House Price Prediction Using the Concept of Machine Learning

This decade has seen a more rapid expansion of machine learning. Machine learning is constantly evolving, resulting in numerous applications and techniques. People frequently utilise the house price index to assess changes in home prices. Information is other than a house price index is required to anticipate single-family house prices because house prices are closely tied to other factors, such as location, area, and population. As a result, this study will combine traditional and advanced machine learning techniques to examine the differences between different advanced models and investigate the impact of various variables on prediction techniques. The proposed concept uses the characteristics or qualities of the homes, such as the number of beds, age, and transportation options from the location, as well as the educational facilities and shopping centres close by. A literature review is done to identify the key parameters and the best models for predicting house values. The results of the investigation justified the usage of linear regression using various machine learning algorithms.