Predict Fuel Efficiency Using Tensorflow in Python

Predict Fuel Efficiency Using Tensorflow in Python

Predicting fuel efficiency using TensorFlow involves creating and training a machine learning model on a dataset that includes features relevant to fuel efficiency. A common example for this kind of task is the Auto MPG dataset, which is often used for regression problems. Below is a step-by-step guide on how to approach this using TensorFlow in Python:

Step 1: Import Required Libraries

import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import pandas as pd import numpy as np import matplotlib.pyplot as plt

Step 2: Load and Prepare the Data

The Auto MPG dataset is available in the UCI Machine Learning Repository. You can load it using Pandas.

url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data' column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight', 'Acceleration', 'Model Year', 'Origin'] dataset = pd.read_csv(url, names=column_names, na_values='?', comment='\t', sep=' ', skipinitialspace=True) # Drop rows with missing values dataset = dataset.dropna() # Convert "Origin" to a one-hot encoding dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'}) dataset = pd.get_dummies(dataset, prefix='', prefix_sep='')

Step 3: Split the Data into Training and Test Sets

train_dataset = dataset.sample(frac=0.8, random_state=0) test_dataset = dataset.drop(train_dataset.index) # Separate the target value (label) from the features train_labels = train_dataset.pop('MPG') test_labels = test_dataset.pop('MPG')

Step 4: Normalize the Data

train_stats = train_dataset.describe().transpose() def norm(x): return (x - train_stats['mean']) / train_stats['std'] normed_train_data = norm(train_dataset) normed_test_data = norm(test_dataset)

Step 5: Build the Model

def build_model(): model = keras.Sequential([ layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]), layers.Dense(64, activation='relu'), layers.Dense(1) ]) optimizer = tf.keras.optimizers.RMSprop(0.001) model.compile(loss='mse', optimizer=optimizer, metrics=['mae', 'mse']) return model model = build_model()

Step 6: Train the Model

EPOCHS = 1000 history = model.fit( normed_train_data, train_labels, epochs=EPOCHS, validation_split=0.2, verbose=0)

Step 7: Evaluate the Model

loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2) print("Testing set Mean Abs Error: {:5.2f} MPG".format(mae))

Step 8: Make Predictions

test_predictions = model.predict(normed_test_data).flatten() plt.scatter(test_labels, test_predictions) plt.xlabel('True Values [MPG]') plt.ylabel('Predictions [MPG]') plt.axis('equal') plt.axis('square') plt.show()

Notes:

  • This is a basic regression model. Depending on your data and requirements, you might need to adjust the complexity of the model, the preprocessing steps, and the training parameters.
  • Always inspect and understand your data before training a model.
  • Experiment with different architectures, hyperparameters, and optimization strategies to improve performance.

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