Industry Use Case

Identify Customers with Higher Likelihood of Credit Card Attrition

This project focuses on the foundational concepts of applying machine learning algorithms and application of decision trees.

Identify Customers with Higher Likelihood of Credit Card Attrition - Application of Decision Tree
  • 3 bar graph
    Difficulty: Advanced

    Designed for those with a technical background or industry experience

  • Asset 1
    Difficulty: Approximately 3 hours

Course Overview

  • Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
  • Due to today’s intense competition and cost-cutting measures, more businesses are beginning to prioritize customer relationship management (CRM). The unknowable future customer behaviors are crucial to CRM. Therefore, it is vital to anticipate client decisions so that the business may move accordingly as soon as possible.
  • Churners are typically referred to as customers who discontinue using the company’s products. Finding the churners can help companies retain the customers.
  • Learn how decision trees can be used to build the customer prediction model to understand the primary drivers underlying attrition and consumers at risk of attrition.

What’s included

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Lifetime Access

Access this case study for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend accessing this case study via desktop

Skills you will learn

Data Mining

Data Management

Data Aggregation

Data Visualization

Machine Learning

ML Model Evaluation and Selection using Decision Trees

Associated Learning Tracks

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