Abstract:
In the ever-changing real estate landscape, a comprehensive understanding of house
prices is crucial for buyers, sellers, and investors alike. While the traditional evaluation method
still serves well, the growing availability of datasets has opened a new way to predict house
prices. This paper will explore the use of predictive analysis algorithms to find house prices
based on data-driven insight to improve the decision-making process when trying to invest or
buy a house.
The study discovered that using a predictive algorithm model, the application managed
to predict the house price with a commendable level of accuracy. By processing the vast dataset
into moderately usable data and comparing several predictive analysis algorithms to find the
best result, the machine learning model was able to find and indicate the correlation between
data in a large-scale dataset resulting in a good prediction for the house prices. With accurate
house price predictions in hand, now users can approach any house transaction with better
clarity and knowledge, ensuring better financial decisions.
