In this dataset you have a collection of purchase card transactions for the Birmingham City Council. This is a historical dataset, you’re able to perform any of the following tasks: (Clustering) Discovering profiles (whether the case) or unusual transactions (anomalies detection) (Forecasting) Try to guess future transactional behaviors. For instance, what would be the next purchase? Expenditures forecasting? (Creativity) State a problem. It’s up to you defining the time window in which your analysis will take place.
Entre las proximas tareas esta el agregar una visualizacion del silhouette (https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py).
Entre las proximas tareas esta el agregar Local Outlier Probabilities (https://github.com/vc1492a/PyNomaly).