🩺 Diabetes Prediction using Machine Learning

📌 Overview

This project aims to predict the likelihood of an individual having diabetes based on various health-related attributes. Utilizing machine learning algorithms, the model analyzes input data to provide accurate predictions, aiding in early diagnosis and management of diabetes.

📂 Project Structure

Diabetes-prediction/
├── data/
│   ├── diabetes_012_health_indicators_BRFSS2015.csv
│   ├── diabetes_prediction_dataset.csv
│   └── pima-data.csv
├── notebooks/
│   ├── PIMA.ipynb
│   ├── dataset_prediction.ipynb
│   └── diabetes.ipynb
├── README.md
├── LICENSE

🧠 Algorithms Implemented

• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier
• Support Vector Machine (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes

📊 Dataset Description

The project utilizes the following datasets:

1. PIMA Indians Diabetes Dataset: Medical data of Pima Indian women aged 21 and above.
2. BRFSS 2015 Dataset: Behavioral Risk Factor Surveillance System data focused on health-related risk behaviors.
3. Custom Diabetes Prediction Dataset: Curated dataset combining various health indicators.

Each dataset includes features such as:

• Pregnancies
• Glucose Level
• Blood Pressure
• Skin Thickness
• Insulin
• BMI
• Diabetes Pedigree Function
• Age
• Outcome (0: Non-diabetic, 1: Diabetic)

🛠️ Installation & Setup

1. Clone the repository:

   git clone https://github.com/anandprakash0/Diabetes-prediction.git
   cd Diabetes-prediction
   
    Create a virtual environment (optional):
   

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Install the required packages:

pip install pandas numpy matplotlib seaborn scikit-learn

Run the Jupyter Notebook:

jupyter notebook

Open any of the .ipynb files to start exploring the analysis and models.

📈 Model Evaluation Metrics

Accuracy Score

Confusion Matrix

Precision, Recall, F1-Score

ROC-AUC Curve

🔍 Exploratory Data Analysis (EDA)

Included in the notebooks:

Missing value treatment

Outlier detection

Feature scaling

Correlation analysis

Data visualization

🚀 Future Enhancements

Add deep learning models (e.g., Neural Networks)

Deploy with Flask or Streamlit

Use more datasets for greater diversity

Add cross-validation and hyperparameter tuning

🤝 Contributing

Contributions are welcome! Fork the repository and submit a pull request. For major changes, open an issue first to discuss what you would like to change. 📄 License

This project is licensed under the MIT License. See the LICENSE file for details.