A collection of neat and practical data science and machine learning projects

Overview

Contributors Forks Stargazers Issues MIT License LinkedIn Codewars


Logo

Data Science

A collection of neat and practical data science and machine learning projects
Explore the docs »

Report Bug · Request Feature

Table of Contents

About The Repository

Product Name Screen Shot

This repository is a collection of my projects and accomplishments from my time spent in the data science and analytics space.

Built With

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

  • npm
npm install [email protected] -g

Installation

  1. Clone the data-science
git clone https://github.com/will-fong/data-science.git
  1. Install NPM packages
npm install

Usage

Please use this as a reference for your own work and feel free to reach out with any questions or comments.

Roadmap

Supervised Learning

  • Regression
  • Classification

Unsupervised Learning

  • Clustering

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Email me: thewillfong[at]gmail.com

Project Link: https://github.com/will-fong/data-science

Owner
Will Fong
Learning space for a technology consultant
Will Fong
A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and A* Search (Manhattan Distance Heuristic)

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

17 Aug 14, 2022
End to End toy example of MLOps

churn_model MLOps Toy Example End to End You might find below links useful Connect VSCode to Git MLFlow Port Heroku App Project Organization ├── LICEN

Ashish Tele 6 Feb 06, 2022
Python based GBDT implementation

Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A

Sberbank AI Lab 20 Sep 21, 2022
Mixing up the Invariant Information clustering architecture, with self supervised concepts from SimCLR and MoCo approaches

Self Supervised clusterer Combined IIC, and Moco architectures, with some SimCLR notions, to get state of the art unsupervised clustering while retain

Bendidi Ihab 9 Feb 13, 2022
jaxfg - Factor graph-based nonlinear optimization library for JAX.

Factor graphs + nonlinear optimization in JAX

Brent Yi 134 Dec 21, 2022
Probabilistic time series modeling in Python

GluonTS - Probabilistic Time Series Modeling in Python GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (

Amazon Web Services - Labs 3.3k Jan 03, 2023
Accelerating model creation and evaluation.

EmeraldML A machine learning library for streamlining the process of (1) cleaning and splitting data, (2) training, optimizing, and testing various mo

Yusuf 0 Dec 06, 2021
Simple Machine Learning Tool Kit

Getting started smltk (Simple Machine Learning Tool Kit) package is implemented for helping your work during data preparation testing your model The g

Alessandra Bilardi 1 Dec 30, 2021
AutoX是一个高效的自动化机器学习工具,它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

English | 简体中文 AutoX是什么? AutoX一个高效的自动化机器学习工具,它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色: AutoX在多个kaggle数据集上,效果显著优于其他解决方案(见效果对比)。 简单易用: AutoX的接口和sklearn类似,方便上手使用。

4Paradigm 431 Dec 28, 2022
A naive Bayes model for cancer classification using a set of documents

Naivebayes text classifcation model for cancer and noncancer documents Author: Alex King Purpose Requirements/files included How to use 1. Purpose The

Alex W King 1 Nov 24, 2021
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

neurodata 3 Dec 16, 2022
scikit-fem is a lightweight Python 3.7+ library for performing finite element assembly.

scikit-fem is a lightweight Python 3.7+ library for performing finite element assembly. Its main purpose is the transformation of bilinear forms into sparse matrices and linear forms into vectors.

Tom Gustafsson 297 Dec 13, 2022
Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

Artsem Zhyvalkouski 64 Nov 30, 2022
This repository contains the code to predict house price using Linear Regression Method

House-Price-Prediction-Using-Linear-Regression The dataset I used for this personal project is from Kaggle uploaded by aariyan panchal. Link of Datase

0 Jan 28, 2022
A collection of Machine Learning Models To Web Api which are built on open source technologies/frameworks like Django, Flask.

Author Ibrahim Koné From-Machine-Learning-Models-To-WebAPI A collection of Machine Learning Models To Web Api which are built on open source technolog

Ibrahim Koné 2 May 24, 2022
MLflow App Using React, Hooks, RabbitMQ, FastAPI Server, Celery, Microservices

Katana ML Skipper This is a simple and flexible ML workflow engine. It helps to orchestrate events across a set of microservices and create executable

Tom Xu 8 Nov 17, 2022
Visualize classified time series data with interactive Sankey plots in Google Earth Engine

sankee Visualize changes in classified time series data with interactive Sankey plots in Google Earth Engine Contents Description Installation Using P

Aaron Zuspan 76 Dec 15, 2022
Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Highly interpretable, sklearn-compatible classifier based on decision rules This is a scikit-learn compatible wrapper for the Bayesian Rule List class

Tamas Madl 482 Nov 19, 2022
stability-selection - A scikit-learn compatible implementation of stability selection

stability-selection - A scikit-learn compatible implementation of stability selection stability-selection is a Python implementation of the stability

185 Dec 03, 2022
Learn Machine Learning Algorithms by doing projects in Python and R Programming Language

Learn Machine Learning Algorithms by doing projects in Python and R Programming Language. This repo covers all aspect of Machine Learning Algorithms.

Ravi Chaubey 6 Oct 20, 2022