Orange3🍊 📊 💡 Orange: Interactive data analysis
Stars: ✭ 3,152 (+15660%)
ChefboostA Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python
Stars: ✭ 176 (+780%)
MetQyRepository for R package MetQy (read related publication here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247936/)
Stars: ✭ 17 (-15%)
sugarcubeMonoidal data processes.
Stars: ✭ 32 (+60%)
AIML-ProjectsProjects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning
Stars: ✭ 85 (+325%)
Awesome Datascience📝 An awesome Data Science repository to learn and apply for real world problems.
Stars: ✭ 17,520 (+87500%)
Machine-Learning-ModelsIn This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Stars: ✭ 30 (+50%)
loloA random forest
Stars: ✭ 37 (+85%)
AsclepiusOpen Price Comparison for US Hospitals
Stars: ✭ 20 (+0%)
conferencias matutinas amloCSVs de las versiones estenográficas de las conferencias matutinas del Presidente Andres Manuel López Obrador ( Mañaneras AMLO )
Stars: ✭ 25 (+25%)
scikit-hubnessA Python package for hubness analysis and high-dimensional data mining
Stars: ✭ 41 (+105%)
scibloxsciblox - Easier Data Science and Machine Learning
Stars: ✭ 48 (+140%)
TextClassification基于scikit-learn实现对新浪新闻的文本分类,数据集为100w篇文档,总计10类,测试集与训练集1:1划分。分类算法采用SVM和Bayes,其中Bayes作为baseline。
Stars: ✭ 86 (+330%)
MatminerData mining for materials science
Stars: ✭ 251 (+1155%)
cheapmlMachine Learning algorithms coded from scratch
Stars: ✭ 17 (-15%)
ReaperSocial media scraping / data collection tool for the Facebook, Twitter, Reddit, YouTube, Pinterest, and Tumblr APIs
Stars: ✭ 240 (+1100%)
DatascienceCurated list of Python resources for data science.
Stars: ✭ 3,051 (+15155%)
DeepgraphAnalyze Data with Pandas-based Networks. Documentation:
Stars: ✭ 232 (+1060%)
EasyMinerEasy association rule mining and classification on the web
Stars: ✭ 14 (-30%)
Automlpipeline.jlA package that makes it trivial to create and evaluate machine learning pipeline architectures.
Stars: ✭ 223 (+1015%)
imbalanced-ensembleClass-imbalanced / Long-tailed ensemble learning in Python. Modular, flexible, and extensible. | 模块化、灵活、易扩展的类别不平衡/长尾机器学习库
Stars: ✭ 199 (+895%)
wetlandmapRScripts, tools and example data for mapping wetland ecosystems using data driven R statistical methods like Random Forests and open source GIS
Stars: ✭ 16 (-20%)
Semantic-Busobject flow treatment, data transformation
Stars: ✭ 49 (+145%)
dh-coreFunctional data science
Stars: ✭ 123 (+515%)
software-analyticsA repository with my data analysis results of software artifacts
Stars: ✭ 37 (+85%)
dlime experimentsIn this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Stars: ✭ 21 (+5%)
kenchiA scikit-learn compatible library for anomaly detection
Stars: ✭ 36 (+80%)
KaliIntelligenceSuiteKali Intelligence Suite (KIS) shall aid in the fast, autonomous, central, and comprehensive collection of intelligence by executing standard penetration testing tools. The collected data is internally stored in a structured manner to allow the fast identification and visualisation of the collected information.
Stars: ✭ 58 (+190%)
eForestThis is the official implementation for the paper 'AutoEncoder by Forest'
Stars: ✭ 71 (+255%)
Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Stars: ✭ 218 (+990%)
TweetfeelsReal-time sentiment analysis in Python using twitter's streaming api
Stars: ✭ 249 (+1145%)
Medium-Stats-AnalysisExploring data and analyzing metrics for user-specific Medium Stats
Stars: ✭ 27 (+35%)
Suod(MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)
Stars: ✭ 245 (+1125%)
pykitmlMachine Learning library written in Python and NumPy.
Stars: ✭ 26 (+30%)
hierarchical-clusteringA Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.
Stars: ✭ 62 (+210%)
PaperWeeklyAI📚「@MaiweiAI」Studying papers in the fields of computer vision, NLP, and machine learning algorithms every week.
Stars: ✭ 50 (+150%)
Gwu data miningMaterials for GWU DNSC 6279 and DNSC 6290.
Stars: ✭ 217 (+985%)
LasioPython library for reading and writing well data using Log ASCII Standard (LAS) files
Stars: ✭ 234 (+1070%)
perkeA keyphrase extractor for Persian
Stars: ✭ 60 (+200%)
ChirpInterface to manage and centralize Google Alert information
Stars: ✭ 227 (+1035%)
Statistical LearningLecture Slides and R Sessions for Trevor Hastie and Rob Tibshinari's "Statistical Learning" Stanford course
Stars: ✭ 223 (+1015%)
Trajectory-Analysis-and-Classification-in-Python-Pandas-and-Scikit-LearnFormed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…
Stars: ✭ 41 (+105%)
Prefixspan PyThe shortest yet efficient Python implementation of the sequential pattern mining algorithm PrefixSpan, closed sequential pattern mining algorithm BIDE, and generator sequential pattern mining algorithm FEAT.
Stars: ✭ 214 (+970%)
Loan-WebML-powered Loan-Marketer Customer Filtering Engine
Stars: ✭ 13 (-35%)
xgboost-smote-detect-fraudCan we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!
Stars: ✭ 59 (+195%)
iisInformation Inference Service of the OpenAIRE system
Stars: ✭ 16 (-20%)
receiptdIDReceipt.ID is a multi-label, multi-class, hierarchical classification system implemented in a two layer feed forward network.
Stars: ✭ 22 (+10%)