Comparative financial analysis of China Airlines (CAL) versus EVA Airways, including DuPont analysis, capital structure evaluation, and Dividend Discount Model (DDM) valuation. Features dynamic Excel modeling with automated formulas, sensitivity analysis, and strategic recommendations for improving profitability and efficiency.

OTAS framework – Sensory‑informed seating & in‑flight OT alert. Founder: I. M. A. Mulla (Ibrahim Muhammad Amin Mulla). Research prototype for airport screening + aircraft incident response. Decibel mapping, OT override, ding + red flicker. Not clinical use.

A high-performance machine learning pipeline designed to analyze 6,000+ airline reviews. This project uses NLP (TF-IDF) and advanced AI (Random Forest/XGBoost) to predict passenger recommendations with 97% accuracy. It translates raw customer feedback into actionable business insights through 15+ analytical visualizations.

Machine Learning project for customer segmentation, marketing ROI prediction, and personalized airline marketing using social media insights, travel behavior analytics, Random Forest, XGBoost, and K-Means clustering.

Statistical analysis of 15 global airline stocks during COVID-19's two waves (2020) — using Spearman correlation, Wilcoxon Signed-Rank Test, and trend analysis in Excel to examine how adjusted close prices and trading volumes shifted between the panic and recovery phases.

Interactive Tableau dashboard analyzing British Airways customer reviews. visualizes key satisfaction metrics including cabin service, entertainment, and food quality across different aircraft models and traveler types.

This repository houses a machine learning project. Its goal is to predict airline customer satisfaction based on factors like flight distance, in-flight amenities, service quality, and travel class. This aims to assist airlines in understanding the main factors affecting customer satisfaction, enabling them to make data-driven decisions.

Strategic evaluation of a diversified conglomerate’s entry into the domestic airline industry. This IIM Bangalore case study analyzes ecosystem-level value creation, connectivity gaps in Tier-2 tourist circuits, and risk mitigation strategies.

This project applies Natural Language Processing (NLP) techniques to classify US airline tweets into positive, neutral, and negative sentiments. A Random Forest model trained on bag-of-words features achieves strong performance and reveals key customer pain points such as delays, cancellations, and service issues.

📊 Analyze and compare the financial performance of China Airlines and EVA Airways through thorough modeling and strategic recommendations.