Year: 2025
Type: article
Abstract: Accurate real estate price prediction is a critical aspect in property investment, buying, and selling decisions. Machine learning (ML) offers a promising approach to modeling the complex and non-linear relationships between various factors influencing property prices. This study presents a Random Forest-based model trained on a dataset containing historical real estate transactions, location features, property attributes, and market trends. Our model achieved a high prediction accuracy and demonstrated robustness in diverse property conditions. We discuss data preprocessing, feature selection, model training, and evaluation techniques. The results indicate that ML algorithms can significantly enhance decision-making in real estate transactions. Index Terms - Real Estate, Price Prediction, Machine Learning, Random Forest, Data Preprocessing, Feature Engineering, Model Evaluation, Property Attributes, Regression, Location-Based Pricing.
DOI: 10.55041/ijsrem44306
Authors: Mrs. Kavitha K.S, Karthik G, Abhishek Abhishek, Meghana G, Akshatha R
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4409379931