A Machine Learning-Based Framework for Real Estate Price Prediction and Transaction Analysis Using Ensemble Techniques

Abstract

The real estate market is a complex and dynamic domain influenced by various economic, geographical, and structural factors. Accurate prediction of property prices is essential for buyers, sellers, investors, and policymakers to make informed decisions. Traditional valuation methods often rely on manual analysis and domain expertise, which may not effectively capture complex relationships within large datasets. This research proposes a machine learning-based framework for analyzing real estate transactions and predicting property prices using ensemble learning techniques.The proposed system utilizes a Random Forest Regression model to predict house prices based on multiple features, including location, property type, residential classification, assessed value, and listing year. Random Forest is chosen due to its ability to handle nonlinear relationships, reduce overfitting, and provide robust predictions. The system is implemented using Python and integrates data preprocessing, model training, prediction, and visualization within a graphical user interface developed using PyQt5. The dataset is preprocessed by removing missing values and encoding categorical variables using label encoding techniques. This ensures that the data is suitable for machine learning algorithms.The model is trained using a split dataset approach, where a portion of the data is used for training and the remaining for testing. The performance of the model is evaluated using the coefficient of determination (R² score), which measures the accuracy of predictions. The system provides an interactive interface that allows users to input property details and obtain predicted prices in real time. Additionally, it includes visualization features such as price distribution graphs, enabling users to analyze market trends.Experimental results demonstrate that the Random Forest model achieves high prediction accuracy and effectively captures the relationships between input features and property prices. The system offers a scalable and efficient solution for real estate price prediction.This research contributes to the field of real estate analytics by providing a practical tool for automated price prediction and transaction analysis. The proposed system can assist stakeholders in making data-driven decisions, reducing uncertainty, and improving market transparency. Future work can focus on integrating deep learning models and incorporating external factors such as economic indicators and location-based features to enhance prediction accuracy.