Analyzing historical weather data with Python: A machine learning clustering exercise with Scikit-Learn plus a cool map visualization of the clusters with Folium.
There is a 100% reproducible jupyter notebook workflow available in my github site, where you can:
Clusters of similar weather.
If so, you’re in luck! I’ve written a R-Markdown document that compares code in R and Python for the most common and basic functions in exploratory data analysis.
The dataset I used is a real-world dataset from Public Health Scotland that contains information on community pharmacy activity. After running the code, you will find the top 40 prescribed items in Scotland in February 2023.
So, whether you’re a Python user who needs to use R, or an R user who wants to learn Python, I encourage you to check out this exercise. It’s a great way to learn how to use both languages for data analysis. The code is 100% reproducible, so you can run it yourself to see how it works.
Here’s the link: https://github.com/InmaculadaRM/Top40Drugs
If you run the code, were those items what you were expecting? (I was surprised to see a group of drugs that weren’t as fashionable when I used to work in the pharmacy. Can you guess which group?)
Drawings and storytelling in Data Science can add interest, explanability and engagement to subjects and concepts otherwise hard to follow or understand. Here, a summary extracted from the results of my Machine Learning course assignment at the University of Stirling.
When I was a kid, in my natal city, all chicken eggs were white. and very often you were lucky to have two yolks in one egg. Now, most of the eggs are brown and it is very rare to find a two yolks egg.
I have used an invented probability for white and brown eggs to have two yolks in orden to explain conditional probability …mainly for myself as I keep forgetting the concept.
I’m on my first steps of learning HTML, CSS, SVG, D3.js and React with the aim to do interactive data visualizations. This is what I am capable of so far: My first in viz . (Not too much, I know … but it’s just the starting point.)