MODEL_marketing_mix_modelling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"D:/3-Semester/BABI/Advertising.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean=df.loc[:,~df.columns.str.contains('Unnamed')]\n",
    "df_clean.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>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       radio   newspaper       sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   14.022500\n",
       "std     85.854236   14.846809   21.778621    5.217457\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   10.375000\n",
       "50%    149.750000   22.900000   25.750000   12.900000\n",
       "75%    218.825000   36.525000   45.100000   17.400000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      22.1\n",
      "1      10.4\n",
      "2       9.3\n",
      "3      18.5\n",
      "4      12.9\n",
      "5       7.2\n",
      "6      11.8\n",
      "7      13.2\n",
      "8       4.8\n",
      "9      10.6\n",
      "10      8.6\n",
      "11     17.4\n",
      "12      9.2\n",
      "13      9.7\n",
      "14     19.0\n",
      "15     22.4\n",
      "16     12.5\n",
      "17     24.4\n",
      "18     11.3\n",
      "19     14.6\n",
      "20     18.0\n",
      "21     12.5\n",
      "22      5.6\n",
      "23     15.5\n",
      "24      9.7\n",
      "25     12.0\n",
      "26     15.0\n",
      "27     15.9\n",
      "28     18.9\n",
      "29     10.5\n",
      "       ... \n",
      "170     8.4\n",
      "171    14.5\n",
      "172     7.6\n",
      "173    11.7\n",
      "174    11.5\n",
      "175    27.0\n",
      "176    20.2\n",
      "177    11.7\n",
      "178    11.8\n",
      "179    12.6\n",
      "180    10.5\n",
      "181    12.2\n",
      "182     8.7\n",
      "183    26.2\n",
      "184    17.6\n",
      "185    22.6\n",
      "186    10.3\n",
      "187    17.3\n",
      "188    15.9\n",
      "189     6.7\n",
      "190    10.8\n",
      "191     9.9\n",
      "192     5.9\n",
      "193    19.6\n",
      "194    17.3\n",
      "195     7.6\n",
      "196     9.7\n",
      "197    12.8\n",
      "198    25.5\n",
      "199    13.4\n",
      "Name: sales, Length: 200, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "labels=df_clean['sales']\n",
    "features=df_clean.drop(['sales'],axis=1)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJztnXu8HWV577/P3hs2l4KQTdwhQBJiA8ZaazXFRqGmom2NbaEXtTW10fo50ao1tuqRymnFnpNT7bFV+mkrJy3WCBFLvRTa0oqHitdIAcUbgYSGhEvIBhK5KBjZez/nj5m1WXvtmVkza82suazf9/PZn73WrFkzzzuz5n3e97m95u4IIYQQnYyULYAQQohqIgUhhBAiEikIIYQQkUhBCCGEiEQKQgghRCRSEEIIISKRghC1wcz2mtmLy5ZDiGFBCkIMFDM728y+YmYPm9khM/uymf1U2XIJIRYyVrYAYngws+OBfwF+F7gSOBI4Bzhcplyif8xszN2ny5ZD5ItmEGKQnAHg7le4+4y7P+7u17r7NwHM7Glm9h9mdtDMHjSz7WZ2QtSBzGzEzC4ws/8K97/SzBaFnx1lZpeH2x8ysxvNbDLiGBeY2Sc6tl1sZn8Zvn6Nme0xs0fN7E4z2xAjy0Xh+T8a7vsdM1vT9vlSM/ukmT0QHuctbXI+bmYnhe//h5lNh4oUM/tfZvbB8PV6M7s1PP69Zvb2cPs6M7vHzN4VXrO97XKa2cvM7Otm9oiZ3W1mF7V9tsLM3Mw2mdl+M7vPzN6W8hq3vvs6M7sL+I+4my7qixSEGCS7gBkz22ZmLzWzEzs+N+BPgaXAauA04KKYY70FOB94Ybj/d4G/Dj/bCDwl/P4E8Abg8YhjXAGsb+uQR4FXAB8zs2OBvwRe6u7HAc8Hbklo2y8DHwdOAK4G/io85gjwz8A3gFOAc4G3mtnPu/sPgBvDNgD8DLAPeEHb+8+Hry8FXh/K8kzmd8hLgJPC428EtprZmeFn3wd+O5TrZcDvmtn5HbL/LLAK+DnggjY/T9I1bvFCgnv18wnXRtQVd9ef/gb2R9CZfAS4B5gm6EwnY/Y9H/h62/u9wIvD1zuBc9s+Oxl4gsBs+jvAV4BnpZDnS8Bvh69fAvxX+PpY4CHg14CjuxzjIuD/tb1/BvB4+Pp5wF0d+/8h8Pfh6/9JoIjGgAPAZuC9wFEESu2kcL+7gNcDx3cca114HY9t23Yl8Ecxsn4Q+ED4egXgwNPbPv8z4NIU17j13ZVl/6b0V9yfZhBioLj7Tnd/jbufSjASXkrQaWFmTzWzj4cmlEeAywlGxlEsBz4dmpAeIujMZoBJ4DLgM8DHQ9PJn5nZETHH+Rjwm+HrV4XvcffvA68kmH3cZ2b/amZPT2jagbbXjwFHmdlYKOfSlpyhrO8K5YRghrAOeA7wLeCzBKPynwbucPcHw/1+DVgP7DOzz5vZ2rbzfTeUt8U+guuKmT3PzD4XmrceDtvTeU3vjvouydc46ruiYUhBiNJw99sIZhPPDDf9KcGo9FnufjzwWwRmpyjuJjD/nND2d5S73+vuT7j7e9z9GQSmoV8kMLNE8Y/AOjM7FfgVQgURyvcZd38Jwcj5NuBve2jm3cCdHXIe5+7rw8+/ApwZnvvz7n4rsIzAHNQyL+HuN7r7ecBTgX8imCW0ODE0ibVYBuwPX3+MYJZ2mrs/BbiEhdf0tJjvxl7jtv1VDrrBSEGIgWFmTzezt4WdMWZ2GsHo/avhLscB3wMeMrNTgHckHO4SYIuZLQ+PtdjMzgtf/6yZ/XjoU3iEwCwyE3UQd38AuB74e4KOfGd4jEkz++Ww4z0cyhV5jC78J/CImb3TzI42s1Eze6aFob3u/hhwM/AmnlQIXyEwJ30+lOVIM9tgZk9x9yfCNnXK8p5wv3MIFOI/htuPAw65+w/M7CyCWVInf2Rmx5jZjwGvBf4h3B57jcVwIAUhBsmjBDb5G8zs+wSK4dtAK3LmPQSmloeBfwU+lXCsiwlGxtea2aPhsZ4XfrYE+ARBR7qToKO9POFYHwNeTNvsgeDZeBvBaPoQgdnnjWka2Y67zwC/BDwbuBN4EPg7Aid6i88DRxAok9b744AvtO3zamBvaHp7A8HsqsUBAgfyfmA78IZwdkYo85+E1+iPmT/zaD//HcB1wPvd/dpwe9I1FkOAuWuGKERdMbN1wOWhTyfrd1cQKK0jXDkMIgLNIIQQQkQiBSGEECISmZiEEEJEohmEEEKISGpdrO+kk07yFStWlC2GEELUiptvvvlBd1/cbb9aK4gVK1Zw0003lS2GEELUCjPbl2Y/mZiEEEJEIgUhhBAiEikIIYQQkUhBCCGEiEQKQgghRCRSEGJomdo+xY4VO7h+5Hp2rNjB1PapskUSolLUOsxViF6Z2j7F7ZtuZ/axWQAO7zvM7ZtuB2Byw4Llq4UYSjSDEEPJngv3zCmHFrOPzbLnwj0lSSRE9ZCCEEPJ4bsOZ9ouxDAiBSGGkvFl45m2CzGMSEGIoWTllpWMHDP/5z9yzAgrt6wsSSIhqocUhBhKJjdMcubWMxlfPg4G48vHOXPrmXJQC9GGopjE0DK5YVIKQYgENIMQQggRiRSEEEKISKQghBBCRCIFIYQQIhIpCCGEEJFIQQghhIhECkIIIUQkUhBCCCEikYIQQggRiRSEEEKISKQghBBCRCIFIYQQIhIpCCGEEJFIQQghhIhECkIIIUQkUhBCCNHB1PYpdqzYwfUj17NjxQ6mtk+VLVIpFKYgzOzDZna/mX27bdsiM/usme0O/58Ybjcz+0szu8PMvmlmzylKLiGESGJq+xS3b7qdw/sOg8PhfYe5fdPtQ6kkipxBfAT4hY5tFwDXufsq4LrwPcBLgVXh3ybgQwXKJYQQsey5cA+zj83O2zb72Cx7LtxTkkTlUZiCcPcvAIc6Np8HbAtfbwPOb9v+UQ/4KnCCmZ1clGxCCBHH4bsOZ9reZAbtg5h09/sAwv9PDbefAtzdtt894TYhhBgo48vGM21vMlVxUlvENo/c0WyTmd1kZjc98MADBYslhBg2Vm5Zycgx87vGkWNGWLllZUkSlcegFcRUy3QU/r8/3H4PcFrbfqcC+6MO4O5b3X2Nu69ZvHhxocIKIYaPyQ2TnLn1TMaXj4PB+PJxztx6JpMbJssWbeCMDfh8VwMbgfeG/69q2/5mM/s48Dzg4ZYpSgghBs3khsmhVAidFKYgzOwKYB1wkpndA7ybQDFcaWavA+4CXh7ufg2wHrgDeAx4bVFyCSGESEdhCsLdfzPmo3Mj9nXgTUXJIoQQIjtVcVILIYSoGFIQQgghIpGCEEIIEYkUhBBCiEikIIQQQkQiBSGEECISKQghhBCRSEEIIYSIRApCCCFEJFIQQgghIpGCEH2j9XuFaCaDruYqGkZr/d7WEo2t9XsBVcMUouZoBiH6Quv3CtFcpCBEX2j9XiGaixSE6Aut3ytEc5GCEH2h9XuFaC5SEKIvtH6vEM1FUUyib7R+rxDNRDMI0WiUoyFE72gGIRqLcjSE6A/NIERjUY6GEP0hBSEai3I0hOgPKQjRWJSjIUR/SEGIxqIcDSH6QwpCNBblaAjRH4piEo1GORpC9E4pMwgz+30z+46ZfdvMrjCzo8zsdDO7wcx2m9k/mNmRZcgmhBAiYOAKwsxOAd4CrHH3ZwKjwG8A7wM+4O6rgO8Crxu0bEIIIZ6kLB/EGHC0mY0BxwD3AS8CPhF+vg04vyTZhKgMygQXZTJwBeHu9wLvB+4iUAwPAzcDD7n7dLjbPcApUd83s01mdpOZ3fTAAw8MQmQhSqGVCX5432HwJzPBpSTEoCjDxHQicB5wOrAUOBZ4acSuHvV9d9/q7mvcfc3ixYuLE1SIklEmuCibMkxMLwbudPcH3P0J4FPA84ETQpMTwKnA/hJkE6IyKBNclE0ZCuIu4KfN7BgzM+Bc4Fbgc8Cvh/tsBK4qQTYhKoMywUXZlOGDuIHAGf014FuhDFuBdwJ/YGZ3ABPApYOWTYgqoUxwUTalJMq5+7uBd3ds3gOcVYI4QlSSVoLfngv3cPiuw4wvG2fllpWVSfyb2j5VWdlEPiiTeojRA159qpoJrrU2hgMpiCFFD7joh24RVhp4NAMV6xtSFEIp+iE2wiocaCh3oxlIQQwpCqGMRpnL6YiNpBpFA48GIQUxpCiEciHKXE5PXIQVM9H7D/vAo65IQQwpCqFciMxu6Ylba2N8eXkDD83+8kdO6iGl6iGUZSCzWzbiIqzagx9gMAOPJgRdVDGqUApiiKlqCGVZjC8bD8xLEdtFOsoaeCTN/urwG6+qgpOCECJk5ZaVpYx+m0YZA4+6z/6qquDkgxAiRGtY15e6B11UVcFpBiEKp4q21ThkdqsnE+sn2H/J/nmLBNRp9ldV86ZmEKJQFDoqimZq+xQHth2Yv4KMwZKNS2qj7KsaVSgFIQpFoaOiaKJ+YzgcvOZgOQL1QFXNmzIxiUKpqm1VNIem/MaqaN7UDEIUSt2dh6L66DdWHFIQolCqalsVzaEuv7E6ZnpLQYjCaEUvzT42C6PBtn5sq3V8wJJoWnvKoqr2+3bqGqxh7t59r4qyZs0av+mmm8oWQ0TQmRkKwaiuH+WQ5/HKpmntEcnsWLEjOox1+Thr964duDxmdrO7r+m2n2YQohDyjl5qWjRU09ojkqmrI10KQhRC3g9EXR+wOJrWHpFMXR3pUhCiEPJ+IOr6gMXRtPaIZOriSO9ECkIUQt4PRF0fsDiytEfO7PpTB0d6FEqUE4WQd9nnpq1fkbY9VS0DLbJTxUS4biiKSYgKU7XoF9EMCotiMrMRMzu+N7GEEFmQM1uUSSoFYWYfM7PjzexY4FbgdjN7R7GiCSHkzBZlknYG8Qx3fwQ4H7gGWAa8uteTmtkJZvYJM7vNzHaa2VozW2RmnzWz3eH/E3s9vhBNoWnO+TqgoIAnSasgjjCzIwgUxFXu/gTzq69n5WLg39396cBPADuBC4Dr3H0VcF34Xoihpq7RL1mpSqdc15IYRZHKSW1mbwHeCXwDeBnBDOJydz8n8wkD/8U3gJXednIzux1Y5+73mdnJwPXufmbSseSkFqI82lcKHFs0huPMHJrJHGEWV3ZkycYlHLzm4ECj1oYlKCCtk7rnKCYzG3P36R6+92xgK4Ev4yeAm4HNwL3ufkLbft919wVmJjPbBGwCWLZs2XP37dvXk/xCRFGn5VGLJulaRHXq7WSpKxXXKWMsWEK06NnT9SPXR9tGDNbNrivsvIMm1ygmM5s0s0vN7N/C988ANvYo2xjwHOBD7v6TwPfJYE5y963uvsbd1yxevLhHEYRYSJPMC/2abLpdi8hV3NrIUlcqNiKro6MeRK0qBQXMJ60P4iPAZ4Cl4ftdwFt7POc9wD3ufkP4/hMECmMqNC0R/r+/x+ML0RNNKaCXh6Lrdi3ShNmmDcXN0vkWHd6roID5pFUQJ7n7lcAsQGhamunlhO5+ALjbzFr+hXMJzE1X8+SsZCNwVS/HF6JXknIOquJETUMeiq5b/kWaTj1txx/VKWP9HbNXhiUoIC1pS21838wmCCd9ZvbTwMN9nPf3gO1mdiSwB3gtgbK60sxeB9wFvLyP4wuRmfFl45G28NFFo7Uqd5FHcl3ctWh10Cu3rOzqg0g76o4qOzKxfoID2w4scFwPYiSfpiTGsPiq0iqIPyAY4T/NzL4MLAZ+vdeTuvstQJSD5NxejylEv0R1eiPHjGAYM4/NnzC3RuRV7BS6de5piLsWrQ66s1PvJ4qpdbzO/Z/ygqdUshMepvpYqaOYzGwMOJNg8nd7mAtRKgpzFXkTNTLc+eqdtYpsyWu1umEZJadl7npERVxRr1DYtFFMiTMIM/vVmI/OMDPc/VM9SSdERYkaycZ1ClWNbMmr8m0dq48WRbewXmhmfaxuJqZfSvjMASkI0Xi6mVuqiDr3fOkW1gvVHTD0Q6KCcPfXDkoQIapKndai6MUs1ERTUt5t6jY7qPqAoVdSLxhkZi8Dfgw4qrXN3f+kCKGEGBRpO5I6jMh7cZ420eFaRJviHP8Q+B6aoFSjSJtJfQnwSoLwVCMIQV1eoFxCFE6TMqeht/yHpiQHtlNEm+IS6FZfvpq1e9c2UjlA+kS557v7bwPfdff3AGuB04oTS4jiaUrn2EriixvhJplHmrggURFt6ieBrk5Jlp2kNTH9IPz/mJktBQ4BpxcjksiTJtqX86IJnWOa6Jok52keORNVo6g29WJmrLsJL+0M4p/N7ATg/wBfA+4ErihMKpELcSaUXW/cVdsRTZ5UoTBbv6PLbtE13ZynTaw9VKU21X2WmnYGcRsw4+6fDCu5Pgf4p+LEEnFkmRHE/Tj3X7J/LvGrbiOaNKS9RoMMX42SCeh7dJk020njPK1ThFZaqtSmus9S0y4Y9E13f5aZnQ38b+DPgXe5+/OKFjCJYcukzpohG1vbPoI6ZYEmkfUadVMmeZjo4mQaOXqE6YMLl1TJci+GZYGbulLV+5PrehA8Wbn1ZcAl7n4VcGSvwoneyDpdrVIZ5UGxa/OuTNdocsMka/euZd3sugXRKHlFOcXdtyjlANnuRZXMKWIhdb8/aRXEvWb2f4FXANeY2XiG74qcyDpdrVIZ5UEwtX2KmYPRVeh7UYB52Y+znjvLvVB56mpT9/uT1gfxCuAXgPe7+0Phgj7vKE4sEUXW6IyqlVEumqSOuxcF2K/9uGWeijPzjU6M4o973/eiDkl8w0yd708qBeHuj9FWd8nd7wPuK0ooEU0vTtU6lVHul6SOuxcF2E+4ZJo1m8+4+AygGs5UIaJIXWpDlI+qdCYT16GPTYz11N5+opySwk87o4ta/1szjp2v3llpZaHcmuFBCqJmNLVzz4O4Dn3Vxat6Ol4/CrnbbKbzGEUkVBXRkdc98UtkI/WCQVVk2MJci6KKI8JeZapKW5JKX0SF3eYdDpnXokGddJOzKtdfJJM2zFUKYsgpqiNpmkxZ6eaD6Oz4Y3NWely1rqj4+yQ5V1+2uvb3rQiqqDTzzoMQDaWKpQCqKFNWWuGNcXSaoPIu+1FUBm+SnE24b3lT94rBUhBDTj8dSVFVKutenqDF5IbJIP49gs6ONu+EqqLqTCXJWeR9q2tF1LorTSmIIafXjqSfkVG3h33QRfSK7HzSdvx5J1QVlcGbJGdR963Oo/C6D3bkgxhyerX392rjTnO+QfogBnGusmzQgz5vWY7xKlNV2eWkbiBFPfC9HLdXp2raB2ZQnVtVH+C6UsR9y9uBP0iKUJp5XOO0CkJ5EDWhyPjzXnIres0yTjvlHlS+R91NAFWjiPtW50WN8i49Pug8FPkgakLVnF292riz2KkH4ZiswqJBIpm6V0RNqhiclUH3A6UpCDMbNbOvm9m/hO9PN7MbzGy3mf2DmamceBtVG+n26lRN+7APyjFZ985nGKh7RdQ8GXQ/UKaJaTOwEzg+fP8+4APu/nEzuwR4HfChsoSrGlWcZvdiTkg75U4aKeXZMVRp9TERT6+mqyomqfXDoPuBUhSEmZ1KsPjQFuAPzMyAFwGvCnfZBlyEFMQcg1wes2jSPOxZR0pxHUGaDqKJ9a3qXqokD5pYN2rQ/UBZM4gPAv8dOC58PwE85O6tJbbuAU6J+qKZbQI2ASxbtqxgMavDsI10s4yU4jqCh7/88Ly1L5rQQaSh146xaR3qoGahg2TQ/cDAw1zN7BeB9e7+RjNbB7wdeC2ww91/NNznNOAad//xpGMNW5jrMJElPDC2MN4oTy6W20bTQ1h7Dd0tMgS5jJlJP+GxTZpJRVHlMNcXAL9sZuuBowh8EB8ETjCzsXAWcSqwvwTZxABIa/aBdCOlWAdd9OqjHN53OOgMa/TwZ+mwenVkpvleL7OMsmYmvdrrmzaT6oeBRzG5+x+6+6nuvgL4DeA/3H0D8Dng18PdNgJXDVo2UTxZopPShgfGPvCjMUIYtSrbkDWiq9fQ3TTf6yXMsqwQ7V4j1KoWUl4mVcqDeCeBw/oOAp/EpSXLIwog68OXJhciriNYumnpgu0YC8wOVX/4s16zXjvGNN/rZXZSVoh2r+GxVQspL5NSM6nd/Xrg+vD1HuCsMuURxRP78EWYfYBUU/1Oc9ToolEMY/8l+xlbNIYdbcwcmok1OSTJ1UkZtumsHVavjsw03+vFbFNmiPYgqwQ0EZXaEAMltpNumX14UhGMHD2SPQrFYebQzNwsYfrgNCPHjLD6stVMbpiMd8SGD3+SAujV/t6vQumlw+o1dLfb93oJs6xbiHbd5C2SKpmYxBAQZcaIM/tMH5wmis6R8zwbPdHHapljkswo3Wz9vZjH8sgGr1K2dy9mm7plQtdN3iJRNVcxcDpH1XFmnzg6wy6T1n+eoy20MW5U3y3MM2vYZJ6VYpsedpkFXYv+qXKYq6gYg37gOs0YqTr4kKiRcxr/Qbs5Js6M0s3Wn9XUk6ezs4nZ3r2gENTBIhPTkFOF1boizU4xRE31RxfFxbMGpDXHdAvzzGrqUaXY/FEI6mCRgqg5/ZbErsID12nzjctfGF8+vkA5TG2fYvbR2egvEG8/jrpu3RRAVtv0IHwHdV2ruRemtk/1HYUmsiEfRI3JY7WqKq7W1a1d7SYxRojMmB6bGOPsB8/OfHzIt85Np/luYv0EB685WMjiMe3taJq5Jaqt7TS9fEreyAcxBORRjKyKMd9J8fgLOoqYchrTh6IjoFrHjbtu/S7o0qJTMay+bDWQLq8jLU0sRhdHVFtbDGsI6iCQgqgxeThBBx3zndYhHueUTeoo2klScEVnysY5Uu1oy7VD76UddY0ASmpTE2dMVUE+iBrTrxO01VnMPjY7Z/fvJeY7rR08D4d4mk68m4Ir2nkcN7KfORg93elVMWVtRxUCEnoltq0RfimRH1IQNaYfJ+iC5LKZJ7+bVTmk7XTycIgnFuZLmdRUtPM4a4ffq2LK2o6467978+7KO7qrlCw4TEhB1Jh+Mj7zil7Kcpy8TGJRHcXqbatTLwpfdKZsXIc/NjGWayeXtR1x13n64HTlZxXKbi4HRTENKXlFL2U5Tl6ZxVW3ow8ySioLWRISFRXUbBTFJBLJK3opy3HycohXfQH7blVRy1p9Ler6x1HXvIKqDx7qhhTEkJJXZ53lOGnLUBfxkA+6REM/SqwoOaOu//T3piOd53XM9lYZjvyRiWmIyasjzrNDj0yICqu9ji/v/dhFrrecF1Pbp9i5cedA19FuUrJdnsURm45MTA2iqE4rrwJweRaSi8xzCMcw/YwIi1pvOStx97J17th1tAsy+fS6uFAV0Upw+SMFUXGGbdrc7WHuNbFsbNFY5PoSY4uefASKzkxOupfdEgCLNPk0pVJsFasC1B2FuVacKhTTSyKvYnGt40RGRHXQy4jQYw7cvr3oEWjSvUw6h+L906FcifzRDKLiVHnanNfsplshtk6yZoofvutwrOKZOfSkTafoEWjSvYxdOGlUpSTS0iRzWVXQDKLi5FEWoqiS0HEj4l2bd/V9nDls/tuRY0aYWD/RtT1T26e47Xdum0sAi2WEueNMrJ8odASadC+TEgDVwS0k7jc9uWGStXvXpk6aFMlIQVScfqfNRdbfiRsRzxycyae+ksHqy1bPy55dsnEJB7Yd6Nqe3Zt34z9MYa+aYe44B7YdYMnGJYVl6ybdy7hMYaBSZTCqsP5EnWtK1Q2ZmCpOv9Pm3Zt3F+Z4TVpPOq+S41HLk6ZpT5RDeg4jch2J2cdmOXjNwcJCIrPey4e//DAHth2oTIBCVQImhqnMedlIQdSAfpKu4jrKLD6Mqe1T7N68e+5YoxOjnHHxGazcspKdv7Wz7+NnSbbLwyezbnZdUCKkz+NkIWp9iPZ7GtX57r9k/wLzWJkdYVU65ir75ZqGTEwNJjHSyUllImjZ8tsVzczBGXa+NlAMYxPRY4wsPpKkQmydJo32sNTE81nkbnPbB7ledBqTSFL+RydldYRV6Zi11vfgkIJoMN0e3DS22z0X7om25T8RfLbq4lWZfCRZbNhRHev0I9PYkfN7/8jzxbkfwu2DDIlME6qcpZMtqyPMY/2RPPwXCmcdHANXEGZ2mpl9zsx2mtl3zGxzuH2RmX3WzHaH/08ctGxNI82D2y2nIqnjOnzX4UxlmONG0rveuCt6++ZdC0fVT8DIcSNdzze+PH6BGRhs+eg0I+/YexURxVVWR5jb+iN9Opaz/ubKdqrXmYHXYjKzk4GT3f1rZnYccDNwPvAa4JC7v9fMLgBOdPd3Jh1LtZiSSZ1fkFDiO6lEdNY6RrHHGiW2xERWedtliWz7CDDbX12nLKStrxRXE2nJxiUcvOZgZeL6ey37UkadpCbVmcqbytZicvf7gPvC14+a2U7gFOA8YF242zbgeiBRQYhkOqNmoiJ3IHmmsXLLSm77ndsWmplGYOZ7M1w/cj3jy8aZWD/RNeImdjaSRTl0kbdF65ztznUAwr5iEBE4SfWVOkfedUny6jVgogz/RTenukqDd6fUaq5mtgL4AvBM4C53P6Hts++6+wIzk5ltAjYBLFu27Ln79u0bjLANoNcRVWcUkx1r8ATzlUZYcbWT9hFi7AwiHNV3MjYxxuzjs13lTXrQuy2SU+QINvbcYX7HIDqjqnSCZcwgkhazWn3Z6qGeXaSdQZTmpDazHwE+CbzV3R9J+z133+rua9x9zeLFi4sTsIH0anef3DDJ2Q+ezTpfxzpfx5EnHblwRpEi4mbllpULHMwtohzPqy5e1VXebrbtNI76omzUsece0JisSgllZTiWk5zqVa9xVhVKURBmdgSBctju7p8KN0+F/omWn+L+MmRrOt1KEaRx6vUacTO5YZKR4yJ+crPxjudu8nZ70Luao4zCOtCkcw+iI6pSJ1jGmtJJSqkqIbtVp4woJgMuBXa6+1+0fXQ1sDF8vRG4atCyDTtpR5z9RNy0F8fr3N5LDZ1uD3pUJzFP3phENOg/AiZpdDyIjqhqneCg6yQlKSXlUqSjjBnEC4BXAy8ys1vCv/XAe4GXmNlu4CXhezFA0o4440ZmS9+wtHv4ac4PZuz3wkRAgCUblwSRUu3fWz6eaBbLwzwzuWEyl0Sq9Wf2AAANi0lEQVTCXqlaJ1hGyGmcUlIuRTrKiGL6EvF5rucOUhYxn7Qjzn4ibvJaCzvpeHNy7zscRGC5z4skap1vz4V7YmtA5VVWYtXFq3Jtbxbyvtb9UJU6Ti3qEjVWNlqTWsxRZKRJezTN2KIxHGfm0EwuD+bcsROilTpp5UHERbLsfPXO2AiYbjkYsfKVtM51FTpBrRddLSqbByGqy8otK4MaS0+0bTwi2Zaehs7R4/TB6WCtg5xCPVvO7NiwxghaWeAQPYpMml3EEdcZF7mkZzcFUJXlRKvmDxHpkIIQ8zCzectwBjEF/TGoKqBJ5cej9oX4DjSreaYME0rVzDZJaL3oeqJifWKOqMJ8/kPvOywyz9FjkqNzYv1EtHer41eexg6fNSyzjJDSuHPu3ry7cvWH5BSuJ5pBiDmKMgPkNXpMGjEDHNh2INLEZGPGyHEjmX0eWcwzWa7dPJ9JWIeql9pQceecPjg9l/VelVmFnML1RApCzFGUGSCvaJpuo/S4ooT+Q2fsR8Y458FzMp0vi4M37bVbUO4kjK7qpSNPa1KrymprVfGHiPTIxCTmKMoMkFcWbVxneHjf4e4lNTLOgrLmQaS9dpELA4VkNUklJgF2kJczWOWzhwvNIMQcRZoBchk9xpUFH4WxE8YS16HOOgvK6lhPe+2yKrKkWUzUOae/N83MwYUXKQ9ncJ2c4iIfpCDEPCptBogrCz7DvMirTnqZBcX6FMLiflGdf5pr180s1N6Rp+mQO88ZV7E3D2dwVdakFoNDJqYaUJdpfTc5+21H3CpxQOSouUUv5qykEXc/Rf2SzEIjx4wwsX5i7hrt3Lgzc2RUXua8qHulXIbhY+gyqauSWZqWuqyK1U3OLO2Iu0epV8hro9dM3antUwuTBnM8dlQUU+eiS7H0kM2dVb6oezVy9EikGU/Z0PUjbSb1UCmIunS27dSlREE3OZOWG1297cmM6jglMDYxxqqLVwGkL6thsPQNSznjb85I1YZ2xTS6aJTZh2fx6eTnY52vy23Q0W1xoxZF3/s4OUYnRvHHvVbPj4im8gsGlUGV6uOnpS7T+m5yJi032m6uiYvymT44PWd/X7t3bXy5x3Y8yI1IYwrqjFqaOTjTVTkwmu+iPGnu6SCSy+LkmDk0M/A1HUS5DJWCqEtn207VSjbH0U3OJHnblXTSvci0EFDEd5JICj+NZSbfQUdsm0YptEPu9DeMLhqN3G982fjA13QQ5TJUCqIunW07dSlR0E3OifUTid9vmTS63YtUCwHFfKfffToZXz7e06Ajzlkfdw1Xb1tdWIccNQOafXQWjpi/XxV/c6J4hkpB1KWzbaeMpRp7oZucB6852PUYO1bsYGL9RHLHPxIsRr/nwj0s2bhk3vniFucZW9Q9mjtu1BwrRvi7yTroSDJJFXGvu0WORc2A/IfO2PFjlf/NieIZKic15BvFVLeIqDJJW4p75JgRlmxcwtSVU4mhq6192zuuqe1TwQJBP4w+UVK9oy+d9KXoRLsRwAMFYhjTh6a7RlYlOW4HGXSQRrbY+9JnpFSWZ0PP0eDRehAx5JUIpqzSbGSpG3TwmoOc8+A58zoORliQKNeZpDW5YZJdm3fFKpakezR9KCYL25M7yqzZ54P0g6VJbCui/laWZ2MYnqM6K8ChMjHlSR0josqkF59Bu0OUGP9xZ8c6cyh51hF3j/rxT2Vx3A7SD5aUDd7N79GP2TXLs9H05yjPKLcykILokTpGRJXJPPs6BJE5MURVQI37pXbum6ajjbpHg/JPZT1PP9nnSdeiKL/H1Pap+KKKEde96c9R3RWgFESP1DEiqmzmRtq+jnXT61h9+equnWVrBBZVhymqY00zU4m6R4MKBshynn5Hn0nXor2Tyit0de5exRB13ZvwHCUp8borwKHzQeRFXmscDDNp7Pex+Qmj0TWW5h1z3+Egoa7NCZt0j/rxT2WxM6c9T7/F8Vr77PytnZGf591JJeWSxF33uj9H3XwodV9qVTOIHikz/LQuxfs6iZK72+g1thObjXdits9UVl+2uvB7VJSdOY/R5+SGydgih3l3UklyxV33yQ2THL/2+Hnbjl97fG2cuN1MSHUMrW9HM4g+KKM0dl2jPnqVu98R2CDuUVFlsPMafQ5qlB4r7/Lx2Ouw6427eOi6h+Zte+i6h9j1xl2pa2iVSTclXvelVjWDqBBpZgZ1dHpNbZ/qqXQ11GMEVpSdOa+2D2q224u8+7fuz7S9aqTxodS5PEmlFISZ/YKZ3W5md5jZBWXLM0jSminq5vRKcjJDd7nrkElelKO1Dm1vpyd5ExaBqgN1GMD0Q2VMTGY2Cvw18BLgHuBGM7va3W8tV7LBkNZMUTenV7cieGnzDKraKUKxJpw82j5Is2RmeROWka0DdTchdaNKM4izgDvcfY+7/xD4OHBeyTINjLQzg7qNWJJmCFWWOwtVH+lX2Sy5dNPSTNurSJ1NSN2ozAwCOAW4u+39PcDzOncys03AJoBly5YNRrIBkHZmULcRS2yJjZgw1bpS5VlOlc2SLUf0/q37g5nEaKAc6uCgHgaqpCCiloBZUEbM3bcCWyEo1le0UIMii5miyp1RJ3HtapJyqDpVN0ue8TdnSCFUlCqZmO4BTmt7fypQj1CGHKi6maJXmtquOlE3s6SoDpUp921mY8Au4FzgXuBG4FXu/p247/RS7luIYaTOFUVF/tSu3Le7T5vZm4HPEMQwfDhJOQgh0lMns6SoDpVREADufg1wTdlyCCGEqJYPQgghRIWQghBCCBGJFIQQQohIpCCEEEJEUpkw114wsweAfWXLUSAnAQ+WLcQAUXubjdpbHZa7++JuO9VaQTQdM7spTaxyU1B7m43aWz9kYhJCCBGJFIQQQohIpCCqzdayBRgwam+zUXtrhnwQQgghItEMQgghRCRSEEIIISKRgqgoZrbXzL5lZreYWeNqmpvZh83sfjP7dtu2RWb2WTPbHf4/sUwZ8ySmvReZ2b3hPb7FzNaXKWNemNlpZvY5M9tpZt8xs83h9kbe34T21v7+ygdRUcxsL7DG3auaaNMXZvYzwPeAj7r7M8NtfwYccvf3mtkFwInu/s4y5cyLmPZeBHzP3d9fpmx5Y2YnAye7+9fM7DjgZuB84DU08P4mtPcV1Pz+agYhSsHdvwAc6th8HrAtfL2N4CFrBDHtbSTufp+7fy18/Siwk2DN+Ube34T21h4piOriwLVmdrOZbSpbmAEx6e73QfDQAU8tWZ5B8GYz+2ZogmqEyaUdM1sB/CRwA0NwfzvaCzW/v1IQ1eUF7v4c4KXAm0IThWgWHwKeBjwbuA/483LFyRcz+xHgk8Bb3f2RsuUpmoj21v7+SkFUFHffH/6/H/g0cFa5Eg2EqdCe27Lr3l+yPIXi7lPuPuPus8Df0qB7bGZHEHSW2939U+Hmxt7fqPY24f5KQVQQMzs2dHZhZscCPwd8O/lbjeBqYGP4eiNwVYmyFE6rswz5FRpyj83MgEuBne7+F20fNfL+xrW3CfdXUUwVxMxWEswaIFg3/GPuvqVEkXLHzK4A1hGURJ4C3g38E3AlsAy4C3i5uzfCsRvT3nUE5gcH9gKvb9no64yZnQ18EfgWMBtufheBXb5x9zehvb9Jze+vFIQQQohIZGISQggRiRSEEEKISKQghBBCRCIFIYQQIhIpCCGEEJFIQQghhIhECkKImmNmY2XLIJqJFIQYKsxsRVi3/2/D2v3XmtnRZvY0M/v3sDjiF83s6WY2amZ7LOAEM5tt1cQK9/lRM3thW73/r5vZcWa2zsy+YGafNrNbzewSMxsJv/chM7spPPd72uTaa2bvM7P/DP9+NNy+2Mw+aWY3hn8vCLdfZGZbzexa4KMlXEoxBEhBiGFkFfDX7v5jwEPArxEsMP977v5c4O3A37j7DLALeAZwNkGd/3PMbBw41d3vCPd9k7s/GzgHeDw8x1nA24AfJyjY9qvh9gvdfQ3wLOCFZvasNrkecfezgL8CPhhuuxj4gLv/VCjn37Xt/1zgPHd/VR4XRYhONDUVw8id7n5L+PpmYAXwfOAfg7I6AIyH/78I/AxwOvCnwH8DPg/cGH7+ZeAvzGw78Cl3vyc8xn+6+x6YK7NxNvAJ4BVh+fYx4GQC5fPN8FhXtP3/QPj6xcAz2uQ6vlWnC7ja3VsKSYjckYIQw8jhttczwCTwUDgL6OSLwBuApcAfA+8gqKH0BYBwdbR/BdYDXzWzF4ff66xh42Z2OsGM46fc/btm9hHgqPZ9Il6PAGs7FUGoML7ftaVC9IFMTELAI8CdZvZyCKpzmtlPhJ/dQDC7mHX3HwC3AK8nUByY2dPc/Vvu/j7gJuDp4ffOMrPTQ9/DK4EvAccTdOoPm9kkwVof7byy7f+O8PW1wJtbO5hZlBITohCkIIQI2AC8zsy+AXyHYHlM3P0wcDfw1XC/LwLHEVTuBHirmX07/N7jwL+F23cA7yUo8Xwn8Gl3/wbw9fD4HyYwT7UzbmY3AJuB3w+3vQVYE65KdivBbEaIgaBqrkLkjJmtA97u7r+Y4Tt7gTXu/mBRcgmRFc0ghBBCRKIZhBBCiEg0gxBCCBGJFIQQQohIpCCEEEJEIgUhhBAiEikIIYQQkfx/I3kqDgKveOkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in features:\n",
    "    plt.plot(labels,features[x],'mo')\n",
    "    plt.title(\"Sales vs \" + x)\n",
    "    plt.xlabel(x)\n",
    "    plt.ylabel(\"sales\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in features:\n",
    "    plt.hist(features[x])\n",
    "    plt.title(x)\n",
    "    plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data must be positive.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-cd75ec11ea10>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m#We now use boxcox to transform the data so it’s closest to normal:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0max2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m212\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mdf_clean\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'newspaper'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxcox\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[0mprob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprobplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_clean\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'newspaper'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0max2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Probplot after Box-Cox transformation'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\morestats.py\u001b[0m in \u001b[0;36mboxcox\u001b[1;34m(x, lmbda, alpha)\u001b[0m\n\u001b[0;32m   1030\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1032\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Data must be positive.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1033\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1034\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlmbda\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# single transformation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Data must be positive."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#As from the above histogram graph, the data distribution for the newspaper is skrew towards left.\n",
    "#Lets correct it using Box Cox which helps in removing the data skrewness.\n",
    "\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(211)\n",
    "x = df_clean['newspaper']\n",
    "prob = stats.probplot(x, dist=stats.norm, plot=ax1)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_title('Probplot against normal distribution')\n",
    "#We now use boxcox to transform the data so it’s closest to normal:\n",
    "ax2 = fig.add_subplot(212)\n",
    "df_clean['newspaper'], _ = stats.boxcox(x)\n",
    "prob = stats.probplot(df_clean['newspaper'], dist=stats.norm, plot=ax2)\n",
    "ax2.set_title('Probplot after Box-Cox transformation')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_clean['newspaper'], bins='auto')  # arguments are passed to np.histogram\n",
    "plt.title(\"Newspaper after Box cox transformation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_clean['sales'], df_clean['newspaper'], 'ro')  # arguments are passed to np.histogram\n",
    "plt.title(\"Scatter plot b/w sales and newspaper\")\n",
    "plt.xlabel(\"Newspaper\")\n",
    "plt.ylabel(\"Sales\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            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:                     570.2\n",
      "Date:                Thu, 05 Sep 2019   Prob (F-statistic):           1.58e-96\n",
      "Time:                        00:38:18   Log-Likelihood:                -386.19\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",
      "Intercept      2.8894      0.361      7.995      0.000       2.177       3.602\n",
      "TV             0.0457      0.001     32.810      0.000       0.043       0.048\n",
      "radio          0.1876      0.008     22.190      0.000       0.171       0.204\n",
      "newspaper      0.0060      0.040      0.152      0.879      -0.072       0.084\n",
      "==============================================================================\n",
      "Omnibus:                       59.670   Durbin-Watson:                   2.078\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              146.495\n",
      "Skew:                          -1.320   Prob(JB):                     1.55e-32\n",
      "Kurtosis:                       6.258   Cond. No.                         521.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\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, 05 Sep 2019   Prob (F-statistic):           4.83e-98\n",
      "Time:                        00:38:18   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",
      "Intercept      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",
      "                            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, 05 Sep 2019   Prob (F-statistic):           1.47e-42\n",
      "Time:                        00:38:18   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",
      "Intercept      7.0326      0.458     15.360      0.000       6.130       7.935\n",
      "TV             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": [
    "import statsmodels.formula.api as sm\n",
    "model1 = sm.ols(formula=\"sales~TV+radio+newspaper\", data=df_clean).fit()\n",
    "model2 = sm.ols(formula=\"sales~TV+radio\", data=df_clean).fit()\n",
    "model3 = sm.ols(formula=\"sales~TV\", data=df_clean).fit()\n",
    "#sales~TV+radio+newspaper\n",
    "print(model1.summary())\n",
    "print(model2.summary())\n",
    "print(model3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.1</td>\n",
       "      <td>20.555465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.4</td>\n",
       "      <td>12.345362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.3</td>\n",
       "      <td>12.337018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.5</td>\n",
       "      <td>17.617116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.9</td>\n",
       "      <td>13.223908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.2</td>\n",
       "      <td>12.512084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.8</td>\n",
       "      <td>11.718212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.2</td>\n",
       "      <td>12.105516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.709379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.6</td>\n",
       "      <td>12.551697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0    22.1  20.555465\n",
       "1    10.4  12.345362\n",
       "2     9.3  12.337018\n",
       "3    18.5  17.617116\n",
       "4    12.9  13.223908\n",
       "5     7.2  12.512084\n",
       "6    11.8  11.718212\n",
       "7    13.2  12.105516\n",
       "8     4.8   3.709379\n",
       "9    10.6  12.551697"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Actual and predicted values\n",
    "y_pred = model2.predict()\n",
    "df1 = pd.DataFrame({'Actual': labels, 'Predicted': y_pred})  \n",
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters:  Intercept    2.921100\n",
      "TV           0.045755\n",
      "radio        0.187994\n",
      "dtype: float64\n",
      "R2:  0.8971942610828956\n",
      "Standard errors:  Intercept    0.29449\n",
      "TV           0.00139\n",
      "radio        0.00804\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Model 2 Parameters, error, and r square\n",
    "print('Parameters: ', model2.params)\n",
    "print('R2: ', model2.rsquared)\n",
    "print('Standard errors: ', model2.bse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}