Activity-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afc6ca90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Data\n",
      "     Unnamed: 0        TV     Radio  Newspaper     Sales\n",
      "0      0.000000  0.775786  0.762097   0.605981  0.807087\n",
      "1      0.005025  0.148123  0.792339   0.394019  0.346457\n",
      "2      0.010050  0.055800  0.925403   0.606860  0.303150\n",
      "3      0.015075  0.509976  0.832661   0.511873  0.665354\n",
      "4      0.020101  0.609063  0.217742   0.510994  0.444882\n",
      "..          ...       ...       ...        ...       ...\n",
      "195    0.979899  0.126818  0.074597   0.118734  0.236220\n",
      "196    0.984925  0.316199  0.098790   0.068602  0.318898\n",
      "197    0.989950  0.596212  0.187500   0.053650  0.440945\n",
      "198    0.994975  0.956713  0.846774   0.579595  0.940945\n",
      "199    1.000000  0.782550  0.173387   0.073879  0.464567\n",
      "\n",
      "[200 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "#   1928191\n",
    "#   SAURABH SINHA\n",
    "#   ACTIVITY-3\n",
    "#   CSSE-3\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "\n",
    "#https://www.kaggle.com/ishaanv/ISLR-Auto\n",
    "plt.rcParams['figure.figsize'] = (12.0, 9.0)\n",
    "df1 = pd.read_csv('C:\\\\Users\\\\KIIT\\\\Downloads\\\\advertising.csv')\n",
    "df1.describe()\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "df_norm = pd.DataFrame(scaler.fit_transform(df1), columns=df1.columns)\n",
    "print(\"Normal Data\")\n",
    "print(df_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f0cbe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "TV            0\n",
       "Radio         0\n",
       "Newspaper     0\n",
       "Sales         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_norm.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "18a195a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.16038882495584206 R2_SCORE:  0.35671845263128477\n"
     ]
    }
   ],
   "source": [
    "X = df_norm.iloc[:, 2:3].values\n",
    "Y = df_norm['Sales']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, Y_train)\n",
    "\n",
    "def linear():\n",
    "    plt.scatter(X_test, Y_test, color='black')\n",
    "    plt.plot(X_test, lin_reg.predict(X_test), color='blue')\n",
    "    plt.title('(Linear Regression)')\n",
    "    plt.show()\n",
    "    return\n",
    "linear()\n",
    "\n",
    "y_train_predict = lin_reg.predict(X_train)\n",
    "rmse=(np.sqrt(mean_squared_error(Y_train,y_train_predict)))\n",
    "r2=r2_score(Y_train,y_train_predict)\n",
    "print('RMSE :',rmse,'R2_SCORE: ',r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d38e8958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 0.15914568630686315 R2_SCORE:  0.3666516764712209\n"
     ]
    }
   ],
   "source": [
    "degree=4\n",
    "poly_reg = PolynomialFeatures(degree)\n",
    "X_poly = poly_reg.fit_transform(X_train)\n",
    "pol_reg = LinearRegression()\n",
    "pol_reg.fit(X_poly, Y_train)\n",
    "\n",
    "def polymonial():\n",
    "    plt.scatter(X_test, Y_test, color='black')\n",
    "    plt.plot(X_test, pol_reg.predict(poly_reg.fit_transform(X_test)), color='blue')\n",
    "    plt.title('(Polynomial Regression)')\n",
    "    plt.show()\n",
    "    return\n",
    "polymonial()\n",
    "\n",
    "y_train_predict1 = pol_reg.predict(X_poly)\n",
    "rmse1=(np.sqrt(mean_squared_error(Y_train,y_train_predict1)))\n",
    "r2_1=r2_score(Y_train,y_train_predict1)\n",
    "print('RMSE :',rmse1,'R2_SCORE: ',r2_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "c0850f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Jg2JCIElSoyHNQ2BBIElSI7sMJEnSoJgQSJLUyNsOJUnSoJgQSJLUyIRAkiQNigmBJEmNprzLQJIkDYkJgSRJjRxDIEmSBsWEQJKkRs5UKEmSBsWEQJKkRo4hkCRJg2JCIElSI+chkCRJg2JCIElSI8cQSJKkQTEhkCSp0YACAhMCSZJkQiBJUrMa0CACEwJJkmRCIElSq6n5bsAaZEEgSVIjuwwkSdKgmBBIktRoOPmACYEkScKEQJKkZo4hkCRJg2JCIElSoyHddmhCIEmSTAgkSWrlGAJJkjQoJgSSJDUaUEBgQiBJkkwIJElqNqCAwIRAkiSZEEiS1GxqQIMITAgkSZIFgSRJrWoOlkkkWZLk7CTH9u+fmeT8JFNJdp3kHBYEkiQtfq8GLhh5/z3gacBJk57AgkCSpEZVNevLOEm2A/YBDhtp1wVV9YPV+SwWBJIkLWBJliY5c2RZOm2XDwJv4E4+a8m7DCRJajQXTzusqmXAspVtS7IvcFlVnZVk9ztzHQsCSZIaLYC7Dh8D7Jdkb2BDYPMkn6iq563uiewykCRpkaqqg6pqu6q6L3Ag8I2WYgBMCCRJarZQJyZKcgBwCLA18JUk51TVU2Y6xoJAkqQBqKrlwPL+9THAMatzvAWBJEmNFmhA0MQxBJIkyYRAkqRWC3UMQQsTAkmSZEIgSVKrqeEEBCYEkiTJhECSpGYDGkJgQiBJkkwIJElqNsVwIgITAkmSZEIgSVIrxxBIkqRBMSGQJKmR8xBIkqRBMSGQJKmRzzKQJEmDYkIgSVKjAQUEFgSSJLVyUKEkSRoUEwJJkhrVgPoMTAgkSZIJgSRJrRxDIEmSBsWEQJKkRiYEkiRpUEwIJElqVAwnIjAhkCRJJgSSJLUa0hiCVRYESa6G27KQ9H9W/7qqavNZbpskSZojqywIqmqzuWyIJEmLzYAmKpxsDEGSP0nyov71VknuN7vNkiRJc2nsGIIkbwV2BXYEPgasD3wCeMzsNk2SpIVtakARwSQJwQHAfsC1AFX1S8DuBEmSBmSSuwxuqqpKUgBJNpnlNkmStCgM6S6DSRKCo5McCmyR5K+ArwMfnd1mSZKkuTQ2Iaiq9yV5EvA7YAfgLVV1wqy3TJKkBW5AQwgmnpjou8BGdPMQfHf2miNJkubD2C6DJC8BTgeeBjwDODXJi2e7YZIkLXRTVbO+zJVJEoK/Ax5WVZcDJLkb8N/AEbPZMEmSFrohdRlMMqjwEuDqkfdXAz+fneZIkqT5MNOzDF7Xv/wFcFqSL9KNIdifrgtBkqS12tR8N2ANmqnLYMXkQz/ulxW+OHvNkSRJ82Gmhxu9bS4bIknSYjOkqYsneZbB1sAbgJ2ADVesr6o9Z7FdkiRpDk0yqPBI4ELgfsDbgIuBM2axTZIkLQpVs7/MlUkKgrtV1eHAzVX1rap6MbDbLLdLkiTNoUnmIbi5//PSJPsAvwS2m70mSZK0OAzp4UaTFATvTHIX4G+BQ4DNgdfOaqskSdKcmuThRsf2L68C9pjd5kiStHjU2nCXQZJD6CYiWqmqetWstEiSJM25mRKCM+esFZIkLUJrxRiCqvr4XDZE0swC/N0e9+OqG27m0FMuYeP11uFFj7wnW268PldcdxNHnP4Lrr95SBOpSppLk9x2KGkB2H37LfnV1Tfe9v5JO2zFRb++jnec8GMu+vV1PGmHu81j66S101TN/jKJJEuSnJ3k2P79lklOSPLD/s+7jjvHrBUESR6Y5I1J/iXJwf3rB83W9aQh22LDddlpm0055eIrb1v3R9tuymk/vQqA0356FQ/ZdrNVHC1pLfBq4IKR928CTqyqBwAn9u9nNCsFQZI3Ap+iSzlPp5vZMMBRScY2StIdPe0h2/DF8y+7w5PVNttgXX534y0A/O7GW9hsg0nuIpa0JlXVrC/jJNkO2Ac4bGT1/sCKrv+PA3827jyzdZfBXwI7VdXNoyuT/DNwPvDuVVxzKbAU4NBDD4WtHzvDJaS1w05/sCnX3HgrP7/yBrbfauP5bo6kOTb63dhbVlXLRt5/kO6ZQ6Mx4TZVdSlAVV2a5O7jrjNbdxlMAfcAfjpt/bbM8Pjo/gOu+JD1ymMuWNWu0lrj/ltuxM7bbsqDt/lD1luyDhuuuw7Pf/g9uPrGW9i8Twk232Bdru7TAklzZy6G8U77bryDJPsCl1XVWUl2vzPXma27DF4DnJjkh8DP+3X3BrYHXnEnziutdb78/V/z5e//GoDtt9qYJzxgS/7jrF+y/85351H3uQsnXHQ5j7rPXfjupdfMc0slzYPHAPsl2ZvuicSbJ/kE8Ksk2/bpwLbAZeNONOnjj98IPJgJH39cVccl2QF4JHBPuvEDlwBnVNWt464pabwTLrqcFz/inux2ny347XU3c8Tpl8x3k6S1znzPVFhVBwEHAfQJweur6nlJ3gu8gK6L/gXAF8eda5JRSEcCn6YbsPCy/sS/nqCRU8CpE5xf0oR+9Jvr+NFvrgPguptu5UP/9bN5bpGkBerdwNFJ/hL4GfDMcQdMUhDcraoOT/LqqvoW8K0k37qTDZUkadFbSI8yqKrlwPL+9eXAE1bneB9/LElSo6mFVBHcST7+WJIk+fhjSZJaDSggmOgug4+xkgmKqurFs9IiSZI05ybpMjh25PWGwAF04wgkSVqrzfdth2vSJF0Gnxt9n+Qo4Ouz1iJJkjTnWp6G8gC6WQclSVqrDSggmGgMwdXccQzB/9LNXChJkgZiki4DH7IuSdJKDGkegnXG7ZDkxEnWSZKkxWuVCUGSDYGNga2S3JXuAUXQTUx0jzlomyRJC9pw8oGZuwxeSvcY43sAZ3F7QfA74MOz2yxJkjSXVlkQVNXBwMFJXllVh8xhmyRJWhSGNA/B2DEEwFSSLVa8SXLXJH89e02SJElzbZKC4K+q6soVb6rqt8BfzVqLJElaJKZq9pe5MklBsE6SFeMHSLIEWH/2miRJkubaJDMVHg8cneQjdAMqXwYcN6utkiRpERjSGIJJCoI3AkuBl9PdafA14KOz2ShJkjS3xnYZVNVUVX2kqp5RVU8Hzge860CStNarmv1lrkz0cKMkuwDPAZ4N/AT4/Cy2SZIkzbGZZircATiQrhC4HPg0kKraY47aJknSgra2jCG4EPg28KdV9SOAJK+dk1ZJkrQIzOVtgbNtpjEET6d71PE3k3w0yRO4ffpiSZI0IDNNXXwMcEySTYA/A14LbJPk34Bjquprc9NESZIWpiF1GUxyl8G1VXVkVe0LbAecA7xpthsmSZLmziQzFd6mqq6oqkOras/ZapAkSYtFzcEyV1arIJAkScM00TwEkiTp902tTWMIJEnS8JkQSJLUaEABgQmBJEkyIZAkqdlaNQ+BJEkaPhMCSZIaDSggMCGQJEkmBJIkNXMeAkmSNCgmBJIkNRpQQGBCIEmSTAgkSWrmPASSJGlQTAgkSWo0NZyAwIJAkqRWxXAqArsMJEmSCYEkSa0GNKbQhECSJJkQSJLUzNsOJUnSoJgQSJLUaEi3HZoQSJIkEwJJklo5hkCSJA2KBYEkSY2qZn+ZSZINk5ye5Nwk5yd5W7/+oUlOSfLdJF9Osvm4z2JBIEnS4nUjsGdVPRTYBdgryW7AYcCbquqPgGOAvxt3IgsCSZIaTVXN+jKT6lzTv12vXwrYETipX38C8PRxn8WCQJKkBSzJ0iRnjixLp21fkuQc4DLghKo6DfgesF+/yzOBe427jncZSJLUaC5uMqiqZcCyGbbfCuySZAvgmCQ7Ay8G/iXJW4AvATeNu44JgSRJA1BVVwLLgb2q6sKqenJVPRw4CvjxuOMtCCRJalRVs77MJMnWfTJAko2AJwIXJrl7v24d4B+Aj4z7LBYEkiQtXtsC30xyHnAG3RiCY4HnJLkIuBD4JfCxcSdyDIEkSY3me6LCqjoPeNhK1h8MHLw65zIhkCRJJgSSJLUa0rMMLAgkSWo0oHrALgNJkmRCIElSsyF1GZgQSJIkEwJJklqZEEiSpEExIZAkqdGAAgITAkmSZEIgSVIzxxBIkqRBMSGQJKnRgAICEwJJkmRCIElSM8cQSJKkQTEhkCSp0YACAhMCSZJkQiBJUjPHEEiSpEExIZAkqdGAAgITAkmSZEIgSVKzIY0hsCCQJKnRgOoBuwwkSZIJgSRJzYbUZWBCIEmSTAgkSWo1oIDAhECSJJkQSJLUzDEEkiRpUEwIJElqNKCAwIRAkiSZEEiS1MwxBJIkaVBMCCRJamRCIEmSBsWEQJKkRgMKCEwIJEmSCYEkSc0cQyBJkgbFhECSpEYDCghMCCRJkgmBJEnNpqaGExGYEEiSJBMCSZJaDWkMgQWBJEmNvO1QkiQNigmBJEmNBhQQmBBIkiQTAkmSmjmGQJIkDYoFgSRJjapmf5lJkg2TnJ7k3CTnJ3lbv36XJKcmOSfJmUkeOe6z2GUgSdLidSOwZ1Vdk2Q94OQkXwXeDrytqr6aZG/gPcDuM53IgkCSpEbzPYagugZc079dr1+qXzbv198F+OW4c1kQSJK0gCVZCiwdWbWsqpaNbF8CnAVsD3y4qk5L8hrg+CTvoxse8MfjrmNBIElSo7lICPov/2UzbL8V2CXJFsAxSXamKyBeW1WfS/Is4HDgiTNdx0GFkiQNQFVdCSwH9gJeAHy+3/QZYOygQgsCSZJa1RwsM0iydZ8MkGQjuhTgQroxA4/vd9sT+OG4j2KXgSRJi9e2wMf7cQTrAEdX1bFJrgQOTrIucAN3HIOwUhYEkiQ1WgB3GZwHPGwl608GHr4657LLQJIkmRBIktRqvhOCNcmEQJIkmRBIktTKhECSJA2KCYEkSY2GlBBYEEiS1Go49YBdBpIkyYRAkqRmQ+oyMCGQJEkmBJIktTIhkCRJg2JCIElSIxMCSZI0KCYEkiS1Gk5AYEIgSZJMCCRJauYYAkmSNCgmBJIkNTIhkCRJg2JCIElSIxMCSZI0KCYEkiQ1MiGQJEmDYkIgSVKr4QQEJgSSJMmEQJKkZkMaQ2BBIElSoyEVBHYZSJIkEwJJklqZEEiSpEExIZAkqdVwAgITAkmSZEIgSVIzxxBIkqRBMSGQJKmRCYEkSRoUEwJJkhqZEEiSpEExIZAkqZEJgSRJGhQTAkmSWg0nIDAhkCRJJgSSJDVzDIEkSRoUEwJJkhqZEEiSpEExIZAkqdGQEgILAkmSGg2pILDLQJIkmRBIktRsOAGBCYEkSTIhkCSp2XyPIUiyIXASsAHdd/pnq+qtST4N7NjvtgVwZVXtMtO5LAgkSVq8bgT2rKprkqwHnJzkq1X17BU7JHk/cNW4E1kQSJLUaL4TguoacE3/dr1+ua1RSQI8C9hz3LkcQyBJ0iKWZEmSc4DLgBOq6rSRzY8FflVVPxx3HgsCSZJaVc36kmRpkjNHlqV3bELd2o8P2A54ZJKdRzY/Bzhqko9il4EkSQtYVS0Dlk2w35VJlgN7Ad9Lsi7wNODhk1zHhECSpFY1NfvLDJJsnWSL/vVGwBOBC/vNTwQurKpLJvkoJgSSJC1e2wIfT7KE7pf8o6vq2H7bgUzYXQAWBJIktZv/uwzOAx62im0vXJ1z2WUgSZJMCCRJajamj38xMSGQJEkmBJIkNZvnMQRrkgmBJEkyIZAkqZljCCRJ0pCYEEiS1MqEQJIkDYkJgSRJrQZ0l4EFgSRJrewykCRJQ2JCIElSqwF1GZgQSJIkEwJJkpo5hkCSJA2JCYEkSa0cQyBJkobEhECSpFaOIZAkSUNiQiBJUivHEEiSpCExIZAkqZVjCCRJ0pCYEEiS1MoxBJIkaUhMCCRJauUYAkmSNCQmBJIktZpyDIEkSRoQEwJJkloNaAyBBYEkSa0GVBDYZSBJkkwIJElq5sREkiRpSEwIJElq5RgCSZI0JCYEkiS1cgyBJEkaEhMCSZJaOYZAkiQNiQmBJEmtHEMgSZKGxIRAkqRWjiGQJElDYkIgSVIrxxBIkqQhMSGQJKmVYwgkSdKQmBBIktTKMQSSJGlITAgkSWrlGAJJkkTV7C8zSLJhktOTnJvk/CRvG9n2yiQ/6Ne/Z9xHMSGQJGnxuhHYs6quSbIecHKSrwIbAfsDD6mqG5PcfdyJLAgkSWo1z10GVVXANf3b9fqlgJcD766qG/v9Lht3rgVdEBxywIPmuwmSJC1oSZYAZwHbAx+uqtOS7AA8Nsm7gBuA11fVGTOdZyEXBJnvBkgLWZKlVbVsvtshrc2uP/tDs/5dlWQpsHRk1bLR//ar6lZglyRbAMck2Znu+/2uwG7AI4Cjk9y/TxRWfp0ZtklawJKcWVW7znc7JC0cSd4KXAs8ka7LYHm//sfAblX161Ud610GkiQtUkm27pMBkmxEVwhcCHwB2LNfvwOwPvCbmc61kLsMJEnSzLYFPt6PI1gHOLqqjk2yPnBEku8BNwEvmKm7AOwykBYtxxBIWpMsCCRJkmMIJEmSBYG0KCXZq5+S9EdJ3jTf7ZG0+NllIC0y/eChi4AnAZcAZwDPqarvz2vDJC1qJgTS4vNI4EdV9T9VdRPwKbo5yyWpmQWBtPjcE/j5yPtL+nWS1MyCQFp8VjZVqn1/ku4UCwJp8bkEuNfI++2AX85TWyQNhAWBtPicATwgyf362cgOBL40z22StMg5dbG0yFTVLUleARwPLAGOqKrz57lZkhY5bzuUJEl2GUiSJAsCSZKEBYEkScKCQJIkYUEgSZKwIJAmluTWJOck+V6SzyTZ+E6c69+TPKN/fViSB8+w7+5J/rjhGhcn2WrS9dP2uWY1r/WPSV6/um2UtHBYEEiTu76qdqmqnYGbgJeNbuyfQrjaquolY55UuDuw2gWBJK0OCwKpzbeB7fvf3r+Z5JPAd5MsSfLeJGckOS/JSwHS+VCS7yf5CnD3FSdKsjzJrv3rvZJ8J8m5SU5Mcl+6wuO1fTrx2CRbJ/lcf40zkjymP/ZuSb6W5Owkh7LyZx7cQZIvJDkryflJlk7b9v6+LScm2bpf94dJjuuP+XaSB66Rn6akeedMhdJqSrIu8FTguH7VI4Gdq+on/ZfqVVX1iCQbAP+V5GvAw4AdgT8CtgG+Dxwx7bxbAx8FHtefa8uquiLJR4Brqup9/X6fBD5QVScnuTfdjIUPAt4KnFxVb0+yD3CHL/hVeHF/jY2AM5J8rqouBzYBvlNVf5vkLf25XwEsA15WVT9M8ijgX4E9G36MkhYYCwJpchslOad//W3gcLoo//Sq+km//snAQ1aMDwDuAjwAeBxwVFXdCvwyyTdWcv7dgJNWnKuqrlhFO54IPDi5LQDYPMlm/TWe1h/7lSS/neAzvSrJAf3re/VtvRyYAj7dr/8E8Pkkm/af9zMj195ggmtIWgQsCKTJXV9Vu4yu6L8Yrx1dBbyyqo6ftt/ejH9EcSbYB7quvkdX1fUracvEc5En2Z2uuHh0VV2XZDmw4Sp2r/66V07/GUgaBscQSGvW8cDLk6wHkGSHJJsAJwEH9mMMtgX2WMmxpwCPT3K//tgt+/VXA5uN7Pc1uviefr9d+pcnAc/t1z0VuOuYtt4F+G1fDDyQLqFYYR1gRcrx53RdEb8DfpLkmf01kuShY64haZGwIJDWrMPoxgd8J8n3gEPpkrhjgB8C3wX+DfjW9AOr6td0/f6fT3Iut0f2XwYOWDGoEHgVsGs/aPH73H63w9uAxyX5Dl3Xxc/GtPU4YN0k5wHvAE4d2XYtsFOSs+jGCLy9X/9c4C/79p0P7D/Bz0TSIuDTDiVJkgmBJEmyIJAkSVgQSJIkLAgkSRIWBJIkCQsCSZKEBYEkScKCQJIkAf8f7zdB7fBFfqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pd.DataFrame(np.c_[df_norm['TV']],columns=['TV'])\n",
    "Y1 = df_norm.iloc[:,1].values\n",
    "Y = Y1.reshape(200)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "\n",
    "model = LogisticRegression(max_iter=10000)\n",
    "model.fit(X_train, Y_train.astype(int))\n",
    "model.predict(X_test[0:10])\n",
    "\n",
    "p_model = model.predict(X_test)\n",
    "score = model.score(X_test, Y_test.astype(int))\n",
    "cm = metrics.confusion_matrix(Y_test.astype(int), p_model)\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(cm, annot=True, linewidths=.5, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(score)\n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c00819e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.52916566])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.predict([[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8517cf7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58674402])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol_reg.predict(poly_reg.fit_transform([[1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "bda823c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[1]])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}