a system that can predict whether a person has diabetes or not with the help of Machine Learning. This project is done in Python. In this project, we use Support Vector Machine model for the prediction.
Diabetes Detection Machine Learning System This repository contains a machine learning system for predicting whether a person has diabetes or not. The system is implemented in Python using the Scikit-learn library and uses a Support Vector Machine (SVM) model for prediction.
Dataset The dataset used to train and test the model is the Pima Indians Diabetes Database from Kaggle. The dataset includes 768 instances with 8 features, such as age, BMI, blood pressure, and glucose level, among others. The target variable is a binary classification indicating whether the patient has diabetes or not.
Model The model is built using Python and the Scikit-learn machine learning library. The SVM model is chosen for its effectiveness in binary classification problems. The dataset is split into a training set and a test set, with 80% of the instances used for training and 20% for testing.
Usage To use the system, follow these steps:
Clone the repository to your local machine. Install the required Python packages by running pip install -r requirements.txt. Run the Jupyter notebook Diabetes_Detection.ipynb. Follow the instructions in the notebook to load the dataset, train the model, and make predictions on new data. Contributing Contributions to the repository are welcome. To contribute, fork the repository and submit a pull request with your changes.
License The code in this repository is licensed under the MIT license. See the LICENSE file for more information.