Advertising - Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Exercise : Kaggle's Advertising Dataset***\n",
    "### **Himel Sarder**\n",
    "### **info.himelcse@gmail.com**\n",
    "### **GitHub : https://github.com/Himel-Sarder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Supress Warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import sqrt\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>12.0</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>16.5</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>17.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3   12.0\n",
       "3  151.5   41.3       58.5   16.5\n",
       "4  180.8   10.8       58.4   17.9"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(df['TV'],df['Sales'])\n",
    "plt.xlabel('TV')\n",
    "plt.ylabel('Sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:1]\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV\n",
       "0    230.1\n",
       "1     44.5\n",
       "2     17.2\n",
       "3    151.5\n",
       "4    180.8\n",
       "..     ...\n",
       "195   38.2\n",
       "196   94.2\n",
       "197  177.0\n",
       "198  283.6\n",
       "199  232.1\n",
       "\n",
       "[200 rows x 1 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.1\n",
       "1      10.4\n",
       "2      12.0\n",
       "3      16.5\n",
       "4      17.9\n",
       "       ... \n",
       "195     7.6\n",
       "196    14.0\n",
       "197    14.8\n",
       "198    25.5\n",
       "199    18.4\n",
       "Name: Sales, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LinearRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>238.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>135.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>217.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>241.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>206.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>139.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>197.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>216.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>216.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>117.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>229.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>56.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>187.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>149.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>170.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>163.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>112.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>214.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>129.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>195.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>184.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>220.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>219.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>75.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>184.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>123.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>68.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>188.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>120.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>97.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>168.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>222.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>193.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>215.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>175.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>164.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>218.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>120.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>89.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>248.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>202.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>171.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>131.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>210.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>197.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>85.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV\n",
       "126    7.8\n",
       "104  238.2\n",
       "99   135.2\n",
       "92   217.7\n",
       "111  241.7\n",
       "167  206.8\n",
       "116  139.2\n",
       "96   197.6\n",
       "52   216.4\n",
       "69   216.8\n",
       "164  117.2\n",
       "124  229.5\n",
       "182   56.2\n",
       "154  187.8\n",
       "125   87.2\n",
       "196   94.2\n",
       "194  149.7\n",
       "177  170.2\n",
       "163  163.5\n",
       "31   112.9\n",
       "11   214.7\n",
       "73   129.4\n",
       "15   195.4\n",
       "41   177.0\n",
       "97   184.9\n",
       "128  220.3\n",
       "133  219.8\n",
       "82    75.3\n",
       "139  184.9\n",
       "123  123.1\n",
       "83    68.4\n",
       "65    69.0\n",
       "151  121.0\n",
       "162  188.4\n",
       "170   50.0\n",
       "77   120.5\n",
       "32    97.2\n",
       "173  168.4\n",
       "174  222.4\n",
       "85   193.2\n",
       "168  215.4\n",
       "112  175.7\n",
       "171  164.5\n",
       "181  218.5\n",
       "7    120.2\n",
       "46    89.7\n",
       "75    16.9\n",
       "28   248.8\n",
       "29    70.6\n",
       "195   38.2\n",
       "40   202.5\n",
       "153  171.3\n",
       "115   75.1\n",
       "64   131.1\n",
       "59   210.7\n",
       "1     44.5\n",
       "192   17.2\n",
       "136   25.6\n",
       "152  197.6\n",
       "161   85.7"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "126     6.6\n",
       "104    20.7\n",
       "99     17.2\n",
       "92     19.4\n",
       "111    21.8\n",
       "167    17.2\n",
       "116    12.2\n",
       "96     16.7\n",
       "52     22.6\n",
       "69     22.3\n",
       "164    11.9\n",
       "124    19.7\n",
       "182     8.7\n",
       "154    20.6\n",
       "125    10.6\n",
       "196    14.0\n",
       "194    17.3\n",
       "177    16.7\n",
       "163    18.0\n",
       "31     11.9\n",
       "11     17.4\n",
       "73     11.0\n",
       "15     22.4\n",
       "41     17.1\n",
       "97     20.5\n",
       "128    24.7\n",
       "133    19.6\n",
       "82     11.3\n",
       "139    20.7\n",
       "123    15.2\n",
       "83     13.6\n",
       "65     11.3\n",
       "151    11.6\n",
       "162    19.9\n",
       "170     8.4\n",
       "77     14.2\n",
       "32     13.2\n",
       "173    16.7\n",
       "174    16.5\n",
       "85     20.2\n",
       "168    17.1\n",
       "112    17.1\n",
       "171    17.5\n",
       "181    17.2\n",
       "7      13.2\n",
       "46     10.6\n",
       "75      8.7\n",
       "28     18.9\n",
       "29     10.5\n",
       "195     7.6\n",
       "40     16.6\n",
       "153    16.0\n",
       "115    12.6\n",
       "64     16.0\n",
       "59     18.4\n",
       "1      10.4\n",
       "192     5.9\n",
       "136     9.5\n",
       "152    16.6\n",
       "161    13.3\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(df['TV'],df['Sales'])\n",
    "plt.plot(X_train,lr.predict(X_train),color='red')\n",
    "plt.xlabel('TV')\n",
    "plt.ylabel('Sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05454575])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = lr.coef_\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.948683200001357"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = lr.intercept_\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.37414007, 19.94148154, 14.32326899, 18.82329361, 20.13239168,\n",
       "       18.2287449 , 14.54145201, 17.72692398, 18.75238413, 18.77420243,\n",
       "       13.34144544, 19.46693349, 10.01415451, 17.1923756 , 11.70507285,\n",
       "       12.08689312, 15.11418241, 16.23237035, 15.8669138 , 13.1068987 ,\n",
       "       18.65965635, 14.00690363, 17.60692332, 16.60328147, 17.03419291,\n",
       "       18.96511257, 18.93783969, 11.05597839, 17.03419291, 13.66326538,\n",
       "       10.6796127 , 10.71234015, 13.5487193 , 17.22510305,  9.67597085,\n",
       "       13.52144643, 12.25053038, 16.13418799, 19.07965865, 17.48692266,\n",
       "       18.69783838, 16.53237199, 15.92145955, 18.86693021, 13.5050827 ,\n",
       "       11.84143724,  7.87050642, 20.51966653, 10.79961336,  9.03233096,\n",
       "       17.99419817, 16.29237067, 11.04506924, 14.09963141, 18.44147334,\n",
       "        9.3759692 ,  7.88687015,  8.34505447, 17.72692398, 11.62325422])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = lr.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.019296008966231\n"
     ]
    }
   ],
   "source": [
    "print(sqrt(mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The linear model is: Y = 6.94868 + 0.05455X\n"
     ]
    }
   ],
   "source": [
    "print(\"The linear model is: Y = {:.5f} + {:.5f}X\".format(lr.intercept_, lr.coef_[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Sales   R-squared:                       0.816\n",
      "Model:                            OLS   Adj. R-squared:                  0.814\n",
      "Method:                 Least Squares   F-statistic:                     611.2\n",
      "Date:                Wed, 25 Dec 2024   Prob (F-statistic):           1.52e-52\n",
      "Time:                        05:51:11   Log-Likelihood:                -321.12\n",
      "No. Observations:                 140   AIC:                             646.2\n",
      "Df Residuals:                     138   BIC:                             652.1\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          6.9487      0.385     18.068      0.000       6.188       7.709\n",
      "TV             0.0545      0.002     24.722      0.000       0.050       0.059\n",
      "==============================================================================\n",
      "Omnibus:                        0.027   Durbin-Watson:                   2.196\n",
      "Prob(Omnibus):                  0.987   Jarque-Bera (JB):                0.150\n",
      "Skew:                          -0.006   Prob(JB):                        0.928\n",
      "Kurtosis:                       2.840   Cond. No.                         328.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "X = df['TV']\n",
    "y = df['Sales']\n",
    "\n",
    "# Add a constant to the training data\n",
    "X_train_sm = sm.add_constant(X_train)\n",
    "\n",
    "# Refit the model with the updated data\n",
    "lr = sm.OLS(y_train, X_train_sm).fit()\n",
    "\n",
    "print(lr.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = lr.predict(X_train_sm)\n",
    "res = (y_train - y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(res, bins = 15)\n",
    "fig.suptitle('Error Terms', fontsize = 15)                  # Plot heading \n",
    "plt.xlabel('y_train - predictions', fontsize = 15)         # X-label\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}