07_Machine_learning_supervise.ipynb

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    "# L’apprentissage supervisé avec Scikit-Learn\n",
    "Les méthodes d’apprentissage supervisé sont les méthodes actuellement les plus\n",
    "utilisées en data science. Il s’agit d’essayer de prédire une variable cible et d’utiliser\n",
    "différentes méthodes pour arriver à cette fin.\n",
    "Nous allons illustrer ces méthodes de traitement de données avec du code et des\n",
    "cas pratiques."
   ]
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  {
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    "### Les données et leur transformation\n",
    "Ce jeu de données est composé de 3333 individus et de 18 variables. Il est stocké dans un fichier csv, nommé telecom.csv, accessible dans le répertoire Data. On le récupère en utilisant Pandas :"
   ]
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
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   "source": [
    "churn = pd.read_csv(\"./Data/telecom.csv\")"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   State           3333 non-null   object \n",
      " 1   Account Length  3333 non-null   int64  \n",
      " 2   Area Code       3333 non-null   int64  \n",
      " 3   Phone           3333 non-null   object \n",
      " 4   Int'l Plan      3333 non-null   object \n",
      " 5   VMail Plan      3333 non-null   object \n",
      " 6   VMail Message   3333 non-null   int64  \n",
      " 7   Day Mins        3333 non-null   float64\n",
      " 8   Day Calls       3333 non-null   int64  \n",
      " 9   Day Charge      3333 non-null   float64\n",
      " 10  Eve Mins        3333 non-null   float64\n",
      " 11  Eve Calls       3333 non-null   int64  \n",
      " 12  Eve Charge      3333 non-null   float64\n",
      " 13  Night Mins      3333 non-null   float64\n",
      " 14  Night Calls     3333 non-null   int64  \n",
      " 15  Night Charge    3333 non-null   float64\n",
      " 16  Intl Mins       3333 non-null   float64\n",
      " 17  Intl Calls      3333 non-null   int64  \n",
      " 18  Intl Charge     3333 non-null   float64\n",
      " 19  CustServ Calls  3333 non-null   int64  \n",
      " 20  Churn?          3333 non-null   object \n",
      "dtypes: float64(8), int64(8), object(5)\n",
      "memory usage: 546.9+ KB\n"
     ]
    }
   ],
   "source": [
    "churn.info()"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>27.47</td>\n",
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       "      <td>103</td>\n",
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       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
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       "      <th>3</th>\n",
       "      <td>OH</td>\n",
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       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
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       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
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       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0    KS             128        415  382-4657         no        yes   \n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  Eve Charge  \\\n",
       "0             25     265.1        110       45.07  ...         99       16.78   \n",
       "1             26     161.6        123       27.47  ...        103       16.62   \n",
       "2              0     243.4        114       41.38  ...        110       10.30   \n",
       "3              0     299.4         71       50.90  ...         88        5.26   \n",
       "4              0     166.7        113       28.34  ...        122       12.61   \n",
       "\n",
       "   Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "0       244.7           91         11.01       10.0           3         2.70   \n",
       "1       254.4          103         11.45       13.7           3         3.70   \n",
       "2       162.6          104          7.32       12.2           5         3.29   \n",
       "3       196.9           89          8.86        6.6           7         1.78   \n",
       "4       186.9          121          8.41       10.1           3         2.73   \n",
       "\n",
       "   CustServ Calls  Churn?  \n",
       "0               1  False.  \n",
       "1               1  False.  \n",
       "2               0  False.  \n",
       "3               2  False.  \n",
       "4               3  False.  \n",
       "\n",
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   "source": [
    "## Les données\n",
    "\n",
    "Ce jeu de données n’a pas de données manquantes et nous allons devoir effectuer\n",
    "quelques transformations pour l’adapter à nos traitements. Nous voyons par exemple qu’il est composé de trois colonnes object.\n",
    "\n",
    "Nous pouvons afficher les statistiques descriptives pour les colonnes object :"
   ]
  },
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       "      <td>False.</td>\n",
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       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0    KS             128        415  382-4657         no        yes   \n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge  ...  Eve Calls  Eve Charge  \\\n",
       "0             25     265.1        110       45.07  ...         99       16.78   \n",
       "1             26     161.6        123       27.47  ...        103       16.62   \n",
       "2              0     243.4        114       41.38  ...        110       10.30   \n",
       "3              0     299.4         71       50.90  ...         88        5.26   \n",
       "4              0     166.7        113       28.34  ...        122       12.61   \n",
       "\n",
       "   Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "0       244.7           91         11.01       10.0           3         2.70   \n",
       "1       254.4          103         11.45       13.7           3         3.70   \n",
       "2       162.6          104          7.32       12.2           5         3.29   \n",
       "3       196.9           89          8.86        6.6           7         1.78   \n",
       "4       186.9          121          8.41       10.1           3         2.73   \n",
       "\n",
       "   CustServ Calls  Churn?  \n",
       "0               1  False.  \n",
       "1               1  False.  \n",
       "2               0  False.  \n",
       "3               2  False.  \n",
       "4               3  False.  \n",
       "\n",
       "[5 rows x 21 columns]"
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <td>3333</td>\n",
       "      <td>51</td>\n",
       "      <td>WV</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phone</th>\n",
       "      <td>3333</td>\n",
       "      <td>3333</td>\n",
       "      <td>332-6934</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Int'l Plan</th>\n",
       "      <td>3333</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>3010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VMail Plan</th>\n",
       "      <td>3333</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>2411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn?</th>\n",
       "      <td>3333</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "      <td>2850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count unique       top  freq\n",
       "State       3333     51        WV   106\n",
       "Phone       3333   3333  332-6934     1\n",
       "Int'l Plan  3333      2        no  3010\n",
       "VMail Plan  3333      2        no  2411\n",
       "Churn?      3333      2    False.  2850"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn.describe(include=\"object\").transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DyGDiULxHqjm"
   },
   "source": [
    "## Transformation des données\n",
    "\n",
    "Pour les variables binaires, il nous suffit de les recoder avec Scikit-Learn pour obtenir des données exploitables. Par\n",
    "ailleurs, il existe une autre variable qualitative dans notre jeu de données, Area Code,\n",
    "qui est numérique mais avec trois modalités :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yA6GW401Hqjn",
    "outputId": "0269af94-69f4-4646-fc11-19d3f8809f30"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "415    1655\n",
       "510     840\n",
       "408     838\n",
       "Name: Area Code, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn[\"Area Code\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       415\n",
       "1       415\n",
       "2       415\n",
       "3       408\n",
       "4       415\n",
       "       ... \n",
       "3328    415\n",
       "3329    415\n",
       "3330    510\n",
       "3331    510\n",
       "3332    415\n",
       "Name: Area Code, Length: 3333, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn[\"Area Code\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>3330</th>\n",
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       "</table>\n",
       "<p>3333 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      AK  AL  AR  AZ  CA  CO  CT  DC  DE  FL  ...  SD  TN  TX  UT  VA  VT  WA  \\\n",
       "0      0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "1      0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "2      0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "3      0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "4      0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ..  ..   \n",
       "3328   0   0   0   1   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "3329   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "3330   0   0   0   0   0   0   0   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "3331   0   0   0   0   0   0   1   0   0   0  ...   0   0   0   0   0   0   0   \n",
       "3332   0   0   0   0   0   0   0   0   0   0  ...   0   1   0   0   0   0   0   \n",
       "\n",
       "      WI  WV  WY  \n",
       "0      0   0   0  \n",
       "1      0   0   0  \n",
       "2      0   0   0  \n",
       "3      0   0   0  \n",
       "4      0   0   0  \n",
       "...   ..  ..  ..  \n",
       "3328   0   0   0  \n",
       "3329   0   1   0  \n",
       "3330   0   0   0  \n",
       "3331   0   0   0  \n",
       "3332   0   0   0  \n",
       "\n",
       "[3333 rows x 51 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(churn[\"State\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IWI2gqn4Hqju"
   },
   "source": [
    "## La préparation des données\n",
    "\n",
    "Nous allons utiliser le processus de traitement classique pour transformer nos\n",
    "données avec Scikit-Learn. Dans ce cas, nous n’avons pas de données manquantes,\n",
    "nous travaillons donc sur la transformation des variables qualitatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LKM32yDuHqjv"
   },
   "outputs": [],
   "source": [
    "# les méthodes de prétraitement\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "\n",
    "# les outils de machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E2leNBiNHqj1"
   },
   "source": [
    "LabelEncoder va nous permettre de transformer les valeurs textuelles en entiers.\n",
    "Nous pouvons utiliser pour chaque variable qualitative :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wPz7kmr_Hqj3"
   },
   "source": [
    "## Nouveauté : ColumnTransformer\n",
    "\n",
    "On peut maintenant tranformer plusieurs colonnes simultanément\n",
    "\n",
    "Essayez d'utliser cet outil :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xqlH9HxnHqj4"
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "# on utilisera un pipeline pour enchaîner les traitements\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9cbGuk5THqj7"
   },
   "outputs": [],
   "source": [
    "# on définit les colonnes et les transformations pour \n",
    "# les colonnes quantitatives\n",
    "col_quanti = ['Day Mins', 'Day Calls', 'Day Charge',\n",
    "       'Eve Mins', 'Eve Calls', 'Eve Charge', 'Night Mins', 'Night Calls',\n",
    "       'Night Charge', 'Intl Mins', 'Intl Calls', 'Intl Charge',\n",
    "       'CustServ Calls']\n",
    "\n",
    "transfo_quanti = Pipeline(steps=[\n",
    "    ('imputation', SimpleImputer(strategy='median')),\n",
    "    ('standard', StandardScaler())])\n",
    "\n",
    "# on définit les colonnes et les transformations pour\n",
    "# les variables qualitatives\n",
    "col_quali = ['Area Code', \"Int'l Plan\", 'VMail Plan']\n",
    "\n",
    "transfo_quali = Pipeline(steps=[\n",
    "    ('imputation', SimpleImputer(strategy='constant', fill_value='manquant')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
    "\n",
    "# on définit l'objet de la classe ColumnTransformer\n",
    "# qui va permettre d'appliquer toutes les étapes\n",
    "preparation = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('quanti', transfo_quanti , col_quanti),\n",
    "        ('quali', transfo_quali , col_quali)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2P-dl0gQHqj_"
   },
   "outputs": [],
   "source": [
    "churn_transfo = preparation.fit_transform(churn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "colab_type": "code",
    "id": "p0MO2rgJLYHZ",
    "outputId": "ddfa391b-517a-47a7-874e-36348085c316"
   },
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <th>6</th>\n",
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       "      <td>0.144867</td>\n",
       "      <td>-0.107549</td>\n",
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       "      <td>0.147825</td>\n",
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       "      <td>-1.573383</td>\n",
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       "      <td>-1.573900</td>\n",
       "      <td>-0.756869</td>\n",
       "      <td>0.198935</td>\n",
       "      <td>-0.755571</td>\n",
       "      <td>0.703121</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>0.697156</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.196596</td>\n",
       "      <td>-1.466936</td>\n",
       "      <td>2.196759</td>\n",
       "      <td>-2.742865</td>\n",
       "      <td>-0.608159</td>\n",
       "      <td>-2.743268</td>\n",
       "      <td>-0.078551</td>\n",
       "      <td>-0.567714</td>\n",
       "      <td>-0.078806</td>\n",
       "      <td>-1.303026</td>\n",
       "      <td>1.024263</td>\n",
       "      <td>-1.306401</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.240090</td>\n",
       "      <td>0.626149</td>\n",
       "      <td>-0.240041</td>\n",
       "      <td>-1.038932</td>\n",
       "      <td>1.098699</td>\n",
       "      <td>-1.037939</td>\n",
       "      <td>-0.276311</td>\n",
       "      <td>1.067803</td>\n",
       "      <td>-0.276562</td>\n",
       "      <td>-0.049184</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.045885</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>3328</th>\n",
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       "      <td>-0.433386</td>\n",
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       "      <td>1.299506</td>\n",
       "      <td>0.286880</td>\n",
       "      <td>1.547039</td>\n",
       "      <td>-0.874374</td>\n",
       "      <td>1.547188</td>\n",
       "      <td>-0.120832</td>\n",
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       "      <td>-0.125496</td>\n",
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       "      <th>3329</th>\n",
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       "      <td>0.942714</td>\n",
       "      <td>-0.938353</td>\n",
       "      <td>-2.264816</td>\n",
       "      <td>-0.938172</td>\n",
       "      <td>-0.189297</td>\n",
       "      <td>1.170023</td>\n",
       "      <td>-0.188670</td>\n",
       "      <td>-0.228304</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>-0.231645</td>\n",
       "      <td>1.092641</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3330</th>\n",
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       "      <td>0.019193</td>\n",
       "      <td>1.731930</td>\n",
       "      <td>-2.114211</td>\n",
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       "      <td>-0.177431</td>\n",
       "      <td>-0.465494</td>\n",
       "      <td>-0.175486</td>\n",
       "      <td>1.383778</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>1.387123</td>\n",
       "      <td>0.332354</td>\n",
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       "      <th>3331</th>\n",
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       "      <td>0.227466</td>\n",
       "      <td>0.625153</td>\n",
       "      <td>-0.816080</td>\n",
       "      <td>-0.808966</td>\n",
       "      <td>-0.815203</td>\n",
       "      <td>-1.219628</td>\n",
       "      <td>1.885562</td>\n",
       "      <td>-1.221396</td>\n",
       "      <td>-1.876211</td>\n",
       "      <td>2.243356</td>\n",
       "      <td>-1.876950</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3332</th>\n",
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       "      <td>0.626149</td>\n",
       "      <td>1.003202</td>\n",
       "      <td>1.280309</td>\n",
       "      <td>-0.909370</td>\n",
       "      <td>1.279915</td>\n",
       "      <td>0.801482</td>\n",
       "      <td>-1.181033</td>\n",
       "      <td>0.800110</td>\n",
       "      <td>1.240482</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>1.241169</td>\n",
       "      <td>-1.188218</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6   \\\n",
       "0     1.566767  0.476643  1.567036 -0.070610 -0.055940 -0.070427  0.866743   \n",
       "1    -0.333738  1.124503 -0.334013 -0.108080  0.144867 -0.107549  1.058571   \n",
       "2     1.168304  0.675985  1.168464 -1.573383  0.496279 -1.573900 -0.756869   \n",
       "3     2.196596 -1.466936  2.196759 -2.742865 -0.608159 -2.743268 -0.078551   \n",
       "4    -0.240090  0.626149 -0.240041 -1.038932  1.098699 -1.037939 -0.276311   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "3328 -0.432895 -1.167924 -0.433386  0.286348  1.299506  0.286880  1.547039   \n",
       "3329  0.942447 -2.164631  0.942714 -0.938353 -2.264816 -0.938172 -0.189297   \n",
       "3330  0.018820  0.426808  0.019193  1.731930 -2.114211  1.732349 -0.177431   \n",
       "3331  0.624778  0.227466  0.625153 -0.816080 -0.808966 -0.815203 -1.219628   \n",
       "3332  1.003042  0.626149  1.003202  1.280309 -0.909370  1.279915  0.801482   \n",
       "\n",
       "            7         8         9         10        11        12   13   14  \\\n",
       "0    -0.465494  0.866029 -0.085008 -0.601195 -0.085690 -0.427932  0.0  1.0   \n",
       "1     0.147825  1.059390  1.240482 -0.601195  1.241169 -0.427932  0.0  1.0   \n",
       "2     0.198935 -0.755571  0.703121  0.211534  0.697156 -1.188218  0.0  1.0   \n",
       "3    -0.567714 -0.078806 -1.303026  1.024263 -1.306401  0.332354  1.0  0.0   \n",
       "4     1.067803 -0.276562 -0.049184 -0.601195 -0.045885  1.092641  0.0  1.0   \n",
       "...        ...       ...       ...       ...       ...       ...  ...  ...   \n",
       "3328 -0.874374  1.547188 -0.120832  0.617898 -0.125496  0.332354  0.0  1.0   \n",
       "3329  1.170023 -0.188670 -0.228304 -0.194831 -0.231645  1.092641  0.0  1.0   \n",
       "3330 -0.465494 -0.175486  1.383778  0.617898  1.387123  0.332354  0.0  0.0   \n",
       "3331  1.885562 -1.221396 -1.876211  2.243356 -1.876950  0.332354  0.0  0.0   \n",
       "3332 -1.181033  0.800110  1.240482 -0.194831  1.241169 -1.188218  0.0  1.0   \n",
       "\n",
       "       15   16   17   18   19  \n",
       "0     0.0  1.0  0.0  0.0  1.0  \n",
       "1     0.0  1.0  0.0  0.0  1.0  \n",
       "2     0.0  1.0  0.0  1.0  0.0  \n",
       "3     0.0  0.0  1.0  1.0  0.0  \n",
       "4     0.0  0.0  1.0  1.0  0.0  \n",
       "...   ...  ...  ...  ...  ...  \n",
       "3328  0.0  1.0  0.0  0.0  1.0  \n",
       "3329  0.0  1.0  0.0  1.0  0.0  \n",
       "3330  1.0  1.0  0.0  1.0  0.0  \n",
       "3331  1.0  0.0  1.0  1.0  0.0  \n",
       "3332  0.0  1.0  0.0  0.0  1.0  \n",
       "\n",
       "[3333 rows x 20 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(churn_transfo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VW05lNO3HqkC"
   },
   "source": [
    "## Prédire l’attrition des clients\n",
    "Lorsqu’on veut prédire une variable binaire, on devra avoir une colonne du type\n",
    "binaire. On préfère généralement un codage 0/1 afin de garder un type entier simple à gérer. \n",
    "\n",
    "Les variables explicatives x auront été préparées de manière intelligente afin de bien appliquer nos modèles.\n",
    "\n",
    "On crée donc x et y :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dJoqH9gPHqkD"
   },
   "outputs": [],
   "source": [
    "y = churn[\"Churn?\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SUtj0ZckHqkK"
   },
   "outputs": [],
   "source": [
    "x = churn_transfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "bjA3TeybMVJF",
    "outputId": "4bac0384-978f-41d8-bffc-a980f601db6f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False.    0.855086\n",
       "True.     0.144914\n",
       "Name: Churn?, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ssBNoGt5HqkO"
   },
   "source": [
    "## Séparation des données\n",
    "\n",
    "Pour la séparation, on utilise la fonction train_test_split() de Scikit-Learn.\n",
    "\n",
    "Cette fonction permet de créer automatiquement autant de structures que nécessaire\n",
    "à partir de nos données. \n",
    "\n",
    "Elle utilise une randomisation des individus et ensuite une séparation en fonction d’un paramètre du type test_size :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OMBJbgbvHqkP"
   },
   "outputs": [],
   "source": [
    "# on importe la fonction\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BAy75ShaHqkT",
    "outputId": "8a19aeb7-f978-4e00-b0de-755d30c0930f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2499, 20)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "36LV3XfCHqkW",
    "outputId": "ef5d6a98-df6e-45c9-b6b1-9e9dba2b6014"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(834, 20)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HylGIvOdHqkc"
   },
   "source": [
    "## Le choix et l’ajustement de l’algorithme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "iuGpEjYNHqke"
   },
   "source": [
    "Tout au long de ce Notebook, nous allons essayer d'ajouter un nouveau modèle, il s'agit du modèle logistique\n",
    "```python\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "83073nv1Hqkf"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NqiTRj4VHqkk"
   },
   "source": [
    "Ensuite, on crée un objet à partir de la classe du modèle en lui fournissant les\n",
    "hyperparamètres dont il a besoin :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fJeFK8Z4Hqkl"
   },
   "outputs": [],
   "source": [
    "modele_rf = RandomForestClassifier(n_estimators = 100)\n",
    "modele_knn = KNeighborsClassifier(n_neighbors = 10)\n",
    "modele_logit = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_wYtImDbHqks"
   },
   "source": [
    "Dans ce cas, on prend les hyperparamètres par défaut.\n",
    "\n",
    "On peut ensuite ajuster notre modèle en utilisant les données :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "gSGwQ6MhHqks",
    "outputId": "4ae832ae-0a25-4e04-ebe2-3291f3cfaca0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modele_rf.fit(x_train,y_train)\n",
    "modele_knn.fit(x_train,y_train)\n",
    "modele_logit.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VbZb1sIhHqkv"
   },
   "source": [
    "Une fois qu’on a estimé les paramètres du modèle, on va pouvoir extraire des\n",
    "informations. De nouveaux attributs de chaque classe apparaissent, ils se terminent par le symbole underscore _ :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VhHaq7vZHqkw",
    "outputId": "fb0663ed-2ac8-45d9-8a99-64a5e7a21c37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.15064509, 0.03344692, 0.13389221, 0.06722332, 0.02769072,\n",
       "       0.064706  , 0.03861744, 0.03219171, 0.0401124 , 0.04757009,\n",
       "       0.05302349, 0.04931653, 0.13193163, 0.00516066, 0.00528532,\n",
       "       0.00491115, 0.03552698, 0.03870951, 0.01856153, 0.02147732])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modele_rf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LP_-2GKbHqkz"
   },
   "source": [
    "Ce qui va nous intéresse avant tout, c’est de prédire avec notre modèle. Pour cela nous allons utiliser la méthode .predict() :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oP4OPfRNHqkz"
   },
   "outputs": [],
   "source": [
    "y_predict_rf = modele_rf.predict(x_test)\n",
    "y_predict_knn = modele_knn.predict(x_test)\n",
    "y_predict_logit = modele_logit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "qODhYGNOPvyf",
    "outputId": "b485f905-f3a6-4ae7-80b9-4332b60e905b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9484412470023981"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_predict_rf == y_test).sum()/len(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MTX_APBRHqk2"
   },
   "source": [
    "On obtient ainsi une valeur prédite pour les éléments de notre échantillon de\n",
    "validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I4cUFsusHqk3"
   },
   "source": [
    "## Les indicateurs pour valider un modèle\n",
    "La partie validation d’un modèle d’apprentissage supervisé est extrêmement\n",
    "importante. L’objectif d’un modèle d’apprentissage supervisé est de prédire une\n",
    "valeur la plus proche possible de la réalité. Nous différencions trois types d’indices\n",
    "en fonction du type de variable cible. Tous les indicateurs de qualité du modèle sont\n",
    "stockés dans le module *metrics* de Scikit-Learn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IZbMxPv0Hqk3"
   },
   "source": [
    "## Le pourcentage de bien classés\n",
    "Il s’agit de l’indicateur le plus connu. On le nomme accuracy. Il est calculé à partir du rapport entre le nombre d’individus bien classés et le nombre total d’individus dans l’échantillon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8573141486810552"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_predict_logit == y_test).sum()/len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "colab_type": "code",
    "id": "pQGQijPdHqk4",
    "outputId": "e7fec79c-74d2-4fb8-ee02-60120d6e5ef4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pourcentage de bien classés pour le modèle RF : 0.948\n",
      "Pourcentage de bien classés pour le modèle kNN :0.865\n",
      "Pourcentage de bien classés pour le modèle logit :0.857\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, recall_score\n",
    "\n",
    "accuracy_modele_rf = accuracy_score(y_test,y_predict_rf)\n",
    "accuracy_modele_knn = accuracy_score(y_test,y_predict_knn)\n",
    "accuracy_modele_logit = accuracy_score(y_test,y_predict_logit)\n",
    "print(\"Pourcentage de bien classés pour le modèle RF : %.3f\" %(accuracy_modele_rf))\n",
    "print(\"Pourcentage de bien classés pour le modèle kNN :%.3f\" %(accuracy_modele_knn))\n",
    "print(\"Pourcentage de bien classés pour le modèle logit :%.3f\" %(accuracy_modele_logit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tMvDIOgaHqk9",
    "outputId": "9ee747ec-e046-4e09-adff-a6a1232cf630"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False.    0.855086\n",
       "True.     0.144914\n",
       "Name: Churn?, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn[\"Churn?\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False.    706\n",
       "True.     128\n",
       "Name: Churn?, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yvSBf_VRHqlC"
   },
   "source": [
    "## La matrice de confusion\n",
    "Il s’agit d’un autre indicateur important pour juger de la qualité d’un modèle, il n’est pas défini par une seule valeur mais par une matrice dans laquelle on peut lire le croisement entre les valeurs observées et les valeurs prédites à partir du modèle. \n",
    "\n",
    "Pour calculer cette matrice, on pourra utiliser :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "id": "nENy3V42HqlD",
    "outputId": "f0fdfba4-0b31-481f-cb27-dd7a2737e837"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrice de confusion pour le modèle RF :\n",
      "[[702   4]\n",
      " [ 39  89]]\n",
      "Matrice de confusion pour le modèle kNN :\n",
      "[[705   1]\n",
      " [112  16]]\n",
      "Matrice de confusion pour le modèle logit :\n",
      "[[687  19]\n",
      " [100  28]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix_rf=confusion_matrix(y_test,y_predict_rf)\n",
    "confusion_matrix_knn=confusion_matrix(y_test,y_predict_knn)\n",
    "confusion_matrix_logit=confusion_matrix(y_test,y_predict_logit)\n",
    "print(\"Matrice de confusion pour le modèle RF :\",\n",
    "confusion_matrix_rf, sep=\"\\n\")\n",
    "print(\"Matrice de confusion pour le modèle kNN :\",\n",
    "confusion_matrix_knn, sep=\"\\n\")\n",
    "print(\"Matrice de confusion pour le modèle logit :\",\n",
    "confusion_matrix_logit, sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fOw_b2p5HqlH"
   },
   "source": [
    "## Le rappel (recall), la précision et le f1-score\n",
    "\n",
    "Scikit-Learn possède des fonctions pour chacun de ces indicateurs, mais il peut\n",
    "être intéressant d’utiliser une autre fonction qui les affiche pour chaque classe :\n",
    "\n",
    "tp / (tp + fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5BVCg4DIHqlH",
    "outputId": "d6c96129-8533-4da2-9e56-8a11a280379d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rapport pour le modèle RF :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      False.       0.95      0.99      0.97       706\n",
      "       True.       0.96      0.70      0.81       128\n",
      "\n",
      "    accuracy                           0.95       834\n",
      "   macro avg       0.95      0.84      0.89       834\n",
      "weighted avg       0.95      0.95      0.94       834\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(\"Rapport pour le modèle RF :\",\n",
    "      classification_report(y_test,y_predict_rf) ,sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lkt5YjxYHqlN",
    "outputId": "7ad14d4f-cfae-42eb-f9ee-0199e315dc96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rapport pour le modèle kNN :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      False.       0.86      1.00      0.93       706\n",
      "       True.       0.94      0.12      0.22       128\n",
      "\n",
      "    accuracy                           0.86       834\n",
      "   macro avg       0.90      0.56      0.57       834\n",
      "weighted avg       0.87      0.86      0.82       834\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Rapport pour le modèle kNN :\",\n",
    "      classification_report(y_test,y_predict_knn) ,sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sSr3bgeeHqlR",
    "outputId": "05ee83a9-60d7-462f-9ca3-e30f8c3cd35a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rapport pour le modèle logit :\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      False.       0.87      0.97      0.92       706\n",
      "       True.       0.60      0.22      0.32       128\n",
      "\n",
      "    accuracy                           0.86       834\n",
      "   macro avg       0.73      0.60      0.62       834\n",
      "weighted avg       0.83      0.86      0.83       834\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Rapport pour le modèle logit :\",\n",
    "      classification_report(y_test,y_predict_logit) ,sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GbEjwZSyHqlU"
   },
   "source": [
    "## L’aire sous la courbe ROC\n",
    "La courbe ROC est un indicateur important mais on préfère souvent une valeur plutôt\n",
    "qu’une courbe afin de comparer nos modèles. Pour cela, on utilise l’aire sous la courbe\n",
    "ROC (AUC). Cette aire est calculée directement à partir de la courbe ROC. Ainsi, un\n",
    "modèle aléatoire aura une AUC de 0.5 et un modèle parfait aura une AUC de 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "c92gUIK1HqlU",
    "outputId": "3cbede92-5987-4fe5-ab87-4681423d10af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aire sous la courbe ROC pour le modèle RF : 0.8808870396600568\n",
      "Aire sous la courbe ROC pour le modèle kNN : 0.8037358356940509\n",
      "Aire sous la courbe ROC pour le modèle logit : 0.8012902797450425\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "auc_modele_rf = roc_auc_score(y_test, modele_rf.predict_proba(x_test)[:,1])\n",
    "auc_modele_knn=roc_auc_score(y_test,modele_knn.predict_proba(x_test)[:,1])\n",
    "auc_modele_logit=roc_auc_score(y_test,modele_logit.predict_proba(x_test)[:,1])\n",
    "\n",
    "print(\"Aire sous la courbe ROC pour le modèle RF :\" ,auc_modele_rf)\n",
    "print(\"Aire sous la courbe ROC pour le modèle kNN :\" ,auc_modele_knn)\n",
    "print(\"Aire sous la courbe ROC pour le modèle logit :\" ,auc_modele_logit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wdITvbfHHqlX"
   },
   "source": [
    "## Passer en production votre modèle d’apprentissage supervisé\n",
    "\n",
    "### Persistance de modèle avec Scikit-Learn\n",
    "\n",
    "Python possède plusieurs outils pour la persistance d’objets, c’est-à-dire pour stocker\n",
    "des objets dans des fichiers. Les objets de Scikit-Learn sont aussi dans cette\n",
    "situation. On utilise un format pickle qui aura l’extension .pkl.\n",
    "\n",
    "Par exemple, si nous voulons sauvegarder le dernier pipeline de traitement, nous\n",
    "allons utiliser :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b8uyQTRoHqlY",
    "outputId": "2aa894d3-f8d1-4588-f90d-ad1282e591ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./Data/modele_rf.pkl']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(modele_rf, './Data/modele_rf.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "saXPTxLOHqlb"
   },
   "source": [
    "Une fois ce modèle stocké, on peut très bien le réutiliser dans un autre cadre. Si\n",
    "nous créons un nouveau notebook, nous allons utiliser :\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jhK3DRayHqlc"
   },
   "outputs": [],
   "source": [
    "import joblib\n",
    "#___ = joblib.load('./data/modele_grid_pipe.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "p1UrpaT-Hqlg"
   },
   "source": [
    "On peut ensuite appliquer le modèle avec tous les paramètres qui ont été appris :\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cjgoPB4PHqlh"
   },
   "source": [
    "```python\n",
    "___.predict(x_test)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L7kckQxvHqli"
   },
   "source": [
    "L’utilisation d’un fichier Pickle dans un notebook est une technique assez simple et courante."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ajustement des hyper-paramètres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dico_grid = {\"n_estimators\":[10,50, 100, 200], \"max_depth\":[3,5,7, None] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_grid = GridSearchCV(RandomForestClassifier(),\n",
    "                          dico_grid, \n",
    "                          scoring = \"accuracy\", \n",
    "                          cv = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=RandomForestClassifier(),\n",
       "             param_grid={'max_depth': [3, 5, 7, None],\n",
       "                         'n_estimators': [10, 50, 100, 200]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_grid.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': None, 'n_estimators': 200}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.948376923076923"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.027317</td>\n",
       "      <td>0.000874</td>\n",
       "      <td>0.003100</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 10}</td>\n",
       "      <td>0.8576</td>\n",
       "      <td>0.8720</td>\n",
       "      <td>0.8768</td>\n",
       "      <td>0.863782</td>\n",
       "      <td>0.867546</td>\n",
       "      <td>0.007392</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.117950</td>\n",
       "      <td>0.002852</td>\n",
       "      <td>0.008304</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 50}</td>\n",
       "      <td>0.8704</td>\n",
       "      <td>0.8672</td>\n",
       "      <td>0.8800</td>\n",
       "      <td>0.878205</td>\n",
       "      <td>0.873951</td>\n",
       "      <td>0.005312</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.224446</td>\n",
       "      <td>0.002224</td>\n",
       "      <td>0.014434</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 100}</td>\n",
       "      <td>0.8720</td>\n",
       "      <td>0.8816</td>\n",
       "      <td>0.8768</td>\n",
       "      <td>0.879808</td>\n",
       "      <td>0.877552</td>\n",
       "      <td>0.003635</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.439990</td>\n",
       "      <td>0.008732</td>\n",
       "      <td>0.026907</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 3, 'n_estimators': 200}</td>\n",
       "      <td>0.8720</td>\n",
       "      <td>0.8736</td>\n",
       "      <td>0.8752</td>\n",
       "      <td>0.878205</td>\n",
       "      <td>0.874751</td>\n",
       "      <td>0.002293</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.030444</td>\n",
       "      <td>0.001026</td>\n",
       "      <td>0.003080</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 10}</td>\n",
       "      <td>0.9216</td>\n",
       "      <td>0.9024</td>\n",
       "      <td>0.9184</td>\n",
       "      <td>0.913462</td>\n",
       "      <td>0.913965</td>\n",
       "      <td>0.007280</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.136057</td>\n",
       "      <td>0.003718</td>\n",
       "      <td>0.008218</td>\n",
       "      <td>0.000133</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 50}</td>\n",
       "      <td>0.9072</td>\n",
       "      <td>0.9232</td>\n",
       "      <td>0.9104</td>\n",
       "      <td>0.921474</td>\n",
       "      <td>0.915569</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.271632</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.015081</td>\n",
       "      <td>0.000081</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 100}</td>\n",
       "      <td>0.9056</td>\n",
       "      <td>0.9216</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.913967</td>\n",
       "      <td>0.005904</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.540357</td>\n",
       "      <td>0.010112</td>\n",
       "      <td>0.029287</td>\n",
       "      <td>0.001509</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 5, 'n_estimators': 200}</td>\n",
       "      <td>0.9040</td>\n",
       "      <td>0.9312</td>\n",
       "      <td>0.9104</td>\n",
       "      <td>0.915064</td>\n",
       "      <td>0.915166</td>\n",
       "      <td>0.010056</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.034954</td>\n",
       "      <td>0.001245</td>\n",
       "      <td>0.003269</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 10}</td>\n",
       "      <td>0.9184</td>\n",
       "      <td>0.9392</td>\n",
       "      <td>0.9408</td>\n",
       "      <td>0.921474</td>\n",
       "      <td>0.929969</td>\n",
       "      <td>0.010106</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.158709</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>0.008992</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 50}</td>\n",
       "      <td>0.9296</td>\n",
       "      <td>0.9376</td>\n",
       "      <td>0.9408</td>\n",
       "      <td>0.934295</td>\n",
       "      <td>0.935574</td>\n",
       "      <td>0.004145</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.319393</td>\n",
       "      <td>0.004576</td>\n",
       "      <td>0.016617</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>7</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 100}</td>\n",
       "      <td>0.9216</td>\n",
       "      <td>0.9488</td>\n",
       "      <td>0.9408</td>\n",
       "      <td>0.935897</td>\n",
       "      <td>0.936774</td>\n",
       "      <td>0.009898</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.623009</td>\n",
       "      <td>0.007550</td>\n",
       "      <td>0.030876</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>7</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': 7, 'n_estimators': 200}</td>\n",
       "      <td>0.9296</td>\n",
       "      <td>0.9472</td>\n",
       "      <td>0.9440</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.939575</td>\n",
       "      <td>0.006737</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.001517</td>\n",
       "      <td>0.003248</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 10}</td>\n",
       "      <td>0.9264</td>\n",
       "      <td>0.9456</td>\n",
       "      <td>0.9504</td>\n",
       "      <td>0.921474</td>\n",
       "      <td>0.935969</td>\n",
       "      <td>0.012275</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.194603</td>\n",
       "      <td>0.004526</td>\n",
       "      <td>0.009945</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>None</td>\n",
       "      <td>50</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 50}</td>\n",
       "      <td>0.9264</td>\n",
       "      <td>0.9568</td>\n",
       "      <td>0.9552</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>0.946779</td>\n",
       "      <td>0.012149</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.395227</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.018709</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>None</td>\n",
       "      <td>100</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 100}</td>\n",
       "      <td>0.9312</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>0.9568</td>\n",
       "      <td>0.942308</td>\n",
       "      <td>0.947577</td>\n",
       "      <td>0.011569</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.791609</td>\n",
       "      <td>0.014834</td>\n",
       "      <td>0.036357</td>\n",
       "      <td>0.000909</td>\n",
       "      <td>None</td>\n",
       "      <td>200</td>\n",
       "      <td>{'max_depth': None, 'n_estimators': 200}</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>0.9584</td>\n",
       "      <td>0.9568</td>\n",
       "      <td>0.942308</td>\n",
       "      <td>0.948377</td>\n",
       "      <td>0.009506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.027317      0.000874         0.003100        0.000159   \n",
       "1        0.117950      0.002852         0.008304        0.000218   \n",
       "2        0.224446      0.002224         0.014434        0.000487   \n",
       "3        0.439990      0.008732         0.026907        0.000495   \n",
       "4        0.030444      0.001026         0.003080        0.000106   \n",
       "5        0.136057      0.003718         0.008218        0.000133   \n",
       "6        0.271632      0.001649         0.015081        0.000081   \n",
       "7        0.540357      0.010112         0.029287        0.001509   \n",
       "8        0.034954      0.001245         0.003269        0.000069   \n",
       "9        0.158709      0.001702         0.008992        0.000152   \n",
       "10       0.319393      0.004576         0.016617        0.000394   \n",
       "11       0.623009      0.007550         0.030876        0.000236   \n",
       "12       0.040300      0.001517         0.003248        0.000069   \n",
       "13       0.194603      0.004526         0.009945        0.000189   \n",
       "14       0.395227      0.003959         0.018709        0.000254   \n",
       "15       0.791609      0.014834         0.036357        0.000909   \n",
       "\n",
       "   param_max_depth param_n_estimators  \\\n",
       "0                3                 10   \n",
       "1                3                 50   \n",
       "2                3                100   \n",
       "3                3                200   \n",
       "4                5                 10   \n",
       "5                5                 50   \n",
       "6                5                100   \n",
       "7                5                200   \n",
       "8                7                 10   \n",
       "9                7                 50   \n",
       "10               7                100   \n",
       "11               7                200   \n",
       "12            None                 10   \n",
       "13            None                 50   \n",
       "14            None                100   \n",
       "15            None                200   \n",
       "\n",
       "                                      params  split0_test_score  \\\n",
       "0       {'max_depth': 3, 'n_estimators': 10}             0.8576   \n",
       "1       {'max_depth': 3, 'n_estimators': 50}             0.8704   \n",
       "2      {'max_depth': 3, 'n_estimators': 100}             0.8720   \n",
       "3      {'max_depth': 3, 'n_estimators': 200}             0.8720   \n",
       "4       {'max_depth': 5, 'n_estimators': 10}             0.9216   \n",
       "5       {'max_depth': 5, 'n_estimators': 50}             0.9072   \n",
       "6      {'max_depth': 5, 'n_estimators': 100}             0.9056   \n",
       "7      {'max_depth': 5, 'n_estimators': 200}             0.9040   \n",
       "8       {'max_depth': 7, 'n_estimators': 10}             0.9184   \n",
       "9       {'max_depth': 7, 'n_estimators': 50}             0.9296   \n",
       "10     {'max_depth': 7, 'n_estimators': 100}             0.9216   \n",
       "11     {'max_depth': 7, 'n_estimators': 200}             0.9296   \n",
       "12   {'max_depth': None, 'n_estimators': 10}             0.9264   \n",
       "13   {'max_depth': None, 'n_estimators': 50}             0.9264   \n",
       "14  {'max_depth': None, 'n_estimators': 100}             0.9312   \n",
       "15  {'max_depth': None, 'n_estimators': 200}             0.9360   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  mean_test_score  \\\n",
       "0              0.8720             0.8768           0.863782         0.867546   \n",
       "1              0.8672             0.8800           0.878205         0.873951   \n",
       "2              0.8816             0.8768           0.879808         0.877552   \n",
       "3              0.8736             0.8752           0.878205         0.874751   \n",
       "4              0.9024             0.9184           0.913462         0.913965   \n",
       "5              0.9232             0.9104           0.921474         0.915569   \n",
       "6              0.9216             0.9120           0.916667         0.913967   \n",
       "7              0.9312             0.9104           0.915064         0.915166   \n",
       "8              0.9392             0.9408           0.921474         0.929969   \n",
       "9              0.9376             0.9408           0.934295         0.935574   \n",
       "10             0.9488             0.9408           0.935897         0.936774   \n",
       "11             0.9472             0.9440           0.937500         0.939575   \n",
       "12             0.9456             0.9504           0.921474         0.935969   \n",
       "13             0.9568             0.9552           0.948718         0.946779   \n",
       "14             0.9600             0.9568           0.942308         0.947577   \n",
       "15             0.9584             0.9568           0.942308         0.948377   \n",
       "\n",
       "    std_test_score  rank_test_score  \n",
       "0         0.007392               16  \n",
       "1         0.005312               15  \n",
       "2         0.003635               13  \n",
       "3         0.002293               14  \n",
       "4         0.007280               12  \n",
       "5         0.006890                9  \n",
       "6         0.005904               11  \n",
       "7         0.010056               10  \n",
       "8         0.010106                8  \n",
       "9         0.004145                7  \n",
       "10        0.009898                5  \n",
       "11        0.006737                4  \n",
       "12        0.012275                6  \n",
       "13        0.012149                3  \n",
       "14        0.011569                2  \n",
       "15        0.009506                1  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(model_grid.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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