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.

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Table of Contents (8 chapters)
Preface
About the Book
Installing Libraries
1
1. Introduction to Jupyter Notebooks
1. Introduction to Jupyter Notebooks
Introduction
Basic Functionality and Features of Jupyter Notebooks
Jupyter Features
Summary
Free Chapter
2
2. Data Exploration with Jupyter
2. Data Exploration with Jupyter
Introduction
Our First Analysis – the Boston Housing Dataset
Summary
3
3. Preparing Data for Predictive Modeling
3. Preparing Data for Predictive Modeling
Introduction
Machine Learning Process
Approaching Data Science Problems
Understanding Data from a Modeling Perspective
Introducing the Human Resource Analytics Dataset
Summary
4
4. Training Classification Models
4. Training Classification Models
Introduction
Understanding Classification Algorithms
Summary
5
5. Model Validation and Optimization
5. Model Validation and Optimization
Introduction
Assessing Models with k-Fold Cross Validation
Dimensionality Reduction with PCA
Summary
6
6. Web Scraping with Jupyter Notebooks
6. Web Scraping with Jupyter Notebooks
Introduction
Internet Data Sources
Introduction to HTTP Requests
Data Workflow with pandas
Summary
Appendix
1. Introduction to Jupyter Notebooks
2. Data Exploration with Jupyter
3. Preparing Data for Predictive Modeling
4. Training Classification Models
5. Model Validation and Optimization
6. Web Scraping with Jupyter Notebooks

Introduction

In the previous chapters, we walked through the steps that we need to take in a data science project before we can train a machine learning model. This included the planning phase, that is, identifying business problems, assessing data sources for suitability, and deciding on modeling approaches.

Having decided on a general modeling approach, we should be careful to avoid the common pitfalls of training ML models as we proceed with modeling. Firstly, remember that training data is very important. In fact, increasing the amount of training data can have a larger impact than model selection on scoring performance. One issue is that there may not be enough data available, which could make patterns difficult to find and cause models to perform poorly on testing data. Data quality also has a huge effect on model performance. Some possible issues include the following:

  • Non-representative training data (sampling bias)
  • Errors in the record sets (such as recorded...
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