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classification

A landcover classification tool based for humans. Classifier does "traditional" supervised and unsupervised learning. Image segmentation and soon also object detection

This repository contains data and code utilized for the metric-learning based classification of the “human E3 ligome”–an extensive set of catalytic human E3s–by integrating multi-layered data, including protein sequences, domain architectures, 3D structures, functions, and expression pattern. Manuscript: https://www.biorxiv.org/content/10.1101/2025.03.09.642240v1.full.pdf

Practical Introduction to Data Science - A Data Science task using R on two datasets (Historic weather/climate and Self-reported happiness statistics UK)

A data mining project analyzing hate crime patterns in the United States from 2017 to 2025, using clustering, predictive modeling, and association rule mining.

Using Natural Language Processing (NLP) on job ads for applications in Econometrics.

Projet personnel de classification des maladies des feuilles d'arbre. Article technique disponible sur mon blog : https://boulayc.gitlab.io/blog/

Egg classification using PyTorch using ResNet50 and AlexNet

Library and tools for similarity measurement, classification and clustering of digital content and segmentation images from digitized document

Compute phylogenetic trees from distance matrix using BioNJ algorithm. It will produce Newick file and Weighted newick if needed. There is also a render using Equal-angle algorithm and SVG output for testing. It's preferred to use a JS render like 'phylotree.js' (demo of phylotree.js here http://phylotree.hyphy.org/).

Repository to store train coffee diseases classification code

A Live Evaluation of Computational Methods for Metagenome Investigation

An example of tabular data regression (parameter estimates) using TabNet

Evaluación de distintos modelos para clasificar aguas entre potables y no potables

Generates a model predicting substrate class (rock, mixed, sand, or mud) using random forest and a stack of environmental predictors. Model is tested against withheld testing observations. Model can be run using a 1-step or 2-step approach. The 2-step approach generates a binary rock/not-rock layer and a mixed/sand/mud layer and the two are combined. This approach was created to reduce the prevalence of predicted rock in the output raster. Substrate index layers (densities per class) can also be produced.

My attempts to solve homework from the Moscow Institute of Physics and Technology course

Collection of completed data-mining (university course) on python