Libraries for predictive biostatistics in Python

In predictive statistics, the hypothesis is a bit different. The main question is – Can we find associations between variables? To answer this, one of the best packages to start with is statsmodels. We can use it to create predictive models and perform predictive hypothesis tests. We will be using the diabetes dataset to try to find associations between cholesterol and triglycerides in diabetic subjects.

Let’s start with coding:

import statsmodels.api as sm
import pandas as pd
data=pd.read_csv(r'C:\Users\MEDIN\Downloads\Dataset of Diabetes .csv')

The next step is to filter based on the diabetes presence status. After the filtering, only subjects with diabetes will remain in the data:

# Filter the data based on 'CLASS' column
filtered_data = data[data['CLASS'] == 'Y']
# Extract the Chol and TG variables
Chol = filtered_data['Chol']
TG = filtered_data['TG&apos...