Classification Algorithms

This repository contains example implementations and demos of simple classification algorithms (Jupyter notebooks):

• KNN.ipynb — Custom K-Nearest Neighbors implementation with synthetic datasets, visualizations, and experiments (k selection).
• Backpropagation_Logic_Gates_&_Linear_data.ipynb — Small feedforward network for logic gates and a linear regression demo.

Requirements

Install dependencies (macOS / terminal):

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Example requirements (if you don't have a file):

numpy
matplotlib
scikit-learn
jupyter

Usage

Open and run the notebooks with Jupyter:

jupyter lab
# or
jupyter notebook

Execute a notebook headless:

jupyter nbconvert --to notebook --execute KNN.ipynb --output executed_KNN.ipynb

Notes

• KNN.ipynb defines a minimal KNearestNeighbors class (fit, predict, predict_proba) and includes plotting for three synthetic datasets.
• The backpropagation notebook shows a tiny neural network for AND/OR/XOR examples and a linear regression toy example.

Contributing

• Add improvements on a feature branch and open a pull request.
• Include tests or example runs for reproducibility.

License

Add a LICENSE file or specify a license before distributing.