Predict-Fuel-Efficiency-using-TensorFlow

The TensorFlow solution can be found here.

Data source

Attribute Information

mpg	continuous
cylinders	multi-valued discrete
displacement	continuous
horsepower	continuous
weight	continuous
acceleration	continuous
model year	multi-valued discrete
origin	multi-valued discrete
car name	string (unique for each instance)

Observations

• Found na in Horsepower column
• Cylinders is the categorical column
• Manipulated data using pd.get_dummies
• Using the pairplot, it's understood that regression is the best approach

Model 1

horsepower_normalizer = tf.keras.layers.Normalization(input_shape=[1, ], axis=None)

horsepower_normalizer.adapt(np.array(X_train.Horsepower))

horsepower_model = tf.keras.Sequential([
    horsepower_normalizer,
    tf.keras.layers.Dense(units=1)
])

horsepower_model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 normalization_1 (Normalizat  (None, 1)                3         
 ion)                                                            
                                                                 
 dense (Dense)               (None, 1)                 2         
                                                                 
=================================================================
Total params: 5
Trainable params: 2
Non-trainable params: 3
_________________________________________________________________

Results

	Mean absolute error [MPG]
horsepower_model	4.211570
linear_model	2.553518
model	2.284585
reloaded	2.284585