Partial dependency plots in R for xgboost, lightGBM and ranger objects

Implements the alpha-balanced focal loss, gradient, and hessian for use with LightGBM in R.

Custom metrics for LightGBM in R

LightGBM is a gradient boosting framework that uses tree based learning algorithms and it has so many features like parallel processing,low memory usage. in this post i have implemented simple lightgbm example in r o…

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Predicting diabetes using machine learning in R with Naive Bayes, XGBoost, and LightGBM.

Capstone 2: This is an independent project for the Springboard Data Science career track.

Implementation and evaluation of TabPFN as a base learner in meta-learner frameworks (S, T, X, R, DR, Z) for causal effect estimation and uplift modeling. Benchmarked on semi-synthetic and real-world RCT datasets against LightGBM, linear models, Causal Forests, and CausalPFN.

Creating SMOTE data & Use LightGBM in R

Dockerized ML model with train, predict and infer APIs using R.

LightGBM Binary Classifier Algorithm in R as per Ready Tensor specifications

Lending Club loan default data analysis and prediction using LightGBM in R

LIGHTGBM REGRESSOR

Using publically avaible Hartford Police Department data, predicting police incidents using LightGBM in R

Using LightGBM, we attempt to forecast whether or not it rains for certain major cities in Australia

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This is a dockerized LightGBM Classifier for binary and multiclass classification implemented in R. The implementation contains a simple preprocessing pipeline which handles missing data and categorical features. The main prediction model is a LightGBM classifier.

In-class Kaggle comp to predict binary outcomes. Using R for h2o, xgboost and Keras, Python for LightGBM and sklearn's voting classifier

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SPY-Time-Series-Forecasting: A comparative analysis of statistical (ARIMA, Holt’s) vs. machine learning (LightGBM, Prophet) models for forecasting S&P 500 daily prices, utilizing macroeconomic feature engineering and expanding-window cross-validation in R.

Predicting ERP implementation duration (in weeks) using machine learning models like Random Forest, XGBoost, and LightGBM. Features include team size, project complexity, and client attributes. Final model achieved 0.59 R², aiding better planning, resource allocation, and client communication.

This R package contains the repository of the methodological process, technical details and workflow of XGBoost & LightGBM and the variables used as inputs to predict the probabilities of the presence of dengue published in the article Risk of dengue transmission in the non-endemic area of ​​Mexico.

CatBoost achieved an impressive R² = 0.9215 in predicting asteroid collision risk using Near-Earth Object data. It outperformed LightGBM by over 10%, thanks to its native handling of categorical features and efficient early stopping. The project shows CatBoost’s strength in accurate, high-performance orbital risk modeling

Developed a machine learning-based system using TabNet and LightGBM to optimize inventory in smart supply chains. The model predicts demand, reduces stockouts and overstocking, and enhances decision-making using ERP data. Evaluated using RMSE and R² for high accuracy and adaptability.

Hi, I'm Santhosh Raj S I'm an MBA student specializing in Supply Chain Analytics. I build data-driven tools that solve real supply chain problems — from demand forecasting to inventory optimization. Technical Skills Languages: R, Python ML & Forecasting: XGBoost, LightGBM, Prophet, ARIMA, ETS, TBATS,

Built a machine learning model to predict used car prices in the Indian market, achieving 65.59% R² score using XGBoost with Optuna hyperparameter tuning and SHAP explainability. Engineered 12 advanced features and compared 4 ensemble algorithms (XGBoost, CatBoost, LightGBM, RandomForest) on a cleaned dataset of 3,577 vehicles.

This project aims at predicting the evolution of ECB's deposit rate 12-month ahead using macroeconomic variables and models such as Linear Regression, Ridge Regression, Random Forest and LightGBM. We also use an ensemble stacking model to combine those models and "diversify" their predictive power based on learned weights. Programmed in R.

This is a project about predicting motel price in Da Nang city, Vietnam. It involves scraping data from 7 different websites and utilizing the following models: Random Forest regression model, LightGBM model. The evaluation metrics used for assessment are Root Mean Square Error, R-squared, Mean Absolute Error, and Mean Absolute Percentage Error.