MLA-C01 Question 132
Single answerA retail company wants to implement a machine learning solution to predict future sales for each of its stores based on historical data, seasonal trends, and marketing campaigns. Which machine learning algorithm is the most appropriate for solving this business problem?
- A
Linear Regression
- B
K-Means Clustering
- C
Random Forest Classifier
- D
Principal Component Analysis (PCA)
Show answer and explanation
Correct answer: A
Explanation
The task involves predicting a continuous numerical value (future sales), which is a regression problem. Among the given options, Linear Regression is the most appropriate algorithm for solving this type of problem because it is specifically designed for regression tasks. Other options either address different types of machine learning problems (e.g., clustering or classification) or are used for data preprocessing rather than prediction.
- A. Correct.
Linear Regression is suitable for predicting continuous numerical values, such as future sales, based on historical data and other features. It is the most appropriate choice for this scenario.
- B. Incorrect.
K-Means Clustering is an unsupervised learning algorithm used for grouping data into clusters. It is not suitable for predicting continuous numerical values like sales.
- C. Incorrect.
Random Forest Classifier is used for classification tasks, where the goal is to predict categorical outcomes. Predicting sales, which is a regression problem, is not a proper use case for this algorithm.
- D. Incorrect.
Principal Component Analysis (PCA) is a dimensionality reduction technique used to reduce the number of features in a dataset. While it can be used as a preprocessing step, it is not an algorithm designed for making predictions.