Flight Delay Prediction
Machine learning project that analyzes and predicts flight delays based on carrier information, airport details, and flight statistics. Optimized Random Forest model achieves ~0.99 R² score with ~1.9 MAE.
Overview
A comprehensive machine learning project that analyzes and predicts flight delays using carrier information, airport details, and flight statistics. The system uses an optimized Random Forest model that achieves exceptional performance with approximately 0.99 R² score and 1.9 Mean Absolute Error, making it highly accurate for real-world flight delay predictions.
Challenge
Flight delays are driven by many operational factors across carriers and airports. The challenge was to clean and analyze large flight statistics data and predict delay minutes accurately enough to be useful for planning.
Solution
Optimized Random Forest Regressor trained on carrier, airport, and flight statistics.
Architecture
Random Forest Regressor · scikit-learn · Pandas / NumPy analysis pipeline
Results
- R² Score: ~0.99
- MAE: ~1.9
- Model: Random Forest
- Achieved exceptional ~0.99 R² score
- Low Mean Absolute Error (~1.9)
- Optimized Random Forest model
- Comprehensive data analysis
- Production-ready prediction system
Technologies
- Python
- Machine Learning
- Random Forest
- scikit-learn
- Data Analysis
- Pandas
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