A web app for Flight Delay Prediction using Random Forest Classifier
Airline Flight Delay Intelligence — 6.2M+ flights, 14 airlines, 628 airports, seasonal patterns, on-time performance, delay cause analysis | Python | Pandas | Matplotlib
End-to-end flight delay prediction system using GCP, BigQuery ML, and Looker Studio — trained on 745K flights with weather and spatio-temporal features.
Predeparture U.S. flight delay prediction with BTS data and chronological evaluation
ML model predicts total flight delays using real-world data (carrier, weather, airport, etc.) with Linear, RF & GB regressors. Includes feature engineering & model comparison.
C2C — Cancellation to Compensation. A durable agent that stays with an airline disruption claim until it is actually resolved.
Predicts US flight delays with a Random Forest (539k flights, 67% accuracy). FastAPI backend and a departure-board-style web app.
End-to-end batch ETL pipeline ingesting 7 years of U.S. domestic flight data from the BTS and transforming 55M+ records to serve exec-level delay analytics.
Intelligent platform for US flight delay prediction using XGBoost, SHAP explainability, K-Means clustering, and RAG with Qwen 3 (Hugging Face). Delivers explained recommendations via FastAPI + Dash dashboard.
Flight-delay risk intelligence with local analysis and an optional live route board.
ML-powered flight delay predictor. Random Forest on 10k flights + live Open-Meteo weather. Know before you board.
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