https://github.com/georgiifirsov/machinelearningmodels

Library with implementation of several machine learning models. It is written in Python 3 with numpy.
https://github.com/georgiifirsov/machinelearningmodels

ai artificial-intelligence machine-learning ml pyhton pyhton3

Last synced: 10 days ago
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Library with implementation of several machine learning models. It is written in Python 3 with numpy.

• Host: GitHub
• URL: https://github.com/georgiifirsov/machinelearningmodels
• Owner: GeorgiiFirsov
• License: gpl-3.0
• Created: 2019-12-28T16:36:12.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2019-12-29T13:53:45.000Z (almost 5 years ago)
• Last Synced: 2024-08-10T04:23:16.072Z (3 months ago)
• Topics: ai, artificial-intelligence, machine-learning, ml, pyhton, pyhton3
• Language: Jupyter Notebook
• Homepage:
• Size: 505 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# MachineLearningModels

This repository contains machine learning models implementation in Python 3.

You can look for some examples in files Example\.ipynb.

### Currently implemented

- [Generalized linear regression (with polynomials)](./ExampleLinearRegression.ipynb)

- [Multiple linear regression](./ExampleMultipleLinearRegression.ipynb)

### Usage

Usage of these models is really simple - you just need to import required model class and fit it with your data.

#### Generalized linear regression

```python

from MachineLearning.LinearRegression import LinearRegression

from MachineLearning.Metrics import mean_squared_error

model = LinearRegression(degree=3)

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print(mean_squared_error(y_pred, y_test))

```

#### Multiple linear regression

```python

from MachineLearning.MultipleLinearRegression import MultipleLinearRegression

from MachineLearning.Metrics import mean_squared_error

model = MultipleLinearRegression()

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

print(mean_squared_error(y_pred, y_test))

```