MACHINE LEARNING CLASSIFIERS

The project aims to compare three machine learning classifiers—Naive Bayes, Random Forest, and Support Vector Machines (SVM)—to evaluate their accuracy, training time, and efficiency on a given dataset. The goal is to identify the most suitable classifier based on the dataset's characteristics.

Recommendation:
-For complex datasets with non-linear relationships, Random Forest or SVM are preferable.
-Naive Bayes is ideal for faster execution on smaller datasets.

The choice of classifier depends on the dataset’s complexity, with Random Forest offering the highest accuracy but a risk of overfitting, while Naive Bayes is the most time-efficient for simpler tasks.