Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring

Abstract

Apartment rental prices are influenced by various factors. The aim of this study is to analyze the different features of an apartment and predict the rental price of it based on multiple factors. An ensemble learning based prediction model is created to reach the goal. We have used a dataset from bProperty.com which includes the rental price and different features of apartments in the city of Dhaka, Bangladesh. The results show the accuracy and prediction of the rent of an apartment, also indicates the different types of categorical values that affect the machine learning models. Another purpose of the study is to find out the factors that signify the apartment rental price in Dhaka. To help our prediction we take on the Advance Regression Techniques (ART) and compare to different features of an apartment for establishing an acceptable model. The following algorithms are selected as the base predictors -- Advance Linear Regression, Neural Network, Random Forest, Support Vector Machine (SVM) and Decision Tree Regressor. The Ensemble learning is stacked of following algorithms -- Ensemble AdaBoosting Regressor, Ensemble Gradient Boosting Regressor, Ensemble XGBoost. Also, Ridge Regression, Lasso Regression, and Elastic Net Regression has been used to combine the advance regression techniques. Tree-based algorithms generate a decision tree from categorical 'YES' and 'NO' values, Ensemble methods to boosting up the learning and prediction accuracy, Support Vector Machine to extend the model for both classification and regression approach and lastly advance linear regression to predict the house price with different features values.