You're reading from The Data Analysis Workshop

Product typeBook

Published inJul 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781839211386

Edition1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Science

Authors (3):

Gururajan Govindan

Shubhangi Hora

Konstantin Palagachev

View More author details

9. Analysis of the Energy Consumed by Appliances

Overview

This chapter aims to display the application of general data analysis techniques to a specific use case—analyzing the energy consumed by household appliances. By the end of this chapter, you will be able to analyze individual features of the dataset to assess whether the data is skewed. You will also be equipped to perform feature engineering by creating new features from existing ones, and also to conduct Exploratory Data Analysis (EDA) and design informative visualizations.

Introduction

In the previous chapter, we took a look at the retail industry through the dataset of an online retail store based out of the UK. We applied a variety of techniques, such as breaking down the date-time column into individual columns containing the year, month, day of the week, hour, and so on, and creating line graphs to conduct a time series analysis to answer questions such as 'Which month was the most popular for the store?'

This chapter guides you through the data-specific analysis of a real-world domain and situation. This chapter focuses on a dataset containing information regarding the energy consumption of household appliances. The true goal of this dataset is to understand the relationships between the temperature and humidity of various rooms of a house (as well as outside the house) to then predict the energy consumption (usage) of appliances. However, in this chapter, we are just going to analyze the dataset to reveal patterns between the features...

Summary

We have reached the end of this chapter and have successfully analyzed the amount of energy consumed by household appliances based on temperature, humidity, and other external weather conditions. We applied several data analysis techniques, including feature engineering and designing boxplots for specific features, to gain a better understanding of the information that the data contains. Additionally, we also plotted distributions of skewed data to observe them better.

In the next chapter, we will come to the end of our data analysis journey by applying our techniques to one last dataset. We will be analyzing and assessing the air quality of multiple localities in Beijing, China. Be ready to apply all your data analysis knowledge gained so far on this last dataset.

The rest of the chapter is locked

You have been reading a chapter from

The Data Analysis Workshop

Published in: Jul 2020Publisher: PacktISBN-13: 9781839211386

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

undefined

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $15.99/month. Cancel anytime

Authors (3)

Gururajan Govindan

Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.
Read more about Gururajan Govindan

Shubhangi Hora

Shubhangi Hora is a data scientist, Python developer, and published writer. With a background in computer science and psychology, she is particularly passionate about healthcare-related AI, including mental health. Shubhangi is also a trained musician.
Read more about Shubhangi Hora

Konstantin Palagachev

Konstantin Palagachev holds a Ph.D. in applied mathematics and optimization, with an interest in operations research and data analysis. He is recognized for his passion for delivering data-driven solutions and expertise in the area of urban mobility, autonomous driving, insurance, and finance. He is also a devoted coach and mentor, dedicated to sharing his knowledge and passion for data science.
Read more about Konstantin Palagachev

Other recommended products

Related to this chapter

Python Feature Engineering Cookbook

Feature engineering is invaluable for developing and enriching your machine learning models. In this book, you will work with the best Python tools to streamline your feature engineering pipelines, feature engineering techniques and simplify and improve the quality of your code.

BookJan 2020372 pages

Hands-On Gradient Boosting with XGBoost and scikit-learn

This practical XGBoost guide will put your Python and scikit-learn knowledge to work by showing you how to build powerful, fine-tuned XGBoost models with impressive speed and accuracy. This book will help you to apply XGBoost’s alternative base learners, use unique transformers for model deployment, discover tips from Kaggle masters, and much more!

BookOct 2020310 pages

Data Science for Marketing Analytics

Data Science for Marketing Analytics opens doors to looking at data with a different approach and new tools. Drawing on machine learning and data science concepts, this book broadens the range of tools that you can use to transform the market analysis process.

BookMar 2019420 pages

Forecasting Time Series Data with Facebook Prophet

This book will help you get to grips with the task of time series forecasting using the leading open source forecasting tool available to the public, Facebook Prophet. You will learn how to implement the advanced features of Prophet to build forecast models and understand why and how to modify each of the default parameters to improve results.

BookMar 2021270 pages

The Supervised Learning Workshop

Taking an engaging and practical approach, The Supervised Learning Workshop teaches you how to predict the output of new data, based on the relationship and behavior of?existing datasets. You’ll learn at your own pace and use Python libraries and Jupyter to build intelligent predictive models.?

BookFeb 2020532 pages

Big Data Analysis with Python

Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control the data avalanche for you. With this book, you'll learn effective techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems.

BookApr 2019276 pages

Hands-On Exploratory Data Analysis with Python

This book provides practical knowledge about the main pillars of EDA including data cleaning, data preparation, data exploration, and data visualization. You can leverage the power of Python to understand, summarize and investigate your data in the best way possible. The book presents a unique approach to exploring hidden features in your data.

BookMar 2020352 pages

Data Science for Marketing Analytics

This book on marketing analytics with Python will quickly get you up and running using practical data science and machine learning to improve your approach to marketing. You'll learn how to analyze sales, understand customer data, predict outcomes, and present conclusions with clear visualizations.

BookSep 2021636 pages

Python for Finance Cookbook

Python is becoming the number one language for data science and also quantitative finance. This book provides you with solutions to common tasks from the intersection of quantitative finance and data science, using modern Python libraries.

BookJan 2020432 pages

The Data Science Workshop

The Data Science Workshop equips you with the basic skills you need to start working on a variety of data science projects. You’ll work through the essential building blocks of a data science project gradually through the book, and then put all the pieces together to consolidate your knowledge and apply your learnings in the real world.

BookAug 2020824 pages5

Personalised recommendations for you

Based on your interests and search pattern

Et al.

Ever wonder why speech recognition systems don't understand the Scottish accent, or what would happen if an astronaut only ate mac 'n' cheese, or other spurious reflections you'd have at a bar? We did, then collated those deliberations into absurd research articles with fake figures and methodologies inspired by even more fictionally absurd studies.

BookAug 2023230 pages5

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages4

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages1

Generative AI with LangChain

This book is a comprehensive introduction to LLMs and LangChain, demystifying the basic mechanics of LangChain, its functionalities, and the myriad of applications it can be integrated into.

BookDec 2023360 pages5

Mastering Tableau 2023

This book is a comprehensive resource to mastering your Tableau skills and becoming a BI expert. As you progress, you will learn how to build advanced dashboards and improve your storytelling to derive key business insight, as well as make you well-versed with advanced functionalities of Tableau in the business intelligence domain.

BookAug 2023684 pages

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages5

Building AI Applications with ChatGPT APIs

This guide covers all ChatGPT API features for effortless creation of robust AI powered apps. With its help, you’ll be able to leverage ChatGPT’s cutting-edge NLP models to take your app development skills to the next level. You’ll also work on ten exciting projects that will give you the practical know-how that you can apply to your existing applications.

BookSep 2023258 pages2

Data Engineering with AWS

Embark on a journey to master data engineering pipelines on AWS! Our book offers a hands-on experience of AWS services for ingesting, transforming, and consuming data. Whether you're an absolute beginner or someone with basic data engineering experience, this guide is an indispensable resource.

BookOct 2023636 pages5

Modern Data Architecture on AWS

Every organization wants an agile, performant, and cost-effective data platform that meets all their current and future business needs. Purpose-built AWS analytics services and their features play a big part in building such a modern data platform. This book brings to you all the design and architectural patterns that’ll help you achieve this goal.

BookAug 2023420 pages5

Practical Guide to Applied Conformal Prediction in Python

Discover the power of Conformal Prediction with the "Practical Guide to Applied Conformal Prediction in Python." Master the latest techniques to quantify uncertainty in machine learning and computer vision models, and seamlessly apply them to your industry applications.

BookDec 2023240 pages

TinyML Cookbook

With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.

BookNov 2023664 pages