12-12-2022, 01:31 AM
Credit Risk Prediction Project From Scratch in Python
Project Based Learning on Machine Learning
New
Rating: 0.0 out of 5
(0 ratings)
726 students
48min of on-demand video
Description
This course consist of two parts: Problem statement explanation and Solution explanation with source code.
Part 1: 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 2: 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.
https://www.udemy.com/course/credit-risk-prediction-project-from-scratch-in-python/
Enjoy!
Project Based Learning on Machine Learning
New
Rating: 0.0 out of 5
(0 ratings)
726 students
48min of on-demand video
Description
This course consist of two parts: Problem statement explanation and Solution explanation with source code.
Part 1: 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 2: 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.
https://www.udemy.com/course/credit-risk-prediction-project-from-scratch-in-python/
Enjoy!