fyp-jupyter

Features and purpose

Complete data science research workflow for Jupyter notebooks covering CRISP-DM methodology from data loading through model validation, with MLflow experiment tracking integration, phase-based workflow guidance (Exploration, Systematic Experimentation, Analysis, Documentation), and skill integration points. Use when working on FYP data science projects requiring systematic data preprocessing, EDA, feature engineering, modeling, statistical validation, experiment tracking, or needing guidance on what to work on at each project phase. Includes MLflow setup for tracking 30+ experiment runs, weekly work planning for 10-week FYP timeline, and clear decision framework for when to use which skill (fyp-jupyter, crossvit-covid19-fyp, fyp-statistical-validator, tar-umt-fyp-rds, tar-umt-academic-writing).

Quick start

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.