MACHINE LEARNING RECIPES
DATA CLEANING PYTHON
DATA MUNGING
PANDAS CHEATSHEET
ALL TAGS
# What is Jarque Bera test?

# What is Jarque Bera test?

This recipe explains what is Jarque Bera test

What is Jarque Bera test? How to perform it in python.

The Jarque-Bera test tests the goodness of fitting of data, whether the data have skewness and kurtosis that matches with a normal distribution curve.

To conduct the jarque-bera test we directly use the inbuilt jarque_bera() function which is available in sci-py Library.

```
import numpy as np
import scipy.stats as stats
import pandas as pd
```

```
df= pd.read_csv('/content/sample_data/california_housing_train.csv')
df.head()
```

```
#perform Jarque-Bera test
stats.jarque_bera(df)
```

The test statistic is 2009089.7744870293 and the corresponding p-value is 0.0. The p-value is less than 0.05, we reject the null hypothesis. Now we have sufficient evidence to say that this data has skewness and kurtosis which is different from a normal distribution.

In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.