1- Add FIFA 22 complete player dataset from Kaggle, then read the data from players_22.csv 2- In this data set we will be using the following columns only for clustering: 1. ("overall", "potential", "wage_eur", "value_eur", "age") 3- Make sure these columns do not have any missing data (hint: use dropna function in Panda) 4- Scale data to standardize values using the following formula: data =(( data data.min ())/( data max() data.min ()))10+1 5- Apply kmean clustering to this data set and print out the selected centroids 6- plot the results Question 2: [PLO K1 / CLO 1 / SO 1] [2 marks] 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. Question 3: [PLO K1 / CLO 1 / SO 1] [6 marks] 1- Write your own implementation of kmeans algorithm 2- Generate blobs dataset using the file create_dataset_sklean for testing your code 3- Show the updated cluster assignment in every iteration (reuse the animation codes from the given code of kmean in the lab) 4- Compare the result of your own implementation of kmeans algorithm with the given kmean implementation, then compare your implementation and the given implementation to the kmean in sklean (set the nini =1 )