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)

6. Building Your Own Program

Activity 6.01: Performing the Preparation and Creation Stages for the Bank Marketing Dataset



To ensure the reproducibility of the results available at, make sure that you use a random_state of 0 when splitting the datasets and a random_state of 2 when training the models.

  1. Open a Jupyter Notebook and import all the required elements:
    import pandas as pd
    from sklearn.preprocessing import LabelEncoder
    from sklearn.model_selection import train_test_split
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.neural_network import MLPClassifier
    from sklearn.metrics import precision_score
  2. Load the dataset into the notebook. Make sure that you load the one that was edited previously, named bank-full-dataset.csv, which is also available at
    data = pd.read_csv("bank-full-dataset.csv")

    The output is as follows:

    Figure 6.8: A screenshot showing...