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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.

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.

Curriculum For This Mini Project

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