Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Alink | 3,479 | 16 | 3 months ago | 19 | November 03, 2023 | 53 | apache-2.0 | Java | ||
Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform. | ||||||||||
Disentangled Attribution Curves | 23 | 3 years ago | mit | Python | ||||||
Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees" | ||||||||||
Data Science End To End | 22 | a year ago | mit | Jupyter Notebook | ||||||
A Respository to get you job ready as a Data Scientist | ||||||||||
50 Days Of Statistics For Data Science | 15 | 2 years ago | Jupyter Notebook | |||||||
This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository. | ||||||||||
Aps2020 | 11 | 2 years ago | gpl-3.0 | R | ||||||
Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics | ||||||||||
Monotonic Optimal Binning | 9 | 6 months ago | 4 | August 03, 2023 | mit | Python | ||||
Monotonic Optimal Binning algorithm is a statistical approach to transform continuous variables into optimal and monotonic categorical variables. | ||||||||||
Describer_ml | 8 | 8 months ago | 28 | January 17, 2023 | 2 | mit | Python | |||
A set of descriptive statistics and hypothesis tests across different types of data | ||||||||||
Target Likelihood Encoding | 8 | 5 years ago | Python | |||||||
Generate target statistics | ||||||||||
Outrank | 8 | 5 months ago | 4 | bsd-3-clause | Python | |||||
A Python library for efficient feature ranking and selection on sparse data sets. | ||||||||||
Avito Demand Prediction Challenge | 7 | 5 years ago | gpl-3.0 | Jupyter Notebook | ||||||
It is a Competition for Regression Challenge held by Kaggle, It is based on a Avito Dataset whose size is 123GB which can be accessed from Kaggle, I have done Data Pre-processing, feature engineering, feature extraction, data visualization, machine learning, stacking and boosting |