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Predicting Credit Card Spend Using Decision Trees
This project is curated to help learners to apply decision tree regressor to predict credit card spend.
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Difficulty: IntermediateFoundational knowledge or experience in statistics or analytics is recommended.
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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
Flexible Scheduling
Start learning online immediately, at your own pace
Desktop Only
We recommend completing this project on a desktop
Skills You Will Learn
Associated Learning Tracks
- 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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