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Predicting Credit Card Approvals

Build a machine learning model to predict if a credit card application will get approved.

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  • 12 tasks
  • 9,715 participants
  • 1,500 XP

Project Description

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this project, you will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.

The recommended prerequisites for this project are:

The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.

Project Tasks

  • 1Credit card applications
  • 2Inspecting the applications
  • 3Handling the missing values (part i)
  • 4Handling the missing values (part ii)
  • 5Handling the missing values (part iii)
  • 6Preprocessing the data (part i)
  • 7Splitting the dataset into train and test sets
  • 8Preprocessing the data (part ii)
  • 9Fitting a logistic regression model to the train set
  • 10Making predictions and evaluating performance
  • 11Grid searching and making the model perform better
  • 12Finding the best performing model
Sayak Paul

GDE in Machine Learning | Intel Software Innovator

Sayak is a GDE in Machine Learning, an Intel Software Innovator, and a Data Science Educator. Full-stack data science and machine learning interpretability are the subjects he loves the most. He enjoys applying deep learning to solve real-world problems. Sayak also blogs about a wide range of topics in data science and machine learning. Check out his site to find out more about him and how to contact him.

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  • Python LogoPython
  • Topics

    Data ManipulationMachine LearningImporting & Cleaning DataApplied Finance