supervised-classification

Remote Sensing Data Analysis in R 🛰

References regarding geospatial artificial intelligence (#geoAI) and geospatial machine learning (#geoML)

Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

Emotion recognition from Speech & Text using different heterogeneous ensemble learning methods

A reproducible, scalable and data-driven workflow for Sentinel-2 land cover classification — outperforming traditional desktop tools like QGIS SCP.

Minimax Classification with 0-1 Loss and Performance Guarantees

Détecter les faux billets à partir du jeu de données englobant statistiques sur 6 caracthéristiques des billets

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

Repository for an Introduction to Modern NLP Methods. 4 x 3(+) hours sessions (including lectures, tutorials & hands-on exercises).

中国地质大学（武汉）地理信息工程学院开设的一门选修课。An optional curriculum held by School of GI Engineering, CUG

A Time-Series Analysis on the Urban Growth of Denver since 1986. This project utilizes Google Earth Engine in conjunction with the geemap package developed by Dr. Quisheng Wu. Supervised classification was conducted on Landsat images of the greater Denver Metro area and corroborated with Census data to analyze the growth of Denver's urban center…

Benchmarking classical machine learning models for multi-class classification of low-magnitude seismic events (USGS, M1-4) under severe class imbalance.

Machine learning project for classifying breast tumors as malignant or benign using the Breast Cancer Wisconsin dataset. Includes data preprocessing, model training (Naive Bayes, Logistic Regression, Decision Tree), evaluation, and visualizations.

Comprehensive multivariate analysis of diabetes data using Factor Analysis, LDA, and Hierarchical Clustering. Custom implementations in Python with medical interpretations.

LaTeX source for the article 'Random Forest Classifier Algorithm of GRASS GIS for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique' (Lemenkova, Coasts 2024, 4(1):127-149).

GRASS GIS shell scripts comparing machine-learning classifiers (LDA, GNB, DTC, SVM) with MaxLike clustering for land-cover classification of a Landsat 8-9 OLI/TIRS time series (2014-2024) over coastal and desert Eritrea. Figures for Lemenkova, Engineering Today 2025, 4(2):13-27.

LaTeX source for the article 'Machine Learning Algorithms of Remote Sensing Data Processing for Mapping Changes in Land Cover Types over Central Apennines, Italy' (Lemenkova, J. Imaging 2025, 11(5):153).

Repositorio creado para mi primer proyecto de Machine Learning, hecho durante mi tiempo en el bootcamp de Data Science de The Bridge. Estudio sobre la calidad del agua, con aprendizaje supervisado

LaTeX source for the article 'Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh' (Lemenkova, Water 2024, 16(8):1141).

Code for predicting the severity of earthquake impact on buildings through various experiments, utilizing models like Logistic Regression, SVM, XGBoost, Neural Networks, and Random Classifier. It employs Grid Search and Randomized Search for optimal configuration and relies on feature correlations as primary predictors, adjustable with a threshold.