This is a repository to extract different metrics from the OpenManage Enterprise service running in a Dell cluster

History of BuyVM/BuyShared/Frantech stock data scraped from buyvmstock.com & buyvm.hasstock.net

A toolkit for machine learning from time series

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

This project aims to compare traditional Machine Learning methods for tabular data classification, such as Ensemble methods, Decision Trees, and Naive Bayes, with NLP classification methods like Multinomial Naive Bayes, RNNs, and Transformers. We are utilizing survey data from the CDC via the Behavioral Risk Factor Surveillance System (BRFSS)

Univariate Time-Series Anomaly Detection algorithms from TSB-UAD

The aim of this project is to create a more nuanced understanding of the interactions between socio-demographic characteristics, in-game behaviours, and global-scale environmental consciousness.

sciBASIC# is a kind of dialect language which is derive from the native VB.NET language, and written for the data scientist.

Topic Modelling for Humans

MANGA Plus Top 40

UnityPy is python module that makes it possible to extract/unpack and edit Unity assets

Apriori Algorithm for Association Rule Mining

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

The Accelerator is a tool for fast and reproducible processing of eBay-scale datasets on a single computer.

The Open Source Time-Series Data Historian

Chrome Extension, download photos, videos from Instagram post, tv, reels, stories

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Conducted Data Mining utilizing Unsupervised Machine Learning K-means Clustering technique to analyze employee absenteeism data. The goal was to uncover hidden trends to better understand absenteeism causes and propose targeted solutions for mitigation.

Auction schedules data miner