🩺 Diabetes Prediction

A machine learning project for predicting diabetes using clinical and demographic features.

Overview • Dataset • Installation • Usage • Project Structure • Contributing • License

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Overview

This repository contains exploratory data analysis (EDA) and machine learning models to predict whether a patient is likely to have diabetes based on various health indicators. The project demonstrates an end-to-end data science workflow including data cleaning, visualization, feature engineering, model training, and evaluation.

Dataset

File	Description
data/diabetes_dataset__2019.csv	Primary dataset with clinical measurements (2019)
data/diabetes_prediction_dataset.csv	Extended dataset for prediction modeling

Features

The datasets include features such as:

• Demographics: Age, Gender, BMI
• Medical History: Hypertension, Heart Disease, Smoking History
• Clinical Measurements: Blood Glucose Level, HbA1c Level
• Target: Diabetes diagnosis (binary classification)

Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup

1. Clone the repository

   git clone https://github.com/<your-username>/diabetes-prediction.git
   cd diabetes-prediction

2. Create a virtual environment (recommended)

   python -m venv .venv
   
   # Windows
   .\.venv\Scripts\Activate.ps1
   
   # macOS/Linux
   source .venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Usage

Running the Notebooks

Launch Jupyter and open the notebooks:

jupyter notebook
# or
jupyter lab

Notebook	Purpose
notebooks/diabetes_notebook__2019.ipynb	EDA and analysis of the 2019 dataset
notebooks/diabetes_prediction_notebook.ipynb	ML model training, evaluation, and prediction

Quick Start

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

# Load data
df = pd.read_csv('data/diabetes_prediction_dataset.csv')

# Prepare features and target
X = df.drop('diabetes', axis=1)
y = df['diabetes']

# Train model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier()
model.fit(X_train, y_train)

print(f"Accuracy: {model.score(X_test, y_test):.2%}")

Project Structure

diabetes-prediction/
├── 📄 README.md                              # Project documentation
├── 📄 LICENSE                                # MIT License
├── 📄 requirements.txt                       # Python dependencies
├── 📄 .gitignore                             # Git ignore rules
├── � data/
│   ├── diabetes_dataset__2019.csv            # Dataset (2019)
│   └── diabetes_prediction_dataset.csv       # Prediction dataset
├── � notebooks/
│   ├── diabetes_notebook__2019.ipynb         # Analysis notebook
│   └── diabetes_prediction_notebook.ipynb    # Prediction notebook
└── 📁 docs/
    ├── Research paper.docx                   # Research paper
    └── GptZero.png                           # Additional documentation

Results

Model performance on the diabetes prediction dataset:

Model	Accuracy
XGBoost 🏆	96.23%
Random Forest	95.64%
Decision Tree	94.51%
KNN	88.84%
Logistic Regression	87.92%
SVM	87.29%

Best model: XGBoost with 96.23% accuracy

Contributing

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

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

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

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