1465 Open source projects that are alternatives of or similar to Chefboost

A curated list of data mining papers about fraud detection.

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.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

🍊 📊 💡 Orange: Interactive data analysis

A collection of research papers on decision, classification and regression trees with implementations.

LAMA - automatic model creation framework

Building Decision Trees From Scratch In Python

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

A collection of notebooks of my Machine Learning class written in python 3

数据科学竞赛知识、代码、思路

Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

An implementation of "Network Representation Learning with Rich Text Information" (IJCAI '15).

I will update this repository to learn Machine learning with python with statistics content and materials

Multi-class confusion matrix library in Python

Collection of stats, modeling, and data science tools in Python and R.

TSrepr: R package for time series representations

MLBox is a powerful Automated Machine Learning python library.

eBay's TSV Utilities: Command line tools for large, tabular data files. Filtering, statistics, sampling, joins and more.

Курс "Прикладные задачи анализа данных" (ВМК, МГУ имени М.В. Ломоносова)

👨🏽‍🏫You can learn about anything over here. What Giveaways I do and why it's important in today's modern world. Are you interested in Giveaway's?🔋

Dex : The Data Explorer -- A data visualization tool written in Java/Groovy/JavaFX capable of powerful ETL and publishing web visualizations.

A community run, 5-day PyTorch Deep Learning Bootcamp

📝 Подборка ресурсов по машинному обучению

A python library for decision tree visualization and model interpretation.

Kaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

CleverCSV is a Python package for handling messy CSV files. It provides a drop-in replacement for the builtin CSV module with improved dialect detection, and comes with a handy command line application for working with CSV files.

потоковый курс "Машинное обучение и анализ данных (Machine Learning and Data Mining)" на факультете ВМК МГУ имени М.В. Ломоносова

Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.

PHP-ML - Machine Learning library for PHP

Python Data Science and Machine Learning Library for the Ethereum and ERC-20 Blockchain

YouTube Like Count Predictions using Machine Learning

Allstate Kaggle Competition ML Capstone Project

📢 Ready to learn or review your knowledge!

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

This is the implementation of Sparse Projection Oblique Randomer Forest

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

Text Classification Algorithms: A Survey

Codes and dashboards for 4th place solution for Kaggle's Home Credit Default Risk competition

Open solution to the Data Science Bowl 2018

A searchable compilation of Kaggle past solutions

General Assembly's 2015 Data Science course in Washington, DC

Explore high-dimensional datasets and how your algo handles specific regions.

The Accelerator is a tool for fast and reproducible processing of large amounts of data.

Python class to scrape data from rightmove.co.uk and return listings in a pandas DataFrame object

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.

[UNMAINTAINED] Automated machine learning for analytics & production

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

Topic Modelling for Humans

The "Python Machine Learning (1st edition)" book code repository and info resource

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

An efficient Python implementation of the Apriori algorithm.

Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm. Capstone Project for Machine Learning Engineer Nanodegree by Udacity.

👨🏽‍🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋

Introduction to Data Science