Customer Churn Prediction Model
Question
You are a machine learning specialist at a music subscription company.
Your company needs to understand its customer churn rate better.
They plan to leverage this information to spend their marketing budget on attempting to retain customers that are likely to leave the service in the near future.
Your task is to group your customer subscribers into categories based on which subscribers may or may not cancel their subscription in the near future (3 months)
You have already performed data engineering and have subscriber data that is labeled.
Which type of model is best suited to your task?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect.
The key to this scenario is that you have labeled data, and you are trying to group your customers into categories.
The linear regression algorithm type is better suited to predicting a numeric/continuous value, such as estimating the value of a house.
Option B is incorrect.
Clustering is best suited to grouping similar objects when you have unlabeled data.
You have labeled your data.
Option C is correct.
The classification algorithm type is best suited to grouping similar objects when you have labeled data.
This is the case described in the scenario.
Option D is incorrect.
The unsupervised learning algorithm type is best suited to clustering, dimension reduction, pattern recognition, and anomaly detection.
These types of algorithms are used when you have unlabeled data.
You have labeled your data.
References:
Please see the Amazon SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html),
The Amazon SageMaker developer guide titled Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html#algorithms-built-in-unsupervised-learning)
The task at hand requires categorizing subscribers into two groups - those who are likely to cancel their subscription in the near future and those who are not. The given dataset is already labeled which means that it has a target variable that indicates whether a subscriber has churned or not.
Linear regression (option A) is not well-suited for this task as it is a supervised learning model that is best used for predicting continuous numerical values, not binary outcomes. Therefore, it would not be able to categorize the subscribers into the two groups that we require.
Unsupervised learning (option D) is also not the best choice as it does not use labeled data and is typically used to identify patterns or groupings in data that are not pre-defined. It is not suitable for this task as we already have labeled data and a clear goal of categorizing subscribers into two groups based on their likelihood of churning.
Clustering (option B) is a type of unsupervised learning algorithm that groups data points based on their similarities. However, it is not the best choice for this task as we already have labeled data, and we need to predict the likelihood of churn. Clustering can be used to find patterns in unlabeled data, but it does not give us information on whether a customer is likely to churn or not.
The best choice for this task is Classification (option C), a supervised learning model used to predict binary outcomes such as whether a subscriber is likely to cancel their subscription or not. Classification algorithms take in labeled data and use it to learn how to classify new, unlabeled data. By using the labeled data, a classification model can learn patterns and features that differentiate customers who are likely to churn from those who are not.
Therefore, option C - Classification - is the best choice for this task.
