Book Image

Learning Predictive Analytics with Python

By : Ashish Kumar, Gary Dougan
Book Image

Learning Predictive Analytics with Python

By: Ashish Kumar, Gary Dougan

Overview of this book

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
Table of Contents (19 chapters)
Learning Predictive Analytics with Python
Credits
Foreword
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
A List of Links
Index

Random sampling – splitting a dataset in training and testing datasets


Splitting the dataset in training and testing the datasets is one operation every predictive modeller has to perform before applying the model, irrespective of the kind of data in hand or the predictive model being applied. Generally, a dataset is split into training and testing datasets. The following is a description of the two types of datasets:

  • The training dataset is the one on which the model is built. This is the one on which the calculations are performed and the model equations and parameters are created.

  • The testing dataset is used to check the accuracy of the model. The model equations and parameters are used to calculate the output based on the inputs from the testing datasets. These outputs are used to compare the model efficiency in the light of the actuals present in the testing dataset.

This will become clearer from the following image:

Fig. 3.37: Concept of sampling: Training and Testing data

Generally, the...