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https://github.com/jhonsnow456/ml-algorithms

An ongoing repository that contains machine Learning models on various topics which do precise analysis and verification over the given dataset for each model done through data visualization techniques.
https://github.com/jhonsnow456/ml-algorithms

data-science practise python visualization

Last synced: 9 months ago
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An ongoing repository that contains machine Learning models on various topics which do precise analysis and verification over the given dataset for each model done through data visualization techniques.

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README

          

# ML-Algorithms

Welcome to the ML-Algorithms repository! This repository contains implementations of various machine learning algorithms using Python. The purpose of this repository is to provide an accessible resource for individuals who are interested in learning about and experimenting with machine learning algorithms.

## Contents

The repository contains the following machine learning algorithms:

- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- K-Means Clustering
- Principal Component Analysis
- Support Vector Machine
- Neural Networks

Each algorithm has its own Python script and is accompanied by a Jupyter Notebook that demonstrates its usage.

## Getting Started

To use this repository, you should have Python 3 installed on your machine. You will also need to install the following packages:

- numpy
- pandas
- matplotlib
- scikit-learn

Once you have installed these packages, you can clone this repository to your local machine using the following command:

```
git clone https://github.com/jhonsnow456/ML-Algorithms.git
```

## Usage

To use the algorithms in this repository, simply navigate to the directory containing the algorithm you wish to use and run the Python script. You can also run the Jupyter Notebook for each algorithm to see an example of how it can be used.

## Contributing

If you would like to contribute to this repository, please fork the repository and submit a pull request with your changes. We welcome contributions from all individuals and are happy to review any pull requests.

## License

This repository is licensed under the MIT license. Please see the `LICENSE` file for more information.