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Built for the implementation of Keras in Tensorflow. Behaves similarly to GridSearchCV and RandomizedSearchCV in Sci-Kit learn, but allows for progress to be saved between folds and for fitting and scoring folds in parallel.
The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset
Various Regression models including linear, polynomial, ridge, lasso and elastic net were experimented with to find which model best predicted health insurance costs. The models were evaluated using cross-validation, from which the best models were optimized using randomized search. The best model was then evaluated on the test data.