The Kidney Stone Prediction Classifier is a binary classification model developed to predict whether a patient is likely to have kidney stones based on various numerical features. This model aids healthcare professionals in making informed decisions for early detection and improved treatment outcomes.
The model has been trained on a dataset consisting of 414 entries, with a separate test dataset containing 276 entries. The dataset includes diverse information such as age, medical history, and other relevant factors, contributing to the model's ability to make accurate predictions.
The model is implemented in Python, leveraging popular libraries such as Pandas for data manipulation and Scikit-learn for machine learning functionalities. The code is well-organized, making it easy for developers and healthcare professionals to understand and potentially customize for specific use cases.
- Python 3.x
- Pandas
- Scikit-learn
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Install the required dependencies:
pip install pandas scikit-learn
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Clone the repository:
git clone https://github.com/shib1111111/Kidney-Stone-Prediction-Classifier.git
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Navigate to the project file "Kidney_Stone_Prediction_Classifier.ipynb" and run the scripts:
The model's performance has been evaluated on a separate test dataset to ensure its reliability. Metrics such as accuracy, precision, recall, and F1 score are provided in the evaluation results.
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I welcome contributions to enhance this repo. Feel free to open issues or submit pull requests.
This project is licensed under the MIT License.
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✨ --- Designed & made with Love by Shib Kumar Saraf ✨