Flight Price Prediction for Enhanced Recommendations via Machine Learning Web Application

Key Objectives:
1. Evaluate 6 machine learning algorithms for flight cost estimation.
2. Select the best-performing algorithm based on performance metrics (MSE, RMSE, MAE, R2).
3. Implement feature engineering to enhance model accuracy.
4. Deploy the chosen model as an online web application.

Outcomes:
1. Random Forest Regressor outperformed other algorithms with the best R2 score.
2. Feature engineering improved model accuracy and predictive power.
3. Random Forest Regressor selected for deployment in online web application.

Performance Metrics:
1. Mean Squared Error (MSE)
2. Root Mean Squared Error (RMSE)
3. Mean Absolute Error (MAE)
4. R-squared (R2) score

Technologies Used:
1. Machine Learning algorithms
2. Flask (web application framework)
3. Python (programming language)

Diagrams:

Fig.1: ML Model Comparison with Performance Metrices

Fig.2: Project Methodology Diagram

Fig.3: Model Deployment Web Application