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.
Project Overview
Dataset / Input Data
Dataset Name / SourceFlight Delay Dataset
FormatCSV
Size / InfoFlight records with carrier, airport, and flight statistics
Model / Approach
Algorithm / Model
Random Forest Regressor
Tools / Frameworks
Tech Stack
Python
Machine Learning
Random Forest
scikit-learn
Data Analysis
Pandas
Results / Output
Performance Metrics
R² Score~0.99
MAE~1.9
ModelRandom Forest
Highlights / Improvements
- Achieved exceptional ~0.99 R² score
- Low Mean Absolute Error (~1.9)
- Optimized Random Forest model
- Comprehensive data analysis
- Production-ready prediction system
