https://github.com/shiblisec/shibli2700.github.io

Zeus is an Open Source Machine Learning Library which is easy to use and study.
https://github.com/shiblisec/shibli2700.github.io

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Zeus is an Open Source Machine Learning Library which is easy to use and study.

• Host: GitHub
• URL: https://github.com/shiblisec/shibli2700.github.io
• Owner: shiblisec
• License: gpl-2.0
• Created: 2018-05-06T09:54:33.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-07-25T04:01:45.000Z (over 6 years ago)
• Last Synced: 2023-08-17T05:17:22.553Z (over 1 year ago)
• Language: Python
• Size: 24.4 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Zeus - An Open Source Machine Learning Library

Zeus is created to provide an in-depth knowledge of some commonly used Machine Learning Algorithms.

## Prerequisites

- Numpy

- Python 3

You can install numpy using pip.

```

$ pip install numpy

```

## Installing Zeus

Download or clone the Zeus Repository from the following [Link](https://github.com/shibli2700/shibli2700.github.io)

Run the following command on your terminal to install Zeus

```

$ python setup.py build

```

## linear_model example

You can perform Linear Regression, Multiple Linear Regression using the linear_model package included in Zeus.

```python

from zeus.linear_regressors import regressors

regressor = regressors.LIRegressor()

x_train = [[1],[1.5],[3],[4.5]]

y_train = [[10000],[20000],[30000],[45000]]

regressor.train(x_train, y_train)

regressor.predict(5)

```

## Decision Tree Classifier Example

Decision Tree Classification can be done using the class DTreeClassifier() of the tree package.

```python

from zeus.tree import classifiers

classifier = classifiers.DTreeClassifier()

#training data having features such as Ear size, Body Size

training_data = [["Long","Big","Dog"],["Short","Small","Cat"],["Long","Small","Dog"]]

#test data

test_data = [["Short","Big"],["Big","Short"]]

node = classifier.train(training_data)

classifier.predict(test_data, node)

```

### Output

```

[{'Cat': '100%'}, {'Dog': '100%'}]

```