Flat Price Prediction using Linear and Random Forest Regression based on Machine Learning Techniques - Jui, Julakha Jahan; Molla, M. M. Imran; Bari, Bifta Sama; Rashid, Mamunur; Hasan, Md Jahid (Springer Singapore, 2020)
Flat price prediction is an important topic of real estate. Flat price in a city depends on different criteria such as, the crime rate of that location, total populations on that area, number of bedrooms, bathrooms, the total size of the flat, location of the flat, etc. People feel confused and face different harassments with unreliable information during purchasing a flat in a city. By taking consideration of this scenario, we have proposed here flat price prediction framework. In this study, we have used our own data set that we have collected from Dhaka, Bangladesh. Two regression algorithms namely the linear regression and the regression tree/random forest regression have been used for building the prediction model. We have also checked the validity of the model using boxplot analysis, residual analysis, error checking and cross-validation. Finally, the performance of two methods has been compared which shows that the random forest regression model gives the best prediction result than the linear regression model.