Book Image

Hands-On Data Preprocessing in Python

By : Roy Jafari
5 (2)
Book Image

Hands-On Data Preprocessing in Python

5 (2)
By: Roy Jafari

Overview of this book

Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who’s developed college-level courses on data preprocessing and related subjects. With this book, you’ll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you’ll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.
Table of Contents (24 chapters)
1
Part 1:Technical Needs
6
Part 2: Analytic Goals
11
Part 3: The Preprocessing
18
Part 4: Case Studies

Exercises

  1. The chapter asserts that before using KNN, you will need to have your independent attributes normalized. This is certainly true, but how come we were able to get away with no normalization when we performed KNN using visualization? (See Figure 7.3.)
  2. We did not normalize the data when applying Decision Trees to the loan application problem. For practice and a deeper understanding, apply Decision Trees to the normalized data, and answer the following questions:

    a) Did the conclusion of Decision Trees change? Why do you think that is? Use the mechanism of the algorithm to explain.

    b) Did the Decision Trees tree-like structure change? In what ways? Did the change make a meaningful difference in the way that the tree-like structure could be used?

  3. For this exercise, we are going to use the Customer Churn.csv dataset. This dataset is randomly collected from an Iranian telecom company's database over a period of 12 months. A total of 3,150 rows of data, each representing...