Mohamed Ebrahim Hamed

Engineered a comprehensive machine learning solution for the Kaggle Titanic dataset, implementing advanced data preprocessing, exploratory data analysis, and feature engineering techniques. Trained and optimized multiple algorithms (Logistic Regression, Random Forest, Gradient Boosting) achieving 92% accuracy. Demonstrated proficiency in model evaluation, hyperparameter tuning, and cross-validation strategies.

Built a production-ready Convolutional Neural Network (CNN) for real-time facial emotion recognition using TensorFlow/Keras and OpenCV. Implemented image preprocessing, model training with data augmentation, and integrated a user-friendly GUI for live webcam inference. Achieved robust performance across multiple emotion categories with optimized inference speed.

Conducted comprehensive healthcare data analysis across patient demographics, physician performance, and financial metrics. Designed and developed interactive Power BI dashboards with advanced visualizations and KPI tracking. Utilized Python, SQL, and Excel for data cleaning, aggregation, and statistical analysis, delivering actionable insights for healthcare operations and decision-making.

Developed a sophisticated NLP deep learning model for sentiment classification on IMDB movie reviews. Implemented text preprocessing, tokenization, and embedding techniques using TensorFlow/Keras. Trained and evaluated LSTM/GRU architectures with attention mechanisms, achieving 89% validation accuracy. Included comprehensive preprocessing pipelines and model interpretability analysis.

Engineered a custom CNC plotter from scratch using Arduino microcontroller, stepper motors, and mechanical design principles. Implemented G-code parsing, motion control algorithms, and precision calibration for accurate 2D drawing capabilities. Demonstrated expertise in embedded systems, robotics, and hardware-software integration for automation projects.

Developed an advanced recommendation system using Regularized Non-negative Matrix Factorization (NMF) with Gradient Descent optimization. Implemented collaborative filtering techniques for movie rating prediction with robust handling of sparse data. Included model validation, performance metrics analysis, and prediction export functionality for business intelligence applications.

Created interactive 2D games using C++ with BGI graphics library, implementing core game development concepts including game loops, collision detection, sprite animation, and user input handling. Designed engaging gameplay mechanics and visual effects, showcasing proficiency in low-level graphics programming and algorithm optimization.

Performed in-depth ecommerce analytics and business intelligence analysis, building comprehensive dashboards for KPI tracking, customer segmentation, and product performance evaluation. Utilized Excel, Power BI, and Python for data aggregation, statistical analysis, and visualization of sales trends, customer behavior, and revenue optimization opportunities.