Exploring the Logistic Regression Algorithm with Heart Disease Dataset in Python

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Logistic Regression is a popular classification algorithm used in machine learning. In this tutorial, we will explore how to implement the Logistic Regression algorithm using Python’s scikit-learn library. We will use the Heart Disease dataset as an example and cover the necessary steps, including importing and preprocessing the data, training the model, evaluating its performance, and making predictions. By the end of this tutorial, you will have a good understanding of how to use Logistic Regression for classification problems in Python.

Importing the Dataset

The first step is to import the Heart Disease dataset from the UCI Machine Learning Repository website. We can use the pandas library to read the dataset.

import pandas as pd

url = "https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data"

# Read the CSV file from the URL into a pandas dataframe
heart_df = pd.read_csv(url, header=None)

# Print the first 5 rows of the dataframe
print(heart_df.head())

In this example, we are reading the data file from the URL “https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data" into a pandas DataFrame named heart_df. We set the header…