import streamlit as st
import pandas as pd

st.title("⚙️ Model Building & Evaluation")

st.markdown("""
## 🏗️ Model Building:
To determine the most effective model for predicting flight ticket prices, I experimented with multiple regression algorithms:
- **Linear Regression**
- **K-Nearest Neighbors (KNN)**
- **Decision Tree Regressor**
- **Random Forest Regressor**
- **XGBoost Regressor**

Each model was trained using the same preprocessed dataset and evaluated on test data to ensure a fair comparison.

---

## ✅ Model Training & Evaluation:
The following metrics were used to evaluate model performance:
- **Mean Absolute Error (MAE)**
- **Root Mean Squared Error (RMSE)**
- **R² Score**

---

## 🏆 Best Model Selection:
Based on evaluation metrics:
- **Random Forest Regressor** achieved the **highest R² score (0.98)** and the **lowest MAE and RMSE**, 
indicating it is the most accurate and consistent model for predicting flight prices.

The final Random Forest model has been saved and is now ready for deployment in the next page.
""")

if st.button('Go to the Deployment Page'):
    st.switch_page('pages/6_Deployment.py')

if st.button('Go to Feature Engineering'):
    st.switch_page('pages/4_Feature_Engineering.py')