This is an initiative to help understand Statistical methods and Machine learning in a naive manner. You will find scripts, and theoretical contents required to clarify concepts, especially for bio-informatic students.

Internal Validity Indexes for Fuzzy and Possibilistic Clustering

Unsupervised ensemble learning methods for time series forecasting. Bootstrap aggregating (bagging) for double-seasonal time series forecasting and its ensembles.

Hear All Solution In R Language

Gaussian mixture modelling - Unsupervised learning

Customer segmentation is the process of dividing customers into groups based on common characteristics so companies can market to each group effectively and appropriately. This Project uses R language and Kmeans clustering algorithm to segment customers into clusters.

R package for estimation of sparse genetic factors from GWAS

UNSUPERVISED MACHINE LEARNING (CLUSTERING): TCGA data mining for studying the system of interactions between sub-branches of Wnt signalling pathway in colorectal cancer

Analyzing ratings and reviews for restaurants across 31 European cities

Using exploratory data analysis and k-means clustering to analyze competitive balance in soccer/football

seqArchR: Identifying (promoter) sequence architectures de novo using NMF

Unsupervised clustering of transcriptomic and proteomic data for breast cancer patients

visualization lecture

PCA, K-Means and Hierarchical Clustering

A Parkinson's Disease clinical decision support system (cdss) based on the Parkinson's Progression Markers Initiative (PPMI) data.

Simple to use package for OMICS data analysis (featuring limma, WGCNA, GSEA)

Immune cell phenotyping of lung cancer and lung tissue using PCA, tSNE, and other techniques

Sparse factorization framework to integrate GWAS studies and identify shared latent genetic components

Unsupervised machine learning algorithm from scratch part of DS-5230 Unsupervised Machine Learning course

Interactive Multivariate Analysis