Ozone inc GUI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbbe14e0-2c3e-4a38-8570-052b5208e827",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "379bfc0d-f2ff-47af-8d46-98aaf08f921b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs</th>\n",
       "      <th>maxO3</th>\n",
       "      <th>T9</th>\n",
       "      <th>T12</th>\n",
       "      <th>T15</th>\n",
       "      <th>Ne9</th>\n",
       "      <th>Ne12</th>\n",
       "      <th>Ne15</th>\n",
       "      <th>Vx9</th>\n",
       "      <th>Vx12</th>\n",
       "      <th>Vx15</th>\n",
       "      <th>maxO3v</th>\n",
       "      <th>vent</th>\n",
       "      <th>pluie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601</td>\n",
       "      <td>87</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.5</td>\n",
       "      <td>18.4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.6946</td>\n",
       "      <td>-1.7101</td>\n",
       "      <td>-0.6946</td>\n",
       "      <td>84</td>\n",
       "      <td>Nord</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>602</td>\n",
       "      <td>82</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>-3.0000</td>\n",
       "      <td>87</td>\n",
       "      <td>Nord</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603</td>\n",
       "      <td>92</td>\n",
       "      <td>15.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.9544</td>\n",
       "      <td>1.8794</td>\n",
       "      <td>0.5209</td>\n",
       "      <td>82</td>\n",
       "      <td>Est</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604</td>\n",
       "      <td>114</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9848</td>\n",
       "      <td>0.3473</td>\n",
       "      <td>-0.1736</td>\n",
       "      <td>92</td>\n",
       "      <td>Nord</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>605</td>\n",
       "      <td>94</td>\n",
       "      <td>17.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>-2.9544</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>114</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obs  maxO3    T9   T12   T15  Ne9  Ne12  Ne15     Vx9    Vx12    Vx15  \\\n",
       "0  601     87  15.6  18.5  18.4    4     4     8  0.6946 -1.7101 -0.6946   \n",
       "1  602     82  17.0  18.4  17.7    5     5     7 -4.3301 -4.0000 -3.0000   \n",
       "2  603     92  15.3  17.6  19.5    2     5     4  2.9544  1.8794  0.5209   \n",
       "3  604    114  16.2  19.7  22.5    1     1     0  0.9848  0.3473 -0.1736   \n",
       "4  605     94  17.4  20.5  20.4    8     8     7 -0.5000 -2.9544 -4.3301   \n",
       "\n",
       "   maxO3v   vent pluie  \n",
       "0      84   Nord   Sec  \n",
       "1      87   Nord   Sec  \n",
       "2      82    Est   Sec  \n",
       "3      92   Nord   Sec  \n",
       "4     114  Ouest   Sec  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# le dataset (changing decimals to . from , at same tilme)\n",
    "df = pd.read_csv('ozone.txt', sep=\";\", decimal=',')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a2de2ba-26ae-4dca-84ce-02b5c867ad53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#heatmap\n",
    "import seaborn as sns\n",
    "corr = df.corr()\n",
    "\n",
    "# plot the heatmap\n",
    "sns.heatmap(corr, \n",
    "        xticklabels=corr.columns,\n",
    "        yticklabels=corr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69109758-4622-48b6-a55a-129990c0821f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1abc093d7c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x180 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, y_vars=['maxO3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16c3689c-215b-4423-8957-214e67ac17b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pluie', ylabel='maxO3'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for catagorical data\n",
    "sns.boxplot(data=df, y='maxO3', x='pluie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3152ef0a-6b7d-44fa-986a-b4b98a154a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "##class analysis - see doc on smf.ols to help choose the variables ####\n",
    "#lrs = smf.ols('maxO3'-'T12'+'Ne9'+ etc etc (needs import)\n",
    "#and .aic method (?)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4a1c25b2-663d-4146-8e69-f1e7023cb3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs</th>\n",
       "      <th>T9</th>\n",
       "      <th>T12</th>\n",
       "      <th>T15</th>\n",
       "      <th>Ne9</th>\n",
       "      <th>Ne12</th>\n",
       "      <th>Ne15</th>\n",
       "      <th>Vx9</th>\n",
       "      <th>Vx12</th>\n",
       "      <th>Vx15</th>\n",
       "      <th>maxO3v</th>\n",
       "      <th>vent</th>\n",
       "      <th>pluie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.5</td>\n",
       "      <td>18.4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.6946</td>\n",
       "      <td>-1.7101</td>\n",
       "      <td>-0.6946</td>\n",
       "      <td>84</td>\n",
       "      <td>Nord</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>602</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>-3.0000</td>\n",
       "      <td>87</td>\n",
       "      <td>Nord</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603</td>\n",
       "      <td>15.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.9544</td>\n",
       "      <td>1.8794</td>\n",
       "      <td>0.5209</td>\n",
       "      <td>82</td>\n",
       "      <td>Est</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9848</td>\n",
       "      <td>0.3473</td>\n",
       "      <td>-0.1736</td>\n",
       "      <td>92</td>\n",
       "      <td>Nord</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>605</td>\n",
       "      <td>17.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>-2.9544</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>114</td>\n",
       "      <td>Ouest</td>\n",
       "      <td>Sec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obs    T9   T12   T15  Ne9  Ne12  Ne15     Vx9    Vx12    Vx15  maxO3v  \\\n",
       "0  601  15.6  18.5  18.4    4     4     8  0.6946 -1.7101 -0.6946      84   \n",
       "1  602  17.0  18.4  17.7    5     5     7 -4.3301 -4.0000 -3.0000      87   \n",
       "2  603  15.3  17.6  19.5    2     5     4  2.9544  1.8794  0.5209      82   \n",
       "3  604  16.2  19.7  22.5    1     1     0  0.9848  0.3473 -0.1736      92   \n",
       "4  605  17.4  20.5  20.4    8     8     7 -0.5000 -2.9544 -4.3301     114   \n",
       "\n",
       "    vent pluie  \n",
       "0   Nord   Sec  \n",
       "1   Nord   Sec  \n",
       "2    Est   Sec  \n",
       "3   Nord   Sec  \n",
       "4  Ouest   Sec  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matrice X\n",
    "X = df.drop('maxO3', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76d821d7-6f08-4d74-a62c-db527ee98930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     87\n",
       "1     82\n",
       "2     92\n",
       "3    114\n",
       "4     94\n",
       "Name: maxO3, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vecteur y\n",
    "y = df.maxO3\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65d14d9c-32f8-4804-bac0-8e6a4ea6cafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sec      69\n",
       "Pluie    43\n",
       "Name: pluie, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pluie.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e615517-abd5-45b9-b683-48a4bd909c59",
   "metadata": {},
   "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",
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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>obs</th>\n",
       "      <th>T9</th>\n",
       "      <th>T12</th>\n",
       "      <th>T15</th>\n",
       "      <th>Ne9</th>\n",
       "      <th>Ne12</th>\n",
       "      <th>Ne15</th>\n",
       "      <th>Vx9</th>\n",
       "      <th>Vx12</th>\n",
       "      <th>Vx15</th>\n",
       "      <th>maxO3v</th>\n",
       "      <th>vent_Nord</th>\n",
       "      <th>vent_Ouest</th>\n",
       "      <th>vent_Sud</th>\n",
       "      <th>pluie_Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.5</td>\n",
       "      <td>18.4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.6946</td>\n",
       "      <td>-1.7101</td>\n",
       "      <td>-0.6946</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>602</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>-3.0000</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603</td>\n",
       "      <td>15.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.9544</td>\n",
       "      <td>1.8794</td>\n",
       "      <td>0.5209</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9848</td>\n",
       "      <td>0.3473</td>\n",
       "      <td>-0.1736</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>605</td>\n",
       "      <td>17.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>-2.9544</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obs    T9   T12   T15  Ne9  Ne12  Ne15     Vx9    Vx12    Vx15  maxO3v  \\\n",
       "0  601  15.6  18.5  18.4    4     4     8  0.6946 -1.7101 -0.6946      84   \n",
       "1  602  17.0  18.4  17.7    5     5     7 -4.3301 -4.0000 -3.0000      87   \n",
       "2  603  15.3  17.6  19.5    2     5     4  2.9544  1.8794  0.5209      82   \n",
       "3  604  16.2  19.7  22.5    1     1     0  0.9848  0.3473 -0.1736      92   \n",
       "4  605  17.4  20.5  20.4    8     8     7 -0.5000 -2.9544 -4.3301     114   \n",
       "\n",
       "   vent_Nord  vent_Ouest  vent_Sud  pluie_Sec  \n",
       "0          1           0         0          1  \n",
       "1          1           0         0          1  \n",
       "2          0           0         0          1  \n",
       "3          1           0         0          1  \n",
       "4          0           1         0          1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get_dummies for vent and pluie\n",
    "X = pd.get_dummies(data=X, columns=['vent', 'pluie'], drop_first=True)\n",
    "#X = pd.concat([X.drop(['vent', 'pluie'], axis=1), dummy], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a625b136-c8d0-43d1-9089-7cbe65586b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((112, 15), (112,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vérifier les tailles\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1cc3842-8fa7-4c75-8a6c-fdda3b659c6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04dd5477-e7d0-4e7e-a516-043f7937a012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((102, 15), (10, 15), (102,), (10,))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Création des échantillons de test et d'entraînement\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=10)\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b685f009-8351-4df2-8fc9-267dd9404d01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scikit learn model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "95c73ccc-c9fe-482e-8dd0-39f35a0d5834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sc_train=0.7790652494663795 et sc_test=0.5032045277137367\n"
     ]
    }
   ],
   "source": [
    "# évaluation R2\n",
    "sc_train = lr.score(X_train, y_train)\n",
    "sc_test = lr.score(X_test, y_test)\n",
    "print(f\"{sc_train=} et {sc_test=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9fcd89f-4053-476e-bf8c-2531126e1a15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse_train=13.525955476121968 et mse_test=13.075822335212434\n"
     ]
    }
   ],
   "source": [
    "# évaluation RMSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "mse_train = mean_squared_error(y_train, lr.predict(X_train), squared=False)\n",
    "mse_test = mean_squared_error(y_test, lr.predict(X_test), squared=False)\n",
    "print(f\"{mse_train=} et {mse_test=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c89451db-2675-4a81-ad58-0f3130efbc2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_true</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92</td>\n",
       "      <td>87.145702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>78</td>\n",
       "      <td>76.399619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>71</td>\n",
       "      <td>66.175200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>131</td>\n",
       "      <td>119.726540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>70</td>\n",
       "      <td>75.327845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>71</td>\n",
       "      <td>59.880137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>70</td>\n",
       "      <td>89.634678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>101</td>\n",
       "      <td>87.013484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>76</td>\n",
       "      <td>76.241098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>92</td>\n",
       "      <td>63.715063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_true      y_pred\n",
       "2        92   87.145702\n",
       "94       78   76.399619\n",
       "102      71   66.175200\n",
       "77      131  119.726540\n",
       "111      70   75.327845\n",
       "89       71   59.880137\n",
       "27       70   89.634678\n",
       "8       101   87.013484\n",
       "74       76   76.241098\n",
       "45       92   63.715063"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prédictions\n",
    "pred = pd.DataFrame()\n",
    "pred['y_true'] = y_test\n",
    "pred['y_pred'] = lr.predict(X_test)\n",
    "#pred['erreur_absolue'] = abs(pred['y_true'] - pred['y_pred'])\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "433cb0ee-47e2-41e4-bd79-4bb61bf15488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(pred.y_true, pred.y_pred, c='green')\n",
    "\n",
    "p1 = max(max(pred.y_true), max(pred.y_pred))\n",
    "p2 = min(min(pred.y_true), min(pred.y_pred))\n",
    "plt.plot([p1, p2], [p1, p2], 'b-')\n",
    "plt.xlabel('True Values', fontsize=15)\n",
    "plt.ylabel('Predictions', fontsize=15)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c5061a01-e943-451c-a205-dd8f424d8fa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\catri\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "C:\\Users\\catri\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "C:\\Users\\catri\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>maxO3</td>      <th>  R-squared:         </th> <td>   0.779</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.741</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   20.22</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 10 May 2022</td> <th>  Prob (F-statistic):</th> <td>4.97e-22</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:51:21</td>     <th>  Log-Likelihood:    </th> <td> -410.40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   102</td>      <th>  AIC:               </th> <td>   852.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    86</td>      <th>  BIC:               </th> <td>   894.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    15</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>const</th>      <td>   26.1845</td> <td>   21.372</td> <td>    1.225</td> <td> 0.224</td> <td>  -16.302</td> <td>   68.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>obs</th>        <td>   -0.0143</td> <td>    0.017</td> <td>   -0.853</td> <td> 0.396</td> <td>   -0.047</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>T9</th>         <td>   -0.0577</td> <td>    1.330</td> <td>   -0.043</td> <td> 0.965</td> <td>   -2.702</td> <td>    2.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>T12</th>        <td>    1.6838</td> <td>    1.597</td> <td>    1.055</td> <td> 0.295</td> <td>   -1.490</td> <td>    4.858</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>T15</th>        <td>    0.8002</td> <td>    1.253</td> <td>    0.639</td> <td> 0.525</td> <td>   -1.690</td> <td>    3.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ne9</th>        <td>   -1.9505</td> <td>    1.094</td> <td>   -1.782</td> <td> 0.078</td> <td>   -4.126</td> <td>    0.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ne12</th>       <td>   -0.9332</td> <td>    1.520</td> <td>   -0.614</td> <td> 0.541</td> <td>   -3.955</td> <td>    2.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Ne15</th>       <td>    0.3040</td> <td>    1.091</td> <td>    0.278</td> <td> 0.781</td> <td>   -1.866</td> <td>    2.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vx9</th>        <td>    0.3598</td> <td>    1.115</td> <td>    0.323</td> <td> 0.748</td> <td>   -1.856</td> <td>    2.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vx12</th>       <td>    0.9635</td> <td>    1.397</td> <td>    0.689</td> <td> 0.492</td> <td>   -1.814</td> <td>    3.741</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vx15</th>       <td>    0.9434</td> <td>    1.054</td> <td>    0.896</td> <td> 0.373</td> <td>   -1.151</td> <td>    3.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>maxO3v</th>     <td>    0.3360</td> <td>    0.073</td> <td>    4.634</td> <td> 0.000</td> <td>    0.192</td> <td>    0.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>vent_Nord</th>  <td>    2.0126</td> <td>    7.560</td> <td>    0.266</td> <td> 0.791</td> <td>  -13.016</td> <td>   17.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>vent_Ouest</th> <td>    9.0260</td> <td>    9.353</td> <td>    0.965</td> <td> 0.337</td> <td>   -9.566</td> <td>   27.618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>vent_Sud</th>   <td>    8.7375</td> <td>    7.940</td> <td>    1.100</td> <td> 0.274</td> <td>   -7.047</td> <td>   24.522</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pluie_Sec</th>  <td>    1.6358</td> <td>    3.974</td> <td>    0.412</td> <td> 0.682</td> <td>   -6.263</td> <td>    9.535</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.766</td> <th>  Durbin-Watson:     </th> <td>   1.927</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.034</td> <th>  Jarque-Bera (JB):  </th> <td>  12.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.026</td> <th>  Prob(JB):          </th> <td> 0.00247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.680</td> <th>  Cond. No.          </th> <td>1.15e+04</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, 1.15e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  maxO3   R-squared:                       0.779\n",
       "Model:                            OLS   Adj. R-squared:                  0.741\n",
       "Method:                 Least Squares   F-statistic:                     20.22\n",
       "Date:                Tue, 10 May 2022   Prob (F-statistic):           4.97e-22\n",
       "Time:                        14:51:21   Log-Likelihood:                -410.40\n",
       "No. Observations:                 102   AIC:                             852.8\n",
       "Df Residuals:                      86   BIC:                             894.8\n",
       "Df Model:                          15                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         26.1845     21.372      1.225      0.224     -16.302      68.671\n",
       "obs           -0.0143      0.017     -0.853      0.396      -0.047       0.019\n",
       "T9            -0.0577      1.330     -0.043      0.965      -2.702       2.586\n",
       "T12            1.6838      1.597      1.055      0.295      -1.490       4.858\n",
       "T15            0.8002      1.253      0.639      0.525      -1.690       3.290\n",
       "Ne9           -1.9505      1.094     -1.782      0.078      -4.126       0.225\n",
       "Ne12          -0.9332      1.520     -0.614      0.541      -3.955       2.089\n",
       "Ne15           0.3040      1.091      0.278      0.781      -1.866       2.474\n",
       "Vx9            0.3598      1.115      0.323      0.748      -1.856       2.575\n",
       "Vx12           0.9635      1.397      0.689      0.492      -1.814       3.741\n",
       "Vx15           0.9434      1.054      0.896      0.373      -1.151       3.038\n",
       "maxO3v         0.3360      0.073      4.634      0.000       0.192       0.480\n",
       "vent_Nord      2.0126      7.560      0.266      0.791     -13.016      17.041\n",
       "vent_Ouest     9.0260      9.353      0.965      0.337      -9.566      27.618\n",
       "vent_Sud       8.7375      7.940      1.100      0.274      -7.047      24.522\n",
       "pluie_Sec      1.6358      3.974      0.412      0.682      -6.263       9.535\n",
       "==============================================================================\n",
       "Omnibus:                        6.766   Durbin-Watson:                   1.927\n",
       "Prob(Omnibus):                  0.034   Jarque-Bera (JB):               12.005\n",
       "Skew:                           0.026   Prob(JB):                      0.00247\n",
       "Kurtosis:                       4.680   Cond. No.                     1.15e+04\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, 1.15e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using Statsmodel\n",
    "from statsmodels.api import OLS, add_constant\n",
    "lr2 = OLS(y_train, add_constant(X_train))\n",
    "res = lr2.fit()\n",
    "res.summary() #evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "13248366-ef13-43b1-a1a9-fa6b94e758ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\catri\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only.\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2       87.145702\n",
       "94      76.399619\n",
       "102     66.175200\n",
       "77     119.726540\n",
       "111     75.327845\n",
       "89      59.880137\n",
       "27      89.634678\n",
       "8       87.013484\n",
       "74      76.241098\n",
       "45      63.715063\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predictions\n",
    "res.predict(add_constant(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "402258d0-cb9f-4e30-84f4-0549f009a874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs</th>\n",
       "      <th>T9</th>\n",
       "      <th>T12</th>\n",
       "      <th>T15</th>\n",
       "      <th>Ne9</th>\n",
       "      <th>Ne12</th>\n",
       "      <th>Ne15</th>\n",
       "      <th>Vx9</th>\n",
       "      <th>Vx12</th>\n",
       "      <th>Vx15</th>\n",
       "      <th>maxO3v</th>\n",
       "      <th>vent_Nord</th>\n",
       "      <th>vent_Ouest</th>\n",
       "      <th>vent_Sud</th>\n",
       "      <th>pluie_Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601</td>\n",
       "      <td>15.6</td>\n",
       "      <td>18.5</td>\n",
       "      <td>18.4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.6946</td>\n",
       "      <td>-1.7101</td>\n",
       "      <td>-0.6946</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>602</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>17.7</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>-4.0000</td>\n",
       "      <td>-3.0000</td>\n",
       "      <td>87</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603</td>\n",
       "      <td>15.3</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19.5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.9544</td>\n",
       "      <td>1.8794</td>\n",
       "      <td>0.5209</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604</td>\n",
       "      <td>16.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9848</td>\n",
       "      <td>0.3473</td>\n",
       "      <td>-0.1736</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>605</td>\n",
       "      <td>17.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.4</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.5000</td>\n",
       "      <td>-2.9544</td>\n",
       "      <td>-4.3301</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obs    T9   T12   T15  Ne9  Ne12  Ne15     Vx9    Vx12    Vx15  maxO3v  \\\n",
       "0  601  15.6  18.5  18.4    4     4     8  0.6946 -1.7101 -0.6946      84   \n",
       "1  602  17.0  18.4  17.7    5     5     7 -4.3301 -4.0000 -3.0000      87   \n",
       "2  603  15.3  17.6  19.5    2     5     4  2.9544  1.8794  0.5209      82   \n",
       "3  604  16.2  19.7  22.5    1     1     0  0.9848  0.3473 -0.1736      92   \n",
       "4  605  17.4  20.5  20.4    8     8     7 -0.5000 -2.9544 -4.3301     114   \n",
       "\n",
       "   vent_Nord  vent_Ouest  vent_Sud  pluie_Sec  \n",
       "0          1           0         0          1  \n",
       "1          1           0         0          1  \n",
       "2          0           0         0          1  \n",
       "3          1           0         0          1  \n",
       "4          0           1         0          1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fc772b2d-5fdd-4b9e-abe7-bfe64d3b0297",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 21 is out of bounds for axis 0 with size 10",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [21]\u001b[0m, in \u001b[0;36m<cell line: 14>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m ax1 \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39madd_subplot(\u001b[38;5;241m131\u001b[39m, projection\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3d\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     13\u001b[0m ax1\u001b[38;5;241m.\u001b[39mscatter(x1, x2, y)\n\u001b[1;32m---> 14\u001b[0m \u001b[43max1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_trisurf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgreen\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     16\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py:1987\u001b[0m, in \u001b[0;36mAxes3D.plot_trisurf\u001b[1;34m(self, color, norm, vmin, vmax, lightsource, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1985\u001b[0m xt \u001b[38;5;241m=\u001b[39m tri\u001b[38;5;241m.\u001b[39mx[triangles]\n\u001b[0;32m   1986\u001b[0m yt \u001b[38;5;241m=\u001b[39m tri\u001b[38;5;241m.\u001b[39my[triangles]\n\u001b[1;32m-> 1987\u001b[0m zt \u001b[38;5;241m=\u001b[39m \u001b[43mz\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtriangles\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m   1988\u001b[0m verts \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mstack((xt, yt, zt), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m   1990\u001b[0m polyc \u001b[38;5;241m=\u001b[39m art3d\u001b[38;5;241m.\u001b[39mPoly3DCollection(verts, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[1;31mIndexError\u001b[0m: index 21 is out of bounds for axis 0 with size 10"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3D plotting\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "x1 = X['T12']\n",
    "x2 = X['pluie_Sec']\n",
    "#test = np.array([y])\n",
    "#test = test.reshape(1, -1)\n",
    "#pred = lr.predict(test)\n",
    "\n",
    "fig = plt.figure(figsize=(25,25))\n",
    "\n",
    "ax1 = fig.add_subplot(131, projection='3d')\n",
    "\n",
    "ax1.scatter(x1, x2, y)\n",
    "ax1.plot_trisurf(x1, x2, pred, facecolor='green', alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c700b52e-4779-433d-aa7b-2a5e6908fbea",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 2nd method with mean values to make GUI #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0f79714c-1b99-4425-9783-0c2b7c11d56c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs</th>\n",
       "      <th>maxO3</th>\n",
       "      <th>maxO3v</th>\n",
       "      <th>temp_mean</th>\n",
       "      <th>neb_mean</th>\n",
       "      <th>vent_mean</th>\n",
       "      <th>vent_Nord</th>\n",
       "      <th>vent_Ouest</th>\n",
       "      <th>vent_Sud</th>\n",
       "      <th>pluie_Sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601</td>\n",
       "      <td>87</td>\n",
       "      <td>84</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>-0.570033</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>602</td>\n",
       "      <td>82</td>\n",
       "      <td>87</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>-3.776700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603</td>\n",
       "      <td>92</td>\n",
       "      <td>82</td>\n",
       "      <td>17.466667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>1.784900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>604</td>\n",
       "      <td>114</td>\n",
       "      <td>92</td>\n",
       "      <td>19.466667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.386167</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>605</td>\n",
       "      <td>94</td>\n",
       "      <td>114</td>\n",
       "      <td>19.433333</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>-2.594833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obs  maxO3  maxO3v  temp_mean  neb_mean  vent_mean  vent_Nord  vent_Ouest  \\\n",
       "0  601     87      84  17.500000  5.333333  -0.570033          1           0   \n",
       "1  602     82      87  17.700000  5.666667  -3.776700          1           0   \n",
       "2  603     92      82  17.466667  3.666667   1.784900          0           0   \n",
       "3  604    114      92  19.466667  0.666667   0.386167          1           0   \n",
       "4  605     94     114  19.433333  7.666667  -2.594833          0           1   \n",
       "\n",
       "   vent_Sud  pluie_Sec  \n",
       "0         0          1  \n",
       "1         0          1  \n",
       "2         0          1  \n",
       "3         0          1  \n",
       "4         0          1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating new table with means for multi value variables\n",
    "df2 = pd.read_csv('ozone.txt', sep=\";\", decimal=',')\n",
    "\n",
    "temps = df2.loc[: , \"T9\":\"T15\"]\n",
    "df2['temp_mean'] = temps.mean(axis=1)\n",
    "Nuag = df2.loc[: , \"Ne9\":\"Ne15\"]\n",
    "df2['neb_mean'] = Nuag.mean(axis=1)\n",
    "vents = df2.loc[: , \"Vx9\":\"Vx15\"]\n",
    "df2['vent_mean'] = vents.mean(axis=1)\n",
    "#.drop('maxO3', axis=1)\n",
    "df_means = df2.drop(['T9', 'T12', 'T15', 'Ne9', 'Ne12', 'Ne15', 'Vx9', 'Vx12', 'Vx15'], axis=1)\n",
    "\n",
    "df_means = pd.get_dummies(data=df_means, columns=['vent', 'pluie'], drop_first=True)\n",
    "\n",
    "df_means.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab7042c7-78bf-4134-8aa1-a197c8ac6f17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "67cd14bb-c495-451c-98d6-d57175dc5ac6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr2 = df_means.corr()\n",
    "\n",
    "# plot the heatmap\n",
    "sns.heatmap(corr2, \n",
    "        xticklabels=corr2.columns,\n",
    "        yticklabels=corr2.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8fd57438-0cd3-4fc5-8822-a9a5c21a3678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -31.539064080448796\n",
      "Coefficients: \n",
      " [0.33821427 4.37702112]\n"
     ]
    }
   ],
   "source": [
    "#making a regression GUI for obs(date) and neb_mean (clouds)\n",
    "import tkinter as tk \n",
    "from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n",
    "\n",
    "Xn = df_means[['maxO3v','temp_mean']]\n",
    "Yn = df_means.maxO3\n",
    "\n",
    "regr = LinearRegression()\n",
    "regr.fit(Xn, Yn)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "\n",
    "# tkinter GUI\n",
    "root= tk.Tk()\n",
    "\n",
    "canvas1 = tk.Canvas(root, width = 500, height = 300)\n",
    "canvas1.pack()\n",
    "\n",
    "# with sklearn\n",
    "Intercept_result = ('Intercept: ', regr.intercept_)\n",
    "label_Intercept = tk.Label(root, text=Intercept_result, justify = 'center')\n",
    "canvas1.create_window(260, 220, window=label_Intercept)\n",
    "\n",
    "# with sklearn\n",
    "Coefficients_result  = ('Coefficients: ', regr.coef_)\n",
    "label_Coefficients = tk.Label(root, text=Coefficients_result, justify = 'center')\n",
    "canvas1.create_window(260, 240, window=label_Coefficients)\n",
    "\n",
    "# New_old label and input box\n",
    "label1 = tk.Label(root, text='Type old O3: ')\n",
    "canvas1.create_window(100, 100, window=label1)\n",
    "\n",
    "entry1 = tk.Entry (root) # create 1st entry box\n",
    "canvas1.create_window(270, 100, window=entry1)\n",
    "\n",
    "# New_temp label and input box\n",
    "label2 = tk.Label(root, text=' Type mean temp: ')\n",
    "canvas1.create_window(120, 120, window=label2)\n",
    "\n",
    "entry2 = tk.Entry (root) # create 2nd entry box\n",
    "canvas1.create_window(270, 120, window=entry2)\n",
    "\n",
    "def values(): \n",
    "    global New_date #our 1st input variable\n",
    "    New_date = float(entry1.get()) \n",
    "    \n",
    "    global New_cloud #our 2nd input variable\n",
    "    New_cloud = float(entry2.get()) \n",
    "    \n",
    "    Prediction_result  = ('Predicted Max O3: ', regr.predict([[New_date,New_cloud]]))\n",
    "    label_Prediction = tk.Label(root, text= Prediction_result, bg='orange')\n",
    "    canvas1.create_window(260, 280, window=label_Prediction)\n",
    "    \n",
    "button1 = tk.Button (root, text='Predict Max O3',command=values, bg='orange') # button to call the 'values' command above \n",
    "canvas1.create_window(270, 150, window=button1)\n",
    " \n",
    "#plot 1st scatter \n",
    "figure3 = plt.Figure(figsize=(5,4), dpi=100)\n",
    "ax3 = figure3.add_subplot(111)\n",
    "ax3.scatter(df_means['maxO3v'].astype(float),df_means['maxO3'].astype(float), color = 'r')\n",
    "scatter3 = FigureCanvasTkAgg(figure3, root) \n",
    "scatter3.get_tk_widget().pack(side=tk.RIGHT, fill=tk.BOTH)\n",
    "#ax3.legend(['Max O3']) \n",
    "ax3.set_xlabel('old maxO3')\n",
    "ax3.set_title('old vs current maxO3')\n",
    "\n",
    "#plot 2nd scatter \n",
    "figure4 = plt.Figure(figsize=(5,4), dpi=100)\n",
    "ax4 = figure4.add_subplot(111)\n",
    "ax4.scatter(df_means['temp_mean'].astype(float),df_means['maxO3'].astype(float), color = 'g')\n",
    "scatter4 = FigureCanvasTkAgg(figure4, root) \n",
    "scatter4.get_tk_widget().pack(side=tk.RIGHT, fill=tk.BOTH)\n",
    "#ax4.legend(['Max 03']) \n",
    "ax4.set_xlabel('mean temp')\n",
    "ax4.set_title('temp vs maxO3')\n",
    "\n",
    "root.mainloop()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fe61520-8c7e-4407-87ba-2807dab38bfa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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