House Price Prediction in Taipei by Machine Learning Models.

House price in Taipei city is a widely discussed issue. Generally, the price is decided by people's understanding from past dealing-price. In the past decades, many researchers pay attention on the study of house price prediction by computer computation. Recently, different machine learning models are adopted to analyze the actual price registration dataset for predicting house price. This study examines linear regression, MLP (Multi-layer perceptron), and LSTM (Long Short-Term Memory) models on prediction of the actual price registration dataset. Various parameters and combinations are also test in our experiments. Experimental results show that LSTM deep neural network has better prediction than others. In the selection of optimizer, the Adam function exhibits better than SGD or RMSProp functions. In our limited experiments, single-layer deep neural network model leads to better results than different multi-layer deep neural network models. [ABSTRACT FROM AUTHOR]