car_pred.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                        pandas LIBRARY FOR READING CSV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "car=pd.read_csv('CAR_DETAILS_FROM_CAR_DEKHO.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Maruti 800 AC</td>\n",
       "      <td>2007</td>\n",
       "      <td>60000</td>\n",
       "      <td>70000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maruti Wagon R LXI Minor</td>\n",
       "      <td>2007</td>\n",
       "      <td>135000</td>\n",
       "      <td>50000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hyundai Verna 1.6 SX</td>\n",
       "      <td>2012</td>\n",
       "      <td>600000</td>\n",
       "      <td>100000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Datsun RediGO T Option</td>\n",
       "      <td>2017</td>\n",
       "      <td>250000</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Honda Amaze VX i-DTEC</td>\n",
       "      <td>2014</td>\n",
       "      <td>450000</td>\n",
       "      <td>141000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Second Owner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       name  year  selling_price  km_driven    fuel  \\\n",
       "0             Maruti 800 AC  2007          60000      70000  Petrol   \n",
       "1  Maruti Wagon R LXI Minor  2007         135000      50000  Petrol   \n",
       "2      Hyundai Verna 1.6 SX  2012         600000     100000  Diesel   \n",
       "3    Datsun RediGO T Option  2017         250000      46000  Petrol   \n",
       "4     Honda Amaze VX i-DTEC  2014         450000     141000  Diesel   \n",
       "\n",
       "  seller_type transmission         owner  \n",
       "0  Individual       Manual   First Owner  \n",
       "1  Individual       Manual   First Owner  \n",
       "2  Individual       Manual   First Owner  \n",
       "3  Individual       Manual   First Owner  \n",
       "4  Individual       Manual  Second Owner  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                    LabelEncoders for converting categorical data to numeric, or vice-versa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "car.name = le.fit_transform(car.name)\n",
    "car.fuel = le.fit_transform(car.fuel)\n",
    "car.seller_type = le.fit_transform(car.seller_type)\n",
    "car.transmission = le.fit_transform(car.transmission)\n",
    "car.owner = le.fit_transform(car.owner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>name</th>\n",
       "      <th>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>775</td>\n",
       "      <td>2007</td>\n",
       "      <td>60000</td>\n",
       "      <td>70000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1041</td>\n",
       "      <td>2007</td>\n",
       "      <td>135000</td>\n",
       "      <td>50000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>505</td>\n",
       "      <td>2012</td>\n",
       "      <td>600000</td>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118</td>\n",
       "      <td>2017</td>\n",
       "      <td>250000</td>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>279</td>\n",
       "      <td>2014</td>\n",
       "      <td>450000</td>\n",
       "      <td>141000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  year  selling_price  km_driven  fuel  seller_type  transmission  \\\n",
       "0   775  2007          60000      70000     4            1             1   \n",
       "1  1041  2007         135000      50000     4            1             1   \n",
       "2   505  2012         600000     100000     1            1             1   \n",
       "3   118  2017         250000      46000     4            1             1   \n",
       "4   279  2014         450000     141000     1            1             1   \n",
       "\n",
       "   owner  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      2  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4340.000000</td>\n",
       "      <td>4340.000000</td>\n",
       "      <td>4.340000e+03</td>\n",
       "      <td>4340.000000</td>\n",
       "      <td>4340.000000</td>\n",
       "      <td>4340.000000</td>\n",
       "      <td>4340.000000</td>\n",
       "      <td>4340.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>751.591244</td>\n",
       "      <td>2013.090783</td>\n",
       "      <td>5.041273e+05</td>\n",
       "      <td>66215.777419</td>\n",
       "      <td>2.469124</td>\n",
       "      <td>0.794470</td>\n",
       "      <td>0.896774</td>\n",
       "      <td>0.820276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>400.046963</td>\n",
       "      <td>4.215344</td>\n",
       "      <td>5.785487e+05</td>\n",
       "      <td>46644.102194</td>\n",
       "      <td>1.508435</td>\n",
       "      <td>0.458629</td>\n",
       "      <td>0.304289</td>\n",
       "      <td>1.233494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>432.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.087498e+05</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>793.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.500000e+05</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1048.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000e+05</td>\n",
       "      <td>90000.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1490.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>8.900000e+06</td>\n",
       "      <td>806599.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name         year  selling_price      km_driven         fuel  \\\n",
       "count  4340.000000  4340.000000   4.340000e+03    4340.000000  4340.000000   \n",
       "mean    751.591244  2013.090783   5.041273e+05   66215.777419     2.469124   \n",
       "std     400.046963     4.215344   5.785487e+05   46644.102194     1.508435   \n",
       "min       0.000000  1992.000000   2.000000e+04       1.000000     0.000000   \n",
       "25%     432.000000  2011.000000   2.087498e+05   35000.000000     1.000000   \n",
       "50%     793.000000  2014.000000   3.500000e+05   60000.000000     1.000000   \n",
       "75%    1048.000000  2016.000000   6.000000e+05   90000.000000     4.000000   \n",
       "max    1490.000000  2020.000000   8.900000e+06  806599.000000     4.000000   \n",
       "\n",
       "       seller_type  transmission        owner  \n",
       "count  4340.000000   4340.000000  4340.000000  \n",
       "mean      0.794470      0.896774     0.820276  \n",
       "std       0.458629      0.304289     1.233494  \n",
       "min       0.000000      0.000000     0.000000  \n",
       "25%       1.000000      1.000000     0.000000  \n",
       "50%       1.000000      1.000000     0.000000  \n",
       "75%       1.000000      1.000000     2.000000  \n",
       "max       2.000000      1.000000     4.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "car.name = car.name.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                matplotlib.pyplot AND seaborn FOR DATA VISUALISATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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doRga/G4savQh4J184Z655OOuOWciOsIwDGOEmRQIVqVLCHg9WDV38ot6Lvm4a951rM+FEL8uXHcYhmFkzKRAmErpEiaCfNw1x6r4JACwyDMMYylmUiBMpXQJE0E+7pprJ6IjDMMwRphJgTCV0iVMBHmnNSCiBiL6ERH9UXu/lIiut65rDMMwGcykQJhK6RImAhIiv9wLmrjfB+ALQogVROQA8LIQYlmhOtPe3i42bcp7fRXDMNOIkYiZfFIgmGlrZV8nqhgKEW0WQrSrPjOTu6ZWCPEwEX0OAIQQSSJKFaSHDMMweZKPXZpvugQrxDidFnhqZze2HgwiLQA7ActOCODcRQ1FyctjRuTDRFSDzGQriGg1MkVEGIZhLMWKsEirQi0P9Iexq3sIG57dk93vjee1YEFdOebUTvzErxmRvwnAIwDmE9HfANQBuMKSXjEMw4zCirBIs/vMt8ZrdyiGBzceTYEAAA9uPICTmqsmvcjPRybd8Cxkar2eavLvGYZhxoQVYZFm9mmmxms8lcJ72ptx51O7sm1vOLcFiVRxvNtmioZ8SUs5XAXgHAA/APDflvSKYRhmFCNhkaMZb1hkfYV6n3Xl8j6Narx2dMoea7/bmRX4kbZ3PrULFW7nmPs6HsyI/Mgw9A4A/yOE+D2AyZ9MmWGYKY8VYZF2G3Djefr0wTee1wK7QhU7g2qrvysoFw0ZTqSUbYcTxbHkzbhbDhHRDwFcAOAbROSGuUGCYRhmTFhRRaozGMUDz+/XpQ9+4Pn9OLG5UvKdNwW8ygVWjYH8F2M1+IuzGMuMSF8F4E8ALhJCHAFQDeDTVnSKYRgml5GwyNXzajGvrnzc4YgNfg9cjqP7IAJcDlK6gFqb/Ljt8jad1X/b5W1obQpIbSfbYqy8F0NNBLwYimGYiSIeT+G3W9/Czb8blSN+bRsuXz4DLpddaj8SXdMVjKIx4EFrU0AZXQNM/GKsYy2GYncLwzAFJZlMY8ubA3h8eye2vHkEyWT6+H9UBHZ0h7ICD2T85jf/bjt2dIeU7W02QoXHCb/XiQqPMy/Rngw2NIdAMgxTMMyEGhab7lAUVWUuvOukE7Lx7L/afBDdoZjUdjLksx8rk+uqMwwzpTETalhsGvweXHPabPzouT34/lO7cc9f9+Ca02ajwe+W2hotnNrXFx5X24mALXmGYQrGsUINV8wqUqcMiCfT+O6T+nj27z65C+2zq6S2xlZ//vnsVW0nAhZ5hmEKhplQw2LTOxRTinFfOC61rfA4cM1ps7ODwkhMfYVHltAyl0N5DcoUk7kTAbtrGIYpGGZCDYtNTblbueK12iev8RyOp5RWfyQuL3AiEli/plV3DdavaUUR3PEA2JJnmJJiovOY5+Jw2HD5iploqS/PK9SwmETiSdxyaSvWP9aRtc5vubQVkXhSajsUSyqt/qGY3DaeFPjBM7t1i6x+8Mxu3H7lSqtO5ZiwyDNMiWBlVEe+GRiBjNCvmFWVlw/ezH7NkM9g53E6cPezHToxvvvZ3fjGu1dI+5td7VO6YJqr5QVOvUMx7O+L4K6nd0vbiwGLPMOUCFak4wWsC4u0ar/5DnahaEIpxoORhLTPubWZVay5+5xbK4u80bxEU5HmJSbfMxTDMGPiWKlzx4NVYZFW7TffEMZGv9on3+CXffIjuXP+cMOZeHDdqfjDDWcaPiFNtnkJtuQZpkQwSow1nnS8QCYsUhU+ON6wSKvCLfPNE7+o3o+vvnMZvvCbbVnr/KvvXIbFDWoxzrek4GSbl2CRZ5gSYSQxVq5LYbyJsU6o8irDB2dWece1X7PhlvlOKuc72B0MRvDzF/fhm1esQCSehNflwAN/34MTZ1UqhdzMpLaZeQmrYZFnmBLBinS8AOBx2JXhgxcsaRjXfkfcGrk+eZVbw8ykcr6DXXcoik37g9i0/2XddlVlqMmWqsAMLPIMU0Lk61Iww2GDRUO94RgWoGLM+zXj1tjXF8Y3Ht+hq5v6jcd3YHFjhXSu+Q52ZtxbVk1qTwQs8gzDHBOrfP1A/m6NvnBMWTe1PxxTimw+g50Z95YVNWYnChZ5hmGOiVW+fjO47DZl3dSH1q1Wts/Hf27GvWXlQGc1LPIMwxwTq3z9ZhiOG9RNVaQVSKcFntrZja0Hg0gLwE7AshMCOHdRg1Lo83FvTYaBbqywyDMMc1zSaYHBaAJHhhPwOh1Ip8WEiryZuqkH+sPY1T2EDc/u0UUDLagrl2q35stkGOjGiqWBm0Q0i4ieJqJXiaiDiG608ngMwxSekZWp79nwAj76k5fwng3P47dbDhlWfIrHU9i0rx+PbX0Lm/f1I66wtkdIpwX2HB7C82/0Ys/hIaTT6lJKZuqmdodiymggVTGQsTAZqj2ZwWpLPgng34UQLxFRBYDNRPRnIcSrFh+XYZgCYbQytaW+HCtm6XOvm6mbaiYs0YwlHY6rk4kNKxKP5ctUDqG01JIXQnQKIV7SXg8C2AFgppXHZBimsBxrZWouW98KKuumbn1LTlWwt1cdlri3V11BacR/vnpeLebVlRuK6+yqMmW6gllVZVLbaDSJjXv78OiWt7Bxbx+iUfVAYLbaU75PKBPBhPnkiWgOgBMBvJizfR2AdQDQ3Nw8Ud1hGCZPzKxM7TpGVaRc9veHlekSDvSHMb9+7GGJdjvhxvNapBW6Drt+UIhGk3hkWydufmTUU8eaNqxZ1gRPTjEQMyGUZiZ+J4IJEXkiKgfwKwD/JoTQlUIXQmwAsAEA2tvbp5i3i2FKHzMrU5tMTJBWep249m1zcMefX8/u96YLFqLS6xxXfzuDUfxjTx9+ePXJGAgnUO1z4r7n9uLE5krdxOu2zmBW4AHtqeOR7ZhbW4ZT5tbo9tng92B2jReXLp+ZHZAe3XJIGUK5v0898Tu/thxzS7H8HxE5kRH4nwohfm318RiGKSw2G6GuwoVvX7EC4VgSPo8DFR670ipdNiOAW9e2ST755TPkAYEIWYEHMiJ7x59fx88/cqqyH/nmnm8KeHBhWxP+5X83Hy0GclkrGnMGmq6QeiWvaoK2uaoMnzi3RRromhUuoLeCEeXE7/ITAqUn8kREAH4EYIcQ4g4rj8UwjDUc6A/j1bcGJffH7GqfFJLoctmxpq0Jc2vK0BWKodHvxrKmgDTpCgCHB+PqdAmDco1VM7nn+4biWP9oh05k1z/agZ9efyrm1B5tNyOgfupQuaEODAzje0/t0qVV+N5Tu3BSc5XkrjGqIhWOGkcZWYnVlvzbAFwNYBsRvaJt+7wQ4g8WH5dhmOOQb1bF7lAMD248oBO4BzcewEnNVZLIp9MCT+06nFcUSoVHXfC6XFEc20yET6eB/7wrZ15gZLDKHby8TvnpoC8cw3Wnz0XfcDzrZ7/u9LnoU6RV8HudyvOq8BZnWZKlRxVCPAdgcscXMcw0xExIYDyVUuaNSaRky9RMIq9oMokbzm2R9htLyvs1k3s+33mBNwcieOD5/bryfw88vx9za31onVmpa+uy2TCcSEl+dpdNHhBmBDz4yto2fGmUy+ora9swo7I4KRB4xSvDTEPMiLHf7VTnjfmInDemOxTFf79/OXwuD7oHM08I4VhUGYVS6/PgoU2v6kT2oU0H8L33nSjt10yET77zAvUVbgwMx3Xl/zxOG+rK3dI+B2NJPLmjK5N7PpZEmduB+/++B8tmynMNR8IJfP/pXbrz+v7Tu3Bn/YmYXSM1txwWeYaZhpgJCRxOGOSNScgW99waD57dNYz/98g/RoUltuKsFlmMlzT6ccN5C6XKTEsbZeE0E+Hjctlx+fIZmFfry7qils+Q5wVSQijdNWnIQX5pkca7T2rGZ3655ehk7qWtSAt51W9nKKqsHdsVikIuEW49LPIMMw0xk1XRVN6YgRhufkQ/6XnzIx144LpVaKyU8743BVzYoIU6VvmccNihnBdwOGxYs2wG5tT40BWKolETbqOSei6XHe1zqo95DfrDcfxxW6fOOv+fZ9/AfEV+G4/Tgbuf7dDNS9z97G586wpZtpsCXmW4pVHFK6thkWeYaYiZrIrm8q7nH5Z4oD+M3d1h9Ia1ycxeoMbnwoxAmXJC9y87ewqaVqDa58Ily5p01vmN57WgqlyO0w9GEsp5iVA0IbVd0lCBj53TIrmLljT4x9TP8cIizzDTEDO5YGw2woVLGvDQutW6GHVV26aAW+0798t+7r6hOMJxeTKzbyiuC3UErKnMZCdSxrP/7MNynH7A68RDm/QRRg9tOqC05A8GI3h4437Jf98+Ww63BPKP/x8rLPIMM805XlbFdFrgT692499/cdSKvv3KlbikTbaiHTZ1SgGXXR4QIomUUmTvuaZdams0h9AdUldmyic8tNeorOGQHKcfiibwz6tm4zt/Obo695PnL8SgwpIPRuJ498k5/vvLWhGMjC/+f6ywyDPMNMRMCOWew0NZgQcyQvjvv3gFixrOwIIGfY3XnsG4MixxvkKIjbNFyhO6ZS51TH2ZYpFVvrljGo1854qnjoDHmRX4kX5+5y+v43+vWyW1TaaEcjGWqm1HZxAP/kO2+lXx/2OFRZ5hpiFmCmPv7QsrxXhvX1gS+RmVLmVYYlOlS+qDYVikQmTjqZQypj6ekqNb9veFsedwWOcGuumChZhfG9alFahw2/GvZy/ALdpEscdpw/o1rahQLMYaGFavzh0Yli35nkH1E0LPoDwvEYrGlVE7g1HZ6h8rLPIMM8kx47PNdxWrmcLYHqddKcYep2xFx5NQumsSigy+SxvVYZFLVWGRdjteeVOfdOwnL+zF2xbIgeddoSie6OhUxrSPFvnOYDQr8EBGiG95pAP3frAdc+v0g5fPxJNEtc+lfEKo9skDncehjtr5tsLXP1ZY5BlmEpNMpvHHjk7s6hlCWgA7OkM40B/GJa1NktCbccGYKYxdVebETRcslLJFVpXJUSiHjkSV7poTqspw0mx9W4fDhrcvbUBzdRm6QzE0+N1oa6xQDmBEAhcsnaFLOvaVtW1QBdakRSqvmPZQVO0uGlTklPe67MrBq0wx0JU5bconhDJFuoTBWEKZLmEwJj8hjBUWeYaZxLzWHcLBgYgUgfJadwhtOUvvzUSghGP5F8ZeWFeBPb1hrDtrHtICsBHQGPBgYY61CwANfvUq0gaFCyYaTeIvrx/Gbm0A290ziK5QFBcuapDyuSdTIpsmYKSvX/rddmUkjMPmwPrHXtL7xB/rwP3X6n3iFW61de5zy7I4GE2gzGnXXYMyp10pxok0lE8IP7le7mt1mRs7u+S0xNVl8vUaK5ZWhmIYZnwMDCeyycE+fu4CfPjMeXhw4wGlL/hYq1hz8WkCNxoj98PBYAS3P7ETI+7vtABuf2InDgYjUtuFjT7cuqZNV4v11jVtWNgox9S/1jOIQ9oA9v2nduOHz+7BoYEIXusZlNqa8XP3hdVt+8L6tk5nJhJodF9vPK8FTof8eOB1OdDx1gBWz6vBwvpynDavBh1vDcDrlAeEw4ZRO3JfRyKKciOMogb1c8cCW/IMcxysjmM+Ful0Wuk7T6dlEaivUK9MrSuXV1qmRRq3XNqK9Y916FwaQhFPacZ/f6A3grue0edtueuZXVjcWI7KWfp+DEaTePmA7GdvU+Se95mwumvL1bH6tTk5aQhynvxoIgkbqRZXpdE+pxbX/XijLl0DSL4PVWXqLJSVCvfWUCyhrI41pAjNHCss8iVCMYWolJmIOOZjUel1KX3nDyqSg9ltUPrO7Ypu2ojw8oFe3PuhU9A7GENdhRu/eekAls6QXTBm/PdvBaOIJ48OFERAPCnQGYxheU62SIdd4Lwl+uIe69e0wumQBxqnnfDZixfj64+/lm372YsXw6mKv48nlJE4kZxC3olkCi67Da/3DGb94fPrfEgosmACNnW6hmvlsEiX3YbPX7L46EpeyqzkdSluRG25C9ecNlvy9deWy5O0Y4VFvgQothCVMmbymFvBcCKltPQiiuRgh4dicNttOr+x225D71BMShNABJycY5muX9MKlREbjqr7EI7JfagxEK1qRaoAu82u9F0/oIgnr/Y5UO1z6s6t2udEVZksYX6vS7c6dSS75XeuWqlrV+ZyoCs0KIVaqqo9GaVrULmLeofiiCTSuv1+8vyF6A3LYZHxpFC6a05ulq/BWGGRLwGKLUSljJk85kD+IYz50hTwKEWzSZHsymW34WuapTuCx2lTWtwxTVBzBVa1YMfntiv74FP4720g7D0ckp4QVimShZnJcxMcTuEzv9omnZuqvyINXP+2uVlL2mHLvM9lOJ5Slh+894PyitsGv9oFVK+YUK4qc+KGPBdO9Rj473sU/vuxwiJfApgVIiZ/zOQxNxPCmC+pNJSW3oVLG6W2w/H8I2a6DSYyuxWW6VA8aVizVOqvSKF9Tl2O77oNKch9aPC70T47gGtOn6eLZ2+okIWzKxTDwvpyfPis+bqMkar+DkTioJxHEiLCkZy0AoOxJN7e2oArTmnODki/2HgAQ4onlAqPHevXtCoWTskDXZ/Bwqn+YdmSry13GcwfsLuGGYUZIWLMYSaPuRVJtHoG1QP44aEo5tfr91lvMOGoKoJhVN9U9YQwaCKePOO71j9V3vzIdqULpqrMjqva9fHst65pRZVPFs7mai/ed+psKR/MrCqv1NbvdeL1bjksscKjdxnNCLixer48mdoYkAW2dyiBJ3d04odXn4wjwwlUljnx0xf2YkalfHzjhVOy3FaWOfDly1rx5UePDh5fvqwVlQUsFcgiXwKYESLGHA6HDZevmImW+nJ0BaNoDHjQ2qTOY26mEAeQn2vHTN73aDKlXLCjKqcX8Djx6YsW4Vt/2plt++mLFqHSI/vO6/1uXLi0Fu9fPVcXBVOvsLh7TPiu+8Mp5WRmbjw7AMSSaWU+GNXgETMIS8xNfBZJCMPc97kkUimsnFWjmyQ2KoHo9zhwy2Wt2f6ODEh+RboEt90Ov9eRifCJJ+FzOWCzAW6HPNCNFRb5EsCMEDHmcThsWDGr6riuLzOCnK9rp7mqTDmAqyYHjWqWzqstR+tMfdvuwQiqfa6ciUwXugcjmJ+Tj8bvseH8JfrVpreuaUPAq4gWqcjf/dBn4I/OjWcHMnHyKneNavAYzjPxmZk5gQqDEogqP/tQLIm7/2+37j7c/X+78bV3LpPaHjoSxU0Pb5Gu1z3XyKkVxgqLfImQrxAx1mFGkPN17RwYGMb3ntLHnX/vqV04qVnOTe73OpSrTcsVfmOHzY7P/VqeyFSFBA6EU0oXzP9etwrzc9qWuxxK33W5wlVRV5G/e2m2gbumWeGuKfeo3SXlbv11MJpMVa3O7Qsb+NkVETPhWFIZRqry9ZtzhY0NFnmGKRD7+9WCfOKsKsl/nq9rp9ugXqjKBdRU4VHGyTcpyvQZrco8rIjq6DJh8YaicXid+jBOr9OGkCKrosMOfPOK5dm0BnYC5teXQ+WpiCTyd9e47DZ88R1LcNvvd2SvwxffsQSunCfbgDfzRDIygB19QpE7YLQYq0yxGKvGpw4jrfHJrjCeeGWYKcT+/rBSkA/0h/OeJK316a1IMy4gm53Q4HfrBLbB74ZdsWCo1sCKzl0VCmR88qqsinUKn7zTbs871BEAIjmVodavaQVB7u9hg2ggVaqAqjI7/B59TL3f40RVmV68g5G0cnWuqtqTx6kuhuJWpECIp9LZVBQj1+vBjQewcpY8R5ZIpfCdq1YglUZ21a2doPT1jxUWeYY5DvnGvvtcDmVIoCqqIt9J0jk1Ptx+5UqpKpNRfdX/UAjsA9etwuwa/SDjc9qVk4Oq2PdKrx0fP6clmyBsJANkZZnctndI7dZQLQSKJYUyVv/H154itTVKVVDjU7lWUvjMr7Yqr8No91JPKGbwlCQPHHayHU2BoE2QRpNJOGzyvEQ8qU5FEVfko/G5nTh4JIqbf3f0Pty6tlUZtTNWWOQZ5hik0wJ/3d2DwUgK4VgSfeE43hwI48wF9ZLQN1W6cWVOSOD6Na1oqpSFKN9J0nRaoMxN2HD1yejXIltSIo10WkjHPzKcUArsEUUys8FYHNU+p060nA7CYEwW4yPDSWUGyPsVYuz3qAe6CoVbo28orlxJ26covxdPJvHJ8xdK5fcSKdl3ne+Eao3BJHGNIu97KJqAw6ZPgTC31qcs5O11OZSTtCrXUjyZzgr8SNubf6delDZWWOQZ5hi8ORBGVzAmTSS+ORCWrOOBoYTSMn3oI6vRnLPg0+/Jb5J0V08IR4aT2NsbzohLLzCn1oddPSEsmVGpa1vmshs8ScgWt9/jQiQRhddpRyot4Pc6kEyn4ffIAtcfVg8eqkyYgTKnMvZdlZzLzGreQJkLfo89xwVjR4Wiv4YTqjnupYBHPUkcUMSoV7id6AlFsbC+IutWGY4lUOGWJ9WDBoNtUHG9zEzojhUWeaZgFDtJWqFTCgCZR/ofPLNb51/9wTO7Ma92hSTy3QYLl1SrMgMGhTgqvXoxHIqnkEqndeISTSQxpFjF2uB34sbzW+Cw2XFYCNSWu3Dj+S2o98sCm0wJBCPy4FFTJicH8xtGq8jyMRxXx76rrFghgCd3dEkVnFQpEBJJgfWP7ZD68FNFjvYyp3p1au5gNxxPZxc4DYQTqPJlFjip6tECAilB+FROdA8gX68yt7qSVplbHmwDBhkr/V75no0VFnmmIBQ7SZoVKQUAIBRLKP2rqmIRNT61Bakq+5ZKC+UkaSon1a8NQCIFfPG3enFRXdG0ADqPxKVoEdWipXg6jcGI/hwGIwnEFSmM/d78LV4zlulwIqGs4DSckK+tmWigff3DeHybfnXqj5/bi3K3A20nVGbb9Q/HlQucVOkH4gbFue/7kOyycjlsyvmW3OgeIDPBqkr5nEzzxCszybAqSVq+1rnZlAL5PnX4XA5dRkMgk9HwG+9eLrUdiqlT3IYVA0IskcbfdvXg8pOadYm8ZgT0NfLMiEswoo5nV1nRyVQateVOzKn1ZX39Q9E4korC2OF4Svc0I0TmaUZ1DWp8aj93dZk80Lns+VVwAjKuKHWqANk6bqhw4/m9/Xj69V5d2389Z4GuXcCjXuCkOn6/icFrIJxQzrcsUHwPvTk1XoXgGq/MJMUoSVrnOJKkmbHOjeLOu0NyPHkymcaTO7uRSAqEY0kk0wJdoQjOW9QgCX3SIFJCJYY2Ijz1muZ+iCdR5sq4H9pm+qW2gtI4o6UeL+zpQ1oAbxwewhkt9VIRir6huHKlZ59CXHpCMeVEpipaxOe240B/Gjf9Qp/LfVa1eoJUFYWimiAdjieVUTvDikrevSYqKPk9DqV1rEoVkEyncOua1qzbaGReINc6DkXVvvNBxWRqjUE8u+oprabcqZxvqVLEyceSKeW1VUXijBUW+QnGCr/xZKAuz2o8ZjBjnZe5HMpYblWh5V2HQ+gPJyQh2nU4hCVNlbq2Xrc6UuKnitqiteUufOSs+UgkBSAyKx0/ctZ85cIWB9lx6EhUSqJ1Qs7q2Nk1Xlx7xlzsHhXVce0ZczFbsdJzRqUHnzx/Acpczqz//pPnL8AMxURmJC6Ucw3ffLdsQTYYxMmr3EDlbgfu/r8OaUn/7QrL1DB9r2K/dhthZqVH596aWemBXfHb8Tgc8LpsuoikYCQOj0MvdwFv/v5wv8ehHDhUg4zLblMOdG5F0RC3Q/2EonLtjBUW+QkknRZ4amc3th4MZn+wy04I4NxFDVNe6G02KH2L4/mumkn4FU0k8NF/WiD9sKJJ2YI8MqzPLQJkhOjr75LdDwMGj+kDCks6mYZUPk8IAYXRj8GYOn3vD68+WdculRboCsqDwdwaOarDabfB6bDrwvzm1PrgVIhLMJLA1avn4NtPHE1Q9qkLFyGosGLtNuBfz14g+eRVK1P7w2qrv18RWeJ12XHr2jbcPCr+/ta1bcoJyoHhBB7beiibJG1kkvQDq+U88TYiHByI4tO/3Ka7Zs1V+rUFYYMKUsNxRchpNIW/KCZpVesVBoYTytw1t61tk9tGEsrQ0CMRLv83JTnQH8YuRQrUBXXlUuUewDqr38x+8/VdB7wuPL1T/hGcOq9VahuNJrGtM4iuUAyNfjeWNQXgUVhEplZ7kk3pu1bFGw/GkkoXzFBMHhDKDeK+yxX9TacFjgwnpbSxTX45AiNiUPEpGtePCKGoejBonXFy7i4RTaojcaKKLJTVPhduePBl3X6//cRO/EQRrTIcVxcYURXX8DjVYZwep/ydicZTKHfbpQyMUUXkUEqkcfLsWt0k6SfPX4h0Wr62Rtcst3asz+VUVpD6luJpJhhNKCdpVYNiJKF2wUQS8mhf43Oh60hESgOhmsMYKyzyBSBf0ewOxZRfvpOaqySRNxstkm8fzOw3mUzjr2/0wE4ZoQ1G4vjrGz04c369JPTNVWW4uG2m7kfwn+9cJiXnikaTeOK1buw+nMlXsrtnEJ3BKC5c3CAJ/ZwaHx76l1WIxTPhiQ1+D9xOKK2nvnBcWQBCNTEW8DiP+s5HCdHJs5dIbQlpZdw3KULnoslUVuCBzP398qMdykVDMwIefOyc+egZjGWt7o+dMx+NAb2rIt/BADAXiWNmItEoiVZIkUSr2udSLghTiVY8JZQZGFWTyhVuZ9baHTn+d/7yOn5yvToLpKq/4ZwarwPD6sgplRVd5VVP0qomtY0mn6sUawUSqRT+849yNS/Vqt+xUhIiX0w/txnRDBumQJV/LPv6wrj3uTd0QnTvc29gcWOF5KpIpwVe2HMYyXRm4UoilUZXcBir59VJfdjbG8auzgHcf+0q9GjC+fyubixqqJDyq7zRG8JgNIXdPcFsLPX8+nK80RvCosZK/X77wvj8b7bpfgSf/802LJsZQMuo1LU7ukM4dCQiPc3s6A7hxNn6+OhILI6uYByJpMBwLIWewRicdsKCmjh8Xr0Yzqr24PQWfUWi2y5vw8wqVeGUFK45Xe/nvub0uQDJFqSN7Lgrx3d91zPq6IfDRkv6FROUqZTAYDQpXYdcy7TK51QuGKr0yT9dM5E4FQax7xWKJ5QKg+Rcqjj5SDyVd1nBPoPrpZpUNlqQ1R+WBdnI15+b3bK6zIn/VFjyqns7FFMfXxU5NTCsdsGorP6hqLqa19BUykJJRBcD+C4AO4B7hBBfL+T+rYqPzhczk4Ozq33KL19ztWyZBiNxZQxxKCL/AA70hfDmQFTymc7oC2FOnf4RNZ5MoKmqHB+87x86yzSelL+Aw7E0Dg3IgjxLkVdjX29Y+WXd1xvWibyRL7r1atn9sKdvWDlBuqdvGMtO0P9go3GhywAJZDJA3nHlSmm/HqcThwZC0nnNrpb93Eci+Vt7huGDigiMoVhK7VLIKWwBQcp2KgvSjGhGEkmlPzqiiILxuuzKyBZV+KKZjJX1JiZevQYhlF5FH5x2ga+sbZNy7bgc+gG02mfHx85ukdYVVCsqU1WWqe9twCvf2/oKD277/au6weNn/9iP777nRKnt3Bq1JqieVseKpatUiMgO4C4AlwBYCuB9RLS0kMcwEtl9feFCHsaQY00O5jK31oc7rlqZ9U+ODEhza+UbmkojO4k5ss/1j3VAFVnVPZhUWk/dg/IPdjCaVq5IHIzKOzYSIlVe7JEf4WhUP8LhhNpyiSjcD0OxlNIyVR0/GI3jPe3N+NFze/D9p3bjnr/uwXvamxFUpLg18tmq3A9+g1hqv6KCklcbhEbf31sua4VX4Y82cinkzgscMagXqspHMyKaozESzUqvK+uP/vi5C3D9GfPw0KYDCCjSBAwMx1HudmDdWZm2686ah3J3Ji1DLo1GfVDkaDe8Xsr1CpmBZnTbG89rgU8RPRVLAjs7B/Dja1fhe+9biR9fuwo7OweQO+ViI2B+vQcPaO0euHYV5td7oLINB4bj+Pwli3XH//wli5VGV2uTHx8/pyX7XfzRc3vw8XNapDkBAJhbV47br9Rrwu1XrhxzuUgVVlvyqwDsFkLsAQAiehDAWgCvFuoAZkuuFRpTk4M2wsWtjVh8w5noGYyivsLYtWQmhrjHIA2rKj7aVAFnIyFSuJfsNiitvdwkfQGv+tHfr8jhbbR6UmWZep3qUEeVq2LIwMesekQORdSP6SGFJW+3EQKKUm52ku+vUdx1bnKsasPFRfIgkxYp5crUtFCU//Pa8IlzW6QVypVlqkl1J2597NVsCGUqDdzz3B5lWGS1z67M0V6jyFj5VjCKn7+4P7uuwOty4J5n38DHz12A3BpKLrs6hNKlSPVb63NhUVMlPjTqafUra1tRm3NtZ1X50T/cJ/39rCp5XUONz4P1j+qt8x/9bS/ufK9snTscNrxz5UwsbDh+pTabjXBJWyOWNB1fE8aK1SI/E8Cbo94fBKCbvieidQDWAUBzc7PpA5gRWSuYU5OxznPdRUaPWzYbYV5d+XEHIKPi3KrkTWYq3Jhpa1jQQOF+cDvs8Ln0CaR8Ljs8OXF2NpCytqhNIYQNBjnPcxNNAeaEu85EiTrDRTCKtk4HwW4jnV/dbiM4FUJU7lHnV8mtXlThsStdDxWKQdFhsyuLTa87a4HUdigmMLfGjQeuXZWZ1K7wwG5LYSgqTyjXVahTDdf55T4IcdQ6zu7XnlZMUwO1Pjde7xnCDT9/Obst496S7y8Rwe0gnDavBr1DMdSWuxGKxADF96a5phz7+4d1g22Fx47mnFxDDocNK2bWoKMzCJfdBrfThtamKqUYtzb5lYOiUR1lM5Xa8tWEsVL0iVchxAYAGwCgvb1d9V04JmZFttCYsc7NYKY495JGn9J6WtIoX4OlBm2XKtrW+lxKIVKJ/Lz6MhwciOjyhleWuTCvXu/nrvY54bSRbjBw2gjVitWALY0+ZRx1i6KvMyrVA4Iqze8JlQ6lcM6qkn8O8+rVJf3m18v++8aAB72DMdg9doghgZpyF1LpFBor5YF5RqUHhwaiOiFyO22YkTNR7HXa0VTpwn0fOiUrbimRglfhpljY4MMFS/W1WL+ytg0LG+Tr1VLrw5OvH8aungGkBbCrZwgt9eU4b2Gl1DadsmNJU5lOuMvcQCol92Gm34dg5Ih+o8hsz2XZjIDy/i5XuDUWNfixpzeM57UVwiP9XdwgW902G+HMlnrs6wsf9zeZrxhP5TrKlLt4o6A7JzoNwJeFEBdp7z8HAEKIr6nat7e3i02bNpk+zkh0jVWPO8ViJEY9ny/VYCSKHV1hdIdiaPC7saTRhwqv+mlmKBLFq6PaLm30oVzRNp0W+OuuHgxGUzqL6MwWOZc6AByJRPH6qP0ubPShMme/6bTAX17rxvZDwazIt80M4PzF6gVh+exz5Fr9flsn/uPXW7OC8Y13Lcc7ljVJ1yydFnhzIIjuYCorWg0BO2ZVBcbVByvaptMCr7zZh0SKst9vp11g5awaZV9DkSheG7XPxY0++A2OPxyJY3vXYLZtW2MFyhQTiem0wJOvdWPbqHu2bGYA5xncs0gkgW1doex+lzX64TXIqhiPp7D1rWA2Mm75jABcislUwNzvYbpBRJuFEPLCBVgv8g4ArwM4D8AhABsB/LMQokPVfqwiz1iHFQOoVYOyGRGYSobBZOjrZOgDY0zRRF47+NsB/BcyIZT3CiG+atSWRZ5hGMY8xxJ5y33yQog/APiD1cdhGIZhZNihxTAMU8KwyDMMw5QwLPIMwzAlDIs8wzBMCWN5dI0ZiOgwgP3F7sc4qAXQe9xWU5NSPTc+r6lHqZ7beM5rthCiTvXBpBL5qQ4RbTIKY5rqlOq58XlNPUr13Kw6L3bXMAzDlDAs8gzDMCUMi3xh2VDsDlhIqZ4bn9fUo1TPzZLzYp88wzBMCcOWPMMwTAnDIs8wDFPCsMgfByK6l4h6iGj7qG0riOh5ItpGRI8SkV/b7iKi+7TtW4jo7FF/8wwR7SSiV7R/9RN/NkchollE9DQRvUpEHUR0o7a9moj+TES7tP+rtO1ERHcS0W4i2kpEJ43a1we19ruI6IPFOietL4U8r9So+/VIsc5J64vZ81qsfUdjRPSpnH1drH0XdxPRZ4txPjn9KeS57dN+f68QUVFT2o7hvN6vfQe3EdHfiWjFqH2N/Z4JIfjfMf4BOAvASQC2j9q2EcA/aa+vA/AV7fXHANynva4HsBmATXv/DID2Yp/PqHNoAnCS9roCmbz/SwF8E8Bnte2fBfAN7fXbAfwRAAFYDeBFbXs1gD3a/1Xa66qpfl7aZ0PFvk/jOK96AKcA+CqAT43ajx3AGwDmAXAB2AJgaSmcm/bZPgC1xb5fYzyv00d+OwAuGfUbG9c9Y0v+OAghngXQn7N5IYBntdd/BvBu7fVSAE9pf9cD4AiASbloQwjRKYR4SXs9CGAHMjV51wK4X2t2P4DLtddrATwgMrwAoJKImgBcBODPQoh+IcQAMtfj4ok7Ez0FPK9JhdnzEkL0CCE2AsitOL4KwG4hxB4hRBzAg9o+ikYBz21SMYbz+rv2GwKAFwCcoL0e1z1jkR8bHTh6ka8EMFIhcguANUTkIKK5AE4e9RkA3Kc9Rn6JSFGBuEgQ0RwAJwJ4EUCDEKJT+6gLQIP2WlWUfeYxthedcZ4XAHiIaBMRvUBEl1vf4/zI87yMmLT3Cxj3uQGAAPAEEW0monXW9NI8Yziv65F5wgTGec+KXsh7inIdgDuJ6EsAHgEwUr36XgBLAGxCJgfP3wGktM/eL4Q4REQVAH4F4GoAD0xorxUQUTky/fk3IURo9NgjhBBENCVjbAt0XrO1ezYPwFNEtE0I8YZFXc6LUr1fQMHO7QztntUD+DMRvaY9jRcNs+dFROcgI/JnFOL4bMmPASHEa0KIC4UQJwP4OTL+MgghkkKITwohVgoh1gKoRMYPByHEIe3/QQA/Q+YRrKgQkROZL99PhRC/1jZ3j7grtP97tO2HoH8qOUHbZrS9aBTovEbfsz3IzKmcaHnnj4HJ8zJi0t0voGDnNvqe9QD4DYr8OzN7XkS0HMA9ANYKIfq0zeO6ZyzyY0CzEkBENgBfBHC39r6MiHza6wsAJIUQr2rum1ptuxPApQC2K3c+QWjuoh8B2CGEuGPUR48AGImQ+SCA343afg1lWA0gqD1y/gnAhURUpUUJXKhtKwqFOi/tfNzaPmsBvA3AqxNyEgrGcF5GbATQQkRzicgF4L3aPopGoc6NiHzakzK03+GFKOLvzOx5EVEzgF8DuFoI8fqo9uO7Z4WcTS7Ff8hY6p3ITPIcROYx6kZkLPTXAXwdR1cOzwGwE5kJlr8g87gPAD5kIm22IuPP/y4Ae5HP6wxk/JdbAbyi/Xs7gBoATwLYpZ1DtdaeANyFzFPLNoyKFELGfbVb+3dtKZwXMpEO25CZZ9kG4Popdl6N2vc1hEwAwEEAfu2zt2vf3TcAfKGY51XIc0Mm+mSL9q+j2Oc2hvO6B8DAqLabRu1rzPeM0xowDMOUMOyuYRiGKWFY5BmGYUoYFnmGYZgShkWeYRimhGGRZxiGKWFY5BmGYUoYFnmGKTBEZC92HxhmBBZ5ZlpDRLcS0b+Nev9VIrqRiD5NRBu1/N7rR33+Wy35VcfoBFhENEREtxPRFgCnTexZMIwxLPLMdOdeANcA2TQV70UmM2ALMnlPVgI4mYjO0tpfJzI5i9oB3EBENdp2HzL5v1cIIZ6bwP4zzDHhLJTMtEYIsY+I+ojoRGRSvr6MTEGKC7XXAFCOjOg/i4ywv1PbPkvb3odMttFfTWTfGSYfWOQZJpMz5EPI5ES5F8B5AL4mhPjh6EaUKed4PoDThBDDRPQMAI/2cVQIkQLDTDLYXcMwmZS0FyNjwf9J+3edlgccRDRTyzwaADCgCfxiZMoFMsykhi15ZtojhIgT0dMAjmjW+BNEtATA81qBhyEAHwDwOICPEtEOZLKNvlCsPjNMvnAWSmbao024vgTgSiHErmL3h2EKCbtrmGkNES1FJg/+kyzwTCnCljzDMEwJw5Y8wzBMCcMizzAMU8KwyDMMw5QwLPIMwzAlDIs8wzBMCfP/AfiIeJhJiJYhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='year', y='selling_price', data=car)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                    SPLITTING DATASET INTO test AND train DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1=car['selling_price']\n",
    "x1=car[['year','km_driven','fuel','seller_type','transmission','owner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x1, y1, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2371</th>\n",
       "      <td>2017</td>\n",
       "      <td>90000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2006</td>\n",
       "      <td>220000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>2012</td>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>2010</td>\n",
       "      <td>115000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3263</th>\n",
       "      <td>2004</td>\n",
       "      <td>80000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  km_driven  fuel  seller_type  transmission  owner\n",
       "2371  2017      90000     1            1             1      0\n",
       "394   2006     220000     4            1             1      2\n",
       "1225  2012      80000     1            1             1      4\n",
       "1369  2010     115000     4            1             1      2\n",
       "3263  2004      80000     4            1             1      0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>year</th>\n",
       "      <th>km_driven</th>\n",
       "      <th>fuel</th>\n",
       "      <th>seller_type</th>\n",
       "      <th>transmission</th>\n",
       "      <th>owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3033</th>\n",
       "      <td>2008</td>\n",
       "      <td>70000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>2005</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>2017</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3504</th>\n",
       "      <td>2010</td>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3377</th>\n",
       "      <td>2016</td>\n",
       "      <td>72500</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  km_driven  fuel  seller_type  transmission  owner\n",
       "3033  2008      70000     4            1             1      4\n",
       "1380  2005      60000     4            1             1      2\n",
       "227   2017      20000     1            1             1      0\n",
       "3504  2010     100000     1            1             1      4\n",
       "3377  2016      72500     4            0             1      0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2371    600000\n",
       "394     220000\n",
       "1225    175000\n",
       "1369    350000\n",
       "3263     79000\n",
       "Name: selling_price, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3033     110000\n",
       "1380      70000\n",
       "227     1500000\n",
       "3504     130000\n",
       "3377     330000\n",
       "Name: selling_price, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                 sklearn.linear_model FOR LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 13043.67922099, -50757.06989795, 739920.7131889 , 327146.65958548,\n",
       "       421858.51016274])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                                            sklearn.metrics FOR MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174481860155.41364"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-73229440.35108043"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.71637099e+04, -9.54122059e-01, -8.94664075e+04, -3.75921373e+04,\n",
       "       -8.43700758e+05, -1.90745800e+04])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  }
 ],
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