MLS-C01 Question 148
Single answerYou are building a recommendation system for an e-commerce platform. The goal is to predict whether a user will purchase a product based on features such as user demographics, browsing behavior, and product attributes. Which of the following algorithms is the most suitable for this classification problem?
- A
Logistic Regression
- B
K-Means Clustering
- C
Linear Regression
- D
Convolutional Neural Networks (CNNs)
Show answer and explanation
Correct answer: A
Explanation
Logistic Regression is the best choice for this scenario because it is a supervised learning algorithm designed for binary classification tasks. The problem involves predicting whether a user will purchase a product (a yes/no outcome), making Logistic Regression the most suitable algorithm. Other options, such as K-Means and Linear Regression, are not appropriate for this type of task, and CNNs are specialized for image and spatial data, which is not the focus of this problem.
- A. Correct.
Logistic Regression is specifically designed for binary classification tasks and works well when the relationship between the input features and the target variable is linear. It is suitable for predicting whether a user will purchase a product (yes/no).
- B. Incorrect.
K-Means Clustering is an unsupervised learning algorithm used for grouping data points into clusters based on similarity. It is not designed for supervised classification tasks like predicting product purchases.
- C. Incorrect.
Linear Regression is used for predicting continuous numerical outcomes, such as sales revenue or stock prices. It is not suitable for binary classification problems.
- D. Incorrect.
Convolutional Neural Networks (CNNs) are primarily used for image-related tasks, such as image recognition or object detection. While they are powerful, they are not appropriate for this particular structured data classification problem.