opardo Goto Github PK

academic_website

📝 Easily create a beautiful website using Academic, Hugo, and Netlify

adl-fall-19

atleta

OOP Python project. Hypothetical retirement plan for NFL players. Final project for the ITAM's Standards of Actuarial Practice class (Spring 2016).

banks

Web application made using Django. It optimizes the ROI of the investors, given the interest rate and the minimum and maximum investments for a group of banks. Author: Carlos Omar Pardo Gomez.

blacklivesmatter

NLP Analysis of tweets about the murder of black people, which gave rise to the #BlackLivesMatter movement.

dlmrstan

R package. Bayesian linear model in a time series context (dynamic beta for each observation). Uses RStan to develop the MCMC algorithm. Author: Carlos Omar Pardo Gomez.

dotfiles

My set of dotfiles. Includes Zsh, Bash, Git, iTerm, and OS X.

gpdpquantreg

R Package. Bayesian and nonparametric quantile regression, using Gaussian Processes to model the trend, and Dirichlet Processes, for the error. Author: Carlos Omar Pardo Gomez.

insidetheoffice

R project. Exploratory data analysis and visualizations from The Office series (NBC). Final project for the EDAV class. (Columbia University, Fall 2018).

kpishealth

Shiny app. Useful to explore trends in time series, particularly designed for KPIs estimated from survey data. Author: Carlos Omar Pardo Gomez.

modelosmatematicos

Repositorio personal de la clase impartida por el profesor José Luis Morales, ITAM 2015.

nyydl2019

What were the odds of the 2019 New York Yankees injuries?

pythonmlworkshop

Workshop part of the #DatatonXOberarzbacher series. We will focus on Applied Machine Learning in Python. Lecturer: Omar Pardo Gomez

soccerlearning

We are employing CNNs and RNNs to predict what team has the ball possession in a soccer game. Final project for the Applied Deep Learning class (Fall 2019).

thesis

Bachelor thesis: A Bayesian and Nonparametric Quantile Regression Model. Final PDF, TeX files and particular applications' code, using GPDP package. Author: Carlos Omar Pardo Gomez.

wars

Wes Anderson Recommendation System. Using Factorization Machines, finds the users who are more likely to enjoy the Wes Anderson's movies, as well as the right film for them. Final project for the Personalization: Theory and Applications class (Columbia University, Fall 2018).

website

📝 Easily create a beautiful website using Academic, Hugo, and Netlify