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sklearn regression

Georgiy Kantsedal answered on August 6, 2022 Popularity 10/10 Helpfulness 4/10

answer sklearn regression

related linear regression using sklearn

sklearn regression

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>>> import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
>>> # y = 1 * x_0 + 2 * x_1 + 3
>>> y = np.dot(X, np.array([1, 2])) + 3
>>> reg = LinearRegression().fit(X, y)
>>> reg.score(X, y)
1.0
>>> reg.coef_
array([1., 2.])
>>> reg.intercept_
3.0...
>>> reg.predict(np.array([[3, 5]]))
array([16.])

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: python regression

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Contributed on Aug 06 2022

Georgiy Kantsedal

0 Answers Avg Quality 2/10

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linear regression using sklearn

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df = pd.read_csv('bottle.csv')
print(df)
df_binary = df[['Salnty', 'T_degC']]
print(df_binary)
  
# Taking only the selected two attributes from the dataset
df_binary.columns = ['Sal', 'Temp']
#display the first 5 rows
df_binary.head()

Popularity 10/10 Helpfulness 1/10 Language whatever

Source: www.geeksforgeeks.org

Tags: linear-regression linear-regressio

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Contributed on Feb 26 2023

Hafizuddin Syed

0 Answers Avg Quality 2/10

sklearn regression

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sklearn regression

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linear regression using sklearn

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