# Airbnb Price Prediction Using Machine Learning and Sentiment Analysis

## TL;DR

This paper develops a machine learning-based model incorporating sentiment analysis to predict Airbnb rental prices, aiding owners and customers in price evaluation with minimal property information.

## Contribution

It introduces an integrated approach combining machine learning, deep learning, and NLP techniques for Airbnb price prediction, utilizing diverse features and models.

## Key findings

- Model achieves accurate price predictions across various algorithms.
- Sentiment analysis of reviews improves prediction accuracy.
- The approach supports better pricing decisions for owners and customers.

## Abstract

Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using machine learning, deep learning, and natural language processing techniques to aid both the property owners and the customers with price evaluation given minimal available information about the property. Features of the rentals, owner characteristics, and the customer reviews will comprise the predictors, and a range of methods from linear regression to tree-based models, support-vector regression (SVR), K-means Clustering (KMC), and neural networks (NNs) will be used for creating the prediction model.