House Price Prediction Using Machine Learning Algorithm

The proposed system successfully demonstrates an automated framework capable of accurately predicting house prices using machine learning algorithms. The system effectively analyzes various property features such as location, area, number of rooms, and amenities to identify pricing patterns and generate reliable predictions. It accurately captures complex relationships within the data, provides consistent estimation results, and produces structured outputs that simplify real estate decision-making. Experimental results show improved prediction accuracy, reduced error rates, and enhanced performance compared to traditional estimation methods. The outcome highlights the potential of integrating machine learning models to enable scalable real estate price prediction and data-driven decision support in modern property management and investment systems.