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.”

Time series forecasting project for airline passenger demand using statistical models and ML, delivered via Streamlit.

An interactive Tableau dashboard that explores airline customer reviews to uncover passenger satisfaction, sentiment trends, and service quality insights.

Airline Flight Delay Intelligence — 6.2M+ flights, 14 airlines, 628 airports, seasonal patterns, on-time performance, delay cause analysis | Python | Pandas | Matplotlib

Analyzing Frontier Airlines’ customer experience using real Skytrax reviews with route and aircraft context. Data is modeled in Snowflake (dbt), cleaned and analyzed with Python and SQL, and presented in an interactive Mode dashboard to surface clear insights.

Airline Revenue Management Analytics project using SQL, Python (ARIMA forecasting), and Power BI to analyze passenger demand, pricing trends, and route performance.

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).

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.