Vinzy AutoML is a Python package that automates machine learning workflows, including model selection, hyperparameter tuning, and performance evaluation. It supports over 60 models, provides parallel training, and helps quickly identify the best model for classification or regression tasks. With built-in visualizations and comprehensive performance metrics, it makes comparing models simple and intuitive. Ideal for both beginners and experienced data scientists looking to save time and streamline their ML projects.

Vinzy Image Data Importer is a Python package designed to streamline the process of importing and preprocessing image datasets for deep learning projects. By providing an intuitive interface, it simplifies tasks such as converting images to grayscale, resizing, normalizing, and organizing data into training sets with corresponding labels. The package returns the processed data as a tuple containing the image data, labels, and a category-to-index mapping dictionary, facilitating seamless integration into machine learning workflows.

Python package designed to effortlessly convert an entire codebase into a neatly structured Word document. With a single command, it scans your project directory, collects all supported code files, and compiles them into a clean, well-formatted .docx file, perfect for documentation, audits, code reviews, or academic submissions. It removes the manual effort of copying code into documents and ensures consistent formatting throughout, making it an indispensable tool for developers who need a quick and professional way to present or archive their code.

vinzy-datadiff is a Python library for comparing pandas DataFrames. It helps identify differences between two DataFrames at both the row and cell level, detect changes in schema (like added/removed columns or dtype changes), and export comparison results in formats like Excel, CSV, JSON, or HTML. It supports key-based or index-based comparisons and has options for numerical tolerance and string comparison handling.

Vinzy Splitter is a Python package that makes it easy to split datasets into training, validation, and testing subsets. It supports customizable split ratios, optional shuffling, and ensures data is organized efficiently for machine learning workflows. The package simplifies dataset preparation, saving time and reducing errors in the process. Perfect for both beginners and experienced data scientists looking to streamline their ML projects.