Dec 2, 2022

Credit Card Risk Prediction with Machine Learning in Python - Machine learning Project Help

Updated: Dec 3, 2022

Credit Card Risk Prediction using Machine Learning

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We have created a complete playlist of machine learning and deep learning projects videos with detailed explanation. In this video we have explained how to create a machine learning model with python. While building the machine learning project our developer takes care that you will learn from these videos a lot of things like how to perform exploratory data analysis, how to handle missing data, outlier, data visualisation, how to prepare data for building the machine learning model etc.

In this article, we are talking about credit card risk prediction models. Here we will give you complete information about the credit card risk prediction model.

Credit card score is an important thing in the banking sector. Banks become much more careful when lending mortgages, credit cards payments or other commercial objectives. A bank can predict the default transaction with the help of accurate credit risk score. This will also help to determine which person qualifies for the loan at what interest rate and what credit card limits.

Project Idea

The model for credit card risk prediction has to be trained using a dataset that consists of data including data such as age, Ed, employ, address, income, debtinc, creddebt, otherdebt and default. For this project, we take this dataset from kaggle, link given in the below. This project will require training and testing the data model. After using data visualisation techniques, clean the data and handle the missing values. This project is an excellent means to learn how to build models such as random forest, support vector machine and logistic regression.

Dataset

To build the credit risk prediction model we have used a bank loan dataset. The data file bankloan.csv contains the information used to create the model. It consists of 1150 rows and 9 columns. The columns represent the variables, while the rows represent the instances.

The Dataset is composed of four concepts.

Data source
Variables
Instances
Missing values

This dataset uses the following 9 variables:

Age : Age of customer
Ed : Educational level
Employ: Work experience
Address : Address of the customer
Income : Yearly income of the customer
Debtinc : Debt to income ratio
Creddebt : Credit to debt ratio
Othdebt : other Debt
Default : Customer defaulted in the path (1-defaulted, 0-never defaulted)

In our explanation video of Data-driven credit risk prediction model using python, We cover techniques of exploratory analytics, data aggregation and cleansing, feature engineering, more importantly, model building and evaluation. We utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.

The Credit Risk Prediction Project is described in two videos part 1 and part 2.

Part 1 :- Title : Credit Risk Prediction Project Part 1 | AI ML Project Series

Description : This is the introduction part of the CREDIT RISK PREDICTION Project where we provide the details and procedures of the coming project that we will build in Part2 of this Project. This is based on prediction of defaulters in bank credit based on the data provided by the bank using past analysis. The result of this project will be that we will be able to forecast what are the chances of a person with certain credentials that will be a defaulter or a successful player.

Part 1 Video Link

Part 2 :- Title : Credit Risk Prediction Project Part 2 | AI ML Project Series

Description : This is the second part of the CREDIT RISK PREDICTION Project where we create a complete project on Kaggle Community Platform regarding prediction of Credit Failure of customers based on their credentials. We use data cleaning, data plotting and utilised Random Forest Classifier, Support Vector Machine and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.

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