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

Classification models

In the previous chapter, we covered predictive modeling. Classification is a type of predictive modeling; specifically, classification is a regression analysis where the dependent attribute or the target is categorical instead of numerical.

Even though classification is a subset of predictive modeling, it is the area of data mining that has received the most attention due to its usefulness. At the core of many machine learning (ML) solutions in the real world today is a classification algorithm. Despite its prevalent applications and complicated algorithms, the fundamental concepts of classification are simple.

Just as with prediction, for classification, we need to specify our independent attributes (predictors) and the dependent attribute (target). Once we are clear about these and we have a dataset that includes these attributes, we are set to employ classification algorithms.

Classification algorithms, just as with prediction algorithms, seek to find...