featurewizUse advanced feature engineering strategies and select best features from your data set with a single line of code.
Stars: ✭ 229 (+1105.26%)
Mutual labels: feature-selection, feature-extraction, feature-engineering
Market-Mix-ModelingMarket Mix Modelling for an eCommerce firm to estimate the impact of various marketing levers on sales
Stars: ✭ 31 (+63.16%)
Mutual labels: eda, feature-selection, feature-engineering
feature engineFeature engineering package with sklearn like functionality
Stars: ✭ 758 (+3889.47%)
Mutual labels: feature-selection, feature-extraction, feature-engineering
FIFA-2019-AnalysisThis 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
Stars: ✭ 28 (+47.37%)
Mutual labels: eda, feature-selection, feature-engineering
exemplary-ml-pipelineExemplary, annotated machine learning pipeline for any tabular data problem.
Stars: ✭ 23 (+21.05%)
Mutual labels: feature-selection, feature-engineering, feature-scaling
dominance-analysisThis 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.
Stars: ✭ 111 (+484.21%)
Mutual labels: feature-selection, feature-engineering
NVTabularNVTabular 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.
Stars: ✭ 797 (+4094.74%)
Mutual labels: feature-selection, feature-engineering
The Building Data Genome ProjectA collection of non-residential buildings for performance analysis and algorithm benchmarking
Stars: ✭ 117 (+515.79%)
Mutual labels: feature-extraction, feature-engineering
pyHSICLassoVersatile Nonlinear Feature Selection Algorithm for High-dimensional Data
Stars: ✭ 125 (+557.89%)
Mutual labels: feature-selection, feature-extraction
Amazing Feature EngineeringFeature 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.
Stars: ✭ 218 (+1047.37%)
Mutual labels: feature-extraction, feature-engineering
TsfelAn intuitive library to extract features from time series
Stars: ✭ 202 (+963.16%)
Mutual labels: feature-extraction, feature-engineering
Machine Learning Workflow With PythonThis is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation
Stars: ✭ 157 (+726.32%)
Mutual labels: feature-extraction, feature-engineering
tsflexFlexible time series feature extraction & processing
Stars: ✭ 252 (+1226.32%)
Mutual labels: feature-extraction, feature-engineering
Data-ScienceUsing Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.
Stars: ✭ 15 (-21.05%)
Mutual labels: dimensionality-reduction, feature-engineering
BlurrData transformations for the ML era
Stars: ✭ 96 (+405.26%)
Mutual labels: feature-extraction, feature-engineering
NniAn open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Stars: ✭ 10,698 (+56205.26%)
Mutual labels: feature-extraction, feature-engineering
skrobotskrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
Stars: ✭ 22 (+15.79%)
Mutual labels: feature-selection, feature-engineering