Tutorials

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.

Automatic machine learning concepts

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:

  1. Create project
  2. Visualize data
  3. Train models
  4. Evaluate the model
  5. Deploy the model

About the dataset: 

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 nameDetails
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
two months, … 8=payment delay for eight months, 9=payment delay for
nine months and above
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)

1. Create project

2. Visualize data

3. Train models

4. Evaluate the model

5. Deployment

Coming soon


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Machine learning in credit decisions

Leveraging machine learning for smarter lending and obtain insights into the technology behind 100% transparent machine learning models.

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