regression_lineaire.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "La regréssion lineaire permet de determiner la relation \n",
    "entre les données à 2 dimensions dont la courbe possede l'allure d'une droite.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import des librairies\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Periode</th>\n",
       "      <th>Indice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>129.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>129.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>129.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>128.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>127.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Periode  Indice\n",
       "0       67  129.72\n",
       "1       68  129.38\n",
       "2       66  129.03\n",
       "3       66  128.45\n",
       "4       65  127.77"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import du dataset\n",
    "# x c'est la première colone privée de la première ligne.\n",
    "# y c'est la deuxième colone privée de la première ligne.\n",
    "\n",
    "dataset = pd.read_csv('datasets/valeurs_trimestrielles.csv', sep=';')\n",
    "x = dataset.iloc[:, :-1].values \n",
    "y = dataset.iloc[:, -1].values\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# affichage du nuage de point\n",
    "\n",
    "plt.scatter(x, y, color = 'red')\n",
    "#plt.plot([3.0, 105.0], [105.0, 145.0])\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# division du dataset en donnees d'entrainnement et de test\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 1.0/6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# construction du modele d'entrainnemet\n",
    "\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# effectuer une prediction\n",
    "\n",
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualiser les résultats\n",
    "plt.scatter(x_test, y_test, color = 'red')\n",
    "plt.plot(x_train, regressor.predict(x_train), color = 'blue')\n",
    "plt.title('Evolution des loyers')\n",
    "plt.xlabel('Trimestre')\n",
    "plt.ylabel('Loyer')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}