Book Image

Practical Machine Learning Cookbook

By : Atul Tripathi
Book Image

Practical Machine Learning Cookbook

By: Atul Tripathi

Overview of this book

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations. The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more. The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
Table of Contents (21 chapters)
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
14
Case Study - Forecast of Electricity Consumption

Multinomial logistic regression - understanding program choices made by students


Let's assume that high school students are to be enrolled on a program. The students are given the opportunity to choose programs of their choice. The choices of the students are based on three options. These choices are general program, vocational program, and academic program. The choice of each student is based on each student's writing score and social economic status.

Getting ready

In order to complete this recipe we shall be using a student's dataset. The first step is collecting the data.

Step 1 - collecting data

The student's dataset titled hsbdemo is being utilized. The dataset is available at: http://voia.yolasite.com/resources/hsbdemo.csv in an MS Excel format. There are 201 data rows and 13 variables in the dataset. The eight numeric measurements are as follows:

  • id
  • read
  • write
  • math
  • science
  • socst
  • awards
  • cid

The non-numeric measurements are as follows:

  • gender
  • ses
  • schtyp
  • prog
  • honors

How to do it...

Let's get into the...