Prediction and Analysis of Flat Price in Dhaka Using Advanced Regression Techniques

Abstract

Prediction of flat costs could be a crucial space of realty. The literature tries to extract relevant info from historical real estate market data. So as to seek out models that are useful to flat patrons and sellers, machine learning techniques are wont to examine previous property transactions in Dhaka. It is clear that, Gulshan is the costliest area, Mirpur, Mohammadpur, Kallyanpur have similar price and Mohakhali is cheaper compare to other areas. To analysis this model I use different python’s library for instance numpy, pandas, matplotlib, seaborn. Scipy and the like. Additionally, tests show that the Advanced Regression Techniques, which rely on mean squared error assessment, are a competitive strategy. To make this endeavor more effective, I gave it everything I had and got a good final prediction, which is R-square 0.87 and normal distribution for error count.