Exploring predictive insights on heart attacks using supervised learning and feature engineering techniques.
Welcome to our Machine Learning endeavor! Here, we delve into the realms of Artificial Intelligence and Machine Learning, expanding our skills and knowledge. This project presents both a challenge and a valuable learning opportunity, enabling us to explore concepts like supervised learning, feature engineering, and beyond, with the goal of mastering new techniques while solidifying our existing expertise.
Dataset: Heart Attack Prediction Dataset on Kaggle
This dataset contains various health-related features, such as age, sex, cholesterol levels, blood pressure, etc., along with a target variable indicating the likelihood of a heart attack. It consists of real patient data collected from hospitals.
Repository Structure 📁 /data: Contains the datasets from Kaggle. /notebooks: df_dummies.ipynb: Notebook for data preparation. ml_project_EDA.ipynb: Notebook containing exploratory data analysis and visualizations.
- Supervised Learning: We utilize supervised learning techniques to train our model using labeled data, where the target variable (likelihood of a heart attack) is known.
- Feature Engineering: We perform feature engineering to select and transform relevant features from the dataset, enhancing the predictive power of our model.
- Feature Importance: Through analysis, we identify key features that significantly influence the likelihood of a heart attack, providing insights into the underlying factors contributing to heart health.
- Model Performance: We evaluate the performance of our Machine Learning model using appropriate metrics such as accuracy, precision, recall, and F1-score, to assess its effectiveness in predicting heart attacks.
This README provides an overview of our Machine Learning project, outlining the dataset, techniques used, and key findings. For further details, please refer to our project documentation and codebase.
