R code used for the analysis of BSc thesis project about flood damage in municipalities of Bomporto and Bastiglia in 2014

Cross-sectional study that analyzed retrospective data from pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome (SARS) between January 2016 and November 2021.

ML Cleaning for Earth Obs Using Collocated Datasets

Analyzed and predicted energy consumption for residential properties in South Carolina and parts of North Carolina, focusing on high-energy usage months and future demands influenced by temperature increases. Utilized predictive models and a Shiny app for interactive data exploration to offer insights and recommendations for managing peak demand

Random Forest Algorithms to predict climate impact-drivers (CID), a.k.a., climate extreme indices for impact studies, in crop yields of soybean maize using Random Forest and XGBoost in a SHAP (SHapley Additive exPlanations) framework

Proyecto de Titulación: Cálculo de Pérdidas Esperadas basado en 3 modelos de Credit Scoring para una institución financiera del Ecuador

A hybrid classification and prediction model for credit risk analysis in R

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

a workflow for fitting cross-validated XGBOOST models in R and producing various diagnostic and interpretive outputs

Analyses Pros-RODAM microbiota data

Survival learners for the `mlexperiments` R 📦

Learners for the `mlexperiments` R 📦

A small wrapper package that enables full survival curve estimation using xgboost

Application of predictive models on a real data set of the obstetric medicine field and methods of interpretability on the previously fitted XGBoost model.

Binary Classification: Predicting whether Airbnb listings will have a high booking rate.

Employee Rating Prediction using Random Forest and XGBoost in R

Artificial Intelligence-based Prediction of Acute Leukemia: a free and open-source software package built in R, with a user-friendly interface provided via Shiny, that enables clinical hematologists and biologists to diagnose the three main subtypes of acute leukemia based solely on 10 routine biological parameters.

All the Data Science projects I've done, during the course, or after.

GCAP (Gene-level Circular Amplicon Prediction) firstly implements extrachromosomal DNA detection from whole-exome-sequencing (WES) data and absolute copy number profiles. https://shixiangwang.r-universe.dev/gcap