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Flight Price Prediction

1R. BHANU SANKAR,2 CH. S S YESWANTH

1Assistant Professor, Department Of MCA, MCA Final Semester,

1Master of Computer Applications,

1Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India.

Abstract:

This project develops a machine learning solution for predicting flight ticket prices, aiding users in identifying optimal travel times and cost-effective options. The predictive model is trained on extensive Kayak flight data for various international routes (NYC, PAR, RUH, SVO) from Feb-Apr 2022. Methodology involves crucial data preprocessing: converting SAR prices to INR, standardizing durations to minutes, and numerically transforming 'Total stops'. Categorical 'Source' and 'Destination' cities undergo one-hot encoding for robust feature engineering. A powerful Random Forest Regressor model is trained to discern complex price relationships from this processed data. The trained model is integrated into an intuitive Streamlit web application. This app allows users to input flight parameters, facilitating real-time price predictions in INR. The comprehensive system provides valuable insights into potential travel expenditures.

Index Terms: Flight Price Prediction, Machine Learning, Random Forest Regressor, Data Preprocessing, Feature Engineering, One-Hot Encoding, Streamlit Application, Web Application, Kayak (Data Source), SAR to INR Conversion