Update app.py

app.py

@@ -110,10 +110,16 @@ if 'df' in locals():
         df[features], df[target_col], test_size=test_size, random_state=42
     )
 
+    # Encode the target variable (if categorical) using LabelEncoder
+    if y_train.dtypes == 'object':  # Check if the target column is categorical
+        le = LabelEncoder()
+        y_train = le.fit_transform(y_train)
+        y_test = le.transform(y_test)
+
     st.subheader("Select and Train Model")
     model_type = st.selectbox("Choose a model:", ["Logistic Regression", "Decision Tree", "Random Forest"])
     if model_type == "Logistic Regression":
-        model = LogisticRegression()
+        model = LogisticRegression(max_iter=200)  # Increase max_iter if needed
     elif model_type == "Decision Tree":
         model = DecisionTreeClassifier()
     else: