This predictive model, employing the powerful XGBoost (Extreme Gradient Boosting) algorithm, is based on the UCI ML Breast Cancer Wisconsin (Diagnostic) dataset.
The Ames Housing Price Prediction model, utilizing the XGBRegressor algorithm, is designed to predict the sales prices of houses. The model is trained on the Ames Housing dataset, which was compiled by Dean De Cock for the purpose of enhancing data science education. The dataset was obtained from openml.org, providing a comprehensive and valuable resource for developing and training machine learning models in the domain of housing price prediction.
To try this model, select Model 3 in the Privasea client and copy your feature vector as the X vector. The client will then encrypt this vector on your local machine using your locally stored client key, and the encrypted data will be transmitted to the Privanetix nodes.Subsequent inference operations will take place in the encrypted domain on the Privanetix nodes, and the encrypted result will be sent back to your client. Upon arrival, the result will be decrypted using your locally stored client key. The decrypted result will yield a numerical number representing the sale price of the house. You can then compare it with the tag in the provided sale price.
Double click on the cell > CMD or CTRL + C to copy data
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