diff --git a/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb b/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb index ea17293..5869f44 100644 --- a/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb +++ b/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb @@ -69,6 +69,7 @@ "\n", "# Machine Learning\n", "from sklearn.cluster import KMeans\n", + "import sklearn.metrics as metrics\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.model_selection import KFold, train_test_split\n", "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor" @@ -100,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 91, "id": "c9597b48", "metadata": {}, "outputs": [], @@ -119,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 92, "id": "8051b5f4", "metadata": {}, "outputs": [], @@ -163,339 +164,17 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 93, "id": "c427a4b8", "metadata": {}, "outputs": [ { "data": { - "application/vnd.microsoft.datawrangler.viewer.v0+json": { - "columns": [ - { - "name": "index", - "rawType": "int64", - "type": "integer" - }, - { - "name": "ANNEE_CTR", - "rawType": "int64", - "type": "integer" - }, - { - "name": "CONTRAT_ANCIENNETE", - "rawType": "object", - "type": "string" - }, - { - "name": "FREQUENCE_PAIEMENT_COTISATION", - "rawType": "object", - "type": "string" - }, - { - "name": "GROUPE_KM", - "rawType": "object", - "type": "string" - }, - { - "name": "ZONE_RISQUE", - "rawType": "object", - "type": "string" - }, - { - "name": "AGE_ASSURE_PRINCIPAL", - "rawType": "int64", - "type": "integer" - }, - { - "name": "GENRE", - "rawType": "object", - "type": "string" - }, - { - "name": "DEUXIEME_CONDUCTEUR", - "rawType": "bool", - "type": "boolean" - }, - { - "name": "ANCIENNETE_PERMIS", - "rawType": "int64", - "type": "integer" - }, - { - "name": "ANNEE_CONSTRUCTION", - "rawType": "float64", - "type": "float" - }, - { - "name": "ENERGIE", - "rawType": "object", - "type": "string" - }, - { - "name": "EQUIPEMENT_SECURITE", - "rawType": "object", - "type": "string" - }, - { - "name": "VALEUR_DU_BIEN", - "rawType": "object", - "type": "string" - }, - { - "name": "CM", - "rawType": "float64", - "type": "float" - } - ], - "ref": "e76df045-0c83-40e9-a027-c48f278ec1d6", - "rows": [ - [ - "10", - "2019", - "(0,1]", - "MENSUEL", - "[0;20000[", - "C", - "40", - "M", - "False", - "37", - "2017.0", - "ESSENCE", - "VRAI", - "[15000;20000[", - "1072.98" - ], - [ - "34", - "2020", - "(-1,0]", - "MENSUEL", - "[20000;40000[", - "C", - "27", - "M", - "True", - "13", - "2018.0", - "AUTRE", - "FAUX", - "[35000;99999[", - "3750.0" - ], - [ - "36", - "2019", - "(-1,0]", - "MENSUEL", - "[20000;40000[", - "L", - "19", - "M", - "False", - "2", - "2017.0", - "ESSENCE", - "VRAI", - "[0;10000[", - "1838.49" - ], - [ - "78", - "2019", - "(-1,0]", - "MENSUEL", - "[20000;40000[", - "B", - "40", - "M", - "False", - "45", - "2018.0", - "DIESEL", - "FAUX", - "[15000;20000[", - "4892.74" - ], - [ - "89", - "2018", - "(1,2]", - "MENSUEL", - "[20000;40000[", - "C", - "20", - "M", - "False", - "11", - "2014.0", - "ESSENCE", - "FAUX", - "[25000;35000[", - "166.73" - ] - ], - "shape": { - "columns": 14, - "rows": 5 - } - }, - "text/html": [ - "
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26, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -509,7 +188,7 @@ "# Calcul du cout moyen \"théorique\" des sinistres\n", "data_model[\"CM\"] = (data_model[\"CHARGE\"] / data_model[\"NB\"])\n", "data_model = data_model.drop(['CHARGE', 'NB', \"EXPO\"], axis=1)\n", - "data_model.head()" + "data_model.shape" ] }, { @@ -522,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 94, "id": "c8fd3ee1", "metadata": {}, "outputs": [ @@ -606,7 +285,7 @@ "type": "float" } ], - "ref": "b2f9efdd-d035-4c51-9797-2e202b404c15", + "ref": "8d8166c3-6828-4361-92de-ebce2dadb512", "rows": [ [ "count", @@ -1082,7 +761,7 @@ "max NaN 83421.850000 " ] }, - "execution_count": 27, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -1117,17 +796,29 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 95, "id": "1b156435", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(824, 13)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "data_set = data_model.drop(\"CM\", axis=1)" + "data_set = data_model.drop(\"CM\", axis=1)\n", + "data_set.shape" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 96, "id": "0ef0fcc0", "metadata": {}, "outputs": [], @@ -1163,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 97, "id": "e130aae5", "metadata": {}, "outputs": [], @@ -1173,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "id": "c39e2ad0", "metadata": {}, "outputs": [ @@ -2121,7 +1812,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 99, "id": "a16215ab", "metadata": {}, "outputs": [], @@ -2131,7 +1822,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 100, "id": "532ca6c4", "metadata": {}, "outputs": [ @@ -3037,6 +2728,14 @@ "**Question :** quels sont vos commentaires ?" ] }, + { + "cell_type": "markdown", + "id": "67406b54", + "metadata": {}, + "source": [ + "*Réponse*: Aucune des variables ne semblent corrélées." + ] + }, { "cell_type": "markdown", "id": "212209ec", @@ -3066,15 +2765,15 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 101, "id": "b8530717", "metadata": {}, "outputs": [], "source": [ - "encoder = preproc.OneHotEncoder(sparse_output=False, drop='first')\n", + "encoder = preproc.OneHotEncoder()\n", "encoder.fit(vars_categorielles)\n", "vars_categorielles_enc = encoder.transform(vars_categorielles)\n", - "vars_categorielles_enc = pd.DataFrame(vars_categorielles_enc, columns=encoder.get_feature_names_out()) # type: ignore" + "vars_categorielles_enc = pd.DataFrame(vars_categorielles_enc.toarray(), columns=encoder.get_feature_names_out(vars_categorielles.columns)) # type: ignore" ] }, { @@ -3087,7 +2786,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 102, "id": 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" + ], + "text/plain": [ + " ANNEE_CTR AGE_ASSURE_PRINCIPAL ANCIENNETE_PERMIS ANNEE_CONSTRUCTION \\\n", + "0 0.406156 -0.317648 0.067767 0.565370 \n", + "1 1.066260 -1.259689 -1.171975 0.881639 \n", + "2 0.406156 -1.839406 -1.740190 0.565370 \n", + "3 0.406156 -0.317648 0.481014 0.881639 \n", + "4 -0.253948 -1.766941 -1.275287 -0.383438 \n", + ".. ... ... ... ... \n", + "819 -0.914052 0.406998 0.894262 -2.597324 \n", + "820 -0.253948 0.406998 1.565789 0.249100 \n", + "821 0.406156 -1.766941 -1.533567 0.565370 \n", + "822 -0.253948 -1.766941 -1.275287 -1.648516 \n", + "823 1.066260 0.406998 0.067767 0.565370 \n", + "\n", + " CONTRAT_ANCIENNETE_(-1,0] CONTRAT_ANCIENNETE_(0,1] \\\n", + "0 0.0 1.0 \n", + "1 1.0 0.0 \n", + "2 1.0 0.0 \n", + "3 1.0 0.0 \n", + "4 0.0 0.0 \n", + ".. ... ... \n", + "819 0.0 0.0 \n", + "820 0.0 1.0 \n", + "821 0.0 0.0 \n", + "822 0.0 1.0 \n", + "823 0.0 0.0 \n", + "\n", + " CONTRAT_ANCIENNETE_(1,2] CONTRAT_ANCIENNETE_(2,5] \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 1.0 0.0 \n", + ".. ... ... \n", + "819 1.0 0.0 \n", + "820 0.0 0.0 \n", + "821 1.0 0.0 \n", + "822 0.0 0.0 \n", + "823 0.0 1.0 \n", + "\n", + " CONTRAT_ANCIENNETE_(5,10] FREQUENCE_PAIEMENT_COTISATION_ANNUEL ... \\\n", + "0 0.0 0.0 ... \n", + "1 0.0 0.0 ... \n", + "2 0.0 0.0 ... \n", + "3 0.0 0.0 ... \n", + "4 0.0 0.0 ... \n", + ".. ... ... ... \n", + "819 0.0 0.0 ... \n", + "820 0.0 0.0 ... \n", + "821 0.0 0.0 ... \n", + "822 0.0 0.0 ... \n", + "823 0.0 0.0 ... \n", + "\n", + " ENERGIE_ESSENCE EQUIPEMENT_SECURITE_FAUX EQUIPEMENT_SECURITE_VRAI \\\n", + "0 1.0 0.0 1.0 \n", + "1 0.0 1.0 0.0 \n", + "2 1.0 0.0 1.0 \n", + "3 0.0 1.0 0.0 \n", + "4 1.0 1.0 0.0 \n", + ".. ... ... ... \n", + "819 0.0 0.0 1.0 \n", + "820 1.0 1.0 0.0 \n", + "821 0.0 0.0 1.0 \n", + "822 0.0 1.0 0.0 \n", + "823 0.0 1.0 0.0 \n", + "\n", + " VALEUR_DU_BIEN_[0;10000[ VALEUR_DU_BIEN_[10000;15000[ \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 1.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + ".. ... ... \n", + "819 0.0 0.0 \n", + "820 0.0 1.0 \n", + "821 0.0 0.0 \n", + "822 0.0 1.0 \n", + "823 0.0 0.0 \n", + "\n", + " VALEUR_DU_BIEN_[15000;20000[ VALEUR_DU_BIEN_[20000;25000[ \\\n", + "0 1.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 1.0 0.0 \n", + "4 0.0 0.0 \n", + ".. ... ... \n", + "819 0.0 1.0 \n", + "820 0.0 0.0 \n", + "821 1.0 0.0 \n", + "822 0.0 0.0 \n", + "823 1.0 0.0 \n", + "\n", + " VALEUR_DU_BIEN_[25000;35000[ VALEUR_DU_BIEN_[35000;99999[ CM \n", + "0 0.0 0.0 1072.980 \n", + "1 0.0 1.0 3750.000 \n", + "2 0.0 0.0 1838.490 \n", + "3 0.0 0.0 4892.740 \n", + "4 1.0 0.0 166.730 \n", + ".. ... ... ... \n", + "819 0.0 0.0 1216.755 \n", + "820 0.0 0.0 2071.560 \n", + "821 0.0 0.0 5077.640 \n", + "822 0.0 0.0 5228.550 \n", + "823 0.0 0.0 5880.340 \n", + "\n", + "[824 rows x 46 columns]" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Concatenate the transformed variables\n", + "data_model_preprocessed = pd.concat([vars_numeriques_scaled, vars_categorielles_enc], axis=1) # type: ignore\n", + "\n", + "# Add the CM column (target variable) to get 824x46 shape\n", + "data_model_preprocessed['CM'] = data_model['CM'].values\n", + "\n", + "print(data_model_preprocessed.shape)\n", + "data_model_preprocessed" + ] + }, { "cell_type": "markdown", "id": "62d49546", @@ -3116,12 +5962,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 118, "id": "6a1c7907", "metadata": {}, "outputs": [], "source": [ - "train, test = train_test_split(data_model, test_size=0.2, random_state=42)" + "train, test = train_test_split(data_model_preprocessed, test_size=0.2, random_state=42)" ] }, { @@ -3142,11 +5988,443 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "bd26339b", + "execution_count": 121, + "id": "053e013c", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeRegressor(max_depth=5, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeRegressor(max_depth=5, random_state=42)" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree = DecisionTreeRegressor(max_depth=5, random_state=42)\n", + "tree.fit(train.drop(\"CM\", axis=1), train[\"CM\"])" + ] }, { "cell_type": "markdown", @@ -3158,11 +6436,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 125, "id": "c4ca2cf9", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 3683.84\n", + "MSE: 55216550.75\n", + "RMSE: 7430.78\n" + ] + } + ], + "source": [ + "y_pred = tree.predict(test.drop(\"CM\", axis=1))\n", + "\n", + "mae = metrics.mean_absolute_error(test[\"CM\"], y_pred)\n", + "mse = metrics.mean_squared_error(test[\"CM\"], y_pred)\n", + "rmse = metrics.root_mean_squared_error(test[\"CM\"], y_pred)\n", + "\n", + "print(f\"MAE: {mae:.2f}\")\n", + "print(f\"MSE: {mse:.2f}\")\n", + "print(f\"RMSE: {rmse:.2f}\")" + ] }, { "cell_type": "markdown", @@ -3234,7 +6532,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "id": "b515460e", "metadata": {}, "outputs": [], @@ -3257,7 +6555,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "id": "eebb394f", "metadata": {}, "outputs": [], @@ -3267,7 +6565,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "id": "b067126c", "metadata": {}, "outputs": [], @@ -3281,7 +6579,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "6597152c", "metadata": {}, "outputs": [], @@ -3293,7 +6591,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "63ff1c9d", "metadata": {}, "outputs": [], @@ -3353,7 +6651,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "id": "6d58dbc2", "metadata": {}, "outputs": [], @@ -3386,7 +6684,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "47da5172", "metadata": {}, "outputs": [], @@ -3396,10 +6694,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "id": "d4936c46", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meilleurs paramètres: {}\n", + "Meilleure RMSE : inf\n" + ] + } + ], "source": [ "# Meilleurs résultats\n", "print(\"Meilleurs paramètres:\", best_params)\n", @@ -3408,7 +6715,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "id": "3215c463", "metadata": {}, "outputs": [], @@ -3422,7 +6729,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "id": "bb9a5c9b", "metadata": {}, "outputs": [], @@ -3434,7 +6741,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "id": "0f0768ad", "metadata": {}, "outputs": [],