Question 2: [PLO K1/CLO 1/SO 1] marks] [3 In this case study, we have a dataset...

 

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Question 2: [PLO K1/CLO 1/SO 1] marks] [3 In this case study, we have a dataset with all the countries in the world, their location (latitude, longitude) and the continent they belong to. Let's say we don't know the continents and we want to find them using clustering. The algorithm has to find out which are the continents based on the data about countries and their location. 1. download the dataset "countries_continents.csv" which contains the data about countries and continents 2. We want to find out how these countries can be assigned to clusters using the K-Means algorithm 3. The data now contains country names (text), which have to be converted to numbers to be able to run the clustering algorithm. We are not interested in the actual country names, and the continents can be assigned to numbers. 4. Runs the clustering algorithm using the number of clusters given as the actual number of continents in the dataset. 5. Plot and compare the results of Kmeans clustering with the actual true clustering. Question 2: [PLO K1/CLO 1/SO 1] marks] [3 In this case study, we have a dataset with all the countries in the world, their location (latitude, longitude) and the continent they belong to. Let's say we don't know the continents and we want to find them using clustering. The algorithm has to find out which are the continents based on the data about countries and their location. 1. download the dataset "countries_continents.csv" which contains the data about countries and continents 2. We want to find out how these countries can be assigned to clusters using the K-Means algorithm 3. The data now contains country names (text), which have to be converted to numbers to be able to run the clustering algorithm. We are not interested in the actual country names, and the continents can be assigned to numbers. 4. Runs the clustering algorithm using the number of clusters given as the actual number of continents in the dataset. 5. Plot and compare the results of Kmeans clustering with the actual true clustering.