Predict the survival of a horse based on various medical conditions such as number of surgeries, horses age and respiratory rate using decision trees and random forest classifier. We've made the data model-friendly using label encoding by assigning a unique numerical label to each category.
We've build tree decision which provided a transparent and understandable way of making decision on classifying the survival of horse based on the features of the data by following a hierarchical structure to attend nodes that have the final decision. The accuracy of the model has attained 0.66.
Then we decided to ameliorate the modeling by building a random forest classifier which is designed to improve the limitations of individual decision trees and enhance overall predictive performance which made the model's accuracy has augmented to 0.71.