Machine Learning-Based Prediction of Home Prices

Housing market is a complex structure and is being growing rapidly. The price of any property depends on various factors and to correctly predict the price is a challenging part to perform. This project aims to develop a model which accurately predicts the house prices with the use of machine learning techniques, using a detailed dataset containing various housing features such as location, facilities, environment etc. The methodology consists of data preprocessing, feature engineering and model selection to achieve optimizable outcome. Implementation has been done of algorithms namely Linear regression, decision trees to captured the complex relationships and patterns within the generated predictions. We investigate the relationship between various house attributes and their corresponding selling prices Building a model that can unambiguously estimate the fair market value in case of a house based on a set of relevant features. The training and testing procedures was conducted for accessing the robustness of the model. The outcome not only contains precise predictions but also insights of the housing market dynamics. This project contributes to informed decision-making process for buyers, sellers and real estate professionals too.