Implementing logistic regression using scikit-learn

Now that you know all about logistic regression, let's implement it in Python using the scikit-learn library. Let's create a model using naive Bayes classification. We will do so using the following steps:

1. We will first import the dataset and the required libraries using the following code:

# Import libraries
import pandas as pd
# read the dataset
diabetes = pd.read_csv("diabetes.csv")

# Show top 5-records
diabetes.head()

This results in the following output:

In our preceding example, we are reading the Pima Indians Diabetes dataset. This dataset does not give the column names, so we have to do so.

2. In the read_csv() function, we will pass the header to None and names to the column list that was created before reading the CSV file:

# Split dataset in two parts: feature set and target label
feature_set = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp&apos...