Diabetes Prediction using Machine Learning

📌 Project Overview

This project aims to predict whether a patient has diabetes based on a medical dataset using various machine learning techniques. The dataset contains patient medical records, and the model applies supervised learning methods, including deep learning, to make accurate predictions.

📂 Repository Structure

Diabetes-Prediction/
│── Koch_Elliot_Predict_Diabetes_Presentation.pdf  # Project Presentation
│── Koch_Elliot_Predict_Diabetes_Report.pdf        # Final Report
│── Koch_Elliot_Predict_Diabetes.ipynb            # Jupyter Notebook with ML model
│── README.md                                      # Project documentation
│
├── Datasets/
│   ├── data.csv                                   # Main dataset
│   ├── mapping.csv                                # Additional data mapping

📊 Dataset

The dataset consists of medical records with around 50 features, including patient demographics, medical history, and diabetes indicators.

• Data Source: Processed from medical datasets
• Features:
  • Patient demographics (age, gender, ethnicity)
  • Medical indicators (glucose level, insulin usage, etc.)
  • Diabetes classification (diabetic/non-diabetic)

⚙️ Installation & Setup

To run the project, follow these steps with python version 3.11.9:

1. Clone the repository

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

2. Setup virtual environment

   python -m venv .venv
   .venv\Scripts\activate
   where python

3. Install dependencies

   python.exe -m pip install --upgrade pip
   pip install -r requirements.txt

4. Run the jupyter notebook

   jupyter notebook Koch_Elliot_Predict__Diabetes.ipynb

5. Close the virtual environment

   deactivate

🧠 Model Training

The project applies multiple machine learning techniques:

• Supervised Learning: Linear Regression, Random Forest, SVM, Decision Tree, Gradient Boosting Machine (GBM)
• Unsupervised Learning: K-Means Clustering
• Deep Learning: A neural network with hyperparameter tuning using Keras

🏆 Model Performance

Model	Accuracy
Basic Neural Network	99.6%
Optimized Deep Learning	99.87%

🚀 Usage

After running the notebook, the trained model can be used for predictions. On a different Python file, the best model can be loaded with:

   loaded_model = tf.keras.models.load_model("dl_model.h5")

📜 License

This project is open-source and free to use for research and educational purposes.

📧 Contact

For any inquiries, contact Elliot Koch at [kochelliotpro@gmail.com].