Unsupervised learning: Clustering and density estimation
When it comes to determining and explaining information within a large, complicated, or multi-dimensional dataset, it can often be difficult to see patterns and relationships. In the case of supervised learning, where there is input data and output data, it is possible to artificially construct and optimize a model that can, with time and several iterations, predict and improve performance through its own experience by splitting the input data into training and testing sets. Naturally, supervised learning can yield a lot of information and results; however, in the case of unsupervised learning, or when output data is not available, many other methods exist to try and capture the natural structure of the data and make useful observations. This report will attempt to use unsupervised methods to try and infer further information about the cars dataset that was used in the previous exercise.