Predicting Real-Time House Prices: A Machine Learning Approach Using XGBoost Algorithm

The housing market involves consideration of numerous factors by prospective buyers that may change over time. As a result, the market for real estate prediction will constantly fluctuate. Machine Learning is used to analyze and predict real estate property prices. The XGBoost algorithm is used to achieve a high accuracy in forecasting the market values. The Primary Objective is to assist users in finding a suitable price according to their preferred geographical factors. The model provides accurate predictions for future real estate prices by analyzing the previous trend and market. This study sought to predict prices in Bengaluru City using the XGBoost Algorithm. The results of this study showed that the XGBoost Model achieved a prediction accuracy of 91.77%. Such an accurate valuation tool could benefit homeowners and prospective buyers when pricing properties, without requiring the help of a real estate agent.