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)

2. Unsupervised Learning – Real-Life Applications

Activity 2.01: Using Data Visualization to Aid the Pre-processing Process


  1. Import all the required elements to load the dataset and pre-process it:
    import pandas as pd
    import matplotlib.pyplot as plt
    import numpy as np
  2. Load the previously downloaded dataset by using pandas' read_csv() function. Store the dataset in a pandas DataFrame named data:
    data = pd.read_csv("wholesale_customers_data.csv")
  3. Check for missing values in your DataFrame. Using the isnull() function plus the sum() function, count the missing values of the entire dataset at once:

    The output is as follows:

    Channel             0
    Region              0
    Fresh               0
    Milk                0
    Grocery             0
    Frozen              0
    Detergents_Paper    0
    Delicassen          0
    dtype: int64

    As you can see from the preceding screenshot, there are no missing values in the dataset.

  4. Check for outliers...