Linear Perceptron

Recently, in my Mathematics of Data Models course, we learned how a linear perceptron worked. I found it very interesting and decided to write my own linear perceptron in python. This was not assigned for any class and was completely self led. I utilized Seaborn's penguin dataset for this project.

To Run

1. Run pip install -r requirements.txt
2. Run python Main.py

Note

I showed this repository to my professor, and he had a few recommendations that I decided to implement. They are:

• Use Numpy arrays and dot products
  • Understand view vs copy in numpy as well
• Boolean indexing using numpy when judging across all data points
• Boolean indexing for faster label checking
• Batch learning (Run against everything all at once and update it based on all teh information)
• Test how my model compares against scikit learn