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This project aims to develop a deep neural network that can accurately predict the price of a house based on a variety of factors such as location, square footage, and number of bedrooms.
The model will be trained on a dataset of historical housing prices and will use supervised learning techniques to make predictions on new data.
In this wiki, you will find information on the dataset used, the architecture of the model, and the training and evaluation process. You will also find instructions on how to use the model to make your own predictions, as well as information on how to contribute to the project.
Here's an outline of the sections in this wiki:
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Dataset: Information on the dataset used to train and evaluate the model, including a description of the data, the source of the data, and any preprocessing that was done.
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Model Architecture: A description of the neural network architecture used, including the number of layers, the types of layers, and the activation functions used.
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Training and Evaluation: Information on how the model was trained, including the training dataset, the loss function used, and the evaluation metric used.
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Using the Model: Instructions on how to use the trained model to make predictions on new data, including how to input data and how to interpret the output.
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Contributing: Information on how to contribute to the project, including guidelines for submitting bug reports, feature requests, and code contributions.
Thank you for visiting the Deep House Price Identifier Wiki. We hope this project will be useful for you and we will be happy to receive your feedback and contributions.