Practical examples crafted with the Python language.
Most of my code is currently hosted on gitlab.com/remojo, I have examples of practical scripts you can modify for your own processes. You will find a diverse array of general solutions that I have discovered to solve complex analysis requirements.
The repositories that I have listed are mostly my personal notes from listed sources. They are templates that I use to craft software with the Python programming language.
In my sources.txt file you will find a list of references by author name, followed by links to online courses.
The majority of the applications are used to explore core competencies, for example combining text or integer object types. The apps folder contains examples of each to show how to experiment and mix them together.
These apps can be used with a basic installation of Python on your computer. Source code for these applications comes from books and online courses that I have researched, the original authors materials are used to build functions from scratch, then move into crafting professional products using class and exception handling features for example.
Interact with Python via web browser based Jupyter notebooks.
The Jupyter folder contains examples of Python code that can be run interactively within your favorite web browser. You will need to install additional software if you want to operate the notebook on your local device. The code is arranged in blocks that can be triggered individually as you implement each step of your business requirements. If all of your steps work, you can compile these blocks into one at the bottom of the notebook into one regular Python file that can then be easily automated on your server setup for data gathering.
Current Machine Learning research.
The majority of my current working knowledge and notes on Python based Artificial Intelligence (AI) can be found in the ml folder.
The ml folder contains basic research models for scikit-learn which is a Python module for advanced analysis. Machine learning (ML) algorithms can be used to create applications that automatically improve themselves based on data and experience.
I start with a poisonous mushroom example found on the data site Kaggle. The TPS generator spreadsheet can be used to generate a test categorical data set, create a pattern and see if your model can detect it. Most of this code came from pluralsight courses by Janani Ravi, use the TPS reports to replace her wine data set.
The TPS report generator can also be used to add unneeded columns which will require extra steps to remove prior to importing the dataframe. This is to mimic real world production environments where you do not have control over the input file and have to perform cleaning steps.
Browse through my public code repositories for simple solutions to common business requirements. Contact me if you would like to develop a custom solution or training session for your organization.