Abstract
Abstract: Forecasting the appropriate house pricing for real estate customers while taking into consideration their priorities and financial situation is the goal. By examining previous market patterns, price ranges, and approaching changes, future prices may be predicted. Research indicates that house price discrepancies are a common source of concern for both homeowners and the real estate industry. Several interrelated factors influence the price at which real estate sells in places like Bengaluru. The size, location as well as and amenities of the property are significant considerations that could have an impact on the price. The analysis' findings supported the use of boosting algorithms like Extreme Gradient Boost Regression (XG Boost), Support Vector Regression, Multiple Linear Regression (Least Squares), and Machine Learning Lasso and Ridge regression models among other regression techniques in modelling explorations
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