MLA-C01 Question 131
Single answerA retail company wants to predict the next purchase amount of its customers based on their historical shopping data. The dataset includes features such as customer demographics, transaction history, and product preferences. Which machine learning algorithm would be most appropriate for solving this problem?
- A
Linear Regression
- B
K-Means Clustering
- C
Decision Trees for Classification
- D
Principal Component Analysis (PCA)
Show answer and explanation
Correct answer: A
Explanation
The problem involves predicting a continuous numerical value (the next purchase amount), which makes it a regression problem. Linear Regression is a standard and appropriate algorithm for such tasks, as it identifies the relationship between input features and the target variable. Other options like K-Means Clustering, Decision Trees for Classification, and PCA are either unsupervised or used for other machine learning tasks, making them unsuitable for this scenario.
- A. Correct.
Linear Regression is a supervised learning algorithm used for regression tasks, making it suitable for predicting continuous numerical values such as the next purchase amount.
- B. Incorrect.
K-Means Clustering is an unsupervised learning algorithm used for grouping data into clusters, but it cannot be used for predicting continuous values.
- C. Incorrect.
Decision Trees for Classification are typically used for categorical target variables, not for predicting continuous numerical values like purchase amounts.
- D. Incorrect.
Principal Component Analysis (PCA) is a dimensionality reduction technique, not a predictive algorithm, and it is not suited for regression tasks.