Predictive House Prices Using Machine Learning and Techniques

Abstract

House price prediction is a crucial task in
the real estate sector for supporting
buyers, sellers, and investors in making
informed decisions. With the rapid
growth of data, machine learning
techniques have become effective tools
for analyzing housing market trends.
This project focuses on predicting house
prices using advanced models such as
Random Forest, Artificial Neural
Networks (ANN) including LSTM,
XGBoost, and Gradient Boosting by
considering important features such as
location, area, and number of rooms.
Data preprocessing techniques, including
normalization and feature selection, are
applied to improve model performance
and reliability. The models are trained
using historical housing price data and
compared with traditional approaches
such as Linear Regression. Ensemble
methods like Random Forest, XGBoost,
and Gradient Boosting enhance
prediction accuracy, while LSTM
captures sequential patterns in data.
Model performance is evaluated using
metrics like Mean Squared Error.
Experimental results show that the
proposed models achieve higher accuracy
compared to conventional methods. The
system provides a reliable, scalable, and
efficient solution for real estate price
prediction and decision-making