In this project, we will complete a notebook where we will build a classifier to predict whether a loan case will be paid off or not. We load a historical dataset from previous loan applications, clean the data, and apply different classification algorithm on the data. We are expected to use the following algorithms to build our models: k-Nearest Neighbour, Decision Tree, Support Vector Machine, Logistic Regression The result is reported as the accuracy of each classifier, using the following metrics when these are applicable: Jaccard index,F1-score,LogLoass
danielwangxh/Machine-Learning-wiht-Python
In this project, we will complete a notebook where you will build a classifier to predict whether a loan case will be paid off or not. 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 LogLoass
Jupyter Notebook