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

1 vote108 views2 answers
Python print trailing zeros
Python
0 vote97 views1 answer
How to make a distance function in python
Python
0 vote109 views1 answer
Python classmethod
Python
14 votes133 views6 answers
Pandas merge df
Python
12 votes95 views6 answers
Python remove empty string from list
Python
11 votes113 views3 answers
Django q filter
Python
0 vote100 views1 answer
Return dataframe as csv flask
Python
1 vote92 views1 answer
Required validator python WTForms
Python
5 votes126 views3 answers
Python String and Integer concatenation
Python
2 votes99 views4 answers
Decision tree classifier sklearn
Python