Article
PREDICTING HOME PRICES: A BEGINNER’S JOURNEY WITH REGRESSION ANALYSIS USING THE BOSTON HOUSING DATASET
Accurately predicting home prices is vital for buyers, sellers, and real estate professionals, enabling informed decisions, property valuation, and market analysis. Machine learning has become popular for this task due to its ability to analyze complex data patterns and generate predictive models. Traditional real estate pricing relies on agents’ expertise, appraisers, market trends, and comparable sales data. However, this approach can be subjective, prone to errors, and may not fully account for all factors influencing prices or scale well for large datasets. Machine learning offers a data-driven solution to improve prediction accuracy and provide deeper insights into the housing market. The challenge is to use regression analysis to predict home prices accurately. The goal is to develop machine learning models that outperform traditional methods, offering reliable predictions and actionable insights for real estate stakeholders. The proposed system uses the Boston Housing Dataset, a widely used dataset in machine learning, to train and evaluate regression models. Features like crime rate, zoning, and proximity to employment centers are used to predict home prices. Regression algorithms such as Random Forest Regression and XG Boost Regression will be employed to build these models. By leveraging these advanced techniques, the system aims to enhance prediction accuracy, improve decision-making, and provide valuable insights into market trends, addressing the limitations of traditional methods and meeting the growing demand for datadriven approaches in the real estate industry.
Full Text Attachment
