MLS-C01 Question 140
Single answerA retail company wants to build a machine learning solution to predict the future sales of their products based on historical sales data, seasonal trends, and promotions. Which machine learning technique is the most appropriate for this use case?
- A
Classification
- B
Regression
- C
Forecasting
- D
Clustering
Show answer and explanation
Correct answer: C
Explanation
The problem involves predicting future sales based on temporal trends, which makes it a time series forecasting problem. Forecasting is the most suitable machine learning technique for this scenario as it specifically addresses the complexities of temporal data, including seasonality and trends, which are critical for accurate sales predictions over time.
- A. Incorrect.
Classification is used to categorize data into discrete labels or classes, such as predicting whether a customer will churn or not. It is not suitable for predicting continuous values like sales over time.
- B. Incorrect.
Regression predicts continuous values but does not specifically account for time series data and temporal trends, which are key factors in this use case. While it might work in simpler scenarios, it is not the optimal choice here.
- C. Correct.
Forecasting is designed explicitly for time series data where trends, seasonality, and temporal dependencies play a critical role. It is the most appropriate technique for predicting future sales based on historical data and trends.
- D. Incorrect.
Clustering is used to group similar data points together based on their features, such as segmenting customers by purchasing behavior. It is not relevant for predicting future values like sales.