House Price Prediction Using LSTM
| Autor: | Anand, Akhila, Benjamin, Jetty |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| DOI: | 10.5281/zenodo.7949678 |
| Popis: | —Predicting house rents accurately is challenging due to the complex relationships between input features such as the floor, location, and amenities. The algorithm is compared in terms of Mean Squared Error. This paper proposes an approach that uses LSTM neural networks to predict house rents based on amenities. We utilise the mean absolute error (MAE) and the root mean square error (RMSE) to assess the performance of our model. And findings demonstrate that LSTM neural networks can effectively capture the complex relationships between various input features and accurately predict house rents. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|