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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 Streamlit app.
""")
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") |