KNN on cars dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abf03569",
   "metadata": {},
   "source": [
    "# KNN Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82abe436",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings (\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5eb5db7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111</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>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111</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>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>152</td>\n",
       "      <td>154</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>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>109</td>\n",
       "      <td>102</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>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>136</td>\n",
       "      <td>115</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type   body-style  \\\n",
       "0          3                 ?  alfa-romero       gas  convertible   \n",
       "1          3                 ?  alfa-romero       gas  convertible   \n",
       "2          1                 ?  alfa-romero       gas    hatchback   \n",
       "3          2               164         audi       gas        sedan   \n",
       "4          2               164         audi       gas        sedan   \n",
       "\n",
       "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
       "0          rwd           front   64.1    48.8        dohc          130   \n",
       "1          rwd           front   64.1    48.8        dohc          130   \n",
       "2          rwd           front   65.5    52.4        ohcv          152   \n",
       "3          fwd           front   66.2    54.3         ohc          109   \n",
       "4          4wd           front   66.4    54.3         ohc          136   \n",
       "\n",
       "  horsepower  city-mpg  highway-mpg  price  \n",
       "0        111        21           27  13495  \n",
       "1        111        21           27  16500  \n",
       "2        154        19           26  16500  \n",
       "3        102        24           30  13950  \n",
       "4        115        18           22  17450  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"cars.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f46d31f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 15 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   body-style         205 non-null    object \n",
      " 5   drive-wheels       205 non-null    object \n",
      " 6   engine-location    205 non-null    object \n",
      " 7   width              205 non-null    float64\n",
      " 8   height             205 non-null    float64\n",
      " 9   engine-type        205 non-null    object \n",
      " 10  engine-size        205 non-null    int64  \n",
      " 11  horsepower         205 non-null    object \n",
      " 12  city-mpg           205 non-null    int64  \n",
      " 13  highway-mpg        205 non-null    int64  \n",
      " 14  price              205 non-null    int64  \n",
      "dtypes: float64(2), int64(5), object(8)\n",
      "memory usage: 24.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12098b2d",
   "metadata": {},
   "source": [
    "Missing Values are present in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3610b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "?      41\n",
       "161    11\n",
       "91      8\n",
       "150     7\n",
       "134     6\n",
       "128     6\n",
       "104     6\n",
       "85      5\n",
       "94      5\n",
       "65      5\n",
       "102     5\n",
       "74      5\n",
       "168     5\n",
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       "95      5\n",
       "106     4\n",
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       "118     4\n",
       "148     4\n",
       "122     4\n",
       "83      3\n",
       "125     3\n",
       "154     3\n",
       "115     3\n",
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       "119     2\n",
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       "192     2\n",
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       "194     2\n",
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       "108     2\n",
       "110     2\n",
       "164     2\n",
       "145     2\n",
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       "256     1\n",
       "107     1\n",
       "90      1\n",
       "231     1\n",
       "142     1\n",
       "121     1\n",
       "78      1\n",
       "98      1\n",
       "186     1\n",
       "77      1\n",
       "Name: normalized-losses, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"normalized-losses\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e7afa7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68     19\n",
       "70     11\n",
       "69     10\n",
       "116     9\n",
       "110     8\n",
       "95      7\n",
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       "123     4\n",
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       "90      3\n",
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       "?       2\n",
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       "288     1\n",
       "143     1\n",
       "142     1\n",
       "175     1\n",
       "106     1\n",
       "Name: horsepower, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"horsepower\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8acdb342",
   "metadata": {},
   "source": [
    "Missing value is in the form of ? \n",
    "\n",
    "replace it by nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7310109",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"normalized-losses\"].replace(\"?\",np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea965073",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"horsepower\"].replace(\"?\",np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0cf99777",
   "metadata": {},
   "outputs": [
    {
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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>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111</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>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111</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>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>152</td>\n",
       "      <td>154</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>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>109</td>\n",
       "      <td>102</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>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>136</td>\n",
       "      <td>115</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type   body-style  \\\n",
       "0          3               NaN  alfa-romero       gas  convertible   \n",
       "1          3               NaN  alfa-romero       gas  convertible   \n",
       "2          1               NaN  alfa-romero       gas    hatchback   \n",
       "3          2               164         audi       gas        sedan   \n",
       "4          2               164         audi       gas        sedan   \n",
       "\n",
       "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
       "0          rwd           front   64.1    48.8        dohc          130   \n",
       "1          rwd           front   64.1    48.8        dohc          130   \n",
       "2          rwd           front   65.5    52.4        ohcv          152   \n",
       "3          fwd           front   66.2    54.3         ohc          109   \n",
       "4          4wd           front   66.4    54.3         ohc          136   \n",
       "\n",
       "  horsepower  city-mpg  highway-mpg  price  \n",
       "0        111        21           27  13495  \n",
       "1        111        21           27  16500  \n",
       "2        154        19           26  16500  \n",
       "3        102        24           30  13950  \n",
       "4        115        18           22  17450  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "644781e3",
   "metadata": {},
   "outputs": [
    {
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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>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>122.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21</td>\n",
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       "      <td>13495</td>\n",
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       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</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>122.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>152</td>\n",
       "      <td>154.0</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>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>109</td>\n",
       "      <td>102.0</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>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type   body-style  \\\n",
       "0          3              122.0  alfa-romero       gas  convertible   \n",
       "1          3              122.0  alfa-romero       gas  convertible   \n",
       "2          1              122.0  alfa-romero       gas    hatchback   \n",
       "3          2              164.0         audi       gas        sedan   \n",
       "4          2              164.0         audi       gas        sedan   \n",
       "\n",
       "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
       "0          rwd           front   64.1    48.8        dohc          130   \n",
       "1          rwd           front   64.1    48.8        dohc          130   \n",
       "2          rwd           front   65.5    52.4        ohcv          152   \n",
       "3          fwd           front   66.2    54.3         ohc          109   \n",
       "4          4wd           front   66.4    54.3         ohc          136   \n",
       "\n",
       "   horsepower  city-mpg  highway-mpg  price  \n",
       "0       111.0        21           27  13495  \n",
       "1       111.0        21           27  16500  \n",
       "2       154.0        19           26  16500  \n",
       "3       102.0        24           30  13950  \n",
       "4       115.0        18           22  17450  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "si=SimpleImputer(missing_values=np.nan,strategy=\"mean\")\n",
    "df[[\"normalized-losses\",\"horsepower\"]]=si.fit_transform(df[[\"normalized-losses\",\"horsepower\"]])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e25ebedc",
   "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>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>104.256158</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>13227.478049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>31.681008</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>39.519211</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>7902.651615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>48.000000</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>101.000000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>70.000000</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>122.000000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10345.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>116.000000</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>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>288.000000</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       width      height  engine-size  \\\n",
       "count  205.000000         205.000000  205.000000  205.000000   205.000000   \n",
       "mean     0.834146         122.000000   65.907805   53.724878   126.907317   \n",
       "std      1.245307          31.681008    2.145204    2.443522    41.642693   \n",
       "min     -2.000000          65.000000   60.300000   47.800000    61.000000   \n",
       "25%      0.000000         101.000000   64.100000   52.000000    97.000000   \n",
       "50%      1.000000         122.000000   65.500000   54.100000   120.000000   \n",
       "75%      2.000000         137.000000   66.900000   55.500000   141.000000   \n",
       "max      3.000000         256.000000   72.300000   59.800000   326.000000   \n",
       "\n",
       "       horsepower    city-mpg  highway-mpg         price  \n",
       "count  205.000000  205.000000   205.000000    205.000000  \n",
       "mean   104.256158   25.219512    30.751220  13227.478049  \n",
       "std     39.519211    6.542142     6.886443   7902.651615  \n",
       "min     48.000000   13.000000    16.000000   5118.000000  \n",
       "25%     70.000000   19.000000    25.000000   7788.000000  \n",
       "50%     95.000000   24.000000    30.000000  10345.000000  \n",
       "75%    116.000000   30.000000    34.000000  16500.000000  \n",
       "max    288.000000   49.000000    54.000000  45400.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7c4e5f",
   "metadata": {},
   "source": [
    "1. Dataset contains 205 response and 15 features.\n",
    "2. Mean and Median are not equal so data has skewness.\n",
    "3. Here we need to predict the price of car depending upon various attributes.\n",
    "\n",
    "So Price is Target & other attributes are Features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3e51fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df,x=df.iloc[:,-1],y=df[\"make\"])\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13685625",
   "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>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>29</th>\n",
       "      <td>3</td>\n",
       "      <td>145.0</td>\n",
       "      <td>dodge</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>ohc</td>\n",
       "      <td>156</td>\n",
       "      <td>145.0</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>12964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    symboling  normalized-losses   make fuel-type body-style drive-wheels  \\\n",
       "29          3              145.0  dodge       gas  hatchback          fwd   \n",
       "\n",
       "   engine-location  width  height engine-type  engine-size  horsepower  \\\n",
       "29           front   66.3    50.2         ohc          156       145.0   \n",
       "\n",
       "    city-mpg  highway-mpg  price  \n",
       "29        19           24  12964  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"make\"]==\"dodge\") & (df[\"price\"]>10000)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a2986bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(29,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e823a4c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>41</th>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>honda</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>65.2</td>\n",
       "      <td>54.1</td>\n",
       "      <td>ohc</td>\n",
       "      <td>110</td>\n",
       "      <td>101.0</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>12945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    symboling  normalized-losses   make fuel-type body-style drive-wheels  \\\n",
       "41          0               85.0  honda       gas      sedan          fwd   \n",
       "\n",
       "   engine-location  width  height engine-type  engine-size  horsepower  \\\n",
       "41           front   65.2    54.1         ohc          110       101.0   \n",
       "\n",
       "    city-mpg  highway-mpg  price  \n",
       "41        24           28  12945  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"make\"]==\"honda\") & (df[\"price\"]>12500)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cb396785",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(41,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e8972acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>45</th>\n",
       "      <td>0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>isuzu</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>63.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>ohc</td>\n",
       "      <td>90</td>\n",
       "      <td>70.0</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    symboling  normalized-losses   make fuel-type body-style drive-wheels  \\\n",
       "45          0              122.0  isuzu       gas      sedan          fwd   \n",
       "\n",
       "   engine-location  width  height engine-type  engine-size  horsepower  \\\n",
       "45           front   63.6    52.0         ohc           90        70.0   \n",
       "\n",
       "    city-mpg  highway-mpg  price  \n",
       "45        38           43  23000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"make\"]==\"isuzu\") & (df[\"price\"]>15000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adfcd035",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(45,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "207980e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>82</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</td>\n",
       "      <td>mitsubishi</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>ohc</td>\n",
       "      <td>156</td>\n",
       "      <td>145.0</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>12629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</td>\n",
       "      <td>mitsubishi</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>ohc</td>\n",
       "      <td>156</td>\n",
       "      <td>145.0</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>14869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</td>\n",
       "      <td>mitsubishi</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>ohc</td>\n",
       "      <td>156</td>\n",
       "      <td>145.0</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>14489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    symboling  normalized-losses        make fuel-type body-style  \\\n",
       "82          3              122.0  mitsubishi       gas  hatchback   \n",
       "83          3              122.0  mitsubishi       gas  hatchback   \n",
       "84          3              122.0  mitsubishi       gas  hatchback   \n",
       "\n",
       "   drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
       "82          fwd           front   66.3    50.2         ohc          156   \n",
       "83          fwd           front   66.3    50.2         ohc          156   \n",
       "84          fwd           front   66.3    50.2         ohc          156   \n",
       "\n",
       "    horsepower  city-mpg  highway-mpg  price  \n",
       "82       145.0        19           24  12629  \n",
       "83       145.0        19           24  14869  \n",
       "84       145.0        19           24  14489  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"make\"]==\"mitsubishi\") & (df[\"price\"]>12500)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c914f472",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([82,83,84],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc91adf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>124</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</td>\n",
       "      <td>plymouth</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.3</td>\n",
       "      <td>50.2</td>\n",
       "      <td>ohc</td>\n",
       "      <td>156</td>\n",
       "      <td>145.0</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>12764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses      make fuel-type body-style drive-wheels  \\\n",
       "124          3              122.0  plymouth       gas  hatchback          rwd   \n",
       "\n",
       "    engine-location  width  height engine-type  engine-size  horsepower  \\\n",
       "124           front   66.3    50.2         ohc          156       145.0   \n",
       "\n",
       "     city-mpg  highway-mpg  price  \n",
       "124        19           24  12764  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"make\"]==\"plymouth\") & (df[\"price\"]>10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d148c233",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(124,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "160bfd97",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</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>172</th>\n",
       "      <td>2</td>\n",
       "      <td>134.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>65.6</td>\n",
       "      <td>53.0</td>\n",
       "      <td>ohc</td>\n",
       "      <td>146</td>\n",
       "      <td>116.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>17669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>3</td>\n",
       "      <td>197.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>dohc</td>\n",
       "      <td>171</td>\n",
       "      <td>161.0</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>16558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>3</td>\n",
       "      <td>197.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>67.7</td>\n",
       "      <td>52.0</td>\n",
       "      <td>dohc</td>\n",
       "      <td>171</td>\n",
       "      <td>161.0</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>-1</td>\n",
       "      <td>90.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>dohc</td>\n",
       "      <td>171</td>\n",
       "      <td>156.0</td>\n",
       "      <td>20</td>\n",
       "      <td>24</td>\n",
       "      <td>15690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-1</td>\n",
       "      <td>122.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>wagon</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>dohc</td>\n",
       "      <td>161</td>\n",
       "      <td>156.0</td>\n",
       "      <td>19</td>\n",
       "      <td>24</td>\n",
       "      <td>15750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling  normalized-losses    make fuel-type   body-style drive-wheels  \\\n",
       "172          2              134.0  toyota       gas  convertible          rwd   \n",
       "178          3              197.0  toyota       gas    hatchback          rwd   \n",
       "179          3              197.0  toyota       gas    hatchback          rwd   \n",
       "180         -1               90.0  toyota       gas        sedan          rwd   \n",
       "181         -1              122.0  toyota       gas        wagon          rwd   \n",
       "\n",
       "    engine-location  width  height engine-type  engine-size  horsepower  \\\n",
       "172           front   65.6    53.0         ohc          146       116.0   \n",
       "178           front   67.7    52.0        dohc          171       161.0   \n",
       "179           front   67.7    52.0        dohc          171       161.0   \n",
       "180           front   66.5    54.1        dohc          171       156.0   \n",
       "181           front   66.5    54.1        dohc          161       156.0   \n",
       "\n",
       "     city-mpg  highway-mpg  price  \n",
       "172        24           30  17669  \n",
       "178        20           24  16558  \n",
       "179        19           24  15998  \n",
       "180        20           24  15690  \n",
       "181        19           24  15750  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"make\"]==\"toyota\") & (df[\"price\"]>15000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15480db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([172,178,179,180,181],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ad16608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df,x=df.iloc[:,-1],y=\"make\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a328193",
   "metadata": {},
   "source": [
    "All Outliers are Removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "59a80531",
   "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>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>122.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>122.0</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>152</td>\n",
       "      <td>154.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>109</td>\n",
       "      <td>102.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type   body-style  \\\n",
       "0          3              122.0  alfa-romero       gas  convertible   \n",
       "1          3              122.0  alfa-romero       gas  convertible   \n",
       "2          1              122.0  alfa-romero       gas    hatchback   \n",
       "3          2              164.0         audi       gas        sedan   \n",
       "4          2              164.0         audi       gas        sedan   \n",
       "\n",
       "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
       "0          rwd           front   64.1    48.8        dohc          130   \n",
       "1          rwd           front   64.1    48.8        dohc          130   \n",
       "2          rwd           front   65.5    52.4        ohcv          152   \n",
       "3          fwd           front   66.2    54.3         ohc          109   \n",
       "4          4wd           front   66.4    54.3         ohc          136   \n",
       "\n",
       "   horsepower  city-mpg  highway-mpg  \n",
       "0       111.0        21           27  \n",
       "1       111.0        21           27  \n",
       "2       154.0        19           26  \n",
       "3       102.0        24           30  \n",
       "4       115.0        18           22  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,:-1]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d281228",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 193 entries, 0 to 204\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   symboling          193 non-null    int64  \n",
      " 1   normalized-losses  193 non-null    float64\n",
      " 2   make               193 non-null    object \n",
      " 3   fuel-type          193 non-null    object \n",
      " 4   body-style         193 non-null    object \n",
      " 5   drive-wheels       193 non-null    object \n",
      " 6   engine-location    193 non-null    object \n",
      " 7   width              193 non-null    float64\n",
      " 8   height             193 non-null    float64\n",
      " 9   engine-type        193 non-null    object \n",
      " 10  engine-size        193 non-null    int64  \n",
      " 11  horsepower         193 non-null    float64\n",
      " 12  city-mpg           193 non-null    int64  \n",
      " 13  highway-mpg        193 non-null    int64  \n",
      "dtypes: float64(4), int64(4), object(6)\n",
      "memory usage: 22.6+ KB\n"
     ]
    }
   ],
   "source": [
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a46c442d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13495\n",
       "1    16500\n",
       "2    16500\n",
       "3    13950\n",
       "4    17450\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,-1]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50204f84",
   "metadata": {},
   "source": [
    "# Skewness Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0db8698b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'width', 'height', 'engine-size',\n",
       "       'horsepower', 'city-mpg', 'highway-mpg'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colname=x.select_dtypes([\"int64\",\"float64\"]).columns\n",
    "colname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3dc23c19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling\n",
      "0.2147470605758054\n",
      "normalized-losses\n",
      "0.846315331460635\n",
      "width\n",
      "0.9147899492639983\n",
      "height\n",
      "0.007986794777796822\n",
      "engine-size\n",
      "2.0664919440355707\n",
      "horsepower\n",
      "1.576213559993105\n",
      "city-mpg\n",
      "0.5925146641287237\n",
      "highway-mpg\n",
      "0.4682909445793763\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import skew\n",
    "for i in x[colname]:\n",
    "    print(i)\n",
    "    print(skew(x[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a87c2fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "symboling\n",
      "0.2147470605758054\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalized-losses\n",
      "0.846315331460635\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "width\n",
      "0.9147899492639983\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "height\n",
      "0.007986794777796822\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "engine-size\n",
      "2.0664919440355707\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "horsepower\n",
      "1.576213559993105\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city-mpg\n",
      "0.5925146641287237\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highway-mpg\n",
      "0.4682909445793763\n"
     ]
    },
    {
     "data": {
      "image/png": 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QmJgIwDo09dBDD9naJyYmIjc3F0lJScjMzMTGjRuxYcMGLFq0yNbm5Zdfxvfff4/s7GxkZGTgscceQ0ZGhu2cRM4ku6QGr3xzCgDw55uGIjqo//Ruhvm4wstViUaTBZl63rhARJ2jkPKbz507F2VlZVi2bBmKioowcuRIbN++HeHh4QCAoqKiFmsCRUREYPv27Vi4cCHeffddBAcHY/Xq1ZgzZ46tTWVlJf7whz9Ar9dDp9Nh7Nix2L17N6655po+f39EUmo0WfDsJxmobzLj2ihfzJscIXVJPUomCIgJ9cTOrBJk5FViTIin1CURkQMRRJG3UPyWwWCATqdDVVUV5wORw3rzf6exZtd5eLoq8b9nr0OgzroY6H8Otb3QqCMqqTbiHz+cgUwA/jIzGu7qjv1Od9+EsF6ujIik0JnPb8nvAiOinncouwxrU84DAJbfMcoWfvobP60aIV4usIjAsYuVUpdDRA6EAYion6mqa0LSp0chisDdsSGYOap/3wDQvEM87wYjos5gACLqRywWEUmfZqCgsh7hPq546bYRUpfU60aH/LI1Rkm1UepyiMhBMAAR9SP/3p2NH08XQ6WQ4d37xnV4Towjc1crMNhfCwDIyOfWGETUMQxARP3EwewyrNiRBQB4+bYRGDlAJ3FFfScmzBOAdRiM93UQUUcwABH1A8XVDfjTlnSYLSLuHDsA9453rv3sogM9oFbIUFHXhNyyOqnLISIH0P/7x4nsSG/cgm62iNi0Lwcl1Ub4a9UYHeKJLT871352KoUMI4J1OJJXgYz8Sgz0dZO6JCKyc+wBInJwP56+hOzSWqjkMtw3IQwqhXP+s26+G+x4QRVMZm6NQURX5pw/KYn6iXPFNUjJKgEA3DF2APy1/XO9n46I9HODh0aB+iYzsi5VS10OEdk5BiAiB1Xd0IRPU/MhAhg/0BtjLveAOCuZINiuQXpepaS1EJH9YwAickAWUcT/S72IGqMJAR5q3DK6fy922FFjQ70AAFmXqlHXaJK4GiKyZwxARA5o95kSnCupgVIu4Pfjw6CU858yAATqNAj00MBsEXG8oErqcojIjvGnJpGDuVBaix8yLwEAbhszAP4ezjvvpy3jLq8JlJbLRRGJqH0MQEQOpL7RjK2p+bCI1ruemj/s6RcxYV62rTEuGRqkLoeI7BQDEJED+eZYIarqm+DjpsLtY4IhCILUJdkdd7UCwwI9ALAXiIjaxwBE5CAyiwxIz6+EAOsu72qlXOqS7FZsuHUydHp+JcwWbo1BRK0xABE5gPpGM77MKAAAXBvlizAfrnR8JUMCtNCqFag1mpCl55pARNQaAxCRA/jfST2qG0zwdVdjxvAAqcuxe3KZgLGX50el5pZLWwwR2SUGICI7d7GiDqkXrB/id4wdwFveOygu3BsAkKWvRmVdo8TVEJG94U9SIjtmEUV8dbQQIqx3fUVwk88O89WqEenrBhFAKidDE9FvMAAR2bG03ApcrKiHWiHDTSMDpS7H4VwTYe0FSr1QzsnQRNQCAxCRnWo0WZB8yrrg4fXRAfDQKCWuyPEMD/aAm0oOQ4MJWXqD1OUQkR1hACKyU/vPl6LGaIK3mwoTI72lLschKWQyxF6eC3Qoh5OhiegXDEBEdqiu0YTdZ0sAADOiA6CQ8Z9qV10T4Q0BwNniGpRWG6Uuh4jsBH+qEtmh3WdK0NBkQaCHBqNDdFKX49C83VQYGqgFAOzPLpO4GiKyFwxARHamxmjCgcsf1AkjAiDjdhfdNmmQLwDgSF4FGprMEldDRPaAAYjIzhw4X4oms4gQLxcMDdBKXU6/MMjPDf5aNRpNFu4PRkQAGICI7IqxyWzr/blusB83O+0hgiAgfpAPAOBAdhlviSciBiAie3L4QjkamizwdVdheLCH1OX0K2NDveCilKO8thE7TuqlLoeIJKaQugAisjJZLNh7rhQAMGWwH+f+9DCVQoaJkd7YmVWCV7dnory2UdIetvsmhEn2vYmIPUBEduNEQRUMDSZoNQqMDfWUupx+KX6QL5RyARcr6pFdWit1OUQkIQYgIjtxKNu6UN+ECG8ouOFpr3BXK2ybpKacKZG4GiKSEn/KEtkBfVUDcsvrIBOAuIFc9bk3XTvYFzIBOFdcg4sVdVKXQ0QSYQAisgOHcqx3fg0P8uCeX73My1WFMSGeAICfThdLWwwRSYYBiEhixiYz0vMrAQATIn2kLcZJTB/qDwHAaX01e4GInBQDEJHEMi5WotFkga+7GpG+blKX4xR8tWqMDfMEAPyQeUnaYohIEgxARBI7cnll4msGenHhwz40fag/ZAJw5lINcst4RxiRs2EAIpJQabUR+RX1EACM4a3vfcrHXY1xYV4AgB2nLkEUuTo0kTNhACKSUPPcn8EB7tBy8nOf+90wfyhkAnJKa5F1qVrqcoioDzEAEUnEIorIyLcOf40N9ZK4Gufk6arCpMt7hP3vhJ57hBE5EQYgIonkltWhoq4JaoUM0UHc90sqU4f4w1UlR3G10TYfi4j6PwYgIok09/6MCNZBpeA/Ram4qOSYPtQfAJCceQkNTWaJKyKivsCfukQSMFtEnCgwAABiOPlZchMiveHjpkKN0cTFEYmcBAMQkQSyS2tQ32SGm0qOCK79IzmFTIZbxwQDAPafL8UlQ4PEFRFRb2MAIpLAycu9P8ODPSCXce0fezAkQIvoIA9YRODrY4W8LZ6on2MAIupjFlHEySJrABoZrJO4Gvq1m0cFQSETkF1Si+MFVVKXQ0S9iAGIqI9dKKtFrdEEF6UckX7uUpdDv+LtpsLUIX4AgO9O6GE0cUI0UX8leQBas2YNIiIioNFoEBsbiz179lyxfUpKCmJjY6HRaBAZGYl169a12/aTTz6BIAiYPXt2D1dN1HXNw1/RQRz+skfXDfGDl6sSVfVN2JVVInU5RNRLJA1AW7duxYIFC7B06VKkp6djypQpmDlzJvLy8tpsn5OTg1mzZmHKlClIT0/H888/j2eeeQaff/55q7a5ublYtGgRpkyZ0ttvg6jDLKKIk4XWoZWRwVz7xx4p5TLcMto6IXrv2VKUVBslroiIeoOkAWjlypV47LHHMH/+fERHR2PVqlUIDQ3F2rVr22y/bt06hIWFYdWqVYiOjsb8+fMxb948rFixokU7s9mM+++/Hy+//DIiIyOvWofRaITBYGjxIOoNhZX1MDSYoFLIEOXP4S97NSxQi6EBWphFEd9wQjRRvyRZAGpsbERaWhoSEhJaHE9ISMD+/fvbfM2BAwdatb/xxhuRmpqKpqYm27Fly5bBz88Pjz32WIdqWb58OXQ6ne0RGhrayXdD1DGn9db9pgb7u0Mhl3wEmtohCAJuGR0EuUzA2eIanCriL0VE/Y1kP4FLS0thNpsREBDQ4nhAQAD0en2br9Hr9W22N5lMKC0tBQDs27cPGzZswPr16ztcy5IlS1BVVWV75Ofnd/LdEHVM1uUANCxQK3EldDU+7mpMGewLAPj2WBEaTRaJKyKiniT5r6CC0HISqCiKrY5drX3z8erqajzwwANYv349fH19O1yDWq2Gh4dHiwdRTys2NKCgsh6Adc0Zsn/ThvjD00WJyvompJzhhGii/kQh1Tf29fWFXC5v1dtTXFzcqpenWWBgYJvtFQoFfHx8cPLkSVy4cAG33nqr7XmLxfpbm0KhQFZWFgYNGtTD74SoY3ZmWbdYCPFygVajlLga6giVQoZZo4Lwn5/zsOdsCcaFecLHXS11WUTUAyTrAVKpVIiNjUVycnKL48nJyZg0aVKbr4mPj2/VfseOHYiLi4NSqcSwYcNw/PhxZGRk2B633XYbpk+fjoyMDM7tIUn9mGkNQEM5/OVQRgR7IMrfHSaLiG+OFUldDhH1EMl6gAAgKSkJDz74IOLi4hAfH4/33nsPeXl5SExMBGCdm1NQUIDNmzcDABITE/HOO+8gKSkJjz/+OA4cOIANGzZgy5YtAACNRoORI0e2+B6enp4A0Oo4UV8ymszYe846T21YIIdYHYkgCLh1dDBW/3gWWZeqkVlkQHQQ/x8SOTpJA9DcuXNRVlaGZcuWoaioCCNHjsT27dsRHh4OACgqKmqxJlBERAS2b9+OhQsX4t1330VwcDBWr16NOXPmSPUWiDrkUHY56hrN8NAoEKzTSF0OdZKfVo3JUT7YfbYU3xwr5F18RP2AIHKBi1YMBgN0Oh2qqqo4IZp6xKvfnsL6PTmIDfPCnNgQqcuhLjCazPhH8hkYGkyYNSoI10Z1/EaLttw3IayHKiOiZp35/OavMER9YM9Z6/DX4AAufuio1Ao5ZkRbb9DYeboY9Y3cJ4zIkTEAEfWyYkMDTuurIQjAIG5+6tDGhnnBX6tGfZMZu84US10OEXUDAxBRL2ue/DwyWAc3taTT7qib5DIBN40MBAAcOF+GyrpGiSsioq5iACLqZc3DX82rCpNjGxqgRYSvG0wWkbvFEzkwBiCiXiSKoi0AXcsA1C8IgmCbC5SWW8FeICIHxQBE1ItO66tRWmOEi1KO2HAvqcuhHhLh64ZIPzeYRRG7uEUGkUNiACLqRXvOWj8cJ0R6Q62QS1wN9aTrh13uBbrAXiAiR8QARNSL9p0rA4BurxlD9ufXvUC7z7IXiMjRMAAR9ZImswWpF8oBAPGDfCSuhnrD9KH+AKxzgeqMJomrIaLOYAAi6iXHC6pQ22iGp6sS0dz/q1+K9HVDkE6DJrOIQ5fDLhE5BgYgol5y4Lx1+GtChDdkMkHiaqg3CIJgG948eL4MJrNF4oqIqKMYgIh6ycFsawCKj+TwV382KkQHD40C1UYTjl6slLocIuogBiCiXtBosiD1QgUAIH4QJ0D3ZwqZzPb/eN+5MnB/aSLHwABE1AuOXqxEfZMZPm4qDOEGqP3eNQO9oZAJ0BsakF9eJ3U5RNQBXQpAOTk5PV0HUb/SPP9nYqQPBIHzf/o7F5Uco0N0AICfORmayCF0KQBFRUVh+vTp+Oijj9DQ0NDTNRE5vOb5PxMjvSWuhPrKNQOt/6+PXaxCfaNZ4mqI6Gq6FICOHj2KsWPH4rnnnkNgYCCeeOIJ/Pzzzz1dG5FDajRZcCTPOv9nIidAO41Qb1cEemhgsoi2//9EZL+6FIBGjhyJlStXoqCgAJs2bYJer8e1116LESNGYOXKlSgp4aqo5LxOFlahockCL1clovw5/8dZCIKAayKsvUA/XyjnZGgiO9etSdAKhQJ33HEHPv30U7zxxhs4f/48Fi1ahJCQEDz00EMoKirqqTqJHEbz3V+x4d6c/+NkYkI9oZQLKKk2Ir+iXupyiOgKuhWAUlNT8eSTTyIoKAgrV67EokWLcP78efz0008oKCjA7bff3lN1EjmMw5cnwY4fyN3fnY1GKceIYOtk6HQOgxHZtS4FoJUrV2LUqFGYNGkSCgsLsXnzZuTm5uLvf/87IiIiMHnyZPz73//GkSNHerpeIrsmiiJSc60ffHEDOQHaGY0N9QRgnQxtsnBlaCJ7pejKi9auXYt58+bh0UcfRWBgYJttwsLCsGHDhm4VR+RosktrUV7bCLVChpEDuP+XMxrk7w6tRoHqBhPO6GswPJh/D4jsUZcCUHJyMsLCwiCTtexAEkUR+fn5CAsLg0qlwsMPP9wjRRI5iubd38eEekKtkEtcDUlBJggYE+KJvedKkZ5fwQBEZKe6NAQ2aNAglJaWtjpeXl6OiIiIbhdF5KgOX54Azfk/zm1smCcA4LS+mmsCEdmpLgWg9m7vrKmpgUaj6VZBRI6suQeI83+cW5DOBYEeGpgtIk4UVkldDhG1oVNDYElJSQCs6128+OKLcHV1tT1nNptx6NAhxMTE9GiBRI6iuLoBF8rqIAjAuDD2ADm70SE66E814ERBFcYzEBPZnU4FoPT0dADWHqDjx49DpVLZnlOpVBgzZgwWLVrUsxUSOYi0y8NfQwO00LkoJa6GpDZygA47Tl3C+ZIa1BlNcFV3acolEfWSTv2L3LlzJwDg0UcfxT//+U94eHByH1GzX+b/8Ld9Anzd1QjSaVBU1YBTRQYOixLZmS7NAdq0aRPDD9FvpOY2z//h8BdZNS+KyHlARPanwz1Ad955Jz744AN4eHjgzjvvvGLbbdu2dbswIkdSazThZKEBAHuA6BejBujwQ+YlnCuuQX2jGS4qLo1AZC86HIB0Op1tXyOdTtdrBRE5ooz8SpgtIgZ4uiDY00XqcshO+GnVCPTQQG+wDoPFhrN3kMhedDgAbdq0qc0/E9Ev+39x+It+a8QAD+gNDThZWMUARGRHujQHqL6+HnV1dbavc3NzsWrVKuzYsaPHCiNyJM07wHOiK/3WiCBrj/m54ho0mrg3GJG96FIAuv3227F582YAQGVlJa655hq8/fbbuP3227F27doeLZDI3pnMFhzJ4wrQ1LYADzU8XZUwWUScL6mRuhwiuqxLAejIkSOYMmUKAOCzzz5DYGAgcnNzsXnzZqxevbpHCySyd5lF1ahrNEOrUWCIv1bqcsjOCIKA6EDrXbOZRQaJqyGiZl0KQHV1ddBqrT/od+zYgTvvvBMymQwTJ05Ebm5ujxZIZO+ab3+PDfeCTCZIXA3Zo+igywFIXw1LO1sJEVHf6lIAioqKwpdffon8/Hx8//33SEhIAAAUFxdzfSByOkfyKgEAsdz+gtox0NcVaoUMtUYTLlbUS10OEaGLAejFF1/EokWLMHDgQEyYMAHx8fEArL1BY8eO7dECiezdkVzr/J9xvMOH2qGQyTAkwNprzmEwIvvQpQB01113IS8vD6mpqfjf//5nO3799dfjH//4R48VR2Tvig0NKKishyBYN78kao9tGIwBiMgudHl3vsDAQAQGBrY4ds0113S7ICJH0jz8NTRAC62GG6BS+4YEuEMAUFxtRGVdo9TlEDm9LgWg2tpavP766/jxxx9RXFwMi6Xl2hbZ2dk9UhyRvUvPtw5/jeX8H7oKV5UCod6uyCuvw5lLvB2eSGpdCkDz589HSkoKHnzwQQQFBdm2yCByNum5lQCAsWGektZBjmFIgPZyAKqWuhQip9elAPTdd9/h22+/xeTJk3u6HiKH0WS24FhBJQBgHHuAqAOGBmitm6OWWFeFVim6NA2TiHpAl/71eXl5wdubS/6TcztdVI2GJgs8NApE+rpJXQ45gCBPDdzUCjSaLLb1o4hIGl0KQK+88gpefPHFFvuBETmb5u0vxoZxAUTqGJkgYIi/OwAgJatE4mqInFuXhsDefvttnD9/HgEBARg4cCCUypZ3vxw5cqRHiiOyZ+mXAxCHv6gzhgRokZ5fiV1ZJVgyK1rqcoicVpcC0OzZs3usgDVr1uCtt95CUVERRowYgVWrVtn2GWtLSkoKkpKScPLkSQQHB+PPf/4zEhMTbc9v27YNr732Gs6dO4empiYMHjwYzz33HB588MEeq5kI+OUWeE6Aps4Y7G+9HT7rUjWKquoRpHORuiQip9SlAPTSSy/1yDffunUrFixYgDVr1mDy5Mn497//jZkzZ+LUqVMICwtr1T4nJwezZs3C448/jo8++gj79u3Dk08+CT8/P8yZMwcA4O3tjaVLl2LYsGFQqVT45ptv8Oijj8Lf3x833nhjj9RNVFpjRF55HQQBiGEAok5wVSsQ4uWC/Ip67D1birvjQqUuicgpdfkWhMrKSrz//vtYsmQJysutk/mOHDmCgoKCDp9j5cqVeOyxxzB//nxER0dj1apVCA0Nxdq1a9tsv27dOoSFhWHVqlWIjo7G/PnzMW/ePKxYscLWZtq0abjjjjsQHR2NQYMG4dlnn8Xo0aOxd+/err5VolbSL/f+RPm5w4MLIFInDbo8D2jfuVKJKyFyXl0KQMeOHcOQIUPwxhtvYMWKFaisrAQAfPHFF1iyZEmHztHY2Ii0tDTbRqrNEhISsH///jZfc+DAgVbtb7zxRqSmpqKpqalVe1EU8eOPPyIrKwvXXXddu7UYjUYYDIYWD6IrOcL5P9QNUX7WALT3XBlE7g5PJIkuBaCkpCQ88sgjOHv2LDQaje34zJkzsXv37g6do7S0FGazGQEBAS2OBwQEQK/Xt/kavV7fZnuTyYTS0l9+k6qqqoK7uztUKhVuvvlm/Otf/8INN9zQbi3Lly+HTqezPUJD2SVNV2abAB3uKW0h5JDCvF2hUcpQWmPkqtBEEulSADp8+DCeeOKJVscHDBjQbnhpz29XkRZF8YorS7fV/rfHtVotMjIycPjwYbz66qtISkrCrl272j3nkiVLUFVVZXvk5+d36j2QczGZLTiaXwWAW2BQ1yjkMlwT4QMA2MthMCJJdGkStEajaXOYKCsrC35+fh06h6+vL+RyeavAVFxc3KqXp1lgYGCb7RUKBXx8fGzHZDIZoqKiAAAxMTHIzMzE8uXLMW3atDbPq1aroVarO1Q3UdalatQ3maFVK2xDGUSddW2UD3afKcG+c6V47NoIqcshcjpd6gG6/fbbsWzZMtu8G0EQkJeXh7/85S+2u7GuRqVSITY2FsnJyS2OJycnY9KkSW2+Jj4+vlX7HTt2IC4urtVaRL8miiKMRmOH6iK6mubb32PCPLkAInXZ5ChfAMDB7DI0mS1XaU1EPa1LAWjFihUoKSmBv78/6uvrMXXqVERFRUGr1eLVV1/t8HmSkpLw/vvvY+PGjcjMzMTChQuRl5dnW9dnyZIleOihh2ztExMTkZubi6SkJGRmZmLjxo3YsGEDFi1aZGuzfPlyJCcnIzs7G6dPn8bKlSuxefNmPPDAA115q0StpOdxB3jqvuhAD3i7qVDXaEZGfqXU5RA5nS4NgXl4eGDv3r3YuXMn0tLSYLFYMG7cOMyYMaNT55k7dy7KysqwbNkyFBUVYeTIkdi+fTvCw8MBAEVFRcjLy7O1j4iIwPbt27Fw4UK8++67CA4OxurVq1v0OtXW1uLJJ5/ExYsX4eLigmHDhuGjjz7C3Llzu/JWiVppvgV+HNf/oW6QyQRMGuSDb44VYe/ZUowfyP0VifqSIHbyHkyLxYIPPvgA27Ztw4ULFyAIAiIiInDXXXfhwQcfvOIEZkdhMBig0+lQVVUFDw8PqcshO1Je24hxr1iHYY++mACda+fWAPrPobyrNyKncN+EMHzycx7+su044sK98Nkf2x76J6KO68znd6eGwERRxG233Yb58+ejoKAAo0aNwogRI5Cbm4tHHnkEd9xxR7cKJ7J3GfnW4a9Bfm6dDj9Ev9U8Dyg9vxLVDa3XMiOi3tOpIbAPPvgAu3fvxo8//ojp06e3eO6nn37C7NmzsXnz5hbzdoj6kyO5lQA4/4d6Rqi3K8J9XJFbVoefc8pxfXTbd8ASUc/rVA/Qli1b8Pzzz7cKPwDwu9/9Dn/5y1/w8ccf91hxRPYmPZ8rQFPPau4F4npARH2rUwHo2LFjuOmmm9p9fubMmTh69Gi3iyKyR2aLiIzmCdBcAZp6yLWXAxD3BSPqW50KQOXl5e0uUghYt6WoqKjodlFE9ujMpWrUNprhrlZgsL9W6nKon4iP9IEgAGcu1aDY0CB1OUROo1MByGw2Q6Fof9qQXC6HyWTqdlFE9qj59vcxoTrIuQAi9RAvNxVGBusAAPvOsxeIqK90ahK0KIp45JFH2t02gqstU3/WvAP82FDO/6GeNTnKF8cLqrD3bBnuGBsidTlETqFTAejhhx++ahveAUb9FXeAp94yOcoH61LO48D50qtuCE1EPaNTAWjTpk29VQeRXauqa8L5kloA7AGinhcX7g2lXEBhVQNyy+ow0NdN6pKI+r0u7QVG5GyOXL79PcLXDV5uKomrof7GRSW3rS21/3yZxNUQOQcGIKIOSM/l+j/UuyYN8gEA7OdEaKI+wQBE1AFpl+f/xIYzAFHvmDTIuh7QgfNl6OQWjUTUBQxARFfBBRCpL8SEekKjlKGsthFnLtVIXQ5Rv8cARHQVWXrrAohaLoBIvUilkGH8QG8AHAYj6gsMQERX0Tz8FRPmyQUQqVc1D4NxIjRR72MAIrqKI5wATX2keSL0wewymC2cB0TUmxiAiK7iCCdAUx8ZEewBrUaB6gYTThZWSV0OUb/GAER0BaU1RuSW1UEQrENgRL1JIZdhQkTz7fAcBiPqTQxARFfQPPw1xF8LD41S4mrIGfyyHhADEFFvYgAiuoI07v9FfWxSlDUAHc4pR6PJInE1RP0XAxDRFXACNPW1If5a+LipUN9kxtGLlVKXQ9RvMQARtaPRZMGxi9aJqJwATX1FJhMwsXkY7ByHwYh6CwMQUTtOFRlgNFng5apEBHfnpj7EfcGIeh8DEFE7fj38JQhcAJH6TvOCiOl5lahvNEtcDVH/xABE1I5fJkBz+Iv61kAfVwTpNGg0W5B2OYgTUc9iACJqBydAk1QEQUA8h8GIehUDEFEbCivrUVTVALlMwJhQndTlkBPivmBEvYsBiKgNzdtfRAdp4apSSFwNOaPmHqBjFythaGiSuBqi/ocBiKgNzfMuYjn8RRIZ4OmCgT6usIjWRRGJqGcxABG1oTkAcQI0SSmew2BEvYYBiOg3aowmnCiwLoA4fqC3xNWQM+O+YES9hwGI6DeO5FbAIgKh3i4I9nSRuhxyYhMjrQEos8iA8tpGiash6l84u5Ocyn8O5V21zY5TegCAn7u6Q+2JeoufVo2hAVpkXarGwewyzBoVJHVJRP0Ge4CIfuNCaS0AYKAPt78g6XE9IKLewQBE9CtNZgvyK+oBAAO5/xfZAc4DIuodDEBEv3Kxoh5miwitWgEfN5XU5RBhQqQPZAKQXVILfVWD1OUQ9RsMQES/cqHs8vCXrxs3QCW7oHNRYuQA62rkB7I5DEbUUxiAiH7ll/k/rhJXQvQL2zygcxwGI+opDEBEl5ktInLL6wBw/g/Zl8m/WhBRFEWJqyHqHxiAiC4rqqpHo8kCjVKGAA+N1OUQ2cQN9IJSLqCgsh755fVSl0PULzAAEV3269vfZZz/Q3bEVaXA2FDrtiy8HZ6oZzAAEV2WU2Yd/org8BfZoeZ5QPt4OzxRj2AAIgJgEUUugEh2rXk9oH3nSmGxcB4QUXcxABEBKK42or7JDKVc4P5fZJfGhXvBXa1AeW0jThRWSV0OkcNjACLCL/N/wr3dIJdx/g/ZH6VchslR1l6glKwSiashcnwMQET49QKIXP+H7Nd1Q/wAALvPMgARdZfkAWjNmjWIiIiARqNBbGws9uzZc8X2KSkpiI2NhUajQWRkJNatW9fi+fXr12PKlCnw8vKCl5cXZsyYgZ9//rk33wI5OJHzf8hBXDfYGoCO5FWiqr5J4mqIHJukAWjr1q1YsGABli5divT0dEyZMgUzZ85EXl5em+1zcnIwa9YsTJkyBenp6Xj++efxzDPP4PPPP7e12bVrF37/+99j586dOHDgAMLCwpCQkICCgoK+elvkYMprG2FoMEEuCAj1Zg8Q2a9Qb1cM8nOD2SJi/zneDk/UHZIGoJUrV+Kxxx7D/PnzER0djVWrViE0NBRr165ts/26desQFhaGVatWITo6GvPnz8e8efOwYsUKW5uPP/4YTz75JGJiYjBs2DCsX78eFosFP/74Y1+9LXIw2Zd7f0K8XKCUS94pSnRFzcNgKWc4DEbUHZL9tG9sbERaWhoSEhJaHE9ISMD+/fvbfM2BAwdatb/xxhuRmpqKpqa2u4Pr6urQ1NQEb2/vdmsxGo0wGAwtHuQ8zpfUAAAG+btLXAnR1U1tngd0poTbYhB1g2QBqLS0FGazGQEBAS2OBwQEQK/Xt/kavV7fZnuTyYTS0ra7g//yl79gwIABmDFjRru1LF++HDqdzvYIDQ3t5LshRyWKIs6XWHuABvkxAJH9mxjpA7VChsKqBpwrrpG6HCKHJXl/v/CbLQdEUWx17Grt2zoOAG+++Sa2bNmCbdu2QaNpf2+nJUuWoKqqyvbIz8/vzFsgB3bJYESt0QSlXECoN9f/IfunUcpxTYS1R5vDYERdJ1kA8vX1hVwub9XbU1xc3KqXp1lgYGCb7RUKBXx8fFocX7FiBV577TXs2LEDo0ePvmItarUaHh4eLR7kHJqHvwb6uEEhk/z3AaIOmcp5QETdJtlPfJVKhdjYWCQnJ7c4npycjEmTJrX5mvj4+Fbtd+zYgbi4OCiVStuxt956C6+88gr+97//IS4urueLp37DNv+Hw1/kQKYNtQagQznlqG80S1wNkWOS9FfepKQkvP/++9i4cSMyMzOxcOFC5OXlITExEYB1aOqhhx6ytU9MTERubi6SkpKQmZmJjRs3YsOGDVi0aJGtzZtvvom//vWv2LhxIwYOHAi9Xg+9Xo+aGo6VU0tmi4icUs7/IcczyM8dAzxd0Giy4GAON0cl6gpJA9DcuXOxatUqLFu2DDExMdi9eze2b9+O8PBwAEBRUVGLNYEiIiKwfft27Nq1CzExMXjllVewevVqzJkzx9ZmzZo1aGxsxF133YWgoCDb49e3yhMBQEFlPYwmC1yUcgR5tj9HjMjeCIKA64b4AuC2GERdpZC6gCeffBJPPvlkm8998MEHrY5NnToVR44cafd8Fy5c6KHKqL9rvoMm0s8NsitMvCeyR1OH+GHLz/ncFoOoizjrk5zW2eJqAEAU1/8hBzQpyhdymYDsklrkl9dJXQ6Rw2EAIqfU0GS2fWgM9tdKXA1R53lolIgN8wIA7MwqlrgaIsfDAERO6XxJDSwi4OuugrebSupyiLrk+mh/AEDyqUsSV0LkeBiAyCmduWSd/8PeH3JkM4Zb10w7mF2G6gbuDk/UGQxA5HREUbTN/xkSwPk/5LgG+bkjwtcNTWYRe85yd3iizmAAIqdTUmNEZV0T5DIBEb4MQOTYZlweBvuBw2BEncIARE7n7OXhrwgfN6gU/CdAjm1GtHUY7KesYpjMFomrIXIc/OlPTqd5+Gswh7+oH4gN94KnqxKVdU1Iy62Quhwih8EARE6l0WRBdol1+4shAZwATY5PIZfhd0N5NxhRZzEAkVM5V1wNk0WEl6sS/lq11OUQ9YiEEdZhsO9O6CGKosTVEDkGBiByKpl66/DXsCAPCNz+gvqJqUP84aKUo6CyHicKDFKXQ+QQGIDIaVgsIrIuB6DoQA+JqyHqOS4qOaYP8wMAfHeiSOJqiBwDAxA5jaMXK1FjNEGtkGGgr6vU5RD1qJtGBgHgMBhRRzEAkdP4MdO6X9LgAC0UMv7Vp/7ld8P8oVLIkFNai6xL1VKXQ2T3+ClATuOHTOsdMtGBvPuL+h93tQLXDb48DHZcL3E1RPaPAYicQn55HU7rqyGAt79T/zVzZCAAYPvxIg6DEV0FAxA5hf+dsP5GHOHrBje1QuJqiHrHDSMCoFLIcLa4Bqf1HAYjuhIGIHIK2y/fGTNigE7iSoh6j4dGaVsU8b8ZhRJXQ2TfGICo3yuqqkd6XiUEARgRxNvfqX+7PSYYAPD10UJYLBwGI2oPAxD1e83DX7FhXvBwUUpcDVHvmj7MH1q1AgWV9UjL495gRO1hAKJ+77vLAWjmqCCJKyHqfRqlHDdengz934wCiashsl8MQNSvFVc34PCFcgDATZc/FIj6u+ZhsG+PFaHJbJG4GiL7xABE/dp3x/UQRWBMiA4DPF2kLoeoT8RH+sDXXY2Kuib8dLpY6nKI7BIDEPVrX14eArgtZoDElRD1HYVchjnjrH/n/1/qRYmrIbJPDEDUb+WW1SI9rxIyAbh1DOf/kHO5Oy4EALAzqxjF1Q0SV0NkfxiAqN9qXgdlcpQv/LUaiash6ltR/lqMC/OE2SLiiyOcDE30WwxA1C+Joogv060/9G/n8Bc5qbvjQgEAn6bmc2sMot9gAKJ+6XhBFbJLa6FRynDjiACpyyGSxC2jg6BRynC+pBZH8iqlLofIrjAAUb+07XKX/4zoAGg1XPyQnJNWo8TNo6y3xH98KFfiaojsCwMQ9TtGk9l299eccSESV0MkrQcmhgEAvjlWhPLaRomrIbIfDEDU7+w4eQmVdU0I9NDguiF+UpdDJKmYUE+MGqBDo8mCT1PzpS6HyG4wAFG/s/Ww9Yf8PXEhkMsEiashkpYgCHhwYjgA6zCYmRukEgFgAKJ+Jr+8DnvPlUIQfrkDhsjZ3TomGDoXJfLL67H7TInU5RDZBQYg6leau/ivjfJFqLerxNUQ2QcXlRx3x1rnw23clyNxNUT2gQGI+o0m8y9zHO5h7w9RCw9PGgiZAOw5W4pThQapyyGSHAMQ9Rv/O6HHJYMRflo1bhzBnd+Jfi3U2xUzR1m3hHl/T7bE1RBJjwGI+o0P9l8AANx3TRhUCv7VJvqtJ66LBAB8dbQQhZX1EldDJC1+SlC/cPxiFdJyK6CUC7h/QpjU5RDZpdEhnpgY6Q2TRcQmzgUiJ8cARP1Cc+/PzaOC4O/BjU+J2vOHy71A/zmUhwoujEhOjAGIHF5JtRFfH7Xu/P7wpIHSFkNk56YP9cfwIA/UNprx/l7OBSLnxQBEDm/jvhw0mi0YG+aJsWFeUpdDZNcEQcCCGYMBAB/su8BeIHJaDEDk0AwNTfjogHWTxyenRUlcDZFjuGF4AHuByOkxAJFD+/hgHqqNJgz2d8f1w/ylLofIIfy2F4ibpJIzYgAih9XQZMaGvdY7WRKnDoKM+34RddgNwwMwcoC1F2j1j2elLoeozzEAkcPaejgfpTVGDPB0wW0xwVKXQ+RQBEHAkpnRAICPDubiQmmtxBUR9S2F1AUQdUV9oxnv7DwHAEicNghKObM8OZb/HMqTugTcNyEMU4f4IeVMCd76Pgvv3j9O6pKI+gw/NcghfXQwFyXV1t6fudz3i6jLlswaBkEAvj1ehLTcCqnLIeozkgegNWvWICIiAhqNBrGxsdizZ88V26ekpCA2NhYajQaRkZFYt25di+dPnjyJOXPmYODAgRAEAatWrerF6kkKNUYT1qacBwA8O2Mwt70g6oZhgR62neJf+uoEzBZR4oqI+oaknxxbt27FggULsHTpUqSnp2PKlCmYOXMm8vLa7hrOycnBrFmzMGXKFKSnp+P555/HM888g88//9zWpq6uDpGRkXj99dcRGMgNMfujTXtzUF7biAhfN9w5doDU5RA5vD/fNAxajQInCgz4z6Fcqcsh6hOSBqCVK1fisccew/z58xEdHY1Vq1YhNDQUa9eubbP9unXrEBYWhlWrViE6Ohrz58/HvHnzsGLFClub8ePH46233sK9994LtVrdoTqMRiMMBkOLB9mnkmoj1l3u/VkwYzAUnPtD1G2+7mr8341DAQBvfZ+F0hqjxBUR9T7JPj0aGxuRlpaGhISEFscTEhKwf//+Nl9z4MCBVu1vvPFGpKamoqmpqcu1LF++HDqdzvYIDeWcEnu1MvkMahvNGBPqiVtH884vop5y/4RwjBzgAUODCa99myl1OUS9TrIAVFpaCrPZjICAgBbHAwICoNfr23yNXq9vs73JZEJpaWmXa1myZAmqqqpsj/z8/C6fi3rPab0BWw9bh0dfuDma6/4Q9SC5TMDfZ4+CTAC2pRfgx8xLUpdE1KskHz8QhJYfYqIotjp2tfZtHe8MtVoNDw+PFg+yL6Io4tVvM2ERgVmjAhE30Fvqkoj6nZhQT8yfYt0t/vkvjqOqrus960T2TrJ1gHx9fSGXy1v19hQXF7fq5WkWGBjYZnuFQgEfH59eq5V6RnfWPTlRUIU9Z0shlwmIDvSwizVUiPqjpBuG4IdTl5BdWouXvzmJlffESF0SUa+QrAdIpVIhNjYWycnJLY4nJydj0qRJbb4mPj6+VfsdO3YgLi4OSqWy12olaRmbzPjmWCEAYOoQP/i4d2xyOxF1nkYpx1t3j4YgANuOFOCro4VSl0TUKyQdAktKSsL777+PjRs3IjMzEwsXLkReXh4SExMBWOfmPPTQQ7b2iYmJyM3NRVJSEjIzM7Fx40Zs2LABixYtsrVpbGxERkYGMjIy0NjYiIKCAmRkZODcuXN9/v6oZ/x0uhiGBhO83VSYOsRP6nKI+r3YcG88PT0KAPD8tuPIK6uTuCKinidpAJo7dy5WrVqFZcuWISYmBrt378b27dsRHh4OACgqKmqxJlBERAS2b9+OXbt2ISYmBq+88gpWr16NOXPm2NoUFhZi7NixGDt2LIqKirBixQqMHTsW8+fP7/P3R91XVFWPfeetE9xvHR3MLS+I+siz1w9GXLgXaowm/GnLETSaLFKXRNSjBLF5FjHZGAwG6HQ6VFVVcUJ0D+rsvB2zRcTalHMorGzAiGAP3D8hvJcqI3JO900Iu+LzBZX1mPXPPaiqb8IDE8Pw99mj+qgyoq7pzOc3f50mu7X3bAkKKxvgopTjtjFc84eorw3wdMGquTEQBOCjg3n45GfefED9BwMQ2aViQwN+PF0MALh5dBC0Gk5yJ5LC9GH+eO6GIQCAF/57AqkXyiWuiKhnMACR3TGZLfg0NR8mi4ghAe4YG+opdUlETu2p6VGYOTIQTWYRj29ORXZJjdQlEXUbAxDZneRTl1BY1QBXlRx3jg3p1iKXRNR9giDg7XvGYEyIDhV1TXh4088oqeZ+YeTYGIDIrpwrrsGec9a7vuaMC4GHC4e+iOyBq0qBDY+MR5i3K/LL6/Hwxp+5UjQ5NAYgshu1RhP+X5p1H7ZrIrwRHcQ78Ijsia+7Gh/Ouwa+7iqcKjLgoU0/o7qBIYgcEwMQ2QVRFLEtvQDVDSb4adWYNTJI6pKIqA0Rvm74aP4EeLoqcTS/Eo9uOswQRA6JAYjsws8XypFZZIBcJmBuXChUCv7VJLJXwwI98NFjE6DVKJCaW4H73z+E8tpGqcsi6hR+ypDkCivr8e2xIgDAjcMDEOzpInFFRHQ1IwfosOXxifB2U+HYxSrM/fcBFFXVS10WUYcxAJGk6hpN+PhQLkwWEUMDtJgU5St1SUTUQSMH6PDpExMR4KHG2eIazH53H04WVkldFlGHMACRZCyiiE9T81FR1wRvNxXuiQuFjLe8EzmUKH8tPv/jJAz2d8clgxH3rDuAH05dkrosoqtSSF0AOa+fThfjzKUaKGQC7p8QBheVXOqSiJxKZ/fnu5J7x4fh40O5yC6txfzNqZg2xA8zhgdc8Zeaq+1FRtSb2ANEkjhdZMBPl7e6uGPsAATpOO+HyJG5qOR4ZPJAxA/yAQDsOlOCTftyUGM0SVwZUdsYgKjPldUY8enl9X4mRnpjbJiXxBURUU9QyGS4dXQw5o4PhUouw/mSWrzz01nkldVKXRpRKwxA1KfqG83YfCAXDU0WhHm7YtYorvdD1N+MCfHEH6cNgp+7GoYGE97bk43kU5dgslikLo3IhgGI+ozZIuKTw3koqTFC56LEfRPCoJDxryBRfxTgocGT0wZhdIgOFhHYmVWMdbvOQ1/VIHVpRAAYgKgPfXu8EGeLa6CUC3hwYjg8NNzni6g/UyvluHd8GO4dHwpXlRyFVQ14d9c5pGQVw2wRpS6PnBzvAqM+8eH+CziYXQ4BwNy4UC52SORERod4IsLXDV+mFyBTX43vT13CiUIDxoTqMDrEU+ryyEmxB4h6XcqZErz89UkAQMKIQAwP1klcERH1Na1GiQcmhuOucSHQKGUoqKzH7e/uw0v/PQED9xIjCTAAUa/KLDLg6Y+PwCIC48K8cN1grvRM5KwEQcC4cC8snDEEMaGeEEXgwwO5mPF2Cr4+WghR5LAY9R0GIOo1Fyvq8PDGn1FtNOGaCG/MjgmGwJWeiZyeVqPEPXGh+Hj+BET4uqG42og/bUnH3PcO4kQBt9KgvsEARL2iorYRD2/8GcXVRgwN0GL9Q3FQyPnXjYh+MTnKF989OwULZgyGRinDzznluPWdvVj82TGUVBulLo/6OX4iUY9raDJj/uZUnC+pRZBOgw/mjYfOhXd8EVFrGqUcC2YMwY/PTcNtY4IhisDW1HxMX7EL/045D6PJLHWJ1E8xAFGPMpkt+NOWdKTlVsBDo8CH867hNhdEdFUDPF2w+vdj8fkf4zE6RIcaownLvzuNhH/sxv9O6Dk/iHocAxD1GItFxNIvTiD51CWoFDK8//B4DAnQSl0WETmQ2HBvfPnkZKy4ewz8tGrkltUh8aM03LFmP/afL5W6POpHGICoR4iiiL99fRJbU/MhE4DV98bgmghvqcsiIgckkwm4KzYEOxdNw9PTo+CilCMjvxL3rT+EBzccwvGLnChN3SeI7FdsxWAwQKfToaqqCh4eHlKXY/dEUcTy707jvd3ZEARg5T1jcMfYkFbt/nMoT4LqiMjRVTc0YWdWCQ7nlMN8+SNr5AAdbogOgJ9W3a1z3zchrCdKJDvRmc9vrgRN3faPH87ivd3ZAIDX7hjVZvghIuoqrUaJ28YE49ooX/yYeQkZ+ZU4UVCFU4VViAn1xHVD/OCv1UhdJjkYBiDqljW7zmH1j2cBAC/dOhy/v4a/TRFR7/B2U+HuuFBMGeyH5FN6ZOqrcSSvEul5lRge7IFpQ/wxwIs3XVDHMABRl4iiiNU/nsM/fjgDAFh80zA8OjlC4qqIyBkE6jR4MH4gLlbUYVdWCU4VGXCy0PoY7O+O+EE+GBKghYwLr9IVMABRp4miiFe/zcT7e3MAAIsShuCP0wZJXBUROZsQL1c8MDEclwwNSDlTgmMXK3G2uAZni2vg5arENRE+iA33gruaH3XUGv9WUKeYLSKWfnEcnxzOB2Ad9mLPDxFJKcBDg3viQjEjOgAHs8uQlluBiromfH9Sjx8yLyE6UItRIZ4YGqCFSsGbn8mKAYg6rMlswcKtGfjmWBFkAvD6nNG4Jy5U6rKIiABY5wjNGhWEG4YH4NjFKhzKKcPFinqcKDTgRKEBSrmAYYEeGDlAhyg/d7io5FKXTBJiAKIOqaprwlP/OYK950qhlAtYNXcsbh4dJHVZREStKOUyxIZ7ITbcC4WV9Th2sQrHCypRUdeE4wVVOF5QBQFAkKcGOaU1mDTIF2PDPOHpqpK6dOpDXAeoDVwHqKXzJTWY/2Eqckpr4aKUY8394zB9mH+nz8N1gIhIKqIooqCyHscvViFTX43SmtabrepclAj00CBQp0GAhxperip4uqqg1Sh6dUI11yLqOVwHiHrMnrMleOrjIzA0mBCs02D9w3EYEayTuiwiok4RBAEhXq4I8XLFzFFBMNQ3Ibu0BtkltcgurUV5bSOq6ptQVd+ErEvVLV4rE6zhyMNFCa1GCa1GAa1a8cufNdY/u6rkvPPMgTAAUZtEUcSH+y/glW8zYbaIGBfmiX8/GNftVVeJiOyBh4sSMaFeiAn1AgA0NJlxydCAoqoG6A0NKDYYUVnfCEN9EywiUFHXhIq6piueUyYA7r8KRu6/CUk+7mr4uqugkHEitj1gAKJWDA1NWPL5cXx7vAgAcOe4AVh+5yioFZwwSET9k0YpR7iPG8J93FocN1tEVDc0obKuCYaGJlQ3mFDdYEKN8Zc/Vzc0obbRDIsIGBpMMDSY2v0+ckGAr1aFAA8NAj00CPV2Ra3RBDfeqt/neMWphcMXypH0aQbyy+uhkAn4y8xheOzaCAjs1iUiJySXCfC8PBfoSswWETVGaxiqbjChpsEEg/FXf25oQkm1EUaTBZcMRlwyGHEM1k1dP9h/AdFBWowL88KECB9MjvLhhOw+wABEAID6RjNW7MjCxn05EEUg1NsF//r9OMSEekpdGhGR3ZPLBOhclNC5KNttI4oiquqbcMnQgEsGIwoq65FXXoeq+iacKDDgRIEBmw/kQhCA0SGemBLliymDfTEu3AtKOYfNehoDECH51CW8/PVJXKyoBwDcExeCv94yHB6a9v8hExFR5wjCL71JQwN/OT5tqB+O5FUg9UIF9p0rxdniGhzNr8TR/Eq8s/MctBoFpg7xw++G+WPaUH94u7F3qCcwADmx03oDXv/uNHZllQAAgnUavHrnKEwf2vlb3ImIqGuCPV0Q7OmCW0YHAwCKquqx92wp9pwtxe6zJaisa8I3x4rwzbEiCAIwNtQTvxvmj+nD/DE8yINTFLqI6wC1ob+vA3SuuAZrdp7DFxkFEEVAIRMwf0oknrk+Cq6q3svEXAeIiKi1K60DZLaIyMivwE+ni/FjZjFO61veoh+k02DaUH9cP8wfk6J8evVnuCPozOc3A1Ab+mMAEkURP+eUY9O+C/j+lB7N/9dvHhWE5xKGINLPvddrYAAiImqtMwshFlbWY2dWMX7KLMa+86VoaLLYnlMpZIiP9MH10f6YPtQfod6uvVGuXeNCiGRzydCAr48W4tPUfJy5VGM7njA8AE//LgqjQzylK46IiDol2NMF908Ix/0TwtHQZMaB7DLsvNw7VFBZj5QzJUg5UwLgJAb7u+N30f743VB/xIZ7QcGJ1C2wB6gNjtwDJIoiLpTV4cfMS/gh8xIO5ZTbentclHLMHhuMeZMjMDhA2+e1sQeIiKi1ntgKQxRFnC2uwU+nrb1DaXkVMFt++Xj30Chs+6ONC/fCmBDPfrn2kEP1AK1ZswZvvfUWioqKMGLECKxatQpTpkxpt31KSgqSkpJw8uRJBAcH489//jMSExNbtPn888/xwgsv4Pz58xg0aBBeffVV3HHHHb39ViTR0GTGaX01Ui+UIy23AocvVLTa4yY23AuzY4Jx+9gBvLOLiKgfEgQBQwK0GBKgReLUQaiqa0LK2RL8lHkJu85YJ1LvzCrBzss3vcgEYEiAFsMCtRgW5IGhgdY/B3ponGZStaQBaOvWrViwYAHWrFmDyZMn49///jdmzpyJU6dOISysdSLOycnBrFmz8Pjjj+Ojjz7Cvn378OSTT8LPzw9z5swBABw4cABz587FK6+8gjvuuANffPEF7rnnHuzduxcTJkzo67fYbWaLiLJaI4oNRhRXN0BfZUROaQ3Ol9TifEkN8svrYPlNH55KLkPcQC9cHx2AhOEBTjkOTETkzHSuStw2Jhi3jQmG2SLiREEV0nIrcCSvAkdyK1BY1YDT+mrrpOqMQtvrNEoZwrxdLz/cMMDLBX5aNfy1avhdfmjVin4RkiQdApswYQLGjRuHtWvX2o5FR0dj9uzZWL58eav2ixcvxldffYXMzEzbscTERBw9ehQHDhwAAMydOxcGgwHfffedrc1NN90ELy8vbNmypUN19dYQmL6qAd8cK4TRZIGxyWz9r8kCo8kMY5MFDSYzqi8vo15db112vaKuqUU3Zls8XZWIDfNC7EAvjB/ojVEDdNAo7W/bCg6BERG1JsVu8EVV9ThVaMBpfTUyi6z/zS6pafULdVt+u+dZ82aw7moFVAoZlHIZ1AoZlHLB9rVKIYNKbv2zXCZALhPg567GjOEBPfq+HGIIrLGxEWlpafjLX/7S4nhCQgL279/f5msOHDiAhISEFsduvPFGbNiwAU1NTVAqlThw4AAWLlzYqs2qVavarcVoNMJo/GXYqKrKujy5wWDozFu6qqz8Cizbltbp1wkC4OOmgu/lFB7q7YpIXzdE+Lgjws8Vvu7qFmm8sb4WjfU9WXnPqKutvnojIiIn09OfNR3hJgDjB7hg/AAXANa13xpNFhRW1eNiRT0uVtQhv7wOl6oaUFLTiLIaI0prjKgxmmEBUNkAVFZ1r4YxITpcEzKx2+/l15qvZUf6diQLQKWlpTCbzQgIaJn+AgICoNfr23yNXq9vs73JZEJpaSmCgoLabdPeOQFg+fLlePnll1sdDw0N7ejb6XXsOyEi6p8el7oAieQD0C3qnXNXV1dDp9NdsY3kk6B/O44oiuIVxxbbav/b450955IlS5CUlGT72mKxoLy8HD4+PpKNcxoMBoSGhiI/P9/h7kSzZ7yuvYPXtXfwuvYOXtfeYQ/XVRRFVFdXIzg4+KptJQtAvr6+kMvlrXpmiouLW/XgNAsMDGyzvUKhgI+PzxXbtHdOAFCr1VCr1S2OeXp6dvSt9CoPDw/+A+0FvK69g9e1d/C69g5e194h9XW9Ws9PM8lWRVKpVIiNjUVycnKL48nJyZg0aVKbr4mPj2/VfseOHYiLi4NSqbxim/bOSURERM5H0iGwpKQkPPjgg4iLi0N8fDzee+895OXl2db1WbJkCQoKCrB582YA1ju+3nnnHSQlJeHxxx/HgQMHsGHDhhZ3dz377LO47rrr8MYbb+D222/Hf//7X/zwww/Yu3evJO+RiIiI7I+kAWju3LkoKyvDsmXLUFRUhJEjR2L79u0IDw8HABQVFSEv75fpvxEREdi+fTsWLlyId999F8HBwVi9erVtDSAAmDRpEj755BP89a9/xQsvvIBBgwZh69atDrcGkFqtxksvvdRqaI66h9e1d/C69g5e197B69o7HO26cisMIiIicjrcGY2IiIicDgMQEREROR0GICIiInI6DEBERETkdBiAJLZ7927ceuutCA4OhiAI+PLLL1s8L4oi/va3vyE4OBguLi6YNm0aTp48KU2xDmL58uUYP348tFot/P39MXv2bGRlZbVow+vaeWvXrsXo0aNti5zFx8e32HSY17RnLF++HIIgYMGCBbZjvLZd87e//Q2CILR4BAYG2p7nde2agoICPPDAA/Dx8YGrqytiYmKQlvbLPpeOcl0ZgCRWW1uLMWPG4J133mnz+TfffBMrV67EO++8g8OHDyMwMBA33HADqqu5sWh7UlJS8NRTT+HgwYNITk6GyWRCQkICamtrbW14XTsvJCQEr7/+OlJTU5Gamorf/e53uP32220/2HhNu+/w4cN47733MHr06BbHeW27bsSIESgqKrI9jh8/bnuO17XzKioqMHnyZCiVSnz33Xc4deoU3n777Ra7JzjMdRXJbgAQv/jiC9vXFotFDAwMFF9//XXbsYaGBlGn04nr1q2ToELHVFxcLAIQU1JSRFHkde1JXl5e4vvvv89r2gOqq6vFwYMHi8nJyeLUqVPFZ599VhRF/n3tjpdeekkcM2ZMm8/xunbN4sWLxWuvvbbd5x3purIHyI7l5ORAr9cjISHBdkytVmPq1KnYv3+/hJU5lqqqKgCAt7c3AF7XnmA2m/HJJ5+gtrYW8fHxvKY94KmnnsLNN9+MGTNmtDjOa9s9Z8+eRXBwMCIiInDvvfciOzsbAK9rV3311VeIi4vD3XffDX9/f4wdOxbr16+3Pe9I15UByI41b+r6241cAwICWm34Sm0TRRFJSUm49tprMXLkSAC8rt1x/PhxuLu7Q61WIzExEV988QWGDx/Oa9pNn3zyCdLS0rB8+fJWz/Hadt2ECROwefNmfP/991i/fj30ej0mTZqEsrIyXtcuys7Oxtq1azF48GB8//33SExMxDPPPGPbssqRrqukW2FQxwiC0OJrURRbHaO2Pf300zh27Fibe8Hxunbe0KFDkZGRgcrKSnz++ed4+OGHkZKSYnue17Tz8vPz8eyzz2LHjh3QaDTttuO17byZM2fa/jxq1CjEx8dj0KBB+PDDDzFx4kQAvK6dZbFYEBcXh9deew0AMHbsWJw8eRJr167FQw89ZGvnCNeVPUB2rPluhd+m5uLi4lbpmlr705/+hK+++go7d+5ESEiI7Tiva9epVCpERUUhLi4Oy5cvx5gxY/DPf/6T17Qb0tLSUFxcjNjYWCgUCigUCqSkpGD16tVQKBS268dr231ubm4YNWoUzp49y7+zXRQUFIThw4e3OBYdHW3bt9ORrisDkB2LiIhAYGAgkpOTbccaGxuRkpKCSZMmSViZfRNFEU8//TS2bduGn376CRERES2e53XtOaIowmg08pp2w/XXX4/jx48jIyPD9oiLi8P999+PjIwMREZG8tr2EKPRiMzMTAQFBfHvbBdNnjy51bIiZ86csW1i7lDXVbLp1ySKovXOj/T0dDE9PV0EIK5cuVJMT08Xc3NzRVEUxddff13U6XTitm3bxOPHj4u///3vxaCgINFgMEhcuf364x//KOp0OnHXrl1iUVGR7VFXV2drw+vaeUuWLBF3794t5uTkiMeOHROff/55USaTiTt27BBFkde0J/36LjBR5LXtqueee07ctWuXmJ2dLR48eFC85ZZbRK1WK164cEEURV7Xrvj5559FhUIhvvrqq+LZs2fFjz/+WHR1dRU/+ugjWxtHua4MQBLbuXOnCKDV4+GHHxZF0XpL4UsvvSQGBgaKarVavO6668Tjx49LW7Sda+t6AhA3bdpka8Pr2nnz5s0Tw8PDRZVKJfr5+YnXX3+9LfyIIq9pT/ptAOK17Zq5c+eKQUFBolKpFIODg8U777xTPHnypO15Xteu+frrr8WRI0eKarVaHDZsmPjee++1eN5RrqsgiqIoTd8TERERkTQ4B4iIiIicDgMQEREROR0GICIiInI6DEBERETkdBiAiIiIyOkwABEREZHTYQAiIiIip8MARERERE6HAYiIumzatGlYsGBBu88LgoAvv/yyw+fbtWsXBEFAZWVlt2sjIroShdQFEFH/VVRUBC8vL6nLICJqhQGIiHpNYGCg1CUQEbWJQ2BE1C0WiwV//vOf4e3tjcDAQPztb3+zPffbIbD9+/cjJiYGGo0GcXFx+PLLLyEIAjIyMlqcMy0tDXFxcXB1dcWkSZOQlZUFAKiqqoJcLkdaWhoAQBRFeHt7Y/z48bbXbtmyBUFBQbavFy9ejCFDhsDV1RWRkZF44YUX0NTUBAC4cOECZDIZUlNTW3z/f/3rXwgPD0d7WyU+8sgjmD17Nl577TUEBATA09MTL7/8MkwmE/7v//4P3t7eCAkJwcaNG22vuXDhAgRBwCeffIJJkyZBo9FgxIgR2LVrV4tzf/XVVxg8eDBcXFwwffp0fPjhhxwWJOoFDEBE1C0ffvgh3NzccOjQIbz55ptYtmwZkpOTW7Wrrq7GrbfeilGjRuHIkSN45ZVXsHjx4jbPuXTpUrz99ttITU2FQqHAvHnzAAA6nQ4xMTG20HDs2DHbfw0GAwDrPKKpU6fazqXVavHBBx/g1KlT+Oc//4n169fjH//4BwBg4MCBmDFjBjZt2tTi+2/atAmPPPIIBEFo933/9NNPKCwsxO7du7Fy5Ur87W9/wy233AIvLy8cOnQIiYmJSExMRH5+fovX/d///R+ee+45pKenY9KkSbjttttQVlYGwBqS7rrrLsyePRsZGRl44oknsHTp0nZrIKJukHYzeiJyZFOnThWvvfbaFsfGjx8vLl68WBRFUQQgfvHFF6IoiuLatWtFHx8fsb6+3tZ2/fr1IgAxPT1dFEVR3LlzpwhA/OGHH2xtvv32WxGA7XVJSUniLbfcIoqiKK5atUq86667xHHjxonffvutKIqiOGTIEHHt2rXt1vzmm2+KsbGxtq+3bt0qenl5iQ0NDaIoimJGRoYoCIKYk5PT7jkefvhhMTw8XDSbzbZjQ4cOFadMmWL72mQyiW5ubuKWLVtEURTFnJwcEYD4+uuv29o0NTWJISEh4htvvCGKoiguXrxYHDlyZIvvtXTpUhGAWFFR0W49RNR57AEiom4ZPXp0i6+DgoJQXFzcql1WVhZGjx4NjUZjO3bNNddc9ZzNw1nN55w2bRr27NkDi8WClJQUTJs2DdOmTUNKSgr0ej3OnDnTogfos88+w7XXXovAwEC4u7vjhRdeQF5enu352bNnQ6FQ4IsvvgAAbNy4EdOnT8fAgQORl5cHd3d32+O1116zvW7EiBGQyX75ERoQEIBRo0bZvpbL5fDx8Wl1LeLj421/VigUiIuLQ2Zmpu0a/Xo470rXiIi6hwGIiLpFqVS2+FoQBFgsllbtRFFsNaQktjPH5tfnbH5N8zmvu+46VFdX48iRI9izZw+mTZuGqVOnIiUlBTt37oS/vz+io6MBAAcPHsS9996LmTNn4ptvvkF6ejqWLl2KxsZG2/lVKhUefPBBbNq0CY2NjfjPf/5jG3ILDg5GRkaG7ZGYmHjF993Ra/Fbze+xM9eIiLqHd4ERUZ8YNmwYPv74YxiNRqjVagBoNfm4I5rnAb3zzjsQBAHDhw9HcHAw0tPT8c0337To/dm3bx/Cw8NbzKPJzc1tdc758+dj5MiRWLNmDZqamnDnnXcCsPbQREVFdbrGKzl48CCuu+46AIDJZEJaWhqefvppANZrtH379hbtu3KNiOjq2ANERH3ivvvug8ViwR/+8AdkZmbi+++/x4oVKwDgipON2zJt2jR89NFHmDp1KgRBgJeXF4YPH46tW7di2rRptnZRUVHIy8vDJ598gvPnz2P16tW2oa5fi46OxsSJE7F48WL8/ve/h4uLS7fe65W8++67+OKLL3D69Gk89dRTqKiosPU4PfHEEzh9+jQWL16MM2fO4NNPP8UHH3wAoPPXiIiujAGIiPqEh4cHvv76a2RkZCAmJgZLly7Fiy++CAAt5gV1xPTp02E2m1uEnalTp8JsNrfoAbr99tuxcOFCPP3004iJicH+/fvxwgsvtHnOxx57DI2NjbYw0ltef/11vPHGGxgzZgz27NmD//73v/D19QUARERE4LPPPsO2bdswevRorF271tZ71dxrRkQ9QxA5wExEEvn444/x6KOPoqqqqld7XTri1VdfxSeffILjx4/3yvkvXLiAiIgIpKenIyYmplN1rVu3rtXt9ETUPZwDRER9ZvPmzYiMjMSAAQNw9OhRLF68GPfcc4+k4aempgaZmZn417/+hVdeeUWyOpqtWbMG48ePh4+PD/bt24e33nrLNkeIiHoOAxAR9Rm9Xo8XX3wRer0eQUFBuPvuu/Hqq69KWtPTTz+NLVu2YPbs2b0+/NURZ8+exd///neUl5cjLCwMzz33HJYsWSJ1WUT9DofAiIiIyOlwEjQRERE5HQYgIiIicjoMQEREROR0GICIiIjI6TAAERERkdNhACIiIiKnwwBERERETocBiIiIiJzO/w+NeX67wZZ7lAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in x[colname]:\n",
    "    print(i)\n",
    "    print(skew(x[i]))\n",
    "    sns.distplot(x[i])\n",
    "    plt.figure()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "798af411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),cmap=\"Blues\",annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f4d1ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "highway-mpg         -0.707051\n",
       "city-mpg            -0.682415\n",
       "symboling           -0.096215\n",
       "normalized-losses    0.129980\n",
       "height               0.147417\n",
       "width                0.730630\n",
       "horsepower           0.771608\n",
       "engine-size          0.871044\n",
       "price                1.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"price\"].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37ac3372",
   "metadata": {},
   "source": [
    "From above we can see that there is need of removing skewness for normalized losses only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5e28c55c",
   "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>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>152</td>\n",
       "      <td>154.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>5.099866</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>109</td>\n",
       "      <td>102.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>5.099866</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>ohc</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses         make fuel-type   body-style  \\\n",
       "0          3           4.804021  alfa-romero       gas  convertible   \n",
       "1          3           4.804021  alfa-romero       gas  convertible   \n",
       "2          1           4.804021  alfa-romero       gas    hatchback   \n",
       "3          2           5.099866         audi       gas        sedan   \n",
       "4          2           5.099866         audi       gas        sedan   \n",
       "\n",
       "  drive-wheels engine-location  width  height engine-type  engine-size  \\\n",
       "0          rwd           front   64.1    48.8        dohc          130   \n",
       "1          rwd           front   64.1    48.8        dohc          130   \n",
       "2          rwd           front   65.5    52.4        ohcv          152   \n",
       "3          fwd           front   66.2    54.3         ohc          109   \n",
       "4          4wd           front   66.4    54.3         ohc          136   \n",
       "\n",
       "   horsepower  city-mpg  highway-mpg  \n",
       "0       111.0        21           27  \n",
       "1       111.0        21           27  \n",
       "2       154.0        19           26  \n",
       "3       102.0        24           30  \n",
       "4       115.0        18           22  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[\"normalized-losses\"]=np.log(x[\"normalized-losses\"])\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "60fb2574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020674302162479046"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(x[\"normalized-losses\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a977d6e2",
   "metadata": {},
   "source": [
    "Skewness is reduced for normalized-losses from 0.846 to 0.020"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f91c721",
   "metadata": {},
   "source": [
    "# Handling Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a9b2991e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['make', 'fuel-type', 'body-style', 'drive-wheels', 'engine-location',\n",
       "       'engine-type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catcol=x.select_dtypes(object).columns\n",
    "catcol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1e5c1323",
   "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>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4.804021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>152</td>\n",
       "      <td>154.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>5.099866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>109</td>\n",
       "      <td>102.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>5.099866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>136</td>\n",
       "      <td>115.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses  make  fuel-type  body-style  drive-wheels  \\\n",
       "0          3           4.804021   0.0        1.0         0.0           2.0   \n",
       "1          3           4.804021   0.0        1.0         0.0           2.0   \n",
       "2          1           4.804021   0.0        1.0         2.0           2.0   \n",
       "3          2           5.099866   1.0        1.0         3.0           1.0   \n",
       "4          2           5.099866   1.0        1.0         3.0           0.0   \n",
       "\n",
       "   engine-location  width  height  engine-type  engine-size  horsepower  \\\n",
       "0              0.0   64.1    48.8          0.0          130       111.0   \n",
       "1              0.0   64.1    48.8          0.0          130       111.0   \n",
       "2              0.0   65.5    52.4          5.0          152       154.0   \n",
       "3              0.0   66.2    54.3          3.0          109       102.0   \n",
       "4              0.0   66.4    54.3          3.0          136       115.0   \n",
       "\n",
       "   city-mpg  highway-mpg  \n",
       "0        21           27  \n",
       "1        21           27  \n",
       "2        19           26  \n",
       "3        24           30  \n",
       "4        18           22  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "oe=OrdinalEncoder()\n",
    "x[catcol]=oe.fit_transform(x[catcol])\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9269457",
   "metadata": {},
   "source": [
    "# Linear Model Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "48678cf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0a0def2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linreg=LinearRegression()\n",
    "linreg.fit(xtrain,ytrain)\n",
    "ypred=linreg.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "81cfaaed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7895546813751259\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4f7ddfb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:0.8967228933546582\n",
      " Testing Accuracy:0.7895546813751259\n"
     ]
    }
   ],
   "source": [
    "train=linreg.score(xtrain,ytrain)\n",
    "test=linreg.score(xtest,ytest)\n",
    "\n",
    "print(f\"Training Accuracy:{train}\\n Testing Accuracy:{test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dda1cc9",
   "metadata": {},
   "source": [
    "Model has:- Low Bais & High Variance\n",
    "\n",
    "Model has Overfitting:- Which  was evident since it has more complexity (more features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "af12944d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  442.43598287,  -863.74227817,  -122.81244081, -1105.10038981,\n",
       "         -93.83829291,  1845.72101506, 13868.40760353,   775.77851733,\n",
       "         292.66184187,   -20.71849431,    86.99182135,    18.75711617,\n",
       "         132.5036889 ,  -225.54362234])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fad2561a",
   "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>coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>442.435983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
       "      <td>-863.742278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <td>-122.812441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel-type</th>\n",
       "      <td>-1105.100390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <td>-93.838293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <td>1845.721015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-location</th>\n",
       "      <td>13868.407604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>width</th>\n",
       "      <td>775.778517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>292.661842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-type</th>\n",
       "      <td>-20.718494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine-size</th>\n",
       "      <td>86.991821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>18.757116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city-mpg</th>\n",
       "      <td>132.503689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway-mpg</th>\n",
       "      <td>-225.543622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   coefficients\n",
       "symboling            442.435983\n",
       "normalized-losses   -863.742278\n",
       "make                -122.812441\n",
       "fuel-type          -1105.100390\n",
       "body-style           -93.838293\n",
       "drive-wheels        1845.721015\n",
       "engine-location    13868.407604\n",
       "width                775.778517\n",
       "height               292.661842\n",
       "engine-type          -20.718494\n",
       "engine-size           86.991821\n",
       "horsepower            18.757116\n",
       "city-mpg             132.503689\n",
       "highway-mpg         -225.543622"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(linreg.coef_,index=x.columns,columns=[\"coefficients\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85aa60df",
   "metadata": {},
   "source": [
    "# KNN Model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5067316",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e89c5fca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7719698858865982\n",
      "Training Accuracy:0.8971106670428599\n",
      " Testing Accuracy:0.7719698858865982\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn=KNeighborsRegressor(n_neighbors=5)\n",
    "knn.fit(xtrain,ytrain)\n",
    "ypred=knn.predict(xtest)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(ytest,ypred))\n",
    "\n",
    "train=knn.score(xtrain,ytrain)\n",
    "test=knn.score(xtest,ytest)\n",
    "\n",
    "print(f\"Training Accuracy:{train}\\n Testing Accuracy:{test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de21df6",
   "metadata": {},
   "source": [
    "# Hyperparameter tunning for KNN Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ec861274",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.9920198385790082 0.770595736726086\n",
      "2 0.9667734288597374 0.793140980277814\n",
      "3 0.9285113270353763 0.8555455670010874\n",
      "4 0.9142238127954221 0.8126971280293032\n",
      "5 0.8971106670428599 0.7719698858865982\n",
      "6 0.8741985941875313 0.7207179966265236\n",
      "7 0.8467266652587366 0.7078131984303446\n",
      "8 0.8313229380266819 0.6896663325104762\n",
      "9 0.8112533767578095 0.6754051473229536\n",
      "10 0.8083918186664512 0.6940193390232522\n",
      "11 0.7930032866342238 0.6943548843481856\n",
      "12 0.7756295900690054 0.6852489215152071\n",
      "13 0.7628515579650961 0.681067946227296\n",
      "14 0.7613931827657955 0.6928852580430046\n",
      "15 0.7572693833568804 0.6719750027392384\n",
      "16 0.7469476772319044 0.6679400959150021\n",
      "17 0.7373416592186033 0.668266728554425\n",
      "18 0.7337338251260143 0.6641867590268251\n",
      "19 0.7310082048955067 0.6651265745160191\n",
      "20 0.7241978253909188 0.6528945258038061\n",
      "21 0.7127202569451518 0.6422874714946131\n",
      "22 0.7055801807608112 0.6342388309985207\n",
      "23 0.6931447057876625 0.6274395506385456\n",
      "24 0.6823088423274932 0.618204462155286\n",
      "25 0.6742361891614834 0.6115597106294335\n",
      "26 0.6620693841688665 0.5986890291314725\n",
      "27 0.6551042246755524 0.5817721601389836\n",
      "28 0.64905986214926 0.5777519669451165\n",
      "29 0.646053384536002 0.5901225446342772\n",
      "30 0.6373120001252763 0.5894287751513392\n"
     ]
    }
   ],
   "source": [
    "for i in range (1,31):\n",
    "    knn=KNeighborsRegressor(n_neighbors=i)\n",
    "    knn.fit(xtrain,ytrain)\n",
    "    train=knn.score(xtrain,ytrain)\n",
    "    test=knn.score(xtest,ytest)\n",
    "    print(f\"{i} {train} {test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a5868d",
   "metadata": {},
   "source": [
    "Best possible result is when n_neighbors=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d3ef9a81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8555455670010874\n",
      "Training Accuracy:0.9285113270353763\n",
      " Testing Accuracy:0.8555455670010874\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn=KNeighborsRegressor(n_neighbors=3)\n",
    "knn.fit(xtrain,ytrain)\n",
    "ypred=knn.predict(xtest)\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "print(r2_score(ytest,ypred))\n",
    "\n",
    "train=knn.score(xtrain,ytrain)\n",
    "test=knn.score(xtest,ytest)\n",
    "\n",
    "print(f\"Training Accuracy:{train}\\n Testing Accuracy:{test}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c730d872",
   "metadata": {},
   "source": [
    "Accuracy is improved with respect to linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b0a3c3b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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