Exploring House Price Forecasting through Machine Learning and Data Preprocessing

Predicting house prices accurately is crucial in real estate, influencing decisions for buyers, sellers, and investors. Machine learning has emerged as a potent tool in this domain, leveraging historical sales data, property features, and economic indicators to forecast future prices. However, the efficacy of machine learning models hinges on data quality, necessitating meticulous preprocessing steps such as cleansing, normalization, and feature engineering. Through techniques like data filtration and normalization, preprocessing refines data suitability for algorithms, enhancing predictive accuracy. In this study, we illustrate the transformative impact of preprocessing on predictive models' reliability. By contrasting accuracy tables for preprocessed and non-preprocessed datasets, we demonstrate the tangible benefits of preprocessing in refining predictive outcomes. The findings highlight the symbiotic relationship between machine learning algorithms and preprocessing techniques, emphasizing their crucial role in enhancing predictive capabilities in the dynamic real estate market landscape.