Linear regression with python

Question

I need to do the linear regression calculation, but I read that there is no possibility to use/install scipy in windows. Is there any other scipy-like library to perform this kind of calculation? Or if there is any way to install scipy in windows, it is also welcome! thanks.

Answer 1

According to the own documentation of scipy it doesn’t turn very well in Windows because it has some dependencies that only work in linux and mac

Alternatively, I recommend using sklearn, she is a very good lib working with Learning machine, and also has good documentation plus several examples.

to install it you can:

pip install -U scikit-learn

or if using anaconda:

conda install scikit-learn

I made an example using the numpy (to work with arrays) and the matplot (to work with graphics) To install:

pip install numpy

python -m pip install -U pip setuptools

python -m pip install matplotlib

In anaconda usually these libs already come installed.

Below is an example of creating a linear regression with the sklearn

    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn import linear_model

    #Logica x = x*10 + acc
    #acc = acc + 5
    #acc inicia em 0

    #dataSet treino
    #1 - 10 + 0 = 10
    #2 - 20 + 5 = 25
    #3 - 30 + 10 = 40
    #4 - 40 + 15 = 55
    x_train = np.array([ [1], [2], [3], [4] ]);
    y_train = np.array([ 10, 25, 40, 55 ]);

    #dataSet teste
    #5 - 50 + 20 = 70
    #6 - 60 + 25 = 85
    #7 - 70 + 30 = 100
    #8 - 80 + 35 = 115
    x_test = np.array([ [5], [6], [7], [8] ])
    y_test = np.array([ 70, 85, 100, 115 ])

    #cria o modelo e faz o treinamento (fit)
    model = linear_model.LinearRegression().fit(x_train, y_train)

    #exibe algumas informações
    print('Coeficientes: \n', model.coef_)
    print("Erro médio quadrado: %.2f" % np.mean((model.predict(x_test) - y_test) ** 2))
    print('variância de score: %.2f' % model.score(x_test, y_test))

    #monta o plot para exibição do resultado
    plt.scatter(x_test, y_test,  color='black')
    plt.plot(x_test, model.predict(x_test), color='blue', linewidth=3)
    plt.xticks(())
    plt.yticks(())
    plt.show()

This code will generate a graph in this way:

An example of the documentation of the sklearn with linear regression applied on diabetes tests:

Linear Regression Example

Answer 2

From what I read on scipy runs on Windows yes.

[Google Translate] For most users, especially on Windows, the easiest way to install Scipy stack packages is to download one of these Python distributions, which include all major packages: Anaconda, Enthought Canopy, Python(x, y), Winpython, Pyzo

Try one of those:

• Anaconda: A free distribution for the Scipy stack. Compatible with Linux, Windows and Mac.
• Enthought Canopy: Free and commercial versions include the core Scipy stack packages. Supports Linux, Windows and Mac.
• Python(x, y): A free distribution including the Scipy stack, based around the Spyder IDE. Windows only.
• Winpython: A free distribution including the Scipy stack. Windows only.
• Pyzo: A free distribution based on Anaconda and the interactive IEP development environment. Supports Linux, Windows and Mac.