I am running a research-oriented machine-learning project focused on the theory and practice of supervised models. The work goes beyond standard data analysis; I need to probe the mathematical foundations, implement ideas quickly in Python, and evaluate them rigorously. The core of the study revolves around Bayesian decision theory, parametric modelling, and classic regression/classification techniques. You’ll be juggling probability theory, multivariate analysis, numerical computation, and thorough model-selection logic while keeping the codebase clean and reproducible with Pandas, NumPy, and allied scientific-Python tools. What I want from this collaboration is concrete, inspectable output that can stand up to peer review: • Well-commented Python modules and Jupyter notebooks implementing and comparing candidate algorithms • Clear documentation of assumptions, derivations, and analytical reasoning used to reach each result • An evaluation report that benchmarks each approach using agreed-upon metrics and explains why certain models outperform others Accuracy, mathematical clarity, and thoughtful experimentation are crucial. If you enjoy turning theoretical ideas into working prototypes and can articulate every decision in the code and the math, this project should fit perfectly.