A Python library for amortized Bayesian workflows using generative neural networks.

Model BIC posterior probability

An IDE-agnostic Software model comparison tool that allows the user to choose the type of matching; similarity based or signature based, and specify the similarity threshold, modelling language of the models to be compared, and the granularity of comparison.

This repository provides a practical implementation of the Friedman and Nemenyi statistical tests specifically tailored for multi-label classification problems. These techniques are essential for evaluating and comparing the performance of multiple algorithms in experiments where each instance may belong to several classes simultaneously.

Explore advanced neural networks for crafting captivating headlines! Compare LSTM 🔄 and Transformer 🔀 models through interactive notebooks 📓 and easy-to-use wrapper classes 🛠️. Ideal for content creators and data enthusiasts aiming to automate and enhance headline generation ✨.

Probabilistic Programming and Nested sampling in JAX

This project demonstrates the use of both Linear and Polynomial Regression models to predict salaries based on the position level of employees. By using a dataset that contains position levels and their corresponding salaries, this project showcases the differences between linear and polynomial models in fitting data and making predictions.

Modular Assessment of Rainfall-Runoff Models Toolbox - Matlab code for 47 conceptual hydrologic models

Repositorio para el curso intersemestral "Temas Selectos en Estadística" para la Facultad de Psicología, UNAM.

💸 Predicting U.S. Retail Sales from FRED Data using Neural Networks

loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)

Goal is to identify an appropriate pipeline to obtain good performance on the dataset, using LSTM neural network approaches.

This project analyzes Brent oil prices from 1987-2022, detecting structural changes and associating them with major events to provide data-driven insights for the energy industry.

This project focuses on the prediction of lung cancer using multiple machine learning models and comparing their performances. The dataset used for this project consists of various features related to lung cancer, which were preprocessed, normalized, and then used for training different classifiers.

This project includes a python script that creates graphs by reading data from CSV files of models trained with YOLO.

Solution in the form of a tutorial article wherein the key decisions made in conducting a CFA are validated through recent literature and presented within a dynamic document framework.

A project performing NLP sentiment analysis on social media sources about the Russia-Ukraine war and making recommendations to improve Ukraine's image.

End-to-End Machine Learning project I made as a machine learning intern @ Mentorness

The goal of the project is to predict Life Expectancy using various factors and to determine the relationship that exists between them.

This project develops a predictive model and a decision support system for evaluating the risk associated with Home Equity Line of Credit (HELOC) applications. It features an interactive interface for financial institutions, integrating multiple models for transparent and effective decision-making.