Stroke Prediction Web App using Streamlit

This repository contains the code for a web application built with Streamlit for predicting the likelihood of a stroke based on various features. The app utilizes a machine learning model trained on a dataset with several features related to health and lifestyle.

Features in the Dataset

The dataset used for training the model includes the following features:

1. Age: The age of the individual.
2. Hypertension: Indicates whether the individual has hypertension (0 for no, 1 for yes).
3. Heart Disease: Indicates whether the individual has heart disease (0 for no, 1 for yes).
4. Marital Status: The marital status of the individual.
5. Work Type: The type of work the individual is engaged in.
6. Residence Type: The type of residence (urban or rural).
7. Average Glucose Level: The average glucose level in the individual's blood.
8. Body Mass Index (BMI): The body mass index of the individual.
9. Smoking Status: The individual's smoking status.
10. Stroke: The target variable indicating whether the individual had a stroke (0 for no, 1 for yes).

These features are used to predict the likelihood of a stroke for a given individual. The web app provides an intuitive interface for users to input their information and receive a prediction regarding their stroke risk.The dataset is publicly available on kaggle https://www.kaggle.com/datasets/jillanisofttech/brain-stroke-dataset.

Here's a link to the web app https://stroke-prediction007.streamlit.app/

Usage

To run the web app locally, make sure you have Streamlit installed. Then, simply run the following command:

streamlit run app.py

Contributions

Contributions to improve the web app or the underlying machine learning model are welcome. Feel free to fork the repository and submit a pull request with your changes.

License

This project is licensed under the MIT License - see the LICENSE file for details. A stroke prediction app using Streamlit is a user-friendly tool designed to assess an individual's risk of experiencing a stroke. By inputting relevant health data such as age, blood pressure, cholesterol levels, and lifestyle factors, the app utilizes predictive algorithms to calculate the user's likelihood of having a stroke. The app's intuitive interface and interactive features make it easy for users to understand their risk factors and take proactive measures to prevent stroke.