MLA-C01 Question 130
Single answerA retail company wants to implement a machine learning solution to predict the likelihood of a customer purchasing a specific product given their browsing history, demographic data, and past purchase behavior. Which machine learning algorithm would be the most appropriate for this business problem?
- A
Linear Regression
- B
Logistic Regression
- C
K-Means Clustering
- D
Principal Component Analysis (PCA)
Show answer and explanation
Correct answer: B
Explanation
The business problem requires a classification algorithm to predict a binary outcome (whether or not a customer will purchase a product). Logistic Regression is specifically designed for such tasks and is the most appropriate choice in this scenario. Other algorithms like Linear Regression, K-Means, or PCA are used for different purposes and do not directly address the requirements of this problem.
- A. Incorrect.
Linear Regression is used for predicting continuous numerical values, such as sales revenue or temperature. It is not suitable for classification tasks like predicting the likelihood of a purchase (a binary or categorical outcome).
- B. Correct.
Logistic Regression is a classification algorithm used to predict categorical outcomes, such as whether a customer will make a purchase (Yes/No). This makes it the most appropriate choice for the given problem.
- C. Incorrect.
K-Means Clustering is an unsupervised learning algorithm used for grouping data points into clusters based on their similarity. It is not suitable for predicting outcomes like purchase likelihood.
- D. Incorrect.
Principal Component Analysis (PCA) is a dimensionality reduction algorithm used to reduce the feature space of a dataset. While it can help preprocess the data, it is not directly used for classification tasks.