Moustafa Mohamed
Software Engineering Student | Data Analysis & AI Enthusiast | Python, SQL, Machine Learning | Deep Learning
View My Projects Download My Resume
Software Engineering Student | Data Analysis & AI Enthusiast | Python, SQL, Machine Learning | Deep Learning
View My Projects Download My ResumeHello! I'm Moustafa Mohamed, a second-year Software Engineering student with a passion for Data Science, AI, and Software Development. My curiosity for intelligent systems led me to explore machine learning, data analysis, and deep learning, where I enjoy solving complex problems and building AI-driven solutions. I'm continuously expanding my expertise to create innovative and impactful technologies.
My programming journey began with C and C++, providing a solid foundation in problem-solving and computational thinking. As my interest in technology expanded, I advanced my skills in Python, JavaScript, and SQL, alongside essential libraries like NumPy, Pandas, Seaborn, Matplotlib, and Scikit-learn. Through a combination of practical experience and continuous learning, I have developed expertise in data analysis, machine learning, and software development, applying these skills to solve real-world challenges. Committed to professional growth, I strive to remain at the cutting edge of technology and contribute to innovative solutions that drive meaningful impact.
Python
C
C++
JavaScript
SQL
NumPy
Pandas
Seaborn
Matplotlib
Scikit-learn
Jupyter Notebook
Data Preprocessing
Machine Learning
Model Evaluation
Deep Learning
TensorFlow
PyTorch
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Prompt Engineering
Large Language Models
AI Development
Git
Conducted comprehensive analysis of Titanic passenger data to identify survival patterns. Implemented data cleaning, feature engineering, and visualization techniques to uncover key insights.
Analyzed salary distributions and trends across San Francisco city employees. Created interactive visualizations to showcase pay disparities and job title distributions.
Developed a machine learning model to classify SMS messages as spam or ham. Implemented NLP techniques and achieved 98% accuracy with XGBoost classifier.
Built a CNN model to classify 36 different fruits and vegetables with 92% accuracy. Implemented image augmentation and transfer learning techniques for improved performance.
Applied unsupervised learning to segment mall customers into distinct groups based on spending patterns. Visualized clusters to provide actionable business insights.
A Python tool that scrapes and summarizes website content using both Gemini AI and LLaMA 3.2. Extracts main text from any URL, cleans irrelevant elements, and generates concise markdown summaries. Supports both cloud-based (Gemini) and local (LLaMA via Ollama) AI processing.
Feel free to reach out to me via email or connect with me on LinkedIn, GitHub, or Kaggle.