You work as a machine learning specialist for a company that runs a car rating website. Your company wants to build a price prediction model that is more accurate than their current model, which is a linear regression model using the age of the car as the single independent variable in the regression to predict the price. You have decided to add the horsepower, fuel type, city mpg (miles per gallon), drive wheels, and a number of doors as independent variables in your model. You believe that adding these additional independent variables will give you a more accurate prediction of price.
Which type of algorithm will you now use for your prediction?

Answer options:

A.Logistic Regression
B.Decision Tree
C.Naive Bayes
D.Multivariate Regression 

Answer: D
Option A is incorrect. Logistic regression is used for problems where you are trying to classify and estimate a discrete value (on or off, 1 or 0) based on a set of independent variables. In your problem, you are trying to estimate a continuous numerical value: price, not a binary classification.
Option B is incorrect. A decision tree can be used as a classification algorithm or a regression algorthm, however, this problem involves multiple independent variables which leads us to the more relevant answer: Multivariate Regression.
Option C is incorrect. Naive Bayes is another classification algorithm, so it is not a good fit for your continuous numerical value prediction problem.
Option D is correct. You are trying to predict the price of a car (dependent variable) based on a number of independent variables (horsepower, fuel type, city mpg, drive wheels, and a number of doors, etc.) The Multivariate Regression algorithm is the best choice for this type of problem. (See the article Data Science Simplified Part 5: Multivariate Regression Models)
Reference:
Please see the Amazon Machine Learning developer guide titled Regression Model Insights, and the article titled Commonly Used Machine Learning Algorithms (with Python and R codes)