Predicting Credit Card Spend Using Decision Trees

This project is curated to help learners to apply decision tree regressor to predict credit card spend.

Build a Regression Tree for Predicting Spend on Credit Card
  • 2 bar graph
    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended.

  • Asset 1
    Duration: Approximately 2 hours

Case Overview

  • Credit cards or “plastic money” is a form of digital money lending service provided by banks and other financial institutions.
  • This card’s primary goal is to allow customers to borrow more money effortlessly and efficiently at checkout.
  • This ease may cause issues for customers as they must reimburse the banks the borrowed amount plus the specified interest rates.
  • This case explores how Credit Card companies utilize analytics to identify their worthy potential customers.

What’s included


Lifetime Access

Access this project for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Data Preprocessing

Data Management

Data Visualization

Data Aggregation

Feature Engineering

Oversampling Techniques

Data Tree Regressor

Machine Learning

Associated Learning Tracks

Case Context

  • The goal here is to have a better understanding of how consumers spend their money every month.
  • Utilize that information to determine which clients are eligible to be targeted for new credit card sales.
  • As spending is tied to Interchange related income, it is critical to assess a prospect’s spending potential.

  • Firstly, we visualize the data and several indicators to determine their correlations and associations among each other.
  • Classification model was built to predict new data class categories after splitting the data sample.
  • Decision tree algorithm was applied that are capable of learning by analyzing data patterns.

  • The case can be used by credit card companies to develop a predictive model for predicting customer satisfaction with credit card services and inviting prospective customers and offering them lucrative offers.
  • This case also be used by banking sector to devise marketing strategies that can be directed towards customer retention preventing profit loss.
  • The analysis can also assist the industry in evaluating their customer based on their expenditures and monthly saving.

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