machine-learning-tools

A lightweight, developer-friendly ML experiment tracking library that automatically records model runs, parameters, metrics, training time, and dataset information using local storage. It provides a CLI and web-based dashboard for organizing, analyzing, and managing experiments without requiring external tracking services or cloud infrastructure.

Open-source Python library for comprehensive ML model evaluation, covering performance, robustness, explainability, calibration, fairness, data quality, and complexity.

A modular Python tool that loads messy CSV/Excel files, cleans them automatically, generates analytical statistics, and produces a polished PDF report. Includes a CLI interface, advanced cleaning engine, and portfolio-grade architecture.

Smart text chunker for LLM preprocessing (sections → paragraphs → sentences → hard splits).

A fast PyQt‑based image annotation tool with customizable hotkeys, per‑folder labels, CSV export, and “next untagged” navigation — ideal for prepping ML training datasets.

A lightweight Chrome extension bringing 100% local, cell-level version control and git-like diffs to Google Colab.

A desktop app for drawing bounding boxes on images, labeling them, and exporting data for object detection or classification workflows. Built with PyQt5 and Pillow.

Automated Exploratory Data Analysis (EDA) and Statistical Hypothesis Testing Engine. Intelligently routes parametric/non-parametric tests, checks multicollinearity, and renders visual insights.