The purpose of this repository is myself practice of Logistic Regression. I am a student that is learning, let me know if you find any errors, the original code is from examples and exercises found in books, tutorials and other sources all mentioned in this file. I am just practicing what I have learned, the proper authors and creators of the algorithms/code are the ones mentioned in the file.
I will be using Streamlit to develop an App where I will be sharing what I've learned. To run the app, first clone the repository, install the project dependencies, then run the app with streamlit.
Before installing the requirements, it's recommended to create a virtual environment.
pip install -r requirements.txt
streamlit run logistic_regression.py
- Tutorial Python Engineer :https://www.youtube.com/watch?v=JDU3AzH3WKg
- Book: Data Science from Scratch, Joel Grus
- https://ml-cheatsheet.readthedocs.io/en/latest/forwardpropagation.html#
- https://scikit-learn.org/stable/datasets/index.html#breast-cancer-dataset
- https://www.youtube.com/watch?v=JDU3AzH3WKg&t=807s
- https://ml-cheatsheet.readthedocs.io/en/latest/logistic_regression.html
- https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc
- Maximum likelihood https://www.youtube.com/watch?v=XepXtl9YKwc
- Coefficient https://www.youtube.com/watch?v=vN5cNN2-HWE