Credit Risk Detection and Prediction with Descriptive and Predictive Analysis
Build an algorithm that can effectively detect & predict credit risk!
Credit risk is always been a crucial factor for financial institutions. As the technologies have advanced, banks have started looking & researching effective ways to model credit risks. The progress in AI & ML has given the financial sector a new possibility. The latest machine learning techniques can now help people to create accurate & effective models that can detect & predict credit risk. The model can be based on the data set involving financial statements, defaulters, or any other parameter.
Considering its importance, we have released this e-book that will help you learn to perform descriptive & predictive analysis for detecting & predicting credit risk of the customer. With this e-book, you will explore the world of descriptive & predictive analytics which are also essential concepts in machine learning & data science.
Why This E-Book?
This e-book will be a one-stop solution for performing both detection & prediction for credit risk with data science & machine learning. Descriptive analytics will include different attributes of the data and understanding the data which respect to its meaningful features. While predictive analytics will help you to predict what might happen in the future. You will work with a dataset that will include customer details & the number of times they have defaulted the payment.
This E-Book Includes:
Knowing your data
Predicting the default rate
Functions for data visualizations & analysis
Exploratory data analysis
Functions for the contingency table
Implementing functions for data visualization
Box plots for credit risks