LR.pdf

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b0d6081",
   "metadata": {},
   "source": [
    "Machine Learning Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91f5159b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Machine Learning - learning from the current data and implementing scenarios for the future by running some algorithms.\n",
    "\n",
    "# Machine Learning has 3 types namely:\n",
    "    # Supervised Machine Learning.\n",
    "    # Unsupervised Machine Learning.\n",
    "    # Reinforcement Machine Learning.\n",
    "    \n",
    "# Supervised Machine Learning: In Supervised ML, we have independent variables (x) and dependent variable (y).\n",
    "    # Classified\n",
    "      # Based on certain value if one is predicting the category(class) of the value in future, it is Classified ML.\n",
    "        \n",
    "    # Regression\n",
    "      # Based on certain value if one is predicting the value in future, it is Regression ML.\n",
    "        \n",
    "# Unsupervised Machine Learning: \n",
    "    # Clustering: In Unsupervised ML for Clustering we don't have dependent variable (Y) and we group the data based on \n",
    "                  # independent variables (X)\n",
    "\n",
    "# Reinforced Machine Learning\n",
    "    # Forecasting: In Reinforced ML, for Forecasting one predicts the future data of the column (attribute) based on the\n",
    "                   # past data of the same column (attribute). Here we have just a dependent variable column (Y)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03d3ca5",
   "metadata": {},
   "source": [
    "Machine Learning process flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22b24c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Get data: Get data from different sources\n",
    "\n",
    "# 2. Clean, prepare & manipulate data: \n",
    "    # a. Handling Null values and outliers.\n",
    "    # b. Feature selection/variable selection based on domain knowledge.\n",
    "            # In other words, by using Correlation try to remove columns that are not essential.\n",
    "    # c. Converting categorical data to Numerical \n",
    "    # d. Scaling the data\n",
    "\n",
    "# 3. EDA (Exploratory Data Analysis): Generating reports and graphs.\n",
    "\n",
    "# 4. Splitting the data: Creating train and test data.\n",
    "\n",
    "# 5. Build a model using train data (which is 70-80% of the whole data)\n",
    "\n",
    "# 6. Evaluation/Test model: Test model using the test data (20 -30% of the whole data)\n",
    "\n",
    "# 7. Optimise the model to increase it's accuracy.\n",
    "\n",
    "\n",
    "# The steps from Get data till Splitting the data are same for all ML algorithms.\n",
    "# Training the model onwards steps vary depending on the ML algorithm used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9551a411",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f657088d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concept of Linear regression\n",
    "\n",
    "# The equation used in Linear Regression is\n",
    "\n",
    "    # y = b0 + b1x1 + b2x2 + b3x3 ......bnxn\n",
    "    \n",
    "# Here b0 = y-intercept: value of y at x=0, where b0, b1 & b2 are co-efficents of linear regression.\n",
    "# Here x1, x2, x3....= independent variables & y = dependent variable\n",
    "\n",
    "# So here using the past data we already have y and x. This by applying Linear regression can help us find\n",
    "# b0, b1 & b2. \n",
    "\n",
    "#Using these coefficents for a given values of x, we can then predict the value of y in future!\n",
    "\n",
    "# Linear Regression works on the concept of \"Best-fit-line\".\n",
    "# Best-fit-line is the line that pass closest to all the datapoints in the X-Y co-ordinate system. (Similar to Trendline)\n",
    "# So equation of this best fit line is nothing but the eqution of linear regression above:\n",
    "     # y = b0 + b1x1 + b2x2 + b3x3 ......bnxn\n",
    "# With the help of this best-fit-line one can predict y for any given value of x.\n",
    "\n",
    "# To check which particular independent variable (x1, x2,x3...xn) impacts the dependent variable(y) more, we need to \n",
    "# check which coefficient obtained after LinearRegression has the highest value. Higher the coefficient more the impact.\n",
    "\n",
    "# Evaluation of a Regression model is done using Root Mean Square Error (RMSE) & R squared. Lesser the RMSE better is the \n",
    "# model.\n",
    "# Actual Value - Predicted value = error\n",
    "# So one can also infer that lesser RMSE infers to lesser error and hence more enhanced model.\n",
    "# To define a RMSE, it is a measure of the goodness of fit of LinearRegression model.\n",
    "\n",
    "\n",
    "# R squared tells you how well the regression model is predicting as compared to the mean model.\n",
    "# It lies between (0-1).\n",
    "# If R squared is close to 1 → very good model.\n",
    "# If R squared is close to 0.5 → Needs tuning.\n",
    "# If R squared is close to 0 → Not a good model.\n",
    "# If R squared is less than 0 → Mean model is better than the regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db15dd99",
   "metadata": {},
   "source": [
    "1. Import all the libraries & read the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a049ee85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9657c353",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"advertising.csv\", index_col= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64bbd525",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\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>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>230.1</th>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44.5</th>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.2</th>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151.5</th>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180.8</th>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.2</th>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94.2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177.0</th>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283.6</th>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232.1</th>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Radio  Newspaper  Sales\n",
       "TV                            \n",
       "230.1   37.8       69.2   22.1\n",
       "44.5    39.3       45.1   10.4\n",
       "17.2    45.9       69.3   12.0\n",
       "151.5   41.3       58.5   16.5\n",
       "180.8   10.8       58.4   17.9\n",
       "...      ...        ...    ...\n",
       "38.2     3.7       13.8    7.6\n",
       "94.2     4.9        8.1   14.0\n",
       "177.0    9.3        6.4   14.8\n",
       "283.6   42.0       66.2   25.5\n",
       "232.1    8.6        8.7   18.4\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3afcb326",
   "metadata": {},
   "source": [
    "2. Assumption checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12728fd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.1 Linearity : Independent variable (x) & dependent variable (Y) should have a linear relation.\n",
    "                 # Can be checked using Correlation either by heatmap/scatter plot.\n",
    "    \n",
    "# 2.2 No multi colinearity: There should not exist a linear realtion between the independent columns (x). In such cases, one\n",
    "                           # has to drop one of those columns.\n",
    "    \n",
    "# 2.3 No auto correlation: Auto correlation means if the values in a column are dependent on their previous values from the\n",
    "                          # the same column, in such case the coulmn is auto-correlated. No Auto-correlation for any \n",
    "                          # column should exist.(Only time-series data is auto correlated)\n",
    "        \n",
    "# 2.4 No outliers: Outliers are the extreme values in the dataset that skew away from the original datapoints.No outliers \n",
    "                  # should be present. Outliers can be identified using boxplot. \n",
    "\n",
    "# 2.5 No null values: Dataset should not contain null values. \n",
    "\n",
    "# 2.6 Character to numbers (only applies when we have categorical columns.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6dd3d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
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Gxkau3MbGBklJSQWG2bp1a2zfvh0DBw6Enp4ebG1tYWZmhlWrVhXrcpkMEBERKVLhnAF/f3+kp6fLHf7+/oV+tEQikQ9FEPKVvXXjxg1MnDgRc+bMQXh4OI4dO4bY2Fj4+PgU63K5tJCIiKgE6evrQ19f/731LC0toa2tna8XIDk5OV9vwVsBAQFo06YNvvrqKwBAo0aNYGhoCFdXV3z99dews7MrUozsGSAiIlIkFVR3FJGenh6cnZ0RGBgoVx4YGIjWrVsXeE5GRga0tOR/lWtrawPI61EoKvYMEBERKVLTpkN+fn7w8PBAs2bN4OLignXr1iEuLk7W7e/v74/4+Hhs3boVANCrVy+MGjUKa9euhbu7OxITEzF58mS0aNEC9vb2Rf5cJgNERESK1JQMDBw4EKmpqViwYAESExPRoEEDHD16FI6OjgCAxMREuT0HPD098fz5c6xevRpTpkyBmZkZOnXqhG+++aZYnysRitOPUIKyU+6qOwR6o4K9q7pDICpzXpxaqu4Q6D8M2gwp0fYzVhRvAt67VJz0k8raKinsGSAiIlJUNr4nlxomA0RERIr4oCIiIiISE/YMEBERKSrmMwU0HZMBIiIiRcV82qCm4zABERGRyLFngIiISBGHCdSDa9vLjsyEIHWHQG8sbDZb3SHQGwuH/KHuEOg/Ft0r2X0GBK4mICIiIjEpMz0DREREZQaHCYiIiEROZKsJmAwQEREpElnPAOcMEBERiRx7BoiIiBSJbDUBkwEiIiJFHCYgIiIiMWHPABERkSKuJiAiIhI5DhMQERGRmLBngIiISIHYnk3AZICIiEgRhwmIiIhITNgzQEREpEhkPQNMBoiIiBRxaSEREZHIiaxngHMGiIiIRI49A0RERAoEkfUMMBkgIiJSJLJkgMMEREREIseeASIiIkXcgZCIiEjkOExAREREYsKeASIiIkUi6xlgMkBERKRAEMSVDHCYgIiISOTYM0BERKSIwwREREQix2SAiIhI3LgdcRHl5ubi0KFDiIqKgkQiwUcffYQ+ffpAW1tblfERERFRCVMqGbh9+zZ69uyJhw8fok6dOhAEATExMXBwcMAff/yBmjVrqjpOIiKi0iOyngGlVhNMnDgRNWrUwIMHDxAREYFLly4hLi4O1atXx8SJE1UdIxERUemSqvDQAEr1DJw5cwYhISEwNzeXlVlYWGDJkiVo06aNyoIjIiKikqdUMqCvr4/nz5/nK3/x4gX09PQ+OCgiIiJ1EtsEQqWGCT7++GOMHj0aoaGhEAQBgiAgJCQEPj4+6N27t6pjJCIiKl1SQXWHBlAqGVi5ciVq1qwJFxcXGBgYwMDAAG3atIGTkxNWrFih6hiJiIioBCk1TGBmZobffvsNt27dQnR0NARBQL169eDk5KTq+IiIiEqfhkz8U5UP2nSoVq1aqFWrlqpiISIiKhPENmegyMmAn58fFi5cCENDQ/j5+b2z7rJlyz44MCIiIiodRU4GLl26hOzsbNn/F0YikXx4VGrgM2Y4pvj5wM7OGtdvxGDKlLk4e+5CofXbubbCt9/ORf16tZGQ8Ajffb8W637ZJnt/mMcAbNzwQ77zDI1rICsrCwAwZ7Yf5syeIvd+UlIyqlT9n4quSnzCIq9i0459uBF9G49T07AiYDY6t2ut7rDKleZD3dB2TE8YWZvhcUw8/lywDfcv3iywbtVmtdF1+uewrGkH3Qr6eBqfgrAdJ3B+wzFZnSaftkO/78bkO3dBHU/kZGWX2HWUBy2HuqHtmI9hbG2G5Jh4/LFga6H3wrFZHbhPHwSrmvaye3FhxwkEb/hTVud/n7bDp9/55Dt3bp3h4rsXHCYo2KlTpwr8//Lgs896Y9n38/DFhBkIPn8Ro0Z64Pcjv6Jh4w548CAhX/1q1Rxw5PA2rN+wA8M9J6C1S3OsXrUYj1NScfDgUVm99PRnqNegndy5bxOBt65dj4Z7t0Gy17m5uSq+OnHJzHyFOk410LdHV/jO/Frd4ZQ7DT5uhe5zPPD77E2IC4tB8yGdMHTzVKzuMhXpCan56r/OzELo1r+QFBWH7MwsVG1WB70Xj8DrjCyE7/z335FXzzKwsvOXcueK7pdPMTX8uBV6zBmGI7M34n5YDJoP6Yzhm6dhRZevCrkXrxDy5l68zsyCY7M66LvYG9kZWbi486Ss3qtnGfihs/yXFDHeCw4TiJDvpFHYuGkXNm7aCQCY8uVcdO3aHj5jhmHmrCX56o8Z7YG4B/GY8uVcAEB09G04OzfGFF8fuWRAEAQ8evT4nZ+dk5P73jpUdK4uzeHq0lzdYZRbrUd2R8Se04jYfRoA8OeCX+HUrhGaD3XD30t356ufdP0+kq7fl71++jAF9bo1h2PzunLJgAABLx6nl3j85UmbkT0Qvuc0wt7ci6MLtqFWu0ZoOdQNfxVwLxKv30eiwr2o3605HJvXkUsGeC/eYM9Awfr161fkRg8cOKBUMOqgq6uLpk0b4Ztvf5QrDww8A5dWzQo8p1VLZwQGnpEr+yvwNEZ4DYKOjg5ycnIAAEZGhrhzKxTa2tq4fPk65s5fisjI63Ln1XKqjrh74cjKeo0LFy9h1uwliI2NU+EVEqmGtq427BpUR9DaI3Llt4Ouoqpz0SYS29Z3hINzLZz4bq9cuV5FA/idXQGJthaSbtzHiWV75ZIIkqetqw37BtXxz9rDcuV596J2kdqwq++Iqs618fd3e+TK9Soa4MuzK6ClrYXEG/fx97K9ckkElU9FTgZMTU1l/y8IAg4ePAhTU1M0a5b3CzM8PBxPnz4tUtKQlZWVr7tcEAS1zDewtDSHjo4Okh+lyJUnJ6fAxta6wHNsbK2RnKxQ/1EKdHV1YWlpjqSkZNy8eRsjRvri2rVomBgbYcKEkfjn9G9o2qwLbt+OBQBcuHAJniMm4datu7CxtsIM/4kIOvMbGjXphLS0JyVzwURKqljJGNo62vm+Nb58nA4jS9NCzsoz5fwqGJobQ0tHG6eW75f1LABAyp0EHPzyZzy6+QAGRhXQyqsbRu6bizXd/ZF271FJXIrGK+xevCjCvZh6fhUMzU2gpaONk8v3y3oWgLx7sf/Ln/Do5gPoG1VAa69uGL1vHlZ390fqvaSSuJQyS2DPQME2bdok+/9p06ZhwIAB+Omnn2SPLM7NzcW4ceNgYmLy3rYCAgIwf/58uTKJlhEk2u8/t6QIgvz4kEQiyVf27vry5aEXIhB6IUL2/rngi7h44TjGj/OCr98cAMCx4/92k15DNM6HhCEmOhjDPD7D8hXrPuh6iEqOwt8LiUSxJJ8Nny2AnqEBHP7nhC7TBiLt/iNcPXweAPDw0m08vHRbVjcuLAY+fyxCq+HuODp/q4pjL18Uf+5F+T71y3/uhfu0QUi9n4Qrb+7Fg0u38UDhXoz/YxFaDe+KP8R2L5gMvN/GjRtx9uxZWSIAANra2vDz80Pr1q3x7bffvvN8f3//fMsTK1nUVSaUD5aSkoacnBzY2FrJlVtZWSC5kLH8R0nJsLFRqG9tiezsbKSmFvyNXhAEhIVFopZT9UJjycjIxLVr0XB6Rx0idcl48hy5ObkwsjKTKze0NMHLlHePMT99mPd3KfnmAxhZmqLjpH6yZECRIAiIv3wXFtVtVRJ3efT2XhhbyfcCGFqa4sV77sWTN/fi0Zt70WlSf1kyoEgQBDy8fBeWvBflnlLbEefk5CAqKipfeVRUFKTS96dT+vr6MDExkTvUtSQxOzsbERFX4NZZfta/m1s7nA8JK/CckNBwuLnJ1+/i1h7h4Vdk8wUK0rhxfSQmJRf6vp6eHurWrYWkJHaNUtmTm52LxGuxqNm2gVx5zbYNERd+q+gNSQBtfd13VrGrVxXPkzlUVpjc7FwkXIuFU9uGcuVObRsgLjymyO1IJBLovPdeOOJ58lNlwtRoglR1hyZQqmfAy8sLI0aMwO3bt9GqVSsAQEhICJYsWQIvLy+VBlgafljxC7ZsWoHw8MsICQ3HKO+hqOpQGT+vy9s3YNHX02FvbwevEZMAAD+v24ZxY73w3dK5WL9xO1q1dMYIr0EY4jFe1ubsWb4IDY3ArduxMDExxhfjR6BJ4/qYOHGmrM7SJbPx+x+BiHsQD2srS8yYMQkmJkbYuk1+chUVXUZGJuIe/rscND7hEaJj7sDUxBh2hcwBoaILXv8n+i0bi/grsXgQcQvNBneCqb0FLm4/AQBwmzoQJjaVcGDKTwCAFh5dkJ6Qgsd38u6JY/M6aDOqJ0K3/CVrs8Okfnh46RZSY5Ogb1wRrTy7wraeI36fs7nUr0+TnFt/FJ8uG4f4K3cRF3ELzQd3gqm9JS68uRddpw6EiY059k1ZCwBo6dEF6Qmpcvei7aieOL/luKzNTpP6Ie7SbaTGJsHAuAJcPN1hV88RR8R4LzTkl7iqKJUMfPfdd7C1tcUPP/yAxMREAICdnR2mTp2KKVOmvOfssmfv3sOwMK+EWTN9YWdnjWvXb6JXbw/ExcUDAGxtbVDVwV5W/969B+jV2wPffTcPY8cOR0LCI0z2nSO3rNDM1BRr1yyFra0V0tOfIzLyGjp26o+LYZGyOpWr2OHXbT/C0tIcjx+nIvRCBNq49pJ9LhXftehbGDFhmuz10lV5cy/6dHfDolma92ezrLn2ewgqmBmhw6RPYGxlhuSYh/jV61ukx+dNqDW2NoNpZQtZfYmWBG5TB6KSgxWkOVKkxT1C4NJdCNv+71I2A5OK6L14JIysTPHqeQaSbtzHxoELEX/5bqlfnya5+nsIKpoZoeOkfjC2MsOjmIfY6rUUT99xL7oq3IvjS3fJEjkg7170XewNYyszvHqegcQb9/HLwIV4ePlOqV8flS6J8K5ZckXw7NkzACjSxMF30dGr/EHnk+pkJgSpOwR6Y2Gz2eoOgd7Ife80SSpNi+7tKNH2H3dpr7K2rBSWopdFH7zp0IcmAURERGWNpoz1q4rSycC+ffuwZ88exMXF4fXr13LvRUREFHIWERFR2Se2ZECp1QQrV66El5cXrK2tcenSJbRo0QIWFha4e/cuunfvruoYiYiIqAQplQysWbMG69atw+rVq6Gnp4epU6ciMDAQEydORHo697QmIiINJ0hUd2gApZKBuLg4tG6d91jYChUq4Pnz5wAADw8P7Ny5U3XRERERqYHY9hlQKhmwtbVFamreIzIdHR0REhICAIiNjX3nFr5ERERU9iiVDHTq1AlHjuQ9uczb2xu+vr7o0qULBg4ciE8++USlARIREZU2QSpR2aEJlFpNsG7dOtm2wz4+PjA3N8fZs2fRq1cvJgNERKTxNKV7X1WU6hnQ0tKCjs6/ecSAAQMwY8YM3Lp1C7VrF+1Z2kRERJTfmjVrUL16dRgYGMDZ2RlBQe/eCC4rKwszZ86Eo6Mj9PX1UbNmTWzcuLFYn1msZODp06cYMmQIrKysYG9vj5UrV0IqlWLOnDmoWbMmQkJCih0AERFRWSMIEpUdxbF7925MnjwZM2fOxKVLl+Dq6oru3bsjLi6u0HMGDBiAEydOYMOGDbh58yZ27tyJunWL9yTgYg0TzJgxA//88w+GDx+OY8eOwdfXF8eOHcOrV69w9OhRtG+vuu0biYiI1EVdwwTLli2Dt7c3Ro4cCQBYvnw5jh8/jrVr1yIgICBf/WPHjuHMmTO4e/cuzM3NAQDVqlUr9ucWq2fgjz/+wKZNm/Ddd9/h8OHDEAQBtWvXxsmTJ5kIEBERFSArKwvPnj2TO7KysvLVe/36NcLDw9G1a1e58q5duyI4OLjAtg8fPoxmzZph6dKlqFy5MmrXro0vv/wSmZmZxYqxWMlAQkIC6tWrBwCoUaMGDAwMZNkLERFReaHK1QQBAQEwNTWVOwr6lp+SkoLc3FzY2NjIldvY2CApKanAOO/evYuzZ8/i2rVrOHjwIJYvX459+/Zh/PjxxbreYg0TSKVS6Orqyl5ra2vD0NCwWB9IRERU1qlyyxx/f3/4+fnJlenr6xdaXyKRn2cgCEK+srekUikkEgm2b98OU1NTAHlDDZ9++il+/PFHVKhQoUgxFisZEAQBnp6esot49eoVfHx88iUEBw4cKE6zREREZYoq9wfQ19d/5y//tywtLaGtrZ2vFyA5OTlfb8FbdnZ2qFy5siwRAICPPvoIgiDg4cOHqFWrVpFiLNYwwfDhw2FtbS3r5hg6dCjs7e3zdX8QERFR8ejp6cHZ2RmBgYFy5YGBgbJHAChq06YNEhIS8OLFC1lZTEwMtLS0UKVKlSJ/drF6BjZt2lSc6kRERBpJXTsH+vn5wcPDA82aNYOLiwvWrVuHuLg4+Pj4AMgbcoiPj8fWrVsBAIMHD8bChQvh5eWF+fPnIyUlBV999RVGjBhR5CECQMkdCImIiMozdT1mZ+DAgUhNTcWCBQuQmJiIBg0a4OjRo3B0dAQAJCYmyu05YGRkhMDAQEyYMAHNmjWDhYUFBgwYgK+//rpYnysRysiThXT0Kqs7BHojM+Hdu11R6VnYbLa6Q6A3clEm/qmkNxbd21Gi7cc27qKytqpfDnx/JTVjzwAREZECTXnAkKowGSAiIlJQ3G2ENZ1SDyoiIiKi8oM9A0RERArE9ghjJgNEREQKpBwmICIiIjFhzwAREZECsU0gZDJARESkgEsLiYiIRK5sbMdXejhngIiISOTYM0BERKSAwwREREQix6WFREREJCrsGSAiIlLApYVEREQix9UEREREJCrsGSAiIlIgtgmETAaIiIgUiG3OAIcJiIiIRI49A0RERArENoGQyQAREZECzhkg0VvYbLa6Q6A3ZoctVHcI9EajeoPUHQL9x6ISbp9zBoiIiEhU2DNARESkgMMEREREIiey+YMcJiAiIhI79gwQEREp4DABERGRyHE1AREREYkKewaIiIgUSNUdQCljMkBERKRAAIcJiIiISESKnQzk5ORAR0cH165dK4l4iIiI1E4qqO7QBMUeJtDR0YGjoyNyc3NLIh4iIiK1k3KY4P1mzZoFf39/pKWlqToeIiIitRMgUdmhCZSaQLhy5Urcvn0b9vb2cHR0hKGhodz7ERERKgmOiIiISp5SyUDfvn1VHAYREVHZwaWFRTB37lxVx0FERFRmaEr3vqoovbTw6dOnWL9+vdzcgYiICMTHx6ssOCIiIip5SvUMXLlyBW5ubjA1NcW9e/cwatQomJub4+DBg7h//z62bt2q6jiJiIhKjdiGCZTqGfDz84Onpydu3boFAwMDWXn37t3xzz//qCw4IiIidZCq8NAESiUDFy9exJgxY/KVV65cGUlJSR8cFBEREZUepYYJDAwM8OzZs3zlN2/ehJWV1QcHRUREpE6cQFgEffr0wYIFC5CdnQ0AkEgkiIuLw/Tp09G/f3+VBkhERFTapBLVHZpAqWTgu+++w+PHj2FtbY3MzEy0b98eTk5OMDY2xqJFi1QdIxEREZUgpYYJTExMcPbsWZw8eRIRERGQSqVo2rQp3NzcVB0fERFRqRPbswmUSgbe6tSpEzp16qSqWIiIiMoEDXnYoMoovenQiRMn8PHHH6NmzZpwcnLCxx9/jL///luVsREREakFlxYWwerVq9GtWzcYGxtj0qRJmDhxIkxMTNCjRw+sXr1a1TESERFRCVJqmCAgIAA//PADvvjiC1nZxIkT0aZNGyxatEiunIiISNNIJeKaM6BUz8CzZ8/QrVu3fOVdu3YtcP8BIiIiTSKo8NAESiUDvXv3xsGDB/OV//bbb+jVq9cHB0VERESlR6lhgo8++giLFi3C6dOn4eLiAgAICQnBuXPnMGXKFKxcuVJWd+LEiaqJlIiIqJRoysQ/VVEqGdiwYQMqVaqEGzdu4MaNG7JyMzMzbNiwQfZaIpEwGSAiIo2jKTsHqopSyUBsbKyq4yAiIiI1+aBNh4iIiMoj7kBYRA8fPsThw4cRFxeH169fy723bNmyDw6MiIhIXTRlFYCqKJUMnDhxAr1790b16tVx8+ZNNGjQAPfu3YMgCGjatKmqYyQiIqISpNTSQn9/f0yZMgXXrl2DgYEB9u/fjwcPHqB9+/b47LPPVB0jERFRqeIjjIsgKioKw4cPBwDo6OggMzMTRkZGWLBgAb755huVBkhERFTa+GyCIjA0NERWVhYAwN7eHnfu3JG9l5KSoprIiIiI1ERsOxAqNWegVatWOHfuHOrVq4eePXtiypQpuHr1Kg4cOIBWrVqpOkYiIiIqQUr1DCxbtgwtW7YEAMybNw9dunTB7t274ejoKLfpkCbxGTMct26ex4tndxAa8ifatmnxzvrtXFshNORPvHh2BzHRwRg9ykPu/WEeA5DzOj7foa+vL6tzOyakwDorVywqkWvUZM2HusE36AfMvrkJPke+hmPzOoXWrdqsNkbum4vpl37C7OhNmHDiW7h4yz9Lo8mn7bDg3vZ8h46+bklfimiERV7F+Klz0bH3EDRo0x0n/glWd0jlzude/RF48RAi44KwL3ALnFs2KbSulbUFvl27EEeD9+J6Ugj8F/oWWM/YxAizl3yFf64eRWRcEH4/uxvtOrcuoSsou8Q2Z0CpnoEaNWrI/r9ixYpYs2aNygJSh88+641l38/DFxNmIPj8RYwa6YHfj/yKho074MGDhHz1q1VzwJHD27B+ww4M95yA1i7NsXrVYjxOScXBg0dl9dLTn6Feg3Zy574dXgGAVq17QFtbW/a6Qf26OH5sF/bv/70ErlJzNfi4FbrP8cDvszchLiwGzYd0wtDNU7G6y1SkJ6Tmq/86MwuhW/9CUlQcsjOzULVZHfRePAKvM7IQvvOUrN6rZxlY2flLuXNzsrJL/HrEIjPzFeo41UDfHl3hO/NrdYdT7nTv44bpC/2wcNpSRFy4jIHDPsHPu5ajV9uBSIx/lK++rr4e0lKf4OflmzBszOcFtqmrq4MNe1cjLSUNk7yn41FCMmwr2+Dli4ySvpwyR1PG+lXlgzYdCgsLQ1RUFCQSCT766CM4OzurKq5S5TtpFDZu2oWNm3YCAKZ8ORddu7aHz5hhmDlrSb76Y0Z7IO5BPKZ8ORcAEB19G87OjTHF10cuGRAEAY8ePS70c1NS0uReT/3qC9y+HYsz/5xXxWWVG61HdkfEntOI2H0aAPDngl/h1K4Rmg91w99Ld+ern3T9PpKu35e9fvowBfW6NYdj87pyyYAAAS8ep5d4/GLl6tIcri7N1R1GuTXcZzAO7DiMfdt/AwAEzP4BbTq2wiDP/vhhUf4vaAkPEhEwK28PmH6fF/xAuX6De8O0kgkG9/RGTk5u3nkPk0roCqgsUWqY4OHDh3B1dUWLFi0wadIkTJw4Ec2bN0fbtm3x4MEDVcdYonR1ddG0aSME/n1Grjww8AxcWjUr8JxWLZ0RGChf/6/A03B2bgQdnX/zKyMjQ9y5FYp7d8Pw28EtaNKk/jvjGDK4HzZvyf/LTcy0dbVh16A67gRdlSu/HXQVVZ1rFakN2/qOcHCuhXuhUXLlehUN4Hd2BaacX4UhG76EbX1HlcVNVJJ0dXVQv3FdnDsdKld+7nQo/te8kdLtdnJ3RWTYVcxeMhVB1//E4TM7MXqSJ7S0lPpVodG4mqAIRowYgezsbERFRSEtLQ1paWmIioqCIAjw9vZWdYwlytLSHDo6Okh+JL8KIjk5BTa21gWeY2NrjeRkhfqPUqCrqwtLS3MAwM2btzFipC8+6e+FoR7j8CorC/+c/g1OTtULbLNPn24wMzPBlq17VHBV5UfFSsbQ1tHO9w3+5eN0GFmavvPcKedXYc7NzfA5/DUubA2U9SwAQMqdBBz88mdsH/U99k1cjZysbIzcNxfm1WxK4jKIVMrM3Aw6OjpIeSw/TJb6OA2W1hZKt1vFsTLcP+4EbW1tjPncFz/9sBFeY4fAx9frQ0PWOIJEdYcmUGqYICgoCMHBwahT599JXHXq1MGqVavQpk2b956flZUlN3YO5HWpSyTq+6kJgvwCEIlEkq/s3fXly0MvRCD0QoTs/XPBF3HxwnGMH+cFX785+dob4TkIx46fQmJi/rE+AvIt0JFI3rtkZ8NnC6BnaACH/zmhy7SBSLv/CFcP5w3BPLx0Gw8v3ZbVjQuLgc8fi9BquDuOzt+q4tiJSki+vxbv/nfrfbS0tJCa8gRzpiyGVCrFjSvRsLK1gvf4oVjzvWZODqeiUapnoGrVqsjOzj/RKicnB5UrV37v+QEBATA1NZU7BOlzZUL5YCkpacjJyYGNrZVcuZWVBZILGe9/lJQMGxuF+taWyM7ORmrqkwLPEQQBYWGRqFVAz0DVqpXRubMrNmzcoeRVlF8ZT54jNycXRlZmcuWGliZ4mfLu8f6nDx8j+eYDhO86hfMbjqHjpH6F1hUEAfGX78Kiuq0qwiYqUU/TniInJydfL4C5ZSWkPk4r5Kz3e/woBffvxkEq/bdz+25MLKxsLKGrK67n2qlzmGDNmjWoXr06DAwM4OzsjKCgoCKdd+7cOejo6KBJkybF/kylkoGlS5diwoQJCAsLk2WhYWFhmDRpEr777rv3nu/v74/09HS5Q6JlrEwoHyw7OxsREVfg1ll+1r+bWzucDwkr8JyQ0HC4ucnX7+LWHuHhV5CTk1PoZzVuXB+JScn5yj2HD0RycgqOHj2hxBWUb7nZuUi8FouabRvIldds2xBx4beK3pAE0H7PskG7elXxPLngZI6oLMnOzsH1y9Fo3V5+CXTr9i1w6eIVpduNuHAZVatVkeulrVazKpKTHiM7u/B/28ojdSUDu3fvxuTJkzFz5kxcunQJrq6u6N69O+Li4t55Xnp6OoYNG4bOnTsX8xPzKJXqeXp6IiMjAy1btpRNmMvJyYGOjg5GjBiBESNGyOqmpeXPUvX19eXW2wNQ6xDBDyt+wZZNKxAefhkhoeEY5T0UVR0q4+d12wAAi76eDnt7O3iNmAQA+HndNowb64Xvls7F+o3b0aqlM0Z4DcIQj/GyNmfP8kVoaARu3Y6FiYkxvhg/Ak0a18fEiTPlPlsikWD4sIHY9ute5Obmlt5Fa5Dg9X+i37KxiL8SiwcRt9BscCeY2lvg4va85Mlt6kCY2FTCgSk/AQBaeHRBekIKHt/JWxbq2LwO2ozqidAtf8na7DCpHx5euoXU2CToG1dEK8+usK3niN/nbC716yuvMjIyEffw36W58QmPEB1zB6YmxrArZD4OFd2Wn3ZgyY/zcS0yCpFhVzHA4xPYVbHF7i0HAAC+M8fBxs4a07+YJzunboO8SbcVDSuikmUl1G1QC9mvc3AnJhYAsGvzfgwdOQAzFk3B9vV74FjDAaMne+LXXziX6UMUNDRe0O9BIG8fH29vb4wcORIAsHz5chw/fhxr165FQEBAoZ8xZswYDB48GNra2jh06FCxY1QqGVi+fLkyp5VZe/cehoV5Jcya6Qs7O2tcu34TvXp7IC4uHgBga2uDqg72svr37j1Ar94e+O67eRg7djgSEh5hsu8cuWWFZqamWLtmKWxtrZCe/hyRkdfQsVN/XAyLlPtst86ucHSsgk2buYqgMNd+D0EFMyN0mPQJjK3MkBzzEL96fYv0+LxJnMbWZjCt/G93qURLArepA1HJwQrSHCnS4h4hcOkuhG0/KatjYFIRvRePhJGVKV49z0DSjfvYOHAh4i/fLfXrK6+uRd/CiAnTZK+XrloHAOjT3Q2LZk1RV1jlxp+//Q0zc1OMm+INKxtL3Iq+A5/PfWVLAa1sLGFXWX5C7MGT22X/36DJR+jVvxvi4xLg1qwvACApIRkjB0zE9AWTcej0djxKeoxt63Zj/SrxzaNR5TbCAQEBmD9/vlzZ3LlzMW/ePLmy169fIzw8HNOnT5cr79q1K4KDC9+0a9OmTbhz5w5+/fVXfP21cnt6SIQPmW2iQjp6759rQKVjhn0HdYdAb8wOW6juEOiNRvUGqTsE+o+o5Asl2v6KqkNV1pbPrQ1F6hlISEhA5cqVce7cObRu/e+uj4sXL8aWLVtw8+bNfG3funULbdu2RVBQEGrXro158+bh0KFDiIyMLFaMSs0ZiIiIwNWr/677/u2339C3b1/MmDEDr1+/VqZJIiKiMkOVcwb09fVhYmIidxQ0RPCW4rB5YavtcnNzMXjwYMyfPx+1a9f+oOtVKhkYM2YMYmJiAAB3797FwIEDUbFiRezduxdTp079oICIiIjEyNLSEtra2khKkt/1MTk5GTY2+fdAef78OcLCwvDFF19AR0cHOjo6WLBgAS5fvgwdHR2cPHky3zmFUSoZiImJkS1d2Lt3L9q3b48dO3Zg8+bN2L9/vzJNEhERlRnqWE2gp6cHZ2dnBAYGypUHBgbKDRu8ZWJigqtXryIyMlJ2+Pj4oE6dOoiMjJQ9ULAolJpAKAiCbB3q33//jY8//hgA4ODggJSUlHedSkREVOapazKdn58fPDw80KxZM7i4uGDdunWIi4uDj48PgLyl+fHx8di6dSu0tLTQoIH8smtra2sYGBjkK38fpZKBZs2a4euvv4abmxvOnDmDtWvXAgBiY2ML7MogIiKi9xs4cCBSU1OxYMECJCYmokGDBjh69CgcHfOenZKYmPjePQeUodRqgitXrmDw4MF48OAB/Pz8MHdu3tP7JkyYgNTUVOzYUfyd9LiaoOzgaoKyg6sJyg6uJihbSno1wVJH1a0mmHr/V5W1VVKU6hlo1KgRrl27lq/822+/hba29gcHRUREpE6a8rRBVVFqAuHMmTMRGBiIzMxMuXIDAwPo6r57y1ciIiIqW5TqGQgPD8eqVauQlZWFpk2bokOHDmjfvj3atm0LIyMjVcdIRERUqsrEbnylSKmegWPHjuHJkyc4ffo0+vTpg0uXLmHgwIEwNzdHq1atVB0jERFRqZJCUNmhCZR+JqW2tjZcXFxgbm6OSpUqwdjYGIcOHcKdO3dUGR8RERGVMKV6BtauXYtBgwbBzs4Orq6u+Ouvv+Dq6orw8HA8fvxY1TESERGVKnU9wlhdlOoZGD9+PKysrDBlyhT4+PjAxMRE1XERERGpjWZ07quOUj0DBw4cwJAhQ7Br1y5YW1ujZcuWmDZtGv7880+8ePFC1TESERGVKvYMFEHfvn3Rt29fAEB6ejqCgoKwb98+9OnTBxKJJN+jGomIiKjsUnoCYVpaGs6cOYPTp0/j9OnTuHbtGiwsLNC+fXtVxkdERFTqpPmfGFyuKb0D4Y0bN2Bubo527dph1KhR6NChQ7EfjEBERFQWacqSQFVRKhkYPXo0f/kTERGVE0olA1988QUA4PXr14iNjUXNmjWho6P0iAMREVGZIq5+ASVXE2RmZsLb2xsVK1ZE/fr1ZY9TnDhxIpYsWaLSAImIiEqb2FYTKJUMTJ8+HZcvX8bp06dhYGAgK3dzc8Pu3btVFhwRERGVPKX69g8dOoTdu3ejVatWkEj+nXJZr149bkdMREQajxMIi+Dx48ewtrbOV/7y5Uu55ICIiEgTiSsVUHKYoHnz5vjjjz9kr98mAL/88gtcXFxUExkRERGVCqV6BgICAtCtWzfcuHEDOTk5WLFiBa5fv47z58/jzJkzqo6RiIioVGnKxD9VUapnoHXr1jh37hwyMjJQs2ZN/PXXX7CxscH58+fh7Oys6hiJiIhKlRSCyg5NoPTmAA0bNsSWLVtUGQsREVGZoBm/wlWnWMmAlpbWeycISiQS5OTkfFBQREREVHqKlQwcPHiw0PeCg4OxatUqCILY8ikiIipvxDZnoFjJQJ8+ffKVRUdHw9/fH0eOHMGQIUOwcOFClQVHRESkDoLIBgqUmkAIAAkJCRg1ahQaNWqEnJwcREZGYsuWLahataoq4yMiIqISVuxkID09HdOmTYOTkxOuX7+OEydO4MiRI3yCIRERlRtiezZBsYYJli5dim+++Qa2trbYuXNngcMGREREmk5TlgSqSrGSgenTp6NChQpwcnLCli1bCl1aeODAAZUER0RERCWvWMnAsGHD+OwBIiIq98TVL1DMZGDz5s0lFAYREVHZIbZhAqVXExAREVH5oPR2xEREROWVpqwCUBUmA0RERArEtukQkwEiIiIFYusZ4JwBIiIikSszPQMvTi1Vdwj0xsIhf6g7BHqjUb1B6g6B3rhyY5e6Q6BSxGECIiIikeMwAREREYkKewaIiIgUSAUOExAREYmauFIBDhMQERGJHnsGiIiIFIjt2QRMBoiIiBSIbWkhhwmIiIhEjj0DRERECsS2zwCTASIiIgWcM0BERCRynDNAREREosKeASIiIgWcM0BERCRygsi2I+YwARERkcixZ4CIiEgBVxMQERGJnNjmDHCYgIiISOTYM0BERKRAbPsMMBkgIiJSILY5AxwmICIiEjn2DBARESkQ2z4DTAaIiIgUiG01AZMBIiIiBWKbQMg5A0RERCLHngEiIiIFYltNwGSAiIhIgdgmEHKYgIiISOTYM0BERKRAbMMEKukZyM3NRWRkJJ48eaKK5oiIiNRKUOF/mkCpZGDy5MnYsGEDgLxEoH379mjatCkcHBxw+vRpVcZHREREJUypZGDfvn1o3LgxAODIkSOIjY1FdHQ0Jk+ejJkzZ6o0QCIiotImFQSVHcW1Zs0aVK9eHQYGBnB2dkZQUFChdQ8cOIAuXbrAysoKJiYmcHFxwfHjx4v9mUolAykpKbC1tQUAHD16FJ999hlq164Nb29vXL16VZkmiYiIygxBhUdx7N69W/bF+tKlS3B1dUX37t0RFxdXYP1//vkHXbp0wdGjRxEeHo6OHTuiV69euHTpUrE+V6lkwMbGBjdu3EBubi6OHTsGNzc3AEBGRga0tbWVaZKIiEj0li1bBm9vb4wcORIfffQRli9fDgcHB6xdu7bA+suXL8fUqVPRvHlz1KpVC4sXL0atWrVw5MiRYn2uUqsJvLy8MGDAANjZ2UEikaBLly4AgNDQUNStW1eZJomIiMoMVa4myMrKQlZWllyZvr4+9PX15cpev36N8PBwTJ8+Xa68a9euCA4OLtJnSaVSPH/+HObm5sWKUamegXnz5mH9+vUYPXo0zp07J7sgbW3tfBdBRESkaaQQVHYEBATA1NRU7ggICMj3mSkpKcjNzYWNjY1cuY2NDZKSkooU9/fff4+XL19iwIABxbpepfcZ+PTTTwEAr169kpUNHz5c2eaIiIjKDFXuQOjv7w8/Pz+5MsVegf+SSCT5YlEsK8jOnTsxb948/Pbbb7C2ti5WjEr1DOTm5mLhwoWoXLkyjIyMcPfuXQDA7NmzZUsOiYiIKO8Xv4mJidxRUDJgaWkJbW3tfL0AycnJ+XoLFO3evRve3t7Ys2ePbB5fcSiVDCxatAibN2/G0qVLoaenJytv2LAh1q9fr0yTREREZYYqhwmKSk9PD87OzggMDJQrDwwMROvWrQs9b+fOnfD09MSOHTvQs2dPpa5XqWRg69atWLduHYYMGSK3eqBRo0aIjo5WKhAiIqKyQl07EPr5+WH9+vXYuHEjoqKi4Ovri7i4OPj4+ADIG3IYNmyYrP7OnTsxbNgwfP/992jVqhWSkpKQlJSE9PT0Yn2uUnMG4uPj4eTklK9cKpUiOztbmSY1wu6TF7H52HmkPH2OmpWtMfXzrmha27HQ+rtOXMSukxeRkPIUtuamGPVxW/Rq07gUIy4/Wg51Q9sxH8PY2gzJMfH4Y8FW3L94s8C6js3qwH36IFjVtIduBX08jU/BhR0nELzhT1md/33aDp9+55Pv3Ll1hiMnq/z+GVaFz736Y8Q4D1jZWOD2zbsImPUDwkMjC6xrZW2BqfMno37junCs4YBff9mNgNk/5KtnbGKEyTPGokvPjjAxNcbDuAQsnbsC/5wo2gxqerewyKvYtGMfbkTfxuPUNKwImI3O7Qr/pknqM3DgQKSmpmLBggVITExEgwYNcPToUTg65v2uSUxMlNtz4Oeff0ZOTg7Gjx+P8ePHy8qHDx+OzZs3F/lzlUoG6tevj6CgIFlwb+3duxf/+9//lGmyzDt24TqW7jyOmR490MTJAftOR2DcDztw8OtxsLMwzVd/z6kwrNx/AnM8e6FBNXtcjY3Hgs2/w9jQAB2a1FHDFWiuhh+3Qo85w3Bk9kbcD4tB8yGdMXzzNKzo8hXSE1Lz1X+d+QohW/9CUlQcXmdmwbFZHfRd7I3sjCxc3HlSVu/Vswz80HmK3LlMBN6tex83TF/oh4XTliLiwmUMHPYJft61HL3aDkRi/KN89XX19ZCW+gQ/L9+EYWM+L7BNXV0dbNi7GmkpaZjkPR2PEpJhW9kGL19klPTliEZm5ivUcaqBvj26wnfm1+oORyOo8xHG48aNw7hx4wp8T/EXvKoeAaBUMjB37lx4eHggPj4eUqkUBw4cwM2bN7F161b8/vvvKgmsrNl2/Dw+cf0f+rVrCgCYOtgdwdfvYM+pMEz6tHO++r8HX8GnHZzRrUV9AEAV60q4eicem44GMxkopjYjeyB8z2mE7T4NADi6YBtqtWuElkPd8NfS3fnqJ16/j8Tr92Wvnz5MQf1uzeHYvI5cMiBAwIvHxetKE7vhPoNxYMdh7Nv+GwAgYPYPaNOxFQZ59scPi9bkq5/wIBEBs5YBAPp93qvANvsN7g3TSiYY3NMbOTm5eec9LNoyKioaV5fmcHVpru4wNAqfWlgEvXr1wu7du3H06FFIJBLMmTMHUVFROHLkiGwDovIkOycXUfcT4VK/ply5S/0auHz7QYHnvM7JhZ6OfK6lr6eDa7HxyH7zDx69n7auNuwbVMftoCty5beDrqKqc+0itWFX3xFVnWvjXmiUXLleRQN8eXYFpp5fBY8NX8KufuFDPpT3Db5+47o4dzpUrvzc6VD8r3kjpdvt5O6KyLCrmL1kKoKu/4nDZ3Zi9CRPaGmp5KGqRFQESu8z4O7uDnd3d6XOLWg3JuF1NvT1dJUNp0Q9eZ6BXKkAC1NDuXILE0OkpL8s8JzWDWrgYNAldGpaBx852uHGvUQcOhuJnFwpnr7IgJWZcWmErvEqVjKGto52vm/wLx6nw8gy//DMf009vwqG5ibQ0tHGyeX7ZT0LAJByJwH7v/wJj24+gL5RBbT26obR++ZhdXd/pN7jt9KCmJmbQUdHBymP5YdmUh+nwdLaQul2qzhWRsu2zfD7/uMY87kvqtVwwOwlU6Gjo40133OpMqmHOocJ1EHpZOBDBAQEYP78+XJlM70+wSzv/uoIp8gUt3wQBKCwfSBG92qHlPSX8Fi0EYIgwNzECL3bNMbmP4P5jUcJin8ti7D/Bn75bAH0DA3g8D8nuE8bhNT7Sbhy+DwA4MGl23hw6basblxYDMb/sQithnfFH/O3qjDyckjhZkgkkg/6h1NLSwupKU8wZ8piSKVS3LgSDStbK3iPH8pkgNRGbMMERU4GKlWqVKQdkAAgLS3tne8XtBuTEH6gqKGUukrGFaGtJcnXC5D2/CUsTAwLPMdATxcLRvTG7GE9kfbsJSzNjLD/TAQMDfRQyahiaYRdLmQ8eY7cnFwYW8n3AhhamuJFyrvH+588fAwAeHTzAYwsTdFpUn9ZMqBIEAQ8vHwXltVtVRN4OfQ07SlycnLy9QKYW1ZC6uN3/51/l8ePUpCTkwOpVCoruxsTCysbS+jq6iA7O0fptomoaIqcDCxfvlxlH1rQAxpeldEhAgDQ1dHGR452CLlxF52d/30QU8j1u+jwv3dPBtTV0YaNuQkA4FjoNbRrXBtaWkVLqgjIzc5FwrVYOLVtiBvHw2TlTm0bICowvMjtSCQS6Oi/+8+YXT1HPLpZ8BwQArKzc3D9cjRat2+Bv4+elpW3bt8CJ4/9o3S7ERcu4+N+7nI9DNVqVkVy0mMmAqQ2xd0fQNMVORkQ+3MHPNxdMPOXg6hXzQ6Na1bB/jMRSExLx2cdnAEAK/adQPKT51g0qi8A4F5SKq7FxqNhjcp49vIVtv0Vgtvxj7FwZF/1XYSGOrf+KD5dNg7xV+4iLuIWmg/uBFN7S1zYfgIA0HXqQJjYmGPflLxHfLb06IL0hFQ8vpMAAHBsXgdtR/XE+S3HZW12mtQPcZduIzU2CQbGFeDi6Q67eo44MmdzqV+fJtny0w4s+XE+rkVGITLsKgZ4fAK7KrbYvSWvZ8935jjY2Flj+hfzZOfUbVALAFDRsCIqWVZC3Qa1kP06B3diYgEAuzbvx9CRAzBj0RRsX78HjjUcMHqyJ379ZU+pX195lZGRibiHCbLX8QmPEB1zB6YmxrCzLd4e9mIh5ZyB4snMzMy30ZCJicmHNlvmdGtRH+kvMrDu8D94nP4CTpWt8ePkwbC3NAMApKS/QFLav93WUqkUW4+H4H5SCnS0tdG8bjVsneGFym/qU9Fd/T0EFc2M0HFSPxhbmeFRzENs9VqKp/EpAABjazOYVv6361qiJUHXqQNRycEK0hwp0uIe4fjSXbj4JnkAAAOTiui72BvGVmZ49TwDiTfu45eBC/Hw8p1Svz5N8udvf8PM3BTjpnjDysYSt6LvwOdzX9lSQCsbS9hVlt9D/eDJ7bL/b9DkI/Tq3w3xcQlwa9YXAJCUkIyRAyZi+oLJOHR6Ox4lPca2dbuxfhXnbqjKtehbGDFhmuz10lXrAAB9urth0awphZ0mamLrGZAISsz8efnyJaZNm4Y9e/YgNTX/pi+5ucVfOvfq3Pb3V6JSsXDIH+oOgd44kHH7/ZWoVFy5sUvdIdB/6FrWKNH269u0VFlb1x+Fvr+Smik1rX3q1Kk4efIk1qxZA319faxfvx7z58+Hvb09tm5lNk9ERJpNKggqOzSBUsMER44cwdatW9GhQweMGDECrq6ucHJygqOjI7Zv344hQ4aoOk4iIqJSI7ZhAqV6BtLS0lC9enUAefMD3i4lbNu2Lf75R/lZxURERFT6lEoGatSogXv37gEA6tWrhz178mb9HjlyBGZmZqqKjYiISC3ENkygVDLg5eWFy5cvA8jbQOjt3AFfX1989dVXKg2QiIiotAkq/E8TKDVnwNfXV/b/HTt2RHR0NMLCwlCzZk00btxYZcERERFRyStWz0BoaCj+/PNPubKtW7eiffv28PHxwY8//pjvAURERESahsME7zBv3jxcufLvo2SvXr0Kb29vuLm5wd/fH0eOHEFAQIDKgyQiIipNYhsmKFYyEBkZic6dO8te79q1Cy1btsQvv/wCX19frFy5UjaZkIiIiDRDseYMPHnyBDY2/241eubMGXTr1k32unnz5njwgA96ISIizSYI0vdXKkeK1TNgY2OD2Ni8h4u8fv0aERERcHFxkb3//Plz6OqW3acPEhERFYUUgsoOTVCsnoFu3bph+vTp+Oabb3Do0CFUrFgRrq6usvevXLmCmjVrqjxIIiKi0qTEY3s0WrGSga+//hr9+vVD+/btYWRkhC1btkBPT0/2/saNG9G1a1eVB0lEREQlp1jJgJWVFYKCgpCeng4jIyNoa2vLvb93714YGRmpNEAiIqLSpind+6qi1KZDpqamBZabm5t/UDBERERlgdiGCZTajpiIiIjKD6V6BoiIiMozTdk5UFWYDBARESnQlJ0DVYXDBERERCLHngEiIiIFYptAyGSAiIhIgdiWFnKYgIiISOTYM0BERKSAwwREREQix6WFREREIie2ngHOGSAiIhI59gwQEREpENtqAiYDRERECjhMQERERKLCngEiIiIFXE1AREQkcnxQEREREYkKewaIiIgUcJiAiIhI5LiagIiIiESFPQNEREQKxDaBkMkAERGRArENEzAZICIiUiC2ZIBzBoiIiESOPQNEREQKxNUvAEgEsfWFlJCsrCwEBATA398f+vr66g5H9Hg/yg7ei7KD94IKw2RARZ49ewZTU1Okp6fDxMRE3eGIHu9H2cF7UXbwXlBhOGeAiIhI5JgMEBERiRyTASIiIpFjMqAi+vr6mDt3LifllBG8H2UH70XZwXtBheEEQiIiIpFjzwAREZHIMRkgIiISOSYDREREIsdkgIiISOSYDJAoeHp6om/fvrLXHTp0wOTJk9UWD5E6bN68GWZmZuoOg8ogJgNFJJFI3nl0794durq6+PXXXws8f8yYMWjUqFEpR615PD09ZT9THR0dVK1aFWPHjsWTJ09U+jkHDhzAwoULVdpmWfD257dkyRK58kOHDkEikagpKlKV5ORkjBkzBlWrVoW+vj5sbW3h7u6O8+fPqzs00nBMBoooMTFRdixfvhwmJiZyZbt27ULPnj2xadOmfOdmZmZi165d8Pb2VkPkmqdbt25ITEzEvXv3sH79ehw5cgTjxo1T6WeYm5vD2NhYpW2WFQYGBvjmm29UnkCVN9nZ2eoOodj69++Py5cvY8uWLYiJicHhw4fRoUMHpKWlqTs00nBMBorI1tZWdpiamkIikeQr8/b2xqlTp3Dv3j25c/ft24dXr15h6NCh6glew7z9xlOlShV07doVAwcOxF9//QUAyM3Nhbe3N6pXr44KFSqgTp06WLFihdz5ubm58PPzg5mZGSwsLDB16lQobqehOEzw5MkTDBs2DJUqVULFihXRvXt33Lp1q8SvtSS4ubnB1tYWAQEBhdYJDg5Gu3btUKFCBTg4OGDixIl4+fIlAGDVqlVo2LChrO7bXoUff/xRVubu7g5/f38AwOXLl9GxY0cYGxvDxMQEzs7OCAsLA/Bvt/ShQ4dQu3ZtGBgYoEuXLnjw4IGsrTt37qBPnz6wsbGBkZERmjdvjr///lsu3mrVqmHhwoUYPHgwjIyMYG9vj1WrVsnVSU9Px+jRo2FtbQ0TExN06tQJly9flr0/b948NGnSBBs3bkSNGjWgr6+f789FWfb06VOcPXsW33zzDTp27AhHR0e0aNEC/v7+6NmzJwBg2bJlaNiwIQwNDeHg4IBx48bhxYsX72z3yJEjcHZ2hoGBAWrUqIH58+cjJydH9v68efNkPRH29vaYOHFiiV4nqQeTARXq0aMHbG1tsXnzZrnyjRs3om/fvrCwsFBPYBrs7t27OHbsGHR1dQEAUqkUVapUwZ49e3Djxg3MmTMHM2bMwJ49e2TnfP/999i4cSM2bNiAs2fPIi0tDQcPHnzn53h6eiIsLAyHDx/G+fPnIQgCevTooZHfHrW1tbF48WKsWrUKDx8+zPf+1atX4e7ujn79+uHKlSvYvXs3zp49iy+++AJAXqJ0/fp1pKSkAADOnDkDS0tLnDlzBgCQk5OD4OBgtG/fHgAwZMgQVKlSBRcvXkR4eDimT58uu18AkJGRgUWLFmHLli04d+4cnj17hkGDBsnef/HiBXr06IG///4bly5dgru7O3r16oW4uDi5uL/99ls0atQIERER8Pf3h6+vLwIDAwEAgiCgZ8+eSEpKwtGjRxEeHo6mTZuic+fOct+ab9++jT179mD//v2IjIxUwU+79BgZGcHIyAiHDh1CVlZWgXW0tLSwcuVKXLt2DVu2bMHJkycxderUQts8fvw4hg4diokTJ+LGjRv4+eefsXnzZixatAhA3heZH374AT///DNu3bqFQ4cOySWKVI4IVGybNm0STE1NC3xv2rRpgqOjoyCVSgVBEIS7d+8KEolEOH78eClGqLmGDx8uaGtrC4aGhoKBgYEAQAAgLFu2rNBzxo0bJ/Tv31/22s7OTliyZInsdXZ2tlClShWhT58+srL27dsLkyZNEgRBEGJiYgQAwrlz52Tvp6SkCBUqVBD27NmjuosrBcOHD5ddZ6tWrYQRI0YIgiAIBw8eFN7+dffw8BBGjx4td15QUJCgpaUlZGZmClKpVLC0tBT27dsnCIIgNGnSRAgICBCsra0FQRCE4OBgQUdHR3j+/LkgCIJgbGwsbN68ucB4Nm3aJAAQQkJCZGVRUVECACE0NLTQ66hXr56watUq2WtHR0ehW7ducnUGDhwodO/eXRAEQThx4oRgYmIivHr1Sq5OzZo1hZ9//lkQBEGYO3euoKurKyQnJxf6uWXdvn37hEqVKgkGBgZC69atBX9/f+Hy5cuF1t+zZ49gYWEhe634b5erq6uwePFiuXO2bdsm2NnZCYIgCN9//71Qu3Zt4fXr16q9ECpz2DOgYt7e3rh//z5OnjwJIK9XoEqVKnBzc1NzZJqjY8eOiIyMRGhoKCZMmAB3d3dMmDBB9v5PP/2EZs2awcrKCkZGRvjll19k3yLT09ORmJgIFxcXWX0dHR00a9as0M+LioqCjo4OWrZsKSuzsLBAnTp1EBUVVQJXWDq++eYbbNmyBTdu3JArDw8Px+bNm2XfNI2MjODu7g6pVIrY2FhIJBK0a9cOp0+fxtOnT3H9+nX4+PggNzcXUVFROH36NJo2bQojIyMAgJ+fH0aOHAk3NzcsWbIEd+7ckfs8xZ9/3bp1YWZmJvvZvnz5ElOnTkW9evVgZmYGIyMjREdH5+sZ+O89ffv6bRvh4eF48eIFLCws5K4rNjZWLh5HR0dYWVl94E9Wffr374+EhAQcPnwY7u7usnvxtjfy1KlT6NKlCypXrgxjY2MMGzYMqampsiEgReHh4ViwYIHcz2zUqFFITExERkYGPvvsM2RmZqJGjRoYNWoUDh48KDeEQOUHkwEVq1WrFlxdXbFp0yZIpVJs2bIFXl5e0NLij7qoDA0N4eTkhEaNGmHlypXIysrC/PnzAQB79uyBr68vRowYgb/++guRkZHw8vLC69evlf48oZBxY0EQNHoGfrt27eDu7o4ZM2bIlUulUowZMwaRkZGy4/Lly7h16xZq1qwJIG+o4PTp0wgKCkLjxo1hZmaGdu3a4cyZMzh9+jQ6dOgga2/evHm4fv06evbsiZMnT6JevXr5hmUK+jm+Lfvqq6+wf/9+LFq0CEFBQYiMjETDhg2LdE/ftiGVSmFnZyd3TZGRkbh58ya++uorWX1DQ8Oi/fDKsLfzLubMmYPg4GB4enpi7ty5uH//Pnr06IEGDRpg//79CA8Pl83zKGy4SyqVYv78+XI/s6tXr+LWrVswMDCAg4MDbt68iR9//BEVKlTAuHHj0K5dO40cPqN301F3AOWRt7c3xo4diz59+uDhw4fw8vJSd0gabe7cuejevTvGjh2LoKAgtG7dWm51wX+/+ZmamsLOzg4hISFo164dgLwx7rdjyAWpV68ecnJyEBoaitatWwMAUlNTERMTg48++qgEr6zkLVmyBE2aNEHt2rVlZU2bNsX169fh5ORU6HkdOnTApEmTsG/fPtkv/vbt2+Pvv/9GcHAwJk2aJFe/du3aqF27Nnx9ffH5559j06ZN+OSTTwDk/fzDwsLQokULAMDNmzfx9OlT1K1bFwAQFBQET09PWf0XL17km4QLACEhIflev22jadOmSEpKgo6ODqpVq1b0H1A5UK9ePRw6dAhhYWHIycnB999/L/vy8d+5NAVp2rQpbt68+c4/CxUqVEDv3r3Ru3dvjB8/HnXr1sXVq1cL/ftEmolfV0vAZ599Bl1dXYwZMwadO3cW3T9OqtahQwfUr18fixcvhpOTE8LCwnD8+HHExMRg9uzZuHjxolz9SZMmYcmSJTh48CCio6Mxbtw4PH36tND2a9WqhT59+mDUqFE4e/YsLl++jKFDh6Jy5cro06dPCV9dyWrYsCGGDBkiN/N+2rRpOH/+PMaPH4/IyEjcunULhw8flhuKadCgASwsLLB9+3ZZMtChQwccOnQImZmZaNu2LYC8ZbNffPEFTp8+jfv37+PcuXO4ePGiXBKlq6uLCRMmIDQ0FBEREfDy8kKrVq1kyYGTkxMOHDgg66EYPHgwpFJpvms5d+4cli5dipiYGPz444/Yu3evLClxc3ODi4sL+vbti+PHj+PevXsIDg7GrFmzZCsbNF1qaio6deqEX3/9FVeuXEFsbCz27t2LpUuXok+fPqhZsyZycnKwatUq3L17F9u2bcNPP/30zjbnzJmDrVu3ynp3oqKisHv3bsyaNQtA3mqQDRs24Nq1a7I2K1SoAEdHx9K4ZCpN6p60oIneNYHwrdGjRwsAhB07dpROUOXEfyfA/df27dsFPT094d69e4Knp6dgamoqmJmZCWPHjhWmT58uNG7cWFY3OztbmDRpkmBiYiKYmZkJfn5+wrBhwwqdQCgIgpCWliZ4eHgIpqamQoUKFQR3d3chJiam5C60hBT087t3756gr68v/Pev+4ULF4QuXboIRkZGgqGhodCoUSNh0aJFcuf1799f0NbWFtLT0wVBEASpVCqYm5sLzZo1k9XJysoSBg0aJDg4OAh6enqCvb298MUXXwiZmZmCIPz7d2X//v1CjRo1BD09PaFTp07CvXv3ZG3ExsYKHTt2FCpUqCA4ODgIq1evznd/HB0dhfnz5wsDBgwQKlasKNjY2AjLly+Xi/fZs2fChAkTBHt7e0FXV1dwcHAQhgwZIsTFxQmCkDeB8L9/TjTNq1evhOnTpwtNmzYVTE1NhYoVKwp16tQRZs2aJWRkZAiCIAjLli0T7OzsZH+Gt27dKgAQnjx5IghCwf92HTt2TGjdurVQoUIFwcTERGjRooWwbt06QRDyJp62bNlSMDExEQwNDYVWrVoJf//9d2leNpUSiSBo0EJbItIomzdvxuTJk9/ZM1MU1apVw+TJk7mFNFEJ4TABERGRyDEZICIiEjkOExAREYkcewaIiIhEjskAERGRyDEZICIiEjkmA0RERCLHZICIiEjkmAwQERGJHJMBIiIikWMyQEREJHL/B8agfz5O47mPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.1,2.2, & 2.3 (Check for Linearity, Multi colinearity & Auto correlation)\n",
    "\n",
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3777182e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The threshold for linear correlation = 0.1\n",
    "# The relation in heatmap below 0.1 is NOT correlated\n",
    "# The relation in heatmap above 0.1 is linearly correlated\n",
    "# The relation in heatmap = 0 is NOT correlated\n",
    "\n",
    "# In the boxplot above \n",
    "# Independent col : Newspaper, Radio, TV \n",
    "# Dependent col : Sales\n",
    "\n",
    "# So in the plot above, the correlation of Sales wrt to Newspaper, Radio & TV are 0.16,0.35 & 0.9 (leftmost col in \n",
    "# the boxplot) and all of them are greater than 0.1 hence there exists linearity and correlation. Linearity verified.\n",
    "\n",
    "# Now let us check for 'Multi correlation',(look at 1st 3 cols in the plot above) the correlation between Newspaper\n",
    "# & TV for one scenario is 0.057 > 0.1. Hence there exists Multi correlation b/w Newspaper & TV.However, one should not\n",
    "# eliminate the columns since we have lack of columns. This is called Feature selection/variable selection. \n",
    "\n",
    "# Also since the values in every independent coulmn above are not anyhow correlated to their previous value we have no \n",
    "# autocorrelation as well.(Only time-series data is auto correlated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2aa9ced",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.4 Check for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2b078e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x23ce52fbb90>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5308ed0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5314e50>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5315950>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce500c510>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce4fb77d0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce531d210>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce531dd10>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x23ce5309a50>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce530a510>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5316490>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5262750>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce4fb6b10>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce4fb5e90>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce531e790>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce531f290>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x23ce52e2350>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5314410>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce500d190>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce531c690>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x23ce530af50>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce500eb90>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5316b50>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce531fe90>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x23ce52ef9d0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce500ded0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce5317810>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce528d450>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68515356",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new = data[data[\"Newspaper\"]< 90]    # Fetching data below 90 so that we \n",
    "                                          # exclude the outlier in Newspaper column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e1d9e16",
   "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",
       "    <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>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.0</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>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "0    230.1   37.8       69.2   22.1\n",
       "1     44.5   39.3       45.1   10.4\n",
       "2     17.2   45.9       69.3   12.0\n",
       "3    151.5   41.3       58.5   16.5\n",
       "4    180.8   10.8       58.4   17.9\n",
       "..     ...    ...        ...    ...\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1   14.0\n",
       "197  177.0    9.3        6.4   14.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   18.4\n",
       "\n",
       "[198 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4512aa97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x23ce5393d10>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a0a10>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a4d50>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a5810>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53b5910>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53b6410>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53c2690>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53c3210>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x23ce53a16d0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a22d0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a6290>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a6c90>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53b6e90>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53b79d0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53c3c10>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53d0790>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x23ce5393110>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a42d0>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53b4f10>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53c1b50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x23ce53a2e10>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53a7850>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53c0450>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53d1290>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x23ce51a6c90>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53b4310>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53c0f50>,\n",
       "  <matplotlib.lines.Line2D at 0x23ce53d1d10>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "365a97e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.5 Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67c6f7ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TV           0\n",
       "Radio        0\n",
       "Newspaper    0\n",
       "Sales        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us check the total number of null values in each column.\n",
    "\n",
    "data_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92c19fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we have got No null values from above, we have no need to do Null Value treatment on data_new\n",
    "# However, if we have got some null values, we can call the following Null_Value_Treatment function where we \n",
    "# replace integer and float columns having null values with mean and rest other coulmns having null values with mode.\n",
    "\n",
    "def Null_Value_Treatment(data_new):\n",
    "    for i in data_new.columns:\n",
    "        if data_new[i].dtypes == \"int64\" or data_new[i].dtypes == \"float64\":\n",
    "            data_new[i].fillna(data_new[i].mean(),inplace=True)\n",
    "        else:\n",
    "            data_new[i].fillna(data_new[i].mode()[0],inplace=True)\n",
    "    return data_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cb281e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Null_Value_Treatment(data_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1a08b5e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique value for coulmn: TV\n",
      "\n",
      "[230.1  44.5  17.2 151.5 180.8   8.7  57.5 120.2   8.6 199.8  66.1 214.7\n",
      "  23.8  97.5 204.1 195.4 281.4  69.2 147.3 218.4 237.4  13.2 228.3  62.3\n",
      " 262.9 142.9 240.1 248.8  70.6 292.9 112.9  97.2 265.6  95.7 290.7 266.9\n",
      "  74.7  43.1 228.  202.5 177.  293.6 206.9  25.1 175.1  89.7 239.9 227.2\n",
      "  66.9 100.4 216.4 182.6 262.7 198.9   7.3 136.2 210.8 210.7  53.5 261.3\n",
      " 239.3 102.7 131.1  69.   31.5 139.3 216.8 199.1 109.8  26.8 129.4 213.4\n",
      "  16.9  27.5 120.5   5.4 116.   76.4 239.8  75.3  68.4 213.5 193.2  76.3\n",
      " 110.7  88.3 134.3  28.6 217.7 250.9 107.4 163.3 197.6 184.9 289.7 135.2\n",
      " 222.4 280.2 187.9 238.2 137.9  25.   90.4  13.1 255.4 225.8 241.7 175.7\n",
      " 209.6  78.2  75.1 139.2 125.7  19.4 141.3  18.8 224.  123.1 229.5  87.2\n",
      "   7.8  80.2 220.3  59.6   0.7 265.2   8.4 219.8  36.9  48.3  25.6 273.7\n",
      "  43.   73.4 193.7 220.5 104.6  96.2 140.3 243.2  38.   44.7 280.7 121.\n",
      " 171.3 187.8   4.1  93.9 149.8  11.7 131.7 172.5  85.7 188.4 163.5 117.2\n",
      " 234.5  17.9 206.8 215.4 284.3  50.  164.5  19.6 168.4 276.9 248.4 170.2\n",
      " 276.7 165.6 156.6 218.5  56.2 287.6 253.8 205.  139.5 191.1 286.   18.7\n",
      "  39.5  75.5 166.8 149.7  38.2  94.2 283.6 232.1]\n",
      "\n",
      "Unique value for coulmn: Radio\n",
      "\n",
      "[37.8 39.3 45.9 41.3 10.8 48.9 32.8 19.6  2.1  2.6  5.8 24.  35.1  7.6\n",
      " 32.9 47.7 39.6 20.5 23.9 27.7  5.1 15.9 16.9 12.6  3.5 29.3 16.7 27.1\n",
      " 16.  28.3 17.4  1.5 20.   1.4  4.1 43.8 49.4 26.7 37.7 22.3 33.4  8.4\n",
      " 25.7 22.5  9.9 41.5 15.8 11.7  3.1  9.6 41.7 46.2 28.8 28.1 19.2 49.6\n",
      " 29.5  2.  42.7 15.5 29.6 42.8  9.3 24.6 14.5 27.5 43.9 30.6 14.3 33.\n",
      "  5.7 43.7  1.6 28.5 29.9  7.7 20.3 44.5 43.  18.4 40.6 25.5 47.8  4.9\n",
      " 33.5 36.5 14.  31.6 21.  42.3  4.3 10.1 17.2 34.3 46.4 11.   0.3  0.4\n",
      " 26.9  8.2 38.  15.4 20.6 46.8 35.   0.8 36.9 26.8 21.7  2.4 34.6 32.3\n",
      " 11.8 38.9  0.  49.  12.   2.9 27.2 38.6 47.  39.  28.9 25.9 17.  35.4\n",
      " 33.2 14.8  1.9  7.3 40.3 25.8 13.9 23.3 39.7 21.1 11.6 43.5  1.3 18.1\n",
      " 35.8 36.8 14.7  3.4 37.6  5.2 23.6 10.6 20.9 20.1  7.1 30.2  7.8  2.3\n",
      " 10.   5.4 21.3 45.1 28.7 12.1 41.1 42.  35.6  3.7  8.6]\n",
      "\n",
      "Unique value for coulmn: Newspaper\n",
      "\n",
      "[69.2 45.1 69.3 58.5 58.4 75.  23.5 11.6  1.  21.2 24.2  4.  65.9  7.2\n",
      " 46.  52.9 55.8 18.3 19.1 53.4 49.6 26.2 19.5 12.6 22.9 40.8 43.2 38.6\n",
      " 30.   0.3  7.4  8.5  5.  45.7 35.1 32.  31.6 38.7  1.8 26.4 43.3 31.5\n",
      " 35.7 18.5 49.9 36.8 34.6  3.6 39.6 58.7 15.9 60.  41.4 16.6 37.7  9.3\n",
      " 21.4 54.7 27.3  8.4 28.9  0.9  2.2 10.2 11.  27.2 31.7 19.3 31.3 13.1\n",
      " 89.4 20.7 14.2  9.4 23.1 22.3 36.9 32.5 35.6 33.8 65.7 16.  63.2 73.4\n",
      " 51.4 33.  59.  72.3 10.9  5.9 22.  51.2 45.9 49.8 17.9  5.3 29.7 23.2\n",
      " 25.6  5.5 56.5  2.4 10.7 34.5 52.7 14.8 79.2 46.2 50.4 15.6 12.4 74.2\n",
      " 25.9 50.6  9.2  3.2 43.1  8.7 43.   2.1 65.6 59.7 20.5  1.7 12.9 75.6\n",
      " 37.9 34.4 38.9  9.  44.3 11.9 20.6 37.  48.7  9.5  5.7 50.5 24.3 45.2\n",
      " 30.7 49.3  5.4 84.8 21.6 19.4 57.6  6.4 18.4 47.4 17.  12.8 41.8 20.3\n",
      " 35.2 23.7 17.6  8.3 27.4 71.8 19.6 26.6 18.2  3.7 23.4  5.8  6.  13.8\n",
      "  8.1 66.2]\n",
      "\n",
      "Unique value for coulmn: Sales\n",
      "\n",
      "[22.1 10.4 12.  16.5 17.9  7.2 11.8 13.2  4.8 15.6 12.6 17.4  9.2 13.7\n",
      " 19.  22.4 24.4 11.3 14.6 18.  17.5  5.6 20.5  9.7 17.  15.  20.9 18.9\n",
      " 10.5 21.4 11.9 17.8 25.4 14.7 10.1 21.5 16.6 17.1 20.7  8.5 16.1 10.6\n",
      " 23.2 19.8 16.4 10.7 22.6 21.2 20.2 23.7  5.5 23.8 18.4  8.1 24.2 14.\n",
      " 16.  11.  13.4 22.3 18.3 12.4  8.8  8.7  6.9 14.2  5.3 17.3 13.6 21.7\n",
      " 12.9 16.7  7.3 19.4 22.2 11.5 16.9 17.2 19.7 21.8 12.2  9.4 15.9  6.6\n",
      " 15.5  7.  15.2 24.7  1.6 17.7  5.7 19.6 10.8 11.6  9.5 20.8  9.6 10.9\n",
      " 19.2 20.1 12.3 10.3 18.2 20.6  3.2 15.3 13.3 19.9  8.  20.   8.4  7.6\n",
      " 27.  16.8 17.6 26.2  6.7  5.9 14.8 25.5]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check for special characters \n",
    "\n",
    "for i in data_new.columns:\n",
    "    print(f\"Unique value for coulmn: {i}\\n\\n{data_new[i].unique()}\\n\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c93fc307",
   "metadata": {},
   "outputs": [],
   "source": [
    "# So here we can infer that we have no special characters as well. \n",
    "# Hence Null value & Special character check. Verified\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5aa130e",
   "metadata": {},
   "source": [
    "3. Splitting the data using train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a27ea0f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data_new[[\"TV\",\"Radio\",\"Newspaper\"]]    ## While assigning multiple parameters we use\n",
    "                                            ## mutiple brackets\n",
    "y = data_new[[\"Sales\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34c3e188",
   "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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper\n",
       "0    230.1   37.8       69.2\n",
       "1     44.5   39.3       45.1\n",
       "2     17.2   45.9       69.3\n",
       "3    151.5   41.3       58.5\n",
       "4    180.8   10.8       58.4\n",
       "..     ...    ...        ...\n",
       "195   38.2    3.7       13.8\n",
       "196   94.2    4.9        8.1\n",
       "197  177.0    9.3        6.4\n",
       "198  283.6   42.0       66.2\n",
       "199  232.1    8.6        8.7\n",
       "\n",
       "[198 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b1292a5a",
   "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>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales\n",
       "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",
       "\n",
       "[198 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac38f804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198, 3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape ##Indicates 3 columns in x for the given example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6dc0815f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape  ##Indicates 1 column in y for the given example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "915a7825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "761b36b3",
   "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 = 0.2) \n",
    "\n",
    "\n",
    "# Interpreting the above code.\n",
    "# Here we are using 'sklearn' library which is using another sub library 'model_selection' to import \n",
    "# a splitting fn 'train_test_split' which splits the independent variable 'x' & dependent variable 'y'into train \n",
    "# and test sizes based on the ratio 70-80 % for train size of each and 20 % (0.2) for the test size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5a92ffaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(158, 3)\n",
      "(40, 3)\n",
      "\n",
      "(158, 1)\n",
      "(40, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print()\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4499d717",
   "metadata": {},
   "outputs": [],
   "source": [
    "# So if you compare this split with the original shape of x and y, we have \n",
    "# x.shape = 198 splitted in x_train = 158, x_test = 40\n",
    "# y.shape = 198 splitted in y_train = 158, y_test = 40"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dee0e2e6",
   "metadata": {},
   "source": [
    "4. Importing LinearRegression function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9f680a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once done with splitting the data, we have to use Linear Regression on the trained data using fit function.\n",
    "# But before that we need to call LinearRegression function by importing it from sklearn.linear_model library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "377b4146",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f88dc6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" 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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "13dd0cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = b0 + b1x1 + b2x2 + b3x3 ...Equation for Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "abbaffdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.05381805 0.10159698 0.00186049]]\n"
     ]
    }
   ],
   "source": [
    "print(lm.coef_)     ##Here lm.coef will return all the coefficients (b1, b2 & b3)\n",
    "                    ##that we obtain after performing LinearRegression using LinearRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cddc9df6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here the value of coefficient b2=0.10159698 is higher than the other coefficients.\n",
    "# From this we can conclude that higher b2 corresponds to larger impact on y.\n",
    "# Hence for the above example, investment in adversising for Radio will give us higher return at Sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b99cc2f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.76105011]\n"
     ]
    }
   ],
   "source": [
    "print(lm.intercept_)  # returns the value of y-intercept (b0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de44abf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To summarise the above result,\n",
    "# I/p to a LinearRegression ---> x_train, y_train\n",
    "# O/p of a LinearRegression ---> b0, b1, b2, b3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "53971b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5043f3ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 =250\n",
    "x2= 14\n",
    "x3 = 67\n",
    "\n",
    "y = 4.76105011 + (0.05381805*x1)+(0.10159698*x2)+(0.00186049*x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fedf9c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.76257316"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4161fe",
   "metadata": {},
   "source": [
    "5. Predicting the y_pred based on x_test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cd08052e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred= lm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9ab209c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8.08582129],\n",
       "       [11.23169063],\n",
       "       [16.4456282 ],\n",
       "       [19.39121801],\n",
       "       [10.79384298],\n",
       "       [16.45502156],\n",
       "       [17.8845832 ],\n",
       "       [ 8.6647108 ],\n",
       "       [ 9.93272352],\n",
       "       [17.15031685],\n",
       "       [ 8.10690585],\n",
       "       [20.07766535],\n",
       "       [10.66075919],\n",
       "       [10.79062784],\n",
       "       [16.79853713],\n",
       "       [18.14214081],\n",
       "       [21.2493726 ],\n",
       "       [24.70913009],\n",
       "       [15.15215266],\n",
       "       [ 8.80348485],\n",
       "       [ 6.17083387],\n",
       "       [21.60137297],\n",
       "       [13.00028066],\n",
       "       [19.17527852],\n",
       "       [15.76206398],\n",
       "       [17.11989612],\n",
       "       [18.01167241],\n",
       "       [22.10680312],\n",
       "       [21.23545151],\n",
       "       [17.98361023],\n",
       "       [23.26366751],\n",
       "       [18.81344571],\n",
       "       [13.31106296],\n",
       "       [20.83832031],\n",
       "       [ 9.69984471],\n",
       "       [12.15133425],\n",
       "       [13.88517389],\n",
       "       [13.10215136],\n",
       "       [15.45188371],\n",
       "       [16.85842284]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d2cba0",
   "metadata": {},
   "source": [
    "6. Constructing a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fd064e99",
   "metadata": {
    "scrolled": true
   },
   "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>actual sales</th>\n",
       "      <th>predicted sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>7.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>41.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.085821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>23.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>11.231691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>149.7</td>\n",
       "      <td>35.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.3</td>\n",
       "      <td>16.445628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>182.6</td>\n",
       "      <td>46.2</td>\n",
       "      <td>58.7</td>\n",
       "      <td>21.2</td>\n",
       "      <td>19.391218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>97.5</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>10.793843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>206.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>19.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>16.455022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>234.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>84.8</td>\n",
       "      <td>16.9</td>\n",
       "      <td>17.884583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>50.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.664711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>75.5</td>\n",
       "      <td>10.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>9.932724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>193.2</td>\n",
       "      <td>18.4</td>\n",
       "      <td>65.7</td>\n",
       "      <td>20.2</td>\n",
       "      <td>17.150317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5.4</td>\n",
       "      <td>29.9</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>8.106906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>219.8</td>\n",
       "      <td>33.5</td>\n",
       "      <td>45.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>20.077665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>89.7</td>\n",
       "      <td>9.9</td>\n",
       "      <td>35.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>10.660759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>36.9</td>\n",
       "      <td>38.6</td>\n",
       "      <td>65.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>10.790628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>206.9</td>\n",
       "      <td>8.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>17.9</td>\n",
       "      <td>16.798537</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>18.4</td>\n",
       "      <td>18.142141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>255.4</td>\n",
       "      <td>26.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>19.8</td>\n",
       "      <td>21.249373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>276.9</td>\n",
       "      <td>48.9</td>\n",
       "      <td>41.8</td>\n",
       "      <td>27.0</td>\n",
       "      <td>24.709130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>147.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>19.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>15.152153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>25.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>43.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.803485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>4.1</td>\n",
       "      <td>11.6</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.170834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>220.3</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>21.601373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>149.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>24.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>13.000281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>184.9</td>\n",
       "      <td>43.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>19.175279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>197.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>15.762064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>218.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>27.4</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.119896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>166.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>19.6</td>\n",
       "      <td>18.011672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>250.9</td>\n",
       "      <td>36.5</td>\n",
       "      <td>72.3</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22.106803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>248.4</td>\n",
       "      <td>30.2</td>\n",
       "      <td>20.3</td>\n",
       "      <td>20.2</td>\n",
       "      <td>21.235452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>202.5</td>\n",
       "      <td>22.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>16.6</td>\n",
       "      <td>17.983610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>261.3</td>\n",
       "      <td>42.7</td>\n",
       "      <td>54.7</td>\n",
       "      <td>24.2</td>\n",
       "      <td>23.263668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>228.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>26.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.813446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>102.7</td>\n",
       "      <td>29.6</td>\n",
       "      <td>8.4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.311063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>290.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>17.8</td>\n",
       "      <td>20.838320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>90.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>23.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.699845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>48.3</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>11.6</td>\n",
       "      <td>12.151334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>74.7</td>\n",
       "      <td>49.4</td>\n",
       "      <td>45.7</td>\n",
       "      <td>14.7</td>\n",
       "      <td>13.885174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>85.7</td>\n",
       "      <td>35.8</td>\n",
       "      <td>49.3</td>\n",
       "      <td>13.3</td>\n",
       "      <td>13.102151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>142.9</td>\n",
       "      <td>29.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.451884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>163.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>52.9</td>\n",
       "      <td>16.9</td>\n",
       "      <td>16.858423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  actual sales  predicted sales\n",
       "56     7.3   28.1       41.4           5.5         8.085821\n",
       "6     57.5   32.8       23.5          11.8        11.231691\n",
       "194  149.7   35.6        6.0          17.3        16.445628\n",
       "53   182.6   46.2       58.7          21.2        19.391218\n",
       "13    97.5    7.6        7.2          13.7        10.793843\n",
       "167  206.8    5.2       19.4          17.2        16.455022\n",
       "165  234.5    3.4       84.8          16.9        17.884583\n",
       "170   50.0   11.6       18.4           8.4         8.664711\n",
       "191   75.5   10.8        6.0          11.9         9.932724\n",
       "85   193.2   18.4       65.7          20.2        17.150317\n",
       "78     5.4   29.9        9.4           5.3         8.106906\n",
       "133  219.8   33.5       45.1          19.6        20.077665\n",
       "46    89.7    9.9       35.7          10.6        10.660759\n",
       "134   36.9   38.6       65.6          10.8        10.790628\n",
       "43   206.9    8.4       26.4          17.9        16.798537\n",
       "199  232.1    8.6        8.7          18.4        18.142141\n",
       "109  255.4   26.9        5.5          19.8        21.249373\n",
       "175  276.9   48.9       41.8          27.0        24.709130\n",
       "19   147.3   23.9       19.1          14.6        15.152153\n",
       "44    25.1   25.7       43.3           8.5         8.803485\n",
       "155    4.1   11.6        5.7           3.2         6.170834\n",
       "128  220.3   49.0        3.2          24.7        21.601373\n",
       "157  149.8    1.3       24.3          10.1        13.000281\n",
       "139  184.9   43.9        1.7          20.7        19.175279\n",
       "96   197.6    3.5        5.9          16.7        15.762064\n",
       "181  218.5    5.4       27.4          17.2        17.119896\n",
       "193  166.8   42.0        3.6          19.6        18.011672\n",
       "93   250.9   36.5       72.3          22.2        22.106803\n",
       "176  248.4   30.2       20.3          20.2        21.235452\n",
       "40   202.5   22.3       31.6          16.6        17.983610\n",
       "61   261.3   42.7       54.7          24.2        23.263668\n",
       "23   228.3   16.9       26.2          20.5        18.813446\n",
       "63   102.7   29.6        8.4          14.0        13.311063\n",
       "35   290.7    4.1        8.5          17.8        20.838320\n",
       "107   90.4    0.3       23.2          12.0         9.699845\n",
       "135   48.3   47.0        8.5          11.6        12.151334\n",
       "37    74.7   49.4       45.7          14.7        13.885174\n",
       "161   85.7   35.8       49.3          13.3        13.102151\n",
       "26   142.9   29.3       12.6          15.0        15.451884\n",
       "95   163.3   31.6       52.9          16.9        16.858423"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame is used for constructing a table \n",
    "\n",
    "new_df = pd.DataFrame()\n",
    "new_df = x_test\n",
    "new_df['actual sales']= y_test\n",
    "new_df['predicted sales']= y_pred\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb3a4eca",
   "metadata": {},
   "source": [
    "7. Evaluating Root Mean Square Error and R square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "329a18d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89.88614375458548"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(x_train,y_train)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "719049ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9069156913074427\n",
      "RMSE: 1.6344492185626285\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "import numpy as np\n",
    "\n",
    "r2=r2_score(y_test,y_pred)\n",
    "print(\"R-squared:\",r2)\n",
    "\n",
    "rmse=np.sqrt(mean_squared_error(y_test,y_pred))\n",
    "print(\"RMSE:\",rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63ae1c4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As seen above R square is close to 1 indicating a very good model. Also RMSE is low, which alos confirms the\n",
    "# above model being a good one.\n",
    "\n",
    "# R square greater than 0.80 is a good model. R square is the measure of comparison which tells how\n",
    "# the machine learning model  is better than the mean model.\n",
    "\n",
    "# RMSE is the difference between the actual and predicted value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aca2a67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Ridge and Lasso are used for tuning the model by playing around with the beta (lm.coefficients)\n",
    "    Lasso is used \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f24c6ea",
   "metadata": {},
   "source": [
    "8. How to save a model (for future use)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3abb3f23",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "327dd41c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lm,\"model.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d87e37",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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