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

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.

Basic machine learning projects and algorithm implementation in Python3

Code developed for the Multivariate Statistics Spring 2019 course practice sessions.

Análisis Multivariado I

Probabilistic OPLS discriminant analysis

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

This repository contains R Markdown reports about multivariate data analysis including Principal component analysis, Factor analysis, Discriminant analysis, classification and clustering.

The code for Roweisposes method for 3D action recognition

Fit four different neural networks: (a) Two distinct single hidden layer neural networks. (b) Two distinct neural networks with two hidden layers. Compare the accuracy of these four Neural networks among them. Also compare it to other classification methods.

Data Mining & Data Analysis coursework - UTC - SY09 - Spring 2017

R code for linear discriminant analysis

Code to predict energy output of solar panels by outside temperature and weather conditions.

Using labelled classifed data to infer a learning algorithm in R

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

This repository contains the lab work of the course Machine Learning (IE 406).

RDA implementation compatible with Scikit-learn API

Comparing Classification Methods. We will code some Discriminant Analysis Methods and compare them to Support Vector Machines (SVMs).

Simple machine learning model using scikit-learn