329 Open source projects that are alternatives of or similar to 50-days-of-Statistics-for-Data-Science

Exemplary, annotated machine learning pipeline for any tabular data problem.

Feature engineering package with sklearn like functionality

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

Market Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales

This is a project based on the FIFA World Cup 2019 and Analyzes the Performance and Efficiency of Teams, Players, Countries and other related things using Data Analysis and Data Visualizations

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

This repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"

DeltaPy - Tabular Data Augmentation (by @firmai)

A miniature lisp-like language for querying JSON-like structures. Tuned for clientside ML feature extraction.

Automatic feature engineering using deep learning and Bayesian inference using TensorFlow.

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Fast k-Nearest Neighbors Classifier for Large Datasets

Online feature-extraction and classification algorithm that learns representations of input patterns.

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

Data transformations for the ML era

Stock for Deep Learning and Machine Learning

Comprehensive toolkit for generating various numerical features of protein sequences

A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning

A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

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.

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

A collection of non-residential buildings for performance analysis and algorithm benchmarking

An intuitive library to extract features from time series

Versatile Nonlinear Feature Selection Algorithm for High-dimensional Data

Library for multi-dimensional, multi-sensor, uni/multivariate time series data analysis, unsupervised feature selection, unsupervised deep anomaly detection, and prototype of explainable AI for anomaly detector

Features selector based on the self selected-algorithm, loss function and validation method

📢 Ready to learn or review your knowledge!

Complete-Life-Cycle-of-a-Data-Science-Project

Automatic feature engineering using Generative Adversarial Networks using TensorFlow.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Flexible time series feature extraction & processing

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

Solutions and figures for problems from Reinforcement Learning: An Introduction Sutton&Barto

Library for VLSI CAD Design Useful parsers and solvers' api are implemented.

Federated Principal Component Analysis Revisited!

Parametric UMAP embeddings for representation and semisupervised learning. From the paper "Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning" (Sainburg, McInnes, Gentner, 2020).

This repository explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply…

mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale.

Audio feature extraction and classification

A repository of resources for understanding the concepts of machine learning/deep learning.

This project demonstrates how to apply machine learning algorithms to distinguish "good" stocks from the "bad" stocks.

WAL enables programmable waveform analysis.

Streaming, Memory-Limited, r-truncated SVD Revisited!

Comparison of dimensionality reduction methods

Automatic machine learning for tabular data. ⚡🔥⚡

A simplified version of featuretools for Spark

A Python Library for Similarity-based Dimensionality Reduction

Ensemble topic modelling with pLSA

Uncertainty-aware principal component analysis.

Efficient Algorithms for L0 Regularized Learning

An open source python library for automated feature engineering based on Genetic Programming

ZCA whitening in python

Extract video features from raw videos using multiple GPUs. We support RAFT and PWC flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, ResNet features.

Statistical Inference and Sure Independence Screening via Ball Statistics

A comprehensive dimensional reduction framework to recover the latent topology from high-dimensional data.

Library for working with KiCAD file formats

Gdstk (GDSII Tool Kit) is a C++/Python library for creation and manipulation of GDSII and OASIS files.

Feature extraction by using SITF+BoF.