152 Open source projects that are alternatives of or similar to MLDay18

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

Heart Disease prediction using 5 algorithms

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Online machine learning methods

A fast and easy to use decision tree learner in java

[ICML 2019, 20 min long talk] Robust Decision Trees Against Adversarial Examples

several methods for text classification

Solution of the Titanic Kaggle competition

Statistical inference on machine learning or general non-parametric models

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Predicting IPL match results. https://kuharan.github.io/IPL-ML-2018/

A complete one stop solution for all the problems of Rural area people. 👩🏻‍🌾

exploratory data analysis using random forests

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

Interactive GUI (developed in Python) for calibration and conditional verification of numerical weather prediction model outputs.

Receipt.ID is a multi-label, multi-class, hierarchical classification system implemented in a two layer feed forward network.

Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine

A curated list of data mining papers about fraud detection.

🌳 Stacked Gradient Boosting Machines

文本分类基准测试

Handwritten Digit Recognition using Machine Learning and Deep Learning

It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.

A curated list of gradient boosting research papers with implementations.

(BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset

Implementations of machine learning algorithm by Python 3

java math expression parser is faster than JEP

ThunderGBM: Fast GBDTs and Random Forests on GPUs

A super-fast and scalable Random Forest library based on fast histogram decision tree algorithm and distributed bagging framework. It can be used for binary classification, multi-label classification, and regression tasks. This library provides both Python and command line interface to users.

Generalized Random Forests

In 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).

Library for MQL5 (MetaTrader) with Python, Java, Apache Spark, AWS

Jupyter Notebooks and miscellaneous

Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka

Machine Learning algorithms coded from scratch

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

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Machine Learning library for the web and Node.

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

Scripts, tools and example data for mapping wetland ecosystems using data driven R statistical methods like Random Forests and open source GIS

🏭 JavaScript Machine Learning Toolkit

A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.

gesture recognition toolkit

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

eForest: Reversible mapping between high-dimensional data and path rule identifiers using trees embedding

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

ML-powered Loan-Marketer Customer Filtering Engine

Random Forests for Density Estimation in Python

This is the official implementation for the paper 'AutoEncoder by Forest'

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html

bitcoin prediction algorithms

Seminar work "Decision Trees - An Introduction" with presentation, seminar paper, and Python implementation

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

A random forest

Machine Learning library written in Python and NumPy.

🚦 Declarative Finite-State Machines in Go