{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Evaluation Python / Machine Learning année 2017 - solution\n",
    "\n",
    "Le répertoire [_data/2017](https://github.com/sdpython/teachpyx/tree/main/_data/2017) contient deux fichiers csv simulés aléatoirement dont il faudra se servir pour répondre aux 10 questions qui suivent. Chaque question vaut deux points. Le travail est à rendre pour le lundi 20 février sous la forme d'un notebook envoyé en pièce jointe d'un mail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 1\n",
    "\n",
    "*Deux fichiers sont extraits de la base de données d'un médecin. Un fichier contient des informations sur des personnes, un autre sur les rendez-vous pris par ces personnes. Quels sont-ils ?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>idc</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>Cendrillon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>Cendrillon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>304895f0-f686-4b0e-8854-a705bb5a6982</td>\n",
       "      <td>Balthazar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>3f0d31d2-0ef4-4e7e-b876-07d10225cc8c</td>\n",
       "      <td>Balthazar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>f29273f4-a76c-4158-b5f5-b3e5a080a0c7</td>\n",
       "      <td>Balthazar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender                                   idc        name\n",
       "0   37       0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  Cendrillon\n",
       "1   41       0  f44b004b-b01e-4835-b86d-1a42846c6d93  Cendrillon\n",
       "2   46       1  304895f0-f686-4b0e-8854-a705bb5a6982   Balthazar\n",
       "3   42       1  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c   Balthazar\n",
       "4   41       1  f29273f4-a76c-4158-b5f5-b3e5a080a0c7   Balthazar"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "persons = pandas.read_csv(\n",
    "    \"https://raw.githubusercontent.com/sdpython/teachpyx/main/_data/2017/persons.txt\",\n",
    "    sep=\"\\t\",\n",
    ")\n",
    "persons.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>idc</th>\n",
       "      <th>idr</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-07 19:47:45.068274</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>644b1236-b9ee-4ef5-8ca7-d1adadb547c8</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-17 19:47:45.068274</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>aff6ac9e-5dd0-434e-9888-f724f6d40969</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-07 19:47:45.068274</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>9ca87507-aa95-49a9-88b3-86ec9fbc44d6</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         date                                   idc  \\\n",
       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "2  2016-12-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "3  2016-12-17 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "4  2017-01-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "\n",
       "                                    idr  price  \n",
       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0  \n",
       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0  \n",
       "2  644b1236-b9ee-4ef5-8ca7-d1adadb547c8   75.0  \n",
       "3  aff6ac9e-5dd0-434e-9888-f724f6d40969   80.0  \n",
       "4  9ca87507-aa95-49a9-88b3-86ec9fbc44d6   80.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend = pandas.read_csv(\n",
    "    \"https://raw.githubusercontent.com/sdpython/teachpyx/main/_data/2017/rendezvous.txt\",\n",
    "    sep=\"\\t\",\n",
    ")\n",
    "rend.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000, 4), (2537, 4))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "persons.shape, rend.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Le second fichier est plus volumineux et contient une variable *price* qui ne peut pas être reliée aux personnes. Le premier fichier est celui des personnes, le second celui des rendez-vous. La variable *idc* est présente dans les deux tables. C'est elle qui identifie les personnes dans les deux tables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 2\n",
    "\n",
    "*On souhaite étudier la relation entre le prix moyen payé par une personne, son âge et son genre. Calculer le prix moyen payé par une personne ?*\n",
    "\n",
    "La table des rendez-vous contient toutes l'information nécessaire. La question était un peu ambiguë. On peut déterminer le prix moyen payé par personne, ou le prix moyen des prix moyens... On va répondre à la première option car la seconde n'a pas beaucoup d'intérêt et c'est très proche du prix moyen par rendez-vous, ce que la question aurait sans doute formulé dans ce sens si telle avait été son intention. On groupe par *idc* et on fait la moyenne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idc</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>003b0195-2acb-4f46-b7fa-28cf266a8f60</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009e689c-51a1-4cef-99ca-a4ba364eba8d</th>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00a213c2-1174-4359-8a67-fe710ec1b439</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00e42818-aade-4758-a5f6-c78a6f235ea5</th>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0153b785-9acd-4d28-aad1-62f8bf2faea3</th>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      price\n",
       "idc                                        \n",
       "003b0195-2acb-4f46-b7fa-28cf266a8f60   80.0\n",
       "009e689c-51a1-4cef-99ca-a4ba364eba8d   80.0\n",
       "00a213c2-1174-4359-8a67-fe710ec1b439   70.0\n",
       "00e42818-aade-4758-a5f6-c78a6f235ea5   70.0\n",
       "0153b785-9acd-4d28-aad1-62f8bf2faea3   75.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr = rend[[\"idc\", \"price\"]].groupby(\"idc\").mean()\n",
    "gr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3\n",
    "\n",
    "*Faire la jointure entre les deux tables.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>idc</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>Cendrillon</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>Cendrillon</td>\n",
       "      <td>78.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>304895f0-f686-4b0e-8854-a705bb5a6982</td>\n",
       "      <td>Balthazar</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>3f0d31d2-0ef4-4e7e-b876-07d10225cc8c</td>\n",
       "      <td>Balthazar</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>f29273f4-a76c-4158-b5f5-b3e5a080a0c7</td>\n",
       "      <td>Balthazar</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender                                   idc        name      price\n",
       "0   37       0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  Cendrillon  70.000000\n",
       "1   41       0  f44b004b-b01e-4835-b86d-1a42846c6d93  Cendrillon  78.333333\n",
       "2   46       1  304895f0-f686-4b0e-8854-a705bb5a6982   Balthazar  75.000000\n",
       "3   42       1  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c   Balthazar  95.000000\n",
       "4   41       1  f29273f4-a76c-4158-b5f5-b3e5a080a0c7   Balthazar  90.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join = persons.merge(gr.reset_index(), on=\"idc\")\n",
    "join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Cette jointure est assez simple puisque la colonne partagée porte le même nom dans les deux tables. On peut néanmoins se poser la question de savoir s'il y a des personnes qui n'ont pas de rendez-vous associé et réciproquement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join = persons.merge(gr.reset_index(), on=\"idc\", how=\"outer\")\n",
    "join.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Visiblement, ce n'est pas le cas puisqu'une jointure incluant les éléments sans correspondances dans les deux tables n'ajoute pas plus d'éléments à la jointure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4\n",
    "\n",
    "*Tracer deux nuages de points (age, prix moyen) et (genre, prix moyen) ?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'age / price'}, xlabel='age', ylabel='price'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join.plot(x=\"age\", y=\"price\", kind=\"scatter\", title=\"age / price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "On peut aussi utiliser un module comme [seaborn](http://seaborn.pydata.org/) qui propose des dessins intéressants pour un statisticatien."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "\n",
    "g = seaborn.jointplot(join, x=\"age\", y=\"price\", kind=\"reg\", scatter_kws={\"s\": 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='price'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join.plot(x=\"gender\", y=\"price\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "On ne voit pas grand chose sur ce second graphe. Une option est d'ajouter un bruit aléatoire sur le genre pour éclater le nuage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gx', ylabel='price'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy\n",
    "\n",
    "bruit = join.copy()\n",
    "bruit[\"gx\"] = bruit.gender + numpy.random.random(bruit.shape[0]) / 3\n",
    "bruit.plot(x=\"gx\", y=\"price\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Il n'y a rien de flagrant. On peut faire un graphe moustache."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'price'}, xlabel='[gender]'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join[[\"price\", \"gender\"]].boxplot(by=\"gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "C'est mieux. Un dernier. Le diagramme violon, plus complet que le précédent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='price'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.violinplot(x=\"gender\", y=\"price\", data=join, inner=\"quart\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 5\n",
    "\n",
    "*Calculer les coefficients de la régression* $prix\\_moyen \\sim age + genre$.\n",
    "\n",
    "Une régression. Le premier réflexe est [scikit-learn](https://scikit-learn.org/stable/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(join[[\"age\", \"gender\"]], join[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.52440734, -4.36373925])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.05057671902867"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "On utilise maintenant [statsmodels](https://www.statsmodels.org/stable/index.html) qui est plus complet pour toute ce qui est un modèle linéaire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.579</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.579</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   686.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th> <td>3.25e-188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:12:30</td>     <th>  Log-Likelihood:    </th> <td> -3149.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   6306.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   997</td>      <th>  BIC:               </th> <td>   6320.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   61.0506</td> <td>    0.640</td> <td>   95.432</td> <td> 0.000</td> <td>   59.795</td> <td>   62.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.5244</td> <td>    0.015</td> <td>   35.302</td> <td> 0.000</td> <td>    0.495</td> <td>    0.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender</th>    <td>   -4.3637</td> <td>    0.358</td> <td>  -12.194</td> <td> 0.000</td> <td>   -5.066</td> <td>   -3.662</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>300.096</td> <th>  Durbin-Watson:     </th> <td>   1.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 719.506</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.618</td>  <th>  Prob(JB):          </th> <td>5.77e-157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.608</td>  <th>  Cond. No.          </th> <td>    151.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.579   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.579   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     686.6   \\\\\n",
       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} & 3.25e-188   \\\\\n",
       "\\textbf{Time:}             &     11:12:30     & \\textbf{  Log-Likelihood:    } &   -3149.8   \\\\\n",
       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               } &     6306.   \\\\\n",
       "\\textbf{Df Residuals:}     &         997      & \\textbf{  BIC:               } &     6320.   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &      61.0506  &        0.640     &    95.432  &         0.000        &       59.795    &       62.306     \\\\\n",
       "\\textbf{age}       &       0.5244  &        0.015     &    35.302  &         0.000        &        0.495    &        0.554     \\\\\n",
       "\\textbf{gender}    &      -4.3637  &        0.358     &   -12.194  &         0.000        &       -5.066    &       -3.662     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 300.096 & \\textbf{  Durbin-Watson:     } &     1.999  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   719.506  \\\\\n",
       "\\textbf{Skew:}          &   1.618 & \\textbf{  Prob(JB):          } & 5.77e-157  \\\\\n",
       "\\textbf{Kurtosis:}      &   5.608 & \\textbf{  Cond. No.          } &      151.  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.579\n",
       "Model:                            OLS   Adj. R-squared:                  0.579\n",
       "Method:                 Least Squares   F-statistic:                     686.6\n",
       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):          3.25e-188\n",
       "Time:                        11:12:30   Log-Likelihood:                -3149.8\n",
       "No. Observations:                1000   AIC:                             6306.\n",
       "Df Residuals:                     997   BIC:                             6320.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     61.0506      0.640     95.432      0.000      59.795      62.306\n",
       "age            0.5244      0.015     35.302      0.000       0.495       0.554\n",
       "gender        -4.3637      0.358    -12.194      0.000      -5.066      -3.662\n",
       "==============================================================================\n",
       "Omnibus:                      300.096   Durbin-Watson:                   1.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              719.506\n",
       "Skew:                           1.618   Prob(JB):                    5.77e-157\n",
       "Kurtosis:                       5.608   Cond. No.                         151.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "\n",
    "lr = ols(\"price ~ age + gender\", data=join)\n",
    "res = lr.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou encore (après avoir ajouté une constante)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.579</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.579</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   686.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th> <td>3.25e-188</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:12:33</td>     <th>  Log-Likelihood:    </th> <td> -3149.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>   6306.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   997</td>      <th>  BIC:               </th> <td>   6320.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>    <td>    0.5244</td> <td>    0.015</td> <td>   35.302</td> <td> 0.000</td> <td>    0.495</td> <td>    0.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender</th> <td>   -4.3637</td> <td>    0.358</td> <td>  -12.194</td> <td> 0.000</td> <td>   -5.066</td> <td>   -3.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cst</th>    <td>   61.0506</td> <td>    0.640</td> <td>   95.432</td> <td> 0.000</td> <td>   59.795</td> <td>   62.306</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>300.096</td> <th>  Durbin-Watson:     </th> <td>   1.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 719.506</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.618</td>  <th>  Prob(JB):          </th> <td>5.77e-157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.608</td>  <th>  Cond. No.          </th> <td>    151.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.579   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.579   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     686.6   \\\\\n",
       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} & 3.25e-188   \\\\\n",
       "\\textbf{Time:}             &     11:12:33     & \\textbf{  Log-Likelihood:    } &   -3149.8   \\\\\n",
       "\\textbf{No. Observations:} &        1000      & \\textbf{  AIC:               } &     6306.   \\\\\n",
       "\\textbf{Df Residuals:}     &         997      & \\textbf{  BIC:               } &     6320.   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{age}    &       0.5244  &        0.015     &    35.302  &         0.000        &        0.495    &        0.554     \\\\\n",
       "\\textbf{gender} &      -4.3637  &        0.358     &   -12.194  &         0.000        &       -5.066    &       -3.662     \\\\\n",
       "\\textbf{cst}    &      61.0506  &        0.640     &    95.432  &         0.000        &       59.795    &       62.306     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 300.096 & \\textbf{  Durbin-Watson:     } &     1.999  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   719.506  \\\\\n",
       "\\textbf{Skew:}          &   1.618 & \\textbf{  Prob(JB):          } & 5.77e-157  \\\\\n",
       "\\textbf{Kurtosis:}      &   5.608 & \\textbf{  Cond. No.          } &      151.  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.579\n",
       "Model:                            OLS   Adj. R-squared:                  0.579\n",
       "Method:                 Least Squares   F-statistic:                     686.6\n",
       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):          3.25e-188\n",
       "Time:                        11:12:33   Log-Likelihood:                -3149.8\n",
       "No. Observations:                1000   AIC:                             6306.\n",
       "Df Residuals:                     997   BIC:                             6320.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "age            0.5244      0.015     35.302      0.000       0.495       0.554\n",
       "gender        -4.3637      0.358    -12.194      0.000      -5.066      -3.662\n",
       "cst           61.0506      0.640     95.432      0.000      59.795      62.306\n",
       "==============================================================================\n",
       "Omnibus:                      300.096   Durbin-Watson:                   1.999\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              719.506\n",
       "Skew:                           1.618   Prob(JB):                    5.77e-157\n",
       "Kurtosis:                       5.608   Cond. No.                         151.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.api import OLS\n",
    "\n",
    "join2 = join.copy()\n",
    "join2[\"cst\"] = 1\n",
    "lr = OLS(join2[\"price\"], join2[[\"age\", \"gender\", \"cst\"]])\n",
    "res = lr.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "On peut aussi définir la régression sous la forme de formule avec le module [patsy](http://patsy.readthedocs.io/en/latest/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression(fit_intercept=False)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression(fit_intercept=False)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression(fit_intercept=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from patsy import dmatrices\n",
    "\n",
    "y, X = dmatrices(\"price ~ age + gender\", join, return_type=\"matrix\")\n",
    "y = numpy.ravel(y)\n",
    "lr = LinearRegression(fit_intercept=False)\n",
    "lr.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([61.05057672,  0.52440734, -4.36373925]), 0.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1., 37.,  0.],\n",
       "       [ 1., 41.,  0.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 6\n",
    "\n",
    "*On souhaite étudier le prix d'une consultation en fonction du jour de la semaine. Ajouter une colonne dans la table de votre choix avec le jour de la semaine.*\n",
    "\n",
    "On convertit d'abord la colonne date (chaîne de caractères au format date) avec la fonction [to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             object\n",
       "idc              object\n",
       "idr              object\n",
       "price           float64\n",
       "date2    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend[\"date2\"] = pandas.to_datetime(rend.date)\n",
    "rend.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Et on récupère le jour de la semaine avec [weekday](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.weekday.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>idc</th>\n",
       "      <th>idr</th>\n",
       "      <th>price</th>\n",
       "      <th>date2</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-07 19:47:45.068274</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>644b1236-b9ee-4ef5-8ca7-d1adadb547c8</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2016-12-07 19:47:45.068274</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-12-17 19:47:45.068274</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>aff6ac9e-5dd0-434e-9888-f724f6d40969</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2016-12-17 19:47:45.068274</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-07 19:47:45.068274</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>9ca87507-aa95-49a9-88b3-86ec9fbc44d6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2017-01-07 19:47:45.068274</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         date                                   idc  \\\n",
       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "2  2016-12-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "3  2016-12-17 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "4  2017-01-07 19:47:45.068274  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "\n",
       "                                    idr  price                      date2  \\\n",
       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
       "2  644b1236-b9ee-4ef5-8ca7-d1adadb547c8   75.0 2016-12-07 19:47:45.068274   \n",
       "3  aff6ac9e-5dd0-434e-9888-f724f6d40969   80.0 2016-12-17 19:47:45.068274   \n",
       "4  9ca87507-aa95-49a9-88b3-86ec9fbc44d6   80.0 2017-01-07 19:47:45.068274   \n",
       "\n",
       "   weekday  \n",
       "0        4  \n",
       "1        4  \n",
       "2        2  \n",
       "3        5  \n",
       "4        5  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend[\"weekday\"] = rend.date2.dt.weekday\n",
    "rend.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 7\n",
    "\n",
    "*Créer un graphe moustache qui permet de vérifier cette hypothèse.*\n",
    "\n",
    "On réutilise le code d'une question précédente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'price'}, xlabel='[weekday]'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rend[[\"price\", \"weekday\"]].boxplot(by=\"weekday\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "C'est clairement plus cher le dimanche."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 8\n",
    "\n",
    "*Ajouter une colonne dans la table de votre choix qui contient 365 si c'est le premier rendez-vous, le nombre de jour écoulés depuis le précédent rendez-vous. On appelle cette colonne $delay$. On ajoute également la colonne* $1/delay$.\n",
    "\n",
    "Pour commencer, on convertit la date en nombre de jours depuis la première date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>idc</th>\n",
       "      <th>idr</th>\n",
       "      <th>price</th>\n",
       "      <th>date2</th>\n",
       "      <th>weekday</th>\n",
       "      <th>constant</th>\n",
       "      <th>jour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>14 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>a65f721a-de11-4a01-be71-b26e2da3ac00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>14 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         date                                   idc  \\\n",
       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "\n",
       "                                    idr  price                      date2  \\\n",
       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
       "\n",
       "   weekday                   constant    jour  \n",
       "0        4 2016-11-18 19:47:45.068274 14 days  \n",
       "1        4 2016-11-18 19:47:45.068274 14 days  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend[\"constant\"] = rend[\"date2\"].min()\n",
    "rend[\"jour\"] = rend[\"date2\"] - rend[\"constant\"]\n",
    "rend.head(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "On convertit en entier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>14 days</td>\n",
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       "      <td>2016-12-02 19:47:45.068274</td>\n",
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       "      <td>4</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>14 days</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                         date                                   idc  \\\n",
       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "\n",
       "                                    idr  price                      date2  \\\n",
       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
       "\n",
       "   weekday                   constant    jour  jouri  \n",
       "0        4 2016-11-18 19:47:45.068274 14 days     14  \n",
       "1        4 2016-11-18 19:47:45.068274 14 days     14  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend[\"jouri\"] = rend.jour.apply(lambda d: d.days)\n",
    "rend.head(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "On trie par patient et jour puis on effectue la différence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "      <th>constant</th>\n",
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       "      <td>-26.0</td>\n",
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       "      <td>2016-12-02 19:47:45.068274</td>\n",
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       "      <td>65.0</td>\n",
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       "      <td>4</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>14 days</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         date                                   idc  \\\n",
       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "1  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "\n",
       "                                    idr  price                      date2  \\\n",
       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
       "1  a65f721a-de11-4a01-be71-b26e2da3ac00   65.0 2016-12-02 19:47:45.068274   \n",
       "\n",
       "   weekday                   constant    jour  jouri  diff  \n",
       "0        4 2016-11-18 19:47:45.068274 14 days     14 -26.0  \n",
       "1        4 2016-11-18 19:47:45.068274 14 days     14   0.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = rend.sort_values([\"idc\", \"jouri\"])[\"jouri\"].diff()\n",
    "rend[\"diff\"] = diff\n",
    "rend.head(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Il reste à traiter le premier jour ou plutôt le premier rendez-vous. On le récupère pour chaque patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "                                    idc                        date  j365\n",
       "0  003b0195-2acb-4f46-b7fa-28cf266a8f60  2016-12-02 19:47:45.068274   365\n",
       "1  009e689c-51a1-4cef-99ca-a4ba364eba8d  2016-11-26 19:47:45.068274   365"
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     "execution_count": 36,
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   ],
   "source": [
    "first = rend[[\"idc\", \"date\"]].groupby(\"idc\", as_index=False).min()\n",
    "first[\"j365\"] = 365\n",
    "first.head(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Puis on fait une jointure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                    idc  jouri                        date  \\\n",
       "0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
       "1  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
       "2  f44b004b-b01e-4835-b86d-1a42846c6d93     19  2016-12-07 19:47:45.068274   \n",
       "3  f44b004b-b01e-4835-b86d-1a42846c6d93     29  2016-12-17 19:47:45.068274   \n",
       "4  f44b004b-b01e-4835-b86d-1a42846c6d93     50  2017-01-07 19:47:45.068274   \n",
       "\n",
       "    j365  \n",
       "0  365.0  \n",
       "1  365.0  \n",
       "2  365.0  \n",
       "3    NaN  \n",
       "4    NaN  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tout = rend.merge(first, on=[\"idc\", \"date\"], how=\"outer\")\n",
    "tout[[\"idc\", \"jouri\", \"date\", \"j365\"]].head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Il ne reste plus qu'à remplacer les NaN par *jouri*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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       "      <td>365.0</td>\n",
       "      <td>365.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>365.0</td>\n",
       "      <td>365.0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>2</td>\n",
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       "      <td>365.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    idc  jouri                        date  \\\n",
       "0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
       "1  4ba0b473-f8ca-4466-a65b-40e9b8ba5029     14  2016-12-02 19:47:45.068274   \n",
       "2  f44b004b-b01e-4835-b86d-1a42846c6d93     19  2016-12-07 19:47:45.068274   \n",
       "3  f44b004b-b01e-4835-b86d-1a42846c6d93     29  2016-12-17 19:47:45.068274   \n",
       "4  f44b004b-b01e-4835-b86d-1a42846c6d93     50  2017-01-07 19:47:45.068274   \n",
       "5  304895f0-f686-4b0e-8854-a705bb5a6982      1  2016-11-19 19:47:45.068274   \n",
       "6  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c     30  2016-12-18 19:47:45.068274   \n",
       "7  f29273f4-a76c-4158-b5f5-b3e5a080a0c7      2  2016-11-20 19:47:45.068274   \n",
       "\n",
       "    j365  delay  \n",
       "0  365.0  365.0  \n",
       "1  365.0  365.0  \n",
       "2  365.0  365.0  \n",
       "3    NaN   29.0  \n",
       "4    NaN   50.0  \n",
       "5  365.0  365.0  \n",
       "6  365.0  365.0  \n",
       "7  365.0  365.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tout[\"delay\"] = tout.j365.fillna(tout.jouri)\n",
    "tout[[\"idc\", \"jouri\", \"date\", \"j365\", \"delay\"]].head(n=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Finalement, il faut ajouter une colonne $1/delay$. Comme des patients ont parfois deux rendez-vous le même jour, pour éviter les valeurs nulles, on ajoute la colonne $1/(1+delay)$. On aurait pu également pour éviter les valeurs nulles considérer le temps en secondes et non en jour entre deux rendez-vous. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>delay</th>\n",
       "      <th>delay1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.019608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   delay    delay1\n",
       "0  365.0  0.002732\n",
       "1  365.0  0.002732\n",
       "2  365.0  0.002732\n",
       "3   29.0  0.033333\n",
       "4   50.0  0.019608"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tout[\"delay1\"] = 1 / (tout[\"delay\"] + 1)\n",
    "tout[[\"delay\", \"delay1\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8 - réponse plus courte\n",
    "\n",
    "On garde l'idée de la fonction [diff](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.diff.html) et on ajoute la fonction [shift](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shift.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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>date</th>\n",
       "      <th>idc</th>\n",
       "      <th>idr</th>\n",
       "      <th>price</th>\n",
       "      <th>date2</th>\n",
       "      <th>weekday</th>\n",
       "      <th>constant</th>\n",
       "      <th>jour</th>\n",
       "      <th>jouri</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>003b0195-2acb-4f46-b7fa-28cf266a8f60</td>\n",
       "      <td>6d65b2b5-f34d-4440-8442-679175554db4</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>14 days</td>\n",
       "      <td>14</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-26 19:47:45.068274</td>\n",
       "      <td>009e689c-51a1-4cef-99ca-a4ba364eba8d</td>\n",
       "      <td>065474ed-6ba3-4ee1-aa83-b89e97d5a2aa</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2016-11-26 19:47:45.068274</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>8 days</td>\n",
       "      <td>8</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-12-20 19:47:45.068274</td>\n",
       "      <td>009e689c-51a1-4cef-99ca-a4ba364eba8d</td>\n",
       "      <td>38a02a6a-8c04-4630-aaa6-2077077a9f87</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2016-12-20 19:47:45.068274</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>32 days</td>\n",
       "      <td>32</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-12 19:47:45.068274</td>\n",
       "      <td>009e689c-51a1-4cef-99ca-a4ba364eba8d</td>\n",
       "      <td>f4cf0df4-0b6a-4b0f-93c7-d12499026fab</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2017-01-12 19:47:45.068274</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>55 days</td>\n",
       "      <td>55</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>00a213c2-1174-4359-8a67-fe710ec1b439</td>\n",
       "      <td>c1b3381b-9663-429d-a8fb-3b24c20a125b</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>14 days</td>\n",
       "      <td>14</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         date                                   idc  \\\n",
       "0  2016-12-02 19:47:45.068274  003b0195-2acb-4f46-b7fa-28cf266a8f60   \n",
       "1  2016-11-26 19:47:45.068274  009e689c-51a1-4cef-99ca-a4ba364eba8d   \n",
       "2  2016-12-20 19:47:45.068274  009e689c-51a1-4cef-99ca-a4ba364eba8d   \n",
       "3  2017-01-12 19:47:45.068274  009e689c-51a1-4cef-99ca-a4ba364eba8d   \n",
       "4  2016-12-02 19:47:45.068274  00a213c2-1174-4359-8a67-fe710ec1b439   \n",
       "\n",
       "                                    idr  price                      date2  \\\n",
       "0  6d65b2b5-f34d-4440-8442-679175554db4   80.0 2016-12-02 19:47:45.068274   \n",
       "1  065474ed-6ba3-4ee1-aa83-b89e97d5a2aa   80.0 2016-11-26 19:47:45.068274   \n",
       "2  38a02a6a-8c04-4630-aaa6-2077077a9f87   80.0 2016-12-20 19:47:45.068274   \n",
       "3  f4cf0df4-0b6a-4b0f-93c7-d12499026fab   80.0 2017-01-12 19:47:45.068274   \n",
       "4  c1b3381b-9663-429d-a8fb-3b24c20a125b   70.0 2016-12-02 19:47:45.068274   \n",
       "\n",
       "   weekday                   constant    jour  jouri   diff  \n",
       "0        4 2016-11-18 19:47:45.068274 14 days     14  365.0  \n",
       "1        5 2016-11-18 19:47:45.068274  8 days      8  365.0  \n",
       "2        1 2016-11-18 19:47:45.068274 32 days     32   24.0  \n",
       "3        3 2016-11-18 19:47:45.068274 55 days     55   23.0  \n",
       "4        4 2016-11-18 19:47:45.068274 14 days     14  365.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rend2 = rend.sort_values([\"idc\", \"jouri\"]).reset_index(drop=True).copy()\n",
    "rend2[\"diff\"] = rend2[\"jouri\"].diff()\n",
    "rend2.loc[rend2.idc != rend2.idc.shift(1), \"diff\"] = 365\n",
    "rend2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 9 \n",
    "\n",
    "*Calculer les coefficients de la régression* $prix \\sim age + genre + delay + 1/delay + jour\\_semaine$.\n",
    "\n",
    "L'âge ne fait pas partie de la table *tout*. Il faut faire une jointure pour le récupérer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "mat = tout.merge(persons, on=\"idc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Ensuite retour à [scikit-learn](https://scikit-learn.org/stable/) et plutôt le second [statsmodels](https://www.statsmodels.org/stable/index.html) pour effectuer des tests sur les coefficients du modèle. On regarde d'abord les corrélations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>delay</th>\n",
       "      <th>delay1</th>\n",
       "      <th>weekday</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030852</td>\n",
       "      <td>-0.010694</td>\n",
       "      <td>-0.023011</td>\n",
       "      <td>0.032413</td>\n",
       "      <td>0.614708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.030852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>-0.026779</td>\n",
       "      <td>-0.000064</td>\n",
       "      <td>-0.198835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delay</th>\n",
       "      <td>-0.010694</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.616038</td>\n",
       "      <td>0.039766</td>\n",
       "      <td>0.049796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delay1</th>\n",
       "      <td>-0.023011</td>\n",
       "      <td>-0.026779</td>\n",
       "      <td>-0.616038</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025400</td>\n",
       "      <td>-0.054954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>0.032413</td>\n",
       "      <td>-0.000064</td>\n",
       "      <td>0.039766</td>\n",
       "      <td>0.025400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.448584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0.614708</td>\n",
       "      <td>-0.198835</td>\n",
       "      <td>0.049796</td>\n",
       "      <td>-0.054954</td>\n",
       "      <td>0.448584</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age    gender     delay    delay1   weekday     price\n",
       "age      1.000000  0.030852 -0.010694 -0.023011  0.032413  0.614708\n",
       "gender   0.030852  1.000000  0.008700 -0.026779 -0.000064 -0.198835\n",
       "delay   -0.010694  0.008700  1.000000 -0.616038  0.039766  0.049796\n",
       "delay1  -0.023011 -0.026779 -0.616038  1.000000  0.025400 -0.054954\n",
       "weekday  0.032413 -0.000064  0.039766  0.025400  1.000000  0.448584\n",
       "price    0.614708 -0.198835  0.049796 -0.054954  0.448584  1.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x7fb3214280d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.clustermap(\n",
    "    mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]].corr(), figsize=(5, 5)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Si le jeu de données n'est pas trop volumineux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fb3029bf820>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.pairplot(\n",
    "    mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]], plot_kws={\"s\": 10}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Un dernier pour la route."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fb2fad7ac20>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ODSFvw+rbePLlz9l8zGxyyTBhtmJXIVaOTTbbdjQ1rTPalM5IfjVW3cg0b0pYeku7WH7p66onfQwTaKbKjUV+nFqyvcAlSTTDRJapsq+ttzF49DvRTp8+jWXLlqFjx47YunUrJk+ejOeeew5ZWVniNPfddx9Wr16Nbdu2Yf78+di5cycGDx6Muro6k8u8cuUK6urqoFZLD3ZqtRrFxcUm5wGAzMxMhIWFiX9xcXEW6+6qoNdUSBNeVbLG/5Vr1bLxdbgkm8cabY0OPx6/ZHE9tbKM37Ub0qA4eemaXevU23lCut4zV6UXMVevSbclJd508pSaFnvjlYjcy96Y1V8sHiwqNboB5SrtI0Pcs2IiN3PGOfb0Zcfahdbo25vnS65LhhdeqcSKXYWYsDoX2XkayThTCbNDRaWSYfqyPHnujGS6/nhmrn62aoy6kWnO+sxche1iIufy6CSaTqdDr169MHfuXPTs2ROTJk3CxIkTsXz5cnGa0aNH4y9/+QsSExMxfPhwfPvtt/j111+xY8cOp9Zl5syZKCsrE/+Kioqsz2SFwgn1sldpVQ3KqmrsmkcHIMBPWlulrPIqf+lXyV82gc7aY3VmDOwkfUed/KJlQMfWWDk2GRMHxJu94zYqOc5imXxPY8QrETUee2PWEy4OW4YEursKRG7hjHOsKsCvEWp2s73ZJqKZ2WlsOX5cr6kzWZbfrHXGzVtnJb8ao25kmrclLNkuJnIuj06ixcTEoFu3bpJhXbt2xblz58zO06FDB7Rq1QoFBaYfE2/VqhX8/Pyg0UjvGGg0GkRHR5tdrkqlQmhoqOTPEmsJsuAAJYYkxkiGqUOlHSL4W/l0ggOViAi2/Itc+TJS4iOh00lv28sTYnLhwf4Y26+9ZFg7WTKrc7R0f/y5m3RfDusRa3klZkxP74yM1AQktglFRmoCsqcNwtDEGLRqHii+jyatmxqvDO1m9lHqtG5qq4k28i32xisRuZe9McuLQyL3ccY59uFGuqGpb29mpCaYnUZ+/DB1s/Wujq0lw/TlxmhTOiv5xfau63hbwpLtYsvk18vWrsHJMb60nz266nfeeSfy8/Mlw06cOIF27dqZnef333/H1atXERMTY3J8YGAgevfujW3btonDdDodtm3bhn79+jmn4oBRgiyhtTTp9FT/Dlg6ppckITRneKJkmuWPJYtJsohgf6MGwVN3dsCB19Il05yZN1RSLpg71CjpNKxHG8lynhmUgJhQFRSo71Rg5dhkyfgFDyUZJbNeGdJVMk1GaoLkxL10TC/J9A35fff09M745tkB4jKWjumF3Ff/bNcLna0l2qhpk3/n5WUib6PyV1gsezv9xWJSXDhaNw9ESKDS7sZYaJA/ggOVCA5UIlC2f9q1bCae19QtVAjwUxidx/lUMzlLiOw9s/Kyt5PHTkLrEExP74yhiabb6noqfwWCA/wQHuyP0GB/BPoZ7xd9h1n6mNW3FeUJJUvJJVPJJ3kb3bDN6ew2pTOTX2zvuoavJyyT20VYLDcW+THB2jHCWZY/lmyx3Fjcdf0h7yTCVR3Z/HVggsWyN/Ho3jl//fVX3HHHHXjjjTcwatQo/PLLL5g4cSI++ugjjBkzBteuXcMbb7yBkSNHIjo6GqdOncLLL7+MiooKHDlyBCpV/ZNd99xzD0aMGIGMjAwAwLp16zBu3Dh8+OGH6Nu3LxYuXIj169fj+PHjRu9KM8eWnsMMe5FcOqaXTV3YWntJpbO6wbW2HFtelulNL9Skps2WeOX3mXyNu7prdwZ7euc0ZBjHB4tKJee57DyN+O6jUclxJl8uzvMeuUv32d+hslqHkEAljr452N3VsYst8Zr23g6cuVqJ9pEhyJ42SByuj6kLpTfw2/kyKJUKdIxqbjJG9dNbimMiss6WmH1w2R4cvVCG7rFh+GryHS6rm/z62VXcdX5vau0KZ+Uy3M2jk2gA8O2332LmzJk4efIk4uPjMXXqVLF3zqqqKgwfPhwHDhxAaWkpYmNjce+99+Ktt96SJMPat2+P8ePH4/XXXxeHLVmyBO+++y6Ki4uRlJSERYsWISUlxeZ6OdrAJyLXY7wSeRfGLJH3YLwSeRfGLFHDeHwSzVPx4EPkPRivRN6FMUvkPRivRN6FMUvUML710gUiIiIiIiIiIqJGwCQaERERERERERGRFUyiERERERERERERWcEkGhERERERERERkRUen0Rr3749FAqF0d+UKVMAADdu3MCUKVMQGRmJ5s2bY+TIkdBoNBaXKQgCZs+ejZiYGAQHByMtLQ0nT550xeYQEREREREREZEX8nd3Baz59ddfUVdXJ5Z/++03/PnPf8ZDDz0EAHjxxRexadMmfPnllwgLC0NGRgYeeOAB7N692+wy33nnHSxatAhZWVmIj4/HrFmzkJ6ejry8PAQFBTmt7gu25mPniUsY2CkK09M7IztPg72FV5ESH4m0bmoAwIPL9uDohTJ0jw3DV5PvMCpPWbtfnGfpmF5GywSAfnOzUVyuRXSoCjl/TzNahql5Or+6GdpaASp/BfLfHoL4GZsgAFAAKJw3FN1nf4fKah1CApU4+uZgADCqv3w9Pd/YipKqWkQE++PAa+lG4wEYbY+87vLxpvajvGxqPdaY+izkw0xNQ76r/YxN4v/PzBvqxpoQOUdT+U4v2JqPbw5dwI2aOgQF+GFYj1gkxYVjfW4Rfim8irKqWui7IQ/0UyDAT4GqGh0AQKkAAvyVCFUFoKqmDqoAP/grgMvXqqETBCgV9dOr/JXiOTMsOADDerQRz6dEzuDr8Wpq+xZszceS7QWS6ZQKQCfcLPsrgWaB/ujQujn8lQocvVCGOkFAWHAgHk6OAwCxTZgUF469hVex/fglnC+tanC7sCHYhiRv565jUoeZm6AT6o8FpzNdt153bW9TW6+7Pl9nUwiCIFifzHO88MIL+Pbbb3Hy5EmUl5ejdevW+Oyzz/Dggw8CAI4fP46uXbsiJycHt99+u9H8giAgNjYW06ZNw/Tp0wEAZWVlUKvVWLVqFUaPHm1TPax1DSxvGAxNjMGmIxfF8sqxyVi+8xRyz5aIw0IClais1onliGB/lFTViuWE1iEouFwpljNSE/D/9hXhYrlWHOavBGpvLgIxoSrJ+IzUBKzYdQraWts/9pBAJf4xuhcmrM41Wxd5o0deTm4XAXVokGQfqPwVknrIy0MTYxDfKkSyH5PbRUj2mXz7kttFWG0wZedpJNuycmwyAEiGZaQmSNarn6YxGkTuamg1pQaetXg1PJHo+eJFDDUd3v6dthazeqYuwl0lIzWBiTRyCl+PV1PbJ29nNRZ1CxXmjEjEhgPnjW7S6plqF1q6mWrqhq8ty3OFptS2I8d5artYn2DRc1WixV3b29TW667PtzF4/JNohqqrq7FmzRpMnToVCoUC+/btQ01NDdLS0sRpunTpgrZt25pNohUWFqK4uFgyT1hYGFJSUpCTk2M2iabVaqHV3kzWlJeXW6zrv34qlJQNk0cA8MqGI9BUaCXDDBNoACQJNACSpBUAk8mwWukiJAkmAFi+s8BoGmsqq3WSxoCpuuhkOTl5OfdsCfxlPx6W111e/vG4pv7RONlyDMm3Tz9e3ogwbPDU1El3wPrcIrSLbCYZti63SFKes/kYCq/Ub/OKXYVOaxAZNrTMLdfUk4SuWK83szdeici9HI3Z1T+faaQaWffNoQtMolGT5Ixz7Me7TzuzSmZpKrSSNuymIxeBtfslyS95Mk9fNtVOmrJ2v9imN7UsoP6Gq7zsijaWr7ftyHHe0i62dj1J3s2XPl+PfyeaoY0bN6K0tBTjx48HABQXFyMwMBDh4eGS6dRqNYqLi00uQz9crZaeVCzNAwCZmZkICwsT/+Li4izW9XpNncXx8gSaI+x5mkzP3gSaM9kbKDpBQI0DFdY3IlbsKsSE1bl4cNkebDpyEVeuVWPTkYvYeeKSZPpDRaVIiY+ULkT2gObVa9LPS95AcpSphpYh/VMWR86XY8n2AizYmu+S9Xo7e+OViNzLkZjNztOgXHazyZXOl15327qJ3MkZ59iqavc1SH84Jm3vn758zahsrp30Q570vcvyMgCo/P0slhuLr7ftyHFsFxM5l1cl0T7++GMMHjwYsbGxLl/3zJkzUVZWJv4VFRVZn4kkerWNsGv60KAAJMVJ5/FXKsxMfZO80XCwSPr0mjz5WFVTh7Ruaqwcm4yJA+Kxcmwy+sqSap3ULSRlo6Sbg+TLkZflCT95ubHW6+0Yr0TexZGYdfcFojtvShG5k7efY3Xy2JU3LRXm20k6QTqzvAwA2to6i+XG4uttO3Kct8cskafxmp9znj17FtnZ2fj3v/8tDouOjkZ1dTVKS0slT6NpNBpER0ebXI5+uEajQUxMjGSepKQks+tXqVRQqVQ21zc82B+lFu6Qx4SqoKnQNvgxRvm7x2whf/eYNYr//TX0ekHecYL8p5lyD/dpa9RxQK1OwMGiUnEa+faHB/sjJT4SK3bd/DltgL8StQZ3POXvjRvQsTUAIK2bWnzsXX5x1rNtOP468Banv2dCn7wzt9yBnaJw5Hy5pOyK9Xo7e+OViNzLkZiVH+tdLSTQNU+XEHkaZ5xjFQDc9UueuJbSV3h0aNVc0rbs0Kq52XZSXMsQ8fUe+rKc/NjkqmSWr7ftyHFsFxM5l9c8ifbJJ58gKioKQ4fefPlc7969ERAQgG3btonD8vPzce7cOfTr18/kcuLj4xEdHS2Zp7y8HHv37jU7jyMWPJQkKa8cm4yYUBUUqE+g5fw9DYNvjZFMExMqPbglt5M+hTU0UTp9RmqC0TISWktP5vL3kIUEKpH/9hCo/Otvu6n8FUbriQiW5laHJMbgo/+9WN9cXUMCpSuSPzCmH//V5Dtw7K3B+GryHUZ1TWgdgozUBCS2CZW8sNlwnozUBMk8zwySlhc8lGT0VNlTd3aQTPPXgQkYmhiDVs0DMTQxxuRLYU3dzUvrpsYrQ7s5vVFiabnT0zub3CeNvV4iIk+X1k1tdP5ypX+M7um2dRN5u95Oit2hiTFo1zIY6hbSdml8K+Pklt4rQ7pKyvK2pb5sqp0kn1de1s9n2A51ZTuLbTtyBvn1o7zcWOTXk/JyY9FfF5srNxb59bINP7jy6vX6Eq/onVOn0yE+Ph6PPPII5s2bJxk3efJkbN68GatWrUJoaCieffZZAMCePXvEabp06YLMzEyMGDECADB//nzMmzcPWVlZiI+Px6xZs3D48GHk5eUhKCjIpjrZ0nOYLT3kyHv4kZflyzD1knn5PPJpus/+DpXVOoQEKnH0zcEm62H4tNdXk+8w2fOQvC5p7+3AmauVaB8Zguxpg4zmsWW98mXYQl4PW/azIy/nZw9HvsOWeHVXV89EjcWbv9O29s4J1J8Dd528jMjmKkSGBOLM1UoEKBUoraqBThCg8vdDZHMVrmtrUXK9Gv5KJdRhQbiurUVVTR0GdGyNET3bYP3/OpT5o7JaPC/1bBuOlPhIHCwqxc4Tl9C2ZQhiw4N4XiCn8/V4NbV9Dy7bg8O/l0InCOIvBBQA/JRAgJ8S0WHBAICrlVrcqNbBTwnc3UWNET3bmGyfydt6hu04wHIP6/a0+dg+JG9nS8wm/H0TanX1CbSCua47Jtly/dgYOr+6GdpaASp/BfLfHuKy9ep7rHR1T5XuWq83n+sMeUUS7fvvv0d6ejry8/PRqVMnybgbN25g2rRp+Pzzz6HVapGeno5//vOfkp9zKhQKfPLJJ2KHBIIg4LXXXsNHH32E0tJS9O/fH//85z+Nlm2JPQ18InIvxiuRd2HMEnkPxiuRd2HMEjWMVyTRPBEPPkTeg/FK5F0Ys0Teg/FK5F0Ys0QN4zXvRCMiIiIiIiIiInIXJtGIiIiIiIiIiIisYBKNiIiIiIiIiIjICibRiIiIiIiIiIiIrGASjYiIiIiIiIiIyAp/d1fAmvbt2+Ps2bNGw5955hksXboUgwYNws6dOyXjnn76aSxfvtzsMgVBwGuvvYYVK1agtLQUd955J5YtW4aOHTs6vf4NlZ2nwd7Cq0iJj0RaN7VDy1iwNR87T1zCwE5RmJ7e2eRy5eUpa/eL5aVjepmcRz5N51c3Q1srQOWvQP7bQ9BvbjaKy7WIDlUh5+9pAIC093bgzNVKtI8MQfa0QUbLMLXeB5ftwdELZegeG4avJt9htD0939iKkqpaRAT748Br6Wa32do+kdfN2vaa+mysfV62fBbkHu1nbBL/f2beUDfWhMg5msp3Wn4MXbA1H98cuoAbNXUoqapGbZ2A4AAl/JRK3KjVwU8BKBXA9Wod/JRAZIgKQQF+aN1ChbNXK1GprUOIyg/tIkNwuUKLiJBAZKQmAADmbD6GK9e0uKtja/EcReQMvh6vprZvytr92Hr0Imp19cPVLVRIbt8Sv50vhVKpREmlFte0tWiu8kdEiArnS66jVlcfz62aB2FYj1gAENtVSXHhZttT1tqF9rCl3ca2HXk7U9dXriC/nnSVDjM3QSfUtw9OZ7ruGOyuY3/32d+hslqHkEAljr452GXr9ZVznUIQBMHdlbDk8uXLqKurE8u//fYb/vznP2P79u0YNGgQBg0ahE6dOuHNN98Up2nWrJnF7nrnz5+PzMxMZGVlIT4+HrNmzcKRI0eQl5eHoKAgm+rVWF0DG550AWDC6lxx3MqxySZPxJYSYgeLSrFke4E4bUZqApLiwiXLzUhNkEyT3C4CuWdLxPLQxBiM6NlGMo98GgUAS1+kmFAVQlT+KLhcKQ4LCVSislonmeZiuVayXk35Dcl65NOo/BXQ1t5cc0SwP8bc3t5om//fviIxoTeyd5zR+C2/XZTUTb4e+fbKyyvHJgMw/rw2HDgvfhbxrUKsfhbmPmNrHGmsuWoeT2AtXg0P6HrefGAn8vbvtK3n2Ow8jeQY6q9UoFbnmmbN0MQYJtLIKXw9Xk1t39DEGGw6crHR6qRvl+0tvIoLpTck68pITTBKpJlq38hv4uqns9Zus2UaIneyFrP6BJqeqxJp+gSanqsSafoEmp6rEmnuOvbrE2h6rkqkefu5zpDHP4nWunVrSXnevHm45ZZbMHDgQHFYs2bNEB0dbdPyBEHAwoUL8eqrr+L+++8HAKxevRpqtRobN27E6NGjnVd5OxmedFfsKkRSXLhk/PrcIhwsKpXcSZPPY5gQW7Gr0GgdS7cXoIdsuct3FkjKhskhAPjut4vYka+xOI21S5b6hJRWMswweG9Oc5OpxpV8GsMDLQCUVNVKElUAJOWL5Vqj8Uu3FxjVX74e+fbKy+tzi9Ausplk2JzNx1B4pVLcFj+FdB3/+qkQ/Tu2MlqOYeLNlgs0+XfAlsaaqXkAWHw60ZH1EBE1pjmbj0nKrkqgAcCuk5ddti4iX/Pdb42XQAOAt77Nw9k/rpsct/PEJUkSzVT7ZvnOU2JbL/dsCR5ctgdfTb7DZBtT3hbaW3jVqMz2EnkTwwSaqXJjkV/XycuNRd50cGFTwi3k1+DyMlnn8Uk0Q9XV1VizZg2mTp0KheJmRmLt2rVYs2YNoqOjMWzYMMyaNQvNmjUzuYzCwkIUFxcjLS1NHBYWFoaUlBTk5OSYTaJptVpotTcTK+Xl5U7aqpvW5xZJyudLpCf/k5eu4fu8+mTWkfP16z+hqZBM8/Hu0xbXIQDI10jrXmslbnSCbweXM46Tlyq0OH35mmSYPoGmVydb0fWaOpy8JJ1nd8FlcV9vOnIRWLvfaiJN/r1Zn1tktbEmbwQaJvxMJWNXjk12aD3u4op4JSLncSRms/M0RsdZV6q44ZqLCiJP44xzbGNfpJpLoAFA25YhkrKppNfRC2WSYfpySaX0Jqu8DAAqfz+LZVO89Ul/8g5sFxM5l1d1LLBx40aUlpZi/Pjx4rBHH30Ua9aswfbt2zFz5kx8+umneOyxx8wuo7i4GACgVktPUGq1WhxnSmZmJsLCwsS/uLi4hm2MDdpESBOB127USMrfHDpvlISpsiHZpa2xLyGmsD6J1/NvYCSUVdXgnIUGmykKADqd9LOokn028oads8gbfVeuScvfHDovKcsTaJ7OHfFKRI5zJGYb6/hoKx+/UU1klrefY2vqpG0t/StUDMvdY8Mkw/TlYT3aSIbLywCgra2zWJbTPwm3YlchJqzORXaexuL0RPby9pgl8jRelUT7+OOPMXjwYMTGxorDJk2ahPT0dCQmJmLMmDFYvXo1NmzYgFOnTjl13TNnzkRZWZn4V1Tk/KTCqGTpAS0jNQErxyZj4oB4rBybbJRUiwhRQSe7lWct4RUa5A9/pX1psfatQqxP5MWUCiDAz75QkE+t0wmIaBZotFxD8r3eItjfqPHVQbav5Q07U+TfG3nZFPl67+oo/dl0RIjKKetxF1fEKxE5jyMxa8vxkYicz9fOsWnd1JL2dlo3Nb6afAeS20UgOECJ5HYR4jvRpqd3RkZqAhLbhJp8txpgOilniakn4YicyddilsjdvObnnGfPnkV2djb+/e9/W5wuJSUFAFBQUIBbbrnFaLz+3WkajQYxMTHicI1Gg6SkJLPLValUUKmMEwvOpD+Jyx/nNnysW94hgLzjgN6yF94ntA6RvDB/wP+SJYbvG7P2Ev1XhnTFhgPnJfNEBPtLfh8v7yRATuWvQFxEM0ld/JXSn5IqFdLH+011RiCfR06pAHq1ldbf2no6tApB5+hQyfbJOyyICVX9r6e3WpOdFwzrEWvUScAzg6QdNgyRvUR37O3txcaX4XvuTPVQaom5740lptY7wkKnFqOS4xxaj7u4Il6JyHkcidm0bupGfzm5JcntItyyXiJ3c8Y5Vt6OdCVTNwHTuqmN2jX6xJnc9PTOFnv4tLe9lBIfKXmPMW8QkLOxXUzkXB7fO6fe66+/jg8//BBFRUXw9zef+9u9ezf69++PQ4cO4bbbbjMaLwgCYmNjMX36dEybNg1A/e/Co6KisGrVKps7Fmis3jmtMfXOBHm33fKyqaSMfJi8bGo98mnkvRbJy6a6KE57bwfOXK1E+8gQZE8bZFTuNzdb7EUz5+9pNs1jqktieV3ky5Uvw9T2maqLIVPdpVvqKTWtm9qpXaw3Nl96P4ct8eorXS4T6Xnzd9qec+yCrflYl1sEbU0dlIr690/6K4AKbS10AiAIN396qQDQLNAPldXSn1cpACj+d4NF5a/AwE5R4s0Dw3ODpvyGUW99RM7g6/FqavvkPQDKKVB/c/j2DpH410+FuF5TB38l0ELljxu1OnSPDcPtHSLFdlVSXDj2Fl6Fyt8P2to68V99UsqT2zS+1OYiz+fJ7WKul+v1Bl6RRNPpdIiPj8cjjzyCefPmicNPnTqFzz77DEOGDEFkZCQOHz6MF198EX/605+wc+dOcbouXbogMzMTI0aMAADMnz8f8+bNQ1ZWFuLj4zFr1iwcPnwYeXl5CAoKsqlO7kqiEZH9GK9E3oUxS+Q9GK9E3oUxS9QwXvFzzuzsbJw7dw5PPvmkZHhgYCCys7OxcOFCVFZWIi4uDiNHjsSrr74qmS4/Px9lZTd72Xn55ZdRWVmJSZMmobS0FP3798eWLVtsTqAREREREREREVHT4hVPonkiZvCJvAfjlci7MGaJvAfjlci7MGaJGsareuckIiIiIiIiIiJyBybRiIiIiIiIiIiIrGASjYiIiIiIiIiIyAom0YiIiIiIiIiIiKzw6N456+rq8Prrr2PNmjUoLi5GbGwsxo8fj1dffRUKhQIAIAgCXnvtNaxYsQKlpaW48847sWzZMnTs2NHispcuXYp3330XxcXF6NGjBxYvXoy+ffu6YrPIQHaeBnsLryIlPhJp3dRGZQBYsDUfO09cwsBOUZie3tmobGqeB5ftwdELZegeG4avJt9htF75MkwNm7J2v7jcpWN6GS0j7b0dOHO1Eu0jQ5A9bZDZ5VraXlv2gallyutmarnW1mttn9gyjy9pP2OT+P8z84a6sSZEztHUv9NT1u7HD3nFqKkTEOCvwG1twtEyJBAnL12DTqdD6xZBOHu1ElAo8HBynNExOztPgyXbC1BSWY1b24QhNjyoyRwPyfV8PV5NbZ++nXGh9AZ+zNdAW6NDaJA/VP5+KL9RA6VCAaVSgc7qFujZNhwHzpWK7a6ebcOREh+Jg0WlZttd9rRjbGmfETUl3Wd/h8pqHUIClTj65mCXrTfh75tQqwP8lUDBXNcdC61dOzYWa9eOjaXzq5uhrRWg8lcg/+0hLltvv7nZKC7XIjpUhZy/p7lsvc7m0b1zzp07F++//z6ysrLQvXt35Obm4oknnsCcOXPw3HPPAQDmz5+PzMxMZGVlIT4+HrNmzcKRI0eQl5eHoKAgk8tdt24dxo4di+XLlyMlJQULFy7El19+ifz8fERFRdlUN1t6NfG1k6+922PqYGQ47K8Db8GE1bni9BmpCViyvUAsrxybjINFpZJhye0ikHu2RCwPTYzBpiMXJfMs33lKMk1yuwiculSBkqpaRAT7Y8zt7SXLzEhNAADJsITWISi4XGl2PfLxCa1DcN+tMUbLlW+P4fauHJsMABb3gXy9GakJKLxSKRkm3ycrxyZj3nfHxIbmjMFdjdYrT87Jt9/UfjWX0PMG1uLVsHGv54sXMdR0ePt32p6ew7LzNHhlwxGUXK+GQgFoax1v1vgrgfaRIThztRK1OvPTRQT748Br6Q6vh8iQr8erqe2Tt4mcISZUhRqdgJT4SIzo2cZk28eU7DyN1faZpfmJvI21mNUn0PRclUjTJ9D0XJVIe3DZHqNrR1ck0hZszTe6dnTF9ZU+gabnqkRav7nZuFiuFcsxXpxI8+gk2v/93/9BrVbj448/FoeNHDkSwcHBWLNmDQRBQGxsLKZNm4bp06cDAMrKyqBWq7Fq1SqMHj3a5HJTUlLQp08fLFmyBACg0+kQFxeHZ599FjNmzLCpbtYOPqZOyM44+TqSmHNGMs+W7TFcj6lEFgDJMKUC0Bl8++TlpLhw5F0oQ3Wd7V/RkEAlqmp0kuVYExyoRFW1haulRqJUAK2bq6Cp0Fqf+H8UAJoH+aPiRq1d8xjujvBgfzwmSySq/BWSg6l8nvhWIRiaaJwk9JZEGpNo1NR4+3fa1iSa/NzkSkykkbP4erya2j55u8PZQoP9UV51s600cUA8XhnazeS0w5fuxsGiUrGcFBeOqBYqfJ+nEYfd202Nj/6XXLOFr91IJ9/iqe1irpfrbSzOfhDEo9+Jdscdd2Dbtm04ceIEAODQoUP46aefMHhwfSa8sLAQxcXFSEu7mcEMCwtDSkoKcnJyTC6zuroa+/btk8yjVCqRlpZmdh4A0Gq1KC8vl/xZsrfwqsWyI/QXCyt2FWLC6lxkG5zcnTmPKda2R76efQbJMgDYf64Eh38vlQyTJ7rk5dNXrtmVQAOAymr7EmgA3JJAA+q3154EGlCf2NLp7KuvfHeUVtXim0MXJMPkDVn5POdLrmNdbpFkmLzsSeyNVyJyL0djdr0bj0MlVbbfzCDyJc44xzZmAg2AJIEGACp/P7PTllRWG5Uvydpn8rIljrbX52zKc7idTmQJ28XUlOmf+DtyvhxLthdgwdb8Bi/To5NoM2bMwOjRo9GlSxcEBASgZ8+eeOGFFzBmzBgAQHFxMQBArZbe4VGr1eI4uStXrqCurs6ueQAgMzMTYWFh4l9cXJzFuqfER1osO8KRxJyzknnWtke+XHnTSBCAZoHmGzCm3Kips2v6pkInKBo0vwJAWVW11ekM1eoEo3nsXYYr2RuvRORejsRsdp5G8qQIEbmGN55jT2gqzI5r3UJlVI6SDZOXLbG37e2sG95E5nhjzBI5i/zhEXnZER6dRFu/fj3Wrl2Lzz77DPv370dWVhYWLFiArKwsl9dl5syZKCsrE/+Kiizf/U7rpsbKscmYOCDeaT/ldCQx56xknrXtkS83OFD61WoR7I++VqaR81MooGxYvsgnRYeZftefrYIC/Iye1vOzsp+VivrPQzqP53449sYrEbmXIzHrjCe8GyLQ2oGTyEc54xzrSdGjra0zKo9KliYZ5GVL7G17N8avV4gMsV1MTVlESKDFsiM8unfOl156SXwaDQASExNx9uxZZGZmYty4cYiOjgYAaDQaxMTEiPNpNBokJSWZXGarVq3g5+cHjUZ6l0ej0YjLM0WlUkGlsv0uFFCfeHLmexD0iSx73rHgyDyWlmVufvl65B0CjL29PZLiwiVPDTx1ZweLnQbc3UWN+FYhFl94HxKolLz4MiLYH0EBfpKXFvorIXlJpfw9HAmtQ9A5OlSyXHtFBPujuk4nqYv8vWJyCgDRoSpJXa29Jy4mVIVXhkg7CrD2XhH5Prq7S31HAobbe9+tMdCU3xA7fQCk769LiouAOjRIMs/dXTz3HR+OxCsRuY8jMZsSH4kVuwobqUbW3fancLetm8idnHGOHSJrzzlLoJ8S1XXGr72wlAQb2CkKR86XS8oNaT/bO6/8WOaMX68QGWK7mJqyjNQEo478GsqjOxaIjIzE22+/jcmTJ4vDMjMz8cknn+DEiRNixwLTp0/HtGnTANS/KDEqKspqxwJ9+/bF4sWLAdS/Y6pt27bIyMhwWscCZPoFfvIXrcrLU9buF8tLx/QyuRz5PD3f2Cr2vKl/ybO8+1x5T6Fp7+0Qe6/MnjYIAIzWLe+OXb4eU+uVdwctX2/8jE0QUJ9AK/zfixTldZXXzVRXwPJ9IF+vfBmm9qupYYZM9a5qbR5PZUu8yj9vIm/nzd9pezoW2Ft4FRdKb+DcH5Vo2zIE50urcPrKNVRU1UpuZIQEKuGnVKJSWz9c/kSuAkBYsD+uaWtRq6sv94gLR/+EVth05CKKy6qgEwQIQn0CzRU9d1HT4evxamr7pqzdbzGRplQAKv/6DqMMKVD/VP3dXaIQ3yoE6349BygUeDg5DtPTO4ttlfaRIejZNtymRJa7ex9nRwTkSp7cLuZ6ud7G4OxjrEcn0caPH4/s7Gx8+OGH6N69Ow4cOIBJkybhySefxPz58wEA8+fPx7x585CVlYX4+HjMmjULhw8fRl5eHoKC6n/2ds8992DEiBHIyMgAAKxbtw7jxo3Dhx9+iL59+2LhwoVYv349jh8/bvSuNHOYRCPyHoxXIu/CmCXyHoxXIu/CmCVqGI/+OefixYsxa9YsPPPMM7h06RJiY2Px9NNPY/bs2eI0L7/8MiorKzFp0iSUlpaif//+2LJli5hAA4BTp07hypUrYvnhhx/G5cuXMXv2bBQXFyMpKQlbtmyxOYFGRERERERERERNi0c/iebJmMEn8h6MVyLvwpgl8h6MVyLvwpglahiP7p2TiIiIiIiIiIjIEzCJRkREREREREREZIXDSbRPP/0Ud955J2JjY3H27FkAwMKFC/Gf//zHaZUjIiIiIiIiIiLyBA4l0ZYtW4apU6diyJAhKC0tRV1dHQAgPDwcCxcudGb9iIiIiIiIiIiI3M6h3jkXL16MFStWYPjw4Zg3b544PDk5GdOnT3da5erq6vD6669jzZo1KC4uRmxsLMaPH49XX30VCoUCADB+/HhkZWVJ5ktPT8eWLVssLnvp0qV49913UVxcjB49emDx4sXo27ev0+puSnaeBnsLryIlPhJp3dgTqLdx1efnyevx5e/wg8v24OiFMnSPDcNXk+9wd3WIGmzK2v1ivC4d08vd1XE5S8er7DwN1ucWAQBGJccBgDit4f997ThHnsvXz0H95majuFyL6FAVcv6eBkAah53ULXBCU4FLFVpEtVBhVHKc1fjz5TYJkbst2JqPnScuYWCnKExP7+zu6jQ6dx1P3LVed32+vrKfHeqdMzg4GMePH0e7du3QokULHDp0CB06dMDJkydx2223oaqqqsEVA4C5c+fi/fffR1ZWFrp3747c3Fw88cQTmDNnDp577jkA9Uk0jUaDTz75RJxPpVIhIiLC7HLXrVuHsWPHYvny5UhJScHChQvx5ZdfIj8/H1FRUTbVzZZeTQw/LACYsDpXHLdybDLSuqmNvsDyD9haWb4eNiJsY+8+y87TGH1+szYeMWoQdpi5CToBUCqA05lDjRqNphqRPd/YipKqWkQE++Pdh5KM1vP8F/tRWa1DSKASR98cDMC4sS1fbvfZ30nmaT9jk7jMM/OGmtwe+Xpsmcdbvm/W4vXBZXuQe7ZELCe3i/DJixhqOqas3Y9NRy6K5aGJMV6VSLOn5zD9hfilCi0ullXhenUd/BVASVWtOI1SAQT4KaCtta/J4/+/5/WT4iLw14G3YMn2ApRUVmNYj9gmcVFBruHt5yBr8dpvbjYulmvFckyoCm8NT5S0KaxR+SugCvDDjeo61OkEKBRAre7m+NYtVHg4Oc7muPTVtrOvXKBS47IWswu25mPJ9gKxnJGa4LJznvz6wxXcdY3jrvW66/P1pf3sUBKtW7duyMzMxP333y9Joi1evBiffPIJ9u/f36BK6f3f//0f1Go1Pv74Y3HYyJEjERwcjDVr1gCoT6KVlpZi48aNNi83JSUFffr0wZIlSwAAOp0OcXFxePbZZzFjxgyblmHt4CP/sO7tpsb3eRqxPHFAPFT+fpIv8NDEGMlFT0ZqgtEX3LC8cmwyAOPk3IYD5yVPH3jyk03uWK+5QLKU0NQnmMyJCVVBU6GFzkI0KQAYjo4JVeFGTZ3kQs+akEAlusaESRrb/kppQ9IWKn/HLiYN1+OvBArmuubk1lDW4tXwhK3nqhM3UWPw9u+0rUk0eUPQlVx5UUG+zdfj1dT2hQf7o9SO9o+tMlITAAA7T1yCyt8P2to6DOwUhaS4cKs3tr2dL12gUuPy1HYx18v1est6Hfo559SpUzFlyhTcuHEDgiDgl19+weeff47MzEysXLmyQRUydMcdd+Cjjz7CiRMn0KlTJxw6dAg//fQT3n//fcl0O3bsQFRUFCIiInD33Xfj7bffRmRkpMllVldXY9++fZg5c6Y4TKlUIi0tDTk5OWbrotVqodXevItWXl5use76x9P1LlVoJeWU+Ej8Y9sJybC9hVcl5XWyZcjL8ukBYM7mYyi8UgkA2HTkIjQGdzdX7Cq0+cRm7yOehidQe9bTUI6sV37BtWR7AQ4WlYrDj5wvR+GVSjGhuWJXodV6GN5hNUeesrJlHrnKap0kgQbYn0ADYHcCzdR6HFmvq9gbr0TkXo7ErDsTaACwYtcpJtGoSXLGObYxEmhAfVzK2zhHzpcbjC9EUly4ZPz63CKTbUdzN2k99akr+XXB3sKrLqmfu9ZLtmO7mMi5HOpYYMKECZg/fz5effVVXL9+HY8++iiWLVuGf/zjHxg9erTTKjdjxgyMHj0aXbp0QUBAAHr27IkXXngBY8aMEae57777sHr1amzbtg3z58/Hzp07MXjwYLGzA7krV66grq4OarX04K5Wq1FcXGy2LpmZmQgLCxP/4uLiLNZdnjS7WCr9ievBolKo/P0kw8KDAyTlazdqJOWyqmpJWeXvZ7SM8yXXJeVDv5dKyqYSb3L6C5Mj58uxZHsBFmzNtzqPqcSUK5g6cVtz+vI1o/I3h85Lhv14XAPyXvbGKxG5l70xm52ncWsCDQCqHbgZQeQLPPkca8tNQnlb2RT9TdoVuwoxYXUusv/3axJzwz2B/ik7c2VfWy/ZzpNjlsgbOZREA4AxY8bg5MmTuHbtGoqLi/H777/jqaeecmbdsH79eqxduxafffYZ9u/fj6ysLCxYsEDSkcDo0aPxl7/8BYmJiRg+fDi+/fZb/Prrr9ixY4dT6zJz5kyUlZWJf0VFRRanj2qhkpSraqRJvZ0nLkFbKx0WHOiHlWOTMXFAPFaOTUbzIGlSzU8h/bi0tXVGywgKlCbVWjYLlJRtObHtPHHJYtmUkkqtxXJjceTErQrwMypHhEg/rwB/+0JDYdfUNzWT1YWcw954JSL3sjdmbblh0thuaR3i7ioQuYUzzrGOtpusUdqw4DYRzSRlfecihszdpHXk5q2rpHVTS64jXPU0mLvWS7Zju5jIuRxKohUWFuLkyZMAgGbNmokv4z958iTOnDnjtMq99NJL4tNoiYmJePzxx/Hiiy8iMzPT7DwdOnRAq1atUFBg+g51q1at4OfnB41GeudIo9EgOjra7HJVKhVCQ0Mlf5bIT8gDOraWlAd2isLATlFGw9K6qfHK0G5I66bGw7Jl3N1FOn1KfKRR0mjs7e0l5TkjEu0+sZmqlzXDerSxWG4sjpy45fv14eQ48R0aevL9mCC7WAoJlIZO73YRUPlLW27WGnIKAE/2j5cMky9DTuWvsDqNnDwfmGyirtbYO7272RuvRORe9sas/Nw3NDGmMatnJCLYH9nTBrl0nUSewhnn2MZKQg++VXosaN1CZXR8yEhNsNp2NHeT1tOfujK8jmgK6yXbsF1smfyazZZkPHkP+XWsM65rHepYYODAgXjyyScxbtw4yfA1a9Zg5cqVTnsKLDIyEm+//TYmT54sDsvMzMQnn3yCEydOmJzn999/R9u2bbFx40b85S9/MTlNSkoK+vbti8WLFwOo71igbdu2yMjIcFrHAoDxOxNMvWfM2rvHrPXeaWo9znhXgyPd3npTV8im6mptP8p7xDTVHX3nVzdDWytA5a9A/ttDjNYTP2MTBNQn0Ar/90JD+TSGvXUeeC3daJmm1iOfR95bpy11lfeGY2q97ugxxxlsiVdv3TYic7z5O+3IOVb/E8+8C2WorrP/p5bBAUqEBwfg8rX6TmJ6tY0Qj/UHi0oBCIhoFoiH+7T1+HMceR9fj1dT25f23g4UXK60unylov6v7n/vYm0WqERwoD+qqmtRVaNDcIAf7kxohVHJcWbb2460jb3tnWhEtvLkdrG71tth5ibohPpjzelM39/eprZeU9e1DeFQEi00NBT79+9HQoL06Z2CggIkJyejtLS0wRUD6nvezM7Oxocffoju3bvjwIEDmDRpEp588knMnz8f165dwxtvvIGRI0ciOjoap06dwssvv4yKigocOXIEKlX9T/TuuecejBgxAhkZGQCAdevWYdy4cfjwww/Rt29fLFy4EOvXr8fx48eN3pVmjq09hxGR+zFeibwLY5bIezBeibwLY5aoYRzqnVOhUKCiosJoeFlZmdkX+jti8eLFmDVrFp555hlcunQJsbGxePrppzF79mwAgJ+fHw4fPoysrCyUlpYiNjYW9957L9566y0xgQYAp06dwpUrV8Tyww8/jMuXL2P27NkoLi5GUlIStmzZYnMCjYiIiIiIiIiImhaHnkQbNmwYgoOD8fnnn8PPr/7F6HV1dXj44YdRWVmJ7777zukV9TTM4BN5D8YrkXdhzBJ5D8YrkXdhzBI1jENPos2fPx933XUXOnfujAEDBgAAdu3ahfLycvz4449OrSAREREREREREZG7OdQ7Z7du3XD48GGMGjUKly5dQkVFBcaOHYvjx4/j1ltvdXYdiYiIiIiIiIiI3MqhJ9EAIDY2FnPnznVmXYiIiIiIiIiIiDySzUm0w4cP49Zbb4VSqcThw4ctTnvbbbc1uGJERERERERERESewuafcyYlJYk9XCYlJaFnz55ISkoy+uvZs6dTK1hRUYEXXngB7dq1Q3BwMO644w78+uuv4nhBEDB79mzExMQgODgYaWlpOHnypNXlLl26FO3bt0dQUBBSUlLwyy+/OLXeRERERERERETkO2x+Eq2wsBCtW7cW/+8qEyZMwG+//YZPP/0UsbGxWLNmDdLS0pCXl4c2bdrgnXfewaJFi5CVlYX4+HjMmjUL6enpyMvLQ1BQkMllrlu3DlOnTsXy5cuRkpKChQsXIj09Hfn5+YiKinJa3bPzNNhbeBUp8ZFI66Z2yjJMLdPaemyZx5G6Ltiaj50nLmFgpyhMT+9sVLZlmfJ5bNkH9o53ZJnezpbP4sFle3D0Qhm6x4bhq8l3GM3Tb242isu1iA5VIefvaQBgcpivaD9jk/j/M/OGurEmRM7R1L7T2XkaLNlegJLKarRuoYK2tg5tW4bgfGkVTl+5BpWfEg/3aQsAkmPdlLX7xeOjpvwGDv9eimaBfugbH4lO6hY4oakAAIxKjvPJ8wV5Bl+PV1Pbl52nwZzNx3D2aiV0Qv04BYBmgUoEB/ojJT4SS8f0Eucz13a9UHoD5/6otNiWlPP1dqCrcX/6Hluu0RqD/PrEVdLe24EzVyvRPjIE2dMGuWy9Pd/YipKqWkQE++PAa+kuW6+7ttdd63X2OVYhCIJgzww1NTV4+umnMWvWLMTHxze4ApZUVVWhRYsW+M9//oOhQ29ubO/evTF48GC89dZbiI2NxbRp0zB9+nQAQFlZGdRqNVatWoXRo0ebXG5KSgr69OmDJUuWAAB0Oh3i4uLw7LPPYsaMGTbVzVrXwNl5GkxYnSuWV45NNnlSsZTISIoLlywjIzUBS7YXSJYJwGg9AMQTmbnx1pa74cB5cRn6Boxh3QBI5kluF4HcsyVieWhiDDYduWhx+xdszZcsIyM1weggbW0/2rqf7VmmYf3ccfKQs6VhYjjNwaJSyX419Vks33lK8nnFhKpwsVwrlkMClais1knGA5BME+NFiTRr8Wp4YNXzxYsYajq8/TttLWYBSJJfI3q2kRzXbaUAYE8jSKkA2kWG4JUhXXmhSE7j6/FqavtWjk22KWYjgv1xo1aHIH8lSqpqxeHyto1eUlw4MlITLN4gdqTtSOZxf3ofazFryzVaY3hw2R7J9UlyuwiXJNLS3tuBgsuVYjmhtWsSPPoEmp6rEmnu2l53rbcxzrF2dywQEBCA//f//h9mzZrVoBXbora2FnV1dUZPlAUHB+Onn35CYWEhiouLkZZ280I+LCwMKSkpyMnJMZlEq66uxr59+zBz5kxxmFKpRFpaGnJycszWRavVQqu9mUAoLy+3WPf1uUVGZfkJxfBAkXu2BP3mZotJiiPny5EUFy6ZfvXPZyTlOZuPIa2r9Mm59blF+D5PAwBYsasQ8a1CJOOXbC9An/YRkmE7T1wyWm7hlfov+KYjF4G1+xHfKkQ8mB45X47WzQMl8xy9UCYp//fkZavbv062j9blFiEpLlzS4NlbeFUyzd7Cq5LlWBuvZ9iQsmUew5PHkfP1n7W8bs56Ak4+zFwidcWuQpMNE8PGy4pdhQgNlob1Zlkj85UNR3C1UisZZpgcAyBJoJkab26Yp7A3XonIveyN2Slr94sX0JuOXMTugssWpzfHrruIAHQCUHilEhNW5/JCkZosZ5xjX9lwxKbp9BeYVTXSdompBBoAHCwqxYTVuZIbxPL2k2FyQF9mLDuO+9Pz2Ruz//qp0KjsiiTawaISi+XGYpjYMVVuLIYJNFPlxuKu7XXXehuDze9EMzR8+HBs3LjRyVUx1qJFC/Tr1w9vvfUWLly4gLq6OqxZswY5OTm4ePEiiouLAQBqtfRArVarxXFyV65cQV1dnV3zAEBmZibCwsLEv7i4uAZuHXD491JJWZ6UOH3lmqR8Q5bYKC6rgsrfTzLs5CXpPOdLr8vKVThwTrre05el85y9Kv1C7zp5Gd8cuiAZVnK9WlKuqZPWrVIrPQgckm0rAGhr6iTlazdqMGF1LlbsKsSE1bnIztMYbZ+8rH/azlwZuJlk0i/X2jIA48TiN4cuSJaxYGu+UV2tkdcjO09jNGzK2v1Ysr0AR86XY8n2AszZfEyyDHlyFjBOJF67Id338ovESxVa1Org0xojXomo8dgbs/IbNaUuangakh97iZoKZ5xj5TfznE3ebjVsP50vkbWNZWWyD/en57M3Zq/LrtHk5cYivz7x9esV8l4OJdE6duyIN998Ew8++CAyMzOxaNEiyZ8zffrppxAEAW3atIFKpcKiRYvwyCOPQKl0qOoOmzlzJsrKysS/oiLjZIahUclxFssAENFM+jSXQjZe5e+HlWOTMXFAPFaOTUabiGDJ+OiwYGhrZYkoWfLKTyldapvwYJyRJcnkTx3Jf+Ab2VwFpWw5Otk0shya0fjr1cYH3w6tm0vKzYMCJOW9hVeNtk9etoX8QkdbWyfZr6bulul/sqoXESL9rORJNlsupkw9nWjqqThDV69Zb2TKk4DWfqDt59rQcQt745WI3MvemG3VXCUpB7rhwGbqBgxRU+CMc2xkiMr6RA0gb7cZahPRzGKZ7MP96fnsjVn5Nam8TNTUOdTq/PjjjxEeHo59+/bho48+wgcffCD+LVy40KkVvOWWW7Bz505cu3YNRUVF+OWXX1BTU4MOHTogOjoaAKDRSJ8C0mg04ji5Vq1awc/Pz655AEClUiE0NFTyZ0laN7XVRM2cEYmS8pDEGEn54f+9xPiVod3q/x3SVTL+lSFdjRrxbcKlibbOamk9M1ITjOYJCZR+DaJaSBs2rwzpio5R0oRXa9kFTHSotBwjK9/VsTXkMlITJOWHZYnGlPhIq0+aWUtCmZpH/7NJ/X41ZXp6Z2SkJiCxTSgyUhOM6ipPsjl6MWVt+wbI9pupZKz8uxbgJz3VyU986d1jEC77yacsR2o0T0igEsntpD8Dlpc9ib3xSkTuZW/Mys+Hk+7q0JjVMzI0MYY/V6ImyxnnWHkb2F4JrUOMhg1NjBHbQvJ2m2H7ST5OXib7cH96PntjtresjS8vNxb5Nam87GvrjZBdj8nLvrZeeX5AXvYmdncsIKefXaFwTY66pKQE8fHxeOeddzBx4kTExsZi+vTpmDZtGoD633hHRUVZ7Vigb9++WLx4MYD6jgXatm2LjIwMp3UsYCtL78My9dtza+/UAix3NKCfx9ILmU3NY+qlofLOBwyXaapsy/bb2/uorS8zdVVPqdbmN1VXa98Be9dj+K4goL5RCUDyWcjrYqpziee/2I/Kah1CApU4+uZgAO7rMaehPPUFqkSNxdu/07acYy311PfDsWJU1woID/bHgoeSsHznKRy9UIY24cFoHhSAkkotbm0Tjpo6HX4pvIrr1XUI8FMYPZltqFmgH0IC/fBwn7ZetS/J87nrZdrO4ug5NjtPI7Y19PyVQICfEt1jw9CzbTgOnCsVe3Jr+b+ny/Q95U5Zux8/HtcgwF+Jsbe3N9k5laX2I3uTdB7uT+9iyznWHW1+d3VS4c7OMdzVO6e71ttvbjaKy7WIdmEHdY1xjnU4ifbxxx/jgw8+wMmTJwHU/8TzhRdewIQJExpUIbmtW7dCEAR07twZBQUFeOmllxAUFIRdu3YhICAA8+fPx7x585CVlYX4+HjMmjULhw8fRl5entghwT333IMRI0YgIyMDALBu3TqMGzcOH374Ifr27YuFCxdi/fr1OH78uNG70sxxVhKtMTjrhffOWK4reGq9THFVXZ2VwPQVtsSrp/TGSuQs3vyddtc5Vn8cVPn7QVtbJ/7ri8dF8izeepMKaPg51pfbH0SeyNeuY715veQazj7HOpREmz17Nt5//308++yz6NevHwAgJycHS5YswYsvvog333yzwRXTW79+PWbOnInff/8dLVu2xMiRIzFnzhyEhYUBqH8S7rXXXsNHH32E0tJS9O/fH//85z/RqVMncRnt27fH+PHj8frrr4vDlixZgnfffRfFxcVISkrCokWLkJKSYnO9PPngQ0RSjFci78KYJfIejFci78KYJWoYh5JorVu3Fl/wb+jzzz/Hs88+iytXrjitgp6KBx8i78F4JfIujFki78F4JfIujFmihnHorXk1NTVITk42Gt67d2/U1rq+m3kiIiIiIiIiIqLG5FAS7fHHH8eyZcuMhn/00UcYM2ZMgytFRERERERERETkSRzuz/Tjjz/G999/j9tvvx0AsHfvXpw7dw5jx47F1KlTxenef//9hteSiIiIiIiIiIjIjRxKov3222/o1au+x79Tp04BAFq1aoVWrVrht99+E6dTKBROqCIREREREREREZF7OZRE2759u7PrYVZFRQVmzZqFDRs24NKlS+jZsyf+8Y9/oE+fPgCA8ePHIysrSzJPeno6tmzZYnG5S5cuFXvn7NGjBxYvXoy+ffs22nYQEREREREREZH3cvjnnK4yYcIE/Pbbb/j0008RGxuLNWvWIC0tDXl5eWjTpg0A4L777sMnn3wizqNSqSwuc926dZg6dSqWL1+OlJQULFy4EOnp6cjPz0dUVFSjbo8rZOdpsLfwKlLiI5HWTd1oy5BPY63cWOtpLNbWY2q8I/O4giPrdVdd3aXf3GwUl2sRHapCzt/T3F0dogZrat/pBVvzsfPEJQzsVH8e1/9/enpnybjCK5XisW3pmF6SZVg63xwsKpUsk8iZfD1eTW1fdp4GS7YXoKSyGsN6xAKoj9uq6jqUVtWYjFEiosbQ1K57mpr2MzaJ/z8zb2iDl6cQBEFo8FIaSVVVFVq0aIH//Oc/GDr05sb27t0bgwcPxttvv43x48ejtLQUGzdutHm5KSkp6NOnD5YsWQIA0Ol0iIuLw7PPPosZM2bYtAxP7Ro4O0+DCatzxfLKscl2HwhsWYZ8mozUBCzZXmC2rF+G4QEKQIPXs3JssskLG8MLJvkFlC0XP9b2ganx1rbHGZ+NIxxZr7vq2lisxWu/udm4WK4VyzE+ehFDTYe3f6ftPccu2JovOTcYSm4XgdyzJSbHDU2MQXyrEOw8cQltW4Zg05GL4jj5+cZQRmoCE2nkNL4er6a2763hiZJ2hjkRwf7w81OaTKilvbcDZ65Won1kCLKnDbKrzva2C4l8iadex7qLr1332KIpJQ0NE2h6DU2kefSTaLW1tairq0NQUJBkeHBwMH766SexvGPHDkRFRSEiIgJ333033n77bURGRppcZnV1Nfbt24eZM2eKw5RKJdLS0pCTk2O2LlqtFlrtzQZAeXm5o5vVqPYWXjUq23L33LAxoa2tM1qGPLjW5xZJyqt/PiMpf7z7tKSsvxDRH6BW7CpEUly40TLldZWvZ52sPGfzMRReqQQAHDl/8zPRr+/I+XL8fPqqeAGlnyYpLtziU2TyCyd9WT9eXq/1uUVoF9lMMky+30x9Nq54ss7cei0xtX3edIC1N14NG/emykTextu+0w09x35z6ILZceYSaADww7FiVNfW30s0PIdYW+Y3h87zwpucxtfj1dT2mUtQy5VU1QJAfYJ77X4xkZb23g4UXK5v/xVcrkTaeztsTqQZJt31cc94Jl/mLdex7uLItZI3M0warthV2CSShs7m0Um0Fi1aoF+/fnjrrbfQtWtXqNVqfP7558jJyUFCQgKA+p9yPvDAA4iPj8epU6fw97//HYMHD0ZOTg78/PyMlnnlyhXU1dVBrZZ+UdRqNY4fP262LpmZmXjjjTecu4GNQOUv3eYD50qNEkjyhoK8MZHcLsLkMg2TPYeKSiXTlP+vkaN3o1onKZdUVuOVDUckw+TL2F1wGd/naSR1NSJ7cFKfQNNbtqMAf4qQJrP2n5NeQK3OOYMl2+vrqz9wANIEX2iwNDROX7kmGa9uIf3J8MlL19BJ3UIyTP5ZpMRHYsWuQknZFQcxU+v1dd4Sr0RUr6ExGxESiLN/XLd7Pn0CzZTi8ioL67P82ggiX+aMc+z5Evvj9bvfLmLOpjykxEfitKz9Jy9bsvPEJaMyk2jky9gutqypXSs1taRhY1C6uwLWfPrppxAEAW3atIFKpcKiRYvwyCOPQKmsr/ro0aPxl7/8BYmJiRg+fDi+/fZb/Prrr9ixY4dT6zFz5kyUlZWJf0VFRdZncgP5U2RnrkobFfKGg6lh8nm0tXVismfFrkJMWJ2L8hvSpJmcn+ybdWubMFytlN6JlF+6VNVIE287T1wySky1iwyxuN46AVAqLfcKW1MnXfPewqtGBxOjysnKVTXS/azTCUb7Xl5O66bGyrHJmDggXkyWmTqIOZup9VozKjnOYtnTeUu8ElG9hsZsRmqC0+skP1cYahMe7PT1EXkLZ5xjmwcF2D2PToDYDpU39QL8LLf9DLVtGWKxTORr2C62zJFrJW8mTxL6etKwMXh8Eu2WW27Bzp07ce3aNRQVFeGXX35BTU0NOnToYHL6Dh06oFWrVigoMP2YeKtWreDn5weNRiMZrtFoEB0dbbYeKpUKoaGhkj9PZC0o9C9ctjTM1DLkyZ3oMOlPbMNlT25FNAuUlGPDgxApu3Ov8pc2eDq0kjZiTP20VF42pWNUc0lZnni7u4vx9sq3uUPr5hbLAzq2lpSH9Yi16YCU1k2NV4Z2Ew/OrjqIyddry/TefDLxlnglonoNjVnDY9bQxBib57N02S0/Jxk694ftT70Q+RpnnGPlbTV7NQuUtjujQ21PbMeGB1ksE/katouts/dayZt5+3WeJ/Don3MaCgkJQUhICEpKSrB161a88847Jqf7/fffcfXqVcTEmG5EBwYGonfv3ti2bRuGDx8OoL5jgW3btiEjI6Oxqu8y+qAwfMdWvJWXp+qHGU4zwsR7ugwfc31lSFdsOHBenGZEzzaSFzI+3Ket5H0X+kSV4TRLH+0tWcbSMb2MXvSanaeRrHdgpyjJTz0TWoeI78QA6l8gPSo5TvxZqL6u8netmdo+w/0GwKhDAwBW96t83zvyeXmKtG5qj6pPYwoJVKLS4CfIIYEef3+ByCL5y/TlP9P3RYbHLP3xuW3LENTU6STnBENDEmMknQkMTYzBuT8qxeP6lLX7sbfwKsKDAyTnGlM3pIgc5evxqvJXQGvw02mVv8KorSYXGuSPTuoWZt9pOKBja0ns6nv3tEVT++kWEZFcU7rO81cCtTppuaE8undOANi6dSsEQUDnzp1RUFCAl156CUFBQdi1axe0Wi3eeOMNjBw5EtHR0Th16hRefvllVFRU4MiRI1Cp6p98uueeezBixAgxSbZu3TqMGzcOH374Ifr27YuFCxdi/fr1OH78uNG70sxpir2aWHsBvny8qekdeYm+teU+uGwPjl4oQ/fYMHw1+Q6H12Pv9pL3sCVeu8/+DpXVOoQEKnH0zcEuriGR85k6NnoLZ59jDY/n8psqth7r2ZsfNSZfj9fOr26GtlaAyl+B/LeHALgZlxdKb+DcH5Vo2zIEseFBZtuNgPRmZkNikm08asqa4nUsNW0Jf9+EWl19Aq1gbsN65gS8IIm2fv16zJw5E7///jtatmyJkSNHYs6cOQgLC0NVVRWGDx+OAwcOoLS0FLGxsbj33nvx1ltvSZJh7du3x/jx4/H666+Lw5YsWYJ3330XxcXFSEpKwqJFi5CSkmJzvXjwIfIejFci78KYJfIejFci78KYJWoYj0+ieSoefIi8B+OVyLswZom8B+OVyLswZokahi/+ISIiIiIiIiIisoJJNCIiIiIiIiIiIiuYRCMiIiIiIiIiIrKCSTQiIiIiIiIiIiIrmEQjIiIiIiIiIiKywq1JtP/+978YNmwYYmNjoVAosHHjRsl4QRAwe/ZsxMTEIDg4GGlpaTh58qRkmj/++ANjxoxBaGgowsPD8dRTT+HatWsW13vjxg1MmTIFkZGRaN68OUaOHAmNRuPszSMXyc7TYM6mPGTn2f4ZmppHPsxZy7WXfBkLtuZj2OJdWLA1v1HXY8t6fVn32d+h/YxN6D77O3dXhcgper6xFe1nbELPN7a6uyqNwtJxUH78MizLxxkuxxnnASJHpL23Awl/34S093a4uyqNot/cbMTP2IR+c7MlwxsSYw8u24Ous77Dg8v22D0vY5uIqOloP2OT+OcMCkEQBKcsyQHfffcddu/ejd69e+OBBx7Ahg0bMHz4cHH8/PnzkZmZiaysLMTHx2PWrFk4cuQI8vLyEBQUBAAYPHgwLl68iA8//BA1NTV44okn0KdPH3z22Wdm1zt58mRs2rQJq1atQlhYGDIyMqBUKrF7926b686ugR2TnafB3sKrSImPRFo3tVHZkeVNWJ0rlleOTTa5XMMyAKN55MMyUhOwZHuBZJoNB86Ly1g6ppfJdciXe7CoFDtPXMLATlGYnt7Z6j6R12NoYgw2HbkoqVdSXLjJ+tuzH+V1lW+vqfWaq783sBav3Wd/h8pqnVgOCVTi6JuDXVlFIqfq+cZWlFTViuWIYH8ceC3djTWyj7WYNXW81R+jw4MDUHC5UhyX3C4CuWdLTK5HfqwzZOo84Mh5isiatPd2SL6zCa1DkD1tkPsqZCdr8dpvbjYulmvFckyoCjl/T8OUtfsl8ZfcLgLa2jq0bRmC2PAgpMRHYt53x3DmaiXaR0r3yYPL9kjiOrldBL6afIdN9TXXdiRqKngdS02JqcTZmXlDG7RM/wbN3UCDBw/G4MGmL1QFQcDChQvx6quv4v777wcArF69Gmq1Ghs3bsTo0aNx7NgxbNmyBb/++iuSk+sTCYsXL8aQIUOwYMECxMbGGi23rKwMH3/8MT777DPcfffdAIBPPvkEXbt2xc8//4zbb7+9kbbW91lLiBk2WlbsKpRcoKzYVWhzI2bB1nwxMaWtrZOM21t4FQAsrude2Tr2Fl7F2avXJcO+OXRBUp6z+RgKr9Q3cDcduQiNQeNNX/f1uUVm5zlyvhwAjBJR8n0ir9uPx6V3SNf9es5om9fnFuH7/91JNbcfDffZ9PTO4n7S23nikqS86+Rl2f4479VJNGsME2imykTexjCBZqrs7eTHMMPj7ZVr1ZJx+8wk0ADgh2PFZset+/Wc0Tr+se2ExZsiRI4wTKCZKns7wwSavrxga75RAlvfrtK3mVbsKhTHFVyuRNp7O8RE2v5z0riWly0xTI7ry0yiERGRrdyaRLOksLAQxcXFSEtLE4eFhYUhJSUFOTk5GD16NHJychAeHi4m0AAgLS0NSqUSe/fuxYgRI4yWu2/fPtTU1EiW26VLF7Rt2xY5OTlmk2harRZa7c1GQHl5uTM202fIk0GmEjlzNh+TlD/efVpSXp9bZLURs2Brvtj4OXK+HMntIiTjVf5+eGXDEcmwZTukjaXdBVck5QulN3DykvQnwGVV0ouwoj+kDVr5RdmS7QUoq6qRDDtzRTrP6p/PGF14yRty8noE+CtRVWOQ0FEooPL3sziPPpmnT2geLCqV7DMASImPlDROB3aKEscBQHCAH8pv3LzoViq96/WJjFci72JvzMqPYcVlVWantfS4fXWt+bEl16XnAWs3RYiaCmecY+U3K21x+opzkovnS6sslsl+Df1lCTUutouJnMtjr4yLi+vvDqvV0gOxWq0WxxUXFyMqKkoy3t/fHy1bthSnMbXcwMBAhIeHm12uKZmZmQgLCxP/4uLi7N0knyZ/KkBeBoCr16R3IrU10qd9LlVIx5uyTva019ELZdJl1tbhaqV0OXWya6SqGumTXOf+qIROJ53ouuxJpFrZg0nyy67TV64ZLUM+zXWt8ZMgJZXSizSdTsDKscmYOCAeK8cmY0BCa8n4vu1b4oSmQjJMvl9PXrqGCatzsWJXISaszjV6muKbQ+eR1k0tWU9SXLhkmmYqaX49LDjAqO6ejPFK5F3sjVn5MSw6LNjpddJZyL45kgAg8hXOOMdGhATaPU+An0L8f+vmKsk4edmSNuHBFstkH/2NdH27k++Z8zxsFxM5l8cm0TzNzJkzUVZWJv4VFRVZn6kJ0b/Py1wZAAZ0lCaE5A2eqBY2NIBkr/AL8Jd+hVPiIxEpW67KXyEpd2gVIikP7BSFYT2kP/0NCrQvNFR+SqNlBMrWK09MATCaZ1iPWKR1U+OVod2Q1k2N2PAgyXh5GQBUAdIn0+TJPCik9YgIqd8/huuRJz11OmnW0KbPxoMwXom8iyMxa3gMe2VIV4fWG+hn/lhv6aLckQQAka9wxjk2IzXB7nmiQ28mu3rIbv7Jy/as25G60E223Egn92K7mMi5PDaJFh0dDQBGvWZqNBpxXHR0NC5dkr7Lqba2Fn/88Yc4janlVldXo7S01OxyTVGpVAgNDZX80U3ypwJMPcq9dEwvDE2MQavmgRiaGIM5IxIl40clW78r8nCftpLy2NvbG613znDpcpc+2luy3uxpg5CRmoDENqHiC/Onp3eWDBt7e3vJMhJah1gsP9ynrdEyJg24xaiucvJ55D8PMpWclO+nh2VleWJOPt5UY1G+nmE92kjKtnw2nsTeeA2RJU3lZSJvExHsb7HsaRp6jjU8Bw1NjJGMiwlVGZXF4/RdHYzGBQcokdwuwugcZYgX3eRMvh6vps6x+piVPwlviWH7Rt4usaedYkublWxny410ci9exxI5l1t75zSkUCgkvXMKgoDY2FhMnz4d06ZNA1D/++2oqCisWrVK7FigW7duyM3NRe/evQEA33//Pe677z78/vvvZjsWaN26NT7//HOMHDkSAJCfn48uXbpYfCeaHHs1cQ5H3qEgf0m+s5ZrbT1T1u6X9M5pSz1smcYaU9tirZdTR3pBdXbPqZ7ElnjV99DJnjnJV+h76PS2njmBhp9j5cfeB5ftwdELZegeG2bUg5+l47S892RfOSaS5/H1eLV0jtXH2YXSGzj3R6Wkd05LvZz7UjvF2/Gz8C68jqWmxrCHzob2zAm4OYl27do1FBTUv/C8Z8+eeP/995GamoqWLVuibdu2mD9/PubNm4esrCzEx8dj1qxZOHz4MPLy8hAUVP+ztsGDB0Oj0WD58uWoqanBE088geTkZHz22WcAgPPnz+Oee+7B6tWr0bdvXwDA5MmTsXnzZqxatQqhoaF49tlnAQB79uyxue48+BB5D8YrkXdhzBJ5D8YrkXdhzBI1jFufF8/NzUVqaqpYnjp1KgBg3LhxWLVqFV5++WVUVlZi0qRJKC0tRf/+/bFlyxYxgQYAa9euRUZGBu655x4olUqMHDkSixYtEsfX1NQgPz8f169fF4d98MEH4rRarRbp6en45z//6YItJiIiIiIiIiIib+QxP+f0NszgE3kPxiuRd2HMEnkPxiuRd2HMEjUM355NRERERERERERkBZNoREREREREREREVjCJRkREREREREREZAWTaERERERERERERFa4NYn23//+F8OGDUNsbCwUCgU2btwoGS8IAmbPno2YmBgEBwcjLS0NJ0+elEzTvn17KBQKyd+8efMsrvfGjRuYMmUKIiMj0bx5c4wcORIajcbZm0dulJ2nwZxNecjOM/25Whtv6zRyC7bmY9jiXViwNd/kMpyxTACYsnY/kt/+AVPW7je5XFPz2Lt9tizDlzS17SXfJz9ONCWWjon2HJcdOWYTOaLf3GzEz9iEfnOz3V2VRvHgsj3oOus7PLhsjzhMH18LtuYzBomIqNH0fGMr2s/YhJ5vbHXK8tzaO+d3332H3bt3o3fv3njggQewYcMGDB8+XBw/f/58ZGZmIisrC/Hx8Zg1axaOHDmCvLw8BAUFAahPoj311FOYOHGiOF+LFi0QEhJidr2TJ0/Gpk2bsGrVKoSFhSEjIwNKpRK7d++2ue7s1cRzZedpMGF1rlheOTYZad3UNo+3dRq5BVvzsWR7gVgemhiDTUcuiuWM1ATJeP0ys/M02Ft4FSnxkUjrpsaCrfnYeeISBnaKAgDJPBmpCSi8UilZbnK7COSeLbG43sIrleI6lo7pZXL7AIjTHCwqNVrv9PTOkrpNT+9scX94EmvxKv/s9NtL5K2mrN0vOQ4MTYzB0jG93Fgj+zhyjtUfS1X+fhaPxYbMHZf1yzM8Tg5NjMG5Pyq97vhHnq/f3GxcLNeK5ZhQFXL+nubGGtnHWrw+uGyPpJ2S3C4Cfx14iyS+9Nq1bIbq2jqUVtWgTXgwCi5XiuNMtecM20/2aMi8RN6O17HUlPR8YytKqmrFckSwPw68lt6gZfo3tFINMXjwYAwePNjkOEEQsHDhQrz66qu4//77AQCrV6+GWq3Gxo0bMXr0aHHaFi1aIDo62qZ1lpWV4eOPP8Znn32Gu+++GwDwySefoGvXrvj5559x++23N3CryN32Fl41Khs2kKyNt3UauW8OnZeU/3vyssXx63OLAEBsRK7YVSi52DtyvhytmwdK5tl54hIult2QDDtYVCop/3j8kqS8+uczKP/fgWPTkYvA2v2oqdNJplmyvUBczopdhQgOVBotQz+dvm4AfOZC0vAiWl/2lW2jpmmzLGkkL/saecLLkPyYaGh1zhlJeX1ukXhhLT8PGB6bAd85/pH7GSbQTJW9nWECTV+Wn3f1zv5xXfy/YQINqD83m0pyr9hVaNPNTr2GzEtERN7FMIFmquwIj30nWmFhIYqLi5GWdvNOXFhYGFJSUpCTkyOZdt68eYiMjETPnj3x7rvvorbW/I7Zt28fampqJMvt0qUL2rZta7RcQ1qtFuXl5ZI/8kwp8ZENKgOAyt/PqGztJwU3aqWJKZ1OsDj+UoXWqBH5w7FiSflatfS73LZlCAKUCsmwWtl6aurqJOXrWukyfjyuwaUKaQP99JVr0rpW62TlOqz7X9JPT172JIxXaurkj5i77ZFzGzU0ZuUJL0NVNXVmx12XHWO/z9Ngxa5CTFidiwulN8zMBXxz6IJd9SPyJc44x8rbHbbIu3BzPXM2H5OMk5ctWS9rv8jLRL6G7WIi5/LYJFpxcX0yQa2W3hlSq9XiOAB47rnn8MUXX2D79u14+umnMXfuXLz88ssWlxsYGIjw8HCLy5XLzMxEWFiY+BcXF+fAVpErpHVTY+XYZEwcEO/w3UVtrfSi64SmAhNW54oXV6YSaUH+0nDSyX4pXVVtfCFXUlktHSC70g3wky6zpk6H0qoai3VXKqTzBPhbLgPGiTaFNE+HNhHNjOpqVHcPwngl8i4NjVlLCS9H/Xa+1Oy4sirPPf4RNTannGMdyOxXGzxFf+Wa9GagvGyJ/EaivEzka9guJnIuj02i2Wrq1KkYNGgQbrvtNvz1r3/Fe++9h8WLF0Orde4JcebMmSgrKxP/iop418qTpXVT45Wh3Uwm0Ez9VFPO1NNp1uYZ1qONpBwdFiwpqwKkT7dFtVBhWI9YybA/d5P+LLlDq+ZG65EnweQ5sTYR0vV2VkvfdTD29vaIaqEyWq6krrIn8V4Z0hXe9GwL45XIuzQ0Zs/9UWl2nJ/C7CijGxWGIkLMHyd1nnv4I2p0zjjHdmht3L5p3TzQqE1jyHDcXR1bS8bJy5bI20DW2kRE3o7tYiLncus70SzRv+NMo9EgJiZGHK7RaJCUlGR2vpSUFNTW1uLMmTPo3Nn4fSXR0dGorq5GaWmp5Gk0jUZj8b1qKpUKKhVPsr4gJT4SK3YVSspy+qfZ9O/GAep/5mNpHv37cfQv3k+KC5e8o+fh5DjJzzdHJceJST7Dl/WPMHjZLQDJMkYlx6GTuoVkOX8dKO04YETPNpJ5MlITcLCoVLKO7DyNZHuS4qSdE9zdJQojeraRvHRXPk1SXITxzvUQ9saryl8Bba0gKRN5s5BAJSoNfpYdEujZ98waeo4d2ClKfFeZ3ORBCWbfv3R3F7XkWAfA7PHX0AA7LtiJrFEqpIlZpYefguyN14TWIZL3myW0DkFGaoJRfD3cpy0A4/eU6qV3v3k9sHRML2DtfkmnSbYalRwnaQONSuZTOeTbeB1L5Fxu7Z3TkEKhkPTOKQgCYmNjMX36dEybNg1AfU8iUVFRWLVqlaRjAUNr167F2LFjceXKFUREGF/kl5WVoXXr1vj8888xcuRIAEB+fj66dOmCnJwcmzsWYK8m3s2RXpmcMY+z1mutl0xb1iOfZooNjdEHl+3B0Qtl6B4bhq8m32FT/T2BLfHa+dXN0NYKUPkrkP/2EBfXkMj5us/+DpXVOoQEKnH0TdOd+HgqR86xhsfFpLhws8feDQfO23zh7eh8RPbqMHMTdEJ9Au105lB3V8cutsRr2ns7cOZqJdpHhiB72iAA9fG1ZHsBSiq1GNajjdieMYxlee/izsLeOakp43UsNTXtZ2wS/39mXsPPsW5Nol27dg0FBfV3m3r27In3338fqampaNmyJdq2bYv58+dj3rx5yMrKQnx8PGbNmoXDhw8jLy8PQUFByMnJwd69e5GamooWLVogJycHL774IgYPHoysrCwAwPnz53HPPfdg9erV6Nu3LwBg8uTJ2Lx5M1atWoXQ0FA8++yzAIA9e/bYXHcefIi8B+OVyLswZom8B+OVyLswZokaxq0/58zNzUVqaqpYnjp1KgBg3LhxWLVqFV5++WVUVlZi0qRJKC0tRf/+/bFlyxYEBQUBqH809YsvvsDrr78OrVaL+Ph4vPjii+JyAKCmpgb5+fm4fv1ml9kffPABlEolRo4cCa1Wi/T0dPzzn/900VYTEREREREREZG38Zifc3obZvCJvAfjlci7MGaJvAfjlci7MGaJGsaz3zRMRERERERERETkAZhEIyIiIiIiIiIisoJJNCIiIiIiIiIiIiuYRCMiIiIiIiIiIrLCrUm0//73vxg2bBhiY2OhUCiwceNGyfh///vfuPfeexEZGQmFQoGDBw8aLePGjRuYMmUKIiMj0bx5c4wcORIajcbiegVBwOzZsxETE4Pg4GCkpaXh5MmTTtwyIvI22XkazNmUh+w8y8cPIiIiIiIi8g7Ovs7zd8pSHFRZWYkePXrgySefxAMPPGByfP/+/TFq1ChMnDjR5DJefPFFbNq0CV9++SXCwsKQkZGBBx54ALt37za73nfeeQeLFi1CVlYW4uPjMWvWLKSnpyMvLw9BQUFO2z4iT5Gdp8HewqtIiY9EWje1TdOYmmfK2v3isKVjerlyExpVdp4GE1bnAgBW7CrEyrHJZvcTkbdYsDUfO09cwsBOUZie3tnd1XEpS8c8S8c6AFaPlUSNwdfjNe29HThztRLtI0OQPW0QgPpYXJ9bBAAYlRwHoD7+VP5+0NbWWY1DW9o2RETUtDXGdZ5CEATBGZVrKIVCgQ0bNmD48OFG486cOYP4+HgcOHAASUlJ4vCysjK0bt0an332GR588EEAwPHjx9G1a1fk5OTg9ttvN1qWIAiIjY3FtGnTMH36dHE5arUaq1atwujRo22qL7sGpsbi7Eah4YEDgMkDh3yajNQELNleIJlnw4Hz2HTkojhsaGKM1yTSrMVrypxsaCq0YlndQoW9r6S5sopETrVga74khjNSE7zqwrwh51hLxzxrxzpDK8cmAwATbNTofD1e097bgYLLlWI5oXUIZgzuKolFc8xd7NjStiEi03gdS01Jwt83o1Z3M+Xlr1SgYO6QBi3TrU+iNdS+fftQU1ODtLSbF7tdunRB27ZtzSbRCgsLUVxcLJknLCwMKSkpyMnJMZtE02q10GpvXmSXl5c7cUuI6pnKlAPWL9wsPVmxt/CqZNq9hVeNptHfCdZb9+s5SXl9bhF+Pi1dzq6Tlxu0rY3J3ng1TKCZKhN5m50nLhmVPfmi3JnnWHPHPABGx7pvDl0wu5z1uUX4/n+P/a/YVSgO59Oq5Gyrfz5jVPaleDVMoOnL8jg1Z31ukclYsxTnRCTF61hqygwTaKbKjvDqjgWKi4sRGBiI8PBwyXC1Wo3i4mKz8+insXUeAMjMzERYWJj4FxcX17DKE5kgbxSuzy3ChNW5WLGrEBNW55r8Hbc+8aafZsHWfElZ5e8nmT4lPtJonpOXrkmmKauqlZRPXroGpUK6XnnZkzBeqakb2CnKYtnTODNm9U+NmSpfkiXIy6qqHVqHrQkAIltUyM658rKncUa8yuPUHHnMmpvf1uURNUVsFxM5l1cn0Vxp5syZKCsrE/+Kioqsz0RkJ2uNQFMXbvJh8idQtLV1WDk2GRMHxItPT8jn0cky8n6yI4NOJyAsOEAyTF72JIxXauqmp3dGRmoCEtuEesVPw5wZs2nd1EbHPL2oFirJtPKbke1aNhPn07+jyRResJMzKRSWy57G3nhNbhdhVNbHabuWzSzOK49ZPUtxTkRSbBcTOZdX/5wzOjoa1dXVKC0tlTyNptFoEB0dbXYe/TQxMTGSeQzftyanUqmgUpk+kRM5i75RaPgOnu8Nnj4zdeGWEh8p+anRwE5ROHK+XDI+rZta0sCUzzOsR6zkfSx3d1FL3n82rEcsAEimGdajjUPb6Ar2xmtMqAoXy7WSMpG3m57e2eOTZ3rOPsfKj3l6o5LjJMfUAR1bGx3rDPeZ/HjMd6JRY+jVNgK5Z0skZU9mb7x+NfkOPLhsD45eKEP32DB8NfkOABDjyPDdZq2bB+LytZtPiFpKZpuLcyKS4nUsNWWNcZ3n1Um03r17IyAgANu2bcPIkSMBAPn5+Th37hz69etncp74+HhER0dj27ZtYtKsvLwce/fuxeTJk11VdSKz5I1Cw4s4U41FeeItrZsaSXHhDZ4n3kxPYb7Ye1jO39PQb242isu1iA5VIefv7FSAyBeZOvaZO9bppzc8hvKCnRqDuSSTLzG3TaZikr1uEhGRszTGdZ5be+e8du0aCgrqn2zp2bMn3n//faSmpqJly5Zo27Yt/vjjD5w7dw4XLlzA0KFD8cUXX6Bz586Ijo4WnyibPHkyNm/ejFWrViE0NBTPPvssAGDPnj3ierp06YLMzEyMGDECADB//nzMmzcPWVlZiI+Px6xZs3D48GHk5eUhKCjIprqzVxMi78F4JfIujFki78F4JfIujFmihnHrk2i5ublITU0Vy1OnTgUAjBs3DqtWrcLXX3+NJ554Qhyv7znztddew+uvvw4A+OCDD6BUKjFy5EhotVqkp6fjn//8p2Q9+fn5KCsrE8svv/wyKisrMWnSJJSWlqJ///7YsmWLzQk0IiIiIiIiIiJqWtz6JJo3YwafyHswXom8C2OWyHswXom8C2OWqGHYOycREREREREREZEVTKIRERERERERERFZwSQaERERERERERGRFUyiERERERERERERWeHWJNp///tfDBs2DLGxsVAoFNi4caNk/L///W/ce++9iIyMhEKhwMGDB42WMWjQICgUCsnfX//6V4vrFQQBs2fPRkxMDIKDg5GWloaTJ086ccuIiIiIiIiIiMiXuDWJVllZiR49emDp0qVmx/fv3x/z58+3uJyJEyfi4sWL4t8777xjcfp33nkHixYtwvLly7F3716EhIQgPT0dN27ccHhbiIiIyLdk52kwZ1MesvM07q4K+Th+14iIiLyDvztXPnjwYAwePNjs+McffxwAcObMGYvLadasGaKjo21apyAIWLhwIV599VXcf//9AIDVq1dDrVZj48aNGD16tG2VJyKfsmBrPnaeuISBnaIwPb2zu6tDRG6WnafBhNW5AIAVuwqxcmwy0rqp3Vwr8kX8rhERETWe7DwN9hZeRUp8pFPOrz7xTrS1a9eiVatWuPXWWzFz5kxcv37d7LSFhYUoLi5GWlqaOCwsLAwpKSnIyckxO59Wq0V5ebnkj8hbOOsOt7fcKbc3XhdszceS7QU4cr4cS7YXYMHWfBfVlIgAzzzH7i28arFM5Cze9l1zJF7NtR+8pV1B5M088RxL5Cr6G1UrdhViwupcp5xvvD6J9uijj2LNmjXYvn07Zs6ciU8//RSPPfaY2emLi4sBAGq1NAOpVqvFcaZkZmYiLCxM/IuLi3POBhA1MmcdOBrjANRY7I3XnScuWSwTUePyxHNsSnykxTKRs3jbd83eeDXXfvCmdgWRN/PEcyyRq6zPLbJYdoTXJ9EmTZqE9PR0JCYmYsyYMVi9ejU2bNiAU6dOOXU9M2fORFlZmfhXVNTwnU/kCs66w+1Nd8rtjdeBnaIslomocXniOTatmxorxyZj4oB4/ryOGpW3fdfsjVdz7QdvalcQeTNPPMcSeTO3vhOtMaSkpAAACgoKcMsttxiN1787TaPRICYmRhyu0WiQlJRkdrkqlQoqlcq5lSVygZT4SKzYVSgpu3M5rmBvvOrfgcZ3ohG5h6eeY9O6qT0+oUG+wZu+a/bGq7n2gze1K4i8maeeY4lcYVRyHL43eNJ5VHLDn8T0uSTawYMHAUCSIDMUHx+P6OhobNu2TUyalZeXY+/evZg8ebKLaknkOvo73A19maKzluOppqd3ZvKMiIjIycy1H3y9XUFERO7XGOcatybRrl27hoKCArFcWFiIgwcPomXLlmjbti3++OMPnDt3DhcuXAAA5OfXv+w7Ojoa0dHROHXqFD777DMMGTIEkZGROHz4MF588UXcdddduO2228TldunSBZmZmRgxYgQUCgVeeOEFvP322+jYsSPi4+Mxa9YsxMbGYvjw4S7dfiJXcdYdbm+6U05ERESewVz7ge0KIiJqbM4+17g1iZabm4vU1FSxPHXqVADAuHHjsGrVKnz99dd44oknxPGjR48GALz22mt4/fXXERgYiOzsbCxcuBCVlZWIi4vDyJEj8eqrr0rWk5+fj7KyMrH88ssvo7KyEpMmTUJpaSn69++PLVu2ICgoqDE3l4iIiIiIiIiIvJRCEATB3ZXwRuXl5QgLC0NZWRlCQ0PdXR0isoDxSuRdGLNE3oPxSuRdGLNEDeNz70RzFX3usby83M01IfJMLVq0gEKhcHc1ADBeiazxpHgFGLNE1nhSzDJeiSzzpHgFGLNEltgSr0yiOaiiogIAEBfX8N4diHyRJ93dYrwSWeZJ8QowZoms8aSYZbwSWeZJ8QowZokssSVe+XNOB+l0Oly4cKHBdxbKy8sRFxeHoqIijzq4ejvu18Zhz371pLtutsQrvzOuw33tGt4ar4D1mOV3yLPw83A9T4pZxmvDcR9Z5837yJPiFWC72BRur29zdpuYT6I5SKlU4k9/+pPTlhcaGtokvsCuxv3aOLxtv9oTr962bd6M+9o1vHE/2xqz3rhtvoyfR9PEeHUe7iPruI8aju1i87i9vs1Z26t0Ql2IiIiIiIiIiIh8GpNoREREREREREREVjCJ5mYqlQqvvfYaVCqVu6viU7hfG4cv71df3jZPw33tGr68n31527wRPw+yhN8P67iPrOM+cq2mtr+5vb7N2dvLjgWIiIiIiIiIiIis4JNoREREREREREREVjCJRkREREREREREZAWTaERERERERERERFYwiUZERERERERERGQFk2gukJmZiT59+qBFixaIiorC8OHDkZ+fL5nmxo0bmDJlCiIjI9G8eXOMHDkSGo3GTTX2TvPmzYNCocALL7wgDuN+dcz58+fx2GOPITIyEsHBwUhMTERubq44XhAEzJ49GzExMQgODkZaWhpOnjzpxhrbZunSpWjfvj2CgoKQkpKCX375xeL0X375Jbp06YKgoCAkJiZi8+bNLqqp97NnX69atQoKhULyFxQU5MLaeqf//ve/GDZsGGJjY6FQKLBx40ar8+zYsQO9evWCSqVCQkICVq1a1ej1bAz2xjLZx1ntlnPnzmHo0KFo1qwZoqKi8NJLL6G2ttaVm0IuwvOrdfbsoxUrVmDAgAGIiIhAREQE0tLSmsRxztFj+xdffAGFQoHhw4c3bgWbiKZyjrXlXOfLTF07+yJr17WOYBLNBXbu3IkpU6bg559/xg8//ICamhrce++9qKysFKd58cUX8c033+DLL7/Ezp07ceHCBTzwwANurLV3+fXXX/Hhhx/itttukwznfrVfSUkJ7rzzTgQEBOC7775DXl4e3nvvPURERIjTvPPOO1i0aBGWL1+OvXv3IiQkBOnp6bhx44Yba27ZunXrMHXqVLz22mvYv38/evTogfT0dFy6dMnk9Hv27MEjjzyCp556CgcOHMDw4cMxfPhw/Pbbby6uufexd18DQGhoKC5evCj+nT171oU19k6VlZXo0aMHli5datP0hYWFGDp0KFJTU3Hw4EG88MILmDBhArZu3drINXUuR75fZB9ntFvq6uowdOhQVFdXY8+ePcjKysKqVaswe/Zsd2wSNSKeX62zdx/t2LEDjzzyCLZv346cnBzExcXh3nvvxfnz511cc9dx9Nh+5swZTJ8+HQMGDHBRTX1bUzrH2nKu81Xmrp19jS3XtQ4RyOUuXbokABB27twpCIIglJaWCgEBAcKXX34pTnPs2DEBgJCTk+OuanqNiooKoWPHjsIPP/wgDBw4UHj++ecFQeB+ddTf/vY3oX///mbH63Q6ITo6Wnj33XfFYaWlpYJKpRI+//xzV1TRIX379hWmTJkiluvq6oTY2FghMzPT5PSjRo0Shg4dKhmWkpIiPP30041aT19g777+5JNPhLCwMBfVzjcBEDZs2GBxmpdfflno3r27ZNjDDz8spKenN2LNnM/e7xc1nCPtls2bNwtKpVIoLi4Wp1m2bJkQGhoqaLVa124ANSqeX61r6HGrtrZWaNGihZCVldVYVXQ7R/ZRbW2tcMcddwgrV64Uxo0bJ9x///0uqKlva8rnWPm5zleZu3b2Rdauax3FJ9HcoKysDADQsmVLAMC+fftQU1ODtLQ0cZouXbqgbdu2yMnJcUsdvcmUKVMwdOhQyf4DuF8d9fXXXyM5ORkPPfQQoqKi0LNnT6xYsUIcX1hYiOLiYsl+DQsLQ0pKisfu1+rqauzbt09SZ6VSibS0NLN1zsnJMfpOpaene+w2egpH9jUAXLt2De3atUNcXBzuv/9+HD161BXVbVJ84Tvt6PeLGsaRdktOTg4SExOhVqvFadLT01FeXs749iE8v1rnjOPW9evXUVNTI8agr3F0H7355puIiorCU0895Ypq+rymfo6Vn+t8lblrZ19k7brWUUyiuZhOp8MLL7yAO++8E7feeisAoLi4GIGBgQgPD5dMq1arUVxc7IZaeo8vvvgC+/fvR2ZmptE47lfHnD59GsuWLUPHjh2xdetWTJ48Gc899xyysrIAQNx3hhdG+rKn7tcrV66grq7OrjoXFxd71TZ6Ckf2defOnfGvf/0L//nPf7BmzRrodDrccccd+P33311R5SbD3He6vLwcVVVVbqqVfRz5flHDONpuMfd9048j38Dzq3XOOG797W9/Q2xsrM9e9Dqyj3766Sd8/PHHTrkgpnpN+Rxr6lzniyxdO/sia9e1jvJ3Uv3IRlOmTMFvv/2Gn376yd1V8XpFRUV4/vnn8cMPP/Al5E6k0+mQnJyMuXPnAgB69uyJ3377DcuXL8e4cePcXDvyRf369UO/fv3E8h133IGuXbviww8/xFtvveXGmhER2y1E7jVv3jx88cUX2LFjB9u7/1NRUYHHH38cK1asQKtWrdxdHfIBTeFc1xSvnRvrupZPorlQRkYGvv32W2zfvh1/+tOfxOHR0dGorq5GaWmpZHqNRoPo6GgX19J77Nu3D5cuXUKvXr3g7+8Pf39/7Ny5E4sWLYK/vz/UajX3qwNiYmLQrVs3ybCuXbvi3LlzACDuO3kvbJ68X1u1agU/Pz+76hwdHe1V2+gpHNnXcgEBAejZsycKCgoao4pNlrnvdGhoKIKDg91UK/s44/tFtmtIu8Xc900/jnwDz6/WNeS4tWDBAsybNw/ff/+9T78A3N59dOrUKZw5cwbDhg0TrwFWr16Nr7/+Gv7+/jh16pSrqu5Tmuo51ty5ztdYu3auq6tzdxWdztp1raOYRHMBQRCQkZGBDRs24Mcff0R8fLxkfO/evREQEIBt27aJw/Lz83Hu3DnJ0xkkdc899+DIkSM4ePCg+JecnIwxY8aI/+d+td+dd95p1L3ziRMn0K5dOwBAfHw8oqOjJfu1vLwce/fu9dj9GhgYiN69e0vqrNPpsG3bNrN17tevn2R6APjhhx88dhs9hSP7Wq6urg5HjhxBTExMY1WzSfKF77Qzvl9knTPaLf369cORI0ckPbr98MMPCA0NNWrQkvfi+dU6R49b77zzDt566y1s2bIFycnJrqiq29i7j7p06WJ0DfCXv/xF7H06Li7OldX3GU3tHGvtXOdrrF07+/n5ubuKTmftutZhTu+qgIxMnjxZCAsLE3bs2CFcvHhR/Lt+/bo4zV//+lehbdu2wo8//ijk5uYK/fr1E/r16+fGWnsneQ8j3K/2++WXXwR/f39hzpw5wsmTJ4W1a9cKzZo1E9asWSNOM2/ePCE8PFz4z3/+Ixw+fFi4//77hfj4eKGqqsqNNbfsiy++EFQqlbBq1SohLy9PmDRpkhAeHi72HPf4448LM2bMEKffvXu34O/vLyxYsEA4duyY8NprrwkBAQHCkSNH3LUJXsPeff3GG28IW7duFU6dOiXs27dPGD16tBAUFCQcPXrUXZvgFSoqKoQDBw4IBw4cEAAI77//vnDgwAHh7NmzgiAIwowZM4THH39cnP706dNCs2bNhJdeekk4duyYsHTpUsHPz0/YsmWLuzbBIda+X9Rwzmi31NbWCrfeeqtw7733CgcPHhS2bNkitG7dWpg5c6Y7NokaEc+v1tm7j+bNmycEBgYKX331lSQGKyoq3LUJjc7efSTH3jmdoymdY2051/k6X++d05brWkcwieYCAEz+ffLJJ+I0VVVVwjPPPCNEREQIzZo1E0aMGCFcvHjRfZX2UvIDAferY7755hvh1ltvFVQqldClSxfho48+kozX6XTCrFmzBLVaLahUKuGee+4R8vPz3VRb2y1evFho27atEBgYKPTt21f4+eefxXEDBw4Uxo0bJ5l+/fr1QqdOnYTAwEChe/fuwqZNm1xcY+9lz75+4YUXxGnVarUwZMgQYf/+/W6otXfZvn27yXOLft+OGzdOGDhwoNE8SUlJQmBgoNChQwfJecibWPp+UcM5q91y5swZYfDgwUJwcLDQqlUrYdq0aUJNTY2Lt4ZcgedX6+zZR+3atTMZg6+99prrK+5C9n6PDDGJ5jxN5Rxry7nO1/l6Ek0QrF/XOkIhCILQsGfZiIiIiIiIiIiIfBvfiUZERERERERERGQFk2hERERERERERERWMIlGRERERERERERkBZNoREREREREREREVjCJRkREREREREREZAWTaERERERERERERFYwiUZERERERERERGQFk2hERERERERERERWMIlGREQeZfz48Rg+fLi7q0HkswYNGoQXXnjBpmlXrVqF8PDwRq0PEd3E+CTyfY7E7o4dO6BQKFBaWtoodSLbMYlGREREREREOHr0KEaOHIn27dtDoVBg4cKF7q4SEZFHYRKNiIh8iiAIqK2tdXc1iIiIvM7169fRoUMHzJs3D9HR0e6uDhGRx2ESjTzWli1b0L9/f4SHhyMyMhL/93//h1OnTonj9+zZg6SkJAQFBSE5ORkbN26EQqHAwYMHxWl+++03DB48GM2bN4darcbjjz+OK1euuGFriLxPRUUFxowZg5CQEMTExOCDDz6Q/MxEq9Vi+vTpaNOmDUJCQpCSkoIdO3aI8+sfVd+6dSu6du2K5s2b47777sPFixfFaerq6jB16lQxzl9++WUIgiCph06nQ2ZmJuLj4xEcHIwePXrgq6++EsfrH2//7rvv0Lt3b6hUKvz000+Num+IvEVlZSXGjh2L5s2bIyYmBu+9955kvLU4ljt16hTuv/9+qNVqNG/eHH369EF2drY4/s0338Stt95qNF9SUhJmzZrltO0i8gWeGJ99+vTBu+++i9GjR0OlUjlnQ4k83Lfffovw8HDU1dUBAA4ePAiFQoEZM2aI00yYMAGPPfYYAOCnn37CgAEDEBwcjLi4ODz33HOorKwUp7U3di9fvozk5GSMGDECWq0WALB582Z06tQJwcHBSE1NxZkzZyTzXL16FY888gjatGmDZs2aITExEZ9//rk4fvXq1YiMjBSXpzd8+HA8/vjjDu0nqsckGnmsyspKTJ06Fbm5udi2bRuUSiVGjBgBnU6H8vJyDBs2DImJidi/fz/eeust/O1vf5PMX1pairvvvhs9e/ZEbm4utmzZAo1Gg1GjRrlpi4i8y9SpU7F79258/fXX+OGHH7Br1y7s379fHJ+RkYGcnBx88cUXOHz4MB566CHcd999OHnypDjN9evXsWDBAnz66af473//i3PnzmH69Oni+Pfeew+rVq3Cv/71L/z000/4448/sGHDBkk9MjMzsXr1aixfvhxHjx7Fiy++iMceeww7d+6UTDdjxgzMmzcPx44dw2233dZIe4XIu7z00kvYuXMn/vOf/+D777/Hjh077I5jQ9euXcOQIUOwbds2HDhwAPfddx+GDRuGc+fOAQCefPJJHDt2DL/++qs4z4EDB3D48GE88cQTjbuxRF6G8UnkGQYMGICKigocOHAAALBz5060atVKkvjauXMnBg0ahFOnTuG+++7DyJEjcfjwYaxbtw4//fQTMjIyxGntid2ioiIMGDAAt956K7766iuoVCoUFRXhgQcewLBhw3Dw4EFMmDBBktADgBs3bqB3797YtGkTfvvtN0yaNAmPP/44fvnlFwDAQw89hLq6Onz99dfiPJcuXcKmTZvw5JNPOnP3NT0CkZe4fPmyAEA4cuSIsGzZMiEyMlKoqqoSx69YsUIAIBw4cEAQBEF46623hHvvvVeyjKKiIgGAkJ+f78qqE3md8vJyISAgQPjyyy/FYaWlpUKzZs2E559/Xjh79qzg5+cnnD9/XjLfPffcI8ycOVMQBEH45JNPBABCQUGBOH7p0qWCWq0WyzExMcI777wjlmtqaoQ//elPwv333y8IgiDcuHFDaNasmbBnzx7Jep566inhkUceEQRBELZv3y4AEDZu3OicjSfyERUVFUJgYKCwfv16cdjVq1eF4OBgu+I4LCzM4nq6d+8uLF68WCwPHjxYmDx5slh+9tlnhUGDBjlhi4h8hzfEZ7t27YQPPvjAzi0j8k69evUS3n33XUEQBGH48OHCnDlzhMDAQKGiokL4/fffBQDCiRMnhKeeekqYNGmSZN5du3YJSqVSqKqqsit2jx8/LsTFxQnPPfecoNPpxGlnzpwpdOvWTTL/3/72NwGAUFJSYnYbhg4dKkybNk0sT548WRg8eLBYfu+994QOHTpI1kX283dj/o7IopMnT2L27NnYu3cvrly5Ap1OBwA4d+4c8vPzcdtttyEoKEicvm/fvpL5Dx06hO3bt6N58+ZGyz516hQ6derUuBtA5MVOnz6NmpoaSVyFhYWhc+fOAIAjR46grq7OKI60Wi0iIyPFcrNmzXDLLbeI5ZiYGFy6dAkAUFZWhosXLyIlJUUc7+/vj+TkZPEnnQUFBbh+/Tr+/Oc/S9ZTXV2Nnj17SoYlJyc3ZJOJfM6pU6dQXV0tibGWLVvaHceGrl27htdffx2bNm3CxYsXUVtbi6qqKvFJFwCYOHEinnzySbz//vtQKpX47LPP8MEHHzTCFhJ5L8YnkWcZOHAgduzYgWnTpmHXrl3IzMzE+vXrxV9KxMbGomPHjjh06BAOHz6MtWvXivMKggCdTofCwkKcPn3aptitqqrCgAED8Oijjxp14HHs2DHJsQEA+vXrJynX1dVh7ty5WL9+Pc6fP4/q6mpotVo0a9ZMnGbixIno06cPzp8/jzZt2mDVqlUYP348FApFQ3dXk8YkGnmsYcOGoV27dlixYgViY2Oh0+lw6623orq62qb5r127hmHDhmH+/PlG42JiYpxdXaIm5dq1a/Dz88O+ffvg5+cnGWeYuA4ICJCMUygURu88s7YeANi0aRPatGkjGSd/V0tISIjNyyUi2+PY0PTp0/HDDz9gwYIFSEhIQHBwMB588EHJuXnYsGFQqVTYsGEDAgMDUVNTgwcffLBRt4XI1zA+iVxr0KBB+Ne//oVDhw4hICAAXbp0waBBg7Bjxw6UlJRg4MCBAOpj8+mnn8Zzzz1ntIy2bdvi8OHDNsWuSqVCWloavv32W7z00ktG7Vxr3n33XfzjH//AwoULkZiYiJCQELzwwguSeO/Zsyd69OiB1atX495778XRo0exadMmu9ZDxphEI4909epV5OfnY8WKFRgwYAAASF4U3rlzZ6xZswZarVa8kDZ8vwMA9OrVC//v//0/tG/fHv7+/KoT2aNDhw4ICAjAr7/+irZt2wKof3LsxIkTuOuuu9CzZ0/U1dXh0qVLYozaKywsDDExMdi7dy/uuusuAEBtbS327duHXr16AQC6desGlUqFc+fOiY0XIrLNLbfcgoCAAOzdu1eM45KSEpw4cQIDBw50KI53796N8ePHY8SIEQDqLybkLzv29/fHuHHj8MknnyAwMBCjR49GcHCwU7eNyNsxPok8i/69aB988IHY5hw0aBDmzZuHkpISTJs2DUD9NWZeXh4SEhJMLsfW2FUqlfj000/x6KOPIjU1FTt27EBsbCwAoGvXrpJ3mQHAzz//LCnv3r0b999/v9jZgU6nw4kTJ9CtWzfJdBMmTMDChQtx/vx5pKWlIS4uzo69QqawYwHySBEREYiMjMRHH32EgoIC/Pjjj5g6dao4/tFHH4VOp8OkSZNw7NgxbN26FQsWLAAA8fHUKVOm4I8//sAjjzyCX3/9FadOncLWrVvxxBNPiD2vEJFpLVq0wLhx4/DSSy9h+/btOHr0KJ566ikolUooFAp06tQJY8aMwdixY/Hvf/8bhYWF+OWXX5CZmWnXHa7nn38e8+bNw8aNG3H8+HE888wzKC0tldRj+vTpePHFF5GVlYVTp05h//79WLx4MbKyshphy4l8R/PmzfHUU0/hpZdewo8//ojffvsN48ePh1JZ3/xzJI47duyIf//73zh48CAOHTokno/lJkyYgB9//BFbtmzhC4yJTPDU+KyursbBgwdx8OBBVFdX4/z58zh48CAKCgqcvxOIPEhERARuu+02rF27FoMGDQIA3HXXXdi/f7+Y3AaAv/3tb9izZw8yMjJw8OBBnDx5Ev/5z3/EjgXsiV0/Pz+sXbsWPXr0wN13343i4mIAwF//+lecPHkSL730EvLz8/HZZ59h1apVknk7duyIH374AXv27MGxY8fw9NNPQ6PRGG3Xo48+it9//x0rVqzg+dhZ3PxONiKzfvjhB6Fr166CSqUSbrvtNmHHjh0CAGHDhg2CIAjC7t27hdtuu00IDAwUevfuLXz22WcCAOH48ePiMk6cOCGMGDFCCA8PF4KDg4UuXboIL7zwAl+mSGSD8vJy4dFHHxWaNWsmREdHC++//77Qt29fYcaMGYIgCEJ1dbUwe/ZsoX379kJAQIAQExMjjBgxQjh8+LAgCKZfeLxhwwbB8NRTU1MjPP/880JoaKgQHh4uTJ06VRg7dqzYsYAgCIJOpxMWLlwodO7cWQgICBBat24tpKenCzt37hQE4WbHApZetErUVFVUVAiPPfaY0KxZM0GtVgvvvPOOMHDgQOH5558XBMH+OC4sLBRSU1OF4OBgIS4uTliyZIlkeYYGDBggdO/e3QVbSeSdPDE+CwsLBQBGfwMHDmyEPUDkWZ5//nkBgHDs2DFxWI8ePYTo6GjJdL/88ovw5z//WWjevLkQEhIi3HbbbcKcOXPE8fbGbk1NjfDAAw8IXbt2FTQajSAIgvDNN98ICQkJgkqlEgYMGCD861//krR3r169Ktx///1C8+bNhaioKOHVV181akPrPf7440LLli2FGzduOGlPNW0KQbDj5TREHmzt2rV44oknUFZWxsfSiRpBZWUl2rRpg/feew9PPfWUu6tDRB5MEAR07NgRzzzzjORJciJyP8YnUdNyzz33oHv37li0aJG7q+IT+KIo8lqrV69Ghw4d0KZNGxw6dAh/+9vfMGrUKCbQiJzkwIEDOH78OPr27YuysjK8+eabAID777/fzTUjIk92+fJlfPHFFyguLsYTTzzh7uoQkQHGJ1HTUVJSgh07dmDHjh345z//6e7q+Awm0chrFRcXY/bs2SguLkZMTAweeughzJkzx93VIvIpCxYsQH5+PgIDA9G7d2/s2rULrVq1cne1iMiDRUVFoVWrVvjoo48QERHh7uoQkQHGJ1HT0bNnT5SUlGD+/Pno3Lmzu6vjM/hzTiIiIiIiIiIiIivYOycREREREREREZEVTKIRERERERERERFZwSQaERERERERERGRFUyiERERERERERERWcEkGhERERERERERkRVMohEREREREREREVnBJBoREREREREREZEVTKIRERERERERERFZ8f8BRsKzKFDLC8MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1250x250 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat = mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\"]]\n",
    "g = seaborn.PairGrid(\n",
    "    feat.sort_values(\"price\", ascending=False),\n",
    "    x_vars=feat.columns[:-1],\n",
    "    y_vars=[\"price\"],\n",
    ")\n",
    "g.map(seaborn.stripplot, size=3, orient=\"h\", palette=\"Reds_r\", edgecolor=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Régression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.08109837e-01, -4.41245429e+00,  5.42852787e-04, -1.60797483e+01,\n",
       "        2.12155016e+00])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\"]], mat[\"price\"])\n",
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.612</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.612</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   800.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:15:05</td>     <th>  Log-Likelihood:    </th> <td> -8257.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.653e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2531</td>      <th>  BIC:               </th> <td>1.656e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   55.4146</td> <td>    0.545</td> <td>  101.618</td> <td> 0.000</td> <td>   54.345</td> <td>   56.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.5081</td> <td>    0.010</td> <td>   48.918</td> <td> 0.000</td> <td>    0.488</td> <td>    0.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender</th>    <td>   -4.4125</td> <td>    0.250</td> <td>  -17.683</td> <td> 0.000</td> <td>   -4.902</td> <td>   -3.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay</th>     <td>    0.0005</td> <td>    0.001</td> <td>    0.562</td> <td> 0.574</td> <td>   -0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay1</th>    <td>  -16.0797</td> <td>    4.842</td> <td>   -3.321</td> <td> 0.001</td> <td>  -25.575</td> <td>   -6.585</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekday</th>   <td>    2.1216</td> <td>    0.061</td> <td>   34.623</td> <td> 0.000</td> <td>    2.001</td> <td>    2.242</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>55.147</td> <th>  Durbin-Watson:     </th> <td>   1.969</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  57.558</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.358</td> <th>  Prob(JB):          </th> <td>3.17e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.823</td> <th>  Cond. No.          </th> <td>9.20e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.2e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.612   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.612   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     800.0   \\\\\n",
       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
       "\\textbf{Time:}             &     11:15:05     & \\textbf{  Log-Likelihood:    } &   -8257.2   \\\\\n",
       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.653e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        2531      & \\textbf{  BIC:               } & 1.656e+04   \\\\\n",
       "\\textbf{Df Model:}         &           5      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &      55.4146  &        0.545     &   101.618  &         0.000        &       54.345    &       56.484     \\\\\n",
       "\\textbf{age}       &       0.5081  &        0.010     &    48.918  &         0.000        &        0.488    &        0.528     \\\\\n",
       "\\textbf{gender}    &      -4.4125  &        0.250     &   -17.683  &         0.000        &       -4.902    &       -3.923     \\\\\n",
       "\\textbf{delay}     &       0.0005  &        0.001     &     0.562  &         0.574        &       -0.001    &        0.002     \\\\\n",
       "\\textbf{delay1}    &     -16.0797  &        4.842     &    -3.321  &         0.001        &      -25.575    &       -6.585     \\\\\n",
       "\\textbf{weekday}   &       2.1216  &        0.061     &    34.623  &         0.000        &        2.001    &        2.242     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 55.147 & \\textbf{  Durbin-Watson:     } &    1.969  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   57.558  \\\\\n",
       "\\textbf{Skew:}          &  0.358 & \\textbf{  Prob(JB):          } & 3.17e-13  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.823 & \\textbf{  Cond. No.          } & 9.20e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 9.2e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.612\n",
       "Model:                            OLS   Adj. R-squared:                  0.612\n",
       "Method:                 Least Squares   F-statistic:                     800.0\n",
       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        11:15:05   Log-Likelihood:                -8257.2\n",
       "No. Observations:                2537   AIC:                         1.653e+04\n",
       "Df Residuals:                    2531   BIC:                         1.656e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     55.4146      0.545    101.618      0.000      54.345      56.484\n",
       "age            0.5081      0.010     48.918      0.000       0.488       0.528\n",
       "gender        -4.4125      0.250    -17.683      0.000      -4.902      -3.923\n",
       "delay          0.0005      0.001      0.562      0.574      -0.001       0.002\n",
       "delay1       -16.0797      4.842     -3.321      0.001     -25.575      -6.585\n",
       "weekday        2.1216      0.061     34.623      0.000       2.001       2.242\n",
       "==============================================================================\n",
       "Omnibus:                       55.147   Durbin-Watson:                   1.969\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               57.558\n",
       "Skew:                           0.358   Prob(JB):                     3.17e-13\n",
       "Kurtosis:                       2.823   Cond. No.                     9.20e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 9.2e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "\n",
    "lr = ols(\"price ~ age + gender + delay + delay1 + weekday\", data=mat)\n",
    "res = lr.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 10\n",
    "\n",
    "*Comment comparer ce modèle avec le précédent ? Implémentez le calcul qui vous permet de répondre à cette question.*\n",
    "\n",
    "Nous pourrions comparer les coefficients $R^2$ (0.57, 0.61) des régressions pour savoir quelle est la meilleur excepté que celle-ci ne sont pas calculées sur les mêmes données. La comparaison n'a pas de sens et il serait dangeraux d'en tirer des conclusions. Les valeurs sont de plus très proches. Il faut comparer les prédictions. Dans le premier cas, on prédit le prix moyen. Dans le second, on prédit le prix d'une consultation. Il est alors possible de calculer une prédiction moyenne par patient et de comparer les erreurs de prédiction du prix moyen. D'un côté, la prédiction du prix moyen, de l'autre la prédiction du prix d'une consultation agrégé par patient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.52440734, -4.36373925]), 61.05057671902867)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_moy = LinearRegression()\n",
    "lr_moy.fit(join[[\"age\", \"gender\"]], join[\"price\"])\n",
    "lr_moy.coef_, lr_moy.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>idc</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>pred_moy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>Cendrillon</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.453648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>Cendrillon</td>\n",
       "      <td>78.333333</td>\n",
       "      <td>82.551278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>304895f0-f686-4b0e-8854-a705bb5a6982</td>\n",
       "      <td>Balthazar</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>80.809575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>3f0d31d2-0ef4-4e7e-b876-07d10225cc8c</td>\n",
       "      <td>Balthazar</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>78.711946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>f29273f4-a76c-4158-b5f5-b3e5a080a0c7</td>\n",
       "      <td>Balthazar</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>78.187538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender                                   idc        name      price  \\\n",
       "0   37       0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  Cendrillon  70.000000   \n",
       "1   41       0  f44b004b-b01e-4835-b86d-1a42846c6d93  Cendrillon  78.333333   \n",
       "2   46       1  304895f0-f686-4b0e-8854-a705bb5a6982   Balthazar  75.000000   \n",
       "3   42       1  3f0d31d2-0ef4-4e7e-b876-07d10225cc8c   Balthazar  95.000000   \n",
       "4   41       1  f29273f4-a76c-4158-b5f5-b3e5a080a0c7   Balthazar  90.000000   \n",
       "\n",
       "    pred_moy  \n",
       "0  80.453648  \n",
       "1  82.551278  \n",
       "2  80.809575  \n",
       "3  78.711946  \n",
       "4  78.187538  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_moy = lr_moy.predict(join[[\"age\", \"gender\"]])\n",
    "join[\"pred_moy\"] = pred_moy\n",
    "join.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "On calcule l'erreur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.874448844571756"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err1 = ((join.pred_moy - join.price) ** 2).sum() / join.shape[0]\n",
    "err1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='pred_moy'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join.plot(x=\"price\", y=\"pred_moy\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5.08109837e-01, -4.41245429e+00,  5.42852787e-04, -1.60797483e+01,\n",
       "         2.12155016e+00]),\n",
       " 55.41460950333407)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrc = LinearRegression()\n",
    "feat = mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\", \"price\", \"idc\"]].copy()\n",
    "lrc.fit(feat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\"]], feat[\"price\"])\n",
    "lrc.coef_, lrc.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>delay</th>\n",
       "      <th>delay1</th>\n",
       "      <th>weekday</th>\n",
       "      <th>price</th>\n",
       "      <th>idc</th>\n",
       "      <th>predc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>4</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>82.855082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>4</td>\n",
       "      <td>65.0</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>82.855082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>80.644421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>86.334615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>86.566717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  delay    delay1  weekday  price  \\\n",
       "0   37       0  365.0  0.002732        4   75.0   \n",
       "1   37       0  365.0  0.002732        4   65.0   \n",
       "2   41       0  365.0  0.002732        2   75.0   \n",
       "3   41       0   29.0  0.033333        5   80.0   \n",
       "4   41       0   50.0  0.019608        5   80.0   \n",
       "\n",
       "                                    idc      predc  \n",
       "0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  82.855082  \n",
       "1  4ba0b473-f8ca-4466-a65b-40e9b8ba5029  82.855082  \n",
       "2  f44b004b-b01e-4835-b86d-1a42846c6d93  80.644421  \n",
       "3  f44b004b-b01e-4835-b86d-1a42846c6d93  86.334615  \n",
       "4  f44b004b-b01e-4835-b86d-1a42846c6d93  86.566717  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predc = lrc.predict(feat[[\"age\", \"gender\", \"delay\", \"delay1\", \"weekday\"]])\n",
    "feat[\"predc\"] = predc\n",
    "feat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "On agrège les secondes prédictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predc</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>003b0195-2acb-4f46-b7fa-28cf266a8f60</th>\n",
       "      <td>85.048055</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009e689c-51a1-4cef-99ca-a4ba364eba8d</th>\n",
       "      <td>83.945104</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00a213c2-1174-4359-8a67-fe710ec1b439</th>\n",
       "      <td>72.345309</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00e42818-aade-4758-a5f6-c78a6f235ea5</th>\n",
       "      <td>65.277461</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0153b785-9acd-4d28-aad1-62f8bf2faea3</th>\n",
       "      <td>79.298313</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          predc  price\n",
       "idc                                                   \n",
       "003b0195-2acb-4f46-b7fa-28cf266a8f60  85.048055   80.0\n",
       "009e689c-51a1-4cef-99ca-a4ba364eba8d  83.945104   80.0\n",
       "00a213c2-1174-4359-8a67-fe710ec1b439  72.345309   70.0\n",
       "00e42818-aade-4758-a5f6-c78a6f235ea5  65.277461   70.0\n",
       "0153b785-9acd-4d28-aad1-62f8bf2faea3  79.298313   75.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg = feat[[\"idc\", \"predc\", \"price\"]].groupby(\"idc\").mean()\n",
    "agg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.697836056479865"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err2 = ((agg.predc - agg.price) ** 2).sum() / agg.shape[0]\n",
    "err2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Le second modèle est clairement meilleur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='predc'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Jsu4sB7pbVVoLPe/lzcrLvi6FQJ7BYIcoyAVC/oHwANx6yILiihoM7BYvml+ilpYHlpqHr1zdGwCSicF6dx/pCVCNeEgb1QpDDS3vxQCE3MVlLKIgFij5B8JSz7S/FuLNr0tw//JCr+9+keuJrPdhq3dJRE9+SlZSFOIlAqH4iLCA3jrtzbwiLmMFJwY7REEsUH5xG518quVzezKnRk+QpDdALbU0OFQltlfTeNmjW/i9wVt5RZxFCk5cxiIKYoHwi9sThea0fG41y3x6c0akWjPIFQjU+16+3cKvndaCfd7KK9Lz34z8H2d2iIJYIDRe9MTsk9rPbVTzTLmgUeuMhN73cieJWo7adhpquTuT5o1u9d7cnUbewZkdoiBn9E4no3lq9knN5/bGLh+tMxJ6Zxb0JlHL0dJOQ61ASpj3xu408g4GO0RBzt9/cWvp6SRQswSi5nOrDbSMCIq07D7SE6B6ImhU005Dy7+lQOuNJZe0ToGFwQ5RO+HNbcVaqOnpJGxD15M/Ive5hR1MYom99juYPJ375By86QlQs5KiMLRHPArLalyODe3RWed/e3XtNNTyZr0cdwRCbSrShjk7RORTano6CYzetaV2B5OntnUr5a9ozU85eOqcxOv1usantp2GWoGQMA94rzUFeQ+DHSLyKbU9nTxRM0htcrSntnUb+VDdcvA06pqaRY/VNTWLdj1XoradhlpFR8/KHt9VJn/cGwKlNhVpw2CHiHzqxtxk2VkTYQnLE7u2jMzZUeJcp0bNQ1VLbRstM2RaLLtvEEY7VbMenZOEZfcN0nwtNd3jfS1QalORNszZISKf2zBnJG5fss1h9iQ+Igwb5oy0fe+JJRC1O5/ceW+p/I9fDe4qe83H1u7B/hNXl5+Uckb0dj1XYmSCe15mAtYXV0oeH5GdKHnMWzy1hZ98izM7RORzGQkRKF44Hh/OHIon/isHH84ciuKF45GRcDXIkKudM7h7PMqqz+taYlBTU8WdekVSS1UrdpTJjuvnSsc8G6XlLXe6nqthRH2bXw3tJhkshHYw4e7BGbqvbRRPbOEn3+PMDhH5jVE5SbINQMW2ZMeYQ7HrWA1mrCgCoH3XjNqZCz3bweW2Wu86VoPo8BCcu9gi+rPOz1Sl7dkFCvkwOzVuE/eU1TOH4t73C1xeXzNzmA9G4ypQkqhJG7+f2Tl37hzmzp2L7t27w2w2Y/jw4SgqKrIdt1qtWLhwIdLS0mA2mzFu3DiUlJT4cMRE7Ye3+yUJgcnmp8dgxYwhGNIjHvVOSbl6E3yVaqpYNW+0Vs7/kAp05EjnjMiPz18WX979rlR0hmzJd0d8NCJHgVB1nLTz+2DnoYcewtdff40PP/wQ+/btw/jx4zFu3DicOHECAPDqq6/i7bffxrJly1BQUIDIyEhMmDABFy5c8PHIiYKXp5pnqg2eMhMj0b1zBIrKatzeNaP2s8xeLd424TerpdsmeOIX7Km6C6KfbVim/DbwYRq3iXtCoOx0YruI4OPXy1hNTU345z//iX/9618YPXo0AOAPf/gDPv30UyxduhSLFi3CW2+9heeffx6TJk0CAKxatQopKSlYv3497r33Xl8OnyhoGV3yX08RN7UF6pSqLav5LKWWBuSXiu8kyi+VbpuglP8hx2QCxCab5n2yD4Dr/clKisLw7ATR9g7Ds7VvE/eEQCkq6O9Vx0k7v57ZaW5uRktLCzp16uTwutlsxrZt23D06FFUVVVh3LhxtmOxsbEYNmwY8vPzJa978eJF1NfXO3wRkTqe+Ov8/g92is6a3L98p+TPKOVWdI4IU5yxUftZ1OTDiHHnF+zg7vK7p8SW65ZOFd8mvnSq9m3izoxYsgy0fBhvNB0l7/DrmZ3o6Gjk5eVh0aJF6N27N1JSUrB27Vrk5+ejZ8+eqKqqAgCkpKQ4/FxKSortmJjFixfjj3/8o0fHThSsjP7rvNTSgH2V4pV/fzxRLzlrorRt/PWvShRnbNR/Fn35MEozO71To3GgyvWzD89OwEcP34CjZ85jZ2k15l+ZzbEnlrDsiRkJI1snqG3PQWQ0v57ZAYAPP/wQVqsVXbp0QXh4ON5++21MmTIFHTroH/r8+fNRV1dn+6qoqDBwxETBzei/zv/yrfyGgiUix4VZhqfHXyOaW/HU+BxVMzZqP4uefJhSSwP2KRT6e2hUlktBxfiIMLxyZ38AbTMLqbGdxH7URixh2cgZCSOrPHuqEjWREr+e2QGA7OxsbNmyBefPn0d9fT3S0tLwq1/9CllZWUhNTQUAnDp1CmlpabafOXXqFK6//nrJa4aHhyM8PNzTQyfyK2o6hathdG5IqcID7rClwfa/pWYZNjw6AtXnL9k+2+aDp2WvKczYqC0qqOUzi41Rygffl7o8/GsaL+N3n/yIjx6+AYBvl36M7lIeKDk7FHz8fmZHEBkZibS0NNTU1GDjxo2YNGkSMjMzkZqaik2bNtnOq6+vR0FBAfLy8nw4WiL/4YmdU1K7tBV2b4sa1ytF9vj4a1Nt/1tqluG1jYccZjK0BAhqd96ozYcRG6MUsSUsANhxpNqhCamvtkIb3Toh0HJ2KHj4/czOxo0bYbVakZubi8OHD+OZZ55Br169MGPGDJhMJsydOxcvvvgicnJykJmZiQULFiA9PR2TJ0/29dCJ/MIja/a4zEhsLbFg9prdttkDLfTuTJLSr2usquNaZhnUzNjYz3SpyXOpbbqEfSdqHV7bd6IW9U2XbbkrUmN0FmIyISspEiWnGyTPKbArAqinoKERjA5O1M6kERnN72d26urqMGfOHPTq1QvTpk3DyJEjsXHjRoSFtf1yefbZZ/HYY49h1qxZGDJkCBoaGvDll1+67OAiao9KLQ2iSy+A4+yBFkb/ta+2gaXW95WasXlxch/Rma7OER1l81wmL9kuuuR0+5Jttu+Vxmg/jilDu8meY8XV3KSyaumgSIxRxR49MavEGjbkC34/s3PPPffgnnvukTxuMpnwwgsv4IUXXvDiqIgCQ4FCl+kCHS0EjP5rPzVa/g+T9FizrveVqnj8u3/uQ6HTVnKlGkFbDp6WTaz9vsSCUTlJimP80539MCwrwTaz9MJn0ud+sue46C4suTEbuXNKYPSsEmvYkC/4fbBDRO6QbxKgp6Wh0UsRyQq7jRKjw23vG2cOQ22Ta9ARZ3bdtiyWO7PtsMWl5xSgnHCrZvZpVE6S4r251242JyspCnlZCaJLgrHmMOw5Jv+eYmOevXqPy/WEKs9rZ2lfsgQ8F5xkJjLIIe/x+2UsItIvXSGQ6Bpv1nXdFyf3QYzZ8W+lGHMoXprcV/O1lH4JCV2ySy0NooEOANQ2OW5blioWqNSwWmoJ7vqucbI/N7Db1QKAWpZplt3nmvQ8uHs86pouu4xdacxqcqncodQ7jMifcWaHKIgpFbVrVnr6S3h+/U8uDTjrm5rx3Pr9mttFVNY1yR4/UdN2XMuSnNrcGWdC3ynnGYcbc5MRaw5DnUiwFWsOc+jUrmUmROzcsurztg7uagjLd57qeu6JpTEib+PMDlEQ88RWX6PbRRyqkk++PXRK2J6tfklO6XNL/eKb98k+ya35uSlRoj8j9bqWwn72syZKY7fnWHXYM13PjSwqaM+oJGoiNTizQ0SaGF0YrrbpkvzxK0HHsMzOsufdYFfFuEJhjH26xGDfCemeeGKNQAvLakTPLSyrUbXdXqyoo9SsyfDsBBSUnlVcyhKqDmcmRtoSuaV00bFkaXRRQYAzReQbnNkhCmJGbxMHjJ8tijN3lD0eb9fVe4hEc8wh3eMdHrpKFZQHdY/H5qfHYPGd/USPO89SuXMf5Yo6Ss2aWK1wyftReu/Kuguy5x2vkV8uFOOJfz+PrNkj2vR19prdmq9FpBaDHaIg5ollLKNrr8RHyv81Hx95NRgKCxX/leX8eudI+QAqMSpcU98pd+6jVEDz0KoiyeXA/NJq/HFSnysBmXzS99X3Nn4Zy+h/P56o+0SkBoMdoiAm9HQSo6ePlcDIwnBJCnV2EqLatp5reVD+sn+67DVvvXLc0+0L5PKbiiSWxQTCcuCUod1VBZd6mpWKjdc+j8bowFZNkjmRJzBnhyjIGdnHSqC048g5P0WuCanaredacoWykqIwtEdnFJa57lAa2qOzprYSgP6dTnp3hQFXPzfg+XYRcnk0Rr635Zx8fpal4aLmaxKpwWCHKIgZ3cfKmXNhOLGHZnxEmEP1Yedk1Kpz8rkmwtZ0rbMw708b7PKQFt7bnpqHuUVhjNUSD2ktO6uc2ZcFULOd3Z3EcbkdV6tmDjWsqGBStPzyYtKVWTwiozHYIQpiRu+cUiL20HRus+C800lLwT4t1Na7UXOe2qU2Z3IzRwO6xWHXMemlLLElNLmqw3qX5NTuuDKi4rERS21EejBnhyiIeTonxZ5Ufooz551ON+Ymy54vFOzTuzNIbb0bufO0bHt39uyEa+CU8gKTCZg1Kkv2mlrpza/xxI4ruTF6IoeMSAmDHaIgpucBqLfYm9b8FOEhukVhm/j3V2YdvBm4ORP6WInJy5J/SN//10KXStXNrVY8+fFe2ffUE2ToSRz39n1dOtW1RcbonCQsnTrI0PchssdlLKIg9+Lkvpi0ZJvDcpJYHys9xd7sE4+15qcID1GjmmxKBRxSydFySdNiXrmrv8t9jI8Iw6t39Zf8Gblu6Q0XW2TfT0+QISzJbT1kQXFFDQZ2i3doZSHG6MauasfIrufkTQx2iILc8+v3q+pjpZSkak8sMBrSQ11ujfNDNFUhH8a+MrCWnUFSwduLk/vi+fX7NVfwVXsf7SkFct0TzDh+9oJhQYbe6sSe3u0lhl3PyZsY7BAFMbXJp0rn/W9hOYbZLdeIBUZyybb2TCZg3sRc2/fJCoX9EqOvJv9qmRWQCt4mLdnmErRIBXUCvW0TlJKv59/SGx8VVBgWZDyyZo9LLSKhOvFHD98g+XOcbaFgx2CHKIip3Y2ldN68T/YBaJsleGr8NaIPfrV1e5pbrZjywU788PsJAPTljCjNCsgFJ2LLSkpBi95dbTfmJrtsvRfER4RhYt80TOybZkiQoaboopokbQY5FIyYoEwUxNQGEmrzbbYfPoPn1u1ze1x1Tc22xGNP0FvMTyop2J0k3g1zRtr6ewniI8KwYc5I2/daOqRLYXViImmc2SEKYllJUYg1h6GuyXVmIdYcplhJ2FmL1Yr9ldLdwrXYdOAURuUkeaQWkN6/4qSCFmHLtNjMidKW6YyECBQvHI/vSyzYU64uaVgf+e5XbhTMJgp4nNkhCmKllgbRQAcA6pouO2wvF9u2LKVvlxjR7exxZvmmnvYSrxTi88TW51bNP6EctFxqFr/qZYnXnY3KScJvb77GQ4GOe7WAiIIdgx2iIKamp5NASFJt67TdT/bn5k/sjRiz48RwZHgIaiUCKzG3KjTrlKNUC0jPL7amS82S1yy1NEgmYBcdq/GLbt3u1AIiCnYMdoiCmvzihdjCR1un7W4ueSaC+Igw/H9bS112NDVcbBY9X4x9M87PfqyUPfdzu+O1jZcwbXkhbnp9C2asKMLY177DtOWFqHNKANYzs1NcUSd5TS1Boy8tu0+8YN+y+1iwj9o3BjtEQUxvL6JSS4NkMbyaxsuibSFaVSaFjM5JwvvTBtu+P3tefjboTMPVTtlytYDsudOAE7i6XVugtxGot1mZmUMkisEOEbnQu5sJcP2lEmIyYUj3eKyYMQSbnx6DVTOHOhS4G5srn8Nyc++23llSvbece20ZRdiuDehvBGpPbxsOLdQGg0TtDXdjEQUxvTud3JkZ6dMlBvtOXN2xJRTJk6rgm6HwXl3j245r+SzuBGv2CkqrkZkYiXSFwodd482Sx/RWNdZKb+FDLdfX0l6DyJ8w2CEKYkpTt6EdxLcry/VL6p0WLbv9/MnxueiREOlQJK/U0oA9FTWiD0q1QYyWXVvuLmMJhE+ulAPk3OjTnpY2HO7wxBZ+wHvBGpEncRmLKIjpeUgLyy2/vjHLZcdVjDkU82/pLXtNIaAZe6V6sFJCsdogRksHd6lzJWI7ScJ2bb3b47259Oap7uWPrNnjMmPknNNE5O8Y7BAFMS0PQOedTlM/KHBJUq5vasab3xxSfF8hYHp45S5Dc0jEagFJ9ZISO3dkzyTkZSW4BEFinIsuDs8WT+aWq8+jZrbFKFqCQbXUtKAgCgRcxiIKQGrzJ4oUtkzvKjsr29zTWYvVqtjwc9aqXSg53SB7DfscEi3LL1oaVgrnbj10GsUVtbbKxeXV5zFpyXbJ3WYCoeiicP1Gia31TTJb7j012yLF6O7lalpQMH+HAgGDHaIAojV/Il/hYbX9yBncPThDMrlVj8MygY49Pbk4AjUNK6Xu1eWWVpcaQUpjLLU0YO/xOtFzio/XSSb/yuU+jeiZaHigYHz3cragoODAZSyiAKJ1a3HPxCjZ612THA3AuN1LgPoHoBDEVCi89/EafWMTu1fbDluQX1ot2/9LbIyf/XhS9jy5wohalt6MYkRjUYAtKCh4MNghChB6kl37dI2VvWbv9BgA6ncvCfkfQ7rHqxy19DWEB/He47Wy5+8pl182EyN1r9QWPnQe49nz8kUDz9oVPnTmi0J/RtX0yUqKwmCJ/9ZDusdzCYsCBoMdIgN4o2CcnmRXLTudpPoq2RNmJMJC9f/qcJ7VSFUo2JceK13DRoq7M1XOYxybmyx7/k29pY97s9Cf2nYaWnSU+G/tzr8BIm/z65ydlpYW/OEPf8Dq1atRVVWF9PR0PPDAA3j++edhurLj4IEHHsDKlSsdfm7ChAn48ssvfTFkame8WYNET25LVlIU4iPCRJNx4yPCHP4yl9qgNKBbHB6/OcehZo7UDh2g7S8o+y3vISYTBnaPwyNje4rmkJw6Jz9rUlUv36pBjNK9Eh1jtzg8cpP4GG/MTUZMp1DUX3DN9YnpFCrZyVxtoT+jCvYZXdNHzW4szu5QIPDrYOeVV17B0qVLsXLlSvTp0we7du3CjBkzEBsbi8cff9x23sSJE7FixQrb9+HhyqXbiYwwe/Ue5Ds1gdxaYsFvVu/G2lk3GPpeUsmuHQBce2U5yplSjyv7h63UQ624vBZVdRdswZTSrMm16TEORQdH9EzEU+OvwdlG8aWeo2fkE5qPWrTPlsklBg/L6ozQDh1EdyzJBai9UmNQWOa6u61Xqvi9B5Tv1f7KOvz+Xz9JBstagiBPVFD2VKFCIm/z62Bnx44dmDRpEm699VYAQI8ePbB27VoUFhY6nBceHo7U1FRfDJHasVJLg0ugI8gv9cxfvWJbi1sB7K+sx9jXvnOZVVL7sFI6b/4n+wC0PYifGp8je665Y4jD9/tO1GLSku22753HqHSPspJcj6sJAuS2YcdGhGnasVRqaRANdACgsOys5H9rpRmmv35/FMUVtQ6vbS2x4OFVu9ApLETTjKEnAhNvb50n8hS/XnQdPnw4Nm3ahEOH2oqY/fDDD9i2bRtuueUWh/O+++47JCcnIzc3F7Nnz0Z1tfx224sXL6K+vt7hi0irAoUaNjslAiF3CFuLNz89Bn27xLhUBHbOBflJYru04MCVGRi1vwi2lljw+3/9LHvO7jLHhGLnmSXnMaYo5OykxFw9LpWT8kNFrUvOlHCvVj04FE/8Vw4+nDnUoQmplh1LeosDyhX6G9w93iXQERSWncU2p1kapTwfTwQmnihUSOQLfj2zM2/ePNTX16NXr14ICQlBS0sLXnrpJUydOtV2zsSJE3HnnXciMzMTR44cwf/8z//glltuQX5+PkJCQkSvu3jxYvzxj3/01segoCW/y0ZjZwIXcrMX5dXnsf+Ea5DuvGShdqeTUlsJe0rXVLqW8xirzsnn5FTWNdn+t1hOytYSi+gMiBVWw/KpTtfJj/GMTN6R1AzTmNxE2QKNzvdRaTlKS36WFkYXKiTyBb8Odv7xj39gzZo1+Oijj9CnTx/s3bsXc+fORXp6OqZPnw4AuPfee23n9+vXD/3790d2dja+++473HzzzaLXnT9/Pp588knb9/X19cjIyPDsh6GgMyxTfvfSMJ01SOSSnsUe4GKEJYuMePm/9oXZAKMaZ2ohjDFEISwM69A276S28KEwA9Jw4RL2VDjObG0tsWDGikJ8MmeEprEeqJKf/f2psg53Q/x3iFShv7WF5ZrGIJBajlKbn6WV8YUKibzPr4OdZ555BvPmzbMFNP369cOxY8ewePFiW7DjLCsrC4mJiTh8+LBksBMeHs4kZnJbVlIUhvboLJrLMbRHZ90PBKWtykotHYCrSxb353XHih1lkufdl9cDgHRCrycJYzyjUMPGcmXWRO12cmEGRMqeilrND/7OkR1ljydGKf8+ca76rFSwT4rUcpSnk4nVVK0m8ld+nbPT2NiIDh0chxgSEoLWVumJ8uPHj6O6uhppaWmeHh4RQiTaaEu9rkSpcKDYMYf3dcqlyEqKQkwn8b9pYjqFOjy8Xpzc16XLuR5qPrr9ssqhqnOy5x461XbcyNmn1fllms7voDD71EFFY1FncrWNYs2hmvNklH6Zh+r8N0kUDPw62Lntttvw0ksv4fPPP0dZWRnWrVuHN954A3fccQcAoKGhAc888wx27tyJsrIybNq0CZMmTULPnj0xYcIEH4+egp2a3VhaGV0Mr9TSIFobBgDqLzQ7jPH59ftV9YwakBEnezwqXDlgEpZVAKBKIR/m5JXjQk6KEbTeZ6U8pWIdVZ4BYNl9gzDaqUbP6JwkfPboKM0tJpRypZrVlo8mCkJ+vYz1zjvvYMGCBXjkkUdw+vRppKen49e//jUWLlwIoG2W58cff8TKlStRW1uL9PR0jB8/HosWLeIyFXmcL7b6yvlw5lCX4nZqx6g2HyY+IgxPj78GU5cXSp7TIBFcSb13amwnlFZLjzMttu3/y3I5KfZCTCZkxHdC2dkmyXMGdtPW7kIp9ylD5383uXwYrXky3CZOJM2vg53o6Gi89dZbeOutt0SPm81mbNy40buDIrrCFw+XIT3isedYrWgHbbEqvmp3Eamd6ahvasbiL/8je47anV3C/fn1jdnYUSq9jf83Y3pqGuOInon41eCumCOzTbu3RBFGKUq5T/dfyX3SSyofRkuejLc7rBMFEr9exiLyZ56oQaL0QJ8+vIem5Q2127rVzii1WK2iW961cL4/SrMiXePV7Rj70539sPnpMVg1c6hiMKM1EM1KikI/iWv2S4/xm0DCFx3WiQKBX8/sEPk7o2uQKD3Q+6THYtXMdNXLG3Xn5Zd9zl3J0fHmbizn+1NwVL74YkFpNTITI2VnLgZ2j0NK7NXig1lJURienSDaAmN4doKuflSdOorX7XKuGO1LtU2XsO9ErcNr+07Uor7psuG92ogCCYMdIje4W4PE+YGblRSFWHMo6kQShWPNV3dPqV3eKK9RX/lXLHAz0p/u7IdhWQki45bfJWQfeomNMcYciqKyGsxYUQTAriaRRMzW3NKKacsLNRUbLLU0oKhMPAm5sKxG1VZ2o5p9ypm8ZLtLXlNN42XcvmQbiheO98h7EgUCBjtEBiivPo8fj9citINJ1YNMqnDgU+NzRAMdAKhratZcH0Zq27kgznz14W5VqAjtrpTYTqJjr1boel5z/moDUefg8t1vD2NPea3D+dsPn8HMlUWS1YkLy2pc1u+VOoOraQ0i9d9FrkikkbMtWw6eli0q+H2JRbI7O1GwY7BD5IZj1edd/pqOjwjDhjkjkZEgvSQlVTjwZJ38TIzcQ1VMD4Vz7Y+LjclIp+ouiAZralta2MtMjITVakWRSEDTYrXKtmEAtLdicKc1yOzVe1xKFGwtseA3q3dj7awbZK+rhZr7yGCH2ismKBO5QW7ZQIpc4cCS0/K1eTxVFk5qTEaa98k+W+POOrt7FtNJfnbDfvbJnrs1icRINfTU2xrEE7WYpFzfNU72uNbt9kTBhMEOkU5qlg3EuPOQ1tpva59C13PhuJYxpce6V8PKuXv3EIW2CYN6iB/3RD8vqV1aQsKzGCHhWYya5S+j3JibLFl0MT4ijLM61K4x2CHSSc/yC6D8kJaqUCz3UJViaZDPhzl1ZWu6lsDB3NG91W/7JSNAW86OPbmt/4O7y89iOP/iU1MuYOlU8WrHS6cOknkn/ctfemyYM9Il4BGWVYnaM+bskN/zxi4WPfQuGygVfxPbcSQktGo1rlcK9lZIz+6MvzZV8zWPWOSXXjpAXWFBoYLy4TMNsucdOi3dO0tu6/9ja4tF7/GwrM4I7dBBc7kAPTvv9C5/6ZWREIHihePxfYkFe8prMLBbPGd0iMBgh/yYt3ax6HVjbjJCO5hEew6FdjDJPmTkHtJaH6pyweCjN+fgta8PSf7sI2O1VSdWQ20FZaExZc/EKNnzrkmOljwmt4NM6h6/OLkPnl//k8pRutJa1Vip3o8njMpJYpBDZIfBDvktqR1LcluEvanU0iDZXLG51Sq7TVxNQKP0UFUTDG45eFr2MwjbkT2R/6JEuHdKwZFc0rTcv5G3p1wv+jO/++c+FDrl0njy39XSqYMMm6kjIn0Y7JBfkmpMqbxF2HuMaASqZZbAmZpg0OjtyCEmE7p27oRj1dJNNtUSkoGPKixjHZVYNlP6N/Lwyl0uNXi2HbZALD715L8rdwtP6uGvS79EvsJgh/ySJzqKG83TjUDlHlhqg0G1eUVql7FizKEY0j3e7WAnPiLM9pnizB0VzxWjNGaxGjwSE3E2nvx35U5gq5a/L/0S+QqDHfJLvugorlVWUhTizGGobXLdfh5nDtP9YFPzwDI6GFS7jFXf1IytJe5vl65pvGwLyOIj5R/C8ZHiwZA3t54HCn9f+iXyFW49J7/kiY7iRiu1NIgGOgBQ23RZd8G42av3uMzaCBV3BWqDwc0KOTubDpwCIH2/nbVYrTitsFVcLaGAX41Cs9KzOraeD+mhfev54O7xKKs+b2ihP2+SK1Zpv9WfqD1isEN+650pAzCiZ6LDa+50FDeamm7dWqmtuKs2GKyX6LMlaLhw9bjY/fakM1eCptom8WBGUCtSuLHU0oDNB0/j6QnXiP4b+WDaEMnlrzhzGEY65SnFmEOx61hbM1GxKs+BQM1sH1F7xWUs8lu+SOzURn23brXUNpwstTTgniFd0XS52aEbt3MwqNTc0z6HxdONQJ39VFmPu6GcsxNnF7SILfGJBTVl1eclq1vXNl3GHyf1sZ337ubD2HOs1uGcQFz6CYSlXyJfYbBDfs8biZ16pMd2kj3eNd6s46ryAUfTpRZMW17o8LAf0j0eDwzvgWu7xIpsX5evYZOZpK8RaHhoB1xsVltRR1xiVFuQc02qQp2dlKt1dsTG6BzUbD98BmfPyy+1lVWfx9jc5LZmomXizUT9ZdefWkrFKgPlcxB5ApexiHRSetRL1eCRkx4rHyCt23Pc5WG/p7wWf991XPRh9sv+abLX+2X/dADaG4G6G+gAwK1X3lttlWG1Y2yxWrG/sl72HGGWQ8/Sj7CE5o85MP6+9EvkK5zZIdJJ6S8FoUKwFkohxD6Rh7gwCyEUCLRXofAwP17TiMzESE0VlKPCQ9BwsUX1+WKG9oi3BWeeGCMgPU77be9aln4CYVu3/y/9EvkGZ3aIdKqsuyB7/HiN9lo07mynvn95oUti7eqdx2R/5sP8MgDqfxHER4ThD7f10TnCNqNzkvD+tCG279fvPSF7/ro9xwFo/2UlFZAJ294Bbbv+5LZ1+5vMxEiMzU1moEN0BYMdIt2M72gt9/Dt1yVG8eedt6g3XFTYjXXluNKMUl5WZ/z5v/ujeOF4JESHK45DzJ0DumDz02OwauZQh5mQU3UKndnrL6oaoyDEZELfdPl7Zb88pWbph9u6iQIbgx0inZTya7roSlCWfvj+ZnS2qp+336KuOMa4tpkkpRml/NKzeOb//YhpywvRWeeSzdDMzqIzDRHhIbI/F9kxVNUYBSN6JuKlO/rJnmO/PCUs/Wx+egxWzBgiGpBxWzdRYNOVs/Pvf/8bISEhmDBhgsPrGzduRGtrK2655RZDBkfkz75TKNj33X9O6+o8LTx8tx6yoLiiBgO7xWNUThJKLfI9pOwJW9TrLsjXiqltbKtxI7WTx9nWEguaW1tFz+0A+dkXIdHYWf+usfjmgPS97J8RKzvGEJMJA7vF4ZGbetpyVLTcK4Hcrj9P5Gd5EntjETnSNbMzb948tLS4rodbrVbMmzfP7UERBYLiilrZ485NKNWqbbyEacsLMe2vhXjz6xJbLk5CZLhkoTxnwqM3I15+NiTDbrZEbVHBHUeq8fR412J+I3OSMLRHZ9GfGZ6dIPnQvU6hf9d1GVePS816fTB9iEOOitEzMZ7YeecJwr+dm17fEtAFEomMpmtmp6SkBNdee63L67169cLhw4fdHhRRIIgIk19+ieiob5X4kTV7sOOIYxXlrSUWPLCiULJQnjNhFqVXarTseb3Trua22O/kWbnjKP62Qzq5+eeT9aK7fuoaL+OxtcWiO5akaAkk1O42MnomJlAK9rE3FpE4XcFObGwsSktL0aNHD4fXDx8+jMhI//g/PZGn3ZCdgB2l0hWP87K1t14otTS4BDoCpZkkgf0syk6Flhb5R6px9+AMh9esVivUVod2XvrRs/X5tMKutjMivbiUCk0aPRMTCAX7hCRqZ4FYIJHIaLr+9Jw0aRLmzp2LI0eO2F47fPgwnnrqKdx+++2GDY7In6VEy1dQTomRPy5Gqd+WktE5SVg6dZDdK+pnMOyXQP62o0z23Bsk8m8EVpXFCQFgp0KLjHyJ4E+OmpkdrcUB/b1gH5OoiaTpmtl59dVXMXHiRPTq1Qtdu3YFABw/fhyjRo3Ca6+9ZugAifyVmoe086yJMv2Jrh/OHOqSEJ2t8Jd8z+SrrRrUtovIy5LOv9FXeM/4fBelmZ1XvviPQ5VlNcUBy8+ex44jjvdnx5EzOFHTiNiIWHeGa4hAWWoj8gVdMzuxsbHYsWMHPv/8czzyyCN46qmnsGnTJnz77beIi4szeIhE/ulUnXzRwFP12osKDssUT/BVQ2xppkUhkLjc2hYWqG3FMDonCcvuGyR5XE/hvcnXd5F9zzsGyh8Xo/Tg/9mpErWa4oB3vLvD5R43t1px+5LtmsfnCVoKJBK1N7rbRZhMJowfPx7jx483cjxEASMiXP7/PpEdtdejyUqKQl5WAvJLXZdueqdF48DJc5I/K5Z0e73CTqeB3eIBKC+BTB6QjrsGdnWZOdpy8DT2Hq/FwG7x6BJn1pUzcmNuMmI6haL+gmsBxJhOobq270vl2HQwtXV6d575URrj3wvLJfN8mlut+HhXhY5ZPOO9M2WAS4K4Py21EfmK6mDn7bffVn3Rxx9/XNdgiAJJhkLRwIzO+ooKLrtvkOiOpnuGdMWjH0nPPriz/VlpJmR9cSXWF1falntqmy5h8pLtDrvDohSKA5ZVSyfIfv7YKNy+ZJvD9eIjwrBhzkgNn8KR2IP/2vQY7D8h3SRUaoz5CrlU24+c8Ytgh72xiMSpDnbefPNNh+8tFgsaGxtty1a1tbWIiIhAcnIygx0KGnLF2a5JlW9J0DNFftu3FKkHllKhPLGcjM0HXWda7G060Fb4UEtRwd+s3o3/VNW7bINXag4qlzMSbQ5Fvy5xDoFJvy5xiDHrb7Apdh+tVituen2L5jHmZSZgfXGl5M+N0LHzzpOUdqsRtTeqc3aOHj1q+3rppZdw/fXX48CBAzh79izOnj2LAwcOYODAgVi0aJEnx0vkFWqKsynl1yjtWFLivKNJT06GSSFnJ8TuUmqLCuaXVsvW+3FeTVOTM+LJJpv2TTH15rX8amg3ydo8oR1MfjGrQ0TSdCUoL1iwAO+88w5yc3Ntr+Xm5uLNN9/E888/b9jgWlpasGDBAmRmZsJsNiM7OxuLFi1yeAhYrVYsXLgQaWlpMJvNGDduHEpKSgwbA7VPah6+Qn6NGLkdS0rkAi2xgGRAtzj8anBX0S3UWmaf7HtEPTC8h66xA45VmQHlnBFvN9nUu4V8tURRvjUzhxk2NiLyDF0JyidPnkRzs2syYUtLC06dOuX2oASvvPIKli5dipUrV6JPnz7YtWsXZsyYgdjYWNtS2auvvoq3334bK1euRGZmJhYsWIAJEybg559/RqdO2uuckPsCvS+PluJsz/2iFyY77dIJ7WDCglt7635/pSq4wtLM/so6rNpRhqKyGuw6VgPAdQt1eqz8/we6iuQdZSZGIjc1SuRsdV6c3Bdd4yNU54yoqQ9j5L8jvXkt735XihCTyaWo4JLvjmBYtnuzeETkWbpmdm6++Wb8+te/xp49e2yv7d69G7Nnz8a4ceMMG9yOHTswadIk3HrrrejRowf++7//G+PHj0dhYSGAtlmdt956C88//zwmTZqE/v37Y9WqVaisrMT69eslr3vx4kXU19c7fJH7/K0vj9aicQItxdmmLi8Q3Y485YOdusaodpYjMzESHxcdx55jtQ7nOc8+6a0kPCxT/8O7a3yEw9KREl/Vh9EyRm/PPhGRsXQFO3/961+RmpqKwYMHIzw8HOHh4Rg6dChSUlLwwQcfGDa44cOHY9OmTTh06BAA4IcffsC2bdtsXdWPHj2KqqoqhwArNjYWw4YNQ35+vuR1Fy9ejNjYWNtXRgbX243gybwLLdwNutQ+fLccPI26JtcZTgCoa2rG9yKzQ0pj/PmkfOAtBFpqH756A4mspCgMl5it6K3QbysYK/WyOjFRYNMV7CQlJeHf//43/vOf/+Djjz/Gxx9/jAMHDuDf//43kpOTDRvcvHnzcO+996JXr14ICwvDgAEDMHfuXEydOhUAUFVVBQBISUlx+LmUlBTbMTHz589HXV2d7auiosKwMbdX/vCXrzBL8vCqXYpBl9ysj9ok1s0HT8uOZ9MB6SVdqcBw2ZYjEj/RRkiS9cbDd+nUQRjtVONmdE4S/nRXf9mf0zoTEwiBBKsTEwU23UUFAaBHjx6wWq3Izs5GaKhblxL1j3/8A2vWrMFHH32EPn36YO/evZg7dy7S09Mxffp03dcVZqPIOJ7Mu1DKARJrUeBMCLp+qKjB61+VKLYzUFOcrV5iVkfQIFIkr9TSgIKjZyVzguRqwADA8Zq2qsxKf6UID193/rvI5bYY2RQzEAKJQGgESkTSdEUojY2NeOyxx7By5UoAwKFDh5CVlYXHHnsMXbp0wbx58wwZ3DPPPGOb3QGAfv364dixY1i8eDGmT5+O1NRUAMCpU6eQlpZm+7lTp07h+uuvN2QMpI4nHlhq+yyp7ekEAM+t2+9Shdg++VegJonVqrCt2z4dRk1ApkbTpRZMW14oeR3nh6+ahphKxGq2aK3U6+mkdW8kxbM6MVHg0hXszJ8/Hz/88AO+++47TJw40fb6uHHj8Ic//MGwYKexsREdOjj+ug4JCUHrlX4+mZmZSE1NxaZNm2zBTX19PQoKCjB79mxDxkDqeOIvX6VdSYD0zikp+ytdZ07kWgXIFWfLTJTfsZSZdPXntARkcr7YdxJ7ymslj8eYQ/HS5L627/UmKCsRgsGth06juKKtXYRYWwc1Aas7s0/6Go/qw+rERIFLV87O+vXr8Ze//AUjR46EyS6voU+fPjhyRD7nQIvbbrsNL730Ej7//HOUlZVh3bp1eOONN3DHHXcAaOvPNXfuXLz44ovYsGED9u3bh2nTpiE9PR2TJ082bBykjt76JWLU5gApPSgFISYT+naRrzmjNTek9vwl2eNCQrTaJpsCqQTgARlxKDpWI3ud+qZmPLd+v+17I2Z2xAgJ1tP+WoQ3vy7B/csLRZPA1SStuzMr6IukeC27uIjIP+ia2bFYLKKJyOfPn3cIftz1zjvvYMGCBXjkkUdw+vRppKen49e//jUWLlxoO+fZZ5/F+fPnMWvWLNTW1mLkyJH48ssvWWPHB4z8y1ftX/tKD0rBiJ6JeGp8DiYt2SF5jtaltj3lNbLHdx07C0B9QCZ45b/747WNhzT3xgJcZ6kqFTqzn6jR3pkdAGav3uPSrFRoJbF21g0AtNUr0sPT1yei4KEr2Bk8eDA+//xzPPbYYwBgC3A++OAD5OXlGTa46OhovPXWW3jrrbckzzGZTHjhhRfwwgsvGPa+5B4j+vKo/WtfbvlsYLc4PHJTT48l1iZFyye5p0R3UvVZnMfRv2ucrt5Y9oRg8FCVdJd0ACg5JX8ccM2HKbU0iHZlB9paSQhBhtqAVe8ylreLERJR4NIV7Lz88su45ZZb8PPPP6O5uRn/9//+X/z888/YsWMHtmyRbrJHpJaWHCC5xFHnvA0jk0xv6p2Crw9Ibz8f0yvZ9lmGZydgxxH5ztnO43AnaBSCwVqFHWNyPa6k8mHG5Lrm5tjbWVqtatZNGKPepbZA2MVFRP5BV7AzcuRI/PDDD1i8eDH69euHr776CgMHDkR+fj769etn9BipnVIbmGhZPlNzrtqdPVoe0lJpNgMyYvH4uGsMaasAuAaDSo1A5VJ2HlmzxyVA21piwYla+XEIl1QbsOpNouZ2cCJSS3Owc/nyZfz617/GggUL8P7773tiTEQA1O/4EWiZCRE7V2wmY3D3eMwY3gPXdol1Ob/q3AXZ9xDyZeSWfYor6lTnNqlZDnOpBSRS68eeVK2gUkuD5EzUEYt8Ivcwu+aoagJWd2ZofLEdfMvB09h7XPnfIxH5D83BTlhYGP75z39iwYIFnhgPkY03txUD4jt7dh2TbrJ5fdc42esN7BYPQH9uifMMk9RMRgcTcG16DN6ZMtDlOtGd5O9TVCfxXwEFR+WX3LISI1EqUn16eHaCLa9HGLvSTJrcMp9wPSne3A5+rPo8Ji/Z7rD0Fx8Rhg1zRiIjQV1eFhH5hq6t55MnT5ZttElkBG9uK1azPdz5vavq5Gd2hONaZy7kenuJbe8f2TMJa2beIPqQvyGzs+x750l265bfVXnfDd3EW0nc2U907J0jOspu15a67Sp363tlO7hzoAO05TzdvmSbx96TiIyhK2cnJycHL7zwArZv345BgwYhMtLxF8zjjz9uyOCo/fLGtmX7WRM1+TDO752vMPux/cgZ3D04Qza3ZEC3OFt9H+HzKG3r1jKTkRwrX4IhUWJH2TCFIGlsrxQ8ODLLZRzTlhcqFoJ0pnZ3l15GVFfecvC0ZDJ3TeNlfF9i4ZIWkR/TFewsX74ccXFx2L17N3bv3u1wzGQyMdght3lqW7HU0ti9Q7qqvobw3j0VKihfk3y1OKBYbkmMORS7jtVgxooi2zieGp+j6sGvNj9Jz04nITgY0DUWxcfrXI7nZV1dWrIfh94A1dv/rfUsg+49Xit7fE95DYMdIj+mK9g5evSo7X9br/ylamQxQSJPbSuWWho723hR9TWEACFBoc5OfGRH2/92zi15d/Nh7DlW6zKOkwpFAIVt3Wpp2ekkFhzER4Q5zGgIwYLAftZEb9Di7f/WcrNMUtTmZxGRf9Ldqnz58uV48803UVJSAqBtaWvu3Ll46KGHDBsckZHkZh6Uuo3bEwKEQ1XyP3NYpGBfZmIkrFYrispcqy+3WK0oOS1fOFDsTwq5ZRotgYRYcFDf1Ix+XWIw7toUh91HYoHRkB7yD3xv1r0xehn0xtxkl8BPEB8RxlkdIj+nK9hZuHAh3njjDTz22GO2isn5+fl44oknUF5ezmrG5Dal3UAFGmc4AOXlkr7pMThw8pxiD6urBfukC/IB0gX7tLaPsGe/rVvNMk2Fwnsdr2m07Z6SCg72najHvivBoHB9scBoz7FaxEeEob6pWVPdm4KjZ2XHqHU2C/DM0tiGOSNx+5JtoruxiMi/6dqNtXTpUrz//vtYvHgxbr/9dtx+++1YvHgx3nvvPbz77rtGj5HaIcs5+Sablgb1y04CpVmOl+/o57LTyVl8RJhdwT75pVuxgn1bDp7GloPSVZcBIDpc/G+QOHOYQ37MlPd2ugQoW0ssmL3mah7d+r0nZN9r3Z6242oDsO2Hz+ChVUWSTVprGi9jYLc4h9eV697IB5d6Fsg9sTQWbQ5Fvy5xDq/16xKHGLPxZRCIyFi6gp3Lly9j8ODBLq8PGjQIzc3yRcyI1DH+Aagk2tyWV7PqwSGS59Q0XrZ1XLcqjLHVLhg4Vn0eA174CtNXFOFvO46Jnh9iMmFIj3icuyj+/6Hapsv4oaLWtrX7gETfqx1Hqm1jbFAoKnjuYtsshdr+XS0SS3D2mppbVF1LMCxTavv7leNZ8sfFCDvgQpxyCUNMJozOSdKV8OyLDutEZAxdwc7999+PpUuXurz+3nvvYerUqW4PiigpWn7LdEKUfHKwGDVLGwDQolDbRTgvztxR9rz4iKvHxWq0OIsxh+KeQfK7wp5bv8/lgSum4MqOrn5dY2XPu+5K4q1UcKDHz5WOuUy+CgjEahLpra4sVYfJPgeIiPyXWwnKX331FW644QYAQEFBAcrLyzFt2jQ8+eSTtvPeeOMN90dJ7Y5SnZcbdPy1r3ZpQ/0SiLqKd3I1WuzVNF7GR4UVsueoTaQWRnadwi6i6zKuHhfbHi9lSPd47CmvdazkjLbdX86trHy19dzI6srssE4U2HQFO/v378fAgQMBAEeOHAEAJCYmIjExEfv377edx+3opJfaxFo5atstOCfQqj1POUG5Le9IqUaLveIK9efKEYJBLVvP7YODnaVnMP+T/ZI/t+C2a/HaxkMOgdG16THYXykdjHl767nAne7xAnZYJwpsuoKdzZs3Gz0OIgebFZJ4vz1wWnK7r9wuJbWNI9WcF6eQmCosYynVaDGafdE/PQ/pzMRI21KdlOrzl1xmTaxWK256fYum9wLagsu8rATRYor2n8WX2GGdKLDpXsYi8qTOkfI5OZ2jpPNllIrJtXVSt6C4oka0c7UwI/THSX0AQHIJ5JrUaMjJSWk7fmNuMkI7mBxmUTzFueifXmqDJOdZE70BQXOr+BxUi8TrvuCLDutEZAwGO+SXftk/DW98fUjmeLrLa6WWBhQcrZYtJvdDRS1e/+qQ6KyPFVZN7QXU7iIqtTR4JdABgH0nalHfdNk2Xr25JllJUbJF9KQCFz0BQamlQXKHV2FZjdu9sYzizQ7rRGQsBjvkl7KSomCCeAqwCXB4yIgtW0l5bv0+HKh03LJtv1vIeUZoW4kFUz/YiXf+z0CPJbfa6xpvxvEa+ZYRcoQu3MULxwPQ1xsLaAtA5BpfSgUgegICTxQV9CSr2lbsROQ3GOyQX9py8LTkXicr4NBlWmzZSorYbiZh1kdMK4D9lfUY+9p3LrM8n/14Uva9PvuxEo/dlIPTdRdUjW10ThIy4jthjcyOrI4hJlxS2Btv34W7UqHX1gmJwErtjJBUqwptScHer6mkh5GNRYnIu3TV2SHyNKUE5U0HTgGQrn/iLMRkQt/0GLfG5Fyd+OgZ+T5WRy1tSb5V5+SDnckD0rH56TFYNXMowkJDZM9VCnQEe8qFZSH5UEHqako5O50jOtqKG85YUYSxr32HacsLUadii70zTxQV9AQWFSQKXAx22rlSSwM2Hzztd0XROkfKF+xLvFJUUO0S0YieiXjpjn5uj8u+OrFSUcG4K3/tK+3GumtgV9ssyNhcYxpKCl2402PlizN2jTeLvq5Ugfj1rw4Z9uDPSorC8GzxgGZ4tn/sxmJRQaLAxmCnnaptvGTYX+aeIJaAbO/WK8eVZiAW39nPNmtyXUac7AM8T+UMglCdOD5SfulCCNiEjtlinDtm35ibjJhO4qvLUeHysz5i19S7jAVIVyB+avw1hj/4l04dhNFOu+JG5yRh6dRBmq/lCWqrbxORf2LOTjultD07UCjtGpoytJvDa3K7heyXqOToSU/V0jG7V2o0CkV2J12bFoODp86hrkm635XrNfUtYwHSycZKS4x6qgn7+04nFhUkCmwMdtohYUremVJZf28qOOpaYM7h+JUdOkq7htYWluMGu8J0Ug/VUksDdhyRf0+B1NKPnIyECBQvHI/vSyzYU361vo+wjGg/DrFAB4Dk64I//3d/3D04w+E1vctY9px3H3nywW9EtWNPYFFBosDGYKcdCow+P+pmJJQ+y/xP9gFw3TXj/FDVsj386tZw7bMmo3KSMConybaM6Lyz554h8o1A5YjV8tHSLsKZ3O6j9vjgZ1FBosDFYKcdCoQpebWNQJU+i0BpiU7tdYCrIc4+hZ5X+4/XSR6btWqXy0zN1hILaq/009JDLPTSU2dH2E7+7ubD2HOs1uGYcB+VHvxSW9IDmb8vtRGRNAY77VAwTclLfRZnzkt07jyMha3QloaLsuedkthyLrdU9eMJ6QBJ7bjsVSrU+LEvYKimOKNwH882uvbGykyMlJyxCqZaNCwqSBR4GOy0U/4+Ja9lqU3ss0jZX1mH3//rJ5eH8a8Ga18+GpgRj70V0sHJ4O7is1Of/Vip+b0EvVOjcaDqnMvrUlu0LQo1fqrtAjYtxRmF+++8HBgsie9iWFSQKHAx2Gmn/H1KXstSm/1n2Vl6BvM/2S/5c6t2lIkuzTRekt7h5Ex40I/KTcJfd5RJnjciJ9HltVJLA36UWd5S8sp/98drG8V7e4lJipZvqCrUK5JKWpcittQZCInv7gjmQI4o2DHYaef8efdLnDkMtU2uO63izOKNKIXP8sW+U6JLdAO6xYk2nGyxWrHrWA1iOoWi/oJy0CPkuWgJyLT075IT3UlbkKq2OrGWBG2pRqCBkfiuT7AHckTBjkUFyS+VWhpEAx0AqG26LFu4TqoY3ozhPWTfU02gA1zdwaSl8q+WJSI5QvG6zMRIjM1NVnzAVigEIMdr2o5rSdAWGoE6C4TEd71YVJAosHFmh/yS2jo7YqSW6P5eWG7I2Owf2hcklr8uXGqx/W+tS0Rq31uN9XtPyB5ft+cERuUkqU70FojN0gRT4ruzYA7kiNoDv5/Z6dGjB0wmk8vXnDlzAABjxoxxOfab3/zGx6Mm9+mv/Ctwnv1QasipRGgrIVyv1NKAPRIJynsqam2zH1qWiLzv6p0UmxGTIvVwl5pV85fEd72UeoUFciBH1B74/cxOUVERWlqu/pW8f/9+/Nd//Rfuvvtu22sPP/wwXnjhBdv3ERHqp+TJPxlR+deZUkPOa9Ni8PPJesnjzg9tpV1Vn/9YiUdvytG0RKREa97LDZkJWFcsPc687KuBifOMmFBnR8ssjb8nvrvD33cwEpE0vw92kpIcmwP+6U9/QnZ2Nm688UbbaxEREUhNTfX20MiD9FT+Vaqdk6EQdISHiM8m9U6Nxrv3DXK5Zr5Ce4n8I2fw6E05sv27tNK6XJKsEDQmiuzWEhK9B2bE6364+2viuzuCOZAjCnZ+H+zYu3TpElavXo0nn3wSJrvp5DVr1mD16tVITU3FbbfdhgULFsjO7ly8eBEXL16tL1JfL/3XPPmGuzudxOqfKC0nFUtsCRerawMAFy63iL4uaLyStyPXv0vMNSlROHSqweX1oT06a364upNrwoe7uGAM5IiCnd/n7Nhbv349amtr8cADD9he+z//5/9g9erV2Lx5M+bPn48PP/wQ9913n+x1Fi9ejNjYWNtXRkaG7PnkfVlJUYjuFCJ6LKZTiOpCdvbcWU4S220j1KiRkhzdNquiNWfnZG2T6OsHT2kPyoUt/GKktvA7U7vzi4jIXwVUsLN8+XLccsstSE9Pt702a9YsTJgwAf369cPUqVOxatUqrFu3DkeOHJG8zvz581FXV2f7qqio8MbwSYNSSwPOXRCfOam/0GJL/hV2OjnvILKvfyIQlpPExHSSn+QUmwGxnJNvFyEcVxtkhZhM6NslBucuin/uuqZmfK9xV5c7W/iJiIJFwAQ7x44dwzfffIOHHnpI9rxhw4YBAA4fPix5Tnh4OGJiYhy+yL989uNJheNtSbda6p/ILSfVX2jGkO7xLrttTAC6dzbb6tHYqz6vEOw0aNv9NaJnIkaJVF22t6dcvKeWFDVb+ImIgl3ABDsrVqxAcnIybr31Vtnz9u7dCwBIS0vzwqhIq1JLAzYfPK04o3D0jGvOisNxS9vPa8lJUQqMHhjew2XbtBXAsbNNuH95IQa88BUqqq9eIzFSfhkr6coyl9L7PvFfOdj89BismjkUNyhUPB7YLV72uLNDEvlGgpJT8seJiIJBQCQot7a2YsWKFZg+fTpCQ68O+ciRI/joo4/wi1/8AgkJCfjxxx/xxBNPYPTo0ejfv78PR0zOtDZRNCnU2bnSsUFTITulwOjaLrFYdV06jp45j1++/T3OX3JcTqppvIzb/rINe38/HoD4TiZ7SVdydpTe9/brutjGqbRjrGu8tryj2ib5qtBG7BAjIvJ3ATGz880336C8vBwPPvigw+sdO3bEN998g/Hjx6NXr1546qmncNddd+HTTz/10UhJitokYkGMWT4Oj7LLsZEqZPfU+GscZpHUFoYrLK12CXQEtU2XbXkzSoUNheBLLlfIuc+U2mU5tTNkmYnywVFWEpOOiSj4BcTMzvjx42EVKWGfkZGBLVu2+GBEpIWeJopjc5Pxtx3HJK95c+8U2/923iLdOaIjXv/qECYt2W47R5hFenFyH0xast1hRiPGHIqXJvdV3azz2wOnMSonCdd3jcM3B05LnicsOcnlCgl9poTPrzQL1DkiDNOWF6qeIUuJlq+zkxIjf5yIKBgExMwOBTY9TRT1LOcIW6Rf/+qQ5CzS8+t/Qr3T0k59UzOeW79fdbPOzlEdAQCtCnM7rVcCdC2fX2n26fWvSjTNkB2okt+u/lOleG0htdTOMBER+VJAzOxQYNNT2E5tKwZnSrNIYuSOifll/7bSB0oP+FIdSdSAdFuCp8bnYNKSHS4/LzdD1jmyo+x7K9UKkqI1B4uIyJc4s0OGc/5rX08TxbPnL8m+x5kG8W3fnm66OaR7vG28SknUwsetUBiT87Z2q8SM0fGz4sUGBWIzZNcp9AO7LkP+uJRH1uxxCRC3llgwe81uXdcjIvIkzuyQYeT+2pfLlRHTO1W+9lGf9FjR1z0ZvcdHhOGNe663fa+URB3TqW2GY+/xWtnz9pTXYFTO1R5wUsncjZfkd1aJzZDp6TGmpNTSgB0SfcF2HKkWnWEiIvIlzuyQYeR2XMnlyojR08Byy8HTWLf3hMZRX5WTHOUy+2TPebxKAdm16W3Hlbqt29fOkasIvetYDYb0cC18KDdDdrpOvrDhGYUq0GJYqJCIAg1ndsgQ7uTKiM0EKEXhoR2uPvCPVZ/HZKdZIz2uy4jFmYaLktdxHq+egEyJ0jLc9OE9YA47rroTedU5+WCnsk5+aUyc/PKd9rkiIiLPYrBDhnAnV6as2jXYUXoIn6i5elxNoBNiMiElNhyVtdIP/x1Hql1mn+TGqzYg07KMpZTM3Cc9FqtmpqvuRK5lVkmtYZmdZY/fkCVfBVrOloOnsfd4LQZ2i3dY2iMicgeDHTKEOx3FxXJNDlXJt4s4dKXNwZaDp1XN6MSYQ/HkuBw8/f/2SZ4jFwjZE8arNh8mVaHWTXqs2fa/s5KiMDw7QTQnZnh2gi2wyUyUD3IERldkFsaYl5WAfJHlqrysBF35OmKzc/ERYdgwZyQyEvT/2yIiApizQwZR2nEldqwDgL7p4nkvUjuSbMevHFaaNRHUNzXj77uOqzpXinNujNp8GK3LXSL1M2Vfl1Nw9Kzs8Z0682uW3TcIo51mXkbnJGHZfYN0XU9sdq6m8TJuX7JN1/WIiOwx2CHDSLVteGfKANFjrQD2V9Zj7GvfYdryQtTZPezG5ibLvtdNvduOK82aCFqsVhSVaesY7sx599hOhUTd/CuzM1rq7JRaGkRnTAAgv7Rac/E+i0LOTrXEFn4lQtXqzU+PwYoZQ2yNTPXU2JGbnatpvNqeg4hILy5jkWGc2zY455MIxx5buwc/V9bDftezsGtr1cyhAIAbc5MRZw5DbZPrQzDOHGbL51CaNTGSsBtLGOO5C/L5PQ0X2saupVmpmmrLWpaJkhSSpPUWFRSoXU6To3VrPhGRVpzZIcMJbRvEHoJWqxX7TzgGOoDjTifBp4+OdGmgGR8Rhk8fHWn73p1cIa2cxyi/Jwkw2S3bvTi5j0tdHrE6Q3qqTcsZlimfLDzMjWRio3giiZqIyB6DnXbOE72N5K6ppU9UtDkU/brEORzv1yUOMearAZBSdWJBiMmE7gYlugpjVNv1HIDqOkN6qk3LyUqKQpxZfGkpzhzmF8X/bsxNlu0Kz1kdInIXg512qrbxEqYtL8RNr2/BjBVFonkznrimlpmLGX8rEm1JMONvhbbv1SYoj+iZiN9NyJU9p196jGxRQecx/pdd53UxE/qkApAvFLi1xIL/LSx3CAzlcp+0KrU0iC4FAkBt02W/aeC5YY74LN6GOSMlfoKISD3m7LRTctWOhZwUX16z1NKA4vJa0WN7ymtthf2UlkDuHNAFdwzsglE5SSi1yG9nf+mOfnjtq0OyTUHtt38PUag3M7hH23Gl2ax5n7Rth7dvpCmX+6SF0TlAnpKREIHihePxfYkFe8prWGeHiAzFmZ12SGmmQc9f+2qvqXYZ68P8Y7Lnrc4vA9C2BGJfTdnZJ8UncP/yQkxbXogDlfWy16xuvGTbYdQ7LVr0HPuPp7Ztgtq8IiEwFMjlPqlldA6Qp43KScJvb76GgQ4RGYrBTjukJW9GinNejtprqn34/qdKPjA5cOV4qaVBVTPL7YfPYMWOMlXvbbVaceDkOdFzHLd/q2ubIJWH48ydYFOKUKRQjP0sFRFRMOMyVjvkzl/7Up3N7x2aIXtNYfZF7TbsqHD5f5rR4W35HWrbVAiNNNVQu/STrrDtvWv81crI70wZgMfWFssukTlf3yhGFikkIgpEnNlph9zZ8SOVl/PKF/+Rfc8fKmpt/1tNAm4Xu0BBTHpc23GlKsZaqF120touAnAswrf4zn6qrm8Eo4sUEhEFIs7stFNiMw1KO37kOpsrzYaUWq4+VNUk4I7NTcbfdkjn7QgVlJW6emtx5ko1YbWzT3pmyIQifF/sq1JVZNBdgZKgTETkSQx22ik9O37c6WyelST94BdzY24yYjqFol6kSnFMp1BbAqvSbix7ncJMuHBZeu3G/oieYFALT19fEGgJykREnsBgp53TUu7fnWrFt/ZP1/wzuSnRKBLJs8lNubpTSihIp6bzuVygAzimG6sJBtXsxpK6t0qNToG2mbRjZxvd2nqupVUFEVGwYs4OqaaU6zO0h3jdmaE9Omt+qJZaGkQDHQAoOlbjkGsiVpBOj4QrfaLsd5rJb/9WtxtLjFxNIqMLPhpZpJCIKBBxZoc0eXFyX0xass1hJkXo8RRjDnNZmhEK5WmlJdfEviDdJ3uOY11xpeb3A9raJ0xbXig6frFu3sMUigreINF3Si73aWuJBQ+v2oU9x2odjrlT8NHIIoVERIGIwQ5p8vz6/ZI9nlbNHGrYQ1Vpl9WZcxddXhuVk4TmVqvuYOftb0tc6utsLbFg9prd+OjhG1zOz0qKQqw5FHVNrnlFseZQyc+uFMgVlbnOaNnX4NF7T43oUE5EFIi4jEWqqa2SbETl3wMKRQV/lqiG7E5ekVQhwR1HxLdol1oaRAMdAKhrapbc1u3OGNUUfCQiIkcMdto5LV3PPVF5WcqhKvHAQ1BySvy4XF5RTrL+4KtApFaN3vshN8bB3eNlr8ndU0RE2nEZq52SqoQslZ8CeKbystT7NV5qkX2vhkviMyqA9Lbufl1iUHL6iOx1pYglG7tzP+S2nj+2tpi7p4iIDMRgp53yRNdzOQ+v2uWSi9KWjFuEf/xmuMv5A7vFY+/xOsnrDe4unRwslZC7trBc9/ilko21st9OLpXf5K0aPERE7QWDnXZIaTeQVBKs2h1SzvVhSi0Nokm3AFBYViP6fqNyk/BXmcadI3ISJY8JnBNylXZP9U2PwX6RXKAh3eNF74eWOjtyM1vO1+buKSIiYzHYaYf0thBQWrbpHNFRdOt2vy7RMj8FfP5jJR69KcfhNaVkMqGxqBZZSVHIy0oQ7RWVl5UAqabkYaFSo1FfZ0fPTBp3TxERGYMJyu2Q3lwTpaKCr391SPSBvq74hOz7nWm45PJapcLW8+M1TbLHpbxyV3+XAoTxEWF4dGxP7DgiPlOz40g1vheZCVMbkCntYltbWM6GnEREHsRgpx3KSoqSnBkJ7WCSnU2Qqsb71PhrJB/olXWuNXHs3Xylqae9oqNnZX9md5n8cSlSdYIWf3FA9ufuX17oUsV4p8IyVv6V4ElpJm3+J/vcrpJMRETS/D7Y6dGjB0wmk8vXnDlzAAAXLlzAnDlzkJCQgKioKNx11104deqUj0ft37YcPI3mVvFmBs2tVtFZDIGQT7LqwaF44r9y8OGVQoJnG11nZ+xFdgwRv545zNbU017pmQbZ6x2xaJ8JkZthEcvVcSYsO12lbilNbV0d1+sTEZER/D7YKSoqwsmTJ21fX3/9NQDg7rvvBgA88cQT+PTTT/Hxxx9jy5YtqKysxJ133unLIfu9vcdrZY/vKRdPJgZg69s07a+FePPrEtuMR5PMVnAAeOH2vqLLR589OlL0fHOYeHAkiOio/Z+u0gxL3y4xLkt09pyLJ06+Xr656R0DuwCQXv5Tuj4RERnD74OdpKQkpKam2r4+++wzZGdn48Ybb0RdXR2WL1+ON954AzfddBMGDRqEFStWYMeOHdi5c6evh+63ru8aJ3t8YDfpwnZSibbvbpavX9OxYwf06+L4vv26xCHGLF7TJy3WLHs9peNilP6xPzq2p8sSnRihWOCNuckIkYhfQkxwmLESW/5Tuj4RERnD74Mde5cuXcLq1avx4IMPwmQyYffu3bh8+TLGjRtnO6dXr17o1q0b8vPzJa9z8eJF1NfXO3y1JzfmJiPWLL4RL9YcKrqsBLi3DLRqR5nkbiQxMRLjE0R10r6RsFXheHhYyJUluiGy5wkJ3KWWBrRItDZvscJhhkZY/tv89BgsvrOvqusTEZExAirYWb9+PWpra/HAAw8AAKqqqtCxY0fExcU5nJeSkoKqqirJ6yxevBixsbG2r4yMDA+O2j/lpsRoeh1QXgaSU1RWo9hTy97YXNekZXs3907RPAa1u6dGX5Msu+tMSODW0y4iMzESU4Z2V3V9IiIyRkAFO8uXL8ctt9yC9HT5XAkl8+fPR11dne2roqLCoBEGhlJLAwoldjMVlp31SANLOWJ9pzIU3qtrvPaxKM3s2CdtS+06s69i7G67CKXrExGRMQKmqOCxY8fwzTff4JNPPrG9lpqaikuXLqG2ttZhdufUqVNITU2VvFZ4eDjCw8M9OVyPca5OrIfeooJCoq1Y36aspAiUnNaXazLvk334974qhz5Zn/1YKfszYoUIlWgJTjxdxZhVkomIvCdgZnZWrFiB5ORk3HrrrbbXBg0ahLCwMGzatMn22sGDB1FeXo68vDxfDNNjhF1QN72+BTNWFLlVl8UTMxJThnaTvWaExNZzwdYSC2av2W37XmlHUqlFfmu6GKWiiGLBRmZiJMbmJutuF6FE7vpERGSMgJjZaW1txYoVKzB9+nSEhl4dcmxsLGbOnIknn3wSnTt3RkxMDB577DHk5eXhhhtu8OGIjWdk486spCjER4ShRiRQio8IE33wKjWwVGqyqdTFHGirVCz0yVJ6+GclRSleT4yRTTYt5+SLJZ5pkD9ORETeERDBzjfffIPy8nI8+OCDLsfefPNNdOjQAXfddRcuXryICRMm4N133/XBKD1Hb+NOueuJBToAUNN42eF6Ug0snxrvuISUHttJ9fvLEZpn/rJ/Ot74ukTyvFv768vbMnL5KCla/jMnRAXmUikRUbAJiGBn/PjxsFrF9/h26tQJS5YswZIlS7w8Ku/Rk2Mjl9uj5XpiM0pbSywuwc+Ya9TVkFEisZPbcEY02VQK8LrGa68FRERExguIYKe9O63QFPOM3XKK1EyMffKv2pwdqRklZ9sPn8HJOvnGnFmJkShVURn4hqwEAMBfvj0se96Sb0vw2j3XK17PXXJBo5bdXURE5DsMdgJA1Tn5YKfSLtAwMrdHbV2dFqsVJaflE4alKiXby8tKsAUUSgnIh3UkKNtT2tWmJmhUW7eHiIh8K2B2Y7Vnats7yFU4ti/ep7YYnta6Or3TokVfH5ARh70VtbI/OzonCcvuG2T7Xq5lBQAM7t5Z09gEane1yQWNgkqF2awTNfLHiYjIOxjsBIAbc5NdmmgK4iOudg1XG8SoXRZT28BS8Mpd/THaqdXE6JwkzByVKftzf7qzH1bNHGqbMQGAnFTxwEmQnaxvN5aaIEZt0Gg5J9/p3cLdWEREfoHBToDYMGekaNfwDXOudg1Xm4ujZVlMTQNLoU5NN4n3z1CodjzsSp6OvUNV52R/5vAp+eNAW9Cy+eBpW3Bi9MxXUnRH2fOSuBuLiMgvMGcnQGQkRKB44Xh8X2LBnvIaDOwW79KwMyspCnlZCcgXKWZnnw+jpeu581bthIiOeO2rQ6J1ah5bWyw6awJAsvLyiJ6JojkzVoV9Wc4Biz2pfJt7hnSVvaawC01t0Dgs0zVIsycWxBERkfcx2Akwo3KSJLuSA4DUipP96zfmJiPOHIbaJtdaO3HmMNHr22/VFqtTo1QLaMOjIwBAdTG/3qnSDUkBoE96rOQxqaWqpsvNstcUghi5thj2wVlWUhSGZydgxxHX4HJ4dgKrIhMR+QkGO0Gk1NIg+uAFHKsTA0C3zmbUnnANdrp1VlcbxrnukdLST/X5S5qK+end1i0XdBWV1WBI93jsKa9VnGFSW2l56dRBLucJu7aIiMg/MNgJImqLBZZaGvDjiXrRc348US9bkVltRWVnwqyJ2mJ+FoW8omqJ5F+le3D3oK447FRBOsYcipcm93U4T22lZTb0JCLyf0xQDiJqc03UdBSXIrVE9PpXJZqbbMqT3wEmlbGjdA8+3nMc9U2Oy1n1Tc14bv1+0fPVNupkQ08iIv/FYCeIqO3qffa8/JZpqQaWSruZfn1jFmLMjpOFYrMm6sgnKEuFQnL3YHD3eBSV1SjuxiIiouDCYCeIlFoacM+QrhjYPc7hdedck7G5ybLXubl3iujrSktEi/99QNOsiTx9MzuA+Hb5ET0TMWN4D9lrClvKiYgouDBnJwiI5dEM6R6PB4b3wLVdYl2WVm7MTUZ0pxCcu9Dicq2YTiGSu72Uloj2V7rmAentzK53ZgeQzqNRakEhLPMREVFw4cxOEBDLo9lTXou/7zouGWBkSjzYe8gEJHJLRH27yG8VF5s1cS76Zy8pWr6jeIKKgn3OeTRql/mIiCi4MNgJcGqrAjv/jORurOP1srkrUktESnk59rMmavpTDcuU7311g86CfVLj51ZxIqLgxWWsAKd2u7k9NbuxHr1JfCu53FZrtVWS1XRmr1D4XMdrGnXNxHCrOBFR+8OZnQCndru5vbPnXYsJ2jvTIL9bCxDfaq1m1kTtTNTe47Wy77+nvEZxjFrHT0REwYkzOwFObWsDe70VOor3SXfNvym1NODY2UbZmRA1syZqZ6K09O8iIiKSw2AnCKhtbSBIjpVP/k2Mvpr8K1Ux+Z0pAxDr1IVdIFclWe1MVIbCeV0VOqkTEREJGOwEAa15KFqWvqTya2auLMKcm3pqznlROxOlJxeJiIhIDHN2gojaPJSspCjES8zKxEeE2X5eLr9m17EayZ1UStTk9ujJRSIiIhLDmZ12qNSpEaa9msbLtgKASrMrAuedVEq4I4qIiLyJMzvtkJolIkB5dkWgt7eU3EyU2jH6C7kCiURE5Fuc2WmHPLVEZGQeTaAsY+lJ4CYiIu/izE6AMWIGQW3bBLXLWAIhADFijGqKCvoDuQKJRETkHzizEyCMnkFQs11d7TIW0JbYHB8RhmnLC1WPUa52j5qiglINS71FSOB2pr/5KREReQKDnQChpsWCFmqShLOSopCXlYD80mrF69U0XsbDK3dhT3mt4hjVBG6pCo1A02PNimPyNG6PJyIKDFzGCgB6mn2qpbRd3WmlS1bRsRpVY5y9eo/LjMjWEgt+s3q37XsthQ99JVDyioiI2jsGOwHAVzuTSi0N2HFEeVZHDWGMpZYGyZmi/NJqW1Ck9A8ztIOGKMxD1OY+ERGRbzHYCQC+mkFQm6AcYjJhSA/5XlXCGAuOnpU9b+eVQKhV4T2bW60KZ3iHmgKJRETkW8zZCQB6mn0aQW2CsvBwf2xtsYoxygcpwhxJoCwRsUAiEZH/48xOgHhxch/EmB1j0xhzKF6a3Ndj7ym3TDOkRzxWzBiCzU+PwaqZQxEbEaZqlmNYZoLsew7LSlB8b39cIlLbqoOIiLzPZLVa/WM9wIfq6+sRGxuLuro6xMTE+Ho4oqYtL5ScNdGzG0utusbLLlvUlba8K81y/J/3d4rmAg3PTsBHD99g+768+jwmLdnu0NoiPiIMG+aMREYCu54TEbV3ap/ffj+zc+LECdx3331ISEiA2WxGv379sGvXLtvxBx54ACaTyeFr4sSJPhyx8Ty5G+vvheWY+/difLyrQvS4sEyz+ekxLjM5UpRmOZZOHYTRTjVyRuckYenUQQ6vPb/+J9Q3NTu8Vt/UjOfW71fz0YiIiAD4ec5OTU0NRowYgbFjx+KLL75AUlISSkpKEB/vmAw7ceJErFixwvZ9eLjvtyUbyRP1XPYdr8Ud7+6wJfquL67E/E/2YcOcEbi2S6zL+ZmJxuWiqMlzYcE+IiIyil8HO6+88goyMjIcApnMzEyX88LDw5GamurNoXmVJ5J17QMdQXOrFbcv2Y7DL/9C8/X0kAugWLCPiIiM4tfLWBs2bMDgwYNx9913Izk5GQMGDMD777/vct53332H5ORk5ObmYvbs2aiulq8Nc/HiRdTX1zt8+TOjk3X/XlguuXW7udUquaTlTYGyG4uIiPyfXwc7paWlWLp0KXJycrBx40bMnj0bjz/+OFauXGk7Z+LEiVi1ahU2bdqEV155BVu2bMEtt9yClpYWyesuXrwYsbGxtq+MjAzPjN+AhpgCI+u55B+VDwa3Hzkje9wdau9JoO3GIiIi/+XXu7E6duyIwYMHY8eOHbbXHn/8cRQVFSE/P1/0Z0pLS5GdnY1vvvkGN998s+g5Fy9exMWLF23f19fXIyMjw7DdWEY37bRnRD2XvxeW43ef7JM8/uf/7o+7BxsbAOq5J3p2ghERUfuhdjeWX+fspKWl4dprr3V4rXfv3vjnP/8p+TNZWVlITEzE4cOHJYOd8PBwjyYxG920054RicJDMjvLHh/cQ/64HnruCQv2ERGREfx6GWvEiBE4ePCgw2uHDh1C9+7dJX/m+PHjqK6uRlpamqeHJ8qT28SN4u1eW+7eExbsIyIid/h1sPPEE09g586dePnll3H48GF89NFHeO+99zBnzhwAQENDA5555hns3LkTZWVl2LRpEyZNmoSePXtiwoQJPhmzr5p2auHt5N9AuCdERBS8/DrYGTJkCNatW4e1a9eib9++WLRoEd566y1MnToVABASEoIff/wRt99+O6655hrMnDkTgwYNwvfff++zWjuBsIsoKykK8RI5L/ERYYbPoATCPSEiouDl1zk7APDLX/4Sv/zlL0WPmc1mbNy40csjkuerpp1alFoaHFow2KtpvGx4wb5AuCdERBS8/HpmJ1AZuU3cE3yxrOTv94SIiIKX38/sBCJ/30Xki2Ulf78nREQUvBjseJCR/aSM5MtlJX+9J0REFLy4jNVOvTi5L2LMjrFujDkUL03u66MREREReQaDnXbq+fX7Ud/U7PBafVMznlu/30cjIiIi8gwGO+1QIBQ+JCIiMgqDnXaIRf6IiKg9YbDTDin9Rw/tYFI4g4iIKHAw2GmHKusuyB4/XtPkpZEQERF5Hree+6FSSwOOnW10qxaN/DWsoj8j4LwOEREFEwY7fqS28RIeX7sXW0ssttdG5yThnSkDECvRy0rPNYZlJsheY1iW/HEiIqJAwmUsP/L42r3YfviMw2vbD5/BY2uLDb1GVlIUhmeLBzTDsxPcLvpXamnA5oOnuauLiIj8Amd2/ISwHdyZ/XZwpSBEyzWWTh2Ex9YWi84A6WXEzBQREZHRGOz4CTXbwZWCHS3XEHpVbT10GsUVtRjYLR6jcpK0DdqJ3KzSqplD3bo2ERGRXgx2/IQRzTm1XMPoWRgjZqaIiIg8gTk7fkJozhlictwLFWIyYXROkqpAQcs1jMgPssdChURE5K8Y7PiRd6YMwIieiQ6vjeiZqCmPRs01PNEuwoiZKSIiIk/gMpYfEfJojp45j7Lq87rq7Ki5hhH5Qc6EWaXth884BFEhJhNG9EzkEhYREfkMZ3b8UGZiJMbmJrsVIMhdw1PtIoyYmSIiIjIaZ3baoVaF482t8hWWpRgxM0VERGQ0BjvtkKcbgWYmMsghIiL/wWWsdshTMztERET+iMFOO8SdU0RE1J4w2GmHjKjpQ0REFCgY7LRT3DlFRETtBROU2ynunCIiovaCwU6AKbU04NjZRsOCE+6cIiKiYMdgJ0AY3biTiIiovWDOToAwunEnERFRe8FgJwB4onEnERFRe8FgJwCoadxJRERE4hjsBAAWASQiItKPwU4AYBFAIiIi/RjsBAgWASQiItLH74OdEydO4L777kNCQgLMZjP69euHXbt22Y5brVYsXLgQaWlpMJvNGDduHEpKSnw4Ys8QigBufnoMVswYgs1Pj8GqmUO57ZyIiEiBXwc7NTU1GDFiBMLCwvDFF1/g559/xuuvv474+HjbOa+++irefvttLFu2DAUFBYiMjMSECRNw4cIFH47cczITIzE2N5lLV0RERCqZrFan/cx+ZN68edi+fTu+//570eNWqxXp6el46qmn8PTTTwMA6urqkJKSgr/97W+49957RX/u4sWLuHjxou37+vp6ZGRkoK6uDjExMcZ/ECIiIjJcfX09YmNjFZ/ffj2zs2HDBgwePBh33303kpOTMWDAALz//vu240ePHkVVVRXGjRtney02NhbDhg1Dfn6+5HUXL16M2NhY21dGRoZHPwcRERH5jl8HO6WlpVi6dClycnKwceNGzJ49G48//jhWrlwJAKiqqgIApKSkOPxcSkqK7ZiY+fPno66uzvZVUVHhuQ9BREREPuXXvbFaW1sxePBgvPzyywCAAQMGYP/+/Vi2bBmmT5+u+7rh4eEIDw83aphERETkx/x6ZictLQ3XXnutw2u9e/dGeXk5ACA1NRUAcOrUKYdzTp06ZTtGRERE7ZtfBzsjRozAwYMHHV47dOgQunfvDgDIzMxEamoqNm3aZDteX1+PgoIC5OXleXWsRERE5J/8ehnriSeewPDhw/Hyyy/jnnvuQWFhId577z289957AACTyYS5c+fixRdfRE5ODjIzM7FgwQKkp6dj8uTJvh08ERER+QW/DnaGDBmCdevWYf78+XjhhReQmZmJt956C1OnTrWd8+yzz+L8+fOYNWsWamtrMXLkSHz55Zfo1KmTD0dORERE/sKv6+x4i9p9+kREROQ/gqLODhEREZG7/HoZy1uEya36+nofj4SIiIjUEp7bSotUDHYAnDt3DgBYSZmIiCgAnTt3DrGxsZLHmbODtuKFlZWViI6OhslkMuy6Qs+tiooK5gK5gffRGLyPxuB9NAbvozHa+320Wq04d+4c0tPT0aGDdGYOZ3YAdOjQAV27dvXY9WNiYtrlP0Kj8T4ag/fRGLyPxuB9NEZ7vo9yMzoCJigTERFRUGOwQ0REREGNwY4HhYeH4/e//z2bjrqJ99EYvI/G4H00Bu+jMXgf1WGCMhEREQU1zuwQERFRUGOwQ0REREGNwQ4REREFNQY7REREFNQY7BjgxIkTuO+++5CQkACz2Yx+/fph165dtuNWqxULFy5EWloazGYzxo0bh5KSEh+O2D/16NEDJpPJ5WvOnDkAgAsXLmDOnDlISEhAVFQU7rrrLpw6dcrHo/YvLS0tWLBgATIzM2E2m5GdnY1FixY59I3hv0d1zp07h7lz56J79+4wm80YPnw4ioqKbMd5H8Vt3boVt912G9LT02EymbB+/XqH42ru29mzZzF16lTExMQgLi4OM2fORENDgxc/hW8p3cNPPvkE48ePR0JCAkwmE/bu3etyDf6+dMRgx001NTUYMWIEwsLC8MUXX+Dnn3/G66+/jvj4eNs5r776Kt5++20sW7YMBQUFiIyMxIQJE3DhwgUfjtz/FBUV4eTJk7avr7/+GgBw9913AwCeeOIJfPrpp/j444+xZcsWVFZW4s477/TlkP3OK6+8gqVLl+Ivf/kLDhw4gFdeeQWvvvoq3nnnHds5/PeozkMPPYSvv/4aH374Ifbt24fx48dj3LhxOHHiBADeRynnz5/HddddhyVLlogeV3Pfpk6dip9++glff/01PvvsM2zduhWzZs3y1kfwOaV7eP78eYwcORKvvPKK5DX4+9KJldzyu9/9zjpy5EjJ462trdbU1FTrn//8Z9trtbW11vDwcOvatWu9McSA9dvf/taanZ1tbW1ttdbW1lrDwsKsH3/8se34gQMHrACs+fn5Phylf7n11lutDz74oMNrd955p3Xq1KlWq5X/HtVqbGy0hoSEWD/77DOH1wcOHGh97rnneB9VAmBdt26d7Xs19+3nn3+2ArAWFRXZzvniiy+sJpPJeuLECa+N3V8430N7R48etQKwFhcXO7zO35euOLPjpg0bNmDw4MG4++67kZycjAEDBuD999+3HT969Ciqqqowbtw422uxsbEYNmwY8vPzfTHkgHDp0iWsXr0aDz74IEwmE3bv3o3Lly873MdevXqhW7duvI92hg8fjk2bNuHQoUMAgB9++AHbtm3DLbfcAoD/HtVqbm5GS0sLOnXq5PC62WzGtm3beB91UnPf8vPzERcXh8GDB9vOGTduHDp06ICCggKvjzkQ8felKwY7biotLcXSpUuRk5ODjRs3Yvbs2Xj88cexcuVKAEBVVRUAICUlxeHnUlJSbMfI1fr161FbW4sHHngAQNt97NixI+Li4hzO4310NG/ePNx7773o1asXwsLCMGDAAMydOxdTp04FwH+PakVHRyMvLw+LFi1CZWUlWlpasHr1auTn5+PkyZO8jzqpuW9VVVVITk52OB4aGorOnTvz3qrE35eu2PXcTa2trRg8eDBefvllAMCAAQOwf/9+LFu2DNOnT/fx6ALX8uXLccsttyA9Pd3XQwko//jHP7BmzRp89NFH6NOnD/bu3Yu5c+ciPT2d/x41+vDDD/Hggw+iS5cuCAkJwcCBAzFlyhTs3r3b10MjIo04s+OmtLQ0XHvttQ6v9e7dG+Xl5QCA1NRUAHDJgj916pTtGDk6duwYvvnmGzz00EO211JTU3Hp0iXU1tY6nMv76OiZZ56xze7069cP999/P5544gksXrwYAP89apGdnY0tW7agoaEBFRUVKCwsxOXLl5GVlcX7qJOa+5aamorTp087HG9ubsbZs2d5b1Xi70tXDHbcNGLECBw8eNDhtUOHDqF79+4AgMzMTKSmpmLTpk224/X19SgoKEBeXp5XxxooVqxYgeTkZNx666221wYNGoSwsDCH+3jw4EGUl5fzPtppbGxEhw6O/7cOCQlBa2srAP571CMyMhJpaWmoqanBxo0bMWnSJN5HndTct7y8PNTW1jrMoH377bdobW3FsGHDvD7mQMTflyJ8nSEd6AoLC62hoaHWl156yVpSUmJds2aNNSIiwrp69WrbOX/605+scXFx1n/961/WH3/80Tpp0iRrZmamtampyYcj908tLS3Wbt26WX/3u9+5HPvNb35j7datm/Xbb7+17tq1y5qXl2fNy8vzwSj91/Tp061dunSxfvbZZ9ajR49aP/nkE2tiYqL12WeftZ3Df4/qfPnll9YvvvjCWlpaav3qq6+s1113nXXYsGHWS5cuWa1W3kcp586dsxYXF1uLi4utAKxvvPGGtbi42Hrs2DGr1aruvk2cONE6YMAAa0FBgXXbtm3WnJwc65QpU3z1kbxO6R5WV1dbi4uLrZ9//rkVgPV///d/rcXFxdaTJ0/arsHfl44Y7Bjg008/tfbt29caHh5u7dWrl/W9995zON7a2mpdsGCBNSUlxRoeHm69+eabrQcPHvTRaP3bxo0brQBE709TU5P1kUcescbHx1sjIiKsd9xxh8P/uclqra+vt/72t7+1duvWzdqpUydrVlaW9bnnnrNevHjRdg7/Parz97//3ZqVlWXt2LGjNTU11TpnzhxrbW2t7Tjvo7jNmzdbAbh8TZ8+3Wq1qrtv1dXV1ilTplijoqKsMTEx1hkzZljPnTvng0/jG0r3cMWKFaLHf//739uuwd+XjkxWq11pVSIiIqIgw5wdIiIiCmoMdoiIiCioMdghIiKioMZgh4iIiIIagx0iIiIKagx2iIiIKKgx2CEiIqKgxmCHiIiIghqDHSIKSmVlZTCZTNi7d6+vh0JEPsYKykQUlFpaWmCxWJCYmIjQ0FBfD4eIfIjBDhEFnUuXLqFjx46+HgYR+QkuYxGR3xszZgweffRRPProo4iNjUViYiIWLFgA4W+1Hj16YNGiRZg2bRpiYmIwa9Ys0WWsn376Cb/85S8RExOD6OhojBo1CkeOHLEd/+CDD9C7d2906tQJvXr1wrvvvuvtj0pEHsC5XSIKCCtXrsTMmTNRWFiIXbt2YdasWejWrRsefvhhAMBrr72GhQsX4ve//73oz584cQKjR4/GmDFj8O233yImJgbbt29Hc3MzAGDNmjVYuHAh/vKXv2DAgAEoLi7Gww8/jMjISEyfPt1rn5OIjMdlLCLye2PGjMHp06fx008/wWQyAQDmzZuHDRs24Oeff0aPHj0wYMAArFu3zvYzZWVlyMzMRHFxMa6//nr8z//8D/73f/8XBw8eRFhYmMt79OzZE4sWLcKUKVNsr7344ov497//jR07dnj+QxKRx3AZi4gCwg033GALdAAgLy8PJSUlaGlpAQAMHjxY9uf37t2LUaNGiQY658+fx5EjRzBz5kxERUXZvl588UWHZS4iCkxcxiKioBAZGSl73Gw2Sx5raGgAALz//vsYNmyYw7GQkBD3B0dEPsVgh4gCQkFBgcP3O3fuRE5OjupgpH///li5ciUuX77sMruTkpKC9PR0lJaWYurUqYaNmYj8A5exiCgglJeX48knn8TBgwexdu1avPPOO/jtb3+r+ucfffRR1NfX495778WuXbtQUlKCDz/8EAcPHgQA/PGPf8TixYvx9ttv49ChQ9i3bx9WrFiBN954w1MfiYi8hDM7RBQQpk2bhqamJgwdOhQhISH47W9/i1mzZqn++YSEBHz77bd45plncOONNyIkJATXX389RowYAQB46KGHEBERgT//+c945plnEBkZiX79+mHu3Lke+kRE5C3cjUVEfm/MmDG4/vrr8dZbb/l6KEQUgLiMRUREREGNwQ4REREFNS5jERERUVDjzA4REREFNQY7REREFNQY7BAREVFQY7BDREREQY3BDhEREQU1BjtEREQU1BjsEBERUVBjsENERERB7f8HDd8/Uxt19jUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg.plot(x=\"price\", y=\"predc\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "La seconde régression utilise une information dont on ne dispose pas au niveau agrégé : le jour de la semaine et un précédent graphe a clairement montré que c'était une variable importante. Un dernier graphe pour comparer les deux prédictions en montrant les prédictions triées par prix à prédire."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>idc</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "      <th>pred_moy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>6423a722-4769-4a7c-8d1d-266538c2a07a</td>\n",
       "      <td>Balthazar</td>\n",
       "      <td>60.0</td>\n",
       "      <td>66.12617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  age  gender                                   idc       name  price  \\\n",
       "0      0   18       1  6423a722-4769-4a7c-8d1d-266538c2a07a  Balthazar   60.0   \n",
       "\n",
       "   pred_moy  \n",
       "0  66.12617  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = join.sort_values(\"price\").reset_index(drop=True).reset_index(drop=False)\n",
    "temp.head(n=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='index'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp.plot(x=\"index\", y=[\"price\", \"pred_moy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>predc</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>64.54544</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index     predc  price\n",
       "0      0  64.54544   60.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp2 = agg.sort_values(\"price\").reset_index(drop=True).reset_index(drop=False)\n",
    "temp2.head(n=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='index'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = temp.plot(x=\"index\", y=\"price\", figsize=(14, 4), ylim=[60, 120])\n",
    "temp.plot(x=\"index\", y=\"pred_moy\", linewidth=1, ax=ax, ylim=[60, 120])\n",
    "temp2.plot(x=\"index\", y=\"predc\", ax=ax, linewidth=0.6, ylim=[60, 120])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "C'est finalement un peu plus visible sur le graphe précédent (nuage de points) mais aussi un peu trompeur du fait de la superposition des points. Une dernière remarque. En machine learning, nous avons l'habitude d'apprendre un modèle sur une base d'apprentissage et de tester les prédictions sur une autre. Dans notre cas, nous avons appris et prédit sur la même base. Ce type de tester est évidemment plus fiable. Mais nous avons comparé ici deux erreurs d'apprentissage moyennes et c'est exactement ce que l'on fait lorsqu'on compare deux coefficients $R^2$.\n",
    "\n",
    "Un dernier graphe pour la route obtenu en triant les erreurs par ordre croissant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb2fa805210>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp[\"erreur\"] = temp.pred_moy - temp.price\n",
    "temp2[\"erreur2\"] = temp2.predc - temp2.price\n",
    "ax = temp[[\"erreur\"]].sort_values(\"erreur\").reset_index(drop=True).plot()\n",
    "temp2[[\"erreur2\"]].sort_values(\"erreur2\").reset_index(drop=True).plot(ax=ax)\n",
    "ax.plot([0, 1000], [0, 0], \"r--\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Le second modèle fait des erreurs moins importantes surtout côté négatif. Il sous-estime moins la bonne valeur."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Traitement spécifique de la variable catégorielle weekday\n",
    "\n",
    "Le second modèle ne prend pas en compte le dimanche comme jour de la semaine. *weekday* est une variable catégorielle. Contrairment au genre, elle possède plus de deux modalités. Il serait intéressant de la traiter comme un ensemble de variable binaire et non une colonne de type entier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "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>lu</th>\n",
       "      <th>ma</th>\n",
       "      <th>me</th>\n",
       "      <th>je</th>\n",
       "      <th>ve</th>\n",
       "      <th>sa</th>\n",
       "      <th>di</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lu     ma     me     je     ve     sa     di\n",
       "0  False  False  False  False   True  False  False\n",
       "1  False  False  False  False   True  False  False\n",
       "2  False  False   True  False  False  False  False\n",
       "3  False  False  False  False  False   True  False\n",
       "4  False  False  False  False  False   True  False"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pandas.get_dummies(mat.weekday)\n",
    "dummies.columns = [\"lu\", \"ma\", \"me\", \"je\", \"ve\", \"sa\", \"di\"]\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On supprime une modalité pour éviter d'avoir une matrice corrélée avec la constante et on ajoute des variables au modèle de régression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>date</th>\n",
       "      <th>idc</th>\n",
       "      <th>idr</th>\n",
       "      <th>price</th>\n",
       "      <th>date2</th>\n",
       "      <th>weekday</th>\n",
       "      <th>constant</th>\n",
       "      <th>jour</th>\n",
       "      <th>jouri</th>\n",
       "      <th>diff</th>\n",
       "      <th>...</th>\n",
       "      <th>delay1</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>ma</th>\n",
       "      <th>me</th>\n",
       "      <th>je</th>\n",
       "      <th>ve</th>\n",
       "      <th>sa</th>\n",
       "      <th>di</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>b7db0ac9-86a1-46f9-98ac-f1f8eb54072d</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2016-12-02 19:47:45.068274</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-11-18 19:47:45.068274</td>\n",
       "      <td>14 days</td>\n",
       "      <td>14</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Cendrillon</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         date                                   idc  \\\n",
       "0  2016-12-02 19:47:45.068274  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "\n",
       "                                    idr  price                      date2  \\\n",
       "0  b7db0ac9-86a1-46f9-98ac-f1f8eb54072d   75.0 2016-12-02 19:47:45.068274   \n",
       "\n",
       "   weekday                   constant    jour  jouri  diff  ...    delay1  \\\n",
       "0        4 2016-11-18 19:47:45.068274 14 days     14 -26.0  ...  0.002732   \n",
       "\n",
       "   age  gender        name     ma     me     je    ve     sa     di  \n",
       "0   37       0  Cendrillon  False  False  False  True  False  False  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat2 = pandas.concat([mat, dummies.drop(\"lu\", axis=1)], axis=1)\n",
    "mat2.head(n=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.918</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.917</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2812.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:15:41</td>     <th>  Log-Likelihood:    </th> <td> -6293.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.261e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2526</td>      <th>  BIC:               </th> <td>1.267e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    10</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>  <td>   59.0371</td> <td>    0.272</td> <td>  217.342</td> <td> 0.000</td> <td>   58.504</td> <td>   59.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma[T.True]</th> <td>   -0.0840</td> <td>    0.208</td> <td>   -0.405</td> <td> 0.686</td> <td>   -0.491</td> <td>    0.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>me[T.True]</th> <td>    0.3003</td> <td>    0.216</td> <td>    1.392</td> <td> 0.164</td> <td>   -0.123</td> <td>    0.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>je[T.True]</th> <td>    0.1607</td> <td>    0.219</td> <td>    0.735</td> <td> 0.463</td> <td>   -0.268</td> <td>    0.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ve[T.True]</th> <td>   -0.1748</td> <td>    0.212</td> <td>   -0.825</td> <td> 0.409</td> <td>   -0.590</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sa[T.True]</th> <td>    0.1849</td> <td>    0.210</td> <td>    0.879</td> <td> 0.379</td> <td>   -0.227</td> <td>    0.597</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>di[T.True]</th> <td>   20.0978</td> <td>    0.211</td> <td>   95.203</td> <td> 0.000</td> <td>   19.684</td> <td>   20.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>        <td>    0.5005</td> <td>    0.005</td> <td>  104.318</td> <td> 0.000</td> <td>    0.491</td> <td>    0.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender</th>     <td>   -4.0722</td> <td>    0.115</td> <td>  -35.321</td> <td> 0.000</td> <td>   -4.298</td> <td>   -3.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay</th>      <td>    0.0003</td> <td>    0.000</td> <td>    0.661</td> <td> 0.509</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay1</th>     <td>  -20.7385</td> <td>    2.236</td> <td>   -9.275</td> <td> 0.000</td> <td>  -25.123</td> <td>  -16.354</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>257.849</td> <th>  Durbin-Watson:     </th> <td>   2.034</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 484.437</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.672</td>  <th>  Prob(JB):          </th> <td>6.40e-106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.666</td>  <th>  Cond. No.          </th> <td>9.20e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.2e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.918   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.917   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     2812.   \\\\\n",
       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
       "\\textbf{Time:}             &     11:15:41     & \\textbf{  Log-Likelihood:    } &   -6293.7   \\\\\n",
       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.261e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        2526      & \\textbf{  BIC:               } & 1.267e+04   \\\\\n",
       "\\textbf{Df Model:}         &          10      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                    & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}  &      59.0371  &        0.272     &   217.342  &         0.000        &       58.504    &       59.570     \\\\\n",
       "\\textbf{ma[T.True]} &      -0.0840  &        0.208     &    -0.405  &         0.686        &       -0.491    &        0.323     \\\\\n",
       "\\textbf{me[T.True]} &       0.3003  &        0.216     &     1.392  &         0.164        &       -0.123    &        0.723     \\\\\n",
       "\\textbf{je[T.True]} &       0.1607  &        0.219     &     0.735  &         0.463        &       -0.268    &        0.590     \\\\\n",
       "\\textbf{ve[T.True]} &      -0.1748  &        0.212     &    -0.825  &         0.409        &       -0.590    &        0.240     \\\\\n",
       "\\textbf{sa[T.True]} &       0.1849  &        0.210     &     0.879  &         0.379        &       -0.227    &        0.597     \\\\\n",
       "\\textbf{di[T.True]} &      20.0978  &        0.211     &    95.203  &         0.000        &       19.684    &       20.512     \\\\\n",
       "\\textbf{age}        &       0.5005  &        0.005     &   104.318  &         0.000        &        0.491    &        0.510     \\\\\n",
       "\\textbf{gender}     &      -4.0722  &        0.115     &   -35.321  &         0.000        &       -4.298    &       -3.846     \\\\\n",
       "\\textbf{delay}      &       0.0003  &        0.000     &     0.661  &         0.509        &       -0.001    &        0.001     \\\\\n",
       "\\textbf{delay1}     &     -20.7385  &        2.236     &    -9.275  &         0.000        &      -25.123    &      -16.354     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 257.849 & \\textbf{  Durbin-Watson:     } &     2.034  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   484.437  \\\\\n",
       "\\textbf{Skew:}          &  -0.672 & \\textbf{  Prob(JB):          } & 6.40e-106  \\\\\n",
       "\\textbf{Kurtosis:}      &   4.666 & \\textbf{  Cond. No.          } &  9.20e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 9.2e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.918\n",
       "Model:                            OLS   Adj. R-squared:                  0.917\n",
       "Method:                 Least Squares   F-statistic:                     2812.\n",
       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        11:15:41   Log-Likelihood:                -6293.7\n",
       "No. Observations:                2537   AIC:                         1.261e+04\n",
       "Df Residuals:                    2526   BIC:                         1.267e+04\n",
       "Df Model:                          10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     59.0371      0.272    217.342      0.000      58.504      59.570\n",
       "ma[T.True]    -0.0840      0.208     -0.405      0.686      -0.491       0.323\n",
       "me[T.True]     0.3003      0.216      1.392      0.164      -0.123       0.723\n",
       "je[T.True]     0.1607      0.219      0.735      0.463      -0.268       0.590\n",
       "ve[T.True]    -0.1748      0.212     -0.825      0.409      -0.590       0.240\n",
       "sa[T.True]     0.1849      0.210      0.879      0.379      -0.227       0.597\n",
       "di[T.True]    20.0978      0.211     95.203      0.000      19.684      20.512\n",
       "age            0.5005      0.005    104.318      0.000       0.491       0.510\n",
       "gender        -4.0722      0.115    -35.321      0.000      -4.298      -3.846\n",
       "delay          0.0003      0.000      0.661      0.509      -0.001       0.001\n",
       "delay1       -20.7385      2.236     -9.275      0.000     -25.123     -16.354\n",
       "==============================================================================\n",
       "Omnibus:                      257.849   Durbin-Watson:                   2.034\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              484.437\n",
       "Skew:                          -0.672   Prob(JB):                    6.40e-106\n",
       "Kurtosis:                       4.666   Cond. No.                     9.20e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 9.2e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = ols(\n",
    "    \"price ~ age + gender + delay + delay1 + ma + me + je + ve + sa + di\", data=mat2\n",
    ")\n",
    "res = lr.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On vérifie que le coefficient pour dimanche n'est clairement significatif contrairement aux autres dont la probabilité d'être nul est élevée. Le médecin appliquerait une majoration de 20 euros le dimanche. Le coefficient $R^2$ est aussi nettement plus élevé. On construit les prédictions d'un second modèle en ne tenant compte que de la variable dimanche."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5.00892234e-01, -4.07229120e+00,  2.97614199e-04, -2.07004497e+01,\n",
       "         2.00395644e+01]),\n",
       " 59.07951294272968)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrc = LinearRegression()\n",
    "feat2 = mat2[[\"age\", \"gender\", \"delay\", \"delay1\", \"price\", \"idc\", \"di\"]].copy()\n",
    "lrc.fit(feat2[[\"age\", \"gender\", \"delay\", \"delay1\", \"di\"]], feat2[\"price\"])\n",
    "lrc.coef_, lrc.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>delay</th>\n",
       "      <th>delay1</th>\n",
       "      <th>price</th>\n",
       "      <th>idc</th>\n",
       "      <th>di</th>\n",
       "      <th>predc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>False</td>\n",
       "      <td>77.664596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>65.0</td>\n",
       "      <td>4ba0b473-f8ca-4466-a65b-40e9b8ba5029</td>\n",
       "      <td>False</td>\n",
       "      <td>77.664596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>75.0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>False</td>\n",
       "      <td>79.668165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>80.0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>False</td>\n",
       "      <td>78.934710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>80.0</td>\n",
       "      <td>f44b004b-b01e-4835-b86d-1a42846c6d93</td>\n",
       "      <td>False</td>\n",
       "      <td>79.225084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  delay    delay1  price                                   idc  \\\n",
       "0   37       0  365.0  0.002732   75.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "1   37       0  365.0  0.002732   65.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "2   41       0  365.0  0.002732   75.0  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "3   41       0   29.0  0.033333   80.0  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "4   41       0   50.0  0.019608   80.0  f44b004b-b01e-4835-b86d-1a42846c6d93   \n",
       "\n",
       "      di      predc  \n",
       "0  False  77.664596  \n",
       "1  False  77.664596  \n",
       "2  False  79.668165  \n",
       "3  False  78.934710  \n",
       "4  False  79.225084  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predc = lrc.predict(feat2[[\"age\", \"gender\", \"delay\", \"delay1\", \"di\"]])\n",
    "feat2[\"predc\"] = predc\n",
    "feat2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "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>predc</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>idc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>003b0195-2acb-4f46-b7fa-28cf266a8f60</th>\n",
       "      <td>80.103904</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009e689c-51a1-4cef-99ca-a4ba364eba8d</th>\n",
       "      <td>80.812446</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00a213c2-1174-4359-8a67-fe710ec1b439</th>\n",
       "      <td>67.581598</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00e42818-aade-4758-a5f6-c78a6f235ea5</th>\n",
       "      <td>66.890947</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0153b785-9acd-4d28-aad1-62f8bf2faea3</th>\n",
       "      <td>74.158351</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          predc  price\n",
       "idc                                                   \n",
       "003b0195-2acb-4f46-b7fa-28cf266a8f60  80.103904   80.0\n",
       "009e689c-51a1-4cef-99ca-a4ba364eba8d  80.812446   80.0\n",
       "00a213c2-1174-4359-8a67-fe710ec1b439  67.581598   70.0\n",
       "00e42818-aade-4758-a5f6-c78a6f235ea5  66.890947   70.0\n",
       "0153b785-9acd-4d28-aad1-62f8bf2faea3  74.158351   75.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg2 = feat2[[\"idc\", \"predc\", \"price\"]].groupby(\"idc\").mean()\n",
    "agg2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.626505427547838"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "err2d = ((agg2.predc - agg2.price) ** 2).sum() / agg2.shape[0]\n",
    "err2d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nettement, nettement mieux."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='predc'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg2.plot(x=\"price\", y=\"predc\", kind=\"scatter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='index'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp2 = agg2.sort_values(\"price\").reset_index(drop=True).reset_index(drop=False)\n",
    "ax = temp2.plot(x=\"index\", y=\"price\", figsize=(14, 4), ylim=[60, 120])\n",
    "temp2.plot(x=\"index\", y=\"predc\", linewidth=1, ax=ax, ylim=[60, 120])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Une variable catégorielle en une seule colonne ?\n",
    "\n",
    "Un jeu de données peut rapidement croître s'il est étendu pour chaque variable catégorielle. On peut utiliser le module [category_encoders](https://github.com/scikit-learn-contrib/categorical-encoding/tree/master/category_encoders) ou [statsmodels](https://www.statsmodels.org/stable/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xadupre/.local/lib/python3.10/site-packages/category_encoders/base_contrast_encoder.py:126: FutureWarning: Intercept column might not be added anymore in future releases (c.f. issue #370)\n",
      "  warnings.warn(\"Intercept column might not be added anymore in future releases (c.f. issue #370)\",\n",
      "/home/xadupre/.local/lib/python3.10/site-packages/category_encoders/base_contrast_encoder.py:126: FutureWarning: Intercept column might not be added anymore in future releases (c.f. issue #370)\n",
      "  warnings.warn(\"Intercept column might not be added anymore in future releases (c.f. issue #370)\",\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>intercept</th>\n",
       "      <th>weekday_0</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.566947</td>\n",
       "      <td>0.545545</td>\n",
       "      <td>-0.408248</td>\n",
       "      <td>0.241747</td>\n",
       "      <td>-0.109109</td>\n",
       "      <td>0.032898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.566947</td>\n",
       "      <td>0.545545</td>\n",
       "      <td>-0.408248</td>\n",
       "      <td>0.241747</td>\n",
       "      <td>-0.109109</td>\n",
       "      <td>0.032898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.377964</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>-0.564076</td>\n",
       "      <td>0.436436</td>\n",
       "      <td>-0.197386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.188982</td>\n",
       "      <td>-0.327327</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.080582</td>\n",
       "      <td>-0.545545</td>\n",
       "      <td>0.493464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.188982</td>\n",
       "      <td>-0.327327</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>0.080582</td>\n",
       "      <td>-0.545545</td>\n",
       "      <td>0.493464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weekday  intercept  weekday_0  weekday_1  weekday_2  weekday_3  weekday_4  \\\n",
       "0        4          1  -0.566947   0.545545  -0.408248   0.241747  -0.109109   \n",
       "1        4          1  -0.566947   0.545545  -0.408248   0.241747  -0.109109   \n",
       "2        2          1  -0.377964   0.000000   0.408248  -0.564076   0.436436   \n",
       "3        5          1  -0.188982  -0.327327   0.408248   0.080582  -0.545545   \n",
       "4        5          1  -0.188982  -0.327327   0.408248   0.080582  -0.545545   \n",
       "\n",
       "   weekday_5  \n",
       "0   0.032898  \n",
       "1   0.032898  \n",
       "2  -0.197386  \n",
       "3   0.493464  \n",
       "4   0.493464  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from category_encoders import PolynomialEncoder\n",
    "\n",
    "encoder = PolynomialEncoder(cols=[\"weekday\"])\n",
    "encoder.fit(rend[[\"weekday\"]], rend[\"price\"])\n",
    "pred = encoder.transform(rend[[\"weekday\"]])\n",
    "conc = pandas.concat([rend[[\"weekday\"]], pred], axis=1)\n",
    "conc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou associer la valeur de la cible à prédire pour chaque jour de la semaine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>76.112532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>76.195373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>77.047478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>76.677116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>76.049724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>76.630728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>97.078804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weekday      price\n",
       "0        0  76.112532\n",
       "1        1  76.195373\n",
       "2        2  77.047478\n",
       "3        3  76.677116\n",
       "4        4  76.049724\n",
       "5        5  76.630728\n",
       "6        6  97.078804"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy = rend[[\"weekday\", \"price\"]].copy()\n",
    "gr = copy.groupby(\"weekday\", as_index=False).mean()\n",
    "gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>delay</th>\n",
       "      <th>delay1</th>\n",
       "      <th>price</th>\n",
       "      <th>idc</th>\n",
       "      <th>weekday</th>\n",
       "      <th>price_label</th>\n",
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       "  </thead>\n",
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       "      <td>37</td>\n",
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       "      <td>75.0</td>\n",
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       "      <td>37</td>\n",
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       "      <td>0.002732</td>\n",
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       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>85.0</td>\n",
       "      <td>7ffb8a8b-a829-41f1-9572-2985ee9ba05e</td>\n",
       "      <td>4</td>\n",
       "      <td>76.049724</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0153b785-9acd-4d28-aad1-62f8bf2faea3</td>\n",
       "      <td>4</td>\n",
       "      <td>76.049724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>70.0</td>\n",
       "      <td>47e415f0-f4e9-40c5-a59d-adc4c0621c8a</td>\n",
       "      <td>4</td>\n",
       "      <td>76.049724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  gender  delay    delay1  price                                   idc  \\\n",
       "0   37       0  365.0  0.002732   75.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "1   37       0  365.0  0.002732   65.0  4ba0b473-f8ca-4466-a65b-40e9b8ba5029   \n",
       "2   57       1  365.0  0.002732   85.0  7ffb8a8b-a829-41f1-9572-2985ee9ba05e   \n",
       "3   30       0  365.0  0.002732   75.0  0153b785-9acd-4d28-aad1-62f8bf2faea3   \n",
       "4   28       1   28.0  0.034483   70.0  47e415f0-f4e9-40c5-a59d-adc4c0621c8a   \n",
       "\n",
       "   weekday  price_label  \n",
       "0        4    76.049724  \n",
       "1        4    76.049724  \n",
       "2        4    76.049724  \n",
       "3        4    76.049724  \n",
       "4        4    76.049724  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat3 = mat[[\"age\", \"gender\", \"delay\", \"delay1\", \"price\", \"idc\", \"weekday\"]]\n",
    "feat3 = feat3.merge(gr, on=\"weekday\", suffixes=(\"\", \"_label\"))\n",
    "feat3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On apprend avec la variable *weekday* :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.612</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.612</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   800.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:15:56</td>     <th>  Log-Likelihood:    </th> <td> -8257.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.653e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2531</td>      <th>  BIC:               </th> <td>1.656e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   55.4146</td> <td>    0.545</td> <td>  101.618</td> <td> 0.000</td> <td>   54.345</td> <td>   56.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.5081</td> <td>    0.010</td> <td>   48.918</td> <td> 0.000</td> <td>    0.488</td> <td>    0.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender</th>    <td>   -4.4125</td> <td>    0.250</td> <td>  -17.683</td> <td> 0.000</td> <td>   -4.902</td> <td>   -3.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay</th>     <td>    0.0005</td> <td>    0.001</td> <td>    0.562</td> <td> 0.574</td> <td>   -0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay1</th>    <td>  -16.0797</td> <td>    4.842</td> <td>   -3.321</td> <td> 0.001</td> <td>  -25.575</td> <td>   -6.585</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>weekday</th>   <td>    2.1216</td> <td>    0.061</td> <td>   34.623</td> <td> 0.000</td> <td>    2.001</td> <td>    2.242</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>55.147</td> <th>  Durbin-Watson:     </th> <td>   0.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  57.558</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.358</td> <th>  Prob(JB):          </th> <td>3.17e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.823</td> <th>  Cond. No.          </th> <td>9.20e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.2e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.612   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.612   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     800.0   \\\\\n",
       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
       "\\textbf{Time:}             &     11:15:56     & \\textbf{  Log-Likelihood:    } &   -8257.2   \\\\\n",
       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.653e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        2531      & \\textbf{  BIC:               } & 1.656e+04   \\\\\n",
       "\\textbf{Df Model:}         &           5      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &      55.4146  &        0.545     &   101.618  &         0.000        &       54.345    &       56.484     \\\\\n",
       "\\textbf{age}       &       0.5081  &        0.010     &    48.918  &         0.000        &        0.488    &        0.528     \\\\\n",
       "\\textbf{gender}    &      -4.4125  &        0.250     &   -17.683  &         0.000        &       -4.902    &       -3.923     \\\\\n",
       "\\textbf{delay}     &       0.0005  &        0.001     &     0.562  &         0.574        &       -0.001    &        0.002     \\\\\n",
       "\\textbf{delay1}    &     -16.0797  &        4.842     &    -3.321  &         0.001        &      -25.575    &       -6.585     \\\\\n",
       "\\textbf{weekday}   &       2.1216  &        0.061     &    34.623  &         0.000        &        2.001    &        2.242     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 55.147 & \\textbf{  Durbin-Watson:     } &    0.442  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   57.558  \\\\\n",
       "\\textbf{Skew:}          &  0.358 & \\textbf{  Prob(JB):          } & 3.17e-13  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.823 & \\textbf{  Cond. No.          } & 9.20e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 9.2e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.612\n",
       "Model:                            OLS   Adj. R-squared:                  0.612\n",
       "Method:                 Least Squares   F-statistic:                     800.0\n",
       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        11:15:56   Log-Likelihood:                -8257.2\n",
       "No. Observations:                2537   AIC:                         1.653e+04\n",
       "Df Residuals:                    2531   BIC:                         1.656e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     55.4146      0.545    101.618      0.000      54.345      56.484\n",
       "age            0.5081      0.010     48.918      0.000       0.488       0.528\n",
       "gender        -4.4125      0.250    -17.683      0.000      -4.902      -3.923\n",
       "delay          0.0005      0.001      0.562      0.574      -0.001       0.002\n",
       "delay1       -16.0797      4.842     -3.321      0.001     -25.575      -6.585\n",
       "weekday        2.1216      0.061     34.623      0.000       2.001       2.242\n",
       "==============================================================================\n",
       "Omnibus:                       55.147   Durbin-Watson:                   0.442\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               57.558\n",
       "Skew:                           0.358   Prob(JB):                     3.17e-13\n",
       "Kurtosis:                       2.823   Cond. No.                     9.20e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 9.2e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = ols(\"price ~ age + gender + delay + delay1 + weekday\", data=feat3)\n",
    "res = lr.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Et on compare avec la variable *price_label* :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>price</td>      <th>  R-squared:         </th> <td>   0.917</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.917</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5611.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 25 Jan 2024</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:15:58</td>     <th>  Log-Likelihood:    </th> <td> -6298.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2537</td>      <th>  AIC:               </th> <td>1.261e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2531</td>      <th>  BIC:               </th> <td>1.264e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>  -14.9859</td> <td>    0.664</td> <td>  -22.576</td> <td> 0.000</td> <td>  -16.288</td> <td>  -13.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>         <td>    0.5000</td> <td>    0.005</td> <td>  104.173</td> <td> 0.000</td> <td>    0.491</td> <td>    0.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>gender</th>      <td>   -4.0767</td> <td>    0.115</td> <td>  -35.344</td> <td> 0.000</td> <td>   -4.303</td> <td>   -3.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay</th>       <td>    0.0003</td> <td>    0.000</td> <td>    0.601</td> <td> 0.548</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>delay1</th>      <td>  -20.8060</td> <td>    2.237</td> <td>   -9.303</td> <td> 0.000</td> <td>  -25.192</td> <td>  -16.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>price_label</th> <td>    0.9696</td> <td>    0.008</td> <td>  122.209</td> <td> 0.000</td> <td>    0.954</td> <td>    0.985</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>249.245</td> <th>  Durbin-Watson:     </th> <td>   2.035</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 459.156</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.660</td>  <th>  Prob(JB):          </th> <td>1.98e-100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.613</td>  <th>  Cond. No.          </th> <td>9.47e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 9.47e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &      price       & \\textbf{  R-squared:         } &     0.917   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.917   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     5611.   \\\\\n",
       "\\textbf{Date:}             & Thu, 25 Jan 2024 & \\textbf{  Prob (F-statistic):} &     0.00    \\\\\n",
       "\\textbf{Time:}             &     11:15:58     & \\textbf{  Log-Likelihood:    } &   -6298.8   \\\\\n",
       "\\textbf{No. Observations:} &        2537      & \\textbf{  AIC:               } & 1.261e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        2531      & \\textbf{  BIC:               } & 1.264e+04   \\\\\n",
       "\\textbf{Df Model:}         &           5      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                      & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept}    &     -14.9859  &        0.664     &   -22.576  &         0.000        &      -16.288    &      -13.684     \\\\\n",
       "\\textbf{age}          &       0.5000  &        0.005     &   104.173  &         0.000        &        0.491    &        0.509     \\\\\n",
       "\\textbf{gender}       &      -4.0767  &        0.115     &   -35.344  &         0.000        &       -4.303    &       -3.851     \\\\\n",
       "\\textbf{delay}        &       0.0003  &        0.000     &     0.601  &         0.548        &       -0.001    &        0.001     \\\\\n",
       "\\textbf{delay1}       &     -20.8060  &        2.237     &    -9.303  &         0.000        &      -25.192    &      -16.420     \\\\\n",
       "\\textbf{price\\_label} &       0.9696  &        0.008     &   122.209  &         0.000        &        0.954    &        0.985     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 249.245 & \\textbf{  Durbin-Watson:     } &     2.035  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } &   459.156  \\\\\n",
       "\\textbf{Skew:}          &  -0.660 & \\textbf{  Prob(JB):          } & 1.98e-100  \\\\\n",
       "\\textbf{Kurtosis:}      &   4.613 & \\textbf{  Cond. No.          } &  9.47e+03  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 9.47e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   R-squared:                       0.917\n",
       "Model:                            OLS   Adj. R-squared:                  0.917\n",
       "Method:                 Least Squares   F-statistic:                     5611.\n",
       "Date:                Thu, 25 Jan 2024   Prob (F-statistic):               0.00\n",
       "Time:                        11:15:58   Log-Likelihood:                -6298.8\n",
       "No. Observations:                2537   AIC:                         1.261e+04\n",
       "Df Residuals:                    2531   BIC:                         1.264e+04\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept     -14.9859      0.664    -22.576      0.000     -16.288     -13.684\n",
       "age             0.5000      0.005    104.173      0.000       0.491       0.509\n",
       "gender         -4.0767      0.115    -35.344      0.000      -4.303      -3.851\n",
       "delay           0.0003      0.000      0.601      0.548      -0.001       0.001\n",
       "delay1        -20.8060      2.237     -9.303      0.000     -25.192     -16.420\n",
       "price_label     0.9696      0.008    122.209      0.000       0.954       0.985\n",
       "==============================================================================\n",
       "Omnibus:                      249.245   Durbin-Watson:                   2.035\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              459.156\n",
       "Skew:                          -0.660   Prob(JB):                    1.98e-100\n",
       "Kurtosis:                       4.613   Cond. No.                     9.47e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 9.47e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = ols(\"price ~ age + gender + delay + delay1 + price_label\", data=feat3)\n",
    "res = lr.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aussi bien qu'avec la variable *dimanche*."
   ]
  }
 ],
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