Learn how to predict car rental demand using machine learning. Discover the ideal features and labels to enhance your rental forecasting accuracy and optimize business strategies.
Table of Contents
Question
Your company rents cars and is developing a machine learning system to predict the number of daily rentals.
What would be the correct choice of features and labels for this scenario?
A. Label: brand of the car; feature: size of the engine
B. Label: day of the week; feature: number of cars available
C. Label: brand of the car; feature: day of the week
D. Label: number of car rentals; feature: day of the week
Answer
D. Label: number of car rentals; feature: day of the week
Explanation
The correct label for this scenario is the number of car rentals, and the correct feature is the day of the week. The day of the week indicates that different days might see varying rental demands due to weekend effects and holidays.
In machine learning, we differentiate between labels and features when building a model. Here’s a breakdown:
- Label: This represents the target variable we want to predict. In this case, the label is the number of car rentals, the value we are trying to forecast.
- Feature: These are the input variables that the model will use to make predictions for the label. The day of the week is a relevant feature because it can influence rental demand. For instance, weekends and holidays might see a surge in rentals compared to weekdays.
Other possible features include:
- Weather conditions: Sunny or rainy weather could influence rental decisions.
- Upcoming events: Local events or festivals might lead to increased or decreased rental needs.
- Historical rental data: Past rental trends for the same day or similar periods can provide valuable insights.
- Car characteristics: Factors such as car type (sedan, SUV), size, and price might influence rental choices.
Using the brand of the car as the label and the size of the engine as the feature is not the best choice. The label represents a categorical variable and predicting it would be a classification problem. In this scenario, the goal is to predict the number of rentals, so the label should be a continuous variable which is the number of rentals.
Using the day of the week as the label and the number of cars available as the feature is not the best choice. While the label (day of the week) is relevant to predicting rentals, the number of cars available is not the most suitable feature for this task. This scenario focuses on a regression problem, aiming to predict a continuous value (number of car rentals). The number of cars available does not directly influence how many people will rent cars on a particular day. It is more relevant to a classification problem, where we might predict if a car will be available for rent on a specific day based on existing reservations.
Using the brand of the car as the label and the day of the week as the feature is not the best choice. The label is categorical, making it suitable for classification problems. The scenario here involves predicting the quantity (number of rentals), so the label should be a continuous variable.
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