Abstract:
The overall aim of this study is to analyze the effectiveness of different machine
learning and deep learning models for apartment price prediction in terms of their
accuracy and error measures, so as to determine how well these models can predict
apartment prices. The experimental setup was done using exhaustive preprocessing and
utilized the Scikit-Learn, TensorFlow frameworks for training the model. Also
considered were Random Forest, Decision Tree, K-Nearest Neighbor, Gradient
Boosting, Extreme Gradient Boosting and Convolutional Neural Network (CNN),
Artificial Neural Network (ANN) among others. They also used evaluation indicators
like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared
Error (MSE), Accuracy (R²). It was found that the Extreme Gradient Boosting
Regressor had the highest accuracy of 97.53%, indicating its reliability in forecasting
apartment prices. Following this closely was Decision Tree Regressor with an accuracy
rate of 96.68%, proving that it is still efficient even though it is a simpler model. On
top of that, Random Forest Regressor has also shown good performance with an
accuracy score of 96.15% owing to its ensemble nature. Conversely; reduced prediction
accuracies were observed for both K-Nearest Neighbor Regressor and Artificial Neural
Network (ANN) having R² values at 87.55% and 89.73% respectively. Despite their
lesser accuracy, these models still offered useful insights and could be further
improved. The Convolutional Neural Network (CNN) demonstrated competitive
performance, suggesting its potentials on intricate patterns in the data. This study points
to the best fit of Decision Tree Regressor and Extreme Gradient Boosting Regressor
among other models for forecasting apartment prices. These results indicate that
selection of a model and preprocessing are important in predicting real estate prices
accurately.
