Media+Company.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Media Company Case Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement: A digital media company (similar to Voot, Hotstar, Netflix, etc.) had launched a show. Initially, the show got a good response, but then witnessed a decline in viewership. The company wants to figure out what went wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing all required packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing dataset\n",
    "media = pd.read_csv('mediacompany.csv')\n",
    "media = media.drop('Unnamed: 7',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Views_show</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Views_platform</th>\n",
       "      <th>Ad_impression</th>\n",
       "      <th>Cricket_match_india</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3/1/2017</td>\n",
       "      <td>183738</td>\n",
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       "      <td>3/2/2017</td>\n",
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       "      <td>1010867575</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/4/2017</td>\n",
       "      <td>240061</td>\n",
       "      <td>1492913</td>\n",
       "      <td>1855353</td>\n",
       "      <td>1079194579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3/5/2017</td>\n",
       "      <td>446314</td>\n",
       "      <td>1594712</td>\n",
       "      <td>2041418</td>\n",
       "      <td>1357736987</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date  Views_show  Visitors  Views_platform  Ad_impression  \\\n",
       "0  3/1/2017      183738   1260228         1706478     1060860448   \n",
       "1  3/2/2017      193763   1270561         1690727     1031846645   \n",
       "2  3/3/2017      210479   1248183         1726157     1010867575   \n",
       "3  3/4/2017      240061   1492913         1855353     1079194579   \n",
       "4  3/5/2017      446314   1594712         2041418     1357736987   \n",
       "\n",
       "   Cricket_match_india  Character_A  \n",
       "0                    0            0  \n",
       "1                    0            0  \n",
       "2                    0            0  \n",
       "3                    1            0  \n",
       "4                    0            0  "
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's explore the top 5 rows\n",
    "media.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting date to Pandas datetime format\n",
    "media['Date'] = pd.to_datetime(media['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2017-03-01</td>\n",
       "      <td>183738</td>\n",
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       "      <td>1706478</td>\n",
       "      <td>1060860448</td>\n",
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       "      <th>1</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>193763</td>\n",
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       "      <td>2017-03-03</td>\n",
       "      <td>210479</td>\n",
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       "      <td>1010867575</td>\n",
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       "      <td>2017-03-04</td>\n",
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       "      <td>1357736987</td>\n",
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      ],
      "text/plain": [
       "        Date  Views_show  Visitors  Views_platform  Ad_impression  \\\n",
       "0 2017-03-01      183738   1260228         1706478     1060860448   \n",
       "1 2017-03-02      193763   1270561         1690727     1031846645   \n",
       "2 2017-03-03      210479   1248183         1726157     1010867575   \n",
       "3 2017-03-04      240061   1492913         1855353     1079194579   \n",
       "4 2017-03-05      446314   1594712         2041418     1357736987   \n",
       "\n",
       "   Cricket_match_india  Character_A  \n",
       "0                    0            0  \n",
       "1                    0            0  \n",
       "2                    0            0  \n",
       "3                    1            0  \n",
       "4                    0            0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'Timestamp' and 'datetime.date'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-2f8bf3f46236>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0md0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2017\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0md1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmedia\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mdelta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0md1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0md0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mmedia\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mdelta\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0md1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'Timestamp' and 'datetime.date'"
     ]
    }
   ],
   "source": [
    "# Deriving \"days since the show started\"\n",
    "from datetime import date\n",
    "\n",
    "d0 = date(2017, 2, 28)\n",
    "d1 = media.Date\n",
    "#delta = d1[0] - d0\n",
    "#media['day']= delta\n",
    "#d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Views_show</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Views_platform</th>\n",
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       "      <td>2017-03-01</td>\n",
       "      <td>183738</td>\n",
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       "      <th>1</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>193763</td>\n",
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       "      <td>1690727</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>210479</td>\n",
       "      <td>1248183</td>\n",
       "      <td>1726157</td>\n",
       "      <td>1010867575</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>240061</td>\n",
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       "      <th>4</th>\n",
       "      <td>2017-03-05</td>\n",
       "      <td>446314</td>\n",
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       "      <td>2041418</td>\n",
       "      <td>1357736987</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "        Date  Views_show  Visitors  Views_platform  Ad_impression  \\\n",
       "0 2017-03-01      183738   1260228         1706478     1060860448   \n",
       "1 2017-03-02      193763   1270561         1690727     1031846645   \n",
       "2 2017-03-03      210479   1248183         1726157     1010867575   \n",
       "3 2017-03-04      240061   1492913         1855353     1079194579   \n",
       "4 2017-03-05      446314   1594712         2041418     1357736987   \n",
       "\n",
       "   Cricket_match_india  Character_A  \n",
       "0                    0            0  \n",
       "1                    0            0  \n",
       "2                    0            0  \n",
       "3                    1            0  \n",
       "4                    0            0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'day'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'day'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-aefb54dae748>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Cleaning days\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmedia\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmedia\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mmedia\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmedia\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmedia\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmedia\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'day'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3022\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3023\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3024\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3025\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3080\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3081\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3082\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3083\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3084\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'day'"
     ]
    }
   ],
   "source": [
    "# Cleaning days\n",
    "media['day'] = media['day'].astype(str)\n",
    "media['day'] = media['day'].map(lambda x: x[0:2])\n",
    "media['day'] = media['day'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Views_show</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Views_platform</th>\n",
       "      <th>Ad_impression</th>\n",
       "      <th>Cricket_match_india</th>\n",
       "      <th>Character_A</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>183738</td>\n",
       "      <td>1260228</td>\n",
       "      <td>1706478</td>\n",
       "      <td>1060860448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>193763</td>\n",
       "      <td>1270561</td>\n",
       "      <td>1690727</td>\n",
       "      <td>1031846645</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>210479</td>\n",
       "      <td>1248183</td>\n",
       "      <td>1726157</td>\n",
       "      <td>1010867575</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>240061</td>\n",
       "      <td>1492913</td>\n",
       "      <td>1855353</td>\n",
       "      <td>1079194579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-05</td>\n",
       "      <td>446314</td>\n",
       "      <td>1594712</td>\n",
       "      <td>2041418</td>\n",
       "      <td>1357736987</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Views_show  Visitors  Views_platform  Ad_impression  \\\n",
       "0 2017-03-01      183738   1260228         1706478     1060860448   \n",
       "1 2017-03-02      193763   1270561         1690727     1031846645   \n",
       "2 2017-03-03      210479   1248183         1726157     1010867575   \n",
       "3 2017-03-04      240061   1492913         1855353     1079194579   \n",
       "4 2017-03-05      446314   1594712         2041418     1357736987   \n",
       "\n",
       "   Cricket_match_india  Character_A  day  \n",
       "0                    0            0    1  \n",
       "1                    0            0    2  \n",
       "2                    0            0    3  \n",
       "3                    1            0    4  \n",
       "4                    0            0    5  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2cb873240>"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JeeIFTFqk3KESwvJi7VSuSDwS9NTrWYwcryGsvwbulVKeCpwF7ABuAh6QUq4HHjB/B3g7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\n7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cbf74f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# days vs Views_show\n",
    "media.plot.line(x='day', y='Views_show')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fCPO6AriiRfogcEyL9Cda5dFurY4oJ3vF/3jyKiqwNf+OO+64w00MNVKl2kad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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cbaee3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter Plot (days vs Views_show)\n",
    "colors = (0,0,0)\n",
    "area = np.pi*3\n",
    "plt.scatter(media.day, media.Views_show, s=area, c=colors, alpha=0.5)\n",
    "plt.title('Scatter plot pythonspot.com')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m9Y5Y9/QWYVlYX1+xKmaJ7iRmpLdekRnyuRJIFO923OBFYu1ySlVx6pbzXDMy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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cbae8080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for days vs Views_show and days vs Ad_impressions\n",
    "\n",
    "fig = plt.figure()\n",
    "host = fig.add_subplot(111)\n",
    "\n",
    "par1 = host.twinx()\n",
    "par2 = host.twinx()\n",
    "\n",
    "host.set_xlabel(\"Day\")\n",
    "host.set_ylabel(\"View_Show\")\n",
    "par1.set_ylabel(\"Ad_impression\")\n",
    "\n",
    "color1 = plt.cm.viridis(0)\n",
    "color2 = plt.cm.viridis(0.5)\n",
    "color3 = plt.cm.viridis(.9)\n",
    "\n",
    "p1, = host.plot(media.day,media.Views_show, color=color1,label=\"View_Show\")\n",
    "p2, = par1.plot(media.day,media.Ad_impression,color=color2, label=\"Ad_impression\")\n",
    "\n",
    "lns = [p1, p2]\n",
    "host.legend(handles=lns, loc='best')\n",
    "\n",
    "# right, left, top, bottom\n",
    "par2.spines['right'].set_position(('outward', 60))      \n",
    "# no x-ticks                 \n",
    "par2.xaxis.set_ticks([])\n",
    "# Sometimes handy, same for xaxis\n",
    "#par2.yaxis.set_ticks_position('right')\n",
    "\n",
    "host.yaxis.label.set_color(p1.get_color())\n",
    "par1.yaxis.label.set_color(p2.get_color())\n",
    "\n",
    "plt.savefig(\"pyplot_multiple_y-axis.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Views_show</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Views_platform</th>\n",
       "      <th>Ad_impression</th>\n",
       "      <th>Cricket_match_india</th>\n",
       "      <th>Character_A</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>183738</td>\n",
       "      <td>1260228</td>\n",
       "      <td>1706478</td>\n",
       "      <td>1060860448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>193763</td>\n",
       "      <td>1270561</td>\n",
       "      <td>1690727</td>\n",
       "      <td>1031846645</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>210479</td>\n",
       "      <td>1248183</td>\n",
       "      <td>1726157</td>\n",
       "      <td>1010867575</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>240061</td>\n",
       "      <td>1492913</td>\n",
       "      <td>1855353</td>\n",
       "      <td>1079194579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-05</td>\n",
       "      <td>446314</td>\n",
       "      <td>1594712</td>\n",
       "      <td>2041418</td>\n",
       "      <td>1357736987</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Views_show  Visitors  Views_platform  Ad_impression  \\\n",
       "0 2017-03-01      183738   1260228         1706478     1060860448   \n",
       "1 2017-03-02      193763   1270561         1690727     1031846645   \n",
       "2 2017-03-03      210479   1248183         1726157     1010867575   \n",
       "3 2017-03-04      240061   1492913         1855353     1079194579   \n",
       "4 2017-03-05      446314   1594712         2041418     1357736987   \n",
       "\n",
       "   Cricket_match_india  Character_A  day  weekday  \n",
       "0                    0            0    1        4  \n",
       "1                    0            0    2        5  \n",
       "2                    0            0    3        6  \n",
       "3                    1            0    4        7  \n",
       "4                    0            0    5        1  "
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Derived Metrics\n",
    "# Weekdays are taken such that 1 corresponds to Sunday and 7 to Saturday\n",
    "# Generate the weekday variable\n",
    "media['weekday'] = (media['day']+3)%7\n",
    "media.weekday.replace(0,7, inplace=True)\n",
    "media['weekday'] = media['weekday'].astype(int)\n",
    "media.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running first model (lm1) Weekday & visitors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['Visitors','weekday']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Representing LinearRegression as lr(Creating LinearRegression Object)\n",
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit the model to the training data\n",
    "lm.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.485\n",
      "Model:                            OLS   Adj. R-squared:                  0.472\n",
      "Method:                 Least Squares   F-statistic:                     36.26\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           8.01e-12\n",
      "Time:                        10:27:35   Log-Likelihood:                -1042.5\n",
      "No. Observations:                  80   AIC:                             2091.\n",
      "Df Residuals:                      77   BIC:                             2098.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -3.862e+04   1.07e+05     -0.360      0.720   -2.52e+05    1.75e+05\n",
      "Visitors       0.2787      0.057      4.911      0.000       0.166       0.392\n",
      "weekday    -3.591e+04   6591.205     -5.448      0.000    -4.9e+04   -2.28e+04\n",
      "==============================================================================\n",
      "Omnibus:                        2.684   Durbin-Watson:                   0.650\n",
      "Prob(Omnibus):                  0.261   Jarque-Bera (JB):                2.653\n",
      "Skew:                           0.423   Prob(JB):                        0.265\n",
      "Kurtosis:                       2.718   Cond. No.                     1.46e+07\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.46e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_1 = sm.OLS(y,X).fit()\n",
    "print(lm_1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create Weekend variable, with value 1 at weekends and 0 at weekdays\n",
    "def cond(i):\n",
    "    if i % 7 == 5: return 1\n",
    "    elif i % 7 == 4: return 1\n",
    "    else :return 0\n",
    "    return i\n",
    "\n",
    "media['weekend']=[cond(i) for i in media['day']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Views_show</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Views_platform</th>\n",
       "      <th>Ad_impression</th>\n",
       "      <th>Cricket_match_india</th>\n",
       "      <th>Character_A</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>183738</td>\n",
       "      <td>1260228</td>\n",
       "      <td>1706478</td>\n",
       "      <td>1060860448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>193763</td>\n",
       "      <td>1270561</td>\n",
       "      <td>1690727</td>\n",
       "      <td>1031846645</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>210479</td>\n",
       "      <td>1248183</td>\n",
       "      <td>1726157</td>\n",
       "      <td>1010867575</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>240061</td>\n",
       "      <td>1492913</td>\n",
       "      <td>1855353</td>\n",
       "      <td>1079194579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-05</td>\n",
       "      <td>446314</td>\n",
       "      <td>1594712</td>\n",
       "      <td>2041418</td>\n",
       "      <td>1357736987</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Views_show  Visitors  Views_platform  Ad_impression  \\\n",
       "0 2017-03-01      183738   1260228         1706478     1060860448   \n",
       "1 2017-03-02      193763   1270561         1690727     1031846645   \n",
       "2 2017-03-03      210479   1248183         1726157     1010867575   \n",
       "3 2017-03-04      240061   1492913         1855353     1079194579   \n",
       "4 2017-03-05      446314   1594712         2041418     1357736987   \n",
       "\n",
       "   Cricket_match_india  Character_A  day  weekday  weekend  \n",
       "0                    0            0    1        4        0  \n",
       "1                    0            0    2        5        0  \n",
       "2                    0            0    3        6        0  \n",
       "3                    1            0    4        7        1  \n",
       "4                    0            0    5        1        1  "
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running second model (lm2) visitors & weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['Visitors','weekend']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.500\n",
      "Model:                            OLS   Adj. R-squared:                  0.487\n",
      "Method:                 Least Squares   F-statistic:                     38.55\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           2.51e-12\n",
      "Time:                        10:27:35   Log-Likelihood:                -1041.3\n",
      "No. Observations:                  80   AIC:                             2089.\n",
      "Df Residuals:                      77   BIC:                             2096.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -8.833e+04   1.01e+05     -0.875      0.384   -2.89e+05    1.13e+05\n",
      "Visitors       0.1934      0.061      3.160      0.002       0.071       0.315\n",
      "weekend     1.807e+05   3.15e+04      5.740      0.000    1.18e+05    2.43e+05\n",
      "==============================================================================\n",
      "Omnibus:                        1.302   Durbin-Watson:                   1.254\n",
      "Prob(Omnibus):                  0.521   Jarque-Bera (JB):                1.367\n",
      "Skew:                           0.270   Prob(JB):                        0.505\n",
      "Kurtosis:                       2.656   Cond. No.                     1.41e+07\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.41e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_2 = sm.OLS(y,X).fit()\n",
    "print(lm_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running third model (lm3) visitors, weekend & Character_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['Visitors','weekend','Character_A']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.586\n",
      "Model:                            OLS   Adj. R-squared:                  0.570\n",
      "Method:                 Least Squares   F-statistic:                     35.84\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           1.53e-14\n",
      "Time:                        10:27:35   Log-Likelihood:                -1033.8\n",
      "No. Observations:                  80   AIC:                             2076.\n",
      "Df Residuals:                      76   BIC:                             2085.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const       -4.722e+04   9.31e+04     -0.507      0.613   -2.33e+05    1.38e+05\n",
      "Visitors        0.1480      0.057      2.586      0.012       0.034       0.262\n",
      "weekend      1.812e+05   2.89e+04      6.281      0.000    1.24e+05    2.39e+05\n",
      "Character_A  9.542e+04   2.41e+04      3.963      0.000    4.75e+04    1.43e+05\n",
      "==============================================================================\n",
      "Omnibus:                        0.908   Durbin-Watson:                   1.600\n",
      "Prob(Omnibus):                  0.635   Jarque-Bera (JB):                0.876\n",
      "Skew:                          -0.009   Prob(JB):                        0.645\n",
      "Kurtosis:                       2.488   Cond. No.                     1.42e+07\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.42e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_3 = sm.OLS(y,X).fit()\n",
    "print(lm_3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create lag variable\n",
    "media['Lag_Views'] = np.roll(media['Views_show'], 1)\n",
    "media.Lag_Views.replace(108961,0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Views_show</th>\n",
       "      <th>Visitors</th>\n",
       "      <th>Views_platform</th>\n",
       "      <th>Ad_impression</th>\n",
       "      <th>Cricket_match_india</th>\n",
       "      <th>Character_A</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Lag_Views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>183738</td>\n",
       "      <td>1260228</td>\n",
       "      <td>1706478</td>\n",
       "      <td>1060860448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-03-02</td>\n",
       "      <td>193763</td>\n",
       "      <td>1270561</td>\n",
       "      <td>1690727</td>\n",
       "      <td>1031846645</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>183738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-03-03</td>\n",
       "      <td>210479</td>\n",
       "      <td>1248183</td>\n",
       "      <td>1726157</td>\n",
       "      <td>1010867575</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>193763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-03-04</td>\n",
       "      <td>240061</td>\n",
       "      <td>1492913</td>\n",
       "      <td>1855353</td>\n",
       "      <td>1079194579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>210479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-05</td>\n",
       "      <td>446314</td>\n",
       "      <td>1594712</td>\n",
       "      <td>2041418</td>\n",
       "      <td>1357736987</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>240061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Views_show  Visitors  Views_platform  Ad_impression  \\\n",
       "0 2017-03-01      183738   1260228         1706478     1060860448   \n",
       "1 2017-03-02      193763   1270561         1690727     1031846645   \n",
       "2 2017-03-03      210479   1248183         1726157     1010867575   \n",
       "3 2017-03-04      240061   1492913         1855353     1079194579   \n",
       "4 2017-03-05      446314   1594712         2041418     1357736987   \n",
       "\n",
       "   Cricket_match_india  Character_A  day  weekday  weekend  Lag_Views  \n",
       "0                    0            0    1        4        0          0  \n",
       "1                    0            0    2        5        0     183738  \n",
       "2                    0            0    3        6        0     193763  \n",
       "3                    1            0    4        7        1     210479  \n",
       "4                    0            0    5        1        1     240061  "
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running fourth model (lm4) visitors, Character_A, Lag_views & weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['Visitors','Character_A','Lag_Views','weekend']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.740\n",
      "Model:                            OLS   Adj. R-squared:                  0.726\n",
      "Method:                 Least Squares   F-statistic:                     53.46\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           3.16e-21\n",
      "Time:                        10:27:36   Log-Likelihood:                -1015.1\n",
      "No. Observations:                  80   AIC:                             2040.\n",
      "Df Residuals:                      75   BIC:                             2052.\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const        -2.98e+04   7.43e+04     -0.401      0.689   -1.78e+05    1.18e+05\n",
      "Visitors        0.0659      0.047      1.394      0.167      -0.028       0.160\n",
      "Character_A  5.527e+04   2.01e+04      2.748      0.008    1.52e+04    9.53e+04\n",
      "Lag_Views       0.4317      0.065      6.679      0.000       0.303       0.560\n",
      "weekend      2.273e+05    2.4e+04      9.467      0.000    1.79e+05    2.75e+05\n",
      "==============================================================================\n",
      "Omnibus:                        1.425   Durbin-Watson:                   2.626\n",
      "Prob(Omnibus):                  0.491   Jarque-Bera (JB):                0.821\n",
      "Skew:                          -0.130   Prob(JB):                        0.663\n",
      "Kurtosis:                       3.423   Cond. No.                     1.44e+07\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.44e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_4 = sm.OLS(y,X).fit()\n",
    "print(lm_4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2cc0301d0>"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YSnLqroI61DiZe3ElVQ0NcSW2wMmuqnw00TG4D+tA0wef8rXHJ9D0zgfJv3+qJg/2633p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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cb8d36a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))        # Size of the figure\n",
    "sns.heatmap(media.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running fifth model (lm5) Character_A, weekend & Views_platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['weekend','Character_A','Views_platform']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.602\n",
      "Model:                            OLS   Adj. R-squared:                  0.586\n",
      "Method:                 Least Squares   F-statistic:                     38.24\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           3.59e-15\n",
      "Time:                        10:27:37   Log-Likelihood:                -1032.3\n",
      "No. Observations:                  80   AIC:                             2073.\n",
      "Df Residuals:                      76   BIC:                             2082.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "const          -1.205e+05   9.97e+04     -1.208      0.231   -3.19e+05    7.81e+04\n",
      "weekend         1.781e+05   2.78e+04      6.410      0.000    1.23e+05    2.33e+05\n",
      "Character_A     7.062e+04    2.6e+04      2.717      0.008    1.89e+04    1.22e+05\n",
      "Views_platform     0.1507      0.048      3.152      0.002       0.055       0.246\n",
      "==============================================================================\n",
      "Omnibus:                        4.279   Durbin-Watson:                   1.516\n",
      "Prob(Omnibus):                  0.118   Jarque-Bera (JB):                2.153\n",
      "Skew:                           0.061   Prob(JB):                        0.341\n",
      "Kurtosis:                       2.206   Cond. No.                     2.03e+07\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.03e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_5 = sm.OLS(y,X).fit()\n",
    "print(lm_5.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running sixth model (lm6) Character_A, weekend & Visitors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['weekend','Character_A','Visitors']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.586\n",
      "Model:                            OLS   Adj. R-squared:                  0.570\n",
      "Method:                 Least Squares   F-statistic:                     35.84\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           1.53e-14\n",
      "Time:                        10:27:37   Log-Likelihood:                -1033.8\n",
      "No. Observations:                  80   AIC:                             2076.\n",
      "Df Residuals:                      76   BIC:                             2085.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const       -4.722e+04   9.31e+04     -0.507      0.613   -2.33e+05    1.38e+05\n",
      "weekend      1.812e+05   2.89e+04      6.281      0.000    1.24e+05    2.39e+05\n",
      "Character_A  9.542e+04   2.41e+04      3.963      0.000    4.75e+04    1.43e+05\n",
      "Visitors        0.1480      0.057      2.586      0.012       0.034       0.262\n",
      "==============================================================================\n",
      "Omnibus:                        0.908   Durbin-Watson:                   1.600\n",
      "Prob(Omnibus):                  0.635   Jarque-Bera (JB):                0.876\n",
      "Skew:                          -0.009   Prob(JB):                        0.645\n",
      "Kurtosis:                       2.488   Cond. No.                     1.42e+07\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.42e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_6 = sm.OLS(y,X).fit()\n",
    "print(lm_6.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running seventh model (lm7) Character_A, weekend, Visitors & Ad_impressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['weekend','Character_A','Visitors','Ad_impression']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.803\n",
      "Model:                            OLS   Adj. R-squared:                  0.792\n",
      "Method:                 Least Squares   F-statistic:                     76.40\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           1.10e-25\n",
      "Time:                        10:27:38   Log-Likelihood:                -1004.1\n",
      "No. Observations:                  80   AIC:                             2018.\n",
      "Df Residuals:                      75   BIC:                             2030.\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const         -2.834e+05   6.97e+04     -4.067      0.000   -4.22e+05   -1.45e+05\n",
      "weekend        1.485e+05   2.04e+04      7.296      0.000    1.08e+05    1.89e+05\n",
      "Character_A   -2.934e+04   2.16e+04     -1.356      0.179   -7.24e+04    1.38e+04\n",
      "Visitors          0.0144      0.042      0.340      0.735      -0.070       0.099\n",
      "Ad_impression     0.0004   3.96e-05      9.090      0.000       0.000       0.000\n",
      "==============================================================================\n",
      "Omnibus:                        4.808   Durbin-Watson:                   1.166\n",
      "Prob(Omnibus):                  0.090   Jarque-Bera (JB):                4.007\n",
      "Skew:                           0.476   Prob(JB):                        0.135\n",
      "Kurtosis:                       3.545   Cond. No.                     1.32e+10\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.32e+10. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_7 = sm.OLS(y,X).fit()\n",
    "print(lm_7.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running eight model (lm8) Character_A, weekend & Ad_impressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['weekend','Character_A','Ad_impression']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.803\n",
      "Model:                            OLS   Adj. R-squared:                  0.795\n",
      "Method:                 Least Squares   F-statistic:                     103.0\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           1.05e-26\n",
      "Time:                        10:27:38   Log-Likelihood:                -1004.2\n",
      "No. Observations:                  80   AIC:                             2016.\n",
      "Df Residuals:                      76   BIC:                             2026.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "const         -2.661e+05   4.74e+04     -5.609      0.000   -3.61e+05   -1.72e+05\n",
      "weekend         1.51e+05   1.88e+04      8.019      0.000    1.14e+05    1.89e+05\n",
      "Character_A    -2.99e+04   2.14e+04     -1.394      0.167   -7.26e+04    1.28e+04\n",
      "Ad_impression     0.0004   3.69e-05      9.875      0.000       0.000       0.000\n",
      "==============================================================================\n",
      "Omnibus:                        4.723   Durbin-Watson:                   1.169\n",
      "Prob(Omnibus):                  0.094   Jarque-Bera (JB):                3.939\n",
      "Skew:                           0.453   Prob(JB):                        0.139\n",
      "Kurtosis:                       3.601   Cond. No.                     9.26e+09\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.26e+09. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_8 = sm.OLS(y,X).fit()\n",
    "print(lm_8.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Ad impression in million\n",
    "media['ad_impression_million'] = media['Ad_impression']/1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running seventh model (lm7) Character_A, weekend, Visitors, ad_impressions_million & Cricket_match_india"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['weekend','Character_A','ad_impression_million','Cricket_match_india']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.803\n",
      "Model:                            OLS   Adj. R-squared:                  0.793\n",
      "Method:                 Least Squares   F-statistic:                     76.59\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           1.02e-25\n",
      "Time:                        10:27:39   Log-Likelihood:                -1004.0\n",
      "No. Observations:                  80   AIC:                             2018.\n",
      "Df Residuals:                      75   BIC:                             2030.\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                 -2.633e+05    4.8e+04     -5.484      0.000   -3.59e+05   -1.68e+05\n",
      "weekend                1.521e+05    1.9e+04      7.987      0.000    1.14e+05     1.9e+05\n",
      "Character_A           -3.196e+04   2.19e+04     -1.457      0.149   -7.57e+04    1.17e+04\n",
      "ad_impression_million   363.7938     37.113      9.802      0.000     289.861     437.727\n",
      "Cricket_match_india   -1.396e+04   2.74e+04     -0.510      0.612   -6.85e+04    4.06e+04\n",
      "==============================================================================\n",
      "Omnibus:                        5.270   Durbin-Watson:                   1.161\n",
      "Prob(Omnibus):                  0.072   Jarque-Bera (JB):                4.560\n",
      "Skew:                           0.468   Prob(JB):                        0.102\n",
      "Kurtosis:                       3.701   Cond. No.                     9.32e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.32e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_9 = sm.OLS(y,X).fit()\n",
    "print(lm_9.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running seventh model (lm7) Character_A, weekend & ad_impressions_million"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Putting feature variable to X\n",
    "X = media[['weekend','Character_A','ad_impression_million']]\n",
    "\n",
    "# Putting response variable to y\n",
    "y = media['Views_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Views_show   R-squared:                       0.803\n",
      "Model:                            OLS   Adj. R-squared:                  0.795\n",
      "Method:                 Least Squares   F-statistic:                     103.0\n",
      "Date:                Fri, 09 Mar 2018   Prob (F-statistic):           1.05e-26\n",
      "Time:                        10:27:39   Log-Likelihood:                -1004.2\n",
      "No. Observations:                  80   AIC:                             2016.\n",
      "Df Residuals:                      76   BIC:                             2026.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "const                 -2.661e+05   4.74e+04     -5.609      0.000   -3.61e+05   -1.72e+05\n",
      "weekend                 1.51e+05   1.88e+04      8.019      0.000    1.14e+05    1.89e+05\n",
      "Character_A            -2.99e+04   2.14e+04     -1.394      0.167   -7.26e+04    1.28e+04\n",
      "ad_impression_million   364.4670     36.909      9.875      0.000     290.957     437.977\n",
      "==============================================================================\n",
      "Omnibus:                        4.723   Durbin-Watson:                   1.169\n",
      "Prob(Omnibus):                  0.094   Jarque-Bera (JB):                3.939\n",
      "Skew:                           0.453   Prob(JB):                        0.139\n",
      "Kurtosis:                       3.601   Cond. No.                     9.26e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.26e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Unlike SKLearn, statsmodels don't automatically fit a constant, \n",
    "#so you need to use the method sm.add_constant(X) in order to add a constant. \n",
    "X = sm.add_constant(X)\n",
    "# create a fitted model in one line\n",
    "lm_10 = sm.OLS(y,X).fit()\n",
    "print(lm_10.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making predictions using lm10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions using the model\n",
    "X = media[['weekend','Character_A','ad_impression_million']]\n",
    "X = sm.add_constant(X)\n",
    "Predicted_views = lm_10.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "mse = mean_squared_error(media.Views_show, Predicted_views)\n",
    "r_squared = r2_score(media.Views_show, Predicted_views)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean_Squared_Error : 4677651616.25\n",
      "r_square_value : 0.802643446858\n"
     ]
    }
   ],
   "source": [
    "print('Mean_Squared_Error :' ,mse)\n",
    "print('r_square_value :',r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Views')"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eXpMam3QSksYpIIBuplIloB4mOINHmJoqzCCQJKIqJC1VEqeAPAGtjBer6tmq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\nyQpWgCwWi8WSFf4fQ1XaNxIIR7sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cb8cb4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Actual vs Predicted\n",
    "c = [i for i in range(1,81,1)]\n",
    "fig = plt.figure()\n",
    "plt.plot(c,media.Views_show, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(c,Predicted_views, color=\"red\",  linewidth=2.5, linestyle=\"-\")\n",
    "fig.suptitle('Actual and Predicted', fontsize=20)              # Plot heading \n",
    "plt.xlabel('Index', fontsize=18)                               # X-label\n",
    "plt.ylabel('Views', fontsize=16)                               # Y-label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Views_show-Predicted_views')"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OhWOOqXyM/3IuWuRCdd2inxVZhMrfUD76CF58Ebbc0omLT6I46KB40TRVlx3Y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\nZbXzYuXKtqnpnm7dYlf1yCP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dnbRxubSsP0h3VL/4RTye+6MfuYnMyTTwtdeGp58udVRZ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\nK9r/AcKm8K5J8QygAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cc62b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Error terms\n",
    "c = [i for i in range(1,81,1)]\n",
    "fig = plt.figure()\n",
    "plt.plot(c,media.Views_show-Predicted_views, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "fig.suptitle('Error Terms', fontsize=20)              # Plot heading \n",
    "plt.xlabel('Index', fontsize=18)                      # X-label\n",
    "plt.ylabel('Views_show-Predicted_views', fontsize=16)                # Y-label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making predictions using lm6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Making predictions using the model\n",
    "X = media[['weekend','Character_A','Visitors']]\n",
    "X = sm.add_constant(X)\n",
    "Predicted_views = lm_6.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "mse = mean_squared_error(media.Views_show, Predicted_views)\n",
    "r_squared = r2_score(media.Views_show, Predicted_views)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean_Squared_Error : 9815432480.45\n",
      "r_square_value : 0.585873408098\n"
     ]
    }
   ],
   "source": [
    "print('Mean_Squared_Error :' ,mse)\n",
    "print('r_square_value :',r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Views')"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rfy5kOqJNldFeNJRSVyilFiulFs9xK415bNsYG2PBfT/PbUtKAWUyvHXNlTlN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cc6ae438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Actual vs Predicted\n",
    "c = [i for i in range(1,81,1)]\n",
    "fig = plt.figure()\n",
    "plt.plot(c,media.Views_show, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(c,Predicted_views, color=\"red\",  linewidth=2.5, linestyle=\"-\")\n",
    "fig.suptitle('Actual and Predicted', fontsize=20)              # Plot heading \n",
    "plt.xlabel('Index', fontsize=18)                               # X-label\n",
    "plt.ylabel('Views', fontsize=16)                               # Y-label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Views_show-Predicted_views')"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gpZd6b3dTfMuvZiGeS2jhwsZFxB3Muttu5t6XSJWnUFtsyM9Hn1SlxAnLkUfG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\n5SRlZmbF+hPorPZ1B/cMSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2cd97d908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Error terms\n",
    "c = [i for i in range(1,81,1)]\n",
    "fig = plt.figure()\n",
    "plt.plot(c,media.Views_show-Predicted_views, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "fig.suptitle('Error Terms', fontsize=20)              # Plot heading \n",
    "plt.xlabel('Index', fontsize=18)                      # X-label\n",
    "plt.ylabel('Views_show-Predicted_views', fontsize=16)                # Y-label"
   ]
  }
 ],
 "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": 2
}