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-# PAPER TITLE
+# Analyzing Dropout Rates in Alcohol Recovery Programs: A Machine Learning Approach
 
 The current Github repository contains the main material that has been used in the paper “title”, by “Authors”. The paper has been focused on the application of a set of machine learning algorithms over a dataset of patients that is derived from the Electronic Health Records (EHR) of patients who received treatment at public addiction centers in Andalusia. The EHR system is managed by the Information System of the Andalusian Plan on Drugs (SiPASDA), which maintains a centralized dataset for all addiction centers. The EHR stores various information following the standards outlined by the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA, 2012).
 
@@ -13,7 +13,7 @@ The material in this repository consists in:
     * [explainability](https://medal.ctb.upm.es/internal/gitlab/compara/mldropoutalcohol/tree/master/code/shap): It contains X python files with the code that has been done to execute SHAP and get Explainability results.
 
 
-* [results](https://medal.ctb.upm.es/internal/gitlab/compara/mldropoutalcohol/tree/master/results):It contains the supplementary material referred to the results shown in the paper.
+* [results](https://medal.ctb.upm.es/internal/gitlab/compara/mldropoutalcohol/tree/master/results): It contains the supplementary material referred to the results shown in the paper.
     
     * [models_results.xlsx](https://medal.ctb.upm.es/internal/gitlab/compara/mldropoutalcohol/blob/master/results/models_results.xlsx): This excel file shows a summary of all the results for all the algorithms that have been tested. It contains the average results of the 10-fold cross-validation.