The House Price Prediction using Machine Learning

Abstract

Machine Learning (ML) has profoundly impacted various domains, including speech recognition, healthcare, and automotive safety. Acknowledging its pervasive influence, our project aims to harness ML's capabilities for housing price prediction. In the volatile real estate market, prospective buyers strive to make informed decisions within budget constraints, often hindered by the absence of reliable future market trend forecasts. Our project's primary goal is to provide accurate house price predictions, mitigating potential financial losses. To achieve this, we are developing a housing cost prediction model employing ML algorithms such as Linear Regression, Decision Tree Regression, K-Means Regression, and Random Forest Regression. This model empowers individuals to invest in real estate without intermediaries. Our research highlights Random Forest Regression as the most accurate model, offering a promising avenue for confident real estate investment.