Explanatory Model Analysis. Explore, Explain and Examine Predictive Models

Use AutoAI to detect fraud

Official implementation of the paper *PDE-Driven Spatiotemporal Disentanglement*

Machine learning to predict future number Covid19 Daily Cases (7-day moving average). Long Short Term Memory (LSTM) Predictor and Reinforcement Learning (RL) Prescription with Oxford Dataset

we proposed a software defect predictive development models using machine learning techniques that can enable the software to continue its projected task.

Supporting code for https://www.biorxiv.org/content/10.1101/796714v4

Portfolio of course work for my Master's in Data Science.

Plotly Dash HTML Python Flask site for user to interact with a trained machine learning model to predict the round-trip cost of flights — based on 9 million 2018 Domestic Flight Prices in the United States.

Course Project for UCSD ECE143: Programming for Data Analytics

Machine Learning Introductory Course

Recurrent Dynamic Graph Mapper using GNN

Mix of good tools for portfolio analysis

Trabajo de Fin de Grado del Grado de Ingeniería Informática, realizado en la Escuela Técnica Superior de Ingeniería Informática de la Universidad Politécnica de Madrid.

This project aims to analyze trends in the Paycheck Protection Program and to generate predictive models based on demographics to predict the likelihood of receiving a loan.

Reproduce Predictive Models of Fire via Deep learning Exploiting Colorific Variation (ICAIIC2019) with Pytorch

PRESS: Predictive State Smoothing in Python (tf.keras)

Our Economic Forecasting Model leverages Genetic Algorithms and Random Forests to provide farmers, policymakers, and businesses with cutting-edge insights for informed, profitable decisions in the ever-changing world of agriculture.

An explainable inductive learning model on gene regulatory and toxicogenomic knowledge graph (under development...)

This repository contains code for predicting the price of mobile phones using regression models. It includes the implementation of various regression techniques such as Random Forest Regression, Decision Tree Regressor, Linear Regression, and Lasso Regression.

A few-shot learning approach to forecasting the evolution of the brain connectome.