Machine Learning Approach for House Price Prediction
M. Jagan Chowhaan
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.
D. Nitish
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.
G. Akash
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.
Nelli Sreevidya
*
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.
Subhani Shaik
Department of IT, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
In our ecosystem, real estate is clearly a distinct industry. Predicting house prices, significant housing characteristics, and many other things is made a lot easier by the capacity to extract data from raw data and extract essential information. Daily fluctuations in housing costs are still present, and they occasionally rise without regard to calculations. According to research, changes in property prices frequently have an impact on both homeowners and the real estate market.
To analyze the key elements and the best predictive models for home prices, literature research is conducted. The analyses' findings supported the usage of artificial neural networks, support vector regression, and linear regression as the most effective modeling techniques. Our results also imply that real estate agents and geography play important roles in determining property prices. Finding the most crucial factors affecting housing prices and identifying the best machine learning model to utilize for this research would both be greatly aided by this study, especially for housing developers and researchers.
Keywords: House price prediction, linear regression, machine learning