Eclipse-ML (Machine Learning Research Toolkit)

Project Overview

An open-source Python toolkit published on PyPI designed to streamline the academic and empirical machine learning research workflow through automated data profiling, structured pipeline evaluation, and reproducible artifact generation.

Key Features

• Automated Data Profiling: Comprehensive summary diagnostics, feature distributions, missingness profiling, and exploratory data visualization.
• Scikit-learn Pipeline Integration: Modular estimators, preprocessing transforms, cross-validation runners, and hyperparameter search routines.
• Model Comparison & Benchmarking: Standardized evaluation matrices across regression and classification tasks.
• Publication-Ready Figures: Automated export of high-resolution, vector-quality research charts and tabular performance reports.

Links

• PyPI Package: pypi.org/project/eclipse-ml
• GitHub Repository: github.com/md-naim-molla/eclipse-ml

Tech Stack

• Language & Libraries: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
• Distribution: PyPI (pip install eclipse-ml)