Predicting Airbnb Prices in Dubai using Machine Learning

The Airbnb rental market in Dubai is dynamic and full of varying factors like location, equipment, and demand time shortages, which makes it challenging to have fair pricing for the different levels of Airbnb properties in Dubai. The goal of the project was to construct a powerful machine-learning model for the forecast of the Airbnb rental price in Dubai The method used by the project was systematic from the beginning to the end stages, which included the data collection from Kaggle, the data preprocessing which involved the handling of missing values, outlier elimination, data type conversion, and exploratory data analysis (EDA) which helped in obtaining insights into the distribution of listing attributes, the correlation between variables, and the market segmentation based on property characteristics. Subsequently, feature engineering solutions were applied, which allowed selection and transformation into new features such as amenities and property attributes in order to increase the model's precision. Out of the machine learning algorithms, namely Linear Regression, Random Forest Regression, Gradient Boosting Regression, and ensemble model trained and evaluated on the pre-processed data, it was the Random Forest Regression model that emerged as the best among the Hyperparameter tuning further improved the model's performance, So, it was clear that the adoption of optimization approaches boosted the performance of the model very much. The research report provides a very significant source of information for the proprietors, property managers, and investors in the Airbnb rentals market in Dubai, maintaining their decision-making rationale data-driven and strategy-based regarding rents.