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Stroke Prediction Binary Classification Project

Project Overview

The Stroke Prediction Binary Classification project aims to develop a machine learning model that predicts the likelihood of a stroke occurring in patients based on various health and lifestyle factors. This project utilizes a binary classification approach, where the output indicates whether a patient is at risk of having a stroke (1) or not (0).

Objectives

Data Analysis and Preprocessing

  • Analyze and preprocess health-related data to prepare it for modeling.
  • Handle missing values and encode categorical variables.

Model Implementation

  • Implement various machine learning algorithms to classify stroke risk.
  • Compare the performance of different models using appropriate metrics.

Evaluation

  • Evaluate and compare the performance of different models using appropriate metrics.
  • Assess the accuracy, precision, recall, and F1-score of trained models.

Dataset

The project uses a dataset that includes features such as:

  • Age
  • Hypertension
  • Heart Disease
  • Marital Status
  • Work Type
  • Residence Type
  • Average Glucose Level
  • Body Mass Index (BMI)
  • Smoking Status

Technologies Used

Programming Language

  • Python

Libraries

  • Pandas: for data manipulation
  • NumPy: for numerical operations
  • Scikit-learn: for machine learning algorithms and evaluation metrics
  • Matplotlib and Seaborn: for data visualization

Installation

To set up the project, follow these steps:

  1. Clone the repository:

git clone

  1. Navigate to the project directory:

cd Stroke-Prediction-Binary-Classification

Usage

  1. Load the dataset using the provided Jupyter Notebook.
  2. Explore and preprocess the data (handling missing values, encoding categorical variables).
  3. Split the dataset into training and testing sets.
  4. Train various classification models (e.g., Logistic Regression, Random Forest, Support Vector Machine).
  5. Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Thanks to all contributors and resources that made this project possible.