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

Linear regression

The name linear regression will tell you all you need to know about it—the regression part tells you this method performs regression analysis, and the linear part tells you the method assumes linear relationships between attributes.

To find a possible relationship between attributes, linear regression assumes and models a universal equation that relates the target (the dependent attribute) to the predictors (the independent attributes). This equation is depicted here:

This equation uses a parameter approach. In this equation N stands for the number of predictors shows the linear regression universal equation.

The working of linear regression is very simple. The method first estimates the βs so that the equation fits the data best, and then uses the estimated βs for prediction.

Let's learn this method with an example. We will continue solving the number of MSU applications in the following example.

Example...