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Objective: to predict future payment behavior of clients from application, demographic and historical credit behavior data. | Data Source: Kaggle | The software analytics program: Python

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KSomkul/Predicting-Home-Credit-Clients-Repayment-Abilities

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Predicting-Home-Credit-Clients-Repayment-Abilities

Project Overview

The goal is to predict whether or not an applicant will be able to repay a loan based of their payment behavior and demographic.

Methodology

Part 1 - Defind

  • Why is borrowers repayment ability a problem?

Part 2 - Discovery

  • Load the data
  • Data Quality Check
  • Explore the data (EDA)

Part 3 - Develop

  • Engineer features
  • Encoding features
  • Split data to train and test set
  • Logistic Regression
  • Decision Tree
  • Random Forest
  • Gradient Boosting
  • ROC Graph
  • Confustion Matrix

Software Used

  • Python (Jupiter Notebook), packages; NumPy, Pandas, Matplotlib, Seaborn, Sklearn

  • Tableau

Data Source

Kaggle

About

Objective: to predict future payment behavior of clients from application, demographic and historical credit behavior data. | Data Source: Kaggle | The software analytics program: Python

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