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Predict Credit Default using Random Forest, Logistic Regression and Gradient Boosting in R

Improve on the state of the art in credit scoring by predicting the probability that somebody will experience financial distress in the next two years.
4.84.8

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What will you learn

  • Data Preparation
  • Cool Visualization in R
  • Feature Engineering
  • Choosing Robust Machine Learning Algorithm
  • Logistic Regression
  • Random Forest
  • Gradient Boosting
  • Neural Network

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.

Project Description

Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit.

Instructors

 
Shubham

Statistical Analyst SME

"He is currently associated with International Store Analytics lab. He is passionate about analytics"