no se como lo hize pero se logro.

This analysis examines spatial autocorrelation patterns in synthetic raster data using the Getis-Ord Gi* statistic to identify hotspots and coldspots, and measures spatial inequality with the Gini index. It involves generating a synthetic raster (representing NDVI), visualizing it as an NDVI map, creating a shapefile, extracting raster values, computing spatial neighbors, calculating Gi* statistics, classifying hotspots, visualizing hotspots with a thematic map, and exporting outputs as shapefiles and rasters. The analysis highlights spatial clustering and inequality in a simulated dataset.

Integrantes: Oscar Maturana, Jovvanis Petro, Alan Petro, Angel Durango y Jose Julian Pacheco

This RMarkdown file contains the report of the data analysis done for the project on building and deploying a stroke prediction model in R. It contains analysis such as data exploration, summary statistics and building the prediction models. The final report was completed on `r date()`. **Data Description:** According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This data set is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relevant information about the patient.