Metadata-Version: 2.1
Name: py4svd-regressor
Version: 0.0.1
Summary: SVD Based Linear Regression Python Library
Home-page: https://github.com/luthfi118/py4svd_regressor
Author: Mgs. M. Luthfi Ramadhan
Author-email: luthfir96@gmail.com
License: UNKNOWN
Description: # py4svd-regressor
        
        SVD Based Linear Regression Python Library
        
        ## Getting Started
        
        This project is simply an implementation of a Linear Regression algorithm based on SVD in python programming language
        
        ### Prerequisites
        
        Numpy
        
        ### Installation
        
        The easiest way to install py4svd-regressor is by using pip
        
        ```
        pip install py4svd-regressor
        ```
        
        ### Usage
        2 public methods are provided namely ```learn``` and ```predict```. The ```learn``` method used to train the model. It takes 2 arguments, the data, and its target. The ```predict``` method used to predict the given data, it takes 1 argument, it is the  data user wanted to predict. It returns the resulting prediction. The weight and intercept are stored on attributes namely ```w``` and ```b``` respectively
        ```
        from py4svd_regressor.regression import Svd_Regressor
        from sklearn.datasets import make_regression
        from matplotlib import pyplot
        
        x, y = make_regression(n_samples=200, n_features=1, noise=5)
        model = Svd_Regressor()
        model.train(x, y)
        pyplot.scatter(x, y)
        pyplot.plot(x, model.w*x + model.b, color='red')
        pyplot.show()
        y_predict = model.predict(x)
        ```
        <a href="https://ibb.co/1bmf57T"><img src="https://i.ibb.co/QXKFRpM/svd-regression.png" alt="svd-regression" border="0"></a>
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
