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

Example 3 – level I and II data cleaning

In this example, we want to use Electric_Production.csv to make predictions. We are specifically interested in being able to predict what the monthly electricity demand will be 1 month from now. This 1-month gap is designed in the prediction model so that the predictions that come from the model will have decision-making values; that is, the decision-makers will have time to react to the predicted value.

We would like to use linear regression to perform this prediction. The independent and dependent attributes for this prediction are shown in the following diagram:

Figure 10.7 – The independent and dependent attributes needed for the prediction task

Let's go through the independent attributes shown in the preceding diagram:

  • Average demand of the month over the years: For instance, if the month we want to predict demands for is March 2022, we want to use the average of the demands for every...