A Survey On Price Prediction Model for Airbnb listing using Machine Learning

Authors

  • Prajapati Pradip   M.Tech Computer Engineering Department, Silver Oak College of Engineering and Technology-Ahmedabad, Gujarat, India
  • Prof. Monali Suthar  Assistant professor ,Computer Engineering Department , Silver Oak College Of Engineering And Technology, Ahmedabad, Gujarat, India

DOI:

https://doi.org//10.32628/IJSRSET229228

Keywords:

Machine Learning, LASSO Regression, MSE, Tunning, Prediction, Gradient Boosting Algorithm

Abstract

People who rent their homes through Airbnb find it hard to figure out how much they should charge for them. They have to figure out how many people they can put in each home. Other than that, customers have to figure out the price of a home or apartment based on what they know about that home or apartment and what it has to offer them. A price prediction model is used with machine learning algorithms to add value for both the customer and the person who owns it in this study. To figure out the right price, I'll look at different features of the rentals and the traits of the owner. To make better predictions, I will also look at the reviews of different customers. To do this, I will use a lot of different techniques, like linear regression, tree-based models and the SVM technique.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-04-30

Issue

Volume 9, Issue 2, March-April-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prajapati Pradip , Prof. Monali Suthar, " A Survey On Price Prediction Model for Airbnb listing using Machine Learning , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 2, pp.167-171, March-April-2022. Available at doi : https://doi.org/10.32628/IJSRSET229228