Book Image

The Applied Data Science Workshop - Second Edition

By : Alex Galea
Book Image

The Applied Data Science Workshop - Second Edition

By: Alex Galea

Overview of this book

From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects.
Table of Contents (8 chapters)

Summary

In this chapter, we learned about the SVM, KNN, and Random Forest classification algorithms and applied them to our preprocessed Human Resource Analytics dataset to build predictive models. These models were trained to predict whether an employee will leave the company, given a set of employee metrics.

For the purposes of keeping things simple and focusing on the algorithms, we built models that depend on only two features, that is, the satisfaction level and last evaluation value. This two-dimensional feature space also allowed us to visualize the decision boundaries and identify what overfitting looks like.

In the next chapter, we will introduce two important topics in machine learning: k-fold cross validation and validation curves. In doing so, we'll discuss more advanced topics, such as parameter tuning and model selection. Then, to optimize our final model for the employee retention problem, we'll explore feature extraction with the dimensionality reduction...