Heart Disease Prediction Classification Model

A Logistic Regression model for heart disease risk classification (Accuracy ≈ 80%). Full technical guide available on Medium: https://medium.com/@jeremiahomueti/build-your-first-machine-learning-model-step-by-step-no-experience-needed-090545175d9b

1. Overview and Objective

This project documents the development and evaluation of a supervised machine learning model designed to predict the presence of heart disease in a patient. The goal is to perform a binary classification (0 = no disease, 1 = disease) using clinical data.

The project demonstrates an end-to-end Machine Learning workflow, including data analysis, preparation, model training, and performance evaluation.

2. Technical Article and Documentation

A comprehensive, step-by-step technical article detailing the development process, conceptual explanations (e.g., train-test split, supervised learning), and code walkthrough is available on Medium.

Full Article: https://medium.com/@jeremiahomueti/build-your-first-machine-learning-model-step-by-step-no-experience-needed-090545175d9b

3. Methodology and Algorithms

• Learning Type: Supervised Learning.
• Algorithm: Logistic Regression, chosen for its effectiveness in binary classification tasks.
• Data Split: The dataset was partitioned into a training set and a testing set using an 80% / 20% split.

4. Key Performance Results

The model's performance was evaluated using the unseen test set (20% of the data).

• Final Accuracy Score: Approximately 80%.

5. Required Libraries

This project was developed in a Jupyter Notebook using the Python programming language. The following libraries were used and are required to run the notebook:

• Data Manipulation: pandas, numpy.
• Visualization: matplotlib, seaborn.
• Machine Learning: scikit-learn (for data splitting, model training, and evaluation).