sisi-y/-kenya_diabetes_classifier

Kenya Diabetes Risk Classifier A machine learning project that predicts diabetes risk using age, BMI, and glucose level. Built with Python and scikit-learn as part of my AI/ML engineering journey. # 🇰🇪 Kenya Diabetes Risk Classifier A Machine Learning project that predicts whether a person is at risk of diabetes based on: - Age - BMI (Body Mass Index) - Glucose Level This project demonstrates end-to-end ML workflow including: - Data preprocessing - Model training - Model evaluation - Prediction --- ## 🎯 Project Goal To build a simple but practical healthcare risk prediction model tailored to a Kenyan context using structured health data. --- ## 🧠 Model Overview - **Algorithm Used:** Logistic Regression - **Type:** Supervised Learning (Binary Classification) - **Target Variable:** Diabetes Risk (0 = No Risk, 1 = At Risk) --- ## 📊 Features Used | Feature | Description | |---------------|-------------| | Age | Age in years | | BMI | Body Mass Index | | Glucose Level | Blood glucose measurement | --- ## ⚙️ Tech Stack - Python - NumPy - Pandas - Matplotlib - Scikit-learn - Google Colab --- ## 🚀 How It Works 1. Load dataset 2. Clean and preprocess data 3. Split into training and testing sets 4. Train Logistic Regression model 5. Evaluate accuracy 6. Predict risk for new inputs --- ## 📈 Example Prediction ```python new_patient = [[45, 31.2, 165]] prediction = model.predict(new_patient)