ML for advertising .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.read_csv('advertising.csv')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
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       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>196</td>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>197</td>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>198</td>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>200</td>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
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      "text/plain": [
       "     Unnamed: 0     TV  radio  newspaper  sales\n",
       "195         196   38.2    3.7       13.8    7.6\n",
       "196         197   94.2    4.9        8.1    9.7\n",
       "197         198  177.0    9.3        6.4   12.8\n",
       "198         199  283.6   42.0       66.2   25.5\n",
       "199         200  232.1    8.6        8.7   13.4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\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>57.879185</td>\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>1.000000</td>\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>50.750000</td>\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>100.500000</td>\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>150.250000</td>\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>200.000000</td>\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",
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      ],
      "text/plain": [
       "       Unnamed: 0          TV       radio   newspaper       sales\n",
       "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
       "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
       "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
       "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
       "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
       "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
       "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
       "max    200.000000  296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      "Unnamed: 0    200 non-null int64\n",
      "TV            200 non-null float64\n",
      "radio         200 non-null float64\n",
      "newspaper     200 non-null float64\n",
      "sales         200 non-null float64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "a.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'TV', 'radio', 'newspaper', 'sales'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>TV</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>24.2</td>\n",
       "      <td>8.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>214.7</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.8</td>\n",
       "      <td>35.1</td>\n",
       "      <td>65.9</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>97.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>204.1</td>\n",
       "      <td>32.9</td>\n",
       "      <td>46.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>195.4</td>\n",
       "      <td>47.7</td>\n",
       "      <td>52.9</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>67.8</td>\n",
       "      <td>36.6</td>\n",
       "      <td>114.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>281.4</td>\n",
       "      <td>39.6</td>\n",
       "      <td>55.8</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>69.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>147.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>19.1</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>218.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>53.4</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>237.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>49.6</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>228.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>26.2</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>62.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>18.3</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>262.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>142.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>240.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>22.9</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>248.8</td>\n",
       "      <td>27.1</td>\n",
       "      <td>22.9</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>70.6</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.8</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>164.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>47.4</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>19.6</td>\n",
       "      <td>20.1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>168.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>222.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>276.9</td>\n",
       "      <td>48.9</td>\n",
       "      <td>41.8</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>248.4</td>\n",
       "      <td>30.2</td>\n",
       "      <td>20.3</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>170.2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>35.2</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>276.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>23.7</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>165.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>156.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>8.3</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>218.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>27.4</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>56.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>29.7</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>287.6</td>\n",
       "      <td>43.0</td>\n",
       "      <td>71.8</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>253.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>205.0</td>\n",
       "      <td>45.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>139.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>26.6</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>191.1</td>\n",
       "      <td>28.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>286.0</td>\n",
       "      <td>13.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>18.7</td>\n",
       "      <td>12.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>39.5</td>\n",
       "      <td>41.1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>75.5</td>\n",
       "      <td>10.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>17.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>31.6</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>166.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>149.7</td>\n",
       "      <td>35.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "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\n",
       "5      8.7   48.9       75.0    7.2\n",
       "6     57.5   32.8       23.5   11.8\n",
       "7    120.2   19.6       11.6   13.2\n",
       "8      8.6    2.1        1.0    4.8\n",
       "9    199.8    2.6       21.2   10.6\n",
       "10    66.1    5.8       24.2    8.6\n",
       "11   214.7   24.0        4.0   17.4\n",
       "12    23.8   35.1       65.9    9.2\n",
       "13    97.5    7.6        7.2    9.7\n",
       "14   204.1   32.9       46.0   19.0\n",
       "15   195.4   47.7       52.9   22.4\n",
       "16    67.8   36.6      114.0   12.5\n",
       "17   281.4   39.6       55.8   24.4\n",
       "18    69.2   20.5       18.3   11.3\n",
       "19   147.3   23.9       19.1   14.6\n",
       "20   218.4   27.7       53.4   18.0\n",
       "21   237.4    5.1       23.5   12.5\n",
       "22    13.2   15.9       49.6    5.6\n",
       "23   228.3   16.9       26.2   15.5\n",
       "24    62.3   12.6       18.3    9.7\n",
       "25   262.9    3.5       19.5   12.0\n",
       "26   142.9   29.3       12.6   15.0\n",
       "27   240.1   16.7       22.9   15.9\n",
       "28   248.8   27.1       22.9   18.9\n",
       "29    70.6   16.0       40.8   10.5\n",
       "..     ...    ...        ...    ...\n",
       "170   50.0   11.6       18.4    8.4\n",
       "171  164.5   20.9       47.4   14.5\n",
       "172   19.6   20.1       17.0    7.6\n",
       "173  168.4    7.1       12.8   11.7\n",
       "174  222.4    3.4       13.1   11.5\n",
       "175  276.9   48.9       41.8   27.0\n",
       "176  248.4   30.2       20.3   20.2\n",
       "177  170.2    7.8       35.2   11.7\n",
       "178  276.7    2.3       23.7   11.8\n",
       "179  165.6   10.0       17.6   12.6\n",
       "180  156.6    2.6        8.3   10.5\n",
       "181  218.5    5.4       27.4   12.2\n",
       "182   56.2    5.7       29.7    8.7\n",
       "183  287.6   43.0       71.8   26.2\n",
       "184  253.8   21.3       30.0   17.6\n",
       "185  205.0   45.1       19.6   22.6\n",
       "186  139.5    2.1       26.6   10.3\n",
       "187  191.1   28.7       18.2   17.3\n",
       "188  286.0   13.9        3.7   15.9\n",
       "189   18.7   12.1       23.4    6.7\n",
       "190   39.5   41.1        5.8   10.8\n",
       "191   75.5   10.8        6.0    9.9\n",
       "192   17.2    4.1       31.6    5.9\n",
       "193  166.8   42.0        3.6   19.6\n",
       "194  149.7   35.6        6.0   17.3\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]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.drop(['Unnamed: 0'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'plt.figure(figsize=(16,8))\\nplt.scatter(data[\\'TV\\'],data[\\'sales\\'],c=\\'blue\\')\\nplt.xlabel(\"money spent on TV ads($)\")\\nplt.ylabel(\"sales($)\")\\nplt.title(\"TV Vs Sales\")\\nplt.show()'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''plt.figure(figsize=(16,8))\n",
    "plt.scatter(data['TV'],data['sales'],c='blue')\n",
    "plt.xlabel(\"money spent on TV ads($)\")\n",
    "plt.ylabel(\"sales($)\")\n",
    "plt.title(\"TV Vs Sales\")\n",
    "plt.show()'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=a['TV'].values.reshape(-1,1)\n",
    "Y=a['sales'].values.reshape(-1,1)\n",
    "\n",
    "reg=LinearRegression()\n",
    "\n",
    "reg.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04753664043301975\n",
      "7.032593549127695\n",
      "The linear model is Y=7.0326+0.047537X\n"
     ]
    }
   ],
   "source": [
    "print(reg.coef_[0][0])\n",
    "print(reg.intercept_[0])\n",
    "print(\"The linear model is Y={:.5}+{:.5}X\".format(reg.intercept_[0],reg.coef_[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.611875050850071"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17.97077451]\n",
      " [ 9.14797405]\n",
      " [ 7.85022376]\n",
      " [14.23439457]\n",
      " [15.62721814]\n",
      " [ 7.44616232]\n",
      " [ 9.76595037]\n",
      " [12.74649773]\n",
      " [ 7.44140866]\n",
      " [16.53041431]\n",
      " [10.17476548]\n",
      " [17.23871025]\n",
      " [ 8.16396559]\n",
      " [11.66741599]\n",
      " [16.73482186]\n",
      " [16.32125309]\n",
      " [10.25557777]\n",
      " [20.40940417]\n",
      " [10.32212907]\n",
      " [14.03474068]\n",
      " [17.41459582]\n",
      " [18.31779199]\n",
      " [ 7.6600772 ]\n",
      " [17.88520856]\n",
      " [ 9.99412625]\n",
      " [19.52997632]\n",
      " [13.82557947]\n",
      " [18.44614092]\n",
      " [18.85970969]\n",
      " [10.38868036]\n",
      " [20.95607553]\n",
      " [12.39948025]\n",
      " [11.653155  ]\n",
      " [19.65832525]\n",
      " [11.58185004]\n",
      " [20.85149492]\n",
      " [19.72012288]\n",
      " [10.58358059]\n",
      " [ 9.08142275]\n",
      " [17.87094757]\n",
      " [16.65876324]\n",
      " [15.44657891]\n",
      " [20.98935118]\n",
      " [16.86792445]\n",
      " [ 8.22576322]\n",
      " [15.35625929]\n",
      " [11.2966302 ]\n",
      " [18.43663359]\n",
      " [17.83291826]\n",
      " [10.21279479]\n",
      " [16.53041431]\n",
      " [11.80527225]\n",
      " [17.31952254]\n",
      " [15.71278409]\n",
      " [19.52046899]\n",
      " [16.48763133]\n",
      " [ 7.37961102]\n",
      " [13.50708398]\n",
      " [17.05331735]\n",
      " [17.04856369]\n",
      " [ 9.57580381]\n",
      " [19.45391769]\n",
      " [18.4081116 ]\n",
      " [11.91460652]\n",
      " [13.26464711]\n",
      " [10.31262174]\n",
      " [ 8.52999772]\n",
      " [13.65444756]\n",
      " [18.31779199]\n",
      " [17.3385372 ]\n",
      " [16.49713866]\n",
      " [12.25211667]\n",
      " [ 8.30657551]\n",
      " [13.18383482]\n",
      " [17.17691262]\n",
      " [ 7.83596277]\n",
      " [ 8.33985116]\n",
      " [12.76075872]\n",
      " [ 7.28929141]\n",
      " [12.54684384]\n",
      " [10.66439288]\n",
      " [18.43187992]\n",
      " [10.61210257]\n",
      " [10.28409975]\n",
      " [17.18166628]\n",
      " [16.21667248]\n",
      " [10.65963921]\n",
      " [12.29489965]\n",
      " [11.2300789 ]\n",
      " [12.25211667]\n",
      " [13.41676436]\n",
      " [ 8.39214147]\n",
      " [17.38132017]\n",
      " [18.95953663]\n",
      " [12.13802873]\n",
      " [14.79532693]\n",
      " [16.4258337 ]\n",
      " [15.82211837]\n",
      " [20.80395828]\n",
      " [13.45954734]\n",
      " [17.60474238]\n",
      " [21.12245377]\n",
      " [20.3523602 ]\n",
      " [15.96472829]\n",
      " [18.3558213 ]\n",
      " [13.58789626]\n",
      " [ 8.22100956]\n",
      " [11.32990584]\n",
      " [ 7.65532354]\n",
      " [19.17345152]\n",
      " [17.76636696]\n",
      " [18.52219954]\n",
      " [15.38478127]\n",
      " [16.99627338]\n",
      " [10.74995883]\n",
      " [10.60259525]\n",
      " [13.6496939 ]\n",
      " [10.66439288]\n",
      " [13.00794925]\n",
      " [ 7.95480437]\n",
      " [13.74952084]\n",
      " [ 7.92628239]\n",
      " [17.68080101]\n",
      " [12.88435399]\n",
      " [17.94225253]\n",
      " [11.17778859]\n",
      " [ 7.40337934]\n",
      " [10.84503211]\n",
      " [17.50491544]\n",
      " [ 9.86577732]\n",
      " [ 7.0658692 ]\n",
      " [19.63931059]\n",
      " [ 7.43190133]\n",
      " [17.48114712]\n",
      " [ 8.78669558]\n",
      " [ 9.32861328]\n",
      " [ 8.24953154]\n",
      " [20.04337204]\n",
      " [ 9.07666909]\n",
      " [15.82211837]\n",
      " [10.52178296]\n",
      " [16.2404408 ]\n",
      " [17.51442276]\n",
      " [12.00492614]\n",
      " [11.60561836]\n",
      " [13.7019842 ]\n",
      " [18.44614092]\n",
      " [18.5935045 ]\n",
      " [ 8.83898589]\n",
      " [ 9.15748138]\n",
      " [20.37612852]\n",
      " [12.78452704]\n",
      " [16.4258337 ]\n",
      " [15.17562006]\n",
      " [15.95997462]\n",
      " [ 7.22749377]\n",
      " [11.49628409]\n",
      " [14.15358229]\n",
      " [ 7.58877224]\n",
      " [13.29316909]\n",
      " [15.23266402]\n",
      " [11.10648363]\n",
      " [15.98849661]\n",
      " [14.80483426]\n",
      " [12.60388781]\n",
      " [18.17993573]\n",
      " [ 7.88349941]\n",
      " [16.86317079]\n",
      " [17.2719859 ]\n",
      " [20.54726042]\n",
      " [ 9.40942557]\n",
      " [14.8523709 ]\n",
      " [ 7.9643117 ]\n",
      " [15.0377638 ]\n",
      " [17.60474238]\n",
      " [20.19548929]\n",
      " [18.84069503]\n",
      " [15.12332975]\n",
      " [20.18598196]\n",
      " [14.9046612 ]\n",
      " [14.47683144]\n",
      " [17.41934948]\n",
      " [ 9.70415274]\n",
      " [20.70413134]\n",
      " [19.09739289]\n",
      " [16.77760484]\n",
      " [13.66395489]\n",
      " [16.11684554]\n",
      " [20.62807271]\n",
      " [ 7.92152873]\n",
      " [ 8.91029085]\n",
      " [10.6216099 ]\n",
      " [ 7.85022376]\n",
      " [14.96170517]\n",
      " [14.14882862]\n",
      " [ 8.84849321]\n",
      " [11.51054508]\n",
      " [15.44657891]\n",
      " [20.51398478]\n",
      " [18.06584779]]\n"
     ]
    }
   ],
   "source": [
    "predictions=reg.predict(X)\n",
    "#plt.figure(figsize=(16,8))\n",
    "#plt.scatter(a['TV'],a['sales'],c='black')\n",
    "#plt.plot(a['TV'],predictions,c='blue',linewidth=2)\n",
    "#plt.xlabel(\"MOney spent on Tv ads ($)\")\n",
    "#plt.ylabel(\"sales ($)\")\n",
    "#plt.show()\n",
    "print(predictions)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OLS -Ordinary Least square regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            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, 03 Jul 2019   Prob (F-statistic):           1.47e-42\n",
      "Time:                        17:24:48   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",
      "const          7.0326      0.458     15.360      0.000       6.130       7.935\n",
      "x1             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",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "x=np.column_stack((a['TV'],a['radio'],a['newspaper']))\n",
    "y=a['sales']\n",
    "X2= sm.add_constant(X)\n",
    "est=sm.OLS(y,X2)\n",
    "est2=est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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