250 Open source projects that are alternatives of or similar to ml-simulations

An End to End Customer Churn Prediction solution using AWS services.

Open solution to the Santander Value Prediction Challenge 🐠

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Different clustering approaches applied on different problemsets

Data and Code to reproduce results for my talk at Paris: R in Insurance 2017 Conference

Maximum Covariance Analysis in Python

[UNMAINTAINED] Automated machine learning for analytics & production

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019

Serverless Inferencing on Kubernetes

Advanced High Performance Data Science Toolbox for R by Laurae

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

No description or website provided.

REST web service for the true real-time scoring (<1 ms) of Scikit-Learn, R and Apache Spark models

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.

H2O.ai Machine Learning Interpretability Resources

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

Open solution to the Home Credit Default Risk challenge 🏡

This project is to convert PortraitFCN+ (by Xiaoyong Shen) from Matlab to Tensorflow, then refine the outputs from it (converted to a trimap) using KNN and ResNet, supervised by Richard Berwick.

Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

Stacked Generalization (Ensemble Learning)

Pure Java implementation of XGBoost predictor for online prediction tasks.

Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

Machine learning models for time series analysis

Playing with MNIST. Machine Learning. Generative Models.

A python library for decision tree visualization and model interpretation.

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.

Provide an input CSV and a target field to predict, generate a model + code to run it.

a solution for competition of kaggle `Human or Robot`

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

A repository to keep track of all the code that I end up writing for my blog posts.

A data-driven tool to identify the best candidates for a marketing campaign and optimize it.

Workshop on tidytranscriptomics: Performing tidy transcriptomics analyses with tidybulk, tidyverse and tidyheatmap

[Done] Master version: developed the stacked regression (score 0.11, top 5%) based on (xgboost, sklearn). Branch v1.0: developed linear regression (score 0.45) based on Tensorflow

Comparison tools

Bayesian Optimization and Grid Search for xgboost/lightgbm

Implemented an A/B Testing solution with the help of machine learning

Top 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market

Nyoka is a Python library to export ML/DL models into PMML (PMML 4.4.1 Standard).

Secure collaborative training and inference for XGBoost.

Material of the Kaggle Berlin meetup group!

Machine Learning University: Decision Trees and Ensemble Methods

Common library for serving TensorFlow, XGBoost and scikit-learn models in production.

Predict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.

Serverless Inferencing on Kubernetes

To identify the driver's drowsiness based on real-time camera image and image processing techniques. 졸음운전 감지 시스템. OpenCV

Tutoriais de Python, Data Science, Machine Learning e Deep Learning - Sigmoidal

Trading Algorithm by XGBoost

Machine Learning Operator & Controller for Kubernetes

Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms

Attempt to predict future stock prices based on Google Trends data.

Implementing Content based and Collaborative filtering(with KNN, Matrix Factorization and Neural Networks) in Python

https://events.kaspersky.com/hackathon/

A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.

Quora Kaggle Competition : Natural Language Processing using word2vec embeddings, scikit-learn and xgboost for training