Exploratory Power BI analysis of gaming industry trends including revenue, players, genres, developers, platforms, and growth.

This project aims to build a game recommendation system for Steam users using machine learning techniques. It utilizes a custom dataset of Steam IDs to retrieve user-specific information such as owned games, playtimes, and game tags through the Steam Web API. The collected data is then processed and used to train a machine learning model.

A distributed system that provides a service to store, retrieve, and analyse the game data of popular multiplayer online survival games such as CS: GO, PUBG, Fortnite, etc.

A powerful recommendation system for Steam games, combining Content-Based and Collaborative Filtering techniques. Built with Python, Scikit-learn, and Streamlit to deliver accurate, real-time game recommendations. Perfect for gamers and data scientists interested in building intelligent recommendation engines.

Análisis prospectivo del mercado global de videojuegos (1980-2016). Implementación de EDA y pruebas de hipótesis para la optimización de inversión publicitaria mediante Python y estadística descriptiva.

Cleaned and visualized 20K+ messy sales records for gaming products, building interactive dashboards in Tableau to reveal insights by product, region, and channel.

🕹️ Decoding the "Achiever" quest in PS4 gaming using Bartle’s Taxonomy. A behavioral analytics project transitioning from KNIME to Python, featuring Decision Tree models with 100% classification accuracy. 🧙‍♂️✨

EDA in R on Steam game data to analyze price distribution and its relationship with user reviews.

Explorer un dataset de 10 000 jeux vidéo populaires afin d’identifier les tendances clés de l’industrie. 🎮