test

CardiaquePrediction_KERNEL_86.88%Succes.py

@@ -0,0 +1,75 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import pickle
+
+# Charger le dataset
+file_path = "dataset4.csv"  # Assurez-vous que le fichier est bien dans le bon répertoire
+datasetMedical = pd.read_csv(file_path)
+
+# Afficher des informations sur le dataset
+datasetMedical.info()
+print("Aperçu du dataset :", datasetMedical.head())
+
+# Définir les colonnes catégoriques
+colonnes_categoriques = ['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal']
+
+# Vérifier quelles colonnes existent dans le dataset
+colonnes_presentes = [col for col in colonnes_categoriques if col in datasetMedical.columns]
+
+# Appliquer la transformation seulement sur les colonnes existantes
+if colonnes_presentes:
+    datasetMedical[colonnes_presentes] = datasetMedical[colonnes_presentes].astype('category')
+    datasetMedical = pd.get_dummies(datasetMedical, columns=colonnes_presentes)
+    print("✅ Transformation réussie des variables catégoriques :", colonnes_presentes)
+else:
+    print("⚠️ Aucune colonne catégorique disponible pour la transformation.")
+
+# Séparer les features (X) et la cible (y)
+y = datasetMedical['target']
+X = datasetMedical.drop(['target'], axis=1)
+
+# Séparer les données en training et test sets
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)
+
+# Vérifier que X_train et X_test ont bien les mêmes colonnes
+missing_cols = set(X_train.columns) - set(X_test.columns)
+if missing_cols:
+    print(f"⚠️ Colonnes manquantes dans X_test après le split : {missing_cols}")
+    for col in missing_cols:
+        X_test[col] = 0  # Ajouter des colonnes manquantes pour éviter les erreurs
+
+# Normalisation des données
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X_train = sc.fit_transform(X_train)
+X_test = sc.transform(X_test)
+
+# Entraînement du modèle SVM
+from sklearn.svm import SVC
+classifier = SVC(kernel='rbf', random_state=0)
+classifier.fit(X_train, y_train)
+print("✅ Modèle entraîné avec succès")
+
+# Sauvegarde du modèle et du scaler
+with open('modele.pkl', 'wb') as model_file:
+    pickle.dump(classifier, model_file)
+    print("✅ Modèle enregistré avec succès")
+
+with open('scaler.pkl', 'wb') as scaler_file:
+    pickle.dump(sc, scaler_file)
+    print("✅ Scaler enregistré avec succès")
+
+# Faire des prédictions
+y_pred = classifier.predict(X_test)
+print("Échantillon de prédictions :", y_pred[:10])
+print("Échantillon de vraies valeurs :", y_test[:10].values)
+
+# Calcul du pourcentage de bonne classification
+#total_predictions = conf_matrix.sum()
+#correct_predictions = conf_matrix.diagonal().sum()
+#accuracy_percentage = (correct_predictions / total_predictions) * 100
+
+#accuracy_percentage

app.py

@@ -0,0 +1,82 @@
+import numpy as np
+from flask import Flask, request, render_template
+import pickle
+import os
+from sklearn.preprocessing import StandardScaler
+
+app = Flask(__name__)
+
+# Définir les chemins absolus pour éviter les erreurs de chargement
+model_path = os.path.join(os.getcwd(), 'modele.pkl')
+scaler_path = os.path.join(os.getcwd(), 'scaler.pkl')
+
+# Chargement du modèle et du scaler avec gestion des erreurs
+try:
+    model = pickle.load(open(model_path, 'rb'))
+    scaler = pickle.load(open(scaler_path, 'rb'))
+    print("✅ Modèle et scaler chargés avec succès")
+except Exception as e:
+    print(f"❌ Erreur lors du chargement du modèle ou du scaler : {e}")
+    model = None
+    scaler = None
+
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        form_values = request.form.to_dict()
+        print("📩 Données reçues :", form_values)
+
+        # Récupérer les colonnes attendues par le scaler
+        expected_columns = list(scaler.feature_names_in_)  # Récupérer les noms des colonnes du scaler
+        print("✅ Colonnes attendues :", expected_columns)
+
+        # Initialiser un dictionnaire avec toutes les valeurs à 0
+        input_data = {col: 0 for col in expected_columns}
+
+        # Mettre à jour avec les valeurs envoyées par le formulaire
+        for key, value in form_values.items():
+            if key in input_data:
+                try:
+                    input_data[key] = float(value)
+                except ValueError:
+                    print(f"⚠️ Erreur de conversion pour {key} : {value}")
+                    return render_template('index.html',
+                                           prediction_text="❌ Erreur : Veuillez entrer des nombres valides")
+
+        # Transformer les données en tableau numpy
+        final_features = np.array([input_data[col] for col in expected_columns]).reshape(1, -1)
+
+        # Vérifier que le scaler est bien chargé
+        if scaler:
+            final_features = scaler.transform(final_features)
+        else:
+            return render_template('index.html', prediction_text="❌ Scaler non chargé")
+
+        # Vérifier que le modèle est bien chargé
+        if model:
+            prediction = model.predict(final_features)
+            output = prediction[0]
+        else:
+            return render_template('index.html', prediction_text="❌ Modèle non chargé")
+
+        # Affichage plus clair du résultat
+        if output == 0:
+            result_text = "✅ Aucun risque détecté : Pas de crise cardiaque"
+        else:
+            result_text = "⚠️ Attention : Risque de crise cardiaque détecté"
+
+        return render_template('index.html', prediction_text=result_text)
+
+    except Exception as e:
+        print(f"❌ Erreur interne : {e}")
+        return render_template('index.html', prediction_text=f'❌ Erreur interne : {e}')
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

dataset4.csv

@@ -0,0 +1,304 @@
+age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target
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