Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Applied Ml | 24,242 | 2 days ago | 3 | mit | ||||||
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. | ||||||||||
Nni | 12,923 | 8 | 22 | 6 hours ago | 51 | June 22, 2022 | 281 | mit | Python | |
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. | ||||||||||
Metarank | 1,773 | 4 days ago | 92 | apache-2.0 | Scala | |||||
A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine | ||||||||||
Osint_collection | 1,441 | 4 months ago | 2 | |||||||
Maintained collection of OSINT related resources. (All Free & Actionable) | ||||||||||
Pygooglenews | 1,137 | a month ago | 26 | mit | Python | |||||
If Google News had a Python library | ||||||||||
Test Tube | 719 | 9 | 3 | 10 months ago | 64 | December 01, 2019 | 27 | mit | JavaScript | |
Python library to easily log experiments and parallelize hyperparameter search for neural networks | ||||||||||
Code_search | 474 | 6 months ago | 27 | mit | Jupyter Notebook | |||||
Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning" | ||||||||||
Hyperactive | 439 | 4 | 5 days ago | 66 | May 04, 2022 | 11 | mit | Python | ||
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models. | ||||||||||
Awesome_python_scripts | 240 | 7 months ago | 57 | mpl-2.0 | Jupyter Notebook | |||||
🚀 Curated collection of Awesome Python Scripts which will make you go wow. Dive into this world of 360+ scripts. Feel free to contribute. Show your support by ✨this repository. | ||||||||||
Uci Ml Api | 189 | 2 years ago | 3 | mit | Python | |||||
Simple API for UCI Machine Learning Dataset Repository (search, download, analyze) |
Model | Accuracy |
---|---|
Random Forest with Randomized search CV | 82.09 |
Logistic Regression with Grid search CV | 83.18 |
Support Vector Machine with Grid search CV | 82.50 |
K Nearest Neighbors with Grid search CV | 77.40 |
Bagging with Base estimator as Random Forest | 84.10 |
Bagging with Base estimator as Logistic Regression | 83.10 |
AdaBoost Classifier | 83.60 |
MultilLayer Perceptron Classifier | 83.40 |
check out our project report to find out why we used these models