This is a repository to extract different metrics from the OpenManage Enterprise service running in a Dell cluster
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Updated
Jun 10, 2024 - Python
This is a repository to extract different metrics from the OpenManage Enterprise service running in a Dell cluster
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.
Univariate Time-Series Anomaly Detection algorithms from TSB-UAD
C# KQL query engine with flexible I/O layers and visualization
History of BuyVM/BuyShared/Frantech stock data scraped from buyvmstock.com & buyvm.hasstock.net
Simulations for the paper "Inter node Hellinger Distance based Decision Tree by Pritom Saha Akash, Md. Eusha Kadir, Amin Ahsan Ali, Mohammad Shoyaib"
A unified framework for machine learning with time series
Agent for https://datatug.app developed in Go language
Data mining, machine learning, and deep learning sample code
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.
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
This project focuses on implementing and assessing multiple techniques to classify heart signals (Phonocardiogram and Electrocardiogram). Two main routes that have been explored are the signal processing route and the image processing route. To carry out the experiments, the PhysioNet/CinC-2016 dataset and PhysioNet/CinC-2017 dataset are used.
Predictor for stock and ETF prices
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
A toolkit for machine learning from time series
Implement Decision Tree classifier to classify the data.
This data preprocessing involves loading the dataset, dropping irrelevant columns, handling missing ', and filling missing values in 'Age' and 'Score' with the mean. Additionally, it converts categorical values to numeric for the gender column and applies feature scaling/normalization to the age and score columns using MinMaxScaler.
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