"NTTDATA" - - Optimization of Hospital Spending

This project follows the taglines established by NTT data for their challenge presented to the UPC Datathon of 2023.

This project is split into two parts: an analysis of the purchases to find ways to optimize spending and a second one dedicated to the creation of a prediction model capable of predicting the fluctuation of prices during the following year. During the first part, the analysis was based on the 'TIPOCOMPRA' variable to find its effects on the prices of the purchases and what could be better for the hospitals going forward. During the second part, the prediction model was based using a modified data file after applying k-clustering to the types of products and the hospitals. This prediction model is based on a random forest regression model.

This project's team members are: Mireia Fernández, Laia Álvarez, Alexandra González and Rodrigo Bonferroni.