House price prediction web service using machine learning (P-0008) (MFN 8645)

Abstract

In this era, we know that many economic factors may have more or less impact on the real estate price variation. In addition, the banker and investor are also interesting to know the real estate price future change. Due to which house price prediction models has got a lot of importance. Different machine learning models have been used for this purpose resulting in more innovative and new ideas to gain more accuracy and less error. A model is created using machine learning which can predict the optimal price of house based on different features. Before creating the model, the dataset of Islamabad is collected or extracted. Factors including location, area, and number of bedrooms, number of bathrooms, garage, and number of dining rooms, lawn, garage, main road/street, servant quarter, interior exterior and condition of construction are considered. In addition, the web service is designed to provide easiness to the users and customers to get any kind of information related to house prices. The web service will be linked to the price prediction model and the database through which the user can get any kind of data. The web framework which is used in this project is Django due to its reliability and easiness. The model is composed