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Diabetes Prediction

📌 Project Overview

This project aims to predict the likelihood of a patient having diabetes based on health parameters. By utilizing machine learning models, it provides insights into potential diabetes risks and assists in early diagnosis.

🚀 Features

  • Data preprocessing and exploratory data analysis (EDA)
  • Feature selection and engineering
  • Machine learning model development
  • Model evaluation and optimization

🛠 Tech Stack

  • Python
  • Pandas, NumPy
  • Scikit-learn
  • Matplotlib, Seaborn
  • Jupyter Notebook

📂 Dataset

The dataset includes various medical parameters such as:

  • Age
  • Blood Pressure
  • Glucose Levels
  • BMI
  • Insulin Levels
  • Diabetes Pedigree Function

📊 Machine Learning Models Used

  • Logistic Regression
  • Random Forest Classifier
  • Support Vector Machine (SVM)
  • XGBoost

🔥 Results

The models are evaluated based on accuracy, precision, recall, and AUC-ROC score. The best-performing model provides reliable predictions for diabetes risk.

📁 Repository Structure

📂 Diabetes-Prediction
👉 📂 data (Dataset & processed data)
👉 📂 notebooks (Jupyter Notebooks)
👉 📂 models (Trained models)
👉 📂 images (Code and Results Screenshots)
👉 📄 README.md (Project documentation)

🖼 Code and Results

Include images of code and results in the images folder. Example:

💜 How to Run the Project

  1. Clone the repository: 
    git clone https://github.com/rohitinu6/Diabetes-Prediction.git
  2. Navigate to the project folder: 
    cd Diabetes-Prediction
  3. Install dependencies: 
    pip install -r requirements.txt
  4. Run the Jupyter Notebook or Python scripts to train and test models.

📞 Links

🗝 Tags

Machine Learning Diabetes Prediction Health Analytics Data Science Python EDA

📝 License

This project is licensed under the MIT License.

💡 For any queries or collaboration opportunities, feel free to connect! 🚀