If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?
A . Unsupervised learning
B . Regressor
C . Classifier
D . Clustering estimator

Answer: B

Explanation:

Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.

Classification is the supervised learning task for modeling and predicting categorical variables.

Examples include predicting employee churn, email spam, financial fraud, or student letter grades.

Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.

Reference: https://elitedatascience.com/machine-learning-algorithms