This module allows users to analyze k-means & hierarchical clustering, and visualize results of Principal Component, Correspondence Analysis, Discriminant analysis, Decision tree, Multidimensional scaling, Multiple Factor Analysis, Machine learning, and Prophet analysis.

The software package SiteGA for de novo motif search in ChIP-seq data

Materiales de las clases prácticas de AID y Aprendizaje Automático

Data Mining project (Fall2023) involving the classification and clustering of Sars-Cov-2 gene expression RNA-seq data

Distinguishing and finding similarities between different customer groups using Cluster Analysis and Discriminant Analysis for a hypothetical company based on a credit card dataset.

Simple machine learning model using scikit-learn

Material from the course of Data Analysis at ENSEM - Université de Lorraine.

A MATLAB toolbox for supervised linear dimension reduction (SLDR) including LDA, HLDA, PLSDA, MMDA, HMMDA and SDA

Проведение бинарной и многоклассовой классификаций эмоций людей на фотографиях

Classification, sampling, and model selection methods. Ch. 4-6 exercises (An Introduction to Statistical Learning: https://www.statlearning.com/)

Probabilistic OPLS discriminant analysis

Using labelled classifed data to infer a learning algorithm in R

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Linear Discriminant Analysis ~ a dimensionality reduction as well as a classification technique — with applications in document understanding

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well in noisy and contaminated datasets.

Multivariate data analysis using R Studio.

Code for the paper E. Raninen and E. Ollila, “Coupled regularized sample covariance matrix estimator for multiple classes,” in IEEE Transactions on Signal Processing, vol. 69, pp. 5681–5692, 2021, doi: 10.1109/TSP.2021.3118546.

This is a scanner designed to recognise DNA motifs within a long stretch of DNA. It uses two models for discrimination, one model representing the target and the second model representing the background.

Performed statistical-EDA and normalization analysis on digitized mass images with 10 nuclei features (radius, texture) Predicted malignant - benign cancer using Logistic, LDA-QDA, KNN, Lasso-Ridge classifiers with 0.89, 0.88, 0.92, 0.96 and 0.97 accuracies respectively along with decision boundaries and ROC curves

knn, Regression (LASSO, Ridge), Logistic, Principal component Analysis (PCA), Discriminant Analysis (LDA, QDA), Trees, Random Forest, Boosting