| import streamlit as st |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| from sklearn.model_selection import train_test_split |
| from sklearn.preprocessing import LabelEncoder |
| from sklearn.ensemble import RandomForestClassifier |
| from sklearn.metrics import accuracy_score, classification_report |
|
|
|
|
| def load_data(): |
| df = pd.read_csv(r"C:\Users\pavilion\Desktop\INNOMATICS_PROJECTS(SL)\Student_Depresion_Prediction(Machine_Learning)\student_depression_dataset.csv") |
| |
| df.drop(columns=['id', 'City', 'Work Pressure', 'Job Satisfaction'], inplace=True, errors='ignore') |
|
|
| if 'Profession' in df.columns: |
| df = df[df['Profession'].str.lower() == 'student'] |
| df.drop(columns=['Profession'], inplace=True, errors='ignore') |
|
|
| return df |
|
|
|
|
| def preprocess_data(df): |
| label_encoders = {} |
| categorical_cols = df.select_dtypes(include=['object']).columns.tolist() |
| |
| for col in categorical_cols: |
| le = LabelEncoder() |
| df[col] = le.fit_transform(df[col]) |
| label_encoders[col] = le |
|
|
| return df, label_encoders |
|
|
|
|
| def train_model(X_train, y_train): |
| model = RandomForestClassifier(n_estimators=100, random_state=42) |
| model.fit(X_train, y_train) |
| return model |
|
|
|
|
| def main(): |
| st.set_page_config(page_title="Student Depression Prediction", layout="wide") |
| |
| st.markdown("<h1 style='text-align: center;'>🎓 Student Depression Prediction</h1>", unsafe_allow_html=True) |
| st.write("This app predicts whether a student is likely to experience depression based on various factors.") |
|
|
| df = load_data() |
| df, label_encoders = preprocess_data(df) |
|
|
| X = df.drop(columns=['Depression']) |
| y = df['Depression'] |
|
|
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y) |
| model = train_model(X_train, y_train) |
| y_pred = model.predict(X_test) |
|
|
| accuracy = accuracy_score(y_test, y_pred) |
| classification_rep = classification_report(y_test, y_pred) |
|
|
| if st.button("🚀 Train Model"): |
| st.subheader("✅ Model Trained Successfully!") |
| st.write(f"**Accuracy:** {accuracy:.2f}") |
| st.text("Classification Report:") |
| st.text(classification_rep) |
|
|
| feature_importances = model.feature_importances_ |
| feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances}).sort_values(by='Importance', ascending=False) |
|
|
| st.subheader("🔍 Feature Importance") |
| fig, ax = plt.subplots() |
| ax.barh(feature_importance_df['Feature'], feature_importance_df['Importance'], color='skyblue') |
| ax.set_xlabel("Importance") |
| ax.set_ylabel("Feature") |
| ax.set_title("Feature Importance in Random Forest") |
| st.pyplot(fig) |
|
|
| |
| st.subheader("📊 Predict Depression Risk") |
| input_data = {} |
| |
| for col in X.columns: |
| if col in label_encoders: |
| input_data[col] = st.selectbox(f"Select {col}", label_encoders[col].classes_) |
| input_data[col] = label_encoders[col].transform([input_data[col]])[0] |
| 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())) |
|
|
| |
| if st.button("🔮 Predict"): |
| input_df = pd.DataFrame([input_data]) |
| prediction = model.predict(input_df)[0] |
| |
| if prediction == 1: |
| st.error("😨 **The student is at risk of depression.**") |
| st.subheader("📌 Recommended Actions:") |
| st.markdown(""" |
| 1. **Seek Professional Help** 👩⚕️ - Consult a therapist or counselor. |
| 2. **Stay Connected with Loved Ones** 🏡 - Talk to trusted friends or family. |
| 3. **Engage in Physical Activities** 🏃♂️ - Exercise regularly to improve mood. |
| 4. **Practice Mindfulness & Relaxation** 🧘♂️ - Try meditation, yoga, or deep breathing. |
| 5. **Manage Academic Stress** 📚 - Break tasks into smaller steps and seek support. |
| """) |
| else: |
| st.success("🥳 **The student is not at risk of depression.**") |
| st.subheader("✅ Wellness Tips:") |
| st.markdown(""" |
| 1. **Maintain a Balanced Lifestyle** ⚖️ - Manage studies, social life, and self-care. |
| 2. **Get Enough Sleep** 😴 - Ensure 7–9 hours of quality sleep for better mental health. |
| 3. **Engage in Hobbies & Creative Activities** 🎨 - Pursue music, reading, or painting. |
| 4. **Stay Active & Exercise** 🚴♂️ - Physical activity boosts energy and reduces anxiety. |
| 5. **Support Others & Build Strong Friendships** 🤝 - Encourage open discussions and check on friends. |
| """) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
