Book Image

Applied Supervised Learning with Python

By : Benjamin Johnston, Ishita Mathur
Book Image

Applied Supervised Learning with Python

By: Benjamin Johnston, Ishita Mathur

Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

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Table of Contents (9 chapters)
Applied Supervised Learning with Python
Applied Supervised Learning with Python
Preface
Preface
Free Chapter
1
Python Machine Learning Toolkit
Python Machine Learning Toolkit
Introduction
Supervised Machine Learning
Jupyter Notebooks
pandas
Data Quality Considerations
Summary
2
Exploratory Data Analysis and Visualization
Exploratory Data Analysis and Visualization
Introduction
Summary Statistics and Central Values
Missing Values
Distribution of Values
Relationships within the Data
Summary
3
Regression Analysis
Regression Analysis
Introduction
Regression and Classification Problems
Linear Regression
Multiple Linear Regression
Autoregression Models
Summary
4
Classification
Classification
Introduction
Linear Regression as a Classifier
Logistic Regression
Classification Using K-Nearest Neighbors
Classification Using Decision Trees
Summary
5
Ensemble Modeling
Ensemble Modeling
Introduction
Overfitting and Underfitting
Bagging
Boosting
Summary
6
Model Evaluation
Model Evaluation
Introduction
Evaluation Metrics
Splitting the Dataset
Performance Improvement Tactics
Summary
Appendix
Appendix
Chapter 1: Python Machine Learning Toolkit
Chapter 2: Exploratory Data Analysis and Visualization
Chapter 3: Regression Analysis
Chapter 4: Classification
Chapter 5: Ensemble Modeling
Chapter 6: Model Evaluation

Summary

In this chapter, we introduced the concept of supervised machine learning, along with a number of use cases, including the automation of manual tasks such as identifying hairstyles from the 1960s and 1980s. In this introduction, we encountered the concept of labeled datasets and the process of mapping one information set (the input data or features) to the corresponding labels.

We took a practical approach to the process of loading and cleaning data using Jupyter notebooks and the extremely powerful pandas library. Note that this chapter has only covered a small fraction of the functionality within pandas, and that an entire book could be dedicated to the library itself. It is recommended that you become familiar with reading the pandas documentation and continue to develop your pandas skills through practice.

The final section of this chapter covered a number of data quality issues that need to be considered to develop a high-performing supervised learning model, including missing data, class imbalance, and low sample sizes. We discussed a number of options for managing such issues and emphasized the importance of checking these mitigations against the performance of the model.

In the next chapter, we will extend upon the data cleaning process that we covered and will investigate the data exploration and visualization process. Data exploration is a critical aspect of any machine learning solution, as without a comprehensive knowledge of the dataset, it would be almost impossible to model the information provided.

Previous Section
End of Chapter 1
Next Chapter