Four projects tell the technical story: collecting real-world data, cleaning and modeling it, visualizing business insights, and building machine-learning systems.
Scraped Egyptian Premier League data, organized the records into three related tables, cleaned and validated the results with Python, exported structured files with OpenPyXL, and published the finished dataset on Kaggle.
Analyzed approximately 20,000 transactions and $22.2M in revenue. Cleaned and transformed Excel data with Power Query, designed a star schema, and built DAX measures for sales, transactions, average value, growth, rankings, and price-per-gram analysis.
Cleaned and analyzed a LinkedIn job-postings dataset, used Python visualization tools to explore the data, and created an interactive Power BI dashboard to communicate hiring trends and insights.
Led development of a deep-learning model trained on a custom dataset of 8,500 videos. Built the preprocessing, feature-extraction, training, and evaluation workflow, then integrated the model into a school-management system for real-time translation.