Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

Build multiple binary classification models like Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LGBM etc. and compare models performance. Choose best model for helping a telecom company combat customer churn.

Showcases work focused on utilizing statistical techniques and machine learning algorithms to forecast future trends and outcomes.

Visual analysis and diagnostic tools to facilitate machine learning model selection.

This project focuses on developing a predictive model using machine learning techniques to analyze a given dataset. The model is designed to provide accurate predictions based on historical data. It includes scripts for data preparation, model training, and making predictions, along with structured approach to project organization and documentation

Regression model building and forecasting in R

Lightweight and modular MLOps library targeted at small teams or individuals

Use a trusted LLM to evaluate new LLM's answers, given datasets and evaluation criteria

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

This project develops a predictive model to estimate diamond prices based on characteristics like carat, cut, color, and clarity. It covers data preprocessing, feature engineering, model selection, training, and evaluation. The final product is a web app where users can input diamond attributes to get accurate and instant price predictions.

R Package With Shiny App to Perform and Visualize Clustering of Count Data via Mixtures of Multivariate Poisson-log Normal Model

My portfolio of algorithms I used to analysize various shapes and forms of data with statistical and machine learning tools.

BAS R package https://merliseclyde.github.io/BAS/

ML hyperparameters tuning and features selection, using evolutionary algorithms.

Model BIC posterior probability

EvalML is an AutoML library written in python.

This is the website repository for the Stata packages lassopack & pdslasso. Please visit:

PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference

Measure and visualize machine learning model performance without the usual boilerplate.