Andres Segura-Tinoco's Projects
Free hands-on course with the implementation (in Python) and description of several computational, mathematical and statistical algorithms.
The README of my profile.
This is the current version of my personal website. It was create with the Beautiful Jekyll template.
Analysis and prediction of the impact of Covid-19 on certain diseases and key indicators in the Colombian health system. The project was approached with data science and machine learning techniques.
Project in which we explore different algorithms and compression approaches such as Huffman coding or entropy change. The distribution of the symbols of the compressed files (image, electronic book, etc.) is also analyzed.
Ongoing project that performs the regression of a continuous variable (oil rate) from a set of associated variables using different ML and DL techniques, such as linear regression, time series or neural networks.
Data Science Project with Python to analyze and discover insights of the detailed attributes of each player registered in the latest edition of FIFA 19 database.
Visual Analytics project to analyze and discovery insights of video game sales in recent years, with the high-level API plotly.express. The dataset contains a list of video games with sales greater than 100,000 copies.
Example of a CRUD web application, using the Django framework.
Descriptive text analysis of the words contained in the Dictionary of the Spanish language (DSL).
Visualización que muestra el consolidado de entradas de extranjeros a Colombia desde el 2012 hasta el 2018, discriminado por nacionalidad y género.
El presente proyecto tiene como objetivo principal, crear una visualización web que muestre la tendencia de la temperatura de la tierra a través del tiempo (1880-2018), para impactar y concientizar a las personas del Calentamiento Global.
Hugging face notebooks.
A simple app to choose randomly students for questions on my class
Practical course, which starting from Data Science offers examples (with Python code) and explanation (in Twitter threads) on concepts and techniques of Machine Learning, Deep Learning and NLP.
Examples of maps with Gmaps, which is a complement to Jupyter to visualize data on Google maps in Jupyter notebooks.
Data science project in R to create a predictive model of the CHD risk, based on supervised learning.
Data analytics project in R to create a predictive model for the detection of objectivity in sports articles, based on co-training.
Free hands-on course with the implementation (in Python) and description of several Natural Language Processing (NLP) algorithms and techniques, on several modern platforms and libraries.
This is the repository of the first version of my personal website.
El presente proyecto tiene como objetivo principal, crear una visualización web que permita identificar la dependencia que existe entre el precio del barril de petróleo y la producción diaria de petróleo en Colombia.
Visualización web de la producción petrolera fiscalizada mensual de Colombia para el año 2017, que permite analizar y agrupar dicha información por diferentes entidades (Departamento, Municipio, Operador y Campo).
Analysis of the interaction between Twitter users during the ACM RecSys 2021 and 2022 conferences.
Visualización del tipo Network que muestra los registros de obras protegidas por el Derecho de Autor (copyrights) realizado por solicitantes colombianos en el exterior para el 2 trimestre del 2018. Los datos están actualizados hasta el 10 Julio de 2018.
Recommender systems with collaborative filtering created with Apache Mahout framework. The system uses a Music Recommendation dataset for research purposes as input, but you can train it and predict recommendations with any other dataset.
Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.
Getting Started with Ruby on Heroku
Seriously Unnecessary Baffling Obscure Perplexing Terms In MAchine Learning
Program that preprocesses a collection of documents to calculate the frequency of the most common terms and identify the keywords of each document. The first time will do it without using the stemming technique and without removing the stopwords. The second time will use these techniques.
Program that use a direct graph and the PageRank algorithm (both simple and weighted). Use of the TF-IDF algorithm to calculate the relevance of the terms of the document collection.