Classifying Heart Disease

  • Guided Project
  • 0.3 hours
  • Intermediate
  • Python

Practice creating and optimizing a logistic regression model in Python to detect heart disease from real patient data.

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Overview

In this project, you'll take on the role of a medical researcher working with the famous Cleveland Clinic Foundation heart disease dataset. Applying skills from the Logistic Regression Modeling in Python course, you'll go through the complete machine learning workflow of data exploration, data splitting, model creation, and model evaluation to develop a logistic regression classifier for detecting heart disease. This hands-on project allows you to practice key classification concepts on real patient data. You'll gain experience in exploratory data analysis, feature selection, model training and optimization, and performance assessment using metrics like accuracy, sensitivity and specificity. If you're new to logistic regression, consider taking the Logistic Regression Modeling in Python course first. Objective: Develop a logistic regression model to predict heart disease in patients and evaluate its real-world performance, strengthening your skills in classification for healthcare applications.

What You'll Learn

  • ✓ Create a logistic regression model from a dataset
  • ✓ Evaluate how well the classification model fits the data
  • ✓ Interpret the model coefficients
  • ✓ Evaluate the predictive power of the logistic regression model

Before You Start

  • ✓ Differentiating between classification and regression problems
  • ✓ Creating and interpreting logistic regression models and their coefficients
  • ✓ Calculating and interpreting evaluation metrics for classification models such as accuracy, sensitivity, and specificity
  • ✓ Using logistic regression models to make predictions on new data and convey results

Project Steps

8 steps

  1. 1 Introduction
  2. 2 Exploring the Dataset
  3. 3 Dividing the Data
  4. 4 Building the Model
  5. 5 Interpreting the Model Coefficients
  6. 6 Final Model Evaluation
  7. 7 Drawing Conclusions
  8. 8 Next Steps
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