Book Image

The Machine Learning Workshop - Second Edition

By : Hyatt Saleh
Book Image

The Machine Learning Workshop - Second Edition

By: Hyatt Saleh

Overview of this book

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.
Table of Contents (8 chapters)

5. Artificial Neural Networks: Predicting Annual Income

Activity 5.01: Training an MLP for Our Census Income Dataset


  1. Import all the elements required to load and split a dataset, to train an MLP, and to measure accuracy:
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.neural_network import MLPClassifier
    from sklearn.metrics import accuracy_score
  2. Using the preprocessed Census Income Dataset, separate the features from the target, creating the variables X and Y:
    data = pd.read_csv("census_income_dataset_preprocessed.csv")
    X = data.drop("target", axis=1)
    Y = data["target"]

    As explained previously, there are several ways to achieve the separation of X and Y, and the main thing to consider is that X should contain the features for all instances, while Y should contain the class label of all instances.

  3. Divide the dataset into training, validation, and testing sets, using a split ratio of 10...