Linear regression in python

Scikit learn linear regression

from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
reg.coef_
reg.intercept_
reg.predict(np.array([[3, 5]]))

Source: scikit-learn.org

Linear regression in python

from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X, y)

Python linear regression

from sklearn.linear_model import LinearRegression
#x, y = np.array([0,1,2,3,4,5])

model = LinearRegression().fit(x.reshape(-1,1), y.reshape(-1,1))
r_sq = model.score(x.reshape(-1,1), y.reshape(-1,1))
q = model.intercept_
m = model.coef_

y_fit = np.array([i*m[0] for i in x]+q[0])

Python linear regression

>>> from scipy import stats
>>> import numpy as np
>>> x = np.random.random(10)
>>> y = np.random.random(10)
>>> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

Source: docs.scipy.org

Python linear regression

import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('tips')
sb.regplot(x = "total_bill", y = "tip", data = df)
plt.show()

Source: tutorialspoint.com

Similar codes

21 votes233 views5 answers
Base64 python decode
Python
-1 vote166 views1 answer
Let’s add a docstring to the greeting method. How about, “Outputs a message with the name of the person”.
Python
0 vote158 views1 answer
Use a callable instead, e.g., use `dict` instead of `{}`
Python
3 votes199 views3 answers
Simple jwt django
Python
0 vote159 views3 answers
Autoextract scrapy spider
Python
1 vote241 views1 answer
Django queryset multiple filters
Python Django
0 vote158 views1 answer
Remove prefix from string python
Python
0 vote169 views1 answer
Remove alphabetic characters python
Python
0 vote201 views1 answer
Dictionary to a dataframe pandas arrays must all be same length
Python
-2 votes151 views1 answer
Python multiplayer
Python