Learn how a bike rental company can use historical data and machine learning to create a predictive model that estimates the daily rental demand for bikes.
Table of Contents
Question
A bike rental company can use historic data to train a model that predicts daily rental demand in order to make sure sufficient staff and cycles are available. True or False?
A. True
B. False
Answer
A. True
Explanation
A regression model can fulfil this task.
The correct answer is A. True. A bike rental company can use historic data to train a model that predicts daily rental demand in order to make sure sufficient staff and cycles are available. This is an example of using machine learning to create a predictive model based on historical data. Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. A predictive model is a mathematical function that estimates the probability or outcome of an event based on input data. Historical data is data that has been collected in the past and can be used to analyze trends, patterns, or relationships.
To train a predictive model, a bike rental company can use historical data such as weather, season, day of the week, holidays, events, and previous rental demand to identify the factors that influence the number of customers who rent bikes on a given day. The model can then learn the relationship between these factors and the rental demand using a machine learning algorithm such as linear regression, decision tree, or neural network. The model can then be tested and validated on new or unseen data to evaluate its accuracy and performance. Once the model is trained and validated, it can be used to predict the rental demand for future days based on the input data. This can help the bike rental company to plan ahead and optimize their resources, such as staff, cycles, inventory, and maintenance.
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