Machine Learning Project

Heart Disease Detection Machine Learning Project

Project Overview

This machine learning project focuses on predicting whether individuals have heart disease based on a variety of health readings. The project involved collecting a dataset with medical information and applying various machine learning techniques to develop a model capable of accurate predictions.

Key Components

• Data Collection: The dataset included multiple health indicators such as age, cholesterol levels, blood pressure, and more, relevant to diagnosing heart disease.
• Exploratory Data Analysis (EDA): EDA was performed to understand the patterns and trends in the data, identify outliers, and prepare it for modeling.
• Feature Selection and Preprocessing: Important features were selected, and the data was normalized to ensure consistent performance across different models.
• Model Training: Multiple machine learning algorithms were tested, including decision trees, logistic regression, random forest, and support vector machines (SVM). The goal was to select the most accurate model.
• Model Evaluation: Performance was evaluated using metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques were applied to ensure the model’s reliability.

Technologies Used

• Scikit-learn: For implementing machine learning algorithms.
• Pandas and NumPy: For data manipulation and analysis.
• Matplotlib and Seaborn: For data visualization and EDA.

How to Run Locally

1. Clone the repository to your local machine.
2. Explore the Jupyter notebooks to follow the workflow from data analysis to model training.
3. Train and test the models using the provided code.
4. Evaluate the performance of each model and identify the best-performing one.

Github link

https://github.com/GiosSiebe/AI-Oefeningen/tree/main

Conclusion

This project demonstrates the application of machine learning to healthcare, showcasing my ability to develop predictive models for diagnosing heart conditions based on medical data.