Branch-AI/one_exercise_per_file/week03/day03/ex02/audit/readme.md

1. This question is validated if the input DataFrames are:

X_train_scaled shape is (313, 5) and the first 5 rows are:

	cylinders	displacement	horsepower	weight	acceleration
0	1.28377	0.884666	0.48697	0.455708	-1.19481
1	1.28377	1.28127	1.36238	0.670459	-1.37737
2	1.28377	0.986124	0.987205	0.378443	-1.55992
3	1.28377	0.856996	0.987205	0.375034	-1.19481
4	1.28377	0.838549	0.737087	0.393214	-1.74247

The train target is:

	mpg
0	18
1	15
2	18
3	16
4	17

X_test_scaled shape is (79, 5) and the first 5 rows are:

	cylinders	displacement	horsepower	weight	acceleration
315	-1.00255	-0.554185	-0.5135	-0.113552	1.76253
316	0.140612	0.128347	-0.5135	0.31595	1.25139
317	-1.00255	-1.05225	-0.813641	-1.03959	0.192584
318	-1.00255	-0.710983	-0.5135	-0.445337	0.0830525
319	-1.00255	-0.840111	-0.888676	-0.637363	0.813262

The test target is:

	mpg
315	24.3
316	19.1
317	34.3
318	29.8
319	31.3

2. This question is validated if the mean absolute error on the test set is smaller than 10. Here is an architecture that works:

# create model
model = Sequential()
model.add(Dense(30, input_dim=5, activation='sigmoid'))
model.add(Dense(30, activation='sigmoid'))
model.add(Dense(1))
# Compile model
model.compile(loss='mean_squared_error',
                optimizer='adam', metrics='mean_absolute_error')

The output neuron has to be Dense(1) - by defaut the activation funtion is linear. The loss has to be mean_squared_error and the input_dim has to be 5. All variations on the others parameters are accepted.

Hint: To get the score on the test set, evaluate could have been used: model.evaluate(X_test_scaled, y_test).