This project develops a machine learning pipeline to analyze and predict air traffic trends using historical aviation data.

Full-stack Aviation BI Dashboard: A Docker-deployed Metabase instance utilizing SQL to monitor Astra aircraft fleet health. Features a 'Maintenance Command Center' tracking 4,200+ airframe hours, fuel efficiency, and real-time alerting for engine component inspections (ATA 72/73) within a 50-hour critical window.

Collection of interactive Tableau dashboards showcasing data visualization expertise across entertainment, real estate, and aviation industries. Features Netflix content analysis, housing market trends, and British Airways customer satisfaction metrics with advanced filtering and drill-down capabilities.

PySpark-based analytics project exploring U.S. flight delays, cancellations, route performance, and airport trends through data visualization and interactive dashboards.

Local Generative AI prototype for operational risk analysis and executive reporting using Ollama, Docker, and Open WebUI.

Interactive dashboard for analyzing Vietnam's aviation sector. Built with React, TypeScript, D3.js, and Python. Visualizes flight density, pricing trends, passenger routes, and airline metrics. Ideal for travelers, airline strategists, and policymakers.

Data analytics and forecasting system to predict passenger traffic, detect bottlenecks, and optimize airport operations.

SQL Server data analytics project analyzing air traffic passenger trends, airport performance, airline activity, and travel demand using SQL for business insights.

Workforce management tool for predicting ground handling delays and optimizing staff allocation for airport operations.

An end-to-end, containerized data pipeline built with Apache Airflow and PostgreSQL to track commercial aviation reliability. Utilizing a strictly typed Star Schema, it ingests flight events to calculate auditable, industry-standard A15/D15 On-Time Performance (OTP) and Completion Factor metrics for fact-backed reporting.

USA flights analysis

✈️ FlightWise AI is an end-to-end Machine Learning project that predicts flight delays using XGBoost and aviation analytics. Features advanced feature engineering, model comparison, interactive Streamlit dashboards, delay risk prediction, and operational recovery recommendations.

Documentation of an end-to-end data lifecycle for airline operations. Focuses on relational integrity, multi-source ingestion protocols, and Row-Level Security (RLS) for departmental data isolation.

AI-powered platform for intelligent operations in healthcare, pharma, and aviation.

Auditing minimum connection time standards at U.S. hub airports using BTS On-Time Performance and DB1B data. January 2019, Python.

Interactive Power BI dashboard analysing 34M U.S. flights across airline, airport, cancellation, diversion, and delay-cause performance from 2017–2022.

This project focuses on the intersection of real-time data engineering and aviation analytics. Built to bridge the gap between raw API streams and structured data insights, it tracks global flight movements, schedules, and historical data while navigating the technical challenges of external API management.

This project presents analytical view of Indian domestic airline flights, covering pricing trends, booking behavior, class distribution, city-wise connections, and timing analysis. The dashboard is built using Microsoft Power BI, leveraging interactive visuals to help understand customer patterns, flight pricing strategies, and operational insights

Visualizing busiest airline routes (2015–2019) using Python + Tableau.