• Ammar Anwar
• Arbaz Ansari
• Siddhesh Ghadigaonkar
• Dr. Varsha Shah
• Prof. Shiburaj Pappu

Data mining is now commonly used in the real estate market. Real Estate is a clear industry in our ecosystem. The ability to extract data to extract relevant information from raw data makes it very useful to predict house prices, important housing features, and much more. Housing prices continue to change from day to day and are sometimes raised rather than based on calculations. Research has shown that fluctuations in housing prices often affect homeowners and the housing market. Literature research is done to analyze the relevant factors and the most effective models for predicting housing prices. The findings of this analysis confirmed the use of Artificial Neural Network, Support Vector Regression, and Linear Regression as the most efficient models compared to others. In addition, our findings also suggest that spatial and real estate agents are key factors in predicting house prices. This study will be of great benefit, especially to housing developers and researchers, to find the most important criteria for determining housing prices and identify the best machine learning model used to conduct research in this field.

House price prediction, Machine Learning, Linear regression

"House Price Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.f615-f619, April-2022, Available :http://www.jetir.org/papers/JETIR2204579.pdf

"House Price Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppf615-f619, April-2022, Available at : http://www.jetir.org/papers/JETIR2204579.pdf