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Exercise 2 Regression example
The goal of this exercise is to learn to train a neural network to perform a regression on a data set. The data set is Auto MPG Dataset and the go is to build a model to predict the fuel efficiency of late-1970s and early 1980s automobiles. To do this, provide the model with a description of many automobiles from that time period. This description includes attributes like: cylinders, displacement, horsepower, and weight.
https://www.tensorflow.org/tutorials/keras/regression
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Preprocess the data set as follow:
- Drop the columns: model year, origin, car name
- Split train test without shuffling the data. Keep 20% for the test set.
- Scale the data using Standard Scaler
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Train a neural network on the train set and predict on the test set. The neural network should have 2 hidden layers and the loss should be mean_squared_error. The expected mean absolute error on the test set is maximum 10. Hint: inscrease the number of epochs Warning: Do no forget to evaluate the neural network on the SCALED test set.