https://github.com/michaelgathara/machine-learning
https://github.com/michaelgathara/machine-learning
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/michaelgathara/machine-learning
- Owner: Michaelgathara
- Created: 2022-05-22T02:14:41.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-11T04:47:27.000Z (over 1 year ago)
- Last Synced: 2025-01-26T05:16:43.233Z (4 months ago)
- Language: Jupyter Notebook
- Size: 2.22 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning
Built using Python 3.8 and Juyper Notebook.
Package Manager: https://pip.pypa.io/en/stable/
Get Python https://www.python.org
Learn more about Juypter https://jupyter.org/
There are some uses of Conda environments here and there as well
## Other works
- You can find some group projects I did with friends under the Github Organization [Computer Talkers](https://github.com/Computer-Talkers)
- I wrote a Neural Network from scratch for both GPUs and CPUs [here](https://github.com/Michaelgathara/GPU)
- Notebooks my team at the Economic Development Partnership of Alabama used to analyze where students were going post-grad from Alabama colleges [here](https://github.com/MCDC-Team-4/retain-alabama)
## Table of Contents
[Reference Notebooks](https://github.com/Michaelgathara/machine-learning/tree/main/reference-notebooks)
Reference Notebooks are Data Science/Machine Learning Juypter Noteboooks I was provided during my internship with the [University of Alabama Department of Physics](https://www.uab.edu/cas/physics/) and [The Economic Development Partnership of Alabama](https://edpa.org/). They cover some basics such as data cleaning and Pandas, as well as intermediate topics such as model validation.
[Regression Algorithms](https://github.com/Michaelgathara/simple-ml/tree/main/regression)
1. [Linear Regression](https://github.com/Michaelgathara/simple-ml/blob/main/regression/linear_regression.ipynb)
1a. **Dataset**: [Tesla Stock Data](https://www.kaggle.com/datasets/timoboz/tesla-stock-data-from-2010-to-2020?resource=download)
1b. **Attempt**: Predicting daily highs
1c. **Result**: Model Score: $$R^2 = 0.999803851997443$$ -Highly inflated, may be overfitted here
1d. **Usefulness**: Little to no usefulness due to the enigmatic nature of the stock market
## Datasets
[Kaggle](https://www.kaggle.com/)
## References
[Andrew Ng x Standford University Coursera Machine Learning Course](https://www.coursera.org/learn/machine-learning)
[Reference Notebooks](https://github.com/Michaelgathara/machine-learning/tree/main/reference-notebooks)