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).
Algorithm | Jaccard | F1-score | LogLoss |
---|---|---|---|
KNN | 0.6 | 0.75 | NA |
Decision Tree | 0.57 | 0.73 | NA |
SVM | 0.72 | 0.84 | NA |
LogisticRegression | 0.74 | 0.85 | 0.56 |
By a narrow margin, Logistic Regression is the best performing model based on comparable metrics.