R Tutorial: Introduction to the Pokemon data

Key Takeaways

so far you have applied what you've learned about the k-means algorithm to synthetic data in this final set of exercises you apply your learning to a real-world data set this data set is about 800 Pokemon from the Pokemon games sorry this isn't from Pokemon go this may not be a data set where you have already built intuition this is normal in data science you may want to gain some intuition about the data by researching Pokemon in the Pokemon games or as we all have you do by poking around in the data the data was originally collected by Alberto Broadus and is hosted on kaggle at the address on the screen the data contains six features for each Pokemon hitpoints attack defense special attack special defense and speed this is unlabeled data because there's not a single outcome that we want to predict just some measurements of each pokemons abilities for the data curious more information on Pokemon and these features can be found at the second address on the screen along with exploring the data this is another way to build intuition about the data in the next set of exercises you'll have multiple steps to complete their typical and handling real-world data the first is determining which variables to use for clustering it is important to consider which features should be used in the clustering exercise sometimes trying multiple subsets of features is an important step to find patterns in the data the next and something will delay to later chapter is scaling the data if the features being used in modeling are of different units or scale scaling the data to common measurement is often completed in order to improve the insights gained from unsupervised learning in this example you'll be finding homogeneous subgroups of Pokemon the number of clusters is not known beforehand so you have to make a determination in real-world data a nice clean elbow on the scree plot rarely exists so as an analyst you'll have to use some judgment in this step finally a common output of any analysis exercise is a visual representation of the outcomes this can also be helpful to gain some additional intuition from the data and the resulting models this may seem like a lot but will guide you through step by step providing hits and templates all along the way let's practice