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Create a sequential model

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Reference no: EM133228703

Need help with this dataset. Note: needed to use sklearn and numpy

1. Load breast cancer data from sklearn. [Hints: import sklearn; from sklearn.datasets import load_breast_cancer; breast_cancer = load_breast_cancer()].

2. Split the data into X_train, X_test, y_train and y_test. [Hints: from sklearn.model_selection import train_test_split; X_train, X_test, y_train, y_test = train_test_split(breast_cancer.data, breast_cancer.target, random_state=1). The parameter random_state=1 is for reproducibility

3. How to create one hot encoding for y_train and y_test.

4. How to create a sequential model with one hidden layer and one output layer.

5. Compile the network.

6. Fit the model using X-train and y_train.

7. Plot the training and validation accuracies and losses.

8. Scale the X_train and X_test using MinMaxScaler of sklearn.

9. How to create a new model using the scaled data.

10. Plot the accuracies and losses of the new model.

Reference no: EM133228703

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