This project implements a machine learning model using Support Vector Machine (SVM) to predict the presence of diabetes based on various health attributes. The model processes a diabetes dataset to classify individuals as diabetic or non-diabetic by analyzing key health indicators.
- Python: Programming language used for data analysis and model development.
- Pandas: Library for data manipulation and analysis.
- NumPy: Library for numerical operations in Python.
- Scikit-learn: Machine learning library used for model training and evaluation.
- Seaborn: Visualization library based on Matplotlib for informative statistical graphics.
- Matplotlib: Plotting library used for creating static, animated, and interactive visualizations.
The dataset used in this project is the Diabetes Dataset, which contains several health-related features such as glucose levels, body mass index (BMI), age, and more. The outcome variable indicates whether an individual has diabetes (1) or not (0). You can download the dataset from Kaggle.
To run this project, ensure you have Python installed on your machine. Follow these steps to set up the environment:
- Clone the repository:
git clone https://github.com/yourusername/diabetes-prediction.git cd diabetes-prediction