15th August 2020 - exam questions discussed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MLAP Midterm Exam - Key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making dataframe from CSV files\n",
    "advertising_df= pd.read_csv(r\"C:\\Users\\Home\\Desktop\\MLAP - Getting Started with Python\\MLAP MT 10 Aug 2020 - Part - B\\Advertising.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# information of dataset procurred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Sno        200 non-null    int64  \n",
      " 1   TV         199 non-null    float64\n",
      " 2   Radio      199 non-null    float64\n",
      " 3   Newspaper  199 non-null    float64\n",
      " 4   Day        197 non-null    object \n",
      " 5   Sales      200 non-null    float64\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "advertising_df.info()\n",
    "# datatype and missing values and the numbers of data collected stated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Day</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>Weekend</td>\n",
       "      <td>10.1</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>Weekdays</td>\n",
       "      <td>8.0</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>Weekdays</td>\n",
       "      <td>10.3</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>Weekdays</td>\n",
       "      <td>25.4</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>Weekdays</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>48.9</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Weekdays</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "      <td>Weekdays</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>120.2</td>\n",
       "      <td>19.6</td>\n",
       "      <td>11.6</td>\n",
       "      <td>Weekdays</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Weekdays</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>199.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>Weekdays</td>\n",
       "      <td>13.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sno     TV  Radio  Newspaper       Day  Sales\n",
       "0    1  230.1   37.8       69.2   Weekend   10.1\n",
       "1    2   44.5   39.3       45.1  Weekdays    8.0\n",
       "2    3   17.2   45.9       69.3  Weekdays   10.3\n",
       "3    4  151.5   41.3       58.5  Weekdays   25.4\n",
       "4    5  180.8   10.8       58.4  Weekdays   13.6\n",
       "5    6    8.7   48.9       75.0  Weekdays    5.9\n",
       "6    7   57.5   32.8       23.5  Weekdays   14.4\n",
       "7    8  120.2   19.6       11.6  Weekdays    6.6\n",
       "8    9    8.6    2.1        1.0  Weekdays   19.6\n",
       "9   10  199.8    2.6       21.2  Weekdays   13.4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df.head(10)\n",
    "# the first 10 rows shown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# advertising_df (\"TV\")= pd.to_numeric(advertising_df[\"TV\"] , error = 'coerce')\n",
    "\n",
    "# when we want to change the dataset to numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df.shape\n",
    "\n",
    "# 200 entries\n",
    "# 6 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# missing values and deleting null rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sno          0\n",
       "TV           1\n",
       "Radio        1\n",
       "Newspaper    1\n",
       "Day          3\n",
       "Sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "advertising_df=advertising_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sno          0\n",
       "TV           0\n",
       "Radio        0\n",
       "Newspaper    0\n",
       "Day          0\n",
       "Sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 197 entries, 0 to 199\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Sno        197 non-null    int64  \n",
      " 1   TV         197 non-null    float64\n",
      " 2   Radio      197 non-null    float64\n",
      " 3   Newspaper  197 non-null    float64\n",
      " 4   Day        197 non-null    object \n",
      " 5   Sales      197 non-null    float64\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 10.8+ KB\n"
     ]
    }
   ],
   "source": [
    "advertising_df.info()\n",
    "\n",
    "# 3 rows were missing so we deleted them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</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>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>101.375635</td>\n",
       "      <td>146.242132</td>\n",
       "      <td>23.152284</td>\n",
       "      <td>30.375635</td>\n",
       "      <td>14.102030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.818691</td>\n",
       "      <td>85.439138</td>\n",
       "      <td>14.922793</td>\n",
       "      <td>20.972370</td>\n",
       "      <td>5.192302</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>52.000000</td>\n",
       "      <td>74.700000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>10.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>102.000000</td>\n",
       "      <td>149.700000</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>151.000000</td>\n",
       "      <td>218.400000</td>\n",
       "      <td>36.500000</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>100.900000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sno          TV       Radio   Newspaper       Sales\n",
       "count  197.000000  197.000000  197.000000  197.000000  197.000000\n",
       "mean   101.375635  146.242132   23.152284   30.375635   14.102030\n",
       "std     57.818691   85.439138   14.922793   20.972370    5.192302\n",
       "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
       "25%     52.000000   74.700000    9.900000   12.900000   10.400000\n",
       "50%    102.000000  149.700000   22.300000   25.900000   12.900000\n",
       "75%    151.000000  218.400000   36.500000   45.100000   17.400000\n",
       "max    200.000000  296.400000   49.600000  100.900000   27.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>197.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>146.242132</td>\n",
       "      <td>23.152284</td>\n",
       "      <td>30.375635</td>\n",
       "      <td>14.102030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.439138</td>\n",
       "      <td>14.922793</td>\n",
       "      <td>20.972370</td>\n",
       "      <td>5.192302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.700000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>10.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.700000</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.400000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>100.900000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       Radio   Newspaper       Sales\n",
       "count  197.000000  197.000000  197.000000  197.000000\n",
       "mean   146.242132   23.152284   30.375635   14.102030\n",
       "std     85.439138   14.922793   20.972370    5.192302\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.700000    9.900000   12.900000   10.400000\n",
       "50%    149.700000   22.300000   25.900000   12.900000\n",
       "75%    218.400000   36.500000   45.100000   17.400000\n",
       "max    296.400000   49.600000  100.900000   27.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df[[\"TV\", \"Radio\", \"Newspaper\", \"Sales\"]]. describe()\n",
    "\n",
    "# only imporatant metric variables described"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_features=[\"TV\", \"Radio\", \"Newspaper\", \"Day\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Weekend', 'Weekdays', 'Holiday'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df[\"Day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Holiday  Weekdays  Weekend\n",
       "0        0         0        1\n",
       "1        0         1        0\n",
       "2        0         1        0\n",
       "3        0         1        0\n",
       "4        0         1        0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(advertising_df[\"Day\"])[0:5]\n",
    "\n",
    "#holiday is reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = [\"Day\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "advertising_df_encoded=pd.get_dummies(advertising_df[x_features],\n",
    " columns = categorical_features,\n",
    "  drop_first = True) #N-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Day_Weekdays</th>\n",
       "      <th>Day_Weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>0</td>\n",
       "      <td>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>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Day_Weekdays  Day_Weekend\n",
       "0    230.1   37.8       69.2             0            1\n",
       "1     44.5   39.3       45.1             1            0\n",
       "2     17.2   45.9       69.3             1            0\n",
       "3    151.5   41.3       58.5             1            0\n",
       "4    180.8   10.8       58.4             1            0\n",
       "..     ...    ...        ...           ...          ...\n",
       "195   38.2    3.7       13.8             1            0\n",
       "196   94.2    4.9        8.1             1            0\n",
       "197  177.0    9.3        6.4             1            0\n",
       "198  283.6   42.0       66.2             0            0\n",
       "199  232.1    8.6        8.7             0            1\n",
       "\n",
       "[197 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TV', 'Radio', 'Newspaper', 'Day_Weekdays', 'Day_Weekend'], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df_encoded.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "day_sales_df = advertising_df.groupby(\"Day\")[\"Sales\"].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Holiday</td>\n",
       "      <td>13.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Weekdays</td>\n",
       "      <td>14.033113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Weekend</td>\n",
       "      <td>14.438095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Day      Sales\n",
       "0   Holiday  13.175000\n",
       "1  Weekdays  14.033113\n",
       "2   Weekend  14.438095"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_sales_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f723fb60c8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.barplot(y=\"Sales\",\n",
    "          x=\"Day\",\n",
    "          data=day_sales_df,\n",
    "          order = day_sales_df.sort_values(\"Sales\")[\"Day\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  9., 20., 46., 34., 29., 22., 17., 12.,  7.]),\n",
       " array([ 1.6 ,  4.14,  6.68,  9.22, 11.76, 14.3 , 16.84, 19.38, 21.92,\n",
       "        24.46, 27.  ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(advertising_df.Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f72433dac8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.distplot(advertising_df.Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([26., 15., 20., 18., 19., 17., 22., 26., 19., 15.]),\n",
       " array([  0.7 ,  30.27,  59.84,  89.41, 118.98, 148.55, 178.12, 207.69,\n",
       "        237.26, 266.83, 296.4 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(advertising_df.TV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([30., 20., 21., 17., 18., 21., 14., 20., 21., 15.]),\n",
       " array([ 0.  ,  4.96,  9.92, 14.88, 19.84, 24.8 , 29.76, 34.72, 39.68,\n",
       "        44.64, 49.6 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(advertising_df.Radio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([42., 32., 36., 29., 23., 18.,  7.,  7.,  2.,  1.]),\n",
       " array([  0.3 ,  10.36,  20.42,  30.48,  40.54,  50.6 ,  60.66,  70.72,\n",
       "         80.78,  90.84, 100.9 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(advertising_df.Newspaper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f724317a88>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.boxplot(y=advertising_df.Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'BOX PLOT FOR DAY TYPES')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.boxplot(x=advertising_df.Day,\n",
    "    y=advertising_df.Sales,\n",
    "          order = advertising_df.Day.unique().sort())\n",
    "plt.title(\"BOX PLOT FOR DAY TYPES\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f72465c048>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(advertising_df.TV, advertising_df.Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "advertising_df_correl = advertising_df[[\"TV\", \"Radio\", \"Newspaper\", \"Sales\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>10.1</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>8.0</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>10.3</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>25.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>13.6</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>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>8.4</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>10.8</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>15.2</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>4.8</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>14.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>197 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "0    230.1   37.8       69.2   10.1\n",
       "1     44.5   39.3       45.1    8.0\n",
       "2     17.2   45.9       69.3   10.3\n",
       "3    151.5   41.3       58.5   25.4\n",
       "4    180.8   10.8       58.4   13.6\n",
       "..     ...    ...        ...    ...\n",
       "195   38.2    3.7       13.8    8.4\n",
       "196   94.2    4.9        8.1   10.8\n",
       "197  177.0    9.3        6.4   15.2\n",
       "198  283.6   42.0       66.2    4.8\n",
       "199  232.1    8.6        8.7   14.7\n",
       "\n",
       "[197 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df_correl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055658</td>\n",
       "      <td>0.095794</td>\n",
       "      <td>0.047781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radio</th>\n",
       "      <td>0.055658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.356668</td>\n",
       "      <td>0.001630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newspaper</th>\n",
       "      <td>0.095794</td>\n",
       "      <td>0.356668</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.047781</td>\n",
       "      <td>0.001630</td>\n",
       "      <td>-0.027103</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     Radio  Newspaper     Sales\n",
       "TV         1.000000  0.055658   0.095794  0.047781\n",
       "Radio      0.055658  1.000000   0.356668  0.001630\n",
       "Newspaper  0.095794  0.356668   1.000000 -0.027103\n",
       "Sales      0.047781  0.001630  -0.027103  1.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df_correl.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f7246d0988>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(advertising_df_correl.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_features = advertising_df_encoded.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sm.add_constant(advertising_df_encoded)\n",
    "y = advertising_df[\"Sales\"]\n",
    "train_x, test_x, train_y, test_y = train_test_split(x,y,train_size = 0.7,random_state = 87)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>               <td>OLS</td>         <td>Adj. R-squared:</td>    <td>-0.009</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>       <td>Sales</td>             <td>AIC:</td>         <td>843.3373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2020-08-15 15:10</td>        <td>BIC:</td>         <td>860.8572</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>         <td>137</td>         <td>Log-Likelihood:</td>    <td>-415.67</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>              <td>5</td>           <td>F-statistic:</td>      <td>0.7536</td> \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>           <td>131</td>       <td>Prob (F-statistic):</td>   <td>0.585</td> \n",
       "</tr>\n",
       "<tr>\n",
       "      <td>R-squared:</td>            <td>0.028</td>            <td>Scale:</td>         <td>26.445</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>        <th>Coef.</th>  <th>Std.Err.</th>    <th>t</th>     <th>P>|t|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>9.3158</td>   <td>3.6073</td>  <td>2.5824</td>  <td>0.0109</td> <td>2.1796</td>  <td>16.4520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>           <td>0.0027</td>   <td>0.0072</td>  <td>0.3752</td>  <td>0.7082</td> <td>-0.0116</td> <td>0.0170</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>        <td>0.0044</td>   <td>0.0315</td>  <td>0.1393</td>  <td>0.8894</td> <td>-0.0580</td> <td>0.0668</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Newspaper</th>    <td>-0.0166</td>  <td>0.0224</td>  <td>-0.7409</td> <td>0.4601</td> <td>-0.0610</td> <td>0.0278</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Day_Weekdays</th> <td>4.8040</td>   <td>3.1830</td>  <td>1.5093</td>  <td>0.1336</td> <td>-1.4928</td> <td>11.1008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Day_Weekend</th>  <td>5.3015</td>   <td>3.1354</td>  <td>1.6908</td>  <td>0.0932</td> <td>-0.9011</td> <td>11.5042</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td>Omnibus:</td>    <td>6.007</td>  <td>Durbin-Watson:</td>   <td>1.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Prob(Omnibus):</td> <td>0.050</td> <td>Jarque-Bera (JB):</td> <td>5.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Skew:</td>     <td>0.424</td>     <td>Prob(JB):</td>     <td>0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Kurtosis:</td>   <td>2.502</td>  <td>Condition No.:</td>   <td>2133</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                 Results: Ordinary least squares\n",
       "=================================================================\n",
       "Model:              OLS              Adj. R-squared:     -0.009  \n",
       "Dependent Variable: Sales            AIC:                843.3373\n",
       "Date:               2020-08-15 15:10 BIC:                860.8572\n",
       "No. Observations:   137              Log-Likelihood:     -415.67 \n",
       "Df Model:           5                F-statistic:        0.7536  \n",
       "Df Residuals:       131              Prob (F-statistic): 0.585   \n",
       "R-squared:          0.028            Scale:              26.445  \n",
       "------------------------------------------------------------------\n",
       "               Coef.   Std.Err.     t     P>|t|    [0.025   0.975]\n",
       "------------------------------------------------------------------\n",
       "const          9.3158    3.6073   2.5824  0.0109   2.1796  16.4520\n",
       "TV             0.0027    0.0072   0.3752  0.7082  -0.0116   0.0170\n",
       "Radio          0.0044    0.0315   0.1393  0.8894  -0.0580   0.0668\n",
       "Newspaper     -0.0166    0.0224  -0.7409  0.4601  -0.0610   0.0278\n",
       "Day_Weekdays   4.8040    3.1830   1.5093  0.1336  -1.4928  11.1008\n",
       "Day_Weekend    5.3015    3.1354   1.6908  0.0932  -0.9011  11.5042\n",
       "-----------------------------------------------------------------\n",
       "Omnibus:              6.007        Durbin-Watson:           1.906\n",
       "Prob(Omnibus):        0.050        Jarque-Bera (JB):        5.512\n",
       "Skew:                 0.424        Prob(JB):                0.064\n",
       "Kurtosis:             2.502        Condition No.:           2133 \n",
       "=================================================================\n",
       "* The condition number is large (2e+03). This might indicate\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df_model_1 = sm.OLS(train_y, train_x).fit()\n",
    "advertising_df_model_1.summary2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Libraries are already imported \n",
    "probPlot = sm.ProbPlot(advertising_df_model_1.resid); \n",
    "# Returns Residual ie difference between observed and fitted values.\n",
    "probPlot.ppplot(line='45');                # Sets the Normal line to 45 degree \n",
    "plt.title(\"PPPlot for residual analysis for Regression model\") #Sets the title of Graph \n",
    "plt.show() #Used to show Plot Residual vs Normal Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getStandVals( vals ):          #Defining Function to calculate Std Values    \n",
    "    return (vals - vals.mean())/vals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(getStandVals(advertising_df_model_1.fittedvalues),getStandVals(advertising_df_model_1.resid)) \n",
    "#Generate Scatter plt errors vs prediction \n",
    "plt.title(\"Scatter Plot\") #Set title\n",
    "plt.xlabel(\"Predicted\")   #X-Axis Title \n",
    "plt.ylabel(\"Residuals\")   #Y-Axis Title \n",
    "plt.show()                #Show Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "advertising_df[\"z_score_sales\"] = zscore(advertising_df.Sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Day</th>\n",
       "      <th>Sales</th>\n",
       "      <th>z_score_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Sno, TV, Radio, Newspaper, Day, Sales, z_score_sales]\n",
       "Index: []"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising_df[(advertising_df.z_score_sales>3.0)|(advertising_df.z_score_sales<-3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.regressionplots import influence_plot \n",
    "#impoting package \n",
    "fig, ax = plt.subplots( figsize=(8,6) )   #Set the size \n",
    "influence_plot( advertising_df_model_1, ax = ax )    #making influence plot \n",
    "plt.title( \"Leverage and Residuals\")      #Setting title \n",
    "plt.show();     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np \n",
    "ad_influence = advertising_df_model_1.get_influence() \n",
    "(c, p) = ad_influence.cooks_distance \n",
    "plt.stem( np.arange( len( train_x) ), np.round( c, 3 ), markerfmt=\",\" );\n",
    "plt.title( \"Figure 3 - Cooks distance for all observations in advertising dataset\" );\n",
    "plt.xlabel( \"Row index\") \n",
    "plt.ylabel( \"Cooks Distance\"); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_y = np.power(advertising_df_model_1.predict(test_x[train_x.columns]),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "189.39248519324147"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "\n",
    "np.sqrt(metrics.mean_squared_error(pred_y, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sales = beta0 + beta1(tv)+ beta2(radio)+beta3(newpaper)+ beta4(day_weekdays)+ beta5(day_weekend)"
   ]
  }
 ],
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