Flight Delay Prediction System
Created a two-stage predictive model to forecast flight delays upon arrival and delay duration using departure weather data.
Project Overview
Created a flight delay prediction system during my internship at Solarillion Foundation using machine learning to forecast delays and their duration. The project involved building a two-stage predictive model: first predicting whether a delay will occur, then estimating the delay duration using weather data at departure locations. I worked with historical flight data and weather information, implementing data cleaning, preprocessing, and feature engineering techniques. The system uses various weather parameters to make predictions and helps understand the correlation between weather conditions and flight delays. This project provided hands-on experience with real-world data science challenges, predictive modeling, and the practical applications of machine learning in the aviation industry.
Key Features
- ✓Two-stage prediction model
- ✓Weather data integration
- ✓Delay probability forecasting
- ✓Duration estimation algorithms
- ✓CSV and JSON data processing
- ✓Data cleaning and preprocessing
- ✓Real-time prediction capabilities
- ✓Airline decision support system
Technical Challenges
- ⚡Weather data correlation with delays
- ⚡Multi-stage model optimization
- ⚡Data quality and preprocessing
- ⚡Real-time prediction accuracy
Technologies Used
Project Info
Collaboration
Research Institute
Screenshots
