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Project 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.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- 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
Projects steps
Step 1: Introduction
Step 2: Exploring the Dataset
Step 3: Dividing the Data
Step 4: Building the Model
Step 5: Interpreting the Model Coefficients
Step 6: Final Model Evaluation
Step 7: Drawing Conclusions
Step 8: Next Steps
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