NEST

Nest is a web application that is built to predict the cost of construction of a house with some features of the house. Our system uses the Streamlit library to develop the frontend of the webapp which consists of a login page a home page and a page for predicting the price of house.The datasets consists of textual and image data related to houses such as the number of bedrooms, bathroom, area, and zip code and images of the house bedroom, bathroom, kitchen, and frontal view are fed into the model and house price is predicted. The data is trained using a Multilayer Perceptron Model that has been built using the Keras Functional API.Dense model is used to extract features from text data textual data is passed through dense layers and saved as input model1. CNN model is used to extract features from image data and saved as input model2. The input model1 and input model2 are further merged together and again passed through Dense Layers and finally through the flatten layer. Training of the model is stopped using the early stopping callback method and the best model weights are saved and exported.