Abstract:
As the housing market grows, it's important to estimate pricing for both businesses and
individuals. Nonetheless, a number of variables influence changes in home prices.
Bangladesh is an overpopulated country, therefore a number of interrelated factors affect
the price at which real estate is sold.The property's amenities, location, and size are crucial
factors that could affect the cost.The objective of this investigation is to predict the prices of
property in Bangladesh by employing a variety of machine learning algorithms. In order to
guarantee precise predictions and robust model training, we assembled a comprehensive
dataset of 18,835 property listings from Bproperty & Bikroy.com. Random Forest, Support
Vector Machine (SVR), Decision Tree, XGBoost, CatBoost, and LightGBM are among the
algorithms implemented in this investigation. The performance of these models was
assessed using a variety of metrics, including R-squared, Mean Absolute Error (MAE),
Root Mean Absolute Error (RMAC), and Mean Squared Error (MSE). The CatBoost and
XGBoost models achieved the highest R-squared value of 91%, indicating superior
accuracy, but XGBoost performed slightly better with less RMSE, MAE, MSE value. While
the DecisionTrees model yielded the lowest R-squared value of 82%, indicating relatively
poorer performance. The value of our findings and the potential for future research in
property market analytics are underscored by the effectiveness of advanced machine
learning models, particularly CatBoost, in predicting property prices within the Bangladeshi
market. This information is valuable for stakeholders.
