Victor Cuspinera

Victor Cuspinera

Data Scientist with Actuary and Economics background.

Projects

Project development course

Published:

In January 2022, I start teaching the Project Development course of the Master in Data Science at UDG. I took this as a Medium-term personal project and, after two years of inherit this course, I selected different data science material and tools for the students to learn and use it for future projects and, in some cases, for their thesis. These material includes collaborative yhe use of version control software GitHub, an overview of Markdown and User Interface commands, perform EDA and use tools for web scraping, interactive graphics, text analysis, wordclouds and maps with Python.
$\bigstar$ Click here o see the GitHub public repository with the material of the course.

Choosing a baby name using Python

Published:

Getting a name for a baby is not as trivial as people could think, or at least not for my wife and me. We look for names for our baby girl everywhere and we pick some options. However, I started to wonder if we were looking in the right places and if there could be a way to measure which would be the best name for our daughter. So, I found three databases with names from Spanish and English-speaking countries, analyzed trends and top frequent names, and wrote down a list of all possible names. Finally, I developed an tool that transforms the names using the International Phonetic Alphabet and measures how well a first name sounds with a middle name and/or family names in both English and Spanish, by returning a score to make it easier finding possible names for a baby.
$\bigstar$ Click here o see the GitHub repository with the complete analysis of this project.

Canada response to COVID-19

Published:

The idea of this project was born on March 11th of 2020, when the Prime Minister of Canada, Justin Trudeau, announced a series of policies to help Canadians cope with the COVID-19. The objective is to measure the impact of the Government policies to help Canadians cope with the COVID-19, studying people’s perception by making sentiment analysis on users’ tweets, before and after the Prime Minister’s announcement.
$\bigstar$ Click here to look at the original GitHub repository of this project.

Guide to use Haver Analytics

Published:

Haver Analytics is a company that collects Economic, Financial and Monetary time series in real time from international and official primary sources as Central Banks and Governments. Some institutions, as Banco de Mexico, have a subscription to consult the Haver Analytic’s databases. The aim of this project is to share a Guide to use Haver Analytics, as R bookdown, which initial objective is helping my colleagues from Banco de Mexico to retrieve information from these databases using R, Python and tools developed by Haver.
$\bigstar$ Click here to see the Guide to use Haver Analytics.

Guide and tips to use SIE API

Published:

Banco de Mexico publishes the archive of national economic databases through the Economic Information System, known as SIE. Also, this Central Bank has developed an API that allows Developers, Analysts and Researchers to consult automatically these time series. To complement this effort, as a personal project, I prepared a guide to disclosure the SIE API, explaining how to retrieve information using the siebanxicor R package developed by Banco de Mexico, as well as a custom function to explore the selected series and a Dashboard to look into currency time series.
$\bigstar$ Click here to look the GitHub repository with the complete guide, tips and dashboard to use the SIE API with R.

Feature selection - R and Python packages

Published:

If you’ve ever encountered a dataset with a myriad number of features, you know it could be very difficult to work with all of them. Some features may not even be important or relevant and could even cause optimization bias. One approach to this problem is to select a subset of these features for your model. Feature selection will reduce both complexity and time training an algorithm, as well as improve the accuracy of your model – if we select them wisely. However, this is not a trivial task and to that end we have created two packages addressing the feature-selection, in Python and R programming languages.
$\bigstar$ In the next links you can access to the original feature-selection sites: package for Python and package for R.

Bike Sharing Machine Learning Model

Published:

Demand forecasting is an important aspect for many companies in carrying out their operations. In this project we analyze the bike-sharing dataset from UCI Machine Learning Repository, and build a regression learning machine model using the Random Forest Regressor algorithm to predict the count of bike rentals based on time and weather-related information. For this project we use Docker, making it easier to replicate the analysis.
$\bigstar$ Click here to look at the original GitHub repository of this project.