🩺 Diabetes Risk Prediction

A comprehensive machine learning project that predicts diabetes risk using health indicators and symptoms. This project follows a complete ML engineering workflow from data exploration to deployment-ready models.

🎯 Project Overview

• Objective: Develop an accurate binary classification model to predict diabetes risk
• Dataset: Early stage diabetes risk prediction dataset from UCI ML Repository
• Models: 8 different classification algorithms compared systematically
• Approach: Complete ML engineering workflow with proper train/validation/test splits

📊 Key Features

• Comprehensive EDA: Deep data exploration with visualizations
• Multiple Models: Decision Tree, Naive Bayes, Logistic Regression, KNN, SVM, Neural Network, Random Forest, Gradient Boosting
• Proper Validation: 60/20/20 train/validation/test split to prevent overfitting
• Medical Metrics: Focus on sensitivity/specificity for healthcare applications
• Production Ready: Model serialization and deployment guidelines

🚀 Quick Start

1. Clone the repository

   git clone https://github.com/ralphcajipe/diabetes-prediction.git
   cd diabetes-prediction

2. Install dependencies

   pip install pandas numpy scikit-learn matplotlib seaborn jupyter

3. Run the notebook

   jupyter notebook diabaetes_risk_prediction.ipynb

📁 Project Structure

diabetes-prediction/
├── diabaetes_risk_prediction.ipynb  # Main analysis notebook
├── dataset/
│   └── diabetes_data_upload.csv     # Dataset file
├── models/                          # Saved model artifacts (generated)
│   ├── best_diabetes_model.pkl
│   ├── feature_scaler.pkl
│   └── feature_names.pkl
└── README.md                        # Project documentation

🔬 Methodology

1. 🎯 Scoping Phase

• Define clear success criteria (>90% accuracy, balanced precision/recall)
• Establish baseline performance metrics

2. 📊 Data Phase

• Exploratory Data Analysis (EDA) with comprehensive visualizations
• Data preprocessing and feature encoding
• Strategic train/validation/test splitting (60/20/20)

3. 🤖 Modeling Phase

• Systematic evaluation of 8 classification algorithms
• Model comparison with validation metrics
• Error analysis focusing on medical implications

4. 🚀 Deployment Phase

• Model serialization for production use
• Deployment strategy and monitoring guidelines

📈 Model Performance

The project evaluates multiple algorithms:

• Decision Tree Classifier
• Gaussian Naive Bayes
• Logistic Regression
• K-Nearest Neighbors
• Support Vector Machine
• Neural Network (MLP)
• Random Forest
• Gradient Boosting

Results available after running the notebook

🏥 Medical Significance

• Low False Negative Rate: Minimizes missed diabetes cases
• Interpretable Predictions: Clear confidence scores for clinical decisions
• Feature Importance: Identifies key health indicators

📚 Dataset Information

Source: UCI Machine Learning Repository

Features: Age, Gender, Polyuria, Polydipsia, Sudden Weight Loss, Weakness, Obesity, and more

Target: Binary classification (Diabetes: Yes/No)

🛠️ Technologies Used

• Python 3.x
• Pandas - Data manipulation
• NumPy - Numerical computations
• Scikit-learn - Machine learning algorithms
• Matplotlib/Seaborn - Data visualization
• Jupyter Notebook - Interactive development

📝 License

This project is open source and available under the MIT License.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.