Home / Current Issue / Paper 1708798
House Price Prediction Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Machine Learning
Abstract
House price prediction is an essential problem in the real estate industry. Accurately forecasting housing prices helps buyers, sellers, and investors make informed decisions. Traditional valuation methods lack precision and adaptability to dynamic market factors. This research paper explores machine learning techniques to predict house prices using multiple features like location, area, number of bedrooms, and amenities. Various regression algorithms, including Linear Regression, Decision Tree, and Random Forest, are implemented and compared. The Random Forest algorithm emerged as the best-performing model with high accuracy and low error rates.
Keywords
House Price Prediction, Machine Learning, Regression, Random Forest, Feature Engineering, Real Estate
References
[1] Kaggle - House Prices: Advanced Regression Techniques. https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques
[2] Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media.
[3] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
[4] Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
[5] Tan, P. N., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining. Pearson
[6] -./;<JKT`a—˜™š¢£¤îÜͽ¬½¬Ÿ½��vj[I7I"hI1lhI1l5�6�CJOJQJaJ"hI1lh'$¤5�6�CJOJQJaJh^~ h˜gCJOJQJaJh^~ CJOJQJaJh³sh½}OJQJh³sh³s6�OJQJ]�h½}h½}5�CJKHaJhh³s5�CJKHaJh!h³sh³s5�CJH*KHaJhh³sh³s5�CJKHaJhhI1lhnRâOJQJnHtH"h8Ðh½}5�CJ(OJQJ\�aJ("h³sh³s5�CJ(OJQJ\�aJ(./a˜™š34§¨µ¶ðæ×͸ª –†x [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]
[7] „dë¤^„gdnRâ$ &Fdë¤a$gd˜g $¤a$gdnRâ $¤a$gdI1l [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]
How to cite this paper
@article{1708798,
author = {Dipak Jadhav, Aadesh Ghule, Prof. M . D. Sarjare},
title = {House Price Prediction Using Machine Learning },
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {11},
pages = {1828-1830},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708798.pdf},
abstract = {House price prediction is an essential problem in the real estate industry. Accurately forecasting housing prices helps buyers, sellers, and investors make informed decisions. Traditional valuation methods lack precision and adaptability to dynamic market factors. This research paper explores machine learning techniques to predict house prices using multiple features like location, area, number of bedrooms, and amenities. Various regression algorithms, including Linear Regression, Decision Tree, and Random Forest, are implemented and compared. The Random Forest algorithm emerged as the best-performing model with high accuracy and low error rates.},
keywords = {House Price Prediction, Machine Learning, Regression, Random Forest, Feature Engineering, Real Estate},
month = {May},
}