Here are some projects based on static methods of information processing. There are many different methods that you can use to process your information, depending on your needs.

a generalized linear models python library implementation from scratch

Machine Learning feature engineering through Scikit-Learn

Coursework, Stata code, and notes for PBHS 32700: Biostatistical Methods (Spring 2024, University of Chicago). Topics include contingency tables, logistic regression, Poisson and negative binomial models, and survival analysis using Kaplan-Meier, Cox, and parametric models. The course emphasizes categorical and time-to-event analysis using Stata.

Cite as: Alba C, M Mittal "Socio-cultural behavioral traits in modeling the prediction of COVID-19 infection rates" Journal of Humanities and Applied Social Sciences Vol. 3 No. 5, pp. 339-355.

Curso de Regression Models do Coursera

Benchopt benchmark for GLMs

Stochastic modelling of urban travel demand

Supervised Machine Learning algorithms for Regression in R and Python

Extended Elo rating system implementation based on the equivalence with logistic regression.

Hypothesis Testing Assignment - Ordered choice model. Using probit, logit and Poisson models to solve problems based on economic theory.

Previsione dei punteggi ottenuti nell'intero campionato NBA 2015/2016, utilizzando la regressione di Poisson che tiene conto della forza offensiva e difensiva delle squadre coinvolte in ogni partita

Estimate the relationship between the number of bikes shared around the London network on a given day, depending on weather and other characteristics to predict the capacity needed to satisfy demand at any given point. Requires R and familiarity with non-linear regression models

Convenient functions for fitting Poisson distributions, regressions and processes to data-sets

Poisson regression in R

Prototype models for FPL player performance predictions

This project aims to develop a Poisson regression model to predict the number of rings in a dataset. Poisson regression is a type of generalized linear model used for modeling count data, where the target variable represents the number of occurrences of an event within a given time or space. In this case, the target variable is the count of rings.