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...