Remote Sensing Data Analysis in R 🛰

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

Minimax Classification with 0-1 Loss and Performance Guarantees

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.

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

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…

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

This repository includes all the scripts developed during the Final Degree Project aiming to provide new insights in risk stratification of Brugada Sindrome through AI-based approaches.

Detecting deforestation in the Brazilian Amazon by using NDVI and Woodiness index

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

Projet d'entreprise : En partenariat avec le groupe VitamineT

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.

Exploratoy Data Analysis,Logistic Regression,Penalized Logistic Regression (LASSO), LDA, Decision Trees, Bagging, Random Forest

A tiny collection of Python routines for machine learning

R package (R6 class) based on a Gaussian Naive Bayes for supervised classification

Exam project of Data Technology and Machine Learning course @unimib18/19.

A simple convolutional neural network (CNN) to classify ten different types of leaf diseases in tomatoes using Deep Learning frameworks and libraries.

Understanding and predicting the factors leading to employees leaving and finding relations between them. Also finding the importance of a feature according to ML models.