EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import clear_output,display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the train and test datasets\n",
    "\n",
    "train=pd.read_csv(\"train.csv\")\n",
    "test=pd.read_csv(\"test.csv\")\n",
    "sample_sub=pd.read_csv(\"sample_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512087, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11004</td>\n",
       "      <td>0.007</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13650</td>\n",
       "      <td>0.007</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11655</td>\n",
       "      <td>0.007</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12054</td>\n",
       "      <td>0.007</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6804</td>\n",
       "      <td>0.007</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10395</td>\n",
       "      <td>0.007</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16023</td>\n",
       "      <td>0.007</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14385</td>\n",
       "      <td>0.007</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16485</td>\n",
       "      <td>0.007</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13377</td>\n",
       "      <td>0.007</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Day_No  Course_ID Course_Domain Course_Type  Short_Promotion  \\\n",
       "0   1       1          1   Development      Course                0   \n",
       "1   2       2          1   Development      Course                0   \n",
       "2   3       3          1   Development      Course                0   \n",
       "3   4       4          1   Development      Course                0   \n",
       "4   5       5          1   Development      Course                0   \n",
       "5   6       6          1   Development      Course                0   \n",
       "6   7       7          1   Development      Course                1   \n",
       "7   8       8          1   Development      Course                1   \n",
       "8   9       9          1   Development      Course                1   \n",
       "9  10      10          1   Development      Course                1   \n",
       "\n",
       "   Public_Holiday  Long_Promotion  User_Traffic  Competition_Metric  Sales  \n",
       "0               1               1         11004               0.007     81  \n",
       "1               0               1         13650               0.007     79  \n",
       "2               0               1         11655               0.007     75  \n",
       "3               0               1         12054               0.007     80  \n",
       "4               0               1          6804               0.007     41  \n",
       "5               0               1         10395               0.007     62  \n",
       "6               0               1         16023               0.007    122  \n",
       "7               0               1         14385               0.007    114  \n",
       "8               0               1         16485               0.007    121  \n",
       "9               0               1         13377               0.007    100  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>512077</th>\n",
       "      <td>548018</td>\n",
       "      <td>873</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11739</td>\n",
       "      <td>0.07</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512078</th>\n",
       "      <td>548019</td>\n",
       "      <td>874</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10311</td>\n",
       "      <td>0.07</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512079</th>\n",
       "      <td>548020</td>\n",
       "      <td>875</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9429</td>\n",
       "      <td>0.07</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512080</th>\n",
       "      <td>548021</td>\n",
       "      <td>876</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9639</td>\n",
       "      <td>0.07</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512081</th>\n",
       "      <td>548022</td>\n",
       "      <td>877</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9072</td>\n",
       "      <td>0.07</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512082</th>\n",
       "      <td>548023</td>\n",
       "      <td>878</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.07</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512083</th>\n",
       "      <td>548024</td>\n",
       "      <td>879</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10542</td>\n",
       "      <td>0.07</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512084</th>\n",
       "      <td>548025</td>\n",
       "      <td>880</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13671</td>\n",
       "      <td>0.07</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512085</th>\n",
       "      <td>548026</td>\n",
       "      <td>881</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.07</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512086</th>\n",
       "      <td>548027</td>\n",
       "      <td>882</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11445</td>\n",
       "      <td>0.07</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Day_No  Course_ID       Course_Domain Course_Type  \\\n",
       "512077  548018     873        600  Software Marketing     Program   \n",
       "512078  548019     874        600  Software Marketing     Program   \n",
       "512079  548020     875        600  Software Marketing     Program   \n",
       "512080  548021     876        600  Software Marketing     Program   \n",
       "512081  548022     877        600  Software Marketing     Program   \n",
       "512082  548023     878        600  Software Marketing     Program   \n",
       "512083  548024     879        600  Software Marketing     Program   \n",
       "512084  548025     880        600  Software Marketing     Program   \n",
       "512085  548026     881        600  Software Marketing     Program   \n",
       "512086  548027     882        600  Software Marketing     Program   \n",
       "\n",
       "        Short_Promotion  Public_Holiday  Long_Promotion  User_Traffic  \\\n",
       "512077                0               0               1         11739   \n",
       "512078                0               0               1         10311   \n",
       "512079                0               1               1          9429   \n",
       "512080                0               0               1          9639   \n",
       "512081                0               0               1          9072   \n",
       "512082                0               0               1          8904   \n",
       "512083                0               0               1         10542   \n",
       "512084                0               0               1         13671   \n",
       "512085                0               0               1          8904   \n",
       "512086                1               0               1         11445   \n",
       "\n",
       "        Competition_Metric  Sales  \n",
       "512077                0.07    144  \n",
       "512078                0.07    133  \n",
       "512079                0.07    114  \n",
       "512080                0.07    121  \n",
       "512081                0.07    111  \n",
       "512082                0.07    114  \n",
       "512083                0.07    145  \n",
       "512084                0.07    167  \n",
       "512085                0.07    107  \n",
       "512086                0.07    152  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((512087, 11), (36000, 9))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                       0\n",
       "Day_No                   0\n",
       "Course_ID                0\n",
       "Course_Domain            0\n",
       "Course_Type              0\n",
       "Short_Promotion          0\n",
       "Public_Holiday           0\n",
       "Long_Promotion           0\n",
       "User_Traffic             0\n",
       "Competition_Metric    1764\n",
       "Sales                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                      0\n",
       "Day_No                  0\n",
       "Course_ID               0\n",
       "Course_Domain           0\n",
       "Course_Type             0\n",
       "Short_Promotion         0\n",
       "Public_Holiday          0\n",
       "Long_Promotion          0\n",
       "Competition_Metric    120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, Day_No, Course_ID, Course_Domain, Course_Type, Short_Promotion, Public_Holiday, Long_Promotion, User_Traffic, Competition_Metric, Sales]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=list(train.columns)\n",
    "train[train.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, Day_No, Course_ID, Course_Domain, Course_Type, Short_Promotion, Public_Holiday, Long_Promotion, User_Traffic, Competition_Metric, Sales]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols=list(train.columns)\n",
    "train[train.duplicated(cols.remove(\"Competition_Metric\"))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are no duplicate rows in our dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                      int64\n",
       "Day_No                  int64\n",
       "Course_ID               int64\n",
       "Course_Domain          object\n",
       "Course_Type            object\n",
       "Short_Promotion         int64\n",
       "Public_Holiday          int64\n",
       "Long_Promotion          int64\n",
       "User_Traffic            int64\n",
       "Competition_Metric    float64\n",
       "Sales                   int64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID                      int64\n",
       "Day_No                  int64\n",
       "Course_ID               int64\n",
       "Course_Domain          object\n",
       "Course_Type            object\n",
       "Short_Promotion         int64\n",
       "Public_Holiday          int64\n",
       "Long_Promotion          int64\n",
       "Competition_Metric    float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train.dtypes)\n",
    "print(\"---------------\")\n",
    "display(test.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Our Test Dataset does not have User_Traffic**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "882"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Day_No\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We have data for 882 days**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895,\n",
       "       896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908,\n",
       "       909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921,\n",
       "       922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934,\n",
       "       935, 936, 937, 938, 939, 940, 941, 942], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"Day_No\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"Day_No\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We have to predict for the next 60 days of data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking into each Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sales-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    512087.000000\n",
       "mean        120.826924\n",
       "std          54.355258\n",
       "min           0.000000\n",
       "25%          84.000000\n",
       "50%         111.000000\n",
       "75%         146.000000\n",
       "max         682.000000\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Sales\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def univ_numerical(data,name):\n",
    "    \n",
    "    fig,ax=plt.subplots(1,2,figsize=(14,7))\n",
    "    var=data[name]\n",
    "    skewness=var.skew()\n",
    "    kurtosis=var.kurt()\n",
    "    range=(var.min(),var.max())\n",
    "    \n",
    "    fig.suptitle(f\"{name}\\nSkewness= {skewness}  Kurtosis={kurtosis}\\nRange={range}\")\n",
    "    \n",
    "    sns.kdeplot(var,ax=ax[0])\n",
    "    sns.boxplot(var,ax=ax[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "univ_numerical(train,\"Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Slightly positively skewed and leptokurtic**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Day_No', 'Course_ID', 'Course_Domain', 'Course_Type',\n",
       "       'Short_Promotion', 'Public_Holiday', 'Long_Promotion', 'User_Traffic',\n",
       "       'Competition_Metric', 'Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ID-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              1\n",
       "1              2\n",
       "2              3\n",
       "3              4\n",
       "4              5\n",
       "           ...  \n",
       "512082    548023\n",
       "512083    548024\n",
       "512084    548025\n",
       "512085    548026\n",
       "512086    548027\n",
       "Name: ID, Length: 512087, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "list1=[]\n",
    "for i,j in enumerate(train.ID):\n",
    "    \n",
    "    if(i+1==j):\n",
    "        list1.append(False)\n",
    "    else:\n",
    "        list1.append(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>943</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12663</td>\n",
       "      <td>0.186</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>944</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16905</td>\n",
       "      <td>0.186</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>945</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15141</td>\n",
       "      <td>0.186</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>946</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14490</td>\n",
       "      <td>0.186</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>947</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11025</td>\n",
       "      <td>0.186</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512082</th>\n",
       "      <td>548023</td>\n",
       "      <td>878</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512083</th>\n",
       "      <td>548024</td>\n",
       "      <td>879</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10542</td>\n",
       "      <td>0.070</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512084</th>\n",
       "      <td>548025</td>\n",
       "      <td>880</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13671</td>\n",
       "      <td>0.070</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512085</th>\n",
       "      <td>548026</td>\n",
       "      <td>881</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512086</th>\n",
       "      <td>548027</td>\n",
       "      <td>882</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11445</td>\n",
       "      <td>0.070</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511205 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Day_No  Course_ID       Course_Domain Course_Type  \\\n",
       "882        943       1          2         Development      Course   \n",
       "883        944       2          2         Development      Course   \n",
       "884        945       3          2         Development      Course   \n",
       "885        946       4          2         Development      Course   \n",
       "886        947       5          2         Development      Course   \n",
       "...        ...     ...        ...                 ...         ...   \n",
       "512082  548023     878        600  Software Marketing     Program   \n",
       "512083  548024     879        600  Software Marketing     Program   \n",
       "512084  548025     880        600  Software Marketing     Program   \n",
       "512085  548026     881        600  Software Marketing     Program   \n",
       "512086  548027     882        600  Software Marketing     Program   \n",
       "\n",
       "        Short_Promotion  Public_Holiday  Long_Promotion  User_Traffic  \\\n",
       "882                   0               1               1         12663   \n",
       "883                   0               0               1         16905   \n",
       "884                   0               0               1         15141   \n",
       "885                   0               0               1         14490   \n",
       "886                   0               0               1         11025   \n",
       "...                 ...             ...             ...           ...   \n",
       "512082                0               0               1          8904   \n",
       "512083                0               0               1         10542   \n",
       "512084                0               0               1         13671   \n",
       "512085                0               0               1          8904   \n",
       "512086                1               0               1         11445   \n",
       "\n",
       "        Competition_Metric  Sales  \n",
       "882                  0.186     97  \n",
       "883                  0.186    122  \n",
       "884                  0.186    106  \n",
       "885                  0.186    109  \n",
       "886                  0.186     80  \n",
       "...                    ...    ...  \n",
       "512082               0.070    114  \n",
       "512083               0.070    145  \n",
       "512084               0.070    167  \n",
       "512085               0.070    107  \n",
       "512086               0.070    152  \n",
       "\n",
       "[511205 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[list1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "548087"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[\"ID\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "512087"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "548027"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"ID\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Day_No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           1\n",
       "1           2\n",
       "2           3\n",
       "3           4\n",
       "4           5\n",
       "         ... \n",
       "512082    878\n",
       "512083    879\n",
       "512084    880\n",
       "512085    881\n",
       "512086    882\n",
       "Name: Day_No, Length: 512087, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Day_No\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17113"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "882*600-train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of Course_ID\n",
       "1      882\n",
       "2      882\n",
       "3      882\n",
       "4      882\n",
       "5      882\n",
       "      ... \n",
       "596    882\n",
       "597    882\n",
       "598    882\n",
       "599    882\n",
       "600    882\n",
       "Name: Day_No, Length: 600, dtype: int64>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1=train.groupby(\"Course_ID\").nunique()[\"Day_No\"]\n",
    "l1.unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day_No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Day_No\n",
       "Course_ID        \n",
       "530           881"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1=pd.DataFrame(l1)\n",
    "l1[l1[\"Day_No\"]==881]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Course_ID 530 has made sales for 881 days out of 882**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day_No</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Day_No\n",
       "Course_ID        \n",
       "9             698\n",
       "14            698\n",
       "29            698\n",
       "33            698\n",
       "41            698\n",
       "...           ...\n",
       "560           698\n",
       "570           698\n",
       "576           698\n",
       "592           698\n",
       "595           698\n",
       "\n",
       "[93 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l1[l1[\"Day_No\"]==698]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are 93 course IDs which have made sales on 698 out of 882 days only.<br>\n",
    "Why is this happening? It is possible that these courses were taken off the website on these days**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2=[]\n",
    "for i,j in enumerate(train[train.Course_ID==9][\"Day_No\"]):\n",
    "    l2.append(i+1==j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7601    546\n",
      "Name: Day_No, dtype: int64 7602    731\n",
      "Name: Day_No, dtype: int64\n",
      "11827    546\n",
      "Name: Day_No, dtype: int64 11828    731\n",
      "Name: Day_No, dtype: int64\n",
      "24873    546\n",
      "Name: Day_No, dtype: int64 24874    731\n",
      "Name: Day_No, dtype: int64\n",
      "28217    546\n",
      "Name: Day_No, dtype: int64 28218    731\n",
      "Name: Day_No, dtype: int64\n",
      "35089    546\n",
      "Name: Day_No, dtype: int64 35090    731\n",
      "Name: Day_No, dtype: int64\n",
      "36669    546\n",
      "Name: Day_No, dtype: int64 36670    731\n",
      "Name: Day_No, dtype: int64\n",
      "50597    546\n",
      "Name: Day_No, dtype: int64 50598    731\n",
      "Name: Day_No, dtype: int64\n",
      "53059    546\n",
      "Name: Day_No, dtype: int64 53060    731\n",
      "Name: Day_No, dtype: int64\n",
      "56403    546\n",
      "Name: Day_No, dtype: int64 56404    731\n",
      "Name: Day_No, dtype: int64\n",
      "64157    546\n",
      "Name: Day_No, dtype: int64 64158    731\n",
      "Name: Day_No, dtype: int64\n",
      "66619    546\n",
      "Name: Day_No, dtype: int64 66620    731\n",
      "Name: Day_No, dtype: int64\n",
      "72609    546\n",
      "Name: Day_No, dtype: int64 72610    731\n",
      "Name: Day_No, dtype: int64\n",
      "74189    546\n",
      "Name: Day_No, dtype: int64 74190    731\n",
      "Name: Day_No, dtype: int64\n",
      "77533    546\n",
      "Name: Day_No, dtype: int64 77534    731\n",
      "Name: Day_No, dtype: int64\n",
      "82641    546\n",
      "Name: Day_No, dtype: int64 82642    731\n",
      "Name: Day_No, dtype: int64\n",
      "84221    546\n",
      "Name: Day_No, dtype: int64 84222    731\n",
      "Name: Day_No, dtype: int64\n",
      "88447    546\n",
      "Name: Day_No, dtype: int64 88448    731\n",
      "Name: Day_No, dtype: int64\n",
      "93555    546\n",
      "Name: Day_No, dtype: int64 93556    731\n",
      "Name: Day_No, dtype: int64\n",
      "94253    546\n",
      "Name: Day_No, dtype: int64 94254    731\n",
      "Name: Day_No, dtype: int64\n",
      "102889    546\n",
      "Name: Day_No, dtype: int64 102890    731\n",
      "Name: Day_No, dtype: int64\n",
      "108879    546\n",
      "Name: Day_No, dtype: int64 108880    731\n",
      "Name: Day_No, dtype: int64\n",
      "111341    546\n",
      "Name: Day_No, dtype: int64 111342    731\n",
      "Name: Day_No, dtype: int64\n",
      "124387    546\n",
      "Name: Day_No, dtype: int64 124388    731\n",
      "Name: Day_No, dtype: int64\n",
      "134787    546\n",
      "Name: Day_No, dtype: int64 134788    731\n",
      "Name: Day_No, dtype: int64\n",
      "141659    546\n",
      "Name: Day_No, dtype: int64 141660    731\n",
      "Name: Day_No, dtype: int64\n",
      "144121    546\n",
      "Name: Day_No, dtype: int64 144122    731\n",
      "Name: Day_No, dtype: int64\n",
      "145701    546\n",
      "Name: Day_No, dtype: int64 145702    731\n",
      "Name: Day_No, dtype: int64\n",
      "148163    546\n",
      "Name: Day_No, dtype: int64 148164    731\n",
      "Name: Day_No, dtype: int64\n",
      "148861    546\n",
      "Name: Day_No, dtype: int64 148862    731\n",
      "Name: Day_No, dtype: int64\n",
      "153969    546\n",
      "Name: Day_No, dtype: int64 153970    731\n",
      "Name: Day_No, dtype: int64\n",
      "159077    546\n",
      "Name: Day_No, dtype: int64 159078    731\n",
      "Name: Day_No, dtype: int64\n",
      "173887    546\n",
      "Name: Day_No, dtype: int64 173888    731\n",
      "Name: Day_No, dtype: int64\n",
      "180759    546\n",
      "Name: Day_No, dtype: int64 180760    731\n",
      "Name: Day_No, dtype: int64\n",
      "202625    546\n",
      "Name: Day_No, dtype: int64 202626    731\n",
      "Name: Day_No, dtype: int64\n",
      "205969    546\n",
      "Name: Day_No, dtype: int64 205970    731\n",
      "Name: Day_No, dtype: int64\n",
      "209313    546\n",
      "Name: Day_No, dtype: int64 209314    731\n",
      "Name: Day_No, dtype: int64\n",
      "211775    546\n",
      "Name: Day_No, dtype: int64 211776    731\n",
      "Name: Day_No, dtype: int64\n",
      "226585    546\n",
      "Name: Day_No, dtype: int64 226586    731\n",
      "Name: Day_No, dtype: int64\n",
      "235221    546\n",
      "Name: Day_No, dtype: int64 235222    731\n",
      "Name: Day_No, dtype: int64\n",
      "237683    546\n",
      "Name: Day_No, dtype: int64 237684    731\n",
      "Name: Day_No, dtype: int64\n",
      "241909    546\n",
      "Name: Day_No, dtype: int64 241910    731\n",
      "Name: Day_No, dtype: int64\n",
      "245253    546\n",
      "Name: Day_No, dtype: int64 245254    731\n",
      "Name: Day_No, dtype: int64\n",
      "246833    546\n",
      "Name: Day_No, dtype: int64 246834    731\n",
      "Name: Day_No, dtype: int64\n",
      "247531    546\n",
      "Name: Day_No, dtype: int64 247532    731\n",
      "Name: Day_No, dtype: int64\n",
      "255285    546\n",
      "Name: Day_No, dtype: int64 255286    731\n",
      "Name: Day_No, dtype: int64\n",
      "266567    546\n",
      "Name: Day_No, dtype: int64 266568    731\n",
      "Name: Day_No, dtype: int64\n",
      "277849    546\n",
      "Name: Day_No, dtype: int64 277850    731\n",
      "Name: Day_No, dtype: int64\n",
      "279429    546\n",
      "Name: Day_No, dtype: int64 279430    731\n",
      "Name: Day_No, dtype: int64\n",
      "281009    546\n",
      "Name: Day_No, dtype: int64 281010    731\n",
      "Name: Day_No, dtype: int64\n",
      "297583    546\n",
      "Name: Day_No, dtype: int64 297584    731\n",
      "Name: Day_No, dtype: int64\n",
      "298281    546\n",
      "Name: Day_No, dtype: int64 298282    731\n",
      "Name: Day_No, dtype: int64\n",
      "309563    546\n",
      "Name: Day_No, dtype: int64 309564    731\n",
      "Name: Day_No, dtype: int64\n",
      "310261    546\n",
      "Name: Day_No, dtype: int64 310262    731\n",
      "Name: Day_No, dtype: int64\n",
      "317133    546\n",
      "Name: Day_No, dtype: int64 317134    731\n",
      "Name: Day_No, dtype: int64\n",
      "321359    546\n",
      "Name: Day_No, dtype: int64 321360    731\n",
      "Name: Day_No, dtype: int64\n",
      "323821    546\n",
      "Name: Day_No, dtype: int64 323822    731\n",
      "Name: Day_No, dtype: int64\n",
      "326283    546\n",
      "Name: Day_No, dtype: int64 326284    731\n",
      "Name: Day_No, dtype: int64\n",
      "329627    546\n",
      "Name: Day_No, dtype: int64 329628    731\n",
      "Name: Day_No, dtype: int64\n",
      "337381    546\n",
      "Name: Day_No, dtype: int64 337382    731\n",
      "Name: Day_No, dtype: int64\n",
      "339843    546\n",
      "Name: Day_No, dtype: int64 339844    731\n",
      "Name: Day_No, dtype: int64\n",
      "340541    546\n",
      "Name: Day_No, dtype: int64 340542    731\n",
      "Name: Day_No, dtype: int64\n",
      "342121    546\n",
      "Name: Day_No, dtype: int64 342122    731\n",
      "Name: Day_No, dtype: int64\n",
      "342819    546\n",
      "Name: Day_No, dtype: int64 342820    731\n",
      "Name: Day_No, dtype: int64\n",
      "343517    546\n",
      "Name: Day_No, dtype: int64 343518    731\n",
      "Name: Day_No, dtype: int64\n",
      "344215    546\n",
      "Name: Day_No, dtype: int64 344216    731\n",
      "Name: Day_No, dtype: int64\n",
      "346677    546\n",
      "Name: Day_No, dtype: int64 346678    731\n",
      "Name: Day_No, dtype: int64\n",
      "355313    546\n",
      "Name: Day_No, dtype: int64 355314    731\n",
      "Name: Day_No, dtype: int64\n",
      "368359    546\n",
      "Name: Day_No, dtype: int64 368360    731\n",
      "Name: Day_No, dtype: int64\n",
      "370821    546\n",
      "Name: Day_No, dtype: int64 370822    731\n",
      "Name: Day_No, dtype: int64\n",
      "371519    546\n",
      "Name: Day_No, dtype: int64 371520    731\n",
      "Name: Day_No, dtype: int64\n",
      "382801    546\n",
      "Name: Day_No, dtype: int64 382802    731\n",
      "Name: Day_No, dtype: int64\n",
      "383499    546\n",
      "Name: Day_No, dtype: int64 383500    731\n",
      "Name: Day_No, dtype: int64\n",
      "385961    546\n",
      "Name: Day_No, dtype: int64 385962    731\n",
      "Name: Day_No, dtype: int64\n",
      "388423    546\n",
      "Name: Day_No, dtype: int64 388424    731\n",
      "Name: Day_No, dtype: int64\n",
      "390003    546\n",
      "Name: Day_No, dtype: int64 390004    731\n",
      "Name: Day_No, dtype: int64\n",
      "403931    546\n",
      "Name: Day_No, dtype: int64 403932    731\n",
      "Name: Day_No, dtype: int64\n",
      "412567    546\n",
      "Name: Day_No, dtype: int64 412568    731\n",
      "Name: Day_No, dtype: int64\n",
      "415911    546\n",
      "Name: Day_No, dtype: int64 415912    731\n",
      "Name: Day_No, dtype: int64\n",
      "417491    546\n",
      "Name: Day_No, dtype: int64 417492    731\n",
      "Name: Day_No, dtype: int64\n",
      "425245    546\n",
      "Name: Day_No, dtype: int64 425246    731\n",
      "Name: Day_No, dtype: int64\n",
      "435645    546\n",
      "Name: Day_No, dtype: int64 435646    731\n",
      "Name: Day_No, dtype: int64\n",
      "438107    546\n",
      "Name: Day_No, dtype: int64 438108    731\n",
      "Name: Day_No, dtype: int64\n",
      "446743    546\n",
      "Name: Day_No, dtype: int64 446744    731\n",
      "Name: Day_No, dtype: int64\n",
      "452732    546\n",
      "Name: Day_No, dtype: int64 452733    731\n",
      "Name: Day_No, dtype: int64\n",
      "460486    546\n",
      "Name: Day_No, dtype: int64 460487    731\n",
      "Name: Day_No, dtype: int64\n",
      "464712    546\n",
      "Name: Day_No, dtype: int64 464713    731\n",
      "Name: Day_No, dtype: int64\n",
      "469820    546\n",
      "Name: Day_No, dtype: int64 469821    731\n",
      "Name: Day_No, dtype: int64\n",
      "473164    546\n",
      "Name: Day_No, dtype: int64 473165    731\n",
      "Name: Day_No, dtype: int64\n",
      "477390    546\n",
      "Name: Day_No, dtype: int64 477391    731\n",
      "Name: Day_No, dtype: int64\n",
      "486026    546\n",
      "Name: Day_No, dtype: int64 486027    731\n",
      "Name: Day_No, dtype: int64\n",
      "491134    546\n",
      "Name: Day_No, dtype: int64 491135    731\n",
      "Name: Day_No, dtype: int64\n",
      "505062    546\n",
      "Name: Day_No, dtype: int64 505063    731\n",
      "Name: Day_No, dtype: int64\n",
      "507524    546\n",
      "Name: Day_No, dtype: int64 507525    731\n",
      "Name: Day_No, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for i in l1[l1[\"Day_No\"]==698].index:\n",
    "    print(train[train.Course_ID==i][np.array(l2)].tail(1)[\"Day_No\"],train[train.Course_ID==i][~np.array(l2)].head(1)[\"Day_No\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There is a jump from 546 to 731 where no sales for some 93 odd courses were made**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Course_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Course_ID.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 600)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Course_ID\"].unique().min(),train[\"Course_ID\"].unique().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There are a total of 600 courses for which sales have been provided to us**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Course_Domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Development', 'Software Marketing', 'Finance & Accounting',\n",
       "       'Business'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Course_Domain\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**So, we have courses of Four domains- Development, Software Marketing, Finance and Accounting, Business. Let us check the count of each course**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2309b513cc8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby(\"Course_ID\")[\"Course_Domain\"].unique().apply(lambda x:x[0]).value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Around half of our courses are from Development**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Course_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Course', 'Program', 'Degree'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"Course_Type\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Course_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Course</th>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Program</th>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Degree</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Course_Type\n",
       "Course           310\n",
       "Program          288\n",
       "Degree             2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=pd.DataFrame(train.groupby(\"Course_ID\")[\"Course_Type\"].unique().apply(lambda x:x[0]).value_counts())\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2309bc88ec8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=arr.index,y=arr[\"Course_Type\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Short_Promotion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Short_Promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.619756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.380244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Short_Promotion\n",
       "0         0.619756\n",
       "1         0.380244"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=pd.DataFrame(train[\"Short_Promotion\"].value_counts(normalize=True))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=b.index,y=\"Short_Promotion\",data=b)\n",
    "plt.xlabel(\"Short_Promotion\")\n",
    "plt.ylabel(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis- Short_Promotion is causing an increase in Sales**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Public_Holiday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Public_Holiday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.968361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.031639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Public_Holiday\n",
       "0        0.968361\n",
       "1        0.031639"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=train[\"Public_Holiday\"].value_counts(normalize=True)\n",
    "a=pd.DataFrame(a)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2309b54be48>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=a.index,y=\"Public_Holiday\",data=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    857\n",
       "1     25\n",
       "Name: Public_Holiday, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Day_No\")[\"Public_Holiday\"].unique().apply(lambda x:x[0]).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**25 days out of a total of 882 days are Public Holidays**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis- On Public Holidays, more sales are made**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Long_Promotion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2309bcd8e08>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train[\"Long_Promotion\"].value_counts(normalize=True).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    301\n",
       "0    299\n",
       "Name: Long_Promotion, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Course_ID\")[\"Long_Promotion\"].unique().apply(lambda x:x[0]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Course_ID==5].Long_Promotion.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Half of the courses are on Promotion all the time, half of them are not.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis- Courses which are being promoted all the time have higher sales**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User_Traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    512087.000000\n",
       "mean      15375.101198\n",
       "std        7727.231205\n",
       "min         168.000000\n",
       "25%       10584.000000\n",
       "50%       13776.000000\n",
       "75%       18123.000000\n",
       "max      100002.000000\n",
       "Name: User_Traffic, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"User_Traffic\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         11004\n",
       "882       12663\n",
       "1764      11256\n",
       "2646      23625\n",
       "3528      14490\n",
       "          ...  \n",
       "507677    11298\n",
       "508559    10626\n",
       "509441     8505\n",
       "510323    14805\n",
       "511205    69258\n",
       "Name: User_Traffic, Length: 599, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Day_No==1][\"User_Traffic\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No. of people who land on Course Page are either the people who are using the course or for buying purposes.<br>**\n",
    "\n",
    "Hypothesis-\n",
    "1. User Traffic will be more on Public Holidays\n",
    "2. Higher User Traffic will lead to higher Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "univ_numerical(train, \"User_Traffic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Competition_Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzIAAAHdCAYAAADGuExTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdedgcVZn38e+djbCvEYQAAURGQDbBBUUijiAMoCCyyAj4isior8uMC4MO2+u4Mo6KMzIyKODKgIKoLMMoYREdCQgossimBAEDGLYESNL3+8epflLpPFtIQqdS38919fU83ae6+lT1Vr8+59SJzESSJEmSmmRMvysgSZIkSYvLICNJkiSpcQwykiRJkhrHICNJkiSpcQwykiRJkhrHICNJkiSpcQwykqRRi4hLIuLIYcpPj4h/ej7rtCxFxJMRsXm/6yFJWpRBRpL6JCLeFhHTq4PlB6qQ8Jp+16srIk6KiG/Vb8vMvTPz7Kr8qIi4pqf82Mz8f8uwTkdFREbEF3puf3N1+1mjXM+0iDh6pOUyc7XMvPs5VleStAwZZCSpDyLi74EvAp8C1gc2Af4deFM/69UQdwGHRMS42m1HAHcsrQfoWbckaTlkkJGk51lErAmcArw3M3+QmU9l5tzM/FFmfiQiVoqIL0bEn6rLFyNipeq+UyNiRkR8NCL+XLXkvDki9omIOyLi0Yg4vvZYJ0XE+RFxbkQ8ERE3RMT2tfINI+L7ETEzIu6JiPdXt78ROJ4SGJ6MiJuq26dFxNER8RLgdOBVVfmsqvysiPhkbf3viog7q3pdFBEb1soyIo6NiN9HxF8i4t8iIkaxCx8EfgPsVa1nHWBX4KKe/fzKiLg2ImZFxE0RMbW6/Z+B3YCvVHX/Sq0+742I3wO/r932our/lSPiXyLiDxHxWERcExErj6K+kqRlwCAjSc+/VwETgQuGKP848EpgB2B74OXAJ2rlG1T33wg4ATgD+FvgZZQD9BN6xnW8CTgPWAf4DnBhRIyPiDHAj4CbqnW9HvhgROyVmZdSWovOrbpXbV9bH5l5K3As8IuqfK3ejYiIPYBPAwcDLwT+AHyvZ7F9gV2q7TyYKpyMwjmUVhiAQ4EfAs/UHnsj4CfAJ6vt/jDw/YiYlJkfB64G3lfV/X219b4ZeAWw9SCPeSplH+9arfOjQGeU9ZUkLWUGGUl6/q0LPJyZ84YoPxw4JTP/nJkzgZOBt9fK5wL/nJlzKcFgPeBLmflEZt4C3AJsV1v++sw8v1r+C5QQ9EpKgJiUmadk5rPVWJAzKMFgaTgc+Hpm3pCZzwD/SGnBmVJb5jOZOSsz/whcQQlvo3EBMLVq3TqCEmzq/ha4ODMvzsxOZl4OTAf2GWG9n87MRzNzTv3GKvT9H+ADmXl/Zs7PzGur7ZIk9YFBRpKef48A6w0zDmNDSutF1x+q2wbun5nzq/+7B9wP1crnAKvVrt/X/SczO8CMan2bAhtWXa9mVd3DjqeM2VkaFtqOzHySsu0b1ZZ5sPb/7J56D6kKGj+htFStl5k/71lkU+CtPdv2GkrL0HDuG+L29SgB8K7R1E+StOw5mFGSnn+/AJ6mdGM6f5DyP1EOxG+prm9S3fZcbdz9p2pZmFytbx5wT2ZuOcT9coT1jlTe3Y7uY69KaY26f6QKj9I5wM8oLVa97gO+mZnvGuK+Q9V9qNsfpjxnW1C64kmS+swWGUl6nmXmY5SxLf9WDdRfpRqzsndEfA74LvCJiJgUEetVy35ruHWO4GURcWDVAvRByliSXwK/Ah6PiI9VA9nHRsS2EbFLdb+HgClV+BnMQ8DkiJgwRPl3gHdExA7VyQo+BfxvZt67BNtSdyXwBuC0Qcq+BewXEXtV2zWxOlHC5FrdRz0/TNWS9XXgC9UJEsZGxKu6J2GQJD3/DDKS1AeZ+QXg7yldo2ZSWhDeB1xIGaA+HbiZcnauG6rbnqsfAocAf6GMtTmwOkvafGA/yriUeyitDv8JrFnd77zq7yMRccMg6/0ZpdXowYh4eJBt/CnwT8D3gQcorRlLa/wNWfw0Mx8dpOw+ykkOjmfB/v0IC773vgQcVJ0t7cujfMgPU56P64BHgc/i96gk9U1kjtQzQJLUVBFxEvCizPzbftdFkqSlyV+SJEmSJDWOQUaStFyJiNOriSp7L6f3u26SpOWHXcskSZIkNY4tMpIkSZIaxyAjLSMRcVREXNPvekh67iLi+Ij4z37XQ5K0KIOMtAQi4jURcW1EPBYRj0bEz2tzcGgIEfG1iLg9IjoRcdQg5ZtHxI8j4omIeLiaW2Wode0XEb+txlBcGxFb18qOioj5PeMsptbKp0TEFRExOyJui4i/7ln3hyLiwer5/fpgc4ZExO4RkRHxydpt20bEZVXdF+m/GxHrRMQFEfFURPwhIt5WKzu+p75zqv20XlW+UUT8sHq9zYiIY3vWPTYiPhkRf6r2368jYq2qLKqy+6ttmhYR29Tu+7mIuC8iHq/q9fGede8QEddX++v6iNihVnZJT72fjYjfDPW89ax3akTMqF2fEBE/qN5Pa4xmHYOs897e5/O5yMxPZebRS7qerog4NSJ+Xz03t0XEET3le0TEDdVzcHdEHFMrm1q9Fur7+cghHme9av89EhGzIuIXEfHqnmWGfH1X+29O7XH+ezHuO9L7asj3d3Xfi6OcFvvBiPhKlPmPRtw/VfmkiPhOtc1/iYhvj2bfR8RuseiYrIyIt4xmm2vLLPJ5MNI2S1oCmenFi5fncAHWAGYBhwFjgZWBPYHtqvKjgGv6Xc/l8QK8F3g9Za6Uo3rKJgB3UeZYWRWY2N2ng6xnS+Bx4DXAOOAfgTuBcaN5DoBfAF+onru3VM/npKpsL8qkidsAawPTgM/03H88cCNlcslP1m7fCngnZR6THORxvwucC6xW1f0xYJsh6ngS8LPa9SuAL1aPvT1lPpPX1co/SZnfZVMggG2BiVXZwcCfKBNBjgU+DdzQU+9Vq/83oswRc2DtefkD8CFgJeD91fUJQ9R7GnDCKF8PU4EZ1f8rAT+utmHV5/Da6j739wJ/3e/X+iD1Oxn4K8oPia+gzO2za+319Bjw7uq52wV4Eti+dz+N4nEmVs/nmGpdb65eK939M+zre7j9N4r7Dve+Gvb9DVwMnFXdvgFl3p73j2b/VMtcXT32mtXyO45m3w/xmnyi9n5Yks+DUX+mefHiZfEufa+AFy9NvQA7A7OGKT+K2kE08HngGmDN6vr/AW6tvkwvAzatbj8ZOK36fzzwFPC56vrKwNPVF+kUIIEjgT9SJjP8eO3xxgDHVV+gjwD/BaxTlU2kzHz+SHWQcR2wfq3ed1df4vcAhy/DfXgNiwaZY4CrR3n/9wE/6dnmOcDrB3sOeu77YsoM96vXbrsaOLb6/zvAp2plrwce7FnHccDnKAdenxzkMV5ET5CpDmSeBV5cu+2bvQdF1e1RPX9HVtdXq57zSbVlvgZ8s/p/bcqB3RZDbPPHgP+qXd8GeHqIZTeiHER+tLq+J3A/1Uliqtv+CLxxkPtOAeYDm43yeZwKzABWAf6b8n5YuVa+0P6l54CectD9McoEos9QgmKnei08WduG/SnhbBblQPQlPfvm/up1f3vtNXQS8K2R3jdL8B64CPiH6v/1q+d3lVr5dcBhg233YjzGGMrEpwm8YDSvb4YPMkPel5HfV8O+vymfifvUrn8e+I9R7p89q3qPXdx9P0jZN4BvjGaba7cN+nkw0jZ78eLluV/sWiY9d3cA8yPi7IjYOyLWHmyhiBgTEWcA2wF7ZuZjEfFmyozjBwKTKF/0363uciXlgAXKL44PArtX118F3J6Zf6k9xGsov7y+HjghIl5S3f5+yq+wuwMbUgLTv1VlR1J+sdwYWBc4FpgTEasCXwb2zszVgV0pvzB2u9HNGubymsXbfUN6JXBv1VXp4Sjdn146xLJRXXqvb1u7bcdqPXdExD/VuqlsA9ydmU/Ulr2pur1bflNP2foRsS5ARGxKCaOnLOb2vRiYn5l3DPG4dbtRDt6+X9u++t/u/93tfSkwjzJj/YPVNr+3tuz3gBdFxIsjYjzldXBp/QEj4riIeJISLFalHMBR1e/mzKx3lbt5iHofQTlwu2eQsqGsBFxCCer7Z+acxbgvlJbRvwHWyszDKCFrv8xcLTM/FxEvprzHPkh5z10M/ChKN7atKKF4l+p1vxflgLjXoO8bgIj492HeGzcPVuGIWJnyHr8FIDMfqur4jihdBF9FaVmrj7V7QUQ8FBH3RMS/Vu/ZIVWP/TTloP0/M/PPVdGwr+/KtyNiZkT8d0RsX7t9uPuO9L4a6f39JeDQiFglIjYC9qZ6jY5i/7ySEkLPjtKl7rqI2J1B9O77nrJVgIOAs0e5zSN9HizOZ5qkxWCQkZ6jzOx2aUrgDGBmRFwUEevXFhtP+eJdh3JQNbu6/d3ApzPz1sycB3wK2KH6MvwFsGX1Bfla4Exgo4hYjRJKruypysmZOSczb6J8uW5fe4yPZ+aMzHyG8svyQdWB/FzKgdiLMnN+Zl5fbQ+UX7K3jYiVM/OBzOweZF2TmWsNc1laJzaYDBxKCVQbAj8BfhgREwZZ9nJg9yhjByZQwuEEyi/7AFdRDvJfQOnichjwkapsNUo3lbrHgNWHKO/+3y3/MvBPmfnkYm7fSI9bdyRwfvcxqoPDnwP/FBETI2InynZ1t3cy5UD7xcBmlIOxkyLiDVX5A5TQfDvlAPytlK5iAzLzM1VddqK0FHXrujj1PoLyq/TiWJ0S1M+uXq+L68uZed8wAegQSuvd5Zk5FziV0sK5K6X1aCVg64gYn5n3ZuZdg6xjyPdNZr5nmPfGdkPU6XTKe/ay2m3fBU6gtGpcTXkP31eV3QbsALwQ2AN4GaUb1ZCqx14DeBsLB6KRXt+HU1rWNqV0Z7wsqrFWI9x3pNfJSO/vKymh4XFKmJ4OXFhb13D7ZzKlVeYKSre0f6nWvR6LGmzfd72F0sJd/6xdks+DxflMk7QYDDLSEqiCyFGZOZlywLwhZfxC14so4yROzsxna7dvCnyp+4stpe96ABtVB2LTKaHltZQv02uBVzN4kHmw9v9syhdu9zEuqD3GrZQDtvUpB6iXAd+LMij8c9UB3FOUA75jgQci4icR8VfPeQcBPYNnNxnFXeZQuoNdUu2zUykHjy/pXTAzb6Mc7H+FcpC+HvA7ygEQmXl3Zt6TmZ3M/A3l19KDqrs/STnAq1uD0rVosPLu/09ExH6UrjPnjmJ7eo30uMDAL8ZvZeFfhaEcYG4G3Ad8Ffg21fZStQ4Ap1Th9mZKK8w+1e0nUn6F3pjSTepk4GfVL9ADsvh1tb6TF7Per6EcRJ4/yLYP52HKwd7ZEbHXYt4Xyv4YzoaUMT0AZGanus9GmXknpaXmJODPEfG9iNhwkHUM+r55DnUlIj5P+cw4uNvKVb3XzqUEwQmUA/qPRsTfVHV+MDN/V72e7wE+yoLX85Ay8+nM/C5wXK1lZcjXd3Wfn1evodmZ+WlKV7rdRnHfkV4nQ76/I2IMZf/+gNIauB6lu+RnR7N/qnXfm5lnZubczPwe5TnuPcnBIvu+x5HAOT1lS/J5MOrPNEmLxyAjLSXVQfVZLNyt6VbgHcAlVfeVrvuAd/f8artyZl5blV9J+cV1R0of8Csp3V1eTmllGI37KF3E6o8xMTPvr77kT87MrSm/SO9LOTggMy/LzDdQfvW9jdLaNNRZfeqX3QarRNW1p3v54yjqfTOllWtUMvP8zNw2M9elHKhvStlngy7Ogm5ZtwCbR0S9RWF7FnQ1uYUFrVvdsocy8xFKN76dq+5bD1LC3wcj4oejqPIdwLiI2HKIx+06kBJwpy20AZl/yMx9M3NSZr6CckD0q6q424VpqP23PXBu1Uo3LzPPohwobj3E8uOALar/bwG2i4h6t7btBqn3kcAPnkNLFZn5A+BdwPkR8bpa0VMsaHWCEpQWufsI1/9EeW0A5QxulEB3f/XY38nM11TLJNXBc0/9hnzfRMTpw7w3FtpHEXEypcvUnrWWUCifHbdX78FOZt5O+fV+70G2t7uNMUTZYMZTTvQAw7++R3qs4e470vtquPf3OpTn5CuZ+Uy1vm+wIIiPtH9G/OwYZt93yzemdO09p6doST4PFuszTdJiyOVgoI4XL028UM5+8w/A5Or6xpRuP2dU14+iGmhOObi7j2oQNnAA8FuqM1VRugO9tbbuPSldK35aXe92tbiltswUypfjuNpt04Cjq/8/VF3ftLo+CXhT9f/rKOMpxlIOHm6q6rs+ZUD0qpQfOk4Gpi2DfTeB0iLwc8qB60RgTFW2FaVl6a+r+n2IMuB9qLNjvaxabhLl19rv1Mr2ZsFJDP6q2ucn1sp/Sfl1dGL1nNTPrvRGSmvX1pSD/Z9RDcindCfZoHY5F/hXFpxMIap1bl09RxOBlWqP+z1KF5lVKb8WL3LWMsqg91MG2d6XVI8/AfhbSktGffD/VcB/ULpKvQT4MwsGrp9I6V60fvX8vp0SEtaqrr+72taghOYHWHDGqO5Zyz5Qrft99Jy1jNJVaxawx2K+Hqay8OD9Iymv91dX199FCdXrVPv7lyw62P+ve9b5S+CY2vWtqm19PeWA/sOUk1pMqMr2qLZrAvB14KzqfiexYLD/oO+bxdzWfwR+D7xwkLItKL/871E9B1tQzsL3rtp+2qQq25jSheobQzzOKyldXydUz8vHKK0iG47i9b0J5XXZfZ9+BJgJrDvSfUfxvhr2/V09J8dRQvRawAXAt0e5f9ahjAU8slr3QZQfA9Ybad/X6n48cNUgty/J58FifaZ58eJl9Je+V8CLl6ZeKGd1+i/KL7pPVX//A1ijKj+Khc9a9i7Kgd+U6vrbKWeFepwScr5eW3Y1Sn/8E6vrQTkg/WptmSkMH2TGUE73eTvlAOYuqrPuUMaK3F7V+yFK3+1xlFaYKykH1t0zO229DPbdtKru9cvUWvmB1QHK49Wy29TKLgGOr12/ptq+R6v9v2qt7NRq+56iHCCdAozv2YfTKF0/bmfRg+G/r+7/OOWX4ZWG2J6zWPgsRd3npn65t1a+DqXf/1OUQelvG+S1NY8yFqP3sT5IOah8qtr2nQe576WUA767KS1/3bKJlBM+PFBt0w1UZx2rXi+XVvvxSUrL0fEsfJayHYHrq/11A7VT29ZeV3+o32eUr4ep9JyNi/J+mUUJVBMpB4ePU37d/hAjB5k3Vft2FvDh6rYDKF0PH6Mai1Hdvh2lVav7OvoxCw74T2JBkBn0fbOY25qU8R1P1i711/PBlMD9BKXL4GdZEPL/nvI5M5vymXEaC58dbOC9QemGelNtm64EXjua1zfViR2q7XwE+Okgr7Mh3xuM/L4a7v29Q3XbXygh/TyqM62NtH+q8t0on6tPUrro7jbafV8tcxvwziGeu+f0eTDSNnvx4uW5XyIzkSRJkqQmcYyMJEmSpMYxyEiSJElqHIOMJEmSpMYxyEiSJElqHIOMJA0iIraOiOn9rofURBGxf0R8r9/1kLRiM8hIWuoi4t6ImFNNBvhgRJwVEav1u16L6f9RTt88qIhYKSL+tZrh/S8R8e9DzfIeES+OiB9GxMyIeDQiLqtPkBoRR0bE9RHxeETMqGaMH1cr/1ZEPFCV3xERR9fKto6I6VUd/hIR/xMRW9fKL+mZnPHZiPhNVfaCiPhutQ2PRcTPI+IVPXV/W0T8ISKeiogLI2Kd0dSrKl+l2i8PV+u/qla2UjWJ5EPVPvlRRGxUK58WEU/X6n37EPv2GxGREfGi2m29E1LOj4jTqrLDe8pmV/d/We3+O0XEVVX5QxHxgcEe+7mKiNdFxBXVPrl3kPIdIuLqqnxGRJywGOuOiPhsRDxSXT4XEYNOmhkRx/fsizkR0YmI9WrL/HVE3FA9//dFxMG1sj2qsscj4u6IOKZblpkXAdtGxHajrbskLS6DjKRlZb/MXI0yL8SOlMnoGiEiXkiZ/PDCYRY7DtiZMtv4i4GdgE8MsexawEWUifHWp8xZ8sNa+SqU+WHWA15BmbTxw7XyT1PmH1qDMmHpJ2sH3n+iTPy3TnX/iygTbgKQmXtn5mrdC3AtZW4OKPMVXUeZVHQd4GzgJ1GFzojYhjI3z9ures8G/n2U9QL4WrXel1R/P1Qr+wDwKsocLhtS5ns5rWe/va9W9616yoiI11AmRVxIz/auT5nP5Lyq7Ns95e+hzLdzQ7XO9Sjz6fwHsC7wIsrkpIslIqZGxLQhip+iTLr5kSHKv0OZ2HQdynwwfxcR+4/yoY8B3kyZeX47YF/KRKeLyMxP9eyLz1ImwH242oatq7p8nDJp7w6UeYSIEtovoOynNSmz2X8hIravPcR3q/pI0jJhkJG0TGXmg8BllIMgACLibyLi19UvufdFxEm1sinVL+RHRsQfq1/zP14rXzkizq5aH26NiI9GxIxa+YYR8f0orR/3RMT7n0O13wDckJlPD7PMfsCXM/PRzJxJmRzx/wyxD36VmWdWy86lzPq9VUSsW5V/NTOvzsxnM/N+4NuUmdW7978lM5/pXq0uW1RlszLz3iyTggUwn3LwvYiImEKZMPCb1X3vzswvZOYDmTk/M7/GgpnuAQ4HfpSZV2Xmk8A/AQdGxOoj1StKi9P+wDGZObNa//W16mwGXJaZD1X7+XuUiRhHJUqL1WnA+0ZY9CDKZLJXD1F+JHBOLphU7e+ren07M5/JzCcy89ba4/5VRFxetSLdXm+hGK3q9fBNSoAazBTKbPbzM/MuysSno903RwL/kpkzqtfSv1Am5x1W1WrzdkqY7foE8B+ZeUlmzsvMR6r6QAlZawDfzOI64FZg69r9pwF/M8p6S9JiM8hIWqYiYjKwN2VW666ngCMoLRV/Q/nF+c09d30N5YD69cAJEfGS6vYTKQd6m1MCx9/WHmsM8CPKjOYbVff9YETsVZW/LSJmDXPZpFrVSykzkg+7adWlfn1yRKw5wv0AXgs8mJmPDFN+y0IPVrpozabMPP4AcHFP+SzgacrB/aeGWO8RwNWZec9ghRGxAyXIdJ+rbSj7EoDqIPZZSgvUSPV6BfAH4OQqjP4mIt5Se7gzgVdXwXMVSmi6pKdKn67u+/OImNpT9iHgqsy8eYht7eoNKvXt3ZSyr8+p3fxK4NGIuDYi/hyly9sm1fKrApdTWileABwG/HvVcrU0fRE4IiLGV4HwVcD/jPK+Cz1n1f+jqd9ulNar79dueyVA9dw9EKUr4ToAmfkQpcXlHRExNiJeBWxKCV1dtwJTImKNUdZdkhaLQUbSsnJhRDwB3Ef5RfzEbkFmTsvM32RmpzoQ/S6lC03dyZk5JzNvohyMdbusHAx8KjP/kpkzKC0hXbsAkzLzlKp1427gDODQ6nG/k5lrDXP5Y7WetYAnRti+S4APRMSkiNgA6Lb8rDLcnapg92+UX/4HK38HpcvaQuNzMvM9wOqUA84fAM/0lK9F6eLzPuDXQzz8EcBZQzzuGpSWmpMz87Hq5tWAx3oWfayqx0j1mkzpdvcYpevY+4Cza4H0DuCPwP3A45TuZ6fUHudjlLC6EaWL2o8iotvaszGlu9SwY0eqALI7C7cy1A0W7CZTws8HgE2AeyivTyjdtO7NzG9ULRQ3UA78DxquHs/Bj6t1zqEExDOrFo/R6H3OHgNWG2qcTM2RwPlVy1vXZEorzVuALYGVWbj733cpz8EzlBavj2fmfbXy7ntorVHWXZIWi0FG0rLy5sxcHZgK/BVl/AYAEfGKKIOdZ0bEY8Cx9fLKg7X/Z1MO0KAcFNcPlur/bwpsWG9lAY6n/NK8OP5C7WA9Fh4g3m01+GdKYLiRMu7kQmAuJbQNKiImUcZb/HtmfneQ8jcDnwH27o5TqKu6Gl1DOcD8u0HKnwJOB86JiBf0rPs1wAbA+YM87sqUlqxfZuana0VPUroP1a1BT8gbol5zKPvjk1WovBK4AtizKv8qMJEyDmVVSgi6pLbO/626dT2TmWcDPwf2qYq/CJxSC1xDOQK4ZqgWqKq8N+TMAS7IzOuqLm8nA7tWLW2bAq/oeX0dTtmvRMRxtdt/DLymZ9kRVS0el1JC3URgY2CviHjPaO7Pos/ZGsCTg7VI1R5zZeCtDL4vvpGZd1QB51NUz0FE/BVwLmUfTqC0+nw0IupdybrvoVFtuyQtLoOMpGWqOoA9i4VbGL5DGZS+cWauSTn4HukX464HKAfMXRvX/r8PuKenlWX1zOwefPWesar30u1adjO17lO58ADxvavb5mTm+zJzo8zcHHgEuD4z5w9W6YhYmxJiLsrMfx6k/I2U1qP9MvM3I+yDcQwyyL0yhtIqtFHP7UcCP+j5xZ2IWIkSwu5n0UHht7CgJYyI2BxYidKaMlK9RurytT1wVpZxQ89Qful/edTOmNWjOwYISpfBz0c5I1438P4iIt7Wc5/Bgkp3W15NCcW9we7m6rHqj0v12PcBV/a8vlbLzL8DyMzPdG+ntN5cU1926F2xkM2B+Zl5TtXqM4MyfmifEe7XtdBzVv1/yxDLdh0IPEoZ01LXuy/qtgVuz8zLqpbV24GfULqRdr2E0oL1+CjrLkmLxSAj6fnwReAN1RgMKL/UPpqZT0fEy4HeA9Dh/BfwjxGxdpTT9dYHe/8KeDwiPhblpABjI2LbiNgFFj1j1SCXbteyy4GdImLiUJWIiI2q8R0REa+kDIQ/cYhl16Cc8ODnmXncIOV7UAb4vyUzf9VT9oKIODQiVqu2Zy/K2IyfVeVviIgdq7I1gC9QWpTqA9S7v7if1bPu8ZQD+TnAEZnZ6anat4H9ImK3anzIKZQw9MRI9aKcdeuPlOdqXBUcplb7AcrZ0o6IiDWrerwH+FNmPhwRa0XEXhExsbrv4ZSxLN37vphygL4DC04isR/lLFrdbduVEua6Z2jrdSTw/czs7UL4DeCAKKdAHk95Xq/JzG4ry4sj4u3V+JXxEbFLrbvcqETEmOq1Nb5cjYkRMaEqvqO67W3VchtQzgh2U+3+GYuOGeo6B/j77usT+AeG6E5YM9Q4om9QxsBsHmUc08co+wBKa+SWUU7BHFG6/e3LwuNzdmfRcU+StPRkphcvXtMEwnkAACAASURBVLws1QtwL/DXPbd9lXLgCKX//x8oXZR+DHwF+FZVNoXyK/C42n2nAUdX/69KGcsxi3Kw/gngrtqyG1L67j9IOaD/ZW9dRrkN5wGHDFP+2mo7Z1NODHB4T/klwPHV/0dW2/QUpetP97JJVX4FMK+n7JKqbBJwZbW9jwO/Ad5Ve5y3UsZRPAnMpAy2366nLodV+zt6bt+9qtfsnsferbbM2yiB5CnKKaPXGU29qmW2AX5R3fd3wAG1snUpQenP1TquAV5eW/d11etjVvUcvmGY5yKBF/Xc9h+UM2oNtvzEar2vH6L87ygtVH+hdLnbuFa2FaXlYSalFe5nwA6DrGMq5VTGg61/KgvO8ta9TKuV71Ft/2OU1/EZwCpV2eRqv6w7xLoD+BylheXR6v+olfc+vxtVr70XDbG+k6ttnUl5361dKzsY+G1VnxmU0zePqZX/Btj++frc8eLFS/sukTlkt9llLiK+TvkF58+Zue1SWN+llLOsXJOZ+9Zuv5oFfXVfAPwqM3vPkCSpgSLi74BDM7P3ZAFLut6tKd2SXp79/KCUaiLib4FtMnO5npcpIvYD3p6Zi316akkarX4HmddSfh06ZykFmddT+oa/ux5kepb5PvDDzDxnsHJJy7cok1VuTvmlf0vKr+Nfycwv9rVikiTpedXXMTKZeRWl6XtARGwREZdGxPURcXV1ZpTRru+nDHPK1CiTuO3B8LN1S1q+TaB0G3qC0q3nhyw827wkSWqBcf2uwCC+Bhybmb+PiFdQDlD2WErrPgD4aXoGFamxMvMPlDMmSZKkFluugkxErAbsCpwXC+buWqkqO5CFJ0vruj8z9xrlQxwG/OeS1lOSJElSfy1XQYbS1W1WZu7QW5CZP6BMmPacRMS6wMsprTKSJEmSGmy5mkem6vJ1T0S8FcqJ9CNi+xHuNlpvBX6cZaZmSZIkSQ3W1yATEd+lnHloq4iYERHvBA4H3hkRN1FmI37TYqzvasrcD6+v1lfvcnYoZW4JSZIkSQ3X19MvS5IkSdJzsVx1LZMkSZKk0ejbYP/11lsvp0yZ0q+HlyQB119//cOZOanf9Vge+T0lSf033PdU34LMlClTmD59er8eXpIERMQf+l2H5ZXfU5LUf8N9T9m1TJIkSVLjGGQkSZIkNY5BRpIkSVLj9G2MzGDmzp3LjBkzePpp56wUTJw4kcmTJzN+/Ph+V0WSJEnLmeUqyMyYMYPVV1+dKVOmEBH9ro76KDN55JFHmDFjBptttlm/qyNJkqTlzHLVtezpp59m3XXXNcSIiGDddde1dU6SJEmDWq6CDGCI0QBfC5IkSRrKchdkJEmSJGkkBhlJkiRJjWOQGcSDDz7IoYceyhZbbMHWW2/NPvvswx133PG81uHee+/lO9/5zsD16dOn8/73vx+AadOmce211w6UnX766ZxzzjlL7bGnTZtGRHDmmWcO3PbrX/+aiODUU08d9r4XXnghv/vd74YsX9p1lSRJUjsZZHpkJgcccABTp07lrrvu4ne/+x2f+tSneOihh57XevQGmZ133pkvf/nLwKJB5thjj+WII45Yqo//0pe+lHPPPXfg+ve+9z223377Ee83XJCZN2/eMqmrJEmS2me5Ov1y3ck/uoXf/enxpbrOrTdcgxP322bYZa644grGjx/PscceO3DbDjvsQGbykY98hEsuuYSI4BOf+ASHHHII06ZN48QTT2T99dfnxhtv5MADD+SlL30pX/rSl5gzZw4XXnghW2yxBUcddRQTJ07klltu4aGHHuILX/gC++67L/Pnz+e4445j2rRpPPPMM7z3ve/l3e9+N8cddxy33norO+ywA0ceeSQ77rgjp556Kl/5ylc4/fTTGTt2LN/61rc47bTT+OlPf8pqq63Ghz/8YW688UaOPfZYZs+ezRZbbMHXv/511l57baZOncorXvEKrrjiCmbNmsWZZ57JbrvtNuR+2GSTTXj88cd56KGHeMELXsCll17KPvvsM1B+11138d73vpeZM2eyyiqrcMYZZ/Doo49y0UUXceWVV/LJT36S73//+7zzne9k11135ec//zn7778/TzzxxEBd77zzTo499lhmzpzJ2LFjOe+889hiiy2W/ImWJEnSCm+5DTL98tvf/paXvexli9z+gx/8gBtvvJGbbrqJhx9+mF122YXXvva1ANx0003ceuutrLPOOmy++eYcffTR/OpXv+JLX/oSp512Gl/84heB0spy5ZVXctddd/G6172OO++8k3POOYc111yT6667jmeeeYZXv/rV7LnnnnzmM5/h1FNP5cc//jFQWmEApkyZwrHHHjsQBgB++tOfDtTziCOO4LTTTmP33XfnhBNO4OSTTx54/Hnz5vGrX/2Kiy++mJNPPpn/+Z//GXZfHHTQQZx33nnsuOOO7LTTTqy00koDZccccwynn346W265Jf/7v//Le97zHn72s5+x//77s++++3LQQQcNLDtr1iyuvPJKAE466aSB2w8//HCOO+44DjjgAJ5++mk6nc6oniNJkiRpuQ0yI7WcPN+uueYaDjvsMMaOHcv666/P7rvvznXXXccaa6zBLrvswgtf+EIAtthiC/bcc0+gdM+64oorBtZx8MEHM2bMGLbccks233xzbrvtNv77v/+bm2++mfPPPx+Axx57jN///vdMmDBhsev42GOPMWvWLHbffXcAjjzySN761rcOlB944IEAvOxlL+Pee+8dcX0HH3wwhxxyCLfddhuHHXbYQHe2J598kmuvvXahdT/zzDNDrueQQw5Z5LYnnniC+++/nwMOOACAiRMnjryBkiRJUmW5DTL9ss022wyEirrMHPI+9ZaKMWPGDFwfM2YM8+bNGyjrnRclIshMTjvtNPbaa6+FyrotMEtTt15jx45dqF5D2WCDDRg/fjyXX345X/rSlwaCTKfTYa211uLGG28c1eOuuuqqi9w23P6UJEmSRuJg/x577LEHzzzzDGecccbAbddddx1rr7025557LvPnz2fmzJlcddVVvPzlL1+sdZ933nl0Oh3uuusu7r77brbaaiv22msvvvrVrzJ37lwA7rjjDp566ilWX311nnjiiUHXM1TZmmuuydprr83VV18NwDe/+c2B1pnn6pRTTuGzn/0sY8eOHbhtjTXWYLPNNuO8884DSii56aabhq1brzXWWIPJkydz4YUXAqVFZ/bs2UtUV6lt7vzzk0y/99F+V0OSpL4wyPSICC644AIuv/xytthiC7bZZhtOOukk3va2t7Hddtux/fbbs8cee/C5z32ODTbYYLHWvdVWW7H77ruz9957c/rppzNx4kSOPvpott56a3baaSe23XZb3v3udzNv3jy22247xo0bx/bbb8+//uu/LrSe/fbbjwsuuIAddthhILR0nX322XzkIx9hu+2248Ybb+SEE05Yov2x66678uY3v3mR27/97W9z5plnsv3227PNNtvwwx/+EIBDDz2Uz3/+8+y4447cddddw677m9/8Jl/+8pfZbrvt2HXXXXnwwQeXqK5S25x5zd2859s39LsakiT1RfSri8/OO++c06dPX+i2W2+9lZe85CV9qc+ydtRRRy0yCF4jW5FfE9KS+tj5N3PlHTP55fGvf87riIjrM3PnpVitFcZg31OSpOfXcN9TtshIUkN1MukZeidJUms42P95ctZZZ/W7Cou47LLL+NjHPrbQbZttthkXXHBBn2okaXEkMMYkI0lqqeUuyGTmImf30rKx1157LXK2tOWJZzaThmeLjCSpzZarrmUTJ07kkUce8QBWZCaPPPKI88tIw8i0RUaS1F7LVYvM5MmTmTFjBjNnzux3VbQcmDhxIpMnT+53NaTlVieTMeYYSVJLLVdBZvz48Wy22Wb9roYkNUInF51oV5KktliuupZJkkYvHSMjSWoxg4wkNZRjZCRJbWaQkaSGcoyMJKnNDDKS1FCdTAKTjCSpnQwyktRQZbB/v2shSVJ/GGQkqaEcIyNJajODjCQ1VGYyxk9xSVJL+RUoSQ1VBvvbIiNJaieDjCQ1VCdxqL8kqbUMMpLUUJ1MwhYZSVJLGWQkqcGcR0aS1FYGGUlqKMfISJKWZ6eddhqnnXbaMlu/QUaSGqrT8fTLkqTl16WXXsqll166zNZvkJGkhuqko/0lSe1lkJGkhioTYva7FpIk9YdBRpIaKnGMjCSpvQwyktRQnXSMjCSpvQwyktRQZR6ZftdCkqT+MMhIUkN1EifElCS1lkFGkpoq08H+kqTWMshIUkM5RkaS1GYGGUlqqI4tMpKkFjPISFJDOUZGktRmBhlJaqjMxBgjSWorg4wkNVQ6RkaS1GIGGUlqqE4mY/wUlyS1lF+BktRQZUJMW2QkSe1kkJGkhrJrmSSpzQwyktRQHQf7S5JazCAjSQ2V4DwykqTWMshIUkOVCTFNMpKkdjLISFJDdTpOiClJai+DjCQ1VGbatUyS1FoGGUlqqE6CDTKSpLYaMchExMYRcUVE3BoRt0TEBwZZJiLiyxFxZ0TcHBE7LZvqSpK6EsfISJLaa9wolpkH/ENm3hARqwPXR8Tlmfm72jJ7A1tWl1cAX63+SpKWkdIiY5CRJLXTiC0ymflAZt5Q/f8EcCuwUc9ibwLOyeKXwFoR8cKlXltJ0gDHyEiS2myxxshExBRgR+B/e4o2Au6rXZ/BomFHkrQUdRK7lkmSWmvUQSYiVgO+D3wwMx/vLR7kLjnIOo6JiOkRMX3mzJmLV1NJ0kI6mQ72lyS11qiCTESMp4SYb2fmDwZZZAawce36ZOBPvQtl5tcyc+fM3HnSpEnPpb6SpEraIiNJarHRnLUsgDOBWzPzC0MsdhFwRHX2slcCj2XmA0uxnpKkHrbISJLabDRnLXs18HbgNxFxY3Xb8cAmAJl5OnAxsA9wJzAbeMfSr6okqc4WGUlSm40YZDLzGgYfA1NfJoH3Lq1KSZJG1vGsZZKkFluss5ZJkpYfpWuZSUaS1E4GGUlqqEwcIyNJai2DjCQ1lGNkJEltZpCRpIZyjIwkqc0MMpLUUJ1MYvhzsUiStMIyyEhSQ3USW2QkSa1lkJGkBipnvcezlkmSWssgI0kNVOUYB/tLklrLICNJDdSpkoxdyyRJbWWQkaQG6lQtMjbISJLayiAjSQ3UcYyMJKnlDDKS1GCOkZEktZVBRpIayDEykqS2M8hIUgN1PGuZJKnlDDKS1EALxsj0uSKSJPWJQUaSGig75a+D/SVJbWWQkaQGShwjI0lqN4OMJDWQY2QkSW1nkJGkBvKsZZKktjPISFIDdYOMo/0lSW1lkJGkBsqBrmX9rYckSf1ikJGkBkrHyEiSWs4gI0kN5BgZSVLbGWQkqYEWTIhpkpEktZNBRpIaaGCsf3+rIUlS3xhkJKmBFnQtM8pIktrJICNJDTQw2N9PcUlSS/kVKEkNZIuMJKntDDKS1ECdgfkwDTKSpHYyyEhSA2X3rGV9rockSf1ikJGkBuo4IaYkqeUMMpLUQIkTYkqS2s0gI0kN1OmUv46RkSS1lUFGkhpowVnL+lwRSZL6xCAjSQ2UnrVMktRyBhlJaiBbZCRJbWeQkaQGqhpkPGuZJKm1DDKS1EDdFhlzjCSprQwyktRAOdC1zCQjSWong4wkNVBnYLB/f+shSVK/GGQkqYE6HVtkJEntZpCRpAbqDvY3x0iS2sogI0kN1HGMjCSp5QwyktRA3QkxDTKSpLYyyEhSA3n6ZUlS243rdwUkSYtvQYtMf+shSdJQZs+evUzXb5CRpAZa0CJjkpEkLZ+6c54tK3Ytk6QGcoyMJKntDDKS1EADLTJ9rockSf1ikJGkBurYIiNJajmDjCQ1UHrWMklSyxlkJKmBbJGRJLWdQUaSGqjbIjPGT3FJUkv5FShJDdRtkQmH+0uSWsogI0kN1D1rmRNiSpLayiAjSQ3UnWLMCTElSW1lkJGkBkpbZCRJLWeQkaQGWtC1zCQjSWong4wkNVCnU/6aYyRJbWWQkaQGskVGktR2BhlJaqAFg/37Wg1JkvrGICNJDZS2yEiSWs4gI0kN1J0Q0yAjSWorg4wkNVB3jIw5RpLUVgYZSWqgbouMQUaS1FYGGUlqIsfISJJaziAjSQ3kGBlJUtsZZCSpgRbMI9PnikiS1CcGGUlqoIExMphkJEntZJCRpAbqziMTfopLklpqxK/AiPh6RPw5In47RPnUiHgsIm6sLics/WpKkurSMTKSpJYbN4plzgK+ApwzzDJXZ+a+S6VGkqQROUZGktR2I7bIZOZVwKPPQ10kSaPkWcskSW23tHpXvyoiboqISyJim6W0TknSELotMpIktdVoupaN5AZg08x8MiL2AS4EthxswYg4BjgGYJNNNlkKDy1J7ZROiClJarklbpHJzMcz88nq/4uB8RGx3hDLfi0zd87MnSdNmrSkDy1JrbVgsH9/6yFJUr8scZCJiA0iyk+CEfHyap2PLOl6JUlDc4yMJKntRuxaFhHfBaYC60XEDOBEYDxAZp4OHAT8XUTMA+YAh2baeVuSlqXuGBlzjCSprUYMMpl52AjlX6GcnlmS9DwZmBDTJCNJainnhJakBuqk42MkSe1mkJGkBkrS8TGSpFYzyEhSA5UWGYOMJKm9DDKS1ECdTAf6S5JazSAjSQ2U6RnLJEntZpCRpAbqdBwjI0lqN4OMJDVQ4hgZSVK7GWQkqYEcIyNJajuDjCQ1UHrWMklSyxlkJKmBbJGRJLWdQUaSGqiTDvaXJLWbQUaSGqh0Let3LSRJ6h+DjCQ1UCchbJGRJLWYQUaSGigzMcZIktrMICNJDeQYGUlS2xlkJKmBOo6RkSS1nEFGkhooHSMjSWo5g4wkNVBmMsZPcElSi/k1KEkN1MkkHO4vSWoxg4wkNZBjZCRJbWeQkaQG8qxlkqS2M8hIUgMlYI6RJLWZQUaSGihtkZEktZxBRpIaqNOxRUaS1G4GGUlqIMfISJLaziAjSQ3UcUJMSVLLGWQkqZHS0y9LklrNICNJDVTmkTHJSJLayyAjSQ3UyXSwvySp1QwyktRAjpGRJLWdQUaSGqjMI9PvWkiS1D8GGUlqoHSMjCSp5QwyktRAHVtkJEktZ5CRpAbqZBKYZCRJ7WWQkaQGKoP9+10LSZL6xyAjSU3kGBlJUssZZCSpgTqZjPETXJLUYn4NSlIDlcH+tshIktrLICNJDdTJftdAkqT+MshIUgOlLTLLtalTpw5cJKntltVnoUFGkhoowXlkJEmtZpCRpAZyjMzyq/eXR1tlJLXR8/FZaJCRpAbqdCAMMpKkFjPISFIDdTKdEHMZiIhjImJ6REyfOXNmv6sjSRqGQUaSGijTMTLLQmZ+LTN3zsydJ02a1O/qSJKGYZCRpAZKHCMjSWo3g4wkNVAnMcgsp6ZNmzbsdUlqg+fjs9AgI0kN5BgZSVLbjet3BSRJiy/Ts5Ytz2yFkaQFltVnoi0yktRAZR6ZftdCkqT+MchIUgOlY2QkSS1nkJGkBnKMjCSp7QwyktRAtshIktrOICNJDdTJxBgjSWozg4wkNVAZ7G+UkSS1l0FGkhooE8b4CS5JajG/BiWpgTrOIyNJajmDjCQ1UDpGRpLUcgYZSWogx8hIktrOICNJDdRJGGOOkSS1mEFGkhooMx0jI0lqNYOMJDWQE2JKktrOICNJDdTJxBwjSWozg4wkNZBjZCRJbWeQkaQG8qxlkqS2M8hIUgMlTogpSWo3g4wkNVBm2rVMktRqBhlJaqBO4mB/SVKrGWQkqYEcIyNJajuDjCQ1UKZjZCRJ7WaQkaSGyUzA0y9LktptxCATEV+PiD9HxG+HKI+I+HJE3BkRN0fETku/mpKkrk7JMXYtkyS12mhaZM4C3jhM+d7AltXlGOCrS14tSdJQOlWLjDFGktRmIwaZzLwKeHSYRd4EnJPFL4G1IuKFS6uCkqSFdYPMGPuWSZJabGmMkdkIuK92fUZ12yIi4piImB4R02fOnLkUHlqS2qfKMZ5+WZLUaksjyAz2VZqDLZiZX8vMnTNz50mTJi2Fh5ak9knHyEiStFSCzAxg49r1ycCflsJ6JUmD6HjWMkmSlkqQuQg4ojp72SuBxzLzgaWwXknSIBYM9jfJSJLaa9xIC0TEd4GpwHoRMQM4ERgPkJmnAxcD+wB3ArOBdyyrykqSFpx+2Z5lkqQ2GzHIZOZhI5Qn8N6lViNJ0rAWTIhpkpEktdfS6FomSXoeLRjs3996SJLUTwYZSWoY55GRJMkgI0mNMzBGpr/VkCSprwwyktQw3TEy4RgZSVKLGWQkqWE6TogpSZJBRpKaJnFCTEmSDDKS1DC2yEiSZJCRpMbpONpfkiSDjCQ1TdoiI0mSQUaSmmZgHhlzjCSpxQwyktQwVYOMLTKSpFYzyEhSw3QG5pHpc0UkSeojg4wkNYwTYkqSZJCRpMZZcPrl/tZDkqR+MshIUsMsGOxvkpEktZdBRpIaJm2RkSTJICNJTdNxjIwkSQYZSWqabouMMUaS1GYGGUlqGMfISJJkkJGkxhk4a5mf4JKkFvNrUJIaxnlkJEkyyEhS43QcIyNJkkFGkpomHSMjSZJBRpKaZmCMjEFGktRiBhlJapgFLTJ9rogkSX1kkJGkhhkYI2OLjCSpxQwyktQwC85a1ueKSJLURwYZSWoYx8hIkmSQkaTG6ThGRpIkg4wkNU3VIOMYGUlSqxlkJKlhbJGRJMkgI0mNs2Cwv0lGktReBhlJaphOp/y1RUaS1GYGGUlqmAVdy0wykqT2MshIUsMsGOzf12pIktRXBhlJapi0RUaSJIOMJDVNd0JMc4wkqc0MMpLUMI6RkSTJICNJjdNtkfGsZZKkNhvX7wpIkhaP88hIkppgWX9PGWQkqWFyoEXGICNJWn6tssoqy3T9di2TpIbpjpExxkiS2swgI0kN07FFRpIkg4wkNc1Ai4w5RpLUYgYZSWqabouMpy2TJLWYQUaSGmbBPDJ9rogkSX1kkJGkhumOkQmH+0uSWswgI0kNY4uMJEkGGUlqHCfElCTJICNJjVP1LLNFRpLUagYZSWqYTqfbtcwkI0lqL4OMJDXMwGB/c4wkqcUMMpLUMB3HyEiSZJCRpKbJ7oSY5hhJUosZZCSpYRLHyEiSZJCRpIbpDLTIGGQkSe1lkJGkhlkwRqbPFZEkqY8MMpLUMOlZyyRJMshIUtM4j4wkSQYZSWqcqkHGICNJajWDjCQ1THeMjKdfliS1mUFGkhqmMzBGxiQjSWovg4wkNUxmOtBfktR6BhlJaphOpuNjJEmtZ5CRpIbJdHyMJEkGGUlqmE5CYJKRJLWbQUaSGsYxMpIkGWQkqXEcIyNJkkFGkhqn4xgZSZIMMpLUNGWwv0lGktRuowoyEfHGiLg9Iu6MiOMGKT8qImZGxI3V5eilX1VJEpSuZY71lyS13biRFoiIscC/AW8AZgDXRcRFmfm7nkXPzcz3LYM6SpJq0jEykiSNqkXm5cCdmXl3Zj4LfA9407KtliRpKI6RkSRpdEFmI+C+2vUZ1W293hIRN0fE+RGx8VKpnSRpEZ61TJKk0QWZwb4ts+f6j4Apmbkd8D/A2YOuKOKYiJgeEdNnzpy5eDWVJAHlAzgMMpKklhtNkJkB1FtYJgN/qi+QmY9k5jPV1TOAlw22osz8WmbunJk7T5o06bnUV5JazwkxJUkaXZC5DtgyIjaLiAnAocBF9QUi4oW1q/sDty69KkqS6jodx8hIkjTiWcsyc15EvA+4DBgLfD0zb4mIU4DpmXkR8P6I2B+YBzwKHLUM6yxJreYYGUmSRhFkADLzYuDinttOqP3/j8A/Lt2qSZIG03FCTEmSRjchpiRp+ZE4RkaSJIOMJDVMJgYZSVLrGWQkqWEcIyNJkkFGkhrHMTKSJBlkJKlxOs4jI0mSQUaSGscWGUmSDDKS1DSdTIwxkqS2M8hIUsM42F+SJIOMJDVOx9MvS5LU/CDzn1ffzRW3/bnf1ZCkZeqS3zzAN3/5B6DMI2OLjCSp7VaAIHMPP/j1/f2uhiQtU/81/T7OvvZeADKTMY3/9JYkack0/qtwztz5zHl2fr+rIUnLVP2zrgz2t0VGktRuK0SQeXquQUbSim3O3M7AZ12ZELPPFZIkqc8aHWTmd5Jn53WYY5CRtIJ7+tn5A591ZUJMk4wkqd0aHWS6X+qz7VomaQU3e+485sydT2YCtshIktTsIFMFGLuWSVrRzXm2QyY8M6/jPDKSJNHwINMNMA72l7Siq3/edTrOIyNJUqODTLdrmWNkJK3IMnOhzzvHyEiS1PQg86xBRtKKb+78ZH6njI0p42QcIyNJUqODTHeQ/7PzOgNf8pK0oql3n53z7HwSx8hIktToIFMf5G+rjKQV1Zyez7oyj4xBRpLUbo0OMgt9uTvgX9IKqvezroyR6WOFJElaDjQ7yNTCi6dglrSiWqhrWdUi42B/SVLbNTvI2LVMUgvUP9+eribFdLC/JKntGh1knrZrmaQW6P2sS8fISJLU7CAzuxZeZhtkJK2gZi/StcwWGUmSGh1kertbSNKKqP5ZN/vZMkYGTDKSpHZrdpB51jEyklZ8Tz/rGBlJkno1Osg4RkZSGwx2+mXHyEiS2q7RQWbO3PmsOmHswP+StCLqfr6tOmEsc+ZWg/0b/ektSdKSa/RX4exn57P2qhMAW2Qkrbi6g/3XWmXCwGD/cIyMJKnlGh1knp47n7VXqYKMLTKSVlBPz53PxPFjWGXC2GqMDNizTJLUdo0OMnOenc/qE8cxfmwYZCStsOY8O5+Vx49l5Qljq7OWOUZGkqRmB5m581llwlhWHj/WrmWSVljls27cwGddJ/GsZZKk1mt8kJlY/UrpPDKSVlRzqq5l3c+6xBYZSZIaHWSe7na3GD92oZmvJWlFMufZ+azcbX2eO59OB+fDlCS13rh+V2BJzJ5bvtwnVl/uYK8dEgAAHtBJREFUkrQimlP70WbOwISYJhlJ0vLtjW984zJdf6ODTH0ArF3LJK2o5swtJzaZOKGMkRk3ZoxjZCRJy73/+3//7zJdf2O7lnU6yTPzOgu6W9i1TNIK6um5tRYZz1omSdL/b+/O46Mq78WPf74zySRkISELIiGsgogLWHGtiqh16eLSStXa1r6qbe3vVrt57XZvb2/v/XXRWr1Wq1VrW/tTXKu1vSqCLAqUyJogkkBYsrAkZLJPltm+vz/OmWECw1KEJEO+79eLFzPnnJk88yRznvN9nu/zHCCFA5mesBO4DEv3kuWz1DJjzPErtkJj7FwXVRALZIwxxgxxKRvIxEZgbI6MMeZ4F5vsn5nuJarQG47YDTGNMcYMeSkbyMRWKctMt9QyY8zxrTsYiZ/rwDn/2RwZY4wxQ13KBjKxyf2xyf6JIzKLKhtZvqVpoIpmjDEfyuKqRpZu3nsO6w7tXdgEIBK1OTLGGGNMyq5a1p0YyOwzIvPLNyvJG5bOBZOKBqp4xhhzxH71VhWZaV4unFxEKBIlHNX4uS7GAhljjDFDXeoGMm7gkuVzeil7w1GiUcXjEfyBIJGoDnAJjTHmyPg7g/GgJd5p49s7IgPYHBljjDFDXuoGMm7jnunb20vZE3bSL1oCQaIWyBhjUpCq4g/sDWR6EhY2SRyRESySMcYYM7SlbiAT7DtHBpwJsKGwEo4qLV3B+AiNMcakikAwQjAcJRiOEopE4wubJJ7rAJvsb4wxZshL2cn+iXNkMmMpGMEI/kAvAFGF1u7QgJXPGGOORHNnMP64pSu433zAGOukMcYYM9SlfiCTmFoWiuAP7L0IaHaDGmOMSRX+hPOWvzPYJ402M93myBhjjDExqRvIJOSNZ/n2Tor1J/RmNiU8NsaYVJB4DmsOBONzZLLS957rwFYtM8YYY1I2kOlJkm7RHYzQHOh7EWCMMakk8bzlDwSTjj4DNtXfGGPMkJeygUx3KEKaR0j3esiMTfYPRfqkk/ktkDHGpJg+6bGdvQeZ7G+hjDHGmKEtZQOZrmAk3juZuEypPxAkM935WM2WWmaMSTHNgV58aR484ozOxOfIpHvJSNt7yra5/sYYY4a6lA1kekKR+EhM4o3jmgNBThieyfDMNJvsb4xJOf5AkOKcDEZk+fAHgnvTaH1eRCR+vhMbkTHGGDPEpfR9ZGITXxMn+zcHghRk+/CI9EnR6A1HUKXPqj/GGDPQuoMRvB7B5462xM5hPe75LLawSeL5rjsUsdQyY4wxQ17Kjsh0h/amlsVGZrqDEZo6gxRm+yjI9vWZNPuDl9dz+59WDUhZjTHmQG5+YgX//b8fxJ/HApmCbF/f5ZfT3PNdfESm/8tqjDHGDCapOyITisYb9MT7yDQHejm9ZDgiQl1zV/z4ih1tNLb3DEhZjTEmmUhU2bCzrc82f2eQk4pz6AlHqNrdQXcoQkaaJ34DzNiEf5sjY4wxZqhL3RGZYDgewKR7PaR5hK5gLLUsg8JsX/w+MtGoUtvcRXtPmNYuWwDAGDM47GztJhRxzk8x/kBvfEQmllqWuFqZzZExxhhjHKkbyIT2b9wbO3oJRZTCbB+FOT5auoJEo8ru9h6C4SgA2/1dB3pLY4zpVzXu+ag5EKStO0RXMExPKEphTgYF2Rm0dofo7A33uX9M7LHNkTHGGDPUpW4gs08vZabPy46WbgC3NzODSFRp7wmx3R+IH1eT8NgYYwZS4rmp1t+F3x1FLsz2UZjtQxV2tfb0CWQyLbXMGGOMAVJ4jkxPKNqncc/yednR6gQyhTk+vG4r7w8EqU0YhamxERljzCCRmFJW0xwgqlmA0xnTE3Ym+e9o7SY3c++pOssm+xtjjDFAKo/IJKxaBk66xa42N5DJzqAg2wc4KRvb/V2ke4WRuRl9ekAfenszn3tiRf8W3BgzJKkq1z6yjN8v3Rbftr0pQEn+MMDpZImttFiQ44ufw3a1dfc91/kstcwYY4yBFB6R2S+1LN1LKKKAcxEQa+P9nUFq/AFKR2RRnJvRZ3Rm/gcNrN/RRltXiLys9H4tvzFmaNnV1kN5XStZ6V5uu3AC4IzInHJiLsFIlO1NAU4Yngk4qWU9IWdeXyii+53rwCb7G2OMMSk5IhONKt2hSJ+bWyb2WBZm+yjKyQCcEZkafxfjCrMYX5gdn+zfE4qwcVc7ABU7Wvux9MaYoai8zjnPrN/RRjSqqKp7bspmfGEWNc1dNAd6AdzJ/r74a5Od62yOjDHGmKEuJQOZXncFsizf/ukWWT4vmeleRmQ7Iyz+zl5q/AHGFWYztjCLps5eAr1hNu5qJxx1RnAq6vfex2F7U4DXynf210cxxhynXl5dH5+3B1Dunmc6e8NsbepkT0cv3aEI4wqzGFuQTY0/gD8QxJfmIdvnZUTCKHHf1DLntG2pZcYYY4a6lAxkYne6TpY3HuvFzEjzkpuRxqbGTgLBSHxEBpxc9FjvaH5WOuvq9o7I3D9/E3fNXcuejt5++SzGmONPjT/Ad18s5+GF1fFt5XWt5LvBybq6tvjocGxEpqG9lx0t3RRm+xAR0rye+PGJnTZZPicj2OIYY4wxQ93xE8i4jwsT0jEKcnysqWkBYHxhNuMKnRWBavwBKurbKM7N4JIpxVTUO4FMOBJlSVUjAIvd/wECvWFWbW8+hp/IGJPKVmz10+OelwAWVu49j6gq0aiyfkcbHz/9RLJ9XirqW+MLj4wryGJckdPJsra2tU9KWexxYmqZzZExxhhjHKkZyASdC4bMJHe7LnTnxoBzERBL7RhbmMXYWCDT3EV5fSvTx+QzvTSfhvZedrf1sKa2lfaeMACLEgKZB+Zv4obH/kF1Y+ex/WDGmJRTUd/KTY+v4NHFW+LbYoHMrrYeNu7qYGtTJ529Yc4szef0MXmU17dR6+/C6xFKRgxjXIFzbtrR2t0nkCnKds5n+978F2yOjDHGGJPSgczBUstg7+iMR2DMiGEMz0ynINvH+zva2LInwPQxeZwxJh+A8vpWFlY2kuYRPnH6iby7qYlQJEpvOMLLa+oBeHFVXfy9l29p4qoH36GxvefYflhjzKBR19zFFQ8sYW1tS3zbcyud88KLq+qIRJVAb5iyrc1cM3004HSKlNc582Oml+YzfUw+G3e2s7mxg5L8YaR7PfG0V9hnVNl9bHNkjDHGmP2lZiDjpnBkJVmStLBPIOP0Zp6YN4yMNGf/uMIs3t7o9JZOL83n1NHDSfMI5XWtLKps5OzxBXxq+mg6esOs3N7M/A8aaOkKMTovk5fX1BMMR1FVfv56JZW7O3h0yd5e2HV1rVzz8FI2NXT0Ka/NtzFmcFPV/b6nFfXO97lyd3t820Nvb2ZTQyf3vlkFQFcwzGvrdjI6L5OdbT28u3kPy6qbCEai3HR2KaeVDGdhZSPl9a1k+7xMKs5hemk+wUiUdzY1xdNd87LSyRvmzIfpM6qckySQsREZY4wxBjjMQEZErhKRKhGpFpHvJ9mfISLPu/vLRGT80S5oolggk5g3npVkRCZ2ETC+KCu+bVxBVvz1Z4zJIzPdy8mjcpm3YTdVDR1cOnUkF04uIt0rLKps5PmVdZTkD+On155GU2eQhZUNvL2xkfU72ijJH8azZbU0tvcQikT53ksVVNS38f2XK4i6K6K9sraes//vAn6XEPB09IT4r79/QNXuvgHPtqYAveEIxpijoycUoSbhJrgA6+vb+NnrG+Mju+Ckj57zswXM27AbgFAkyj3x7/N6IlGlxh/gL2t3UJI/jH9s9bNiq5//rdhFZ2+Y++ZMpyDbx/Mr61hU1UhORhozxxdw6ckjWVvbwrubmzitJA+vRzhjTB5AfMWymPHu42Sjyn1Sy2KT/bFIxhhjzNB2yEBGRLzAI8DVwDTgZhGZts9htwEtqnoS8ADwy6Nd0ERJU8vSD5xaNrZgb9rGODeFY3xhFvlZzv7ppfls2eNc7MyeOpKcjDTOnVDIX9ftZGl1E3NmjmH21JGMGp7J3PfqePDtTYwtyOLp284hHFUeXbKFJ97dSlVDB9fNGM2a2laeW1nH5oYOfviX98lI83DvvCre29ZMbzjC1/68mt8v3cbnf19GXbOzctGzZbVcev9ibnmijPaeEADVjR186jdL+fFf3yfoLjndHYxw75uVPFNWg6oTLKkqb29sYOnmpj71tKO1m+1NfS/iekIR2rpDR1z3xhwtbV2h/QL3LXs62d3WN11zUWUj727eE38ejSpPLd3GA/M3xV/fE4rwvZcquP63y+I3vW0OBLnhseVcev8S/rpuB+B8p77wVBmPv7OVO+euIRyJsriqkYcWVuPzerj7xXJq/V38Ydk2Knc73+d1da08W1bDwwurSfMIc79yHsW5GTy4YBPPr6xjYnE2F0wq5NNnlrBgYwNvbWjgoslF+NI8zJ46kqg6nRQzSp001pL8YRTFOlkSUsrGuo8PmVoWn+x/pDVvjDHGHB/SDuOYc4BqVd0KICLPAdcCHyQccy3wE/fxS8DDIiIau9I+ymKrAyWbAFuYs/9FwPiEXs9YD2hsbgzA9DF5PFsGYwuymFTsXEzMnjqSpdVNiMCcmaV4PcJnZ47hIXc51XtvOINJxTl85iMlPFNWi0fgylNP4IEbZ7C7vYdfvLGR4twMsjO8PP+187ntjyu5c+4aZpTms3yLn29dPpk/LNvOF596j2tnjObBBZuZUZpPeX0rn3tiBbdfOJF/f/V9FOcGelW7O/jm5ZP5j79uYLO76MDCjY1894qTeWDBJuZ/0ADANdNH852PTeGZshr+uHw74ahy48xS7rpsMvM27OaRRdW0dYe45dxxfP2SSazc3swji7ZQ4w9wy7ljuf2iiWxp7OSpZdv4YGc7151ZwhfPH09TZy/PlNWwtraVK6adwM3njiUYjvLCqjrKtjZz4eQi5swsJd0j8QDwrHEjuP7MEoZnpjNvw26WbN7DKaNy+eQZoxk5PINFlXtYXNXI+KJsrj5tFCUjhrF8i58lVXsozs3gylNPYHxhNqtrWli8aQ85GWlcdspIpozMZf2ONpZs2oPXI1xycjHTThzOpoZOlmxqJBiOcvGUYk4bnUdtcxdLNu2hoyfEBScVMX1MPg3tTgpQY3sv500qZEZpPi1dQZZVN1Hr7+bsCSM4a9wIuoMRllX72bKnkxml+ZwzoYBQJMo/tvjZuKuD08cM59wJhYhA2bZmKuraOHlULudPKiQjzcPK7c2srmlhYnEOF55URE5GGmtqWyjb2kzJiGFcNLmIwmwf6+paWb7FT0G2j1lTihmdP4zy+laWbm4iy+flkpNHMrEom/d3trG4yvnMs6Y4n/mDXe0srHQ+8+ypxcwoHcGmhg7e2tBAa3eQS6eO5LyJhWxvCvD6+t3sautm9tSRzJpSTH1LN3+v2El1YyezTx7JFaeeQFNnkFfW7qCivpVZU4r51PTRdPVGeGFVHSu2+rlwchE3nDUGVXj2vVoWbmzk3IkFfP68cWT5vPxp+Xb+Vr6LGaX53HbRBEbnD+OJd7Yy971apo7K5V9mn8SpJXk8vHAzL6yqpzgng7sum8xFk4v49fxNvLJ2B740D1+9aCKf/kgJv3yzknkbnL/tT00fzR2zJvKz1zeyrNoPwIKNDfz7J6fx8zcqKa9z0reueWQp/3XtaTz09mZqm7s4dfRwvvX8Oupbunm2rJY0j/CN2Sfx8KJqvv1COUs372HqqFx+c/OZfObR5Xz1z6uo8Xdx+SnO93lPZy+/fLOK7lCEW88fz9jCLL4+axI//btzCvzhx6ciItx4dilPLt2GPxBk9tSR7rkln8JsH/5AMH7OERHOGJPPwspGxhYcfESmINmITDy1zCIZY4wxQ5scKtYQkRuAq1T1dvf5F4BzVfUbCce87x5T7z7f4h7TlOw9AWbOnKmrVq06okLPfa+WH/xlPSt+cBmj8jIBeK18J3fNXcur//LReM/nkk17uPWp93js8x/hqtNOBGB1TQufeXQ5//aJU7j9ookAVO5u56oH3+VLF4znJ9ecCjg3xrzkV4u5eEoxT3/5HMCZ6HvxfYsoHZHF29+dRbrXQ62/i0vvX0xGmocF353FiXnD2LKnk6sffJdQNMr/u+1cPnpSkRMU/HYZwXCU7189lTtmTWLV9mZuebKM3nCUT5x+Ig/cOINlW5q448+r6Q1HmXbicJ64dSYrtzXzvZcr6A1HKc7N4P4509m6p5OfvVFJMBwlM93Dty+fQk8oysOLNhOKqBOAnTWG4Znp8YAG4LyJBYwryOalNfVE3G0Ti7M5ZdRw3nh/FwqoOr3Cp5bksdTtCY8qZKZ7OHV0HmtqWxB3m0dgygm5VO7uQIT49rEFWdS6o01pHiEcVYpyMmjqdOYh+LwegpEouZlpdLgrxfnSPPHP0xNyRqAy0jz0hqPx9wDi+0WcsoJzcdcd6tu7n2xbls9LV/DQx2WmOz838evhS/MQiWq83mKfTQRCkb3bPAJpXk98FA2c3vOMtL2f62A/O7Yt2edL3JZYDx4RIlGN15eIU8e94Wi8XkUgx5dGR284Xv8izu+6qTNIulfifztjRgyjrrkbr8d539jvuSph/pcAHxk7goodbQTDUTziXKRfNLmItbWttHWHEAGvCFeffiLlda3xv4l0rzBnZilVuztY7S6R7kvzcNuFE9jV2s2r63bGf//f/tgUguEoDy+sJhiJMizdy398ahqFORnc81I5LV0hsnxefv3ZGUwdlcvtT6+iurGTbJ+XJ289mzPH5vN/nlnDwspGsn1Ox8JpJXnc/1YVv1lYTbbPy9/uvJCJxTnM/6CBrzy9iiyfl/nfmUVJ/jC2NQW48sF3EODde2YzcngmPaEIF9+7iOZAkBU/vIwid17LZx5dzuqaFt770WWMzHXOTd99oZyX19Tz7j2zKXUDl/9ZsJkHFmxi3rcu5uRRuQC8tLqeu18s5+Wvn89Z4woAWFbdxC1PlvGHL50dD46qGzu4/Nfv8MCN07n+zDF8WCKyWlVnfug3Og59mHbKGGPM0XGwdupwApk5wJX7BDLnqOqdCcdscI9JDGTOUVX/Pu/1VeCr7tOTgaoj+0gppwg4YFA3hFm9JGf1kpzVS3Iftl7GqWrx0SrM8URE9gA1A12OfmLfr+SsXpKzeknO6iW5Y9ZOHU5qWT1QmvB8DLDzAMfUi0gakAfsdwdJVX0cePxwSnw8EZFV1uO5P6uX5KxekrN6Sc7q5dgZSgGe/R0lZ/WSnNVLclYvyR3LejmcVctWApNFZIKI+ICbgNf2OeY14Fb38Q3AwmM1P8YYY4wxxhhjDjkio6phEfkGMA/wAk+p6gYR+SmwSlVfA34P/FlEqnFGYm46loU2xhhjjDHGDG2Hk1qGqr4OvL7Pth8nPO4B5hzdoh1Xhlw63WGyeknO6iU5q5fkrF7M0WB/R8lZvSRn9ZKc1Utyx6xeDjnZ3xhjjDHGGGMGm8OZI2OMMcYYY4wxg4oFMv1ERGaIyAoRWSciq0TknIEu02AhIneKSJWIbBCRewe6PIOJiNwtIioiRQNdlsFARO4TkUoRqRCRV0Qk/9CvOn6JyFXud6daRL4/0OUxqc3aqQOzdurArJ3qy9qpvo51O2WBTP+5F/hPVZ0B/Nh9PuSJyGzgWuAMVT0V+NUAF2nQEJFS4GNA7UCXZRCZD5ymqmcAm4AfDHB5BoyIeIFHgKuBacDNIjJtYEtlUpy1U0lYO3Vg1k4lZe2Uqz/aKQtk+o8Cw93Heex/L56h6uvAL1S1F0BVGwe4PIPJA8A9OH87BlDVt1Q17D5dgXNfq6HqHKBaVbeqahB4Dudiy5gjZe1UctZOHZi1U/uwdqqPY95OWSDTf74F3CcidTi9OUM2Qt/HFOAiESkTkSUicvZAF2gwEJFrgB2qWj7QZRnEvgy8MdCFGEAlQF3C83p3mzFHytqp5KydSsLaqcNi7dQxbqcOa/llc3hEZAEwKsmuHwGXAd9W1ZdF5LM49965vD/LN1AOUS9pwAjgPOBs4AURmTgUbqh6iHr5IXBF/5ZocDhYvajqX91jfgSEgWf6s2yDjCTZdtx/b8yHY+1UctZOJWftVHLWTh22Y95O2fLL/URE2oB8VVUREaBNVYcf6nXHOxF5E2fIfrH7fAtwnqruGdCCDSAROR14G+hyN43BSfE4R1V3D1jBBgkRuRW4A7hMVbsOdfzxSkTOB36iqle6z38AoKo/H9CCmZRl7VRy1k7tz9qpg7N2ytEf7ZSllvWfncAs9/GlwOYBLMtg8ipOfSAiUwAf0DSgJRpgqrpeVUeq6nhVHY8zFPsRaxyc1U+A7wHXDOXGwbUSmCwiE0TEB9wEvDbAZTKpzdqp5Kyd2oe1Uwdm7VQfx7ydstSy/vMV4H9EJA3oAb46wOUZLJ4CnhKR94EgcOtQGK43R+xhIAOY73QYs0JV7xjYIg0MVQ2LyDeAeYAXeEpVNwxwsUxqs3YqOWunzD/D2ilXf7RTllpmjDHGGGOMSTmWWmaMMcYYY4xJORbIGGOMMcYYY1KOBTLGGGOMMcaYlGOBjDHGGGOMMSblWCBjjDGDmIg8JSKN7opJhzr2YhFZIyJhEblhn333isgGEdkoIg+59wkxxhhjPpSBbKcskDHGmMHtj8BVh3lsLfAl4NnEjSJyAfBR4AzgNJy7k8/a98XGGGPMEfgjA9ROWSBjjDGDmKq+AzQnbhORSSLypoisFpF3RWSqe+x2Va0Aovu+DZCJcyO/DCAdaDj2pTfm+CAio0TkORHZIiIfiMjr7s0x+7MM40XkcwnPZ4rIQ+7jS9wLwdi+O0Tki0fxZ18iIioityVsO9PddvchXnudiEw7yP6jWlbT/waynbJAxhhjUs/jwJ2qehZwN/Dbgx2sqv8AFgG73H/zVHXjMS+lMccBN73lFWCxqk5S1WnAD4ET+rko44F4IKOqq1T1LvfpJcAFCfseU9Wnj/LPXw/cmPD8JqD8MF53HZA0kBGRtGNUVjPw+qWdSjsKBTXGGNNPRCQH54LlxYT04YxDvOYk4BRgjLtpvohc7PaiGWMObjYQUtXHYhtUdZ047gOuxulN/m9VfV5ELgH+E6c3eQbwF5wg4JvAMOA6Vd0iIn8EeoBTcYKi76jq30XEC/wCJzjJAB5R1d+5204RkXXAn4C1OBeI3wDuACIi8nngTuAyoFNVfyUiM4DHgCxgC/BlVW0RkcVAmfv58oHbVPXdg9RDLTBcRE4AGnFSiV6P7RSRScAjQDHQBXwFKACuAWaJyL8BnwF+DyzHSSN6TURyE8p6klvWYiACzFHVLQf97ZhBpz/bKQtkjDEmtXiAVlWd8U+85npghap2AojIG8B5gAUyxhzaacDqJNs/jROoTAeKgJUiEvtOTce5KGsGtgJPquo5IvJNnEDjW+5x43HmAUwCFrkXc18E2lT1bBHJAJaJyFvA94G7VfWT4KR7gZOqIyKP4QYD7r7LEsr5NE7P+BIR+SnwHwk/P80t18fd7Zcfoi5eAubgBFFrgN6EfY8Dd6jqZhE5F/itql4qIq8Bf1fVl9yyAeSr6iz3+U8S3uMZ4Beq+oqIZGKZQ6mq39op+wMxxpgUoqrtwDYRmQNO2ouITD/Ey2pxekTTRCQd58LJUsuM+XAuBOaqakRVG4AlOBOUAVaq6i5V7cUZBXnL3b4eJ3iJeUFVo6q6GSfgmQpcAXzRHXkpAwqByUdSQBHJwwkalrib/gRcnHDIX9z/V+9TrgN5ASeQuRmYm/BzEnvg1wG/A048yPs8n6SsuUCJqr4CoKo9qtp1GGUyg0x/tlMWyBhjzCAmInOBfwAni0i9O9n2FuA2ESkHNgDXuseeLSL1OBcavxORDe7bvIRzMbUeJ6e9XFX/1s8fxZhUtQE4K8n2gy0NmzhSEU14HqVvNozu8zp13/dOVZ3h/pugqm9xbMTKFeEwsnRUdTcQAj4GvJ2wK94Dn/DvlIO8VSDJNlsSPkUNZDtlqWXGGDOIqerNB9i131KXqrqSvfnFidsjwNeOctGMGSoWAj8Tka+o6hPgXIwBLcCNIvInnLkgFwP/ijOqcrjmuK+fAEwEqoB5wNdFZKGqhtzV0XYAHUDuAd6nAxi+70ZVbRORFhG5yJ3/8gWckaMP48fASFWNxOY/qGq7iGwTkTmq+qK7QMIZqlp+iHInlrXdvQi+TlVfddPqvDYqM/gNZDtlIzLGGGOMMQegqoqTv/8xd/nlDcBPcO6DUYHTe7wQuMcdsfhnVOEEFm/gzC/pAZ4EPgDWiHODwd/hdDxXAGERKReRb+/zPn8DrheRdSJy0T77bgXuE5EKnDk9P/0ny9iHqi5X1VeT7EraAw88B/yriKx1FwQ4mC8Ad7llXQ6M+jBlNcc/cb6fxhhjjDGmv7irlsUnwRtj/nk2ImOMMcYYY4xJOTYiY4wxxhhjEJErgV/us3mbql4/EOUx5lAskDHGGGOMMcakHEstM8YYY4wxxqQcC2SMMcYYY4wxKccCGWOMMcYYY0zKsUDGGGOMMcYYk3IskDHGGGOMMcaknP8PGAZaI+7oxb8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "univ_numerical(train, \"Competition_Metric\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Course_ID\n",
       "1      0.007\n",
       "2      0.186\n",
       "3      0.316\n",
       "4      0.099\n",
       "5      0.027\n",
       "       ...  \n",
       "596    0.070\n",
       "597    0.025\n",
       "598    0.025\n",
       "599    0.122\n",
       "600    0.070\n",
       "Name: Competition_Metric, Length: 600, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=train.groupby(\"Course_ID\")[\"Competition_Metric\"].unique().apply(lambda x: np.round(x[0],10))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "a[a<0]=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    598.000000\n",
       "mean       0.072938\n",
       "std        0.099493\n",
       "min        0.000000\n",
       "25%        0.010000\n",
       "50%        0.034500\n",
       "75%        0.093500\n",
       "max        0.768000\n",
       "Name: Competition_Metric, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2309b4c0748>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "sns.barplot(x=a.index,y=a.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hypothesis- Courses with higher competition have lower sales**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We have come up with some Hypothesis which we will now look into-\n",
    "1. Short_Promotion is causing an increase in sales for Courses\n",
    "2. On Public Holidays, there is more sale\n",
    "3. Courses which are being Long_promoted have higher sales\n",
    "4. User Traffic will be more on public Holidays\n",
    "5. Higher User Traffic will lead to More Sales\n",
    "6. Courses with higher competition metric have lower sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2309c579448>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=train,x=\"Short_Promotion\",y=\"Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>68.829670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>97.836996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157.577381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <td>132.249084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <th>1</th>\n",
       "      <td>232.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">599</th>\n",
       "      <th>0</th>\n",
       "      <td>105.653846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133.723214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">600</th>\n",
       "      <th>0</th>\n",
       "      <td>97.163004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128.755952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Sales\n",
       "Course_ID Short_Promotion            \n",
       "1         0                 68.829670\n",
       "          1                111.937500\n",
       "2         0                 97.836996\n",
       "          1                157.577381\n",
       "3         0                132.249084\n",
       "...                               ...\n",
       "598       1                232.125000\n",
       "599       0                105.653846\n",
       "          1                133.723214\n",
       "600       0                 97.163004\n",
       "          1                128.755952\n",
       "\n",
       "[1200 rows x 1 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp=pd.DataFrame(train.groupby([\"Course_ID\",\"Short_Promotion\"])[\"Sales\"].mean())\n",
    "grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": [
    "grp.loc[:30].unstack().plot(kind='bar',stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "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": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAATa0lEQVR4nO3df7BfdX3n8eerQXSLuoC5QARiwAZ2cWujXqldq2LZbZHtFu3UQupgcGmjM9DR6Q/80RmhdphR1DrjdEWjUKBDMWwRoR26haEi206xTTCGIKDB8iMhvwALKA42yXv/uOcevoRvci8x53tu+D4fM3e+3/M+n3POOzHcl+d3qgpJkgB+qu8GJElzh6EgSWoZCpKklqEgSWoZCpKklqEgSWp1FgpJjk7ytSR3Jbkzyfub+qFJbkry3ebzkKaeJJ9Nsj7J2iSv7ao3SdJwXe4pbAd+v6r+M/AG4JwkJwAfAm6uqsXAzc00wNuAxc3PcuDiDnuTJA1xQFcrrqpNwKbm+xNJ7gKOBE4DTmqGXQ7cAnywqV9RU3fT3Zbk4CQLmvUMNX/+/Fq0aFFXfwRJel5avXr1w1U1MWxeZ6EwKMki4DXAN4DDp3/RV9WmJIc1w44EHhxYbENT220oLFq0iFWrVnXRsiQ9byW5f3fzOj/RnOTFwDXAB6rq8T0NHVJ71jM4kixPsirJqm3btu2rNiVJdBwKSV7AVCBcWVVfacpbkixo5i8Atjb1DcDRA4sfBTy06zqrakVVTVbV5MTE0L0fSdJe6vLqowCXAHdV1Z8OzLoeWNZ8XwZcN1B/d3MV0huAx/Z0PkGStO91eU7hjcCZwB1J1jS1jwAfB65OcjbwAPDOZt4NwKnAeuBJ4D0d9iZJGqLLq4/+geHnCQBOHjK+gHO66keSNDPvaJYktQwFSVLLUJAktUZy85ok7a3zzjuPzZs3c8QRR3DRRRf13c7znqEgaU7bvHkzGzdu7LuNseHhI0lSy1CQJLUMBUlSy3MK0hz1wMd+tu8W5oTtjx4KHMD2R+/37wRY+NE7Ol2/ewqSpJahIElqGQqSpJahIElqGQqSpJZXH0ma0+a/aCewvflU1wwFSXPaH7z63/puYax4+EiS1DIUJEmtzkIhyaVJtiZZN1BbmWRN83Pf9LubkyxK8qOBeZ/vqi9J0u51eU7hMuDPgCumC1V1+vT3JJ8GHhsYf29VLemwH0nSDDoLhaq6NcmiYfOSBPhN4Je62r4k6bnr65zCm4AtVfXdgdoxSb6Z5OtJ3tRTX5I01vq6JHUpcNXA9CZgYVU9kuR1wFeTvKqqHt91wSTLgeUACxcuHEmzkjQuRr6nkOQA4NeBldO1qnqqqh5pvq8G7gWOG7Z8Va2oqsmqmpyYmBhFy5I0Nvo4fPTfgLurasN0IclEknnN92OBxcD3euhNksZal5ekXgX8E3B8kg1Jzm5mncEzDx0BvBlYm+RbwF8B76uqR7vqTZI0XJdXHy3dTf2sIbVrgGu66kWSNDve0SxJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqRWl+9ovjTJ1iTrBmoXJNmYZE3zc+rAvA8nWZ/kniS/0lVfkqTd63JP4TLglCH1z1TVkubnBoAkJwBnAK9qlvlcknkd9iZJGqKzUKiqW4FHZzn8NODLVfVUVf0rsB44saveJEnD9XFO4dwka5vDS4c0tSOBBwfGbGhqkqQRGnUoXAy8ElgCbAI+3dQzZGwNW0GS5UlWJVm1bdu2brqUpDE10lCoqi1VtaOqdgJf5OlDRBuAoweGHgU8tJt1rKiqyaqanJiY6LZhSRozIw2FJAsGJt8BTF+ZdD1wRpIXJjkGWAz88yh7kyTBAV2tOMlVwEnA/CQbgPOBk5IsYerQ0H3AewGq6s4kVwPfBrYD51TVjq56kyQN11koVNXSIeVL9jD+QuDCrvqRJM3MO5olSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLU6uwxF9q/nHfeeWzevJkjjjiCiy66qO92JPXEUBAAmzdvZuPGjX23IalnHj6SJLUMBUlSa+wPH73uD6/ou4U54SUPP8E84IGHn/DvBFj9yXf33YLUC/cUJEktQ0GS1DIUJEmtzkIhyaVJtiZZN1D7ZJK7k6xNcm2Sg5v6oiQ/SrKm+fl8V31puJ0HHsSOF76UnQce1HcrknrU5Ynmy4A/AwbPWt4EfLiqtif5BPBh4IPNvHurakmH/WgPfrj4l/tuQdIc0NmeQlXdCjy6S+3GqtreTN4GHNXV9iVJz12f5xT+F/C3A9PHJPlmkq8neVNfTUnSOOvlPoUkfwRsB65sSpuAhVX1SJLXAV9N8qqqenzIssuB5QALFy4cVcuSNBZGvqeQZBnwq8C7qqoAquqpqnqk+b4auBc4btjyVbWiqiaranJiYmJUbUvSWBhpKCQ5hakTy79WVU8O1CeSzGu+HwssBr43yt4kSR0ePkpyFXASMD/JBuB8pq42eiFwUxKA26rqfcCbgY8l2Q7sAN5XVY8OXbEkqTOdhUJVLR1SvmQ3Y68BrumqF0nS7HhHsySpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklqGgiSpZShIklrPORSS/FSSl3bRjCSpX7MKhSR/meSlSQ4Cvg3ck+QPu21NkjRqs91TOKF54c3bgRuAhcCZnXUlSerFbEPhBUlewFQoXFdV/w5Ud21Jkvow21D4AnAfcBBwa5JXAM96VaYkaf82q/cpVNVngc8OlO5P8tZuWpIk9WW2J5oPT3JJkr9tpk8AlnXamSRp5GZ7+Ogy4O+AlzfT3wE+0EVDkqT+zDYU5lfV1cBOgKqafpfyHiW5NMnWJOsGaocmuSnJd5vPQ5p6knw2yfoka5O8di/+PJKkn8BsQ+GHSV5Gc8VRkjcAj81iucuAU3apfQi4uaoWAzc30wBvAxY3P8uBi2fZmyRpH5ltKPwecD3wyiT/CFwB/O5MC1XVrcCju5RPAy5vvl/O1GWu0/UrasptwMFJFsyyP0nSPjDbq49uT/IW4HggwD3NvQp74/Cq2tSsd1OSw5r6kcCDA+M2NLVNe7kdSdJztMdQSPLru5l1XBKq6iv7sJcMqT3rBrkky5k6vMTChQv34eYlSTPtKfzPPcwrYG9CYUuSBc1ewgJga1PfABw9MO4o4KFnbbRqBbACYHJy0ruqJWkf2mMoVNV7Otjm9Uzd4/Dx5vO6gfq5Sb4M/Dzw2PRhJknSaMzqnAJAkv8BvAp40XStqj42wzJXAScB85NsAM5nKgyuTnI28ADwzmb4DcCpwHrgSaCLQJIk7cGsQiHJ54GfBt4KfAn4DeCfZ1quqpbuZtbJQ8YWcM5s+pEkdWO2l6T+16p6N/D9qvpj4Bd45vF/SdLzwGxD4UfN55NJXg5sB47ppiVJUl9me07hb5IcDFwErG5qX+qmJUlSX2a6T+H1wINV9SfN9IuBO4C7gc90354kaZRmOnz0BeDHAEnezNSVQ19g6rlHK7ptTZI0ajMdPppXVdPPLjodWFFV1wDXJFnTbWuSpFGbaU9hXpLp4DgZ+PuBebO+x0GStH+Y6Rf7VcDXkzzM1BVI/w8gyc8wu0dnS5L2IzM95uLCJDcDC4AbmxvMYGoPY8ZHZ0uS9i8zHgJq3m2wa+073bQjSerTbG9ekySNAUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQa+fOLkhwPrBwoHQt8FDgY+B1gW1P/SFXdMOL2JGmsjTwUquoeYAlAknnARuBa4D3AZ6rqU6PuSZI0pe/DRycD91bV/T33IUmi/1A4g6knsU47N8naJJcmOaSvpiRpXPUWCkkOBH4N+D9N6WLglUwdWtoEfHo3yy1PsirJqm3btg0bIknaS33uKbwNuL2qtgBU1Zaq2lFVO4EvAicOW6iqVlTVZFVNTkxMjLBdSXr+6zMUljJw6CjJgoF57wDWjbwjSRpzvbxSM8lPA/8deO9A+aIkS4AC7ttlniRpBHoJhap6EnjZLrUz++hFkvS0vq8+kiTNIYaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKllKEiSWoaCJKnVy+s4AZLcBzwB7AC2V9VkkkOBlcAipt7T/JtV9f2+epSkcdP3nsJbq2pJVU020x8Cbq6qxcDNzbQkaUT6DoVdnQZc3ny/HHh7j71I0tjpMxQKuDHJ6iTLm9rhVbUJoPk8rLfuJGkM9XZOAXhjVT2U5DDgpiR3z2ahJkCWAyxcuLDL/iRp7PS2p1BVDzWfW4FrgROBLUkWADSfW4cst6KqJqtqcmJiYpQtS9LzXi+hkOSgJC+Z/g78MrAOuB5Y1gxbBlzXR3+SNK76Onx0OHBtkuke/rKq/m+SfwGuTnI28ADwzp76k6Sx1EsoVNX3gJ8bUn8EOHn0HUmSYO5dkipJ6pGhIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElqjTwUkhyd5GtJ7kpyZ5L3N/ULkmxMsqb5OXXUvUnSuOvjHc3bgd+vqtuTvARYneSmZt5nqupTPfQkSaKHUKiqTcCm5vsTSe4Cjhx1H5KkZ+v1nEKSRcBrgG80pXOTrE1yaZJDemtMksZUb6GQ5MXANcAHqupx4GLglcASpvYkPr2b5ZYnWZVk1bZt20bWrySNg15CIckLmAqEK6vqKwBVtaWqdlTVTuCLwInDlq2qFVU1WVWTExMTo2taksZAH1cfBbgEuKuq/nSgvmBg2DuAdaPuTZLGXR9XH70ROBO4I8mapvYRYGmSJUAB9wHv7aE3SRprfVx99A9Ahsy6YdS9SJKeyTuaJUktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1JpzoZDklCT3JFmf5EN99yNJ42ROhUKSecD/Bt4GnAAsTXJCv11J0viYU6EAnAisr6rvVdWPgS8Dp/XckySNjbkWCkcCDw5Mb2hqkqQROKDvBnaRIbV6xoBkObC8mfxBkns672p8zAce7ruJuSCfWtZ3C3om/21OO3/Yr8nn7BW7mzHXQmEDcPTA9FHAQ4MDqmoFsGKUTY2LJKuqarLvPqRd+W9zdOba4aN/ARYnOSbJgcAZwPU99yRJY2NO7SlU1fYk5wJ/B8wDLq2qO3tuS5LGxpwKBYCqugG4oe8+xpSH5TRX+W9zRFJVM4+SJI2FuXZOQZLUI0NBgI8X0dyU5NIkW5Os67uXcWEoyMeLaC67DDil7ybGiaEg8PEimqOq6lbg0b77GCeGgsDHi0hqGAqCWTxeRNJ4MBQEs3i8iKTxYCgIfLyIpIahIKpqOzD9eJG7gKt9vIjmgiRXAf8EHJ9kQ5Kz++7p+c47miVJLfcUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUtN9K8kdJ7kyyNsmaJD+f5L4k83+CdS5JcuoMY85Ksq3Z5reT/M7ebm9vNNt/+cD0l3yqrfaVOfc6Tmk2kvwC8KvAa6vqqSYIDvwJ13kAsASYZOZXwq6sqnOTHAbcmeT6qtoyuK7mpsAunAWso3kUSVX9dkfb0RhyT0H7qwXAw1X1FEBVPVxV089r+t0ktye5I8l/AkhyaJKvNnsVtyV5dVO/IMmKJDcCVwAfA05v9gJOn6mJqtoK3Au8Ytd1JXlRkj9v+vhmkrc22zyr6eWvk/xrknOT/F4z5rYkhzbjljTTa5Ncm+SQJL/BVGhd2fT4H5LckmSyWWZps711ST4x3WeSHyS5MMm3mnUevi/+R9Dzj6Gg/dWNwNFJvpPkc0neMjDv4ap6LXAx8AdN7Y+Bb1bVq4GPMBUA014HnFZVvwV8lKm9gCVVtXKmJpIcCxwLrB+yrnMAqupngaXA5Ule1Iz7L8BvMfUuiwuBJ6vqNUw90uHdzZgrgA82Pd8BnF9VfwWsAt7V9PijgV5eDnwC+CWm9nhen+TtzeyDgNuq6ueAW4GRHvLS/sNQ0H6pqn7A1C/g5cA2YGWSs5rZX2k+VwOLmu+/CPxFs+zfAy9L8h+bedcP/nKdpdOTrAGuAt5bVdMvghlc1+A27wbuB45r5n2tqp6oqm3AY8BfN/U7gEVNbwdX1deb+uXAm2fo6fXALVW1rTl0deXAMj8G/qb5Pvj3Ij2D5xS036qqHcAtwC1J7gCWNbOeaj538PS/8T29M+KHe7H5lVV17pD64LqGbXPaUwPfdw5M72Tv/7vc0/b+vZ5+0Nng34v0DO4paL+U5PgkiwdKS5j6f+K7cyvwrmbZk5g6xPT4kHFPAC/ZR20ObvM4YCFwz2wWrKrHgO8neVNTOhOY3mvYXY/fAN6SZH7z3u2lA8tIs2IoaH/1YqaO0X87yVrgBOCCPYy/AJhsxn6cp/cqdvU14ITZnmieweeAec1ezErgrOkT47O0DPhk0/MSpk6Cw9TL7D8/faJ5enBVbQI+3PwZvgXcXlXX/YR/Bo0ZH50tSWq5pyBJanmySdqNJO8B3r9L+R+r6pw++pFGwcNHkqSWh48kSS1DQZLUMhQkSS1DQZLUMhQkSa3/D8cqQWQ5booQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEHCAYAAABBW1qbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAARRklEQVR4nO3de5BkZX3G8e8jK6IoAdyRuy7GBYM31PGeeCOV4CVCpbQALV0MySZVaDRGQbQiaooqQSsmlvGyBciSIghBDGhplCBIYgWSAZWryEYFFlgZCkUUC1345Y8+89JsZtlxoPvM0t9PFdV93nNOn2eHYR/ec7pPp6qQJAngEX0HkCQtHZaCJKmxFCRJjaUgSWosBUlSs6zvAA/G8uXLa8WKFX3HkKStyqWXXnpbVU3Nt26rLoUVK1YwMzPTdwxJ2qokuX5z6zx9JElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJzVb94TVJD39HHXUUGzZsYNddd+WEE07oO87DnqUgaUnbsGEDN910U98xJoanjyRJjaUgSWosBUlS4zUFaYm64cPP6DvCkrDx9p2BZWy8/Xp/JsATP3DFSF/fmYIkqbEUJEmNpSBJaiwFSVJjKUiSmpGVQpKTk9ya5MqhsY8m+V6Sy5N8McmOQ+uOSbIuybVJ/nBUuSRtXZZvdy+7PHojy7e7t+8oE2GUb0k9BfgkcOrQ2HnAMVW1McnxwDHA0Un2Aw4FngbsDvx7kn2q6p4R5pO0FXj3M3/ad4SJMrKZQlVdBNy+ydjXq2pjt3gxsGf3/CDg81V1d1X9EFgHPH9U2SRJ8+vzmsKfAF/tnu8B3Di0bn039v8kWZ1kJsnM7OzsiCNK0mTppRSSvB/YCJw2NzTPZjXfvlW1pqqmq2p6ampqVBElaSKN/TYXSVYBrwUOqKq5v/jXA3sNbbYncPO4s0nSpBvrTCHJgcDRwOuq6q6hVecChyZ5VJK9gZXAf48zmyRphDOFJKcDLweWJ1kPHMvg3UaPAs5LAnBxVf1FVV2V5EzgaganlY70nUeSNH4jK4WqOmye4ZMeYPvjgONGlUeStGV+olmS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqLAVJUjOyUkhycpJbk1w5NLZzkvOSXNc97tSNJ8knkqxLcnmS54wqlyRp80Y5UzgFOHCTsfcC51fVSuD8bhngVcDK7p/VwKdHmEuStBkjK4Wqugi4fZPhg4C13fO1wMFD46fWwMXAjkl2G1U2SdL8xn1NYZequgWge3xCN74HcOPQduu7MUnSGC2VC82ZZ6zm3TBZnWQmyczs7OyIY0nSZBl3Kfx47rRQ93hrN74e2Gtouz2Bm+d7gapaU1XTVTU9NTU10rCSNGnGXQrnAqu656uAc4bG39K9C+mFwB1zp5kkSeOzbFQvnOR04OXA8iTrgWOBjwBnJjkCuAF4Q7f5V4BXA+uAu4C3jiqXJGnzRlYKVXXYZlYdMM+2BRw5qiySpIVZKheaJUlLgKUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKnppRSS/FWSq5JcmeT0JNsl2TvJJUmuS3JGkm37yCZJk2zspZBkD+AvgemqejqwDXAocDzw8apaCfwEOGLc2SRp0vV1+mgZ8Ogky4DHALcArwTO6tavBQ7uKZskTayxl0JV3QR8DLiBQRncAVwK/LSqNnabrQf2mG//JKuTzCSZmZ2dHUdkSZoYfZw+2gk4CNgb2B3YHnjVPJvWfPtX1Zqqmq6q6ampqdEFlaQJtKyHY/4+8MOqmgVIcjbwYmDHJMu62cKewM09ZJtYRx11FBs2bGDXXXflhBNO6DuOpJ70cU3hBuCFSR6TJMABwNXABcDru21WAef0kG1ibdiwgZtuuokNGzb0HUVSj/q4pnAJgwvKlwFXdBnWAEcD70qyDng8cNK4s0nSpOvj9BFVdSxw7CbDPwCe30McSVKnl1JYSp77nlP7jrAkPO62O9kGuOG2O/2ZAJd+9C19R5B64W0uJEmNpSBJan7jUkjyiCQ7jCKMJKlfCyqFJP+cZIck2zN4++i1Sd4z2miSpHFb6Exhv6r6GYP7EX0FeCLw5pGl0tjdu+323POoHbh32+37jiKpRwt999EjkzySQSl8sqp+nWTe21Bo6/SLlX/QdwRJS8BCZwqfBX7E4D5FFyV5EvCzUYWSJPVjQTOFqvoE8ImhoeuTvGI0kSRJfVnoheZdkpyU5Kvd8n4M7k8kSXoYWejpo1OArzG41TXA94F3jiKQJKk/Cy2F5VV1JnAvQHd763tGlkqS1IuFlsIvkjye7otvkryQwTemSZIeRhb6ltR3AecCv53kW8AU9333gSTpYWKh7z66LMnLgH2BANdW1a9HmkySNHYPWApJ/ngzq/ZJQlWdPYJMkqSebGmm8EcPsK4AS0GSHkYesBSq6q3jCiJJ6t+Cv3ktyWuApwHbzY1V1YdHEUqS1I+FfqL5M8AhwNsZXGh+A/CkEeaSJPVgoZ9TeHFVvQX4SVV9CHgRsNfoYkmS+rDQUvhl93hXkt2BjcDeo4kkSerLQq8pfDnJjsAJwKXd2ImjiSRJ6ssDzhSSPC/JrlX1t1X1U+CxwBXAvwAfX+xBk+yY5Kwk30tyTZIXJdk5yXlJrused1rs60uSFmdLp48+C/wKIMlLgY90Y3cAax7Ecf8B+LeqeirwLOAa4L3A+VW1Eji/W5YkjdGWSmGbqrq9e34IsKaqvlBVfwM8ZTEHTLID8FLgJICq+lU3CzkIWNtttpbBV39KksZoi6WQZO66wwHAN4bWLfgzDpt4MjALfC7Jt5OcmGR7YJequgWge3zCfDsnWZ1kJsnM7OzsIiNIkuazpVI4HfhmknMYvAPpPwCSPIXF3zp7GfAc4NNV9WzgF/wGp4qqak1VTVfV9NTU1CIjSJLms6XbXByX5HxgN+DrVVXdqkcw+CDbYqwH1lfVJd3yWQxK4cdJdquqW5LsBty6yNeXJC3SFk8BVdXF84x9f7EHrKoNSW5Msm9VXcvgtNTV3T+rGFzMXgWcs9hjSJIWZ7HXBR6stwOnJdkW+AHwVgazjzOTHAHcwOBWGpKkMeqlFKrqO8D0PKsOGHcWSdJ9FnqbC0nSBLAUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpKa3UkiyTZJvJ/lyt7x3kkuSXJfkjCTb9pVNkiZVnzOFdwDXDC0fD3y8qlYCPwGO6CWVJE2wXkohyZ7Aa4ATu+UArwTO6jZZCxzcRzZJmmR9zRT+HjgKuLdbfjzw06ra2C2vB/aYb8ckq5PMJJmZnZ0dfVJJmiBjL4UkrwVurapLh4fn2bTm27+q1lTVdFVNT01NjSSjJE2qZT0c8yXA65K8GtgO2IHBzGHHJMu62cKewM09ZJOkiTb2mUJVHVNVe1bVCuBQ4BtV9SbgAuD13WargHPGnU2SJt1S+pzC0cC7kqxjcI3hpJ7zSNLE6eP0UVNVFwIXds9/ADy/zzySNOmW0kxBktQzS0GS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqxl4KSfZKckGSa5JcleQd3fjOSc5Lcl33uNO4s0nSpOtjprAR+Ouq+h3ghcCRSfYD3gucX1UrgfO7ZUnSGI29FKrqlqq6rHt+J3ANsAdwELC222wtcPC4s0nSpOv1mkKSFcCzgUuAXarqFhgUB/CE/pJJ0mTqrRSSPBb4AvDOqvrZb7Df6iQzSWZmZ2dHF1CSJlAvpZDkkQwK4bSqOrsb/nGS3br1uwG3zrdvVa2pqumqmp6amhpPYEmaEH28+yjAScA1VfV3Q6vOBVZ1z1cB54w7myRNumU9HPMlwJuBK5J8pxt7H/AR4MwkRwA3AG/oIZskTbSxl0JV/SeQzaw+YJxZJEn35yeaJUmNpSBJaiwFSVJjKUiSGktBktRYCpKkxlKQJDWWgiSpsRQkSY2lIElqLAVJUmMpSJIaS0GS1FgKkqTGUpAkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQZLUWAqSpMZSkCQ1loIkqVlypZDkwCTXJlmX5L1955GkSbKkSiHJNsA/Aq8C9gMOS7Jfv6kkaXIsqVIAng+sq6ofVNWvgM8DB/WcSZImxrK+A2xiD+DGoeX1wAuGN0iyGljdLf48ybVjyjYJlgO39R1iKcjHVvUdQffn7+acY/NQvMqTNrdiqZXCfH/aut9C1RpgzXjiTJYkM1U13XcOaVP+bo7PUjt9tB7Ya2h5T+DmnrJI0sRZaqXwP8DKJHsn2RY4FDi350ySNDGW1OmjqtqY5G3A14BtgJOr6qqeY00ST8tpqfJ3c0xSVVveSpI0EZba6SNJUo8sBUlSYynIW4toyUpycpJbk1zZd5ZJYSlMOG8toiXuFODAvkNMEktB3lpES1ZVXQTc3neOSWIpaL5bi+zRUxZJPbMUtMVbi0iaHJaCvLWIpMZSkLcWkdRYChOuqjYCc7cWuQY401uLaKlIcjrwX8C+SdYnOaLvTA933uZCktQ4U5AkNZaCJKmxFCRJjaUgSWosBUlSYylIkhpLQVutJO9PclWSy5N8J8kLkvwoyfIH8Zr7J3n1FrY5PMlsd8yrk/zZYo+3GN3xdx9aPtE72+qhsqS+o1laqCQvAl4LPKeq7u6KYNsH+ZrLgP2BaeArW9j8jKp6W5InAFclObeqfjz8Wt0HA0fhcOBKutuRVNWfjug4mkDOFLS12g24raruBqiq26pq7p5Nb09yWZIrkjwVIMnOSf61m1VcnOSZ3fgHk6xJ8nXgVODDwCHdLOCQLYWoqluB/wWetOlrJdkuyee6HN9O8orumId3Wb6U5IdJ3pbkXd02FyfZudtu/2758iRfTLJTktczKK3TuoyPTnJhkulun8O6412Z5Pi5nEl+nuS4JN/tXnOXh+Jfgh5+LAVtrb4O7JXk+0k+leRlQ+tuq6rnAJ8G3t2NfQj4dlU9E3gfgwKY81zgoKp6I/ABBrOA/avqjC2FSPJk4MnAunle60iAqnoGcBiwNsl23XZPB97I4PssjgPuqqpnM7ilw1u6bU4Fju4yXwEcW1VnATPAm7qMvxzKsjtwPPBKBjOe5yU5uFu9PXBxVT0LuAgY6ykvbT0sBW2VqurnDP4CXg3MAmckObxbfXb3eCmwonv+u8A/dft+A3h8kt/q1p07/JfrAh2S5DvA6cCfV9XcF8EMv9bwMb8HXA/s0627oKrurKpZ4A7gS934FcCKLtuOVfXNbnwt8NItZHoecGFVzXanrk4b2udXwJe758M/F+l+vKagrVZV3QNcCFyY5ApgVbfq7u7xHu77HX+g7434xSIOf0ZVvW2e8eHXmu+Yc+4een7v0PK9LP6/ywc63q/rvhudDf9cpPtxpqCtUpJ9k6wcGtqfwf+Jb85FwJu6fV/O4BTTz+bZ7k7gcQ9RzOFj7gM8Ebh2ITtW1R3AT5L8Xjf0ZmBu1rC5jJcAL0uyvPvu7cOG9pEWxFLQ1uqxDM7RX53kcmA/4IMPsP0Hgelu249w36xiUxcA+y30QvMWfArYppvFnAEcPndhfIFWAR/tMu/P4CI4DL7M/jNzF5rnNq6qW4Bjuj/Dd4HLquqcB/ln0ITx1tmSpMaZgiSp8WKTtBlJ3gq8Y5Phb1XVkX3kkcbB00eSpMbTR5KkxlKQJDWWgiSpsRQkSc3/AZRvYaOXh04jAAAAAElFTkSuQmCC\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 i in range(1,6):\n",
    "    sns.barplot(data=train[train.Course_ID==i],x=\"Short_Promotion\",y=\"Sales\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Short Promotion is indeed causing an increase in Sales**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x204ed3e41c8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=train,x=\"Public_Holiday\",y=\"Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>0</th>\n",
       "      <td>86.504084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>0</th>\n",
       "      <td>122.322014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.928571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <td>155.114219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <th>1</th>\n",
       "      <td>115.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">599</th>\n",
       "      <th>0</th>\n",
       "      <td>117.242424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>84.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">600</th>\n",
       "      <th>0</th>\n",
       "      <td>109.721963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.961538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Sales\n",
       "Course_ID Public_Holiday            \n",
       "1         0                86.504084\n",
       "          1                42.320000\n",
       "2         0               122.322014\n",
       "          1                67.928571\n",
       "3         0               155.114219\n",
       "...                              ...\n",
       "598       1               115.857143\n",
       "599       0               117.242424\n",
       "          1                84.333333\n",
       "600       0               109.721963\n",
       "          1                91.961538\n",
       "\n",
       "[1200 rows x 1 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp=pd.DataFrame(train.groupby([\"Course_ID\",\"Public_Holiday\"])[\"Sales\"].mean())\n",
    "grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEMCAYAAAA/Jfb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deZgU1b3/8feHdRQ3QCTooKCiBpDNEQ2QiA4qGq9orhhXxEQxRsI1P3MNxkSNkWhuuBi9JuYhEVcU0YgajIawuJBE2cSRTUFEHUFAoqBXQcXz+6Nq5jY9PTPdM9Oz1Hxez9NPV536VtXp7dunT1edUggBMzNLlhYNXQEzM6t7Tu5mZgnk5G5mlkBO7mZmCeTkbmaWQE7uZmYJVG1yl1QgaYGkVyQtl/TzuPweSW9KWhrf+sXlknS7pDWSSiQNyPeDMDOzXbXKImYHcEII4WNJrYH5kp6Ol/1nCOHRtPhTgB7x7RjgzvjezMzqSbXJPURnOX0cz7aOb1Wd+TQCuC9e70VJ+0jqEkLYUNkK++67b+jWrVv2tTYzMxYvXvx+CKFTpmXZtNyR1BJYDBwK/DaE8JKky4EJkq4D5gDjQwg7gAOAd1JWL43LNqRtcwwwBuDAAw9k0aJFuT0qM7NmTtJblS3L6g/VEMLOEEI/oBAYKKk3cA1wBHA00AH4cdn+Mm0iwzYnhxCKQghFnTpl/OIxM7MayulomRDCh8CzwPAQwoYQ2QHcDQyMw0qBrimrFQLr66CuZmaWpWyOlukkaZ94ejdgGLBKUpe4TMAZwLJ4lSeBUfFRM8cCW6vqbzczs7qXTZ97F+DeuN+9BTA9hDBT0lxJnYi6YZYC34vj/wKcCqwBPgEurvtqm1lNff7555SWlrJ9+/aGroplqaCggMLCQlq3bp31OtkcLVMC9M9QfkIl8QG4IusamFm9Ki0tZc8996Rbt25EP7ytMQshsGXLFkpLS+nevXvW6/kMVbNmZvv27XTs2NGJvYmQRMeOHXP+peXkbtYMObE3LTV5vZzczcwSyMndGqcb9q54s7yRxFVXXVU+P3HiRG644Ya87rNbt24ceeSR9O3bl5NOOon33nuvyvjRo0fz6KPpo53As88+y2mnnQbAk08+yS233JJzXfbYY49d5u+55x7Gjh1b5To33HADEydOBOC6665j9uzZVdatvjXt5O4EYFYn2rZty2OPPcb7779fr/udN28er7zyCkVFRfzyl7+s9fZOP/10xo8fXwc1y82NN97IsGHD6n2/VWnayd3M6kSrVq0YM2YMt956a4Vlb731FsXFxfTp04fi4mLefvttIGpJjxs3jkGDBnHwwQfv0qr+9a9/zdFHH02fPn24/vrrq93/N77xDdasWQPs2op+9NFHGT16dPn87Nmz+frXv85hhx3GzJkzK2wntcW9ceNGzjzzTPr27Uvfvn35xz/+kd2TkeXjT5X6q+KZZ57hiCOOYMiQITz22GPlMQsWLGDQoEH079+fQYMG8dprrwHw9a9/naVLl5bHDR48mJKSkhrVNZWTu5kBcMUVVzB16lS2bt26S/nYsWMZNWoUJSUlnH/++YwbN6582YYNG5g/fz4zZ84sbzHPmjWL1atXs2DBApYuXcrixYt5/vnnq9z3zJkzOfLII6ut47p163juued46qmn+N73vlflESTjxo3juOOO45VXXmHJkiX06tWr0thPP/2Ufv36ld+uu+66rB5/uu3bt3PppZfy5z//mRdeeGGXrqYjjjiC559/npdffpkbb7yRn/zkJwBccskl3HPPPQC8/vrr7Nixgz59+lT7XFTHyd3MANhrr70YNWoUt99++y7l//znPznvvPMAuPDCC5k/f375sjPOOIMWLVrQs2dPNm7cCETJfdasWfTv358BAwawatUqVq9enXGfxx9/PP369WPbtm1cc8011dbx7LPPpkWLFvTo0YODDz6YVatWVRo7d+5cLr/8cgBatmzJ3ntX3m272267sXTp0vLbjTfemNXjT7dq1Sq6d+9Ojx49kMQFF1xQvmzr1q2MHDmS3r1788Mf/pDly5cDMHLkSGbOnMnnn3/OlClTdvmlUhtZjQppZs3DlVdeyYABA7j44spPLE89LK9t27bl09H5i9H9Nddcw2WXXVbt/ubNm8e+++5b6fbTW+bphwQ2xCGd1e2zsuU/+9nPOP7445kxYwbr1q1j6NChAOy+++6ceOKJPPHEE0yfPr3ORsh1y93MynXo0IGzzz6bu+66q7xs0KBBTJs2DYCpU6cyZMiQKrdx8sknM2XKFD7+OLoMxLvvvsumTZsAKC4u5t13361y/c6dO7Ny5Uq+/PJLZsyYscuyRx55hC+//JI33niDtWvXcvjhh1e6neLiYu68804Adu7cybZt26rcb2VyefxHHHEEb775Jm+88QYADz30UPmyrVu3csABBwCUd8OUueSSSxg3bhxHH300HTp0qFE90zm5m9kurrrqql2Omrn99tu5++676dOnD/fffz+33XZbleufdNJJnHfeeXzta1/jyCOP5KyzzuKjjz7iyy+/ZM2aNdUmr1tuuYXTTjuNE044gS5duuyy7PDDD+e4447jlFNO4fe//z0FBQWVbue2225j3rx5HHnkkRx11FHl3SC5yuXxFxQUMHnyZL75zW8yZMgQDjrooPJlV199Nddccw2DBw9m586du6x31FFHsddee1X5iylXKvsp1ZCKiopCjX6KZDr08YatFcus6fFrmzcrV67kq1/9ar3vd9myZUyZMoVJkybV+74bu/Xr1zN06FBWrVpFixaZ29yZXjdJi0MIRZni3XI3s3rRu3dvJ/YM7rvvPo455hgmTJhQaWKvCf+hambNwpYtWyguLq5QPmfOHDp27NgANYqMGjWKUaNG1fl2ndzNrFno2LHjLicLJZ27ZczMEsjJ3cwsgZzczcwSyMndzCyB/IeqmdVIt/FP1en21t3yzWpjPv30U4YPH87cuXORxJVXXlk+XVBQwPTp06u8zujQoUOZOHEiRUUZDw2vkZtvvpm77rqLli1bcvvtt3PyySfz2WefMWzYMObOnUurVg2TZhtfcvfJK2ZWiSlTpvCtb32Lli1b8tBDD7F+/XpKSkpo0aIFpaWltGvXrl7rs2LFCqZNm8by5ctZv349w4YN4/XXX6dNmzYUFxfz8MMPc/7559drncq4W8bMmoypU6cyYsQIIBpuuEuXLuUn/hQWFtK+fXsALr/8coqKiujVq1el48nPmjWLr33tawwYMICRI0eWj4Uzfvx4evbsSZ8+ffjRj35UZX2eeOIJzjnnHNq2bUv37t059NBDWbBgARCNmDl16tQ6edw1UW1yl1QgaYGkVyQtl/TzuLy7pJckrZb0sKQ2cXnbeH5NvLxbfh+CmTUHn332GWvXrqVbt25ANPzvn//8Z/r168dVV13Fyy+/XB47YcIEFi1aRElJCc8991yFi1+8//773HTTTcyePZslS5ZQVFTEpEmT+Ne//sWMGTNYvnw5JSUl/PSnP62yTu+++y5du3Ytny8sLCwfGK13794sXLiwjh597rJpue8ATggh9AX6AcMlHQv8Crg1hNAD+AD4bhz/XeCDEMKhwK1xnJlZrbz//vvss88+5fOFhYW89tpr3HzzzbRo0YLi4mLmzJkDwPTp0xkwYAD9+/dn+fLlrFixYpdtvfjii6xYsYLBgwfTr18/7r33Xt566y322msvCgoKuOSSS3jsscfYfffdq6xTprG5yob8bdmyJW3atOGjjz6q7UOvkWr73ENU+4/j2dbxLQAnAOfF5fcCNwB3AiPiaYBHgTskKTSGEcrMrMnabbfdKozv3rZtW0455RROOeUUOnfuzOOPP87BBx/MxIkTWbhwIe3bt2f06NEV1gshcOKJJ+4yJG+ZBQsWMGfOHKZNm8Ydd9zB3LlzK61TYWEh77zzTvl8aWkp+++/f/n8jh07qhy5Mp+y6nOX1FLSUmAT8DfgDeDDEMIXcUgpcEA8fQDwDkC8fCtQYeAGSWMkLZK0aPPmzbV7FGaWeO3bt2fnzp3liXrJkiWsX78egC+//JKSkhIOOuggtm3bRrt27dh7773ZuHEjTz/9dIVtHXvssfz9738vv27rJ598wuuvv87HH3/M1q1bOfXUU/nNb35TPlzBjBkzMl4p6vTTT2fatGns2LGDN998k9WrVzNw4EAgGsumU6dOtG7dOi/PR3WyOlomhLAT6CdpH2AGkGm80LKWeabLkFRotYcQJgOTIRryN6vamlmjkc2hi3XtpJNOYv78+QwbNoxNmzZx6aWXsmPHDgAGDhzI2LFjKSgooH///vTq1YuDDz6YwYMHV9hOp06duOeeezj33HPL17/pppvYc889GTFiBNu3byeEUH7B8DfeeIO99tqrwnZ69erF2WefTc+ePWnVqhW//e1vadmyJRBdZerUU0/N11NRrZwOhQwhfCjpWeBYYB9JreLWeSGwPg4rBboCpZJaAXsD/6q7KptZczV27FgmTZrEsGHDGD58OMOHD88Yl36lozLPPvts+fQJJ5yQ8Q/PsqNdUi1durQ80ae79tprufbaayuUP/jgg9x8880Z16kP2Rwt0ylusSNpN2AYsBKYB5wVh10EPBFPPxnPEy+f6/52M6sL/fv35/jjj69wJaN8e+CBB+jUqVPW8Z999hlnnHFGlZcBzLdsWu5dgHsltST6MpgeQpgpaQUwTdJNwMtA2UUX7wLul7SGqMV+Th7qbWbN1He+852GrkK12rRpk5cx2nORzdEyJUD/DOVrgYEZyrcDI+ukdmZmViM+Q9XMLIGc3M3MEsjJ3cwsgRrfqJBm1jRkGsG1VturfvTXxjbk75YtWzjrrLNYuHAho0eP5o477ihfNmzYMB555JHywczqm5O7mTUZjW3I34KCAn7xi1+wbNkyli1btsuyCy+8kN/97ncZj4GvD+6WMbMmo7EN+duuXTuGDBmScfyY008/PePYNfXFLXczaxIyDfk7ZMgQXnjhBYqLi7ngggvo3z86anvChAl06NCBnTt3UlxcTElJCX369CnfVuqQv+3ateNXv/oVkyZNYuzYscyYMYNVq1YhiQ8//LDG9W3fvj07duxgy5YtdOxYYXitvHPL3cyahMY45G919ttvv/LBzeqbW+5m1iQ0xiF/q7N9+3Z22223Gq9fG265m1mT0BiH/K1KCIH33nuvvBupvrnlbmY10wAXrm9sQ/4CdOvWjW3btvHZZ5/x+OOPM2vWLHr27MnixYs59thjadWqYdKsk7uZNRmNccjfdevWZSy///77+f73v59xWX1wcjezJiN1yN+yi2LUhwceeCDndXr37k1xcXEeapMdJ3cza1KawpC/AJdeemmD7t9/qJo1Q75+TtNSk9fLyd2smSkoKGDLli1O8E1ECIEtW7ZkPAu2Ku6WMWtmCgsLKS0tZfPmzQ1dFctSQUEBhYWFOa3j5G7WzLRu3brKkRMtGdwtY2aWQE7uZmYJ5ORuZpZA7nM3s6Yv01WhGmB4hMak2pa7pK6S5klaKWm5pP+Iy2+Q9K6kpfHt1JR1rpG0RtJrkk7O5wMwM7OKsmm5fwFcFUJYImlPYLGkv8XLbg0hTEwNltQTOAfoBewPzJZ0WAhhZ11W3MzMKldtyz2EsCGEsCSe/ghYCRxQxSojgGkhhB0hhDeBNcDAuqismZllJ6c/VCV1A/oDL8VFYyWVSJoiqewS3wcA76SsVkqGLwNJYyQtkrTIJ1OYmdWtrJO7pD2APwFXhhC2AXcChwD9gA3Af5eFZli9wnnOIYTJIYSiEEJRp06dcq64mZlVLqujZSS1JkrsU0MIjwGEEDamLP8DMDOeLQW6pqxeCDTMRQTNzGqjCR+Fk83RMgLuAlaGECallHdJCTsTWBZPPwmcI6mtpO5AD6Di6PdmZpY32bTcBwMXAq9KWhqX/QQ4V1I/oi6XdcBlACGE5ZKmAyuIjrS5wkfKmJnVr2qTewhhPpn70f9SxToTgAm1qJeZmdWChx8wM0sgJ3czswRycjczSyAndzOzBHJyNzNLIA/5a2bWWNXiJCq33M3MEsjJ3cwsgZzczcwSyMndzCyBnNzNzBLIR8s0dU14SFJLEL8PGx0n9/riN7+Z1SMndzNrnDI1iMCNoiy5z93MLIGc3M3MEsjJ3cwsgZzczcwSyMndzCyBfLSMNUrdtj9YoWxd/VfDrMlyy93MLIHcck/nY2vNLAGqbblL6ippnqSVkpZL+o+4vIOkv0laHd+3j8sl6XZJaySVSBqQ7wdhZma7yqZb5gvgqhDCV4FjgSsk9QTGA3NCCD2AOfE8wClAj/g2BrizzmttZmZVqja5hxA2hBCWxNMfASuBA4ARwL1x2L3AGfH0COC+EHkR2EdSlzqvuZmZVSqnP1QldQP6Ay8BnUMIGyD6AgD2i8MOAN5JWa00Lkvf1hhJiyQt2rx5c+41NzOzSmWd3CXtAfwJuDKEsK2q0AxloUJBCJNDCEUhhKJOnTplWw0zM8tCVkfLSGpNlNinhhAei4s3SuoSQtgQd7tsistLga4pqxcC6+uqwmZWTzxMdZNWbXKXJOAuYGUIYVLKoieBi4Bb4vsnUsrHSpoGHANsLeu+aVB+o5pZM5JNy30wcCHwqqSlcdlPiJL6dEnfBd4GRsbL/gKcCqwBPgEurtMap/BZjGZmmVWb3EMI88ncjw5QnCE+AFfUsl5mZslUTydK+gxVqx13d5k1Sk7u1rR5uAizjDxwmJlZArnlblaf3I1l9cQtdzOzBHJyNzNLIHfLmDUn7hZqNhpdcveJSWZmteduGTOzBGp0LXfLEx8P3rT49bJacsvdzCyBnNzNzBLIyd3MLIGc3M3MEsjJ3cwsgZzczcwSyMndzCyBnNzNzBLIJzHVhk80MbNGysndzOpPY2gQNZPB09wtY2aWQNUmd0lTJG2StCyl7AZJ70paGt9OTVl2jaQ1kl6TdHK+Km5mZpXLplvmHuAO4L608ltDCBNTCyT1BM4BegH7A7MlHRZC2FkHdTWrP7n8dG8mP/Otaak2uYcQnpfULcvtjQCmhRB2AG9KWgMMBP5Z4xqa1ZXG0N9reeHrQFRUmz9Ux0oaBSwCrgohfAAcALyYElMal5mZJVcjbDjU9A/VO4FDgH7ABuC/43JliA2ZNiBpjKRFkhZt3ry5htUwM7NMatRyDyFsLJuW9AdgZjxbCnRNCS0E1leyjcnAZICioqKMXwBmZg2pKXf31KjlLqlLyuyZQNmRNE8C50hqK6k70ANYULsqmplZrqptuUt6CBgK7CupFLgeGCqpH1GXyzrgMoAQwnJJ04EVwBfAFT5Sxsys/mVztMy5GYrvqiJ+AjChNpUyM7Pa8RmqZmYJ5ORuZpZATu5mZgnkUSGbuKZ8qJZZVTK9t6F5vb9r8/l2y93MLIHccjdr6hrhqe/W8JzczazW3D3Y+Lhbxswsgdxyrydu2VhT4/ds0+aWu5lZArnlbmZWj+rrEE8nd6s/vhydWb1xcreKfGidWZPnPnczswRyyz2NT3k2syRwy93MLIGc3M3MEqjZdMv4hIz8aOjnNandaEl9XFZ/3HI3M0sgJ3czswRqNt0yZo1BQ3djWfPh5N4Y+UxOM6ulapO7pCnAacCmEELvuKwD8DDQjajhcXYI4QNJAm4DTgU+AUaHEJbkp+qWC/9BZ9a8ZNNyvwe4A7gvpWw8MCeEcIuk8fH8j4FTgB7x7RjgzvjezBoBdws1H9X+oRpCeB74V1rxCODeePpe4IyU8vtC5EVgH0ld6qqyZmaWnZoeLdM5hLABIL7fLy4/AHgnJa40LjMzs3pU14dCKkNZyBgojZG0SNKizZs313E1zMyat5oeLbNRUpcQwoa422VTXF4KdE2JKwTWZ9pACGEyMBmgqKgo4xeAmSVLY/hjPx//OzSGx5Wupi33J4GL4umLgCdSykcpciywtaz7xszM6k82h0I+BAwF9pVUClwP3AJMl/Rd4G1gZBz+F6LDINcQHQp5cR7q3Gg0xm9rMzPIIrmHEM6tZFFxhtgAXFHbSpk1JT680Bojn6FqzYZ/aVlz4uRuloFb49bUeVRIM7MEcnI3M0sgJ3czswRyn7tZE+c/ii0Tt9zNzBLIyd3MLIHcLWMV+Ge+WdPn5G71xseOm9Ufd8uYmSWQk7uZWQI5uZuZJZCTu5lZAvkP1UbIfzyaWW255W5mlkBO7mZmCeTkbmaWQE7uZmYJ5ORuZpZATu5mZgnk5G5mlkC1Os5d0jrgI2An8EUIoUhSB+BhoBvR4dlnhxA+qF01zcwsF3XRcj8+hNAvhFAUz48H5oQQegBz4nkzM6tH+eiWGQHcG0/fC5yRh32YmVkVapvcAzBL0mJJY+KyziGEDQDx/X6ZVpQ0RtIiSYs2b95cy2qYmVmq2o4tMziEsF7SfsDfJK3KdsUQwmRgMkBRUVGoZT3MzCxFrVruIYT18f0mYAYwENgoqQtAfL+ptpU0M7Pc1Di5S2onac+yaeAkYBnwJHBRHHYR8ERtK2lmZrmpTbdMZ2CGpLLtPBhCeEbSQmC6pO8CbwMja19NMzPLRY2TewhhLdA3Q/kWoLg2lTIzs9rxGapmZgnk5G5mlkBO7mZmCeTkbmaWQE7uZmYJ5ORuZpZATu5mZgnk5G5mlkBO7mZmCeTkbmaWQE7uZmYJ5ORuZpZATu5mZgnk5G5mlkBO7mZmCeTkbmaWQE7uZmYJ5ORuZpZATu5mZgnk5G5mlkBO7mZmCeTkbmaWQHlL7pKGS3pN0hpJ4/O1HzMzqygvyV1SS+C3wClAT+BcST3zsS8zM6soXy33gcCaEMLaEMJnwDRgRJ72ZWZmaRRCqPuNSmcBw0MIl8TzFwLHhBDGpsSMAcbEs4cDr2XY1L7A+1nsMtu4phbb0PvPV2xD7z9fsQ29/3zFNvT+8xXb0Puvi9iDQgidMkaHEOr8BowE/pgyfyHwPzXYzqK6jGtqsQ29fz8uP67GsH8/rtxjQwh565YpBbqmzBcC6/O0LzMzS5Ov5L4Q6CGpu6Q2wDnAk3nal5mZpWmVj42GEL6QNBb4K9ASmBJCWF6DTU2u47imFtvQ+89XbEPvP1+xDb3/fMU29P7zFdvQ+89nbH7+UDUzs4blM1TNzBLIyd3MLIGc3M3MEsjJ3cwsgZzcqyBpvzxss2Ndb9Mizen1ktRK0mWSnpFUIukVSU9L+p6k1vVUh90lXS3pPyUVSBot6UlJ/yVpjyzWf70+6pkrSQdLmiLpJkl7SPqDpGWSHpHUraHrl61Gn9wlPZ02v5ekmyXdL+m8tGW/S5v/iqQ7Jf1WUkdJN0h6VdJ0SV3SYjuk3ToCCyS1l9QhLXZ4yvTeku6KP2APSuqcsuwWSfvG00WS1gIvSXpL0nFp21wi6aeSDsniOSmSNE/SA5K6SvqbpK2SFkrqnxa7h6QbJS2PYzZLelHS6Azb3Tuu8ypJW+Lbyrhsn+rqFW/Dr1fF5yQfr9f9QD/gBuBU4JvAz4G+wAPV1Sllf5PT5lsq+tL4haTBact+mrb6PUBnoDvwFFAETAQE3Jm27keStsW3jyR9BBxSVp4S1ydlunX8HD8p6ZeSdk/b5tiU1+tQSc9L+lDSS5KOTIt9TNIFyuJLJ35cC4GPgReBVUSDID4DTEnbbgtJ35H0lKIv2MWSpkkamhZX689WvJ2nq4+K5XI6a75uwIBKbkcBG9Ji/wTcApxBdGLUn4C28bIlabHPAD8AxgMlwI+BA+OyJ9JivwTeTLt9Ht+vTYtdkjL9R+Am4CDgh8DjKcteTZmeBxwdTx9G2qnE8X4mAm8DC+Jt7V/J87WA6M12LvAOcFZcXgz8My32CWA00VnC/w/4GdADuBf4ZVrsX+Pn6CspZV+Jy/7m16vxvF7Aa1V8nl5Pm+9Qya0jUJoW+0fgQeBKYDEwKdPzGM8vje8FvMf/HVotoCQt9n+A+4DOqc9hhrqnvlb/TZRojwNuBe5Li12eMv0UcGY8PRT4e1rsu8CjwL+A6cCZQJtKnr+XU6bfrmxZPH830RfsEOA3wI3AicBs4Ae5frZy/XxVdWvwxB4/mJ3A3PgDlX77NNMbKmX+WuDv8Rs1/c1X1YuUvp0fESWXI6t682V4A6ZvZ2nK9CqgVTz9Ylrcq1Vs8+vA7+IPzDxgTC3efK+kzS+M71sAq9KWVZUwXkuZ9uvVwK8XUYtyJNAipawF8G3gpbRt7ATWsusXYdn8Z2mxJSnTrYhOnHkMaJuhrqnP3ZSqHkdcdlT8vhkX13VthpjU52op0DqezvSFkfqeXFjZ40jdLrAn0VhXfwE2EyXnk9JiFxN9oQ8kGqirKC7vkWG76fMvxvdtgZW5frZy/XxVdcsqKN83YBnQo5Jl76TNr0x9Q8dlFwHLgbcqe4MBN1X1osRlhcAjwKT4TVDhzRfHlRK1qq6KPySq5MPxA2AWcALRt/tvgG8Q/Xy+v7I3dUpZS2A4cHda+T+Bk4g+3G8BZ8Tlx1GxhfkPYEg8/W/AX6t4U80CrmbX1lVnotbFbL9ejef1AroBDwObgNfj26a4rHvaNlcDB2b5eq3KEHM90Rfy6rTyPwJ7ZIg/BJhfyf5aECX3F4D1GZavBb4F/DspyTH9/RHPTyBq2R8M/ITo18aBwMXAzLTYJRn21QH4HjA3rbyYaJTalUQt8j/Fz+EmYERa7GLgkHh6APB8yrIVuX62cv18VXXLKijfN+As4PBKlp2RNv9fwLAMccMzvPlurOTNdyjwaBX1+TeiltF7lSy/Pu3WKS7/ChV/Og6NP3AvA68StRjGELdIUuKm5fB89SX6mfc0cARwG/ABUcIcnBbbh6hb4ENgPnBYXN4JGJcW2x74FVEL9gOin7Ar47IOzeT1Or6eXq8P49drUCWv19aqXi+gDdGX5IlEv4IuIPr1cEWGul4B9K2kbj9Im3+AaLju9LhLgM/TytoCo8peX+A84I5K6tAmLfZCokT+/dRYopb0lPj+buJkGL9WczLUazTwElEL+yNgBfBLYO+0uOczPf5KnpOy57asrudX8dyeQNQ19zrRL6FjUl6v/8rw2VpJ9LnK+NnK9fNV1a1RDD8g6RiibxdND/kAAAV/SURBVOltknYDrgH6E79QIYStabGrQghbs4xN3e54om/X6mJ3J2q5DSD6Zq6qDrvH261QhwzbLNv/8izqmutzUNV2U+v64yqeg3HAjBDCO9W8XlnF1Wds/DwcEkJYVtPtNsbHVUXcVKJuk92IvgjaATOIWp0KIVyUEtuWqLtmfQhhdvzH9iCi5DI5hPB5lrF/CNHFd9LrsDvRl9UeRF04xQAhhNFVxGasb7z/c4B3s6hr2aCEZXU9P45dkePjSo/N5bltQ5T8Pw4hPFLNdg8l6uvvCnxB9IXwUOpnMCX2kLTY1ZXFVqaxJPflRC2LL+J/7z8h+vOjOC7/VgPE/i/Rz7Eabzcf28zzdrfG23qD6E+1R0IIFS4kkBb3UBy3OT2unmOnZ6prLtvN9vHX4+N6kOgXS6a6loQQ+khqRfRn4f4hhJ2SRNR9kXrUSVVJOD1Z5ZKwc6lDVrH5+MKowXNQV89teX3jL+3TgOeJjm5aSvTr+Ezg+yGEZ1O2mXVslbJt4ufzxq5/PGT8R74pxjb0/msQ+zJRn+hJwF1Efzg9Q/QTdc9c45pabEPvP8e6LiPqPmhP1B3RIS4voGJfdUl83wrYCLSM5zP9SZlLbC51yCo2j3XN1+PKartEXXxly3YHno2nD6TiH9VZx1Z1ayzHuS+TdHE8/YqkIgBJhxEd3tZUYxt6/7nGhhDClyGEWSGE7wL7E/U1DifqH801rqnFNvT+c4m9i+i/kaVERyA9IukPRMdnT0vbZou4+2BPomSxd1zeFkg/4SmX2FzqkG1svuqar8eVy3ZbpSzbEyCE8HaGuFxjM8v2WyCft/gJuYfop+hLRElnLfAcaX8ENaXYht5/DWIrbRUAu+Ua19RiG3r/NYjdn/jYemAfoj/iBmZY74fxa/4W0ZEqc4A/ELUQr69pbC51yDY2X3XN1+PKdrvAfxCduzGZ6Ivj4ri8E2l/9uYSW9WtUfS5l5G0J9FhTa2ITq7YmITYht5/trGSDgshVHtKeLZxTS22ofefa2wuJO0PEEJYr+iMyGFEx9wvqE1sPuSrrvl6XNluV1Iv4KvAshDCqmq2mXVspdtoTMndzMzqRmPpczczszrk5G5mlkBO7mZmCeTkbk2eoqGCp0l6Q9IKSX+JD/Vs6HoNlTQznh6taPjelyWtlvRXSYMauo6WXE7u1qTFZw3OIDrR45AQQk+iQaQ6V71mtdttWRf1S/NwCKF/CKEH0TDIj0n6ah72Y+bkbk3e8UQDWv2+rCCEsBSYL+nXiq6g86qkb8Ourel4/g7FF8KQtE7SdZLmAyMljYt/CZRImhbHtFN0lZ6FcSt8RE0qHUKYR3Qc85iaPnCzqrSqPsSsUetNNLhbum8RXamoL7AvsFDS81lsb3sIYQiApPVEw+fu0P9dLedaoiFivxOXLZA0O4TwvzWo+xLgshqsZ1Ytt9wtqYYQjaK3Mz5h6zng6CzWezhlugSYKukCopH5IBrzZbykpcCzROONHFjDOqqG65lVyy13a+qWE50anq6yxPkFuzZqCtKWp7bAv0l0sY7TgZ/FZw0K+PcQwms1q+4u+hMNDWtW59xyt6ZuLtBW0qVlBZKOJhoi9duKLvjciShJLyAaA6SnpLaS9iYemjWdpBZA17hv/Gqi8UX2ILroxg/iP3JR2gWus6XogttjiMYhMatzbrlbkxZCCJLOBH4jaTywHVhHdMm1PYBXgABcHUJ4D0DSdKIul9VEQ+xm0hJ4IP4CEHBrCOFDSb8guvxeSZzg1xGNvZ2Nb0saQjR64JtEvwDccre88NgyZmYJ5G4ZM7MEcreMWS1JOpnoQsep3gwhnNkQ9TEDd8uYmSWSu2XMzBLIyd3MLIGc3M3MEsjJ3cwsgf4/1vXq1uBFoGwAAAAASUVORK5CYII=\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"
    },
    {
     "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"
    },
    {
     "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": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEQCAYAAABLMTQcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de3xU1bn/8c/DNQgit1SRoIFCtdwEGhEBLxi0Yq2oFatWkXqhFzn0Qo/FXtRaKfqrB6qnrb5opailIseKWloVRbxwWuRuBEFBRI0gICLoUUDC8/tjr6STySSZTDJJZvJ9v17zyp61n9lr7ZnMM2vW7L22uTsiIpJdmjV0A0REpO4puYuIZCEldxGRLKTkLiKShZTcRUSyUIuGbgBAly5dPD8/v6GbISKSUVauXPm+u+cmWtcoknt+fj4rVqxo6GaIiGQUM3ursnUalhERyUJK7iIiWUjJXUQkCzWKMXcRqT+fffYZxcXF7Nu3r6GbIknKyckhLy+Pli1bJv0YJXeRJqa4uJjDDz+c/Px8zKyhmyPVcHd27dpFcXExPXr0SPpxGpYRaWL27dtH586dldgzhJnRuXPnGn/TUnIXaYKU2DNLKq9X0sndzJqb2WozWxDu9zCzl8xso5k9ZGatQnnrcH9TWJ9f41aJiEit1KTn/j1gfcz924EZ7t4b2A1cHcqvBna7ey9gRogTEZF6lFRyN7M84CvAH8N9A84AHg4h9wHnh+Ux4T5hfaHV5DvFzUdUvIlIWpkZkydPLrt/xx13cPPNN6e1zvz8fPr3788JJ5zAWWedxXvvvVdl/Pjx43n44YcrlD/33HOce+65ADz++OPcdtttNW5Lu3btyt2fPXs2EydOrPIxN998M3fccQcAN954I88880yVbatvyfbcfwNcDxwK9zsDH7r7wXC/GOgWlrsB7wCE9XtCfDlmNsHMVpjZip07d6bYfBGpC61bt+aRRx7h/fffr9d6Fy9ezMsvv0xBQQG/+tWvar298847jylTptRBy2rmlltuYdSoUfVeb1WqTe5mdi6ww91XxhYnCPUk1v27wH2muxe4e0FubsJ5bxpGom8O+vYgWa5FixZMmDCBGTNmVFj31ltvUVhYyIABAygsLOTtt98Gop70pEmTGDZsGD179izXq/71r3/NiSeeyIABA7jpppuqrf/UU09l06ZNQPle9MMPP8z48ePL7j/zzDOccsopfOELX2DBggUVthPb496+fTsXXHABJ5xwAieccAL//Oc/k3syktz/WLHfKp588kmOP/54RowYwSOPPFIWs2zZMoYNG8agQYMYNmwYr732GgCnnHIKa9asKYsbPnw4RUVFKbU1VjI99+HAeWa2BZhLNBzzG6CDmZUeJ58HbA3LxUB3gLD+COCDWrdURNLquuuuY86cOezZs6dc+cSJExk3bhxFRUV84xvfYNKkSWXrtm3bxpIlS1iwYEFZj3nhwoVs3LiRZcuWsWbNGlauXMkLL7xQZd0LFiygf//+1bZxy5YtPP/88/z973/n29/+dpWHB06aNInTTjuNl19+mVWrVtG3b99KYz/99FMGDhxYdrvxxhuT2v94+/bt49prr+Vvf/sbL774YrmhpuOPP54XXniB1atXc8stt/CTn/wEgGuuuYbZs2cD8Prrr7N//34GDBhQ7XNRnWqTu7vf4O557p4PXAI86+7fABYDF4WwK4HHwvLj4T5h/bOuq3CLNHrt27dn3Lhx3HXXXeXK//Wvf3HZZZcBcMUVV7BkyZKydeeffz7NmjWjT58+bN++HYiS+8KFCxk0aBCDBw9mw4YNbNy4MWGdI0eOZODAgezdu5cbbrih2jZefPHFNGvWjN69e9OzZ082bNhQaeyzzz7Ld77zHQCaN2/OEUdU/g28TZs2rFmzpux2yy23JLX/8TZs2ECPHj3o3bs3Zsbll19etm7Pnj2MHTuWfv368YMf/IB169YBMHbsWBYsWMBnn33GrFmzyn1TqY3anKH6Y2Cumd0KrAbuDeX3Ag+Y2SaiHvsltWuiiNSX73//+wwePJhvfvOblcbEHh/RunXrsuXSPpy7c8MNN/Ctb32r2voWL15Mly5dKt1+fM88/tiMhjhev7o6K1v/85//nJEjRzJ//ny2bNnC6aefDsBhhx3GmWeeyWOPPca8efPqbPrzGp3E5O7Pufu5YXmzuw9x917uPtbd94fyfeF+r7B+c520VETSrlOnTlx88cXce++9ZWXDhg1j7ty5AMyZM4cRI0ZUuY0vf/nLzJo1i48//hiAd999lx07dgBQWFjIu+++W+XjjzzySNavX8+hQ4eYP39+uXX/8z//w6FDh3jjjTfYvHkzxx13XKXbKSws5O677wagpKSEvXv3VllvZWqy/8cffzxvvvkmb7zxBgAPPvhg2bo9e/bQrVt03EnpMEypa665hkmTJnHiiSfSqVOnlNoZT2eoikg5kydPLnfUzF133cWf/vQnBgwYwAMPPMCdd95Z5ePPOussLrvsMk4++WT69+/PRRddxEcffcShQ4fYtGlTtcnrtttu49xzz+WMM86ga9eu5dYdd9xxnHbaaYwePZp77rmHnJycSrdz5513snjxYvr378+XvvSlsmGQmqrJ/ufk5DBz5ky+8pWvMGLECI499tiydddffz033HADw4cPp6SkpNzjvvSlL9G+ffsqvzHVlDWG4fCCggIv+yqS6MiUm/dULEuXyo6Mqc82iKTR+vXr+eIXv1jv9a5du5ZZs2Yxffr0eq+7sdu6dSunn346GzZsoFmzxH3uRK+bma1094JE8eq5i0i96NevnxJ7Avfffz8nnXQSU6dOrTSxp0JT/opIk7Br1y4KCwsrlC9atIjOnSucZ1lvxo0bx7hx4+p8u0ruItIkdO7cudzJQtlOwzIiIllIyV1EJAspuYuIZCGNuYtISvKn/L1Ot7fltq/U6faaOvXcRSRjfPrpp5x22mmUlJRw6NAhJk2aRL9+/ejfvz8nnngib775ZpWPP/300+vs9P5S06ZNo1evXhx33HE89dRTABw4cIBTTz2VgwcPVvPo9FHPXUQyxqxZs7jwwgtp3rw5Dz74IFu3bqWoqIhmzZpRXFxM27Zt67U9r776KnPnzmXdunVs3bqVUaNG8frrr9OqVSsKCwt56KGH+MY3vlGvbSqlnruIZIw5c+YwZswYIJpuuGvXrmUn/uTl5dGxY0cAvvOd71BQUEDfvn0rnU9+4cKFnHzyyQwePJixY8eWzYUzZcoU+vTpw4ABA/jRj35UZXsee+wxLrnkElq3bk2PHj3o1asXy5YtA6IZM+fMmVMn+50KJXcRyQgHDhxg8+bN5OfnA9H0v3/7298YOHAgkydPZvXq1WWxU6dOZcWKFRQVFfH8889XuPjF+++/z6233sozzzzDqlWrKCgoYPr06XzwwQfMnz+fdevWUVRUxM9+9rMq2/Tuu+/SvXv3svt5eXllE6P169eP5cuX19He15ySu4hkhPfff58OHTqU3c/Ly+O1115j2rRpNGvWjMLCQhYtWgTAvHnzGDx4MIMGDWLdunW8+uqr5ba1dOlSXn31VYYPH87AgQO57777eOutt2jfvj05OTlcc801PPLIIxx22GFVtinR3FylU/42b96cVq1a8dFHH9V211OiMXcRyQht2rSpML9769atGT16NKNHj+bII4/k0UcfpWfPntxxxx0sX76cjh07Mn78+AqPc3fOPPPMclPyllq2bBmLFi1i7ty5/Pa3v+XZZ5+ttE15eXm88847ZfeLi4s5+uijy+7v37+/ypkr00nJXURSUt+HLnbs2JGSkhL27dtHTk4Oq1at4qijjuLoo4/m0KFDFBUVMWDAAPbu3Uvbtm054ogj2L59O0888UTZhTFKDR06lOuuu45NmzbRq1cvPvnkk7LE/Mknn3DOOecwdOhQevXqBcD8+fNZtmwZ06ZNK7ed8847j8suu4wf/vCHbN26lY0bNzJkyBAgmssmNzeXli1b1svzE6/pJPeGnkpYRGrtrLPOYsmSJYwaNYodO3Zw7bXXsn//fgCGDBnCxIkTycnJYdCgQfTt25eePXsyfPjwCtvJzc1l9uzZXHrppWWPv/XWWzn88MMZM2YM+/btw93LLhj+xhtv0L59+wrb6du3LxdffDF9+vShRYsW/O53v6N58+ZAdJWpc845J11PRbWqnc/dzHKAF4DWRB8GD7v7TWY2GzgNKM2Q4919jUUDTncC5wCfhPJVVdVRL/O5J7tdzecuWa6h5nOvC6tXr2b69Ok88MAD9Vrv5ZdfzowZM8jNzU36MRdeeCHTpk2r8mpRNVHT+dyT6bnvB85w94/NrCWwxMyeCOv+090fjosfDfQOt5OAu8NfEZFaGTRoECNHjqSkpKSsh1wf/vznP9co/sCBA5x//vl1lthTUe3RMh75ONxtGW5VdffHAPeHxy0FOphZ1yriRUSSdtVVV9VrYk9Fq1at0jJHe00kdSikmTU3szXADuBpd38prJpqZkVmNsPMSi+D3g14J+bhxaEsfpsTzGyFma3YuXNnLXZBRETiJZXc3b3E3QcCecAQM+sH3AAcD5wIdAJ+HMIt0SYSbHOmuxe4e0FNxrFERKR6NTqJyd0/BJ4Dznb3bWHoZT/wJ2BICCsGusc8LA/YWgdtFRGRJFX7g6qZ5QKfufuHZtYGGAXcbmZd3X1bODrmfGBteMjjwEQzm0v0Q+oed9+WpvaLSEOp7MiylLenI9LqUjI9967AYjMrApYTjbkvAOaY2SvAK0AX4NYQ/w9gM7AJ+APw3TpvtYg0SY1tyt9du3YxcuRI2rVrx8SJE8utGzVqFLt3766zumqq2p67uxcBgxKUn1FJvAPX1b5pIiLlNbYpf3NycvjlL3/J2rVrWbt2bbl1V1xxBb///e/56U9/Wq9tKqWJw0QkYzS2KX/btm3LiBEjEs4fc9555yWcu6a+NJ3pB0QkoyWa8nfEiBG8+OKLFBYWcvnllzNoUDTIMHXqVDp16kRJSQmFhYVl886Uip3yt23bttx+++1Mnz6diRMnMn/+fDZs2ICZ8eGHH6bc3o4dO7J//3527dpF586da7XvqVDPXUQyQmOc8rc6n/vc59i6tWEOFlTPXUQyQmOc8rc6+/bto02bNik/vjaU3EUkNfV86GJjnPK3Ku7Oe++9VzaMVN+U3EUkYzS2KX8B8vPz2bt3LwcOHODRRx9l4cKF9OnTh5UrVzJ06FBatGiYNKvkLiIZY+LEiUyfPp1Ro0Zx9tlnc/bZZyeMmz17dsLy5557rmz5jDPOSHiN09ILXMdas2ZNWaKPt2XLloTlDzzwAN/9bsOd5qPkLiIZI1Om/IXoAtmFhYVpaE1ylNxFmiB3L7uQc6a56qqrGroJSbn22mvrbFvVXVQpER0KKdLE5OTksGvXrpQShtQ/d2fXrl01vtC2eu4iTUxeXh7FxcXoOgqZIycnh7y8vBo9RsldpIlp2bIlPXr0aOhmSJppWEZEJAspuYuIZCEldxGRLKTkLiKShZTcRUSyULXJ3cxyzGyZmb1sZuvM7BehvIeZvWRmG83sITNrFcpbh/ubwvr89O6CiIjES6bnvh84w91PAAYCZ5vZUOB2YIa79wZ2A1eH+KuB3e7eC5gR4kREpB5Vm9w98nG42zLcHDgDeDiU3wecH5bHhPuE9YWWqec5i4hkqKTG3M2suZmtAXYATwNvAB+6+8EQUgx0C8vdgHcAwvo9QIVrTJnZBDNbYWYrdKaciEjdSiq5u3uJuw8E8oAhwBcThYW/iXrpFSaxcPeZ7l7g7gW5ubnJtldERJJQo6Nl3P1D4DlgKNDBzEqnL8gDSi8UWAx0BwjrjwA+qIvGiohIcpI5WibXzDqE5TbAKGA9sBi4KIRdCTwWlh8P9wnrn3VNPyciUq+SmTisK3CfmTUn+jCY5+4LzOxVYK6Z3QqsBu4N8fcCD5jZJqIe+yVpaLeIiFSh2uTu7kXAoATlm4nG3+PL9wFj66R1IiKSEp2hKiKShZTcRUSykJK7iEgWUnIXEclCSu4iIllIyV1EJAspuYuIZCEldxGRLKTkLiKShZTcRUSykJK7iEgWUnIXEclCSu4iIllIyV1EJAspuYuIZCEldxGRLJTMZfa6m9liM1tvZuvM7Huh/GYze9fM1oTbOTGPucHMNpnZa2b25XTugIiIVJTMZfYOApPdfZWZHQ6sNLOnw7oZ7n5HbLCZ9SG6tF5f4GjgGTP7gruX1GXDRUSkcslcZm8bsC0sf2Rm64FuVTxkDDDX3fcDb4ZrqQ4B/lUH7RWRpuLmIyop31O/7chQyfTcy5hZPtH1VF8ChgMTzWwcsIKod7+bKPEvjXlYMQk+DMxsAjAB4Jhjjkmh6SKNRKIkpAQkDSzpH1TNrB3wV+D77r4XuBv4PDCQqGf/X6WhCR7uFQrcZ7p7gbsX5Obm1rjhIiJSuaSSu5m1JErsc9z9EQB33+7uJe5+CPgD0dALRD317jEPzwO21l2TRUSkOskcLWPAvcB6d58eU941JuwCYG1Yfhy4xMxam1kPoDewrO6aLCIi1UlmzH04cAXwipmtCWU/AS41s4FEQy5bgG8BuPs6M5sHvEp0pM11OlJGRKR+JXO0zBISj6P/o4rHTAWm1qJdIiJSCzU6WqY+5O/7S4WyLfXfDBGRjKbpB0REspCSu4hIFmp0wzINLdGwEGhoSEQyi5K71B+dySlSb5TcReqTPuCknmjMXUQkC6nnLiJNSxP59qSeu4hIFlJyFxHJQhqWqQ1dTEBEGin13EVEspB67iJSf/Rtt96o5y4ikoWaTM9ds02KSFOinruISBZSchcRyULVDsuYWXfgfuAo4BAw093vNLNOwENAPtEIx8Xuvjtcc/VO4BzgE2C8u69KS+ubyJlmIiI1lcyY+0FgsruvMrPDgZVm9jQwHljk7reZ2RRgCvBjYDTRRbF7AycBd4e/InVPR1+IJFTtsIy7byvtebv7R8B6oBswBrgvhN0HnB+WxwD3e2Qp0MHMutZ5y0VEpFI1GnM3s3xgEPAScKS7b4PoAwD4XAjrBrwT87DiUBa/rQlmtsLMVuzcubPmLRcRkUolfSikmbUD/gp83933RkPriUMTlHmFAveZwEyAgoKCCutFpIHpN62MllTP3cxaEiX2Oe7+SCjeXjrcEv7uCOXFQPeYh+cBW+umuSIikoxqk3s4+uVeYL27T49Z9ThwZVi+EngspnycRYYCe0qHb0REpH4kMywzHLgCeMXM1oSynwC3AfPM7GrgbWBsWPcPosMgNxEdCvnNOm1xptJXXGkM9H/YZFSb3N19CYnH0QEKE8Q7cF0t2yUiIrWgM1RFRLKQkruISBZqMrNCZi2NoYpIAuq5i4hkISV3EZEspGEZkUQ03CUZTj13EZEspJ67iEhtNcKpp5XcRUQqk8HDc0ruIpL5MjgJp4vG3EVEspB67iIi9amexufVcxcRyUJK7iIiWUjDMiKNUSM8tE4yi3ruIiJZSD13EZHGqhaHeFab3M1sFnAusMPd+4Wym4FrgZ0h7Cfu/o+w7gbgaqAEmOTuTyXVEvk3HbMrNaEhHEkgmWGZ2cDZCcpnuPvAcCtN7H2AS4C+4TG/N7PmddVYERFJTrXJ3d1fAD5IcntjgLnuvt/d3yS6SPaQWrRPRERSUJsx94lmNg5YAUx2991AN2BpTExxKKvAzCYAEwCOOeaYWjRD6py+5ksjkL/vLwnLt9RvMzJWqkfL3A18HhgIbAP+K5RbglhPtAF3n+nuBe5ekJubm2IzREQkkZR67u6+vXTZzP4ALAh3i4HuMaF5wNaUWydNVwb9qJyoh7ml/pshUk5Kyd3Murr7tnD3AmBtWH4c+IuZTQeOBnoDy2rdShGROtJUPoyTORTyQeB0oIuZFQM3Aaeb2UCiIZctwLcA3H2dmc0DXgUOAte5e0l6mi4iIpWpNrm7+6UJiu+tIn4qMLU2jRIRkdrR9AMiIllI0w80FY3g8MamMtZZFT0HUl+U3EWk9pI8uknHrtcfDcuIiGQhJXcRkSykYRmRBDQ2LplOyb0WNH4oIo2VhmVERLKQeu6S0fTtSSQxJXepnQya4EukKVFyF5GE9KNyZsvo5K5/PpGa0Xum6cjo5J5J9KYSkfqko2VERLKQeu7SdDSCydNE6ouSe4bTcI+I3geJVDssY2azzGyHma2NKetkZk+b2cbwt2MoNzO7y8w2mVmRmQ1OZ+NFRCSxZMbcZwNnx5VNARa5e29gUbgPMJrouqm9gQnA3XXTTBERqYlkLrP3gpnlxxWPIbquKsB9wHPAj0P5/e7uwFIz6xB3MW1Jgr5iimSv+jqrOtUx9yNLE7a7bzOzz4XybsA7MXHFoaxCcjezCUS9e4455pgUmyEikj7JdrQa4zQYdX0opCUo80SB7j7T3QvcvSA3N7eOmyEi0rSlmty3m1lXgPB3RygvBrrHxOUBW1NvnoiIpCLVYZnHgSuB28Lfx2LKJ5rZXOAkYI/G20XSqzEOCUjDqza5m9mDRD+edjGzYuAmoqQ+z8yuBt4GxobwfwDnAJuAT4BvpqHNIllPCVugdgdXJHO0zKWVrCpMEOvAdUnWLSIiaaIzVKUC9RpFMp+SexOhhC3StCi5S62k64QrncglUjtK7iJSa/owbnw0n7uISBZSchcRyUJK7iIiWUjJXUQkCym5i4hkISV3EZEspOQuIpKFlNxFRLKQkruISBbSGarSZGh+HWlK1HMXEclCSu4iIllIyV1EJAvVaszdzLYAHwElwEF3LzCzTsBDQD7RcObF7r67ds0UEZGaqIue+0h3H+juBeH+FGCRu/cGFoX7IiJSj9IxLDMGuC8s3wecn4Y6RESkCrVN7g4sNLOVZjYhlB3p7tsAwt/PJXqgmU0wsxVmtmLnzp21bIaIiMSq7XHuw919q5l9DnjazDYk+0B3nwnMBCgoKPBatkNERGLUqufu7lvD3x3AfGAIsN3MugKEvztq20gREamZlJO7mbU1s8NLl4GzgLXA48CVIexK4LHaNlJERGqmNsMyRwLzzax0O39x9yfNbDkwz8yuBt4Gxta+mSIiUhMpJ3d33wyckKB8F1BYm0aJiEjt6AxVEZEspOQuIpKFlNxFRLKQkruISBZSchcRyUJK7iIiWUjJXUQkCym5i4hkISV3EZEspOQuIpKFlNxFRLKQkruISBZSchcRyUJK7iIiWUjJXUQkCym5i4hkISV3EZEslLbkbmZnm9lrZrbJzKakqx4REakoLcndzJoDvwNGA32AS82sTzrqEhGRitLVcx8CbHL3ze5+AJgLjElTXSIiEsfcve43anYRcLa7XxPuXwGc5O4TY2ImABPC3eOA1xJsqgvwfhJVJhuXabENXX+6Yhu6/nTFNnT96Ypt6PrTFdvQ9ddF7LHunpsw2t3r/AaMBf4Yc/8K4L9T2M6KuozLtNiGrl/7pf1qDPVrv2oe6+5pG5YpBrrH3M8DtqapLhERiZOu5L4c6G1mPcysFXAJ8Hia6hIRkTgt0rFRdz9oZhOBp4DmwCx3X5fCpmbWcVymxTZ0/emKbej60xXb0PWnK7ah609XbEPXn87Y9PygKiIiDUtnqIqIZCEldxGRLKTkLiKShZTcRUTqmZl1MrOO6ayj0Sd3Mzu+kvKWCcq6JChrZmbNwnIrMxtsZp2SrPu7Sca1C9vtEFfeysws5v5IM5tsZqMTbGNAMnXFxB9TWp+Z5ZvZRWbWr4r4AjO7wMy+WtlzGhN7ZNifQWZ2ZA3b1a4m8UlsL6nXKsSeV5fbNbNeZva1RPMixb/WSWyrRcxyu/B6VNoGM8sNz3//qp5Ti5xkZheG1/ek2P+5JNql91fiOuv8/RW2OdfMdgIvAcvNbEcoy69J+5JSkzOeGuIGvB13fyTRSVI7gYVAfsy6VXGx5wPbgW1Ec9u8BDwbHv/VuNgfxt0mE53q+0Pgh3Gxv49ZHgG8DSwG3gHOiVn3MtAxLP8n8E/gZ8DTwLS4bZYAm4BfAn2qeU6mAG8CG4Brwt97gXUJ2noasAJ4BtgNLAD+F3gO6B4XOxBYCqwP8c+EbS8FBqf4evUPj3+H6FCujjHrlsXFDg91rwNOCs/T5vDYk+NiL4y7fQ14r/R+XOzPYpb7AK+H528L0bQYpesWA13C8hUh7o/AK8B/xG3zYHh+rgY6VPOcjAd2he2NDvu0KOzXpXGxfcJ2NwEHiP5n3wRmA0fExZ4V4p4I7fwj8GQoO0vvr0b3/voX8HWgeUxZc6LzgJYm+Xq9kkycuzeO5A7cVcntv4G9cbHLgb5h+SJgIzA03F8dF7saOAroAewFjgvlxxJ3Ki/wEfAQcCNwU7jtLl2Oi10Vs7yYkPiAnrHbBdbGLK8A2oTlFkBRgrb2A6aGf8KXwz9ZfoLnax3QBugc2p0bytvG1hmz3dL1PYD5YflMYGFc7Bpikl1M+VDg5SreqLFv2A/iHrsEOBvoAPwotP3zlbxey4g+DE4meuOPCOWDgf+Niz0Y3kizgD+F20fh76wqXq+/A6PD8hDgn5W8XsuBzmH5sASv1yvAucAcosT9GNGbtE2iNyTRvCCl/4el+39kgu0u5d//p0OA+8LytcDDcbHrK/n/6AGs1/ur0b2/NsbXlWgdFTsusR2YnZVtI/6WlpOYUvBNosSwP8G6S+Put/JwQpS7P2xm64FHwpzxFQ7ad/f3AMzsbXd/LZS9VfpVMkZfYDrRC/gLd//EzK50919U0/b27r4qbHdzmO641F4z6+fua4mSVQ7wKdE/X3z9HuJ+CvzUzIYQJYsXzewddx8WE1vi7p+a2YGwvV1hA/+X4Bt5c3ffGZbfJnrj4e5Pm9lv4mLbuvtL8Rtw96Vm1jam6FfAr4kSbLz4/Wrn7k+G5TvMbCXwZJhMLv71aunurwCY2U53XxLqX2VmbeJiTwZuI0pG97i7m9np7v7NBG2KdbS7PxG2uyxuu5+ZWTd3fxf4GPi/UL6fqIcV6zN3XwAsCNv4KtHr9Tsze8rdL4uJLXH394H3zexjd38j1L89wevVJub/dJmZ3ROW/2BmP4iLbUHUS473LhA7rKL3V+N4f600s98D91q+CGUAAAhXSURBVBF9C4FompYriT4kSj1E1Gmo8HyHfUxKY0nuy4k+Ef8Zv8LMbo4r+szMjir9p3L3dWZWSNSL+3yCxzdz90PAVTFlzYFWsXHu/jZwkZmNAZ42sxlVtPd4MysCDMg3s47uvjv8Q8e+qb4NzDGzl4EdwAozex4YQJQgyzU1rj3LgGVmNhk4NS52lZn9heiNsgi4z8yeBM4AXo2LXWFm94a4MURfFzGzw6iYsJ4ws78D91P+n28c0df9svqBR919ZfwTY2bXVCyyI9x9T9ivxWb2NeCvQPzYbOwb8oa4dfGv13IzOxP4D+BZM/sxid8MAD3N7HGi5zjPzA5z90/CutjX6wfAQjP7K1Hv7dnwvJ5C9I2g3H7FtOVTYB4wz8yOIBquiPW2mU0DDgc2mNl/AY8Ao4iGNGK9YWY/J3q9LiT6NlU6Bh7/fp1FNG47l/Kv1yVEwwil9P5qHO+vcUTDeL8AuoU2FRNNzRL7ehUBd4QPo/I7YTYqvqxSyXbx03kjepMflmTsKOCEBOVHAD+NKzsRyEkQmw9cXkUdhxH1TF+oZP2xcbeWobwLFcd7mxONs36PqPf0dRKM0QKX1eD5akHU47okLA8DfgtcT9T7jo1tCXw3rL+WMN5H9LXz2ATbPge4B/gb0Rv6HmLGOUPMcYSvogkef2T8fhG+1seVHwP8Ia7svET/B0RJ5foqno9uRMl1cyXrT4u7tSttK3Bdgv+j7wAziIYtfgwcn2CbP6rB69We6MNqCtCOaLhjAfB7oGtcbAfg/4X1U4HDY9qV6HnsE7b73+E1nkLcmHIdvb861OH7q20N31+tGvj9Nbyu3l9J1n8KcEwl6wqS3Y6mHxARqQcWHTF1NdE3u25E3zS3Ev1ec6+7f1anFabyyVLXN6Kv+T8j/NCULbENXX9cbM8kYk8k+gHrz0Rf758GPiT6Wj8otveQIG5PfFwSsQPrOLZCW2uy3TrcZjKxdbHddsAtRENIe4iOcFkKjE8lLtNiG6D+K6uIXZvEdh8E7iY6QCEv3IaGsodi4loA3yIaCi0i+vH3CaJhqJbVvY/LtpNsYDpvRIcd3UH0g8QyorHPozM9tqHrTyF2GdFX3EuJxnAvCuWFwL9qGpdpsQ1dfwqxjxEdZplHdLTSz4HeRD/Y/aqmcZkW29D1pxD7WhU58PWY5aQ+BKrNq8kGpvNG+UOfTiEai3yPqAczIVNjG7r+FGJXxyzHH/+8uqZxmRbb0PWnEPty3P3l4W8zYENN4zIttqHrTyF2KdFV6prFlDUj+p3gpZiypD4Eqrs1ujNU3f1Fd/8u0ZjU7USHvGV8bEPXn2TsPjM7y8zGAm5m5wOY2WlEJ4HUNC7TYhu6/prG/p+ZjQjrvwp8AODR0SuWQlymxTZ0/TWNvYTox/T3zOx1M3udf594d0lM3G4zGxt7OKlFZwJ/nejcgOQk+ymQzhswNxtjG7r+FGJPILrAyhPA8cCdRGPD64BhNY3LtNiGrj/F2GVh/RL+fRJRLjCppnGZFtvQ9dc0NpSfRHRyWmeis29/RMWj0fKJjnXfQXRW8+th+SGgR9Lv52QD030L/8iFhMPUYsrPzuTYhq4/hdgvJrlfScVlWmxD159i7Kgk96vauEyLbej6a9jWm4iGZlYA04iOjb8ReIGKh5lW+yFQ3S3pwHTeiE5EeQ14lGi+jzEx6+Lns8iY2IauP4XYSUTzaFS3X0nFZVpsQ9ev/cr6/XqF6Lj8w4ima2gfytsQM10CNfgQqOrW4Ik9ZqdLTyzJDzv1vXA//kekjIlt6Pq1X9qvxlC/9qssNuGP9+H+mrhtVvshUN2tsUw/0NzdPwZw9y1mdjrwsJkdS8UfJTIptqHr135pvxpD/dqvyAH799QXXyottGjKikMxcQfdvQT4xMzecPe9YfufmtkhktRYjpZ5z8wGlt4JT9a5RKcb98/g2IauX/ul/WoM9Wu/IqeGxI5HR9OUakk0eVipAxbNTQNVfwhULdkufjpvRAfpH1XJuuGZGtvQ9Wu/tF+NoX7tV81uQOtKyrsA/ZPdjuaWERHJQo1lWEZEROqQkruISBZSchcRyUJK7pLxzOwoi64g/4aZvWpm/zCzLzSCdp1uZgvC8ngz22lmq81so5k9ZWbDqtuGSKqU3CWjmZkB84Hn3P3z7t4H+AnRVZZqs934S6TVhYfcfZC79ya6/usjZvbFNNQjouQuGW8k0cWq7yktcPc1wBIz+7WZrTWzV8KMeuV60+H+b81sfFjeYmY3mtkSYKyZTQrfBIosuk4pZtbWzGaZ2fLQCx+TSqPdfTEwE5iQ6o6LVKWxnKEqkqp+QIULdRNNozqQaNa+LkQXkn4hie3tc/fSKVy3Es3Ct9/MOoT1PwWedferQtkyM3vG3f8vhbavIrrijkidU89dstUI4EF3L3H37cDzRJcRrM5DMctFwBwzuxw4GMrOAqaY2RqiK93nEF3sOxXxp6eL1Bn13CXTrSO6AEK8yhLnQcp3anLi1sf2wL8CnAqcB/zczPqG7X7N3V9LrbnlDALW18F2RCpQz10y3bNAazO7trTAzE4kumLN182suZnlEiXpZcBbQB8zax3m6ihMtFGLroLTPYyNXw90ILoY8lPAf4QfcjGzQak02qIrK00A/pDK40Wqo567ZDR3dzO7APiNmU0B9hHNq/19omT8MuDA9e7+HoCZzSMactkIrK5k082BP4cPAANmuPuHZvZL4DdAUUjwW4gmikrG1y26JNthRBcu/5q7q+cuaaG5ZUREspCGZUREspCGZURqycy+DNweV/ymu1/QEO0RAQ3LiIhkJQ3LiIhkISV3EZEspOQuIpKFlNxFRLLQ/wd65cxQYMvQygAAAABJRU5ErkJggg==\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"
    },
    {
     "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"
    },
    {
     "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"
    },
    {
     "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 i in range(1,571,30):\n",
    "    grp.loc[i:i+30].unstack().plot(kind='bar',stacked=True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There is decrease in Sales on Public Holidays but we assumed the other way round.**  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2309b542388>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=train,x=\"Long_Promotion\",y=\"Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Long_Promotion</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>153.207483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>94.812925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>113.215420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>96.774376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>102.436508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>1</td>\n",
       "      <td>593</td>\n",
       "      <td>82.810658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>1</td>\n",
       "      <td>595</td>\n",
       "      <td>122.275072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>1</td>\n",
       "      <td>596</td>\n",
       "      <td>84.924036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>1</td>\n",
       "      <td>597</td>\n",
       "      <td>91.241497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>109.198413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Long_Promotion  Course_ID       Sales\n",
       "0                 0          3  153.207483\n",
       "1                 0          4   94.812925\n",
       "2                 0          5  113.215420\n",
       "3                 0          6   96.774376\n",
       "4                 0         18  102.436508\n",
       "..              ...        ...         ...\n",
       "595               1        593   82.810658\n",
       "596               1        595  122.275072\n",
       "597               1        596   84.924036\n",
       "598               1        597   91.241497\n",
       "599               1        600  109.198413\n",
       "\n",
       "[600 rows x 3 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=train.groupby([\"Long_Promotion\",\"Course_ID\"]).mean()[\"Sales\"].to_frame().reset_index()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Long_Promotion\n",
       "0    126.639698\n",
       "1    114.373945\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.groupby(\"Long_Promotion\")[\"Sales\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Long Promotion is not causing an increase in Sales**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Public_Holiday\n",
       "0    15526.375039\n",
       "1    10745.152512\n",
       "Name: User_Traffic, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Public_Holiday\")[\"User_Traffic\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230a5078348>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Public_Holiday\",y='User_Traffic',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**User Traffic is much less on a public holiday, this contrdicts our Hypothesis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation,_=pearsonr(train[train.Course_ID==5][\"User_Traffic\"],train[train.Course_ID==5][\"Sales\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>User_Traffic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.824644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_Traffic</th>\n",
       "      <td>0.824644</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales  User_Traffic\n",
       "Sales         1.000000      0.824644\n",
       "User_Traffic  0.824644      1.000000"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp=train.groupby(\"Course_ID\").mean()[[\"Sales\",\"User_Traffic\"]]\n",
    "grp.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230aaaa9c08>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"User_Traffic\",y=\"Sales\",data=grp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation,_=pearsonr(train[\"User_Traffic\"],train[\"Sales\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8297414753800694"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9184429565725598\n",
      "0.9474412206321607\n",
      "0.9380762095404369\n",
      "0.9517906345925298\n",
      "0.9640989054374156\n",
      "0.9343166604623798\n",
      "0.9400161152441172\n",
      "0.9387959659062846\n",
      "0.9587497535398373\n",
      "0.9529195331278463\n",
      "0.9380517477249786\n",
      "0.9493185885988209\n",
      "0.9438937917483171\n",
      "0.9284441976921964\n",
      "0.9681998992904679\n",
      "0.9507146869888394\n",
      "0.9467882681978479\n",
      "0.9294102622355008\n",
      "0.9509529162891255\n",
      "0.9614761098099929\n",
      "0.932440975215358\n",
      "0.975131302123158\n",
      "0.9519843905955505\n",
      "0.9561595544807335\n",
      "0.9589892623647818\n",
      "0.9224168413078826\n",
      "0.9625739782730818\n",
      "0.9557042308678799\n",
      "0.9510145253072042\n",
      "0.9473497342357821\n",
      "0.9617177632122907\n",
      "0.9553010530943598\n",
      "0.9517974412151644\n",
      "0.9608914861499885\n",
      "0.9609448520598691\n",
      "0.9288142275703994\n",
      "0.9443658835317377\n",
      "0.9479776954826691\n",
      "0.9540156247537248\n",
      "0.9538382175744696\n",
      "0.9646297494598576\n",
      "0.9420708616568125\n",
      "0.9578754263810858\n",
      "0.9488125016092535\n",
      "0.950599514926352\n",
      "0.9530375978645824\n",
      "0.9565111779980731\n",
      "0.9400221405958864\n",
      "0.9756380501424667\n",
      "0.9574055846081547\n",
      "0.9609239983005076\n",
      "0.9583927668182953\n",
      "0.936636651438495\n",
      "0.961297537339823\n",
      "0.936389002312559\n",
      "0.9583068007704396\n",
      "0.9620327880313775\n",
      "0.9402926732713748\n",
      "0.9576739420287849\n",
      "0.9132684656923435\n",
      "0.9372834172864717\n",
      "0.9595351347964237\n",
      "0.9388178112407111\n",
      "0.9331105465239631\n",
      "0.9286514752820751\n",
      "0.9549687049985806\n",
      "0.9530654719695157\n",
      "0.9545944434524196\n",
      "0.9302125763721923\n",
      "0.9341894357013867\n",
      "0.9578896926910694\n",
      "0.9548541774211783\n",
      "0.9626844051445916\n",
      "0.5875312330406599\n",
      "0.9441774488007876\n",
      "0.9156701338650477\n",
      "0.9481248797835791\n",
      "0.9531953932472694\n",
      "0.9580794947924685\n",
      "0.9450415045452832\n",
      "0.9290512014406475\n",
      "0.9536839306843455\n",
      "0.9414427924377258\n",
      "0.944888993193088\n",
      "0.9537560673190038\n",
      "0.9489421099504699\n",
      "0.9539096732066106\n",
      "0.9582375807601746\n",
      "0.9655699296350981\n",
      "0.9553423091124504\n",
      "0.9571161268638769\n",
      "0.9398249109834735\n",
      "0.9543221054996718\n",
      "0.9497965150368064\n",
      "0.9645645048689668\n",
      "0.9615092804758314\n",
      "0.9469879236006711\n",
      "0.9607075029675817\n",
      "0.9317452191069924\n",
      "0.9157661076013817\n",
      "0.960637632490401\n",
      "0.9717505730420491\n",
      "0.9456126398196979\n",
      "0.954558272581075\n",
      "0.9582342775679225\n",
      "0.9520485143498691\n",
      "0.9512412583837866\n",
      "0.9210603052870487\n",
      "0.9476487730158292\n",
      "0.9191154913142908\n",
      "0.975167444909657\n",
      "0.9361235236435788\n",
      "0.9654495152562169\n",
      "0.952678861917476\n",
      "0.9562996500904776\n",
      "0.9586618653590742\n",
      "0.9498254845984134\n",
      "0.9313482211319197\n",
      "0.956644674514157\n",
      "0.9504428888816743\n",
      "0.9289421991415026\n",
      "0.9420489534171166\n",
      "0.9587214010702405\n",
      "0.9491049597382996\n",
      "0.9371944687485518\n",
      "0.9289507067690169\n",
      "0.9270203441911353\n",
      "0.9633951807586206\n",
      "0.9552060035591188\n",
      "0.8931516232035004\n",
      "0.9708015524814295\n",
      "0.9530019019962493\n",
      "0.9440936791335306\n",
      "0.9570437517046186\n",
      "0.9554139524471735\n",
      "0.9541690836660083\n",
      "0.9109104250524798\n",
      "0.9560363978502818\n",
      "0.9709082434774239\n",
      "0.9539526993385488\n",
      "0.960751734913853\n",
      "0.9209858155424115\n",
      "0.9342785460950953\n",
      "0.9415949681823255\n",
      "0.9627942880009427\n",
      "0.9592091820516311\n",
      "0.9417709184706236\n",
      "0.9081287667665396\n",
      "0.9539808075496563\n",
      "0.9626543294587737\n",
      "0.9420246319882225\n",
      "0.9349484962388112\n",
      "0.9386513720691962\n",
      "0.9402821168152975\n",
      "0.965165822406922\n",
      "0.9055624716001887\n",
      "0.9717355195549998\n",
      "0.9151056988436004\n",
      "0.9517604935405044\n",
      "0.9542869590575207\n",
      "0.9616589135965801\n",
      "0.9716287036366443\n",
      "0.946519976891246\n",
      "0.9457617208153202\n",
      "0.9485188434308676\n",
      "0.9347517135371548\n",
      "0.9561348352042702\n",
      "0.9525349896844275\n",
      "0.9672824334682983\n",
      "0.9181990167149237\n",
      "0.9417469397108917\n",
      "0.9536060982793761\n",
      "0.9594993614191001\n",
      "0.9441043103919031\n",
      "0.9548069648497433\n",
      "0.9761928620983258\n",
      "0.9619766912265033\n",
      "0.9260954043902876\n",
      "0.9313741175990475\n",
      "0.956925080511543\n",
      "0.9526101737919659\n",
      "0.974260833773089\n",
      "0.9545744827725702\n",
      "0.9205772455093665\n",
      "0.9322870179609197\n",
      "0.9288286617471428\n",
      "0.959863461627498\n",
      "0.9682001060081886\n",
      "0.8805628584383816\n",
      "0.9559662433602748\n",
      "0.9484923452757974\n",
      "0.9557135351272936\n",
      "0.9623782646176289\n",
      "0.9477504887285095\n",
      "0.9441896587740994\n",
      "0.954303100487206\n",
      "0.9530498796523001\n",
      "0.9629302060303723\n",
      "0.9176650813867001\n",
      "0.9387715890999675\n",
      "0.8804910233944572\n",
      "0.9383471816605538\n",
      "0.9585199041049028\n",
      "0.9484692224160373\n",
      "0.9501865680047279\n",
      "0.9403624256390795\n",
      "0.9343050662008381\n",
      "0.9384018795643301\n",
      "0.9619420255001674\n",
      "0.9205299418429038\n",
      "0.9491883990939113\n",
      "0.9496499530463258\n",
      "0.9559401859246962\n",
      "0.967358689241848\n",
      "0.9263916427605026\n",
      "0.9712476746541813\n",
      "0.9524565409581809\n",
      "0.9554596757850142\n",
      "0.9373992464380819\n",
      "0.957452409427074\n",
      "0.9421981634633355\n",
      "0.9494921641312501\n",
      "0.9565116415243188\n",
      "0.96483927102443\n",
      "0.938092232434226\n",
      "0.9649584337735464\n",
      "0.9586269860088471\n",
      "0.9485848339591778\n",
      "0.9352498152203188\n",
      "0.9642270822527992\n",
      "0.9567847061671264\n",
      "0.9388273197567476\n",
      "0.97086925553249\n",
      "0.9620839904154903\n",
      "0.9145690781611797\n",
      "0.9611533488640713\n",
      "0.9613580643192046\n",
      "0.9182773120493362\n",
      "0.9512657991155673\n",
      "0.9475648674726004\n",
      "0.9299571315053327\n",
      "0.9637137602221223\n",
      "0.8565626535416064\n",
      "0.9567829330819451\n",
      "0.9477137456833388\n",
      "0.9420175784205465\n",
      "0.9574627900661301\n",
      "0.9541269803470099\n",
      "0.935332799378651\n",
      "0.9674358822896342\n",
      "0.929064297733004\n",
      "0.9308342316457845\n",
      "0.930876567509014\n",
      "0.9496903249239357\n",
      "0.9520076170075469\n",
      "0.9187730778667585\n",
      "0.9469234670215743\n",
      "0.9566410087993118\n",
      "0.9390211723197535\n",
      "0.8772934288680083\n",
      "0.9163486402635994\n",
      "0.9585021300463061\n",
      "0.9557438361744385\n",
      "0.9516190885500367\n",
      "0.9405797474940407\n",
      "0.9299923876650338\n",
      "0.9519082152904191\n",
      "0.9365883213049946\n",
      "0.945637975872389\n",
      "0.9581452247863477\n",
      "0.9439807703794207\n",
      "0.9617501260408899\n",
      "0.9600224903461105\n",
      "0.9423167812103286\n",
      "0.9180713757225853\n",
      "0.9453781114842355\n",
      "0.9662913875487231\n",
      "0.9479209941591009\n",
      "0.9646001862443152\n",
      "0.9572380620527312\n",
      "0.9674536242516178\n",
      "0.9389993676163771\n",
      "0.9345057866769506\n",
      "0.9239441278401694\n",
      "0.9262731234681472\n",
      "0.9629138032073109\n",
      "0.9404428122141025\n",
      "0.9316171006375901\n",
      "0.9256433871619056\n",
      "0.9350143782122199\n",
      "0.9315815429005974\n",
      "0.9393400704543963\n",
      "0.9443972565390015\n",
      "0.95662474706747\n",
      "0.9329898091747064\n",
      "0.9513538645275113\n",
      "0.9323610538308191\n",
      "0.9328415400033045\n",
      "0.9555164226093815\n",
      "0.9635969757334079\n",
      "0.9361214618394291\n",
      "0.9376263917018547\n",
      "0.9390982880006074\n",
      "0.9735716270028573\n",
      "0.962702234855439\n",
      "0.9433019364171273\n",
      "0.9424502571293393\n",
      "0.9689933406531874\n",
      "0.9557476752115586\n",
      "0.9322712473550422\n",
      "0.9665748793735544\n",
      "0.9433256962848625\n",
      "0.9472794994382773\n",
      "0.9368903668356102\n",
      "0.9169589265969207\n",
      "0.9107207463025606\n",
      "0.9569344205759398\n",
      "0.9391745679780523\n",
      "0.970624358769063\n",
      "0.9304584667162719\n",
      "0.9435765770413778\n",
      "0.9403749667336658\n",
      "0.9392963070085977\n",
      "0.9424777847365632\n",
      "0.9533959850398153\n",
      "0.954007058259722\n",
      "0.9505498886463882\n",
      "0.9584330775750634\n",
      "0.9720506595344951\n",
      "0.9086964164553873\n",
      "0.9382781277562676\n",
      "0.9369228581229732\n",
      "0.9509017679322613\n",
      "0.9626828149339507\n",
      "0.9400733641601574\n",
      "0.9297349735320507\n",
      "0.9310991966585462\n",
      "0.95002091965391\n",
      "0.9536422842302191\n",
      "0.9551042712200872\n",
      "0.9592333933603789\n",
      "0.9234770721520225\n",
      "0.9481158861221257\n",
      "0.9235261456931199\n",
      "0.9434079118541445\n",
      "0.9583706523393392\n",
      "0.9572869656973135\n",
      "0.9297262631740492\n",
      "0.9558735472226689\n",
      "0.933691853988809\n",
      "0.9533353736531005\n",
      "0.9534919774797659\n",
      "0.9498314460670947\n",
      "0.9394323589666516\n",
      "0.9598401361476863\n",
      "0.9658427963498611\n",
      "0.9455102158746838\n",
      "0.9598099407140401\n",
      "0.942886618972228\n",
      "0.9555946021101658\n",
      "0.9672074111123743\n",
      "0.9650140827015922\n",
      "0.9426841573946798\n",
      "0.9507578092331715\n",
      "0.9467954887544276\n",
      "0.9209110506379907\n",
      "0.9478289431673808\n",
      "0.9511443799333639\n",
      "0.9342167122082465\n",
      "0.9574566391151176\n",
      "0.9328628249933553\n",
      "0.9528150862806988\n",
      "0.9649926503806933\n",
      "0.9596444145124787\n",
      "0.9045436137509206\n",
      "0.9518238245224178\n",
      "0.9445334993468792\n",
      "0.9477139424937244\n",
      "0.9137542067347748\n",
      "0.935542411950047\n",
      "0.9483768873388991\n",
      "0.9100473909071098\n",
      "0.9006704303936108\n",
      "0.9572989042024103\n",
      "0.9514931879882587\n",
      "0.9467327943589059\n",
      "0.9564268059112503\n",
      "0.9553243861343444\n",
      "0.9583315423439349\n",
      "0.9516603054055666\n",
      "0.9480716640431179\n",
      "0.9449073697228606\n",
      "0.9608329075630412\n",
      "0.9807569160727825\n",
      "0.9374945144980149\n",
      "0.950771598278213\n",
      "0.9559126714316158\n",
      "0.9463325590644118\n",
      "0.9576315328140428\n",
      "0.9490166792566729\n",
      "0.9420129128773189\n",
      "0.9314429744377167\n",
      "0.9534740827690737\n",
      "0.9498761197174762\n",
      "0.9414369316987419\n",
      "0.9462617280360922\n",
      "0.9308593567644895\n",
      "0.9294066724148748\n",
      "0.9343561996838441\n",
      "0.9501555231952657\n",
      "0.9384428497916183\n",
      "0.9562574324924032\n",
      "0.9594803141973589\n",
      "0.9436132739316951\n",
      "0.9771741460562908\n",
      "0.9744022941536352\n",
      "0.9458433715361843\n",
      "0.9535025037457312\n",
      "0.9471640101114536\n",
      "0.94660331132116\n",
      "0.9645575569445626\n",
      "0.9649061357217694\n",
      "0.9528143343856856\n",
      "0.9194049653534855\n",
      "0.9191606503499161\n",
      "0.9288465398195911\n",
      "0.9636156881716487\n",
      "0.9447326480368499\n",
      "0.9434438820789115\n",
      "0.9634467043383963\n",
      "0.8118643813523085\n",
      "0.9475737979857287\n",
      "0.9380985038387301\n",
      "0.9292567654696258\n",
      "0.9408199390487002\n",
      "0.9459265403072561\n",
      "0.9683212763830735\n",
      "0.9567426045672507\n",
      "0.96774578325891\n",
      "0.9005529304705255\n",
      "0.9461151923755804\n",
      "0.9538850149490019\n",
      "0.9638635464097447\n",
      "0.9317278902038943\n",
      "0.9484852294120557\n",
      "0.9566202691975212\n",
      "0.9552462402775583\n",
      "0.9462185048809395\n",
      "0.9569097195790027\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9722425072324721\n",
      "0.9555687804779969\n",
      "0.9452313184429204\n",
      "0.9669474651947055\n",
      "0.941103661771296\n",
      "0.9391895688740385\n",
      "0.9460317605106192\n",
      "0.956851023981676\n",
      "0.9521608580904986\n",
      "0.9405186912394601\n",
      "0.9266956252064344\n",
      "0.9677637112261058\n",
      "0.9404942929327171\n",
      "0.9691829518809195\n",
      "0.9554239849597603\n",
      "0.9615252252386337\n",
      "0.9416256787133859\n",
      "0.942538386998292\n",
      "0.9486035636083809\n",
      "0.9397869157891983\n",
      "0.9479932992222126\n",
      "0.9545623518725109\n",
      "0.9546012701256488\n",
      "0.947585525073696\n",
      "0.9348400086566606\n",
      "0.9319517753106186\n",
      "0.9526323812222853\n",
      "0.9494696575612162\n",
      "0.9432246263272527\n",
      "0.9578725120092655\n",
      "0.9593649958882423\n",
      "0.9655935216604532\n",
      "0.9394185638054986\n",
      "0.9574122182580096\n",
      "0.9639700446711557\n",
      "0.9478735691974558\n",
      "0.942806956531231\n",
      "0.9413436655843797\n",
      "0.9390422821845861\n",
      "0.9496642350709918\n",
      "0.7785644339382367\n",
      "0.9709773655376548\n",
      "0.9085021127935629\n",
      "0.9577965487039044\n",
      "0.951677585045603\n",
      "0.9612712280931944\n",
      "0.9400576035508084\n",
      "0.9513194127866491\n",
      "0.9302694214614369\n",
      "0.9521537430250246\n",
      "0.9498400532727242\n",
      "0.9569161906807248\n",
      "0.9749427637828132\n",
      "0.8349481203935782\n",
      "0.9293109821676557\n",
      "0.9625075116943853\n",
      "0.9377718933959596\n",
      "0.9100716909016124\n",
      "0.9633935791905763\n",
      "0.9342854697902978\n",
      "0.9701406615817189\n",
      "0.9446038650878106\n",
      "0.8977617855090245\n",
      "0.9547681667643406\n",
      "0.9472442460486785\n",
      "0.9490445332010862\n",
      "0.9433730179076634\n",
      "0.949848124784004\n",
      "0.9647996330510975\n",
      "0.9585312647924951\n",
      "0.9445057538760022\n",
      "0.9489419607243101\n",
      "0.9439123112462301\n",
      "0.9499641048628988\n",
      "0.9737639073350097\n",
      "0.9674059464879874\n",
      "0.9490239702801062\n",
      "0.9735570793386334\n",
      "0.9123625242799902\n",
      "0.9549061489209636\n",
      "0.9072569657892647\n",
      "0.9528080203485406\n",
      "0.9461392040589343\n",
      "0.9560749583448456\n",
      "0.9599908388994185\n",
      "0.9453566853247343\n",
      "0.9148500516157935\n",
      "0.9504968377921668\n",
      "0.9542282748072729\n",
      "0.9488368951417101\n",
      "0.9451746142851994\n",
      "0.9375726199152141\n",
      "0.9651462482626083\n",
      "0.9295965353854445\n",
      "0.9492155506369168\n",
      "0.9374641761656934\n",
      "0.9304495117839124\n",
      "0.9370503503380038\n",
      "0.9357958834692268\n",
      "0.966003781319274\n",
      "0.9647593155629659\n",
      "0.9558348695207837\n",
      "0.9666873611552906\n",
      "0.9554103837963059\n",
      "0.9235517270298762\n",
      "0.9387882859511546\n",
      "0.9669784582339879\n",
      "0.9592105268264394\n",
      "0.9583054761633261\n",
      "0.957632302118895\n",
      "0.935649569441656\n",
      "0.945190803417105\n",
      "0.9369125502231404\n",
      "0.9585131265723021\n",
      "0.9595886309079313\n",
      "0.9553274104376374\n",
      "0.9467315122085944\n",
      "0.9510849395342609\n",
      "0.9685275424902019\n",
      "0.9650344131710484\n",
      "0.9344627732807196\n",
      "0.9325721073992187\n",
      "0.9533807896985569\n",
      "0.9598318337535212\n",
      "0.9584441365524641\n",
      "0.9450465709895555\n",
      "0.952371569247422\n",
      "0.9426582065367528\n",
      "0.9505707903663164\n",
      "0.9319846941614317\n",
      "0.9319120434424006\n",
      "0.9400058639338777\n",
      "0.9661117343046792\n",
      "0.968288289906879\n",
      "0.9590495144484101\n",
      "0.9334389492433359\n",
      "0.9586961990185048\n",
      "0.9667147655919337\n",
      "0.9443025197952081\n",
      "0.9620015581054797\n",
      "0.9576155836041644\n",
      "0.9669512758751078\n",
      "0.943339460580078\n",
      "0.9331160882941072\n",
      "0.8026959588671174\n",
      "0.9428241304609002\n",
      "0.942805342665492\n",
      "0.9474530842844577\n",
      "0.9756192246637931\n",
      "0.9059791880948098\n",
      "0.8755291264457169\n"
     ]
    }
   ],
   "source": [
    "sum=0\n",
    "for i in range(1,601):\n",
    "    correl,_=pearsonr(train[train.Course_ID==i][\"User_Traffic\"],train[train.Course_ID==i][\"Sales\"])\n",
    "    sum+=correl\n",
    "    print(correl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9455744409019602"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum/600"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**User Traffic is highly correlated to the amount of sales made**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014679921551452813"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation,_=pearsonr(train[\"Competition_Metric\"],train[\"Sales\"])\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.007</td>\n",
       "      <td>85.251701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.186</td>\n",
       "      <td>120.595238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.316</td>\n",
       "      <td>153.207483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.099</td>\n",
       "      <td>94.812925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.027</td>\n",
       "      <td>113.215420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>0.070</td>\n",
       "      <td>84.924036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>0.025</td>\n",
       "      <td>91.241497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>0.025</td>\n",
       "      <td>180.100907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>0.122</td>\n",
       "      <td>116.346939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>0.070</td>\n",
       "      <td>109.198413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>588 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Competition_Metric       Sales\n",
       "Course_ID                                \n",
       "1                       0.007   85.251701\n",
       "2                       0.186  120.595238\n",
       "3                       0.316  153.207483\n",
       "4                       0.099   94.812925\n",
       "5                       0.027  113.215420\n",
       "...                       ...         ...\n",
       "596                     0.070   84.924036\n",
       "597                     0.025   91.241497\n",
       "598                     0.025  180.100907\n",
       "599                     0.122  116.346939\n",
       "600                     0.070  109.198413\n",
       "\n",
       "[588 rows x 2 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xa=train.groupby(\"Course_ID\")[[\"Competition_Metric\",\"Sales\"]].mean()\n",
    "xa=xa[xa[\"Competition_Metric\"]>0]\n",
    "xa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230aaaf19c8>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=\"Sales\",y=\"Competition_Metric\",data=xa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Competition_Metric</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>-0.015797</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Competition_Metric     Sales\n",
       "Competition_Metric            1.000000 -0.015797\n",
       "Sales                        -0.015797  1.000000"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xa.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There is very little correlation with Competiton_Metric and Sales**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Day_No', 'Course_ID', 'Course_Domain', 'Course_Type',\n",
       "       'Short_Promotion', 'Public_Holiday', 'Long_Promotion', 'User_Traffic',\n",
       "       'Competition_Metric', 'Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.251701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120.595238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153.207483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94.812925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>113.215420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>84.924036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>91.241497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>180.100907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>116.346939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>109.198413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Course_ID            \n",
       "1           85.251701\n",
       "2          120.595238\n",
       "3          153.207483\n",
       "4           94.812925\n",
       "5          113.215420\n",
       "...               ...\n",
       "596         84.924036\n",
       "597         91.241497\n",
       "598        180.100907\n",
       "599        116.346939\n",
       "600        109.198413\n",
       "\n",
       "[600 rows x 1 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa=pd.DataFrame(train.groupby(\"Course_ID\").mean()[\"Sales\"])\n",
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Course_ID\n",
       "1               Development\n",
       "2               Development\n",
       "3               Development\n",
       "4               Development\n",
       "5               Development\n",
       "               ...         \n",
       "596             Development\n",
       "597             Development\n",
       "598    Finance & Accounting\n",
       "599             Development\n",
       "600      Software Marketing\n",
       "Name: Course_Domain, Length: 600, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Course_ID\")[\"Course_Domain\"].unique().apply(lambda x:x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa[\"Course_Domain\"]=list(train.groupby(\"Course_ID\")[\"Course_Domain\"].unique().apply(lambda x:x[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Course_Domain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>337.649660</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>306.088435</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>290.574830</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>276.689342</td>\n",
       "      <td>Finance &amp; Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>273.692744</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>53.543084</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>52.730159</td>\n",
       "      <td>Finance &amp; Accounting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>51.165533</td>\n",
       "      <td>Software Marketing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>50.871882</td>\n",
       "      <td>Development</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>50.197279</td>\n",
       "      <td>Finance &amp; Accounting</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales         Course_Domain\n",
       "Course_ID                                  \n",
       "151        337.649660           Development\n",
       "225        306.088435           Development\n",
       "424        290.574830           Development\n",
       "304        276.689342  Finance & Accounting\n",
       "397        273.692744           Development\n",
       "...               ...                   ...\n",
       "132         53.543084           Development\n",
       "444         52.730159  Finance & Accounting\n",
       "153         51.165533    Software Marketing\n",
       "466         50.871882           Development\n",
       "124         50.197279  Finance & Accounting\n",
       "\n",
       "[600 rows x 2 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.sort_values(\"Sales\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Day_No', 'Course_ID', 'Course_Domain', 'Course_Type',\n",
       "       'Short_Promotion', 'Public_Holiday', 'Long_Promotion', 'User_Traffic',\n",
       "       'Competition_Metric', 'Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa[\"No_Days_Sales\"]=list(train.groupby(\"Course_ID\")[\"Day_No\"].nunique())"
   ]
  },
  {
   "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>Sales</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>No_Days_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>337.649660</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>306.088435</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>290.574830</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>276.689342</td>\n",
       "      <td>Finance &amp; Accounting</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>273.692744</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>265.321995</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>265.020408</td>\n",
       "      <td>Finance &amp; Accounting</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>260.778912</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>254.246032</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>243.541950</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>240.555556</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>239.892290</td>\n",
       "      <td>Business</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>236.502268</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>231.273243</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>228.777937</td>\n",
       "      <td>Development</td>\n",
       "      <td>698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>228.021542</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>226.676871</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>221.488662</td>\n",
       "      <td>Development</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>221.111111</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>212.981859</td>\n",
       "      <td>Finance &amp; Accounting</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales         Course_Domain  No_Days_Sales\n",
       "Course_ID                                                 \n",
       "151        337.649660           Development            882\n",
       "225        306.088435           Development            882\n",
       "424        290.574830           Development            882\n",
       "304        276.689342  Finance & Accounting            882\n",
       "397        273.692744           Development            882\n",
       "335        265.321995           Development            882\n",
       "340        265.020408  Finance & Accounting            882\n",
       "192        260.778912           Development            882\n",
       "222        254.246032           Development            882\n",
       "557        243.541950           Development            882\n",
       "314        240.555556           Development            882\n",
       "200        239.892290              Business            882\n",
       "271        236.502268           Development            882\n",
       "274        231.273243           Development            882\n",
       "555        228.777937           Development            698\n",
       "430        228.021542           Development            882\n",
       "48         226.676871           Development            882\n",
       "461        221.488662           Development            882\n",
       "157        221.111111    Software Marketing            882\n",
       "255        212.981859  Finance & Accounting            882"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.sort_values(\"Sales\",ascending=False).head(20)"
   ]
  },
  {
   "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></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>No_Days_Sales</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Business</th>\n",
       "      <th>882</th>\n",
       "      <td>186.592971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Development</th>\n",
       "      <th>698</th>\n",
       "      <td>107.069090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>81.087401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>121.697050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Finance &amp; Accounting</th>\n",
       "      <th>698</th>\n",
       "      <td>101.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>126.099538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Software Marketing</th>\n",
       "      <th>698</th>\n",
       "      <td>114.498225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>119.650122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Sales\n",
       "Course_Domain        No_Days_Sales            \n",
       "Business             882            186.592971\n",
       "Development          698            107.069090\n",
       "                     881             81.087401\n",
       "                     882            121.697050\n",
       "Finance & Accounting 698            101.142857\n",
       "                     882            126.099538\n",
       "Software Marketing   698            114.498225\n",
       "                     882            119.650122"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.groupby([\"Course_Domain\",\"No_Days_Sales\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Day_No', 'Course_ID', 'Course_Domain', 'Course_Type',\n",
       "       'Short_Promotion', 'Public_Holiday', 'Long_Promotion', 'User_Traffic',\n",
       "       'Competition_Metric', 'Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Course_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85.251701</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120.595238</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153.207483</td>\n",
       "      <td>Development</td>\n",
       "      <td>Program</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>94.812925</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>113.215420</td>\n",
       "      <td>Development</td>\n",
       "      <td>Program</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>84.924036</td>\n",
       "      <td>Development</td>\n",
       "      <td>Program</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>91.241497</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>180.100907</td>\n",
       "      <td>Finance &amp; Accounting</td>\n",
       "      <td>Program</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>116.346939</td>\n",
       "      <td>Development</td>\n",
       "      <td>Program</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>109.198413</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales         Course_Domain Course_Type\n",
       "Course_ID                                              \n",
       "1           85.251701           Development      Course\n",
       "2          120.595238           Development      Course\n",
       "3          153.207483           Development     Program\n",
       "4           94.812925           Development      Course\n",
       "5          113.215420           Development     Program\n",
       "...               ...                   ...         ...\n",
       "596         84.924036           Development     Program\n",
       "597         91.241497           Development      Course\n",
       "598        180.100907  Finance & Accounting     Program\n",
       "599        116.346939           Development     Program\n",
       "600        109.198413    Software Marketing     Program\n",
       "\n",
       "[600 rows x 3 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa[\"Course_Type\"]=list(train.groupby(\"Course_ID\")[\"Course_Type\"].unique().apply(lambda x: x[0]))\n",
    "aa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Course_Type\n",
       "Course     113.041589\n",
       "Degree     184.061224\n",
       "Program    128.058375\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.groupby(\"Course_Type\").mean()[\"Sales\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230aa738348>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Course_Type\",y=\"Sales\",data=aa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Course_Domain\n",
       "Business                186.592971\n",
       "Development             119.665465\n",
       "Finance & Accounting    124.136653\n",
       "Software Marketing      118.453217\n",
       "Name: Sales, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aa.groupby(\"Course_Domain\").mean()[\"Sales\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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": [
    "sns.barplot(y=\"Course_Domain\",x=\"Sales\",data=aa)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Day_No', 'Course_ID', 'Course_Domain', 'Course_Type',\n",
       "       'Short_Promotion', 'Public_Holiday', 'Long_Promotion', 'User_Traffic',\n",
       "       'Competition_Metric', 'Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INFERENCES from EDA-\n",
    "\n",
    "1. Short_Promotion causes increase in Sales\n",
    "2. Courses with Long_Promotions have lesser Sales infact\n",
    "3. On Public Holidays, Sales are Less\n",
    "4. Sales and User Traffic are highly correlated\n",
    "5. Competition_Metric doesn't have any affect on Sales\n",
    "6. Business Courses have much higher Sales than other domains\n",
    "7. Degree Courses have much higher Sales than other courses/programmes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Day_No"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "train[(train[\"Day_No\"]<50)].groupby(\"Day_No\")[\"Sales\"].mean().to_frame().plot(kind='line',figsize=(20,7))\n",
    "plt.xticks(range(0,52,2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "train[(train[\"Day_No\"]>50) & (train[\"Day_No\"]<=100)].groupby(\"Day_No\")[\"Sales\"].mean().to_frame().plot(kind='line',figsize=(20,7))\n",
    "plt.xticks(range(50,100,2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day_No</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131.223706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>112.315000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>98.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>94.880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>176.696667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>141.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>125.995000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>125.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Sales\n",
       "Day_No            \n",
       "1       131.223706\n",
       "2       112.315000\n",
       "3       102.875000\n",
       "4       107.440000\n",
       "5        98.490000\n",
       "6        94.880000\n",
       "7       176.696667\n",
       "8       141.360000\n",
       "9       125.995000\n",
       "10      125.900000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(\"Day_No\")[\"Sales\"].mean().to_frame().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train=train.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Week_No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11004</td>\n",
       "      <td>0.007</td>\n",
       "      <td>81</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13650</td>\n",
       "      <td>0.007</td>\n",
       "      <td>79</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11655</td>\n",
       "      <td>0.007</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12054</td>\n",
       "      <td>0.007</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6804</td>\n",
       "      <td>0.007</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512082</th>\n",
       "      <td>548023</td>\n",
       "      <td>878</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>114</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512083</th>\n",
       "      <td>548024</td>\n",
       "      <td>879</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10542</td>\n",
       "      <td>0.070</td>\n",
       "      <td>145</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512084</th>\n",
       "      <td>548025</td>\n",
       "      <td>880</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13671</td>\n",
       "      <td>0.070</td>\n",
       "      <td>167</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512085</th>\n",
       "      <td>548026</td>\n",
       "      <td>881</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>107</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512086</th>\n",
       "      <td>548027</td>\n",
       "      <td>882</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11445</td>\n",
       "      <td>0.070</td>\n",
       "      <td>152</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512087 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Day_No  Course_ID       Course_Domain Course_Type  \\\n",
       "0            1       1          1         Development      Course   \n",
       "1            2       2          1         Development      Course   \n",
       "2            3       3          1         Development      Course   \n",
       "3            4       4          1         Development      Course   \n",
       "4            5       5          1         Development      Course   \n",
       "...        ...     ...        ...                 ...         ...   \n",
       "512082  548023     878        600  Software Marketing     Program   \n",
       "512083  548024     879        600  Software Marketing     Program   \n",
       "512084  548025     880        600  Software Marketing     Program   \n",
       "512085  548026     881        600  Software Marketing     Program   \n",
       "512086  548027     882        600  Software Marketing     Program   \n",
       "\n",
       "        Short_Promotion  Public_Holiday  Long_Promotion  User_Traffic  \\\n",
       "0                     0               1               1         11004   \n",
       "1                     0               0               1         13650   \n",
       "2                     0               0               1         11655   \n",
       "3                     0               0               1         12054   \n",
       "4                     0               0               1          6804   \n",
       "...                 ...             ...             ...           ...   \n",
       "512082                0               0               1          8904   \n",
       "512083                0               0               1         10542   \n",
       "512084                0               0               1         13671   \n",
       "512085                0               0               1          8904   \n",
       "512086                1               0               1         11445   \n",
       "\n",
       "        Competition_Metric  Sales  Week_No  \n",
       "0                    0.007     81      1.0  \n",
       "1                    0.007     79      1.0  \n",
       "2                    0.007     75      1.0  \n",
       "3                    0.007     80      1.0  \n",
       "4                    0.007     41      1.0  \n",
       "...                    ...    ...      ...  \n",
       "512082               0.070    114    126.0  \n",
       "512083               0.070    145    126.0  \n",
       "512084               0.070    167    126.0  \n",
       "512085               0.070    107    126.0  \n",
       "512086               0.070    152    126.0  \n",
       "\n",
       "[512087 rows x 12 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train[\"Week_No\"]=np.ceil(new_train[\"Day_No\"]/7)\n",
    "new_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    512087.000000\n",
       "mean         62.559565\n",
       "std          36.576473\n",
       "min           1.000000\n",
       "25%          31.000000\n",
       "50%          61.000000\n",
       "75%          94.000000\n",
       "max         126.000000\n",
       "Name: Week_No, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train[\"Week_No\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Week_No</th>\n",
       "      <th>Year_No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11004</td>\n",
       "      <td>0.007</td>\n",
       "      <td>81</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13650</td>\n",
       "      <td>0.007</td>\n",
       "      <td>79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11655</td>\n",
       "      <td>0.007</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12054</td>\n",
       "      <td>0.007</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6804</td>\n",
       "      <td>0.007</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512082</th>\n",
       "      <td>548023</td>\n",
       "      <td>878</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>114</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512083</th>\n",
       "      <td>548024</td>\n",
       "      <td>879</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10542</td>\n",
       "      <td>0.070</td>\n",
       "      <td>145</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512084</th>\n",
       "      <td>548025</td>\n",
       "      <td>880</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13671</td>\n",
       "      <td>0.070</td>\n",
       "      <td>167</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512085</th>\n",
       "      <td>548026</td>\n",
       "      <td>881</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>107</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512086</th>\n",
       "      <td>548027</td>\n",
       "      <td>882</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11445</td>\n",
       "      <td>0.070</td>\n",
       "      <td>152</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512087 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Day_No  Course_ID       Course_Domain Course_Type  \\\n",
       "0            1       1          1         Development      Course   \n",
       "1            2       2          1         Development      Course   \n",
       "2            3       3          1         Development      Course   \n",
       "3            4       4          1         Development      Course   \n",
       "4            5       5          1         Development      Course   \n",
       "...        ...     ...        ...                 ...         ...   \n",
       "512082  548023     878        600  Software Marketing     Program   \n",
       "512083  548024     879        600  Software Marketing     Program   \n",
       "512084  548025     880        600  Software Marketing     Program   \n",
       "512085  548026     881        600  Software Marketing     Program   \n",
       "512086  548027     882        600  Software Marketing     Program   \n",
       "\n",
       "        Short_Promotion  Public_Holiday  Long_Promotion  User_Traffic  \\\n",
       "0                     0               1               1         11004   \n",
       "1                     0               0               1         13650   \n",
       "2                     0               0               1         11655   \n",
       "3                     0               0               1         12054   \n",
       "4                     0               0               1          6804   \n",
       "...                 ...             ...             ...           ...   \n",
       "512082                0               0               1          8904   \n",
       "512083                0               0               1         10542   \n",
       "512084                0               0               1         13671   \n",
       "512085                0               0               1          8904   \n",
       "512086                1               0               1         11445   \n",
       "\n",
       "        Competition_Metric  Sales  Week_No  Year_No  \n",
       "0                    0.007     81      1.0      1.0  \n",
       "1                    0.007     79      1.0      1.0  \n",
       "2                    0.007     75      1.0      1.0  \n",
       "3                    0.007     80      1.0      1.0  \n",
       "4                    0.007     41      1.0      1.0  \n",
       "...                    ...    ...      ...      ...  \n",
       "512082               0.070    114     22.0      3.0  \n",
       "512083               0.070    145     22.0      3.0  \n",
       "512084               0.070    167     22.0      3.0  \n",
       "512085               0.070    107     22.0      3.0  \n",
       "512086               0.070    152     22.0      3.0  \n",
       "\n",
       "[512087 rows x 13 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train[\"Year_No\"]=np.ceil(new_train[\"Week_No\"]/52)\n",
    "new_train[\"Week_No\"]=new_train[\"Week_No\"]%52\n",
    "new_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>ID</th>\n",
       "      <th>Day_No</th>\n",
       "      <th>Course_ID</th>\n",
       "      <th>Course_Domain</th>\n",
       "      <th>Course_Type</th>\n",
       "      <th>Short_Promotion</th>\n",
       "      <th>Public_Holiday</th>\n",
       "      <th>Long_Promotion</th>\n",
       "      <th>User_Traffic</th>\n",
       "      <th>Competition_Metric</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Week_No</th>\n",
       "      <th>Year_No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11004</td>\n",
       "      <td>0.007</td>\n",
       "      <td>81</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13650</td>\n",
       "      <td>0.007</td>\n",
       "      <td>79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11655</td>\n",
       "      <td>0.007</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12054</td>\n",
       "      <td>0.007</td>\n",
       "      <td>80</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Development</td>\n",
       "      <td>Course</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6804</td>\n",
       "      <td>0.007</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512082</th>\n",
       "      <td>548023</td>\n",
       "      <td>878</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>114</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512083</th>\n",
       "      <td>548024</td>\n",
       "      <td>879</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10542</td>\n",
       "      <td>0.070</td>\n",
       "      <td>145</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512084</th>\n",
       "      <td>548025</td>\n",
       "      <td>880</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13671</td>\n",
       "      <td>0.070</td>\n",
       "      <td>167</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512085</th>\n",
       "      <td>548026</td>\n",
       "      <td>881</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8904</td>\n",
       "      <td>0.070</td>\n",
       "      <td>107</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512086</th>\n",
       "      <td>548027</td>\n",
       "      <td>882</td>\n",
       "      <td>600</td>\n",
       "      <td>Software Marketing</td>\n",
       "      <td>Program</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11445</td>\n",
       "      <td>0.070</td>\n",
       "      <td>152</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>512087 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Day_No  Course_ID       Course_Domain Course_Type  \\\n",
       "0            1       1          1         Development      Course   \n",
       "1            2       2          1         Development      Course   \n",
       "2            3       3          1         Development      Course   \n",
       "3            4       4          1         Development      Course   \n",
       "4            5       5          1         Development      Course   \n",
       "...        ...     ...        ...                 ...         ...   \n",
       "512082  548023     878        600  Software Marketing     Program   \n",
       "512083  548024     879        600  Software Marketing     Program   \n",
       "512084  548025     880        600  Software Marketing     Program   \n",
       "512085  548026     881        600  Software Marketing     Program   \n",
       "512086  548027     882        600  Software Marketing     Program   \n",
       "\n",
       "        Short_Promotion  Public_Holiday  Long_Promotion  User_Traffic  \\\n",
       "0                     0               1               1         11004   \n",
       "1                     0               0               1         13650   \n",
       "2                     0               0               1         11655   \n",
       "3                     0               0               1         12054   \n",
       "4                     0               0               1          6804   \n",
       "...                 ...             ...             ...           ...   \n",
       "512082                0               0               1          8904   \n",
       "512083                0               0               1         10542   \n",
       "512084                0               0               1         13671   \n",
       "512085                0               0               1          8904   \n",
       "512086                1               0               1         11445   \n",
       "\n",
       "        Competition_Metric  Sales  Week_No  Year_No  \n",
       "0                    0.007     81      1.0      1.0  \n",
       "1                    0.007     79      1.0      1.0  \n",
       "2                    0.007     75      1.0      1.0  \n",
       "3                    0.007     80      1.0      1.0  \n",
       "4                    0.007     41      1.0      1.0  \n",
       "...                    ...    ...      ...      ...  \n",
       "512082               0.070    114     22.0      3.0  \n",
       "512083               0.070    145     22.0      3.0  \n",
       "512084               0.070    167     22.0      3.0  \n",
       "512085               0.070    107     22.0      3.0  \n",
       "512086               0.070    152     22.0      3.0  \n",
       "\n",
       "[512087 rows x 13 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_train.groupby([\"Year_No\",\"Week_No\"])[\"Sales\"].mean().to_frame().plot(kind='line',figsize=(14,7))\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}