partie_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PARTIE 1 : EDA et prétraitement des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A. Nettoyage des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import librairies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from datetime import datetime\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lecture des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examination des colonnes :\n",
    "\n",
    "- *Store*: identifiant unique d'un magasin (les données concernent 20 magasins distincts)\n",
    "\n",
    "- *Weekly_Sales*: les ventes hebdomadaires d'un magasin donné (il est en moyenne de 1,25 millions de dollars)\n",
    "\n",
    "- *Date*: les ventes sont relevées en fin de semaine les vendredis (les données concernent 2010, 2011 et 2012)\n",
    "\n",
    "- *Temperature*: elle est en unité Kelvin utilisée en Amérique\n",
    "\n",
    "- *Fuel_Price*: le prix du gallon d'essence (1 gallon = 3,8 litres)\n",
    "\n",
    "- *CPI*: le Consumer Price Index mesure l'inflation\n",
    "\n",
    "- *Unemployment*: le taux de chômage dans le pays\n",
    "\n",
    "- *Holiday_Flag*: correspond aux jours fériés en Amérique  (il vaut 1 lorsqu'il s'agit d'un jour de fête, autrement il vaut 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18-02-2011</td>\n",
       "      <td>1572117.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.61</td>\n",
       "      <td>3.045</td>\n",
       "      <td>214.777523</td>\n",
       "      <td>6.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>25-03-2011</td>\n",
       "      <td>1807545.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.38</td>\n",
       "      <td>3.435</td>\n",
       "      <td>128.616064</td>\n",
       "      <td>7.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.0</td>\n",
       "      <td>27-07-2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.719581</td>\n",
       "      <td>5.936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1244390.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214.556497</td>\n",
       "      <td>7.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.0</td>\n",
       "      <td>28-05-2010</td>\n",
       "      <td>1644470.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.89</td>\n",
       "      <td>2.759</td>\n",
       "      <td>212.412888</td>\n",
       "      <td>7.092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0    6.0  18-02-2011    1572117.54           NaN        59.61       3.045   \n",
       "1   13.0  25-03-2011    1807545.43           0.0        42.38       3.435   \n",
       "2   17.0  27-07-2012           NaN           0.0          NaN         NaN   \n",
       "3   11.0         NaN    1244390.03           0.0        84.57         NaN   \n",
       "4    6.0  28-05-2010    1644470.66           0.0        78.89       2.759   \n",
       "\n",
       "          CPI  Unemployment  \n",
       "0  214.777523         6.858  \n",
       "1  128.616064         7.470  \n",
       "2  130.719581         5.936  \n",
       "3  214.556497         7.346  \n",
       "4  212.412888         7.092  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('./src/Walmart_Store_sales.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 8) \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Store         150 non-null    float64\n",
      " 1   Date          132 non-null    object \n",
      " 2   Weekly_Sales  136 non-null    float64\n",
      " 3   Holiday_Flag  138 non-null    float64\n",
      " 4   Temperature   132 non-null    float64\n",
      " 5   Fuel_Price    136 non-null    float64\n",
      " 6   CPI           138 non-null    float64\n",
      " 7   Unemployment  135 non-null    float64\n",
      "dtypes: float64(7), object(1)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape, '\\n')\n",
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of distinct Walmart stores: 20\n"
     ]
    }
   ],
   "source": [
    "print('Number of distinct Walmart stores:', dataset['Store'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Données manquantes\n",
    "\n",
    "Il est possible de retrouver les valeurs manquantes pour les colonnes :\n",
    "- *Holiday_Flag* :  puisque nous connaissons la liste des jours fériés en Amérique du Nord  \n",
    "\n",
    "- *Date* : en observant les champs *CPI* et *Fuel_Price*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values per column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Store            0\n",
       "Date            18\n",
       "Weekly_Sales    14\n",
       "Holiday_Flag    12\n",
       "Temperature     18\n",
       "Fuel_Price      14\n",
       "CPI             12\n",
       "Unemployment    15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Number of missing values per column:')\n",
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_mv = dataset.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recherche des valeurs manquantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Holiday_flag manquants\n",
    "\n",
    "Il n'y a pas de jours fériés dans la liste. Nous remplaçons les valeurs NaN par 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>18-02-2011</td>\n",
       "      <td>1572117.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.61</td>\n",
       "      <td>3.045</td>\n",
       "      <td>214.777523</td>\n",
       "      <td>6.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.0</td>\n",
       "      <td>30-04-2010</td>\n",
       "      <td>1498080.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.91</td>\n",
       "      <td>2.780</td>\n",
       "      <td>211.894272</td>\n",
       "      <td>7.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7.0</td>\n",
       "      <td>26-08-2011</td>\n",
       "      <td>629994.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.60</td>\n",
       "      <td>3.485</td>\n",
       "      <td>194.379637</td>\n",
       "      <td>8.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1.0</td>\n",
       "      <td>05-08-2011</td>\n",
       "      <td>1624383.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.65</td>\n",
       "      <td>3.684</td>\n",
       "      <td>215.544618</td>\n",
       "      <td>7.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>14.0</td>\n",
       "      <td>25-03-2011</td>\n",
       "      <td>1879451.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.76</td>\n",
       "      <td>3.625</td>\n",
       "      <td>184.994368</td>\n",
       "      <td>8.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1.0</td>\n",
       "      <td>27-08-2010</td>\n",
       "      <td>1449142.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.22</td>\n",
       "      <td>2.619</td>\n",
       "      <td>211.567306</td>\n",
       "      <td>7.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>9.0</td>\n",
       "      <td>09-07-2010</td>\n",
       "      <td>485389.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.51</td>\n",
       "      <td>2.642</td>\n",
       "      <td>214.656430</td>\n",
       "      <td>6.442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1.0</td>\n",
       "      <td>09-12-2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.93</td>\n",
       "      <td>3.158</td>\n",
       "      <td>218.961846</td>\n",
       "      <td>7.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>9.0</td>\n",
       "      <td>18-06-2010</td>\n",
       "      <td>513073.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.99</td>\n",
       "      <td>2.637</td>\n",
       "      <td>215.016648</td>\n",
       "      <td>6.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>4.0</td>\n",
       "      <td>08-07-2011</td>\n",
       "      <td>2066541.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.59</td>\n",
       "      <td>3.469</td>\n",
       "      <td>129.112500</td>\n",
       "      <td>5.644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "0      6.0  18-02-2011    1572117.54           NaN        59.61       3.045   \n",
       "15     6.0  30-04-2010    1498080.16           NaN        68.91       2.780   \n",
       "43     7.0  26-08-2011     629994.47           NaN        57.60       3.485   \n",
       "48     1.0  05-08-2011    1624383.75           NaN        91.65       3.684   \n",
       "53    14.0  25-03-2011    1879451.23           NaN        41.76       3.625   \n",
       "73     1.0  27-08-2010    1449142.92           NaN        85.22       2.619   \n",
       "90     9.0  09-07-2010     485389.15           NaN        78.51       2.642   \n",
       "109    1.0  09-12-2011           NaN           NaN        43.93       3.158   \n",
       "118    9.0  18-06-2010     513073.87           NaN        82.99       2.637   \n",
       "136    4.0  08-07-2011    2066541.86           NaN        84.59       3.469   \n",
       "\n",
       "            CPI  Unemployment  \n",
       "0    214.777523         6.858  \n",
       "15   211.894272         7.092  \n",
       "43   194.379637         8.622  \n",
       "48   215.544618         7.962  \n",
       "53   184.994368         8.549  \n",
       "73   211.567306         7.787  \n",
       "90   214.656430         6.442  \n",
       "109  218.961846         7.866  \n",
       "118  215.016648         6.384  \n",
       "136  129.112500         5.644  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_holiday_flag = dataset_mv[(dataset_mv['Holiday_Flag'].isna() == True) & (dataset_mv['Date'].isna() == False)]\n",
    "missing_holiday_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_holiday_flag_list = missing_holiday_flag.index.tolist()\n",
    "for i in missing_holiday_flag_list:\n",
    "    dataset_mv.iloc[i, 3] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dates manquantes\n",
    "\n",
    "Nous remarquons que les observations qui partagent le même CPI partagent aussi la même date.\n",
    "\n",
    "--> Nous pouvons déduire et compléter la date manquante :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>13.0</td>\n",
       "      <td>10-12-2010</td>\n",
       "      <td>2461468.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.78</td>\n",
       "      <td>2.817</td>\n",
       "      <td>126.793400</td>\n",
       "      <td>7.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>4.0</td>\n",
       "      <td>10-12-2010</td>\n",
       "      <td>2302504.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.40</td>\n",
       "      <td>2.860</td>\n",
       "      <td>126.793400</td>\n",
       "      <td>7.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4.0</td>\n",
       "      <td>07-01-2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.980</td>\n",
       "      <td>127.191774</td>\n",
       "      <td>6.510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1714309.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.43</td>\n",
       "      <td>3.287</td>\n",
       "      <td>127.191774</td>\n",
       "      <td>8.744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>12.0</td>\n",
       "      <td>12-08-2011</td>\n",
       "      <td>955913.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.04</td>\n",
       "      <td>3.701</td>\n",
       "      <td>129.201581</td>\n",
       "      <td>13.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>13.0</td>\n",
       "      <td>12-08-2011</td>\n",
       "      <td>1970341.38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.95</td>\n",
       "      <td>3.606</td>\n",
       "      <td>129.201581</td>\n",
       "      <td>6.877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "72    13.0  10-12-2010    2461468.35           0.0        36.78       2.817   \n",
       "103    4.0  10-12-2010    2302504.86           0.0        42.40       2.860   \n",
       "57     4.0  07-01-2011           NaN           0.0          NaN       2.980   \n",
       "65    10.0         NaN    1714309.90           NaN        43.43       3.287   \n",
       "68    12.0  12-08-2011     955913.68           0.0        91.04       3.701   \n",
       "128   13.0  12-08-2011    1970341.38           0.0        75.95       3.606   \n",
       "\n",
       "            CPI  Unemployment  \n",
       "72   126.793400         7.795  \n",
       "103  126.793400         7.127  \n",
       "57   127.191774         6.510  \n",
       "65   127.191774         8.744  \n",
       "68   129.201581        13.503  \n",
       "128  129.201581         6.877  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CPI_same = dataset_mv[dataset_mv.duplicated(['CPI'], keep=False)][dataset_mv['CPI'].isna() == False].sort_values(by='CPI')\n",
    "CPI_same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_mv.iloc[65,1] = '07-01-2011'\n",
    "dataset_mv.iloc[65, 3] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous remarquons que les observations qui partagent le même prix de l'essence sont susceptibles de partager la même date. \n",
    "\n",
    "Pour confimer ou infirmer cela, nous pouvons nous aider des valeurs de *CPI* et *Unemployment*. Si les valeurs sont proches il est probable qu'il s'agit de la même date.\n",
    "\n",
    "--> Nous pouvons déduire et compléter les dates manquantes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12-02-2010</td>\n",
       "      <td>1641957.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.51</td>\n",
       "      <td>2.548</td>\n",
       "      <td>211.242170</td>\n",
       "      <td>8.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>8.0</td>\n",
       "      <td>12-02-2010</td>\n",
       "      <td>994801.40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.34</td>\n",
       "      <td>2.548</td>\n",
       "      <td>214.621419</td>\n",
       "      <td>6.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>8.0</td>\n",
       "      <td>27-08-2010</td>\n",
       "      <td>888816.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.92</td>\n",
       "      <td>2.619</td>\n",
       "      <td>214.936279</td>\n",
       "      <td>6.315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1.0</td>\n",
       "      <td>27-08-2010</td>\n",
       "      <td>1449142.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.22</td>\n",
       "      <td>2.619</td>\n",
       "      <td>211.567306</td>\n",
       "      <td>7.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>9.0</td>\n",
       "      <td>18-06-2010</td>\n",
       "      <td>513073.87</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.99</td>\n",
       "      <td>2.637</td>\n",
       "      <td>215.016648</td>\n",
       "      <td>6.384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18-06-2010</td>\n",
       "      <td>364076.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.52</td>\n",
       "      <td>2.637</td>\n",
       "      <td>214.785826</td>\n",
       "      <td>7.343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>5.0</td>\n",
       "      <td>30-07-2010</td>\n",
       "      <td>268929.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.46</td>\n",
       "      <td>2.640</td>\n",
       "      <td>211.927001</td>\n",
       "      <td>6.496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>6.0</td>\n",
       "      <td>30-07-2010</td>\n",
       "      <td>1532308.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.640</td>\n",
       "      <td>212.894485</td>\n",
       "      <td>6.973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>14.0</td>\n",
       "      <td>18-06-2010</td>\n",
       "      <td>2248645.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.62</td>\n",
       "      <td>2.780</td>\n",
       "      <td>182.442420</td>\n",
       "      <td>8.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6.0</td>\n",
       "      <td>30-04-2010</td>\n",
       "      <td>1498080.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.91</td>\n",
       "      <td>2.780</td>\n",
       "      <td>211.894272</td>\n",
       "      <td>7.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>20.0</td>\n",
       "      <td>26-03-2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.49</td>\n",
       "      <td>2.854</td>\n",
       "      <td>204.201755</td>\n",
       "      <td>8.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.0</td>\n",
       "      <td>14-05-2010</td>\n",
       "      <td>1494251.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.78</td>\n",
       "      <td>2.854</td>\n",
       "      <td>210.337426</td>\n",
       "      <td>7.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>3.0</td>\n",
       "      <td>07-01-2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.35</td>\n",
       "      <td>2.976</td>\n",
       "      <td>214.695510</td>\n",
       "      <td>7.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2.0</td>\n",
       "      <td>07-01-2011</td>\n",
       "      <td>1758050.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.69</td>\n",
       "      <td>2.976</td>\n",
       "      <td>211.064888</td>\n",
       "      <td>8.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18-11-2011</td>\n",
       "      <td>398838.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.91</td>\n",
       "      <td>3.308</td>\n",
       "      <td>221.643285</td>\n",
       "      <td>7.197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1.0</td>\n",
       "      <td>18-11-2011</td>\n",
       "      <td>1539483.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.25</td>\n",
       "      <td>3.308</td>\n",
       "      <td>218.220509</td>\n",
       "      <td>7.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>418925.47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.12</td>\n",
       "      <td>3.555</td>\n",
       "      <td>224.132020</td>\n",
       "      <td>6.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2.0</td>\n",
       "      <td>24-02-2012</td>\n",
       "      <td>1861802.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.63</td>\n",
       "      <td>3.555</td>\n",
       "      <td>220.275944</td>\n",
       "      <td>7.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>3.0</td>\n",
       "      <td>19-10-2012</td>\n",
       "      <td>424513.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.44</td>\n",
       "      <td>3.594</td>\n",
       "      <td>226.968844</td>\n",
       "      <td>6.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1.0</td>\n",
       "      <td>19-10-2012</td>\n",
       "      <td>1508068.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.97</td>\n",
       "      <td>3.594</td>\n",
       "      <td>223.425723</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>5.0</td>\n",
       "      <td>19-10-2012</td>\n",
       "      <td>313358.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.17</td>\n",
       "      <td>3.594</td>\n",
       "      <td>224.019287</td>\n",
       "      <td>5.422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.0</td>\n",
       "      <td>19-10-2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.68</td>\n",
       "      <td>3.594</td>\n",
       "      <td>225.050101</td>\n",
       "      <td>5.329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1046782.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.91</td>\n",
       "      <td>3.630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.0</td>\n",
       "      <td>18-05-2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.38</td>\n",
       "      <td>3.630</td>\n",
       "      <td>225.251202</td>\n",
       "      <td>6.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359206.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.630</td>\n",
       "      <td>221.434215</td>\n",
       "      <td>5.943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>3.0</td>\n",
       "      <td>29-07-2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.07</td>\n",
       "      <td>3.682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.0</td>\n",
       "      <td>29-07-2011</td>\n",
       "      <td>275142.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.42</td>\n",
       "      <td>3.682</td>\n",
       "      <td>216.046436</td>\n",
       "      <td>6.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>1.0</td>\n",
       "      <td>27-05-2011</td>\n",
       "      <td>1466046.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.786</td>\n",
       "      <td>215.503788</td>\n",
       "      <td>7.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1797732.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.42</td>\n",
       "      <td>3.786</td>\n",
       "      <td>215.154482</td>\n",
       "      <td>7.931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "44     1.0  12-02-2010    1641957.44           1.0        38.51       2.548   \n",
       "107    8.0  12-02-2010     994801.40           1.0        33.34       2.548   \n",
       "56     8.0  27-08-2010     888816.78           0.0        74.92       2.619   \n",
       "73     1.0  27-08-2010    1449142.92           0.0        85.22       2.619   \n",
       "118    9.0  18-06-2010     513073.87           0.0        82.99       2.637   \n",
       "100    3.0  18-06-2010     364076.85           0.0        83.52       2.637   \n",
       "46     5.0  30-07-2010     268929.03           0.0        82.46       2.640   \n",
       "58     6.0  30-07-2010    1532308.78           0.0          NaN       2.640   \n",
       "145   14.0  18-06-2010    2248645.59           0.0        72.62       2.780   \n",
       "15     6.0  30-04-2010    1498080.16           0.0        68.91       2.780   \n",
       "69    20.0  26-03-2010           NaN           0.0        50.49       2.854   \n",
       "95     1.0  14-05-2010    1494251.50           0.0        74.78       2.854   \n",
       "140    3.0  07-01-2011           NaN           0.0        53.35       2.976   \n",
       "85     2.0  07-01-2011    1758050.79           0.0        44.69       2.976   \n",
       "125    3.0  18-11-2011     398838.97           0.0        63.91       3.308   \n",
       "78     1.0  18-11-2011    1539483.70           0.0        62.25       3.308   \n",
       "9      3.0         NaN     418925.47           0.0        60.12       3.555   \n",
       "45     2.0  24-02-2012    1861802.70           0.0        54.63       3.555   \n",
       "144    3.0  19-10-2012     424513.08           0.0        73.44       3.594   \n",
       "74     1.0  19-10-2012    1508068.77           0.0        67.97       3.594   \n",
       "63     5.0  19-10-2012     313358.15           0.0        69.17       3.594   \n",
       "134    6.0  19-10-2012           NaN           0.0        69.68       3.594   \n",
       "91    17.0         NaN    1046782.52           0.0        68.91       3.630   \n",
       "19     3.0  18-05-2012           NaN           0.0        72.38       3.630   \n",
       "81     5.0         NaN     359206.21           0.0          NaN       3.630   \n",
       "77     3.0  29-07-2011           NaN           0.0        88.07       3.682   \n",
       "121    5.0  29-07-2011     275142.17           0.0        89.42       3.682   \n",
       "123    1.0  27-05-2011    1466046.67           0.0          NaN       3.786   \n",
       "89     2.0         NaN    1797732.56           0.0        76.42       3.786   \n",
       "\n",
       "            CPI  Unemployment  \n",
       "44   211.242170         8.106  \n",
       "107  214.621419         6.299  \n",
       "56   214.936279         6.315  \n",
       "73   211.567306         7.787  \n",
       "118  215.016648         6.384  \n",
       "100  214.785826         7.343  \n",
       "46   211.927001         6.496  \n",
       "58   212.894485         6.973  \n",
       "145  182.442420         8.899  \n",
       "15   211.894272         7.092  \n",
       "69   204.201755         8.187  \n",
       "95   210.337426         7.808  \n",
       "140  214.695510         7.551  \n",
       "85   211.064888         8.028  \n",
       "125  221.643285         7.197  \n",
       "78   218.220509         7.866  \n",
       "9    224.132020         6.833  \n",
       "45   220.275944         7.057  \n",
       "144  226.968844         6.034  \n",
       "74   223.425723           NaN  \n",
       "63   224.019287         5.422  \n",
       "134  225.050101         5.329  \n",
       "91          NaN         5.936  \n",
       "19   225.251202         6.664  \n",
       "81   221.434215         5.943  \n",
       "77          NaN         7.567  \n",
       "121  216.046436         6.529  \n",
       "123  215.503788         7.682  \n",
       "89   215.154482         7.931  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fuel_same = dataset_mv[dataset_mv.duplicated(['Fuel_Price'], keep=False)][dataset_mv['Fuel_Price'].isna() == False].sort_values(by='Fuel_Price')\n",
    "Fuel_same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_mv.iloc[9,1] = '24-02-2012'\n",
    "dataset_mv.iloc[91,1] = '18-05-2012'\n",
    "dataset_mv.iloc[81,1] = '18-05-2012'\n",
    "dataset_mv.iloc[89,1] = '27-05-2011'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_clean = dataset_mv.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suppression des valeurs manquantes\n",
    "\n",
    "Nous modifions le format de la date pour pouvoir l'exploiter (de string à datetime).  \n",
    "\n",
    "Ceci nous permettra de déterminer s'il est possible de retrouver les autres dates manquantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_clean['date_temp'] = ''\n",
    "\n",
    "for index, value in enumerate(dataset_clean['Date']):\n",
    "    if value == value:\n",
    "        dataset_clean['date_temp'][index] = datetime.strptime(value,'%d-%m-%Y').date()\n",
    "    else: \n",
    "        dataset_clean['date_temp'][index] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_clean.drop(['Date'], axis=1, inplace=True)\n",
    "dataset_clean.rename(columns={'date_temp': 'Date'}, inplace=True)\n",
    "reorder_column = ['Store', 'Date', 'Weekly_Sales','Holiday_Flag','Temperature', 'Fuel_Price', 'CPI', 'Unemployment']\n",
    "dataset_clean = dataset_clean.reindex(columns=reorder_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1494251.50</td>\n",
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       "      <td>8.106</td>\n",
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       "    <tr>\n",
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       "      <td>7.787</td>\n",
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       "      <th>48</th>\n",
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       "      <td>215.544618</td>\n",
       "      <td>7.962</td>\n",
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       "      <th>78</th>\n",
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       "      <td>1539483.70</td>\n",
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       "      <td>62.25</td>\n",
       "      <td>3.308</td>\n",
       "      <td>218.220509</td>\n",
       "      <td>7.866</td>\n",
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       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.93</td>\n",
       "      <td>3.158</td>\n",
       "      <td>218.961846</td>\n",
       "      <td>7.866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-03-16</td>\n",
       "      <td>1677472.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.74</td>\n",
       "      <td>3.734</td>\n",
       "      <td>221.211813</td>\n",
       "      <td>7.348</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.730</td>\n",
       "      <td>222.439015</td>\n",
       "      <td>6.908</td>\n",
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       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2012-10-19</td>\n",
       "      <td>1508068.77</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.97</td>\n",
       "      <td>3.594</td>\n",
       "      <td>223.425723</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2124451.54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.69</td>\n",
       "      <td>2.514</td>\n",
       "      <td>210.945160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2011-01-07</td>\n",
       "      <td>1758050.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.69</td>\n",
       "      <td>2.976</td>\n",
       "      <td>211.064888</td>\n",
       "      <td>8.028</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2010-11-12</td>\n",
       "      <td>1916812.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.69</td>\n",
       "      <td>2.728</td>\n",
       "      <td>211.660898</td>\n",
       "      <td>8.163</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1853161.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.70</td>\n",
       "      <td>3.480</td>\n",
       "      <td>214.929625</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "     Store        Date  Weekly_Sales  Holiday_Flag  Temperature  Fuel_Price  \\\n",
       "95     1.0  2010-05-14    1494251.50           0.0        74.78       2.854   \n",
       "44     1.0  2010-02-12    1641957.44           1.0        38.51       2.548   \n",
       "73     1.0  2010-08-27    1449142.92           0.0        85.22       2.619   \n",
       "133    1.0  2010-09-24           NaN           0.0        80.94         NaN   \n",
       "123    1.0  2011-05-27    1466046.67           0.0          NaN       3.786   \n",
       "48     1.0  2011-08-05    1624383.75           0.0        91.65       3.684   \n",
       "78     1.0  2011-11-18    1539483.70           0.0        62.25       3.308   \n",
       "109    1.0  2011-12-09           NaN           0.0        43.93       3.158   \n",
       "13     1.0  2012-03-16    1677472.78           0.0        64.74       3.734   \n",
       "42     1.0         NaN    1661767.33           1.0          NaN       3.730   \n",
       "74     1.0  2012-10-19    1508068.77           0.0        67.97       3.594   \n",
       "112    2.0  2010-02-19    2124451.54           0.0        39.69       2.514   \n",
       "85     2.0  2011-01-07    1758050.79           0.0        44.69       2.976   \n",
       "51     2.0  2010-11-12    1916812.74           0.0        59.69       2.728   \n",
       "34     2.0         NaN    1853161.99           0.0        87.70       3.480   \n",
       "\n",
       "            CPI  Unemployment  \n",
       "95   210.337426         7.808  \n",
       "44   211.242170         8.106  \n",
       "73   211.567306         7.787  \n",
       "133  211.597225         7.787  \n",
       "123  215.503788         7.682  \n",
       "48   215.544618         7.962  \n",
       "78   218.220509         7.866  \n",
       "109  218.961846         7.866  \n",
       "13   221.211813         7.348  \n",
       "42   222.439015         6.908  \n",
       "74   223.425723           NaN  \n",
       "112  210.945160           NaN  \n",
       "85   211.064888         8.028  \n",
       "51   211.660898         8.163  \n",
       "34   214.929625           NaN  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_clean.sort_values(by=['Store', 'CPI']).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ins>Résultat</ins> : \n",
    "\n",
    "Nous constatons qu'il est compliqué de retrouver les valeurs des dates manquantes restantes, et ce même en ordonnant les données par magasin et par date.\n",
    "\n",
    "La date est une donnée essentielle puisqu'elle détermine directement le chiffre d'affaires hebdomadaire d'un magasin.\n",
    "\n",
    "--> Nous supprimons les observations où la date est absente.  \n",
    "--> Nous supprimons les observations où le chiffre d'affaires est absent.\n",
    "\n",
    "Cela concerne 27 observations soit 18% du jeu de données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows to delete: 27\n",
      "Percentage: 18.0 %\n"
     ]
    }
   ],
   "source": [
    "missing_date = dataset_clean[dataset_clean['Date'].isna() == True].index.tolist()\n",
    "missing_sales = dataset_clean[dataset_clean['Weekly_Sales'].isna() == True].index.tolist()\n",
    "rows_to_drop = missing_date + missing_sales\n",
    "\n",
    "print('Number of rows to delete:', len(rows_to_drop))\n",
    "print('Percentage:', round(len(rows_to_drop)/dataset.shape[0]*100,2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(123, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Store            0\n",
       "Date             0\n",
       "Weekly_Sales     0\n",
       "Holiday_Flag     0\n",
       "Temperature     12\n",
       "Fuel_Price      11\n",
       "CPI             10\n",
       "Unemployment    11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_clean = dataset_clean.drop(rows_to_drop, axis=0)\n",
    "dataset_clean.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print(dataset_clean.shape)\n",
    "dataset_clean.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ajout de colonnes relatives à la date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_clean['Year'] = ''\n",
    "dataset_clean['Month'] = ''\n",
    "dataset_clean['Day'] = ''\n",
    "dataset_clean['Dayofweek'] = ''\n",
    "\n",
    "for index, value in enumerate(dataset_clean['Date']):\n",
    "    if value == value:\n",
    "        dataset_clean['Year'][index] = value.year\n",
    "        dataset_clean['Month'][index] = value.month\n",
    "        dataset_clean['Day'][index] = value.day\n",
    "        dataset_clean['Dayofweek'][index] = value.weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dayofweek\n",
       "4    123\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_clean['Dayofweek'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_clean.drop(['Dayofweek'], axis=1, inplace=True)\n",
    "reorder_column = ['Store', 'Date', 'Day', 'Month', 'Year', 'Weekly_Sales','Holiday_Flag','Temperature', 'Fuel_Price', 'CPI', 'Unemployment']\n",
    "dataset_clean = dataset_clean.reindex(columns=reorder_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_imputed = dataset_clean.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputation des données manquantes\n",
    "\n",
    "Temperature, Fuel_Price, CPI et Unemployment sont des données quantitatives continues.\n",
    "\n",
    "Nous remplaçons les données manquantes de ces colonnes par leur valeur moyenne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>123.000000</td>\n",
       "      <td>123</td>\n",
       "      <td>123.0</td>\n",
       "      <td>123.0</td>\n",
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       "      <td>1.230000e+02</td>\n",
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       "      <td>113.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>51.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>mean</th>\n",
       "      <td>9.845528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.249552e+06</td>\n",
       "      <td>0.065041</td>\n",
       "      <td>60.765946</td>\n",
       "      <td>3.304313</td>\n",
       "      <td>178.399782</td>\n",
       "      <td>7.654339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.066615</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.615939e+05</td>\n",
       "      <td>0.247606</td>\n",
       "      <td>17.789311</td>\n",
       "      <td>0.480638</td>\n",
       "      <td>39.998524</td>\n",
       "      <td>1.661474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.689290e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>2.514000</td>\n",
       "      <td>126.111903</td>\n",
       "      <td>5.143000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>45.465000</td>\n",
       "      <td>2.838500</td>\n",
       "      <td>131.637000</td>\n",
       "      <td>6.656750</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.295605e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.790000</td>\n",
       "      <td>3.327000</td>\n",
       "      <td>196.919506</td>\n",
       "      <td>7.477000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.806773e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.430000</td>\n",
       "      <td>3.689750</td>\n",
       "      <td>214.878556</td>\n",
       "      <td>8.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.771397e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>91.650000</td>\n",
       "      <td>4.193000</td>\n",
       "      <td>226.968844</td>\n",
       "      <td>14.313000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Store        Date    Day  Month    Year  Weekly_Sales  \\\n",
       "count   123.000000         123  123.0  123.0   123.0  1.230000e+02   \n",
       "unique         NaN          80   30.0   12.0     3.0           NaN   \n",
       "top            NaN  2011-01-07   12.0    5.0  2010.0           NaN   \n",
       "freq           NaN           3   11.0   17.0    51.0           NaN   \n",
       "mean      9.845528         NaN    NaN    NaN     NaN  1.249552e+06   \n",
       "std       6.066615         NaN    NaN    NaN     NaN  6.615939e+05   \n",
       "min       1.000000         NaN    NaN    NaN     NaN  2.689290e+05   \n",
       "25%       4.000000         NaN    NaN    NaN     NaN  5.670360e+05   \n",
       "50%       9.000000         NaN    NaN    NaN     NaN  1.295605e+06   \n",
       "75%      15.000000         NaN    NaN    NaN     NaN  1.806773e+06   \n",
       "max      20.000000         NaN    NaN    NaN     NaN  2.771397e+06   \n",
       "\n",
       "        Holiday_Flag  Temperature  Fuel_Price         CPI  Unemployment  \n",
       "count     123.000000   111.000000  112.000000  113.000000    112.000000  \n",
       "unique           NaN          NaN         NaN         NaN           NaN  \n",
       "top              NaN          NaN         NaN         NaN           NaN  \n",
       "freq             NaN          NaN         NaN         NaN           NaN  \n",
       "mean        0.065041    60.765946    3.304313  178.399782      7.654339  \n",
       "std         0.247606    17.789311    0.480638   39.998524      1.661474  \n",
       "min         0.000000    18.790000    2.514000  126.111903      5.143000  \n",
       "25%         0.000000    45.465000    2.838500  131.637000      6.656750  \n",
       "50%         0.000000    61.790000    3.327000  196.919506      7.477000  \n",
       "75%         0.000000    75.430000    3.689750  214.878556      8.150000  \n",
       "max         1.000000    91.650000    4.193000  226.968844     14.313000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_imputed.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputed_cols = ['Temperature', 'Fuel_Price', 'CPI',\t'Unemployment']\n",
    "for i, col in enumerate(imputed_cols):\n",
    "    missing_rows_index = dataset_imputed[dataset_imputed[col].isna() == True].index.tolist()\n",
    "    for row in missing_rows_index:\n",
    "        dataset_imputed.iloc[row,i+7] = round(dataset_imputed[col].mean(),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store           0\n",
       "Date            0\n",
       "Day             0\n",
       "Month           0\n",
       "Year            0\n",
       "Weekly_Sales    0\n",
       "Holiday_Flag    0\n",
       "Temperature     0\n",
       "Fuel_Price      0\n",
       "CPI             0\n",
       "Unemployment    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_imputed.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_outliers = dataset_imputed.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recherche de données aberrantes\n",
    "\n",
    "Nous considérons comme valeurs aberrantes les données en dehors de l'intervalle :  \n",
    "\n",
    "[(moyenne - 3x l'écart-type), (moyenne + 3x l'écart-type)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chiffre d'affaires hebdomadaire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interval: [-735229, 3234334]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_val = dataset_outliers['Weekly_Sales'].mean()\n",
    "std_val = dataset_outliers['Weekly_Sales'].std()\n",
    "print(f'Interval: [{round((mean_val - 3*std_val))}, {round((mean_val + 3*std_val))}]')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 1))\n",
    "sns.boxplot(x=dataset_outliers['Weekly_Sales'], ax=ax, whis=3, palette=['yellow'], linecolor='black')\n",
    "plt.xticks([i for i in range(0,3500000,500000)], fontsize=9)\n",
    "plt.xlabel('Weekly sales (million $)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Température"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interval: [10.09, 111.44]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_val = dataset_outliers['Temperature'].mean()\n",
    "std_val = dataset_outliers['Temperature'].std()\n",
    "print(f'Interval: [{round((mean_val - 3*std_val),2)}, {round((mean_val + 3*std_val),2)}]')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 1))\n",
    "sns.boxplot(x=dataset_outliers['Temperature'], ax=ax, whis=3, palette=['dodgerblue'], linecolor='black')\n",
    "plt.xticks([i for i in range(15,100,10)], fontsize=9)\n",
    "plt.xlabel('Temperature (F°)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prix de l'essence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interval: [1.93, 4.68]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_val = dataset_outliers['Fuel_Price'].mean()\n",
    "std_val = dataset_outliers['Fuel_Price'].std()\n",
    "print(f'Interval: [{round((mean_val - 3*std_val),2)}, {round((mean_val + 3*std_val),2)}]')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 1))\n",
    "sns.boxplot(x=dataset_outliers['Fuel_Price'], ax=ax, whis=3, palette=['limegreen'], linecolor='black')\n",
    "plt.xticks([i / 10.0 for i in range(22,46,3)], fontsize=9)\n",
    "plt.xlabel('Fuel price ($/gallon)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interval: [63.43, 293.37]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_val = dataset_outliers['CPI'].mean()\n",
    "std_val = dataset_outliers['CPI'].std()\n",
    "print(f'Interval: [{round((mean_val - 3*std_val),2)}, {round((mean_val + 3*std_val),2)}]')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 1))\n",
    "sns.boxplot(x=dataset_outliers['CPI'], ax=ax, whis=3, palette=['orangered'], linecolor='black')\n",
    "plt.xticks([i for i in range(120,240,20)], fontsize=9)\n",
    "plt.xlabel('CPI index')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taux de chômage\n",
    "\n",
    "Nous remarquons des valeurs aberrantes dans la colonne *Unemployment*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interval: [2.9, 12.41]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_val = dataset_outliers['Unemployment'].mean()\n",
    "std_val = dataset_outliers['Unemployment'].std()\n",
    "print(f'Interval: [{round((mean_val - 3*std_val),2)}, {round((mean_val + 3*std_val),2)}]')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 1))\n",
    "sns.boxplot(x=dataset_outliers['Unemployment'], ax=ax, whis=3, palette=['crimson'], linecolor='black')\n",
    "plt.xticks([i for i in range(5,16,1)], fontsize=9)\n",
    "plt.xlabel('Unemployment rate (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_final = dataset_outliers.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suppression des valeurs aberrantes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous supprimons les valeurs aberrantes détectées précédemment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Store</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2011-05-06</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>1021154.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.40</td>\n",
       "      <td>4.193</td>\n",
       "      <td>129.044433</td>\n",
       "      <td>13.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2010-12-17</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "      <td>1295605.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.77</td>\n",
       "      <td>3.236</td>\n",
       "      <td>126.879484</td>\n",
       "      <td>14.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2011-08-12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>955913.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.04</td>\n",
       "      <td>3.701</td>\n",
       "      <td>129.201581</td>\n",
       "      <td>13.503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2011-05-27</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>2011</td>\n",
       "      <td>964332.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.77</td>\n",
       "      <td>4.087</td>\n",
       "      <td>129.062355</td>\n",
       "      <td>13.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2010-09-10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>2010</td>\n",
       "      <td>903119.03</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83.63</td>\n",
       "      <td>3.044</td>\n",
       "      <td>126.114581</td>\n",
       "      <td>14.180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Store        Date Day Month  Year  Weekly_Sales  Holiday_Flag  \\\n",
       "26    12.0  2011-05-06   6     5  2011    1021154.48           0.0   \n",
       "54    12.0  2010-12-17  17    12  2010    1295605.35           0.0   \n",
       "60    12.0  2011-08-12  12     8  2011     955913.68           0.0   \n",
       "76    12.0  2011-05-27  27     5  2011     964332.51           0.0   \n",
       "111   12.0  2010-09-10  10     9  2010     903119.03           1.0   \n",
       "\n",
       "     Temperature  Fuel_Price         CPI  Unemployment  \n",
       "26         68.40       4.193  129.044433        13.736  \n",
       "54         52.77       3.236  126.879484        14.313  \n",
       "60         91.04       3.701  129.201581        13.503  \n",
       "76         60.77       4.087  129.062355        13.736  \n",
       "111        83.63       3.044  126.114581        14.180  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_final[dataset_final['Unemployment'] > 12.41]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118, 11)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers = dataset_final[dataset_final['Unemployment'] > 12.41].index.tolist()\n",
    "dataset_final.drop(outliers, axis=0, inplace=True)\n",
    "dataset_final.reset_index(drop=True, inplace=True)\n",
    "dataset_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interval: [4.57, 10.2]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_val = dataset_final['Unemployment'].mean()\n",
    "std_val = dataset_final['Unemployment'].std()\n",
    "print(f'Interval: [{round((mean_val - 3*std_val),2)}, {round((mean_val + 3*std_val),2)}]')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 1))\n",
    "sns.boxplot(x=dataset_final[\"Unemployment\"], ax=ax, whis=3, palette=['crimson'], linecolor='black')\n",
    "plt.xticks([i for i in range(5,11,1)], fontsize=9)\n",
    "plt.xlabel('Unemployment rate (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B. Analyse des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes hebdomadaires suivant l'année"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Year</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.268950e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>1.360004e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>1.133822e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Weekly_Sales\n",
       "0  2010  1.268950e+06\n",
       "1  2011  1.360004e+06\n",
       "2  2012  1.133822e+06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(2.5, 3)}, style='white')\n",
    "colors = sns.color_palette('cool')\n",
    "sns.boxplot(x=\"Year\", y=\"Weekly_Sales\", hue=\"Year\", data=dataset_final, palette=colors, linecolor='black', width=.7)\n",
    "plt.title('Weekly sales per year', fontsize=12.5, pad=20)\n",
    "plt.ylabel('Million $', fontsize=10, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.yticks([i for i in range(0,3500000, 500000)], fontsize=10)\n",
    "plt.xticks([i for i in range(0,3)], ['2010', '2011', '2012'], fontsize=10, fontweight='bold')\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()\n",
    "\n",
    "weekly_sales_per_year = dataset_final.groupby('Year')['Weekly_Sales'].mean().to_frame().reset_index()\n",
    "display(weekly_sales_per_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes hebdomadaires suivant le mois"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous observons que les ventes hebdomadaires varient suivant le mois.  \n",
    "\n",
    "Elle sont bien plus élevées en décembre avec 2,27 millions dollars/magasin en moyenne. Octobre, avril et novembre sont les mois qui enregistrent les chiffres les plus bas avec moins de 1 million dollars/magasin.\n",
    "\n",
    "--> <ins>Hypothèses</ins> : Les ventes sont influencées par les saisons et les activités liées à celles-ci. On remarque une alternance entre périodes de grosse consommation et périodes de faible consommation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Month</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2.279542e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.552612e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.438223e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.399005e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1.331006e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>1.289846e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1.193792e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.101871e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9.939129e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>9.501744e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9.351991e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>8.127581e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Weekly_Sales\n",
       "11     12  2.279542e+06\n",
       "0       1  1.552612e+06\n",
       "1       2  1.438223e+06\n",
       "2       3  1.399005e+06\n",
       "6       7  1.331006e+06\n",
       "7       8  1.289846e+06\n",
       "5       6  1.193792e+06\n",
       "4       5  1.101871e+06\n",
       "8       9  9.939129e+05\n",
       "10     11  9.501744e+05\n",
       "3       4  9.351991e+05\n",
       "9      10  8.127581e+05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(9, 3.5)}, style='white')\n",
    "colors = sns.color_palette('cool')\n",
    "sns.boxplot(x=\"Month\", y=\"Weekly_Sales\", hue=\"Month\", data=dataset_final,  palette=colors, linecolor='black', width=.5)\n",
    "plt.title('Weekly sales per month', fontsize=12.5, pad=16)\n",
    "plt.ylabel('Million $', fontsize=10, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.yticks([i for i in range(0,3500000, 500000)], fontsize=10)\n",
    "plt.xticks([i for i in range(0,12)], ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], fontsize=10, fontweight='bold')\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()\n",
    "\n",
    "weekly_sales_per_month = dataset_final.groupby('Month')['Weekly_Sales'].mean().to_frame().reset_index()\n",
    "display(weekly_sales_per_month.sort_values(by='Weekly_Sales', ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes hebdomadaires suivant le jour du mois\n",
    "\n",
    "Nous observons que les ventes hebdomadaires varient suivant le jour du mois.\n",
    "\n",
    "Le 10 et le 16 du mois elles sont particulièrement élevées. En revanche le 13 du mois et le 29 du mois elle sont particulièrement faibles.\n",
    "\n",
    "--> Ceci pourrait s'expliquer par le fait que les américains reçoivent généralement leur salaire en 2 versements mensuels, au début de mois et à la moitié du mois."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10, 3.5)}, style='white')\n",
    "colors = sns.color_palette('cool')\n",
    "sns.barplot(x=\"Day\", y=\"Weekly_Sales\", hue=\"Day\", data=dataset_final, palette=colors, edgecolor='black', width=1)\n",
    "plt.title('Weekly sales depending on the day of month', fontsize=12.5, pad=16)\n",
    "plt.ylabel('Million $', fontsize=10, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.yticks([i for i in range(0,2600000, 500000)], fontsize=10)\n",
    "plt.xticks(fontsize=10, fontweight='bold')\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes hebdomadaires suivant le magasin\n",
    "\n",
    "Les ventes ne sont pas uniformes suivant le magasin. Certains magasins performent mieux que d'autres.\n",
    "\n",
    "Les magasins n°4 et n°14 sont les plus performants. Ils réalisent en moyenne plus de 2 millions dollars/semaine.  \n",
    "En revanche les magasins n°5 et n°3 sont les moins bons. Ils réalisent en moyenne moins de 500000 dollars/semaine.\n",
    "\n",
    "*Note: Après nettoyage du jeu de données n'y a plus de données pour le magasin n°12.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stores: 19\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Number of stores:', len(dataset_final[\"Store\"].unique().tolist()))\n",
    "sns.set(rc={'figure.figsize':(10, 3.5)}, style='white')\n",
    "colors = sns.color_palette('cool')\n",
    "sns.barplot(x=\"Store\", y=\"Weekly_Sales\", data=dataset_final, palette=colors, edgecolor='black', width=1)           \n",
    "plt.title('Weekly sales per store', fontsize=12.5, pad=16)\n",
    "plt.ylabel('Million $', fontsize=10, fontweight='bold')\n",
    "plt.xlabel('Store ID', fontsize=11, fontweight='normal')\n",
    "plt.yticks([i for i in range(0,3000000, 500000)], fontsize=10)\n",
    "plt.xticks([i for i in range(0,20)],[1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,''], fontsize=10, fontweight='bold')\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes hebdomadaires suivant les jours fériés\n",
    "\n",
    "Les ventes sont en moyenne meilleures lors des jours fériés. On observe un écart de 100000 dollars entre un jour normal et un jour férié."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8aWlpquc4JiaGBQsW0KdPH8zNzTVeR0nat2+PtbU1I0eOJDExkcOHD6sOyGrD3Nyc999/nwULFhAbG0tycjKzZs0qMTz8/Pxo2LAhI0eO5PDhwyQlJTFlyhTOnz9Pv379tK7laXr27Mm9e/cYNWoUiYmJxMfH88knn2BlZaXahS9O3bp1adOmDVOmTCEuLo7z588zevRotddXrVq1OHbsGGfOnCElJYVVq1YRGRkJqL8Ou3btysmTJ4mLi1N73efk5DBr1iyio6O5evUqR44c4c8//9RotzLfM4eHt7c32dnZeHt7FxqO+fn5kZWVpTYiAZg3bx6tW7dmwoQJtGvXTnWUP//BNWnShOXLl3P27Fm6dOnC0KFDqVevHqtWrSpyeGlmZsaUKVPYv38/GzduLLJOCwsL3nnnHapWrVqonoKsrKyIiIggLS2Nbt260aVLF65evcrKlSuxsLCgb9++tG3bltGjRxMQEMCSJUsIDQ2ldu3aat95yWdpaUlkZCRWVlYEBwfz3nvvkZGRwYoVK1SfQPPmzePVV19l6NChtGnThh9//JG5c+fi5eVVZI29e/dm1KhRLFmyhE6dOnHhwgXee++9Um0z/3k2NjbmvffeY9q0afTt25fQ0NBSraMklStXZvXq1ZiamtKzZ09Gjx7NwIEDNbpvcT755BM6duzIF198QefOnbl27Rpvv/12sbsdJiYmrFixgkaNGjFs2DACAwO5cOECq1atUjt28rzVqVOHyMhI/v77b7p3705wcDA2NjasW7eu2FFvQbNmzaJly5aMGDGC3r174+DggKurq2r++PHjqVGjBn369CEoKIi9e/cye/ZsALXXob29Pe7u7jRq1Ejt7xYUFMTw4cP59ttvadu2LZ9++il+fn4lfgA/yUgpzQ7kC+7999+nadOmjBgxQt+l6N2YMWNIS0sr8TSnIdq1axeenp5qx74GDBjAyy+/rDquJv5HURTeeecdhgwZUuxxOW090wHTF0VsbCxnz57lxIkTqnQWL6bvvvuOH374gdGjR2NhYcHu3bs5ePAgK1as0HdpBuXBgwfs2bOHgwcPkpWV9dRdJW2Ui/BYvnw5ycnJTJ069YX67YAobO7cucycOZP+/fuTnZ2Ng4MD8+fPx8fHR9+lGRRTU1OmTZsGwJw5c9TOkD0v5Wq3RQjx/Mj1PIQQWpHwEEJoRcJDCKEVCQ8hhFYkPIQQWpHwEEJoRcJDCKEVCQ8hhFYkPIQQWpHwAMLDw7XqHRIdHU27du1o3Lgx7du3Z8eOHWVQnRCGqdyHx9q1a1UX7C2NzZs3M27cOHr37s22bdsICAggNDRU7YLQQvyTlYsfxhUlIyODiRMncujQoSIvMFQSRVGYP38+ffv2pXfv3gAMHTqUI0eOcPjw4VJdUEWIF1W5HXmcOXMGU1NTtmzZgru7e6H5e/fupWvXrri5ueHv788333yjukJTcnIyaWlpdOjQQe0+3333HYMHD9ZJ/ULoW7kdebRq1YpWrVoVOe+3337j008/ZezYsbz++uukpKQwdepUkpOTmT9/PsnJycDj3ibBwcEkJCRQp04dhg4dWuw6hfinKbcjj5IsXbqUbt260aNHD+rWrYufnx+TJ0/ml19+4erVq6qmPWFhYQQEBLBixQqaN2/ORx99VKgjmhD/VOV25FGShIQE4uPj+fe//62aln/Zk6SkJNW1WoODg1XXXW3YsCEJCQmsXLkSX19f3RcthI5JeBQhLy+PgQMHFnmVdRsbG86ePQug1kgIHveHKapdghD/RLLbUoQGDRqQnJys1jfm+vXrzJ49m3v37uHi4kKVKlUKXfL//PnzqvYRQvzTycijCIMGDeLTTz9l0aJFtG/fnuvXrzNu3Djq1KmDjY0NAAMHDmTx4sXY2tri5ubGtm3bOHDgAKtWrdJv8ULoiIRHEdq0acO8efMIDw9n6dKlVKtWjVatWvHZZ5+plvnoo48wNzdn3rx5ZGRkUL9+fRYuXKjqoyvEP51cAFkIoRW9HvPIzc0lICCAQ4cOFbvMrl27aNu2LR4eHvTs2ZMzZ87osEIhRHH0ttuSk5PDyJEjuXDhQrHLXLhwgZEjRzJlyhSaNm3KqlWrGDx4MLt27cLc3LzU2/Ty8iI3N1d13EKIspKZmUnFihU5cuSIvkspM3oZeVy8eJFu3bqpdf0uyoEDB3BwcKBz587UrVuX0NBQMjMzuXjxolbbzcnJ4eHDh1rdV4jSePjwITk5Ofouo0zpZeRx+PBhvL29GTFiRInNhqtVq8bFixc5evQoHh4eREVFYWFhofXp0Jo1awKwe/dure4vhKbefvttfZdQ5vQSHr169dJouXbt2rFnzx569eqFiYkJxsbGhIeHU7Vq1TKuUAjxNAb9JbHbt2+TmZnJhAkT2LBhA506dWLs2LHcunVL36UJUe4ZdHjMnTsXR0dHevfujaurK1OnTsXc3JyffvpJ36UJUe4ZdHicOXMGZ2dn1W1jY2OcnZ1JT0/XY1VCCDDw8KhZsyZJSUlq05KTk6lTp46eKhJC5DO4r6dnZmZiaWmJmZkZ3bp1Y8yYMbi6uuLh4cHGjRtJT08v8teuQgjdMrjw8PPzY8aMGXTt2pV27dpx7949wsPDuX79Og0bNmT16tVYW1vru0whyj29h8e5c+dKvB0UFERQUJAuSxJCaMCgj3kIUZLFixfj7+/P4sWL9V1KuSThIV5I2dnZREdHk5eXR3R0NNnZ2fouqdyR8BAvpIcPH5KXlwc8vmyk/GZJ9yQ8hBBakfAQQmhFwkMIoRUJDyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEViQ8hBBakfAQQmjF4Js+nTt3jp49e+Lm5kaHDh04ePCgDisUQhRHb+GRk5NDaGhoiU2f7t69y4ABA3BwcODnn3/G39+fYcOGyQWQhTAABt30adOmTVSuXJlJkyZhZ2dHSEgIdnZ2nD59WkeVCiGKo5fwyG/6tH79+qcu9/bbb2NiYqKa9tNPP9GyZcuyLlEI8RQG3fQpNTUVNzc3xo8fz549e6hduzZhYWF4enqWcYVCiKcx6LMtWVlZLFu2DBsbGyIiImjWrBnBwcFcu3ZN36UJUe4ZdHiYmJjQsGFDQkJCaNSoEaNGjcLe3p7NmzfruzQhyj2DDg8bGxtee+01tWn29vYy8hDCABh0eDRp0qTQ1dQvXbpE7dq19VSRECKfwYVHZmam6mK2PXr04Ny5cyxcuJArV64wf/58UlNT6dSpk56rFEIYXHj4+fmxfft2AGrXrs3y5cvZu3cvAQEB7N27l2XLlmFra6vnKoUQBt/0ydPTk6ioKF2WJITQgMGNPIQQLwYJDyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEVgy+Y1y+q1ev4uHhodGyQoiyZ9Ad4wqaNGkSWVlZZVyVEEJTBt0xLt+WLVu4d+9eGVclhCgNg+4YB3D79m3mzJnDlClTdFCZEEJTBt0xDmDmzJl06dKFBg0alGFFQojS0vs1TEsSFxfH0aNH2bp1q75LEUI8wWBP1WZnZzNhwgQmTpyImZmZvsspc4sXL8bf35/FixfruxQhNGKw4REfH09qaiohISF4eHjg4eEBwKBBg5gwYYKeq3u+srOziY6OJi8vj+joaFXfGiEMmcHutri5uRETE6M2rXXr1kybNo3mzZvrqaqy8fDhQ/Ly8gDIy8vj4cOHeq5IiKczuPDIzMzE0tISMzMz7OzsCs23tbXF2tpaD5UJIQoyuPDw8/NjxowZdO3aVd+liAJSUlK4efOmvstQuX//vtrtkydPYm5urqdqCsvNzaVixYr6LqNM6T08ntYxTtN5ouykpKTg3LAh9w3oG74VKlTgjTfeUN1+8803DWp3z97entdee03fZZQpvYeHMHw3b97kflYWLUYsptqrhvF9G+VBNlk7v1HdbjM9CiNTwzkrd3H15/ouocxJeAiNVXu1Adb13fRdBgB5OfcoOA6qXs8F40pV9FbPky5VMNV3CWXOYE/VCiEMm4SHEEIrEh5CCK1IeAghtCLhIYTQioSHEEIrEh5CCK1IeAghtFLuviT28OFDjh07pu8y1Bj67zTOnj2r7xKEASp34XH16lU8PT31XYYaQ/+dhhBFKXfhkZeXh/PkBVS2N4zfaAAY5WbDuoWq2+5LNqJUNJzfafwVt4fL4XP0XYYwMHoNj9zcXLp27cr48ePx9vYucpl9+/Yxb948UlJSqFOnDp9++ilvv/32M223sn0DLJ0bP9M6nqv76m0lLBo0AnPD+Z1G1uWL+i5BGCCDbvqUmJjIsGHDCAwMJDo6mh49evDJJ5+QmJiow0qFEEXRy8jj4sWLjBw5EkVRSlxu69at+Pj40LdvXwDs7OzYs2cPO3bswNnZWRelCiGKoZfwyG/6NGLECJo0aVLscl26dOHBgweFpt+9e7cMqxNCaMKgmz7Vr19f7faFCxf4448/6NGjR1mUJYQohRfmS2J//fUXw4cPp2nTps98wFQI8exeiPC4efMm/fr1Q1EUFixYgLHxC1G2EP9oBv89j4yMDNUB0zVr1lC9enU9VySEAAMfeWRlZTFw4ECMjY2JjIzE1tZW3yUJIf7L4EYeBZs+hYeHk5KSwvfff6+aB2BmZoalpaU+yxSi3DO4kYefnx/bt28HYOfOnWRnZxMUFISfn5/q3/Tp0/VcpRBC7yOPkpo+/fLLL7ouRwihIYMbeQghXgwSHobApAKKkREAirExmOh9QCjEU0l4GIKKlVB8/VGMjVF83oGKlfRdkRBPJR9xBkLp0AelQx99lyGExmTkIYTQioSHEEIrGu223Lhxg5o1a6pu//nnn5w6dQpra2tat25tUBfrFULoxlNHHnPnzuXNN98kIyMDgKVLl9K3b19mz55NWFgY7dq14/r162VeqBDCsJQYHmfPnmX58uW0bduWKlWqcPfuXZYuXUrt2rWJjY1l27ZtZGdns2zZMl3VK4QwECXutgQHB2NkZMSRI0fo1KkTd+7cIScnh6ysLPr16wc8vqrXv//9b3799Vd2796tk6KFEPpX4shjzZo1KIqCl5cXI0eOpFKlSpiamrJ582a2bt1K9+7defjwIZ07d5bgEKKcKXHk4eDgwEcffcTSpUvZvn07ZmZmTJkyBRsbG44cOcLXX39NnTp1+Pjjj3VVrxDCQDz1bEtISAg9e/bk2rVr1KtXT/VTeAcHB+bNm8dbb72FmZnhNCgSQuiGRt/zsLGxwc3NTe0aGtWqVaNt27bPFBy5ubkEBARw6NChYpdJSEggKCgId3d3AgMDOX36tNbbE0I8Pwbd9CkrK4sPP/wQLy8voqKi8PDwYPDgwWRlZemwUiFEUfQSHhcvXqRbt26kpKSUuNz27dupVKkSo0ePpn79+owbN44qVarIdT6EMAB6CY/8pk/r168vcbmTJ0/i6emJ0X9/rm5kZETTpk05ceKEDqoUQpSkVL+qTUtLY+nSpVy9epVHjx6pphsZGbF69WqN16Np06fMzEwcHBzUpllbW5e4qyOE0I1ShceoUaM4duxYoen5I4Pn7f79+1SsWFFtWsWKFcnNzS2T7QkhNFeq8Dh79izNmzdn3rx5vPTSS2VVk0qlSpUKBUVubq6cGhbCAJQqPFq0aIGZmZlOggPA1taWmzdvqk27efOm2i98hRD6UarwqFmzJuvXr+fy5cu8+uqrmJiYAI93W2bNmvXci3N3dyciIgJFUTAyMkJRFI4dO8aQIUOe+7aEEKVTqvCIjIwEID4+nvj4eNX05xkeBZs+tWnThq+++orp06fTo0cPfvzxR+7fv0/btm2fy7aEENorVXjo4sdvfn5+zJgxg65du2JhYUF4eDgTJ05kw4YNODk5sWzZMipXrlzmdQghSlaq8Khduza5ubnExsaSlpZG3bp1adWqFaamploXUFLTJwA3Nzc2bdqk9fqFEGWjVOGRnp5O//79SU1NVR2HqFevHitXrpQm1EKUM6X6hum0adO4fv06oaGhREREEBoaSlpaGtOmTSur+oQQBqpUI48jR47QvXt3Bg0aBDw+dXvjxg22bNlSJsUJIQxXqUYeFStWJDk5WW3apUuXqFRJOpwJUd6UauQRGBhIeHg47du3x8HBgaSkJJKSkvjwww/Lqj4hhIEqVXgMHz6c3NxcfvzxR5KSkjA3N6d3794MGzasrOoTQhioUoVHhQoVCAsLY+TIkdy+fRsrKysqVJB2t0KURxq98+fPn0/z5s05cOBAkfONjIwICQl5roUJIQybRuGxZMkSKlWqxJIlS4qcL+EhRPmjUXisWbOGOnXq0LRp07KuRwjNGFcAIyNQFDAyfnxb6JRGz7iXlxcAL7/8cpkWI4SmjE0rYen+LndPxmDp3hpjU/m6gK5pFB6NGjUq8WphRkZGJCQkPLeihNCEVcu+WLXsq+8yyi2NwqNZs2ZlXYcQ4gWjUXh8//33ZV2HEOIFo/Gp2pKU9mxLTk4OkydPJiYmBjMzMwYMGMCAAQOKXHbXrl18/fXXXL9+HWdnZ7744gtcXFw03pYQomxofKq2JKUNj9mzZ3P69GlWr15Neno6YWFhvPLKK7Rp00ZtuQsXLjBy5EimTJlC06ZNWbVqFYMHD2bXrl2Ym5trvD0hxPOn8ana5yUrK4uNGzcSERGBi4sLLi4uXLhwgbVr1xYKjwMHDuDg4EDnzp0BCA0NZe3atVy8eJHGjRs/t5qEEKWn8alaY2Nj8vLynnmDiYmJPHz4EA8PD9U0T09Pli5dSl5eHsbG//uhb7Vq1bh48SJHjx7Fw8ODqKgoLCwsqFu37jPXIYR4NhqFh4uLC5988kmxxz5Kc6o2MzMTKysrtWZONWrUICcnhzt37lC9enXV9Hbt2rFnzx569eqFiYkJxsbGhIeHU7VqVY22JYQoOxqPPGrVqqX6stizKK4LHFCowdPt27fJzMxkwoQJuLu7s27dOsaOHcumTZuwtrZ+5lqEENor1anaTp06PfMGi+sCBxTqBDd37lwcHR3p3bs3AFOnTqVt27b89NNPcg0RIfRMo/BYtGhRifONjIz4+OOPNdqgra0tt2/f5uHDh6qf82dmZhbZie7MmTO8//77qtvGxsY4OzuTnp6u0baEEGVH4/DI/3q6oiiF5pcmPBo2bEiFChU4ceKEajfo6NGjNG7cWO1gKTzuUJeUlKQ2LTk5Wc60CGEANP4poqIo1KtXD29vb3x8fHBxcSn0ZteEubk5nTt3ZtKkSXz55ZfcuHGDFStWMGPGDEC9Y1y3bt0YM2YMrq6ueHh4sHHjRtLT0+nSpUuptyuEeL40Co9vv/2WuLg44uLi+PHHH1m/fj0WFhZ4eXmpwqQ0xo4dy6RJk+jXrx8WFhYMHz6c1q1bA+od49q1a8e9e/cIDw/n+vXrNGzYkNWrV8vBUiEMgEbh0apVK1q1agVARkYGcXFx7Nmzh927d7Nv375S/6rW3NycWbNmFdnf9smOcUFBQQQFBWm8biGEbpTqCiqnTp1SjUCOHz+u+tKYg4NDmRQnhDBcGoVHSEgIhw4d4u+//wbA3t6erl274u3tjbe3t9oXu4QQ5YNG4RETE4ORkRF2dnZ4e3tjY2MDoOrbUpqzLUKIf4ZSnW25fPkyly9fLjRPwkOI8kej8Ni9e3dZ1yGEeMFoFB61a9cu6zqEEC+Y0n/LSwghkPAQQmhJwkMIoRUJDyGEViQ8hBBakfAQQmhFwkMIoRW9hEdOTg6ff/45Xl5e+Pn5sWLFimKXPXfuHD179sTNzY0OHTpw8OBBHVYqhCiOXsKjYNOniRMnsmjRIn755ZdCy929e5cBAwbg4ODAzz//jL+/P8OGDePWrVt6qFoIUZDOwyO/6dO4ceNwcXHB39+fgQMHsnbt2kLLbtq0icqVKzNp0iTs7OwICQnBzs6O06dP67psIcQTSnU9j+ehNE2fDh8+zNtvv42JiYlq2k8//aTTeoUQRdP5yONpTZ8KSk1NpXr16owfP57mzZvTrVs3jh49quOKhRBF0Xl4lKbpU1ZWFsuWLcPGxoaIiAiaNWtGcHAw165d01m9Qoii6Tw8StP0ycTEhIYNGxISEkKjRo0YNWoU9vb2bN68WWf1CiGKpvPwKNj0KV9xTZ9sbGx47bXX1KbZ29vLyEMIA6Dz8CjY9ClfcU2fmjRpUuhq6pcuXZLriwhhAHQeHgWbPsXHxxMbG8uKFSvo27cv8HgUkp2dDUCPHj04d+4cCxcu5MqVK8yfP5/U1NTn0jNXCPFs9PIlsbFjx+Li4kK/fv2YPHlyoaZP27dvBx5fwWz58uXs3buXgIAA9u7dy7Jly7C1tdVH2UKIAnT+PQ8oXdMnT09PoqKidFWaEEJD8sM4IYRWJDyEEFqR8BBCaEXCQwihFQkPIYRWJDyEEFqR8BBCaEXCQwihFQkPIYRWJDyEEFqR8BBCaEXCQwihFQkPIYRWJDyEEFox+I5x+a5evYqHhweHDh3SQYVCiKfRy/U8CnaMS09PJywsjFdeeYU2bdoUe59JkyaRlZWlwyqFECXReXjkd4yLiIjAxcUFFxcXLly4wNq1a4sNjy1btnDv3j0dVyqEKInOd1uK6xh38uRJ8vLyCi1/+/Zt5syZw5QpU3RZphDiKQy6YxzAzJkz6dKlCw0aNNBhlUKIp9H5bktpOsbFxcVx9OhRtm7dqrP6hBCaMdiOcdnZ2UyYMIGJEycW6iQnhNA/nY88CnaMq1Dh8eaL6hgXHx9PamoqISEhavcfNGgQnTt3lmMgQuiZzsOjYMc4Ly8voOiOcW5ubsTExKjdt3Xr1kybNo3mzZvrtGYhRGE6D4+CHeO+/PJLbty4wYoVK5gxYwbweBRiaWmJmZkZdnZ2he5va2uLtbW1rssWQjzBoDvGCSEMl8F3jNN0nhBCt+SHcUIIrUh4CCG0IuEhhNCKhIcQQisSHkIIrUh4CCG0IuEhhNCKhIcQQisSHkIIrUh4CCG0IuEhhNCKhIcQQisSHkIIrRh806d9+/bRqVMnPDw86NChA7t379ZhpUKI4uglPAo2fZo4cSKLFi3il19+KbRcYmIiw4YNIzAwkOjoaHr06MEnn3xCYmKiHqoWQhRk0E2ftm7dio+PD3379gXAzs6OPXv2sGPHDpydnXVduhCiAJ2HR3FNn5YuXUpeXp7adUy7dOnCgwcPCq3j7t27OqlVCFE8g276VL9+fbURxoULF/jjjz/w9fXVVblCiGLoPDxK0/SpoL/++ovhw4fTtGlT3n777TKtUQjxdAbb9Kmgmzdv0q9fPxRFYcGCBWq7NkII/dD5u7Bg06d8RTV9ypeRkUHv3r3Jzc1lzZo1VK9eXZflCiGKofPwKNj0KV9RTZ/g8ZmZgQMHYmxsTGRkJLa2tjquVghRHJ2HR8GmT/Hx8cTGxrJixQrV6djMzEyys7MBCA8PJyUlRdWiITMzk8zMTDnbIoQB0EvflrFjxzJp0iT69euHhYVFoaZPM2bMoGvXruzcuZPs7GyCgoLU7t+lSxdmzpypj9KFEP9l0E2fivrWqRDCMMhpCyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEVgy+6VNCQgJBQUG4u7sTGBjI6dOndVipEKI4Bt30KSsriw8//BAvLy+ioqLw8PBg8ODBZGVl6aFqIURBOg+P/KZP48aNw8XFBX9/fwYOHMjatWsLLbt9+3YqVarE6NGjqV+/PuPGjaNKlSpynQ8hDIDOw6O4pk8nT54kLy9PbdmTJ0/i6emJkZERAEZGRjRt2lTt+qdCCP3Q+ZXEntb0qeDV0TMzM3FwcFC7v7W1NRcuXNBq2zdu3MDU1JT/mz+eexVMtXsA5VBe9n3s7e25uPpzLsnzppEH//cXN3L+2c+VzsOjNE2filu2pOZQJalUqRJGRkbYVCvc4kGUwMIcakjLi9LIzK1Y6LX7T6Pz8ChN06fili2uOdTTHDlyRKv7CSEKM+imT7a2tty8eVNt2s2bN6lZs6ZOahVCFM+gmz65u7tz/PhxFEUBQFEUjh07hru7uy5LFkIUwaCbPrVp04a///6b6dOnc/HiRaZPn879+/dp27atrssWQjzBSMn/WNeh+/fvM2nSJGJiYrCwsCA4OJj+/fsD4OTkpGr6BBAfH8/EiRNJSkrCycmJyZMn06hRI12XLIR4gl7CQwjx4pMfxgkhtCLhIYTQioSHEEIrEh5CCK1IeAghtCLhIYTQioSHEEIr5T48nJyccHV15fz586pphw4dwsnJiYULF+qxMnWtWrXS+MtxV69excnJSfXFO31zcnLC399fbdqRI0dwcnJizJgxGq0j/28ybtw4ABYuXIiTkxObN28ucvmoqCicnJz49ttvn614USyd/6rWED148IDPP/+c9evXY2Jiou9yhAZ8fHwwMTGhYcOG+i6l3Cr3I498p06dYtWqVcXO37BhA+3atcPd3Z327duzbt061Q/28j8Fp06dio+PDz169FB9Us6YMYMPP/yQxo0bExgYSEJCAsOGDcPNzY2AgAASEhKAxz/6++abb2jRogWurq68+eabLF++XKPas7OzmTx5Mj4+PrRs2ZIdO3aozc/NzWXChAn4+vri6upK69atiY6OBiA4OBgnJydVHfB4lOPp6UlOTk4pnsGSPXz4kCtXrqj+3bhxo9AysbGxdO3aFXd3d1q3bs23337LgwcPilzfwYMHmT9/PmfPngUgPT2dQYMG4eHhQdeuXTl37pza8teuXSM4OBhPT0/c3NwICgri9OnT5OTk4OXlhbe3t+qX3qmpqTg5OTFgwIDn9vj/iSQ8gAYNGmBlZcWCBQu4cuVKofkbNmxg/PjxPHz4kD59+mBqasqkSZMKXfV906ZNdOrUiS5duqimrV69GktLS7y8vDh9+jTvvfce9+7d46233uLChQvMmDEDgG3btrFkyRLs7e0JDg7GyMiIOXPmkJqa+tT6Fy9ezA8//ECtWrUICAgoFIIrV65k/fr1NGnShA8++ID//Oc/jB8/npycHDp27AhATEwMAGfOnCEtLY3WrVtTqVKlUj2PJUlPT6d169aqfyNGjFCbv3//foYNG0ZGRga9e/emZs2azJ8/X/X8PM2IESP47bff8PX1xd3dvdA1cb/44gsOHjxIx44d6dq1K6dOnWLmzJlUqlSJd999lzt37vDnn38Cj0MMoEOHDs/hkf9zyW4LYGVlxZAhQxg5ciRffPEFH3/8sdr87777jgoVKvD9999ja2vL0KFDadWqFcuWLSM4OFi1XM+ePRk1ahTweB8doFmzZnz11VckJyfTpk0batSowbJly3j06BG//PIL165dA+Ddd9/FwcGBl19+mZSUFBISEkhPTycjI4NXX321xPq3b99OxYoVWbVqFVWrVsXDw0PtMfTv3x8/Pz/s7OxITEwkLi6O06dPc/v2bfz9/alcuTIxMTF8+umn7Nq1C3j+bxwbGxu++OIL1e1Lly4xf/581e2VK1eiKArffvst7u7uPHr0iPbt2/PDDz8QGhpa4rrT09M5ceIEjRs3Vh3jMDU1ZfXq1aplvv76azIyMrC2tubkyZP8/PPPZGRkANCpUyf+/e9/s3PnTnx9fdm1axdmZmaFjtMIdRIe/xUQEMC2bdvYs2cP1tbWavPS09OxtrbG1tYWAAsLC+zs7IiPj+f27duq5ezt7Qutt06dOsDjSxHk3zY1NcXU1BQTExPVRZ/v3r3LggULiIuLw87OTrVL9OjRo6fWnpmZibW1NVWrVgXgtddeU5t/48YNZs6cSXx8PE5OTvz999/A412JypUr4+/vz+bNm0lKSiI2NhYbGxt8fHyeut3SMDc3p02bNqrbT17VLS0tDUB1UNjExARnZ2eSk5O5fPlyievOzMwE1B/3k8/ByZMnmTlzJhkZGbi7u5OXl6d6bps1a0bt2rWJjY3l448/5vjx47z77rtYWFho92DLCdltKWDy5Mm89NJLhY4ZvPLKK9y6dUv1SXXv3j1SUlKwtLRUu/pZUdesfPICR8UdkJ0xYwa7d+9m+fLlbN68mX/9618a112rVi1u3rzJnTt3gMef6gWFhYVx+PBhdu7cyYYNG6hfv77a/E6dOgEQERHBhQsXaN++faG6y1rt2rUBVMcw8vLyOHfuHEZGRtSqVavE+7788ssAJCUlqaYV/P+9e/cYNmwY8PhYSXh4uNrfysjIiA4dOpCZmcncuXPJy8uTXRYNyMijgJo1azJmzBg+//xztenvv/8+U6dOpW/fvvj7+xMXF8edO3cIDQ19bmdn8kcgixYton79+qxfvx6g2AOGBXXq1In58+fTv39/Xn/9dbZs2VLkur/88kuqVKnC3r171dbt6+tLzZo12bRpE/B4FKZrvXv3Zv/+/Xz00Ud07NiRU6dOcenSJbp37461tTUXL14s9r62trb4+vryxx9/MGTIEGxtbVWPBR6HQ15eHikpKcycOZOkpCTu3LmDqen/rm7esWNHli5dSnR0NNWqVaNFixZl+nj/CWTk8YTAwED8/PzUpvXp04cpU6ZgamrKmjVryMnJYerUqQwePPi5bfeTTz7Bw8ODY8eO8fvvvxMYGAg8PoD5NIMGDeKDDz7g+vXr7Nixg88//1wt1MaNG4ejoyP79u3j9OnTdO7cWW3dxsbGqsCwt7encePGz+1xaapVq1YsWrQIW1tbIiMjycjIIDQ0lAkTJmh0/zlz5tCqVSsOHTrE6dOnCQsLU82rXLkyU6dOxcrKiqioKAC8vb3VRpP169fH1dUVeHz86Z9+5fPnQS4GJIDHuyxz585l+PDhqiF+eTNo0CB+++03vv/++1LtNpZXsttSzh07doz9+/cTGRlJhQoVVKOS8mTbtm2qEZ+9vT3NmjXTd0kvBNltKecyMzOJiIigcuXKTJ8+XXV2qDw5efIkGzZswMnJiblz56ram4qSyW6LEEIrMvIQQmhFwkMIoRUJDyGEViQ8hBBakfAQQmhFwkMIoRUJDyGEViQ8hBBa+X87nj9AugTsZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Holiday_Flag</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.253356e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.347476e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Holiday_Flag  Weekly_Sales\n",
       "0           0.0  1.253356e+06\n",
       "1           1.0  1.347476e+06"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(2, 3)}, style='white')\n",
    "colors = sns.color_palette('cool')\n",
    "sns.barplot(x=\"Holiday_Flag\", y=\"Weekly_Sales\", data=dataset_final, palette=colors, edgecolor='black', width=1)\n",
    "plt.title('Weekly sales depending on holidays', fontsize=11, pad=20)\n",
    "plt.ylabel('Million $', fontsize=9, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.yticks([i for i in range(0,1900000, 200000)], fontsize=10)\n",
    "plt.xticks([i for i in range(0,2)], ['Normal day', 'Holiday'], fontsize=9.5, fontweight='bold')\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()\n",
    "\n",
    "weekly_sales_holiday = dataset_final.groupby('Holiday_Flag')['Weekly_Sales'].mean().to_frame().reset_index()\n",
    "display(weekly_sales_holiday)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes & température"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(6, 4)}, style='white')\n",
    "colors = sns.color_palette('bright')\n",
    "sns.scatterplot(data=dataset_final, x=dataset_final['Temperature'], y=dataset_final['Weekly_Sales'], hue=dataset_final['Holiday_Flag'], palette=colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes & prix de l'essence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(6, 4)}, style='white')\n",
    "colors = sns.color_palette('bright')\n",
    "sns.scatterplot(data=dataset_final, x=dataset_final['Fuel_Price'], y=dataset_final['Weekly_Sales'], hue=dataset_final['Holiday_Flag'], palette=colors)\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes & inflation\n",
    "\n",
    "On observe que lorque l'index d'inflation dépasse 200, les ventes hebdomadaires ne dépassent jamais les 2,5 millions dollars.\n",
    "\n",
    "Au contraire, c'est lorsque l'index d'inflation est au plus bas (140) que les ventes hebdomadaires atteignent des records de plus de 2,5 millions dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(6, 4)}, style='white')\n",
    "colors = sns.color_palette('bright')\n",
    "sns.scatterplot(data=dataset_final, x=dataset_final['CPI'], y=dataset_final['Weekly_Sales'], hue=dataset_final['Holiday_Flag'], palette=colors)\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ventes et taux de chômage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lZgj8cVL3w8csNs8X79AIeGux+vIet/8fuBFs/JgKC0k4RKbUVLh6F6avh66TYOAsOPQvRDwwdWRCGEdZb9i9ULUxz6DRqEqsr7UHK/mLmSXiGqxoDKe/huQ4SIiCgwthbVuI+g+Afaf1P/zCTYg2w2WVB3Fw9pr+8T0njBdLYSMThCLTuZvQ8g31C5dh816YPRxGvATFi5kqMiGMw9oaGlSDw6sg6L76XSjnDd7uUNzJ1NGZkZRE+HshJERmH4u4Dld3Qf1BeOVwjNjGWlWzNTc21qrmSpKe/TrebkYNp1CRfF0AcD8KRs3TTjYyvLcKQs1w6lMIQ9BooEwJaFQd2tWHymUk2cgmPgIubNc//s9GSIolwF8lcbr0DDTPF29vd3jtWd1jVlbQpp5x4ylMJOEQgFo2+fus7rH0dP1jBSkmDq7eURu2zt1Q1R6FEGZIYwX2zvrH7YuDlQ2+nvC/qdmXoqqUgRmDoZiDYcPMD0d7mDoIqpfXvm5lBV9/IPt4noYZTmgJU9C1QepRKamG/fzB4Won++c/qr0koKa2/zcVKpXO+bGhERAeDalp4OECvtJsTAjDcvaGpm+pDaOOHlCmCaSnws39aj9H0zFgY4+jDTzXBM59BT8fhNuh8GxD8K8IpUuY+ovQr1xJ+G2+2svx2xHVU+iFFuq/5pgkWQpJOASgpozrVYGTl3WPN6lpuM+dmAxLv4cVW7WvH7+ozuzvXax+0R+Xmgqnr8Lrs+HMw01eFXxh1UR12sDR3nAxC1GQ0tPhTpiq8xAVA+V91HKDm4upI8tBta6EvLSHS7GV+eZwCRxtU+ndMQw/28t4emf9wSjmoGY0xr5iwljzobSXunVsbOpICg9JOASgquhNHQQ9p0FikvbYiJcgPknXowpGUBgs/l732NU7cD1Id8JxI1idlY9LyLp2PQg6jYejq6BuZcPEK0RBSk+H01fg+Xe1C0v16QBzR2qfmDEnQcmlGLTRl9+PaTKvLfjBiTe6lWVKRStkolE8zuR7OCIjI5kyZQqtWrWifv369O7dm2PHjum9/+3btxk+fDj169cnICCAhQsXkppq4Pn+IiAmAeZ9A1s/hr4doGpZaFUXVk9SG+iOnTfM5w2PVu/sYuP13+fK7ezXUlPh69+0k41Hxz7+EmJyeE4hzMV/ofDs29mrWG74Tc36mWtJ7V8PoZVsZPjsByvO39TxAFHkmTzhGDduHCdPnmT+/Pl8//331KhRg8GDB3PtWvaD0MnJyQwePBiAb775hmnTpvG///2Pzz77zNhhFzrF7OHsdXjpfbUXol9HaFgdpq2FDz6HWhUN83n/PquOH+a0/FFRxx6O+ET4K4cz/kcv6D5xI4q24Ptq+e3kZfVCbw7vVU5d0V/rZtFm9fthbu5Fqtj0WbIl+0ypECZdUrl58yYHDhxg48aNNGjQAIAPP/yQffv28eOPP/LWW29p3X/nzp3cvXuX7777DldXV6pWrcr9+/eZO3cuI0aMwM7OzhRfRqFQ2gve66e6Yv5vl/ZY5dJQqVTBf87QCHXktpafav29Ylv2+5T3gYo6Pre9nbq+V08VwzIlwEF+HMRDKSlw8gr0/Qiu3FHXPF1hyVi1qdGUx14v3tI/Fh2rvx6EKaWkqtj0iXwAKWkg26jEo0w6w+Hu7s6qVavw9/fPvKbRaNBoNERHR2e7/7Fjx6hVqxaurlnVZJo2bUpMTAznzxtozr+IsLZWSylTBmrPNrR6BnZ8qnsPxdNKTFbHXzf/CS38oWc7VQMhQ00/9blL6/jctjbwZnf9z/1Bf3A35w13JnIvUr2zvxsGaWmmjsZ4boVCu7eykg1QtWdem5614dhQ0tJUt9GTl+DIObX36NF3/7X89D/W2x2ssq9amJyHC3Rppn/81XbgJKc5xGNMmnAUL16c1q1ba81M7Ny5k5s3b9KyZcts9w8ODsbHx0frmre3NwBBQUGGDbYI8HaHd/vAmS/g+Gp1lO37GVDBALMboJKGiqXUH+SBs9Xn3z4bvpkG22bDsnFqL4k+FUvBusmqKmAGKyu1+bVhDcPEbKmiY2HPcbWh1u8VaDAEFmxSSwxFwbd7dO/3AfhwjeHK9yclw75/oPEwaDgUmo2E2v1h9U9Zn9OjuDqGqcvoHmBjhlv77e1g7Ku6Z4bKlYQOjYwfkzB/ZvWjfOLECSZPnkyHDh1o06ZNtvGEhASKFy+udc3eXr0dT0yUTmMFwd5OHS01Bh8PlRz0n6nW0pd8r24aDdjbqsQnJy7F4JW20LIunL+hNtfVrqgSFxcpw67ljxPQ/YOsj0MjYOIyOHoePntbLS8UVknJcDCHwnVnrqpkxBAzYrdCVJL36LJIfCKMWaSS6faN4M49WDUBpq1TvYtAHSV9s7uabUx7Qo0cU6noC4dWqLh/+Esl/v07wvje+hOoJ4lLgJAINfvp7KhmNzVmOMMj8sdsEo5du3Yxfvx46tevz6effqrzPg4ODiQlae9Eykg0ihWTVxhL1KERTO4Lc/+XtYHPszhs+Vj1sHgSR3uVIBkrSbJEd8NU90tdNv0BHwwo3AmHrQ3UrqAKT+lSwddw+302/q5/D8aHq6F+VahXVfUwGvGSmmFMSlYzdRt+g7v3YfjzhontaVlZQbVy8PlEdXxXA3i55f97eTsUpqxV37PkFPWGZPZw6NpczQIJy2cWCcfXX3/Nxx9/TKdOnZgzZ47ezZ8+Pj5cunRJ61poaCgAJUvmM6UWJlXCDd7tC4M6q3XuYg7qD01pL+nMWVCiYtS+DX1OXlIvyIWVRgMDnoN53+qumDttkGESrqRkOH5J//jl26q+TSlP+GwcvDpV+9RMtXJqD1Mxx4KPrSA5O6rb0wgJh1emwJFHtuIFh8Og2bD+PbW/TGY6LJ/JE46NGzcyY8YM+vXrx/vvv48mh5+qRo0asXXrVmJiYnB2VnX8Dx06hJOTE9WrVzdWyKKAOTuCc+knlzAX+WNnq/5Y6ytf755DS4zConxJtT+o90cqAQM18zH9dcNV0bWzhUY14Ke/dY9XK6eOozvYQ4fGcO5L2HFEvdNv1wBqVdC9YbowuhmsnWw86t0V0LYelMnFjKcwb/lOOOLi4jKXMTKOq7Zt2xY/P79cP8f169eZNWsW7du3Z/jw4YSFZVW+cXBwwN7enqioKFxdXbGzs+PZZ59l4cKFjB07lvHjx3P79m3mz5/P66+/LkdihdDD01XtFfjtSPYxBzvD1VgxJw72ENgATq1VeyYSksHvYflwJwPOIPQKhNlfQYKOmhQzhmQtFRSzV11p3yxTsJ//fpQq6mdtBSXdzbMdPOhvqQBqpiNGz4ZfYVnyPGl97do12rdvz6pVqwAyE4A5c+bw4osvcvz48Vw/186dO0lOTub3338nICBA6/bxxx9z8uRJAgICOHlSFVuwt7dn9erVpKWl8eqrrzJ9+nRee+01Ro0aldcvQ4giw80Zlo5VtUkeZWMNm2dA6SLS/dLGRm1mbFZbvWOu4GvYZAPUzMpv87UblTk7wsrx0KCq4T5vbLzaKNt1ElR8FfwHwEfrs1czNRc5zeTY2qhN5MLyadLTn9QnVNuoUaO4fv06c+fOpVq1apkJwkcffcS7775LVFQUX331laHiLXCBgYEA7N6928SRCGFY/4WqUyl7Tqjlq+ebq2lqKZBmWOnp6oX+XiQkp6pZlVKeYGvAF9F9p6HtW9mX0Vr4w6aPoKSZ9We5GQx1BupuRzDgOZUwS5dW85SX19A8T7AdO3aMWbNm4e/vz/79+3nw4AE9e/bE2dmZXr16MXr06LxHLIQwuLLe6ta9takjKVo0GjXDYax27Pci1KkkXW8lD5xRDQ7NLeEo7QW/fgqdJ2i3JGhUXe2zkWSjcMhzwpGcnJxZC+Ovv/7C0dExsyx5amoqNuZYpUYIIYqIB/Gq+6w+u45B01rGi+dx6ekQFqWO0Xq6qoTMxgYaV4fT61RdlDth6rhw+ZLmlxyJ/MtzdlC1alV+++03KlSowI4dOwgICMDGxobk5GQ2bNhA1aoGXJgUQgiRI2srdUJGX/2PEm5GDUfLf6Gw5U9Y/6tKNAZ3gRcD1NKejY3qnVTe58nPIyxTnhOOMWPG8MYbb7Bhwwbs7OwYOnQoAB07diQsLIwVK1YUeJBCCPORlqbeoYIq0mZtbdp4DCEiWn2Nicng6qz2XFjK1+ntrupWrP05+5iVlTqtYwr/hULHd7Sb1Y1ZBKt+hJ/nyLHXoiDPCUeLFi348ccfOXPmDHXr1qV0aVU8YcCAATRt2pRq1aoVeJBCCPNwOxQ27YU1P6mS2/07wWvP5r+UtTm6egeG/V9WJ2JPV/hkOHRrVTDlz+MTVaGra0FqWaGCL/h4FtzmXUd7+HCAOqVy/mbWdY0GvngPfE1wKik9Hbbv190Z9+w12HlUzXaIwi3Pp1Qe9eDBA0JDQylbtizW1tZYW8pbgEfIKRVRVMQnqmZtETGqk2cJt7yVjL59T71DvXBT+3rFUrBnkdqQauluh0LAG7ors343HXq0ebrnj4pRjeTGLsnqGOtgBysnqKWFguwBdCdMvZjvOKw2Zb4QoP5r6KPAuoRFwXPj4YSeyqtNa8FPc6TDsyUy6CkVgMOHD/Ppp59y9uxZNBoNmzZtYvXq1ZQsWZJ33303P08phDCgkHBV2nvJ91lr+wF1VNno3Pah2Xk4e7IBcO0ubN4Lb71s+eXo/7mqvwz8uyvVsVKfp5ghOHcDRs7TvpaQBAM+hqOroH4BThCX9lK3jo0L7jnzS0POPxtWGnUfUbjl+c/DwYMHGTx4MA4ODowfP56MCZJq1arx5Zdfsm7dugIPUgiRf8nJsHI7zPtGeyPh/n+gy0QIykWL+sgHaqOfPl/ugPvRTx+rqR38V//Ytbuq90l+xcTB7A36x+d9BwmFtOm1pysMzaEJ3YiXwE1mNwq9PCccCxcuJDAwkK+++ooBAwZkJhwjRoxgyJAhbNq0qcCDFELk3937KtnQ5eItuBH05OfQWKnKpPrYWBeO5lpVcigt7lH86UqDxyXCtTv6x6/cVvcprJ5rAg10zOA0rqkqv4rCL88Jx/nz5+nRowdAtkZrLVq04M6dHH6jhDCAu2Hq7P6py2oNPi3N1BGZl9gE3RUcM1z678nP4eoEI1/SPz7yJfAykxb36ekQ8QAexOb9sS3r6t+8OfYV8H2KmhAuxXS/4GZoXOPpu66as9Il4IeP4cv3oWUdaFUXNk6B72dAqSLSpK6oy3O+7uLiwr1793SOBQUF4eIi82LCOJJTVKnufjPhRrC65u2u+lS0a1C4/3jnhaO9ehHV1UAMwC+Xezha1oXWz6h3+R0bqxmN3ccgPBo6Nsl+/5Bw1YL92z0qhl6BqpCTIVrBZ7gVAj/8Bf9cTadXkxD8/VIp5u5GcTenXD2+bAnYOQ9emJzVVRbg1XYwuOvTHY11tIfxveF/u7Xb0NvaQKfGMKGXqp9RmJUuAX06QNcWas9G8dz9s4hCIs8JR2BgIAsWLKBq1arUrKn6Oms0GoKDg1mxYgVt2rQp6BiF0OlGEDw7Lmu3P0BoBHT/AA6tgIbVTRebOfH1gCFdYemW7GOlvKBSqVw+jyesngRf7IBlP6hjsT3bqed+vPlW0H0YPEdtNM0w7xt4s7s6sunllu8vR6+bwdBhHCwbFkQft614nV4AhyJILv8sqYFTSC5embhkW5wd9b+w29hA05qqq+zVO2qmpIafqnbpUQDvpSqXhl/mwpA5anNq72ehX0f49RCMXgTPNlQ9bsr7FI4lKn1cJdEokvJ8LDYqKor+/ftz6dIlvLy8uHfvHn5+fgQHB+Pr68uGDRvw8LCcWrRyLNYypabCh6thzkbd412bw9cfFuwxQ0t2NwzGLobv/8y6VrEUbP8EapTP3XP8FwrPjoUrj62alisJfy7RrsWx7mcYMlf38+xdrGZLClJSMnywGur6hPJKxEDsrj+2w9XGgbDeR3hthT81/WBUN3U6x1Tt2u+GQXQsHLsIA2dp9z1xd4G/lkJNP9PEJkRe5OU1NM97OFxdXdm0aRPTp0+nUaNGNG/enGrVqjFhwgS2bNliUcmGsFzxiXD4vP7xf67mvG+hqCnlpWo9nPsKfl8Ax1bDX0tyn2wA/HIwe7IBahnj2z1Ze2fuRcLCzfqfZ+kW/WW38+teJGzdBy3LXsuebACkJOC+/x3a1oxiyffwzOv6a0IYQykv1XJ9yJzsTdYiHsCwuYXj1I8Qj8pXfm9nZ8err77Kq6++WtDxCJEr9nZQvVxWNcjHVfBVa+Yii7uLulUrl/fHRj6Ar3bqH9/wGwx8ThUTS0lV7971iXig2rQX9H6F2hXA/fZWvePW138nsHMkH+BKYpJ6sd+1EEq6F2wcufXPNbUPSZeD/8L9KFU6XoinlZQEd8PVsmNyiprd9PEwfhfeXCUcS5cuzfUTajQa3njjjXwHJERu2NqoafGV23W34f5wILg5Gz2sQsvKKucEwd5OFW8CldR0bgYrtuq+76vtVKXTguTlBm3qQaptDv/o1nakpGqoUgam9wzG3zcStwQNxHqCk/GPScQn5Dye8sjG0oRECA6H8AcqkS7haph9MKLwiY1Xe4QGfQJxD3/mbG1g1jAY9By4GzGplYRDWCw/H9j8EQyYlbV8YmcLc0fCM5VNG1thU9xJbfj885Tu8VEvZZ0+cbCDt19Rsx4P4rTvV64kdGhU8PHZ28ILLSA6rBtuhz7UeZ/46n04fceNP8btw3f/UDh6UQ2Uqg8vrYGS/mBlvPYM9XJorF2xVFaZ73uRsGIbzNmglhJBbYj++sOc64YIAapnT6/p2m/MklNgwjJ4pgq0q2+8WHKVcFy4cMHQcQiRZ06O6p30P+tVn4+UVHXssqSHLKcYQrPa6hTFrmPa11v4Q/vHkoiKpeDwSpiyRu2tsLVRjd4m9jZcozc/X4iwKU1CwHQc9k/VHnQtx61aU+iquYXvpnaQ9shaxt0TsDoA3jgNHpUME5wOJT1UEvf46SErK1j+jjoVlJYG3+2BaWu173PsArR/G/YvgzIljBaysDDJKbD8B92zwAAzv4D6VY03G1zge7SvXbtGxYoVC/pphdDJzlYdISzvY+pICj9fT9Vt9NQVtVySlq7KVTesBr6PrUhYWam9ImsmwadvqCOeXq4F1xFVH/cSbqQFjCa1Zmesji1HExtKXOWXOUtbftzvxQeu72knGxmSYuHUl9BmitFmOdyc4YP+KmH7+Ct1cqVRDZgxWO1PAnVtxhe6H/9fqOrNIgmH0CchCS7mUNjvepCaNTPbhCMyMpKFCxdy5MgRkpKSMkubp6enExcXR1RUFOfP53B8QAhhsXw8oZOnKkWdng4OT5hJci6mbsZk5eQOTg2h9CpISyU50Y7Eq9C2ZjD2xw/pf+D1vdDiHbA33qJ2CXe1p6VtPUhKUcXqXB/54x+fpJZU9PnnqmGWqEThUMxe1ZXRt7n+mSrGLZCY52Oxs2fPZvPmzZQvXx5ra2tcXFzw9/cnOTmZ6OhoPvroI0PEKYQwI/Z2T042TM7KGmzscHVSpbQD6jmASw5Vztz8wNrI2/YfKuGuqnC6PvZO0942+7VHVStr2LjM2YM4uHpX7Ss6el7aGuhibQ0DO+ueWdRoVBE+Y9YqynPCsW/fPkaPHs3y5cvp2bMnPj4+LFy4kB07dlCtWjWuXLliiDiFEOKp2BV3g9bv679DszFgY+A1nzzy9YSxL+se8ygOdYro5uh7kWr/QY2+0O4taDoCGg2Dw+cgRc9R46Kqgg/sXqiq3Gbw9YTts42fsOY54YiOjqZePdXar1KlSpw9exYAJycnXn/9dfbu3VugAQohRIEp3VglHY/WDbeygRdWgEcV08Wlh60NDHtR1Th5NOQyJWDXAijnbbrYTOnHA/DpN9o9aUIjoP04tbdFZLGxgaa1VDXg0+vg5Fq1ofu5pmrjvVFjyesD3N3defDgAQB+fn7cv3+fyMhI3NzcKFmyJCEhIQUepBBCFAgnTwiYCPUGqNMpVjbgWw+cS4KdeTb48PGA+W/Cu33VsoGrkzrhUrqIbhYNuq9/I218Ivx2FIa/aNyYLIGPp7qZUp4TjmbNmrFixQqqV69OuXLlcHV15YcffmDQoEH88ccfuLubqGyfEKJQCr4PYVGQmqYqb5byUqdg8s2huLp5mt+Mhj6uzuomdTfUUc9bObyv/eeq8WIReZPnX9u33nqL+/fvM2nSJDQaDcOHD2fOnDk0adKE9evX06NHD0PEKYQoYlJS4OgFaDMG6g6C+oOh4VD4YV/2gmKi6LC3hao57D1oWst4sYi8yfMMR+nSpfnll1+4ceMGAIMGDcLLy4sTJ05Qp04dunXrVtAxCiGKoJshakNg3CMlwO9FwqtT4MAyeWEpqkp6wOxh0ENHQVl3F2hVwJ2IRcHJ18Skg4MD1atXz/z4+eefZ+rUqZJsCCEKRHq66kAbp6ffyIdrVEM5UTS1egaWjNU+0lnTD/YuNlwlW/H0cj3DkZqayq5duyhbtiw1a9YE4NatW8ybN4+rV69SrVo1Ro8ejZ+fn6FiFULkU2iEaglva6PeIRaE1FR1IuDwebj8n6qSWatCwVS+TEqGv8/oHz9zFWITwM3l6T9XoRATCnH3IDkBinmCiy/YmHuhlPzzKA5Du0KXZnA/Wi2zeLmZrvOvyJ1cJRyxsbEMHDiQs2fP8vbbb1OzZk2io6N57bXXiIyMpHXr1ly6dImePXuyZcsWSpcu/eQnFUIYXHg0HDwL738O529CpdIw/XVoW1+VGs+vtDQ4flEdQ8xonAeqxPzv89XneRq2NlCzAvx6WPe4n4/hy6RbjNDz8F1PCHmYodkWUyXaGww2SRdcY7GVtgYWJ1dLKmvWrOHWrVssXbqU119/HYAvvviC+/fvM3XqVD777DO2bt1K1apVWbZsmUEDFkLkTlKyWpZ4YTKcuaaa2128Bb2mqe6j+pYrcuNOGHR9VzvZALgZDEP/DyKecrnDygoGdVaVEnWZMiirO21hEhoB/16HI+fh6h2IedLm2MhbsLZNVrIBkBwHv78LF38yZKhC5FmuEo7ffvuNIUOGEBgYiI2NTeY1JycnunfvDoC1tTW9evXiwIEDhotWCJFrQffh3RW6x2Z+ASHh+X/um8FwP0r32J8nc+7/kVt+PvDDTO11ehtrmDlU9YcobK7cgc4ToM5AaDYCavSDCcshOKd/p7vHIVZPpas9UyD6riFCFSJfcrWkcvv2bWrXrp35cUREBFeuXKF169ZYP/IWpGTJkty/f7/goxRC5Nn9qOwzEBmSU1RCUiGH1iI5Pnd0zuMJSfl73kc52kOHxnB6PfwXomZsKviCt7vxKyQa2t0weG48XHskP0hNhVXbVSfPaa+rfQrZH3hM/5NG/QcpiQUeqxD5lauEw9rampRHCtQfP36c9PR0mjZtqnW/iIgIihUzcmtIIYROtk/47bZ/ij0QOfVgcHMuuHbXtjZQvqS6FWbXg7STjUct3aIqZ/rp2qvgXVvHxYecvM2uN4wo2nK1pFKlShWOHz+e+fGuXbvQaDS0bNlS6347d+6katWqBRuhECJfvNygop4ZjJIeT3daxdsderbTPTZjiKoGKnLv8m39Y3EJOey3KdsM7IvrHmv9Hjj7PnVswnQiotW+q2MX4MptiI41dURPJ1czHK+++ipTpkxBo9GQlpbGjz/+SJMmTahUqRIAiYmJfPXVV/zyyy9Mnz7doAELIXLH1xO+nQ5tx2gvrTjYwaaPoNRT9FXwKK76e1QvBws3Q1QMlPVW+yuea6L2Wojcq5zDqR5HeyjmoGfQtRy8/gd8/Tw8eDhForGCJm+Af++nrAEvTOlWCAz/FH47oj62soJe7WDOSMtN6HOVcHTv3p27d++yevVqEhISqFu3LnPmzMkcb9OmDZGRkXTu3JlXXnnFYMEKIfLmmcpwfA3sOAxHzoF/JXihheoy+rSvRT6e8F5/GNRF7a9wtLfcP4SmVqGUWjK5EZx9bFS3HJJDKyvVfG7EEYgJhsQYKF5GLac4SJESS3UvEvrNhP3/ZF1LS4ONu9TJraVjwdkCdy9o0tPT03N75+TkZB48eICHh/Zc7JdffknlypVp3rx5gQdoaIGBgQDs3r3bxJEIUfBCwmHSCnXUsnxJdZz1n6vw3UcQWB8cCm9tKItz+Ta8/CGcvaY+trKCfh3h46FqtkoUHf9eV6eVdLG2hvNfPX2tm4KSl9fQPPVSsbW1zZZsAPTv3z/btbS0NAYOHMhHH30k1UeFMIH0dNi8F77aqT4+cSlrrNv78O8XUCWHzZ/CuKqUgd/mw70ItQTm5ar2yhR3evJjY+LUYxwdVPt6YdlyOgqdmqqWMC1Rnpu35VZ6ejpHjhwhNtbCd7kIYaGC78PcjbrHUlNV19WJrxk3pqIuKgZS01STMY0m+3hJ97yV546Nh0v/wYwv4fRlqFgaPugPdStJ2XdL5u2mf0yjyV0Sao4MlnAIIUwrJQ3u5lAW59J/uq8/iIPIGPWHzctVSogXhKD7aj1+6RZITFJLJS8EqI22+ZWWBn+chJfeU7NZoPaA7DkOi8bA4K5qX42wPCU9oF4VOHk5+9hLLdXMlyWSLcxCFFKO9uqPlj7PNtD+ODUVLtyEwZ9A5V5Qoy+885mqKiryL+g+9J+pSsrv/weOXoAxiyBwrDqJkF9378Ow/8tKNh41YfnTVZIVpuXtDptnQINq2tc7NoaFY2SGQwhhZrxcYe5I9cL2OB8PaO6vfe16EDQdoWY4QPVeWbEVfj0kbb+fxomLsOdE9utX78CXO2FyH/09Y3JyP0p/UpGUrP49/aQMh8Xy84Wf5qgTKxEPsvb0eOgpu2IJZIZDiEKsQTXY8rF2y/jW9eCPxxKIhESY921WsvGom8Gw+3j264VZSLg6LXLsgnrhTshnhfDEJFj1o/7x9b/kv+/Mk441Sy0Uy+ftDrUqQEAdqF7espMNMLMZjpUrV7J//36++uorvffZvn07EyZMyHZ99+7dlClTxpDhCWFxXIrBiwHQsLrasGhno7qsuj+2oTD8AfxyUP/zfLdHVRZ9vABVXILaUR+fqOoClPJ8ckl1c/fvdeg5Fc7fVB872MHkvqq8eAm33D1Haqr6nthYg5WOzaEZ0tMh13UJHuNZXH/tDidHmZES5sds/jRs2LCBhQsX0rBhwxzvd/HiRRo3bsz8+fO1rus6riuEUEp7qZs+NtYP14Xv6R53c8n+jvnOPZiyBjb8rprBuRSDSX1gcBfL3dR2MwTavQVhj3TCTUiCqWtVUbNBnXWfLsm8b6JKAFZth9NXoX5V+HCgeo7fjma/f7+OOZ9IyEkpL/jyA2g/Ts2kZNBoYN1kqd0hzI/JE46QkBCmTp3K4cOHc1Wv49KlS1SrVo0SJUo88b5CiNzxdoe3XoHh/6d7/M1uYPdIt9KwSBg0W3up5UEcfPC52vsx8TU93U3N3PEL2snGo6atg05N9FdTTU2Fv/6BrpPU/wPsPQlLvoevP4TQSDj1yKkDPx94vUv+9m9kaFQdTq+F1T/B4XNq2v3N7qqrrp0Ffv9F4ZbnhCMxMRF7+yeftdJoNJQqVQo7u5zP1P3777/Y2tqyfft2PvvsM+7cuZPj/S9evEi7dnq6Rgkh8q1LM3i2Iex6rOP56JehWnnta8Hh+vd1/N//1Dt3nd1NzdyjxdEq+KrvhwZ1IiQ2XiVT+ty9D31nZCUbGZJT4K1FsO0TGLMQEpOhXwfo3vrplz3sbFXxto+HqeUtRzuwfZhoxMSrmJ0d1RKLEKaW54SjRYsWdOnShR49elCnTh2997OysmLPnj1PfL527drlOoGIiooiJCSEY8eOsXHjRiIiIqhTpw4TJkygQoUKuf4ahBDZ+XrCl++rEtvf7lHHansHQnmf7JvVrupppQ7qRc5SKyH6V1Jf9+K3VKKw/YCqd/FaeyhdAv69BtZW6v8fFxqhTo7oEhwO1hr4ea4q/OXhUrB91TKXxIDoOLh4E2ZvgPM3oHZFeLcPVCunkg8hTCXPCcfrr7/Otm3b+O6776hQoQLdu3fnxRdfNMoSx+XLaj4yPT2d2bNnk5CQwPLly3nttdf48ccf8fKSzlFCPI2MtvUB+t9LqPvlsEdDowEnfd1NzVzTmrBqAqzYBgfOZF3/7Sg0rgGje0Dr0bBrQfYjp6lpOT93RoVRQ0pOgV/+hj4zsq5d+g9++Au+mQYvBYCNyRfSRVGV5xx71KhR7Ny5kw0bNtCgQQNWrlxJ27ZtGTZsGDt37iQ5OdkQcQLQsGFDDh48yLx586hduzYNGzZk6dKlpKWlsWXLFoN9XiGEtrLeut/lg1qasdRNo+VKqpMjjyYbGY6ch7thapZg3FKIfqRrQ2KS6mGib+miuJNxvidB92HEvOzX09NhxKc5V54VwtDyPalXv359ZsyYwYEDB1i0aBHx8fGMHTuWgIAA5syZ88S9GPnl4eGB5pFt4o6OjpQpU4aQkKco2SeEyJPSJeCX/1MFxB5VrwosGWu5lRCjY3Oum7F5L3RuBj/+nbW5NDVVVRCdsAymDtT9uKVjjXNqJDhcdy0VUMWj8lvzQ4iC8FSTa0FBQWzbto1ff/2VixcvUqFCBdq0acNff/3Fxo0bmT17Np07dy6oWPn222+ZP38+f/zxB8WKFQMgJiaGGzdu8PLLLxfY5xFCPFntCnB4pdrP8V8oVC+nZj5KWvAJ9bQ0tSyhT3Kq2i+RlqaqeYKaNeg1HcKj1QzJN9Ng5Ta4ckftm/hwANSpZJz6JDnV/MjNuC6pqeprjIgGOzso4apquQiRV3n+FYiJiWHnzp1s3bqV48eP4+DgQKdOnZg6dSr169cHYNKkSQwfPpxZs2Y9VcKRmppKeHg4Li4uODg40KpVKz799FMmTpzIW2+9RUJCAvPnz8fDw4Pu3bvn+/MIURSlpMCdMFVRMzgc6lVVCUNui1sBlPFWt8LCvTgM6KSOmOrSpRn8eUolFhmzOMHhKtkAWL4Vvv9TbTLt0lwlYu7Oxpvx8XZX+0QiHmQfK+GWt39bUEnGD/tg4vKs52xUHb54XyVTQuRFvk6pJCUlUbduXT766CM6d+6cOdvwKH9/f86d0/Nbm0tBQUEEBgYye/Zsunfvjq+vL+vXr2fevHn07t2b9PR0WrRowZdffpmro7pCCCUlBQ7+q2pGxMRnXW/9DHz1gf79GUVB52ZQtWz2broVS6kX24+/hC0zs+pxJD22bS00AhZ+l/Vxn/aGjfdRpTxV0a/uH6hZmAzW1rD+Pf01RPT56zQMnat97egFaPsWHFoh1UxF3mjS03X1GtRv7ty5vPzyy1SsWDHH+8XGxuLg4ID101S1MYLAwEBAlUYXoqi4EQT+A1Xthse92V01fbMvwm3p/wuFjb/Dul/UhsuXWqpkbO7/4L2+0KQmuDqr+14Pgpr9siceoO5zaq1xX5gjH6iYFm1WSVNNPxjdHSqUyttMS0g4tBmTPfHK8N106NGmICIWliwvr6F53jQ6ceLEJyYbAE5OTmafbAhRVB29oDvZAFjzs1omKMrKesP4XvDnEtUpd1xPqFlBvch2aJyVbIDaODtziO7nWTQm77MKTyM1FQ6dg9ZjVALUwl/VRWn5pjplk/aEo7uPSkjWn2yA7pM8QuQkV0sq7dq10zoZkhONRsOuXbueKighhGHdyuFQV3xizhsniwpr69xtgHW0Vz1WavqpnivX7qr//2iwOrVjzK6td+9Dv5kqyfj2sbqL/T+GIytzv+fG9uHXH6In+axeXvd1IfTJVcLRuHHjXCccQgjz17im/rFyJbN3hRU58ygOzzVVxcHik9T3z8PARb50CYvM2sD6uJBwdSw2twmHj4eqUPr2kuxjDnaq7LsQeZGrhOOTTz7J9RNGRemp7SuEMBuVS6uS12evZR+bM8K4ywCFiSmOi4aEw+17cPWOOqWy9l3VaE7XLNaTqqE+ysoKeraDf6+r5nAZ3Jxh6ywoV4hOJwnjyPMejuXLl+sd++WXXwq07oYQwjB8PeHHT+DVdlndSjN6qbSXd64W42YwdJ4IjYdB7+kQOBamr4eV49U+lEe5Oue92mlJD5gzEs5/Dds/UftZTq6F5rWlRLrIuzz/yCxevJjU1FTefPPNzGsZLeb37t2bY0M3IYT5KFcSPp8AHw9VHUyLF1MzG7J6ahkiH8DwT7Vb3oNKQkYvhMl9YdT8rOvLxuVv5srNWd2qln2qcIXIe8IxY8YMpkyZQlpaGmPGjGHDhg3Mnz8fKysrpkyZQu/evQ0RpxDCAJyLqZuwPKGR8PtR3WNX7qjmcpVKPax2OhBqlDfuBlYhHpfnhOPll1+mWLFiTJw4ke3bt3Pnzh2ee+45Jk+ebJSOsUIIISBWz7HmR+1bCsUcwUWSSmEG8rUKl1Fd9K233qJ169bMnz//yQ8SQghRYNycwc5Wd8ExUEtmJY3QME6I3MpVwjF58mSd18uVK8eff/7J8OHD8fBQB9Y1Gg2zZs0quAiFEEJk4+MBb3SDBd9lH+vcDLzdjB6SEDnKVcJx+PBhvWO+vr5cvpy1a0nqdQghhOE52sOE3mBvC4u/V5VjbW1U75aPhkhHV2F+cpVw7Nmz58l3EkIIYVQlPWDKQBj6PDyIBycHNfMhhduEOcr3Seq0tDQuXbpEaGgo9evXJyUlBTc3twIMTQghxJPY26kTKUKYu3wlHNu2bWPevHmEhoai0WjYvHkzS5YswdbWlnnz5mFnV4TbTAohhBAimzxXGv3ll1+YNGkSTZs2ZcGCBWR0t2/fvj1//vkny5YtK/AghRDCXKWmqnb2Jy/BP1fgbpipIxLCPOV5hmPFihX06tWLadOmkZqamnm9R48ehIeH89133zF27NiCjFEIIYwqNl4V1kpOARdH8NVTofNBHOw4rCp6ZjRNK+8DX38AjWqoTZymEhIOSSlqU2leS5oLYQh5nuG4fv067du31zlWt25dQkJy6HsthBBm7lYwjPgUqveBGn2h1Wj4+W+Ijs1+33+vQ69p2h1abwbDs+PgRrDRQtZyPwo274V2Y6FSL9VfZes+uK+ni6wQxpLnhMPT05OrV6/qHLt69SqenlJpRghhme6GQacJsHEXpDycwL12F16YDIf+1b5vVIxqlKZLYhJ8uUMttxhTfKLq7NpzKly4qT7/uRvQ4wP4aqeKSwhTyXPC0blzZxYvXsyOHTtISlI/vRqNhrNnz7Js2TI6depU4EEKIYQxnLsBF2/pHnvnMwiJyPo4NgHOXtP/XEfOQ4KeKqCGEhKuPwn6cDUEhRs1HCG05HmFcezYsVy6dImxY8diZaXylX79+hEXF0fDhg156623CjxIIYQwhj9P6R87d0MV18rgaK+ao+nbJFrLDxxsCzC4XAiJ0D+LEZcA9yLBz8eoIQmRKc8Jh52dHatXr+bAgQMcPHiQqKgoXFxcaNy4Ma1bt5ZKo0IIi1XWW/+YSzHtbqvuLjBlELR/O/t9ra1VMS5rI3dntXvCX/QnjQthSPn+8WvRogUtWrQgMTERW1vbzNkOIYSwVIENVFKRomPvxaiXwOex0x71qsDCMTBpRdbMgquzOqViimJc3u5Qykv3rEt5HyjhZvSQhMiUr4Tj2rVrLF68mL///puYmBg2bdrE5s2bqVixIv369SvoGIUQwihKl4Cts6D7B9pdWNvUgzd7gO1jSyTuLjCkK3RtDnfuqWOwvp5QyhNsTDCbUMoLNs9QJ1PiE7OuOznCd9PVuBCmkudfifPnz9OnTx88PT3p2rUr//vf/wCwtrZm1qxZODs7061btwIPVAghDM3BDtrVh3NfwdHzEBoBzWqrpRZ9tSwc7aGCr7qZmkYDDarCP+vhl4Nw7CI0rgHPNYVyOSwXCWEMeU445syZQ+3atVm7di0AGzduBOCDDz4gMTGRL7/8UhIOIYTFsrcznwQiP2xsoGIpNSMjhDnJ88aLU6dOMXDgQGxsbLJtEO3cuTM3btwoqNiEEEKIIi/oviooF3Tf1JE8nTwnHPb29iQkJOgci4yMlMZtQgghRAEIi4KvdkCLUVCxJzQfCV/8qo43W6I8JxwtWrRg8eLFBAdn1e3VaDTExsaydu1amjdvXqABCiGEEEVNfCKs2AoDZ6vZDYBbIfD6J7DsB+2aMJYizwnHhAkTiIuLo1OnTvTp0weNRsMnn3xCp06dCAoKYty4cYaIUwghhCgygsNh1te6xz7ZoF311lLkKuHYvn079++rxSNfX1+2bdvGgAEDSE9Pp1y5csTFxdG1a1e2bNlC2bJlDRqwEEIIUdjdy6FqbFKyOkFlaXJ1SmXixIloNBqqVq1K8+bNadmyJW+88QZvv62jxJ4QQgghnor9E7ZDPmncHOUq4fj+++85evQox44dY+vWraxbtw57e3vq169PQEAALVq0oHr16oaOVQghhCgSvN3U8eZrd7OP+fmocUujSU9PT8/rg65evcqRI0c4fvw4x48fJygoCA8PD5o1a0ZAQIBF1eEIDAwEYPfu3SaORAghhMhy6jK0GwtRMVnXijvBH4vgmSomC0tLXl5D85VwPO7w4cNs3LiR3bt3k5qayvnz55/2KY1GEg4hhBDmKC1NnUw5cAaOX4R6VaFlXVU11lzal+XlNTRf1f7Dw8PZt28fBw8e5PDhwwQHB1OsWDFatmxJQEBAfp5SCCGEEI+wslJNAP18oU8HU0fz9HKVcKSmpnLy5En27dvHvn37uHDhAgC1atXixRdfJCAggGeeeQYbU3QrEkIIkS+pqZCYrHrImMs7ZlF45SpDaNKkCbGxsfj6+tKsWTOGDh1K8+bNcXV1NXR8QgghClh8oiomtfonOHMNGlWHAZ1UC3s72yc/Xoj8yFXCERMTg5ubG61bt6Z58+Y0a9YMZ2dnQ8cmhBCigKWkwJ+n4IXJaoYDYNcxmP8d/D4fWvibNDxRiOUq4di8eTP79u1j//79bNq0CYA6deoQEBBAQEAAderUMWiQovBLS5MpXSGM4e596PNRVrKRITFJXf97OZTyMk1sonDLVcJRu3ZtateuzciRI4mJieHvv/9m//79bN68mcWLF+Pm5kbz5s0za3KULFnS0HGLQuJOGJy4CBt+BzdnGNxFnT33lNU6IQwi+D5Exuge+y9UNQaThEMYQp53eTo7O9OhQwc6dFBbZq9evcqhQ4c4fPgw06ZNIyUlhXPnzhV4oKLwuX0PXngXTl/Juvb5j/D2qzC5ryQdQhhCSmrO46lpxolD5E5yMsQnQTEHsLE2dTRPJ9/HSiIjIzl58iQnTpzg1KlT/Pvvv6SlpcnyisiV1FRY85N2spFhwXfQ61lJOIQwBF8vdSolQUefDo/i4CW/d2YhLkFVGf3sB7hwExrVgCFdVZVRS93Ym+uE48aNG5w4cSLzdv36ddLT06lSpQrNmjVj8ODBNGrUCCcnJ0PGKwqJkAhYuV3/+JqfoGE148UjRFHh4wFzR8KYRdnHPnsbSnkaPyahLSkZfjsCL0+BjNKcf52GJd/Db/OhpYW+r89VwtG0aVOioqJIT0+nVKlSNGvWjFGjRtGsWTM8PeWnU+RdWprK4PWJilW/aBqN8WISoihwtIfX2kPNCjB9LVy5A7UrwrRBUKsCSDkl0wu6DwNmZSUbGZKSof9MOLDMMvfZ5LoOR8Zx2HLlyhksmJUrV7J//36++uorvfeJiIhg5syZ/PXXX2g0Grp06cLEiRNxdHQ0WFyi4LkXh67N4X+7dI/3bW+iZCM1BeLvg8YKnEqYIAAhDM/dBdrWg2dmqZocTg7gKpUOzMbtexATr3vsVgiERRXihGPRIh1zbwVsw4YNLFy4kIYNG+Z4vzFjxhAfH8/69euJjo7m/fffJy4ujjlz5hg8RlFwnBzgwwHw44Hsv1j1qpioMVHEDTi+Bv79DmwcoeloqPocuJQyQTBCGJ67i7oJbZExEB6tNtC6OUMJN+N+/idt3E2z0I29Jp88CwkJYerUqRw+fBg/P78c73vy5EmOHDnCL7/8QqVKlQD46KOPGDJkCOPGjZPjuBamcmk4+jnM/AJ+/FslISNegoHPmSB7j7gOq5pBTEjWta1DoHwr6PkNuPgaOSAhhClcugVjFsPvR9XHz1RRe1vqVQF7O+PEUNZbfa5EHRt7vd0td2OvyUst/fvvv9ja2rJ9+3bq1q2b432PHTtGiRIlMpMNgMaNG6PRaDh+/LihQxUFzNoaqpaF5ePhzBdwZBVM7gNljL2SkZIIfy/UTjYy3PwLgk8bOSAhhCncCIaWb2YlG6BaxLcZA5dvGy8OHw/4dFT26xoNrBxvmcspYAYzHO3ataNdu3a5um9ISAi+vtrvNO3s7HBzcyMoKMgQ4QkjcHJQN5OJC4Mz3+gfP74aKj0LVib/dRHC4JKSIThc/beYg+W+uOXHjwfU/ojHJafAzC9h9SRwNsJ2QUd7eO1ZqF0BZn4FV29DncpqGbpqWcutymxRf0Hj4+Oxs8s+p2Vvb09iYqIJIhKFgybnZMLKVt1HiELuzj2Y9w18/pM6RVbWG2YPh46NVY2Owiw+EX49pH9832mIjjVOwgHg5gKtnoFNlSEuUX1el2LG+dyGYlF5koODA0lJ2Re1EhMTKVbMwv8lhOk4lYD6A/WPNxoOVhZe4k+IJ7gXCYNmw6LNWUfW/wuFvjNg+wHL3aiYW7Y2UDqH5Vxvd3UfY3N1Bl9Py082wMISDh8fH0JDQ7WuJSUlERkZibe3t4miEhbP2hYaDgePStnHanaHEjWNH5MQRnY3DHbr2Qo3eaUaL8xsrGFUN/3j7/Yx/mmVwsaiEo5GjRoRHBzMzZs3M68dOXIEgAYNGpgqLFEYuJWD1/+AF1eBXyuo3BH6/QLPLwNnSWZF4XdKR5uBDKER8CDOeLGYSkVfWDw2ew2gQV2gTT2ThFSomPUejtTUVMLDw3FxccHBwYG6detSv3593n77baZNm0ZcXBxTpkzhpZdekiOxeqSnqyIyF2+pgjG1KkB5H7ULWjzGtSw0HAq1e6olFDsp0y+KjpLu+sesrIx3JNSUXJ1hQCfo2Aj2/6OaprWqq5Y0CvseFmMw64QjKCiIwMBAZs+eTffu3dFoNCxdupTp06czYMAA7O3t6dSpE5MnTzZ1qGYpPR3+uQod31HrsxnqVIKts1TiIXRwkL8souipUV7tE9A1k/FiQNFZTnB2hMpl1E0ULE16+uPV2ouWwMBAAHbv3m3iSAre7XvQZJg64va4rs3hy/elnHFBSEkF0qUHhbBsKSlw+Dx0Gq/d56imH/w0R96gCN3y8hoqfyILsZvBupMNgJ8PqlkPSTh0C7qvEgl7W7U7XZeQcPj3Bqzarv5YD+6qqhL6Sj9DYYFsbKBJDTizHo5egJsh0Lg6VCkrP9OiYEjCUYiFRugfS09XZ7uFttAI+OlvVeTnZrDa8zJ7GDT31+45ERwOo+bBtv1Z137Yp9pGb5xatIolicLDxgb8fNVNiIJmUadURN5UyWEN0qUYuMqeSC3RsTBnIwydq5INgH+vwwuT4Ye/Hi6dPHT8gnaykWHfP/DbEePEK4QQlkQSjkLMxwM6NNI9NrkflJJpUi2hkXDiIrzbF8b1VCWEM0xYllWHICYelnyv/3mWbNFdHlkIIYoySTgKMS83Vft/6PNgZ6uuubvAp2/A653B1tak4ZmdxCS1dPLbETj0ryoCtHqSOg4YGQPhD9T9UtPUcTl94hOf3F5amK/UVLh2B3Yfg81/wJlr2qe8hBD5I3s4CrnSJWDBaJjUBxISwakYlPZUnVpFlpvB0GWiKuWc4e+z0K4+zHsD3lwAdg9/W1ydoG97dU5fl57twEtO1lqklBT4+1/o9p5KMjO82g7mjoCyUu5HiHyThKMIcLSHCrIJTK/kZPjsB5Vs2NqoLo1dm6uNtalp4OGiEg8v16zHdGoKlUrD1Tvaz+XrCQOfk4TOUl0Lgs4T1CzVo77bA7X8YHJf+bcVIr8k4RBFXmgUbPhNLZ1s+BC274c+M1R7bmdHtbTy+UTt47FlvWHXAlj9E6z7RSUmvZ+F0d3Nu17BvUi1VyU6BjxdVTGnR0/fFHX7/8mebGRYtBn6tIcKpYwbkxCFhSQcQjw0sTd8/iPsfOSUSUw8zN2oeitMGQAO9llj5UrClIEw4kVIB0q4Zu2VMUc3gqDXNFVjIcMLAbD0bSgtx3gBuPSf/rHwaEhMNl4sQhQ2smlUFHnertC3AzSuoZ1sPGrRJgjSUUTNxlrV3CjtZd7JRkg4dHtfO9kANZszaXnRaMyVG3Uq6x+rWEqWU4R4GpJwiCLP1hbe6J7zSYSEJIiK0T9u7oLDVV8dXb7dk3ORuKKkSmkoU0L32Ds9Vf0aIUT+SMIhBGp5pHr5nO9TzME4sRhC0H39Y2lpMsORoYS72q8TUCfrmkdxmD0cfDzBU04fCZFvsodDiIfKeoOfD9wIzj4W2NCyu2XmVGrd2lreuWfw9VAnj9rWVzMaSSmQnALHL6plN1v5iylEvskMhxAPlfJSXTFLPzal7l8RPp9g2ac5fDygYXXdY307QEkP48ZjruztoKU/DOykEo17kVCtnKpjI/1xhHg6kq8L8YgafvD3crh2F26FqPLm5bzVdLol83aH76bDoE/gz5PqmpWVKlI2c4g6/isUOztpYCaEIUjCIcRjypTQv3HQ0ILuq3fVsfFqCaekR8Etd5T3gU3T1fM/iFMzNt7uUFya+AkhjEASDiHMxPkb6ujq5dvqYysrGNwFpr2ulkQKgqerugkhhLHJHg4hzMB/ofDs21nJBqjTI5//CKu2q/0EQghhySThEMIMnLuhamXosuC7nI+1CiGEJZCEQwgzcPGW/rHoWFV4TAghLJkkHEKYgZp++sfcnFXHXyGEsGSScAhhBqqX138yZlIfKGXhx3KFEEISDiHMQJkS8PsCqFMp65qtDYzrCQOek6ZhxpaUBDeD4eBZ+PuM+n/pFCvE05FjsUKYiaplYec8VScjLkEdX/XxsOweLpboQRz8cgiGzlX1UED9Gyx/B15oIXVLhMgvSTiEMCPe7uomTOfKHXhtuva1uAQY8DEcWgGNapgmLiEsnSypCCHEQ/GJMO8b9f9lSkCN8uBglzX+yQaITTBNbEJYOpnhEEKIh+ITwckets5SxdjCo6F2RbhyG6asVYXZ4hLASZa5hMgzSTiEEOIhOxt4thH0mqZd+6RNPfjqfdh+QBrdCZFfknAIIcRDoRHQb2b2UvJ7T6qZjikDpCaKEPkleziEEOKhPSf09635cgfYyFs0g4qJg1sh6hhydKypoxEFTRIOIYR46GaI/rHoWEhLN14sRc3l2zB4DlTuDZV6Qd+ZcOGmamIoCgfJ14UoAhKTICgcwiLB3g683aBkAbW8L0xa1gW+0j1WvTwUk+UUg7gRBAGjICwq69rPf8O+03Dsc6hU2nSxiYIjMxxCFHJhUbB0C/gPgCbD4ZlB0G4snLkK6fKOXUutClCljO6xeW9IkmYIaWnwzR7tZCNDdCys2AZJUuW1UJCEQ4hCbtcxmLhcHefMcOGmSjpu5bCEUBSV9lLVXl9uk1VOvlxJ+H4GNKv1lE8eew+CTsE//4Nrf0DUf5LxoZKKHw/oH//lEEQ+MF48wnBkSUWIQiz4Pnzwue6x8Gj4+yyU9zFuTOauvA+smQSzh0NSChQvBqW8nvJJo2/Dpr5w48+sa8W8YMBO8HkGrIruez9bW/Aorn/c3VndR1i+ovtTLkQRkJgM14P0jx89b7xYLIlzMahYCqqXK4BkIzkOdk/RTjYA4sJgfXuVjBRhTg7w9iv6x8f3BncX48UjDEcSDiEKMVsb/W3vAepWNl4sRVZMCJz+WvdYfDiEXSA0Ak5fgV8OwqnLEBxu3BBNrU4leKNb9ut92kPz2saPRxiGLKkIUYj5esKUgTDs/7KPOTtC63pGD6noSUmAVP27HlMjbjNyPWzdl3XNvyL8MAsq+Bo4tug7EHIGruwE13JQrSu4lAa7Ygb+xNq83GDa6zDkedi2H1JS4MWWUM5bjRlLYrJahgy6r/bw+HiAr4fUXyko8m0UohDTaFRL9at3YN63kJKqrpfygi0fQ9kcZj9EAbF1BkcPNZuhwz372vx2VPvamWuqvPpPc6CEm4HiirwJX3SAsEtZ13aMh57fQdXOYGvcGu4exdWtTiWjftpMkTGw5U94a3HWBmt3F/jqA1XaXirMPj1ZUhGikCvhDu/1h/Nfw19L4ejnqs16w2pZJzGEARUvBe2m6xxK963PgVvltU4QZTh2QZVaN4ikONj1vnayAZCeBt/1ggd3DfSJzde/12HoXO3TXBEP4MX34Eaw6eIqTCThEKIIcHZUmyBb+EP9qlC6hJr9EEZgZQ3+veC5BeDgpq5prKDWy8T3+IGhy0vqfeiDuJyfOi0NbofCuRtw7a4qDZ4rsaFw5ls9T5oCN/fn8okKh6gYmL5e91hqKqzYmjU7KPJPllSERYmKURvqjl9UFTPrVVHrrMUsrF14cooqwWAnx/2KBicvaPIm1OwOiQ/A1gGcvLkb5pJjz5Ccjovej4Zt++C9VXAvUs1WdW8Fc0eq2iE5SktWiYU+sWFPeILCJS4RLv+nf/zMNYhPBBfjbm0pdCThEBbjXiTM2QALvsu6ZmMNy9+BV9oa549BcjKERkI64OGS90QnJFxN3a7YDklJMKgLNKpeAEcvhfmztgG3clqXvN2gdyBs3JX97t1bg7e77qdKT1cnWobOzbqWmgqb/lBF3X79VG0Y1su+OJSoDvcu6B73a5njl1LYODlALT/9hfDqV5U9HAVBEg5hMf4+q51sgJrmHDoXGlQz/BHPWyGwfCus/VntZu/eGt7rq/o85GZ5IiQcRi+E7x8px/Dj3yrh+H6mWuYQppeQCHfvw/5/VHIZ4K9OixiirHlxJ5gzEhzsVTfalFQ1U/HaszBrGLg5637c3TCYvFL32Jlranklx4TDuSR0WaLqgDyuYltwK5/nr8WSFXeCDwfCr4ezj9nawNDn1Zsb8XRMvocjLS2NxYsX07JlS5555hmGDh3Kf//pn9vavn071apVy3a7fbtoF88p7O5HwSw9TbUAlm8z7Brrf6HQYRzM3ah6PjyIgy9+Vb1JruVyf93pq9rJRoajF9RRQKlybXoJifDbUajRFwbNhknLocUoePlDuHPPMJ+zlBcsHAPnv4ITa9R/l76d86xXbII6uqnPiUv6xzKVaQqD/4RS9dXHDq7Q5gPo8bVKSIqYGn7wv6naSZ6vJ/z6f+An1XgLhMlnOJYtW8bGjRv55JNP8PHx4f/+7/8YMmQIP/74I3Z2dtnuf/HiRRo3bsz8+fO1rnt4SFelwiwxWb2r0+fGXXWfR9+FJCZBdBw42D39csufp1T77MdFxsCizfDxUPUiYG+ruypiQiJ8tkX/8y/7QfXv0DeFLozjThi8PEUtTzzq77Pq33nmEMPsu3FygIp56IhqZ6t+rhOSdI+X887Fk9g7g18r6L8TkmNBYw3OPmrppwgqXkztgWla6+GeGCt1JLmUl2ywLigmneFISkpi7dq1jBkzhjZt2lC9enUWLFhAcHAwv/32m87HXLp0iWrVqlGiRAmtm7Wc7yvUiheDJjX1j7drkNU6PDkFLt6Ct5dC4Fh4ZQr8cVL1DsmP+ETY+Lv+8W37YM8JaPUmvDgZfj6o2sA/KjVd/4sDqLG0tPzFJwrO78eyJxsZVm6DEEMdU80jH3cY2Fn3mLMjPFMlD0/m5KWWUFzLFNlkI4ONjdpw26Ca+h7Kaa6CZdKE48KFC8TGxtKsWbPMa8WLF6dmzZocPXpU52MuXrxIpUomqgwjTMa5GEwZoLtuhKszvNIm6w/D6StQb7B6gfj3Ovx+FJ4dq1q053QiQB9rq5xnSJwc1Ua9q3fgwBl44V34ZIN2h0snB+jfUf9zvNoOPF3zHpsoWDktm8TEm8/RSAd7tX+o9TPa14s7wY55quutEObGpAlHcLCqpuLrq12/19vbO3PsUVFRUYSEhHDs2DGef/55AgICGDVqFNevXzdKvMK0qpSB3+dD1bJZ15rWgr+WZHU8vRehyngn6phN+Gh9/t6h2tnCKB19HjL0fla7LDWoza2Pf67Wz0ANHXvxSnrAsOfV5jShX3C4qjfx7/Wc9y88jbb19Y/VrqgSR3NRugR8Ox2Or4a178Iv/wen1kHj6lKKW5gnkyYc8fHxANn2atjb25OYmJjt/pcvXwYgPT2d2bNns3DhQhITE3nttdcICyta58aLIkcH9aL9x2I4+yVc+Bq2z1YvBBndvSNi1AyHLunpcORc/j53TT94vUv2601rQbWycERH19UDZ7Q/LuOtjivOGKISpFJeMK4nHFgGfobumWHBklPg8Dm1POY/AOoMVMtXf57KeZkqP6qXg1oVdI8teNP89tjEJqifsz9PwY7DEB6lZmKEMEcmzYMdHNTbhaSkpMz/B0hMTMTRMXsd/4YNG3Lw4EHc3d3RPJw/X7p0KW3atGHLli0MGzbMOIELk/LxUDddnrTcapXPFLuEG8weBkO6qmOxcYnq6OLtezB4ju7H6DpGV9YbJr0GgzqrBMjLVYp/Pcn1IGj7lvas1bW76tTQiTX6E4T8KOWl+pdMX6dqYyQlQ7VysHC0Or5sTi7egpZvqhNcGRZvVideBj4nRaqE+TFpwpGxlBIaGkq5clkFcUJDQ6lWrZrOxzx+GsXR0ZEyZcoQEqKnYosoUtxdVJEeXccCNZq8vWgkJqmp+/vRquhPCTe1cbVJTZUsRMaoTaK6+mBoNPrbaltbP6FGwkPp6Q+LjKWBZ3GwLYKJSXIKrNque4ksJRX+73+q8FtBFmUqV1IdS/1woOpa6lxMf4JrKpExqsnYo8lGhreXwHNNJOEQ5sekSyrVq1fH2dmZw4ezqq1ER0dz7tw5GjVqlO3+3377LU2aNCEuLqthQExMDDdu3KByZQNXfRImlZyiekZcD4JQ3U03AdXKetVE3RVAZw3LffGmsEjVXbX2AGg8TE3ld3xHvasElVC4u8CycWqj3uPmjoSSTzH9fuee2uTabwb0nQFT1qqvvaiJjVdHUvU5cl4dfS5ojvaq9kLlMuaXbIA6cfW77n31pKeromVCmBuTJhx2dnb07duXTz/9lN27d3PhwgXefvttfHx86NChA6mpqdy7d4+EBPUWslWrVqSlpTFx4kQuX77MmTNnGD16NB4eHnTv3t2UX4owoLthaoq79gCo3AsC34bfjug/ceJfQU21j+upjre9GKC6pA59Pnfv+tLTYet++HC1OhKb4fQVaPcW/PfIZFpNP7Vpb9ogCKgDvQLh4HK1ZOKiIxHJ7de7eDNULg3PNYVnG0LLOvDVjrwlHXfuwaF/VcvtU5cN2HnUgBzsoEIp/ePlSoJj9nI9hd6TjlDHZ98CZ9YiHqjZxCc1qxOWzeR7mceMGUNKSgoffPABCQkJNGrUiDVr1mBra8vt27cJDAxk9uzZdO/eHV9fX9avX8+8efPo3bs36enptGjRgi+//BJ7eyl0Xxjdi4SBs2H3saxr527AcxNg6yx4vkX2x9jYqBMts4ZCdLx60crL6YK791WCo0twuEo8yj4sxGhlpbqwvtcP3npFFf6yf8oXwBtB6ojsi+9p14To0x4u/aeOPD5p38eFW/DceO3eEE1rwTfT1D4SU0lN1X20WR8He3j7FfhGR68RgMl9dc8wFXauzqpOxKnLusdbPWPUcPItIhpOXIaZX6hkum5lmDJQ7Ztxzr6NT1g4TXp60S6oHBgYCMDu3btNHInQ5cRFaKRnL7CfD+z/DHwLuObA1btQtbf+8amD1B9FQ0hKhr9Oq+UbXRaNUTU7cjotcScMWr0BN7KfLOeFAFj/Hrga8UU6JQVuhsDmvWoJpE4ldZTYzyd3G2aD7qvltIP/whc71IustTV8MlzNJOmq7FoUHD4HrUer5cZHDeoCc0fk3GnWHMQlwIptMGGZ9nWNBn74GLo2l6JbliAvr6Emn+EQIieHdRw3zXAjWK3fF/SJUntbNcOga0MeqGUUQ9EA3+3RP776J92zOo+6c093sgHw4wFVq8SYCcfRi+pEScbm2q37VGG0n+dC67r6ZzwexMGpKzB5hXoXXKYETOgNjWuopbGS7qroWlH1TGU4tlrNDuw7rZLQSX2gXX3zTzZANTN8b1X26+npMOJTOLxK/ZuLwsPkzduEyEkJN/1j1tZgZ4CU2ddDvbDp4ups2OORaek594wJDn9y18qc9mqkp6vaDcbyXwj0+Sj7SZ6kZHhtOtwK1f249HTYfRzajFYzG4lJqpLriE9V0uVRvGgnG6CW7mpXgNWT4Ojnqiher0DzqxWiz/Wg7LMzGYLD9Sf8wnJJwiHMWoNq+vdE9GgNJQzwx9XaWpUhH/6idt2O0iVgz0K1UdFQ7O2gQ2P9481qPXnPQvlH9pe0qguvtFVHhUHtZzHm7EZoJNzUM9tyL1K9y9XlThi8uUD32LIf1CyNUJwdVf0QLzdTR5I3T6qGai2vToWOLKkIs5CaqnbWO9hrv4Mv7QlbZqp6F4/2sahSRq3hG2pjWUkP9fzjXlUvfhlT+MboHPlCAMz8Mvs7PGtrmPb6k0/a+HjCO73U1PqeE2rj6Est1UmaS7fVuLEkJec8ru80ReSDnMuXn7kGVcrqH89Jaqr6N70epOpZVC+nZgWK6l4QUylfUs1SxeqojFqptPQWKowk4RAmlZQEN0JU9c5jF1SZ8uEvqFLfjvZgZwdt6sG5r9VJlVshagd+rQqGb1BV3EndKpcx7Od5nJ+P6g/zxgLYe1Jdq+mnClxVy8WLrJsztK2nnaRt+kNd371QzXIYi0dxlSDpOu5ob6c/+XlSXxnnfBa1SkmBQ+fU9yYyJut6n/Ywd5R51tworHw9YP1keHWqWkLL4GAHX76fu+J4wrJIwiFMJj0dDp6DTuOz3gn/cRKWbYWtH0OHRmra1cEOKpWCSi+YNFyjql4eNs9QsxxpaeDqkvtCYnfDoPf07J1NI2Ng0Gz4bZ5hlqJ08XKFqQNh/LLsY+N76Z9VcHNWm0N19agp5gAV87lT+L976uft8ZmVDb+rZPednnk7tivyz84OOjWB0+tg+VbVcblZbejfCSr4mDo6YQiScAiTuRumqmg+Pu2emgr9ZsLJtYbdL2Hu3F3yN81/PUh/AaV/rkJYtPESDk9XtZzj6wWLNsH5m6qg2Zvd1f4SfTMKicnq+HH/j7WXlqytYcnY/Ddt23tS/zLOp9/Aa+3lZIQxFXNQs5ULRquNwY72kvAVZpJwCJMJi9J/IiMyRm0oLMoJR37FPaHKZIqekwGGUqEUFHME/4rqxd7eDrzdci4zHxUL45bCmkmq0Nupy6rbbmAD9W7Y2UHNSOTVpf/0j92PMv73Rii2Nk9eRhOWT/6JhcmkPqE88+NLAiJ3qpZRJ1R0lb821ebIku556y3j5KA6wr70nioUVqWM2uOzcJP6ut7rl784mtXSP1aptHH3twhR1EjCYaFi4uHuPdiyD+6EQudm6g9zaQuaDi7hpl78Ih5kH3O0L/gKok8SEwchEarYlJVGfT9LelheiWVvd3j7VZj3TfaxRWPUSRtz5+OhWqx//qNaBvrnatZY5dL5n/mqX1X9jty5l33sk+HGPcEjRFEjCYcFio2HbftgwKys3d3Ltqr+Azs+tZxlCF8P1W219/TsY/PfVOPGEh6tXtw+WJ01M2BtDfPegH4d1SZGc5Sero543otQRZS83dWL9YTeUK8KzPhCneypUwlmD1fXrCygvkExB1U+PjJGnbDJ4F8Rvp+Z/xMMZbxhzyIY8gnse9hR1aM4fDJCnYbKkJamik+lpalTNq5m+u8vhCWRXioW2Evlym2o0U/3lPmgzmpTnaOF9LJ7EKfW6Kevh3PX1RHUKQPVC6QxX+T3noTAsbrH/l4OTWrm7fli41XRq9RUdYTTEMctk1PUKY5Xp6gXR1BLArOGqSTJo7jaB5Ocon4eLLGuQWSMqpwaFqmOKJfI49KMPuHRag9RYpI6AVTaM2uz4t0wdWpl0SYIfwBtnlHf0+rlZclFiMdJL5VCbvdx/e2pN/wOHw6A8hZyrMylmHox/2aqepF2tAc3I+8xiI5VvT30mfcNfPF+7pO4m8Hwwefw3R9qH0q1crD4LdWttSCXZ26FQPtx6kUzQ0KS2mxZtaxqbZ/TxkxL4OasblXzWeRLH4/iuvuNhITDgI9VwbQMO4+o37kDy6ChAcvaC1HYWcDkqnhcWA49BpKSn7wZ0xwVd1J7NoydbIB6kda1pp/hVoj+o5SPu3NPdXrduEu9oD3fHDo2hvvR6qTF4XOqsVriEypw5sbmvdrJxqM+XK1mBUTeXLurnWxkSEmFsYvVzIgQIn9khsMCBTaAKWt0jz1T5cmlr4W24sXU7MO5G7rHA+qASy5nJs5cU3sqlr+jZkRCwlXl0JHzs16sijmoPSqvtM3/slFaGhzNoZPu5dv5r1VRlO04on/s4L/quK4ldGIVwhzJDIcFquALLetkv25lBQvH5NxhVWTnYA/jeoKdrY4xO9XEzVbHmC6/H4W178KXO2DoXFUr4rUZ2u+M4xJU19NHT17klZUVNM5hX0nVsurrEnmTUwJoZysNxYR4GvLrY4FKesDGqWqvRsYfyGa1YN9SqF/FtLFZqkq+qhNsjfJZ1/wrwt4leSuz3LgGnL2u3g13aqxOE6XqqScyfZ1qUpZfPVrr31cyc4gqKy7ypnNT/WN92ksyL8TTkCUVC1XKCz4YAEOfV3s2nBws8xSCubCzU30cdi98WBdEAx4u6phpXtSvqo7Wgqr3kFNly8u3VVXQ/O5bKV8Sfp8PPadl7UFxcoQ5I3Ke/RD6lfKExWNhzELt65VKqwTfUk5/CWGOJOGwYDbWllXoyxKU9Hi6kx32dll9TG4GqxMqujYhguoA+zSnVmxsVJJ0aIU6gpucokqG+3rqXh4ST+biBP06QOu68PVv6ohst1bQqLqq4SGEyD9JOIQoQCXc1LT8Fzvgt2Pw42xY83P2BnUA0wap0zlPq5SXZVQPtRTFndTem09GmDoSIQoX2cMhRAFytId3+6qZi9RUmPctfPGedkLg5gxff6hmOIQQoqiQGQ4hClilUnB4FUxbC9sPqBoeX7yvjt9qNGoWpJSXWhITQoiiQhIOIQqYtTVULwerJ6rS3BlJhuyrEEIUZZJwCGEgzsXUTQghhOzhEEIIIYQRSMIhhBBCCIOThEMIIYQQBicJhxBCCCEMThIOIYQQQhicJBxCCCGEMDhJOIQQQghhcEW+DkdoaCipqakEBgaaOhQhhBDCogQFBWFtnbuyyUV+hsPe3h4bmyKfdwkhhBB5ZmNjg729fa7uq0lPT083cDxCCCGEKOKK/AyHEEIIIQxPEg4hhBBCGJwkHEIIIYQwOEk4hBBCCGFwknAIIYQQwuAk4RBCCCGEwUnCIYQQQgiDk4RDCCGEEAYnCYcQQgghDE4SDiGEEEIYnCQcQgghhDA4STiEEEIIYXCScOgREhJCtWrVst22bNli6tCKjK1bt9K5c2f8/f3p0qULv/76q6lDKjIOHz6s8+e/WrVqBAYGmjq8IiElJYVFixbRtm1b6tWrR58+fTh16pSpwypSYmJimDp1KgEBATRu3Jjx48dz//59U4dlsaQvux4XLlzA3t6eXbt2odFoMq+7uLiYMKqiY9u2bbz//vu89957tGzZkp9//plx48bh4+NDvXr1TB1eoVevXj3279+vde3UqVOMHj2aUaNGmSiqomX58uVs2rSJTz75hLJly/L5558zZMgQfvnlF7y9vU0dXpHw1ltvcfXqVT7++GNKlSrFwoUL6d+/Pz/88AN2dnamDs/iyAyHHpcuXcLPzw9vb29KlCiReXNwcDB1aIVeeno6ixYton///vTp04dy5coxcuRImjdvzpEjR0wdXpFgZ2en9XPv5OTE7Nmz6datGz169DB1eEXCrl276Nq1KwEBAZQvX553332XBw8eyCyHkZw/f579+/fz0Ucf0bp1a6pUqcLcuXMJDQ3l559/NnV4FkkSDj0uXrxIpUqVTB1GkXT9+nXu3LnD888/r3V9zZo1DB8+3ERRFW0rVqwgPj6eSZMmmTqUIsPT05M//viD27dvk5qayrfffoudnR3Vq1c3dWhFwo0bNwBo2LBh5jUnJyfKly8vb3zySRIOPS5dukR4eDh9+vShefPm9O7dm7/++svUYRUJ169fByAuLo7BgwfTrFkzXnnlFfbs2WPiyIqm8PBw1q9fz4gRI3BzczN1OEXG+++/j62tLYGBgfj7+7NgwQIWL15MuXLlTB1akZCxbBUUFJR5LTU1leDgYMLDw00VlkWThEOHlJQUrl27RlRUFKNHj2bVqlU888wzDBs2jIMHD5o6vEIvJiYGgEmTJtG1a1fWrl1LixYtGDVqlHz/TWDjxo24uLjQs2dPU4dSpFy5cgUXFxc+++wzvv32W7p378748eM5f/68qUMrEvz9/alYsSJTp04lJCSEhIQE5s2bR0REBMnJyaYOzyLJplEdbGxsOHz4MNbW1pl7NmrXrs3ly5dZs2YNzZo1M3GEhZutrS0AgwcPplu3bgDUqFGDc+fOsW7dOvn+G9nWrVt56aWXZP+SEQUFBfHOO++wfv36zCl9f39/rly5wpIlS1i2bJmJIyz87OzsWLp0KRMnTqRVq1bY2try/PPP07ZtW6ys5L16fkjCoYeTk1O2a1WqVMm2c18UvJIlSwJQtWpVreuVK1dm7969Joio6Lpw4QL//fdftv00wrBOnz5NcnIy/v7+Wtfr1q0rS7tGVKlSJb7//nsiIyOxsbHB2dmZl19+maZNm5o6NIskaZoOly9fpn79+hw+fFjr+tmzZ6lcubKJoio6atWqhZOTE6dPn9a6funSJVm/NrJjx47h6ekpGxWNzMfHB1Cb1x+VcXpOGF5MTAx9+/blwoULuLm54ezszO3btzl37hwtWrQwdXgWSRIOHSpVqkTFihX56KOPOHbsGFevXmX27NmcOnWKkSNHmjq8Qs/BwYEhQ4bw2Wef8dNPP3Hr1i2WL1/OgQMHGDRokKnDK1LOnTtHtWrVTB1GkVOnTh0aNGjApEmTOHToEDdu3GDhwoUcPHiQYcOGmTq8IsHZ2Zn09HQ+/vhjLl++zJkzZxg5ciRNmzaVZd180qSnp6ebOghzFBYWxrx589i3bx/R0dHUrFmT8ePHax2REoa1bt06vv76a0JCQqhUqRKjR4/m2WefNXVYRcrQoUNxdnZmwYIFpg6lyImKimLhwoXs3buXqKgoqlatyrhx42jcuLGpQysyQkJCmDFjBocOHcLOzo4OHTowYcIEnUvu4skk4RBCCCGEwcmSihBCCCEMThIOIYQQQhicJBxCCCGEMDhJOIQQQghhcJJwCCGEEMLgJOEQQgghhMFJwiGEEEIIg5OEQwghTEBKIImiRhIOIQqJfv360a9fP73j7dq149133zViRE/n8OHDVKtWLVtPI0sXHR3NxIkTOXbsmKlDEcKoJOEQQggjOn/+PNu2bSMtLc3UoQhhVJJwCCGEEMLgJOEQoghq164dixcvZs6cOTRv3pw6deowePBgbty4oXW/Y8eO0bdvX+rWrUvjxo2ZNGkS4eHhmeNbtmzB39+fY8eO0aNHD/z9/enYsSN79uzh2rVrDBgwgLp169K+fXt+/vlnrcdVq1aN06dP061bN+rUqcPzzz/Pjh07coz7zJkzDB48mCZNmlC/fn1GjBjB5cuXAUhJSSEgIIB33nkn2+M6dOjABx98kPm1L126lFmzZtGkSRPq1avHO++8Q2xsLKtWraJVq1Y0aNCA0aNHExERofU8mzZtokuXLtSuXZs2bdqwZMkSUlNTM8ffffddBg4cyPfff0/Hjh2pXbs2L774In/99Reglon69+8PQP/+/XNcAhOisJGEQ4gi6ssvv+TatWvMnj2bmTNncvbsWSZNmpQ5fvToUQYOHIiDgwMLFy7kvffe48iRI/Tv35+EhITM+6WkpPDOO+/Qq1cvli9fjqOjI+PHj2fEiBG0adOGFStW4O3tzaRJkwgODtaKYfjw4QQGBrJ06VIqVKjA2LFj+fPPP3XGe+jQIXr37g3ArFmzmDlzJkFBQfTq1YurV69iY2PDSy+9xK5du4iJicl83PHjx7l58ybdu3fPvLZ27VqCgoJYsGABI0eO5KeffqJHjx7s37+fGTNmMG7cOHbv3s3ixYszH7Ny5Uo+/PBDmjVrxooVK+jTpw+ff/45H374oVacZ8+eZc2aNYwZM4bPPvsMa2trRo8eTVRUFLVq1WLKlCkATJkyhalTp+b1n00Ii2Vj6gCEEKZRvHhxli1bhrW1NQC3bt1iyZIlRERE4O7uzrx586hQoQIrV67MvE/dunXp0qUL33//PX369AEgLS2NESNG8MorrwBqU+Tbb7/NgAEDGDRoEAAuLi706NGDs2fP4uPjkxlDv379eOONNwBo2bIl3bp147PPPqN169bZ4p03bx7ly5dn1apVmfEEBATQvn17Fi9ezKJFi+jRoweff/45O3fupEePHgBs3boVPz8/6tevn/lczs7OLFiwABsbG5o3b84PP/xASEgImzZtwsXFBYB9+/Zx4sQJAB48eMCyZcvo2bNn5kxJQEAAbm5ufPDBBwwaNIgqVapk3nfLli2UK1cOgGLFitG3b18OHTpEx44dqVy5MgCVK1fO/H8higKZ4RCiCNFoNJn/7+/vn/nCDWQmAvHx8cTHx3P69Glat25Neno6KSkppKSkULZsWSpVqsSBAwe0nrdevXqZ/+/p6Qmo5CSDm5sboJKRR3Xr1k0rtvbt2/PPP/9ozaAAxMXFcebMGZ577jmtmIsXL07btm05cuQIABUqVKBBgwZs27YNgISEBH799Vet2Q2AOnXqYGOT9X7Ly8uLChUqZCYbGTE/ePAAgJMnT5KQkEC7du0yvxcpKSm0a9cOQOv74eHhkZlsPP59FaIokxkOIQqJYsWKERkZqXc8KSkJR0fHzI8f/X8AKyv1/iMtLY3o6GjS0tL4/PPP+fzzz7M9l729vdbHzs7O2e7z+PPr4u3trfWxp6cn6enp2RKTBw8ekJ6ejpeXV7bn8PLyykwMAF5++WXee+89goKCOH78OLGxsbz00ktPjLdYsWJ648z4vg4bNkzneGhoaOb/P/51ZyR5cipFFHWScAhRSHh5eXHp0iWdY0lJSYSHh+t8wdbFyckJjUbDwIED6dKlS7bx3CQTuREZGakVU1hYGNbW1pkzIhlcXFzQaDSEhYVle4579+5p3b9Tp07MnDmTHTt2cOzYMVq0aEHJkiWfKs7ixYsD8Omnn+Ln55dtPLffVyGKMllSEaKQaNy4MXfv3uXUqVPZxnbt2kVqaipNmzbN1XM5OztTs2ZNrl27hr+/f+atSpUqLFmypMCKce3atSvz/9PT0/ntt99o0KABdnZ2WvcrVqwYtWvX5tdff9U6FfLgwQP27t1LgwYNtO7buXNnfvrpJw4cOJBtOSU/6tati62tLSEhIVrfDxsbG+bPn8/t27dz/VyPLgkJUZTIDIcQhUTnzp354osvGD58OMOHD6dWrVqkpaVx4sQJVq9eTdeuXbU2Tj7JuHHjGDZsGO+88w4vvPACqamprF27ltOnTzNq1KgCiXnu3LkkJiZSoUIFNm3axNWrV/niiy903vedd95h8ODBDBs2jNdee43k5GRWrVpFUlJS5sbTDC+//DI9e/bE1dWVZ5999qnjdHd3Z8iQISxatIiYmBiaNGlCSEgIixYtQqPRUL169Vw/V8Y+kb179+Lq6pqnxwphySThEKKQsLW15euvv2bFihVs2rSJxYsXY2VlRfny5Xn77bfp27dvnp4vICCANWvWsHTpUsaMGYOtrS21atVi3bp1PPPMMwUS87Rp01i5ciX//fcfNWvWZO3atTRs2FDnfZs1a8a6detYvHgx48aNw87OjoYNGzJnzpzMEyIZnnnmGdzc3OjcuXO22ZL8Gjt2LCVKlGDjxo2sXr0aV1dXmjVrxrhx47Q2mz5JlSpV6Nq1Kxs2bGDfvn389NNPBRKfEOZOky4dhIQQRrZlyxYmT57M7t27KVOmTIE//+nTp3n11VfZtm2bzCAIYSZkhkMIUWgcPnyYw4cPs3XrVgICAiTZEMKMyKZRIUShERERwbp16/Dy8mLmzJmmDkcI8QhZUhFCCCGEwckMhxBCCCEMThIOIYQQQhicJBxCCCGEMDhJOIQQQghhcJJwCCGEEMLgJOEQQgghhMFJwiGEEEIIg5OEQwghhBAG9//F8nOns7fj3AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(6, 4)}, style='white')\n",
    "colors = sns.color_palette('bright')\n",
    "sns.scatterplot(data=dataset_final, x=dataset_final['Unemployment'], y=dataset_final['Weekly_Sales'], hue=dataset_final['Holiday_Flag'], palette=colors)\n",
    "plt.legend('', frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrice de corrélation\n",
    "\n",
    "Nous construisons la matrice de corrélation mieux voir quels facteurs impactent les ventes hebdomadaires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_cols = ['Store', 'Day', 'Month', 'Year', 'Holiday_Flag', 'Temperature', 'Fuel_Price', 'CPI', 'Unemployment', 'Weekly_Sales']\n",
    "corr_df = dataset_final[corr_cols]\n",
    "corr = corr_df.corr()\n",
    "sns.set(rc={\"figure.figsize\":(9, 5)})\n",
    "sns.heatmap(\n",
    "    corr,\n",
    "    annot=True,\n",
    "    annot_kws={\"fontsize\":8}\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Résultats : \n",
    "\n",
    "- On observe que le taux de chômage a une influence positive sur les ventes hebdomadaires (coefficient de 0,20).\n",
    "\n",
    "- L'index d'inflation a une influence négative sur les ventes hebdomadaires (coefficient de -0,35).\n",
    "\n",
    "- La température a également une influence négative sur les ventes hebdomadaires (coefficient de -0,18).\n",
    "\n",
    "- Les autres variables ont une influence négligeable sur les ventes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enregistrement fichier csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_final.to_csv('./src/dataset_for_ml.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}