Predicts flight arrival delays using operational flight features and a Random Forest model. Includes a Streamlit web application for interactive predictions.
“Predictive analytics project analyzing 3M+ U.S. flight records to forecast delays using Random Forest and XGBoost, improving operational decision-making for airlines.”
Data analytics project analyzing airline social media sentiment using Python and Tableau, with actionable business insights and interactive dashboard.
LLM-powered aviation analytics assistant using OpenAI tool calling and Streamlit to analyze airline routes and traffic metrics.
An end-to-end Tableau data analytics portfolio project demonstrating real-world dashboard design, data modeling, and interactive visualization using airline customer review data. This project showcases how professional data analysts build interactive dashboards used for business insights and decision-making.
Time series forecasting project for airline passenger demand using statistical models and ML, delivered via Streamlit.
Airline Flight Delay Intelligence — 6.2M+ flights, 14 airlines, 628 airports, seasonal patterns, on-time performance, delay cause analysis | Python | Pandas | Matplotlib
Power BI dashboard analyzing airline revenue, delays, passenger load, and route profitability
Power BI dashboard analyzing airline loyalty program performance, including enrollments, cancellations, and flight booking trends with DAX-based insights.
Airline revenue optimization using machine learning, SQL, and optimization techniques across dynamic pricing, demand forecasting, seat inventory, and overbooking. Analyzes booking patterns, fare classes, and market factors to identify revenue opportunities and support data-driven pricing and inventory decisions.
SQL, Power BI & Excel analysis of 110,851 U.S. domestic flights (2008-2013), tracking load factor trends, seasonality, and route saturation across 187M+ passengers.
An interactive Tableau dashboard that explores airline customer reviews to uncover passenger satisfaction, sentiment trends, and service quality insights.
Customer segmentation of East–West Airlines frequent flyer data using K-Means and Hierarchical Clustering. The project identifies optimal customer segments based on flying behavior, reward usage, and credit card activity, and provides data-driven business inferences for targeted marketing strategies.
End-to-end machine learning project to predict airline customer satisfaction using XGBoost, Random Forest and Neural Networks, combining EDA, PCA and SHAP explainability to identify the service, customer and travel variables that most strongly influence satisfaction and support data-driven service improvement strategies.
Python analytics project analyzing airline disruptions, delays, cancellations, and passenger impact using a Disruption Severity Index (DSI).
⛈️ StormChain — identifying pilot flight sequences through DFW most vulnerable to weather-driven cascading delays. XGBoost (AUC 0.81) + 1,220 avoid recommendations + live METAR dashboard. Built for the EPPS-American Airlines Data Challenge.
Customer segmentation on airline loyalty data using LRFMC model and K-Means clustering to identify 5 distinct groups based on customer transaction behavior and provide data-driven marketing strategy recommendations.
End-to-end Data Analytics project in Power BI, analyzing 2M+ flight records to identify delay root causes and optimize operational efficiency.
Predictive analytics project using R and SQLite to estimate airline route fares with stepwise and Lasso regression.
Airline Revenue Management Analytics project using SQL, Python (ARIMA forecasting), and Power BI to analyze passenger demand, pricing trends, and route performance.
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