Support Vector Regression

preprocessing

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset = pd.read_csv('_data/04.csv')
x = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
y = y.reshape(len(y), 1)

from sklearn.preprocessing import StandardScaler

sc_x = StandardScaler()
sc_y = StandardScaler()

x = sc_x.fit_transform(x)
y = sc_y.fit_transform(y)

Train

from sklearn.svm import SVR

regressor = SVR(kernel='rbf')
regressor.fit(x, y)

SVR()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Visualize

plt.scatter(sc_x.inverse_transform(x), sc_y.inverse_transform(y), color='red')
plt.plot(sc_x.inverse_transform(x), sc_y.inverse_transform(regressor.predict(x).reshape(-1, 1)))
plt.show()

High resolution

x_grid = np.arange(min(sc_x.inverse_transform(x)), max(sc_x.inverse_transform(x)), 0.1)
x_grid = x_grid.reshape((len(x_grid), 1))
plt.scatter(sc_x.inverse_transform(x), sc_y.inverse_transform(y), color='red')
plt.plot(x_grid, sc_y.inverse_transform(regressor.predict(sc_x.transform(x_grid)).reshape(-1, 1)))
plt.show()