python-scikit-learnUsing quadratic linear regression

Quadratic regression can be achieved by using PolynomialFeatures to prepare dataset for polynomial form:

from sklearn import preprocessing, linear_model
import numpy as np

data = np.array([[1, 1], [2, 4], [3, 9], [4, 15], [5, 21], [6, 36]])
X = np.array(data[:,0]).reshape(-1,1)
y = data[:,1]

poly = preprocessing.PolynomialFeatures(degree=2, include_bias=False)
Xp = poly.fit_transform(X)

model = linear_model.LinearRegression()
model.fit(Xp, y)

y_pred = model.predict(Xp)ctrl + cgithub

`from sklearn import` import module from scikit-learn	`.PolynomialFeatures(` create polynomial object to prepare our dataset for polynomial form
`poly.fit_transform(` transform original dataset to polynomial form	`linear_model.LinearRegression` initialize linear regression model
`.fit(` train transformation model	`.predict(` predict target variable based on given features dataset

Usage example

from sklearn import preprocessing, linear_model
import numpy as np

data = np.array([[1, 1], [2, 4], [3, 9], [4, 15], [5, 21], [6, 36]])
X = np.array(data[:,0]).reshape(-1,1)
y = data[:,1]

poly = preprocessing.PolynomialFeatures(degree=2, include_bias=False)
Xp = poly.fit_transform(X)

model = linear_model.LinearRegression()
model.fit(Xp, y)

print(model.predict(Xp))

output

[ 1.57142857  3.62857143  7.97142857 14.6        23.51428571 34.71428571]

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python-scikit-learnUsing quadratic linear regression

Usage example

output

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