This project was developed with the goal to study in which way kernel functions could better prediction results, and discover ways to fine-tune custom functions in order to to maximize the performance.

This work was done in the context of AA2 UPC class final project, oriented to kernel methods. Most of the work was done with Python’s sklearn and R for preprocessing.

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The final report with conclusions and interactive notebooks with used code can be found at the github repository.