This project aims to analyze heart attack data and develop a machine learning model to predict the likelihood of a person experiencing a heart attack based on various health metrics.
Heart disease is a leading cause of death worldwide, and early prediction can significantly improve patient outcomes. In this project, we analyze a dataset containing several health parameters such as age, sex, cholesterol levels, and exercise habits, among others, to identify patterns and factors associated with heart attacks. We then build a machine learning model to predict the risk of a heart attack for an individual based on these parameters.
We start by cleaning the dataset, handling missing values, and encoding categorical variables as necessary.
We perform exploratory data analysis to gain insights into the distribution of features, correlations, and identify any potential patterns or anomalies in the data.
We use various techniques such as correlation analysis, feature importance, and domain knowledge to select relevant features for training the machine learning model.
We experiment with different machine learning algorithms such as logistic regression, random forest, and gradient boosting to build predictive models. We optimize hyperparameters using techniques like grid search or random search.
This project is licensed under the MIT License.