The phenomenon of the falling or rising of the house prices has attracted interest from the researcher as well as many other real-estate parties. Usually, House price index (HPI) represents the summarized price changes of residential housing. Since housing price is strongly correlated to other factors, prediction needs more accurate methods based on location, house type, size, built year, local amenities, and some other factors which could affect house demand and supply. With limited dataset and data features, a practical and composite data pre-processing, creative feature engineering method is examined in this project. The project is developed using random forest regression model and Gradient boosting regression model to predict individual house price. As a result, to explore various impacts of features on prediction methods, this project will apply both traditional and advanced machine learning approaches to provide an optimistic result for housing price prediction. A web based application using flask and python is also developed in this project.
₹10000 (INR)
NON IEEE -2022