AI-powered property consultant featuring semantic search and chat for nearly 9,000 properties and 600+ blogs across 1,700+ compounds and 40+ locations. RAG-based chatbot for area insights, property comparisons, and XGBoost price prediction. Personalized via dynamic preference tracking stored in MongoDB.

Built a bilingual (English/Arabic) RAG chatbot to query the Faculty of Computer & Artificial Intelligence (USC) internal regulations. Uses Google Gemini with ChromaDB for semantic retrieval and provides accurate, context-aware answers via a web interface.

Developed a web-based application utilizing LLMs, encompassing data cleaning, vector database creation for semantic search, zero-shot text classification (fiction/non-fiction), sentiment analysis for tone-based sorting, and a Gradio interface for user interaction.

Binary classification of colon histopathology images into Colon Adenocarcinoma vs Benign Tissue using transfer learning on the LC25000 dataset. Featuring 10,000 high-resolution images, the model uses EfficientNetB3 with a custom classification head, achieving 99.4% accuracy.

A modern, responsive portfolio built with Next.js and Tailwind CSS, featuring smooth animations, an interactive showcase of projects and certificates, and a contact system.

Built a machine learning model to predict customer conversion in digital marketing campaigns using demographic and engagement data. Deployed a Gradio web app for real-time predictions with a Random Forest model.

A movie sentiment analysis application using three models to predict review sentiment through an interactive Gradio web interface.

This modern, responsive portfolio website showcases Ahmed Ayman's skills and projects as a Data Scientist and ML Engineer, featuring dark/light mode, smooth animations, and an interactive user experience built with HTML, CSS, and JavaScript.