Credit Risk Modeling in Python

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Perhaps you have applied for a loan, or a credit card. You are aware that financial companies may process your information before they make a decision. They could lose their business if they approve you for a loan. This course will show you how to prepare credit application data. Machine learning and business principles will be used to reduce risk and improve profitability. Two data sets will be used in order to simulate credit applications. However, the business value must also be considered. Join me to learn more about credit modeling and the expected value.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Intermediate Python

Introduction to Python for Finance

What You Will Learn

In this course, you will learn how to prepare credit application data

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability

You will use two data sets that emulate real credit applications while focusing on business value

Course Content

Expand all sections

Module 1: Exploring and Preparing Loan Data

Module 2: Logistic Regression for Defaults

Module 3: Gradient Boosted Trees Using XGBoost

Module 4: Model Evaluation and Implementation

Course Instructors

Michael Crabtree

Data Scientist

Michael is a cross-functional data scientist and big data engineer at Ford. He has created several high-value analytical and data products spanning domains such as manufacturing, purchasing, finance,...