Advertising.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bffce61",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import Libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d7d0449d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import data\n",
    "\n",
    "ad_data = pd.read_csv(\"D:\\Artificial Intelligence\\Samatrix Projects\\Advertising\\DataSet\\Advertising.csv\",usecols=[1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "209bc65d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        TV  Radio  Newspaper  Sales\n",
      "0    230.1   37.8       69.2   22.1\n",
      "1     44.5   39.3       45.1   10.4\n",
      "2     17.2   45.9       69.3    9.3\n",
      "3    151.5   41.3       58.5   18.5\n",
      "4    180.8   10.8       58.4   12.9\n",
      "..     ...    ...        ...    ...\n",
      "195   38.2    3.7       13.8    7.6\n",
      "196   94.2    4.9        8.1    9.7\n",
      "197  177.0    9.3        6.4   12.8\n",
      "198  283.6   42.0       66.2   25.5\n",
      "199  232.1    8.6        8.7   13.4\n",
      "\n",
      "[200 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "#understanding of data\n",
    "\n",
    "print(ad_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "981c66bd",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
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       "      <td>230.1</td>\n",
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       "      <td>9.3</td>\n",
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      "text/plain": [
       "      TV  Radio  Newspaper  Sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9773c4d5",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>TV</th>\n",
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       "      <th>Newspaper</th>\n",
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       "        TV  Radio  Newspaper  Sales\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1    9.7\n",
       "197  177.0    9.3        6.4   12.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   13.4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "162d31dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TV         200 non-null    float64\n",
      " 1   Radio      200 non-null    float64\n",
      " 2   Newspaper  200 non-null    float64\n",
      " 3   Sales      200 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "ad_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1820dad",
   "metadata": {},
   "outputs": [
    {
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       "<p>200 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "0    False  False      False  False\n",
       "1    False  False      False  False\n",
       "2    False  False      False  False\n",
       "3    False  False      False  False\n",
       "4    False  False      False  False\n",
       "..     ...    ...        ...    ...\n",
       "195  False  False      False  False\n",
       "196  False  False      False  False\n",
       "197  False  False      False  False\n",
       "198  False  False      False  False\n",
       "199  False  False      False  False\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c558bbf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0\n",
       "Radio        0\n",
       "Newspaper    0\n",
       "Sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbccce39",
   "metadata": {},
   "source": [
    "Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f524424b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   14.022500\n",
       "std     85.854236   14.846809   21.778621    5.217457\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   10.375000\n",
       "50%    149.750000   22.900000   25.750000   12.900000\n",
       "75%    218.825000   36.525000   45.100000   17.400000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4898b9b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#the min value of Radio is zero. so check how many zero values existing in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fcb9dd7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For all other columns, the data cleaning is not required. However the data scaling is required.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2044d6",
   "metadata": {},
   "source": [
    "Analysis of Zero Values in Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2bf1dd2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0\n",
       "Radio        1\n",
       "Newspaper    0\n",
       "Sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ad_data == 0).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f357442d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#only Radio col has zero value  which is possible. Hence we conclude the data cleaning steps are not required."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69bdc705",
   "metadata": {},
   "source": [
    "Relationship - Predictor and Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "42b46c52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhune\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='TV', ylabel='Sales'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Rel betn sales and TV\n",
    "\n",
    "sns.regplot(ad_data.TV,ad_data.Sales ,order=1,ci=1,scatter_kws={'color':'r','s':9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce0d577d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sales', ylabel='Radio'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Relationship between Sales and Radio\n",
    "\n",
    "sns.regplot(ad_data.Sales,ad_data.Radio,order=1,ci=None,scatter_kws={'color':'r','s':9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d0c15547",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bhune\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sales', ylabel='Newspaper'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Relationship between Sales and Newspaper\n",
    "\n",
    "sns.regplot(ad_data.Sales,ad_data.Newspaper,order=1,ci=None,scatter_kws={'color':'b','s':9})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af122cd0",
   "metadata": {},
   "source": [
    "# Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "43510e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a82b4f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=scale(ad_data.TV,with_mean=True,with_std=False).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6f634db8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=ad_data.Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0be0c0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  83.0575],\n",
       "       [-102.5425],\n",
       "       [-129.8425],\n",
       "       [   4.4575],\n",
       "       [  33.7575]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "017137d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0089706847793422e-14"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9c876db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85.63933175679269"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9fa2425",
   "metadata": {},
   "source": [
    "Linear regression(Scaled Data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "157737d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.linear_model as skl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "85552bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = skl.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b9224034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "76d9edaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.032593549127693"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "25ba5251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04753664])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "613de29b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#min rss\n",
    "\n",
    "min_rss = np.sum((reg.intercept_+reg.coef_*X - y.values.reshape(-1,1))**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae91a94",
   "metadata": {},
   "source": [
    "Linear Regression (unscaled data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3078eff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = skl.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "01285524",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=ad_data.TV.values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a53c56fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ad_data.Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0ded42f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3eb85d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.032593549127693"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f8416be5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04753664])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1d6278b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rss\n",
    "\n",
    "min_rss = np.sum((reg.intercept_+reg.coef_*X-y.values.reshape(-1,1))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9590b591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2102.5305831313512"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_rss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a0627b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MSE\n",
    "\n",
    "mse = min_rss/len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "128d270c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.512652915656757"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f903127",
   "metadata": {},
   "source": [
    "R-Sq using Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4c18faaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error ,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fca31972",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sales_pred = reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "64e3646f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1919813484022592"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "r2_score(y,Sales_pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1b15e6",
   "metadata": {},
   "source": [
    "#MSE using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c3cd2bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.512652915656757"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y,Sales_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faa8a4f6",
   "metadata": {},
   "source": [
    "# Regression using Statsmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "536864ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ba356064",
   "metadata": {},
   "outputs": [],
   "source": [
    "est = smf.ols('Sales ~ TV',ad_data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "eec12f0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   312.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 15 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>1.47e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:18:57</td>     <th>  Log-Likelihood:    </th> <td> -519.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1042.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   1049.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    7.0326</td> <td>    0.458</td> <td>   15.360</td> <td> 0.000</td> <td>    6.130</td> <td>    7.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0475</td> <td>    0.003</td> <td>   17.668</td> <td> 0.000</td> <td>    0.042</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.531</td> <th>  Durbin-Watson:     </th> <td>   1.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.767</td> <th>  Jarque-Bera (JB):  </th> <td>   0.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.089</td> <th>  Prob(JB):          </th> <td>   0.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.779</td> <th>  Cond. No.          </th> <td>    338.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.612\n",
       "Model:                            OLS   Adj. R-squared:                  0.610\n",
       "Method:                 Least Squares   F-statistic:                     312.1\n",
       "Date:                Wed, 15 Mar 2023   Prob (F-statistic):           1.47e-42\n",
       "Time:                        23:18:57   Log-Likelihood:                -519.05\n",
       "No. Observations:                 200   AIC:                             1042.\n",
       "Df Residuals:                     198   BIC:                             1049.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      7.0326      0.458     15.360      0.000       6.130       7.935\n",
       "TV             0.0475      0.003     17.668      0.000       0.042       0.053\n",
       "==============================================================================\n",
       "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
       "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
       "Skew:                          -0.089   Prob(JB):                        0.716\n",
       "Kurtosis:                       2.779   Cond. No.                         338.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67e98b9",
   "metadata": {},
   "source": [
    "#Regression RSS AND MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5a32182a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    7.032594\n",
       "TV           0.047537\n",
       "dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "8b645034",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2102.5305831313512"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#RSS\n",
    "\n",
    "((ad_data.Sales - (est.params[0] + est.params[1] * ad_data.TV))**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bbb21046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.512652915656757"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MSE\n",
    "\n",
    "((ad_data.Sales - (est.params[0] + est.params[1]*ad_data.TV))** 2).sum()/len(ad_data.Sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b675d1da",
   "metadata": {},
   "source": [
    "# Single Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0d6a2ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear Regression Sales and Radio\n",
    "\n",
    "\n",
    "est = smf.ols('Sales ~ Radio', ad_data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "62ee7654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      9.3116      0.563     16.542      0.000       8.202      10.422\n",
      "Radio          0.2025      0.020      9.921      0.000       0.162       0.243\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(est.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "abc88dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check the p-value of Intercept and Radio.\n",
    "\n",
    "#It shows that there is a relationship between Sales and Radio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "120ba386",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear Regression Sales and Newspaper\n",
    "\n",
    "est = smf.ols('Sales ~Newspaper',ad_data).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "c1c57a74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     12.3514      0.621     19.876      0.000      11.126      13.577\n",
      "Newspaper      0.0547      0.017      3.300      0.001       0.022       0.087\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(est.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "26d0f869",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check the p value of Intercept and Newspaper.\n",
    "\n",
    "#It shows that there is a relationship between Sales and Newspaper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62668c69",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "78f10c11",
   "metadata": {},
   "outputs": [],
   "source": [
    "est = smf.ols('Sales ~ TV + Radio + Newspaper', ad_data).fit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "5ac2f011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 15 Mar 2023</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:25:43</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   793.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    2.9389</td> <td>    0.312</td> <td>    9.422</td> <td> 0.000</td> <td>    2.324</td> <td>    3.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.809</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.1885</td> <td>    0.009</td> <td>   21.893</td> <td> 0.000</td> <td>    0.172</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Newspaper</th> <td>   -0.0010</td> <td>    0.006</td> <td>   -0.177</td> <td> 0.860</td> <td>   -0.013</td> <td>    0.011</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.414</td> <th>  Durbin-Watson:     </th> <td>   2.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 151.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.327</td> <th>  Prob(JB):          </th> <td>1.44e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
       "Model:                            OLS   Adj. R-squared:                  0.896\n",
       "Method:                 Least Squares   F-statistic:                     570.3\n",
       "Date:                Wed, 15 Mar 2023   Prob (F-statistic):           1.58e-96\n",
       "Time:                        23:25:43   Log-Likelihood:                -386.18\n",
       "No. Observations:                 200   AIC:                             780.4\n",
       "Df Residuals:                     196   BIC:                             793.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.9389      0.312      9.422      0.000       2.324       3.554\n",
       "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
       "Radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
       "Newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
       "==============================================================================\n",
       "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
       "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
       "Kurtosis:                       6.332   Cond. No.                         454.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9510fe73",
   "metadata": {},
   "source": [
    "# Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "30ab3878",
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.782224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.576223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newspaper</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.782224</td>\n",
       "      <td>0.576223</td>\n",
       "      <td>0.228299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     Radio  Newspaper     Sales\n",
       "TV         1.000000  0.054809   0.056648  0.782224\n",
       "Radio      0.054809  1.000000   0.354104  0.576223\n",
       "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
       "Sales      0.782224  0.576223   0.228299  1.000000"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ad_data.corr()"
   ]
  }
 ],
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