Prediction of House Price Using Stacked LSTM Model

House price is used as an economic indicator. Prediction of house price is important as it can be helpful for those who want to buy or plan to buy a house in the future or near future. It can give them an idea of the future price. It is also be helpful for sellers who are willing to sell their house as they can understand the future trend of the price of their houses. House price prediction is beneficial for the investors who want to invest in real estate. It helps to know the future trends of the real estate of a locality. In this paper, we have used deep learning Long Short-term memory model to predict house price. This model performed quiet well. Here our main motive is to see how data size influences a deep learning model. In a single model we have used different data size (specifically 12 different data sizes) and compared the results. It is evident from the results that data size plays a significant role in our model’s performance.