# EDA

EDA is a [package](https://datagrok.ai/help/develop/#packages) for the [Datagrok](https://datagrok.ai) platform. It provides the following exploratory data analysis and machine learning tools:

* Dimensionality reduction
  * Principal component analysis ([PCA](https://en.wikipedia.org/wiki/Principal_component_analysis))
  * Uniform manifold approximation and projection ([UMAP](https://arxiv.org/abs/1802.03426))
  * t-distributed stochastic neighbor embedding ([t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding))
  * Stochastic proximity embedding ([SPE](https://pubmed.ncbi.nlm.nih.gov/12820129/))
* Multivariate analysis
  * Partial least squares regression ([PLS](https://en.wikipedia.org/wiki/Partial_least_squares_regression))
* Supervised machine learning
  * Least-squares support vector machine ([LS-SVM](https://en.wikipedia.org/wiki/Least-squares_support_vector_machine))
  * [Linear regression](https://en.wikipedia.org/wiki/Linear_regression)
  * [Softmax](https://en.wikipedia.org/wiki/Multinomial_logistic_regression) classifier
  * [PLS](https://en.wikipedia.org/wiki/Partial_least_squares_regression) regression
  * [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting)
* Analysis of variances (ANOVA)
  * One-way ANOVA ([link](https://en.wikipedia.org/wiki/One-way_analysis_of_variance))
* Missing data imputation
  * k-nearest neighbors method ([KNN](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm))
