Probability Distribution is another important subject to get deeper knowledge of data you need to work with, and in many times have a better perception of possible outcomes and how good and bad your data is 📊
The goal of this exercise lays down on understand what kind of distributions exist when we talk probability and how to get this information out of the data you are presented. Essential for this task, and as shown in the Jupyter Notebooks is understanding Matplotlib / Seaborn to plot graphics representing the data in their limits like this Binomial Distribution of data 📉
At the same time SciPy is another important library to acknowledge since it helps laydown your data. And of course you'll need to understand what kind of Distributions you can find and work with in the wild, which is exactly what this exercise also helps, vizualise the data in plots to identify trends, median, etc... 🧮 Here I just explored the more common Distributions as Binomial, the Normal / Gaussian, Uniform.