This project aims to predict the likelihood of a stroke using various machine learning algorithms. By analyzing medical and demographic data, we can identify key factors that contribute to stroke risk and build a predictive model to aid in early diagnosis and prevention.

• Introduction
• Problem Statement
• Methodology
  • Data Collection
  • Data Preprocessing
  • Model Development
• Results
• Visualizations
• Deployment
• How to Contribute
• References

Brain stroke, also known as a cerebrovascular accident (CVA), is a medical emergency characterized by the sudden interruption of blood flow to the brain, leading to a range of neurological impairments. Early detection and prompt intervention are crucial in preventing the devastating consequences of strokes and improving patient outcomes. This project utilizes machine learning techniques to predict stroke risk based on various health and demographic factors.

Stroke is a leading cause of death and disability worldwide. The high mortality and long-term care requirements impose a significant burden on healthcare systems and families. By developing a predictive model, we aim to:

1. Reduce the incidence of stroke through early intervention.
2. Alleviate healthcare costs associated with long-term stroke care.
3. Improve the quality of life for individuals at risk.

We acquired datasets from Kaggle, encompassing demographic, clinical, and lifestyle factors. The data includes variables such as age, BMI, blood glucose levels, hypertension status, and more.

Data preprocessing involves handling missing values, encoding categorical variables, and scaling numerical features. Exploratory Data Analysis (EDA) helps in understanding the data distribution and identifying key features.

We employed several machine learning algorithms to develop the predictive model, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, and others. The data was split into training and testing sets to evaluate the model's performance.

The performance of various models was evaluated based on accuracy, precision, recall, and F1 score. The Random Forest and Gradient Boosting Machines (GBM) showed the best performance with the highest accuracy rates.

Below are some visualizations illustrating the correlation between different factors and stroke risk:

The model has been deployed as a web application using Streamlit, making it accessible for real-time stroke risk assessment. You can access the application at Stroke Prediction App.

We welcome contributions from the community. Here are some ways you can contribute:

1. Data Improvement: Provide more comprehensive datasets to enhance the model.
2. Model Enhancement: Experiment with different algorithms and improve the accuracy of predictions.
3. Documentation: Help improve the documentation and add more examples.

1. Fork the repository.
2. Clone the forked repository.

   git clone https://github.com/your-username/stroke-prediction.git

3. Make your changes and commit them.

   git add .
   git commit -m "Description of your changes"

4. Push to the branch.

   git push origin feature-branch

5. Create a pull request describing your changes.