Published Paper Details:

Real estate is a significant investment, and it is essential to know the value of a property before investing hard-earned money. Machine learning techniques have been increasingly used to predict real estate prices in megacities like Mumbai, Chennai, Bangalore, and Pune. This paper focuses on the XGBoost regression algorithm, a powerful technique that can be used to predict housing prices with high accuracy. The XGBoost algorithm is an ensemble method that combines multiple decision trees to create a more robust and accurate model. It is particularly useful in handling non-linear relationships between input variables and real estate prices. The algorithm can efficiently process large datasets with multiple variables, making it an excellent tool for real estate prediction. This study highlights the importance of the XGBoost algorithm in predicting real estate prices accurately. It examines various input variables, including carpet area, number of bedrooms and baths, balcony, amenities, and area type, to build a reliable model. The XGBoost algorithm is evaluated based on various performance metrics, such as mean absolute error, mean squared error, and R-squared, to assess its accuracy and effectiveness. The results show that the XGBoost algorithm outperforms other machine learning techniques such as linear regression, decision trees, random forest, and neural networks, in predicting real estate prices. It can efficiently handle complex non-linear relationships and accurately predict the prices of properties in megacities like Mumbai, Chennai, Bangalore, and Pune. In conclusion, this study demonstrates the effectiveness of the XGBoost algorithm in predicting real estate prices. The algorithm can help investors make informed decisions by providing accurate predictions based on various input variables. The study emphasizes the importance of using advanced machine learning techniques like XGBoost for real estate investments, especially in megacities where property prices are highly volatile.