programing by python

1- Add FIFA 22 complete player dataset from Kaggle, then read the data from players 22.c8V 2- In this data set we will be using the following columns only for clustering: 1. (overall", "potential", "wage, eur", "Yalue, eur", "age") 3- Make sure these columns do not have any missing data (hint: use drepua function in Panda) 4- Scale data to standardize values using the following formula: data=((datadata.minD)/(datasmax0datamin0))10+1 5- Apply kmean clustering to this data set and create the elbow plot using SSE as an objective function for k values from 2 to 10 6- Choose the best value for k, then plot the final 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 haye 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 Kmsans clustering with the actual true clustering