Find p values of regression model using sklearn

Find p values of regression model using sklearn. P Value is a statistical test that determines the probability of extreme results of the statistical hypothesis test.

Recipe Objective - Find p-values of regression model using sklearn?

Regression - Linear Regression is a supervised learning algorithm used for continuous variables. It is the relationship between the dependent and independent variable, where the dependent variable is the response variable denoted as "y" and the independent variable is denoted as "x". y = mx + c Let's understand how it is implemented in Sci-kit learn.

P-Value - P-Value is a statistical test that determines the probability of extreme results of the statistical hypothesis test, taking the Null Hypothesis to be correct.

Build Expedia Hotel Recommendation System using Machine Learning

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Example:-

Step:1 Importing Libraries:-

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

score_df = pd.read_csv('student_scores.csv')
print(score_df.shape)

(25, 2)

score_df.head()


score_df.describe()

Step:2 Plotting the 2-d graph

score_df.plot(x='Hours', y='Scores', style='o')
plt.title('Hours vs Percentage')
plt.xlabel('Hours Studied')
plt.ylabel('Percentage Score')
plt.show()

 

Step:3 Data Preparation

X = score_df.iloc[:, :-1].values
y = score_df.iloc[:, 1].values

Step:4 Getting P-Value

from sklearn.feature_selection import chi2

scores, pvalues = chi2(X, y)
pvalues

array([0.10860225])

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