Feature Engineering and Machine Learning for Accurate House Price Prediction

Abstract

The objective of this investigation, "Feature Engineering and Predictive Modeling for Accurate House Price Valuation," is to develop a comprehensive method for determining the value of a house by utilizing state-of-the-art machine learning techniques. The paper argues in favor of the use of feature engineering to transform fundamental characteristics such as location, size, number of rooms, amenities, and area variables into predictive features. This will enable the utilization of housing datasets to identify valuable information. Some of the methods that are employed and tested to determine the most accurate valuation model include Gradient Boosting, Linear Regression, Decision Trees, and Random Forest. The primary objective of the paper is to enhance the precision of real estate forecasts and reduce the number of pricing errors, thereby enabling the foundation of decisions on data. The findings indicate that the model functions significantly more effectively as a result of the meticulous selection of factors and features. Therefore, it is feasible to develop instruments that are more precise and accessible to a broader audience for the purpose of estimating the value of a home.