Update app.py

app.py

@@ -5,7 +5,69 @@ import matplotlib.pyplot as plt
 from sklearn.tree import plot_tree, export_text
 import seaborn as sns
 from sklearn.preprocessing import LabelEncoder
-import joblib
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve
+
+data = pd.read_csv('exported_named_train.csv')
+X_train = data.drop("Target", axis=1).values
+y_train = data['Target'].values
+
+models={
+    "Logisitic Regression":LogisticRegression(),
+    "Decision Tree":DecisionTreeClassifier(),
+    "Random Forest":RandomForestClassifier(),
+    "Gradient Boost":GradientBoostingClassifier()
+}
+
+for name, model in models.items():
+
+    model.fit(X_train, y_train)
+
+    # Make predictions
+    y_train_pred = model.predict(X_train)
+    y_test_pred = model.predict(X_test)
+
+    # Training set performance
+    model_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy
+    model_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score
+    model_train_precision = precision_score(y_train, y_train_pred) # Calculate Precision
+    model_train_recall = recall_score(y_train, y_train_pred) # Calculate Recall
+    model_train_rocauc_score = roc_auc_score(y_train, y_train_pred)
+
+    # Test set performance
+    model_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy
+    model_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score
+    model_test_precision = precision_score(y_test, y_test_pred) # Calculate Precision
+    model_test_recall = recall_score(y_test, y_test_pred) # Calculate Recall
+    model_test_rocauc_score = roc_auc_score(y_test, y_test_pred) #Calculate Roc
+
+    print(name)
+
+    print('Model performance for Training set')
+    print("- Accuracy: {:.4f}".format(model_train_accuracy))
+    print('- F1 score: {:.4f}'.format(model_train_f1))
+    
+    print('- Precision: {:.4f}'.format(model_train_precision))
+    print('- Recall: {:.4f}'.format(model_train_recall))
+    print('- Roc Auc Score: {:.4f}'.format(model_train_rocauc_score))
+
+    
+    
+    print('----------------------------------')
+    
+    print('Model performance for Test set')
+    print('- Accuracy: {:.4f}'.format(model_test_accuracy))
+    print('- F1 score: {:.4f}'.format(model_test_f1))
+    print('- Precision: {:.4f}'.format(model_test_precision))
+    print('- Recall: {:.4f}'.format(model_test_recall))
+    print('- Roc Auc Score: {:.4f}'.format(model_test_rocauc_score))
+
+    
+    print('='*35)
+    print('\n')
 
 def load_model_and_data():
     # Ici vous chargeriez votre modèle et données
@@ -14,7 +76,7 @@ def load_model_and_data():
     # X = loaded_X
     # y = loaded_y
     # feature_names = X.columns
-    model = joblib.load('DecisionTreeModel.joblib')
+    model = models['Decision Tree']
     data = pd.read_csv('exported_named_train.csv')
     X = data.drop("Target", axis=1)
     y = data['Target']