| 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') |
