#Traffic_kmean_Clusturing - Unsupervised Learning
A K-Means Clustering algorithm allows us to group observations in close proximity to the mean. Specifically, we wish to analyse the frequency of traffic across different routes in London, specifically for bicycles and cars and taxis. The main objective of using K-Means is to separate these observations into different clusters. We do this in order to categorize routes that have different traffic patterns into separate groups. The purpose behind these two algorithms are two-fold. Firstly, the pca algorithm is being used to convert data that might be overly dispersed into a set of linear combinations that can more easily be interpreted.