Project: Diabetes Prediction Using KNN

Overview: This project aims to predict whether a patient has diabetes based on medical predictor variables. It includes an IPython Notebook file (diabetes-prediction-using-knn.ipynb) containing the code for implementing the K-nearest neighbors (KNN) algorithm for prediction.

Instructions:

Dataset: The dataset required for this project is available in the dataset.rar file. Please extract the contents of the dataset.rar file to access the dataset needed for training and testing the model.

Running the Code:

Ensure you have Python installed on your system. Install the required dependencies by running:

Copy code

pip install -r requirements.txt

Open the diabetes-prediction-using-knn.ipynb file in a Jupyter Notebook environment or any compatible platform. Follow the instructions provided within the notebook to execute the code cells and perform the prediction using the KNN algorithm. Contents:

dataset.rar: Compressed file containing the dataset required for the project. diabetes-prediction-using-knn.ipynb: IPython Notebook file containing the code for implementing KNN for diabetes prediction. Note:

Ensure all necessary dependencies are installed before running the code. If you encounter any issues or have questions, please feel free to reach out to the project maintainers. Contributing: Contributions to this project are welcome. If you have any suggestions, enhancements, or bug fixes, please submit a pull request or open an issue on GitHub.

License: This project is licensed under the MIT License. Feel free to use and modify it according to your needs.