1672 Open source projects that are alternatives of or similar to Awesome Fraud Detection Papers

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.

[UNMAINTAINED] Automated machine learning for analytics & production

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

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

R package to interface with OpenML

Machine learning fundamentals lesson in interactive notebooks

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

IPython widgets, interactive plots, interactive machine learning

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

Universal Reddit Scraper - A comprehensive Reddit scraping command-line tool written in Python.

A proofreader for your data

Topic Modelling for Humans

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

Complete machine learning model codes

A curated list of awesome machine learning interpretability resources.

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

Python code for common Machine Learning Algorithms

An open-source, low-code machine learning library in Python

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Materials for GWU DNSC 6279 and DNSC 6290.

An intuitive library to extract features from time series

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

Real-time sentiment analysis in Python using twitter's streaming api

Building Decision Trees From Scratch In Python

Ranking, acceptance rate, deadline, and publication tips

RMDL: Random Multimodel Deep Learning for Classification

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

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

Coding up a Neural Network Classifier from Scratch

📈 Click-Through Rate Prediction using Logistic Regression and Tree Algorithms

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

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.

Machine Learning models from scratch with a better visualisation

H2O.ai Machine Learning Interpretability Resources

Companion code for the book "Mastering Social Media Mining with Python"

mlr3: Machine Learning in R - next generation

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

Super simple text classifier using Naive Bayes. Plug-and-play, no dependencies

simple tutorial on Machine Learning with Scikitlearn

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

Vídeos e códigos do Universo Discreto ensinando o fundamental de Machine Learning em Python. Para mais detalhes, acompanhar a playlist listada.

Multi-class text categorization using state-of-the-art pre-trained contextualized language models, e.g. BERT

All Algorithms implemented in R

Simple machine learning library / 簡單易用的機器學習套件

open source for wechat-official-account (ID: PyDataLab)

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

NFStream: a Flexible Network Data Analysis Framework.

A unified framework for machine learning with time series

A curated list of fraud detection papers using graph information or graph neural networks

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

food image to recipe with deep convolutional neural networks.

ELKI Data Mining Toolkit

A library of extension and helper modules for Python's data analysis and machine learning libraries.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Code of the IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018 [read-only repository]

Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost

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.