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Predict Credit Default | Give Me Some Credit Kaggle

In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.
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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.

Prerequisites

  • R-Studio
  • Language used: R

Project Description

Banks often depend on credit score prediction models to approve or deny a loan request. A good prediction model is necessary for a bank so that they can provide maximum credit without exceeding the risk threshold. This data science project uses credit score dataset which has fairly large volume of data (250K). The predictive models will be build following various approaches - random forests, graident boosting and logistic regression. At the end of the project you will build a predictive model that will automatically score each applicant with a credit score which is human readable and easy to interpret.

Instructors

 
Shubham

Statistical Analyst SME

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

Curriculum For This Mini Project

 
  Problem Statement
00:03:35
  Classification vs Prediction
00:03:25
  Import Data Set
00:02:12
  Data Set Exploration
00:20:05
  Data Validation - Missing Values
00:38:02
  Outliers Overview
00:05:59
  Boxplot to Identify Outliers
00:01:39
  Quantile Method
00:09:27
  Replace Outliers
00:02:06
  What is Linear Regression?
00:15:59
  What is Logistic Regression?
00:06:13
  Convert Data Type to factor
00:13:01
  Build a Regression Model
00:11:09
  Model Summary
00:02:47
  Prediction
00:01:59
  Calculate Event Rate
00:07:20
  ROC Curve method
00:13:28
  Decision Trees
00:14:20
  Prediction using Trees
00:12:20
  Sampling Techniques
00:07:02