The purpose of this final project is train a set of supervised Machine Learning classifiers and determine which the best using the provided datasets: loan_train.csv and loan_test.csv, with the ultimate goal to applying what we've learnt in the course.

The two dataset, already split into training and test subsets, describes a set of loan takers/customers that either have not or have paidoff their loans. The target is to predict whether a customer will pay off or have their loan end up collected. The two cases are represented by the "PAIDOFF" and "COLLECTION" labels. Thus we are working with a binary classification problem.

The following classifiers are considered in this project:

• K-nearest Neighbours
• Decision Tree
• Support Vector Machine
• Logistic Regression

We evaluathe model's accuracy using the dataset stored in loan_test.csv and three metrics Jaccard score, F1 score and LogLoss(in the case of Logistic Regression).

By a narrow margin, Logistic Regression is the best performing model based on comparable metrics.