A Python package implementing informational complexity (ICOMP) criteria for regression models

Time Series Forecasting Methods to forecast Daily Post Publications on Medium

My research at NASA-JPL as a Student Intern presents a unique climate change simulation toolbox that merges multiple climate models and observations using Pseudo-Bayesian Model Averaging (BMA) and other techniques. It allows researchers to select optimal model weights for accurate forecasts and flexibility in addressing various climatic inquiries.

The project involves the multivariate regression analysis of a dataset.

Time Series Model of COVID 19 cases in Gotham City

Linear Regression Models on Montesinho Forest Fire

Applying stepwise selection in R Studio to forecast credit balances and stock market behavior.

Regression Analysis of Log Returns: Intel and Citigroup

The project involves the analysis and forecasting of time series on financial data.

Time Series Forecasting

University Project: using linear regression models to predict secondary market car prices based on a series of features. We will apply variable selection techniques and optimisation in attempt to build the best predictive model.

This project estimates a multiple linear regression of 50 startups and how their expenses on R & D, administration, marketing, and location can be significant or not to their profits.

The repository contains some of the work done by me and 4 colleagues for a university project of the "data analysis for business" class. The project aims at identifying the best deals and strategies to take by rental agencies to maximise profits in the Brazilian House Market. On the other hand, We also analyzed good deals for mid-income households.