Create pages/5 Model Building.py

pages/5 Model Building.py

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+import streamlit as st
+
+# Page Title
+st.title("⚙️ Model Building & Evaluation")
+
+# Model Building Section
+st.markdown("""
+### 🏗️ Model Building:
+The classification model was built using the **K-Nearest Neighbors (KNN) Classifier**, which predicts a student's depression status based on similar instances in the training data.
+
+#### Model Pipeline:
+- **Preprocessing**: Encoding and scaling were handled using `ColumnTransformer` with `OrdinalEncoder` and `StandardScaler`.  
+- **Train-Test Split**: The dataset was split into training and testing sets using `train_test_split` with **stratification** on the target to preserve class balance.  
+- **Model**: Implemented using `KNeighborsClassifier` from scikit-learn.
+""")
+
+# Model Training Section
+st.markdown("""
+### ✅ Model Training:
+- The model was trained on the processed dataset with optimized hyperparameters.  
+- `GridSearchCV` was used to find the best value of `k` (number of neighbors).  
+- Cross-validation ensured the robustness of the trained model.
+""")
+
+# Model Evaluation Section
+st.markdown("""
+### 📊 Model Evaluation:
+
+**Metrics Used:**  
+- Accuracy Score  
+- Classification Report (Precision, Recall, F1-score)  
+- Confusion Matrix  
+
+The trained model demonstrated good performance on the test data and was exported as a `.pkl` file for deployment in the Hugging Face.
+""")
+
+if st.button("Go to Deployment >>"):
+    st.switch_page(r"pages\6 Deployment.py")
+
+if st.button("<< Back"):
+    st.switch_page(r"pages\4 Feature Engineering.py")