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Applied Data Science with Python and Jupyter
Applied Data Science with Python and Jupyter

Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

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Applied Data Science with Python and Jupyter

Introduction


Consider a small food-delivery business that is looking to optimize their product. An analyst might look at the appropriate data and determine what type of food people are enjoying most. Perhaps they find a large amount of people are ordering the spiciest food options, indicating the business might be losing out on customers who desire something even more spicy. This is quite basic, or as some might say, "vanilla" analytics.

In a separate task, the analyst could employ predictive analytics by modeling the order volumes over time. With enough data, they could predict the future order volumes and therefore guide the restaurant as to how many staff are required each day. This model could take factors such as the weather into account to make the best predictions. For instance, a heavy rainstorm could be an indicator to staff more delivery personnel to make up for slow travel times. With historical weather data, that type of signal could be encoded into the model. This prediction...

Preparing to Train a Predictive Model


Here, we will cover the preparation required to train a predictive model. Although not as technically glamorous as training the models themselves, this step should not be taken lightly. It's very important to ensure you have a good plan before proceeding with the details of building and training a reliable model. Furthermore, once you've decided on the right plan, there are technical steps in preparing the data for modeling that should not be overlooked.

Note

We must be careful not to go so deep into the weeds of technical tasks that we lose sight of the goal. Technical tasks include things that require programming skills, for example, constructing visualizations, querying databases, and validating predictive models. It's easy to spend hours trying to implement a specific feature or get the plots looking just right. Doing this sort of thing is certainly beneficial to our programming skills, but we should not forget to ask ourselves if it's really worth...

Training Classification Models


As you've already seen in the previous chapter, using libraries such as scikit-learn and platforms such as Jupyter, predictive models can be trained in just a few lines of code. This is possible by abstracting away the difficult computations involved with optimizing model parameters. In other words, we deal with a black box where the internal operations are hidden instead. With this simplicity also comes the danger of misusing algorithms, for example, by overfitting during training or failing to properly test on unseen data. We'll show how to avoid these pitfalls while training classification models and produce trustworthy results with the use of k-fold cross validation and validation curves.

Introduction to Classification Algorithms

Recall the two types of supervised machine learning: regression and classification. In regression, we predict a continuous target variable. For example, recall the linear and polynomial models from the first chapter. In this chapter...

Summary


In this chapter, we have seen how predictive models can be trained in Jupyter Notebooks.

To begin with, we talked about how to plan a machine learning strategy. We thought about how to design a plan that can lead to actionable business insights and stressed the importance of using the data to help set realistic business goals. We also explained machine learning terminology such as supervised learning, unsupervised learning, classification, and regression.

Next, we discussed methods for preprocessing data using scikit-learn and pandas. This included lengthy discussions and examples of a surprisingly time-consuming part of machine learning: dealing with missing data.

In the latter half of the chapter, we trained predictive classification models for our binary problem, comparing how decision boundaries are drawn for various models such as the SVM, k-Nearest Neighbors, and Random Forest. We then showed how validation curves can be used to make good parameter choices and how dimensionality...

Summary

In this chapter, we have seen how predictive models can be trained in Jupyter Notebooks.

To begin with, we talked about how to plan a machine learning strategy. We thought about how to design a plan that can lead to actionable business insights and stressed the importance of using the data to help set realistic business goals. We also explained machine learning terminology such as supervised learning, unsupervised learning, classification, and regression.

Next, we discussed methods for preprocessing data using scikit-learn and pandas. This included lengthy discussions and examples of a surprisingly time-consuming part of machine learning: dealing with missing data.

In the latter half of the chapter, we trained predictive classification models for our binary problem, comparing how decision boundaries are drawn for various models such as the SVM, k-Nearest Neighbors, and Random Forest. We then showed how validation curves can be used to make good parameter choices...

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Key benefits

  • Get up and running with the Jupyter ecosystem and some example datasets
  • Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests
  • Discover how you can use web scraping to gather and parse your own bespoke datasets

Description

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.

Who is this book for?

Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.

What you will learn

  • Get up and running with the Jupyter ecosystem
  • Identify potential areas of investigation and perform exploratory data analysis
  • Plan a machine learning classification strategy and train classification models
  • Use validation curves and dimensionality reduction to tune and enhance your models
  • Scrape tabular data from web pages and transform it into Pandas DataFrames
  • Create interactive, web-friendly visualizations to clearly communicate your findings
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Publication date : Oct 31, 2018
Length: 192 pages
Edition : 1st
Language : English
ISBN-13 : 9781789958171
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Publication date : Oct 31, 2018
Length: 192 pages
Edition : 1st
Language : English
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Table of Contents

3 Chapters
Jupyter Fundamentals Chevron down icon Chevron up icon
Data Cleaning and Advanced Machine Learning Chevron down icon Chevron up icon
Web Scraping and Interactive Visualizations Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 33.3%
2 star 0%
1 star 0%
Lukas K. Aug 09, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Although this book only covers some of the techniques and approaches usually encountered when reading about data sciences, it covers them really well. And it has a clear structure. I especially liked the broad coverage on Jupyter at the beginning of the book. I can recommend this book to everyone that wants to get into the field of data science. You definitely, need some further material, but it certainly adds knowledge and value.
Amazon Verified review Amazon
wPastor Mar 01, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The iBook version contain blurred images for that reason I bought the paper version.The book is perfect for me as a new guy in data science. The author teachs how to install anaconda and how to uptodate it's libraries. Shows how to work with jupyter notebooks and analize libraries such as pandas, matplotlib, numpy, etc.Excellent book to learn how to clean up data and show the results you are looking for.
Amazon Verified review Amazon
kia Feb 08, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I just purchased the book online. Some of the code examples are so blurred that are unreadable
Amazon Verified review Amazon
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