For this self-paced course, we will explore the public dataset UCI ML data set from Taiwanese Bank. The dataset contains demographic data such as age, education gender, history of past payment and a target variable: default_payment_next_month.
We will explore possible risk factors derived from this dataset that could have been considered when giving out credit cards to the bank’s customer. More specifically, we will create a predictive model to determine what factors contributed to a passenger surviving. In part, this self-paced course will also be an overview of the Evispot ML platform.
You will learn how to load data, explore data details, generate visualizations, train a model, and view experiment results. As well, we will go through the automated documentation that you can generate right after a model is complete.
This self-paced course will focus on steps 1 – 4. We will cover other aspects and functionalities in other self-paced courses.
The typical Evispot ML workflow is to:
A public dataset UCI ML Data set from Taiwanese Bank has been used for this self-paced tutorial using the Evispot ML platform. The dataset contains 25 variables:
Variable name | Details |
---|---|
id | ID of each client |
limit_bal | Amount of given credit in NT dollars (includes individual and family/supplementary credit |
sex | |
education | 1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown |
marriage | 1=married, 2=single, 3=others |
pay_0 | Repayment status in September – April, 2005 -1=pay duly, 1=payment delay for one month, 2=payment delay for |
pay_2 | Repayment status in August, 2005 (scale same as above) |
pay_3 | Repayment status in July, 2005 (scale same as above) |
pay_4 | Repayment status in June, 2005 (scale same as above) |
pay_5 | Repayment status in May, 2005 (scale same as above) |
pay_6 | Repayment status in April, 2005 (scale same as above) |
bill_amt1 | Amount of bill statement in September, 2005 |
bill_amt2 | Amount of bill statement in August, 2005 |
bill_amt3 | Amount of bill statement in July, 2005 |
bill_amt4 | Amount of bill statement in June, 2005 |
bill_amt5 | Amount of bill statement in May, 2005 |
bill_amt6 | Amount of bill statement in April, 2005 |
pay_amt1 | Amount of previous payment in September, 2005 |
pay_amt2 | Amount of previous payment in August, 2005 |
pay_amt3 | Amount of previous payment in July, 2005 |
pay_amt4 | Amount of previous payment in June, 2005 |
pay_amt5 | Amount of previous payment in May, 2005 |
pay_amt6 | Amount of previous payment in April, 2005 |
date | the application date |
amount | the credit card amount |
default.payment.next.month | default payment (this is the variable we want to predict) |
© Evispot 2022 All rights reserved.
Leveraging machine learning for smarter lending and obtain insights into the technology behind 100% transparent machine learning models.
A link to download the file will be sent to your inbox.