Automobile_Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AUTOMOBILES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **It is mini project related to the Automobile dataset to predict the price of a vehicle on the basis of its feature.**\n",
    "+ **Here I have tried to cover as many slides as I can.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why This?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **Being an automobile fanatic, and as I am learning data analysis, so it would be great to analyse this data.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What to expect?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ **This notebook presents the multivariate analysis of the automobile dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud, STOPWORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df  =  pd.read_csv(\"Automobile_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data 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 \n",
      "dtypes: float64(5), int64(5), object(16)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>3.4</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>3.4</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19     3.4              10.0        102      5500       24   \n",
       "4         mpfi  3.19     3.4               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.replace(\"?\",np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Converting ? to the null values so that we can change them for further analyzing**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling             0\n",
       "normalized-losses    41\n",
       "make                  0\n",
       "fuel-type             0\n",
       "aspiration            0\n",
       "num-of-doors          2\n",
       "body-style            0\n",
       "drive-wheels          0\n",
       "engine-location       0\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "engine-type           0\n",
       "num-of-cylinders      0\n",
       "engine-size           0\n",
       "fuel-system           0\n",
       "bore                  4\n",
       "stroke                4\n",
       "compression-ratio     0\n",
       "horsepower            2\n",
       "peak-rpm              2\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**From above we can see the number of null values in each column**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   symboling          205 non-null    int64  \n",
      " 1   normalized-losses  164 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       203 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               201 non-null    object \n",
      " 19  stroke             201 non-null    object \n",
      " 20  compression-ratio  205 non-null    float64\n",
      " 21  horsepower         203 non-null    object \n",
      " 22  peak-rpm           203 non-null    object \n",
      " 23  city-mpg           205 non-null    int64  \n",
      " 24  highway-mpg        205 non-null    int64  \n",
      " 25  price              201 non-null    object \n",
      "dtypes: float64(5), int64(5), object(16)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.astype({'normalized-losses': 'float64',\"bore\":\"float64\",\"stroke\":\"float64\",\"price\":\"float64\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.astype({\"horsepower\":\"float64\",\"peak-rpm\":\"float64\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**We have changed the data types of multiple columns for the analysis**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"bore\"]=temp[\"bore\"].fillna(temp[\"bore\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"stroke\"]=temp[\"stroke\"].fillna(temp[\"stroke\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"horsepower\"]=temp[\"horsepower\"].fillna(temp[\"horsepower\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"peak-rpm\"]=temp[\"peak-rpm\"].fillna(temp[\"peak-rpm\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"price\"]=temp[\"price\"].fillna(temp[\"price\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Now we have filled the null values of each column with the mean of the particular column.**\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling             0\n",
       "normalized-losses    41\n",
       "make                  0\n",
       "fuel-type             0\n",
       "aspiration            0\n",
       "num-of-doors          2\n",
       "body-style            0\n",
       "drive-wheels          0\n",
       "engine-location       0\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "engine-type           0\n",
       "num-of-cylinders      0\n",
       "engine-size           0\n",
       "fuel-system           0\n",
       "bore                  0\n",
       "stroke                0\n",
       "compression-ratio     0\n",
       "horsepower            0\n",
       "peak-rpm              0\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
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       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
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       "      <th>symboling</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.528667</td>\n",
       "      <td>-0.531954</td>\n",
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       "      <td>-0.008689</td>\n",
       "      <td>-0.178515</td>\n",
       "      <td>0.071389</td>\n",
       "      <td>0.273679</td>\n",
       "      <td>-0.035823</td>\n",
       "      <td>0.034606</td>\n",
       "      <td>-0.082201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
       "      <td>0.528667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.074362</td>\n",
       "      <td>0.023220</td>\n",
       "      <td>0.105073</td>\n",
       "      <td>-0.432335</td>\n",
       "      <td>0.119893</td>\n",
       "      <td>0.167365</td>\n",
       "      <td>-0.033577</td>\n",
       "      <td>0.066315</td>\n",
       "      <td>-0.132654</td>\n",
       "      <td>0.295772</td>\n",
       "      <td>0.264597</td>\n",
       "      <td>-0.258502</td>\n",
       "      <td>-0.210768</td>\n",
       "      <td>0.203254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheel-base</th>\n",
       "      <td>-0.531954</td>\n",
       "      <td>-0.074362</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.874587</td>\n",
       "      <td>0.795144</td>\n",
       "      <td>0.589435</td>\n",
       "      <td>0.776386</td>\n",
       "      <td>0.569329</td>\n",
       "      <td>0.488760</td>\n",
       "      <td>0.160944</td>\n",
       "      <td>0.249786</td>\n",
       "      <td>0.351957</td>\n",
       "      <td>-0.360704</td>\n",
       "      <td>-0.470414</td>\n",
       "      <td>-0.544082</td>\n",
       "      <td>0.583168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>-0.357612</td>\n",
       "      <td>0.023220</td>\n",
       "      <td>0.874587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.841118</td>\n",
       "      <td>0.491029</td>\n",
       "      <td>0.877728</td>\n",
       "      <td>0.683360</td>\n",
       "      <td>0.606462</td>\n",
       "      <td>0.129522</td>\n",
       "      <td>0.158414</td>\n",
       "      <td>0.554434</td>\n",
       "      <td>-0.287031</td>\n",
       "      <td>-0.670909</td>\n",
       "      <td>-0.704662</td>\n",
       "      <td>0.682986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>-0.232919</td>\n",
       "      <td>0.105073</td>\n",
       "      <td>0.795144</td>\n",
       "      <td>0.841118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.279210</td>\n",
       "      <td>0.867032</td>\n",
       "      <td>0.735433</td>\n",
       "      <td>0.559152</td>\n",
       "      <td>0.182939</td>\n",
       "      <td>0.181129</td>\n",
       "      <td>0.642195</td>\n",
       "      <td>-0.219859</td>\n",
       "      <td>-0.642704</td>\n",
       "      <td>-0.677218</td>\n",
       "      <td>0.728699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>-0.541038</td>\n",
       "      <td>-0.432335</td>\n",
       "      <td>0.589435</td>\n",
       "      <td>0.491029</td>\n",
       "      <td>0.279210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295572</td>\n",
       "      <td>0.067149</td>\n",
       "      <td>0.171101</td>\n",
       "      <td>-0.055351</td>\n",
       "      <td>0.261214</td>\n",
       "      <td>-0.110137</td>\n",
       "      <td>-0.320602</td>\n",
       "      <td>-0.048640</td>\n",
       "      <td>-0.107358</td>\n",
       "      <td>0.134388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>curb-weight</th>\n",
       "      <td>-0.227691</td>\n",
       "      <td>0.119893</td>\n",
       "      <td>0.776386</td>\n",
       "      <td>0.877728</td>\n",
       "      <td>0.867032</td>\n",
       "      <td>0.295572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850594</td>\n",
       "      <td>0.648485</td>\n",
       "      <td>0.168783</td>\n",
       "      <td>0.151362</td>\n",
       "      <td>0.750968</td>\n",
       "      <td>-0.266283</td>\n",
       "      <td>-0.757414</td>\n",
       "      <td>-0.797465</td>\n",
       "      <td>0.820825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>-0.105790</td>\n",
       "      <td>0.167365</td>\n",
       "      <td>0.569329</td>\n",
       "      <td>0.683360</td>\n",
       "      <td>0.735433</td>\n",
       "      <td>0.067149</td>\n",
       "      <td>0.850594</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.583798</td>\n",
       "      <td>0.203094</td>\n",
       "      <td>0.028971</td>\n",
       "      <td>0.810713</td>\n",
       "      <td>-0.244599</td>\n",
       "      <td>-0.653658</td>\n",
       "      <td>-0.677470</td>\n",
       "      <td>0.861752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>-0.130083</td>\n",
       "      <td>-0.033577</td>\n",
       "      <td>0.488760</td>\n",
       "      <td>0.606462</td>\n",
       "      <td>0.559152</td>\n",
       "      <td>0.171101</td>\n",
       "      <td>0.648485</td>\n",
       "      <td>0.583798</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.055909</td>\n",
       "      <td>0.005201</td>\n",
       "      <td>0.575737</td>\n",
       "      <td>-0.254761</td>\n",
       "      <td>-0.584508</td>\n",
       "      <td>-0.586992</td>\n",
       "      <td>0.532300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>-0.008689</td>\n",
       "      <td>0.066315</td>\n",
       "      <td>0.160944</td>\n",
       "      <td>0.129522</td>\n",
       "      <td>0.182939</td>\n",
       "      <td>-0.055351</td>\n",
       "      <td>0.168783</td>\n",
       "      <td>0.203094</td>\n",
       "      <td>-0.055909</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.186105</td>\n",
       "      <td>0.088264</td>\n",
       "      <td>-0.066844</td>\n",
       "      <td>-0.042179</td>\n",
       "      <td>-0.043961</td>\n",
       "      <td>0.082095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>-0.178515</td>\n",
       "      <td>-0.132654</td>\n",
       "      <td>0.249786</td>\n",
       "      <td>0.158414</td>\n",
       "      <td>0.181129</td>\n",
       "      <td>0.261214</td>\n",
       "      <td>0.151362</td>\n",
       "      <td>0.028971</td>\n",
       "      <td>0.005201</td>\n",
       "      <td>0.186105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.205740</td>\n",
       "      <td>-0.435936</td>\n",
       "      <td>0.324701</td>\n",
       "      <td>0.265201</td>\n",
       "      <td>0.070990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>0.071389</td>\n",
       "      <td>0.295772</td>\n",
       "      <td>0.351957</td>\n",
       "      <td>0.554434</td>\n",
       "      <td>0.642195</td>\n",
       "      <td>-0.110137</td>\n",
       "      <td>0.750968</td>\n",
       "      <td>0.810713</td>\n",
       "      <td>0.575737</td>\n",
       "      <td>0.088264</td>\n",
       "      <td>-0.205740</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.130971</td>\n",
       "      <td>-0.803162</td>\n",
       "      <td>-0.770903</td>\n",
       "      <td>0.757917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>0.273679</td>\n",
       "      <td>0.264597</td>\n",
       "      <td>-0.360704</td>\n",
       "      <td>-0.287031</td>\n",
       "      <td>-0.219859</td>\n",
       "      <td>-0.320602</td>\n",
       "      <td>-0.266283</td>\n",
       "      <td>-0.244599</td>\n",
       "      <td>-0.254761</td>\n",
       "      <td>-0.066844</td>\n",
       "      <td>-0.435936</td>\n",
       "      <td>0.130971</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.113723</td>\n",
       "      <td>-0.054257</td>\n",
       "      <td>-0.100854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>-0.035823</td>\n",
       "      <td>-0.258502</td>\n",
       "      <td>-0.470414</td>\n",
       "      <td>-0.670909</td>\n",
       "      <td>-0.642704</td>\n",
       "      <td>-0.048640</td>\n",
       "      <td>-0.757414</td>\n",
       "      <td>-0.653658</td>\n",
       "      <td>-0.584508</td>\n",
       "      <td>-0.042179</td>\n",
       "      <td>0.324701</td>\n",
       "      <td>-0.803162</td>\n",
       "      <td>-0.113723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971337</td>\n",
       "      <td>-0.667449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>0.034606</td>\n",
       "      <td>-0.210768</td>\n",
       "      <td>-0.544082</td>\n",
       "      <td>-0.704662</td>\n",
       "      <td>-0.677218</td>\n",
       "      <td>-0.107358</td>\n",
       "      <td>-0.797465</td>\n",
       "      <td>-0.677470</td>\n",
       "      <td>-0.586992</td>\n",
       "      <td>-0.043961</td>\n",
       "      <td>0.265201</td>\n",
       "      <td>-0.770903</td>\n",
       "      <td>-0.054257</td>\n",
       "      <td>0.971337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.690526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.082201</td>\n",
       "      <td>0.203254</td>\n",
       "      <td>0.583168</td>\n",
       "      <td>0.682986</td>\n",
       "      <td>0.728699</td>\n",
       "      <td>0.134388</td>\n",
       "      <td>0.820825</td>\n",
       "      <td>0.861752</td>\n",
       "      <td>0.532300</td>\n",
       "      <td>0.082095</td>\n",
       "      <td>0.070990</td>\n",
       "      <td>0.757917</td>\n",
       "      <td>-0.100854</td>\n",
       "      <td>-0.667449</td>\n",
       "      <td>-0.690526</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   symboling  normalized-losses  wheel-base    length  \\\n",
       "symboling           1.000000           0.528667   -0.531954 -0.357612   \n",
       "normalized-losses   0.528667           1.000000   -0.074362  0.023220   \n",
       "wheel-base         -0.531954          -0.074362    1.000000  0.874587   \n",
       "length             -0.357612           0.023220    0.874587  1.000000   \n",
       "width              -0.232919           0.105073    0.795144  0.841118   \n",
       "height             -0.541038          -0.432335    0.589435  0.491029   \n",
       "curb-weight        -0.227691           0.119893    0.776386  0.877728   \n",
       "engine-size        -0.105790           0.167365    0.569329  0.683360   \n",
       "bore               -0.130083          -0.033577    0.488760  0.606462   \n",
       "stroke             -0.008689           0.066315    0.160944  0.129522   \n",
       "compression-ratio  -0.178515          -0.132654    0.249786  0.158414   \n",
       "horsepower          0.071389           0.295772    0.351957  0.554434   \n",
       "peak-rpm            0.273679           0.264597   -0.360704 -0.287031   \n",
       "city-mpg           -0.035823          -0.258502   -0.470414 -0.670909   \n",
       "highway-mpg         0.034606          -0.210768   -0.544082 -0.704662   \n",
       "price              -0.082201           0.203254    0.583168  0.682986   \n",
       "\n",
       "                      width    height  curb-weight  engine-size      bore  \\\n",
       "symboling         -0.232919 -0.541038    -0.227691    -0.105790 -0.130083   \n",
       "normalized-losses  0.105073 -0.432335     0.119893     0.167365 -0.033577   \n",
       "wheel-base         0.795144  0.589435     0.776386     0.569329  0.488760   \n",
       "length             0.841118  0.491029     0.877728     0.683360  0.606462   \n",
       "width              1.000000  0.279210     0.867032     0.735433  0.559152   \n",
       "height             0.279210  1.000000     0.295572     0.067149  0.171101   \n",
       "curb-weight        0.867032  0.295572     1.000000     0.850594  0.648485   \n",
       "engine-size        0.735433  0.067149     0.850594     1.000000  0.583798   \n",
       "bore               0.559152  0.171101     0.648485     0.583798  1.000000   \n",
       "stroke             0.182939 -0.055351     0.168783     0.203094 -0.055909   \n",
       "compression-ratio  0.181129  0.261214     0.151362     0.028971  0.005201   \n",
       "horsepower         0.642195 -0.110137     0.750968     0.810713  0.575737   \n",
       "peak-rpm          -0.219859 -0.320602    -0.266283    -0.244599 -0.254761   \n",
       "city-mpg          -0.642704 -0.048640    -0.757414    -0.653658 -0.584508   \n",
       "highway-mpg       -0.677218 -0.107358    -0.797465    -0.677470 -0.586992   \n",
       "price              0.728699  0.134388     0.820825     0.861752  0.532300   \n",
       "\n",
       "                     stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "symboling         -0.008689          -0.178515    0.071389  0.273679   \n",
       "normalized-losses  0.066315          -0.132654    0.295772  0.264597   \n",
       "wheel-base         0.160944           0.249786    0.351957 -0.360704   \n",
       "length             0.129522           0.158414    0.554434 -0.287031   \n",
       "width              0.182939           0.181129    0.642195 -0.219859   \n",
       "height            -0.055351           0.261214   -0.110137 -0.320602   \n",
       "curb-weight        0.168783           0.151362    0.750968 -0.266283   \n",
       "engine-size        0.203094           0.028971    0.810713 -0.244599   \n",
       "bore              -0.055909           0.005201    0.575737 -0.254761   \n",
       "stroke             1.000000           0.186105    0.088264 -0.066844   \n",
       "compression-ratio  0.186105           1.000000   -0.205740 -0.435936   \n",
       "horsepower         0.088264          -0.205740    1.000000  0.130971   \n",
       "peak-rpm          -0.066844          -0.435936    0.130971  1.000000   \n",
       "city-mpg          -0.042179           0.324701   -0.803162 -0.113723   \n",
       "highway-mpg       -0.043961           0.265201   -0.770903 -0.054257   \n",
       "price              0.082095           0.070990    0.757917 -0.100854   \n",
       "\n",
       "                   city-mpg  highway-mpg     price  \n",
       "symboling         -0.035823     0.034606 -0.082201  \n",
       "normalized-losses -0.258502    -0.210768  0.203254  \n",
       "wheel-base        -0.470414    -0.544082  0.583168  \n",
       "length            -0.670909    -0.704662  0.682986  \n",
       "width             -0.642704    -0.677218  0.728699  \n",
       "height            -0.048640    -0.107358  0.134388  \n",
       "curb-weight       -0.757414    -0.797465  0.820825  \n",
       "engine-size       -0.653658    -0.677470  0.861752  \n",
       "bore              -0.584508    -0.586992  0.532300  \n",
       "stroke            -0.042179    -0.043961  0.082095  \n",
       "compression-ratio  0.324701     0.265201  0.070990  \n",
       "horsepower        -0.803162    -0.770903  0.757917  \n",
       "peak-rpm          -0.113723    -0.054257 -0.100854  \n",
       "city-mpg           1.000000     0.971337 -0.667449  \n",
       "highway-mpg        0.971337     1.000000 -0.690526  \n",
       "price             -0.667449    -0.690526  1.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
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       "      <th>horsepower</th>\n",
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       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>164.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
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       "      <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>122.000000</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>3.329751</td>\n",
       "      <td>3.255423</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>104.256158</td>\n",
       "      <td>5125.369458</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13207.129353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>35.442168</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>0.270844</td>\n",
       "      <td>0.313597</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>39.519211</td>\n",
       "      <td>476.979093</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7868.768212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.000000</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>2.540000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4150.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</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>3.150000</td>\n",
       "      <td>3.110000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4800.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>7788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>115.000000</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>3.310000</td>\n",
       "      <td>3.290000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>5200.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>150.000000</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>3.580000</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>16500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>256.000000</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>3.940000</td>\n",
       "      <td>4.170000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>6600.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>45400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  normalized-losses  wheel-base      length       width  \\\n",
       "count  205.000000         164.000000  205.000000  205.000000  205.000000   \n",
       "mean     0.834146         122.000000   98.756585  174.049268   65.907805   \n",
       "std      1.245307          35.442168    6.021776   12.337289    2.145204   \n",
       "min     -2.000000          65.000000   86.600000  141.100000   60.300000   \n",
       "25%      0.000000          94.000000   94.500000  166.300000   64.100000   \n",
       "50%      1.000000         115.000000   97.000000  173.200000   65.500000   \n",
       "75%      2.000000         150.000000  102.400000  183.100000   66.900000   \n",
       "max      3.000000         256.000000  120.900000  208.100000   72.300000   \n",
       "\n",
       "           height  curb-weight  engine-size        bore      stroke  \\\n",
       "count  205.000000   205.000000   205.000000  205.000000  205.000000   \n",
       "mean    53.724878  2555.565854   126.907317    3.329751    3.255423   \n",
       "std      2.443522   520.680204    41.642693    0.270844    0.313597   \n",
       "min     47.800000  1488.000000    61.000000    2.540000    2.070000   \n",
       "25%     52.000000  2145.000000    97.000000    3.150000    3.110000   \n",
       "50%     54.100000  2414.000000   120.000000    3.310000    3.290000   \n",
       "75%     55.500000  2935.000000   141.000000    3.580000    3.410000   \n",
       "max     59.800000  4066.000000   326.000000    3.940000    4.170000   \n",
       "\n",
       "       compression-ratio  horsepower     peak-rpm    city-mpg  highway-mpg  \\\n",
       "count         205.000000  205.000000   205.000000  205.000000   205.000000   \n",
       "mean           10.142537  104.256158  5125.369458   25.219512    30.751220   \n",
       "std             3.972040   39.519211   476.979093    6.542142     6.886443   \n",
       "min             7.000000   48.000000  4150.000000   13.000000    16.000000   \n",
       "25%             8.600000   70.000000  4800.000000   19.000000    25.000000   \n",
       "50%             9.000000   95.000000  5200.000000   24.000000    30.000000   \n",
       "75%             9.400000  116.000000  5500.000000   30.000000    34.000000   \n",
       "max            23.000000  288.000000  6600.000000   49.000000    54.000000   \n",
       "\n",
       "              price  \n",
       "count    205.000000  \n",
       "mean   13207.129353  \n",
       "std     7868.768212  \n",
       "min     5118.000000  \n",
       "25%     7788.000000  \n",
       "50%    10595.000000  \n",
       "75%    16500.000000  \n",
       "max    45400.000000  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = []\n",
    "for i in temp['make']:\n",
    "    i.split(\",\")\n",
    "    names.append(i)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "words = \" \".join((names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+QQxbHWNYdjOTDMrttuIJNMNE041GZlc03fzTxZbF9xNCqAsKmc0dKzVxxrGqnwQC46dHlmX49Tq+49Czc6+6qYQgPzXRzLZ6tSq5yeOk+4dIdPc0K7BVkuFk5RQrPrPQNFJ9A2i6QefwjlVHSPJTE2dUltHu8apPCnMzIDS6RkbVQgUQhmQb2dmVcza2OMMgIDt+nMzQCL279yFDiRDgVCssHR9DSkklu4hmmGQGR2DZ+m8kgdxqBSlD8tMTdA7voHfPuYSBj1uttsgaeB5L48foGBymb8+5Ks4nBF5NJVMIwLBVSYxAucVC03BrVSpLC6etMwBxsg7Mp7NXVwiJZYWEocDzziCl9ppDYpoSTUhcT0Nu1pw5i9jS4WsDoenololfrzesEoPAc9fNGApdx7DsRuFvQOA6zYC8GYmqz1a5a7ppIXQdvxGHMqOxtho2AN+pb8p6EZqGYUeaSRQzGsN3HGQYYNgRwkbWuDm/ZaEbKjEYron56aaynk4VIxNCQ7dttEY9YOC5bWvVTRPdUPEJFfNrdVk1XUe3bJASz6mrpIWULccJTVO6bRSQB67bElbQTatRSK1077vOKUMFG+3VPWvEt3d3mT/43SM8++MUn/5vO/D9N5YXreuSf/OJwwwOOPynP93FsRObCAydZYxur/Jvf/8wLx1K8Kd/OYrnvbF0KITkk/96jB3bq3zqL3Zy8PAGQbktbOEnhFe9SYFlhfT2uHSkz6yu5rWHpDPj0dPtYJob7eV9dWEaIb09DpkOD3Gy6uHXMTIdHr3dLtZrpMMtbGEz2IrxbeFnFsIy0LtbrdIgW0bWX/0WZm8YaAKjN7USvwPCYo2wvMmdJq9TbBHfFn5moSVsYvt3gKEjDFVkXH3wMN50e33ozyqEqRO9ZAQRVeU96Br15ydxXpx+rZf2ivCqEZ9tBaTTPoYucT1BoWDinTTuJ4nFApKJAE1IanWdQtHYRIBcJQQ6Uj6G0ZiraJ40PiaEJJ3yiUYDfF+QL5iE4anipGp9iXiAYUiCAKpVg1JZ59VKQlhmSDrtYW5SLpDEog0dapKao1EsmoThqde3ei7PE+Q3ocNU0icWDfCD5bWd+lrFoiHxuI9pLutQp1zZzHU++whyFUrf+zEYGtELt2FufwOkn3/CkI5P+Qcvgq5j7egicuHwa72ks4KzTnxBILjg3BLvfecMu3ZWse2QSkXn4OEEf//lQY4ej9FKFJJoJOT2Wxe49cZFBvocdF2SK5g8dSDNV/9pgPkFq+Wc4aEaH7l7kh/8qIt80eQD75lm184KkUhItapz8HCcv//KEGNH2+dKxAPefecsN167RCbj4Tgah44k+NLXBja40SXbhuq85U0LXHR+id4eB9sO8TzB4pLFI49nuOdb/ZTPIgEGAZyzp8J73jnDnl0rch0ei/MPXxnk0JGVvZ/La7TtkDfdtMjttywy0O9gGCGFgsmB51J85esDzM7ZLef099X52IcnePCRTmbnbT7w3in27l6Z68hYnH/4x0EOHm6fKxYNeOfb57j5hiW6Ol1cV+PIWJwvfm0A111fhwP9Dm990wIXXVCkr9chEgnxPcFS1uKxpzr46jf6KZaMs6bDTUGCdH1wIXS23NuNIN0ACAhrb9T4fTvOOvEN9jv87m8fpVAweeDhTsJQcO6+EldfkaO/z+Hf/dFelrImyz9w2w75tY+O86abF5mds3no0U4cV2PH9ipvvX2BfXsq/Oc/38X0zMqNm0z4XHNljo60R3eXS6ls8NCjnQSBmuuqy/PNuRaXVkjTNCUf+8Vxbrt5kflFmx8+0EUQCPburvB7vzO2oWW0b3eZO26bZ2YuwhNPpykUTTIdHpddXOAX3jNNIh7wmf8+whnWUraht9vld3/7KJWqzkOPduL7gnP2lrnisjwD/XX+3R/uY26hVa5f/tAEb719gYUFRca1msb2kRq33byodPhnOxmfjDbPiccCrr4yR3eXSyrpU3c0Hno0g+9r7NtT5vLL8gwO1Pnf/3Avs/MrujcMyUfunuRtb55nccni/oe6cF2NPbsq/N7vHMVZl/hg12iFt79ljrkFm6efSZMrmHSkPC69uMh73zlDKunzl5/dju+/MUpgtvDGxlknvuFtNR54uJP/76+3ky+o4TszHv/mE0c4Z2+Zyy4u8C/3LXeIkNx0XZZbb1rkxYMJPvUXo8zP20ggEgn5+Ttnef97pvnw+yf5k0/vbLspztlb5qFHO/l/PrcyV6bD4w8+cYTz9pXZf0mB793b0zz+ovOL3Hz9EjNzEf7ov+zmxHgUCaSSPh//2Aluui5Lrb72xhU8+kSGqZkIk1NRKlUdKQWaJrnisjy//6/GuPaqLF++Z4ClrMXZwLahOo88nuEvP7udbE7VRqVTPp/8n49w0fklrtif55vf7W3q8Norc7zlTQuMHY3xX/7vXczM2kipHip3vnWeD//CJB+5e5L/+KnduKvcUQHs3V3hsSc7+PR/29Ey1+//6zEuvqDIlZfn+ca3+5rnnLuvzG23LLKwaPNHf7KbsWMxpIREIuBXfnGc229ZxHHadfjUMx382z/cx8RUlEpFR0rQNLjkwgL/6++OcfUVOb741QHm5iNo6SjWtk78pTL+bPs+aTSBNdqNsAzcI/NIb+WJIywdvTuJnlJ93cJSHX+hhHyFFp25oxstZuEeW0CuY/locRtzRzdhsYY3sWr/rQAtEcHoTiKiJtILCLJlglwVNrGvdyOIiIG1s5ew6uKdWFy354M5lEHPxHCPL7YmI3QNozOOnomDoRFWHfz5ErJ68u1ip4LRl8LoSeJO5gjz7Y0XhKlj7epF+gHu2EJLbElETYyeJFrchlAS5Kv4S2XwX53qgLNOfJWqzpfuGWgQkbrJsjmThx7NcO6+MqPbVxRi2yG337JAKOEr9wwwt8qyqNc1vvHtPq69KsvllxbYMVLjyBrXtVrV+dLXWufK5U0eeqST888pM7q9pQc6N1ybxTQl9/6wm+PjK9ZPsWTwjW/3ceVl+XU9rUrV4OVDyZbPwlBw4Nk0UzMRBvodOjPuWSO+Wk3jy/cMNIhILahQNHjg4S4uvqDE6PZqs8GGZUluv3UBIeAfvzHQYhk7js63vtfL9ddkueTCIrt3VXjx5USLDmt1jS9/rX2u+x/q5JILi4yO1FY185Bcf3WWiB3y9Qc7G6SnzimXjcb1ymHo7Xdhrabz0sG1OoTnXkgxPhlldHuV7k6PufkIhBJrTx9Gf5ryQgmCNd1+EzaRi4YJCjWcwyt7UfWuONFLt6N3xJG+6oQsDJ2gWKd24ATBwunvjGjOGTWJXLAN6fq4R+bbvje3dxE5f4jaU8dXnSSw9/RhnzOAMHS1Jl1DCHDHs9SfmzhzQg4k1nAnwjbw54ttpCVMnciFQwjTwDm6srtBi1tELt2O2Z9GBiGEEmHqhHWP+vOTeONLp+xduCE0DfvcQUTEVHpYM47emyRy4Tbco62kZ27LELloGC1mqYeYEAhDw58vUTtwgrB0Zj33TrrUsz3g3LzN9EyEtQxSKJpICdHoytM50+ExvK1GsWhyeGxtLElQqaqbJRYL2LenfZP0/KLF1CbnikRCtg/X8DzBy4fa55qcilIonaTpHQASTZMYeohhhCoLWNMRQmIYZ6/ubmHJYmJyPbmMNrnSSY8dIzXKFZ2XDyXazqnVNX78YpJIJOTcve06XMpajE9F285bT4e2FbJjew3fF7x0MNGWkJiZjZDLnZ4OhVAPMCHAaNT+hRUHf76Inomhd7QXkpuDGYRp4I1nmxaBFrOIXbkTLRGhduAE5XtfpPz9F6k+fgxhGcSu2omWip5ibRvDm8ojXR9zuLOltAMAQ8fclkHWPbyZlYywtaObyIXDBMUalQcOUv7+i5R/8BLu0QX13UXD7WNtEtILcCeyiIiJ2Z9u+17vTqAlonizhRVSNDSi+0cx+9M4B2cp/+Alyt9/kcqDh5F1n+j+HRj9HW1jbRbBUomwVMfoTyMi7b8Da7hL7dxYZRHrPUmil4+ChOqjY+q63fsi9ecn0bsTxK4cRVhnPwd71kcsFA3CYO3FFE2CX71bJx4LiMcC5uZt3HUSC1LCwqKyovp72+uGCkWT4KRzrZCRbYXE4wFBKMgV2i9Kva5Rq2mkU21foWmSkW019l9aYNeOCp2dHrFIoIq2e102eIXAGaNY2liuxjbGJqLRgETCZ3omsmF8bb6hw751dFgsGQRtcbXVc63o0LIkibhPGEIu327dOq5GpabT0/YNaEIlifZfmmf3aJXOTpdYNMCyJL09a9YVSrzxLOZQBnNbJ8HSKsI2NEUyjoe/imTM0W60ZJT6cxO4YysWWVh2QEDsyp3Ye/paLbLTQFhWLrPZn0JPxwhyK63OjK44eiqKN55tkoywdOw9fUjXp/bUccLiitVSe24SLRHBGunCPbZIsHhmlqg3lcPe14853Il7fLHFdTaHOwGpLLjlz/rSGH0pvPEl6i9MNY8PKw411yd+0znY5/TjzxfbrOzNQLoB7mSWyHmDGL0pvBMrc2txG6M3pVzYbON6NixiYehUHzvacj2dQ7MI28Q+ZwBzW0ZZiWcRr0pWd7O2j95wiYJQbGheLxOAvo5F1U4OG0NoisCQ658nYd1tdpomufOtc7zv56exLMn4RJTxyQjFoknd0XjzrQskk2c3I3h6OmzuPz+lDtezSk9nLiEkuqZ25/vr6VCCv05JiyYkb7ltgQ++b4qIHTA+FWV8IkqhaFCv69x60yJdna1xM3+hSFhxMAc7cF6ebrqEeiaOno7hTeUIK87ywjD70kg/WLcGz58rIh0PozcJhnZmcaNQWSrmUAfmtkwL8ZnbOgFwJ1bf6BG0VBRvJt9e7BuEeJM5jMEOjN7kGRNfWKoTLJabMc2gEVcTUQuzL01QqOEvrSLoRiGyN5Vriy8G+SphoYqeiaPFrDN2L/3JHHJvP9Zwp7LIG1bIshXoHZpt6l+YOkZXgrDqtOtAgjeVxd7Xj9Gfxj22eFZbeL2mBczVmo7naSRiQZMEV0MImqRSLL6ypfqewHU0hAaxaHv6VdPAtts/3zVa4UPvn8L1BP/5z3Zy4Lm0SrI0unNcfmnhrBPf6aBW13BdjVgsWPfhACp5A8oafyXwfUHdUa59PLbOm7g0SSTSTiojwzU+cvcESMGn/nInTzzdoWr+Gsu98PxSG/FJx1cWzd5+9O4k/pRqEGsOZUATuKssGQxNuUNBiKy3Jx6k5yPdAGEZCMtA+mcWxPfni4QVF3Mog3NwRo0ZMTH60wSlOv7iimUqoiZoQiVC1klihDX18nIt9griwqHEncgSG0hjDHY0ic/oSyGiFt6RefAbv2kh1JokKw+MNWOFdQ9dE8pNPUPiC4o1giVFxlrSVpau3rDSXXVNlyEsAwwdWXNbElTLkHUfghAtaoIuwD97xPea7oIvFExm520yGY+ebpe2Vu26ZNdolSCAsWMbdNTcJKo1nYUlC9MIGR6qt82V6fBIxNuVf+6+MrFYwNPPpnnqQAdeo+uIRBCNBsTjr239V6lkMD1r05HyG+GANTrUYPfOCmH4ynVYd3TmFyx0XTKyrV2H6ZTfJNnV2LenTCIe8NwLSR57ogPXXdGhHQlJJtbXoTeRVYmO4U5149oG5mAHYanWmqhYXsaG72pd/lyeeeAeVAxvKoeWjKB3qa1uRm8SLW6pta6+eZvxlg0GE43/e4X3sj9bUGS8LQOmrjLeI51Ir5Vk1JqW595gUWfj3cKhcq+FqWMOqt5leiqC0ZVQD47V1m+zjdZG123Nus8iXlPiK5V1Hnuyg4gdcNstC2tcMcn555TZt6fM5HSUg21Fu6eHMBQ8/UwaTYPrr1GZydVzXXZxgfQ6DRaajbNlq0sohOTySwrrxh5/kqhUdR5+PINpqgy5abbqcN+eMuefW2Z2LtKW0T1dhKHg6WfTCAHXXZ1dYzlLLr6gSGdmHR0227Stdasll1xYZGhwfetCxYMqGL0pRNRE70qgxW28yVyrhRAEhDUXoYt1LaimpecGqmD5FcCbUO6buU0lOcxtnUgvbCOZsOoqayVmr5vA0OLqFZXrWl+ngeVYp56OYWRiaHEbvStBsFAiLLf23QsrTrN2arMAACAASURBVLO8pg26hhY1kYFE1l5ZWYs3WyCseYqMDQ1joAN0rZExXvkFSNdTVrNtrJvAEDELdE1Zx2cQczwZXuO9uoJvfrePyy4ucMdtC2gCHni4E9fT2DVa4a53z6Bpki9/beAVu7oADz7ayVtuW2D/JQV+9aPjfP+H3Xiexnn7Srz7zllqNb3N5T50JI7jaFx2cYHrr8ly5Ggc0wy55MIi73r7LNWajmW9lp1IBN/7fi9XXlbgTTcvIqXghw90UXc0RrdXuevdM1hmyFe+PrCJjOup8cgTGd725nkuOr/Ex395nO/e24PrqqLnn79zllpNW0O+cHgsTq2uc9EFRW66LsvLhxOYRsiF55d49zvUOdHoOjpsWA/GZdsx+1Lo3UlkIz7WAqkC/UZfSiVD8tUWK8Ec6kDYBu6JpVd8AwX5KkG2gtGXQu+IYXSrGN3amFhYcQiyFfSueFsyBEPHHO4CL1CJhFcCCe5EFmtnD8ZQhrDsIAxdybrGxfZm89h7+zBHOhVRr9KF0ZVAT8fwF0uveIeGrCsytka70TNxzIEOlalfU04kvQB/roA12oPRl25JxCjLtUs1JJ0pnHWr76wRXxgKHEfbcH9nEKjv1xYhL2VN/uTTO/m1j4xzx+3zvO3N84ShQNclS1mTz/zNCPc/3MVqSyWUNOZa31UIQkHd0dqSFUtZk7/4zA5+81eOc/sti9x28yJBo+nn1/6pn0Q84JYbF5GrdnAcGovztW/28863zfKJxm4KXZeEoeCeb/ZTq2u8/+en2eCNlaeFUIoVudaxzMJQ4Lha257YfMHgv356lF/5yDi33bzAm29dIAgFhi7J5U0+9/lh7v1RV+OlmApSQn35eq2jw+b1XKPDQsHgLz6zg9/61ePcfMMSN12/RBCoZMc/facPISR33L7Qsgvm+HiMr9wzwHvfOcO/+o1jVKs6mq5ecvPN7/axuGTxix+YXHfnjDeTJ+IMYm5XBcTBYpmg2N440xvPYg53Yu/pa56HBKM7iX3uAGGpjntkzfsnNAGaQOgawlRvI1q2PmTQ2Ly9Nj4XhLjjWaKXDGPt6VOEOt5OMvgh9ZemiV29i9iVo9RfniEs1RGWgTXajdGdwB2bV4XMrxBBrkKQq6raPMdvFCS3E2qwWMY9vog12kN0/3a840tIP0BLR4mcO4gMQpyXZlofDppo1tVpDatMmDrCNpp1gG2yS2UZW6Pd2Lt70dJR3LH59ppFqbK3Rl+K6CXDCNsgyJZB0zCHMlij3fhzhXaX/SzgrDUiNc2Qzg4Px9VaCoqXEbEDUimfel1bZ0+mJBoNOWdPmZ2jFUxDMrdg88JLSRYWrbZ6MdMMyXR4uGc4V2fG4+ILivT3OdTrOj9+KcHR43FsW5XX5PJmC2kaRsjunRXOa8T78gWTF19OcHw8hmmEpFI++fypmjCswO7UyVxos3Sgjldc9dQ1QjozHp4nyOVXCoqb5zUaPziO1khUtMoViYTs211m984qphmysGjxwktJ5hbsVh0K6LnEorvXY/Zxl1yuXYenmqsj7XPxBUUGB+o4jsYLLyc5cjSGZUmScZ9cobXJga6H7B6tct45ZeJxn2LR4MWDSY6diKLrkE55FApme1mTgOgVO7F2dIGE2pPHVIZvHWhxVdhsDKSbb3MUgJ+tUH9ugiC7YnUJ2yB27W60eAShCYSlg66pfalBgAwlwUKJ6pPHIJBt8yTedB7CNgirLuV7X1w3qYJQyZjIBdvQ4rZ6x4YA6Ye4xxdxXpxeN6h/JrD29hO9ZASkxDkyR/3A+LrHCUsnct4Q5o5uhC4aJUuCsFyn9uNJ/KlVWXFNEL1iFKMnqXRk6CqO6IdIT+korNSpPjLWLr+hk7j5HPRMDBmEVH70MsHS+m871DvjRC8ZUccuq1qCP5On9tzEK9pR8qp3YN7C5pHaa7H7Ix0c/H+zVCZeg+SIBrs+mMaIaRz6bA55lvYYv1rQUhGMriRSSvyp3MnJQhPo6RhaMtKMoQX5ansJi66sCmFs/LAKax7+bH5di9gY7ECLmIRVd9W2Osk77lykVtO47wedTQtW2AZ6Jq6ypX5AUKip+Nsp7q7rrs/R1+fyT9/oOWU3bhExMPo7EEI0ss8niR0KgZa00dMxRfY1lyBXbY9/CjD6O1RWdRUGBhzueNsi3/5WNzOTBt5Uft0Qgt6dQE9FkV6wbgkNQCLh8973zfHwY12MzfeoGK2UBMU6YbH2irb1wcbEt9WPbwuve4TFOm5xk+UVoWy4fqd4l3IQtsaUThP+Bj37Lr2sSKFg8MP7OpufScdff8/xKbB3X5U9u6t859vdrPP6jxbIuo93fH1LuP1gSVistxRVr38cLUXFy4jZZa7adpz7ljy88Y0rBYLFMsHiyd81HYmG3HhjjqNjUQ69UOIn9Qx+XROfMJUZLgOJZglCRyJD0CPKRA+dNU8DAbolQAMZQOi2Py30iCB01TiaLRAayLBx7NrhdNBMNR6NY9bG8prHCAg9iVzPgBOg2yvHbAitsX4B0peEG/zYNUsgGi/IlkFjzDN9MIoVPWy4/tVzhhCs0ZUwQWjq+pz0OKMh3xrIEIL6lnOxhZ8cXtfE13tVjGi/QehLOs61WXq6TnXaY/D2BDKQnPjHIpVJdacacUH/TXE6zrXRLIFXCll4rMbS07WmK6dHBXs/lmHmvgpWSqP7qhhmQsMrBhz9HwXq88vFnpDcZdF3XYzYoIFmCPxaSP4Fh8nvlps3c6RPZ/DWBPFhE6FDfd5n5kdVSkdWYhKaLei/MUbnJRE0XVCd8ige9WANgZppjYGb46T2WGimwC2EzD9cJftcvXms0KDrsgjdV8aw0qpjsFcKyR6oM/fg6QfJhQZ918XoujSKHhPU5wOmv1+mfHyFcYUB3ZdH6b48ipnUCF1J7gWH2fsrBFWliL5rYiR2Wiw9XaPv+jhWhzoue6DO7P2VJoF3748ycEurhaCZAicXcOivcs3x3jiQxOMBiUSA12i2u94DSAhJKuUTiaoehPnC8jbB9hhtKuUThoJ83jjpWNFo2FJ25/mCpUWzGcvVNHWcHQnxGrHw9q2km4OuSzo6PAxDUiptRBkSy5Kk0z6aJqlU9HV7VAohSXf42Jbq+7hcXrnRWGt3G+ULBrWq3jwuEglJpgKEkJRLBtWq1jbnenhdE59mCTrOt5m5r8LikzX6b45TOuIy9b0yg7fF6b4iSmWqhDBg5F0por0GM/dV8IohyV0Ww+9QcaGlJ5VJLwSYCY3+G2L4dcncjyqEnsTuNvBX3XSpPRa77k5TnfaZ+ucyflUS6dYJqmHzIlkdGrvu7sDNB0x8s4QMJF2XRdn5/jSH/3uO6qQPQhFL/w1xZn5YoTLuERsy6L8x1nJtNFuw4z0pzLjG9L+o+dL7bLa/K4n0Jbkfq3hNfMRk+B1J5h+pURxz0XSIbTMJnDNLKce3mXilkKnvlUFA/00xRu9Kc/CzWdycasLQe02M/pvjzN1foTLlE+nUGbgtjhHXGL+niAzUdUrvtbBSGvMP13ALAelzbAZujVPPBmQPKP3nXnCoTK6QqpnQ2P7uFPV5v916f51DCMkll5a46645urpcqlWdJ55Ir6kBVjfm29+xyLXX5YnFVMfvl16M86Uv9bO4sJzAkuzcWePuD86wbdjBdQQvvZRQWyxXQTdC7rhjiRtuVFnOZNInmQwo5A1eeDHOX312CMfRicUC3vXuea64soBth/i+4JkDKb7ylV6K6yQDN4YkkQi46/1zXH65ctXn5y2eeTpFGLQet3t3lffeNcfwSB1NQD5v8N3vdPPgg5nGlkn16tF33LnAzbfksMyQfN7k4YfSa7aQSoaGHO7+0Ay9Pa562Hd5CAEzMxZf+mI/B55OAZLzzq/wnvfM0T/gommSxUWTb9zTy5NPpk7Z0ft1TXyg3MvFJ2oYUY3ea2IUD7vkX3BI7jCJDihLK7HdIr3PZuxv8xQb1lbpmEtswKD36hi5552VG0sDzdY4/vksfmX5h7USCBYGDNwcp74UMPZ3BfyKIpW1EZquy6LoEcGJrxZxC+qYyqTPeb/TSff+KONTJfSIoPuKKNnn68zcVwEJxTEXK63Tc/VKp5D0XovEiMmhz+WpTHjN9ceHDXqujpF/2UH6YCY1hC4oHHKUVSahcPDMM16BI5n8ThlnSf2K3XzAOR/vpOO8CPMPVTHiGn3Xx1h4tMrsj5RFWUIR9eBtceYe1KnPqXOFJpi+t0L+BaXL8rhHep9FapdF9hkVyPfLIX45bOq55+oUXjlk8rvl132CZS36+11++ZenGDsa4zOfGQIJt92eZdfuKk89pTpdCAHvuHOBW27N8qUv9nPkcIxMxuOu983xa782yZ9+ajvVqk4iEfBLH5siDAV/+qkRKmWdq64u8NY7lpifWynIPvecKu981zyf/9sBnn0mSabT57d/e5wjYzH+6rNDuK4qA7vrfXNceFGJv/u8alPW3+/woQ/PYBiSz35maNNNNYSAt719kauvzvN3Xxhg7EiMbcN13vf+OfQmc0h6+1w+/puTTE3ZfOpPdqi61/1F7v7gDK6n8cjDqnvMNdcUeMedC3zj67089WSKTMbjvXfNEY2tXHzDkPzCB2aJREL+659sp1bTufmWLG97+yJ/81dDHDqkuvWMjNT5+McnefrpJJ//20HCEG6+NcsvfWyKXN7gyOG13ddb8bonPr8SNuNYoStxi0pJgScRjdUnRkw0U5A+1ya5a+WHYqY0zLSObosWi6I05q4ivVYYcY1on8HCY9Um6a2FIlsToQt6rl7VNkmAMATRPgMEWB06ZkKjfMxdMecllI67LcSXGDERhiBzkU3HefbKWmIawhBoliDwJeUTHtUpj50fSJP/sUP2uTrVKW/DWOCp4GQDvOLKj66+GOCWAmKDSrGRHh0zpRPpNRh6y8rbyCI9OpqlYWdWiM+vhFRXWXOhIwlqUsUPxZr95QJ6r46R2mVx9O/zLSU9bwxILr2siGVJvvKlPiYnVQ/Er33V4OJLVop0MxmPG2/M8aMfdnL/jzJIKZiasvF9we998jgXXFDm8cfT7N5dZds2hz/70xFefkntUFpctLjoojLGqmLwXburlMs6Bw6kKBUNVRL0UpzRHfVGfaygp9fhuutz/N0XBnjyyRQgmJ6y2bZNZWJ7enqYm7PXCrQu4vGAa67N8/hjaR64f2X9g4MO73r3Sgeca64uEIsF/N0XBhpELZiZsdgxWuMtb1nkwNNJPE/jhptyjI3F+O53unAcnclJm3gi4Lf/p5XSm3giYHi4zr33djLT6C35+ONp7rhjiXSHj5QCISQ33pSjVtP4ypf7KJfV7/VrX+1j//4i115baBDfxnjdE9/aZELz71U3kh5VzB7tNQhX1Vy5xZDanI9cs7n5ZK6hpgs0U+BtQIzqIJWsEDpEB1pVWJnwqM36IFeSHsEaNy505Jr1q3hdtM9okbe+FOCXwqY15BVDjnw+T+aiCF2XRum6LEJ53GPyO2VqM6dfFiODNckaqR4uut3oiBNRJRRWWleyrELhRafl4REGkmCdxM16z9zUHov+m2JMfLPUjNG+kSAEDG2rUyrpzM2tvAIgmzUbNZEK3T0e8XjAkSOr6tMQzMzYlEoGO3dVefzxFEPbHIJAMD6+0oOxXteYmbEZHlnJvJZLOpFISCwaUCqqQvp0ym/E0hS2DdWJRkOuuabA+eevZFT7+lyi0ZB0h79p4stkPBKJgLGx1vUfO9ra13Dn7iqzszb5Vc1sfV/j8MEY737PfMPFl3R3uTz9dGpVh27B+IlIi6vreRp1RyOd9hsPTPVSK4SkVlXnmaZkx446yZTPL350unkvaTrEYiF9/U77w3YNXvfEtxl4xZDQlRz/akHFpk6Fkygk9CRBPcTOnKRuKgSvEiKWAsa+kN8wExrUQwjBSLSOtUx0zfWXQoKa5NiXik1XcCP4FcnCIzUWn6iR2G4x8nNJtr8rxaHPZk/b8tNtDc0UBI2HhdAFRkyjfEIN5JdVZf7cAxVyz29iT+kmwnSRXp2Rn0uy8HiN7GbGfB1CCHXz+b5o2W0ShuB7qwvfJUKjbSdOEAiCQGBZSmGmGRIEtPRFlBJct3Vn0oEDKW55U5Zf//gkzz6bpK/PZcdonb/568Gm+2rZIVJCuaw3LSFQHbIPHYpRyG/+ljcMVXC9dqeQ22jUsQzLUm/mW0s0jqthGLKx00mi6e1jeZ7WosNqReO+ezv5uXfNo+uSYtHg8suLHHw5zpEjyorTNIlphTiORnlNsuWB+zNMT9un7GD1U0F8pTEXeauk69Ios/dXmkSkmepmPp1SCb8SUj7h0XF+hMUn69TmlPWGBprRKIUJoPCyy/A7kqT22BQOOirzKlSgn1CVorj5kPqST+Y8lZEOHeWed5zb+sQtHHbovSZK58URFh6tNi08rVHasuyma5Z6jIUeSB9KR10KLztkLoqocp+TlcqsA7tbJzpgUD6miC65y8KIa82/6ws+tTmf7v1RFR6oLhOkWktQO735jJhg5OdSVGd8FTN8o3m4DUgJhbxJdF+VaDRoEoxlS6LRgFyDXIpFA8/V6OryWNlHolzIWCxgaUlZSPm8qXa8JAOVGUb1WUwkg5YHZC5n8Phjaa64skgiETAzY/HP39vOiRMrlmI2qzpnP/BAhmefaW31f7oolw18X5BZ03gilfJbGtTOz1mcd14F05Sr6g0lvb0uxaKO4+h4nqBW0+jo8NE0mkSdSPpt++OfeirF9TfkiUZD6vWQb32rm2efSVKrqYeK7wvyOZMwEPzD3/efsrh7PfxUEF91xmf2hxX6b46TGDWpLwRopiDSo1M64jL9/VMUs66CDGH6BxV23Z1mzy91qBu+LrGSGqEnOfblIkjIPlcnudNk9L0pimMuXinEiAoivQbT95YpvOQSupLZH1XZ/vOpZpZ4OW622jqrnPCYf7jG4G2qnMXJBuiWGiv33EqpSs9VUTrOt6lN+/i1ECul03GeTfbZektWerPwCgHDb0tSHleL6bwoQvmYR+GQssSCukp+7Hhvij0fyzQTL3ZGRwYw9vcbW7ttEKjrs8Nk7qEqXftXOoRIX+nzjZLZlRJefjnGrW9a4tLLSjz4gGrXvm9fhb5+t/HeE5ibtTh6NMp11+U58HSSQsHAMCXXXptHE/D8cypuOjYWxXUF11yb52tf7cX3BYODDrt3V1vKR9Jpn5tuyvHVr/by0IMdrBdImBiPMD4e4c1vXmRiPEI2q86PRsMWst0M8nmD8fEIV1xZ5IH7MxQKJpFIwFVXtab6nnwyxdXX5Nm/v8hDD3UQhjAw4LL/8iLPP5ekXNYJQzh0KMbFF5cZGHCYmrIxDMmVVxbbSlauurqAlPCFzw+sWxLj+4Inn0jxix+d5vIrijzxeArfF+g6za2qtZrOyfA6Iz7JOTtVXOLgsQRONqAy4ZGKeezeVUMrWE1X0MkG6HbDGpMw+0CVyqRP50U20R6D0JOUjrhkn12JkcgAysc8nOzJU4i1GZ9Df5Wj65Io8WETMyXwioEqK2lco9CRnPhaiY7zXNLn2ET7DPxqyNKBOpXxFVbLPlcnqId0XRolOmBQGnMpHCozeGuiaYnKAKbvLVM+4ZK5IKLW74YUXnbIv7jiDhYOOpgJjUivQaTXwK+ETHy7pNzQ0+EMqWTMPe/gFgJVo5fQWHisysJjtRZLrnjY5dDncnRdGlVJDwH1hYD8i07TMnWWAsrHvLZ4bGXCwyuHLW5HZdwjsc2EbSvboAJXkn/JecMQHwh+/HyCp59O8aEPz7B/f5EwhI4On+mpFWve8wRf/B/9/PpvTPDJPzjGxHiUjg6P4ZE6//iPvUxMKPKfmoxw7/e7ePNblhgdVe9P6enxmJu1WzKejqtRKuvc9b45brghTyihXtN44ccJHnywA8fRqVR0vvC3g/zqr0/yb/63o8xM2wgBmU6PuTmLT//5yKazup4n+MY9PfzGb03y+588zsRkhK4uD6euUa+vxOlefCHO9/+li1+4e4YrrypQr2vs3FmjWDL4+j29DVdW8t3vdHPOORV+9xMnODIWI5lQcbxioZWGsksmg0MOn/hfTlCra4QhLMxb3P+jDMeOqffDPPZYmtGdNT7y0Wluu22JctkgFgvIdHr8zV8P8uPnT27tvs726kr27qhw11tn+E+f24XnaWhC8tF3TzC9EOGZl1LMLq7TS2wLW3gNEIsFXH5FkdHRKoWCwWOPpdm2zSGV9Lnvvs7mDd/Z5XHFFUUGBh3KJYNnDiQ5ejTaEtsyzZCLLy5x7nkVHFfjqSdS6IbkvPPKfOubPQSB4OZbstx0U46pqYgK9AtlBV50cYlvfbOHr9/Tw3JdYFeXx6WXlRjapkqJZmZsnn8uyczMSjJmMxBCMrK9zpVXFohGQw4djHHoUIzb35zlhz/MMDeriF7XQ845t8pFF5WwLMmJ4xGeeipFqbTaYlOlL1dfXaCz0+PEiSjPHEhy0805XnghzuFDcbp7XD74wRlsO2R21kaGYJiqzjGe8Pmjf7+TxUULUC/42ruvyvnnl0kmA0plnSOHY7z4QhzHURbf67JJQTrhccPlWXRN8qMnO8kXLSJ2wG/dfZw//8Iovi+4YX+WD7x9igef7uSfH+qhWtO58YolIlbIg093spBtz1DtGqnQk3EZ7K3z8IEMuYLJdftzpBIejxzoRNMkF+0rNgLMIY8cyBCEghsvzxIEgh8+3oUfCK6+JEfUDqnUdB45kGHXSIWLzikyPh3jqRfSG76AfAtbONvo63P4v/79GJ/77BBPPL5coKtu/t/4zQkSiYA//g+jpyzcfX1D8sEPzbBvX5U//g+jq3ZhSHaM1vg//s8x/uMfj/LSi4lTDdTERsR3xh2Yhaljb+shsup/wjw9z9n1NX58OIkXCK67NMdaf01KeO5giuNTMX7waDfZgokfCF4aS5Ivmdx61WLbOQC7RypcuK/IY891UCiZXHVJns60y8FjCe66Y5rtQ1W6My7X7c8StQMu2FPiXbfNkiuaICQ3X7WIaYTcecscx6eiHDoeJxYNePdtszz9QgeXnltg1/Dm44Zb2MIrhWlKLCtclVRQ/3Z1ewwMuszPW2fzXTyvCYRQ5SgIWuQ0TcnevVUcR6N0FhoSwyuJ8YVStY5G9emyutO4C/nTCjVt66tx1cV5ejsdpufXc2EFlZqO42oUywZBoLGtr8r1+7N0JL0NX6cYhoKDRxNMzakxB7odXjqa4NCxBHfeModthUzNRYhHA2YXI9hWyO6RKp1pjzAUHJ9SdUpzizZHJ2L4gUZvp0PN0Tk+FeX4dJSBXofDJzb/5NnCFl4J5uctHn00zYc+PMO11xaoVDSSyYBt2+oUCgbf/nY3p+PCvh6hstEd/MZvTvLJPzjO9JSN0CR9vR7dPS7f/lYP09Obq0E8Fc6Y+GQQ4s6vtKwRpo6wTHA2X0x2xYV5lvIWfuMVh5oGPRmXZNynu8NldrFdyEvOLVKp6ZQqBj2d62/Xkiy38VJm8thEjEvOLZCI+RRKJnVHJ2oHLST97Msp6o7G9EKE6Xk1b7jKbShVDQxdcum5BfZur/BP9/VtWs6zDaHr/P/svXeUZNd93/m5L9er2NW5e3qmJw8wyIkESJMQM0jZpKxkHUnW2lpJx7Jkaffs2vLusWWfVfIhRWlta6V1UrACtTRFKjCCCSRFgGACAUwABpNDh+nuyvXqxbt/3Orqrq6qiT1Ag+jvOTgA+tVL937f7/7uLwpdQyYJMnqV5Xq9CqFZyhGThNGVo2JvIYJA4w/+2xSHbmuwa7aFbSVcumTzuc8WOXHCpdG4shfz1QHB8WNpfu1Xd3P4cJ1iMURKwfFjGU6ccLl0yd4089IN2/iEZeLOTijZIgTCNGi8cO66mrkMFwLuOlhlqWRRbRgsLtk8dFeZbDqiXDP42rNDxJHgtn01XjiVIYo18tmQe2+rsFKxaHg6J8/15uRNjbWIE1hoO0J0PeGO/TUK2YhnjuUwzQTTkOQyEeWqqcrcl03uOVQl5cQ8czxPvaFzaE+dIy9lO/aU8RGfw/vqXJh3OHE2/YrZUzKzO0jPTOAvlykfO/mKfYyvBWi2xfC9tyN0ndJzLxBWr1xfbhtbC5vv3NAEespmNTck8cNt7eNlQnbPTtI7J/GXSpSOnNgWfLcQmm0x+sCdCENn5ZljBJUba/69jVcGm+/c0HX0tEPcbBE3WmiOhWbffBevbWxjG9u41bhhwac5Js70KO7uKYRlYGRdNOcmusJvYxvb2MbLhJvyDfuXy8Q1D3f3JFJK4uY19kXYxja2sY1XEDfu1Q1jkmaLqFInafmkdk9uGRufMHTsYoGo6ZEEIZld05gZl6Bap3HuEkkc406M4owNI+OY5qVF/OXepiqabWEVclj5LEbKUZ7UOCZqePgrZfxSta99Teg69rDqeHUlJHGs7tvvGoZBamwYeziPZpokYYi/XKG1uMyV7LIIgeE6WIUcZi6D7tiqfWAYEdYatJZKRPXNjUE0cxl028JfKWMXC7hTY8hE0ry4gL9SxnBTpHdNoTs2YaVG4/w8ycbuOZqGmXWxC3mMrItutbtt+SFhtUZrqUTc6l/NxcxlMFJXz+gJqnVir//ibOYypMZHMDKqAkhUb+ItLJEEoWoLOeCawtCx8lmsQg4jnUIzDGSSELd8glKV1nJpy3wX21jDDQu+pBUgwwh0jSSKab50UTUY3gLQbZv8gd34K0qY2cUCQtcw81mErhE1PLJ7ZkAq4pq5LCvPHieqrQkEI+MydHg/uqM+KJnEKkZG17CG8qSmxmheXKR26lyP4NIsk/zBPWjGlUMMIq/FUqmC3Ni31bbIH9yDXcyDBBnHQAq7WMAZHSK6gmbtjA2T27cLzTRAyvacSISmhLE7PU7t5Dm8+cvXMaJXxuoiUjtzgczOKdWgpwSsTgAAIABJREFUW9cxs2kqx0+Smd2BkU4hhMDKZ9FMk8oLp7qukd0zgzs5htBV6Q6ZqHI3Zk7DGSuSmhqjcuwkYa1XaLsTo6Smxq660FReOE2zj+BLTY6S3bMTzVRCiyTByqZJjQ3TuDCPHNDiUBg6Q4f3YxVyIES7CXkCQmAVcqTGR3BKFSrHT5EEN1gtdhu3BDcs+DTbxN03rby6QgUxN46dJWndeCn0zYY9PIS/XGLlO8cws2ly+2ZJjY+QBCG1k+cIqnWyu2ewhwukRoaorfuo4pZP1GwRVOr4K2Vir0USxeiWSWpqjNT4COnpMfyVMkGpu1pFEoRUT5yGfh+iELgTo5i5DLEf9H5UQpCdncYu5kmCkPrZiwTlmqronM+S2TmFmRscOB3Vm8ReC2++RlCuErcCpEwwUg6ZndOY+QyZ2Wn8UoXE37y5EoZOenqC2qlzamHZuwurkCV3YDdRw2PlmWMYborcgd3YwwV02yJed/+w1iDMNvCXy4S1OokfglCaWGbXDgw3RWZ2B6UjL/b0Wm0uLBHU+oeZ6I5DemYCIQRx0Pu+ZjbdEXqtpRWaFxaIgxDdtnCnxkjvmhooUGUUEzY8ZCLxl0uE9SZJFKHpOvbwEOmZSexigdTkGI2zF29idLex2bhxjc8PabxwDlWETuDsGNtygeNCCBoX5glrDaKmR2pyDDObJmxUac5dBinxFpawi/nOFmcVMoopHz3Ro8XGXouw4WG4KcxsGruY7xF8Mo7x5vv3OLWHhzDSKRI/oHayV1s00i7O6DBISe3Uua7rRPUmSRhSOLRv4McYNZqsfOdY73M3W8R+wPDdt6HbFmYmjb+Zgk8Iokaz87zNSwtYhSyaZdI4frIzB+6OCcxMGs3qFnytxWVal1fYWDokanggJfmDe9SW2jKJNyyuYaVG2CfMRBg6hUN7EZrW2XZvhDs9jmYahNU6leOnOtvSuOkR1uoM3XFAaXQDzAv1U+eV6WHd8RglyDXLJD09jl3M0zh3aTvsaAvhhr26CFSqhSYQQiAMtb3ZSkjCsPNxyTjpaDhhtd4hYRKopj1CN3o0tEFbdxlFnUBW3br2EB4j45LbvwuA6slzfYNh7WIeYehEXovWUqnnuL9SIWp6V7zPoOeOGp4aDyHQrM2vSBbW11pcxl5LbbUjZRMFkIkkCQKVi2lu4IqUPUJvFUGlhkwkmqEjtGvkmBBkdk0rrX+lQu3MhR5NUbNMJdQAb36pxxYnoxhv4cpNx2WSDBRoQakCUiqObDGl4LWOG9/qWuu2ulIS1Zpbzqsrk6RtH1v7f6DLsK7+pkpsDyzUL4Sy12kaQlNlxIWmKS6La1s7NMskv38W3baon7lI63L/D8rMppX2VPf6GsVlFBM1vR4Ntf9NtbawaJsjhGgngAvENT739aBrXNtaUBJGnXEHOlv7K91/1UYoNA2E6F5ctGuTIKmJUdzpcWLPo3rijLJHb4BuW2iWiYwTwgEOn6jeuGZNTRi6eu62+UdbLdohBAKBvOGu79vYbNzUVrd+9Ey7ovZVOnu8Qti4BVF/pGflHwTdtnDGhrGLeXTbVsRuf4xCvw7BoWlk9+zEzGdpLSzTOD83sHDoal5oP3vUKpI+H/F6GBmX1PgIVi6DZlttIdIWfNrmC7xVrBdwrPZTknLgu3ZBCOxCDmdsGCPjolmmWly0tpDWrp1jViFHds8MMk6onDgz0JO7Op9JGA0c01XBLQaMmzB0nOEh7JEhDDelFhpdA7G6SK7+8JoefRsvE25Y8AnLwCzmCJcqSvOTckt5doEBH9y1fjxZcgf2YLgOSRgR1RpErZb6EKIYe3gIeyh39QsJSO+YIDU+TFitUz15tltAdP1WrNnurlAmd+D5gDs1Rmb3DJppKAdNvUHcCkiiCBknpHdMoDubU+Gi98H6/enq4y10tTCkJkeVttv0CMs14iBQWq8QZHZOXZPw0FMOuf2zaLpG5aWzBKXq4Pt2hFmfBbLzAlItlH3knu7Y5A/uxirkkUlCVG/gV+skYUgSxRgpB3dq7OoPvY2XHTe+1TUNNMfCLOaIGy2ErqG5DnGtefWTtziEYZDduwvDdQhKFaonzhK1Wl2aom5b1yT4nJEimV1TxH5A5cUzVw5rkLIj1AZpGFc6ZmbTZHfPqIIRF+ZpnJ9r2zDbzy0EztjwrRN8NwhnbLgd+5dQeemsilVct83XHKvtmb2ytioMg9y+XRhuisaFuauG7Kwu0qLtoOt/0QHHhCCzawprKE/stai8eIawWuta+O2RoW3Bt0Vxw/seGcUYmRTW+BDBYq8R/tUMM53CzLjIOKF25qJyJmzYHourxOgBGJk02b07Aai+dPaaAodXnTEDhZNgzXa0AdZQHmEaxM2WCtT2gy5NRmja9W3RXyY4o0WEpuEvl/HmLvfYNrU+jqceCEFmdhq7WMBfKVE/e/GqJg2lBcegawOdVJpp9F1otHaQPEDj4gJBqdKz2xk0T9t45XHDX0Hih3in5mi+dJHEDwmWKsSNreXcuFEIw2gHpMZ9swWEYWBm01e8hmaZ5A+sOTP8Ph7afggrdaSUHTtXv3sbGbdvOItmGgiUfTCJem1Whuug21ssn1oINEMJiKjtCd4IK5+5qrbnTo6SnhonajSpnjh7TdkSccsn9gOEpmHm+jenMXOZvkJXLSJq8YsHeNlXPcbb2Hq4qeU/brZIPCUYEj/sNua+ipGEansodL03FUoTpKfHMdxU/5Np26z27sTMZfDml2heXLjme/ulCkkQojs27uRY90cnBKmx4YHpWUkQIml7K41ubUOYBumdU1su5Eh5ftX233BTPULGcFO40xNXzMqwhnJkds+QRJFyZgxIbeu5dRR3UhXdyVH0DeOq2xapidG+5yZx0llcjHSvh90eHsIZHrqm59jGy48b08XbaUXrPyJrJEfc8Imqr/5eFFHTI6w3lc1s704a5+eIW77y8o4WVR5wY3BIiTM2TGpMBSFrlknuwGzf38k4ob66JW0j9lo0Ly2SmZ3u5Lf6pUo7cyNHarRI1GxhpHsFr1+qkAmjjoHfm1tURvZ0itT4KEY6RdRsYfY595VE63IJu1jAHi6Q3TODv6y0YzObUcJfU1kX/baOwtDJ7VWZF7Hnq99P9hdWQaWGN9dt92teWsAZGUJPOQzdsR9vfmltrsdH0C0TGUY9pg0ZxwQrFVJTY6RnVJGOsFpXeeJDeVITo8S+f83hN9t4eXH9gk8InOkRwlKd9L7pThiAnrJpvHh+s5/vBiH7hrKs/m2jp1FK2fVTGcXUTp4lf2APZjZN4fZ9q5dFxjGN83P4pQrFuw713Zqt307axfzAp0yimObcYk/qWOP8HJqpk5oYIzU5Sqr9Ia8WVAhKVYbuONBTrCCqN6mdPk929wzOaBFntNh57tj3qb54GmEY5A/Mcn2NeK8MiezvaV71iG78m5Rd928tLmNm02q7OjNJemay89uw1qB64gzu9Hh7Mem+nNA0NNNUgcKORcoZvtKD9gi+2POpvHCa3P5ZjLTbsckiVRZM+dhJ0jOTWIVc962lpH72InrKxirkyO3btXYsSWgtlaidvsDQHfvVNn3rRXu9pnFjFZiFQEtZ6GmHcEWlClkjeRLPJ6punldXy+g4BzJdW+hgrkV4cUBclqWROpxF+pLwXIiMkq5tj2aZaIahcmRXA5s1Dd2xIJE9WyTNtlQ6W3sLlrR8/HKVqN5UgbWOjUySHsGlmYb6GK8CSfue/YzwQqiUuKE8mmmo6iylaidJX0/ZXdko62GkXeyhnHo+KYkaXjtv10foGrptr4WKbAI647r+mqJdoXvDuGq2habrxL7f7QzQBFYug5XPqfeNY6JaE79cRUZRZ0w3etcRKh9Xz5k4B9LdXLnUzZUkijpedWFrpG7PkngxrRfraIap5jq9Vp1l1eyg3k9Xec8bBLwwdOxCHjOXbscEhgSVOkG1DkmiuCUEsXdt2+9tbC4GVWC+sa2ulCRNX9n32hwMlyqbHsOsZwwyDw6hORrCVv/Uvrw8UPBprk7+baOESwHLL11ABt0kTYKwN5wkSQZmnCR+0KMhdCDlwMDYKwXEXjPaW6dBPR6ulCUTNZpEjf4LkIyTq6a8XS/6juuA+oyJH9A3CjGRBOWaKsjQ7/CgMZXKPKDlJc7twx2uaI5O9UsB3on+76qldPJvHyVcDPBPNUiCcGB+dd/3W719FNNaWqG1tNL3+Ma84m1sDdycv3399vAWBC6H8z6X/9tZhK3h3p0n95aRTb/HNr470MWVe/LkvmebK9sYjC0faCRDqYqeNm5Sg9rGdz3WuLJd+HMbV8bWi2bdxja2sY1bjOvW+Ixhi8zDQ0RLAfWvlfp6q6ydKdx78vhnmnjPrsuVFGCO2dh70+gFE2JJcKmlbCy3eJXWh0wyr1NxVY2nS0Qr62w2OlhTDvasi55rFwmoR4RzLYI5n6QWIWyN3KMjRKWAxtNrdd3SDxUwJx1aR2u0TijHgzAFmTcOI1uxGqNE2ZTMKRtrOoWeNVSPknKEf7pBOO/3HUdhCuzdaawZBy1t9OSqylZC9QuXkYHsOsfa5WLvTKGldOJmTHCmSXDeQ0brbqJB9o3DyCih/nQZc8TC2Z9Gz5kkQUJw1sM/3UCGN264vS6unG7iPbeBK+M29p5VriQEl/yXjyuvHwIJ9adLxF1cEW2upHq5cqlFUo8VV75nhGg5oPH19VwZwpy0+3PFi6k/3eaKq2NObuRKiH+6OZgrlsCeTWPtdNDcAVz5/OWu+RzEFf+8B/24EibUv17GHLVw9m0uV15uXLfgS5ox1kwKZ18a71iduLKxdwKk7yuQOpyl9eI6w7whyD5SJPPQEMIUxM0YoQvSDxQIF30qn1okOL+5RvfOrYsmhfdOYo5YVD57mai09szCEOTeMoJ7X0E5bbwEoSlBhSZoPlOh/PEFkGDtcLCmHZrfriBDibA00vcWMMYshCY6ZNZcnfR9ebxjdUXktE7xB6ewphxkAokXq3u4BvKNRWpPLCvSr+daSqPwngmc/WmiUkjSiNDzphICEoKLXs9HoOcN8u8ax9njIkNJEiRojgavH6J1rEblM5dJmqvebIGzL41wVKhF5hEV+iIjiebqZB4awnu+SvmTC12C9XqQNGOsnatcqRFXNpgrNEjfXyB1e5bWC3248rohMARJhyv6y8AVi6H3TWAMK67EG7ny1lHce/N9udL4doXKJ9pcmXYwJx2az6zjyn15jFELIVjjStpQXDlauwauJFSfWKbRwxWdwveO4+y7Alfmur3K182V/WmEpSoTZR5WCsRmcuXlxvULvlZM64U6mUeK2Htdmt/qrj6s50zs3S7RSkBwbo2c6XvyZN9QJJhrUf38ElEpRGhg702T+54RCn93nOUPXexeXW8U68beGLYYeu8E+pBJ+VOLeM9Xu45bMync+woE5z0qn72sbIlCoGd07FmX4JLyTMowIbwc4Oxx1eoYRuh5Ay2jEy34mOM2whLIQKIXTDRbI5xT5ybNGP9kE+9IDf9sU33Imlpt8+8YJfNIEe+FOnF57d3du/M4hzI0vlmm9sQyiZ+gp3XyjymC158q0Tq+JiyEKci/Ywx7NkX1S8t4R2vIMEFL6WQfLpK6M0fiJVQ+s9g9PkWLzMNF6l9dwTtWR0YSY8Si8NhYZ/HyjvX3LF8NHa48XMTek6b57Q1cyZvYsy7RctAlyNL3trlyqUX1C+u4si9N7tE2V/7sYpdQuim0x8MYsRj6e22ufHIR78gGruxM4d6XJzjnUXn8MklzMFeipQBrt4twdGQYoRcMtPQ6rpgCGUrFIVvvCKaBXJl1yb99lOwjRVrHuxcR954czsEMjW+UqX1pjSuFd49j7x3AlXcO4MojRVJ35EiaMZXHL/d8S5nXD/Vy5d2KK96LdVo3yJWXG9dv45PgHa8jw4TU7VkwunVqe6+LltZpvVDvrBiaq5N+sEASJFQ+uUhwziOpRcSViOa3K9SfLGEULdL3FjblpUhUkKwxrFZvPW9S/sRCj9ADFSsodEG46BMt+iT1mKQWEc751J8sEZz1Ou8dLvhoro6WUeuFOaoKCXjH6mhpvbP1Mcfszu9Xz619ZZnG18tEiwFJPSauRnjPV/FPNdFSGsbwurg/XWDtSkEsaX67osYxlp1zQAns9bB2udj70nhHatSfXCEuhST1mOhyQOULS8TVkNTtWfT8hvhCAd7RGvWvlYjLIUk9IjjTVFs0XfTc57pwFa44e9OKKy9u4MoDiivlT23gyrcq1J9qc+W+wYHh14V2oydjxGLofZNrXDnShytpA6EJwgWf6PJVuDLvo7s6enYDV47X0dJGZx7McVuFLi20Ouf25cpz67myLt9aF9i73L5caa5yZUcvV5y9A7jy+SXiWkTqcBY9t0EvEqhzNnLlacUV+2a48jLjhpwb0WWf4GJLqfMj6ybBEKRuyyLDBO/YWjyWMWxhDJmE8z7h0oa4JgmtE3VkkGDvdhHmzaf4yDBBHzIZ+r5J9KxJ+W/m1YrXRwsPL7VI6hHpe/Lk3jqKOWGD3v8ZwnlFTnNUvbM1k1K2l7NNhKFhjFgdO2bcJuz690QDLWtgjNmY0w7WTAqht6sjm+umQoAwNGRCt12u/W5Iidi44My6CEOQNGLsnS727No/5rhN4idKOOc3kDmSyiSxIRopWgkgVluZmymiGS36BJfaXBnu5opzWwYZbODKSJsrcz7R0oagXwmtF1e5kt4UriSrXHnfJHpGp/TXV+FKIyJ979W5Esy3ANGfK1ZbeHW4EhHXroMr1hpXhAAMgUxkH66oIrA9XNntKhPCAK7IVtwlnDvXG8SVUgCxWrReLbihcBYZSryjNexZF+dQVtmaUALBmnQILrYIF9cEnJ7RQRfKHtgnSyGuR8hQfWTC0ZDhzRmvha3UfHPSpnWsjn+6OTBlKFoOKf3VPNk3DZN5sED6vjzBXAvvuVqXJgKoVc5LMMfUVsWctImWA6LlgKQeYe1I4Z9sYAxb6rfNNpk1cPZnSN+fxxixFRG1dgn41Y93PTdjSbjgY+92O2YDEsBQzg6EILzUHRxsDCmSZh4pdux1PUgkmtW91slYdn90nQOrg3lzwkWGEu9IDXuXi3Mo09GCzTF7AFeMdVzp8wrruWLfPFc0R6PwHsUV72iN4MyVuBJQ+st5sm9ex5VLLbznB3ElxhhVJhBzwiZcCoiWVrni4J9SXInK4Tp7GjgHMsoeOIgr6yBXuTLb5kppPVdcEHS24Ku4Vq6IDVzhilyR7Y6LDBy/rYQbjuPzTzaUSnwgTf2pFWQrUellloZ3pNbZQgDXqTHc/CpuTTtESwHhvI+zP036/kKP82A9/NNNpZXMpEjdnsXZ42K/e4zwvjzlTy12hEzSjIkrIXrRQs8aGEUL70iNpBUTXvYxJ2y0lI5RNDvGagD3rjz5d46SNGIa3ywTXmp1hH32zcO4d2woXySh8a0yzl6X3KMjWDtSxNUQY9jC3u3SOlHHe2GDLUUTkED9yZXOQtQDCUGfYxv7+m42/JMNknqEcyBD/WulNlfSCLMPV66CzX5SazpFtOQTzvukDmQIr5UrO1Okbrs6V4xhcwNXlK3YnHAUV4Ysta1e5crdefLvGCNpRD1cyb15mFQfrjS/VcbZo2zl1kwvV7qcjKu9Za7KFdnZ4ay/16Aew1eCpuuqzFuStPuPQJIkaLreadYkdAMZR+1OFkoGCE1rH29X8l4VsJuAGxZ8cTXCP93EPZzFmk4RnPdwDmSI6xH+ye4KLXFN2R30nKE21xsWad01EIYgbkRI/+YzQKLlgOX/7yJCFxR/cIrsm4aJKmGXgXcjpJ/gv9TAP9lAz5ukHyiQeahA/m2jLP/pBWQkkaEkvOxjTacwxmyEqanVNFY2HffunDJc21pnlRW2Rvr+PEITlD++oLTPa0C8ElJ9YpmhvzuBMWKh5w2ShjI4e8dqyNaG9pGVEDSISqESulsIcTWidarNlSml5TkHMsS1iNapDVypR2rblDP6ag962lBRAY1oUzyI0XLA8p9fQhjXyZUTDfyX2lx5sEDmwT5cWQqwJh2l9XW4ogSKe9cqV0Q3V+7LIzQofXyBYANXBr1ttBxSfWKpP1eObuCKhLh6E1y53iEXAnd4GE03iHwf3VJbf79ex7Bt/FoVO5NVNSTjGKFrGJZNksSAIGw2sFwXKSVBvU4Sb1J++Q2fKekY2lMH05gTNsaI1dEE1yNaDohKIeaEgzHUWwjT3usqYXHe68mvvREkfkLSiImWAsp/s4AME/LvHMPa0b+O3cb3isshtS8vE5VC9CETkVqzXQSXWugZA2smRdKIOp7Y4KKHljawdqSQkSRaVts3YQg0VycJks7fVqG5OtZE/2fSXJ3cm4bxzzZZ+qPzLP3BeVY+fInmtypIr3eM/DNNiCWp27MIa4uVQpIorQZwDmYwJ2zMYcWVZCNXlgKiUoA1iCt7XIS1iVxpJSTNSHHl42tcMaevgytf6s+V8FILLasrrtTXceVC68pc8ZVXeD209BW4ktbJvXkY/0wfrrT6cOV0myu3vTxciaOIyF/TLOPAhyQh9DzVVEu0K/zIBNNx0E0VhhM2G22BmCCEtmlCD24ycyO40FKr2qxL6nAWgfIQblwVkmZM4xtlNFtTpBq3EY6G5uo4t2XIPFwkqUc9oTGAWvV1OkZkoQvlHbzG+QrOe1Q+cxnN1ii8exy92G2wdQ5lcO/OYRRNRKqd4J5WVWH0rEFSi5CttQGPFtVE2bMu0cqabSZaDpF+gr3XRfpJh+QySIgrEZqjq4/W1hCWQB8yyb11BH1oQMnztN42gAv0rAqb0TI6WlrZtjbCP9WkdaqJPeuqMZ6wlQfaVddJ3ZFV8WevEFa5Yre5AlfhiqORf+doN1duz5B5RHGlMZArQnEEVKWW6+FKO0xFs5Xdb+PcOIcyuHfl0Ddy5aDiSlzt5kq46COE6NhpO1xZCdpcSSNbCdEqV8KEuKq44mzkyltGr8yVogUa18wV/3QTe7dL/h0buDKyyVyRkla5jFcu4ZVL1C8v0qpWCbwmoac0Wq9UIvJ9It/HK5dpLC3h16rEQYAQGrphEDQ3t87nTeXqrnrksn9H2anCtre3HxrfrqBnDNIPFhj5iRniWoTQ1Ucd1yPKj19eC/9Aqf3ZNxTR8ybCFGpigdTtWYxRGxkkJF5M/anSVeO5vOM19Jyh4gXfNU7pY3MdEprjdicqPW7EECUIR0dP68S1iOoTS10R6VE5JAkSzHGb2peXOx9uUo+IKyHmlDJaJ+0tuwwl9a+XGBq3yb9rnPQDQ8hYYhRMonJI/SvLZN/UW0Murkb45zyc/WnsWbdT90+uhrUcqdL4ernzbDJIqHxqEd45hntnjtTtORI/VvZmS0PogubztZ5YupcL0k9oHa+TeWMRN5MjbHt7+6F5Na585jLRIK5YoqMppg5nMcbaXGm2uVK+CleO1dCzBrm3jFB4bIzSx+bXuDLhkH1DUS1mzV6u1L7UzZW4HCID5QyrrfMUJ7WIuBpiTjr4Jxsd844MJI2vlzHHbPKPjZN+sICMUVwpXYErlQj/vIezP4M9m+7lyvNVGt/o5kr5U4vk3zmGe1eO1OFVrgiEpRaO5nObx5WNpbw27pZlkqz1vN7QMkHKBK9cRsrNLYJy00UKWsfqWJMOwhDK9jTIRhdJqk8s0XqpgXMwgzFkIiNJONdSwbsbhJfQQMsYKpocZcNazRIRQpFd6KLLVS/DhNapJnE17DbCJlD/ehlhapjTDvYeF+95ZdtofLNMXFYCa9WjmMz7hHMtWicaPc+VNCJaL9QxCib+OvuUjCTesRqJF+Md73b5t47XWa5fUHF0BUuF+xyp4h2tIUwNY9wmqa9NuDAFmdcNoedNgrNNZTpIUGEutoY5ZpP7nlH1Xk+t9fKIyyGlv7iENevi7HY74QhxNSK44OGvCyhHSoILntJSwt45S7yY1smmWow2yaPgHa1hTthX5YocxJVLLbwXN4krUZsr5ZCub2qVK5aGOdWHK6XgmrkS1yO8QVw5WiNptrmybny9YzXlNLw9i14we7hibuSKJci8fgg9N4Ar4za5t4wiE2h8bR1XSiGlj1yBK2c3cqVFVA77c6XZ5sqAdLqbxWYLPbjRQqTbuKVw789TeMcY1SeWVY7rBq+nOeUw8qM7COZaLH/oYnde5TZeU0jfXyD/zlGqX2inPQ7iyqUWy3/+2uPKoEKk29VZthoE2DMpZCyVltAn1CNpxipYtR0asI3XKIRKo5ORxD99Ba7EUsUCvgKPuFWxLfi2IBIv7mzLN86Q5uqk78+jpXXCS94tj8HbxtZGhytTA7jyQAHN1QkvtnoyO17L2PKFSF9zkNB8topzIEP+baOkDmaIlpXtSM/oGOMORsEgONvOp93m8msXEprfqeDsT5N/e5srK6tcMTDGbYyCgX+2SX1deaxtbNv4tiyMcZv0PXmsaQctrWLDklZCtBKo0JXjdRJvu9LwNlRkgntPHmuH08mX7eZKjaRP7OdrAZvbbGgbtxzRgk/lM4sqJm11CyPb6WWvTQ5vYwDCba5cN7YF31aG7K3Oso1t9MU2V64L286NbWxjG685bAu+bWxjG685bAu+rYjtgKtXJ7bn7VWDbRvfFoMx4ZB+8zjRUovGE4uvuUj7VyusgzlSDxQJXqjifXNlO8xoi2Nb49tisA/msHance4ooOd7yzJtYwtCFzh35rFmXJy7hxD2q6cE+2sV24JviyFaCZChbJcu71PmextbD4kkXg6QsSRa8pHRdgzJVsf2VneLwT9aIVrwkF6MbG4HKL8qIKH5t5fxj1VU2att88SWx7bg22qIJfHigD4I29iykEFCtLFHxTa2LLYF32ZDF2gZQ7WLTCSJFyNfptQy4epotupoRyKRYdLps3pVaO2+sZamitjFksQ6CLa6AAAgAElEQVRvP/vVThcgXAPN1lTTo+s5d+Pzp3RAqGdvRNfViOhmICwNLa1q7MlYIpub0//l6jdeN+6amrfET5BedG1ZF4ZAc9t8E6rOoGwlXZWgB0JXpe5X51xGEtmK+parv+LzZwyEpQMS6bfnbYsrvduC7yYgLI3M2yfA0Kg/Pocx6uC+fgRj3FGtD2NJUgvxX6jhPb3cn4wCMm+fQB+yu/6c1EPqj89fU18Jfdgi9eAw5s60IqHe7rPaSojLAcGpOq1vlwbe39qdwbm/iDGmOn+pD0iSNCPiJR//aAX/eG+DbZHSsfZlsQ9kMUYdhKuas8tYkjQiwvNNvKeXiTf2xwXsO/I4hwt4314huuiRet0w1v4sesYEAUkrJpr3aD65RHTR6zn/ZqAVTDJvmSCuhjSfWMQ6mCV1XxG9qNqGKhtrQOvZEq1ny323rsLRyD421f7g1xDOezS/vHhNQsvc4ZJ6sIgxkUJzVSMuJXRj4hUf/4UarefL/YW/IbBvz+PcWcAoWsqhIpTmmTQiooUWre+UCM/1NrfScqZyou3LoA+pzoBoqlp4UgsJTtbxvrHcVfBUnQjuw6OY0ykaX1xExhL3kRHFO1cHqQr1hmcbNJ9cIi4FPffeKniVCz6JZUmCUIB8BYKodIG5w0VkDFKlYVL3DKlGzeWAZDlBz5noRRv3YRs9b1L75KX+H1HKQC9aqlKwoSFMTZFGu/o76aM2ue+bQS9aSC9WRvYwQXM0tJyJOe2i50z858p9F2HnzgLpt04gLI2kFhLNe6qftWugZYxOxWj/eLXnXGMyRfZdk0pb8GPiUoAMEjTXQC+Y6HcVMKdSVP/iPPFK90egFyysPRlkECPuGcKcTZPUIqIFD0wNo2hj7ctijKvzo7nNE36apWHOpjH8BCJJ6v4iMk6ISz4koBdMjAmHzOgEmqPTfHKpV4MRAi1toOUsVdrfEghdo9Nf9ioqj7UvQ/bd04iUrgTVYguZqH7BWsbE3J0BXSjB1/MCkH7jGKmHip2uaclKgNBEZ870YZvocquv4LNvy5F+dFxpl62YaEmVrNJzJvqITWrUxhh3qH7sfLfWKwTGuIO5J4NdCrB2ptGLqod0WA7QXB29YGHfVUAftal+5Hyv8NwieFULPtOSfO+PlHj8YwVqlVcuhECYGu7rR4jmPRqfXyC63CaxrZN6/Qip+4vYB3O0nisTntnQNEVC/ROXwBAITWDfVSDz6Pg13hhS9xbRixbRBY/apy+RVEKklAhNIFI65rSL0IXafmw83dFJPTSseiF/Yxnva8vtXiESoWvoeRNzV5rwQv8m29HFJt63S0TzHuH5pnLISIkwNOyDOdJvGUcfsXHuLKiYxD6w9ueQYULji4u0jlSUINQE5oxL5p1TaFmD1ANFah+/uOkJ91rWIPVgkeClGo0vXyaphiDV39NvmcDal8G5t0jraIVkQ68O6cVUPnwONIEwNNKPjuHcUbi2GxuC1OtGEK6Of6RC44lFNXaoedOyJuZOV92zj7anD9s49w6pMvmfn8c/Vm17klXTcb1oY+108Y/1LlYAwYs1Var+TINoTnWrk7LN1weKpB4awZxNY+3JDLxG6t4iST2k9jcXCU43kHGi5v22HJnvGceYTGEfzuN9bfnaxuRlxi0RfLouKQxHCAFOKmHhkkkYaAhNMjoeYTsJi/MmvicwTMjm1RYsm4tZuGTitwRuJmF4LCKOBJfnDcJAIDQYGYtw0wnVik6jrpFy1b0KwxEriwaNuorQKQzH5AoxpSWDWkXjVobVCyFI/Ij6Z+a6DNxJGNF8cglrTwa9aGHuTPcKPlSvENQ311dADYQu0IfbfUpfqhJfXttSyra9xS8PbhijpQ20tIEMEvwjla7VWYYxUSsmWhhssJd+QuNz8z1CUYYxre+UMHelcW7PY+xw+/bIVQ8BrWdKeN9Y7gg2iSR4qU5rsoT7hlGlVVva9dmerhHxSkD98fmud49XAppfWcSccdEyBsaYQ9CnSVGnSZCIr8seqDk6es6EWOIfrSiBu3pNJLHv9zUPrEIvWGpXUAnwj1fX2ZAlMoSo2SS6MLh/c1wKqH+2d96SMKL51BLm7ozS7Hb0F55CCCSSxpcWu47LMKb1TBlrNtOOR83gPb18NeX3FcEtieNLpRP+wU+tcP8jDe5/Y4O3vbeKpknuf6TBm95V5dDdHt/7wyUcV5Ifivj+n1jhgTc02He4RSYX46Qk7/3REnsP+dx+r0ehqCb24UfrvOldNWYP+Ow52EIAbibhoTc1OHRXi/f+WAnLluzaG/C2v1dh7yGf9/5oiZHxW69uhxeafYWE9CLiZV91PswYmyt/284TAGtXBi17feuYDGJkmCAsDWtvBswbeLhBpJYQXVLbU2U37H9t6Se0jlT6anNh27YnLA3h3BqN3n+x2nc7Fq8EJE21eOvZ/m0dbxQyTJTtVheYezN9W0BeCYkXQyLRMybWrnSn9er1PcSAPwdJZwEVqcF8WrUd9z6c7JglNFdHGFszVPiWbXW9puArj2dJJPyDn1qmMBxxx/0eH/2jIaplnXf/UJlde32WFgySBL78eJZWUw2S7SQIAc26xvFnHaplHduRHLzL43/8/nBbgwPLlkQhfOXxDMuLJj/wj5bJFWLue6SBlBBHYJiS2f0+SwubS96NiOa8/mRK6PTH6PR43awVMAH/SAVrTwZzd5r8D++i9XyZ4IWa6jKWXPlGST3Cf7FG6v4i7iOjmDvStJ4rEZxtIBvX6YkWasuPrt5RaKL7gxzwbSaNiKTa3wjecewIOr1yNxUSorn+Gq2MpNpmrs7bZt62LezTbxojdU8Rcyylxv1UXQnhq/AjXmwRnm9g7s6QeWwKa3+d1vNloovN6/dEa2p7jKZq+YnVf4sr9yOOV/yB90pW500TWzZF4pYJvjgSxLEgSSBJBIYBuiEJfIGUELQ0LEfNcLOuE4Vro+y3BH/1p0McvtfjfT9W4vMfz7GyqB41CmG99Ah8Da+hISUksUDTJZYtOX3CZu68yekTNqWlW2/KTF6hYOPgZI36Z+Zw3zCKPmKTfvM47kMjBGcbtJ4tE55vDA6oldD8ymVIJM5dBczdacxdaeJKQPCi8ijGS1duGahlDKx9WcxdadXXNqUjzHY7R/MaWB8lgz3XL8MWKWm+MsZ375srCF2Qum8IY0eKzFSKpB6qLf5zZeXkGTQsQULt03OkHx3H3pfFvj2HfTBLdNnHP17t2T73g160sPZnMXe4aFkTzdGVnVkX16SBylbysoUa3QrcMolQGI44cIdHkgiiUFBe0bl4xuLeh5vMnTeZ2BHwza+6CNHLb8uWTOwIOXfKYnQyZGQ84uJZi2pZ557XNTl70kYIWFro8/hScOKIw449ARdOWzhuQvwyyCR5Fe3qliEB//kK4dkG9m157NvzGKM29m057P1ZglN1Gl9c6PGqrkK2YhpfWMA/UsG5q4C1T/X+Tb1uGOfOAt4zJbynlvoKJ3N3hszbJpRHOUqIVwLilQDZipFBgj5sq63YFSCVL+WVwys1b2FC86uX8Y9XcO4sYB3IoRcsnPuGsA/n8Z8v0/jbywOzd5JySO2vL9La4eLcXcCazWBMOBgTDql7h2g+uUTruT6hMAKce4Zw3zCq7Lt+TLwSKIdcS3XvM2dcjFHnio//ivF9k3DLBF+lZJDOJqTchE//RZ7AFzzxySx3P9Rkz0GfL34yx8plg3Qm4cXnHZIN8zsxHeK4CXPnTY49k0Im8Om/KHDXg01uv8fj0jmThUsGx591CHwNmcCJIw7NhsZ3vu4S+ILb7vHwGhpz5777k/2TWoT39LJyKky7SojtzWIdyKKlDSofOTc4BU5CtNCi/vg82lNLWHuzOHcVMCZTuA+PIHRB44sLXQJKy5tk3zmJVjAJzzVpfGmB+LKvBGT7d6kHilcVfK9pSIiXAxpfXKT59RWs2bTSvGfSOPcXEbauQqAGaVaxJDzbIDzfUGEkB3PYdxbQixaZt02oFqXPdYfDmDMu6UfHEZaGf7RC88klkmrYZVbIvnvqqoLv1Y5bJviiUPCtr6bxW2tqs9fUeeqL2a7fNeo6336y++MIfI2vPN79O4B6Veern+v++zf/NtP5728/tXad57/l8vy3buoVXpWQfkJwqk5wtoF9KEfmnZMYkyms2Qz+0cEe3lUktYjWMyX841XSbxrFubeIfUce75srXdsnazaNljc7nt1+jp1b5ZD4boRsRPhHKgQnaqQeGsZ9ZBTrQBb9aZt48SqpcEnbE/3kEq0jFbKPTWHuTpO6dwj/WKXL1GEfyiNsjXg5oP65+d7FUBOvieoyt8T0GMcwf9Ek2S5S8cohlvgv1oiXA5WOdr0e31ZM6zsqa0FLtdOa1kFLKw910oiIa33sSbrAnHZv5g1ek5BBQuu5MtKLVRqde31CKKmGaosLaFmzxym0yoO4HPQNDxKOjjFq9/z9uw23RPB5DY3P/mWOMNguSXu96Ir6uNrw6QJ9xB7oddTzJlrGgIS+IRvC1dHyZv8MEQH6uAO6UPnGG2x8STsPV3N1JQTXQwP79hzmzGtR8F2d88LW0IesgWEoxoit4haDpG+et5Y3Ea7e/1a6wJhQ29SkFvY0nF+NE9VzZq8TwxCkHiiiFb77TUO3aKu7LfCuCe08WW3IQrM0hKVhTKbUIVcn/XfGSLxIfQBBQniuSby8FtgqbI3c98+Q1CLCi+qYbMWK/MM29m15tKyhwh/O9gZOm9MumXdMEl3yCOeaJOUQGSUIW8eYSuHcngdNRfon9W6tLjzfJGlGaGmDzNsm1Fa4FqGlday9WezbckTL/nenrcgQ2PuzneICwtY7Ql4ftkg/Oob01ZzJVkxwst4VmK4P2+S+f4ZorkV0qUlcDlXGiqlhjDvYhwtgCsIX60TLvYHMzp0FnMP5TuxoXFMZHpprYO5KYx/MQSxVfOQGj37wUl0VuR2xybxtgtbzZaSfoOdN7EM5zNkM8ZKP/l2u9b2qU9a2Cq7UlP2Kv9EFqdep4gLr1wopJcLScO4bav9B/avxuXm89R9CAsQSc6eLudNVv5MSVTJDHY8uetQ/O99X4+sELx/MYh3MtsMn1s6XQYL/bJlGn6T7eNmn+aXLpB8dw9yVxpxJKw9p+76t58t431wh/8O7rm9Mthj6PaOwNNKPjqPlzJ5503ImqQeH239QYxxXzncJPhklCCGw9mWw9mV6xp1IErxYo/65+b6hSNJP0LIm9p0F7Dvap67mCAuVTud9c4XWd0o95wan63jfWFFplIfz2Idy7ZcSyCBRRSEWPPI/sPO6xuRGfvNKQlzpAYcf/OGt/fSvNDQwxhwwNBXQOcBrqhcthGsgm1Fvsv6ofc3G5KQc9AgwLWdiTKYwxp21clixJK6GRBebhBeuENSqKe3DnHLRRyy0VLssU5AQr/iE59rZKINCFwToYw72vgx6UWkIcTUkPN0gvNiEWGKMK41vo/NDy5loOVPda4DxXlga+pijsgEWW5tX4NMUShMVEC36EPYfH2PcAVMjqQQktXXjvm7erwopiZc2BPsKxQljysUYsdfKYYUJSTkgPN8knPMGv68hMMYczKkU+pClnEiaQLZiosUW4bmG4tmg4dIE5o4U1p6MEt6JCkgOTtbVOGuqGIFsxb18HbYQKQPZiAZWX9Eyhtouh4m63isoRZa//ud9t59bXPBJbEvi2jGVhkGSvFq30JJCJkbXJZW6ThTfmGlVIMllYsJI0Gxtbv6xaSQUMjF+KKg29Bu8tsQyJWknptowiLfkfEly6Zg4ETS8W5vDfSth6AmFbEwYCio3MV+mIcmkYqpNgzjezLFQ42yZkmpDJwhv1J2grpMkgvoNzNcgwbdFE0oUNA1+6cfP8ze/eZRH7qyyJbOdrwG6Br/7z1/ikx88wu27ByePXw37Zzw+/GvH+O1fPIVjba7L/P5Ddb7w/zzLr/zMWQz9xsZZAL/wQ5f4xAeP8NYHymzF+Zqd9PnzXznO7/xvJ0k7r96wgzv3NfnC7zzLv/u5M1jmjY/zT79vnk/+1hG+9w0rbPZ8/crPnOWz//453nh3/wov14LJkYA/+bcv8F/+jxPkM5uXibClBZ8uJHfuazIz7rNr4lrLsUuGsiH2JguGm0UmFZNLRxg3ESI1MRyyZ6rFoV1NUpv80Rq6JJ+OcZ34hnUgIeDOfQ2mxwJ2T2/NMuxjQyH7drQ4uLOJm7q2D8k0Eoq5ECG2jiA3NDVf6ZuYL4A79zSYHg3Yt2Nzi70CuE5MLh1j3uBCClDMRRyY8Tgw45F1N0/wbWnnRhgL/sOHJ7n/YJbHn762WmeFbMTv/YuX+G9/Pc6nv1a8xU/48uKZE2l++0PTnJ23Kde23tQlEn73I5M8+1Kav/nK1hz7I6ddPvhn08wvmyxXrq1wxTseKvPjjy3yM7+xj0pj6437zeA//9UEpy45fOQLI6/0o/TFyYsOH/jTHVQbOvPLm1doZIvPouCJbxV44lvXWOAR2Dvd4u59jVf1NmYQqg2D//g/pl7px7gCBE8+n+PJ53Ov9IMMRMPT+d2/mLzm3wsh+Z77y0wUAzRt62h8mwPBN49n+ebx3iyprYKWr/Of/3Ji0697iwSf7NRPsU3Jvh0eUSI4ddEhjAT5TMzsRItq0+DsvL3BaaHItVF97//XDUcFvP5wDduSqpTRBpvFoCtrQm2BRodCTCMhCDVWqgaLJZOoY/AVnd93V5bqvd7acbHhiNKKTCNhZtwnn47xfI1zC/YVnBVywFj0u/eG89a9l3XV91oHAbl0xI4xH8eS1D2NCws2Tb/fM97IfN0I1sZ1tBAyPRowv2Ixv2wiBOya8MmnI84t2JRqxoZ7947h1Z9RHR3KRty9X8VAig2cuhIHHCthejQgl1bbs4ansbBiUW3oG+679n9XG8l+x1cd7oWMmi9DlyxXTC4uWe3Mqc2cL+VsnB5V3AVotDQWVywq9Y3v1Q1Nk0yNBIzkQySwVDaZWx78jNc/X9eHWyL4JoZD3v9zp/noE8Pcva/BD7/9MjIR/OEnxvjQZ0f5wM+f4t4DDWqezgf/dJr//qkxZLtnhhDwM++b5z2PrHRd89/+1518o+/KJLlzb5N3vq7E7bub3HewjqFL/tcfucg/es9C51fVps4v/c4s5xedrnMnhkN+/gcv8dYHygznIww9IYo1SjWd42ddfvX3Z3jhXKpzxgOH6vzyT57jy9/J8YE/3dGTlveu15f4J39/jo9/tch/+tgE6ydKSsF4MeSn33uKN95VJZuOaQWCo6ddPvAnO3jqSLYzDqsYL4Z88BdOkVtn3zg95/CLv7XnCoVFJGNDIT/3g5d4+4NlRgqr7yUo1wxeOJfi1/5whqOnezMr4ljw2MMlfvb759g92cIyEzxf5+hpl/f/8TRfO5rteicB/Phji/zgW5a6rvP+P9nBl57JD3rA68YPvXWJd72+xH/5qwn+9U+e48BOj/PzNv/7f9zNrskW//IfXiCfiThyyuWffXAvZ+bsznMO5yM++AunKGbXQlIuXLb5+d/cS9THbGSbCe95wwoPHKpzeE+DfTs84ljwx//mhS7P55eeyfObfzrdNQ+akLz53go/+wNzHNrpkU7FqrZkS2OxZPKpp4b44J9NE0Zr1/nlnzzHXfsa/PofzvRwXNfgAz9/mskRn3/1n2Y5cT7VdTyOBe9+ZIV/8vfnmJ1Ugq9UM/jEV4f4rQ9Ns1LduAjAD7xliZ94rLsVwO98ZJJPPTXYPCGE5I13V/m5H7jEbbMemfZ7eb56r8efLvD+P9mh+t9sQMaN+eWfPMd7HlmhmFNzsFwx+OSTRX7rQ1M9C1UuHfPBXzjF+NBa0PzlssnPvn8frU3KBrslgs82E+7a12BiOODsnM3vfXSSH3n7ZX7sXYvcta/BUtnk9z46yY++c5Gfeu88n3xyiMvltTSZcws2x866DGUi7j9UZ6wYdlbOfrh9d5P7DqlqsHGi1uRWoFHz1jwJdU8n2SBUbFPyb3/qLO94qMxXn83yJ5/O0Qo0xoYC7trXYHokwAu6tZxcOubegw0uXLb7rj0jhYh7DzT4zku9VUlsM+GXfvw8YaTxx58epdY0uPdAnbc+UOaDv3iKn/q1/Tx/yu26nx8Inj+ZZmI4YMdowP231ZTj5goFTS1D8ss/eY53P1Liqeez/NnjWTxfZ7QQcNe+JtOjAa2+2hvctrvJ6w7XOHLa5RN/O0kiBW+8u8rDd1T5jX96hh/7Nwe5eNnqnCuBi5ctjp5xKWQi7j1YZ2okZCi3uXXuJoYDHr2vQtaN+dtncxw95fJ9jy7zf/5P57BMyYceH2HPVIvHHi7xI2+/zK//0Y7OuUEoOHLKZXo0YHIk4MHb6mTTcdtZ0TsGjp3wd+6pMl4MiBOBEJBIQcPTCdcJPs/f6BuU3LW/wW/+wil0Df7qK0XOzNlYpmT3ZIv7DtbJpOKesKy9O1rcs7/Rl+NCSA7uarJ70se1e4/ftrvJ3fsbfP1Yho89MUw6lfCu15f4h48tkkkl/Mvfne0RFvNLFkdOuxQyMXfvrzMzHjCSv9J8SQ7vbvJbv3gKy5B8/KtDnL7oYBqS2Umf+w7Wybpx39x8TZP89PvmGM5FPP50gTNzDqNDIX/3jSv8xLsX8EPBr//RTNe5UaTmqz7hM14Med3hGpfLJtomOpduqY1vJB/yU7++n7PzNnEs+Oc/doGRQsjPfmAvtYbBxHDA9715mamRoCP4pIRPfHWITz81hGkk/N//yyne84beCPT1+B+fH+FjT6ho+d/8Z6d4zxtW+E8fm+AvvzTc+Y0Ewg2r0cy4z+sP1zh+NsU/ef++TvyaaKv0Q9mI+ZXNM6hapiSKBf/4V/dzYVG9r2NJ/vmPned//nsL/PT75vnF397TRYJy3eA3/vsOdA3u2lfnj//Ni1e9z9RowCN3VnnpgsPPvn9vZ0UV7a50xWzEwoD3mp3w+aNPjvHrfzhDo11Z548/Ncb/+0snePiOGm+8u8qff7bbEP65bxT4wrcKGLrkN372DD/01qV+l75pGIbk7LzNr//hDtKphHsP1rnnQIN/90c7+N2PTjIz5vPIXTXuOaC0/tXtfK2p8/4/2YGmwW27mvzZ/3X8ivep1HX+xX+cRQjYMebz1x84yuWSyc//5upYKsRJb2z3W+6vMJyP+JXfn+G//vVEW8hJdE1pPpqQxJtofp4aDvj3H57iP3x4qqNtfeQLw/zBv3qR97xhhY99aZgnvp1jvYD/yrM5vvp8Dl2T/Ot/fI5/9L39G0Gtx6P3VRgrhPy7/76D3/voZOcddA3SqbhrvNdD12CiGPJPP7CXJ5/LtRcSyVe+k+P3/sVLvOeRFX7vo5Ndjqamr/Hbfz6NpsHuyRYf/rVjNzdIfXBLw1nOLTgslkykhBfPpQgjwbMv/f/svXmQZMl93/fJfHfdfXfPsTOz9wXsAgRAgFgAxEmAIA4eQYGiGAodpGjJlh2SGLIiJFly6AjZlkK2bMuiKdkKU5QIiacIESQIggBB3McCe99z9/Tddb8z03/kq+qqrqqenpmemV5sfxGN3a1671Vmvsxf/o7v75dFtpuG3Hp+xUMIzfTQbiMAQaYESSpHtLRRmGujRBIlsk+aTdOdz6JEEidyxHMghPGD+a4a4sVpBGEsWd5wR0zPG8VvfH4mF3qmn2Es+He/N89W0+atDzdYnN7NhhdoLUgzQZru73XJvF+eM9qvKO/XpHHdqNv80m8v5kLPtHG7ZfHZb9QQAu69YzftwVyjlCBJxU2tyKMVfPfFImlmSNbnr3iEseQbz5bQGuotm82GTbmQGT/vQBu1NhXBk1Tsg64miNOduaO12ZCjZHhOGSL6ruonwsyyci7keqlomRLUWzZbTWfknhvBVtPmk5+dzYWe+Xv5ss+v/9Esgad4/1u2ds96ht/X/toipfGblwvZQJDH9KvRttlsTO7XZ75W6ws9MC6frz5V5tKaR7WUsTQzfs7v/31dO26q4NtuWrlPRBAlAqVhdWvHpI0TicCYZrcDF1Z8vvlsiTuPhfybv/0Cn3jfOgv96N3BtylOBM+fDxieIILL6y6X111maylLszd+CPPFNY+vP1Pm1GLEv/nbL/CnP7DG4kyMtY9+nV/xWF7fMWV7bay3bLQ23KzbBQ1sDCywbmQRJ4LtlvlMaUhSgW2R9/XW44++VWW7ZfNzH7/CP/krZ/n+h5oU/YybRea+vO7m2tKw0PnOi0XiVPDQmQ7iAFb5579dZbNu8+c+ssL//F+/wtte16QYXL1fSsHjz5dGtNwokbS7EsvSt4Vze1NN3TiVfVOgpzmNc37eLoSx4O/90h20upL3v3mbf/yXz3J53eUzX6vxyc/O8uy5woGmyRlNZXTIs0ywUXdwrE4/WnYjiBPB3//Xd9AOJT/0/dv8w58/y5UNl898fYpP/sEsT5+d3K/NhjM2aNLLbLydb0/r4fljajII0kGDQff/7zZA8O3nS/ytf3mK//YnL/Pj717nw2/f5MmXivz652f43S9NjYk43xjqrXGpnGajShJh0sZsRRjfSHFRwRMvFvnv/8/T/HefuMzH37XBh962xVOvFPiNz8/wX/5kmo0xQRQwvtHNCX2+nXUMbqrgO+QFGgDBuSsef/1/vZNH723zE+9Z5wffWOfP/cgKH3/XBv/Xry/xr//zAsk+TUxDjdF7zutJY9L7/GC4YoKLqy6/8C/O8MufbvMT717n3d9X58/+8Aoff+cG//dvLvKvfmtxbP5kdrvPwLgKxo3fYWpupgSf+pNpvvJkhfe/ZZsf+8F1HrmnzZseaPKJ967xd37xVB742r/wG1cusYdJOrzWuZGduz1uFEoLPv2VKb72dJn3vXmbH/vBDd5wb4s33tfiE+9b5+/84h1867kSIxQuzQHnAB8MDjmB+VZAEKeCrz1d4l3LEnsAACAASURBVOvPlDg+F/Oj71rnZz+2wl/7qUu8eNHnD75eY5iWMvlppcLkFCLL0pTGpN1IafhimWKsRnh9MP6RbzxT4pvPljg2G/Oxd2zwcz96hb/6py7x4iWf3/3yFK/WJP3DDaPB/4fPmKDb6+9u8fM/eoX3vmmb//HnzvNn/t69NDsD73nHZTYCx9YE3mRTsFzIxpj1JrHfsTXt8EYKBOyGYLPh8MnPzvJbX5jhdXe3+bmPXeED37/FP/hL5/ipv3s/9darQ6Qc6lzd60HfJLvm9WwcqhdXPf6P/3SMX/zNRQJP8daHmkNXJanxJRX9bCR305Jw78nuxN92Hc1dx0N279Ez1YRjszFbDXtitPX6Yfp1ac3jX/7GEv/y15bwXc1bH24eibx9wmhOXMceYYJkX3u6wl//387w4qWAu090ObkwnHceJRIhGUNX0SzNxlRLk6kmxyZ8f98dHTxH8/z54CYcJGeCPN94pswv/IszPHeuwJljIaeXDmd+9jgcQsGn2UOBv8r3JsplSUNDECPXD99T8LJ8sg1+p/tRPCDPVtjBRt2m1bG4944uizPJ0LPvvaPL2x9pTNQIpYAfeWyTWmnnN6XQfOSxTaarCd99qZhz5Cb0d0DQipHvdxB4WR6EGO1XL5rbjeQBmYg39r5uPq7Whr2/TxLD36uVU2rFQWf+uHtMgYyd4NjOtT0/XKbELg3MuFscS/OWh5pY1s69loSPPrZD+h2H6UrCD79tK9+Ezd90JeVj79wkSQWf+2Z113y8nrGY3K8sn09KGcbAwWBCG8Vebbw2HDK9VHPnsZAfeH2TciGlWsp46IxJF/qp96/x6L0tmh1DWfjMV2s0OqPN/9ITFf7sD6/yZz64ylbT5uXLPq5tIkdfeLxKe4DU/PZHGvyVH1/my0+UefKVAo2Wjeso3nBfm5/54CobDZvPfXM4T/jlywGPP1/kHY82+Kd/9WV++dPzNNoWdx4P+ekfWtuT0hHGgqWZmP/9b7zEf/zsLPW2xZvub/HnP7JCq2PxS7+1OMSFsiS8/y1bHJs1qU+nl0J8V7EwnfA3f+YCjbZNs2Px7LmArz61k1Hxlgeb/LWfusSXn6zw5EsF6i0b19E8cnebn/nQCttNi89+ff/5z5OhOTkf88431KkUTfWZN9xr3tePvWudu493aXRstlsWf/C1Wk7luLWQAt775m1OzkeUixl3LEQEvmJWJPzNP3OR7ZYZwxcuBHzpidHMoK2mzXdeKPLBt27xd//Cef79Z+bohpJCkLGx7fCNZ0tD1/+Dv3QOx9Z8+cky5674JIlgpprw0XdsctexLp/5+hQXVobLuv/+V6f46Q+s8ZPvXacbWXz5iTKurXnXG+t88K1brNcdggmRz426w1/+8WXmaglfe7pM4GV84v3rPHxXmy9+p8Lnv11lUFVdnEl4z/dtUylmlIsZb8ktmg+/fZOFmZhmx2K7ZfO5b1SHkgr+h794nqKvTL+WPeJEMl1J+fDbN7n3ji5f+HaFc1duvFy9yDNfziyFlIsZx+diioEiUIpf+OmLbDYcmh2Lly/7/PG3K9ct+m6K4MuUYKNu54RggyQVrG87fVIsQDeUrNWdoUjdG+9v8dd/6uLQ89a2bN70QJM3PWBeUiu0ePz54ljB94XHq/w/v7PAT75vjb//s+fJlNHeljdcvvNicUjwNdsWC9Mxf/FjK9iWQuUs/TQzu/A//9VjfOeFYUd0GAv+4f97kn/w8+d4431tvv/Bl0hzTtQffqPGr/7BHP/ovzo79DtgiMh//HiVf/Ubi/ytP3uB/+m/eQVbGsfzlU2Hf/4fjvOlJ4aJprat+dmPXeHOYzsmRL1lnjuYIva7X5nKBZ9Bq2sxV0v4Cx8Z7pfhTrr8i08e45u7HNHm/dg02vbYyRTGkrVth9aufj10Z5u/8adH39fr727z+ruNEOzGkqdfKdyQ4OuEFut1h6inLWnz/jbq9hA/bKtp59Fec5llaf78j6xw/6mdOojNfF7++Lt3xvBz36qOFXxxKvln//44izMx73ikzrveUM+pGYJ/93tzfPO50pBGVW/bfPSxDd735u28TcZM7kSS//Llaf7xvz1BtIvZ8M1nS/wvv3Kcn/+xZX7uY1f42Y9eQSlD3fmnv3KcE/MxH3lsYyhrJMkEK1sO/+xXjlMtZfzsx67wFz56Bdsy7/KPH6/yd3/xDpqd4fd1z8kuv/DTo+/r/lOd/hhFieSVS/6Q4Ku3bN73pvW8zmJ+ykCesvb7X63xj/7tyZFMlkbbZn3X+h7Edstmo+6QDqTvSQE/88FV3nhfq/+ZKRgLH3/nRv+zrzxV5ovfqZgD6a8DN6UCsxTGiZ9mO5WCbUtR8BVRLPMXL/oE23bYI4NqXFtftdac1nkK2lhKhqkqe+/JLved6uK7imbH4twVn6dfCYaqH0upmasl3HOyy8J0gu8q4sTw6sxCnUQ9MM7j199taplpbcrnPPVykTgRlAoZcWL8O73cslKgUNrkbFZLGY/e02ZpJqbZtfjOC8bEHSVLm3G0rmJBJIkYKiAghWY279fijOlXkg70q2GPkLltS1MMJld3dmxFwVPEqcgnuOiPdWEf76vdtW6oIvO4uVLwFbalaXV66YhmnIGBar3mM+sqNeH2rmqt++ldS7OJEUp1h2fOBlwe4jxqfFdzajHkzLGQWjlFSiNoX7wY8NIlf4BoPAwpNXcsRDx0Z5tqMaPRtvjOi0UurXo4ucUyOIaWNJWT26Eh+Z9eDHn4rg6Bp7iw4uWb/Gh/bEtRDPbxvkJrIBprMpl6/ZoqpViWyYh58aLPSxeD/poeHLOCr3AsPfDOhse0FCik1LvmhqYYqKsWw01TMUCyn4xXaen5IxzhexCDq+qwRJgOY5sOAK/K0vNHOML3IiSCuztzzCWlq198i1DOfB5oL+LqGyE6v3pwJPiOcIRbDIHAVw62OjxCxtKmTeKAc9MPK44E3xGOcITXHI4E3xGOcBvhZTa1JKCaBNhK9n1ttpKUUw9HWdSSAFdZSC2oJgHF1AUN5dTHz0ykXGioDXyHhkri46lrI24IBKXUYyopUEzdHeqohmLq4mU2xdSlkvhILfrtd5SFpQW1JEDmWqOrLKbjgulX3qdaEnAYzmw6ZDy+IxzhtYNy5lFLAzTgaotEZLwSrBPKlIJyOdWdpm3FFDOXVCjaVkwpM8LwpcIas3GRTCjO+1t4yuFMd5a2FfFCYRWJ4I5wmgv+JpHcf1HY41EVV9kIwFE2G06bS/4WGpiPy1haYmlJoBw2nTa+cvCUQyQTzvtbnAqneaGwRseKmY1LHIuqvBxssOm0KWc+S1GVhh2ib3OG9ZHGd8CQJYvqB+cpvX36aHRvBgQU3lCl9iML2DPu1a8/xPCUzdlgg+eLKzxfWEFqwVK0U67f0RYbTpuXCusEykGjeb6wQmgllFOfthUbvxwQKIdIprnmJfvCq2vtX+hZWpIKxfOFVZ4trnDJ32IuKVFOd45rcLURumtui5mkxGWvztlggyBzkAhikRFkjtEQM4+mFVHMzHsKMpdQJqhDUFbiaGkeMKRvUXh9Bf/e0vUkDB/hahDgnSpQeEMVWX51GywNO6QrE5TQhDJly+lQynxsbZZliiKUCZFMydC07IhUKGKR4WhJ10pwlY1EUEw9GnYXJTSesvGUTSoUqbiWMmeadadFJhVKaDadDonIqGQ7gq8rE1Jh2pUJRceKiWWKFsbcDmVCoBxsLXG0ZNvpEigHgaCQOXSsG683eRA4EnxHOMJtQiKyHc6cgFimWFpg5YJPC1C5Q0wLPaApaUAQSXMYj6ccisqlZUVEMiVQLn7mXLN2pYQmFYMVuzWJzHAH/IT99gAqN1gHM8jbVoyfOfjK+B6bVoijLFxl4WrbCL5DoA8cqOCTSMRAr+SRXD3CESZCMFxWXWhTOELv0/ufCEUqFMXMxdKSrkzo5D7BgnLpWsnVHzLYHi2G1m/vs0HhebWWda0YR1uUMo9QpoSW0WjLmYfUglBeW5tuFg5UMlUo4mHseRubaXF4D5buYz+7z17X3Kzd60aeO+neqx9fcvDYTzmn6/1+MOvoWtu+3+vHXXe982HXd4XMHSo4UsxcEpkNaV17QaMJrYRi6qFy7awjYwqZi6euXbsSCArK6bfH0Raesuleg3kayRSpJcXMo2VFKDShTCilPlkuqA8DDsxJ4uFSExUUmpQUiaSpW1e/8TbAnnUJHirjHveRgYVONOl2QnyhS/h8C9XO/SISqu+bwz0RsPWfr5CuDU8Ae9qh9tFFkpWI+u+vwqA7RWmsokXw+greqQLCFiSrEZ3vNkgujdYtk2Ub70wB73QBe8pBSEHWTonPd+k81UQ1dzmpBVQ/MI9Vstj8zSvY0w6FR6u4x3yQgnQzpvONbeL8t+xZl6mPLhJf6lL/zBrsmn+lx6YJ7i3R+MN1orMdbgTClfj3FvHvLmLVen3JSK6ERC+1iS/u9N897lP90ALh8y2af7wxolJU3j+He9yn/nurJMsDdezy67x7ihQermDVHFQnI3y+RfepJjre1UFL4J7w8e4s4i56+XtXJGsx3WeaxOe7I7/t3hFQee8cjc+tkVyOCB4s499XwipZqE5G9EqH1te3IdNmrrx/Hve4P36uzLjUPrJAshLR+rQpjlDIHI5FVZp2RCFzqaUBl736NZmnHRmzFFXZdjpGyFgJtrYQcM3alUazEFVACzKRMZuUUUKx7ew+YGoyEpGhci10xW2YNloxi1GVph0eisAGHKDgi4jZ1HVSUmJSQJNy+w6mmQT3ZMDUxxeRBYt0K0GHCuFL/HuKBA+UyOoJ0cs7C9+acnEWPYQ7qhwLR+Is+ujE+FwGV44MLKY+toQ17ZDVE6QrKT5aJbi/zPanrhA+3955kITKD84SPFxGh4qskaBTjbPk499bwr+vxOZ/urwjkHPY0w72nId/V5HK++eQrkR1M4QjcGZKRC+04FLeVlfiLHqo7vh3YtccnCUf4d+YESAcQfVD8wQPllHtjKyeoBU4Cx7enQXsWZf48nJf8ApP4ix5JKvR2OfZUw7Ooj92/AuPVvHPFMjaGTrRuMd9/LuLeHcEbP/uCjreeR/uCZ/pnziGsARZM0W1M2TBovCGKoXXVdj+9CrdJxpDz5e+xF3ycBd9Cq+r4t9XRHcVWmmcmgNS0Pr6ztGnpq0ewhk3V8TOXMn9eWtui3Lqc7I7hRZwxWuw5rZAGP9ZItK+Fy0WWd+/lgpFlmtOHStGCU3TivrfhTIGxDVpV0poGnbImttkPi7jKptIprwSbBAJs+mmUvUFlxKKWJq5pPP2aWFa27IiyplvBK+AthWjhaZph4fCvwcHzONr0EIgsJCHUuj1qBBWyWbrUyuEzzTRCoQEq2zjLHjEF/e/u+0Fe9alu9pk67eXydoZQgqCh8pUf2ieynvmiC+FO4JMQefxOtFLbaILXSOcNFgli9oPL+DdZbSnzncaI79jFS2qH5ij890Gncfr5l4psGdc0s1bH0FzFn2C+0vE5zps/fYVVNccfi6dXPCG2Yi2eV0Q4J8psP2pFaKzHbQ2wrv2IwsED1eIznXpPF7vX54sRzQ+t05yOSTdjNEZCAuCBytUPzRP6c01us80IR3VSIpvmSLditn81cskKxFojSzZCEdwPdM8E5oXC2soNFt2B9kvmav7gqFlRTxfXO0LmueLK/1/v+jvCNuWFfFU6bL5ToDSmhcLRqO8Fq5cwwppFiI0mrrdxZzCnD8h39Mveju/u213+3y8RGQj7RPs+AZbVsSTvTYeEhyo4HOwmRcz2Fis6g0cHBocLnNXOsJUWI4UOtWgQWeQbiakmwfneNWxovnFTbK62S01ms53G/h3F/HvK+GeKhA+vVPWPr4wKnCzekrnuw28O4vYcxOKPNqC6GyH5hc3BgSKJrl8e8qAC0uAEOhUG3MzyzWE1JiGB4nuM03CF9t9RTtdj2n+8QYznzhB8HCZ7pMN844x76PzrfrQ/TqD7tMNSm+dQpZtrMAi2+1SAIQtqH96dch8zbZubK6ogQDGWIEghj/fK8AwdL+4NoE37r6+EB75fvLv7m6f3tWmwyT04IAFXwGfpm4RCMNG94R7+yqOj4OGztNNvLuK1D68QPd0ge7TTZKVaNQndIPIGilZY9fiyDTxxS7+/SXcRW9I8AEIV2DVXOwpB+nL3JT2QJjFNxYKwudaB6NFHQDiKyHJ5RD/7hLTP3mc7hMNolc6RqAc8FyIL4Yjz0zXYlQ7xZl1Ea5EpwMqmQSrZGNPu8iShXAl0pMIVyKkgAk1A5KViHT9cPDPjnAwOFDBFxIzK6bwhIuFpHEIgxvhcy22siuU3jpl/DtvqJKsRHS/26D7dMOYZgcAHau+tjGIrGUWogwGVpkA/74SpbdN48y66MTcq5U2/qK9jhfM9ES/3e2ADhVbv7VM6a3TBA+U8D68SNZOCV9o0flWPTcVD+a3xvVbJQoVKqyKPbRZWFWb8mMz+PcWwRJG488ATBBKhZPfe9Y6eKF9hNuLAxV8ETFrehNPO8SkRBzCXVJB+HyL6JU2zjGf4IEy/r0lqh+cJ3iwxNZvX+mbp1eF3CM5w1h8I+uld6r9IBPDO11g6iOLqFhR/+wa8fkuqpOhE4V/T4mpH12a3AbNgZxfLKyD8zpn9ZT676/S+toW/t1FggfLFB8xgZ3GZ9fG+iontmuSpgtjD5wVQhiS1sC4CEcYX+mdRTpPNOh8u05WT1CRAgmzP3MSq7jHUjgIoSdF/90f4fbjwHN+YhJijIlXJKDNwQQLDho60cTnusTnurS+vEnl3bMED1coPFql+fmNgQvNrBdjFplVsmHCwpSBMaV0MqyVWFOGJzVoBgcPlhG+pPVH6yN+KOGKg4mE9c4YFLlEHlzNkoNP/9LGD9b++jadx+tmc/nQAuV3zBC+2N4J7PSatVMpvg9hC+QeAsmqjn4nPIksGLqJTo0WZ895uCcD0rWI+u+toiM1fP2YiPH1QA/2ZXdbSzYc4OZyhBvDgbxxiWRKVJgS1f7fjKhREsWDePzBYowfpxdEQGmsqrMjaBQ7UdK5XQnxFvj3jx5O0/+64uAc84c+E4HEO1M0vr6BYIbwpfmt1rCmKWyBf0/JaDE3CJUHc6yqjdxFWbFnPZyFGz8hCxg7o3Si6T7bIt2IjaAZMPNVN4NMY0+7IzQQ55iPPT35gCL/ruKIRujfWUD6FvHlsC/ghCNAClSoRny53qkAWToAoT80V3aNpQX+/Yen2vIRDkjjs7HwcOnoncWshMSe5C2+TRCOoPqBeZK1mORS11ArMNpO6a1TIIQh/A5oHdHLHQqvr1J66zSqlZFuRAhHGjLrPcWJQQWtNNX3zNGwBelqjHAFxe+r4R7ziV5uD0Vdk+WI4IEywSNVkvXY+KiKFoU3VnFPBux1Lsp+oVopyXKIe6pA+V2zdL61jU409rRD6Qemx2q014PggTLuiYDo5TbpdoLONNKReHcWcOZd0vWYrLEj4NPthGQtxln0KD82TeeJBihDByo/NtNLSx2FNpzM8rtn6T7RQCca57hP+bEZdKIMlSV/N9l2gupmOAsewQNlQ1myBO6JgPI7pjmoE7ejl9sUXleh9LYpVHvXXLl78lw5wq3HgQi+hJQ1vUk2+GY1xByQFnGAsGddCo9UAY3KCa7SEehM0/7mNt0nh/1P4fMtOo/XCV5XYfonjqFihZCgOhmNz65RfsfM2N/pfHsbNEx9ZNFwBS2BsATxxS7131/NSc/5td+t450u4N9ZxDsRoGKF9CTpVsL271yh9uHFG+63TjSNz61T++gixZy0qzONsAThCy3Cl9pU3jV7w78jbEHhDVWKb6qZIE2mEbbpe7Iam74PaF06VKZdH5qn9NZpit9Xy8cLuk82iV5pU3zz1MjvZI2Uxh+tU35smuIbqmgF0pOoMKPx2eHsk6yR0vqTTSo/OMvUxxbJOlk/itv+6jayZBHsob3vF+FzLTrfqRM83Jsr5nf6c+Wd4+fKEW49DkTwaTRZriZ5uAR4xCR0uD1csknQiWbz1y7jLvnYM64xMTVkzZTkUkiyHo0QUnWi2f69VTpPNnCP+QhHkjVSorMdskaS+/FUX2tQnYzWV7YIn2uRrMd0n2nhnvARtiRZj4jPdkYix6qVsfnrl/FOF3DmzWaRbsREZzuoMKP5xQ2y+i5qjIbuU01DhB7DPRuH+GLIxi9fxD1dwK456EyTLIfEF7pYFQdhC9KNGwtIdZ9qGg1uwev7tXScGS37Yjg2Ehu92Gb9/7uIdzrAqjroWBFfDokvhThzLjrVOwEnbTaj+EKX7pMN4rMdvDMFrKqD6mZE5zqGbzeoxGlof3Ob+GIX91QBGUhURxFf6JCsRDhLPlk9HYnsppsJzS9tklwZn1WyGzrRbH96lc4Tg3MlITrb3ZkrsTowDfMI148DPV7Sx2NaVOnoEF+4dHR46AjMRzjCEV47mHS85IGG8jwcmrpNkzaedqmI0hH/6QhHOMKhw4EKvg4h82KGCkUEkg29dfWbjnCEIxzhFuNABV9CyrJeRWKh8v8d4QhHOMJhw4EKviIBCkWX/TmDj3CEIxzhduBAk2gEAgcbwWgJ63Fw/QLHzzzE1Nzxg2zGawaW7bB06n7mjt15u5tySyCkZOHkPSyevBchj/K/rgezS6c5dvpBbOfVfULdjeJANb4MxZycpaLLgKapO9RpTrw+KFbwgiJxdDjT2g47PL9IUCgThe08Mfh7O5LkugGFYoUsS/N6cUe4Fli2Q6FUw7JsxGs8cfhABV+XkHPqUv+/95yYQhAUbpw0+lqGXyi/po6w9IIiQlqQ7f+s2CPswPUCbMdBH/EID9bUDfCZFlUcnPzouckDbFm2mcivoYV7kBBCEBTLr6nxC4rV11R/Dxp+scKhqf1+m3HAZakiLC2piQo2FnXdpMWuqrtCIIUkKFaQlklAF1Jg2c5YU01rjVJXrzcnhMB2PFw/wLKN/0JlKVHYIY3DPfNdhZAIKRGA0gqt9heNFkIgpbW/dgqBbbu4fgHbdhBCkGUpcdQliUK03m8EXCClxPULOK7ff7Zl2ePHDzMOV4O0bBzXx3E9pGWbsVAZaRITR12y9PYdCyikxHE8vKDQ+wRp24hsdBHvt79gTD/XL+A4HkIIlFIkcZc47F5lzgmkZeXFZDQq2389xN7Y7qedlmWb+eJ6SCHz9oXEUXfffQQzfpZlExTMRqmFaYdljS5/jXnvV3ObCGlhOy6u62PZDkJItFakaUwShaRJdCA55jcLByr4FJqElJQUFxtroEhBoVTFL1Zw3QDH87Ftt797l8rTFIq1sc8Mu01WLry4p2DwgiK1mSUjTKU1YP5plFKEnSbb68uEnfH+xkK5xtzSaUDQaW+zevFl9sO8rkwt9AMzzfo6G1fOjb3O9QJqs0sEpRrWUPvMJIu6bbY3lum26mPv7/WxUK7tjJ/j9YWu5xc5edfrx96XJhHL558bK7iEEHiFMuXqDEGhguU4I74frTVZmtBubrK9vnzLBKDjBZQqUzh5fx3HCGQA23E5cefDY1+RUinL554jiSenS1q2Q3V6kVJ1Jl+0O+9Da0UShdQ3V2jVN8bOO9txWTp1H5blkKUxy+efI02unurn+UUWTt6DlBZJ3GX5/PNjBZi0bKrTC5Sqs9i73onWiiSOaGyt0txem7hJW7ZDqTqD6xX642fZuaIhJEun7hs7florVi+/PHEumvcyTaFcw3F9pNhVlDJXAMJOk631y0Td9tjn3G4ceOn5iijR0h22aAzx+CrTCxRK44Ubwuyg49Bb3JNQqs4ws3hqaPcyOxZISyKlRaFUww9KbKxcoLm9NvKMsNNEqQzH9SkUq7ief9WAi5CSYmUaaVlorem2tsdeVyjVmF06PRRF6+2oUlpIaREUK/hBkc21y9Q3rjBuRpYqM1RnJhcrmDh+avL4WY7HwvG7+gsCMBNXq377jCbtUp1exPUCVi6+dE3axvWiUKzkm8qEeocT54Xe0+/pegFzx+/E83dKpmml0FohpIUQRpueWzqNF5TYXDk/ov2lSUTUbVOqziCtgEKpRmNr9ep9Kk/150HYaY0dR9v1mD92p/Hf9tqXWyFCStM+L2B28RR+ocz68tmxz3H9AtPzJyYGMSaNnx5zqHgPQghmFu6gUKoO36MytNb99knLplCewg2KrFx44VAKvwPP3Eh0io2NRvdPagLYXl+mub1T4DMolqlMzQNG8DQ2V8fqWCpLJmp7QbHC7OIppGWjtabd3KS5vU4am9OibNulVJ2hXJ1FWjazi6dQKqPd2Bx6TpYmtBtbVGcWkZZNsTJNvHZp7G/24HoFvKCAEII47BB2RnOSvaDI3LHTuemt6TS3aWyvGdMWjWU5lCrTlKfmkNJiev4EKkvHCudmfZ1wYAK5ns/U7DEQgiQO2Vq7NNY60SqbKKjSOKLbrhOUaoTtBp12gzhso7IUrY3WUK7O9NsXFKuUqzPUN1f2HJuDQKdVJ734Ej3BZ9s20/MnEZZFlqVsrlxAjdN2tCKboH1ZtjMk9KJui/rmKlHYRiuFtCwKxSqV6Xlsx6MyNYfWGRsrF0ZMv1Z9nWJlCiEkpeoMzfr6ni4SKS2KlVpuUme0ds1BMJre3NKZvtCLow71zRWiTgulFFIaF1FlegHH9SlVptFKsb58dmSNxGGH1Usv98dPSsn0/HFsx0NrzebKBdKx2rs2LIFx32hNu7GBXygRhx06rTpRt0WaxGitTftKVarTi9iOi227TM0eZ+XiC4fO7D1QwefjMiOm8lPWNvGFy5Y2ZZ52m5lSCsgFXxJHtBobI8/bCz1B0TN/ttcvs72+PDQB0jgi7LSIum1ml04hpGR6/gRhp0WWDi+OVn2D8tQcVi746psre2o2vUlvBO7WiFYghGR67kTf39jYXGVj9cLQJuHLaQAAIABJREFU4kiJiLotwm6LuWNnkNJiau44Yac5YqrFYYc43PGXpkGJ2uyS8cVlqVlI1zy5NJurlxBrl0mSaOT+nmajVEZt9hhCCArlKeqbq9zsJOwkDofGwHa8XAO00PnmtR/f7yBqM4t9oddt11m99PKw6Z6Yce62GyycvBvH9anU5um2GnR2afRhp0UcdvGCovnzixNdKWA2QdcNAIi6beIxwqUyNUdQrOTXtFi5+BJpMpwMEEddOu0GCyfuxvUCStUZuu0Grfr60HVmM98RrlJa1GYWwQG0ptPa3tMdMAmtxiZx2CWOumMVkjgy3y2euBtp2fiFEpbtjvTjduNAo7oeLg3dIiQG9E0tRBqUqnhBb+duU9+4MkEz1LTq63SaZuI6rk+pOj1yVRR1CDtNtNa4nr8n1UZaFsXSzu7dbozmJPuFEn6xguhrZJcnagTtxhatuhH8tuNSrs1drfsHhjSJzAKYKDQ1ze31/iZgO+5Es/oww3Y8SlVTb1CpjM3VixP9lVHYzjdRY75VpxdGzGejtZl31tP69kKxMm2O3dSaVmNzRAOSlt1/71orNlcvTRQWSdRla+0SoBFCUJ2ev2WEbq2U0ZD38LmH7WbfOhHSwnEPX13OAx2tDiEVUaIiisyIKZr6YM9RHUSxPEVPjW81Nvbc/bXWNAd2xGJ5epQWoTWt7fVcAAgzkSf4ivygjO36aK1z7WzUH1goT5kImta0G1tk2V5BAZ070nV+b+2qvs1biSxN+pFLmftxXm3wC+W+LzPqtIa053FoN7dIkzgPAJV2IuiD1zS2+sKzUKr1tfvdMMRhQ8XJ0qS/CQ+1LyhhO0ZAxGGXsLt3Obduq04ShQghcP0CrlfY8/pbCa1VX2iLPIJ82HCgMzgh5Ype47xaZkWvE92knF2R0zlEnq2wH+dpHBoKgBACx/WGHfo5Ou0GSRzmHLnK2MkOPTM3F7r10d1bCIHn70zEq01iMLt4lib9YMJtSykSAiFNUKhHeZCWPRBjEK9KzvQOFYZcY9nbVM/SpG8KSmnhesHINWkS9U1g23EplKsj1wAEhUrft9Zt18dqcv353GvfVcx4pTKiPAAnhOxbP7cDg/NFWvZIpPww4kBFsYPNnJhCYo7Maur2nilr1wtL2li5uaVUti+KRe86y3aQ0sr9DsN+vp6vbMoLTJCjPMX2ruiuZTt9Im0Sh3Tbo2F/Ia3+y1dZOuJPHNs+rcjSGMf1EEJiO94tS+WTlo0fFPt0I8t2DE9NCMNxFOJQ7tr7h+F49jTwJN7fhmwEnxFmkzbB5va6ie5Ki3J1ltb2+rBQFYJiJS+drzWt+mhQwzx/p3379YeluWDubea3Cj0tMyhW8fwClm3cH7LHhx3gtx5WHDidpa27NDAa2F6ZGzcCMWBuaa33Rf7VWu1MSGFIwOPQbmxSnV7EsixKlWkamytDZnRQrGA7LlprOq36RH7cTvvYHyFa636UUghukc9GUKpMU5tdwvUDdlNH+mN7yCJy1wwxTN/Yb1BkUOua5NeMum2ibrufd+76RaIBDd9x3DxjAuK4S9gdrwgMtW+fhOjB627VxuT5RabmjxMUK6MuD637c2bSGVGHBQcyWhJBiQKucPDwQBsaS0ycBzoOFqPJcFcf4v1UiwGII6PFFSvTOF6AX6zQafaCF8b316ck1PcRid7327/106Q6Pc/0wkmktNBaEXXbdNp14m6HNI1QWYZGI4XF4h33Hkon9fVg/yN99Su1VrTq6/iFMkJIytWZIcHXKwoAxic4SagNzef9molDxOH93XIj8IISCyfu6vsijalvKC1JHOU0KA1opudPXjXgcztxQNuEQCJJdEZKB4nIWXw3ZzFrtZNW1ks3u3oTxc51Wu2x62ua9Q2KeXCiXJ3pCz7H9fADcz5qHHYm+hb1wPMFwiTW76N9O+lv+9/1rxeO61ObPWaEnlJsrS9T31geOy5G23kVa316OD1sv9rRoJan0snUpnazTm02wnY8CuUa1vrlvr+2WJnupye2m5Mrkg+1b59m4mD79g6e3TiEEH0eIBie5fqVs6QT3AZjOZaHCAdiTykU2zRp0aZBmy0aNGiR7j6y7ICgsrT/oqVlYY8JVOyGlTtdwbyUNJk8UcJ2gzjqIoTAHwhyFMq1PlnaUBLGv1yVGRJtjw6xn0CFHLhuMCp2sxCUqv3xCMM22xuXJ24GPT/fqxeaZCBfe5K/bhhi6Lq9OG9ZGtNu9oIcXj+zwfEC3DzIFXVae/psk6jbb5+7r/btXKe1Jolu7omGjuvj+aW+tbO5emGi0NsrE+uw4EAdSUUCgvwsXQtJTUzmwt2I28jQSFr9idLzoewFLyj2d9I46u65Q/bMWK01lmXnVASZU2ggy9KxlISBFtIdILMG+2mfX+ibRGkcke4jIHIjGDRbo25rTz/kYJ7sqxWD88ULSle1EmzH7Qs+lReT2AutgcyNnpZXKNVyN4I2nL89Jn0vewTADYpXHe9egQUwFtDV6DmaAZ39OvYwE/AyY5Ym0WShB0gh97m53D4cqODLUATCx8HOy9BPftF6oAKEZVn792vkaDc2+xOlVJkeS0/pQUhJuWbIq71Mi6tJ3nZzgKNVnsId2L3DdsNkOuyBTmu7T58xCd2T/WNCCEq1uT7Btd3cumpAZDBYI6SJqF0Lhmuy7T32pdrsbefuDQaxTD7otWkUYbfV19q8oDCUCzsOvQIGYITm1SLBcdjtZ274QQnHMzm8Qoi+L2wvRGGnnyrmuv5IPuxuFMq1vtkZhe19MAD0rmDNtW1kWu9fWQlK1deW4GvTJSNjXszgCoftPF1tHJKBsjVeUNq3et9D1G31OVSO6zM9f3LsyxRCmsothTyyFnVHcnXHti/PYwWjjRUrU32N8Wq7NxgfYI+hb9su0wt3jBfOQlCZWqBYNgUc0iSiub0+et0uDJKKHdfDy32P+8Ug6dovlCYIEkG5Nkc5z3i4nVAq7eeWWrZNUNxbMIzcn6U0Nlcw2Q6SmfmTY7l5YAISvYIQKkupb44vHDEIrVWfJG+oUNO4frBn9H/ofqWob66YNSEEU3PHJ75Tv1BmatZUBVIqy++7ykapdH/NGetlQsGQCcjSuC84HcebOHaeX8yLI4hDl587iAO1XzSaTV0H9t7dwPhMorDdZ9TPH7+L+uaVfGfVCGFhWTZKZSN5kmA0gM3Vi8aP4gWUa7O4nk9ze504CgGdpynNUChWIOfUba6c32dpJU1ze4NiZQZp2f10J8Pd2x83cWvtMp5fwAtKFMtTOI6Xt69jBKLjUiybEj9CCLRSbK5c3Jd/L0sTuu0GtmO0sbljp6lvrBitIfctmtpvglZzNI+3226SprGpc+cXmVs6Q2NrtZ+t0Bu7YmUKpRQ6SW9rVFcrRae5jecXEQhmFk5i2Q5hbqbLnEQrpEW7uTlWY25um+hrqTqD6xdYvOM+mttrO8/I3Ro9Xh5otjdX6LYnb+CD6LbqJHGI4/qUazP9aPl+NlqATnOLxtYq1bwIweLJe0xxirapHiSlRVCqUKrO9t0iza31AdbBniNIp7lNqTKNQFCdXkRISadZR6ks52qaNTdOUCdJRNhpmowkaTF77Azba5cMiVrrfnZKuTaHtGziqDtROB4GiL2k8syb/9RNFdlBscJ8Xhapt0PsmDMCELSbW6xcfHGihmVKCJ3pV3PWOZeo94wdUmjMxsr5fU9CMNG1pVP3GZ9Q/pztjWU2Vy7s+xmO6zN37DR+obJn+7I0YXP14tjKLBOf7QUsnrgbxwt2PdvE1BGCOOpw+ZVnxgYuKlNzzCzc0Y8698ofQS9iaFKs1pfP4voBU3MnyNKES688ta/6cwcNy3ZYOHkP/sD76PXXZJQIsjTm4stPTySNS8tmZuGOPi3JQPc1od5nKjOa1Nb6pX0XpgWYnj/RL+ig86yi5XPP7ps7KKTFzPwJylNzI3X4ev/dCzA0t9fzKjX7fbZk/tidfR+kzu1XPTB+Wisun31mLGPB8wvMnzDFG3rX9qyOHrdWa0V94wrddoPFO+5DCFi5+NI1rbuDxMbXf3WsH+e2eqy77QYrF1+kNnsMLyhi5fXQIKeEZKnRfvYQv3HY4cqF56lMzVOqTBuGvtx5RpokdFp1Gpsr15wJoVRGs77RNzm02v/u3UMSh1y58CKV2hyl2sxQAVHTvpRup0Gjp61dy7Ojbn/8TEVru79h9MYviSdXwm1srZNlGdUZU2tPSgthyVwQp6aA68YyUbdNlqUUyx1UXnvtdiBLE1YvvkRtdinPje0dmmMW8U5/JwsqlaWsL5+l225QmZrH9fydcdOaNE2IwjaNzVU67fo1R+FajU0qU/N9t0a7eW1VZLTK2Fg5T7fToDK1gOcX8iwaCVqTqYw47NDYWqXT3Lqmd6GVYm35LHEUUqpOm2LAUprycfn4pWkyUdBHYYeViy8yNXvMWGq9dEY0KlPEcYv6xgrt5iZCWoSdBpbtXnMVnVuB26rx7bRC9CvESikNj02lZGli/Dr7fLlSWqYqbJ5bqrKMNI5uiOPkF8ocO30/IAg7DZbPPXfdC79XqcKyHBCgs4wkiQ6kqrHJ7x0Q+iojTZM+rWbPduVROCNIRE73iXOtblwQ5Pb7bizbMSXjc9+kViqfL/E1HR1gu37/KIDJ/d4/pLQ4fufDuJ6fa8dPX1f5p7yBOI6H7biIvPR8lsZjS4hdczst2xwzIM1a0aqnaMT7EFSiP9+k3LFYRjed2z9fDqXG14fWI/XXrgdKZdesNV0NJs9yckGCa4HOd+ubgZ0Fe+3QWhFHHa5eU+L2C7wesjS54Q3D8N+6JAeYE+0Xyv2Uxm6nue+84AkNPJB1MQ4qS4m611tJ2+QTX90XfXjmy268+uoL3UJYtkOxZDI40jQeG2Q5whEGYQIjkl4dyMO8+F/LOBJ8e6BUncF2TTmhTmPrtjj0j/DqgecXKZRqaK2Jwy7d1v6iwUe49TgSfBPgF8qmVDfGrNrPYTJHeO3Csh1T8CHPa65vrRxKp/5+4d5XQfjfu+LhcPj4bjN6+ZU9PpPrFyhVdpj79c0rt6w23hEOP3ZKzWu0NoGlUmW6n9nTaTVoT6i792qAKFikqyE6yc10WyBciU4U5J8J3wJLQKrMdRJItbkuVgP3aEiU+d6SCFuY56S31wVwJPgwJ5bNLp0e+11ze/2WnCp2hFcPeodCDRaf6PP2wg4bV869erU9S+AcL+LcXab7pVVUPcE5XcKacpFFm84XV7EXA+wTBawZj+TlFqqZIMsO8bN1/O+fJfzaOvZSAWvWQ1Ycul9cRVYdvEenUZsx6XKH9PLtVSSOBB/0aQyWZZt8WZWRxBHN+vqehzYf4YAheFXEAjSGED/Ir0vSmE6rTn1j+XD5gq91TDNN/EIDa9br589nV7roSOG9fgrhSKxZj/RSB93NUNsx2BIhzbXCzgsZrIXoVOHN+wjP5OLrTkr4zWs7TfFm4UjwYZLQL73y1ACxWJNl6VXPPTjCwUGWbUrvnKP1hTVU8+YfWH4jUFnKlQvPG8GXU52yLL0lB61fEwQUf2CGdCshevr6Ai3Ck/hvmSV+odFn5aXLXfzXTZFcbJOuhdgLgTFtPYnwJDiS4M2zxC82e0k1AOjw8Kyn713v5TVB9w+XSeKQNImOhN4thnM8wL2riHBfHVNSZSlpHPXnzKETehg/nP9QFXvq6vUqJ0ErjY4V9mKA6qagQfpGQZCBjTXlkm1GyKqL98g0OlKQaXSYYS/66CgzlYAybb47JLj1Gp+GILMpJy5rfgcNWFqQCX24i/Qf4eZBgHtHoW8uHeFgYE+7WNVrFHq2QNjSbECZCWZ0v7RqzN68lJl9qkT4xBYysHDvqdD90hrdL67Qt6sVdL+yBjK/R0PWSsk2D8+h4tck+ISGIHOwlaRj57XqUofQSlFC42c2QgtaToSjLILMppt/N4jQSqnGptKHrSWL3SItO6HhRiPXvlZgL/mU379A6/NrqE5K8EgNZ9FHJ5r4bJvuE3V0Z4wW6gi8u0t495axKg46VsQXOoRPNVD1gcwGW1D5wAKy4lD/7csjzwreNIX/YIXWH62RnO8M3efdkz+/6iCsXQdrhxmN/7yMauUajwB73sN/qIK96COkIN2IiZ5pEJ/vwMCm379uwcc5HiAcSfXHjg9F/Dpf3yJ88urVfsYPqqDyQ4uoMKP9pQ38B8p4d5cQriRdj+h+t056eXxWhL3o4z/Y6wOkGzHh0w2SC8N98O4rU3zbDO2vbhA9M1y1x5p2qXxokeRSl9bn14Z8bfaCh/9wFXvBR3rDWq7W0P3GJuGTO+apKFj495dxzxSRRRvVyYhfbhE+00R3d96lcAT+IzWc4wHOkg+OIHjjFN69O/UH0/WY5qeXd6K2g22uuTh3lkmXuzvvVMFg45NXmjh3FEFpomfrY69BA9mu5x+ipX1Ngk9qwUwUsOq3UUIz3y0S2imzUYGmHVFMXWIrM/+UGRqYjgPaVm8Bmp63nB3nrxIaJYwwvFmnsr0aIF2Js+QTPFrDnvcg06h2hlVzKL17HvdMcURgCU9S/sAC3v0VVD0hqyfIwKL4AzP4D1Zo/M4y6ZXeEYRgzXpYUy5CipGRtioOzpJvfDT9RkHpHXMEb5oivdwludhFBhbevSWEbxlhdraDjnba5D9UofSeeYQlSNcidKbx7irhP1ih89VN2l/e6C8IWbAQBRvVStGJQtiCbDNGhTuSRXWu34QUUmDPugjPwqo52PM+2XYMUuA/XMW7t0zjU8vELw2kOQrwH6xQeu88CEG6nvfhnhL+AxXaf7JO52ub/UUsixb2ko8sjKkF6QjsRR+1a5NxThaofuyYORP6xRZpqnFPFbAXfbL1mPCJOunqjnZk1RwqH17COR6QbsaoZopVdSi9dwHvgQrNTy2TbedrzBJYNQed5vNnGlQ7JRl4nqonE1N9s/WIbH1vzSy90CG9cHNSL28VrtnUjUVGZGWgwVEWicroWAlaQNdOyIRGasFU7JNIha0k2OYzqaUxaQeg0GRCkeSC8rUO/74yzT9cJfxuHZ1ppC8pvWce/+EqwaM1Ol/aiYoVvm8K/4EKna9t0v7KRp9P5d9XpvzBRUrvnmP7P168bs6UNe0SPFoludSl/msXjY9GgH+hSuVDi6RbCd3Hd9L47DmP0nvm0ZGi/jvLJCuh8QmVbCo/tEDhbTOk6xHRs0Yzis92iM91wBLUfvIEzoJP6/PrZBsDC+8AJoU165LVE7Z+5bzRYgR495SofHiJ0rvm2LrURefC1p41fVCNlManlkk3zSZt1RwqP7JE8bFZkotdkkvXScewBIU3TyF8yfYnLxoNUoOsOkx94iQI6D6+vaPFSSj94BzO8YDW59fMd6lGWAL/9VVK756n9O556r99OfetKVqfNWR7/3VVnOMB0XMt2l/aVdz2Nb7YrsmTrMEIPQABG34HqSVKaFKhSKQiFYpUmn+3tKBtx3StFD+zjaCUGaXEw9YW5SQ/sclOmIoDLC36PySGDglg789vB/TA36TPr6Od6VpE+ETdkEBzra/95Q10pPDvK/c1MhFY+K+rkm0nRuiF5noSTfhMk+RyiHM8wJ69/uKh9rSLcCXJhc6OY1pDcrFrHN7zw8/2H6ogCzadr24awZAap7aqJ7S+sA6ZJnjj1PCs0/T9QOZP39D4jUWm6Xx905j+mYZUEz3XJH6ljT3n4SztFMz0HqogizbtL6+TrkXm+kyTbcR0H99GuBLvgb3L1u8F4QisGRdVT8zz8z6qZkKyEiJLNrK4Uw3bnvNw7yyRXOrS+ebWTvAgVnS/vU1yro17V3H4XQyOZe+DG5yX32u4Jo1PSc22t+MT6dhJ39e3G7s/XwkGq6akNN2dXb3pxDQHzF8vs7izUeOVyjahvWMmFFKb460yZyt1Euv2R4hON6uEVsKV4o7aL4DTjSp1N2LTv/aqGtlWPBL9Us2UrJ5g1RzDho8U1pSDLNuoekLwSG1kMkvPOKitik165Xp6h9EgtTFJByF8CVIM0xMk2McCdKaJL46aQel6hGom2DOGCHsrKSs60aS7zTcFyeUQ774y9qxL/EobJLjHAlAa53gBqzp8Op417YI2G8J1cw4VkGhE0TJZDL3PpUD6FjrVQxq6PeshfEl8oTOquWea+EIX9+4SzqJPunxzT1r7XsKh5PEJBF5mIfRolE8fouCHm0nSMYfwaLjuCLWKxwj0nFIgHNkPLkhXImyJNeVSfNv4g5t1J7vmQ5wGkVwJSVcjvPsrJJdDkkvGx1d8u/m9nskKhrgqXWnaGo7vg4oUdsm+5ZQVnaqhgET/81xwCy8X7EIgCiYVK3ikOlaw6TAzwmk/gm/M0OtYET7XpPTOWYqPzdL91pbxId5dwjkRED3fImvsKA0iMG1T3fH0Kt3Nxm5OR9gbNy74Jr38cTUIx02W/Uyg/PuOnfJSdfv6NPW92nm1Wpv7qaeod/5xrlK/bmuix3wf/lAgLIHOdP90NJ1p0Cbi2/zD1Ylt269mNU4Y6W5G49NXqH7sGJUfXjQLXmmyRkrzMytEL7V2rs10XyAIe7ywFY5Eq7zttxDClmOdOsLJi7amA1IxNXyz+m9dJquPt2Z0PF6QjjzfkmMpOt1vbmHVHIJHavgPVUDlAvGpBu0vrA89Wyf5UQDO+M1COMIUEh0ToT3CZNyw4LO0YK5boBy72FqSyIy1oEvdjUBALfaY6Qa8Uqn3qSpTkc9U5PNKZRutwVGShU6RUuIQW8rcO9hIJTnTrGIr4098ubJ9baauNu2YDn3czEILaDgxq4U2mdDYWnKmUeVKoU3TNSa3rSV31qtcKrVoOwlCm3bPdo0/aNuL+qz9HpY6JWqRh9SwXGhfl6lrVR1wRD8ZHIxpKSvGPOxN8KyRoroZsuygGqlZjHsNQe5LE5YwyeVDPyqwpsYfei5zDaj1h6vE5zroTKPa6ahWlxlz0jnuY897O1HG3k9UHGTRMhHc9gRhfJNofMIRWBUzToOw5zxQ7LRVaZK1CHvBAwHZxtVTz3SWC3tntPFWLX+XuyHNeEQvtWj/yTokGhVmJvCy25rdiCHR2Au+Ed67Chzb8z4o4xsebdxVm/+axQ3bHAKBrSQbfpdLxRaJVJxuVPAyo3pbShJk9tCctpXET026jwBOtMqUE5eVQpttL2Q2DJADZm4qFWfLdVaDTv++a4WTWbSdhMvFFptel/lugdmuqaYhNPipvRNcyT8LUhtLCdBQiT1ONis03JiVQgc/sykmw+TQ1aDN2XIdEDj6+obWXvBwTxZ2PpCGXiGLNvH5Tt88U42E+KU29qxL8MbayMITrhGWfWRGUxOuxL1j4Pk5edg5NuZ4T0nffxg+1yRdjcg24vGmLBA+3UBnmuANU8OmlyXwH60hCzbRs41R7UQb7VI4Eqty/VkGE2EJ/NdVh4SQPe/hnimiWilJL2Fe8/+3d24/chxVHP6qe/oyMz0zO7azTnZNMI4dJxAIQYkSHoIEUoQQSjAPCOWP4w8AEW5CEXlAoCgQwEghQbk4ODb2rr272evcu7tuPNTs7Ix3bG+yTlhn+nvah5naqu6qM6fO+dUpsvfdGKrPHce7VfzrC7xaaaId3ZJghgLsMa9ZVHziJ+fGLjTaI1gsE56ukF3qoFZS1HrmPPMphkqtpciVAeHpyuR7223nbIL6OEOu7M8ym9SAtS42eX8ciPncOLTHp4RhOdmL9QxKkmYWExqfjLsf+wq1Tz0P+W+9PfL0LHBajt2bKkD6htzXTAn73R0B6+XJgHsiQ6qqdOBfxeNpTCfMWa30QEAvkNSzMS9JgBaWDH0oEbbpKmrff5D8oy56R1Kaj4geq6Fbkv4/9/RjWOj+ZQP/RETynQcIzySotRSrLX61hH8ixPQ0rVeWR5m89L028fkayffmCU5G6LbCb4aEj1RRmznByVsywNYlW6JHExoXFjFtOfIcdVuRX+46ozHsk1zu07+4TfXZYzR+cor8ox5WGYJTFaIzVfJrPQZvTalibSG72iM6X6P2wjzpu20X04x98iu9Ty8d2W0+NYSnqzQuLCKX+ojQI36sjlf1nWB8LByQX9sbQ/PlL5Ff72N6Gi9y8VS/GdD69Q3UmpuranVomM4kNH60QH7dtR9+pYooCcyU86mm6zz05PkTROcSFyIY9lPe6JP9pztKcFlp6f553YUbXlogu9RBb0v8RkB03mWXu6+vT/0xUmspuq3cc5UWtZkhSh420wzebu0XGM8QhzN8TjZGI4up5yGB8fCtR8l4HHTtB8bd8pT6arTVSX11b5MY1mWEm1k88uzKqkQ3ONidDQKIdYntsYy2Fob8M8gs51d7yNXUafS+3sBqi1wa0H1jHb0xufUyO5LWr5apPN0kPJs478wDmxn0Vk72YWfCsOdXurRfW3Vtf8NdKK1bkv6bm+iuovHSwsTnvYrvbKa0bquauOkifIFX9qk83aTz2ureCQMDvTc20Ds55W82qTzTBE9gOor+xS36/9jaJ+bdJX2vjZ8ExE/UqXz7uDv51NeotcNnKk2m6fxhlfK35qg8e9wJpduSzh8/doZ4fKpp68awkVF+co7oXIIIfVDGGfsrPfSYobSpof3qKsnzJwhOlQm+XMEODNnVLv2/bdH48eIoLuseqgtn6K7Cr5bcFhaXgxKxT/mpObIPO7R/vzIKX8jlATuv3KD63HGiR52kyWYGuZLS//smcmn6D4PpKDqvrlB9/gTx4zXw69jckF3uwtuf8jTMF4RDe3wP9hKOZTErlR6DkkQgOLfTvON3vClum7jl73sZ7qmoEo+0mmxHKWuVHsozLPRqdzTO3i29sLAvy/yZhKSEIH27RXap47KNxrqM3m1EyKaj6P5pHfHmJiJ098Ja7WJG+75jIH1nt23PlQrKtPMWfMHWz66id+NvgaD2g4cozUfOw1kfu9lLCEr4qjKGAAAEHUlEQVTzEfUXFyg/1SS71Nnbvmrr+v9+x8lehMDmxm3R7/RbJi29v27Qf2t7L+kgzT2p6CE8d4qk9ZubbgvujY17GtqSvtsm/bCDF/susTTMrE/7jl7PaP32Jl55tzindQf6Dez8YmlPpwjEX2tQe+Ek/YtbDN5pwVhiRUQ+yXcfIDqbUJqPkMt7Bk2tprR+d3MUc0UP58VdvLb8Wh95cwlR9t2JnWEBgVn29uAeGL56HtIKMzZj95ISGUzEyrQw+MbFAbWnEUBtLDaW+W5rmMiQ1HdtVFQwVcryaamoAAGsVrsoz+IZQWA8lHCTzgiLFdbFJYfzoTo2DovTJSYyGCWBA+MRaZ/eZyQIsun0RXYvPm8zs79ShrYTCYnSsZDw4Qrpuy2ncbsFKQ2mI11cy9ufmre5uWvCZX/HnATHHiBE8onYnUrafjL9oLQYecDPa7t3tnWMif/nC+LHa058/K+d/X3paeSNlOhczVU43tfY9P9xN6y02IOOY0Y49LLtBi6mJz2DsJCoEDXmSvUDd4ztdKdBO8gpa5/A7L1U6Rk24gELvYRY+YCgokoTcbJY+cSqRFUF+EbQTCOykqZfUuT+3RfJwFcIC4vdGoOSIpGB8zqHC0ILSzvMONmrEgyTMhUVTPRhvTzgbGuOM+05+r6kooKJpe4ZQU0GBMYnMB5VGdD0YqSn3Zb6Pis8siuX8efC0dYKcOPwXeEC/1jo4lGf1MDNKtZJfkTo4c8Fk1lc4QTS8fkEm+ojVcnki8jhDJ+AlWqXzFeUVYD0NUtJm3oejQxS7hkuz+1wLI0JrEcryhj4Pep55IoSCOeJpUODlHuG67U2zSxGeW5BlVVAPXeJhK04JdYlYu2M40EMXy+QXGm0mMsiIu2zEQ/QnqWi9oZ/o9qlX1JUVID0NNdrLeay2LUvnMd3ubFNM4vxrcdapUdg/FFhBd8KGlmMgFGSpp67og29QN53ygK9LckudYifaNB8+WHkygArLSLyKM1HlB5wkpX+m5uFbOKgGBj8u0X4cIXGhUXya31n/IaFBYIHY0TguRjj9uEvmS+4PeJOF2Qff+anxZT+nAgWy9R/+BDpBx16r6//v7sDDKvvfrVOeDZxx+V8F6/TO5J8qU/2QedIV0sWgaD+4gJeUmLnl8vTy3p97p2C4FSZ+IkGwckYEXtYDbankGsp2aWOy5QXTvQ9YfPiz6futQrDd1QQuFiZtUdv0ntMHn07in28Hbti7aMWzN9937vcT8/0PuJ2hu9IntWdSSxHb3HucmuRyfuJo/pMj/L7ngEKPXdBQcHMURi+goKCmaMwfAUFBTNHYfgKCgpmjjtmdQsKCgq+iBQeX0FBwcxRGL6CgoKZozB8BQUFM0dh+AoKCmaOwvAVFBTMHIXhKygomDn+B4br6p3ismhTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wordcloud = WordCloud(background_color=\"#123456\", width=700, height=500).generate(words)\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Univariate Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Brands and the number of cars under those?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cars type and their frequencies')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"make\"].value_counts().plot(kind=\"bar\")\n",
    "plt.title(\"Cars type and their frequencies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As We can see toyota has the maximum number of cars followed by  nissan**\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Find the counts of the cars on the basis of fuel?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf592cb4190>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"fuel-type\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can see mostly cars runs on gas as compared to diesel, it moght be because of the rules made the governments of diffrent country in respect to the environment as diesel cars emit harmful gases which are dangerous for the environment**\n",
    "\n",
    "***`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Count on the basis if aspiration?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf592b03fa0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"aspiration\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Through this we have seen that mostly cars have standard aspiration Internal combustion engines as compared to turbo engines***\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Count on the basis of number of doors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf592d66ac0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"num-of-doors\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can see the cars with four doors are use more as compared to two doors.**\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Count on the basis of body type?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf59418ba30>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"body-style\"].value_counts().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "**People mostly prefer sedan and hatchback as compared to other cars**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Count on the basis of drive wheels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf5941604f0>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SeVV1pO9ZtDRJpoETgH/msRflr/U71crzA9Xl9aIkzwbuYLC3cNSJSTwr5OryD8CzAeO++rymqmYAkvx1VX1woTu0yD33ZZTkrcCbGJz98f7ZNzF4W3h2L4NpyZLcBfwk8DXgBzz2HD6/18F0TEl2zbP6l4BPw/F3umbjPgZJ3lNVb+p7Dg2vewf2OMfj2/vVIsltwF0MPi85eurt64At8NgZP48Xxl1SE5I8BbgCeC3wm1X1pST3Hq/vmI27pKZ0P2L6MwanILioqs7qeaRe+IGqpKZU1SHgkiQXAt/re56+uOcuSQ16St8DSJKWn3GXpAYZd0lqkHGXpAb9H5atvTj8NUpgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"drive-wheels\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can mostly cars used to be forward wheel drive as compared to rear of four wheel drive**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Count on the basis of engine location?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf59313a1f0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"engine-location\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can most of the automobiles have engine in front**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Counts on the basis of engine type?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf5930cbc40>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"engine-type\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Count on the basis of num-of -cylinders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf593063d00>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"num-of-cylinders\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can see mostly cars have four cylinder engine as compared to others**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Counts on the basis of fuel_system?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf592ffd940>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"fuel-system\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can see mostly cars have mpfi fuel system**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Which car has the maximum price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>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>74</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>112.0</td>\n",
       "      <td>...</td>\n",
       "      <td>304</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>45400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    symboling  normalized-losses           make fuel-type aspiration  \\\n",
       "74          1                NaN  mercedes-benz       gas        std   \n",
       "\n",
       "   num-of-doors body-style drive-wheels engine-location  wheel-base  ...  \\\n",
       "74          two    hardtop          rwd           front       112.0  ...   \n",
       "\n",
       "    engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "74          304         mpfi   3.8    3.35               8.0      184.0   \n",
       "\n",
       "    peak-rpm city-mpg  highway-mpg    price  \n",
       "74    4500.0       14           16  45400.0  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[temp[\"price\"] == temp[\"price\"].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can see mercedes benz has the maximum price**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. which car has the minimum price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>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>138</th>\n",
       "      <td>2</td>\n",
       "      <td>83.0</td>\n",
       "      <td>subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>93.7</td>\n",
       "      <td>...</td>\n",
       "      <td>97</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.36</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>5118.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses    make fuel-type aspiration num-of-doors  \\\n",
       "138          2               83.0  subaru       gas        std          two   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "138  hatchback          fwd           front        93.7  ...           97   \n",
       "\n",
       "     fuel-system  bore  stroke compression-ratio horsepower  peak-rpm  \\\n",
       "138         2bbl  3.62    2.36               9.0       69.0    4900.0   \n",
       "\n",
       "    city-mpg  highway-mpg   price  \n",
       "138       31           36  5118.0  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[temp['price']==temp['price'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13. To find  the correlation between 'engine-size' and 'price'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 52347.27627505432)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= temp[[\"engine-size\", \"price\"]].corr()\n",
    "sns.regplot(x=\"engine-size\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can see the price increases with increase in engine size so it is a  positive correlational.Engine size seems like a pretty good to compare with  price since the line is almost a perfect diagonal line**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. To find the correlation between 'highway-mpg' and 'price' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 48130.624038083544)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[['highway-mpg', 'price']].corr()\n",
    "sns.regplot(x=\"highway-mpg\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As the highway-mpg goes up, the price goes down. This indicates an negative corelation between these two variables.**\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  15. To find the correlation between 'peak-rpm' and 'price' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 47414.1)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j4IggCI4IjvjlxjSTO7ZsIOq6nLuok3ee08e5izqJuq71UZm6KLVp67qa7oUxpiLWR2UaqaRAoqq/nu8Li8ga4FvAuYAHbFPVrwbzT74LrAMOAR9X1ZFgm/uA24A0cKeq/igovwJ4BH9BrR8Cd6mqikhH8B5XACeBT6jqofnuazHrl3Vz4Pg44k23O3sKFyy3Ia2m+VgflWmUWvbEpYB/rqoXAVcBnxWRi/HXf39KVTfir7x4L0Dwu5vxhxVfB/yHYElfgK8DtwMbg5+whnQbMKKqFwD/Dniwmh/g3usvYnF3FHEgrYo4sLg7yr3XX1TNtzEtwGaNG1NczQKJqh5V1ReD+6PAa8Aq4Eb8ocMEtx8L7t8IPKaqU6p6EDgAXCkiK4F+VX1GVRW/BpK9Tfha24EPiYhU6zNs3TTAl2+6jMvXLOHc/k4uX7OEL9uIrbZjs8aNmV2pfSQVEZF1wOXAc8A5qnoU/GAjIuFZeRXwbNZmh4OyZHA/vzzcZih4rZSInAGWASfy3v92/BoNa9eunde+W3OBsUSKxsyu5oFERHqB/wr8M1U9O0uFodAvdJby2bbJLVDdBmwD2Lx5s83QMvMyNBJncVc0p6yUPGI2mdW0i5oGEhGJ4geR/5K1CNYxEVkZ1EZWAmH7wGFgTdbmq4G3gvLVBcqztzksIhFgEXCqJh/GLAjlnNzLWca2EWn1LXCZRqlZH0nQV/EN4DVV/bdZv9oB3BrcvxX4QVb5zSLSISLr8TvVdwfNYKMiclXwmp/K2yZ8rZuAp4N+FGNmKLevo5w8YvVOq79z3zB3b9/LS0MjHDs7yUtDI9y9fa/145i6qOWorQ8AnwQ+KCIvBz8fAR4AflNE9gO/GTxGVV8FHgd+CTwJfFZVw3SynwEexu+A/xXwRFD+DWCZiBwAfpdgBJgxhTy0a5BkOjdDbjI9d4bcrZsGuOk9qzg+OsVrb49yfHSKm96zatar/Xqn1X/gidc4HU+iHrgiqAen40keeOK1mryfMdlq1rSlqj+jcB8GwIeKbPNF4IsFyvcAlxYonwR+p4LdBKxJoBWV83+2f3iUM/EkjiO4jpDylBOjCZLp0Tnfa/uLR1jR18HaqMtEMs32F4/wrtWLi75nOc1hlTh4Mu5nYchK2qiecvCkrQdjaq/tMrrte3s0Zx6ADe1sPeX+nyVSHuSlvEGC8lmU00zViLT6aU+ZSqWZTKaZSqVJe3O38tr8GFMNbRdIIo7knHhsidjWU+7/WdT1r9Y9T1FVvOBEG3Nnn3pUTjNVueuKlGugr4O0Bll/8W/T6pcXYxdRplrqMo+k2WTPAyh3aKeZqV5NhOX+n114Tj8HT4wxOpkikfaIuQ59nVHWL++ddbtym6nqOQepJ+biiJ/CJ+SIX16MzY8x1dJ2NZJQeOKxJWKro55Xt+X+n92xZQOxSG6G3Fhk7gy5rbD644nxBPnjFVX98mLqPSDALFxtF0gmk2kGj49xcnwqkyG12U8SraCeTYTl/p+V29xUzqiteosn0pnZu0HXDxqUF2MXUaZa2q5pS4HxRJqJZJpb3rvW0m9XST2bCCv5PyunuamcUVv1lkj5AUEz/+SWF3LHlg18YcerxBMpuoLPVeuLKBshuTC1XSAJqcJ/3/sWt75/Pe9dv5SrL1hG1HEywyfN/NR7uGuoHrNP692XUM7J1nUcVD0U/7sd1kpcp3ijQ70vohox29/UR9sFEgE6Ig6e53HoVJyT41M5v3cdIeI6RF0h6jhEXCHqOkSCclNYPa9ud+4b5vZv7yGR9sPI4ZEJXvj1KbZ9cnPNOvddgcHjY5lO+uW9sZrUtnbuG+bz2/cyOpki5XmcGJ3i89v38qU5sk6Ha+dERDJr56RVWb+seQYEhBNCT45ND3bo74pY5/4CYGfGPGlPmUqmGZtMMRJPcHx0irdOT/DmqTgHT4wzdCrO22cmOTE2xZmJJPFEikTKo90zs9RzuOs//c6LmSASSqSVf/qdF6v+XgC9MZcjpydJpRVXhFRaOXJ6ctYRUeV68Ml9jMSTKBBxHRQYiSd58Ml9s27XCmvn7B8e5cRogpSnORNC9w/PPiHUNL+2q5GAP4/AUzhvSde8tlNVkmklmS48gS0S1GAiQW0mGvFrMlHXwW2DJrN6Xd2OThVu9y9WXikRwfOUtOh0s5H65dU2eGIc8L9n4Xs5EpYXF66d08x9fdkTQsH/bJ7onBNCTfNry0AiDvTHoty+5fyqvm7K80h5+Cuo5HFEZjSTxdzppjPTvIZHJxEHNDzfqf8dOj46WfX38lQJzrcQNFGlFETmrvE2+9o5UVeYSPoXcmHzG8w9IdQ0v7YLJBFHuPjcRdz83jVcuWFp3d7XUyWRKnz1JSJBcBG/VuMIblCrcYO8UO1Qo2lWybSSXQlVIO0xo3mtGiKO+LWR8I2yyltduRNCy2UjxOqn7QLJhhW9/NtPXNbo3cgx3WQGULh5JjvYuE7uQIB2aToLbTqnh33HZjb1bDqnpybvV2wIbSKVrvrJqjvmMpXyUCVnXkgt+mPqLRyQce6iSF0GZNgIsfppu0DSqnKDzUyOSDDiLLgN+2uC2kzMdWrSpt8IS3o66YjEmUpNX7J3RIQlPZ01eb9kkSb8RFqrfrIqfNUeKemqvdmvwOs53NjSv9SXBZIFwlPFmyXQgD8YIBrJDTSuTDedRRxpiWAzNBJn40Bfzr6qas1Se4Qj8sJ+C4LagqdUfThruVftrXIFXq9+HMuhV18WSNpIyvNIFU+9BJAVVHJrNJGgvybiSMMnba5Z0s2hk2Ocncg9ga9bVpu29t6OCOOJlN/clDWSyvPgxGhi3uubzKbcq3a7As/VqAmy7artAsnYVIoX3xyhJxahO+bS0xGhJ+YSiyycpp9KpD0l7SkJig/JLNSM5jpCNO9xrVy9YSnPHTyZyXSbTE+nvKmFT1+znq8+fQDXIZNh11OIOFqT4azlXLXbFXiuRqR/aWdtF0iOnJ7g7u/9fEa56wg9MZfuWISejunbnliE7uA2U571PD8QTT8n6rZG81AlSmlGEwmazbJqNeHjsDktLJ/v8frhK0f9IbhMd0ijfvmd115YwScr7M5rL+TgiTF2/Pxtkml/Mt0N7zqXXftPcHYy1RTDWe0KPJfl0Kuvtgsk/up4M3M0pT3l7GSKs5Opil4/4khWTadAEAqDU/Cc7FpRd3Db0xFp+bklqkoqmBMxNcdz3QLNZ7MFnIMn40FtaPoYpT2vZsvK7tw3zAtvnmHdsu7M1e0Lb55hoK+TqJuo23DW2dgV+EzNPq9mIWm7QOIKvGvVIn778lVsekc/44kU8ak044kU41Np4okU44k041Mp4sHvxoLbePCc8USKeCJdMEV3qkoBKepKEID8YJP2lBNjUyTSHr2xCJeuWsQFA71+EOpw/YCU9fwwOLVCfrBSmtNguklNVTM/ofD+2FQKVwTHoWpNbMX6H1Q1WN+k9sNZ57J10wA3HT7Nwz87yHgiTU/M5dPXrLcT6QLm/w1MXxSr+hk7FM3053mqwcCQsExnlBM01YbbZV4/63Xn+jtqu0DiOMKpeIL/+NNB7vrgxoomJaY9ZTLpB53p4OMHnLEp//FEonCQmkhMB6j8NSHAnwR3eiLJ6YmZ0+Qnkwl2vnGcnW8cn3MfYxEnU8vpjhWuFWXXhgo36UWaYp5K2KS2ekk3vz45DjrdpOQpnLe0m+GzubPNwyY2Ef//3hGCx/59R/y12x1n+r5kyv3bYv0PZyaS/OsbL22K5pNWSHVfb7UaDh1etGSfxDMnahTPC76rwYmdrBN3ZtvMawW3ZO5kMjhnbxMGBiUIFnXO7TdXC0nbBRIgc/X42PNDFQUS15GgRlDZYUx7mhVw/GCUHXwe2z3E2FQKRyTzBU0GCQSX9cb850+lmCzQyZtIeSRSHiPxAnlb5qEz4tAd1naKNNl1xyL0Brc5z8sKUtUISLf/7Q38mx/+krGs3Fq9HS63/+2ZNYGwiQ0oNtdzTst7Ohg8Mcro5PTiUf2dLhcM9HPxqn7+5O9d7teCRBAHzk4mg4mEuZ81nFzo35fp+1llALveGObhnx7MOQH+nYvOmXUfm2XUlqoW7PMqdPLNPqmGJ8nwOdPb5ZZnTqz4KWvCE2v2iRyFvzlwgj/+n68TDfo+3zod5/f+4hX++W9eyNXnL889qRdYWTL/PbP318zUdoEkkfIYGomzpDvK22cnGr07gB+Qejsj9HYW/u/4L8+9yTn9HZkTDfhf9NHJFI/+wyszZWlPc5vfsprhCteagvtT00FsIpEuGJAmUx6TqQSnZs8dOKfOiFOwbyjncVAL8n+X12QXi+CpEnUdYhEPz1Mcp7b5ypb3Rnn58HQUUuDMZJol3RHGKmzCzLd78FROkDxyepJfvHWG3//Ixbzv/GWZb0D2uVoQDp4YI5FKM3h8PBPslnRHiE+lOHLa/57nnwhnOy9OBznJBMDwSniuq+Pdg6d47Pkhjp6dYGV/V93TEQE88je/xhWhI+KiCh0RFy+Z5tvPvMnla5fUdV/aQdsFEvD7MY6dneK8ZbVJqVFtK/u7ODk+lbO+9mTS49z+3OzFriP0dUbp64zmv8S8pNKev4pkEHhy+oiyglEYnDLBKytIjSfSBYfBhgHpZIUBKXvAhKgyNpni/3tyH+8/f1kJtSY/KHXF3MzQ3dk8M3gKJ3i/TNqSoLzavvLUGzk1LYCxqTRfeeoN/nzDVdODRHLO3Yp6HqfiKbJ/fSqe4pxeYWq24XVVtnvwFF99ej8RR+jvjHByfIqvPr2fu/CbkesVZI6enaA/78KsM+o0zcXjQtOWgWRGA2WTu/m9a/jq0/uZSKbpjDpMJj1SnnLze9fU5P0irsOiLodFXZUFpGTamw44UyniybwgVGAAQ3bTXlhTShZIjphdogpJVc5MJHniF2/Pax/9fqP8gJNbG4on0pn+kumsvJoJmp3R0gJSKd4+W3iMW7Hy0JkiKfSLldfKY88PEXEkc9GT3YwMzBpkqqnUiy9THW0ZSCKusKQ7RryOV2qVuHLDUu5iI489P8TbZyc4t0HNBfMVrVJASqS8TMCJJ1J87jsvFpzD4gh8cNMAY5lBDllBLJEm5c0MSOHouxPMPuU/HTbk5/non/w1ApmgM2NAw4xaUdh8lzsAorcjQme0/Oa5RMojIv5+hrUmV6j7Wh+z1QRmCzLV/i7X++Kr3bVdIIlFHNYs6WYimWagp6PRu1OyKzcsbfrAUSuxiEMsEmNxMLcuXSz+K/zeRwqvCBgmvRwLh3Un0jkBJz6VygxwyBllN5XmyEicU7MMVlAImvYquzCZaxzC13f+ym+WKxCkOiIOE3nZJdPq17jqabaaQD2bm1r14qtVtV0gAX/Gr12dtK5i19izXXuLCLGIsDQSY2lPbN7v+e2/OcTjLxwmnkjTFXW44V3v4Pp3rWT3wVM8+Yu3OTE+xaLOGJetWcSKvo4ZAxzCpr3sgQ35FaQCFaYc33vhcMn7m+k/ErjrsZdm1IayJ7+G2RryB0GUkzZotprAY88P1bW5qZ0vvuqt7QKJ5ynLejrs6sTMyyffv45Pvn9dTtnuwVP8+e43GZ9KZYZwjx5Ics+HN8353VJVplJepmYU1n72vnma7704lJMiP+IIF6/spzPqBIMbwiDl16hmC0BjU2leOXK2rM+cnTYou/YzW9qg7g6X/+vy1Tz12jFOjE+xclEXt2T9rdWzuakZRo+1i7YLJLVc2CqczBbOEfB/podPZsrDDfIv9vJPCMUuBrMmLeU8XXLfx8l+b5l+7/x9Crcl6zXDcfSStxOFnhe+30/fOM7DPzuYmZz36WvWs+WdKwqOacjOTRW+Xv5w0sxnnLFPMw9VWN7bEcnM0vXUv3Co1USubT8d5OxE0s/+6wqqcHYiybafDs55whIROqMunVE3p4Z0xXlLuHTVopKbZFSVyaTHrf95Nz0xNzOnwvMgrR4TiTS//Z7Vmb6gMGPDWFbTXdjUV4u0QcfHEvzy6NlMbQfg1HiClKd0x1zeeU4fe4+c5sDxsQI1pena0XzTBs01esxUV9sFEtcRFncXb9oI26mzT7zZs539302fpDPBY4EnapzLzn3D3Pf9VxgLrs5Pjie47/06EJEAABa6SURBVPuv8OWbLqv6hLiumFswPU1XzGWgf/bFrcI0EmlPM5PY8tNIkB2IwhnKHqRV8YLt0p4yNBLPjOaCIMiKX16J+TTJiAhdMZfVi7s5cnqcsak0ybRH1HXo7XA5f0Uft+bVpArx1K9RxRO5I+mm+4pyh3pPP6+0tEFnJpKcycvSMJXyePbgKZ49WNow6jBtUHehya95TXXff+kIyZSHG3WZSnq4jpD2lO/sftMCSQ20ZSDJbyNv9pXlWsEDT7zGybFE5qo2mVamkgkeeOK16geSqMNUKo3nTY9QchzoLmHUk58yharMsM/JPpxX3t8VzQScVHo6+NTK5WsW8fMjp3GCWmky7XFy3OOjf2tRSds7kp2lofxBKF44LDor8IQ1obGpNBNBIBoLmuUK9x/NP21Qqd46M8l1X/3pzLRBebWh/MfZc5PC57dCHrt6abtAkq9VVpZrdm8Mj81oGtGgvNouPKeffW+f4cxEKrPQVH9nhI3n9Ff9vWazYXkPB46PZ/bBb0ITzl/ew/LemSfjcORY2lNSnpdpegtrOikv/J3OuwnupaEzLO2OMp6YrpH0xFxeGjrDJ6v1gUvgiNDb4Q9nrkTY55QTcArVhrLmJE0kUowFQezomUnSnhZsAq1W2qCO7CwNBSa+zszWULu0QY3W9oHkoV2DVV8utRVUuxZW7GK7FhfhV29Yyu5Dp/zFtILUHWcn01xd5yaLe6+/iLu3780057mOsLgjyr3XFx6CHI4c880+LDcMMOms4OJlBZnwd2GT29GzEyzpibG0JzeNTqvO5J4rbdBcdg+e4sEf7WNsMknaA9eBzliEv//etaxd3j1jouyMDOB5k2SnCszHmUp5TFUpbVB3Vi2ot2jaoNyAlB3EumJuQwNS2weS/cOjnIknq7pcai1U88Tf6rWwZwZPsaI3lrcOSIRnBk9xZx33Y+umAb5802U1yf7rOIKDEC1xGsj6ZT0Mj07SFXUzCQnjSY93LO4iFnEyAandiCM4KBKsa7NueU9ZfSSpIEtD9oi5GU12OXOT8jI1TPm57QplaahWHruuqJszMbanSNNcT7FaU/C8crI0tH0gSaS8miyXWk3VPvE3S6bYcg2NxFne28GKvumOdVVtyLKyzbJ40j/+jfP5/Pa9vHV6kpTnEXH84Pqv/u4lmVUSVadrNzm3aX8YbirtN7ktBI89P0RvR4QVWU2Mlcyij7gO/V0O/VVMG5SfCijMV5c9cGE8a2h4dhArFJAmkn7f0sk5sjTMJZM2KKs2NFfNsO0DSdQVJpI0xXKpxVT7xF+L9b2XdUc4GZ85THRZd/W/Yo1YVrYVBmQoQDiCUAqMJhch4gqRWWo5qtMBJpn2gtvpfp1w4EC52j1pYzXTBmUCToHM3vkZwGcu2uffzpo2aKz0gFSzQCIi3wQ+Cgyr6qVB2VLgu8A64BDwcVUdCX53H3Ab/qoRd6rqj4LyK4BHgC7gh8Bdqqoi0gF8C7gCOAl8QlUPzXc/sztuPfWH/y7qqn/H7WyqfeKvyYm4SHW4FsOiq7WsbKnBYee+YT6/fS+jkylSnseJ0Sk+v30vX6rB0OZyPbRrkPhUiskgTUoyrTjM/2JDRIi6fpNaZ5F2tbCvJuVl1WRKqNXUc27HQk/aGKYNqvTaKROQgoCTn6cunjWK7muzvE4taySPAH+Kf7IP3Qs8paoPiMi9weN7RORi4GbgEuAdwE9E5EJVTQNfB24HnsUPJNcBT+AHnRFVvUBEbgYeBD4x351slo7b2VT7xF+L9b0LzR8AGC9SXomtmwa4Hyrqm5hPc+GDT+5jJJ701493HVRhJJ7kwSf3NU0gefHNk0zlVQjjSY+X3jxZ9fdyHCHmCDEKD38NazWptJL0PNLB7eMvDBF1hc6IJW1sFqUGpKjrNCaQqOouEVmXV3wjsDW4/yiwE7gnKH9MVaeAgyJyALhSRA4B/ar6DICIfAv4GH4guRH4o+C1tgN/KiKi8xw32Swdt7O5Y8sG7t6+lyOnJzKjg3o7IvzBb11c1utV40Scr1AVebbySlXaNzGf5sLBE+OA38QTDvV1JCxvDvlBJFTldbdKkl2r6coanTY8OpVTs1b1R2cNj07S2xnJzLkpZ/hzPkvaWD27B0/x3T1DRFes+1vFnlPvPpJzVPUogKoeFZHwL3YVfo0jdDgoSwb388vDbYaC10qJyBlgGXAi/01F5Hb8Wg1r167N+V0zddzORsCfca0Kmp+4ZP6q3klc7O++SQcKDY3EcQUGj49lLiCW98YK/r97qgRjMoK1SCClINKkH65J5desRWAykea8ZT0M9OVmJAibygr105Q6As2SNlYuHEY9PpVCHLdox06zdLYXOi8WmjQcls+2zcxC1W3ANoDNmzfnPKcRHbfz9dCuQfq7opy7aLp9t+lGWRWLbM0zZiFHX0eE/cNjuFnDvo+cnmTjQO+M50Yc8WsjkPMNizTRRLLZ8o81izu2bODz2/dyZGQiZ2RZoZp1xHWIlNBPk/Zym8+qWasx0/nkgjkqRQ9ovQPJMRFZGdRGVgLDQflhILvxcjXwVlC+ukB59jaHRSQCLALmvfbpfL7cjVKLUVbVVmg44mzllfraT97g4Z8dZDyRpifm8ulr1nPntReWvH3mJJNZLTOvPEt3zGUy6eX8FQnQU+e1PmbjBItaFSpvJpPJNIl0MKtfPSbLXFwu7KeB3OazUJhFIOV5JFN+kEmmPQsy8xTmk5tr0Ey9k8XsAG4N7t8K/CCr/GYR6RCR9cBGYHfQDDYqIleJ/0k+lbdN+Fo3AU/Pt38kNNewyUYLF+LK1my1pnr62k/e4KtPH2AimSbi+Mfiq08f4Gs/eaPk1xhLpFm1uJOIK6RVibjCqsWdBQcHDPR1ZrIlQ5g1mZzm0IYr9ofeRMlEH3jiNSaSHlHHoTPiEHX8xbgeeOK1qr+Xn0XAoTsWYVF3lOW9Haxc1MWapd2sX97D2qXdvGNxFyv6OljSHaO3M0Jn1CXiWP6sctRy+O938DvWl4vIYeAPgQeAx0XkNuBN4HcAVPVVEXkc+CWQAj4bjNgC+AzTw3+fCH4AvgF8O+iYP4U/6mveHto1yKKuKCubuNmoFqOsWtnDPzsIqqQzfRX+if3hnx0suVYSNmluWDHdlBVPpGa01YN/des4ghtkelb105KoatPML+mJuYwn/Nxj4YAAkeaqNR08GWRLdqYn/6qnHDxZ/5r1bE1n2TnRkp5HMhUMbQ4mb7ZThoA1i7v49ak4MsdnruWorVuK/OpDRZ7/ReCLBcr3AJcWKJ8kCESVaIVmo1qMsmplY3mrC4b10LFiQ5cKmE9wHkukWdId4eR4MjPXaFlPlBNjU00zv+RDm1bw/ZePZh6H6fA/tGlFXfdjIcjOiTZbs1l25381J202k9u3nO93tidSMEuXW7N0tjdMK3S2Q/Ok4lgo5hOc+zoiHD09Mb2Yl8Kp8SQR1yEVtPcrkPY8kuOJhswveftsgg4XspeN73D98maxYXkP+4fHEJ3OIuEpbFzR0+hdm5dSkm963nTnfzI9XZNJpL1gGHlrBJorNyzlng9v4rt7hjjkpYumS277QFLvZqNym0Iq7Vw2M5UanEcnkzkd2YrfsZ0O8rFl+k/ULz9Qg9T5c/nFkdM5QQT8oPKLI6frvi/F3HPdJu767kucnUhlhmT2d0W457pNjd61qnMcocMp3qyYCgJKMqjBpNIeiXRz1mau3LCUD2xczl987tArxZ7T9oGkns1G5abaCDuXHSGncxloy2ASdR2SKY/sRBxOUF4Lx85Ozf4EybpVSDXgRBBPFk5LUqy8UaKuQ0fUyUysrdX/WbML+2gKNZ2Fec7CPplw9Fkzjzhr+0AC9Ws2KjfVxsM/OxgEEf+PzhFIed68OpcXkg3Le3jj2Cii05ONRPzyWphrdn7+33Uj8n0W6wBupo7heg9sadVavD+3afbaTDhZM9Uk82cskNRRuak2xhP+MNdsjtQmj1UruP7Sc3n92GjOFBANymthtj9LJ+v9Jfi5YMXMSY2mvgNbvvaTN/jKU/szgzLOTqb4ylP7gdavxc81WTM7wIQjzpI1bjazQFJH5aba6In5fTfZk8s8ba6hnfX0zOAp+jsjnJ2cztjc36D8aEt7YzkrJPZ2RIqukNju6jmw5T/uGpyxOqenfnmrB5K5RF1nRp6zUPbyANnDmZOekq5gPZr2bKBskDClhr+C3fSV7lypNj59zXo8JVjn28us9/3pa9bXdoeb1BvHzmaCCIQZm1PsP3a27vvy5Zsu4/I1Szi3v5PL1yzhyw1KLV9smdVmWg/8ji0bSKaVeCLlr+AYLNBUi4EtxbJRFytvF64jdEb9RasWdUVZ1tvBQH8nqxZ3sXaZP1lzzdJuVi7qYnlfB4u7Y/R2ROjIbxLJYzWSOuqOuUylvEwQCdv256pZhFdQrdjeWwtjU+mCV5uj+cOW6qAW/WvljOxb2Rfj8JmZgwJW9sWqum+VsPlQza9Y5ua5WCCpowvP6efgibEZKevXL5+7Tf3Oay9s28CRr9gyyLVaHtkBCr1yLarz5Y7sOzpaeL5IsfJGsflQC5M1bdXRHVs2EIu4nLuok3ee08e5izqJRdy2TXVSrnpnrI9GnBlTeiUor7ZwZJ/id6oq0yP7ZtMKo7bqqSta+P+mWLmpjB3VOtq6aYD7b7iEgb5OzkwkGejr5P4bLrErtHmKFRlfW6y8UuuXdeM6QofrJxvscB1cR1i/rPqdxIMnxv18VCIIgiPSdItotYKejggOuYk2naDcVJ8d1TpbqFX7eq6HMdBbuD9goLc2/QH3Xn8Rd2/fmzM6a3FH1EZnNbGNA30ccsc4OzHdjNzfFWHdMhuaXQtWIzFVsWpx4ZTqxcor4jis6I1mhkM7Ait6o0iNUoBv3TTAp646j5jr4CnEXIdPXXVeTS4I1i/r9tfqCCaWeZ7iKTWp/Sxkd2zZQNTNbUaOutaMXCsWSExVfHzzmnmVZ9u5b5hbtj3LNQ8+zS3bnmXnvuFZn79mSTcR16Er6hJ1ha6oS8R1SpqPMN/3CrfZ/uIRVvR1cNG5fazo62D7i0dK2na+7r3+IhZ3RxHHT1UvDizuttrPfFkzcn1Z05apimcGT3Fuf8eMEWlzTRLcuW+YL+x4lagrLO6KMjw6yRd2vMr9UPSP/uoNS9l96FTQlwCJtMfwaIJb3jv7+tzlvBf4w1WjrmQm0nXHIjVL7bF10wBfvukyGyJbBQu1GbkZWSAxVTE0Emd5b0fOqoGqOmf6i3JO0s8MnmJFb2zeQavcgFDvNWvsBGhajQUSUxXlpr8o5yRdbtAqNyC0wpo1MVdIFFi0vVYj2YzJZn0kpirKTX9Rznr05a5hX+529UztUa7P/Z0L5lVuTDVZIDFVUW7nZjkn6XJP7OVu1wodt3deeyG/e+1G+jsjuI7Q3xnhd6/daNkQTF1IMy6SUkubN2/WPXv2NHo3TJYwt9R8OpfL2aaS7YxpdyLygqpuLvg7CyTGGGPmMlsgsaYtY4wxFbFAYowxpiIWSIwxxlTEAokxxpiKWCAxxhhTEQskxhhjKmKBxBhjTEUskBhjjKmIBRJjjDEVsUBijDGmIhZIjDHGVMQCiTHGmIpYIDHGGFMRCyTGGGMqYoHEGGNMRSyQGGOMqUjLBxIRuU5EXheRAyJyb6P3xxhj2k1LBxIRcYF/D1wPXAzcIiIXN3avjDGmvbR0IAGuBA6o6qCqJoDHgBsbvE/GGNNWIo3egQqtAoayHh8G3pf/JBG5Hbg9eDgmIq/XYd9qZTlwotE70UTseMxkxySXHY9c5R6P84r9otUDiRQo0xkFqtuAbbXfndoTkT2qurnR+9Es7HjMZMcklx2PXLU4Hq3etHUYWJP1eDXwVoP2xRhj2lKrB5LngY0isl5EYsDNwI4G75MxxrSVlm7aUtWUiHwO+BHgAt9U1VcbvFu1tiCa6KrIjsdMdkxy2fHIVfXjIaozuhSMMcaYkrV605YxxpgGs0BijDGmIhZImoSIuCLykoj8ZfD4j0TkiIi8HPx8JOu59wUpYV4XkQ9nlV8hIq8Ev/uaiBQaHt30RORQ8DleFpE9QdlSEfmxiOwPbpdkPX9BHw8oekza+TuyWES2i8g+EXlNRK5u5+9IkeNRv++HqtpPE/wAvwv8OfCXweM/Au4u8LyLgb1AB7Ae+BXgBr/bDVyNP7/mCeD6Rn+uMo/FIWB5XtkfA/cG9+8FHmyX4zHLMWnn78ijwKeD+zFgcTt/R4ocj7p9P6xG0gREZDXwW8DDJTz9RuAxVZ1S1YPAAeBKEVkJ9KvqM+p/I74FfKxmO11/N+L/sRDcfiyrvB2Px2wW9DERkX5gC/ANAFVNqOpp2vQ7MsvxKKbqx8MCSXP4CvAvAC+v/HMi8nMR+WZWNb1QWphVwc/hAuWtSIH/KSIvBOltAM5R1aMAwe1AUN4OxwMKHxNoz+/IBuA48J+D5uCHRaSH9v2OFDseUKfvhwWSBhORjwLDqvpC3q++DpwPvBs4Cvz/4SYFXkZnKW9FH1DV9+Bndf6siGyZ5bntcDyg8DFp1+9IBHgP8HVVvRwYx2/KKqZdj0fdvh8WSBrvA8ANInIIP3vxB0Xkz1T1mKqmVdUD/hN+pmMonhbmcHA/v7zlqOpbwe0w8H38z34sqHoT3A4HT1/wxwMKH5M2/o4cBg6r6nPB4+34J9J2/Y4UPB71/H5YIGkwVb1PVVer6jr8FC9Pq+o/CP8gAv8n8Ivg/g7gZhHpEJH1wEZgd1CVHxWRq4KRFp8CflC/T1IdItIjIn3hfeD/wP/sO4Bbg6fdyvRnW9DHA4ofk3b9jqjq28CQiLwzKPoQ8Eva9DtS7HjU9fvR6NEG9pMzmmIr06O2vg28Avw8+I9fmfW8f4k/0uJ1skZVAJuDL8uvgD8lyFzQSj/47b17g59XgX8ZlC8DngL2B7dL2+F4zHFM2vI7EnyOdwN7gs/+F8CSNv+OFDoedft+WIoUY4wxFbGmLWOMMRWxQGKMMaYiFkiMMcZUxAKJMcaYilggMcYYUxELJMY0gIg8IiI3NXo/jKkGCyTGtAARcRu9D8YUY4HEmHkQkXXBmg+PBsnwtotId7COw18FSRV/lJWq4x+JyPMisldE/quIdBd4zX8d1FCcvPKtIvK/ROTPgVeKvXfw3EMi8v+KyDMiskdE3hPsx69E5B/X5eCYtmWBxJj5eyewTVXfBZwFPgv8CXCTql4BfBP4YvDc/6aq71XVy4DXgNuyX0hE/hg/S+3/o35OpHxX4s9kv7jIe/+TrOcOqerVwE+BR4CbgKuA+yv8vMbMygKJMfM3pKp/Hdz/M+DDwKXAj0XkZeD3mU5+d6mI/FREXgH+PnBJ1uv8AbBYVe/Q4ikmdqu/ZkSx974m63c7gttXgOdUdVRVjwOTIrK4jM9pTEkijd4BY1pQ/kl/FHg1qA3kewT4mKruFZH/Gz+fWuh54AoRWaqqp0TkfcBDwe++gF/jGJ/jvbMfTwW3Xtb98LH9rZuasRqJMfO3VkTCoHEL8CywIiwTkaiIhDWPPuCoiETxayTZngQeAP6HiPSp6nOq+u7gZweF5b/3z6r1oYwplwUSY+bvNeBWEfk5sJSgfwR4UET2Ai8D7w+e+wfAc8CPgX35L6Sq38NfK2KHiHSV8d5fr/CzGFMxy/5rzDyIyDr8VP+XttN7GzMbq5EYY4ypiNVIjDHGVMRqJMYYYypigcQYY0xFLJAYY4ypiAUSY4wxFbFAYowxpiL/G2aiHLXPYZsmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[['peak-rpm','price']].corr()\n",
    "sns.regplot(x=\"peak-rpm\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "**Peak rpm does not seem like a good comparison with the price at all since the line is close to horizontal. Also, the data points are very scattered and far from the line, showing lots of variability. Therefore we can't rely on peak rpm.**\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16. Calculate the mean price on the basis if drive wheels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drive-wheels\n",
       "4wd    10570.569928\n",
       "fwd     9310.818823\n",
       "rwd    19671.422755\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.groupby(\"drive-wheels\")[\"price\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Through this we found out that average price of front wheel drive comes out to be less than the other two**\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17: Calculate the mean price on the basis of drive wheels and body styles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4wd</th>\n",
       "      <th>hatchback</th>\n",
       "      <td>10405.064677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedan</th>\n",
       "      <td>12647.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">fwd</th>\n",
       "      <th>convertible</th>\n",
       "      <td>11595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardtop</th>\n",
       "      <td>8249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hatchback</th>\n",
       "      <td>8396.387755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedan</th>\n",
       "      <td>9930.934363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">rwd</th>\n",
       "      <th>convertible</th>\n",
       "      <td>23949.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardtop</th>\n",
       "      <td>24202.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hatchback</th>\n",
       "      <td>14278.269966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedan</th>\n",
       "      <td>21711.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 price\n",
       "drive-wheels body-style               \n",
       "4wd          hatchback    10405.064677\n",
       "             sedan        12647.333333\n",
       "             wagon         9095.750000\n",
       "fwd          convertible  11595.000000\n",
       "             hardtop       8249.000000\n",
       "             hatchback     8396.387755\n",
       "             sedan         9930.934363\n",
       "             wagon         9997.333333\n",
       "rwd          convertible  23949.600000\n",
       "             hardtop      24202.714286\n",
       "             hatchback    14278.269966\n",
       "             sedan        21711.833333\n",
       "             wagon        16994.222222"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.groupby([\"drive-wheels\",\"body-style\"]).agg({\"price\":np.mean})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Through this we found that mean price of the cars on the basis of drive-wheels and body-style.**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18.Average Price on the basis of features of engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-location</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">front</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">dohc</th>\n",
       "      <th>four</th>\n",
       "      <td>14266.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>21966.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dohcv</th>\n",
       "      <th>eight</th>\n",
       "      <td>13207.129353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">l</th>\n",
       "      <th>four</th>\n",
       "      <td>15489.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>5151.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ohc</th>\n",
       "      <th>five</th>\n",
       "      <td>21207.557214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>9888.771221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>28295.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ohcf</th>\n",
       "      <th>four</th>\n",
       "      <td>8541.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ohcv</th>\n",
       "      <th>eight</th>\n",
       "      <td>38900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>16834.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>twelve</th>\n",
       "      <td>36000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rotor</th>\n",
       "      <th>two</th>\n",
       "      <td>13020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rear</th>\n",
       "      <th>ohcf</th>\n",
       "      <th>six</th>\n",
       "      <td>34528.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     price\n",
       "engine-location engine-type num-of-cylinders              \n",
       "front           dohc        four              14266.833333\n",
       "                            six               21966.000000\n",
       "                dohcv       eight             13207.129353\n",
       "                l           four              15489.090909\n",
       "                            three              5151.000000\n",
       "                ohc         five              21207.557214\n",
       "                            four               9888.771221\n",
       "                            six               28295.000000\n",
       "                ohcf        four               8541.250000\n",
       "                ohcv        eight             38900.000000\n",
       "                            six               16834.875000\n",
       "                            twelve            36000.000000\n",
       "                rotor       two               13020.000000\n",
       "rear            ohcf        six               34528.000000"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[[\"engine-location\",\"engine-type\",\"num-of-cylinders\",\"price\"]].groupby([\"engine-location\",\"engine-type\",\"num-of-cylinders\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "+ **Through this we have seen that the average price was maximum when engine was loacted in the front and type was ohcv and there were 8 number of cylinders.**\n",
    "\n",
    "+ **We can even see through this that there is only one type of car which have rear engine which ahs 6 num of cylinders and engine type is ohcf**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19. Find the cars with the maximum curb weight and there price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>make</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>jaguar</td>\n",
       "      <td>4066</td>\n",
       "      <td>32250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>jaguar</td>\n",
       "      <td>4066</td>\n",
       "      <td>35550.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      make  curb-weight    price\n",
       "47  jaguar         4066  32250.0\n",
       "48  jaguar         4066  35550.0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp[temp[\"curb-weight\"] == temp[\"curb-weight\"].max()].loc[[47,48],[\"make\",\"curb-weight\",\"price\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**From this we found out that the two of the jaguar cars have the maximum weight** \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20. Find the correlation between the horsepower and price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 50794.88097870049)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= temp[[\"horsepower\", \"price\"]].corr()\n",
    "sns.regplot(x=\"horsepower\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**As we can see the price increases with increase in engine size so it is a  positive correlational. Horsepower seems like a pretty good to compare with  price since the line is almost a perfect diagonal line**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 21.To find the corelation of lenght,width and height with the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 47857.894015106795)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LIhxOpPnRmz082Rml6+xobjzkt9hxcTPtWyNsOa9xwayPgM+iPuSnLjS5VExXSJ4FPgpk7awa4IfAL8/6ChVFUaZgJr6P6dTrmk2IcKlzQIwxdJ4cpKOzm+feOVNg1Wxsrqd9WxsfuaSV+ikW73KRLx6lLtoYNsbkNuuMMSMisng6xiiKUpGU0vcx0xDhZCYbgVWaEN7BeJofvnmajn1RjvWNCWFNwMf1m1vYuS3CRa31C2J9BP0WdcGZiUc+0xWSURF5vzHmVQARuRKIz/jdFEVRZkApfR/T3SZLpG0GYmliqbmH8DrG8PrxATr2Rfnp4bOk7TFBuri1gfZtEa7f3FwglPNF0O9aHrXB2YlHPtO9+t8D/klETnk/R4BPzumdFUVRpqCUvo+ptslKGYHVN5riGa9V7cmBsXvuuqCPj17Syi1b29jU2jDn95kppRSPfKYlJMaYl0VkM3AxbvXhg8aY9BSHKYqizJlS9Soptk0WS2VoawpzvC82Zwe6YwyvvNdPx74oP3u3t6Ao45bzGmnfGuHDFzdTE/DN6X1mSijgoy7ooy7kn1Op+MmYVEhE5HpjzC4R+TfjntokIhhj/rksV6UoilJi8rfJwn6L0ZRNMuPwb9+3Zk4icnYkydP7XeujeyiRG28I+/nYpa20b42wflVdKaYwbUIBH/VBP7UhX9nEI5+pLJIPA7uA/6vIcwZQIVEUpWyUshDjjs0tfMl2+Nvnujg5EKOtsYY7rlrL1RtWzPhctmN46UgfHZ1R9nT1FrSqvXxNEzu3RfjQpuaSbh9NxXyLRz4yVTSCiFjA7caY78/PJZWX7du3m7179y70ZSiKMgW7D/bw+cfeYDiRIeM4+C2LhrCfP7398hmLSTzlRmDN1YF+eijBU53dPLW/mzMjydx4U02AG7e0csvWCOevmL+A1nDA3bKqC/rwl1k8ROQVY8z2Ys9N6SMxxjgi8jvAohASRVGqgweePkh/LI3PEvw+C2OgP5bmgacPTktIjDGMegKSnIMDPWM7vNjlWh8vH+krKDL4/vOXsXNbhF++cNW8WR/zKR7TZbpRWz8Skc8BjwK59EtjTF9ZrkpRFpBK7GuxFOk6O4ol5CraioARU5ABXoxsBvpgPE3Gmb3v49RAnCc7ozx94DR9o6nc+PLaADd5zaJWL6uZ9flnQiWKRz7TFZJ/j+sT+e1x4+UpZKMoC0Sl9bVQpo/tGAbjaYbi6VlnoKdth58dPkvHviivHBtrVSvAVeuW077tPK7dsGJeFvNKF498piskl+KKyAdxBeUF3MZWirKoqJS+FgqsX1nLoZ4R0raNwV3MLYFNLYURUMmMzVA8M6cuhMf6YjzptaodiI9lNqyqD3LLZRFu2tpGW2N4DrOZHtUkHvlMV0geBoaAr3s/3+mNfaIcF6UoC8VC97VQxrhla4S/+PGhgjHHuOPgllsfSqRn3QMklXF4/tAZntgXZd+Jwdy4JXDNhpW0b41w9fryt6qtCfqoDfqpD/nnpS1uOZiukFxsjLk87+efiMgb5bggRVlIFqqvxXRZSv6bF7v6aG0MMZzIkLIdgj6L+pCPFw6d5ePvn33ux9HeUZ7YF+VHb55mODEWxdXaGOKWrRFu2tJGc0P5WtWKCOGA5Vke1Sse+UxXSF4TkWuMMXsAROSXgJ+V77IUZWGY774WM2F8OOzZ4SSff+yNWYXDVgPH+2Osqg/R3BDGGIPtGDKO4Xj/zLPQE2mb595xrY8Dp4Zy4z5L+MCFK2nfFuHKC5aXrVWtiFAb9Hlfi0M88pmukPwS8Bsicsz7+XzgLRHpBIwxZltZrk5R5pn56GsxW+YaDlttrF1ey+mhOCG/D9sYMK4gtDVOP1LqcM8IHfui/PjgaUaTY1tgq5fVcMvWNm7c0saKuvK0qrWy4hHyUxvwYS0y8chnukJyU1mvQlEqiFLVdio1sw2HrUbStsMdV63l/qcPkrYN4YBFIu2QcQx3XLV20mNjqQy7Dp6hozPK293DufGAT/jgxlW0b4twxdplZbE+/JZFbchHXdBPOGBVVDvccjLdoo3vlftCFEVRkhmbwViakWSGrWua+Oz1m3jk5eN0D8UnLWlijOHt08N07Ovm2YOnC1rVnr+ilvatbdxwaRtNtaVvVRvwuf6O2qCP8DwXZKwUylYEX0TWAt8B2gAHeMgY8zURWYGb2LgOOAp8whjT7x3zRdze8DZwrzHmGW/8SuDbuJ0ZnwQ+a4wxIhLy3uNKoBf4pDHmaLnmpCgLyfqVtRw+M4o4xrVGjBvFtHFVZQQCzIWJeoBcvWHFpLWwRpIZnn3rNB37ujl8Jtd7j6Df4sMXNbNza4TLVpe+VW25yrFXK+XsppIB/pMx5lURaQBeEZEfAb8JPGuMuV9E7gPuA74gIpcCdwBbgPOAH4vIRcYYG/gb4G5gD66Q3AQ8hSs6/caYjSJyB/AA2idFmQcWInrqvpsv4XOPvcFIMoPtGHyWsCwU4L6bLynr+5YLYwwjyQxDicyMSpgYYzhwaoiOzii73z5DMq9V7YZVddyyNcLHLm2hIVxa6yMccLesFqIoYqVTNiExxkSBqPd4WETeAlYDtwE7vJc9DOwGvuCNP2KMSQJHROQwcLWIHAUajTEvAojId4BfxRWS24CveOd6DPifIiKmFH0xFWUCFir7fcfmFv7s9ssrMhBgJtiOYTiRZiiemVEJk6F4mh+95baqPdo7ltcT9lv8yuYW2rdGuCTSUFLrI5sgWM05HvPBvPR3FJF1wPuAXwCtnshgjImKSPa/YDWuxZHlhDeW9h6PH88ec9w7V0ZEBoGVwNlx7383rkXD+eefX6ppKUuUhcx+r9RAgOkwmx7oxhj2nRikozPKc++cKWhVu7Glnp3bInxkcwt1odItZVnLoy5UXdnlC0nZhURE6oH/DfyeMWZokruFYk+YScYnO6ZwwJiHgIfALSM/1TUrymRo9vvMmE0L24FYimcOnOafXz1ZUK496Le48dJW2rdFuKiErWqz2eXVVpqkUiirkIhIAFdE/iGvm+JpEYl41kgE6PHGTwD5cX1rgFPe+Joi4/nHnBARP9AEaEVipaxUevZ7JZD1fwzE0tNOHnSM4fVjAzyxL8pPD58l4xTe8/kEagMWH7hw1axF5KWuPh55+TjRoTirl9XwHz64nhsvi+i21RwpZ9SWAH8HvGWM+fO8px4HPgXc733/Qd74P4rIn+M62zcBLxljbBEZFpFrcLfGfgP4q3HnehG4Hdil/hGl3FRy9vtCY4xhOJlhcBIByV/MI401tG9t4/Rwko7OKNHBsVa1lheZ5rPAsgRjYDiR4aEXumbV1fClI318/SeHCPksVtUFGYynuf/pt6kN+qt2u7BSKKdF8gHg14FOEXndG/tDXAH5vojcBRwDfg3AGHNARL4PvIkb8fUZL2IL4NOMhf8+5X2BK1Tf9RzzfbhRX4pSVsqV/V6qSLCFiCibrgP9pa4+vrbrED5xLYy3Tw/x+omBgtdsXd1I+9YIf/bDtzGA7UDGMbnqv8dnsIWYLU1SF/Lzr6+dpCbgy/NtWVrZuURM2Wp3saGtdpVKpFRtZfMjyvKtpa/euqUsi2XadhjwEgins5b8zj+8xtG+EWKpQrGxBP7N+1fTvjXCBSvdMvE3/MVzZIpokt+CH/7+hyd8DytPPGqDvlwU1wcf2MWymkBBVJcxbg+TF75w/XSmu6SZU6tdRVHKT6nqaM1XRFkq4zAQTzGSmLoHuu0YfnGklyf2RXmze6joaxwD289fkRMRcAsqjveTZMfHY4nkSpPki0c+6tsqHyokilIBlKqOVrkjyhLpbAjv1ALSPZTgqc4oT+7vpnckNeXrx/s+agI+UplMQRimALVeGRKf5QpmXchHTaC4eORzz3Ub+Pxjb3CyP15g9X2p/dIpr02ZHBUSRalwZuLzKMdddzYCazCeJlVsrymPjO3w83d76eiMsvdof0Gc/pUXLOfN6OA521pZxvs+1q2s50T/KKMpm7Tt5GpaXbCijram8LTE45y5eBcjIiBFcgWUWaFCoigVwER1tNrqAzmfh0/gtWP93PWdl9nUXM99N19yjqCUMqLMGMNQIsNQfOoQ3pP9cTo6ozxzoJv+2Fir2pV1QW66rI1btrYRaarhDx594xzn+kTccdVavrbrEM0hv2ud2A4Z2/C7128sEMrp8uDzXTTVBIg0jZWhV2d7aVAhUZRZUOrIqInqaDXUBEnZDrZjiA4mEQGfCEf7YkXLspQioiwbgTUYT2MX8VFkSWUcfnr4LB2dUV47NiYOlsDV61fQvjXCNRtWFvg07rhqLftPDZzjRBdg7bKavHMI11/awrLaAN/++VFODsTnHB230Imki7m7pQqJosyQctTamqiO1n/+wX6W1QQ4cnY0Z6mkbYOxDT3DCe5/6q1z3nO2ZVRSGYfB+NQRWMd6YznrYyjP2d7SEOLmy9q4+bI2WhrDRY+9esMKfv2XLuDhF98jX0tqAxb/8cMX0hAOFPg8wt73UmxBLaSzfaHqs80XKiSKMkPKFRlVTADWPu8ufinbQfBEBPeu33EMh86MsPtgz5zeN55yHejjS7jnk0zbPHfoLB37TtF5cizyyhK49sKVtG+NcNW6FdPKEL+4rZGagEUs7eRqIIlAS2O4oFd6qRffhUwkffD5LtK2Te/IWP/5xhr/otlWUyFRlDyms/2Qv0UynEhzZjhJMmNzoj8+50V9PNnFzydCIm8/yDGQdgwBSwoWo+lun0zXgd51ZoSOzm5+9OZpRvIitVbUBgj5faQdh9GEjYVMu8zIN154l0TGIWBJLmM9kTHnWFelXnwXso3yoZ5hBmNpLEtyYc1nh1Ok7eGpD64CVEgUxWO6d8DZLRLbMZwaSCBe2K4IJd+uyC5+Dzx9kLfy2sYK7jaX7RgO9QxP+/qnk4EeT9vsPthDR2eUN6Nj7+m3hA9sXMXG5jo6OqP4fUJ9OEDvaJKv7TrEZ9k0YemSUMBHvdfL48RAAp8l+Cy3OKIIGMdwpLfQV1GOxXehqienMg6MC+92xEwZBVctqJAoisd0t6yyVkLPUAIwYNw9/NaGMH6flHy7Irv4Xfrlp4ml7NxWUMCyMIwtRpNd/y9vXDWl/+PQ6WGe6Izy7Fs9xFJjlXrXLK/hlsvauPGyNpbXBvmDR98gY7v+lFxYbtDHIy8fLxCSbC+P2VbUTWUcHAy2bTDGnbMIVbn4BnxCPO1uR2Z9XQBB3+IoFqlCoige043qyVoJ9/z9KxjcRWJVfZjGmgDGmLJFAdUELNK2g4XkFiPD2GJU7PpDPoujvaMTXtNoMsMuz/p45/RYq9qAT/jQpmZ2botw+ZqmgnyNo70jjCQzCIIlQsY2DMTT2M4INcGseEzcCGrDqjoO9YwgZlyoc0OQOx/ak9uWS9sO+VHHxuDqdhWWdbqotZEjZ0cYToxt0zWEA6xfVb/Ql1YSVEgUxSO7ZZWxDWdHkqRsB58lrFtxblTPjs0tvP/85fMaBTTVYpS9/pqAD8fb9oqlMrQ2FEZQGWM42D1Mx74ou97uIZEeW60vWFlL+9YIH7u0laaa4q1qs82lLGtsm8Z2DBnHFORoTMTNl7Xxzq7DBS1yawMWw8kMrx3rJ+M4nB1Oksw4Oesra5G471d9d/FZK7atyb8oK0ZrBxdF8bjnug0MxtOcHIiT9qKkMrahdzTF7oM9RV+ftt3F2hj3ezkXh3uu20DQ76OtKczFrQ20NYUJ+n259/utD60nkXYYSqRJ2zaxVIaMY7jjKrfNz0giw7++dpLf+u4rfOYfX+PJ/d0k0g4hv8WNW1r5+h1X8K1Pbef2K9dMKCLgNpcaswxcURGEkFe6ZDJ2H+zhu3veG9vb8UhkHEYSbjkUv88ie2aDu4UX8lsELAufJVW5HbRjcwtfvXULLQ1hBuNpWhrCZSukuRCoRaIoHjs2t9BcH2IkkcE2hqDPorkhhM8q7veY7yig8e9XH/ITsAx/9K+dRJpq+LUr1/C7v7KRR14+TvdQnLbGGj65fQ21IR/3P3WQ5945U2AFXNhcR/vWCB+9pJX68NRLQdbncXFrA+/1jTIUz1pGQmNdgHUrp96mefD5LgbjaRzGwn4tGSvOGMxzRgt4wj2ULmsAACAASURBVCKLYjuomtskT4UKiaLkMZzMsLGl/pxS4xP5GKa7OJQqqzn7frsP9vClH+zHEqgN+jg9lOAvnz3EZ6/fxJ9/8nIG42l++OZp/vb5Lt7rG7v2cMDi+s0t7NwW4eLWhim3ibLRVvn9y//jhy+c9TbNoZ5hUrbJ9cg2BjIT+DwsAduQq6u12LaDFhMqJIqSRzmyn0uZWJfN//j6rkMAhPzudlJNwEcsleEbL3Txw7dO88KhMzlfBsDFrQ20b2vj+s0tU9apCvot6oJ+6kJ+dxtrHHOxxAoirnJqMjaUH9UkIqxpDNLSEJ73vA9lZqiQKEoe5ch+fvD5LlKZwsS6hvDMEutsxzAUT+caX50ciNPobUdlHIeheIbBRJq0bXjXKz1fF/TxkUtaad/axqYpepwH/Rb1IT+1weLiMZ5ilth0rK6AT/BZbtfD8XVPGkM+Uo4pqDX2Jx/fpsJRBaiQKEoe5fB7vHN6iKFExs3+9sJle0dTZOziTZ7ySWay/T/sgrDXtoYwpwbjxNM2I0m74JhLI420b4uw4+JmaiZxgAd8rnhMZHnMhOlaXdnIs4FYOuev8VuwsaWBL9y0eUGyzpW5o0KiKOMotVO0WLis4xhS9sT5EKNe+ZJEekwkXurq47t73uNo3yjJtFPQPdAS1xl+94c2cOsV50143mxPj7qQL7ctVgpmmsy5enmhf+ULN21e1M7oxY4KiaKUmaDfIp6ycfIS8DCcYwU4jmE4kfHCd8d8CbZj+Ic97/G9l48XRF0BhP0WoYDFBSvq+HdXn1+0RInfsqgLuRFX4YCvLOXMZ5rMuRQtDy0jryhLhN0He7j/qbdydZ82rKrL3S3Plk0tDRztHckLl7UKwmVTGTf3YySRwcnbvjo9lOCp/d08vb+bnuFkbtwSaAoHCAUs2hpr+PNPXn7Oe/osoS7kp94Tj/z5TbUFNZsFb+3y2iLJkv6iobpL0fLQMvKKskTYfbCHzz32BgOxNNnqHod6Rvj8Y2/wp7dfPut/+Imymn/z2gvoHkwUlG/P2A57uvro6Izy0pG+An90TcBiWU2AupAfSwSDoXsonnveEqE25Ms5zYsxVUXd2S54125YwUtH+7y+85CyHc6MpPh3Vxcv4jhdFstdfLlaD1QKKiRKAV//8Tt886dHGE3Z1AV9/IcPrufej1600Jc1Lzz4fBcjyQw+kTF/hnG3m+byD5+/nXO8b5TIsho+ceVaNrU15EQkOhjnyc5untrfTd9oKnes3xKCfgtjDHVBHw3hse2jRNqhramG2qCf+rCfl7p6+cYLRyZddKeqqDvbBe/Frj5aGoKFVleNnxe7+ri3yOunIxCL6S5+obszlhsVEiXH13/8Dl/bdRhL3EiaeNrma7sOAywJMTneH3NDT/OS9ERcK2Gu//COY0jbroM8nXFDXNO2w88O99LRGeWV9/rH3hM3cTCRdhAMy2r8pG2H3lG3F/ryuiCpjIMxcO+vbKStKczugz185f+8OeWiO1U589kueMf7Y6ysC7Gqfqyu10SJnNMViMXUDGohuzPOByokSo5v/vSIJyKuE9gSN0fhmz89siSEZO3yWs6OJDHOWIFAY9zPY7b/8PGUzTP7o/zRv3TmOgJ2DyU5cGqAoN/HaF659lX1QbatXkbnqQEG42n8FiDCmeEULY0hVtUL8ZRNPGWzdkVdwV38dC2JqcqZz3bBm8lx073WxdQMaiG7M84HWrRRyTGashlf+dsSCha7xcw9122gPuTHNgbbcbwvQ0PYP6N/+IztMBBLcbwvRnQwzn9/8k1GPRHJknbGPu9rN6zkT351C9/7rWvoG00R9vu8hd7NOxGB/tEULQ1hltUG+el9H+F7d19TsOge74+dkzNSzJK4qLWRlXVB/D7BNga/T1hZF2RTa2PuM5hNIcqZHDfda823nrIl66nSfiQ7Nrdw+/tXc2Y4yVvdw5wZTnL7+1dXnWU1EWqRKDnqgu6dUr6YOMYdXwrs2NzCn91+eUHU1qbm6UdtxVIZhuKZAuf50d5RTo+kJzzme791TUGf8uhQnKZwgKDPImMbxBIscdvqTmYZTNcimKqc+VThuRP5NmYS1jvda11MzaB2H+zhsVdP0twQ4nzvc3/s1ZNsW7NsUYiJComS4z98cD1f23WYjONgiSsijnHHlwozDU21HcPIuNyPRNrmuXfO0LEvyv5Tk2evZ0UkW6Jk3co6zo4kafYy1x3HYHD9NpNZBtPdOpnOgj/RZzCVb2O6n921G1bw17vfxXYMIb8bJpxfDj/LYmoGpVFbypIh6wdZqlFbWaYTUZRI2wwlCkuXvHtmhCf2RfnxW6cZzStbki2HPp5wwGJ5bbCgRMmnvcq6AZ9wXlOY08NJMrZb8n0yy2gmFsFs8zhKsRhm+5FkHLf7YSZlM5qy+fgVkXPOsZiaQWnUlrKkuPejF81ZOKo59n+yu+7rLmpmOJlhOJHO7dPHU3auVe3B7jEnsN8SPrRpFe1bI+w/McDDe46dIybN9SHeOD5wjjWQLwjvW7ucazes4MWuPv7zD/az9vnSC8R0KcVi+MDTB+kbTWGbwuK/T3R2c9sVPZN+FtWcBa9RW4oyA6o99r/YXfdIMs1f7TrMBavqMMZgjOGd0yN0dEZ59q0e4nn1sNYur6F9W4QbLm1lWW0QgCvXrSDg9/HdPUdJZNxeHM31QYJ+q+hnky8IlfR5lmIx7Do7OiYieUqSts2EzcOq4e9mKhZ71JYKiVJSqn0vOHvXbYzBNgbHca2LkwMxhhNpnn3LtT4O94zkjgn4hA9f1MzObRHiSZtH957gX147yeplNdz1gfXctDXCH+28lP2nhs5ZiCf6bLJW3avH+jHGFR8HiuZSzJcFWNLFUM79cbFs8xRjMVlXxVAhUUpKte8Fn9dUw9Fe18Gbth38lhAOWFji4xN/+yKJvNDT9avqaN/axkcvaaWxJsBLXX381U8OE/RbrKwLEh2M8wf/9AZffnw/F7U2cqhnmLbGcMH7Fftsdh/s4fOPvcFwIlNQpDFgcU4uxXxaLKVYDNevrOXg6ZHxLdvxWyyabZ6JWCzWVTFUSJSSUo17wbbjdh0cTqTZEmlg73t9OQd5ImNIZGzA3b4K+y12XOy2qr0k4raqzda4+ufXTlIT9FEb9DOcSNM7ksZgSKQdeoYTDCcyBHzJguzvYp9N1o/gjFtsMw6EfVKQiT7f2d9zXQzvu/kS7v3eqwwnbQxe33agqTa4aLZ5liIqJEpJqZa9YGPcvIzhRIZYys75Pl443EvAEpLjeoXUBHzcfd0GPnJJC/Uh998mFPDRGHYr7IoIpwbjOWvszHASEbAQUrZDbdDP8toAfaNpaoOTRyEd7hkpcEbnrhk3pwLGcikO9QzT74mOATK2TcI7byWyY3MLX7/z/Tzw9EG6vE6O61fWct/Nlyzau/WlgAqJUlIqfS84mbEZSWQYSWawvUV5MJbmmTe76dgX5Xh/vOD1AgR8EPILt11xHpa45dkba/znNIbKt8ZStoPPEozj+jUAVtWHGE2mOTOcLAivHv/ZZBtWFZMCv08KciliKZt8zTCAbdzxSmUxb/EsVcomJCLyLWAn0GOMucwbWwE8CqwDjgKfMMb0e899EbgLdw/hXmPMM974lcC3gRrgSeCzxhgjIiHgO8CVQC/wSWPM0XLNR5k+C71QjHc+/9aH1nPluhWMJDMkvQgrxxhePzZAR2eUnx4+W3AH77fEdXCL29XQcQyxlM3b0WFu2tqGSPHM6vxEO8eM9R7PbmWdHUmSsmHN8omzm3cf7CkqIFlsr6d51opJTCAYE40rSjkoZ62tbwM3jRu7D3jWGLMJeNb7GRG5FLgD2OId879EJHu79zfA3cAm7yt7zruAfmPMRuAvgAfKNhOlasg6n3uGEzSG/JwaiPFH/7qfp/ZFSaZt+kZT/OMvjvEb33qJzz22j5+8fYa0bagL+bjtivO49/qNiLhFGy0RN1QKWFEX5Dt73ptQRLIlMOqCPk9I3KoAAcv1thzqGaZ7KOlGg3l1tGqDfgI+4cHnu3LnefD5rkn/KR1j6I+l+E//9Dp3PrSHiapOmeqrIqJUMWWzSIwxz4vIunHDtwE7vMcPA7uBL3jjjxhjksARETkMXC0iR4FGY8yLACLyHeBXgae8Y77inesx4H+KiBgzPh5EWUr8zXPv5ioYZxxDyO/DdjI89EIXHfuj/Pzd3tyWFsBl5zWyc1uE6y5qJhzw8VJXH45jvO0iQ8gnrG6qpSHsnzTyLOv0HknaBP0WIpC2HeJph2N9cUJ+y2v4ZDjaGyPst2hrClMfKjzv8f4YAb91TkvdLGnbYAk5B/5EVGE5KqWKmW8fSasxJgpgjImKSHb/YzWwJ+91J7yxtPd4/Hj2mOPeuTIiMgisBM6Of1MRuRvXquH8888v2WSUyiBjO4wmbYaTaY72jtIY9mOM2+9jKJFhMJ4m45icc7cx7OeGLa3csjXCupV1AIgIbxwb4K93Hybgs/AZgyBka/ZOFXl2vD9G30iKtOf1FgGfCDaGcMCitSHMsT5XMARIZhxODSRYWT/WchdcP0v3YIKA5VbnHR+5Ba4PJJFxHfhBn5Cyx4oaioAYt72voswXleJsL3b/ZCYZn+yYcweNeQh4CGD79u1qsSwCjDGMplzHeX613baGMCcH4sTT9jnl7+tDfnZujfCbH1jH68cG+PqPD9M9FGfN8lp++8MX8uje42Qc1xJwF2eDT+D0cIKWhvCkkWcNIT8n8vwsxpDrv55IO5wajOMTyJixP1KDoW80zX//+Nh577luA68e68cxhqDPbW5VgBeXnDW8I01hjvfH3WrBjoPfcosgfuGmzTP9SIHqLm+jLBzzLSSnRSTiWSMRoMcbPwGszXvdGuCUN76myHj+MSdExA80AX3lvHhl4YmnbEaSGUaTmdxCDdA9mODJ/VGO9I4ylMgUHOOWJAkQCvh57tAZagIWz7x5mpDfYlV9iP5Yiq888SYDsRTxlI1lCUGfW203Y0AwfPXWLZMuqFPtqKZtc86djyVCQ8jHvhMD3PvIa7lIrvevbWLvsYFzRQTOTeTzWVzc2sCy2uCco+QqqRyLUl3Mt5A8DnwKuN/7/oO88X8UkT8HzsN1qr9kjLFFZFhErgF+AfwG8FfjzvUicDuwS/0ji5NUxmEkmWEkkclZDOBuaf28q5eOfVH2Hu0vMEcbQn5i6QyWQHN9OJf7kczYPPbqCVoaw+eUcck2mrI8b7vPcjtEBnwWDz7f5RZNnGChHknZ+D2LYyLyTWy/T3K+mvHtjV880l/U3M6Oibh5LdnmUbdd3saLXX2TRntNh2ovb6MsHOUM//0ermN9lYicAP4LroB8X0TuAo4BvwZgjDkgIt8H3gQywGeMMdl9iU8zFv77lPcF8HfAdz3HfB9u1JdSRuZz2yPb52M4mT6nI97J/jgdnVGeOdBNf2ysadTKuiA3XdbGzZe1cd6yGu78xh4aw/5c9rnPEoJ+ixMDiaId+hzHYPmkoJGSYwzxlE3PcGLSu/S1y2s5NRCfsGR8FoPrCLdw/Sd9sTQ+S3Ltjd17IYPBLTOfsU0urwTcul4GqAlYtDSEuXbDCr6z571cXszZkSSfe+wN/uz2y2f8u6n28jbKwlHOqK07J3jqIxO8/r8B/63I+F7gsiLjCTwhUsrPfGx7ZP0eP9rfzcN73iM6GCfSWMMdV63livOX8bPDZ+nojPLqsYHcMQJcvX4F7VsjXHvhSnx57R3Pa6rh1EDMrZvlmFz5kGwnyPFlXBrCfnwWDMYzOMZtMyxAKGBNeZfulnrvnXKOIq6zPCCwuinMe31xAnnmR77F5baXhaw0Gdy8lvqQPycUN/3FcwzE0vi8trzGgYFYmvufemvGv5dqLG+jVAaV4mxXKpxybXtkS5WMJDPEkjZ73u3la7sO4beExrCf7qE4f9zxJlDYO765PsTNW13ro3VcIcRwwEdjTYAdF63i6z9519uugpTt0DOc4tZtbbxybPCcMi4f2dzMD96I5iKlst9XhFzrZTjhZqUnMzYn+uPsPjjWP+PFrj5CfiE5yd6Wz9s2y2BIZhxODyfdqr6eaLmfR+ExGcfJWTl1QR8bmusLPvcjvTF3ft4JRMA4JtcqeCZUS3kbpfJQIVGmxfH+GMl0hiNnR3ML38q6wDnbTtMl6zSPpTIFeR2PvHzc7VFuO5wdSRLPczhbAtdsWMnObRGuWreiwPoQce/UG2v8vHi4N1eC3XEM+bFb9UGL7qEUt79/9TmdIJ/a350L3TV53/tjGYYTwyQyjhfW6z6Xb5EdODU4qYiAKxh2nlJkbENN0Ec8ZQNue+Pss5a4dbWyH43glliB8m03VXp5G6VyUSFRpocxnBkZ80c4Bs6MpFmzzDfJQYUk8iyP/C2cLEfOjvJ2zzDJtF2QP5Et5f6t37wqt5hm8VlCU02AhnAAnyUFW3Bp2znHXzGScnj1WD+vHuvHdgxhv0Vt0Mdjr54kOhDH7xN81lhueTJjk3HG/BTGuA715bVjWek7NrcwnMwwEX4LfGKRssesC0tgzfIafJaQyjgMxtOMpmxqAz78FtSG/G4ZeTsbdRak0fNf5G83bVhVx6GeEcTk+3VgU3PdtH8v+Sx0eRulOlEhUabFqYHiWdQTjWfJRlyNJt3+HuOJp212v32Gjn1R3owOFTxXH/LRVBPw7sbDBSIS8Fk01gRyznRw/Tj3PvIasZRNyG8VTeYDNxnQJ+45MrahdzTFyrogGWMIjCuBYvIsgvzT9Y6mqA2OWQaTxQvaDlg+k3O0uxadcGY4yap6t4vivq/cmHt9NqjhRH+Mtct99I6maPCabY3fbvrCTZtzvUsytptHsrw2MOs8EkWZDSokFUwlJYdNtIFVbDyVcRhNZhhNZSbc+jp0epiOzm6efet0ge9jZV2QlO3QEPJTF/KRSDtkHMP71jbxB4++wenhBGuX1/LbOy4s+CyyzaCyOST2FEULs7WwfJZbT2s4kckt8pIXtWWAkE8QEXdrC6+cu4GTAwk2tdRP+j7g+i8sEQS39ErA50aQZRxT9BzjrYJ8YRm/3bRjcwt/evvluh2lLCgqJBVKJSWH7T7YM+nzdz60h7s+sC5XYbeY5QFuW9ldB3vo2NfN26eHc+MBn/ChTc20b23jirXLePlIP4+8fJzuoThtjTW87/wmfvhmDyG/sLIuSO9o8pzPItsMKst0cioyjoPP8iHiWikbm+s5O5rKhdL6LMFvoDbkYzCeKThvrt24Z4oELCiSPwi4vo6G2gCr/BbdQ0ksPGeIKTzHREy13aTbUcpCo0IyQ+bLSqik5LD86rTFODkQ40uPH+Cz128CXId5dMgN3f3k9jU01QV4Yl+UXQd7CrK1L1hRyy3bItxwSStNtWP5C1dvWMHVG1YgIjSE/XzmH14lPEUIbrYZ1HQxuBZHMmMjuBni9918SW6+2bv7tsYgj+/rPmebTAALw6Ezo9z50B52bovwL69HJ34v3PpYq5eFOTuSynUzbKsPnVPKRVGqDRWSGZDfSzvjOJwdTvL5x97gT2eR/DUVx/tj+AS6zozkFp1V9cEFSQ473h8jaEGqyB13wIKw30csmeRLj+/P9fXwA6OJNF96/EBBQl3Qb/ErFzfTvjXClvMai5Zl91lCYzhAY43rQD85EJ8yUS4zkUNkCrKH3XpZa8F2UZY7H9pDc32QnuHkWJiut+VlRAj7LHqGE5wciBP2u50V8w2MgAWIGxBwZjhJU02ADc1jW1mxVIaWhsLwZUWpNlRIZsADTx+kP5uJ7LO80NA0Dzx9sORC0hDyc6hnBJ81+X56qSlmca1ZVsPJcZ0Ds2QcGElm6B1NF2wnZXAjpLJsaK6jfWuEj13SSn24+J9dwGfRVBugIeQvEJjpJMrNtjxIXdBHQ9hP91Cq6PPH+2Osqg8RDvg4NZBAxPUBZd9vVX0oZyEF/T5WLw9xrC+GzxK3erBj8PuEmoAvV79L8zSUxUY5G1stOrrOjuZqMblZx27mcbY8eSnJ7ZsbZrSfPhfym0I1hf1EB+P84b92ckmkYcKF2gA9w4kJnxdgRV2Ab/z6lXz8fauLikjQb9HSGGbtiloaw4FzrJR7rtuQW4CNMbkaU3NZgIM+IeATNjTXs6o+NKGlt3Z5rZf1HuC8ZWG3eyKuZXJeU00uJDdfKHziCojjGBwMq+pDxNM2m1ob+eqtW2hpCDMYT9PSEJ6yGKSiVANqkVQoIyl73vfT/9ZrChXwWbmSIrZjeO3Y4KTHTZaTaIBlNcGiW1i1QT9NNQFqgpPnokwnUS7gLeLFsLLRWN71+MVNYAx4CY2TlQHJz/auD/nxWcKJ/jjLawM5EcmeY1NrI/dct4EHnj7IOz0jBHxwXkMYv3dt2WtW4VAWGyokM2D9yloOnxktCA91DGxcVfpaRNntnHLvp2dsh9GUzWgywxGvKZST528IByy6h4pva02bPCsqm4HeVBMg6J++QTzVAnyhl5jn9ZVyneECkcYQDTVBb2F3y7b3x9yorLbG0JTWTTERu+3y83js1ZNFt6iy15kfspvtZaICoixWVEhmwH03X8LnHnujIDx0WSiQi/YpJfdct4HPP/YGJ/vjBQ2LvtR+6ZzPnbYdYkmbkVSGZHrMwok01tA7miyojJtIO7Q2hOkeSk54vlV1QXpHU0W3t5bX+oml7XMc6KXmvpsv4d7vvcpIys6VN6kL+viTj287Z2Hf2BxCRBhJZqa1yBcTsW1rlk1qIanloSwllpyQHOwe5s6H9szqDnHH5hb+bB6TvwyAtw2TTT2YLROJRz53XLWWr+06RDxtEw5YbvvahJuVPhmfu+Fi/viJA4yOS6TwixtWu3ZFLeevqC26vVVKggEfQcfkRD6YJ4ilXthVKBRljCUnJH5L5pTcN18LyIPPd9FUEyDSVJMbm2keSbaX+WTikc/VG1bwu2Yj33zhCMf64iRnUJAxGPCRyDgFuRy2gb7RNL/+S6vKLiKl+LwURZkdSzJqqzY4VnCvUjneHyvafGmqPJK07TAYS3NyIM6xvhi9o8lpiUjPUIKHf36Uv3z2EF29ozkRqQ36qJvCGf7Iy8epD/kJ+i23Mq43LuIWG3yxq/wdkGf7eSmKMneWnEWSpdIXmZk0GUpmbGJJe9LaVsWwHcOerl46OqO8dKSvIHv7irXL2LktwuOvn6I/liKZiReNzmptCBIdchMG07ZDwG8hCMYYbGMmDa0tJdqUSVEWjiUrJJW+yEzVZCiRdiOtYil7wtpWExEdjPNkZzdPH+imd2QsEW95bYAbt7TRvjXC6uXuFtE3XuiiMeyntTHMqYHCfJH6oMUf3XIp//DSMc6OJAn5fWTyItqCPmvePmdtyqQoC8eSFJJSJLSVm2Jhp7/5yxdw2ZomjvXGivbzmIy07fDzd3vp2Bfllff6C4oPbl+3PNeqNuAr3O3MRnLVBf2ctyxM32iKZMahLujja598H9df2kpjTYAvP36Axho/Z4dTOOKevSEcmLfPWZsyKcrCIeXMlK5EGtZcbHZ++eGqWWQm6iQ4XU72x+nojPLMgW76Y2ONqVbVB7n5sjZuvixCW9PEuSkvdfXlWt/WBHykbAfbMfzxbZcVLXV+qGeYVMYh6JNcgl41fM6KokyOiLxijNle9LmlJiTbt283e/fuXejLmJS5ikcq4/DCobN0dEZ5/fhAbtwSuHr9CnZui/BL61dOO5/jtWP9PPryCaKDcb3TV5QlymRCsiS3tiqRbBva0eTsxAPgvd5ROjqj/PDA6VyDJ4CWhhDtWyPcdFkbzQ2hSc5QSG3Qz7Jat1rtv71y7ayuSVGUxY8KyQKSdZiPTtDDfLrneP6dM3R0Ruk8Odaq1mcJ125Yyc5tEa68YPmMssnrQ36aagOE/NPvx64oytJFhWSeSWZsRpP2hD3Mp8u7PSM80Rnlx2+dZjSZV+akKUz71gg3bmllZf30rY9sDaxltYFzHO6KoiiToUIyDyTSNrHU3MUjnrL5yds9PLEvysHusVa1fkv44MZVtG+L8L7zl2HNIIvcEqGxJkBj2I9fBURRlFmgQlImSmV5ALxzepiOfVGePdhDLK+M/JrlNezcFuGGS1tZVhuc0Tl9ltu1rzEcwCpDEUVFUZYOKiQlpJTiMZrM8OzBHjr2RTnUM5IbD/iED1/UTPu2CNtWN824htVEXQgVRVFmiwrJHJlLhvl4jDG8FR2mozPKTw72kMirSbJuZS3t29xWtY3j+pdPh1DAR1NNgPqQ/soVRSktuqrMgmyobmwO0Vb5DCfS/OjNHjo6oxzJa9sb8lvsuLiZndsiXBppnJUFkQ3hDQc0AktRlPKgQjJN4im3KGKpxMMYQ+fJQTo6u3nunTMFxRYvbK5j57YIH7mkdVYWhCVCfdjtQqgRWIqilBsVkgkwxpBIO3PKMC/GYCzND9/spqOzm2N9Y1VxawI+rt/cws5tES5qrZ+V9VHuLoSKoijFUCHJwxhD3Nu2iqfskomHYwyvHx+gY1+Unx4+Szqv+9PFbQ20b41w/ebmghLoM0EjsBRFWUiWvJAYY9wcj1RpxQOgbzTFMwe6ebKzm5MD8dx4XdDHRy9ppX1bhI0t9bM+f8Bn5XJANAJLUZSFYkkKieMYYmmbmBdt5ZSwcKVjDK+810/Hvig/e7e3QJi2nNfIzm0RPnxR85yc30G/xbLaoEZgKYpSESy5lShjG97ri1HqqsdnhpM8faCbJzujnB5K5sYbwn5uuLSVW7ZGWL+qbk7vURP0sawmSM0UrW8VRVHmkyUnJI4xJRMR2zG8dKSPJ/ZF+cWR3oJWtZevaaJ9W4TrNjUT9M8tcqo+5KexRkN4FUWpTJackJSC7qEET3d289T+bs6MjFkfy2oC3LDFtT7OXzG39rJaRFFRlGqh6oVERG4Cvgb4gG8aY+4vx/tkbIcXu/roPC/wMgAABs1JREFU2HeKl4/2F/Quv/L8ZbRvO48PbDy3Ve1M0RBeRVGqjaoWEhHxAX8NfAw4AbwsIo8bY94s1XucGojzZGeUpw+cpm80lRtfUZdtVdvGectq5vw+GoGlKEq1UtVCAlwNHDbGdAGIyCPAbcCchCRtO/zs8Fk69kV55dhYq1oBrlq/gvatEa7dsKIkZde1BpaiKNVOta9eq4HjeT+fAH5p/ItE5G7gbu/HkQtbGt6e7RseBf5ptgfPnlXA2fl/25KzGOaxGOYAOo9KoxrmccFET1S7kBTbAzonJMsY8xDwUPkvpzyIyF5jzPaFvo65shjmsRjmADqPSqPa51Ht4UAngLV5P68BTi3QtSiKoixJql1IXgY2ich6EQkCdwCPL/A1KYqiLCmqemvLGJMRkd8BnsEN//2WMebAAl9WOajabblxLIZ5LIY5gM6j0qjqeUipS4UoiqIoS4tq39pSFEVRFhgVEkVRFGVOqJAsMCLyLRHpEZH9eWNfEZGTIvK693VL3nNfFJHDIvK2iNy4MFd9LsXm4Y3/rnetB0Tkf+SNV808ROTRvN/FURF5Pe+5aprHFSKyx5vHXhG5Ou+5aprH5SLyooh0isj/EZHGvOcqdR5rReQnIvKW97/wWW98hYj8SEQOed+X5x1TkXMpivGq4erXwnwB1wHvB/bnjX0F+FyR114KvAGEgPXAu4BvoecwyTx+BfgxEPJ+bqnGeYx7/v8FvlyN8wB+CNzsPb4F2F2l83gZ+LD3+N8Df1wF84gA7/ceNwDveNf7P4D7vPH7gAcqfS7FvtQiWWCMMc8DfdN8+W3AI8aYpDHmCHAYt0zMgjPBPD4N3G+MSXqv6fHGq20eAIhbBO0TwPe8oWqbhwGyd+9NjOVcVds8Lgae9x7/CPi33uNKnkfUGPOq93gYeAu3MsdtwMPeyx4GftV7XLFzKYYKSeXyOyKyzzPts+ZusZIwq+f/0qbNRcCHROQXIvKciFzljVfbPLJ8CDhtjDnk/Vxt8/g94E9F5DjwZ8AXvfFqm8d+4Fbv8a8xlpRcFfMQkXXA+4BfAK3GmCi4YgO0eC+rirlkUSGpTP4GuBC4AojibqfANEvCVBB+YDlwDfB54PveXX21zSPLnYxZI1B98/g08PvGmLXA7wN/541X2zz+PfAZEXkFd5soW5a74uchIvXA/wZ+zxgzNNlLi4xV1FzyUSGpQIwxp40xtjHGAb7BmElbbSVhTgD/bFxeAhzc4nTVNg9ExA/8G+DRvOFqm8engH/2Hv8TVfp3ZYw5aIy5wRhzJa6wv+s9VdHzEJEAroj8gzEm+3s4LSIR7/kIkN3+rei5jEeFpALJ/mF5fBzXlAe3/MsdIhISkfXAJuCl+b6+GfCvwPUAInIREMStcFpt8wD4KHDQGHMib6za5nEK+LD3+Hogu0VXVfMQkRbvuwX8Z+Bvvacqdh6eJf53wFvGmD/Pe+pxXIHH+/6DvPGKnEtRFtrbv9S/cO+ookAa9y7kLuC7QCewD/cPKpL3+j/CvQN7Gy8CpxK+JphHEPh7XCF8Fbi+GufhjX8b+I9FXl818wA+CLyCGw30C+DKKp3HZ3Gjnt4B7ser0FHh8/gg7tbUPuB17+sWYCXwLK6oPwusqPS5FPvSEimKoijKnNCtLUVRFGVOqJAoiqIoc0KFRFEURZkTKiSKoijKnFAhURRFUeaEComilBARGSnDOa8YVwH6KyLyuVK/j6LMFhUSRal8rsDNOVCUikSFRFHKhIh8XkRe9opv/ldvbJ3Xk+IbXl+KH4pIjffcVd5rXxSRPxWR/SISBL4KfNLrI/JJ7/SXishuEekSkXsXaIqKAqiQKEpZEJEbcMtaXI1rUVwpItd5T28C/toYswUYYKwM+v+Hmz1/LWADGGNSwJeBR40xVxhjsrW+NgM3euf/L14dJ0VZEFRIFKU83OB9vYZbHmYzroAAHPn/27tjlAiCIArD74GJoGBmYOIxvIMXMBQ9wKZ6As+gBzAyNdDEzI2MPIG5sMiaGMgz6F4YZXGUdplB/i8aaqCppCm6G6qSLKYsPkjatb0laTPJfY1f9qx/nTKr4lml0d/2n2YP/MLa0AkA/5QlnSU5/xQssyjeOqF3Seta3jb8O1/XYC9jMJxIgNW4kXRU50/I9s6ia+0ySWaS5rb3auig83uuMncDGCUKCbACSW5Vrqemth8lXam/GBxLurA9VTmhvNT4ncrjevexHRgNuv8CI2F7I8lr/T5RGR8wGTgtoBf3qsB47Ns+VdmXT5IOh00H+BlOJACAJryRAACaUEgAAE0oJACAJhQSAEATCgkAoMkHIBRot/FXY8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= temp[[\"length\", \"price\"]].corr()\n",
    "sns.regplot(x=\"length\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 47883.55859388041)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= temp[[\"width\", \"price\"]].corr()\n",
    "sns.regplot(x=\"width\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 47414.1)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= temp[[\"height\", \"price\"]].corr()\n",
    "sns.regplot(x=\"height\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Through these we can see that lenght and width can be used to compare price while the height have no effect on the price as the line is almost equal to horizontal**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 22. Find out the maximum city-mpg of every car and sort them in descinding order and what is the maximum city-mileage and of which car?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "make\n",
       "honda            49\n",
       "chevrolet        47\n",
       "nissan           45\n",
       "toyota           38\n",
       "isuzu            38\n",
       "volkswagen       37\n",
       "plymouth         37\n",
       "dodge            37\n",
       "mitsubishi       37\n",
       "mazda            36\n",
       "subaru           32\n",
       "peugot           28\n",
       "volvo            26\n",
       "audi             24\n",
       "bmw              23\n",
       "renault          23\n",
       "mercedes-benz    22\n",
       "alfa-romero      21\n",
       "saab             21\n",
       "porsche          19\n",
       "mercury          19\n",
       "jaguar           15\n",
       "Name: city-mpg, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.groupby(\"make\")[\"city-mpg\"].max().sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Through this we have found out the maximum city-mpg of diffrent brands and the maximum city mileage of all the cars is of Honda.**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 23.Find the bore and stroke ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp[\"bore/stroke\"] = (temp[\"bore\"]/ temp[\"stroke\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_stroke = temp[temp[\"bore/stroke\"].apply(lambda x: True if x > 1.1 else False)][\"price\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "long_stroke = temp[temp[\"bore/stroke\"].apply(lambda x: True if x < 1.1 else False)][\"price\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_stroke"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "+ **An engine is an oversquare or short-stroke if its cylinders have a greater bore diameter than its stroke length, i.e. the bore to stroke ratio is greater than 1.**\n",
    "+ **An engine is described as undersquare or long-stroke if its cylinders have a smaller bore (width, diameter) than its stroke (length of piston travel) - giving a ratio value of less than 1.1.**\n",
    "+ **manufacturers nowadays  push for more and more efficient engines and higher fuel economy, so Undersquare engines have become more common**\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 24. Find the effect of bore and stroke ratio on price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 47414.1)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f= temp[[\"bore/stroke\", \"price\"]].corr()\n",
    "sns.regplot(x=\"bore/stroke\", y=\"price\", data=temp)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 25. city mileage and highway mileage on the basis of drive wheels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf595896a30>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.groupby(\"drive-wheels\")[[\"city-mpg\",\"highway-mpg\"]].mean().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Through this we have seen that the cars with forward wheels drive has the maximum mean average**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 26.  Average mileage on the basis of fuel type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf595903f10>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.groupby(\"fuel-type\")[[\"city-mpg\",\"highway-mpg\"]].mean().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "**Through this we have seen that diesel cars used to have more average mileage as compared to gas cars**\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 27. Average mileage of diffrent aspiration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf5958fc760>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.groupby(\"aspiration\")[[\"city-mpg\",\"highway-mpg\"]].mean().plot(kind=\"bar\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "+ **We have seen how easily we can get an data analysis report using the pandas library.**\n",
    "+ **With a few lines of code, we have generated an interactive report.**\n",
    "+ **And we have seen that engine size, mileage , lenght , width, horsepower are the features which effects the price.**\n",
    "\n",
    "\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}