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

4. Training Classification Models

Activity 4.01: Training and Visualizing SVM Models with Scikit-learn

Solution:

  1. Create a new Jupyter notebook.
  2. In the first cell, add the following lines of code to load the libraries we'll be using and set up our plot environment for the notebook:
    import numpy as np
import datetime
import time
import os
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
%config InlineBackend.figure_format='retina'
sns.set() # Revert to matplotlib defaults
plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['axes.labelpad'] = 10
sns.set_style("darkgrid")
  3. In the next cell, enter the following code to print the date, version numbers, and hardware information: 
    %load_ext watermark
%watermark -d -v -m -p \
requests,numpy,pandas,matplotlib,seaborn,sklearn

    You should get the following output:

    Figure 4.19: Output of loading all the required libraries

  4. Load the preprocessed Human Resource...
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