This project analyses Bayut real-estate listings from Dubai, DIFC and Downtown to uncover pricing patterns, property characteristics and neighbourhood differences. After cleaning and structuring aggregated data, price per m² and key amenities were compared across districts. The analysis highlights market trends and provides insights useful for both buyers and investors.
This project builds a regression model that predicts the recovery rate of gold from raw ore during extraction and refinement. The analysis covers process parameters, intermediate outputs and final concentrate characteristics. The final model helps mining operations assess ore quality early and avoid launching unprofitable production cycles.
This project explores historical videogame sales, ratings and genre data to understand what makes a game successful across global markets. The analysis highlights regional preferences, platform differences, and key factors shaping sales performance. It also includes hypothesis testing to compare user ratings between platforms and genres in a statistically sound way.
Analyzed real mobile usage data to compare two legacy tariff plans and determine which one brings higher monthly revenue. The project combines data cleaning, exploratory analysis, and statistical testing to identify the more profitable plan and support a data-driven marketing strategy for the operator.
In this project, an apartment-listings dataset was analysed to identify pricing outliers and anomalies that may point to fraudulent listings. Key attributes such as area, ceiling height, distance from city centre and listing date were explored using visualisations and engineered features. The findings deliver actionable insights for real-estate platforms seeking to flag irregular listings and understand pricing patterns.
