Diabetes Prediction using a Support Vector Machine (SVM)

Welcome to the Diabetes Prediction project, where we leverage the Support Vector Machine (SVM) algorithm to forecast the likelihood of diabetes based on health indicators.

Project Overview

In this project, we aim to employ machine learning techniques to predict diabetes occurrence using features like glucose levels, blood pressure, BMI, etc. The SVM algorithm is chosen for its robustness in handling classification tasks, particularly in scenarios with intricate decision boundaries.

Dataset

The diabetic prediction dataset contains features related to health indicators such as glucose levels, blood pressure, BMI, etc., along with the target variable indicating the presence or absence of diabetes. This dataset is used to train and evaluate the SVM model for predicting diabetes.

Implementation

Explore the Diabetic_prediction_using_SVM.ipynb notebook to dive into the Python code for data preprocessing, model training, and evaluation using the SVM algorithm. Follow along to understand the step-by-step process of building a predictive model for diabetes.

Dependencies

• Python 3
• NumPy
• Pandas
• Matplotlib
• Scikit-learn
• LabelEncoding

Getting Started

1. Clone the repository.
2. Navigate to the project directory.
3. Open and run the Diabetic_prediction_using_SVM.ipynb notebook in Jupyter Notebook or JupyterLab.
4. Follow the instructions to preprocess the data, train the SVM model, and evaluate its performance.

Feel free to explore, modify, and experiment with the code to enhance your understanding of machine learning and predictive modeling!

License

This project is licensed under the MIT License. Please look at the LICENSE file for details.