Update app.py

app.py

@@ -10,6 +10,7 @@ from sklearn.ensemble import RandomForestClassifier
 from xgboost import XGBClassifier
 from sklearn.inspection import permutation_importance
 from sklearn.feature_selection import mutual_info_classif
+from sklearn.preprocessing import LabelEncoder
 import io
 import base64
 
@@ -29,7 +30,13 @@ def plot_correlation_matrix(data):
 
 # Function to calculate feature importance
 def calculate_feature_importance(X, y):
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    # Convert non-sequential class labels to sequential integers
+    le = LabelEncoder()
+    y_encoded = le.fit_transform(y)  # Transform y into continuous integers
+
+    # Split the dataset
+    X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)
+    
     scaler = StandardScaler()
     X_train_scaled = scaler.fit_transform(X_train)
     X_test_scaled = scaler.transform(X_test)