House Price Prediction Using Diverse Machine Learning Techniques

Abstract

With the rapid growth of society and evolving market demands, understanding market trends has become increasingly crucial. Accurate prediction of house prices based on current trends is vital for informed decision-making. It enables individuals to plan their financial needs effectively and align them with their goals. As a continually expanding industry, the real estate sector plays a significant role in this context. For investors, identifying market patterns is essential for making strategic investments that can maximize returns. However, the lack of transparency in real estate pricing, often influenced by inflated rates set by intermediaries, poses challenges for clients. The availability of extensive datasets has opened new possibilities for researchers to create predictive models with improved accuracy. Traditional models often face lower precision and overfitting issues, which reduce their effectiveness. In contrast, the proposed system addresses these challenges, offering a robust and efficient model complemented by an intuitive ui. The primary goal of this research is to create an all-encompassing solution that benefits both businesses and individuals, reducing manual efforts while saving time and money. This system utilizes several ML techniques, such as Linear Regression, Lasso Regression, and Decision Tree. These algorithms are integrated using the stacking technique to enhance performance and accuracy. The proposed approach aims to deliver a user-friendly and reliable tool that simplifies real estate decision-making while ensuring precise predictions.