I'm a Computer Science student who prefers building over just reading. My journey started close to the metal with C, went up to Java, and now focuses heavily on data science and machine learning using Python and R.

I don't just import libraries—I write algorithms from scratch to understand the math, then use industry-standard tools to build performant applications. Whether it's simulating quantum mechanics or predicting real-world trends, I treat code as a tool to solve actual problems.

Python • NumPy

A data-driven project that correlates study habits with academic success. I built a linear regression pipeline to predict GPAs based on behavioral data.

• Data Pipeline: Cleaned and preprocessed raw CSV data using Pandas.
• Analysis: Visualized correlations and distributions with Matplotlib.
• Modeling: Implemented prediction logic using scikit-learn for robust results.

Java • OOP • Optimization Algorithms

An experimental framework applying quantum concepts to machine learning optimization. Instead of standard gradient descent, this project uses tunneling and superposition heuristics to escape local minima.

• Architecture: Modular Java design with strict interfaces (QuantumState, ObjectiveFunction).
• Innovation: A creative fusion of physics theory and software engineering patterns.

Python • NumPy • Math

I believe in understanding the "black box." This repo implements logistic regression without high-level ML libraries to predict customer churn.

• Core Logic: Manual implementation of sigmoid functions and gradient descent optimization.
• Performance: comparable accuracy to standard libraries, demonstrating deep understanding of the underlying calculus.

I select the right tool for the job—whether it's statistical analysis, systems programming, or web dashboards.

I'm always open to discussing data pipelines, algorithm design, or new tech stacks.

• 🔗 LinkedIn: linkedin.com/in/yassine-m-297a71276
• 📧 Email: [email protected]
• 🐙 GitHub: github.com/yassinexng