Selecting the Ideal Algorithm for Car Price Prediction
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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)
The most appropriate algorithm for building a price prediction model with multiple independent variables is Multivariate Regression (Option D).
Multivariate regression is a type of regression analysis that involves multiple independent variables to predict a single dependent variable. In this case, the dependent variable is the price of the car, while the independent variables are horsepower, fuel type, city mpg, drive wheels, and number of doors.
Multivariate regression is used when there is a linear relationship between the dependent variable and each of the independent variables. The algorithm works by estimating the coefficients for each of the independent variables that can best predict the dependent variable. These coefficients are then used to create a linear equation that can be used to predict the price of the car.
Logistic regression (Option A) is used when the dependent variable is binary or categorical. For example, if the problem were to predict whether a car is expensive or not, then logistic regression could be used.
Decision trees (Option B) are used for classification problems, where the dependent variable is categorical, and the independent variables are both categorical and numerical. Decision trees work by recursively splitting the data into smaller subsets based on the most significant independent variable until a stopping criterion is met.
Naive Bayes (Option C) is a probabilistic algorithm used for classification problems, where the independent variables are categorical. Naive Bayes assumes that the independent variables are conditionally independent of each other given the dependent variable, which may not be true in this case.
In conclusion, Multivariate Regression is the most appropriate algorithm for building a price prediction model with multiple independent variables, given that the problem is a regression problem, and the independent variables are numerical.
