For this project I created a python Web App that uses a TF-IDF model of the genres and keywords of movies and cosine similarity, that measures the similarity between two non-zero vectors, to recommend five movies to the user, given a movie the user likes. For this task, I used a dataset from TMDB (The Movie Database) that contains 5000 movies, with their respective genres and keywords, between the year 1916 to 2017.

For this project I created a classifier to correctly identify the religion of a country given its geographical location, its area in square kilometers, its population in millions of people, its language and the characteristics of its flag such as its color and the shapes that appear on it, like: circles, crosses, sunstars, crescents, saltires, triangles, etc… For this task, I used the flags dataset from the repository of Machine Learning of the University of California Irvine which contains information of 194 countries.

For this project I analyzed data from the US National Parks Service about endangered species in different parks. Taking the role of a biodiversity analyst, the goal is to ensure the survival of at-risk species to maintain the level of biodiversity within the parks. In that sense, I will identify if there are any patterns in the types of species that become endangered and their relationship to the National Parks.

For this project I analyzed data on the Growth Domestic Product (GDP) and Life Expectancy from the World Health Organization and the World Bank, to try and identify the relationship between the GDP and the life expectancy of six countries. GDP is the total monetary or market value of all the finished goods and services produced within a country’s borders in a specific time period. In that sense, being a broad measure of overall domestic production, it functions as a comprehensive scorecard of a given country’s economic health.