Predicting Children's Talent Based On Hobby Using C4.5 Algorithm And Random Forest

Abstract

A person's talent is closely related to intelligence, hobbies, and interests. These factors are the best features to be used in a dataset to predict a children's talent, such as in an academy, arts, or sports. This research uses the C4.5 and random forest algorithms in 8 different models to predict a children's talent based on a dataset gained from a survey involving 1601 parents. Each model contains four training-testing data ratios, such as 50:50, 60:40, 70:30, and 80:20. We calculate each model prediction performance using 10-fold and 20-fold crossvalidation, with the accuracy, f-score, precision, and recall values as a comparison. The best result for the training evaluation we get is 91.5% for each comparison value from the random forest model (70:30 ratio) using a 20-fold cross-validation. For the testing evaluation, we get 92.7%, 92.8%, 92.8%, and 92.7% from the random forest model (50:50 ratio). The worst testing evaluation we get is 81.7% for each comparison value from the C4.5 model (50:50 ratio) using a 20-fold cross-validation. For the testing evaluation, we get 89.2%, 89.2%, 89.3%, and 89.2% from the C4.5 model (50:50 ratio).