Metadata-Version: 2.1
Name: regressionAlgorithm
Version: 0.1
Summary: Linear Regression Algorithm
Home-page: https://github.com/rizki4106/regressionAlgorithm
Author: Rizki Maulana
Author-email: rizkimaulana348@gmail.com
License: MIT
Download-URL: https://github.com/rizki4106/regressionAlgorithm/v_01.tar.gz
Description: # POLINOMIAL REGRESSION ALGORITHM
        
        ```bash
        pip3 install regressionAlgorithm
        ```
        ## POLYNOMIAL ORDER 1
        
        !['formula'](https://i.postimg.cc/R3qrYY4P/least-squared.png)
        
        ```python
        from regressionAlgorithm import PolynomialOrderOne
        import matplotlib.pyplot as plt
        
        # value
        x = [1,2,3,4,5]
        y = [5,4,3,2,1]
        
        # intial
        rl = PolynomialOrderOne()
        rl.fit(x,y)
        rl.start()
        
        # get data
        print(rl.plot())
        print(rl.line())
        
        # data visualization
        
        plt.figure(figsize=(10,7))
        plt.scatter(x,y)
        plt.plot(x, rl.line())
        plt.savefig("visual.png")
        
        # r squared
        print(rl.r_squared()) # result 1.0
        
        # r value
        print(rl.r_value()) # result -1.0
        ```
        ![graph.png](https://i.postimg.cc/hvd4PJR2/graph.png)
        
        ### PREDICT Y VALUE
        ```python
        ...
        x = 10
        # formula (y = a + b * x) = (y = a + b * 10)
        print(rl.predict(x)) # result -4.0
        ```
        
        ## POLYNOMIAL ORDER 2
        
        !['formula](https://i.postimg.cc/18vhJShn/kuadratik.png)
        
        ```python
        from regressionAlgorithm import PolynomialOrderTwo
        import matplotlib.pyplot as plt
        
        # value
        x = [1,2,3,4,5]
        y = [5,4,3,2,1]
        
        # intial
        q = PolynomialOrderTwo()
        q.fit(x,y)
        q.start()
        
        # result
        print(q.plot())
        print(q.r_square())
        
        # data visualization
        plt.figure(figsize=(10,7))
        plt.scatter(x,y, color="blue")
        plt.plot(x, k.draw_line(), color="red")
        plt.savefig("quadratic.png")
        ```
        ![quadratic.png](https://i.postimg.cc/G2wVdprj/quadratic.png)
        
        ### PREDICT Y VALUE
        
        ```python
        ...
        x_pred = 5
        
        # formula y = a + b * x + c * square(x) -> y = a + b * x_red + c * square(x_pred)
        print(q.predict(x_pred)) # result 0.9999999999997522
        ```
Keywords: linear-regression,machine-learning,supervise-learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
