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.

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Table of Contents (8 chapters)
Preface
About the Book
1
1. Introduction to Scikit-Learn
1. Introduction to Scikit-Learn
Introduction
Introduction to Machine Learning
Scikit-Learn
Data Representation
Data Preprocessing
Scikit-Learn API
Supervised and Unsupervised Learning
Summary
Free Chapter
2
2. Unsupervised Learning – Real-Life Applications
2. Unsupervised Learning – Real-Life Applications
Introduction
Clustering
Exploring a Dataset – Wholesale Customers Dataset
Data Visualization
Mean-Shift Algorithm
DBSCAN Algorithm
Evaluating the Performance of Clusters
Summary
3
3. Supervised Learning – Key Steps
3. Supervised Learning – Key Steps
Introduction
Supervised Learning Tasks
Model Validation and Testing
Evaluation Metrics
Error Analysis
Summary
4
4. Supervised Learning Algorithms: Predicting Annual Income
4. Supervised Learning Algorithms: Predicting Annual Income
Introduction
Exploring the Dataset
The Naïve Bayes Algorithm
The Decision Tree Algorithm
The Support Vector Machine Algorithm
Error Analysis
Summary
5
5. Supervised Learning – Key Steps
5. Supervised Learning – Key Steps
Introduction
Artificial Neural Networks
Applying an Artificial Neural Network
Performance Analysis
Summary
6
6. Building Your Own Program
6. Building Your Own Program
Introduction
Program Definition
Saving and Loading a Trained Model
Interacting with a Trained Model
Summary
Appendix
1. Introduction to Scikit-Learn
2. Unsupervised Learning – Real-Life Applications
3. Supervised Learning – Key Steps
4. Supervised Learning Algorithms: Predicting Annual Income
5. Artificial Neural Networks: Predicting Annual Income
6. Building Your Own Program

4. Supervised Learning Algorithms: Predicting Annual Income

Activity 4.01: Training a Naïve Bayes Model for Our Census Income Dataset

Solution:

  1. In a Jupyter Notebook, import all the required elements to load and split the dataset, as well as to train a Naïve Bayes algorithm:
    import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
  2. Load the pre-processed Census Income dataset. Next, separate the features from the target by creating two variables, X and Y:
    data = pd.read_csv("census_income_dataset_preprocessed.csv")
X = data.drop("target", axis=1)
Y = data["target"]

    Note that there are several ways to achieve the separation of X and Y. Use the one that you feel most comfortable with. However, take into account that X should contain the features of all instances, while Y should contain the class labels of all instances.

  3. Divide the dataset into training, validation, and...
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