200968268_L4_EX2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c9a701a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # importing libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import random\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3cabfa9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel=pd.read_csv(\"D:\\\\Sem 4\\\\ML\\\\week4\\\\Fuel.csv\")\n",
    "fuel.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e6f098d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel = fuel[[\"ENGINESIZE\",\"CYLINDERS\",\"FUELCONSUMPTION_COMB\", \"CO2EMISSIONS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cc50d8ec",
   "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>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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf031a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = fuel.loc[:, fuel.columns != 'CO2EMISSIONS']\n",
    "y1 = fuel[[\"CO2EMISSIONS\"]]\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x1,y1,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb2b0036",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()\n",
    " \n",
    "model.fit(x_train, y_train)\n",
    "y_pred1 = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "59f4a40d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "S\n",
    "acc = []\n",
    "train_acc = []\n",
    "degree = []\n",
    "mse = []\n",
    "rss = []\n",
    "for i in range(2,6):\n",
    "    poly = PolynomialFeatures(degree = i)\n",
    "    X_poly = poly.fit_transform(x_train)\n",
    "\n",
    "    poly.fit(X_poly, y_train)\n",
    "    model1 = LinearRegression()\n",
    "    model1.fit(X_poly, y_train)\n",
    "\n",
    "    y_pred = model1.predict(poly.fit_transform(x_test))\n",
    "    train_error = model1.predict(poly.fit_transform(x_train))\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    \n",
    "    acc.append(r2*100)\n",
    "    te = r2_score(y_train, train_error)\n",
    "    train_acc.append(te*100)\n",
    "    degree.append(i)\n",
    "    MSE = np.square(np.subtract(y_test,y_pred)).mean()\n",
    "    RSS = np.square(np.subtract(y_test,y_pred)).sum()\n",
    "    mse.append(MSE)\n",
    "    rss.append(RSS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6d1dc34f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2yUdMcTymu1AiSAUubKqsZfSEKcTychh/fint8rVNPmpK4pqHEkV6E7OVq086P3x8Gss2VPLY6GF069gu6EiSAVpCiSZdgbdyN744h7/PL+d/Th1AaZ/OQceRDCmOx9i0rZaaumTQUaQZqcBbsaenLOO+fyzkwiN7M2qoTkuKsoTOxowkFXgrNX3pRq57diZH7l/Mz751SNBxJMN2nI2pZZQoUYG3Qqs3VzFmQhld28e469zB5GmbfOQ1nk6/VlfgkaI/ua1MVW09Yx6awtbqOu67oJROqV16Em3b56HodPpI0V0orYi785NnZ/LB0o3cc94Q+u/TPuhI0kISRY3b6bWEEiW6Am9FHnhrIc9MXc7Y4w/kmwP2CTqOtKCC/Fza5bVhne4Fj5R0DzUea2azzWyWmT1mZm3N7Dgzm5p67EEz09V8Fntzfjm//uscThywDz88rm/QcSQAiaJ8beaJmCYL3My6A1cApe4+AGgDnAM8CIxKPbaYHSfUS5ZZuLaCHz46lQO7FjHuuwPJydE2+dYoEY9pCSVi0l1CyQXapa6yC4AKoMbd56c+/jJwegbyyV7aXFXLRQ++T26bHO67oJTCmP6i1FoVF2o7fdQ0WeDuvhwYBywBVgKbgCeBXDMrTT3tDKDnzl5vZmPMrMzMysrLy5sntaSlPulc9fh0Fq+r5K5zB9Ozc0HQkSRAJUX5ugKPmHSWUDoBpwL7Ad2AQuBcYBRwi5m9B2wB6nf2encf7+6l7l5aUlLSbMGlaeMmzeO1uWu4/tuHMmz/4qDjSMAS8RjrK6qpT3rQUaSZpLOEcjyw0N3L3b0WeAY4yt3/6e7D3X0o8Hdg/hd+FmlRf5q+nLvf+JhzvtyL84f1DjqOZIFEPEbSYWOlrsKjIp0CXwIMM7MCaxgSPQKYY2ZdAMwsBvwncE/mYsrumLlsE9dMnMHQPp35+SmHBh1HskRxXPeCR006a+CTgYnAVGBm6jXjgavNbA4wA/iLu7+WyaCSnjVbqhjzUBmJeIy7zhtMfq5u9ZcGCZ1OHzlp3ZLg7tcD13/u4atTPyRLVNfVc+nDU9lYWcvES4/c/gdWBFTgUaR7yiLC3fmv52YxZfEG7jxnMId26xB0JMkyCS2hRI7+fh0Rf3xnEU+WLeOK4/py8uH7Bh1HslCHdnnktTFdgUeICjwC3l6wll+9MIevH9KVq44/MOg4kqXMjOLCmOahRIgKPOQWr6vgskemckBJITefNUjb5OULFce1mSdKVOAhtrW6jtETyjCD+y/4EnFtk5cmJHQ6faSowEMqmXTGPjGdj8sruOucwfQq1jZ5aVoiHtPp9BGiAg+pW16Zz8sfrua/Tj6Yo/omgo4jIZGI51O+tRp3baePAhV4CL0wYyW3v7aAs0p7cuFRfYKOIyGSiMeoqUuypbou6CjSDFTgITN7xSZ+/NQHDOndiV9+51AaphuIpGf70Wo6GzMSVOAhsnZrNWMmTKFjQR73nDeEWG6boCNJyBQXNuzGXFehdfAo0G0LIVFTl+Syh6eydms1Ey85ipIibZOX3ZfQ6fSRogIPiZ//ZTbvLVrPbaMGcVgPbZOXPbPjdHoVeBRoCSUEHnp3MY9OXsKlxxzAqYO6Bx1HQqxzQT5mmocSFSrwLPfuJ+v4xZ9nc1z/Lvz46wcFHUdCLrdNDp0KdDp9VKjAs9jS9ZVc9shUehcXcOuoQbTRNnlpBom4CjwqVOBZqiK1Tb6uPsn9F36J9m3zgo4kEdEw0EpLKFGgAs9CyaTz46c+YP7qLdxxzmD2SxQGHUkiJFGkeShRoQLPQre/toAXZ63iJycdzNEHlgQdRyImoYmEkZFWgZvZWDObbWazzOwxM2trZiPMbKqZTTezt8ysb6bDtgYvzVrFLa/MZ+Tg7nz/q/sFHUciKBGPsbW6jqra+qCjyF5qssDNrDtwBVDq7gOANsAo4G7gXHcfBDwK/CyDOVuFuas286MnpzOoZ0d+fdph2iYvGbHjaDUto4RduksouUA7M8sFCoAVgAPtUx/vkHpM9tD6ihpGTygjHsvl3vOH0DZP2+QlM3YcbqxllLBrciemuy83s3HAEmAbMMndJ5nZRcBfzWwbsBkYtrPXm9kYYAxAr169mi14lNTWJ7n8kams3lzNkxcfSdf2bYOOJBHWWOA6Wi380llC6QScCuwHdAMKzew8YCxwkrv3AP4A3Lyz17v7eHcvdffSkhK9Ibczv3r+Q/75yTpuHHkYg3p2DDqORFyxllAiI50llOOBhe5e7u61wDPAV4CB7j459ZwngKMylDHSHn9vCQ/+czGjh+/HyME9go4jrYCWUKIjnQJfAgwzswJreFdtBPAh0MHMGo9APwGYk6GMkfX+ovX8159mcfSBJVx74sFBx5FWom1eG4piuboCj4B01sAnm9lEYCpQB0wDxgPLgKfNLAlsAL6XyaBRs3zjNi59eAo9OhVw+6gjtE1eWpROp4+GtMbJuvv1wPWfe/jZ1A/ZTdtq6hkzoYzq2iSPjymlQ4G2yUvLSsRjmgkeAdqJ2cLcnasnfsCHKzfzf2cfQd8u8aAjSSuUiMdYV6ECDzsVeAu7642PeX7GSq75Rn+O7d8l6DjSSmkJJRpU4C3olQ9XM27SPE4d1I1LvrZ/0HGkFUvEY2yorKGuPhl0FNkLKvAW8tHqLVz1xHQGdOvAb04/XNvkJVCJohjusL5SV+FhpgJvARsra7hoQhlt89ow/gJtk5fgJQpTm3m2qMDDTAWeYXX1SX7w6DRWbqzi3vMHs2+HdkFHEiFR1LiZR29khplOpc+wX/91Lm8tWMtNpx/OkN6dg44jAnx6N6YKPMx0BZ5BT5Ut5fdvL+TfjurDmV/qGXQcke0a56HoaLVwU4FnyJTFG/jps7P4at8EPztZ2+QluxTFcsnPzdEVeMipwDNg1aYqLnl4Cvt2bMsd5xxBbhv9a5bsYmaUxGOUq8BDTWvgzayqtp4xD5VRWV3HIxd9mY4F+UFHEtmp4ni+llBCTpeGzcjdufbpGcxcvolbRx3BgV2Lgo4kskuJuE6nDzsVeDMa//dPeG76Cv7jhAM54ZCuQccR+UINp9OrwMNMBd5MXp+3hhtfmsvJh+/L5cf2DTqOSJMS8Rjrttbg7kFHkT2kAm8GH5dv5YrHpnHwPu357RnaJi/hUByPUZd0Nm2rDTqK7CEV+F7atK2W0Q+Wkd8mh/suLKUgX+8LSzgkdDZm6KnA90J90rnisWks3VDJ3ecNoXtHbZOX8CjR2Zihp8vFvXDTS3N5c345vz7tMIbup23yEi7F2k4femldgZvZWDObbWazzOwxM2trZv8ws+mpHyvM7LkMZ80qz05bxr1//4Tzh/XmnC/3CjqOyG7bvoSio9VCq8krcDPrDlwBHOLu28zsSWCUuw//1HOeBv6UuZjZ5YOlG/nPp2cybP/O/PcphwQdR2SPdCrIJ8dgXYWWUMIq3TXwXKCdmeUCBcCKxg+YWXvgOOC5Zk+XhdZsrmLMQ2V0KYpx17lDyNM2eQmpnByjc6E284RZk+3j7suBccASYCWwyd0nfeop3wFedffNO3u9mY0xszIzKysvL2+GyMGpqq3n4oensKWqjvsuKKVzobbJS7gl4vmU61CH0GqywM2sE3AqsB/QDSg0s/M+9ZSzgcd29Xp3H+/upe5eWlJSsrd5A+Pu/Oy5WUxbspGbzxzIwfu2DzqSyF4rKdLp9GGWzt//jwcWunu5u9cCzwBHAZhZAhgKvJC5iNnh928vYuKUZVw5oh/fHLBv0HFEmkVxobbTh1k6Bb4EGGZmBdawxXAEMCf1sTOA5929KlMBs8E/Pirnhhc+5BuHduXKEf2CjiPSbBLxmM7FDLF01sAnAxOBqcDM1GvGpz48ii9YPomCRWsr+MGj0ziwaxE3nzmInBxtk5foSBTF2FZbT2VNXdBRZA+ktZHH3a8Hrt/J48c0d6BssqWqlosmlJFjcN8FpRTGtO9JoqX4U6fT9yrWf99ho3vgdiGZdMY+MZ2Fayu489zB9OxcEHQkkWbXeDq9TuYJJxX4Lvzu5Xm8MmcN159yCEcdkAg6jkhGlGg7faipwHfiLx+s4M7XP+bsoT05f1jvoOOIZIxOpw83FfjnzFq+iasnfsCX+nTiF98eoNneEmnFhboCDzMV+KeUb6lmzIQyOhfkc/d5Q8jP1b8eibb83Bw6tMtTgYeU3nZOqalLcunDU1hfWcPES44ikVobFIk6nU4fXipwGrbJ//efZlG2eAO3n30EA7p3CDqSSItJxGO6CyWktEYATPjnYh5/fymXH3sApwzsFnQckRZVEtdEwrBq9QX+zoK1/PL5Dzn+4C78xwkHBR1HpMUltIQSWq26wJesq+SyR6eyf6KQW87SNnlpnYrjMTZtq6WmLhl0FNlNrbbAt1bXMXpCGe4N2+SL2uYFHUkkEI1v2GusbPi0ygJPJp0fPTGdj9Zs4Y5zjqBPojDoSCKBSWgzT2i1ygK/9dWPmPThan568iEM7xfeQyZEmkPj6fS6EyV8Wl2BvzhzJf/36kecMaQH3/tKn6DjiARu+zwUnU4fOq2qwD9csZkfPfkBR/TqyA2naZu8CECiKLWEotPpQ6fVFPi6rdWMnlBGh3Z53HveEGK5bYKOJJIVCvJzaZfXRlfgIdQqdmLW1ie57JGprN1azZMXH0mX9m2DjiSSVRJFOhszjFpFgf/iL7OZvHA9t40axMCeHYOOI5J1EvGYllBCKK0lFDMba2azzWyWmT1mZm2twQ1mNt/M5pjZFZkOuycembyYh99dwsVf259TB3UPOo5IVioujFGuJZTQafIK3My6A1cAh7j7NjN7kobDjA3oCfR396SZdcls1N03+ZN1XP+n2Rx7UAnXfKN/0HFEslZJUT7Tl24MOobspnSXUHKBdmZWCxQAK4BfAee4exLA3ddkJuKeWbahkksfmUqv4gJuO/sI2mibvMguJeIx1ldUk0y6RkqESJNLKO6+HBgHLAFWApvcfRJwAHCWmZWZ2Ytm1i+zUdNXWVPH6AlTqK1Pcv8FpbTXNnmRL1RcmE/SYUOl1sHDpMkCN7NOwKnAfkA3oNDMzgNiQJW7lwL3Ab/fxevHpEq+rLy8vPmS74K7c/VTM5i3ajO3n30E+5fEM/41RcKu8XT6tdpOHyrpvIl5PLDQ3cvdvRZ4BjgKWJb6NcCzwOE7e7G7j3f3UncvLSnJ/Lb1O15bwAszV3Ltif055qCsW5YXyUrbB1rpVsJQSWcNfAkwzMwKgG3ACKAM2AwcCywEvgbMz1TIdE2avYrfvTyf047ozujh+wcdRyQ0GgdaaR5KuDRZ4O4+2cwmAlOBOmAaMB5oBzxiZmOBrcBFmQzalHmrtjD2iekM7NGB/x15mLbJi+yGxitwLaGES1p3obj79cD1n3u4Gji52RPtgQ0VNYyeUEZhLJd7zy+lbZ62yYvsjg7t8sjNMe3GDJnQ78Ssq0/yg8emsmpTFY9fPIx9OmibvMjuMrPU6fQq8DAJfYH/6oU5vL1gHeO+O5DBvToFHUcktBLxmJZQQibU0wiffH8pf3xnEd//6n6cMaRH0HFEQi2h0+lDJ7QFPmXxen763EyG90tw3YnaJi+ytxLxmI5VC5lQFviKjdu4+KGpdO/YjjvOHkxum1D+Y4hklUQ8n/Kt1bh70FEkTaFrvqraei5+aApVtfXcf2EpHQq0TV6kOSTiMWrqkmyprgs6iqQpVAXu7lwzcQazVmzitlGD6NulKOhIIpGx/Wg1LaOERqgK/J43P+HPH6zgx18/iBEHdw06jkikFBc2bubRG5lhEZoCf23uam7621xOGdiNy445IOg4IpGT0On0oROKAl+wZgtXPjadQ7u156bTD9c2eZEMaFxCWauj1UIjFAV+1xsfE8vLYfz5pbTL1zZ5kUzoXJCPma7AwyQUOzFvHHk4S9ZX0q1ju6CjiERWbpscOhXodPowCcUVeH5uDn276GAGkUxLxPN1F0qIhKLARaRlFBdqO32YqMBFZLtEkQo8TFTgIrKdllDCRQUuItsl4jG2VNdRVVsfdBRJgwpcRLZrPBtTyyjhoAIXke12nE6vZZQwSKvAzWysmc02s1lm9piZtTWzP5rZQjObnvoxKMNZRSTDiuOahxImTW7kMbPuwBXAIe6+zcyeBEalPny1u0/MZEARaTlaQgmXdJdQcoF2ZpYLFAArMhdJRIKyfaCVllBCwdI5fcPMrgRuALYBk9z9XDP7I3AkUA28Clzr7v/yv20zGwOMSf32IGDeHmZNAGv38LWSOfq+ZB99T7LT3nxfert7yecfbLLAzawT8DRwFrAReAqYSENprwLygfHAx+7+yz0M1yQzK3P30kx9ftkz+r5kH31PslMmvi/pLKEcDyx093J3rwWeAY5y95XeoBr4AzC0OYOJiMgXS6fAlwDDzKzAGgZxjwDmmNm+AKnHvgPMylhKERH5F03eheLuk81sIjAVqAOm0bBk8qKZlQAGTAcuyWBOUl9Tso++L9lH35Ps1Ozfl7TexBQRkeyjnZgiIiGlAhcRCamsL3Az62lmr5vZh6nt/FcGnam1S41SeM/MPkh9T34RdCZpYGZtzGyamT0fdBZpYGaLzGxmauRIWXN+7jCciVkH/Ie7TzWzImCKmb3s7h8GHawVqwaOc/etZpYHvGVmL7r7u0EHE64E5gDtgw4in3Gsuzf75qqsvwJP3W8+NfXrLTT8x9k92FStW+r+/62p3+alfujd8ICZWQ/gZOD+oLNIy8j6Av80M+sDHAFMDjhKq5f6q/p0YA3wsrvrexK8W4FrgGTAOeSzHJhkZlNSo0WaTWgK3MziNGzpv8rdNwedp7Vz93p3HwT0AIaa2YCAI7VqZvYtYI27Twk6i/yLr7r7YOBE4HIzO7q5PnEoCjy1zvo08Ii7PxN0HtnB3TcCrwPfDDhKa/cV4Ntmtgh4HDjOzB4ONpIAuPvy1M9rgGdpxrEjWV/gqa36DwBz3P3moPMImFmJmXVM/bodcAIwN9BQrZy7X+fuPdy9Dw3z+l9z9/MCjtXqmVlh6uYLzKwQ+DrNOHYkDHehfAU4H5iZWnMF+Im7/zW4SK3evsCDZtaGhouAJ91dt62J/KuuwLMN16HkAo+6+0vN9cm1lV5EJKSyfglFRER2TgUuIhJSKnARkZBSgYuIhJQKXEQkpFTgIiIhpQIXEQmp/wfOTQUy2tjf/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylim(85,93)\n",
    "plt.xticks([2,3,4,5,6])\n",
    "plt.plot(degree,acc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "35d386e1",
   "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": [
    "\n",
    "plt.xticks([2,3,4,5,6])\n",
    "plt.plot(degree,acc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6d4590c4",
   "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": [
    "plt.xticks([2,3,4,5,6])\n",
    "plt.plot(degree,train_acc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "819e40f7",
   "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": [
    "plt.scatter(y_test, y_pred1, color = 'blue')\n",
    "plt.scatter(y_test, y_pred, color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7fd3c98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[85.42086902149778, 89.99229681136023, 91.92336668515816, 49.794175978238144]\n"
     ]
    }
   ],
   "source": [
    "print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "680c347e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[92.01263213388422, 92.85219463005753, 93.78660263236802, 94.55452343445297]\n"
     ]
    }
   ],
   "source": [
    "print(train_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a4d0d5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68dbf3d2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}