COVID-19 and Radio Listeners Data Analysis

I. Data Description – COVID-19

Global Spread Data The COVID pandemic situation impacted us all during the past three years. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus.

Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illnesses. Anyone can get sick with COVID- 19 and become seriously ill or die at any age. The best way to prevent and slow down transmission is to be well informed about the disease and how the virus spreads. Students are offered to research the COVID-19 global data by means of clustering analysis.

The full and most recent dataset is available here.

World Health Organisation Coronavirus disease (COVID-19) Weekly Epidemiological Updates are available here.

Stay Informed and Take Care!

Identify the main groups of countries and their characteristics using

1. K-Means clustering after performing dimensionality reduction (PCA)

2. Agglomerative hierarchical clustering after performing dimensionality reduction (PCA)

3. Critically summarise the business value of the clustering analysis with reference to the given domain.

To understand what exactly a listener prefers listening to on the radio, every detail is recorded online. This recorded information is used for recommending music that the listener is likely to enjoy and to come up with a “focused” marketing strategy that sends out advertisements for music that a listener may wish to buy. However, this results in wasting money on scarce advertising. Suppose that you are provided with data from a music community site, giving you details of each user. This will be further enhanced by getting access to a log of every artist that listed users have downloaded on their computer. With this data, you will
also get information on the demographics of the listed users (such as age, sex, location, occupation, and interests).

The objective of providing this data lies in building a system that recommends newmusic to the users in this listed community. From the available information, it is usually not difficult to determine the support for various individual artists (that is, the frequencies of a specific music genre/artist or song that a user is listening to) as well as the joint support for pairs (or larger groupings) of artists.

You need to count the number of incidences across all your network members and divide it by the number of members. In the mentioned data set, a large chunk of information close to 300,000 records of song (or artists) selections is listed that is per the listening frequency given by 15,000 users. Each row of the data set contains the name of the artist that the user has been listening to. The first user is a German lady, who has listened to 16 artists. This has resulted in the first 16 rows of the data matrix.

First, you need to transform the data given here into an incidence matrix, where each listener is represented by a row, with 0s and 1s across the columns. Thisindicates if a listener has chosen a certain artist or not. Then, calculate the support for each of the listed 1004 artists and display the support for all artists with a support threshold greater than 0.08. The full data dataset is available here.

1. Perform Market Basket analysis for the top 3 baskets (listeners): Split the data according to the listeners (creation of a basket) and apply the Apriori algorithm for each basket.

2. Critically summarise the business value of each basket analysis with reference to the given domain