A Review: Real Estate Price Prediction using Machine Learning with Research and Trends

The complex interactions between location, property attributes, prevailing economic conditions, and market trends define the real estate market, which is dynamic and prone to ongoing changes. In order to make educated judgments, buyers, sellers, investors, and legislators are among the stakeholders in the real estate ecosystem that understand how crucial precise price forecasting is. The goal of this project is to create a reliable and accurate real estate price prediction model by utilizing machine learning. The real estate market is known for its dynamism, which is influenced by a wide range of elements including location, property characteristics, prevailing economic conditions, and market trends. For many parties involved in real estate, including buyers, sellers, investors, and legislators, accurate price forecast is essential. The goal of this research is to use machine learning methods to build a solid and accurate real estate price forecast model. In order to conduct this study, we assemble a large dataset of historical real estate transaction records that includes a wide range of property qualities, geographic data, economic indicators, and market trends. To assure the data's quality and relevancy, we preprocess and sanitise it. In order to extract useful information from the dataset, feature engineering approaches are used. Exploratory data analysis (EDA) is then carried out to learn more about the distribution of the data and the relationships between the variables.