Data Science Project to analyze and discover insights of the attributes of each player registered in the latest edition of FIFA 19 database. Most of the project was done with Jupyter Notebook, so that the reader can see and understand the code implemented.
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Objectivity Detection with R
Detection of objectivity in sports articles, based on co-training
Currently, many sports articles are published daily on the Internet, by various authors, which are often written objectively, on other occasions subjectively, which may not please the reader or change your perception of the facts.
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Prediction of CHD Risk with R
CHD Risk Prediction based on Supervised Learning
In the current era in which we live, there is a clear and irreversible tendency to generate and store large volumes of information, from various sources such as: government agencies, public and private companies, clinics and hospitals, social networks, etc.
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Recommender Systems for Last.fm
User-based and item-based models
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. This project explores the calibration and accuracy of user-based and item-based models.
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Recommender Systems with Surprise
Collaborative filtering approach
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.
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