Predict Fuel Efficiency Using Tensorflow in Python

In this article, we will learn how can we build a fuel efficiency predicting model by using TensorFlow API. The dataset we will be using contain features like the distance engine has traveled, the number of cylinders in the car, and other relevant feature.

Importing Libraries

• Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
• Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
• Matplotlib – This library is used to draw visualizations.
• Sklearn – This module contains multiple libraries having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.
• OpenCV – This is an open-source library mainly focused on image processing and handling.
• Tensorflow – This is an open-source library that is used for Machine Learning and Artificial intelligence and provides a range of functions to achieve complex functionalities with single lines of code.

The dataset can be downloaded from here.

Output:

 

Let’s check the shape of the data.

Output:

(398, 9)

Now, check the datatypes of the columns.

Output:

 

Here we can observe one discrepancy the horsepower is given in the object datatype whereas it should be in the numeric datatype.

Output:

 

Exploratory Data Analysis

As per the df.info() part first we will deal with the horsepower column and then we will move toward the analysis part.

Output:

 

Here we can observe that instead of the null they have been replaced by the string ‘?’ due to this, the data of this column has been provided in the object datatype.

Output:

(398, 9)
(392, 9)

So, there were 6 such rows with a question mark.

Output:

mpg             0
cylinders       0
displacement    0
horsepower      0
weight          0
acceleration    0
model year      0
origin          0
car name        0
dtype: int64

Output:

mpg             127
cylinders         5
displacement     81
horsepower       93
weight          346
acceleration     95
model year       13
origin            3
car name        301
dtype: int64

Output:

 

Here we can observe that the mpg values are highest for the origin 3.

Output:

 

If we will remove the displacement feature then the problem of high collinearity will be removed.

Data Input Pipeline

Output:

((313, 6), (79, 6))

Model Architecture

We will implement a model using the  Sequential API of Keras which will contain the following parts:

• We will have two fully connected layers.
• We have included some BatchNormalization layers to enable stable and fast training and a Dropout layer before the final layer to avoid any possibility of overfitting.
• The final layer is the output layer.

While compiling a model we provide these three essential parameters:

• optimizer – This is the method that helps to optimize the cost function by using gradient descent.
• loss – The loss function by which we monitor whether the model is improving with training or not.
• metrics – This helps to evaluate the model by predicting the training and the validation data.

Let’s print the summary of the model’s architecture:

Output:

 

Model Training

Now we will train our model using the training and validation pipeline.

Output:

Epoch 45/50
10/10 [==============================] - 0s 14ms/step - loss: 2.8792 - mape: 12.5425 - val_loss: 5.3991 - val_mape: 28.6586
Epoch 46/50
10/10 [==============================] - 0s 8ms/step - loss: 2.9184 - mape: 12.7887 - val_loss: 4.1896 - val_mape: 21.4064
Epoch 47/50
10/10 [==============================] - 0s 9ms/step - loss: 2.8153 - mape: 12.3451 - val_loss: 4.3392 - val_mape: 22.3319
Epoch 48/50
10/10 [==============================] - 0s 9ms/step - loss: 2.7146 - mape: 11.7684 - val_loss: 3.6178 - val_mape: 17.7676
Epoch 49/50
10/10 [==============================] - 0s 10ms/step - loss: 2.7631 - mape: 12.1744 - val_loss: 6.4673 - val_mape: 33.2410
Epoch 50/50
10/10 [==============================] - 0s 10ms/step - loss: 2.6819 - mape: 11.8024 - val_loss: 6.0304 - val_mape: 31.6198

Output:

 

Output:

 

The training error has gone down smoothly but the case with the validation is somewhat different.