Objective: Build a classifier to predict whether a loan case will be paid off.
You load a historical dataset from previous loan applications, clean the data, and apply different classification algorithm on the data. You are expected to use the following algorithms to build your models:
-
k-Nearest Neighbour
-
Decision Tree
-
Support Vector Machine
-
Logistic Regression
The results is reported as the accuracy of each classifier, using the following metrics when these are applicable:
-
Jaccard index
-
F1-score
-
LogLoss
This final project will be graded by your peers who are completing this course during the same session. This project is worth 25 marks of your total grade, broken down as follows:
- Building model using KNN, finding the best k and accuracy evaluation (7 marks)
- Building model using Decision Tree, finding the best k and accuracy evaluation (6 marks)
- Building model using SVM, finding the best k and accuracy evaluation (6 marks)
- Building model using Logistic Regression, finding the best k and accuracy evaluation (6 marks)
Load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.