Chapter_3/3.4 The Marketing Plan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 5)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'E:\\programming\\dataset\\Into_to_statstical_learning\\Advertising.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </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    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.iloc[:,1:]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_degree(data,var,target,degree):\n",
    "    poly = PolynomialFeatures(degree)\n",
    "    poly_data = poly.fit_transform(data[var].to_frame())\n",
    "    lin_model = LinearRegression(fit_intercept=False)\n",
    "    lin_model.fit(poly_data,data[target])\n",
    "    pred = lin_model.predict(poly_data)\n",
    "    \n",
    "    return pred\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Is there a relationship between advertising sales and budget?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For tv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 2)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.612\n",
      "Model:                            OLS   Adj. R-squared:                  0.610\n",
      "Method:                 Least Squares   F-statistic:                     312.1\n",
      "Date:                Thu, 18 Jun 2020   Prob (F-statistic):           1.47e-42\n",
      "Time:                        09:28:34   Log-Likelihood:                -519.05\n",
      "No. Observations:                 200   AIC:                             1042.\n",
      "Df Residuals:                     198   BIC:                             1049.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          7.0326      0.458     15.360      0.000       6.130       7.935\n",
      "x1             0.0475      0.003     17.668      0.000       0.042       0.053\n",
      "==============================================================================\n",
      "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
      "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
      "Skew:                          -0.089   Prob(JB):                        0.716\n",
      "Kurtosis:                       2.779   Cond. No.                         338.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(1)\n",
    "tv_data = poly.fit_transform(data['TV'].to_frame())\n",
    "print('shape - ',tv_data.shape)\n",
    "est = sm.OLS(data['sales'],tv_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_1 = fit_degree(data,'TV','sales',1)\n",
    "plt.figure(figsize = (14,8))\n",
    "plt.scatter(data['TV'],data['sales'])\n",
    "plt.plot(data['TV'],pred_1,color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For radio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 2)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.332\n",
      "Model:                            OLS   Adj. R-squared:                  0.329\n",
      "Method:                 Least Squares   F-statistic:                     98.42\n",
      "Date:                Thu, 18 Jun 2020   Prob (F-statistic):           4.35e-19\n",
      "Time:                        09:30:10   Log-Likelihood:                -573.34\n",
      "No. Observations:                 200   AIC:                             1151.\n",
      "Df Residuals:                     198   BIC:                             1157.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          9.3116      0.563     16.542      0.000       8.202      10.422\n",
      "x1             0.2025      0.020      9.921      0.000       0.162       0.243\n",
      "==============================================================================\n",
      "Omnibus:                       19.358   Durbin-Watson:                   1.946\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.910\n",
      "Skew:                          -0.764   Prob(JB):                     1.75e-05\n",
      "Kurtosis:                       3.544   Cond. No.                         51.4\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(1)\n",
    "radio_data = poly.fit_transform(data['radio'].to_frame())\n",
    "print('shape - ',radio_data.shape)\n",
    "est = sm.OLS(data['sales'],radio_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_1 = fit_degree(data,'radio','sales',1)\n",
    "plt.figure(figsize = (14,8))\n",
    "plt.scatter(data['radio'],data['sales'])\n",
    "plt.plot(data['radio'],pred_1,color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### for newspaper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 2)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.052\n",
      "Model:                            OLS   Adj. R-squared:                  0.047\n",
      "Method:                 Least Squares   F-statistic:                     10.89\n",
      "Date:                Thu, 18 Jun 2020   Prob (F-statistic):            0.00115\n",
      "Time:                        09:31:06   Log-Likelihood:                -608.34\n",
      "No. Observations:                 200   AIC:                             1221.\n",
      "Df Residuals:                     198   BIC:                             1227.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         12.3514      0.621     19.876      0.000      11.126      13.577\n",
      "x1             0.0547      0.017      3.300      0.001       0.022       0.087\n",
      "==============================================================================\n",
      "Omnibus:                        6.231   Durbin-Watson:                   1.983\n",
      "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                5.483\n",
      "Skew:                           0.330   Prob(JB):                       0.0645\n",
      "Kurtosis:                       2.527   Cond. No.                         64.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(1)\n",
    "news_data = poly.fit_transform(data['newspaper'].to_frame())\n",
    "print('shape - ',news_data.shape)\n",
    "est = sm.OLS(data['sales'],news_data)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_1 = fit_degree(data,'newspaper','sales',1)\n",
    "plt.figure(figsize = (14,8))\n",
    "plt.scatter(data['newspaper'],data['sales'])\n",
    "plt.plot(data['newspaper'],pred_1,color = 'orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How strong is the relationship?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 4)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.897\n",
      "Model:                            OLS   Adj. R-squared:                  0.896\n",
      "Method:                 Least Squares   F-statistic:                     570.3\n",
      "Date:                Thu, 18 Jun 2020   Prob (F-statistic):           1.58e-96\n",
      "Time:                        09:39:07   Log-Likelihood:                -386.18\n",
      "No. Observations:                 200   AIC:                             780.4\n",
      "Df Residuals:                     196   BIC:                             793.6\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "constant       2.9389      0.312      9.422      0.000       2.324       3.554\n",
      "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
      "radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
      "newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
      "==============================================================================\n",
      "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
      "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
      "Kurtosis:                       6.332   Cond. No.                         454.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "features = data.iloc[:,:3]\n",
    "columns = ['constant'] + list(features.columns)\n",
    "constant = pd.Series([1]*data.shape[0])\n",
    "features = pd.concat([constant,features],axis = 1)\n",
    "features.columns = columns\n",
    "\n",
    "print('shape - ',features.shape)\n",
    "est = sm.OLS(data['sales'],features)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# which media corresponds to sale?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see the p value for the newspaper is very high, but for tv and radio the p value is significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.782224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.576223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>newspaper</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.782224</td>\n",
       "      <td>0.576223</td>\n",
       "      <td>0.228299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     radio  newspaper     sales\n",
       "TV         1.000000  0.054809   0.056648  0.782224\n",
       "radio      0.054809  1.000000   0.354104  0.576223\n",
       "newspaper  0.056648  0.354104   1.000000  0.228299\n",
       "sales      0.782224  0.576223   0.228299  1.000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = data.corr()\n",
    "corr_matrix\n",
    "#radio and newpaper are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 3)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.897\n",
      "Model:                            OLS   Adj. R-squared:                  0.896\n",
      "Method:                 Least Squares   F-statistic:                     859.6\n",
      "Date:                Thu, 18 Jun 2020   Prob (F-statistic):           4.83e-98\n",
      "Time:                        09:40:01   Log-Likelihood:                -386.20\n",
      "No. Observations:                 200   AIC:                             778.4\n",
      "Df Residuals:                     197   BIC:                             788.3\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "constant       2.9211      0.294      9.919      0.000       2.340       3.502\n",
      "TV             0.0458      0.001     32.909      0.000       0.043       0.048\n",
      "radio          0.1880      0.008     23.382      0.000       0.172       0.204\n",
      "==============================================================================\n",
      "Omnibus:                       60.022   Durbin-Watson:                   2.081\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              148.679\n",
      "Skew:                          -1.323   Prob(JB):                     5.19e-33\n",
      "Kurtosis:                       6.292   Cond. No.                         425.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#removing newspaper as a feature\n",
    "features = data.iloc[:,:2]\n",
    "columns = ['constant'] + list(features.columns)\n",
    "constant = pd.Series([1]*data.shape[0])\n",
    "features = pd.concat([constant,features],axis = 1)\n",
    "features.columns = columns\n",
    "\n",
    "print('shape - ',features.shape)\n",
    "est = sm.OLS(data['sales'],features)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Is the relationship linear?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=False, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model = LinearRegression(fit_intercept=False)\n",
    "lin_model.fit(features,data['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.92109991, 0.04575482, 0.18799423])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lin_model.predict(features)\n",
    "residual = data['sales'] - pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x19c6f1f1320>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tlu3dWrvpcV26bP7w8rkjA+s3zZmuj5x6rN47VP3j6BktuurjJ6Uu+CxlJIcbQfgqJbhK42hitYLDagbpcSp2Ix33jUAeT77KQ/CMWFXrhM3M4HhUu4rEyJueLdu7dfGtW/Xtj5+kVx52yHAaSNe+Xs0+9BDdtPJUvXQwp8d37RsOrNd9aMG4Ueandh9IVfA50YU7DQs2IJt8n1KkdTSxWk9ZshoQFrqRHsi54XJ7cR9LnnyVh+AZsan2CTtk+bA0jupkQaGbHkmRN0K5nNP+3v7hwHpma/O4E/3Xf/yYvvHBN+sPr70/FcFnsQv3zNbmWBdsSBv6RbJ8n1JUM3iM8ylLXN+frAaEUU9U130oujJRXMeSJ1/lIXhGbLJ6t1+qEKM6tRSUFLrpKfQdmdzUMDxZcfqU5nEn+q59vdrX26/Vi+dpZmuzjpreosmTGrTzhZ4g+6rYhbuW+0haRzuTVO1+6ZuuVq3gsdRjXqz9cX5/shoQRg0u5HK5yMpEcR1LUiDLQ/CM2GT1br9U1Q6A6jEoydu9v08f/tf/Ht7Xb5ozPTIf+pIfDaZ1vGnOdP31e07Uims6g+2rYhfuWu4jtXxjECVEv/RNV6tW8FjqMS/W/q69vbF9f7IcEI4dXOja25vosSQFsjzUeUZssrYwRbmqHQDVc03rsft6y/ZuXfKjbbp++Sm66wun6+LzThzOh5akT59x3HDgLIXZV8XqytZyH6nlG4Moofqlz0IWcdY2LqacY16o/YXeK5fLlVT3OP804NBDmnTDioW698/e7l37Po2qcSxZHKV0jDwjNlm+2y9FtR8JpiEoCZU2ErWvu/b1DrdhZD700TNadOzhrd4TqpLanmIjObXcR7L6qLxc5fTLNE+oLqdtcR7zqPc6e94sPbe/Tyuu9nuSVItP6RgZTidzLjur17S3t7uOjo7QzUARtZSbW0i1T9D5hUXGXqCq9Tg85AWp1MoVTk6/e/ndRfdV6Ats3H0kLX0u9H4tRyX7rtR+meb9U27b4tymqPe67hMn6wPfvNd7H4c+V6L2mFmnc6593OsEz0DpqhmwhL7ohr4glbKvffZV6O2JU+jvRlR70hDI+6h035X69+V876L2p6TY93ElfSLOYz72vfr6B7RozZ3jfm/zqtN11Iwp415/es+Bkn4fmEih4Jm0DaAM1SyTF/qxXei0kVL2tc++Cr09cUrbJL2Q5SNLVem+K7Vflvq9KxScT25qGJ5EG9fNUiV9Is5jXulkuXpLHUI4TBgE9PJSrr6TUqot5ISOrE1ym2hfZW17iqmlG4Fqq/a+K/V7Vyi4/9XuA7FPUkxrnxg5We5Nc6br3z76Fl3zByfLyUWeo6s1URJg5Bl1L22PvtOmkklu/f057drXq4MDOU1qbNCsqZPV1BT2nr2WJu0x0la+SvddnDWOoxQK7qc0N457rdKAP619Ij+6/4NPLdLO7pcmLEEZ+ikd6gc5z6h7tZQDm5Ry8hr7+3P6n2f36oIRF7y15y/QCbOnBQ+gs5SbWww3ftF8jm+l+y6uHOZCn1Xo/S8+70R97Mr7vD/TV5r7BOdohELOM1AAj75HK3QRLfUitWtf73DgLA3u0wuu6dQNKxbqyOktE/w1fDDSNp5vUFzpvvM5b0T1Jd9+NKNlktaev2DczeeU5pdHzOMcIU5zvjrnaKQNwTPqHo++XxbnSObBgVzkBa9/IBdnk0tWjdHaao7ipTnoCaGUiYCV7LuJzhuVfs/29BzU13/8S61ePE/TWyape+jff7d0ft3dLNXCOTrNI/soHRMGUfeYZPKyOFdNm9TYEDkJqakx7Gln7Da2TZ2sX7/wknZ0xzNZNB80Lbl8sxatuVNLLt+sbc/uHX7fpCen+r5/2ifJlqtao5QTnTcq7Ut9/QO6besurbi6U+9bf49WXN2p27buUk/fQKpWg6vG9yjr5+iJzgnInpJHns2sQdJU59yLCbQHqDoefb8szsBj1tTJkY+dZ00NN0qayzn19Q/oa7/3RnX3HNSPtz6r8950lFZteCi2UehiI58zW5sTHfX2He1MS650EqNx1RqlnOi8UWlfysJoa7W+R1k/R6etpCQq5zUEZGbXmdmhZtYqaaukbWb2+WSbBlRPyFJwaRJnyaqmpgadMHuablixUHd9/jTdsGJh0MmC+Qv9+9bfo/etv0cX37pVF5z22uHAWYqn9FexoCnOkf0ovu+fdDt8JDUaV81RymLnjUr7UqXbUY0R4Wp+j7J8jiZnu/b4jjzPc869aGYflPQfklZJ6pR0aWItA1B1lZSsihpFbGpqSM3kwKgL/fP7+2K/qBUbMUz6Iur7/qEv5rmc069ffEn7e/u1evE8rd30uLZs7y5pNK7QqHVaRimj+tK68xeosWGw7RO1p5LtqNaIcOjvUVZk4SkCSuMbPE8ys0mS3iPpn51zB82MZB2gxpR7wU5LGkAxURf63fv7xl3Uzp43S2amp/ccKLr9hYK3YjcgUZ8X10U0l3MyM914wULt3t83HJBGvX/Ii3nUd2XN0vm6bOM2bdne7RV4TfR9KzYRcORxa2luVH/O6WB/LvYgO9+Xblp5qg70DuiJ5/bry997RF37er37hu+ExrHfRSdXdppAKak0BIV+0lpHG+XzqvNsZp/W4Gjzg5LOlfQqSdc453472eaNRp1nIJ2yUIc1qo1nz5ulz5x5vFZcPZiXffa8Wfr0GcePytMuJ2e4UACS1E1GoYD0qruf0IVnzdVxbVO1p+fgcHtmtEzSY137EmnHRIFXoe/K6sXzdPGtW72+M7v2vqTfvfzuce9x08pTNWvaIUXbl99PbVMn6wvnzNXnb4wv3z1K0n0j6thf8wcn67TLNo373bu+cLqOnt5ScPtK/X5m4aY5Lai2kU2F6jyXvUiKmTU55/orblkJCJ6BdHp6zwEtWnPnuNc3rzpdR82YEqBF4xW60I8MLM1M71338wkDnUoCoiQuooXac8OKhZo1dfKoQPnsebP05XPnDY7QmqnRpIaGhpJSAnxuDPKf09hgo36v0HflxgsWqnVyk1fg9dTz+/XWSzaNe/2uL5yuV72i8Pdt5H5a96EFuvjWrZH77JWHHhJbYJN034g69v/20bdo9fcfiVxgZdahk3V4a3PkMU964RcgaypaJMXMZkv6G0lHOufeaWbzJC2U9K14mwkgi8xMZ8+bpaUL5gzXpN3QuV1m6bmINjSYjmubqhtWLFT/QE5NI5YLzwcGT+85kHjOcBJ1mQu1xzmnPT0HhwPaN82Zro+ceqw+8M17yxopLDbSODKnvNjnFHrUf+T0Fu+gtdEs8j0aJ/jTkftpesukyH32THePXug5GNvoadKpDVHH/us/fkzrzl8wajnrfGpM177e4VH+sce+nO81dcZRj3ynvV8paaOkI4f+/UtJn630w83sHDPbZmb/a2ZfrPT9AITR3Gj61NuP08W3bh2uZPGptx+n5omimSrK5Zwe69qn9677ud566Sa9d93P9VjXvlFVCHwrJBT6vZbmxiC1k4u1e2RAVE51kZFVG3794ksFqyv4fk6hKhKljPa2NDfq0mXzR73Hpcvmq6W5eEA6cj919xyM3Gf5m4C4KkYkXf0j6th37evVEdMP0fXLT9H1y0/R6sXzhnPKd+zpGb5xGLudcVbbAWqZb/B8uHPuBkk5SRpK16hoOq2ZNUr6F0nvlDRP0vuHRrSBmlCri1BE6RtwWnnt/aOCpZXX3q++gfRss09ZLd9AJ+r3vv3xk/Tsi71BFkIo1u6RAVGh0dZCI4tjy8k9091T8O99P2fkpNTNq07XzSsXlTzKO72lWbMPPUQXn3eirl9+ii4+70TNPvQQTW8pHpCO3E9rNz0+LgBfs3S+1m56PNaKEXFsbzGFjv30lsFj/6fffVArru7Ulu3dwz/v7jkoafyxz/piJEC1+Fbb2G9mMyU5STKzUyS9UOFnnyTpf51z/zf0nv8u6TwN1pEGMq3eJtI45wqmDYwVKkfS55G0b7WRqN9zcvrwiEls1VwIoVi7R870z4+2jk0hGFtdRBq82eg52K9fv/CS2qZO1o49PXrp4EDk309qavD6nPwIZqWP+hsaTMfMbNW0QyaV9D0au59amhv13RUL9XR3j3bv7xsenY17tLXU7S2lj/ge+7HpG9L4UeVKy+OR+4x64Rs8/4mkH0h6rZltltQmaVmFn32UpO0j/r1D0skVvieQCvW2opRvXmfImwrfNvoGOmN/zzdfOimF2j0yIMrlclr3oQXD1UXyqz5e9INHdNvWXcMj6L39uciAq7HBdOmy+cMVKs6eN0tffOdvqPfg4AIwx7VNLfg5cY9gVhqQHnFYy/BEx+6eg/rs9Q/EUtu80u9xOX3E59j39Q9oIOf01z/cOnyDELWd5dzY1NtgAeAVPDvn7jezt0maK8kkbXPOHazws6N61LhhKjNbLmm5JL1u9mzpooukpUulTZuk3bul5cul9eulN7xBmjpV+vnPpfe/X7r1Vqm3V/rAB6Qrr5QWLBh8w85O6aMfla67Tpo8WVq8WPrOd6SFC6V9+6SHH375PWfOlE47TdqwYfD/n3lG+uUvX/75EUdI7e3SLbdIZ589+LMnn3z558ccIx1/vHTbbdK73iV1dEg7d7788+OPl448cnBb2Kaa2qbm7n3Sc3P02Yfv0MOvfJ0k6Q2//l8NnHOE9MObM7FNuSmt6rnrZ9q3ZJla79ioKa5fDR/8YORxmnnttdrQJ322/2idfM9GPTX3jfqT1x2pwy/7G2nFiuFt6n7Lqfr5l7+uo2edoDfve16vef5pffHF53Rd7iG1HjMn0W2a+foTdd2rGnTH7d/XNceeqt99ZoveO3+WZnYdK33tqoq/e60nnaqP/epuHbb9CV33xnP0gQd/pIOzZmvKw6+QfrIxaH9q6OxU29A2tTU36wdnvUNN1/+7Dr7uZH3379Zq3pYH9cAbz9Gyn/1I/Xvv0d/un6Vl9/1E97zqDZq19Xltu/0qfeZLF2rO1y/RLbucLl1ypl7z7H/pxWmn6zsr/0qtO3fozre9R998aYtmvv54NZwwV7M2btQtZ5wjdf5CzbueVcvAXDX81d8HOUfkXvEKPXniW7Tpb9bqtlkn6DfcPq08MqeZF35KDd+8QnNnv1K3LHyjGn/4Yw0sOEuH/ecGNTz1q6LHyT3zjB5/zwf0X5//Gz0wZZbcK4/QX0zr0swPv18Nd/207G3as+z9uutzl+icPuknrz1J7/7ZT3XFL+/TV05/tQ773/8p+RzRcMstahv67rknntDffvCjuuwX35MOO0ZTH5+shnW3j/ru5T7xSfX8yzfU+5rXquGoo3TYvZtly5YVPU7d716qH6/+F717X49+MO9tWvazO3Tl1rv15+fO02FbH8r0ubwWr09sUwnbVEDRUnVm9rsFfyjJOXdTsZ8XY2YLJV3knHvH0L//bOg9/7bQ31CqDnFK8jFjFuoeF1POSJLP/gxd0i7JY57F0beo43H98lP0vvX3jPvd65efogN9A8Ml0AqVeqv2d7yS2tKVtDWpPh6yj5T7HQ7dr4GklFuq7l1FfuYklR08S7pP0nFmdqykpyX9vqQPVPB+gLekA52sryhVTtqJz+PeECuSVSsXs5x80dB5olHH40BfdF5zvppI/ntd6uTDJPj24ySWkU5qaeqQq/aVm25WaZtD9wOgVEWrbTjnPlbkfx+v5IOHKnZ8SoMl8B6VdINz7heVvCfgy6fyQiWSnmGftKQCg2rP5h9bLSLpChj5G4gjDhvcvp0v9BSstFLttkWJOh6vnjlFa89fMOq1tecv0Oxph+gVrZOHv9f5gHqkapc1m6gf5yveuKG2jW1rJXXIkyrrFrLiRbn9vpI2p6EfAKXyXmHQzM6V9HpJw2ufOuf+KqF2RSJtA3HhMWNxSaadVHOUKcR2+I6GpiW1Z+x2NDZIX9zw0LgFb766ZP6odqUhTaVYPz7isJaiS3GvWTpfr2tr1ezDWiLeeWJJbn+okdgQK2empR8AUSpdYXCtpCmSTpf0TQ1W2vjvWFsIVFHIR6OVqsaFNcm0k2quSJbECPpgdYY+7ex+adQKblEr7eU/L+rRd1Kj+6WKqhpy29Zdum3rruY0naAAABX6SURBVFG/95V3DYz7u3LLmsWlWD8eeRx27OnRJT/apovPO1FzXtGix7v266q7n9BXl8wv+7OT3P5Qq/ZV0u/LbXNa+gFQCt9Sdac65+ab2UPOub80s6+psnxnIKis5iRXa7QvDYFRHJqbGiOXDS/3Jim//3/9wkvDE+ek0QGybzCQ1hu4UtoVemnmYv145wujF3TZsr1bH7vyvsFFVYaWpq60v4fe/riF6Pdp7QdAMb7B80tD/3/AzI6U9LykY5NpEpC8rAaH1awfXQuBwYyWSfr0GcfrgmtG1zWe0TKprPfL7/+v/d4bJ1xpb6JgIK03cNVoV1xPT4r140LHIZ8SkOb+HnICXbX7fVr7AVCMb/B8i5lNl3SppPs1WGnjisRaBVRBFoNDHnGWZk/PweHAWRrcVxdc01n2zUZ+/xdbQc83GEjrDVzS7Yr76UmhflzoOOQXR0mrNOSSV1Na+wFQjG/w/D+SBpxzG8xsnqQ3S/pecs0CEIVHnKWJ+2Yjv//Xbnpca5bO16oND40LkEsJBtJ6A5dku6r19CSrQVm9rU4qpbcfAIUULVU3wmrn3F4z+y1JZ0m6UtI3EmsVgEghy1hlUdzlxPL7v2tfry7bODgBbdPnTtNNK08dNTKYDwaOmjFFbdMmpz5gq6ZqPj3J4nHg6RKQfr4jz/lee66ktc6575vZRck0CUAhWR1NCyXufMpa2v+h8mp5elIc+wdIP686z2Z2qwZXATxT0gJJPZL+2zn3xmSbNxp1ngGUitXLxguZV1tvOb2lYv8A6VGozrNv8DxF0jmSHnbOPWZmR0h6g3PutvibWhjBM4B6kWTQH3phCm5oimP/AOlQ0SIpzrkDGlHX2Tm3U9LO+JoHpBcXMlRb0qOPofNqmSBWXJb2D+dH1CPfnGeg6tJwUuYRaval4XtUqqQrLpBXizhwfkS98q22AVRV/qS85PLNWrTmTi25fLO2PbtXudzEaUZxKhTE7N7fV9V2oDxp+R6VKumR4aiqLevOX6DGBqV+31RLLufUtbdXT+85oK69veyXCJwfUa8InpFKaTkph368jcqk5XtUqrhL7I2Vrxpy08pTtelzp+ni807Ul7/3iN79z9m4uUhaVm+6qo3zI+oVwTNSKS0n5aSDGIwW92hfqO9RpdtRjXreDQ0mk+n8b92rj115n7Zs76745qJWRmuzetNVbZwfUa/IeUYqpSUnM+46wSgsifzJEN+jOLajWvWk47y5KLTdx7VN1Z6eg5nKOU/LzXvacX5EvfIqVZcWlKqrH2maiJLFCWdZlET5tHK+R5Ue79Bl4EoRZ1uj3uvsebP0mTOP14qrOzMVUGfpGIbG+RG1rKJSdUC1pWkltyyVjcqyJEb7Sv0exXHTlqVRyzhHDqO2e+mCOcOBs/Ry+sN1nzhZH/jmvcFvjAthRNUf50fUI4JnpBYn5fpSSYpFsdGvUr5HcZSIS0vKkY84b1Kjtntma3PkjcSuvb2JleGLQ5pu3gGkDxMGAaRCuZPk4qyMEMeocTUm+8Upf3Nx1Iwpaps2uewAMWq7Z02bHDmhbOzEuzSOzMe1XwDUHkaeAaTCyNG+XC6nASc5NziiXGzUL84FReIYNa7XUcuo7Z7RMmlc+sO6Dy3QP93xy1F/m9aReQCIQvAM1KG0TvJpaDDNbG0uKe84zhzjuHJdS005SuvxKFXUdkcF1BeeNVdbd+4lnxhAJhE8A3UmTZVMopQ6khxnjnGIUeO0H49K+QTUoW8WauXmBUB1kPMM1Jm0LwBR6khy3DnG1c51TdPxqNYiJ2nKJ2Y1QQClYuQZqCE+I2hpL6VW6khy1nOM03I8an0EvJA4c+YB1AdGnoEa4TuClvYldcsZSU7TSGap0nI80jQCXk1puXkBkB0Ez0CN8A1+0lxKLT9yfughTbphxULd+2dv180rF9X06Gdajke9BpFpuXkBkB2kbQA1wjf4SWuaQ72mDaTleGRpcZc4sZoggFIRPAM1opTgJ42rN6Yl9zRE5YU0HI96DSLTcvMCIDsInoEakfXgJw1pA6FGv9NQKq2eg8g03LwAyA6CZ6BGZD34SUPaQIjR7zSlqxBEAsDEmDAI1JAsV51Iw8S5EKPf9VrlAgCyipFnAKMkkULg855pGDkPMfqdhnQVAIC/IMGzmV0q6V2S+iQ9LuljzrnuEG0B8LIkUggKvedxbVO1p+fguEA5ZNpAiLzxNKSrAAD8mXPVX4LUzM6W9BPnXL+ZrZEk59yqif6uvb3ddXR0JN4+oF517e3Vkss3jwvkKsn5jXrPs+fN0mfOPF4rru5MLM+33BH0ak/eS1POMwDgZWbW6ZxrH/t6kJFn59xtI/55j6RlIdqRZWmYnY/ak0QKQdR7Ll0wZzhwzn9GnBPzKglIkx79juq7odNVAAD+0jBh8OOS/rPQD81suZl1mFlHV1dXFZuVXr7LMAOlSmK1taj3nNnanGieb1on4RXqu5IyO9ETAOpNYsGzmd1hZo9E/O+8Eb/zJUn9kq4t9D7OufXOuXbnXHtbW1tSzc2UtAYGyL4kKl5EveesaZMTXRI5rZPw6LsAkH2JpW04584s9nMz+4ikxZLOcCESrzMsrYEBsi+JihdR7zmjZVJiE/NyOScz040XLNTu/X1au+lxbdneraNntMjMlMu5irankpQp+i4AZF+oahvnSFol6W3OuQMh2pBlzM5HkpLI+Y16zyTyfKNyndcsna+r7n5CHzn1WF30g0d04Vlzy56MV+nkPvpuYczjAJAVoXKe/1nSNEm3m9kDZrY2UDsyKQ2LSQCVSmJBl6i0iFUbHtLn33GCLtu4Tbdt3VVRmkSlaRf03WjM4wCQJaGqbbwuxOfWijQsJgGkUaG0CLPR/87lcura2zth/xk7GprL5SpKu6DvRguxLDoAlIsVBjMq9GISQBoVSovY/nyPPveOubps4za1TWvWc/v7JqwxHZWise5DC3T2vFm6beuuUe9fStoFfXc8csEBZEkaStUBQCyi0iLWLJ2vr//4Ma3a8JA+fcZx+vK58yJrTI9NvYgaDV1xdae+fO480i5ilkSJRABICiPPAGpGPi3i+uWnaMeeHnX3HNRlG7dpy/ZuSdJrZ01Vo8lrlLPQaGhjg5F2EbMQy6IDQLkIngHUlIYGU3NTo/70uw+OS99omdQ4/N8TVbwoVhmDtIt4kQsOIEtI2wBQc4pVtfCteEFljOpKovoKACTBsrQ+SXt7u+vo6AjdDAAZUKxusG9NYWoPA0D9MrNO51z72NdJ20AmEMSgVMWqWvhWvKAyRjLozwCyjOAZqVfpqm4A0oP+DCDryHlG6lW6qlva5XJOXXt79fSeA+ra28uqaqhptd6fAdQ+Rp6RerW8gAKjcEiTaqRT1HJ/BlAfGHlG6tXyAgqMwmVfrTw5yN/ILbl8sxatuVNLLt+sbc/uVX9/Ltbtq+X+DKA+EDwj9Wq5ZBijcNlWKODMYgAddSP3D7dv07Zd8W5fLfdnAPWBtA2kXi0voFBsIQ6kX6EnBzevXJS5Kh1RN3JLF8yJXMq8ku2r5f4MoD4w8ozUiXoMXqsLKIQehauVlINQaunJQVQ6xczW5kS2r1b7M4D6wMgzUiVrE+gqnWAVchQua/s6jWrpyUH+Rm7k92HWtMk1s30AEBdWGESqdO3t1ZLLN4+7WKfxMXjWg88s7eu0yvp3YKyxN4MzWibpsa59NbN9AFAKVhhEJmTpMXia8l3LGQHP0r5Oq1rL341aUbGWtg8A4kDwjFTJ0mPwtASf5Y5+Zmlfp1mtL+Fd69sHAKViwiBSJfQEulKkpV5tubWis7SvAQBIC0aekSpZegweNcEqRPBZ7gh4lvY1AABpQfCM1MnKY+K0BJ+VpF9kZV8DAJAWpG0AFUhDvVrSLwAAqB5GnhFUpXWSkZ4RcAAA6gHBM4KptRq5IZF+AQBAdZC2gWDKrRIBxIklygEApWDkGcGkpU4y6hdPPwAApWLkGcGkpU4y6hdPPwAApSJ4RjBUiUBoPP0AAJSKtA0EQ5UIhMYS5QCAUjHyjKDSUCcZ9YunHwCAUjHyDKBu8fQDAFAqgmcAdY0a2QCAUgRN2zCzz5mZM7PDQ7YD9Yn6vgAAoFTBRp7NbI6ksyQ9FaoNqF/U9wUAAOUIOfL8D5K+IInhPlQd9X0BAEA5ggTPZvZuSU875x70+N3lZtZhZh1dXV1VaB3qAfV9AQBAORJL2zCzOyS9MuJHX5L055LO9nkf59x6Seslqb29nVFqxIL6vkhCLue0e38flTsAoIYlFjw7586Met3M3iDpWEkPmpkkHS3pfjM7yTn366TaA4yUr+87NueZ+r61Lcngljx6AKgP5lzYwVwze1JSu3PuuYl+t7293XV0dCTfKNQFRgnrS9LBbdfeXi25fPO4pxk3r1xEKTwAyCAz63TOtY99nRUGUbdY3bC+JD1JlDx6AKgPwYNn59wxPqPOAFCJpIPbfB79SOTRA0DtCR48A0A1JB3c5vPo859BHj0A1KbgOc+lIOcZQLmqMaGPPHoAqB2Fcp6DrTAIANXU0GCaO3uabl65KLHgNp9HDwCoXQTPAOoGwS0AoFLkPAMAAACeCJ4BAAAATwTPAAAAgCeCZwAAAMATwTMAAADgieAZAAAA8ETwDAAAAHgieAYAAAA8ETwDAAAAngieAQAAAE8EzwAAAIAngmcAAADAE8EzAAAA4IngGQAAAPDUFLoBQBblck679/epr39AzU2NmtnarIYGC90sAACQMIJnoES5nNO2Z/fqk9/u0I49PTp6Rouu+HC75s6eRgANAECNI20DKNHu/X3DgbMk7djTo09+u0O79/cFbhkAAEgawTNQor7+geHAOW/Hnh719Q8EahEAAKgWgmegRM1NjTp6Rsuo146e0aLmpsZALQIAANVC8AyUaGZrs674cPtwAJ3PeZ7Z2hy4ZQAAIGlMGARK1NBgmjt7mm5euYhqGwAA1BmCZ6AMDQ2mtmmTQzcDAABUGWkbAAAAgCeCZwAAAMATwTMAAADgieAZAAAA8ETwDAAAAHgieAYAAAA8BQuezeyPzWybmf3CzC4J1Q4AAADAV5A6z2Z2uqTzJM13zvWa2awQ7QAAAABKEWrk+Q8l/Z1zrleSnHO7ArUDAAAA8BYqeD5e0m+b2b1m9lMze0uhXzSz5WbWYWYdXV1dVWwiAAAAMFpiaRtmdoekV0b86EtDnztD0imS3iLpBjN7jXPOjf1l59x6Seslqb29fdzPAQAAgGpJLHh2zp1Z6Gdm9oeSbhoKlv/bzHKSDpfE0DIAAABSK1TaxvckvV2SzOx4Sc2SngvUFgAAAMBLkGobkv5V0r+a2SOS+iR9JCplAwAAAEiTIMGzc65P0vkhPhsAAAAoFysMAgAAAJ4IngEAAABPBM8AAACAJ4JnAAAAwBPBMwAAAOCJ4BkAAADwRPAMAAAAeCJ4BgAAADwRPAMAAACeCJ4BAAAATwTPAAAAgCeCZwAAAMATwTMAAADgieAZAAAA8ETwDAAAAHgieAYAAAA8ETwDAAAAngieAQAAAE8EzwAAAIAngmcAAADAE8EzAAAA4IngGQAAAPBE8AwAAAB4IngGAAAAPBE8AwAAAJ4IngEAAABPBM8AAACAJ4JnAAAAwBPBMwAAAOCJ4BkAAADwRPAMAAAAeAoSPJvZb5rZPWb2gJl1mNlJIdoBAAAAlCLUyPMlkv7SOfebkv5i6N8AAABAqoUKnp2kQ4f++zBJzwRqBwAAAOCtKdDnflbSRjO7TIMB/KmB2gEAAAB4Syx4NrM7JL0y4kdfknSGpAudcxvM7L2SviXpzALvs1zS8qF/7jOzbUm0F1VxuKTnQjcCieH41jaOb+3i2NY2jm/5Xh31ojnnqt0QmdkLkqY755yZmaQXnHOHTvR3yDYz63DOtYduB5LB8a1tHN/axbGtbRzf+IXKeX5G0tuG/vvtkh4L1A4AAADAW6ic509K+icza5L0kl5OywAAAABSK0jw7Jz7maQFIT4bQa0P3QAkiuNb2zi+tYtjW9s4vjELkvMMAAAAZBHLcwMAAACeCJ5RFWb2pJk9nF+SPXR7UBkz+1cz22Vmj4x47RVmdruZPTb0/zNCthHlKXBsLzKzp4f67wNm9jsh24jymdkcM7vTzB41s1+Y2WeGXqf/ZlyRY0v/jRlpG6gKM3tSUrtzjlqTNcDM3ippn6RvO+dOHHrtEknPO+f+zsy+KGmGc25VyHaidAWO7UWS9jnnLgvZNlTOzI6QdIRz7n4zmyapU9J7JH1U9N9MK3Js3yv6b6wYeQZQMufcXZKeH/PyeZKuGvrvqzR40kbGFDi2qBHOuZ3OufuH/nuvpEclHSX6b+YVObaIGcEzqsVJus3MOodWjUTtme2c2ykNnsQlzQrcHsTrU2b20FBaB4/0a4CZHSPpTZLuFf23pow5thL9N1YEz6iWRc65N0t6p6Q/Gno0DCAbviHptZJ+U9JOSV8L2xxUysymStog6bPOuRdDtwfxiTi29N+YETyjKpxzzwz9/y5JN0s6KWyLkIBnh3Lu8rl3uwK3BzFxzj3rnBtwzuUkXSH6b6aZ2SQNBlfXOuduGnqZ/lsDoo4t/Td+BM9InJm1Dk1ekJm1Sjpb0iPF/woZ9ANJHxn6749I+n7AtiBG+aBqyBLRfzPLzEzStyQ96pz7+xE/ov9mXKFjS/+NH9U2kDgze40GR5ulwVUtr3POfTVgk1AhM/uOpNMkHS7pWUlfkfQ9STdIepWkpyT9nnOOiWcZU+DYnqbBR75O0pOSVuTzY5EtZvZbkv5L0sOSckMv/7kGc2PpvxlW5Ni+X/TfWBE8AwAAAJ5I2wAAAAA8ETwDAAAAngieAQAAAE8EzwAAAIAngmcAAADAE8EzANQwM7vSzJaFbgcA1AqCZwAAAMATwTMAZMzQqp0/NLMHzewRM3ufmf2Fmd039O/1Q6uNjf27BWb2UzPrNLONI5Zj/rSZbTWzh8zs36u/RQCQHU2hGwAAKNk5kp5xzp0rSWZ2mKTbnXN/NfTvqyUtlnRL/g/MbJKk/yfpPOdcl5m9T9JXJX1c0hclHeuc6zWz6dXdFADIFkaeASB7HpZ0ppmtMbPfds69IOl0M7vXzB6W9HZJrx/zN3MlnSjpdjN7QNKXJR099LOHJF1rZudL6q/OJgBANjHyDAAZ45z7pZktkPQ7kv7WzG6T9EeS2p1z283sIkmHjPkzk/QL59zCiLc8V9JbJb1b0moze71zjiAaACIw8gwAGWNmR0o64Jy7RtJlkt489KPnzGyqpKjqGtsktZnZwqH3mGRmrzezBklznHN3SvqCpOmSpia+EQCQUYw8A0D2vEHSpWaWk3RQ0h9Keo8G0zmelHTf2D9wzvUNlaz7+lCOdJOkf5T0S0nXDL1mkv7BOdddla0AgAwy51zoNgAAAACZQNoGAAAA4IngGQAAAPBE8AwAAAB4IngGAAAAPBE8AwAAAJ4IngEAAABPBM8AAACAJ4JnAAAAwNP/B/0iZ/n+MhtEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "plt.ylim(-8, 8)\n",
    "sns.scatterplot(x = data['sales'],y = residual)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',linewidth = 0.5,color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see from the graph, that the dispersion is not very well spread, there is some kind of \n",
    "# pattern on the right hand side of the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Is there synergy among the interaction media?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </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    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "      <th>interaction</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",
       "      <td>8697.78</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",
       "      <td>1748.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>789.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>6256.95</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>12.9</td>\n",
       "      <td>1952.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  radio  newspaper  sales  interaction\n",
       "0  230.1   37.8       69.2   22.1      8697.78\n",
       "1   44.5   39.3       45.1   10.4      1748.85\n",
       "2   17.2   45.9       69.3    9.3       789.48\n",
       "3  151.5   41.3       58.5   18.5      6256.95\n",
       "4  180.8   10.8       58.4   12.9      1952.64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['interaction'] = data['TV']*data['radio']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape -  (200, 4)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  sales   R-squared:                       0.968\n",
      "Model:                            OLS   Adj. R-squared:                  0.967\n",
      "Method:                 Least Squares   F-statistic:                     1963.\n",
      "Date:                Thu, 18 Jun 2020   Prob (F-statistic):          6.68e-146\n",
      "Time:                        11:47:09   Log-Likelihood:                -270.14\n",
      "No. Observations:                 200   AIC:                             548.3\n",
      "Df Residuals:                     196   BIC:                             561.5\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "constant        6.7502      0.248     27.233      0.000       6.261       7.239\n",
      "TV              0.0191      0.002     12.699      0.000       0.016       0.022\n",
      "radio           0.0289      0.009      3.241      0.001       0.011       0.046\n",
      "interaction     0.0011   5.24e-05     20.727      0.000       0.001       0.001\n",
      "==============================================================================\n",
      "Omnibus:                      128.132   Durbin-Watson:                   2.224\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1183.719\n",
      "Skew:                          -2.323   Prob(JB):                    9.09e-258\n",
      "Kurtosis:                      13.975   Cond. No.                     1.80e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.8e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "features = data.drop(['newspaper','sales'],axis = 1)\n",
    "columns = ['constant'] + list(features.columns)\n",
    "constant = pd.Series([1]*data.shape[0])\n",
    "features = pd.concat([constant,features],axis = 1)\n",
    "features.columns = columns\n",
    "\n",
    "\n",
    "print('shape - ',features.shape)\n",
    "est = sm.OLS(data['sales'],features)\n",
    "est2 = est.fit()\n",
    "print(est2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can see that by just adding the interaction term we have got the R2 value of almost 97"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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