polynomialLab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df8804f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3eef6f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>MODELYEAR</th>\n",
       "      <th>MAKE</th>\n",
       "      <th>MODEL</th>\n",
       "      <th>VEHICLECLASS</th>\n",
       "      <th>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>TRANSMISSION</th>\n",
       "      <th>FUELTYPE</th>\n",
       "      <th>FUELCONSUMPTION_CITY</th>\n",
       "      <th>FUELCONSUMPTION_HWY</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>FUELCONSUMPTION_COMB_MPG</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MODELYEAR   MAKE       MODEL VEHICLECLASS  ENGINESIZE  CYLINDERS  \\\n",
       "0       2014  ACURA         ILX      COMPACT         2.0          4   \n",
       "1       2014  ACURA         ILX      COMPACT         2.4          4   \n",
       "2       2014  ACURA  ILX HYBRID      COMPACT         1.5          4   \n",
       "3       2014  ACURA     MDX 4WD  SUV - SMALL         3.5          6   \n",
       "4       2014  ACURA     RDX AWD  SUV - SMALL         3.5          6   \n",
       "\n",
       "  TRANSMISSION FUELTYPE  FUELCONSUMPTION_CITY  FUELCONSUMPTION_HWY  \\\n",
       "0          AS5        Z                   9.9                  6.7   \n",
       "1           M6        Z                  11.2                  7.7   \n",
       "2          AV7        Z                   6.0                  5.8   \n",
       "3          AS6        Z                  12.7                  9.1   \n",
       "4          AS6        Z                  12.1                  8.7   \n",
       "\n",
       "   FUELCONSUMPTION_COMB  FUELCONSUMPTION_COMB_MPG  CO2EMISSIONS  \n",
       "0                   8.5                        33           196  \n",
       "1                   9.6                        29           221  \n",
       "2                   5.9                        48           136  \n",
       "3                  11.1                        25           255  \n",
       "4                  10.6                        27           244  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"FUELCONSUMPTION.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8511b342",
   "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>ENGINESIZE</th>\n",
       "      <th>CYLINDERS</th>\n",
       "      <th>FUELCONSUMPTION_COMB</th>\n",
       "      <th>CO2EMISSIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>9.6</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.6</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENGINESIZE  CYLINDERS  FUELCONSUMPTION_COMB  CO2EMISSIONS\n",
       "0         2.0          4                   8.5           196\n",
       "1         2.4          4                   9.6           221\n",
       "2         1.5          4                   5.9           136\n",
       "3         3.5          6                  11.1           255\n",
       "4         3.5          6                  10.6           244"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['ENGINESIZE', 'CYLINDERS' ,'FUELCONSUMPTION_COMB', 'CO2EMISSIONS']]\n",
    "cdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91983088",
   "metadata": {},
   "outputs": [
    {
     "data": {
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PK6uNQ/Ic7ALVg/pO40iTknqXpMW1XNzM+s1sHjAVOEXSu4DlwAnAfwImAZkMjqQlkjZK2rh7d3QDL8ephST/fiV5BIDj/PGhPG7h7TdjxrK1zFi2dojODzz9Mu+ePnGQ//78k7u47p4nBwLhbRXp/x1t4qpzToz8rKTBLoePiV5G4uROa5Nmp3AZ8BeS3gTC2wszs7en/RAz2ytpPfA+M/ubQPyGpG8Dfxa87gXK/wKmBrLKa90A3ADQ3d3t5Y5ObiT59ytJm3UD8a0vduyO9seH8vBz43odxbHhmT08vaJ0L1fp5tp7oI+ONjFx3Bj27u9L5aqpNthlXEc7bxw8FCl3hh9pso+OMLM2MxsbPD8ijUGQNFlSZ/B8HPAe4Img5gFJAnqAnwWn3Al8LMhCWgDs83iC00iytlrIskjHHZpGfk3PXJ5esbhqe+xqukW5ufoOGXv29w24nDY+X/tozX0Rrqhqcqe1SdX7SNI5wO8GL+83s7tSnDYFuDGIK7QBt5rZXZLWBRlNArYAfxwcfzewGNgB7Ac+kfpbOE4OVLvzD2cYzD5qwsBA+a4MNQSNnmpWHnNIyvWvtyNpe5s4eCgi7lHpo3KGBWlSUldS8v+vCkSXSVpoZsurnRc0zZsfIY9suBJkHV2aqLHj5Ei1NtRRPPXSa7znS/dz7xWnR6ZRtgki1seG1RuElMci0hbAxbnJkogyCNXkTmuTZqewGJhnZocAJN0IbKYUMHZGMdWmkg0HHeKyaiorbyt5KsjLj0qjjLsrz6ve4O2Ht/PKG/EZT1EZU1HGKwrvSOpA+tbZnUDodJxYjCrOcCJpKtlw0CEusNwm8fSKxZFjLyupTKOMOyevBfec+V2Rhqw8GyhKRyCx/bZ3JHUgXUrqCmBz0J7iRkp1B9cWq5bT6lSbSjZcdCiih3/cwprXglvrMJqe+V08sOxMnl15NhcvmB55TN51B87wJE320U3AAuAO4HbgNDO7pWjFnNamFRqY1atD0gI++6gJke+XyyunicW1es5rwc3DkHUfO2nIH35bIK+FOCMTJ3dam1ijIOmE4Oe7KWUS7Qoex3izOqcVGpil0aHaCMi4hTqU33vF6UMMQ3n2Uei+Kp+d8MjOfUPOqTavOG7/ECfPYydy3T1PUllVcCiQx1Ht9+iMLKrFFK4AlgBfjHjPgPixTc6IpxUamCXpkBRzqCwMiwrShgYgijj3VWWDuHBecVScI+7+Pk5+4anT6m45kXWHlfR7zFr057Q2sUbBzJYEP89onDrOcKFnfhcbn3950IJ6/smN7V2T1EStWswhPKZapS5Uz26KW0SrjZ2sJK7WIa6uIY0hSyJp9nQlSb9Hn688skiMKUj6kKQjgudXSrpD0pD6A2d0sWZzL7dv6h3Uc+f2Tb0t5VaoN+YQ5R4qH2OZxVUW95lRIz2TdlzlFc5Pr1ic+W78jBMmZ5In/R6zusCc1iZN9tFfmtmvJf0OpdnM3wT+b7FqOa1OK2QfJS3a4zqi/3vHyStJ+o5LF82hoz3d0tcmRfrje+Z3cf7JXYOazRW941r/RHQjyTh5UuwmrQvM4xLDgzR1CuFfxdnADWa2VtI1BerkDAOKyD7KWoiW5NbY3ze0SRsQK68krgp4kLxi5WsD2ttFX//gN8pHX5b749ds7uWWh18YtOO65eEX6D52Uux3P375Wg6WXX6MYMeK9H2Rsv7b5RE/WrO5l8tv2TLwunfvgYHX3i67tUhzy9Qr6R+AjwB3Szo85XnOCCbv7KOku/4omp0We909T9JX0crhEDDhsDEDbaujsoLKdxtX3fnokGv0HTKuujN6NGalQQA4aCV5WrL+2/XM72LFeXNjR2mmYeltWzLJneaRZqfwYeB9wN8ELbCnAEuLVctpdfLOPoq76//MrVv59C1bIncOWQOmeRNnfPYd6GPL598LxFc4h3pHDbupJq80CEnyKJYumsPS1VsH7WY62lX1367aAJy4tiDl5jBuc5Zy0+Y0kDR3/FOAtWb2lKTTgQ8BDxeplNP65HH3WE7cAtsfjI+M2jnUEqTNkzR33EVXONdM5SpeR6LQRTFFanFyp7VJs1O4HeiWdDyl4TY/BL5PqVGeM4rJc3ximk6elamdzZ7rm2a31Kx0zWpjRaPcXn2HLDZtNok80mSd1iGNUThkZgclnQf8HzP7P5I2F62YM7pYumgOV9yyZUilbSW9ew8wY9naQQtPPW6NasHt2UdNGOiIWk5YsZzGKGWtQ0hijKJdRWPKvlRl99fKeQlFxGKS6j0WzprEA08PHeSzcFZtrTWc4kjjPuqTdCHwMSAcrtNR5XjHyczG519ONAjlJM1PBhh/WPQ4yFC+ZnMvS1dvHRTcXrp664CLKqnNBQxuNPfAsjOHGKi8XVw7Vpw9yADA0Oyj7z80tOK5XJ70eymCVZecNsQALJw1iVWXnFbYZzq1kWan8AlK09GuNbNnJc0E/rFYtZzhQJ7zFJK6fFY7L+4O9bU3o+cHhPKrf/TokNTRvn7j6h89OvA9qrW5iKPy93L+yV2sf2J3bi6upPTTuNk2oXx/ld/LzGVrC3PDuQEYHiQaBTN7DPjTstfPAn+ddJ6kscBPgMODz1ltZp8PjMrNwG9QasP9UTN7M0h1/S5wMvAr4CNm9lzmb+Q0hLznKdTqY692Xtx4zTDIu2d/dIZPnDwNUb+X2zf11hWEz5tqv+nyoD54DcFopFqX1FuDn9slbSt7bJe0LcW13wDONLOTgHnA+yQtoGRQvmxmxwN7gE8Fx38K2BPIv0wKw+M0j7wrmmvNxql2XjOCvK1Q6Z3UdiLN77rROjutQ7WdwmXBzw/UcuFg5vKrwcuO4BF2V/0vgfxG4CrgeuDc4DnAauBvJSm4jtNipKr2zUBc988058Ux4bD2SBfShMB33jmuI7IeoHNc7SGzuGBt794DublmqmUWQXLbiQXHHRkZ9K2kkbMxnNahWpfUF4OfzwNIenu146OQ1E7JRXQ88HfA08BeMzsYHLILCP86uoAXgs88KGkfJRfTv2f5TKcxJLlmstJ97CS+v2Fn6mBzuBh2HzuJhSvXRfrr43znofyqc05k6W1bB6VndrSJq845MbXelQv0+BhDBG+5ZpbethWozTWTlFmUhud+lW6xb+RsDKd1SFzkJf0RcDXwOm/dbBhwXNK5ZtYPzJPUCfwAOKFmTd/SZwmlOQ9Mn+7FMc0ijWsmSyA6avBLHAJ+c+JYgKpxjaQ75nrrHKIW6Nfe7Ke9TfTHRXt5q41FLUah3tkFM5etTVWnVktvo2bVizj5kubO/8+Ad5lZzXfsQXuM9cBpQKekMcFuYSoQlqj2AtOAXZLGABMpBZwrr3UDpSI6uru73bXUJI4c3xEZkD1yfMn1kjUQncXtFN5xr9qwM9PsgiiSCvBO+NzdvF6WoTS2XTxxbaluM26BPnTI6Oocx8+DVNcoQrdV0u+xknrjJElHCTIv6ms29w7acdW7G3KaSxqj8DSwP+uFJU0G+gKDMA54D6Xg8XrgDyhlIH2cUoU0wJ3B6weD99d5PKF12ReToRPK0wy4qZe4/xz1+MLL3UFRvN5vnPC5u3ni2sWxxxjwwLLSYMK43kchb/RFu5ri5EXz7Mr03VZDqjX1c6Mw/EhjFJYD/yrpIUoZRQCY2Z/GnwKUeibdGMQV2oBbzewuSY8BNwfttzdTms9A8PMfJe0AXgYuyPZVRhZ5b8eTgpNZiXP1hPJmdjCt1Rde6Q6KI9w55BFXqbe9dyuQtamf09qkMQr/AKwDthO/FgzBzLYBQya0mdkzwCkR8tcpNdsb9eRdA5BHcDIrzepgWk+1cNYCujzmJTtOq5GmzUWHmV1hZt82sxvDR+GajWLyznWvFpwsikZ2MM2rU2vW+oVreuZy8YLpg6amXbxg+iBDG5feWk/aaxK19lWqtQ9RXPwjTu60NmmMwj9JWiJpiqRJ4aNwzUYxebtemlHElXdr7WpU6z1UBGPLRnB2HzuJ35w4diAjqvvYwX8aV51zIh1tg91JWdNey7k4ph11uTzKILe3aVDx2tFHHDbo/Xr6EH3+gycOGUva0S4+/8HavqPTXNK4jy4Mfi4vk6VKSXVqY2JMUdXEGu8u2xTdD6ettpKC1GRprR3nn6+HrMVpcV1VK48Js4/SuPmS0l6zdg9N06a68jM7x3fw6usHB+bq9pvxyuv9fOUj83Ixos1uYe7kS5reRzMboYjzFnFxylrnshw+po0DEYHLw8e0zlTVInYtJx5zROSCe+IxR0Qen0YDoxSjuaZnbqyb76o7Hx2yQIbZSJXMnPy2SB1nTn5brA5JbaphsEFeuHLdkLTXvDPB8pyt4TSXxFVB0nhJV0q6IXg9W1JNrS+cdOyNSfeMkyfxekwmS5y8GUwooG3zhmf2ZJKnzRoKYzFx7ry9B/pSz5quJd5z5ZrtzFp+NzOWrWXW8rurtg+vpqe3sXCiSHOr+G3gTeC3g9e9wDWFaeRkHqze6OsVQVxLinrIGktJmzUUnp/291ctSSCrjmEmWfh+mrkSjfj3X7O5l4Ur1zFz2VoWrlwXawSd1ieNUZhlZv8b6AMws/3EN2J0cmDpojmRgbtaM3fSZAI1+4+6HufRjGVrBx7v+dL9A/Ks85Ers4niCN+P+r3GEVexnVXHWnYWRWeChbGVtLsjp7VJYxTeDCqSDUDSLMqK2JyCyHGwelImUNimYNAEstu25v5H3QjD89RLrw0Yhrg7/7Q7gjjTEJ4f9XuNC97HLfJZdawlk6zoTLBWaBfu5Eea7KPPAz8GpklaBSwE/rBIpUY7eQ9Wh+qBwEa0Kci7IK8a4VzlrAPlK4v8KpfZuEyfcv3j2lrELdpZday1irrIQLDHLEYWabKP7pX0CLCA0s3TZfU0x3OSafQfWVFtCspbdbRFLGZ5Z8BEkSZTJ6Rai4vnUvYEGt/RFtmiYnxH/KY8i47HTR4/YPQq5c2iWdXrTjGkykk0s1+Z2Vozu8sNQvGMjVlA4uStSKWfOe5OudahPK1KVOpvNXlWntkd3ZsyTt4IGlm97hRPpqE5TmN442D0AhInr5es7ZvTEOVnjqLWoTzVmH3UhNyvmZakGQ5RZGl+2Izq9CS8eG1k4UahBYmbz1JlbktdfP6DJ7J09Vb6yuYG1NumIO0OIO/FbPZRE7j3itNrOjeuornIVLusswjynniXF168NnJwozBCqbz7POOEyax/YnfknVwRd3pp21bk0Riumr8/y134RQumR8YVLorpN5QHWYP83pnVKZpYoyBpLvB1SrOT/wn4rJntCd572MyGtL92WoOoTJ/yhaR37wGuuGULMLhHT553eml3AHnc4MbNaM6a8ZQ1EygPsgb5m6FjGnwc58ih2k7heuAqYAPwX4GfSjrHzJ4GvCduC5PGn38IWH7HtsL+cOOa8FUSFcvISuiq6t17gKWr33K91DL9LUsmULNoNR0bmW7sFE+1dJYjzOzHZrbXzP4G+BPgx5IWUF8BqpNAvf3p0/rz88qIiSJL/GNmwsjKLPT1G1f/6FGgOfnzWf/tRsIsAi9eG1lUzXGUNDF8bmbrgfOBfwSOTbqwpGmS1kt6TNKjki4L5FdJ6pW0JXgsLjtnuaQdkp6UtKjmbzXMGW396fO+wwh3H83o+ZT1324k/Ft78drIopr76K+B/0jJfQSURmxKOgv4yxTXPgh8xswekXQEsEnSvcF7Xw52HwNIeielucwnAscA/0/SfzCz5kwwbyKNSvErb8mQt084zWyColm6aM4gtwYUnz+f9d9uJKRzevHayCLWKJjZ98Pnkt4WyF41s53AJUkXNrMXgReD57+W9DiloHUc5wI3m9kbwLOSdlCa5fxgmi/iZOe/nFrKqinCJ9xsgwDNW3CzBu2HezpnM4yvUxxVU1Il/TdKE9cmlF7q18Bfm9nfZ/kQSTOA+cBDlHon/YmkjwEbKe0m9lAyGBvKTttFdSMyYlmzuZfLg+wgKC3Sl1dkC9VDZcZKLQHZJOIK4hrNcF9wa6WR2UAjYbfjvEW1lNQrKc1QON3MnglkxwFflTTJzFLNVAh2GbcDl5vZK5KuB75A6WbyC8AXgU+mVVjSEmAJwPTpxeWPN5Olt22Jldf7hxaV01+ET7iJBbajnmZkA41W4zsSqRZo/ihwXmgQAILnHwY+lubikjooGYRVZnZHcI1fmlm/mR2iVAcR1jv0AuUVOFMD2SDM7AYz6zaz7smTJ6dRY9gRlxRUVLJQEQHZfXU203Nqx7OBnHqoZhTMzF6PEB6glOZeFUkCvgk8bmZfKpNPKTvs94GfBc/vBC6QdLikmcBs4OHkr+DUSxENzTqHUUrlSMOzgZx6qBZT6JV0lpndVy6UdCZBADmBhZR2G9slbQlkfwFcKGkeJffRc8AfAZjZo5JuBR6jlLl06WjMPCqaGcvWDokpFOETjnMfKUhLcu9ScXg2kFMP1YzCnwI/lPRTYFMg66a02J+bdGEz+ynRvcTurnLOtcC1Sdd26iOc6wvUXBl7+Ji2yK6th48pbT5j3UcGz648O3YYTR7k0U9pOOPZQE49xLqPzOxR4F3AT4AZweMnwLuC95wWJW3r6HCu75rNvVxxy5ZB4zivuGVL1XGZ0ydF33WG8kYVjlXedXS0iavOGT6FX0VQ9PhNZ2RTLfvoeOBoM/tWhXyhpF8EPZCcCJrdHCxqMlcUYdO65XdsGxIkSuqNFPcZofyMEyZHdvM844R8kwN+e9YknvvVAU+FrMCzgZxaqeY++gqlGoVKXgne+2AB+gx7ik4HnLlsbe6LXxHTwtY/sTuTvFY2PLOHp1csTj7QcZxUVMs+OtrMtlcKA9mMwjQa5hSdDhi6d5bfsb2qeycNRY5laVQGTDMnjjnOSKSaUeis8p6nMcTQqMUwD0NT5HLaqJhCsyeOOc5Io5pR2ChpSI8jSf+Vt7KRnAoa2ZmzlfPOk2of8lrMfeKY4+RLtZjC5cAPJF3E4JTUwygVnTkRNCrACq2dd55U+3Dc5PGpA+JRtMrEMccZaVTrkvpL4LclnUEpNRVgrZmta4hmw5S126Lr+tZuezHXBSzPvPOiBtZXy4B5Zvf+mq7Z0QZP/VX8TGbHceqjapdUGBius74BuowI4jqD5tUxVJB79lEzBtbXGiA+ZeaknDVxHKecRKPgtBbPRnQ5rZdmDINvl2oyDBue2VOANo7jhLhRaAJXrtne0AU4DY0eBn/hqdMidydJeAqq4xSLG4WcSfLPX7lm+6DFMI8+RM2g3jhE5e4kLZ6C6jjF4kahBqq1sYhb3kJ52G+okpseeqGpRiHr7uX4oyZEZg8dn7LvEgzenZx67b388tdvJp7jKaiOUyxuFDJSbxuLuLviZrpFatm97IhJJ42TJzGmvb3q+63iZnOckY4bhYzUO884LsDaTLdILbuXpB1RVqL6/4dEjRB1HKcYqlU0OxHELV7VFrVyFhx3ZCZ5kYxtLxmiVti9xBlFjyE4TmNxo5CRehevx178dSZ5kbzeX1r0a/lOEw6LdvfEyZNoBcPkOE6BRkHSNEnrJT0m6VFJlwXySZLulfRU8PPIQC5JX5O0Q9I2Se8uSrd6qHfxKrq4rRbigrfVgrrX/v5c2tsGG432NnHt79fm8++KadkRJ3ccpxiK3CkcBD5jZu8EFgCXSnonsAy4z8xmA/cFrwHeD8wOHkuA6wvUrWaGw+L19sOz3a1f0zOXixdMH9gZtEtcvGB61aBuz/wuvvihkwZN9/rih06quco6rjdUET2jHMeJp7BAs5m9CLwYPP+1pMeBLkrznU8PDrsRuB/4bCD/rpkZsEFSp6QpwXVahkY2vKuVV97oTz6oglqK1/Kc7tWooTyO41SnIdlHkmYA84GHKA3vCRf6XwBHB8+7gPI0mF2BrKWMQtwiddNDL7Cqhgpdp0Sj5lA4jlOdwgPNkt4G3A5cbmavlL8X7AoyRRIlLZG0UdLG3bsbfxcZl2XUb1bo0JqQmcvWsnDlurqnrrUajZxD4ThOPIUaBUkdlAzCKjO7IxD/UtKU4P0pwEuBvBcoj2xODWSDMLMbzKzbzLonT268y6bZKZJ5juNsJZKG8jiO0xiKzD4S8E3gcTP7UtlbdwIfD55/HPhhmfxjQRbSAmBfq8UToHVSJPOc+1wrazb3snDlulx2Lz3zuzj/5K5Bwe7zT84vZuE4TjqKjCksBD4KbJe0JZD9BbASuFXSp4DngQ8H790NLAZ2APuBTxSo24ggzt8+vqON/X2HCv3sett9RF1v1YadAy64fjNWbdhJ97GT3DA4TgMpMvvop8Q3zTwr4ngDLi1Kn5FInL99/vROHnj65cTzx3fUvlGst91HJX++euuQmIwFcjcKjtM4vKJ5mFLN3/6vKQwCUNduIu9soTf7o91ycXLHcYrBjcIwIywUW3He3Ng76EYso54t5DgjE++SOswoYhxnLSxdNGdQTAE8W8hxRgK+UxiBNCJptmd+FyvOmzuozUW13UsSC2dNyiR3HKcYfKcwAknrPuoc11HX5+TZ5mLVJadx0dcfHBQgXzhrEqsuOS2X6zuOkw7fKYxA0jbn+8BJUwrWJBszJ79tUJ3CzMlva7JGjjP6cKMwAomqDo7i9k27GqBNOsKRoGFxYDgS9Mo125usmeOMLtwojEAq/f1xHCi4wC0L1UaCOo7TODymMEIp9/fPWLa2ydok45PXHKc18J1CgxkXU0VcLs87Eydut+DTjx3HqcSNQoMIp6G1xXRZLZc/96voquA4eRJx99p+D+44TiVuFBrEEeMOA+C1N6OnopXL42Y2xMlHAsNhzKnjjAbcKDQInyBWHZ+n4DitgQeaI7hyzXZueugF+s1ol7jw1GmZ5xdX0jm+vkKxkU4YFL/unif5+d4DHNM5jqWL5niHVMdpMG4UKgjz5UPCfHmgLsPQzCSahbMmRbbSbrUWEnlWSDuOUxvuPqogKV++1tYQ+w701axTvay65LQhBsBbSDiOE4XvFCpIypf/wElTBu0k0tLsltJuABzHSUORM5q/JeklST8rk10lqVfSluCxuOy95ZJ2SHpS0qKi9EqaK9wekzIaytc/sTvzZ3a0aSBgmqZmIG4gWh2D0hzHcVJR5DLzHeB9EfIvm9m84HE3gKR3AhcAJwbn/L2k5OY9GQnnCvfuPYDx1lzhcsNw4anTIs8N5TVlEZWt+GlqBq770LzIY+LkjuM4eVGYUTCznwDp5kLCucDNZvaGmT0L7ABOyVunanOFQ67pmcvFC6YP6tZ58YLpA0HmODdQu4SI3mn09dvAZ6TJx++Z38VXPjJv0KyCr3xkngdhHccpnGY4JP5E0rbAvXRkIOsCyiO8uwJZrqSdK9x97CR+c+JYBPzmxLF0H/tWkDYun/6LHz6JZ1eezaGYmET4GWnz8Xvmd/HAsjN5duXZPLDsTDcIjuM0hEYHmq8HvkDJW/IF4IvAJ7NcQNISYAnA9OnTM3342I62yM6gY8uc9aGLKdxRhC4mGJwyWZ5Pf8YJk7nunif59C1baJMig9XhDiNtPv6azb115ezXe77jOKOThhoFM/tl+FzS14G7gpe9QLkzf2ogi7rGDcANAN3d3Zmy/984GN0qulwe52L6zK1b+fQtWwYW2AeWnQkMNSJRBqFyJ5CUj59kmJJYs7mXpau30tdvA+cvXb019fmO44xeGuo+klQ+6uv3gTAz6U7gAkmHS5oJzAYezvvzD8WYkHJ5nIup3ywyOB1lROCtGEMts4vTxD6qcfWPHh0wCCF9/cbVP3o0tQ6O44xOCtspSLoJOB14h6RdwOeB0yXNo+Q+eg74IwAze1TSrcBjwEHgUjOL7hxXB+0xrp3y4PAxneMSG8+FC3TP/K5YI3LIjGdXnl2TnmljH3Hs2R9dKLdnfx8zlq3NrXWH4zgjjyKzjy40sylm1mFmU83sm2b2UTOba2a/ZWbnmNmLZcdfa2azzGyOmf1TETolpZsCnHHC5FTXChfouGykeorV4vok5dU/yUddOo4Tx6gqh0pKN4X0xWnhoh9nRNIalyji+iSl7Z+UthWHj7p0HKeSUdfm4pqeuVXdJmlcNOWB4zgjUkvlc0hcn6S0/ZOuOudElt62lb64IEqAj7p0HKeSUbVTSENScVpl4Lhe/38WHdK6pHrmd3Hdh04aKH6LI66lh+M4o5dRt1NIYumiOYPSQaG0M4jLIIoLTFdbwJNqCOJ0yDJwpjzt9aKvPxjZOnvBcUcOkTmOM7rxnUIFPfO7WHHe3EEtJqqllGadGJam/1JWHZLIe+az4zgjF98pRJBl2EvWiWHVahCKKiwrwsXlOM7IxI1CDUS5f8IK5yTiaiDK5fVWNFdSi4vLcZzRibuPMpLG/VONpHkNUH9FcyVZXVyO44xe3ChkpN4FO2myG+Tv7sk7RuE4zsjF3UcZqXfB7opx5ZTPUyjC3ZMlTuI4zujFdwoZqbeGII0rx909juM0CzcKGal3wU7jynF3j+M4zUI2jFsddHd328aNGxv+uT7AxnGc4YykTWbWHfWexxRqoF7/fBqj4obHcZxm4EYhB7Is4GlqEPKuU3Acx0mLxxTqJGvdQpqU1rzrFBzHcdLiRqFOsi7gaVJavS2F4zjNojCjIOlbkl6S9LMy2SRJ90p6Kvh5ZCCXpK9J2iFpm6R3F6VX3mRdwNOktBYxzc1xHCcNRe4UvgO8r0K2DLjPzGYD9wWvAd4PzA4eS4DrC9QrV7Iu4F6n4DhOK1PkjOafAJVN/M8Fbgye3wj0lMm/ayU2AJ2SphSlW55kXcC9TsFxnFam0dlHR5vZi8HzXwBHB8+7gPKBwbsC2Yu0OFlbZ4fnJC3w3pbCcZxm0LSUVDMzSZkr5yQtoeRiYvr06bnrVQu+gDuOM1JodPbRL0O3UPDzpUDeC0wrO25qIBuCmd1gZt1m1j158uRClXUcxxltNNoo3Al8PHj+ceCHZfKPBVlIC4B9ZW4mx3Ecp0EU5j6SdBNwOvAOSbuAzwMrgVslfQp4HvhwcPjdwGJgB7Af+ERRejmO4zjxFGYUzOzCmLfOijjWgEuL0sVxHMdJh1c0O47jOAMM69bZknZTckPVwjuAf89RnaIYDnq6jvngOuaD65jMsWYWmakzrI1CPUjaGNdPvJUYDnq6jvngOuaD61gf7j5yHMdxBnCj4DiO4wwwmo3CDc1WICXDQU/XMR9cx3xwHetg1MYUHMdxnKGM5p2C4ziOU8GoMwpRw39aDUnTJK2X9JikRyVd1mydKpE0VtLDkrYGOl7dbJ3ikNQuabOku5qtSxySnpO0XdIWSRubrU8UkjolrZb0hKTHJZ3WbJ3KkTQn+P2Fj1ckXd5svSqR9Ongb+Znkm6SNLbZOpUz6txHkn4XeJXS/IZ3NVufKIJmgVPM7BFJRwCbgB4ze6zJqg0gScAEM3tVUgfwU+CyYB5GSyHpCqAbeLuZfaDZ+kQh6Tmg28xaNr9e0o3Av5jZNyQdBow3s71NVisSSe2Ummqeama11jLljqQuSn8r7zSzA5JuBe42s+80V7O3GHU7hZjhPy2Fmb1oZo8Ez38NPE5pvkTLEAxEejV42RE8Wu4OQ9JU4GzgG83WZTgjaSLwu8A3AczszVY1CAFnAU+3kkEoYwwwTtIYYDzw8ybrM4hRZxSGG5JmAPOBh5qsyhACt8wWSi3Q7zWzltMR+Arw58ChJuuRhAH/LGlTMDOk1ZgJ7Aa+HbjiviFpQrOVqsIFwE3NVqISM+sF/gbYSWmI2D4z++fmajUYNwotjKS3AbcDl5vZK83WpxIz6zezeZTmX5wiqaXccZI+ALxkZpuarUsKfsfM3k1pXvmlgZuzlRgDvBu43szmA6/x1oz1liJwbZ0D3NZsXSqRdCSl8cMzgWOACZIubq5Wg3Gj0KIEfvrbgVVmdkez9alG4EZYD7yvyapUshA4J/DX3wycKel7zVUpmuAOEjN7CfgBcEpzNRrCLmBX2W5wNSUj0Yq8H3jEzH7ZbEUi+D3gWTPbbWZ9wB3AbzdZp0G4UWhBgiDuN4HHzexLzdYnCkmTJXUGz8cB7wGeaKpSFZjZcjObamYzKLkT1plZS92VAUiaECQUELhk3gu0VHacmf0CeEHSnEB0FtAyiQ8VXEgLuo4CdgILJI0P/s7PohQzbBlGnVEIhv88CMyRtCsY+NNqLAQ+SunONkyvW9xspSqYAqyXtA34N0oxhZZN+WxxjgZ+Kmkr8DCw1sx+3GSdovgfwKrg33we8FfNVWcogVF9D6U78JYj2GmtBh4BtlNag1uqunnUpaQ6juM48Yy6nYLjOI4TjxsFx3EcZwA3Co7jOM4AbhQcx3GcAdwoOI7jOAO4UXCcjEg6RtLqAq9/jqSWrBZ2Rj6ekuo4juMM4DsFZ9Qg6eJgBsQWSf8QtFdG0quSrg1mQ2yQdHQgnxW83i7pGkmvBvIZ4TwOSX8o6Q5JP5b0lKT/XfZ575X0oKRHJN0W9LKq1OlPg7kZ2yTdXHbNvw2el88HOCDpPwcV0N8KvstmSecW/9tzRgtuFJxRgaT/CHwEWBg08esHLgrengBsMLOTgJ8AlwTyrwJfNbO5lHr/xDEvuPZc4CMqDUl6B3Al8HtBo7uNwBUR5y4D5pvZbwF/XPmmmc0L9P3L4Br/CnyOUsuOU4AzgOtavGOpM4wY02wFHKdBnAWcDPxbqeUM4yi1/AZ4EwhbdGyi1CYB4DSgJ3j+fUotj6O4z8z2AUh6DDgW6ATeCTwQfN5hlNqrVLKNUuuINcCaqItLmg1cB5xhZn2S3kup0d+fBYeMBabTYj10nOGJGwVntCDgRjNbHvFen70VXOsn+9/FG2XPw/NFqR/UhQnnnk1peM0Hgc9JmjtI6ZLL6VbgEjN7MRQD55vZkxn1dJxE3H3kjBbuA/5A0lEAkiZJOjbhnA3A+cHzCzJ+3gZgoaTjg8+bIOk/lB8gqQ2YZmbrgc8CE4HKuMO3gG+b2b+Uye4B/kfQZRNJ8zPq5jixuFFwRgXBfOsrKU032wbcS6nTazUuB64Ijj8e2Jfh83YDfwjcFJz/IHBCxWHtwPckbQc2A18rH3EZGK0/AD5ZFmzuBr5AafzpNkmPBq8dJxc8JdVxYpA0HjhgZibpAuBCM/NMH2dE4zEFx4nnZOBvAzfNXuCTzVXHcYrHdwqO4zjOAB5TcBzHcQZwo+A4juMM4EbBcRzHGcCNguM4jjOAGwXHcRxnADcKjuM4zgD/HwvPjTeWi/9BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cdf['ENGINESIZE'], cdf['CO2EMISSIONS'])\n",
    "plt.xlabel('engine size')\n",
    "plt.ylabel('CO2 emissions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd78467e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msk = np.random.rand( len(cdf) ) < 0.8\n",
    "train_set = cdf[msk]\n",
    "test_set = cdf[~msk]\n",
    "\n",
    "plt.scatter(train_set['ENGINESIZE'], train_set['CO2EMISSIONS'])\n",
    "plt.scatter(test_set['ENGINESIZE'], test_set['CO2EMISSIONS'], color = 'red')\n",
    "plt.xlabel('cylinders')\n",
    "plt.ylabel('CO2 emissions')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d2218c2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.    2.    4.  ]\n",
      " [ 1.    2.4   5.76]\n",
      " [ 1.    1.5   2.25]\n",
      " ...\n",
      " [ 1.    3.    9.  ]\n",
      " [ 1.    3.2  10.24]\n",
      " [ 1.    3.2  10.24]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "\n",
    "train_x = np.asanyarray(train_set[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train_set[['CO2EMISSIONS']])\n",
    "\n",
    "test_x = np.asanyarray(test_set[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test_set[['CO2EMISSIONS']])\n",
    "\n",
    "poly = PolynomialFeatures(degree = 2)\n",
    "train_x_poly = poly.fit_transform(train_x)\n",
    "print(train_x_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eeba934b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coef is:  [[ 0.         51.49567082 -1.63615932]]\n",
      "intercept is:  [104.59516796]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(train_x_poly, train_y)\n",
    "print(\"coef is: \", regr.coef_)\n",
    "print(\"intercept is: \", regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "78aef23b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "XX = np.arange(0, 10, 0.1)\n",
    "YY = regr.intercept_[0] + regr.coef_[0][1]*XX + regr.coef_[0][2]*np.power(XX, 2)\n",
    "plt.scatter(cdf['ENGINESIZE'], cdf['CO2EMISSIONS'])\n",
    "plt.plot(XX, YY, '--r')\n",
    "plt.xlabel('engine size')\n",
    "plt.ylabel('CO2 emissions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6f0d56ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the R2 score is: 0.75\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "test_x_poly = poly.fit_transform(test_x)\n",
    "eval_y = regr.predict(test_x_poly)\n",
    "R2 = r2_score(test_y, eval_y)\n",
    "print(\"the R2 score is: %.2f\" % R2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c12cf46",
   "metadata": {},
   "source": [
    "# practice\n",
    "#### test for cubic instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "feec5c95",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model\n",
    "train_x = np.asanyarray(train_set[['ENGINESIZE']])\n",
    "train_y = np.asanyarray(train_set[['CO2EMISSIONS']])\n",
    "\n",
    "test_x = np.asanyarray(test_set[['ENGINESIZE']])\n",
    "test_y = np.asanyarray(test_set[['CO2EMISSIONS']])\n",
    "\n",
    "poly3 = PolynomialFeatures(degree = 3)\n",
    "train_x_poly = poly3.fit_transform(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c77939d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coef3 is:  [[ 0.         30.8311044   4.00604053 -0.46388015]]\n",
      "intercept3 is:  [126.73324237]\n"
     ]
    }
   ],
   "source": [
    "regr3 = linear_model.LinearRegression()\n",
    "regr3.fit(train_x_poly, train_y)\n",
    "\n",
    "print(\"coef3 is: \", regr3.coef_)\n",
    "print(\"intercept3 is: \", regr3.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "988d9bdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "XX3 = np.arange(0, 10, 0.1)\n",
    "YY3 = regr3.intercept_[0] + regr3.coef_[0][1]*XX3 + regr3.coef_[0][2]*np.power(XX3, 2) + regr3.coef_[0][3]*np.power(XX3, 3)\n",
    "plt.scatter(cdf['ENGINESIZE'], cdf['CO2EMISSIONS'])\n",
    "plt.plot(XX3, YY3, '--r')\n",
    "plt.xlabel('engine size')\n",
    "plt.ylabel('CO2 emissions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a8997ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score is: 0.75\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "test_x_poly3 = poly3.fit_transform(test_x)\n",
    "eval_y = regr3.predict(test_x_poly3)\n",
    "R2 = r2_score(test_y, eval_y)\n",
    "\n",
    "print(\"R2 score is: %.2f\" % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "myenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}