Replacing Lewa!

Part 2: Determining the number of clusters and applying K-Means

K-Means requires us to determine the number of clusters chosen and choosing the correct number is integral to the algorithm. An ideal way to figure out the right number of clusters would be to calculate the Within-Cluster-Sum-of-Squares (WCSS).WCSS is the sum of squares of the distances of each data point in all clusters to their respective centroids. The goal is to minimise the sum.

Part 3: Finding strikers in the same cluster as Lewandowski and using PCA to visualise some similar strikers.

Finding players in Lewandowski's cluster is easily achieved through a few smart loops. I made sure to filter out players who were not out and out strikers in the list.

Knowing that our data has many features (38 to be precise) we can't visualise the results as of now. Hence we must lower these 38 dimensions into 2 and make sure that these 2 dimensions capture most of the variance in the 38 features and projecting into this vector space causes minimal loss of information. This in a nutshell is dimensionality reduction and can be achieved using the PCA Algorithm. I will not go deep into the PCA, especially it's derivation since it is replete with linear algebra which everyone may not be familiar with, but for those readers who love exploring the mathematical nuances refer to this comprehensive article describing everything about the PCA.

Here are the simple steps for PCA

Here is the result!