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. This study aims to propose a system for
“House price prediction” by using Machine Learning. This house price prediction
is an approach that can precisely estimate the price of a new house based on
its attributes using previous data on house features (such as square footage,
number of bedrooms and bathrooms, location, etc.) and their corresponding
prices.
To analyze the key elements and the best predictive models
for home prices, literature research was conducted. Five algorithms namely
linear regression, support vector machine, Lasso regression, Random Forest and
XGBoost have been applied in this study to predict house prices using a dataset
of real estate properties. Exploratory data analysis (EDA) was conducted for
the house price prediction project. The analyses' findings supported the usage
of artificial neural networks, support vector regression, and linear regression
as the most effective modeling techniques. Results also showed that Random
Forest and XGBoost can handle high-dimensional datasets, capture complex
relationships, and effectively manage feature interactions by their superior
performance. This study's 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.
Author(s)details:-
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.
Please See the book
here :- https://doi.org/10.9734/bpi/strufp/v3/202
