machine learning

Automated pipeline for decensoring of doujinshis, using HentAI and DeepCreamPy. Supply an Imgur album link, nhentai link or nhentai id, wait 15-30 minutes, and download significantly decensored images.

Just me learning ML.

Bandicoot: user-friendly C++ library for GPU accelerated linear algebra, integrating with CUDA and OpenCL

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.

Compute confidence intervals for ranks to compare the results of algorithms.

Research, code, algorithms and all related to artificial intelligence

The main project for OctoMY™ - Ready-to-run robot software

Notebooks for Pandas, Spark and Python experiments.

Environment for evaluation of explainability methods in data streams

GitLab is an open source end-to-end software development platform with built-in version control, issue tracking, code review, CI/CD, and more. Self-host GitLab on your own servers, in a container, or on a cloud provider.

Resources for interns and new team members at Tangible AI

A tool to annotate radiology images.

This project aims to leverage an extensive dataset of flight booking records sourced from the "Ease My Trip" website to develop predictive models for estimating ticket prices. With a focus on incorporating various influencing factors such as airline choice, travel class, booking lead time, and other significant attributes, our goal is to create a robust analytical framework that enables stakeholders to gain insights into flight pricing dynamics.

My personal webpage

This project aims to leverage the Ames Housing Price Dataset to develop an accurate predictive model for estimating residential property sale prices in Ames, Iowa. By analyzing various property features and their relationships to sale prices, the project seeks to uncover insights that can inform both buyers and sellers in the housing market.

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

Neural networks, NLP sentiment analysis, Logistic Regression Classification, ...

This project explores the application of Support Vector Machines (SVM) in classifying human cell records to predict the malignancy of breast cancer samples. By training an SVM model on a dataset of breast cancer features, the goal is to accurately differentiate between benign and malignant cases. This research aims to contribute to the development of early detection and diagnosis methods for breast cancer.