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 ยป

Report Bug ยท 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
MLR - Machine Learning Research

Machine Learning Research 1. Project Topic 1.1. Exsiting research Benmark: https://paperswithcode.com/sota ACL anthology for NLP papers: http://www.ac

Charles 69 Oct 20, 2022
A Streamlit demo to interactively visualize Uber pickups in New York City

Streamlit Demo: Uber Pickups in New York City A Streamlit demo written in pure Python to interactively visualize Uber pickups in New York City. View t

Streamlit 230 Dec 28, 2022
Open-Source CI/CD platform for ML teams. Deliver ML products, better & faster. โšก๏ธ๐Ÿง‘โ€๐Ÿ”ง

Deliver ML products, better & faster Giskard is an Open-Source CI/CD platform for ML teams. Inspect ML models visually from your Python notebook ๐Ÿ“— Re

Giskard 335 Jan 04, 2023
A toolkit for geo ML data processing and model evaluation (fork of solaris)

An open source ML toolkit for overhead imagery. This is a beta version of lunular which may continue to develop. Please report any bugs through issues

Ryan Avery 4 Nov 04, 2021
Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational model)

Sum-Square_Error-Business-Analytical-Tool- Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational m

om Podey 1 Dec 03, 2021
A scikit-learn based module for multi-label et. al. classification

scikit-multilearn scikit-multilearn is a Python module capable of performing multi-label learning tasks. It is built on-top of various scientific Pyth

802 Jan 01, 2023
fastFM: A Library for Factorization Machines

Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat

1k Dec 24, 2022
Machine learning algorithms implementation

Machine learning algorithms implementation This repository consisits of implementation of various machine learning algorithms. The algorithms implemen

Karun Dawadi 1 Jan 03, 2022
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

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Distributed (Deep) Machine Learning Community 23.6k Jan 03, 2023
Python bindings for MPI

MPI for Python Overview Welcome to MPI for Python. This package provides Python bindings for the Message Passing Interface (MPI) standard. It is imple

MPI for Python 604 Dec 29, 2022
MICOM is a Python package for metabolic modeling of microbial communities

Welcome MICOM is a Python package for metabolic modeling of microbial communities currently developed in the Gibbons Lab at the Institute for Systems

57 Dec 21, 2022
Factorization machines in python

Factorization Machines in Python This is a python implementation of Factorization Machines [1]. This uses stochastic gradient descent with adaptive re

Corey Lynch 892 Jan 03, 2023
XManager: A framework for managing machine learning experiments ๐Ÿง‘โ€๐Ÿ”ฌ

XManager is a platform for packaging, running and keeping track of machine learning experiments. It currently enables one to launch experiments locally or on Google Cloud Platform (GCP). Interaction

DeepMind 620 Dec 27, 2022
GAM timeseries modeling with auto-changepoint detection. Inspired by Facebook Prophet and implemented in PyMC3

pm-prophet Pymc3-based universal time series prediction and decomposition library (inspired by Facebook Prophet). However, while Faceook prophet is a

Luca Giacomel 314 Dec 25, 2022
It is a forest of random projection trees

rpforest rpforest is a Python library for approximate nearest neighbours search: finding points in a high-dimensional space that are close to a given

Lyst 211 Dec 29, 2022
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Dec 28, 2022
This is a curated list of medical data for machine learning

Medical Data for Machine Learning This is a curated list of medical data for machine learning. This list is provided for informational purposes only,

Andrew L. Beam 5.4k Dec 26, 2022
inding a method to objectively quantify skill versus chance in games, using reinforcement learning

Skill-vs-chance-games-analysis - Finding a method to objectively quantify skill versus chance in games, using reinforcement learning

Marcus Chiam 4 Nov 19, 2022
Case studies with Bayesian methods

Case studies with Bayesian methods

Baze Petrushev 8 Nov 26, 2022
Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai

Machine Learning Engineering for Production (MLOps) Specialization on Coursera (offered by deeplearning.ai) Programming assignments from all courses i

Aman Chadha 173 Jan 05, 2023