clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/medical_insurance.csv\", sep=\",\")\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "le_smoker = LabelEncoder()\n",
    "df[\"smoker\"] = le_smoker.fit_transform(df[\"smoker\"])\n",
    "\n",
    "le_sex = LabelEncoder()\n",
    "df[\"sex\"] = le_sex.fit_transform(df[\"sex\"])\n",
    "\n",
    "oh_region = OneHotEncoder()\n",
    "encoded_region = oh_region.fit_transform(df[[\"region\"]])\n",
    "encoded_region = pd.DataFrame(encoded_region.toarray(), columns=[\"northeast\", \"northwest\", \"southeast\", \"southwest\"])\n",
    "df.drop(columns=[\"region\"], inplace=True)\n",
    "df = pd.merge(df, encoded_region, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Découpage en trois zones de charges :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nombre de personnes avec des charges < 10000 :  711\n",
      "Nombre de personnes avec des charges entre 10000 et 30000 :  463\n",
      "Nombre de personnes avec des charges > 30000 :  162\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df[df[\"charges\"] < 10000][\"charges\"], color=\"blue\", bins=30)\n",
    "sns.histplot(df[(df[\"charges\"] >= 10000) & (df[\"charges\"] <= 30000)][\"charges\"], color=\"green\", bins=30)\n",
    "sns.histplot(df[df[\"charges\"] > 30000][\"charges\"], color=\"red\", bins=30)\n",
    "plt.xlabel(\"Charges\")\n",
    "plt.ylabel(\"Nombre de personnes\")\n",
    "plt.title(\"Histogramme des charges\")\n",
    "plt.show()\n",
    "# On affiche le nombre de chaque groupe\n",
    "print(\"Nombre de personnes avec des charges < 10000 : \", len(df[df[\"charges\"] < 10000]))\n",
    "print(\"Nombre de personnes avec des charges entre 10000 et 30000 : \", len(df[(df[\"charges\"] >= 10000) & (df[\"charges\"] <= 30000)]))\n",
    "print(\"Nombre de personnes avec des charges > 30000 : \", len(df[df[\"charges\"] > 30000]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On tente de prédire les coûts d'assurance en fonction des autres variables\n",
    "# En trois groupes : 0-10000, 10000-30000, >30000\n",
    "df['charges_group'] = pd.cut(df['charges'], bins=[0, 10000, 30000, np.inf], labels=[0, 1, 2])\n",
    "df = df.drop(columns=['charges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 6/6 [00:00<00:00, 14.39it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['NaiveBayes' '0.7014925373134329']\n",
      " ['SVC' '0.7052238805970149']\n",
      " ['KNeighbors' '0.7201492537313433']\n",
      " ['AdaBoost' '0.7761194029850746']\n",
      " ['DecisionTree' '0.8134328358208955']\n",
      " ['RandomForest' '0.9029850746268657']]\n"
     ]
    }
   ],
   "source": [
    "# On compare différents algorithmes de classification\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "models = {\n",
    "    \"AdaBoost\": AdaBoostClassifier(),\n",
    "    \"DecisionTree\": DecisionTreeClassifier(),\n",
    "    \"KNeighbors\": KNeighborsClassifier(),\n",
    "    \"NaiveBayes\": GaussianNB(),\n",
    "    \"RandomForest\": RandomForestClassifier(),\n",
    "    \"SVC\": SVC()\n",
    "}   \n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "results = []\n",
    "for model_name, model in tqdm(models.items()):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    results.append((model_name, accuracy))\n",
    "    \n",
    "results = np.array(results)\n",
    "results = results[np.argsort(results[:, 1])]\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(results[:, 0], results[:, 1].astype(float))\n",
    "plt.xlabel(\"Modèle\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Accuracy des différents modèles\")\n",
    "plt.show()\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2], dtype=int64), array([154,  84,  30], dtype=int64))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On regarque ce que réponds le RandomForestClassifier\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "np.unique(y_pred, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "162 fits failed out of a total of 300.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "93 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "58 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'int' instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "8 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'int' instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "3 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:976: UserWarning: One or more of the test scores are non-finite: [0.91385768 0.91947566 0.91853933 0.91479401 0.91573034 0.91573034\n",
      "        nan        nan 0.91573034 0.91479401 0.91385768 0.91947566\n",
      " 0.91573034 0.917603   0.91573034 0.91666667 0.91853933        nan\n",
      " 0.91666667        nan 0.917603   0.91853933        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan 0.917603          nan 0.91385768 0.91666667        nan\n",
      "        nan        nan        nan        nan 0.91573034        nan\n",
      " 0.91385768 0.91479401 0.91666667 0.91573034 0.917603          nan\n",
      " 0.91385768 0.91666667        nan 0.91573034 0.91573034 0.91479401\n",
      "        nan        nan 0.91853933        nan        nan        nan\n",
      " 0.917603   0.91573034        nan        nan        nan        nan\n",
      " 0.91666667 0.91666667        nan        nan        nan        nan\n",
      "        nan 0.91853933 0.917603   0.91853933        nan        nan\n",
      " 0.91947566        nan        nan        nan        nan        nan\n",
      " 0.91479401        nan        nan        nan        nan 0.917603\n",
      " 0.91853933        nan        nan        nan 0.91385768        nan\n",
      " 0.91666667        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 10}\n"
     ]
    }
   ],
   "source": [
    "# On peut voir que le RandomForestClassifier est le meilleur modèle pour prédire les coûts d'assurance\n",
    "# On va maintenant faire du random grid search pour trouver les meilleurs hyperparamètres\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from tqdm import tqdm\n",
    "\n",
    "n_estimators = [int(x) for x in np.linspace(start=100, stop=500, num=10)]\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num=11)] + [None]\n",
    "min_samples_split = [2, 5, 10]\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "max_features = ['log2', 'sqrt', 'int', 'auto']\n",
    "\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "                'max_features': max_features,\n",
    "                'max_depth': max_depth,\n",
    "                'min_samples_split': min_samples_split,\n",
    "                'min_samples_leaf': min_samples_leaf}\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf_random = RandomizedSearchCV(estimator=rf, param_distributions=random_grid, n_iter=100, cv=3, verbose=2, random_state=42, n_jobs=-1)\n",
    "rf_random.fit(X_train, y_train)\n",
    "\n",
    "print(rf_random.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meilleurs paramètres pour le modèle de régression logistique : \n",
    "{'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 40}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "540 fits failed out of a total of 1080.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "270 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'min_samples_leaf' parameter of RandomForestClassifier must be an int in the range [1, inf) or a float in the range (0.0, 1.0). Got 0 instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "270 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_depth' parameter of RandomForestClassifier must be an int in the range [1, inf) or None. Got 0 instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:976: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.91573034 0.91947566 0.91666667 0.91947566 0.91853933 0.91947566\n",
      " 0.91947566 0.92041199 0.91573034 0.917603   0.91666667 0.91666667\n",
      " 0.917603   0.917603   0.91853933 0.91853933 0.91947566 0.91853933\n",
      " 0.91666667 0.917603   0.91666667 0.917603   0.91853933 0.91666667\n",
      " 0.91853933 0.917603   0.91853933 0.91853933 0.917603   0.917603\n",
      " 0.91666667 0.91666667 0.91947566 0.91853933 0.91479401 0.91853933\n",
      " 0.91666667 0.91947566 0.917603   0.917603   0.91666667 0.917603\n",
      " 0.917603   0.91666667 0.91666667 0.917603   0.917603   0.91666667\n",
      " 0.917603   0.917603   0.91666667 0.917603   0.91666667 0.917603\n",
      " 0.91853933 0.91666667 0.917603   0.91853933 0.917603   0.917603\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.92041199 0.92041199 0.92041199 0.91666667 0.917603   0.91853933\n",
      " 0.917603   0.92134831 0.917603   0.91853933 0.91666667 0.91853933\n",
      " 0.917603   0.92041199 0.91853933 0.917603   0.91947566 0.91853933\n",
      " 0.92041199 0.917603   0.91853933 0.91853933 0.91666667 0.91947566\n",
      " 0.91947566 0.91666667 0.92041199 0.91666667 0.917603   0.91853933\n",
      " 0.917603   0.91853933 0.917603   0.91853933 0.917603   0.91853933\n",
      " 0.91853933 0.917603   0.91853933 0.91666667 0.91666667 0.91573034\n",
      " 0.91853933 0.91666667 0.91666667 0.91666667 0.917603   0.917603\n",
      " 0.91666667 0.91666667 0.917603   0.917603   0.917603   0.91853933\n",
      " 0.917603   0.91666667 0.917603   0.91666667 0.917603   0.917603\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.91947566 0.91947566 0.917603   0.91947566 0.91666667 0.91666667\n",
      " 0.91573034 0.917603   0.91853933 0.91385768 0.91853933 0.917603\n",
      " 0.91573034 0.91853933 0.917603   0.92041199 0.91666667 0.91947566\n",
      " 0.91947566 0.91853933 0.91479401 0.91947566 0.917603   0.91666667\n",
      " 0.91666667 0.91573034 0.91666667 0.917603   0.91853933 0.917603\n",
      " 0.91853933 0.91666667 0.91853933 0.917603   0.91666667 0.917603\n",
      " 0.91666667 0.917603   0.91666667 0.917603   0.91573034 0.91573034\n",
      " 0.917603   0.917603   0.91666667 0.91479401 0.91947566 0.917603\n",
      " 0.91666667 0.91853933 0.91573034 0.91573034 0.91666667 0.91666667\n",
      " 0.91573034 0.91666667 0.91666667 0.91573034 0.91666667 0.917603  ]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 4, 'n_estimators': 527}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9067164179104478"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grid search sur les meilleurs hyperparamètres\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': np.linspace(rf_random.best_params_['n_estimators'] - 50, rf_random.best_params_['n_estimators'] + 50, 10).astype(int),\n",
    "    'max_features': [rf_random.best_params_['max_features']],\n",
    "    'max_depth': [rf_random.best_params_['max_depth'] + 10, rf_random.best_params_['max_depth'], rf_random.best_params_['max_depth'] - 10] + [None],\n",
    "    'min_samples_split': [rf_random.best_params_['min_samples_split'] - 1, rf_random.best_params_['min_samples_split'], rf_random.best_params_['min_samples_split'] + 1],\n",
    "    'min_samples_leaf': [rf_random.best_params_['min_samples_leaf'] - 1, rf_random.best_params_['min_samples_leaf'], rf_random.best_params_['min_samples_leaf'] + 1]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf_grid = GridSearchCV(estimator=rf, param_grid=param_grid, cv=3, n_jobs=-1)\n",
    "rf_grid.fit(X_train, y_train)\n",
    "\n",
    "print(rf_grid.best_params_)\n",
    "y_pred = rf_grid.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9067164179104478\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(**rf_grid.best_params_)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test sur une saisie utilisateur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On tente de prédire les coûts d'assurance en fonction des autres variables pour un individu saisit par l'utilisateur\n",
    "age = int(input(\"Entrez l'âge de la personne : \"))\n",
    "sex = input(\"Entrez le sexe de la personne (h : Homme, f : Femme) : \") == \"h\"\n",
    "smoker = input(\"Est-ce que la personne fume (o : Oui, n : Non) : \") == \"o\"\n",
    "bmi = float(input(\"Entrez l'IMC de la personne : \"))\n",
    "children = int(input(\"Entrez le nombre d'enfants de la personne : \"))\n",
    "region = input(\"Entrez la région de la personne (ne : Northeast, nw : Northwest, se : Southeast, sw : Southwest) : \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>northeast</th>\n",
       "      <th>northwest</th>\n",
       "      <th>southeast</th>\n",
       "      <th>southwest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>True</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age   sex   bmi  children  smoker  northeast  northwest  southeast  \\\n",
       "0   21  True  22.5         0   False       True      False      False   \n",
       "\n",
       "   southwest  \n",
       "0      False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\n",
    "    'age': age,\n",
    "    'sex': sex,\n",
    "    'bmi': bmi,\n",
    "    'children': children,\n",
    "    'smoker': smoker,\n",
    "    'northeast': region == 'ne' or region == 'NE' or region == 'Ne' or region == 'nE',\n",
    "    'northwest': region == 'NW' or region == 'nw' or region == 'Nw' or region == 'nW',\n",
    "    'southeast': region == 'SE' or region == 'se' or region == 'Se' or region == 'sE',\n",
    "    'southwest': region == 'SW' or region == 'sw' or region == 'Sw' or region == 'sW'\n",
    "}\n",
    "\n",
    "input_df = pd.DataFrame(data, index=[0])\n",
    "input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le coût d'assurance de cette personne est inférieur à 10000\n"
     ]
    }
   ],
   "source": [
    "# On prédit les coûts d'assurance\n",
    "model.predict(input_df)\n",
    "if model.predict(input_df) == 0:\n",
    "    print(\"Le coût d'assurance de cette personne est inférieur à 10000\")\n",
    "elif model.predict(input_df) == 1:\n",
    "    print(\"Le coût d'assurance de cette personne est compris entre 10000 et 30000\")\n",
    "else:\n",
    "    print(\"Le coût d'assurance de cette personne est supérieur à 30000\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clusterisation des données\n",
    "\n",
    "Pour voir la repartition des données, nous allons utiliser la méthode de clusterisation KMeans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/medical_insurance.csv\", sep=\",\")\n",
    "df = df.drop_duplicates()\n",
    "\n",
    "le_smoker = LabelEncoder()\n",
    "df[\"smoker\"] = le_smoker.fit_transform(df[\"smoker\"])\n",
    "\n",
    "le_sex = LabelEncoder()\n",
    "df[\"sex\"] = le_sex.fit_transform(df[\"sex\"])\n",
    "\n",
    "oh_region = OneHotEncoder()\n",
    "encoded_region = oh_region.fit_transform(df[[\"region\"]])\n",
    "encoded_region = pd.DataFrame(encoded_region.toarray(), columns=[\"northeast\", \"northwest\", \"southeast\", \"southwest\"])\n",
    "df.drop(columns=[\"region\"], inplace=True)\n",
    "df = pd.merge(df, encoded_region, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n",
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# On tente de la clustering sur les données\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "X = df.drop(columns=['charges'])\n",
    "y = df['charges']\n",
    "\n",
    "inertia = []\n",
    "\n",
    "for k in range(1, 10):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(X)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "    \n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, 10), inertia)\n",
    "plt.xlabel(\"Nombre de clusters\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.title(\"Inertia en fonction du nombre de clusters\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On peut voir que l'inertie diminue fortement jusqu'à 10 clusters, puis diminue plus lentement\n",
    "# On utilise la méthode du coude pour déterminer le nombre de clusters optimal\n",
    "from kneed import KneeLocator\n",
    "\n",
    "kl = KneeLocator(range(1, 10), inertia, curve=\"convex\", direction=\"decreasing\")\n",
    "kl.elbow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sacha\\anaconda3\\envs\\SY09\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  super()._check_params_vs_input(X, default_n_init=10)\n"
     ]
    }
   ],
   "source": [
    "# On remarque donc 3 clusters\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "\n",
    "y_pred = kmeans.fit_predict(X)\n",
    "\n",
    "df['cluster'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# On affiche les clusters\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(data=df, x='age', y='charges', hue='cluster')\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Charges\")\n",
    "plt.title(\"Clusters en fonction de l'âge et des charges\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On remarque que les personnes se répartissent en 3 clusters distincts. En regardant les données, on peut voir que les clusters correspondent à des groupes d'âge et de bmi différents."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "SY09",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}