsMsYUGGMOAfF2PIcx7XuusGNgx5xc+4+n6pV9+NLpgmIOpOa4r2sKQATGj4dvvoHCQiYP6073dq34+6o4fdpQqgG5kjS6A+WnpiTbZQ7rGGOKgWwgqIp7nZUHAVl2DEfvNUVEdojIUhHp4aixInKviMSISExqqv7WWa/sNRq7j5zEGNz7pAEwaRKcPAlr1uDj5cH9Y/qwNSmLHw/o1iJKNRRXkoajSfgVf5VzVqe+ygH+A4QaY4YA33L2yebcysbMN8ZEGWOigoMb7vjRFqe01EoaPXuyM8UeBHd30rjySvD3h+XLAbh5RAidA3x57dv9+rShVANxJWkkA+V/qw8BjjirIyJeQCCQUcW9zsrTgHZ2jHPeyxiTbowpsMv/CYxwoe2qvqSmQkEB9OrFzuQsugT40cldg+BlWrWCCRNgxQooLcXP25OHrwhnU0Im3+494d62KdVMuZI0NgHh9qwmH6yB7RUV6qwAZtivbwJWGetXvRXAVHt2VRgQDmx0FtO+Z7UdAzvmZwAi0rXc+00E9tbso6o6KbdGY2dKNoPc/ZRRZvJkSEmBzZsBuGVkD3oHt+alr/bqug2lGkC1ScMeX3gIWIn1g3qJMWa3iDwnIhPtaguAIBGJB2YCs+17dwNLgD3A18CDxpgSZzHtWLOAmXasIDs2wCMisltEtgOPAHfW7aOrGrGTRl63EA6mnWZgtwA3N8h23XXg6Xmmi8rb04PZEy7gQOppFsfoKnGl6ps0577fqKgoExMT4+5mNA9/+Qs88QQ79x7mhve288b04VwzuGv19zWGceOsPbH27AHAGMMtb63nYNppVj1xOQF+3m5uoFLnFxHZbIyJcnRNV4Qr1yQmQkAA+3KsXzL6d2nr5gaVc9NNsHcvbNsGgIjwu+sHkHG6gFdXxrq5cUo1L5o0lGvsNRr7j5/C18uDXkGNvB16VW65xTpN8P2zE+qGhLTjjotD+XB9IluTMt3YOKWaF00ayjX2Go19x04R3rkNnh6NvB16VTp0gIkT4eOPoejsFum/vrofndv68eS/d1Kkg+JK1QtNGso15Z40+nduIoPg5c2YYU0L/uqrM0Vt/bz5w8SB7Dt2ijfWHHBj45RqPjRpqOqdOgWZmWT16M3xkwX079LG3S2qbPx46NwZ3nvv3OKBnZk4tBt/+18cW7SbSqk606Shqmfvbrsv2DrmtX+XJvik4e0N06fD55/DibML+0SEF24cRJcAPx5btI2cguIqgiilqqNJQ1XPXqOxv1UQAP07N6GZU+Xde681pjFv3jnFAX7evDY1kuTMXJ5Zvku3GFGqDjRpqOqVPWkYfwJbedM5wNfNDXKif39rQHzePMjNPefSyNAOPHZlP5ZtTeHD9YluaqBS5z9NGqp6iYng7c3+U6X079wWawf7JuqJJyA9/Zzpt2UeGtuXcRd04rn/7GFzYoYbGqfU+U+ThqpeYiImJITY46ea1qI+Ry69FKKj4a9/hZKScy55eAh/vSWS7u1b8auPtnDiVL6bGqnU+UuThqpeUhJHwwdxKr+Yfk09aYhYTxvx8bBoUaXLga28efO2EZzML+Khj7fq+g2lakiThqpeYiKxvQYAcEFTTxoAU6bAsGHw1FOQl1fp8oCuAcyZMoSNCRm8+OU+NzRQqfOXJg1VtaIiOHKE2OBQAPp1Og+ShoeHtcFiUhL87W8Oq0yK7M6do0J5Z90hPtuW0sgNVOr8pUlDVS0lBUpLifUPpmugH4H+58mOsWPHwg03wJ//fM66jfKevm4A0aEdmP2vnew7drKRG6jU+UmThqqavUYj1vjTr6muz3DmlVcgPx8efdThZW9PD/4xfRht/by4/8PNZOcVOaynlDpLk4aqWlISxeJBfN55Mp5RXv/+8PvfWwPiS5c6rNKprR9v3Dac5Mw8Zi3doQv/lKqGS0lDRCaISKyIxIvIbAfXfUVksX19g4iElrv2pF0eKyLjq4tpHwG7QUTi7Jg+Fd7rJhExIuLwgBBVzxITSejQjcISc/49aQDMng0jR8KvfuW0m/Iu3kQAACAASURBVGpErw78Znx/vt59jE83JzdyA5U6v1SbNETEE5gHXANEANNEJKJCtXuATGNMX2AuMMe+NwLr/O+BwATgdRHxrCbmHGCuMSYcyLRjl7WlLdZRrxtq93FVjSUlEdtnCNDEDl5ylZeXtdDv1Clrb6pix3tP/XJ0by7uHcQfV+wmMf10IzdSqfOHK08a0UC8MeagMaYQWARMqlBnElC2BHcpME6sZcOTgEXGmAJjzCEg3o7nMKZ9zxV2DOyYk8u9z/PAy4CuymosiYnEhg7EQ6Bvpya4u60rBgyAN96Ab7+FWbMcVvHwEP7y86F4eAgzl2ynpFS7qZRyxJWk0R04XO77ZLvMYR1jTDGQDQRVca+z8iAgy45xznuJyDCghzHmcxfarOpLYiKxwaGEdmyNn7enu1tTe3fdBY88Yq0U/+ADh1W6tWvFHycOZHNiJp9sTGrkBip1fnAlaTjaaKjir2HO6tRLuYh4YHV7/bqKdloNEblXRGJEJCY1NbW66qoqxljdU62Dz79BcEdefdWainvvvbBpk8MqNw7rziV9g3j5632cOKkPtEpV5ErSSAZ6lPs+BDjirI6IeAGBQEYV9zorTwPa2THKl7cFBgFrRCQBuAhY4Wgw3Bgz3xgTZYyJCg4OduHjKafS0sgrKiVRWp2fg+AVeXvDkiXQtSvceCMcO1apiojwwuTBFBSX8tzne9zQSKWaNleSxiYg3J7V5IM1sL2iQp0VwAz79U3AKmPNXVwBTLVnV4UB4cBGZzHte1bbMbBjfmaMyTbGdDTGhBpjQoH1wERjTEwtP7dyRWIicR17YJDm8aQB0LEjLF8OmZnWdiMFBZWqhHVszUNj+/L5jqOsi09zQyOVarqqTRr2+MJDwEpgL7DEGLNbRJ4TkYl2tQVAkIjEAzOB2fa9u4ElwB7ga+BBY0yJs5h2rFnATDtWkB1buUNSErEdrdP6msWTRpmhQ61jYX/80RrncOC+y3sT0r4VL3yxVwfFlSrHq/oqYIz5EviyQtnvy73OB252cu+fgD+5EtMuP4g1u6qq9oxxpd2qjuxBcF8voVdQa3e3pn7dfDM8+SS8+KK1ueH9959z2dfLk99OuIBHFm7l31uSuTmqh5NASrUsuiJcOZeURGyX3oR3aounRxM+eKm2nn8err3WetrYsqXS5RuGdGVoj3a8+t9Y8gpLHARQquXRpKGcS0wktlMY/bsEuLslDcPT05p+26kTTJsGp89d1Cci/O66ARw/WcCCtQfd1EilmhZNGsqpzCMnOOEXQP8u5+miPlcEBcGHH0JcnMONDUeGdmDcBZ2Y//1BTubrhoZKadJQTsXav3g32yeNMmPHWntULVgAK1dWuvzYlf04mV/Mu2sTGr9tSjUxmjSUY6dPE+vTHoD+zWnmlDPPPgv9+sGDD1Y67W9wSCBXDujM22sP6vbpqsXTpKEcs6fbBnqW0jnA192taXi+vvD663DggDWjqoLHrgznVH4x76475IbGKdV0aNJQjiUmEhvci/6B3lj7SLYA48ZZO+G+9BLEx59zaVD3QK6O6Mw7aw+RU+B4p1ylWgJNGsohc/AQ+4N70b97e3c3pXG9+qq13cizz1a69KsxfTiZX8wi3cxQtWCaNJRDRxKPcsq3Nf17d3Z3UxpXly7WLKqFC2HHjnMuDevZngvDOrBg7SGKSkrd1ECl3EuThnJo/7FTAPTv2sxnTjnym99AQAD87neVLt1/eR+OZufzn+0V9+xUqmXQpKEc2mdPt21We065qn17+O1v4T//gfXrz7k0pn8w/Tu35a3vDup54qpF0qShHNqPP11L8whs5e3uprjHI49YyeOVV84pFhHuu7w3scdPsSZWz2tRLY8mDVVZTg772nahv08LXpPQpg088AAsW1ZpJtUNQ7vRLdCPN7874KbGKeU+mjRUJcWHEjgQ1IP+Hfzc3RT3eughaybV3LnnFHt7enD3pWFsOJTB1qRMNzVOKffQpKEqSdibQKGXN/1DOri7Ke7VpQvcfju8+y6knXsY09TongT4eTH/e93IULUsmjRUJfsSrL76fgN6urklTcDMmda2Im+9dU5xG18vbr+4F1/vPsahtNNOblaq+dGkoSrZn3oaj9JS+vbXg4eIiLBWis+fDyXnnqlx56gwvD09ePsHfdpQLYdLSUNEJohIrIjEi8hsB9d9RWSxfX2DiISWu/akXR4rIuOri2mfG75BROLsmD52+f0islNEtonIWhGJqMsHV87ty/Mg9HQafj4uHezY/N13HyQlVdoBN7itLz8b1p2lm5NJz6l81rhSzVG1SUNEPIF5wDVABDDNwQ/se4BMY0xfYC4wx743ApgKDAQmAK+LiGc1MecAc40x4UCmHRvgE2PMYGNMJPAy8NdafmZVjf3ShgtKTrq7GU3HpEnQuTO8+WalS78YHUZBcSkfrdetRVTL4MqTRjQQb4w5aIwpBBYBkyrUmQS8b79eCowTa5e7ScAiY0yBMeYQEG/HcxjTvucKOwZ2zMkAxpjyP8VaA7qyqgHkFhaT6N+efn66TcYZPj5w993wxRdw+PA5l/p2asvY/sF8uD6B/CI9ElY1f64kje5A+X8pyXaZwzrGmGIgGwiq4l5n5UFAlh2j0nuJyIMicgDrSeMRR40VkXtFJEZEYlJTdfFVTcUfPIYRDy7o2MrdTWlafvlLMAbefrvypdG9ScspZPnWFDc0TKnG5UrScLQvdsXf8p3Vqa9y64Ux84wxfYBZQOWNgaw6840xUcaYqODgYEdVVBX27bW6Wfr16ujmljQxYWHWgPiHH1rJo5yL+wQR0TWAt9ceorRUH4BV8+ZK0kgGyk+jCQEq7tZ2po6IeAGBQEYV9zorTwPa2TGcvRdY3VmTXWi7qqH9SWn4FeXT64Je7m5K03P77XDoEKxbd06xiHDvZb2JP5HDd/v16VY1b64kjU1AuD2ryQdrYHtFhTorgBn265uAVcbazW0FMNWeXRUGhAMbncW071ltx8CO+RmAiISXe7/rgLiafVTliti0fMLTDuPZu7e7m9L0/Oxn4O9vPW1UcN2QrnQN9NPFfqrZqzZp2OMLDwErgb3AEmPMbhF5TkQm2tUWAEEiEg/MBGbb9+4GlgB7gK+BB40xJc5i2rFmATPtWEF2bICHRGS3iGyz36MsSal6FFvgSb+TxyAoyN1NaXratIEbb4QlS6Dg3Cm23p4e3DkqlJ8OprMrJdtNDVSq4Ulz3t45KirKxMTEuLsZ543M04UMe/4bno77L7/819/c3ZymaeVKmDAB/vUv68mjnOy8Ika9+D+uiujMa1OHuamBStWdiGw2xkQ5uqYrwtUZe49Zs5r7BeqiPqfGjbP2pHLQRRXYyptbRvbk8x1HScnKc0PjlGp4mjTUGXsPWzu2RnQPdHNLmjAvL7j1VmvNRnp6pcu/GB0GwHzdNl01U5o01Bl7DxylY04mwX115lSVbr8dioqssY0KurVrxc+Gd2fRpsOkntKtRVTzo0lDnbH36CkGpB6Cvn3d3ZSmbehQGDQIPvrI4eX7L+9DYUkp76w71MgNU6rhadJQABSVlBKXY4g4oUmjWiJw223w449woHI3VO/gNlw7uCsf/pRIdl4LPv1QNUuaNBQAB1NPU4gw4OQR6NTJ3c1p+qZPt5KHk6eNB8b0IaegmA9/SmjUZinV0DRpKAD2HrVmTg3wN9YPQ1W1kBAYO9ZKGg6mrQ/sFsjY/sG8sy6B3MJiBwGUOj9p0lCAlTR8Sorp3UVnTrnsttsgPh42bXJ4+cGxfck4XciijYcdXlfqfKRJQwGw50g24WmJePft4+6mnD9+9jPw9YVPPnF4OSq0A9FhHZj//UEKinXbdNU8aNJQAOxNyWLAiYPQR5OGywID4frrYdEiKHbcBfXQ2L4cO5nPv7fotumqedCkoUg9VUBaXgkDdOZUzd16Kxw/DqtXO7w8OrwjQ0ICmbc6nsJiPdhKnf80aaizg+CaNGru2mutJw4nXVQiwsyr+pGcmcfiTXokrDr/adJQZ5PGqWPQrZubW3Oe8fODKVOsDQzzHO83dXm/YKJDO/D3VfHkFerYhjq/adJQ7EzJpnvhSdqHhoCH/i9RY7feCqdOWftROSAiPDG+P6mnCvjgp4RGbZpS9U1/Qih2pmQz5Fg8DBjg7qacn8aMsXa+ddJFBRAd1oHL+wXzxncHOJmvq8TV+UuTRguXnVtEYnougw/u0KRRW56eMHWq9aSRmem02hNX9ycrt4gFP+ieVOr8pUmjhdtpnzI35FicJo26mD4dCgvh3/92WmVwSCDXDOrC2z8cJON0YSM2Tqn641LSEJEJIhIrIvEiMtvBdV8RWWxf3yAioeWuPWmXx4rI+Opi2ueGbxCRODumj10+U0T2iMgOEfmfiOj+3fVgR0oWAIOOH9CkURcjRkB4OHz8cZXVZl7Vj7yiEt5YE99IDVOqflWbNETEE5gHXANEANNEJKJCtXuATGNMX2AuMMe+NwKYCgwEJgCvi4hnNTHnAHONMeFAph0bYCsQZYwZAiwFXq7dR1bl7UrJpif5tCvK0+m2dSFiDYivWQPJyU6rhXduy43DQvjgp0SOZec3XvuUqieuPGlEA/HGmIPGmEJgETCpQp1JwPv266XAOBERu3yRMabAGHMIiLfjOYxp33OFHQM75mQAY8xqY0yuXb4eCKn5x1UV7UjOZvCpI1bC8PFxd3POb7ffbm1e6GTn2zKPXRmOMfDKythGaphS9ceVpNEdKL/jWrJd5rCOMaYYyAaCqrjXWXkQkGXHcPZeYD19fOWosSJyr4jEiEhMampqtR+uJcs4XUhyZh5DkvZo11R96NMHRo+G995zuPNtmR4d/LnrklD+tSWZncnZjdc+peqBK0nD0T7ZFf9FOKtTX+Vn30jkNiAKeMVBXYwx840xUcaYqODgYEdVlK1sEHzwrvWaNOrLjBkQGwsbNlRZ7cEr+hLU2ofnP9+DqSLBKNXUuJI0koEe5b4PAY44qyMiXkAgkFHFvc7K04B2doxK7yUiVwJPAxONMXoAcx3tTLYGwQce1ZlT9ebmm6FVK+tpowoBft48flU/NiZk8PWuY43TNqXqgStJYxMQbs9q8sEa2F5Roc4KYIb9+iZglbF+fVoBTLVnV4UB4cBGZzHte1bbMbBjfgYgIsOAt7ASxonafVxV3o7kbMJ8SwksOK1Jo74EBFjbiixa5HRbkTJTR/agX+c2vPjVPt06XZ03qk0a9vjCQ8BKYC+wxBizW0SeE5GJdrUFQJCIxAMzgdn2vbuBJcAe4GvgQWNMibOYdqxZwEw7VpAdG6zuqDbApyKyTUQqJi5VA8YYth7OYkiJ9bTBBRe4t0HNyZ13QnZ2lWs2ALw8PfjddREkZeTy3rqERmmaUnUlzbk/NSoqysTExLi7GU1SUnoul72ymuczNnL7yvcgMdHdTWo+SkuhXz9r88fvv6+2+l3vbiQmIZPVvxlDxza+jdBApaomIpuNMVGOrumK8BYqJjEDgBHbf4ChQ93cmmbGwwPuuw9++AF27662+tPXDSC3qIS53+xvhMYpVTeaNFqomMRM2vp60j/mexg2zN3NaX7uvNNa9zJ/frVV+3Zqy+0X9WLhxiR2pegUXNW0adJooTYnZBIZ6IFnaYkmjYYQHGwNiL//PuTmVlv98Sv70d7fh2c+20VpafPtMlbnP00aLVB2XhH7T5wiqjDNKoiMdG+Dmqv777cGxBcurLZqoL83T147gK1JWXy6+XC19ZVyF00aLdCWpEyMgajDu6F9e+ilez82iNGjrfGiuXOrXCFeZsrw7owMbc9LX+0jK1d3wVVNkyaNFmhzQiaeHkLkljXWU4Y4Woiv6kwEZs60BsP/+18XqgvPTRrEyfxiXtZ9qVQTpUmjBYpJzCCiS1tab9uiXVMNbepU6NoV/vIXl6oP6BrAjItDWbgxie2Hsxq4cUrVnCaNFqaopJTth7MZEWAgP18HwRuajw88/DB88w3s2OHSLY9dFU7HNr4889kuSnRQXDUxmjRamO2Hs8grKiE6197vSJNGw7vvPvD3hzlzXKoe4OfN764bwI7kbD7ZmNTAjVOqZjRptDDfx6XhITDq4Bbw9YX+/d3dpOavQwd46CFrFtXevS7dMnFoNy7pG8Scr/ZxNLvqPayUakyaNFqYtXGpDAlpR7stG2HwYPD2dneTWobf/MZ62njuOZeqiwgv3jiE4tJSnl62S7dPV02GJo0WJDuviG2Hs7isTwfYuBEuusjdTWo5Ona0xjYWL3ZpaxGAnkH+PHF1f1btO8GK7RVPI1DKPTRptCA/HUij1MClZMHp03DJJe5uUsvyxBPQujU884zLt9x1SRiRPdrxhxW7Sc/RI2SU+2nSaEG+j0ujtY8nw2I3WQWXXureBrU0QUEwaxYsWwarV7t0i6eH8PJNQ8gpKOaP/9nTwA1UqnqaNFqQtXFpXNynI97r1kLPnhAS4u4mtTy//rW1Av/RR6G42KVb+nVuy0Njw1mx/Qjf7jnewA1UqmqaNFqIxPTTJGXkMrpvEKxbp08Z7tKqFbz6KuzcCf/8p8u3/WpMH/p3bsvvlu8iO6+oARuoVNU0abQQa2JTARjtXwBHjuh4hjtNmQJjxsBTT1l/Fy7w8fLglZuHkJpTwLOf7WrY9ilVBZeShohMEJFYEYkXkdkOrvuKyGL7+gYRCS137Um7PFZExlcX0z43fIOIxNkxfezyy0Rki4gUi8hNqBr5cudRwju1ofdu+yRDfdJwHxF46y1rRf5997m0mSHAkJB2PHJFOMu3HeGzbSkN3EilHKs2aYiIJzAPuAaIAKaJSESFavcAmcaYvsBcYI59bwQwFRgITABeFxHPamLOAeYaY8KBTDs2QBJwJ/BJ7T5qy3XiZD4bEzK4dnBXWLsWAgJg4EB3N6tl69cP/vxn+Pxz+Ogjl297cGwfhvVsx++W7+JIli76U43PlSeNaCDeGHPQGFMILAImVagzCXjffr0UGCciYpcvMsYUGGMOAfF2PIcx7XuusGNgx5wMYIxJMMbsAEpr+VlbrK93H8MYuG6InTRGjQJPT3c3Sz3yiNVN+PDDcPCgS7d4eXrw2i2RlJYaHlm4leIS/eegGpcrSaM7UP5UmGS7zGEdY0wxkA0EVXGvs/IgIMuO4ey9qiQi94pIjIjEpKam1uTWZuuLHUfp26kN/YqyrYVlY8e6u0kKrMT94YdWd9VNN1ndVS7oFdSaP/9sMDGJmfxFzxVXjcyVpOHosIWKnbDO6tRXucuMMfONMVHGmKjg4OCa3NosnThldU1dN7grfPmlVXj99e5tlDorLMxKHFu3WvtTuTi+MSmyO9Oie/LGmgOsjj3RwI1U6ixXkkYy0KPc9yFAxSkfZ+qIiBcQCGRUca+z8jSgnR3D2XupGvh6V7muqS++gNBQGDDA3c1S5V1/PTz9NCxYAH/9q8u3PXtDBAO6BvDYom0kpJ1uwAYqdZYrSWMTEG7PavLBGtheUaHOCmCG/fomYJWxdlhbAUy1Z1eFAeHARmcx7XtW2zGwY35W+4+nlm9NsbqmAr3h22/huuv0pL6m6Lnn4Oabra1GFi1y6RY/b0/eum0EIvDLD2I4la/rN1TDqzZp2OMLDwErgb3AEmPMbhF5TkQm2tUWAEEiEg/MBGbb9+4GlgB7gK+BB40xJc5i2rFmATPtWEF2bERkpIgkAzcDb4mIa7u+tWC7UrLZkpTFtOiesGYN5OZaSUM1PR4e8MEHcNllcMcd1lOhC3oG+fP6rcM5mHaaxxdv00ObVIOT5rzlclRUlImJiXF3M9xm1tIdrNh+hPVPjSPwtzOt7o/0dGtVsmqaMjPhqqusU/4+/RQmVZyo6Nj7Pybw7Ird3H5RL56bNBDRp0lVByKy2RgT5eiarghvprJyC1m+LYXJw7oR6Odl/eY6bpwmjKaufXurG3H4cGtG1YcfunTbjFGh3HdZbz5cn8g/VsU3cCNVS+ZVfRV1Pvo0JpmC4lJuvygUNm2CQ4esbStU09euHfz3v3DjjVZX1f798Mc/Wl1YVZg14QJSTxXwl2/2E9DKmxmjQhunvVU5eRJSUuD48bNTiv39oUsXa8NMf3/3tk/VmCaNZqi4pJQP1ycyMrQ9Ed0C4Ll3rCeMn//c3U1TrgoIgK++ggcegBdesLqr3nnH2l7dCQ8PYY69jfqzK3ZTUmq4+9KwxmtzSYk1dXjVKvjuO6vNycnO63t4WMcNR0XBhAkwfnyVn081DZo0mqGlm5NJysjlqWsHWIPfCxdaM3MCAtzdNFUTPj7WTrhDhlizqiIjrS1HLr/c6S3enh7Mmz6chz/ZynOf76GguJT7L+/dcGMcmZnw2WewYoU12SIz0yqPiLA2ZRw40NoKvnNn66nCGMjJsZ484uJg2zYrOX74obXYcfx4uPNOmDxZjyJuojRpNDO5hcXM/XY/w3u2Y/zAzvDxx1YXwV13ubtpqjZEzm43MnWq9YP4l7+El1+2urEc8Pb04P9uHcavl2xnztf7OJyZy3MTB+LlWU9DmFlZsHy5NVD/zTdQVAQ9eljdaePGwRVXWN1Priopgc2brcOpPvrIeiLu0QNmzoRf/ALatKmfdqv6YYxptn9GjBhhWpr/+99+02vW52bToXSrYOxYY3r3NqakxL0NU3WXk2PME08Y4+FhTMeOxvztb8bk5zutXlJSauZ8tdf0mvW5ue3t9SbtlPO61crONubDD425/npjvL2NAWN69bLas3GjMaWltY9dXnGxMZ9/bsxll1nv0b69Mb/7nTEnTtRPfOUSIMY4+bnq9h/sDfmnpSWNtFP5ZuDvvza/eH+TVbBvn/VX/Pzz7m2Yql9bthgzbpz1d9ujhzGvvmpMVpbT6os3Jpnwp780I1/4xqyLT3X9fU6dMmbhQmMmTzbG1/fs+/3618Zs2FB/icKZn34y5sYbjRExxt/fSlDHjjXseypjjCaNFqG0tNTc894m0/epL0zc8ZNW4bRp1j82/YfW/JSWGrNypTGXX279M27d2pjp04354gtjcnMrVd+dkm3GvrrahM7+3DyzfKfJyi2sHLO42JhNm4x58UVjrrzSGD8/K3bXrsY88ogx69a554l1715jbrvNesJq1cqYxx835ujRxm9HC1JV0tDFfc1E2eKuZ66P4J5Lw2DXLmsAddYsePFFdzdPNaTNm2H+fGuMITMTfH2t7e9HjIDBg639xjp3Jte3FS+vP84H20/QwdeDmWEe3FR4GJ+4/daAdEzM2YHswYOt8YnJk60Du5rCVvr798Of/mSN03l7WwdY/fa30K2bu1vW7FS1uE+TRjOw58hJJr++jkv7dmTBjChrpsyUKdYisUOHoEMHdzdRNYaCAli92vp7X7PG+sWhoKBStV2devP7q3/Flu4D6J59gl9uWcGNnCBwSIQ1iH3FFdZsp6YqPt46wOqDD6yJAjfeaCWQsWOrXcuiXKNJoxk7lHaaqfN/whj46tHRBLXxhZ9+sn7T/MMf4Nln3d1E5S7FxdYP2JQUOHoU8vKsmU4+PpiAQL6T9vw92YMtx/Pw9fLgmkFdmDCoC5eGB9PG9zyYWHnwIMybB++9BxkZ0LevNbPsZz+zXqta06TRTCWknWbq/PUUlpTyyS8v5IIuAdYc+MhI64fDzp26NkNVyRjDrpSTLI5JYsW2I5zML8bbUxjcPZDIHu0Z2C2AnkH+9OzgT3AbXzw8muCeVvn5sHSpde762rVWWb9+cO211gado0bpyvMa0qTRDK2NS+PxJdaupmcSBsDdd8P771vdE6NHu7WN6vxSVFLK5sRMVseeYEtiJjuSsykoPnucrI+XByHtWxHU2ocAP28CWnkT4OdFGz8v/Lw88fP2xM/bA19vT3y9POzvPfHzssr8vD3O1Cu77uvlUb+J6OBB67CxL76wuuoKCqzxmIEDrZXnI0fCsGHWmTL6C5VTmjSakbzCEl77337mf3+QPsFteH36cPp1bmtdXLDAWgz19NPW1hNK1UFRSSmHM3JJysjlcEYuhzPzOJyRS2ZuISfzijlVUGR9zS+iLjuy+3h5lEsyVmLxLZdg/Lw98PXytM71NFBqDKbsK2UzQK3vS8tfLy7GZGbR6mQmrTNSaX38CP6nsmiXn0O3kyfo5m3o3rU9XXp3x3fABVYiGTDAGs9p4bsEa9JoBgqKS1i86TD/WBXPiVMFTIvuye+vj6CVjz2r5e234d57rW21P/9ct2BQjcYYQ1GJoaC4hPyiUvKLSs68PrfM+lrp++ISCirUrVi/oLgEA3iIIGVfBaTse4+yMsFDrNdlDzB5RSXkFpRwurCY03lF5BSVVvoMPTOPckFqAhekHmJAbioD2nrQK6Qj0rcvhIdbf/r2bTGTSqpKGufBaFfLVVpq2J6cxfKtKXy2/QhZuUVEh3bgH7cOJzrM/p+3uBheegmeeQauuQb+/W9NGKpRiQg+XoKPlwdt/dzdmurlF5VwLDufI1l5pGTlkZKZR1xiIHuPdOXb3Isoxco2gQWnGbJ3P0NXfc2Qo/9H5NH9dPKVswmk7GtYmPWnS5cW8YSiSaMJKSguIe54DluSMolJyGRtfBoZpwvx8fLg6ojOTIvuyag+QWc3n9u8Ge6/35pfP20avPuuNUdfKeWUn7cnoR1bE9qxdbnSfoDV/Rt34hS7j5xkR3IW25M688bxYZTYHTJdSvMYkp3C0KTdDF23nMFH4wgssM9n9/Oz1sSUJZGKf9q3b9TP2VBc6p4SkQnA3wBP4G1jzEsVrvsCHwAjgHTgFmNMgn3tSeAeoAR4xBizsqqY9lnii4AOwBbgdmNMYVXv4UxT7J4qLinl+KkCjmTlkZyZy4ETp4k7cYq44zkkZuSeOa6zU1tfLu3bkcv7BzOmXycC/e2nh/R066yFt96ytp8ODramHd58sxs/lVLNV15hCXuOy1nXRwAACbtJREFUZrP9cDbbk7PYkZzNobTTZ673bgUDOE2f0yfoczyRPgl76L1nM/5px88NFBgIvXs7TiihoU3qgLQ6jWmIiCewH7gKSAY2AdOMMXvK1XkAGGKMuV9EpgI3GmNuEZEIYCEQDXQDvqUspTuJKSJLgH8bYxaJyJvAdmPMG87eo6q21yVpGHtQraTUUGoMxaWGknJ/So2hqMTqcz1t95eW9ZvmFpaQk19MRm4h6TkFZJwuJC2nkNRTBRw7mX/OOc6eHkJokD/hndoS3rkN/Tq3ZVjPdnSXQmTrVjhyxDqTYO9e63yCbdus7aV79YKHH4Z77nG626lSqmFk5xaxI8VKINsPZ7H/+CmSMnLPmRDQvpUXXXyhiymgS8FJgrPTCEg/TuCxZAJSEgk8lUmrogJ8SorwKSnCt30gPt274RvSDd+g9ni0a4dH+3Z4tG+PtG+PtG9n7fjr42N1Qfv4nH3t5WXtFlxcfPZPhw5WoqqFuiaNi4E/GGPG298/CWCMebFcnZV2nZ9ExAs4BgQDs8vXLatn31YpJvASkAp0McYUl39vZ+9hqvgAtU0ab313gBe/2lfj+yry8hCC2vjQobUvHdv4ENzGl+7tW9GtXSu6t7O+9uzgj4+Xg1Wsa9ZYK1zLdO1qTRu85BLrzIHo6KaxtYNSCrC6lxPTczlwIoeDaac5mp3Hsex8jmbnc/xkPmk5hXWK///tnW2MHVUZx3//dKEVqLYF0bVFu01qYyGotQ02ENMYoliI0QTi8sVGjZCCBuMHbdOEROIX6ReCmtJGicY3ii+kTRNSW6AJSNwtSFta6NIFmtB0Q9MYaDS+IY8fznPp3Lt3budu577M9fklkz3zzDkz5z9zZp+Zc+Y+R/Y2xzZ/gSGbPpDflC1bUvf1TI51ngPhC4HXMusngGvy8vg/+zeBS93+54ayCz3dbJ+XAm+Y2VtN8ucd43S2IpJuA27z1b9JmjiHvssa91EmL5e1o6mptOzdm6b+rKejGrpI6OgvBkHHIGgA19HWJy7r16dlZnwob0MRp9Hsc4DGp/u8PHn2ZgFiWuUvWg/MbBuwrUnepkh6Js+jVoVB0ACho98YBB2DoAH6S0eR6F4ngCsy64uAk3l5vOvoPcBfW5TNs58G5vk+Go+Vd4wgCIKgSxRxGvuBpZJGJF0IjAI7G/LsBNZ5+mbgcR9r2AmMSprtX0UtBcbz9ullnvB94PvccY5jBEEQBF3inN1TPn7wDWA36fPYB83siKR7SBN17AR+CvxC0iTp6X/Uyx7xr6FeAN4C7jSz/wI026cf8rvAQ5K+Dzzn+ybvGCVQuCurjxkEDRA6+o1B0DEIGqCPdAx0GJEgCIKgXGLGkiAIgqAw4TSCIAiCwgyc05B0i6Qjkt6WtLJh20ZJk5ImJH02Y7/BbZOSNmTsI5LGJB2TtN0H7fGB/e2ef0zS4m7paySv7r1E0oOSTkk6nLEtkLTHz+UeSfPdLkn3e/0PSVqRKbPO8x+TtC5j/4Sk573M/VL5UeIkXSHpCUkvenu6q6I65kgal3TQdXzP7W237Xbvnw5omSXpOUm7KqzhuF/zA5KecVul2pTHoh+cBfgIsAzYB6zM2JcDB4HZwAjpd3ezfHkZWAJc6HmWe5mHgVFPPwCs9/QdwAOeHgW290hrbt17fA0+BawADmds9wIbPL0B+IGn1wKPkn6H80lgzO0LgFf873xPz/dt48BqL/Mo8LkOaBgGVnh6LinszfIK6hBwiacvAMa8fm217ZncPx3Q8m3g18AuX6+ihuPAZQ22arWpTpyYfliY7jQ2Ahsz67v95K4Gdjfm85N+Ghhy+zv5amU9PeT51AONTeve63PvdVlMvdOYAIY9PQxMeHorKe5YXT7gVmBrxr7VbcPA0Yy9Ll8H9ewgxUqrrA7gIlIQ0Gvabdvt3j8dqPsi4DHg08Cumdyfvdbg+z7OdKdRqTY1cN1TLWgWDmVhC3vhkCZALaRJt8mrez/yPjObAvC/l7u93euy0NON9o7h3RsfJz2lV06Hd+scAE4Be0hP1e227Xb1lc19wHeAWuClmdyfvdYAKYrFHyU9qxTyCCrWpio5n4akvcD7m2zaZGY7mtihByFNukC/1ON8aDcETVc1S7oE+D3wLTM706KLuG91WPpt1MckzQMeIXXh5h27rJBApSHpJuCUmT0raU3N3OK4fachw7VmdlLS5cAeSa0io/Zlm6qk0zCz62dQrFU4lJYhTfxppVlIkxPqbUiTIiFe+oXXJQ2b2ZSkYdJTL7QONbOmwb7P7Yua5C8dSReQHMavzOwPbq6cjhpm9oakfaT+8Xbbdrv3T5lcC3xe0lpgDvBu0ptHlTQAYGYn/e8pSY+Qpo2oVpvqRL9dPyxMH9O4kvpBsFdIA2BDnh7h7CDYlV7mt9QPtN3h6TupH2h7uEcac+ve64XpYxqbqR/su9fTN1I/2Dfu9gXAq6SBvvmeXuDb9nve2mDf2g7UX6RJv+5rsFdNx3uBeZ5+F/AkcFO7bXsm90+H2tUazg6EV0oDcDEwN5N+Grihcm2qUxe3VwvwRZLH/RfwOvUDXJtI/bkTZL4qIH2l8JJv25SxLyF9jTDpDXS22+f4+qRvX9JDvU3r3uNr8BtgCviPX4uvkfqUHwOO+d9aIxfwY6//89Q7+q/6OZ4EvpKxrwQOe5kf0YGPEIDrSK/2h4ADvqytoI6rSeF4Dvmx7p5p2273/ulQ21rDWadRKQ1e34O+HKkdp2ptKsKIBEEQBIX5f/p6KgiCIDhPwmkEQRAEhQmnEQRBEBQmnEYQBEFQmHAaQRAEQWHCaQRBl5F0j6SZ/EA1CHpOfHIbBF1E0izzKY+DoIrEm0YQlISkxZKOSvq5z3/wO0kX+RwKd0t6CrhF0s8k3exlVkl62ue7GJc01wMMbpa03/dze4+lBcE7hNMIgnJZBmwzs6uBM6S5HQD+aWbXmdlDtYw+adB24C4z+yhwPfAP0i/o3zSzVcAq4OuSRropIgjyCKcRBOXympn9ydO/JIUjgeQcGlkGTJnZfgAzO2Mp+N5ngC97OPMxUpiJpZ2tdhAUo5JRboOgj2kcJKyt/71JXjXJX7N/08x2l1mxICiDeNMIgnL5oKTVnr4VeKpF3qPAByStAvDxjCHSjHLrPTQ7kj4s6eJOVjoIihJOIwjK5UVgnaRDpBDWW/Iymtm/gS8BP5R0kDSr3hzgJ8ALwF8kHSZN5xm9AkFfEJ/cBkFJ+LSwu8zsqh5XJQg6RrxpBEEQBIWJN40gCIKgMPGmEQRBEBQmnEYQBEFQmHAaQRAEQWHCaQRBEASFCacRBEEQFOZ//nE5EQzaLjQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "v=testDf['highway-mpg']\n",
        "fit = np.polyfit(v,y,3)\n",
        "prFit = np.poly1d(fit)\n",
        "print(prFit)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "3         2\n-1.557 x + 204.8 x - 8965 x + 1.379e+05\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "pr = PolynomialFeatures(degree=2, include_bias=False)\n",
        "xPoly = pr.fit_transform(z)"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scale = StandardScaler()\n",
        "scale.fit(z)\n",
        "xScale = pr.fit_transform(z)"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "input = [('scale',StandardScaler()),('polynomial',PolynomialFeatures(degree=2)),('model',LinearRegression())]\n",
        "pipe = Pipeline(input)\n",
        "pipe.fit(z,y)\n",
        "yHat2 = pipe.predict(z)\n",
        "yHat2"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "lm.score(x,y)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.4965911884339175"
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": []
    },
    {
      "metadata": {},
      "cell_type": "raw",
      "source": [
        "%%capture\n",
        "! pip install ipywidgets\n",
        "from IPython.display import display\n",
        "#from IPython.html import widgets \n",
        "from IPython.display import display\n",
        "from ipywidgets import interact, interactive, fixed, interact_manual, widgets"
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "y_data = testDf['price']\n",
        "x_data=testDf.drop('price',axis=1)\n",
        "\n",
        "lre=LinearRegression()\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.25, random_state=1)\n",
        "\n",
        "xTrainDf = x_train[['length','curb-weight','wheel-base']]\n",
        "xTestDf = x_test[['length','curb-weight','wheel-base']]\n",
        "\n",
        "lre.fit(xTrainDf, y_train)\n",
        "print(lre.score(xTrainDf, y_train))\n",
        "print(lre.score(xTestDf, y_test))\n"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "0.685576622900421\n0.7914069795542525\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "pr = PolynomialFeatures(degree=5)\n",
        "x_train_pr = pr.fit_transform(xTrainDf)\n",
        "x_test_pr = pr.fit_transform(xTestDf)\n",
        "print(x_train_pr.shape)\n",
        "print(x_test_pr.shape)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "(150, 56)\n(51, 56)\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "poly=LinearRegression().fit(x_train_pr,y_train)\n",
        "poly"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
        "    width = 12\n",
        "    height = 10\n",
        "    plt.figure(figsize=(width, height))\n",
        "\n",
        "    ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n",
        "    ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n",
        "\n",
        "    plt.title(Title)\n",
        "    plt.xlabel('Price (in dollars)')\n",
        "    plt.ylabel('Proportion of Cars')\n",
        "\n",
        "    plt.show()\n",
        "    plt.close()"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n",
        "    width = 12\n",
        "    height = 10\n",
        "    plt.figure(figsize=(width, height))\n",
        "    \n",
        "    \n",
        "    #training data \n",
        "    #testing data \n",
        "    # lr:  linear regression object \n",
        "    #poly_transform:  polynomial transformation object \n",
        " \n",
        "    xmax=max([xtrain.values.max(), xtest.values.max()])\n",
        "\n",
        "    xmin=min([xtrain.values.min(), xtest.values.min()])\n",
        "\n",
        "    x=np.arange(xmin, xmax, 0.1)\n",
        "\n",
        "\n",
        "    plt.plot(xtrain, y_train, 'ro', label='Training Data')\n",
        "    plt.plot(xtest, y_test, 'go', label='Test Data')\n",
        "    plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n",
        "    plt.ylim([-10000, 60000])\n",
        "    plt.ylabel('Price')\n",
        "    plt.legend()"
      ],
      "execution_count": 119,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "yhat_test=poly.predict(x_train_pr)\n",
        "Title='Distribution  Plot of  Predicted Value Using Test Data vs Data Distribution of Test Data'\n",
        "DistributionPlot(y_test, yhat_test, \"Actual Values (Test)\", \"Predicted Values (Test)\", Title)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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PBLXRLfE1cocIks5hwNNmdqyZlTWzU4LjsQ7/wRt+DF4E7jazVgBmVt3Mzg/m/RP/U/FvgiYYN3PoB33k9nPxXziHA/9yzoXXtlXFJ1CbzawmcH8+ReUA3cwPV1gd3/QotI3VwFjgr8H7qIyZNTGzWD8bvw/0MbMzguNxLP78HhHsczMz6xEco91BjEUZtvRd/LGsYWb18R36Ci1IYofhf+Gobd5xQfIcqh1uHFwXtlDE60JQbgMzexjf4fJPwayq+C/v6/Dtwh/C16SHrAVC2wffVro8vvPjATM7G99GOD9v45vvDCDsi1wB+5bfvtQyf8+GZ/D9D7bGEddaoG7wXg+pCmxxzm0P3hNXF7TtsBiONrPb8Odq1F89zewy801d9gf75/DvyR+AsmYW68t3fu4M3reN8OfcO8H0HKC7mdU3sxr4Wt5wMc8V55vHjQL+bGZVzKwJcAvwZrTlC/AVUMaC4QuD8/gMEjNKyt/xn2m/CK4DlYL3eR0za2h+mM1K+C/1Ozj4fXK8xT+c4pFB3O8DnzrnQpVqVYHNzrkdZtYG3/wmZCv+ehJ+jKsGcWw2s7rAH4u22xKNknRJhE/MbBu+BuBP+GGlfhtj2abAF/hONv8FXnDOjQ/m/QVfy7TZzG4vxPb/ge9Itwb/wXsz+Bos4Hp8G8yV+AvJirD1QhfUDWY2PUq5w4KyJ+A7GO3G18aljPPtve/FX0hX42uTw9spPwC8HhyzC6IUkbZ9cL6N9hn4eFfhX5/H+emL0o34n5LX4F+/v+dT3O3A//CjDmwMyinjnNuJb04wMTgGnYI2nI8DI8z//Dob394b59x64Hx8e+gN+PNxYhy78zq+RvqNiOnP4BOn9fhOazGHZXTO/QufbMwCpuG/xIS7DJ9AfodvjjES338jWllz8MngX/DH47/4ETweDBapgN/H9fjjW9QPz4fw75ml+PftSHxyUBS34s+Dqfhk7jN85z3wnXzH40esmAA85Zz7Npj3KPBo8PrG+pJwgpmFRpyYhP91o4tz7j/B/E+CchfjOyKuxyfsISPwNbQbzeyb4Dy5PVhvA74z8pgC9m8Cvr1wdfyxCslv36KZH+zLAvw5cb1z7s+Qd/7mF9dM/IhJucHxqonvAHlVUOZgfkp4YylrfszuHUF5pwN9nXNvxVj+rCDmbfjz8QLn+6BsAp7A/wKw2cyyCthuuH8G256Kv06/GTZ9NP498i2+yVS4QcBlZrbJzJ6IUu61wWMu/heIV4FY+xWTc243fr/Pw78OT+NH14q3SV5+Zc/DXzP/EpS9DP85Br6J4H34hHwd0Ar/+sJPo0ytM7P84vhH8FqtwlfaDMP/whNyC3BTcL48TfDFP4jNBdsfHbymvfDXmePx16wv+amZlyRAaFQNERGRfJnZ74D+zjl1DBMRSTLVpIuISFRmdoyZdQ5+dm+Gb4Nf5LsiiohI/A7Xu32JiEjxHYEfZaYx/sYqI4AX0hqRiMjPhJq7iIiIiIiUMGruIiIiIiJSwqi5SxS1a9d2jRo1SncYIiIiInKYmzZt2nrnXJ3I6UrSo2jUqBFTp05NdxgiIiIicpgzs6h33FZzFxERERGREkZJuoiIiIhICaMkXURERESkhFGbdBERERFg7969rFixgt27d6c7FDkMVaxYkQYNGlC+fPm4lleSLiIiIgKsWLGCqlWr0qhRI8ws3eHIYcQ5x4YNG1ixYgWNGzeOax01dxEREREBdu/eTa1atZSgS8KZGbVq1SrUrzRK0kVEREQCStAlWQp7bilJFxEREREpYZSki4iIiIiUMErSRUREREqQUaNGYWbMmzevwGVfe+01Vq1aVeRtjR8/nrPOOuugaTt27KBWrVps2bLloOnnnHMO7777bqHKSrRnnnmGN954gxtuuIGsrCxatmxJpUqVyMrKIisri5EjRxaqvOnTp/PZZ5/l/f/hhx/y8MMPJzrsIlGSLiIiIlKCDB8+nC5dujBixIgCly1ukh5NlSpVOOOMM/jwww/zpm3ZsoWvv/466Ul4fvbt28ewYcO46KKLGDx4MDk5OYwZM4YmTZqQk5NDTk4O5513XqHKjEzS+/bty/vvv18ihuFUki4iIiIS6dZb4bTTEvt3660Fbnb79u1MnDiRoUOHHpKkP/HEE7Ru3ZrMzEzuuusuRo4cydSpU7n44ovJyspi165dNGrUiPXr1wMwdepUTjvtNAAmT57MqaeeStu2bTn11FOZP39+vnEMGDDgoO2PGjWK3r17U7ly5bjKeuCBB3jqqafy/s/IyGDZsmUAvPnmm3Ts2JGsrCyuvfZa9u/fz/79+xk4cCAZGRm0bt2aQYMGHVLmuHHjyM7Oply5/EcQX7hwIb169aJdu3Z069aNBQsWADBixAgyMjLIzMyke/fu7Nq1i4ceeoi33norrxbezOjatStjxozJdxupoHHSRUREREqIDz/8kN69e3PSSSdRs2ZNpk+fTnZ2Np9++ikffvghkyZNonLlymzcuJGaNWvy/PPP89RTT9G+fft8y23evDkTJkygXLlyfPHFF/zxj3/k/fffj7l87969ueqqq9iwYQO1atVixIgR3HTTTUUqK9zcuXN55513mDhxIuXLl+f666/nrbfeolWrVqxcuZLZs2cDsHnz5kPWnThxIu3atStwG9dccw2vvvoqTZo0YeLEidx4442MHTuWBx98kPHjx1OvXj02b95MpUqVuO+++5g9ezbPPPNM3vrt27fnq6++4je/+U1c+5QsSU3Szaw38CxQFnjVOfdYxPwKwBtAO2ADcKFzblkw727gSmA/cLNz7vP8yjSzxsAIoCYwHbjUOfejmR0PvA4cFaxzl3Mu/V+PREREpOQKS9pSafjw4dwa1Lj379+f4cOHk52dzRdffMFvf/tbKleuDEDNmjULVe6WLVu4/PLLWbhwIWbG3r17813+iCOO4Oyzz2bkyJH069ePnJwczjjjjCKVFe7f//4306ZNo0OHDgDs2rWLunXr8utf/5olS5Zw00030adPn7xthVu9ejUtWrTIt/zNmzfz7bff0q9fv7xp+/btA6Bz585cdtllnH/++fkm4HXr1k14E6KiSFqSbmZlgcFAT2AFMMXMPnbOfRe22JXAJufciWbWH3gcuNDMWgL9gVbAscAXZnZSsE6sMh8HBjnnRpjZi0HZQ4B7gHedc0OCcscAjZK13yIiIiJFsWHDBsaNG8fs2bMxM/bv34+Z8cQTT+Cci2uc7XLlynHgwAGAg9pV33vvvXTv3p1Ro0axbNmyvGYw+RkwYACPPPIIzjn69u2bdzv7eMoKjyM8Fuccl19+OX/5y18OWWfmzJl8/vnnDB48mHfffZdhw4YdNL9SpUoFthV3zlG7dm1ycnIOmffKK68wadIkRo8eTWZmJrNmzYpaxu7du6lUqVK+20mFZLZJ7wgscs4tcc79iK/l7huxTF98LTfASOB082dgX2CEc26Pc24psCgoL2qZwTo9gjIIyjwneO6AasHz6kD6vxqJiIiIRBg5ciSXXXYZubm5LFu2jOXLl9O4cWO+/vprzjjjDIYNG8bOnTsB2LhxIwBVq1Zl27ZteWU0atSIadOmARzUBGXLli3Ur18f8J1N49G9e3cWLlzI4MGDGTBgQKHKatSoEdOnTwd858ylS5cCcPrppzNy5Eh++OGHvP3Izc1l/fr1HDhwgH79+vHwww/nrRuuRYsWLFq0KN+Ya9SowTHHHMOoUaMAOHDgADNnzgRgyZIldOrUiYcffpgaNWqwcuXKQ44fwIIFC8jIyCjo8CRdMpP0+sDysP9XBNOiLuOc2wdsAWrls26s6bWAzUEZkdt6ALjEzFbga9FvihasmV1jZlPNbOq6devi30sRERGRBBg+fDjnnnvuQdP69evH22+/Te/evTn77LNp3749WVlZeZ0yBw4cyHXXXZfXcfT+++/nlltuoWvXrpQtWzavnD/84Q/cfffddO7cmf3798cVT5kyZejXrx8bNmygW7duhSqrX79+bNy4kaysLIYMGcJJJ/kGES1btuSRRx7hjDPOoE2bNvTs2ZPVq1ezcuVKTjvtNLKyshg4cGDUmvYzzzyTCRMmFBj3iBEjePHFF8nMzKRVq1aMHj0agNtuu43WrVvTunVrfvnLX5KRkUGPHj2YOXMmbdu2zRu+8csvv+RXv/pVXMcomcw5l5yCzc4Hejnnrgr+vxTo6Jy7KWyZOcEyK4L/F+Nryx8C/uucezOYPhSfYJeJVmbY8icG048DxjjnWpvZ74P9/KuZnQIMBTKccz/9BhOhffv2burUqYk8HCIiIlLCzZ07t8A2z5Je5557Lk888QRNmzZNSvmrVq1i4MCBjB07NinlRzvHzGyac+6Qnr/JrElfARwX9n8DDm1qkreMmZXDN0fZmM+6saavB44Kyojc1pXAuwDOuf8CFYHaxdgvEREREUmDxx57jNWrVyet/OXLlx80dGQ6JTNJnwI0NbPGZnYEviPoxxHLfAxcHjw/DxjnfNX+x0B/M6sQjNrSFJgcq8xgnS+DMgjK/Ch4/j1wOoCZtcAn6WrPIiIiIlLKNGvW7KCmN4l28skn06ZNm6SVXxhJG93FObfPzG4EPscPfTjMOTfHzB4CpjrnPsY3PfmHmS3C16D3D9adY2bvAt8B+4AbnHP7AaKVGWzyTmCEmT0CzAjKBvg/4BUzuw3fiXSgS1YbHxERERGRBEham/TSTG3SRUREfn7UJl2SraS0SRcRERERkSJI6h1HRaSY/vlPGD0aOnaErl2hSROI42YWIiIiUrqpJl2kpHr3XTj7bBg6FK64Apo2hfr1/fMdO9IdnYiIJEHZsmXJysoiIyOD888/P+/mRUUxfvx4zjrrLAA+/vhjHnvssZjLbt68mRdeeKHQ23jggQcOGQ1l/PjxnHLKKQdN27dvH/Xq1ct3ZJZoZSXaeeedx5IlSzj55JPJysri+OOPp06dOmRlZZGVlcWyZcsKVd4HH3zAvHnz8v6/9dZb4xrLPR5K0kVKopEj4aKLoHNn2LABZs+GIUPgtNPgtdfg9tvTHaGIiCRBpUqVyMnJYfbs2RxxxBG8+OKLB813znHgQMxbvcR09tlnc9ddd8WcX9QkPZpu3bqxYsWKgxLeL774goyMDI455piEbKMo5syZw/79+znhhBOYNGkSOTk5PPTQQ1x44YXk5OSQk5NDo0aNClVmZJJ+0003Rb0RU1EoSRcpaUaNggEDoFMn39ylalVo1Qquuw7efhv+7//gxRd9MxgREUmKW2/19SKJ/Lv11sLF0LVrVxYtWsSyZcto0aIF119/PdnZ2SxfvpyxY8dyyimnkJ2dzfnnn8/27dsB+Oyzz2jevDldunThgw8+yCvrtdde48YbbwRg7dq1nHvuuWRmZpKZmck333zDXXfdxeLFi8nKyuKOO+4A4Mknn6RDhw60adOG+++/P6+sRx99lGbNmvHLX/6S+fPnHxJ3mTJlOP/883nnnXfypo0YMYIBAwYA8Morr9ChQwcyMzPp169f1F8LTjvtNEKDeKxfvz4ved6/fz933HFHXlwvvfQSAKtXr6Zbt255v0J89dVXh5T51ltv0bdv3wKP+6effpp3bC+88EJ2BL9e33HHHbRs2ZI2bdpw55138tVXXzFmzBhuu+22vFr4Jk2asHr1ahJx93ol6SIlySefwAUXQIcOMGaMT9AjPfIIZGb6Zi9r16Y+RhERSbp9+/bx6aef0rp1awDmz5/PZZddxowZM6hSpQqPPPIIX3zxBdOnT6d9+/Y8/fTT7N69m6uvvppPPvmEr776ijVr1kQt++abb+YXv/gFM2fOZPr06bRq1YrHHnuMJk2akJOTw5NPPsnYsWNZuHAhkydPJicnh2nTpjFhwgSmTZvGiBEjmDFjBh988AFTpkyJuo0BAwYwYsQIAPbs2cOYMWPo168fAL/5zW+YMmUKM2fOpEWLFgwdOjRqGdEMHTqU6tWrM2XKFKZMmcIrr7zC0qVLefvtt+nVqxc5OTnMnDmTrKysQ9adOHEi7dq1y7f8H374gccee4x///vfTJ8+nTZt2vDss8+ydu1axowZw5w5c5g1axZ33303Xbt25Ve/+hWDBg06qBa+bQRK/7cAACAASURBVNu2fPPNN3HvUyzqOCpSUuza5RPvNm3g00+hWrXoy1WoAG+9Be3b++VHj1ZnUhGRBHvmmfRsd9euXXkJZteuXbnyyitZtWoVDRs2pFOnTgB8++23fPfdd3Tu3BmAH3/8kVNOOYV58+bRuHFjmjZtCsAll1zCyy+/fMg2xo0bxxtvvAH4NvDVq1dn06ZNBy0zduxYxo4dS9u2bQHYvn07CxcuZNu2bZx77rlUrlwZ8M1oounQoQPbt29n/vz5zJ07l06dOlGjRg0AZs+ezT333MPmzZvZvn07vXr1ivv4jB07llmzZjFy5EgAtmzZwsKFC+nQoQNXXHEFe/fu5ZxzzomapK9evZo6derkW/4333zDd999x6mnngr4Y9ulSxdq1qxJmTJluPrqq+nTp09eW/9o6taty6pVq2LOj5eSdJGS4u23Yf1632G0evX8l23VCp54Am6+2Td9+d3vUhOjiIgkVahNeqQqVarkPXfO0bNnT4YPH37QMjk5OViCKm2cc9x9991ce+21B01/5pln4t5G//79GTFiBHPnzs1r6gIwcOBAPvzwQzIzM3nttdcYP378IeuWK1cur+397t27D4rrueeei5rYT5gwgX/+859ceuml3HHHHVx22WUHza9UqdJBZUXjnKN379784x//OGTe1KlT+de//sWIESMYMmQIY8eOjVrG7t27qVSpUr7biYeau4iUBM75aps2bXzDxXjceCP06uXbqC9cmNTwRESk5OjUqRMTJ05k0aJFAOzcuZMFCxbQvHlzli5dyuLFiwEOSeJDTj/9dIYMGQL4Nt5bt26latWqbNu2LW+ZXr16MWzYsLy27itXruSHH36gW7dujBo1il27drFt2zY++eSTmHEOGDCAN998k3Hjxh1U475t2zaOOeYY9u7dy1tvvRV13UaNGjFt2jSAvFrzUFxDhgxh7969ACxYsIAdO3aQm5tL3bp1ufrqq7nyyiuZPn36IWW2aNEi75jFcuqpp/Kf//yHJUuWALBjx468XxC2bt3KWWedxaBBg5gxYwbAIcctFFNGRka+24mHknSRkmDcOD+Cy623xt90xQyGDYMDB9L3u6yIiKRcnTp1eO211xgwYABt2rShU6dOzJs3j4oVK/Lyyy/Tp08funTpQsOGDaOu/+yzz/Lll1/SunVr2rVrx5w5c6hVqxadO3cmIyODO+64gzPOOIOLLrqIU045hdatW3Peeeexbdu2vM6UWVlZ9OvXj65du8aMs2XLllSuXJkePXoc9EvAww8/zMknn0zPnj1p3rx51HVvv/12hgwZwqmnnsr69evzpl911VW0bNmS7OxsMjIyuPbaa9m3bx/jx48nKyuLtm3b8v7773PLLbccUmafPn2i1tqHq1evHkOHDuXCCy8kMzOTU089lQULFrBlyxb69OlDZmYmPXr04Omnnwb8F5E///nPeR1H9+zZw7Jly/KaCRWHOeeKXcjhpn379i7Uo1gkJc4+G779Fr7/HipWLNy6l14KH38Mq1ZB2EVQREQKJ9ot2+XwsWvXLrp3787EiRMpW7ZsUrbx3nvv8d133x00Gk64aOeYmU1zzrWPXFY16SLptnCh7/z5u98VPkEHuPZa2LoVgl70IiIicqhKlSrx4IMPsnLlyqRtwznHbbfdlpCylKSLpNtzz0G5ckXv/Nm5M7RsCcFYsSIiUnRqYXB469WrF8cff3zSyr/ggguoFmN0tsKeW0rSRdJp82bfrrx/fzj66KKVYeZr06dMgaAji4iIFF7FihXZsGGDEnVJOOccGzZsoGIhfjHXEIwi6TRsGOzYAVE6uBTKpZfCnXf62vSIW0iLiEh8GjRowIoVKxJyt0iRSBUrVqRBgwZxL6+Oo1Go46ikxIEDcOKJ0KABTJhQ/PJ++1sYOdJ3II12p1IREREpcdRxVKSkmTEDli6Fq65KTHnXXgvbt/ubIomIiEippiRdJF0++8w/9u6dmPJOPtnfDOmll/zNkURERKTUUpIuki6ffgrt2kHduokpL9SBdMYMUHMtERGRUk1Jukg6bNoE//0vnHlmYsu9+GKoXBleey2x5YqIiEhKKUkXSYd//9t3HE1UU5eQ6tWhZ09/cyQ1eRERESm1lKSLpMNnn/mE+uSTE192nz7w/fcwe3biyxYREZGUUJIukmrO+SS9Z09/p9FE+9Wv/OM//5n4skVERCQllKSLpNqcObByZeKbuoTUrw9t2ypJFxERKcWUpIukWmjoxV69kreNPn3gm29g48bkbUNERESSRkm6SKp9+ilkZPg7jSbLWWf5jqmhLwQiIiJSqihJF0ml7dvhq68SP/RipA4doE4dNXkREREppZSki6TSl1/C3r3Ja48eUqaM/yLw2Wewf39ytyUiIiIJpyRdJJU++wyqVIHOnZO/rT59fJv0b79N/rZEREQkoZSki6SKc749eo8eUKFC8rd3xhl+iMfRo5O/LREREUkoJekiqbJoESxdmvymLiFHHQVduqhduoiISCmkJF0kVSZO9I/du6dum336wP/+5+9AKiIiIqWGknSRVJk0CapVg2bNUrfNPn3845gxqdumiIiIFJuSdJFUmTzZD41YJoVvu+bN4YQT1ORFRESklFGSLpIKu3bBrFnQsWNqt2sGPXvChAkailFERKQUUZIukgo5ObBvX+qTdIBu3WDrVv8lQUREREoFJekiqTB5sn9MR5Letat/nDAh9dsWERGRIlGSLpIKkydDgwZw7LGp3/Zxx0GjRkrSRUREShEl6SKpMHlyemrRQ7p1g6++8jdUEhERkRJPSbpIsm3c6G9klM4kvWtXWLcO5s9PXwwiIiISNyXpIsk2ZYp/THdNOqjJi4iISCmhJF0k2SZN8kMhtmuXvhiaNoV69XyTFxERESnxlKSLJNvkydCihb/baLqY+SYvqkkXEREpFZSkiySTc+nvNBrSrRt8/z3k5qY7EhERESmAknSRZMrN9R02S0qSDmryIiIiUgooSRdJptBNjE4+Ob1xAGRkQPXqavIiIiJSCihJF0mmyZOhQgVo3TrdkUDZstCli2rSRURESgEl6SLJNHkyZGdD+fLpjsTr2hXmzYMffkh3JCIiIpIPJekiybJvH0ybVjLao4eoXbqIiEipoCRdJFm++w527ixZSXq7dlCpkpJ0ERGREk5JukiyhDqNlqQk/YgjoFMndR4VEREp4ZSkiyTLzJlQtSo0aZLuSA7WtSvk5MC2bemORERERGJQki6SLP/7nx/20CzdkRysUyd/k6Vp09IdiYiIiMSgJF0kGZyD2bN9kl7SdOjgH0PNcURERKTEUZIukgxr18KGDSUzSa9dG044QUm6iIhICaYkXSQZZs/2jyXhJkbRdOwIkyalOwoRERGJIalJupn1NrP5ZrbIzO6KMr+Cmb0TzJ9kZo3C5t0dTJ9vZr0KKtPMGgdlLAzKPCKYPsjMcoK/BWa2OZn7LAL8lKSXxJp0gJNPhhUrYNWqdEciIiIiUSQtSTezssBg4EygJTDAzFpGLHYlsMk5dyIwCHg8WLcl0B9oBfQGXjCzsgWU+TgwyDnXFNgUlI1z7jbnXJZzLgt4DvggWfsskmf2bKhbF+rUSXck0YWGhZwyJb1xiIiISFTJrEnvCCxyzi1xzv0IjAD6RizTF3g9eD4SON3MLJg+wjm3xzm3FFgUlBe1zGCdHkEZBGWeEyWmAcDwhO2hSCyhkV1KqrZtoWxZtUsXEREpoZKZpNcHlof9vyKYFnUZ59w+YAtQK591Y02vBWwOyoi6LTNrCDQGxkUL1syuMbOpZjZ13bp1ce6iSBQHDsCcOSU7Sa9UCdq0UZIuIiJSQiUzSY82OLSLc5lETQ/XHxjpnNsfZVmccy8759o759rXKalNFKR0yM2FHTtKdpIOvsnL5Mn+S4WIiIiUKMlM0lcAx4X93wCI7KWWt4yZlQOqAxvzWTfW9PXAUUEZsbbVHzV1kVQo6Z1GQzp2hK1bYcGCdEciIiIiEZKZpE8BmgajrhyBT5I/jljmY+Dy4Pl5wDjnnAum9w9Gf2kMNAUmxyozWOfLoAyCMj8KbcTMmgE1gP8mYT9FDhZK0lu1Sm8cBTn5ZP+oJi8iIiIlTtKS9KB9+I3A58Bc4F3n3Bwze8jMzg4WGwrUMrNFwO+Bu4J15wDvAt8BnwE3OOf2xyozKOtO4PdBWbWCskMG4DuiRjaBEUm82bOhYUOoVi3dkeSveXM48kgl6SIiIiWQKW89VPv27d3UqVPTHYaUVm3awPHHw+jR6Y6kYN27+/bzStRFRETSwsymOefaR07XHUdFEmnvXpg3r+S3Rw/p2BFycmD37nRHIiIiImGUpIsk0sKFPlEvTUn63r0wc2a6IxEREZEwStJFEqm0jOwSos6jIiIiJZKSdJFEmj3b38mzefN0RxKf+vXhmGOUpIuIiJQwStJFEmn2bGjaFCpWTHck8TH76aZGIiIiUmIoSRdJpNmzS09Tl5COHf0NjTZtSnckIiIiElCSLpIoO3fCokWlL0nv0ME/athRERGREkNJukiizJ0LzpW+JD072z/OmJHeOERERCSPknSRRCltI7uE1Krl75A6fXq6IxEREZGAknSRRJk9GypUgCZN0h1J4WVnK0kXEREpQZSkiyTKnDl+6MVy5dIdSeG1a+dvxLR1a7ojEREREZSkiyTOggWlZ3z0SGqXLiIiUqIoSRdJhB9/hKVL4aST0h1J0YSSdDV5ERERKRGUpIskwpIlcOBA6U3S69WDY49Vki4iIlJCKEkXSYT58/1jaU3SQZ1HRUREShAl6SKJsGCBf2zaNL1xFEd2NsybBzt2pDsSERGRnz0l6SKJsGAB1KkDNWqkO5Kiy872TXZmzUp3JCIiIj97StJFEmHBAmjWLN1RFI86j4qIiJQYStJFEmHBgtLdHh2gQQOoXVtJuoiISAmgJF2kuLZuhTVrSn+SbqbOoyIiIiWEknSR4gp1Gi3tSTr4JH32bNizJ92RiIiI/KwpSRcpriQl6WvXwn33wfHHQ48e8MYbKRh4JTsb9u3zibqIiIikjZJ0keJasMA3FWnSJCHFzZkDV17pk/NHHoFWreD77+Hyy+Hoo/28iRMTsqlDqfOoiIhIiaAkXaS4FiyAhg2hYsViFeOcT8AzMmD4cP983jz49FNYuBC++gouvBDefRe6dIE//zlB8Yc74QSoXl1JuoiISJopSRcprgQNv/j88zBsGNxyi685f+GFn1rQmPnE/NVXfR/VSy6BP/3JL5NQ6jwqIiJSIihJFykO52D+/GK3R585E26/Hc46CwYN8iMhxlKlik/mzz4bbrgB3nyzWJs+VHa2D2jv3gQXLCIiIvFSki5SHGvWwPbtxUrSd+yA/v2hVi34+999ZXZBypeHd96B7t1h4ED4+OMib/5Q2dl+dJd58xJYqIiIiBSGknSR4kjAyC633OIr4998M/8a9EgVK8JHH0G7dnDBBTBuXJFDOFio8+i0aQkqUERERApLSbpIcRQzSX/nHRg6FO6+2w+zWFhVq/qOpU2bQt++kJtbpDAO1rQpVK4MOTkJKExERESKQkm6SHEsWAAVKsBxxxV61aVL4ZproFMneOCBoodQsyaMHu2bx994o38slrJloU0b3y5dRERE0kJJukhxLFjga57Lli30qv/3f/5x+HDfxrw4GjaEBx/0yfqoUcUrC4CsLF+TXuyMX0RERIpCSbpIcRRxZJfcXN+e/IYboFGjxIRyyy2QmQk33QRbtxazsKws2LzZjwUpIiIiKackXaSo9u2DxYuLlKQPGeIfr7suceGUKwcvvwyrV8O99xazsKws/6h26SIiImmhJF2kqJYt84l6IZP0Xbv8TYnOOQeOPz6xIXXsCNdfD889B1OnFqOg1q2hTBkl6SIiImmiJF2kqIo4ssuIEbBhg+/kmQyPPgpHHw3XXuu/QxRJ5cp+v5Ski4iIpIWSdJGiKkKS7pyv5W7VCk47LTlhVa8Ozz4L06fD888Xo6BQ51ERERFJOSXpIkU1fz4cdVSh7kD07bcwY4avRY/nzqJFdd55cOaZcP/9sGlTEQvJyvJNejZvTmRoIiIiEgcl6SJFtWABNGtWqGz7ued8TfcllyQxLnxIjz3mR3kZNKiIhYQ6j2q8dBERkZRTki5SVKEx0uO0ejW89x789rdw5JFJjCvQpo2vUX/mGdi4sQgFZGb6RzV5ERERSTkl6SJFsXs3rFwJJ54Y9yovv+w7cl5/fRLjinD//bB9O/z1r0VY+eijoV49JekiIiJpoCRdpCiWLfO9QJs0iWvxH3+EF1/07cQLUflebBkZcP758Le/wfr1RShAnUdFRETSQkm6SFEsXuwfTzghrsU/+gjWrEnesIv5uf9+2LEDnnqqCCtnZcGcOf5bhoiIiKSMknSRoliyxD/GmaSPGgV16kCvXkmMKYaWLaF/fz8c47p1hVw5Kwv27oW5c5MSm4iIiESnJF2kKBYv9jf8qVevwEX37YNPP4U+faBs2RTEFsV99/k7nT75ZCFXDI3woiYvIiIiKaUkXaQolizxtehxDL/4zTd+qPGzzkpBXDE0bw4XXQSDB8PatYVYsWlTqFRJSbqIiEiKKUkXKYolS+LuNDp6NJQvDz17JjmmAtx7rx+UplC16WXL+rEclaSLiIiklJJ0kcJy7qea9DiMHg2/+AVUq5bkuApw0km+bfpLLxXyJqKhEV6cS1psIiIicjAl6SKFtWaNb+AdR0364sW+z+Wvf52CuOJwxx1+3PSXXirESllZPqtfvjxpcYmIiMjBlKSLFFYhhl8cPdo/9umTxHgKISsLfvlLePZZ2LOnECuBmryIiIikkJJ0kcIKDb8YR0366NHQokXczddT4o47YPVqePvtOFdo3dp3kFWSLiIikjJK0kUKa/Fin7Q2bJjvYlu3wn/+k95RXaLp2RMyM/3NjQ4ciGOFKlV8g3Yl6SIiIimjJF2ksJYsgeOOgwoV8l3sX//y9wEqaUm6Gdx+O3z3nR+/PS6hzqMiIiKSEkrSRQpr8eK42qN/8gnUqAGnnpqCmArpwgv994y4h2PMyoKlSws5LIyIiIgUlZJ0kcKKY4z0/fthzBg480woVy5FcRVC+fJw662+Oc6UKXGsEOo8OmtWUuMSERERT0m6SGFs3+5v2VlATfqUKbBuXclr6hLu6quhevU4a9M1wouIiEhKKUkXKYylS/1jATXpo0f7m3X26pWCmIqoalW47jp4//2fBqyJ6eijoV49JekiIiIpoiRdpDBC2WwBNemjR0PnzlCzZgpiKoabb/ZfJgYNimPhzEwl6SIiIimS1CTdzHqb2XwzW2Rmd0WZX8HM3gnmTzKzRmHz7g6mzzezXgWVaWaNgzIWBmUeETbvAjP7zszmmFm8o0OLHCp0I6N8atKXL4eZM0t2U5eQY4+Fiy6Cv/8dNm0qYOGsLJgzB378MSWxiYiI/JwlLUk3s7LAYOBMoCUwwMxaRix2JbDJOXciMAh4PFi3JdAfaAX0Bl4ws7IFlPk4MMg51xTYFJSNmTUF7gY6O+daAbcmaZfl52DJEt+Qu0aNmIv85z/+sWfPFMVUTLfdBjt2wCuvFLBgVpZP0OfNS0lcIiIiP2fJrEnvCCxyzi1xzv0IjAD6RizTF3g9eD4SON3MLJg+wjm3xzm3FFgUlBe1zGCdHkEZBGWeEzy/GhjsnNsE4Jz7IQn7Kj8Xixf7WnSzmItMnAjVqvkbdZYGmZnQowf87W9+XPeY1HlUREQkZZKZpNcHlof9vyKYFnUZ59w+YAtQK591Y02vBWwOyojc1knASWY20cy+NbPe0YI1s2vMbKqZTV23bl2hdlR+RpYsKbA9+sSJ0KmTb+tdWvz+97ByJYwcmc9CJ50ElSopSRcREUmBZCbp0aoaXZzLJGo6QDmgKXAaMAB41cyOOmRh5152zrV3zrWvU6dOlOLkZ2//fj+6Sz5J+ubNMHu27zRampx5JjRrBk8/DS7yXRpStqz/eUBJuoiISNIlM0lfARwX9n8DYFWsZcysHFAd2JjPurGmrweOCsqI3NYK4CPn3N6g6cx8fNIuUjgrV/r2IPl0Gv3vf32SW9qS9DJlfNv0qVPh66/zWTAryyfpMTN5ERERSYRkJulTgKbBqCtH4DuCfhyxzMfA5cHz84BxzjkXTO8fjP7SGJ9UT45VZrDOl0EZBGV+FDz/EOgOYGa18c1fChoVWuRQoZFd8qlJnzjRVziffHKKYkqgSy+FWrV8bXpMWVl+GJjly/NZSERERIoraUl60D78RuBzYC7wrnNujpk9ZGZnB4sNBWqZ2SLg98BdwbpzgHeB74DPgBucc/tjlRmUdSfw+6CsWkHZBMtuMLPv8In8Hc65DcnabzmMhcZIz6cmfeJEn8ceeWSKYkqgypXhd7+Djz6CRYtiLKTOoyIiIilhTj9bH6J9+/Zu6tSp6Q5DSpo//hGefBJ27YJy5Q6ZvXevH53x6qvh2WfTEF8CrF4NDRvCtdfCc89FWWD7dj90zYMPwr33pjw+ERGRw42ZTXPOtY+crjuOisRryRKfwUZJ0MFXLu/aVfrao4c75hh/c6Nhw2Lc3OjII6FpU9Wki4iIJJmSdJF4FTD8YqjDZWlO0sEPx7hzJ7z4YowFQp1HRUREJGmUpIvEK3QjoxgmTvQV7fUj7wZQyrRpA2ec4W9utHt3lAWysvwXli1bUh6biIjIz4WSdJF4bNkCGzfGrEl3zifpXbqkOK4kueMOWLMG3nwzysxQ59FZs1Iak4iIyM+JknSReCxd6h8bN445e82a0t/UJeT006FtW3jqKThwIGKmRngRERFJOiXpIvHIzfWPjRpFnT1xon88XJJ0M1+bPn8+jB4dMfPoo6FuXSXpIiIiSaQkXSQey5b5x4YNo87++ms/MmGrVqkLKdnOP9/v7pNPRswwU+dRERGRJFOSLhKP3FyoVAlq1446e+JEOOUUf7fRw0W5cn6kl6+/hv/+N2JmVhbMnu0HhxcREZGEU5IuEo/cXN/UxeyQWZs2wZw5h0+n0XBXXAE1akSpTc/MhB9/hHnz0hKXiIjI4U5Jukg8li2L2dQlVMt8uLRHD3fkkXD99fDhh7BgQdgMdR4VERFJKiXpIvEI1aRHMXGib+bSsWNqQ0qVm26CI46Av/41bOJJJ0HFikrSRUREkkRJukhBtm+HDRti1qRPnOiHK6xSJcVxpUi9enD55fD667B6dTCxXDlo3VpJuoiISJIoSRcpSGj4xShJ+r59MHny4dnUJdwf/uD39aC26aERXpxLW1wiIiKHKyXpIgXJZ4z0uXNh1y7o0CG1IaVakyZwySXw4ouwdm0wMSvL34V1xYq0xiYiInI4UpIuUpB8xkifMcM/tm2bunDS5Y9/hD17/F1IAXUeFRERSSIl6SIFyc31PSePPvqQWTNm+OHTmzVLQ1wpdtJJMGAAvPACrFuHb5NupiRdREQkCZSkixQkNxeOPx7KHPp2mT7dDxl+ON3EKD/33OOb9/z1r0DVqnDiiUrSRUREkkBJukhBYoyRfuCAz09/Dk1dQpo3hwsvhOefh/Xr8U1eZs5Md1giIiKHHSXpIgXJzY2apC9dClu3/rySdPC16Tt3wqBB+CR98WJ/IERERCRhlKSL5Gf3blizJurILj+nTqPhWrWC886D556DjU2CYW1mzUpvUCIiIocZJeki+fn+e/8YpSZ9+nTfFj0jI8UxlQD33gvbtsEz33byE9QuXUREJKGUpIvkJ58bGc2Y4WuVK1ZMcUwlQOvW0K8fPDP0SH6o1UJJuoiISIIpSRfJTz43Mpox4+fX1CXco4/Czp3Gg5UeU5IuIiKSYErSRfKzbJlv01K//kGTV6/2d978OSfpzZrBNdfASyv7MH/WHti7N90hiYiIHDaUpIvkJzfXJ+jlyh00+efaaTTS/fdDpQoHuGvvQzB/frrDEREROWwoSRfJT25u1KYu06f7x6ys1IZT0tSrB3dds4kPOZev3lmZ7nBEREQOG0rSRfIT40ZGM2b4m21Wq5b6kEqa2x6uSX1WcMfQ5jiX7mhEREQOD0rSRWLZuxdWroyZpP/cm7qEVK5WjocbDWPS6oa89166oxERETk8KEkXiWXlSjhw4JDmLps2+buNKkn/yWU9VtC67BzuvtuxZ0+6oxERESn9lKSLxLJsmX+MqEkPjTaYnZ3acEqystmZPLn/9yxZYgwenO5oRERESj8l6SKxxBgjXSO7RJGdTS/Gcma7tdx/Pyxfnu6ARERESjcl6SKxhJL04447aPKMGXDssVC3bhpiKqnatIEyZRjceTj798NNN6U7IBERkdJNSbpILMuWwTHHQIUKB01Wp9EoqlSB5s1pvHQcDzwAH30Eo0alOygREZHSS0m6SCxRxkjfuRPmzlV79Kiys2H6dG67zVes33QTbN2a7qBERERKJyXpIrFEGSP9f//zA76oJj2K7GxYuZLyG9fyyiuwahXcc0+6gxIRESmdlKSLRHPggO/9GJGkq9NoPtq184/Tp9OxI9xwAzz/PEyenN6wRERESiMl6SLRrF7tb2YUZWSXGjWi3t9IsrL84/TpADz6qG/Sf801/lCKiIhI/JSki0STzxjpWVlglvqQSrxq1aBp07wkvVo1eO45mDkTHn88zbGJiIiUMkrSRaIJDb8YlqQfOABz5kDr1mmKqTQIOo+GnHsuDBgADzwA336bvrBERERKGyXpItFESdK//x527IBWrdIUU2mQne1/hdi4EfC/OAwZAg0awEUXabQXERGReClJF4lm2TKoXduP/x2YPds/KknPR2hsylAPW6B6dXjrLf+954Yb0hSXiIhIKaMkXSSa3NxD2qPPmeMflaTnIzTsTViTF4DOneG+++DNN33CLiIiIvlTki4STZQbGc2ZA/Xrw1FHpSekUqFWLf/lJiJJB/jTn3yyWy/7BAAAIABJREFU/rvfwZIlaYhNRESkFFGSLhLJuZg16apFj0NE59GQcuV8TboZXHyxhmUUERHJj5J0kUjr1sGuXYeM7DJ3rpL0uGRnw4IFUXuJNmoEL73kR3r5wx9SH5qIiEhpoSRdJFJoZJew5i5Ll/q8XUl6HEKdR2fOjDq7f3+4+WZ45hlfsy4iIiKHUpIuEinKjYzUabQQQkl6lCYvIU89Bb/4BVx99UEDwYiIiEhASbpIpChjpIeGX2zZMg3xlDZHHw3HHptvkl6+PLz7rh/l8txzYf36FMYnIiJSCihJF4mUm+sH9w4bxmXOHDjuOH+re4lDjM6j4erWhVGjYM0auPBC2LcvRbGJiIiUAkrSRSItWxZ1ZJeMjPSEUyplZ8N338HOnfku1r49vPgijBsHd96ZothERERKASXpIpEixkjfvx/mzVN79ELJzvZD4sToPBpu4EC48UZ4+ml4/fXkhyYiIlIaKEkXCRdljPTFi2HPHiXphdKunX+cNi2uxZ9+Gnr0gGuugW++SWJcIiIipYSSdJFwmzf78b01skvx1K8P9erBlClxLV6+PLz3Hhx/vO9IGuq7KyIi8nOlJF0kXJQx0kNJeosWqQ+n1DKDDh3iTtIBataETz7xv1r07QvbtycxPhERkRJOSbpIuBjDLzZqBEcemZ6QSq0OHXxj/m3b4l6leXMYMQL+9z+47DLfrF1EROTnSEm6SLgYNzJSU5ci6NDBt/GPs116SO/evo36qFFw331Jik1ERKSEU5IuEi43FypX9nfZAfbuhfnzNfxikXTo4B8L0eQl5Oab4aqr4NFH4e23ExyXiIhIKZDUJN3MepvZfDNbZGZ3RZlfwczeCeZPMrNGYfPuDqbPN7NeBZVpZo2DMhYGZR4RTB9oZuvMLCf4uyqZ+yylXGiMdDMAFi3yibpq0ougdm3fTqgISboZDB4M3brBFVfA5MmJD09ERKQkS1qSbmZlgcHAmUBLYICZRd5U/Upgk3PuRGAQ8HiwbkugP9AK6A28YGZlCyjzcWCQc64psCkoO+Qd51xW8PdqEnZXDhcRwy9qZJdiKmTn0XBHHAHvvw/HHOM7kq5YkeDYRERESrBk1qR3BBY555Y4534ERgB9I5bpC4RuXzISON3MLJg+wjm3xzm3FFgUlBe1zGCdHkEZBGWek8R9k8NVxI2M5szxtbrNm6cvpFKtQwf/68S6dUVavXZtP+LL9u1wzjkF3sBURETksJHMJL0+sDzs/xXBtKjLOOf2AVuAWvmsG2t6LWBzUEa0bfUzs1lmNtLMjosWrJldY2ZTzWzquiImFFLKbd8OGzYcUpN+wgm+mboUQahd+tSpRS4iIwOGD4fp0/3dSZ1LTGgiIiL/z96dx0dV3f8ff50kJCyyLyIQdgSDiEJAECGEpcjigltRa61frdZCa3/UKtZ9/Yp7bbXVVltb9atWrStqq2wulU1lCyQTliCgGGQJa0KS8/vj3GiELAOZO3dm8n4+Hvcxkzv3nvuJpvWTk8/5nFjmZ5Juqjh38H9eq7smUucB3gC6WmtPAN7ju5n7719s7RPW2kxrbWbbtm2rukQSXTXtF1XqUgcDB7o/RRxhyUuFSZNg5ky34dHdd0coNhERkRjmZ5K+Eag8a90J2FzdNcaYFKA5sK2Ge6s7vxVo4Y3xvWdZa7+x1hZ75/8MDKzTdyWJ66CNjEpKIBRSkl4nTZu6WqE6JukA11wDF1zg2jLOmxeB2ERERGKYn0n6IqCX13UlFbcQ9PWDrnkduMR7fy4w21prvfNTvO4v3YBewMLqxvTumeONgTfmawDGmGMqPe8MYFWEv09JFAf1SM/Lg9JStV+ss4rFo3WsUzEGHn8cevaECy884jJ3ERGRuOBbku7Vh08D3sUlxi9aa1caY243xpzhXfYk0NoYkw9MB2Z4964EXgRygHeAqdbasurG9Ma6DpjujdXaGxvgl8aYlcaYpcAvgZ/49T1LnCsocC1F2rcH1NklYgYNgi1bItKepWlTePFFt3Tg4ou1I6mIiCSulNovOXLW2lnArIPO3Vzp/X7gvGruvQu4K5wxvfNrcd1fDj5/PXD94cYu9VBBAXTuDEnud9eVK93b3r0DjiveVd7UKL3KdduHpX9/ePhhuOoquPdemHHIDgwiIiLxTzuOilSo2MjIs2qV6+zSsGFwISWE/v0hJSUidekVrrwSzj8fbrwRPvwwYsOKiIjEDCXpIhUO2sgoN1f90SOiYUM44YSIJunGwJ//7Nb4XnCBK38RERFJJErSRQD274evvvq2s0tZmVs4qiQ9QgYNcr3SI1hE3qyZq0/fsgWuvjpiw4qIiMQEJekiABs2uFdvJn3DBiguVj16xAwaBDt3Qn5+RIcdMAB++1t49ll4662IDi0iIhIoJekicMhGRqtXuy81kx4hlRePRtj117sOPD/7GRQVRXx4ERGRQChJF4FDNjLKzXVfaiY9QjIyoHFjWLAg4kOnpcGTT8KmTer0IiIiiUNJugi4zi7JydCxI+CS9JYtoU2bYMNKGCkpkJkJn3ziy/Annwy/+hX88Y8wf74vjxAREYkqJeki4GbSO3VyySSu3KVPH9dFRCJk6FD47DPYt8+X4e+4A7p1g8sv9+0RIiIiUVNrkm6MaWKMSfLeH2uMOcMY08D/0ESiqIr2iyp1ibChQ6G0FJYs8WX4Jk1cW8ZQCG6/3ZdHiIiIRE04M+nzgYbGmI7A+8ClwN/8DEok6iptZFRUBF9+qUWjETd0qHv97399e8To0XDZZXDffbBihW+PERER8V04Sbqx1u4FzgZ+b62dDGT4G5ZIFB044FYdatGov9q1c1u4+pikA9x7LzRtCtde6+tjREREfBVWkm6MGQpcBFR0Ik7xLySRKNu0yW2yc1D7RSXpPhg61CXp1vr2iFat4IYb4O234f33fXuMiIiIr8JJ0q8Grgf+Za1daYzpDszxNyyRKFq/3r16SXpurmv00qNHcCElrKFD3c6uFS0vfTJtmvvX+ZvfRHSTUxERkaipMUk3xiQDp1trz7DWzgSw1q611v4yKtGJREMVPdK7d4fU1OBCSlhRqEsHaNgQ7r7bNZN59llfHyUiIuKLGpN0a20ZMDBKsYgEo2ImPT0d+K79ovjghBPcpkY+J+kAU6bAwIGu9EUtGUVEJN6EU+7ymTHmdWPMxcaYsysO3yMTiZaCAjjmGEhLo6zMtfBTPbpPUlJg0KCoJOlJSXD//fDFF/DII74/TkREJKLCSdJbAd8Ao4DTvWOSn0GJRFVBwbelLgUFUFysmXRfDR0Kn38elentkSNh0iRX+rJ1q++PExERiZhau7RYay+NRiAigVm/HgYPBtR+MSoqb2p06qm+P27mTOjXz+1I+rvf+f44ERGRiAhnx9GGxpipxpjHjDFPVRzRCE7Ed+Xlrh5C7RejZ8gQ9xqFkheAjAy3wdEf/wgbN0blkSIiInUWTrnLP4D2wDhgHtAJ2OVnUCJR8+WXbjOjSp1dWrWCNm2CDSuhtWvn+ltGKUkHuP569/vYww9H7ZEiIiJ1Ek6S3tNaexOwx1r7NDAR6OdvWCJRUkWP9N69wZjgQqoXorCpUWXdurluL48/Dtu2ReWRIiIidRJOkn7Ae91hjDkeaA509S0ikWiq6JFeqdxFi0ajIEqbGlV23XWwezc89ljUHikiInLEwknSnzDGtARuBF4HcoB7fY1KJFoqJek7d7q8UfXoURClTY0q69cPJk50i0f37o3aY0VERI5IrUm6tfYv1trt1tr51tru1tp21to/RSM4Ed+tX+8K0Js0UWeXaOrXD5o0iWqSDjBjhmvF+JSWvouISIyrNkk3xkw3xlxWxflfGGN+5W9YIlFSUPC9enRQuUtUpKS4tpcffRTVx556Kgwb5jY5OnCg9utFRESCUtNM+v/gOrsc7AnvM5H4V2kjo9WrITkZuncPNqR6Y/hwt6lRUVFUHztjhvvX/sILUX2siIjIYakpSbfW2pIqThYD6n0h8c/aQ2bSe/SA1NSA46ovRoxwfRE//jiqj50wAY4/3m1yFKXmMiIiIoetxpp0Y8zR4ZwTiUuFhW5r+ko90lWPHkVDhriyl/nzo/rYpCS49lpYsQJmzYrqo0VERMJWU5J+H/CWMSbLGNPUO0YCbwD3RyU6ET9V6uxSVgahkOrRo6pJE8jMjHqSDq5nepcucN99UX+0iIhIWKpN0q21fwduAm4H1gPrgNuAW7xNjUTiW6WNjAoKoLhYM+lRN2IELFzo/qIRRQ0awFVXwbx5sGpVVB8tIiISlhrLXay1b1trs6y1ra21bbz3b0crOBFfVZpJX73avVWSHmUjRrg2KwsWRP3Rl17qkvUnnoj6o0VERGoVzmZGIompoACaN4cWLcjLc6eUpEfZsGFgTCAlL+3aweTJ8PTTUZ/IFxERqZWSdKm/1q//trNLKOTy9TZtgg2p3mnRAvr3DyRJB7jySti+HV56KZDHi4iIVKumzYyu9l6HRS8ckSiq1CM9Lw+OPdZN6kqUjRjh2jCWHNLx1XfZ2dCrFzz+eNQfLSIiUqOaZtIv9V5/H41ARKLqoB7poZBL1iQAI0a4epNPP436o42BK65wG5+uWBH1x4uIiFSrpiR9lTFmPdDbGLOs0rHcGLMsSvGJ+GPHDrfTZZcu7N8PGzYoSQ/M8OHuNaCSl5/8xG1gpdl0ERGJJTW1YLwAGALkA6dXOiZ5ryLxq6KzS9eurF3rJtaVpAekXTvXoD6gJL1NGzj3XPjHP2Dv3kBCEBEROURtLRi/stb2B74EmnrHZmttQTSCE/FNpR7poZB7qyQ9QCNGwIcfQllZII+/8krYuRNeeCGQx4uIiByi1u4uxpgsIAQ8CjwG5BljRvgdmIivKvVIr2i/qCQ9QCNGuCx5+fJAHj98OBx3nEpeREQkdoTTgvFB4AfeRkYjgHHAQ/6GJeKzggJo3BjatCEUciUPLVsGHVQ9NsL7vT+gkpeKBaQLFsDSpYGEICIi8j3hJOkNrLW5FV9Ya/OABv6FJBIFFT3SjVFnl1iQnu7aYQaUpAP8+MeQlgZ/+UtgIYiIiHwrnCR9sTHmSWPMSO/4M7DE78BEfKX2i7FnxAiXpFsbyONbtYLTT3d16aWlgYQgIiLyrXCS9KuAlcAvgauBHOBnfgYl4jtvI6M9e2DTJiXpMSE7GwoLA21YfuGFLoT33w8sBBERESCMJN1aW2ytfdBae7a1drK19iFrbXE0ghPxxe7d8M030KUL+fnu1LHHBhuSAKNHu9f33gsshAkToHlzeO65wEIQEREBwptJF0kslTq7qP1iDElPh969A03S09Jcz/RXXlHPdBERCZaSdKl/Km1kVJGk9+wZXDhSyZgxMG8elJQEFsKFF7o/trz5ZmAhiIiIKEmXeuigjYzat4emTQONSCqMGQN79rheiAHJyoIOHVTyIiIiwQpnM6NjjTF/Nsb82xgzu+KIRnAivigogNRUaN+evDzVo8eUkSMhKSnQkpfkZJgyBWbNgu3bAwtDRETquXBm0v8JfArcCPym0iESnwoKoHNnSEpS+8VY06IFDBoUaJIOruTlwAF4+eVAwxARkXosnCS91Fr7R2vtQmvtkorD98hE/OJtZFRUBF9/rSQ95owZ48pdiooCC2HAAPcXlmefDSwEERGp58JJ0t8wxvzcGHOMMaZVxeF7ZCJ+8Xqkq7NLjBozBsrKYO7cwEIwBi66yK1h3bgxsDBERKQeCydJvwRX3vIxbqfRJcBiP4MS8c3+/fDVV9ClC3l57pSS9BgzdCg0ahR4ycsFF7jNT194IdAwRESkngpnM6NuVRzdoxGcSMRt2OBeK/VIV/vFGJOWBiNGBJ6k9+rlyuPV5UVERIIQTneXBsaYXxpjXvKOacaYBtEITiTiDuqRnp7uJm0lxowZA6tWwaZNgYZx4YXw6aewenWgYYiISD0UTrnLH4GBwGPeMdA7JxJ/DtptVKUuMWrMGPf6/vuBhvHDH7r69JdeCjQMERGph8JJ0gdZay+x1s72jkuBQX4HJuKL9etdI+yOHZWkx7ITToA2bQIveTnmGBgyBP71r0DDEBGReiicJL3MGNOj4gtjTHegzL+QRHxUUACdOvHNzhS2bdNGRjErKQlGj3ZJurWBhnLWWa7kpWI5g4iISDSEk6T/BphjjJlrjJkHzAZ+Hc7gxpjTjDG5xph8Y8yMKj5PM8a84H2+wBjTtdJn13vnc40x42ob0xjTzRsj5I2ZetCzzjXGWGNMZjixS4IqKPjeolHNpMewMWPgyy9h5cpAw5g82b2++mqgYYiISD0TTneX94FewC+9o7e1dk5t9xljkoFHgfFABnCBMSbjoMsuA7Zba3sCDwEzvXszgClAX+A04DFjTHItY84EHrLW9gK2e2NXxNLUi31BbXFLgvM2MlKSHgfGeb+bz5oVaBi9ekHfvkrSRUQkuqpN0o0xo7zXs4GJQE+gBzDRO1ebwUC+tXattbYEeB4486BrzgSe9t6/BIw2xhjv/PPW2mJr7Tog3xuvyjG9e0Z5Y+CNeVal59wB3AvsDyNuSVQHDrhuIV5nl6Qk6K5morErPR3694e33go6Es46C+bPh2++CToSERGpL2qaSc/yXk+v4pgUxtgdgS8qfb3RO1flNdbaUmAn0LqGe6s73xrY4Y3xvWcZY04C0q21b9YUrDHmCmPMYmPM4sLCwjC+PYk7GzdCefm3M+ldukBqau23SYAmToSPPoLt2wMNY/JktwnqG28EGoaIiNQj1Sbp1tpbvLe3W2svrXzgZqZrY6oaNsxrInLeGJOEK6OptYbeWvuEtTbTWpvZtm3b2i6XeFSp/WJenhaNxoWJE112/O9/BxrGgAFuYl8lLyIiEi3hLBx9uYpz4XQN3gikV/q6E7C5umuMMSlAc2BbDfdWd34r0MIbo/L5psDxwFxjzHpgCPC6Fo/WU16Sbrt0VfvFeHHyydC6deAlL8a4kpd334U9ewINRURE6omaatL7GGPOAZobY86udPwEaBjG2IuAXl7XlVTcQtDXD7rmdeAS7/25wGxrrfXOT/G6v3TDLVxdWN2Y3j1zvDHwxnzNWrvTWtvGWtvVWtsV+AQ4w1q7OIz4JdGsXw/A12np7NqlJD0uJCfDaafB22+7GfUATZ4M+/e7RF1ERMRvNc2k98bVnrfg+/XoA4Cf1jawVx8+DXgXWAW8aK1daYy53RhzhnfZk0BrY0w+MB2Y4d27EngRyAHeAaZaa8uqG9Mb6zpgujdWa29ske8UFMAxxxDakAYoSY8bEyfC1q2wcGGgYQwfDq1aqeRFRESiI6W6D6y1rxlj3gSus9befSSDW2tnAbMOOndzpff7gfOqufcu4K5wxvTOr8V1f6kpnpHhxC0JqqAAunYlL899qSQ9Towb51rxvPUWDB0aWBgpKXD66fDaa65RUIMGgYUiIiL1QI016dbaMmBslGIR8VelHukpKdC1a9ABSVhatYJhwwKvSwdXl75jB8ybF3QkIiKS6MJZOPqxMeYPxpjhxpgBFYfvkYlEUnk5fPHFtz3Su3d3ibrEiYkT4fPPXZ/7AP3gB9CokUpeRETEf+Ek6afgdv68HXjAO+73MyiRiPvyS1ej4M2kq9Qlzkyc6F4D3n20cWO3jvXVV93vfSIiIn6pNUm31mZXcYyKRnAiEeN1dilP70J+vpL0uNO3L3TuHDMlL5s2wWefBR2JiIgkslqTdGNMc2PMgxW7cRpjHjDGNI9GcCIR4/VI39y4J3v3aiOjuGOMm01/7z3XBzFAp53mXt9+O9AwREQkwYVT7vIUsAs43zuKgL/6GZRIxHlJemhfJ0Az6XFp4kS3k1DAqzbbtYNBgwKvvBERkQQXTpLew1p7i7V2rXfcBnT3OzCRiFq/Htq0IbSxEaAkPS6NGuWKwmNg1eb48bBgAXzzTdCRiIhIogonSd9njDm14gtjzDBgn38hifjA65EeCkFaGqSnBx2QHLZGjWDCBPjXvwLffXTCBLdw9N//DjQMERFJYOEk6VcBjxpj1htjCoA/AFf6G5ZIhBUUfNvZpUcPtzeOxKFzzoEtW+DjjwMNIzMTWrdWXbqIiPgnnO4un1tr+wMnAP2stSdZa5f5H5pIhFj7bZKel6dFo3Ft4kT3p5CXXw40jORkt4D0nXfUilFERPwRTneX1saYR4C5wBxjzO+MMa19j0wkUr7+Gvbto6xzN9asUT16XGvaFMaNg1decb98BWj8eCgshCVLAg1DREQSVDh/9H8eKATOAc713r/gZ1AiEeX1SP/iqOMoKVGSHvfOOcftHrtoUaBhjBvnOkOqy4uIiPghnCS9lbX2DmvtOu+4E2jhd2AiEbNuHQChMteUSEl6nDv9dEhJCbzkpU0bOPlkJekiIuKPcJL0OcaYKcaYJO84Hwh+2z+RcHkz6Xm7OwBK0uNey5YwerRL0mOg5GXRIlf2IiIiEknhJOlXAs8BJd7xPDDdGLPLGFPkZ3AiEbFuneuRviGNxo2hQ4egA5I6O+ccWLMGlgW7hn3CBPd7wrvvBhqGiIgkoHC6uzS11iZZa1O8I8k719Ra2ywaQYrUyfr10K0boZCbRTcm6ICkzs46y/XRfOmlQMMYMMDtQKpWjCIiEmlhdYs2xpxhjLnfOyb5HZRIRK1b9+1GRip1SRBt20JWVuB16UlJ37ViDHh/JRERSTDhtGC8B7gayPGOq71zIrGvvBwKCjjQuQfr1ilJTyjnnAOrVrkjQOPHw7ZtgTebERGRBBPOTPoEYKy19ilr7VPAad45kdj35ZdQUsL6pv0oLdVGRgll8mT3GvBs+g9+4GbU1eVFREQiKdzN0Su3XGzuRyAivvA6u4SMy841k55AOnSAU0+F//u/QLu8tGoFQ4YoSRcRkcgKJ0n/X+AzY8zfjDFPA0uAu/0NSyRCKnqk708HlKQnnIsugpwcWLo00DBOOw0+/RS2bg00DBERSSA1JunGGAN8CAwBXvGOodba56MQm0jdVcykb2tNs2ZuvaEkkPPOgwYN4NlnAw1j7Fg3mT97dqBhiIhIAqkxSbfWWuBVa+2X1trXrbWvWWu/ilJsInW3fj20b09oXYraLyai1q3dys3nngu0vUpmJjRvDv/5T2AhiIhIggmn3OUTY8wg3yMR8cO6ddCtG3l5WjSasH70I9i8GebNCyyElBTIznZJesCboIqISIIIJ0nPxiXqa4wxy4wxy40xwW7zJxKu9espTu/Jhg2qR09YkyZB06bwzDOBhjFmDBQUuI1QRURE6iqcJH080B0YBZwOTPJeRWJbWRls2MDaFgMoL1eSnrAaNYJzz3WtGPftCyyMsWPd63vvBRaCiIgkkGqTdGNMQ2PMr4Df4Hqjb7LWFlQcUYtQ5Eht2gSlpYRSjgOUpCe0iy6CoiJ4883AQujVC9LTVZcuIiKRUdNM+tNAJrAcN5v+QFQiEokUr/1iXmk3QEl6Qhs50vVND7DLizFuNn327EDXsIqISIKoKUnPsNb+yFr7OHAuMDxKMYlERkX7xZ1H07q123RGElRyMlxwgdtRaNu2wMIYMwZ27IAlSwILQUREEkRNSfqBijfW2tIoxCISWevWgTGEtjTVLHp98KMfwYED8M9/BhbC6NHuVXXpIiJSVzUl6f2NMUXesQs4oeK9MaYoWgGKHLH166FjR0L5SUrS64P+/SEjA/7xj8BCaNfOhaG6dBERqatqk3RrbbK1tpl3NLXWplR63yyaQYockXXr2Nu5Dxs3qh69XjAGLrkEPvoI8vICC2PsWPj4Y9izJ7AQREQkAYTTglEkPq1fT36rwYA2Mqo3fvxjt7PQk08GFsKYMVBSAh98EFgIIiKSAJSkS2I6cAA2biTUqB+gmfR6o317OP10+Nvf3M9AAIYPh9RU1aWLiEjdKEmXxPTFF1BeTqi8J6AkvV65/HL4+uvAeqY3bgzDhqkuXURE6kZJuiQmr0d6aG9Hjj7a7Rov9cS4cdCxI/zlL4GFMHYsLFsGW7YEFoKIiMQ5JemSmCp6pG9tqVn0+iY5Gf7nf+Cdd9xfVAIwZox7ff/9QB4vIiIJQEm6JKZ16yA5mbwNaVo0Wh9deimUl7va9AAMGAAtW6rkRUREjpySdElM69dT1PE4tmwxmkmvj7p1c9PZTz7pkvUoS06G7GyYPRusjfrjRUQkAShJl8S0fj357U4BtGi03rr8cigoCKzmJDsbNmz4dnmEiIjIYVGSLolp3TpCTU4ElKTXW2edBa1aBdYzPTvbvc6ZE8jjRUQkzilJl8RTXAybN5OXfBwAPXsGHI8EIy3NbW70r39BYWHUH5+RAe3aKUkXEZEjoyRdEk9BAQCh4nQ6dXJ9q6WeuvJKt/1nAO0YjXGz6XPmqC5dREQOn5J0STwVPdJ3tFWpS33Xp49bQPrHP0JpadQfn50NmzdDKBT1R4uISJxTki6JZ80aAEKbj1KSLjB1quuX/sYbUX+06tJFRORIKUmXxLN2LdsaduCb7UlK0gUmTYLOneHRR6P+6F69oEMH14pRRETkcChJl8SzZg2hY0YAaCMjgZQUuOoq14px1aqoPtoYGDUK5s5VXbqIiBweJemSeNauJdRiEKD2i+K57DJITQ1kNj07G77+GnJyov5oERGJY0rSJbFY62bSU/uSlATduwcdkMSEtm1hyhR4+mkoKorqo1WXLiIiR0JJuiSWwkLYs4dQaVc6d3atskUAt4B09274+9+j+thu3aBLFyXpIiJyeJSkS2LxOrvk7WyvUhf5vsGDYdAgV/IS5QLx7GxXl15eHtXHiohIHFOSLoll7VosEPrqKC0alUNNmwarV8N770X1saNGwbZtsHx5VB8rIiJxTEm6JJY1ayikHUW7kzWTLof64Q/h6KPhwQej+tiKunS1YhQRkXApSZfEsnYtobanAOrsIlVIS3O16e+8AytXRu1o55/wAAAgAElEQVSxnTpBz56qSxcRkfApSZfEsmYNoVYnA0rSpRpXXQWNGsFDD0X1sdnZMH8+lJVF9bEiIhKnlKRLYlm7lry040lJga5dgw5GYlKbNnDJJfDMM7BlS9Qem50NO3fCZ59F7ZEiIhLHfE3SjTGnGWNyjTH5xpgZVXyeZox5wft8gTGma6XPrvfO5xpjxtU2pjGmmzdGyBsz1Tv/M2PMcmPM58aYD40xGX5+zxKgfftg82ZCZT3o1g0aNAg6IIlZv/oVFBfDY49F7ZHqly4iIofDtyTdGJMMPAqMBzKAC6pIkC8DtltrewIPATO9ezOAKUBf4DTgMWNMci1jzgQestb2ArZ7YwM8Z63tZ609EbgXiO6KMYmedesACO1W+0WpRe/ecPrpLknfty8qj2zfHvr0gXnzovI4ERGJc37OpA8G8q21a621JcDzwJkHXXMm8LT3/iVgtDHGeOeft9YWW2vXAfneeFWO6d0zyhsDb8yzAKy1lbcXbAJEt0GyRM+aNVgg/+tmStKldtOnw9atruwlSrKy4IMPVJcuIiK18zNJ7wh8Uenrjd65Kq+x1pYCO4HWNdxb3fnWwA5vjEOeZYyZaoxZg5tJ/2VVwRpjrjDGLDbGLC4sLDyMb1Nixtq1fMkx7Nmn9osShqwsGDDAtWOM0i5DWVlQVASffx6Vx4mISBzzM0k3VZw7eBa7umsidd69sfZRa20P4DrgxqqCtdY+Ya3NtNZmtm3btqpLJNatWUNeoxMBtJGR1M4Y+PWv3eZGb78dlUdmZblXlbyIiEht/EzSNwLplb7uBGyu7hpjTArQHNhWw73Vnd8KtPDGqO5Z4MpjzjqC70Xiwdq1hNoMAdR+UcJ03nmQng733ReVx3Xo4PqlK0kXEZHa+JmkLwJ6eV1XUnELQV8/6JrXgUu89+cCs6211js/xev+0g3oBSysbkzvnjneGHhjvgZgjKmcrk0EQhH+PiVWrFlDqOEJpKa6vEukVg0auNn0efPgv/+NyiMr6tKjVGEjIiJxyrck3asPnwa8C6wCXrTWrjTG3G6MOcO77EmgtTEmH5gOzPDuXQm8COQA7wBTrbVl1Y3pjXUdMN0bq7U3NsA0Y8xKY8zn3jMqfimQRFJeDuvWEbI96dEDkpODDkjixuWXQ6tWMHNmVB6XlQXbt8Py5VF5nIiIxCnjJqGlsszMTLt48eKgw5DDsXEjpKeT0f4bjj25Fa++GnRAElduvRVuuw1WroQMf7dS2LABunSB3/0OflnlMnYREalPjDFLrLWZB5/XjqOSGNaupZRk8re2oHfvoIORuDNtGjRuDPfe6/ujOneGbt1g7lzfHyUiInFMSbokhjVrWE9XDpQmKUmXw9emDfz0p/Dss26q22dZWTB/vurSRUSkekrSJTGsXUtukitT6NMn4FgkPk2f7l4f9H9T4qws+OYbyMnx/VEiIhKnlKRLYlizhtwWJwNoJl2OTOfOcNFF8Oc/u51IfaR+6SIiUhsl6ZIY1q4lt1F/WreG1q2DDkbi1rXXwt698Ic/+PqYrl1dm1Al6SIiUh0l6ZIY1qxhdfmxmkWXusnIgDPPhEcegaIi3x5jjJtNnzcP1GBLRESqoiRd4l9REWzdSu6uDkrSpe5uusk1Mn/0UV8fM3IkfP015Ob6+hgREYlTStIl/q1dy06asWX3UVo0KnU3cCCMH+8WkO7e7dtjKurS1YpRRESqoiRd4t/ateTiptA1ky4RcdNNbvHon/7k2yN69IAOHVSXLiIiVVOSLvEvP19JukTW0KEwZgzcf79bSOoD1aWLiEhNlKRL/AuFyG08gORk6N496GAkYdx8M2zZ4loy+iQrC778EvLzfXuEiIjEKSXpEv9CIVY3OpHu3SE1NehgJGEMH+6y6Hvvhf37fXmE+qWLiEh1lKRL/MvLI7esl0pdJPJuugk2b4annvJl+N694eijlaSLiMihlKRLfNu9m7IvtxDa3V6dXSTyRo2CU06Be+6BkpKID6+6dBERqY6SdIlv+flsoDPFpSmaSZfIM8bNpn/xBfz97748IivLDb9unS/Di4hInFKSLvEtL0+dXcRf48ZBZibcfTccOBDx4VWXLiIiVVGSLvEtFFKSLv4yxnV6WbcOnnsu4sNnZECbNkrSRUTk+5SkS3zLy2N1k4G0aAFt2wYdjCSsSZPgxBPhrrugrCyiQxsDI0YoSRcRke9Tki7xLRQit8Hx9Onjkh0RX1TUpodC8MILER8+KwvWr4cNGyI+tIiIxCkl6RLfQiFyS7qp1EX8d9ZZcPzxcOedUF4e0aFVly4iIgdTki7xa/t2dm3dz+a9LZWki/+SkuCGG2DVKnj55YgO3a8ftGypJF1ERL6jJF3iVyhEHscCWjQqUXLeee6H7Y47IjqbnpTk6tLnzo3YkCIiEueUpEv8UvtFibbkZLjxRli+HP71r4gOnZUFa9bApk0RHVZEROKUknSJX6EQqzmOpCRLz55BByP1xpQpcOyxcOutEZ1NV126iIhUpiRd4lcoRG6Tk+jWzZCWFnQwUm+kpMAtt8CKFfDSSxEbtn9/aN5cSbqIiDhK0iV+5eWRm3ScSl0k+n74QzjuOLjttoj1TU9OhlNPVZIuIiKOknSJT9ZSnpdP3r50JekSfcnJrtwlJwdefDFiw2ZlQW4ufPVVxIYUEZE4pSRd4lNhIRt3NWNfaaqSdAnGuee6vum33gqlpREZcuRI9zp/fkSGExGROKYkXeKTOrtI0JKSXIKelwf/938RGfKkk6BpU7ViFBERJekSr0IhVtMHgD59Ao5F6q/Jk92Kz9tvj8hsekoKDBumunQREVGSLvEqL49ccxzNmlmOPjroYKTeSkpyi0fz8+GZZyIyZFaWK3UvLIzIcCIiEqeUpEt8CoVY2XAgGRkGY4IORuq1M86AAQPcbPqBA3UerqJfuurSRUTqNyXpEp9CIXLKjqVv36ADkXrPGJegr1sHTz9d5+EyM6FxY5W8iIjUd0rSJf6Ul7M1bxtfl7QkIyPoYESACRNg8GC4804oKanTUA0awCmnKEkXEanvlKRL/Nm8mVX7uwIoSZfYYIyrTS8ogL/+tc7DjRwJy5fDtm11D01EROKTknSJP3l5rMTVuShJl5gxbhwMHepm04uL6zRUVhZYq7p0EZH6TEm6xJ9QiBwyOKpJOenpQQcj4qmoTd+4Ef7ylzoNNWgQNGyokhcRkfpMSbrEn1CInKTjyeirzi4SY0aPhuHD4e67Yf/+Ix4mLc1NyitJFxGpv5SkS/zJzWVlUj8yMpShS4ypqE3fvBkef7xOQ2Vlweefw44dEYpNRETiipJ0iTvbVmzmq9K2qkeX2JSd7Y6774Y9e454mIq69A8/jGBsIiISN5SkS3zZt49V6xsBqEe6xK4774Svv4ZHHjniIU4+GVJTVfIiIlJfKUmX+JKXRw7HAersIjHslFNg4kS4994jrldp1AiGDFGSLiJSXylJl/iyahUr6UvjhuV07hx0MCI1uPNOl6A/8MARD5GVBUuWQFFRBOMSEZG4oCRd4suqVeTQl+MyIEk/vRLLTjwRzjsPHn4YCguPaIisLCgvh48+inBsIiIS85TmSHzJySEnuR99j9ePrsSB22+HvXvhnnuO6PahQ6FBA5W8iIjUR8p0JK7sWLGRTWXtVY8u8aFPH/jxj+HRR90mR4epcWO3sZGSdBGR+kdJusSP0lJWhVIALRqVOHLLLa5m5c47j+j2rCxYvLhO3RxFRCQOKUmX+LFmDTllxwJK0iWOdO0KP/0pPPkkrF172LdnZUFpKXz8ceRDExGR2KUkXeKH19mlUVoZXbsGHYzIYbjhBkhJgVtvPexbhw2D5GSVvIiI1DdK0iV+rFpFDhn06e2SFpG40aED/OIX8MwzkJNzWLcedRRkZsLcuf6EJiIisUlJusSPVavISe5HRj9l6BKHrrvOZdw333zYt44cCQsXukYxIiJSPyhJl7hRtLyAL8o6qh5d4lPr1jB9Orz8stuh6DBkZ8OBA+qXLiJSnyhJl/hQXs6q1QaAvn0DjkXkSE2fDq1awY03HtZtp57qStpnz/YpLhERiTlK0iU+bNxIzv5ugDq7SBxr1syVvbzzDnz4Ydi3NWkCJ5+sJF1EpD5Rki7xISeHHDJISy2ne/eggxGpg2nToH171/HF2rBvGzXK9UvfudPH2EREJGYoSZf44LVf7NOrXJ1dJL41bgw33QTz57sZ9TBlZ7s9kT74wMfYREQkZihJl/iwahU5SceTcUJK0JGI1N3ll0P37jBjhsu8wzB0KKSlwZw5PscmIiIxwdck3RhzmjEm1xiTb4yZUcXnacaYF7zPFxhjulb67HrvfK4xZlxtYxpjunljhLwxU73z040xOcaYZcaY940xXfz8nsUfu1esp6C8s+rRJTGkpsJdd8GyZfDcc2Hd0rAhnHKK6tJFROoL35J0Y0wy8CgwHsgALjDGHJxiXQZst9b2BB4CZnr3ZgBTgL7AacBjxpjkWsacCTxkre0FbPfGBvgMyLTWngC8BNzrx/crPrKW1SvLAC0alQRy/vkwYIDr9FJcHNYto0bB0qXwzTc+xyYiIoHzcyZ9MJBvrV1rrS0BngfOPOiaM4GnvfcvAaONMcY7/7y1tthauw7I98arckzvnlHeGHhjngVgrZ1jra3YAuQToJMP36v4qbCQFUXpgNovSgJJSoJ77oGCAvjTn8K6JTvbrTWdN8/n2EREJHB+JukdgS8qfb3RO1flNdbaUmAn0LqGe6s73xrY4Y1R3bPAza6/XVWwxpgrjDGLjTGLCwsLa/3mJIpWrWIp/WmUVkbPnkEHIxJBY8fCmDFw551QVFTr5YMGuXaMqksXEUl8fibppopzB/cbq+6aSJ3/7kHG/AjIBO6r4lqstU9YazOttZlt27at6hIJSk4OS+nP8X1K1dlFEs8998DWrXD//bVempoKw4erLl1EpD7wM0nfCKRX+roTsLm6a4wxKUBzYFsN91Z3fivQwhvjkGcZY8YANwBnWGvDK/6UmGFzVrGUE+mfmRp0KCKRN3Ag/PCH8MAD8NVXtV6enQ05OWFdKiIicczPJH0R0MvrupKKWwj6+kHXvA5c4r0/F5htrbXe+Sle95duQC9gYXVjevfM8cbAG/M1AGPMScDjuAT9a5++V/HRps8L2UYr+p9Y1R9MRBLAnXdCSQncfnutl44a5V7nzvU3JBERCZZvSbpXHz4NeBdYBbxorV1pjLndGHOGd9mTQGtjTD4wHZjh3bsSeBHIAd4Bplpry6ob0xvrOmC6N1Zrb2xw5S1HAf80xnxujDn4FwWJcUtzGgDQv3/AgYj4pWdPuOIK+POfIRSq8dKTToLmzVXyIiKS6Iw9jG2p64vMzEy7ePHioMMQgK1bubvtg9zA3ezY4ZITkYT01VcuWZ84EV54ocZLzzzTlbzUks+LiEgcMMYssdZmHnxeO45KbFu2jKX0p2v7fUrQJbG1bw/Tp8OLL0ItkwTZ2ZCfD198UeNlIiISx5SkS2xbupSl9Kf/ifpRlXrgmmugTRu47jrXEL0aFXXpasUoIpK4lPlITNv76WpC9KL/4LSgQxHxX7NmbgfS2bPhP/+p9rLjj3e5/PvvRzE2ERGJKiXpEtNWLNxLOclaNCr1x89+Bl27wowZUF5e5SVJSa7k5b33apxwFxGROKYkXWLXgQMsXXMUoM4uUo+kpcEdd8Bnn8Hzz1d72dixsHkzrFoVxdhERCRqlKRL7MrLY2lZX45qeIBu3YIORiSKLrwQTjwRfvtb2L+/ykvGjnWvNVTFiIhIHFOSLrHLWzR6Qp8SkvSTKvVJUpLbgbSgAB55pMpLunaFXr2UpIuIJCqlPhKz7NJlLOME+p/cMOhQRKJv1CiYNAnuugsKC6u8ZOxYt/NoSUl0QxMREf8pSZeYtf6TryiiOf1PSg46FJFg3Hcf7NkDt91W5cdjx7qPP/kkynGJiIjvlKRLzFq63P14atGo1Ft9+sCVV8Kf/gSrVx/ycXY2JCer5EVEJBEpSZfYtHUrS7enY4ylX7+ggxEJ0K23QpMmcO21h3zUvDkMHgz//nf0wxIREX8pSZfYtGwZS+lPzw57adIk6GBEAtS2revy8sYbVW4xOnYsLF4M27cHEJuIiPhGSbrEJq+zS/8BqkcX4eqroXNn+PWvD9ngaOxYd2r27IBiExERXyhJl5hUtDiPtfSg/2B1dhGhYUP43/91Gxw988z3Pjr5ZGjaVHXpIiKJRkm6xKTli9wGLlo0KuKZMgUGDXKlL3v3fnu6QQMYOVJJuohIolGSLrGntJSla5sCStJFvpWUBA8+CJs2uddKxo6FtWvdISIiiUFJusSe3FyWlvWlReNi0tODDkYkhpx6Kpx9NtxzD3z11benx451r5pNFxFJHErSJfZ4nV36ZxzAmKCDEYkxM2e6LUZvvvnbU717Q3q6knQRkUSiJF1iTtlny1hOP/qf3CjoUERiT8+eMHUqPPkkrFgBgDFuNv3996GsLOD4REQkIpSkS8zJ/2Qre2mi9osi1bnpJreT0TXXfHtq7FjYscP1TBcRkfinJF1izqLlru3ioEEBByISq1q1cuUu774Ls2YBMHq0m1HX7qMiIolBSbrElsJCFu7oRZPUEjIygg5GJIZNnQp9+sD/+39QUkLbtpCZ+W3OLiIicU5JusSWxYtZyGAG9tlLsqpdRKrXoAE89BDk5cHvfw/AxImwYAEUFgYcm4iI1JmSdIkpJf9dwuecyOCRjYMORST2nXaay8xvvx22bGHCBLDWVcGIiEh8U5IuMWX5nEKKacigYalBhyISHx58EPbtgxtuYOBAOPpoeOutoIMSEZG6UpIuscNaFn6eBsDgwQHHIhIvjj0Wrr4annqKpM+WMH68m0kvLQ06MBERqQsl6RI7Nmxg4e7jaHvUXrp0CToYkThy443Qti1cfTUTxlu2b4dPPgk6KBERqQsl6RI7Fi1iIYMZ3L9EO42KHI7mzeHuu+Gjj/jB7ldITlaXFxGReKckXWLGrg+XsorjGDzqqKBDEYk/P/kJDBhA81t+xalDy1SXLiIS55SkS8xYMncXliQGDU0JOhSR+JOcDL/7HWzcyMSG77NsGXzxRdBBiYjIkVKSLrGhrIyFq5oC2mlU5IideipccAET5s8A4O23A45HRESOmJJ0iQ25uSws6U/3trto0yboYETi2MyZZCStpkvjr1XyIiISx5SkS2yoWDSaWR50JCLxLT0dc/0MJu79J+/9u4zi4qADEhGRI6EkXWLCV3NX8wWdGTSqadChiMS/a65hQtvF7N2fzLzZZUFHIyIiR0BJusSERR+56b7BQ/QjKVJnjRuT/cAkGrKPWfetDDoaERE5AsqIJHjFxSxc04ZkU8ZJJwUdjEhiaPyjs8lutYy35jWBrVuDDkdERA6TknQJ3rJlLCwfyPGdd9GkSdDBiCQIY5h4VWfyy3uQ9/OHg45GREQOk5J0CZxdsJBFDGLwKclBhyKSUCb99BgAXvtnMSxYEHA0IiJyOJSkS+DWzC5gO60YNFI7jYpEUpcuMPCkMl5ucAFMnQplWkQqIhIvlKRL4BYusAAMPtkEHIlI4jn73GQWHBjAxiVfwZ//HHQ4IiISJiXpEqxdu1i4uRONGhygb9+ggxFJPOec415f7fkb+O1vtYhURCROKEmXYC1ezEIGMfDYXaSkBB2MSOLp3RsyMuDllpfDrl1w/fVBhyQiImFQki6B2j/nv3zKAAZnNQ46FJGEdfbZMH9JEwp/+lt48kktIhURiQNK0iVQC98qpJiGZI1rGHQoIgnrnHOgvBxe73s9tG+vRaQiInFASboEp6SEuctaYShn+PCggxFJXP37Q7du8MqshvDAA7BkiRaRiojEOCXpEpwlS5hXegondC2iZcuggxFJXMa4kpf//Ad2jp8CWVlaRCoiEuOUpEtgimd/xH8ZysixqUGHIpLwzj4bDhyAt2YZePRRKCrSIlIRkRimJF0Cs+iNr9hHY7LGa9GoiN+GDIFjjoFXXgH69oWrr9YiUhGRGKYkXYJRVsa8z5oBqB5dJAqSkmDyZHj7bdi7F7jlFreI9Oc/h9LSoMMTEZGDKEmXYHz+OfNKhtAvfQdt2gQdjEj9cPbZLkF/912gWTN46CH49FP4wx+CDk1ERA6iJF0CcWDOh3zEMLJGawcjkWgZMQJatfJKXgDOPx/Gj4cbb4QNGwKNTUREvk9JugRi8eub2UsTsiYeFXQoIvVGgwZw5pnwxhtQXIxr+/LYY2AtTJvmXkVEJCYoSZfoKy9n3uImgJvZE5HoOf982LkTZs3yTnTtCrfd5jL3b6fYRUQkaErSJfpycpi7bzAZHXbQrl3QwYjUL2PGwNFHwz/+Uenkr34FJ54Iv/iFy+BFRCRwStIl6kpnz3f16Nn68ROJtpQUuPBCePNN2Lat0sknnoAtW+CGGwKNT0REHGVJEnWfvr6R3TRl5OlNgw5FpF66+GK3sdGLL1Y6OWiQq0t/7DH45JPAYhMREUdJukSXtcxd0AiAEVkm4GBE6qcTT3T7GX2v5AXgjjugQwe44gqXxYuISGB8TdKNMacZY3KNMfnGmBlVfJ5mjHnB+3yBMaZrpc+u987nGmPG1TamMaabN0bIGzPVOz/CGPOpMabUGHOun9+vhCEUYt7uAfQ+egft2wcdjEj9ZIybTf/4Y1izptIHzZq5nunLl8ODDwYWn4iI+JikG2OSgUeB8UAGcIExJuOgyy4DtltrewIPATO9ezOAKUBf4DTgMWNMci1jzgQestb2ArZ7YwNsAH4CPOfH9ymHp3T2fD7kVEaODDoSkfrtootcsv7MMwd9cNZZ7rjtNli7NpDYRETE35n0wUC+tXattbYEeB4486BrzgSe9t6/BIw2xhjv/PPW2mJr7Tog3xuvyjG9e0Z5Y+CNeRaAtXa9tXYZUO7XNyrh+/ylfIpoTtYZzYMORaRe69QJsrNdycsh7dF//3u3mPTnP1fvdBGRgPiZpHcEvqj09UbvXJXXWGtLgZ1A6xrure58a2CHN0Z1z6qRMeYKY8xiY8ziwsLCw7lVwnXgAPM+TAYga6Tq0UWCdvHFrtzlkHWinTrBXXfBu+/C888HEpuISH3nZ5JeVRZ28JRMdddE6nzYrLVPWGszrbWZbdu2PZxbJVwff8yc4qH0PGY3HToEHYyInHMONGpUxQJScLPogwe7Hurf9moUEZFo8TNJ3wikV/q6E7C5umuMMSlAc2BbDfdWd34r0MIbo7pnScD2vfZvZjOK005PDToUEQGaNnXl5y+8ACUlB32YnOx6p2/b5hJ1ERGJKj+T9EVAL6/rSipuIejrB13zOnCJ9/5cYLa11nrnp3jdX7oBvYCF1Y3p3TPHGwNvzNd8/N7kCLz/8g720ZjTz1GSLhIrLr7Y5eGzZlXxYf/+8Nvfuqn2N9+MemwiIvWZb0m6Vx8+DXgXWAW8aK1daYy53RhzhnfZk0BrY0w+MB2Y4d27EngRyAHeAaZaa8uqG9Mb6zpgujdWa29sjDGDjDEbgfOAx40xFddLNBUU8MaGEzgqrYSsrKCDEZEKY8fC0UfD3/9ezQU33AD9+sGVV8L27VGNTUSkPjNWK/cPkZmZaRcvXhx0GAnFPvZHOk09gyFjm/Hyv7XTqEgs+c1v4OGHoaCAqteLfPqpq0+/+GL461+jHp+ISCIzxiyx1mYefF47jkpUfPZ8LpvpyOkXHBV0KCJykJ/9DMrK4PHHq7lgwACYMQP+9rdq6mJERCTSlKSL//bt443/tsFQzoSJar0oEmt69IAJE1ySfsgC0go33QR9+8IVV8COHVGNT0SkPkqp/RKROpo3jzdLxzHkuJ20a9cy6GhEpArTpsH48fDyy3DBBVVckJbmZtKHDIFf/rKGInY5YtbC6tWwYoWr/9+xwx27d0O7dtCxozs6dYJjj3UbTolIwtL/wsV3m1/4gMXcxV1TDgQdiohU4wc/gF694A9/qCZJB8jMhBtvhNtug0mT4PzzoxpjQsrNhbfegg8+gA8/hK1bv/95Sgo0bgxFRd8/37QpZGXB6NHuOP54MPpLpUgiUZIu/rKWt950i5NPn9wg4GBEpDpJSTB1qmuJ/umnrgy9SjfcAO+84wrZhw1zM7tyeEpK4F//gj/9CebOdee6d4eJE2H4cPfLUJs20KKFS9CNgf37YfNm2LTJrfD94AN4//3vWmP26AGXXw4/+Qm0bx/UdyYiEaTuLlVQd5cIys3ljD65LGuVzbqtTTXRIxLDduxwlRTnnw9PPVXDhaEQnHQSDB0K777rMnypXVER3H+/K/7/+mvo1s21trzoIvcP/kgUFMB//gPPPAPz5rmZ9zPOgKuucjPs+j9dkZin7i4SiH2vvst7jOH00/XfCpFY16KF67L43HPwzTc1XNirFzz0ELz3HjzySNTii1ulpfDHP0LPnnDHHa6u/+23IT8frrvuyBN0gC5d3Az63Lmunv1Xv4L5810D/GHDXAKvyTiRuKQkXXw1+9kv2UdjJl2g3ugi8WDqVCguhiefrOXCyy93M7YzZsDy5VGJLS7NmgUnnAA//zkcdxwsWgSvvQannRb5v0D07g333QcbN7pfCr74wi02GD4cZs+O7LNExHdK0sU/mzbxxvKuHJVazMiRQQcjIuE4/ngYORIee8z1Tq+WMfCXv7jp9ylTYM+eaIUYH7ZtgwsvdHXmpaXw6qtutjvzkL9oR15amlszkJ8Pjz4K69e70pczznDnRCQuKEkX39jnX+BNJvKDkQdISws6GhEJ17RprtS5Yk1itdq2dbXQq1a5mWKVVThvv+1+2/nnP+H2211LxTPPjH7NX1qa+/eSnw8zZ8KcOZCR4Upsdu2KbiwictiUpItvPv/LYjbRSbuMisSZM8+E9HRXOVFr3j1mDNxyi+ub/te/RiW+mPhXBscAACAASURBVLV7t9vsacIEaNUKFixwm0ClpgYbV8OGcO21kJfnZvfvvdf1Wf/nP/WLlUgMU5Iu/li9mldX98YYy4QJQQcjIocjJcWVmn/0kVsbWqsbb3TJ+tSpsGyZ7/HFpNWr4eSTXQnQtdfCkiU19LEMyDHHuA2pPvkEOnRwbXwmT3atHUUk5ihJF1+UP/c8/+BiRg0roV27oKMRkcN12WVuNv3mm8OYbE1OhmefhZYt4dxzD914J9G9+CIMGgSFha6bysyZxHSN38knu1n+e+91LTQzMtwvF5pVF4kpStIl8qzlwydzWUd3fnJlDP+HSkSqlZbm9i365BO3d1Gt2rWD55+HNWtc55f6kPCVlLiWhz/8IfTr53aBGj066KjCk5ICv/mN+8vHiSfCT3/qYtfCUpGYoSRdIm/RIv62eSxHpR1g8uSggxGRI3Xppa4Nd1iz6QAjRsA997ha5zvu8D2+QBUWuhKf3/0Orr7adW6pS7/zoPTq5dozPv64K9E54QR44AHXkUZEAqUkXSJuz9Mv8U/O4/zzLE2aBB2NiByp1FS37nHxYnjrrTBvuuYa+PGP3WLSF1/0Nb7ALF3qylsWLXJlPg8/HPzi0LpISnILXnNy3CZI11zjdpOtr+sLRGKEknSJrNJSXnlmL7tpyiU/jeP/aIkI4PLt7t0PYzbdGHjiCTj1VLjkEli40PcYo+rll+GUU9xM8wcfuG4piaJjR9fP/YUXXA/OzEy47TZX1iMiUackXSJrzhyeLjqL7kfv5tRTgw5GROqqQQM3m/7ZZ26jzLCkpcErr7huImee6Xa+jHfl5S5hPfdcV3++aFF0NiaKNmNc15ecHDjvPLj1Vhg82P0AiEhUKUmXiCp44l1mM4pLrkiL+I7XIhKMH/0IevZ0FSzl5WHe1LYtvPEG7N3rdrrcudPXGH21Z49bHHrrre5PC3Pnul9AElmbNq6U59VXYcsWl6jffLNm1UWiSGmURM6uXfzj9eZYkvjx/zQIOhoRiZCUFJegL1t2mGXmffu6G1ascBv87N7tW4y+KSiAYcPcXwbuv9/1GW/YMOiooufMM2HlSlfWc8cdMHCgW6QgIr5Tki4RY//6N54umcLIAUV07Rp0NCISSRdc4Dr1TZ9+mJPi48a51owLFrgZ9X37fIsx4j780C0QXbcO3nwTfv1rVw5S37RqBU8/7f4ysm0bDBkCv/0tFBcHHZlIQlOSLpFRXs7H935IPr245BfNgo5GRCIsOdmtB92yBa6//jBvPucc+PvfXZnI5Mmxn9xZC3/4A4waBS1auF8wxo8POqrgTZrkZtV//GP43/+Fk05y29KKiC9Sgg5AEsSsWfxt0xiapB3g3HNV6iKSiAYNgl/8Ah55BC6+2HXpC9uFF8L+/W4r0/POg5deis22hXv2uHaEzz0HEyfCP/7hdlKNsrIy+Pprt+Z240Z3bNrkJrK3b3fHjh1uc9fiYneUlLjX8nL3S1VKyndHo0bQrNn3jzZtoH17dxx9tCuz79LFna/2DwYtWsBTT7nFpVde6br4XHGF648fwD8nkURmbH3YFe4wZWZm2sWquTsse7Mncsy8/2Pyj47ib3/XH2hEEtWuXa7UvHlzt/fNYefZjz0GU6dCdrZrZxhLiV1enpv1X7nS1V9ffz1+r4AvKXEl+ytXwurVkJvrXkOhQ9dopqa6ypOWLb87mjd3zXTS0tznqaku5LIy1yWyrAwOHHBVRkVF7ti1y5UsFRa6ZP9gRx0F3bq51ps9ekBGhvt3npHhkvtv7d7tFtM+9JBbKPy737nkvT6WBInUgTFmibX2kHZRmkmXuluxgufnHk0Rzbjk0qCDERE/NW0Kjz7qysvvv9+VJh+Wn//cDXLZZa7f+FtvuWwwSNbCM8/AtGmu5+S777pNfXx4TE4O/Pe/bu3lkiVuMW5FMp6S4pLiPn3cJH63bm4T044d3WuNM9xHqLjYlTB99RVs3gzr17sS/HXrID/f/aPYv/+769PT3aakmZmQmXkUmdfcT/uLLnKz6VOmuIW1jz3mgheROtFMehU0k354yi67goy/XkPj47vz6dIUTaKI1APnnefWEa5Y4dozHrb58119elKSa8B+yikRjzEsW7e6so1XXnFdXJ57Djp3jsjQ1rp/PnPnwrx57lsuLHSfNW/uGqUMHOgS3hNOcAl6gxirFiwrc4n7ypXfHUuXul82KtpxduwI/7+9ew+Pqjr7Pv69E85yElABRQqiCAgSEQURQUBFVKzWV32rgrRKrVqrlsdDfatVW1vt9fhW61kUtdoHKYiiFZWDoHJGEDSgHKWEiIhIBEQgyXr+uPc0Q5wEhiTMDPl9rmtfs2fvPXuvmQWTe9a+11o9Tg70semc9ubtdA6Lybr7LrjppvR7QyJpqKyWdAXpCShIT8LGjYxpeSOX7HqRMWP8D7eIHPjy86FDB89TnzRpH1t4ly/3oRnXrvVE96uv3r+pEm+8AVdd5Tkf997ro7dkZ1folFu3wuTJfoPgzTf9cwKP+/v2hT59oHdvD8gzeS6Jbdvgo4/8jsC8eT4Qzpo1vq9xza303jWVPi2Wc9q9Z5AztAs1dN9epEwK0pOgIH3vhT/eR87/G8T3bTqQu7x2Rf++iUgGeeIJ+OUv4S9/gREj9vEkX3/tEwVNmQL9+8PIkVT5GK6rV8Ntt/kY7l26eOfQLl32+XQbN8L48d4Xdto0T19p2BDOPNN/g5x+etW/pXSwZo3fLZg+Hd6buJXl+fUBqF9jO71ONfqcWYcBA+CEEyr8W0jkgKIgPQkK0vfSrl38q/nPOXfTC4waBVdemeoCicj+FILH12PHeqP0oEH7eKLiYnj6afiv//L1P//Zc9cru6l582a47z7v4JidDbfc4p1Da9dO+lSxwHzMGHj3XU8LadfO5/455xwf9KS6Z3rkL9/G+7e+zvTXCngv9CY3dAS8w2u/fjBggKf+H3VUigsqkmIK0pOgIH3vhMcep9d1x7PukBxWrKtb7f8giVRH27Z5+sbKlTB7tqfA7LN//9s7IL79to/BPWKE59BV9Mvlyy9h1Cjv6bppEwwdCn/4gydTJ2HnTk9jef55fyws9MD84ou9mMcfr4FNElq1CkaMYMP4D5hy6E+Z3PkmJi07krVr/cNq06YkYO/XD5o2TXF5RfYzBelJUJC+F7ZsYdqRQzh983ge+Vvguuv1l0mkuvr3vz03vWFDn/enSZMKnCwEeOklD6I/+8yHNfnVrzxfPZnhGouLPVn+qadgwgSPqAcMgAce8B8ASViwwGP8f/zDY/zmzeGyy3zo95wcBeZ7bcoU+PWvITeXMOAMlt/0GJNWt2PyZJg61YeHNPN0mFjQ3qsX1KmT6oKLVC0F6UlQkL4X7rqLM+/pxeImfVmdV4u6dVNdIBFJpZkzSzpGTpxIxTsKFhfDW2/Bgw96cJed7XnjPXv60q2bz9Bj5mkxhYU+9EisJ+O8eT6USrNm3nJ+1VU+tuFe2rYNRo/2vPv58z0j5sc/9lOdcUYlvL/qqrDQP9Q77/So/Lrr4M47KWzUlHnzvNPtpEk+TGVhoQfovXuXBO3HH5/ZHW5FElGQngQF6XuQn8+8tpdw0o73uf9+T+sUERk1Cn72Mx9u/OGHK7GFedEiT3yfNcub6rduLfvYrCyfdefEE+Hssz1JPImc848/hief9L6k334Lxx3nIzRefrlPtimVZONGD9SffNLHzb/tNm9lj1p8tm71DqixoD0311/WrJn3Lz7jDA/cW7dO4XsQqSQK0pOgIH0Phg/ngpHnML3huaxZm02DBqkukIiki9/8xhu/r77a57Sp9BbnoiIffPzjj72ptbjYU2QA2reHrl19yswkbN8O//ynx4szZ3pMH5v1/pRTlM5SpZYs8QD99dc9tenee+GKK34w/MsXX5QE7JMn+3OAo48uaWU//XT9kJLMpCA9CQrSy7FkCbOPu4qeYSZ33gl3353qAolIOgkBfvc7+OMfYeBAH/0kXX/If/qpB+bPP+9DpR9zjAfmQ4eq8+J+9957PrrP3LnQuTPcf7//A0rwCyk2c2ssaJ82zdOTsrI8I6pXLx9d59RTPe4XSXcK0pOgIL1su869gBMm/oHNzY9lyadqRReRxJ5+2sdQ79zZR0Jp2TLVJXI7dvjQiU884ekUNWvChRd6cN63r1rNUyoET2u6/XYfLqhfP+/o261buS/budOzoKZO9UmVZs8uyYhq3Xr3oL1TJ+W0S/pRkJ4EBellmDqVP/WfxG/5E6+9BoMHp7pAIpLOJk70tJGDD/bZN487LnVl+fRTz5kfNcr7k7Zp46M9DhsGhx2WunJJAjt3+i2Oe+7x3PVLLvHbtu3b79XLCwth8WIP2GNLLD2mcWPo0cPTmHr2hJNO8lGJRFJJQXoSFKQnUFDAig7n0Xn9OwwaXJNxr2q6OBHZs4ULfXKfr7/2foF33AGNGu2faxcU+Agtzz3nravZ2XDeeXDNNZ7DrBbVNPftt96S/te/eseBoUO9s2mS07eGAJ9/7sH6++97/+PcXN9u5j8eY0F7z56e5647KrI/KUhPgoL0HwpXDOGMF4cyt15fli7LTnYOEBGpxr74An77Ww+WDznE+wZedVXVTA2/dau34I8bB6+9Bt9/74O9DBvmI7Q0b17515QqtmGDz0L72GPeUXj4cP+116LFPp+yoMBTZGbN8mX2bN8G3h8hvrW9e/ek+yKLJEVBehIUpJcyZgx/v+R1hvB3Hn3UZ+sWEUnWhx/CTTd5a2bnzj5866BBFZz8CJ9QdOJEeOUVeOcdzztv1sxnAR02zEdjVMvoASAvzye5euYZ70xw/fVw662V0su3uBiWLi0J2mfN8udQ0iE1vrW9bVv9m5LKoyA9CQrS46xbx8ZOfTh223yO7taIGTNNt4hFZJ+F4MH0Lbf4bPHZ2d6hb/BgOPdcaNeu/DSUbdtg+XIPombO9GXVKt/XqpV3Ar3wQu8sWBUt9ZIGVq70HPUXX/Qm7ptughtvTG5G2r2wadPure1z5sCWLb7v0ENLAvaePf2HYL16lXp5qUYUpCdBQXqkuJhw5lkMmfYzRtulLFhgdO6c6kKJyIGguNgnBX39dZgwwYc9Bw/QmzXztJhDDvFOfV9/7a3l69fvPo9R8+YlrZunn+7Tyat1sxpZssRz1MeN83E+b7jBA/YqGj+zqMhz2eNb25ct8301avhsqPGt7a1b69+j7B0F6UlQkB554AEeunUdN/KQxkQXkSq1erWnquTleQryV1/5UlDgQXvz5j4KS/PmcOSRCoIkzuLFngYzdqw3Z193nc+qdeihVX7pjRs9nz0WtM+d63d7YPcfkT17+kiSdepUeZEkAylIT4KCdOCll3j98tGcz2v8+AJj7FiluYiISBpbssRn0Ro92qeNveYanyCpAh1Mk1VY6HeF4lvbV670fTVrQk5OSeDeqxcahEEABelJqfZB+ltvsfDc39E7vEeHrrWZ/n6Wcu1ERCQzLFsG993nOes1asDVV8OIEX7rJQU2bNg9aJ83z0eUBB9NMjbZUq9emmypulKQnoRqHaTPmcO6vpdxctEMsg47hDlzs/ZnI4SIiEjlWLUK/vQnH/szBPjJT+Dmm+Hkk1NarF27YNEimDGjZLKl9et9X+PG3soeC9q7d1eH1OpAQXoSqm2QvnQpW3udxWnb3mRFrY58MCOLLl1SXSgREZEKWLsWHnnEZzEtKPDo9+ab4fzz02IIoBC8T0YsaJ8xwzuogt8I6NatJGjv1Wu/pNrLfqYgPQnVMkifNYtvzr+SiwpGMq3wVN54wzj77FQXSkREpJJs3QrPPuszmK5eDW3a+NCNw4b56DBpZNMmT42JBe1z5/r4/+AzosYH7e3bqwN1plOQnoRqF6S/8ALLr7qf85jAqtCWZ0cZl1+e6kKJiIhUgaIin472wQc9Am7UyKfAHT4cjjkm1aVLaMcOWLCgJGj/4AMfmhR89KNTTvHA/dRTfSjS2rVTW15JjoL0JFSbIL2oCG6/nWl/mcuFNSaQ1bA+41/NonfvVBdMRERkP5gzx4P1V17xoVn69oVf/AIuuCCtI90QvH9sLKd9xgyf5Au82CedVNIh9ZRTKn2eJ6lkCtKTUC2C9HXr4OqrGTmxJb/MepKjj8nijX8ZbdumumAiIiL72fr1MGoUPP20p8I0bQqXXgpXXOERbwbkk3z5pc/AGwvaP/zQf3eAjxoTP4pMmzYZ8ZaqDQXpSTigg/TCQnjkEfLveJRbv7+bF4t/yllnwcsv+x0/ERGRaqu4GKZMgWee8ZSY77/3JPDLL4eLL4Zjj011Cffad9/5cI+xoH3mTO83Cz50fHzQ3rWrd1KV1FCQnoQDNkifNYsdv7iBhz4+nXuzf8/O7Lrccotx1136zykiIrKbggIYNw5eeAGmT/dtnTr5UI4/+Ql07pxRzdHFxT5qTHxe+5o1vu+gg3xkyljQ3qMHNGyY2vJWJwrSk3BABekhwNSphP9+kIkTAzfWeITlhW0ZPDjw4IPGUUeluoAiIiJpLi8Pxo/3oP399z3ibd0aBg6Es8+G/v2hfv1UlzJpeXkesMeC9kWL/K1lZUGXLiWt7d27Q9u2GfWbJKMoSE/CARGkf/cdjBnDN38ZyYtLcngq+1o+KerAMe2KeehvWQwcmOoCioiIZKAvv4QJE+DNN2HyZB/asWZNn4XotNOgTx9fP+igVJc0aVu2wOzZJUH77NmwbZvva9jQ02JycnwEmZwcz/6pWTO1ZT4QKEhPQsYG6d98A2+8QeG415j51rc8s+MyxtglfB/q0L1bMcOvyWLIEKhVK9UFFREROQDs3OkR7cSJMG2aj5NYVOQ5pDk53gR94om+dOiQcbmlhYWweLF3Ql240JdFi2D7dt9fu7Zn/eTkeADfoYMvhx2mVvdkKEhPQsYE6fn5MHs2YeYsPpm6gcmLDmFKcV/esz5sCQ1oUK+Qy4dkc/VwIycn1YUVERE5wG3Z4j00p0/32Yg+/NC3AdSp4xFsp07QsaM/tmvnQ63UrZvaciehqMiHf1ywoCRwX7jQ2wljGjf2t9q+PRx1VMnStq0PnKMAfncpCdLNbCDwEJANjAwh/LnU/trAC0A34GvgkhDC59G+24GfA0XADSGEt8s7p5m1AUYDTYAFwBUhhJ3lXaMsaROk79jht9XWr4f8fL5fupr8RV+xcskOlqypR+63rcilE7l0ooDGABzdajv9B9Wh/wBj4MCMTJETERE5MBQX+wDm8+d7VJub60te3u7HtWzpEeyRR/r64Yf742GHecTbuLEPdt6gQVpGuCH4yM5Ll+6+LFsGX3yx+7H16vlba9HCH0uvt2wJzZv7W83KSs372d/2e5BuZtnAMuAMIA+YB/zfEMKSuGOuBbqEEK4xs0uBC0IIl5hZR+B/gJOAlsBkIDYNWMJzmtkY4JUQwmgzewJYFEJ4vKxrlFf2VATp+fk+2lNBAWyetZSCt2ZRsLMOX9OUfFqST0s20XS31zQ9aDudjtlFp+4H0b1HNv37+/9vERERSWMFBR7Frlrly8qV/piX59Hujh2JX5eV5eMlH3ywB+6NGnkOa40aP1xq1vTH4mJPy9m1yx+bNIGRI/fbW/3uu93f5tq1Hrjn55cs3333w9eZeR58o0ZlL/Xre8pNrVr+GL/EbzPzHxKxkLes9f79989n8sP3mjhIr8rkqJOAFSGEVVEBRgPnA0vijjkf+H20PhZ4xMws2j46hLADWG1mK6LzkeicZrYU6Af8NDrm+ei8j5d1jZBmeT6rV8O11/p6zRrtaVzzCBo13cXBDYo4qnkRvY8o4vD222l5VF1at/a7ZIceWhezzLlFJiIiIniE2aOHL6WF4Lkj69bBxo2+vnlzyWP8ekGBR7iFhbsvu3aVPGZne8Beq5Y/tmq1X99qvXpw3HG+JBKCZwTl55cE7+vXl7y9+CU/33/bxJ7HJmuqDFlZnsqTTqoySD8cWBv3PA84uaxjQgiFZlYANI22zy712sOj9UTnbApsDiEUJji+rGtsjC+ImQ0HhkdPt5rZZ3v9TivZrkL4qhC+2o4n6HyeqpL8QDNKfW6SEVRvmUn1lplUb5mp+tTbJ5+kZcrMPqq0eisuTunH0jrRxqoM0hO91dKt12UdU9b2RNlJ5R2/t+UghPAU8FSCYyViZvMT3Y6R9KZ6y0yqt8ykestMqrfMdKDXW1Wm5OcB8fdUjgDyyzrGzGoAjYBN5by2rO0bgcbROUpfq6xriIiIiIikpaoM0ucBR5tZGzOrBVwKTCh1zARgaLR+ETA1yhWfAFxqZrWjUVuOBuaWdc7oNe9G5yA652t7uIaIiIiISFqqsnSXKP/7euBtfLjEZ0MIuWZ2DzA/hDABeAb4e9QxdBMedBMdNwbvZFoIXBdCKAJIdM7okrcCo83sD8DC6NyUdQ3ZJ0oHykyqt8ykestMqrfMpHrLTAd0vWkyIxERERGRNFNNhokXEREREckcCtJFRERERNKMgnTZIzMbaGafmdkKM7st1eWpjszsWTPbYGafxG1rYmaTzGx59HhwtN3M7OGovhab2QlxrxkaHb/czIbGbe9mZh9Hr3k4mlRMKsjMWpnZu2a21MxyzezX0XbVXRozszpmNtfMFkX1dne0vY2ZzYnq4OVoAAOiQQ5ejupgjpn9KO5ct0fbPzOzs+K263u1iphZtpktNLM3oueqtzRnZp9H32Mfmdn8aJu+J0MIWrSUueAddFcCbYFawCKgY6rLVd0W4DTgBOCTuG0PALdF67cB90frg4CJ+BwBPYA50fYmwKro8eBo/eBo31ygZ/SaicDZqX7PB8ICtABOiNYbAMuAjqq79F6iz7J+tF4TmBPVxxjg0mj7E8Avo/VrgSei9UuBl6P1jtF3Zm2gTfRdmq3v1Sqvv5uBfwBvRM9Vb2m+4NM2Niu1rdp/T6olXfbkJGBFCGFVCGEnMBo4P8VlqnZCCO/xw/H9zweej9afB34ct/2F4Gbjcwi0AM4CJoUQNoUQvgEmAQOjfQ1DCLOCf5u9EHcuqYAQwhchhAXR+hZgKT4LsuoujUWf/9boac1oCUA/YGy0vXS9xepzLNA/aqk7HxgdQtgRQlgNrMC/U/W9WkXM7AjgHGBk9NxQvWWqav89qSBd9uRwYG3c87xom6TeYSGEL8CDQeDQaHtZdVbe9rwE26USRbfSc/BWWdVdmotSJj4CNuB/7FcCm0MIhdEh8Z/1f+on2l8ANCX5+pSK+ytwC1AcPW+K6i0TBOAdM/vQzIZH26r992SVjZMuB4xEeVsatzO9lVVnyW6XSmJm9YFxwI0hhG/LSYdU3aWJ4HNzdDWzxsB4oEOiw6LHZOsnUQOZ6q2CzOxcYEMI4UMz6xvbnOBQ1Vv66RVCyDezQ4FJZvZpOcdWm+9JtaTLnuQBreKeHwHkp6gssrsvo9t4RI8bou1l1Vl5249IsF0qgZnVxAP0l0IIr0SbVXcZIoSwGZiG5742NrNY41b8Z/2f+on2N8LT05KtT6mYXsBgM/scT0Xph7esq97SXAghP3rcgP8oPgl9TypIlz2aBxwd9Y6vhXeumZDiMombAMR6rw8FXovbPiTqAd8DKIhuFb4NnGlmB0e95M8E3o72bTGzHlE+5pC4c0kFRJ/nM8DSEMKDcbtUd2nMzA6JWtAxs7rAALw/wbvARdFhpestVp8XAVOj3NcJwKXRKCJtgKPxDmz6Xq0CIYTbQwhHhBB+hH+mU0MIl6F6S2tmdpCZNYit499vn6DvSY3uomXPC96Tehmek3lHqstTHRfgf4AvgF14q8DP8dzJKcDy6LFJdKwBj0b19TFwYtx5foZ3gloBDIvbfiL+pbgSeIRoNmItFa63U/HbqouBj6JlkOouvRegC7AwqrdPgDuj7W3xYG0F8E+gdrS9TvR8RbS/bdy57ojq5jPiRpTQ92qV12FfSkZ3Ub2l8RLVz6JoyY19rvqeDF5IERERERFJH0p3ERERERFJMwrSRURERETSjIJ0EREREZE0oyBdRERERCTNKEgXEREREUkzCtJFRDKEmRWZ2Udm9omZ/dPM6pVx3Juxcb4reL0cMxsZrQ82s9sqcK7fm9mIPRzznJldFK1PM7MT9/V6cec818zuruh5RET2NwXpIiKZY3sIoWsI4ThgJ3BN/M5oco+sEMKg4DNlVtRvgb8BhBAmhBD+XAnnrBJmll3Grn/hs1Am/EEjIpKuFKSLiGSm94F2ZvYjM1tqZo8BC4BWZva5mTUDMLMhZrbYzBaZ2d+jbYeY2TgzmxctvUqfPJoBsEsIYVH0/EozeyRaf87MHjazmWa2Ktb6neAcd5jZZ2Y2GWgft72rmc2OyjU+mh2wTGb2uJnNN7Pc+Fbx6H3eaWYfAP/HzG4wsyXReUcDBJ8MZBpw7l5/siIiaaBGqgsgIiLJMbMawNnAW9Gm9vjsetdG+2PHdcJnTuwVQthoZk2i4x8C/n8I4QMzOxKfTrtDqcvEZugrSwt8RtVj8Wm6x5YqYzd82vQc/G/NAuDDaPcLwK9CCNPN7B7gLuDGcq51RwhhU9RaPsXMuoQQFkf7vg8hnBpdMx9oE0LYUSrdZz7QGxhTzjVERNKKgnQRkcxR18w+itbfB54BWgJrQgizExzfDxgbQtgIEELYFG0fAHSMBfNAQzNrEELYEvfaFsBX5ZTl1RBCMbDEzA5LsL83MD6E8B2AmU2IHhsBjUMI06PjnsenZi/PxWY2HP+b1QLoCMSC9JfjjlsMvGRmrwKvxm3fgH9OIiIZQ0G6iEjm2B5C6Bq/IQq0t5VxvAEhwfYsoGcIYXt51wLqlLN/R6nrJJLo2kkxszbACKB7COEbM3uuVLni3/s5wGnAYOB3ZtYphFAYHV/eexURSTvKSRcROXBNwVuhmwLEpbu8A1wfO8jMuiZ47VKgXQWu/R5wgZnVjfLbstyI1QAAAQ1JREFUzwMIIRQA35hZ7+i4K4DpZZwDoCEeiBdELfZnJzrIzLKAViGEd4FbgMZA/Wj3MZSfuiMiknbUki4icoAKIeSa2R+B6WZWBCwErgRuAB41s8X434H3KDVSTAjhUzNrlCANZm+vvcDMXgY+Atbg6TkxQ4EnohFXVgHDyjnPIjNbCORGx84o49Bs4MUoncbwnPvYCDenA7cn+x5ERFLJvOO7iIjI7szsJmBLCGFkqsuyr6LW93+EEPqnuiwiIslQuouIiJTlcXbPPc9ERwK/SXUhRESSpZZ0EREREZE0o5Z0EREREZE0oyBdRERERCTNKEgXEREREUkzCtJFRERERNKMgnQRERERkTTzv/yzjJ6M01Y+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "tags": []
      },
      "cell_type": "code",
      "source": [
        "RigeModel=Ridge(alpha=0.1)\n",
        "RigeModel.fit(x_train_pr, y_train)\n",
        "yhat = RigeModel.predict(x_test_pr)\n",
        "print('predicted:', yhat[0:4])\n",
        "print('test set :', y_test[0:4].values)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "predicted: [ 8.57923010e+03  5.04997953e+03  9.58621988e+03 -2.21726227e+00]\ntest set : [ 9549  6229 10245  7295]\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "Rsqu_test = []\n",
        "Rsqu_train = []\n",
        "dummy1 = []\n",
        "ALFA = 10 * np.array(range(0,1000))\n",
        "for alfa in ALFA:\n",
        "    RigeModel = Ridge(alpha=alfa) \n",
        "    RigeModel.fit(x_train_pr, y_train)\n",
        "    Rsqu_test.append(RigeModel.score(x_test_pr, y_test))\n",
        "    Rsqu_train.append(RigeModel.score(x_train_pr, y_train))"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "width = 12\n",
        "height = 10\n",
        "plt.figure(figsize=(width, height))\n",
        "\n",
        "plt.plot(ALFA,Rsqu_test, label='validation data  ')\n",
        "plt.plot(ALFA,Rsqu_train, 'r', label='training Data ')\n",
        "plt.xlabel('alpha')\n",
        "plt.ylabel('R^2')\n",
        "plt.legend()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x20448543bc8>"
          },
          "metadata": {},
          "execution_count": 123
        },
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "RigeModel=Ridge(alpha=10)\n",
        "RigeModel.fit(x_train_pr, y_train)\n",
        "RigeModel.score(x_test_pr, y_test)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7230892984015707"
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "alphaParameters= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000]}]\n",
        "RR=Ridge()\n",
        "GridTest = GridSearchCV(RR, alphaParameters,cv=4)\n",
        "GridTest.fit(x_data[['curb-weight', 'engine-size', 'highway-mpg']], y_data)\n",
        "BestRR=GridTest.best_estimator_\n",
        "BestRR"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "Ridge(alpha=1000, copy_X=True, fit_intercept=True, max_iter=None,\n      normalize=False, random_state=None, solver='auto', tol=0.001)"
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "BestRR.score(x_test[['curb-weight', 'engine-size', 'highway-mpg']], y_test)\n"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7827790663033924"
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "alphaParameters= [{'alpha': [0.001,0.1,1, 10, 100, 1000, 10000, 100000, 100000], 'normalize': [True,False]}]\n",
        "RR=Ridge()\n",
        "GridTest = GridSearchCV(RR, alphaParameters,cv=4)\n",
        "GridTest.fit(x_data[['curb-weight', 'engine-size', 'highway-mpg']], y_data)\n",
        "BestRR=GridTest.best_estimator_\n",
        "BestRR.score(x_test[['curb-weight', 'engine-size', 'highway-mpg']], y_test)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7807736716836969"
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python_defaultSpec_1599510320962",
      "display_name": "Python 3.7.6 64-bit ('base': conda)",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6-final",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}