HOUSE PRICE PREDICTION USING MACHINE LEARNING

This paper presented a machine learning-based house price prediction system using Random Forest Regressor. The proposed model achieved high accuracy and reliable predictions using a large housing dataset. The results show that the system can assist buyers, sellers, and real estate professionals in making data-driven decisions. The research confirms that Random Forest Regressor is an effective algorithm for regression-based prediction tasks in the real estate domain.

[1] Priya Gour, Sudhanshu Vashistha and Pradeep Jha, “Twitter Sentiment Analysis Using Naive Bayes based Machine learning Technique", 2nd International Conference on Sentiment Analysis and Deep Learning (ICSADL 2022), Springer - Advances in Intelligent Systems and Computing Series 1432, 2023. [2] H. Arora, G. K. Soni, R. K. Kushwaha and P. Prasoon, "Digital Image Security Based on the Hybrid Model of Image Hiding and Encryption", 2021 IEEE 6th International Conference on Communication and Electronics Systems (ICCES), pp. 1153-1157, 2021. [3] M. Jain, H. Rajput , N. Garg and P. Chawla, "Prediction of House Pricing using Machine Learning with Python", 2020 IEEE International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 570-574, 2020 [4] Himanshu Arora, Shilpi Mishra and Manish Dubey, "Development of the Framework for the Solution of the Security Problems in Data Transmission Involving Advanced Asymmetric Algorithm", International Journal of Emerging Technology and Advanced Engineering, vol. 8, no. 4, pp. 18-20, April 2018. [5] Tan F, Cheng C, Wei Z., "Time-Aware Latent Hierarchical Model for Predicting House Prices", In2017 IEEE International Conference on Data Mining (ICDM), pp. 1111- 1116, 2017.