Update app.py

app.py

@@ -6,7 +6,7 @@ from sklearn.preprocessing import LabelEncoder
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score, classification_report
 
-# Load dataset
+
 def load_data():
     df = pd.read_csv(r"C:\Users\pavilion\Desktop\INNOMATICS_PROJECTS(SL)\Student_Depresion_Prediction(Machine_Learning)\student_depression_dataset.csv")
     
@@ -20,7 +20,7 @@ def load_data():
 
     return df
 
-# Preprocess data
+
 def preprocess_data(df):
     label_encoders = {}
     categorical_cols = df.select_dtypes(include=['object']).columns.tolist()
@@ -32,13 +32,13 @@ def preprocess_data(df):
 
     return df, label_encoders
 
-# Train model
+
 def train_model(X_train, y_train):
     model = RandomForestClassifier(n_estimators=100, random_state=42)
     model.fit(X_train, y_train)  
     return model
 
-# Streamlit App
+
 def main():
     st.set_page_config(page_title="Student Depression Prediction", layout="wide")
     
@@ -75,7 +75,7 @@ def main():
     ax.set_title("Feature Importance in Random Forest")
     st.pyplot(fig)
 
-    # User Input for Prediction
+   
     st.subheader("📊 Predict Depression Risk")
     input_data = {}
     
@@ -86,7 +86,7 @@ def main():
         else:  
             input_data[col] = st.number_input(f"Enter {col}", min_value=int(X[col].min()), max_value=int(X[col].max()), value=int(X[col].mean()))
 
-    # Prediction Button
+    
     if st.button("🔮 Predict"):
         input_df = pd.DataFrame([input_data])
         prediction = model.predict(input_df)[0]
@@ -112,6 +112,6 @@ def main():
             5. **Support Others & Build Strong Friendships** 🤝 - Encourage open discussions and check on friends.
             """)
 
-# Run the Streamlit app
+
 if __name__ == '__main__':
     main()