Diabetes Health Prediction and Analysis 🎉

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Welcome to the Diabetes Health Prediction and Analysis project! This repository contains a comprehensive pipeline for predicting diabetes diagnosis using various machine learning and deep learning models, along with an in-depth exploratory data analysis and feature engineering steps.

🚀 Project Overview

This project aims to provide a thorough analysis of diabetes-related health data, develop predictive models, and evaluate their performance. The key components of the project include:

• 📊 Data Preprocessing
• 🔍 Exploratory Data Analysis (EDA)
• 🛠️ Feature Engineering
• 🧠 Model Training
• 📈 Model Evaluation
• 📑 Comprehensive Reports

📂 Project Structure

Here's an overview of the project directory structure:

Diabetes_Health_Prediction_and_Analysis/
├── data/
│   ├── raw/
│   │   └── diabetes_data.csv
│   ├── processed/
│   │   ├── X_train.csv
│   │   ├── X_train_engineered.csv
│   │   ├── X_test.csv
│   │   ├── X_test_engineered.csv
│   │   ├── y_train.csv
│   │   └── y_test.csv
├── app/
│   ├── app.py
│   ├── templates/
│   │   └── index.html
│   └── static/
│       └── styles.css
├── models/
│   ├── logistic_regression.pkl
│   ├── random_forest.pkl
│   └── xgboost.pkl
├── notebooks/
│   └── exploratory_data_analysis.ipynb
├── scripts/
│   ├── plots/
│   ├── reports/
│   ├── data_preprocessing.py
│   ├── feature_engineering.py
│   ├── model_training.py
│   ├── model_evaluation.py
│   └── model_performance_report.py
├── tests/
│   ├── models/
│   ├── test_data_preprocessing.py
│   ├── test_feature_engineering.py
│   ├── test_model_training.py
├── requirements.txt 
└── README.md

🔧 Setup and Installation

To get started with this project, follow the steps below:

1. Clone the repository:

   git clone https://github.com/ThecoderPinar/Diabetes_Health_Prediction_and_Analysis.git
   cd Diabetes_Health_Prediction_and_Analysis

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Run the data preprocessing script:

   python scripts/data_preprocessing.py

5. Run the feature engineering script:

   python scripts/feature_engineering.py

6. Train the models:

   python scripts/model_training.py

7. Evaluate the models:

   python scripts/model_evaluation.py

8. Generate comprehensive model performance reports:

   python script/comprehensive_model_report.py

🚀 Usage

• Exploratory Data Analysis: Check the notebooks/exploratory_data_analysis.ipynb notebook for detailed data analysis and visualizations.
• Scripts: All scripts for data preprocessing, feature engineering, model training, and evaluation are located in the scripts/ directory.
• Tests: To ensure code quality and correctness, tests are included in the tests/ directory. Run them with pytest.

📊 Models

The following models are trained and evaluated in this project:

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Logistic Regression

ROC Curve:

The ROC curve illustrates the true positive rate (sensitivity) versus the false positive rate (1-specificity) for different threshold settings. A higher area under the curve (AUC) indicates better model performance.

Confusion Matrix:

The confusion matrix provides a summary of the prediction results on the classification problem. It shows the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) predictions.

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Random Forest

ROC Curve:

The ROC curve illustrates the true positive rate (sensitivity) versus the false positive rate (1-specificity) for different threshold settings. A higher area under the curve (AUC) indicates better model performance.

Confusion Matrix:

*The confusion matrix provides a summary of the prediction results on

🎯 Performance Metrics

The performance of the models is evaluated using the following metrics:

• Accuracy
• Precision
• Recall
• F1 Score
• ROC AUC Score
• Confusion Matrix

Logistic Regression

• Accuracy (Doğruluk): %78.99
• Precision (Kesinlik): %73.19
• Recall (Duyarlılık): %70.63
• F1 Score: %71.89
• ROC AUC: %83.86

Confusion Matrix:

[[196  37]
 [ 42 101]]

Model dosyası:

models/logistic_regression.pkl

Random Forest

• Accuracy (Doğruluk): %91.22
• Precision (Kesinlik): %94.35
• Recall (Duyarlılık): %81.82
• F1 Score: %87.64
• ROC AUC: %97.69

Confusion Matrix:

[[226   7]
 [ 26 117]]

Model dosyası:

models/random_forest.pkl

Explanations:

1. Accuracy: The ratio of correctly predicted instances to the total instances.
2. _Precision:_ The ratio of true positive predictions to the total predicted positives. It measures the accuracy of positive predictions.
3. Recall: The ratio of true positive predictions to the actual positives. It measures the model's ability to identify positive instances.
4. F1 Score: The harmonic mean of precision and recall. It provides a balance between precision and recall.
5. ROC AUC: The area under the ROC curve. It summarizes the model's ability to distinguish between classes.

Confusion Matrix:

• True Positive (TP): 117 - The number of actual positive cases correctly identified by the model.
• True Negative (TN): 226 - The number of actual negative cases correctly identified by the model.
• False Positive (FP): 7 - The number of actual negative cases incorrectly identified as positive by the model.
• False Negative (FN): 26 - The number of actual positive cases incorrectly identified as negative by the model.

Explanations:

1. Accuracy: The ratio of correctly predicted instances to the total instances.
2. Precision: The ratio of true positive predictions to the total predicted positives. It measures the accuracy of positive predictions.
3. Recall: The ratio of true positive predictions to the actual positives. It measures the model's ability to identify positive instances.
4. F1 Score: The harmonic mean of precision and recall. It provides a balance between precision and recall.
5. ROC AUC: The area under the ROC curve. It summarizes the model's ability to distinguish between classes.

Confusion Matrix:

• True Positive (TP): 117 - The number of actual positive cases correctly identified by the model.
• True Negative (TN): 226 - The number of actual negative cases correctly identified by the model.
• False Positive (FP): 7 - The number of actual negative cases incorrectly identified as positive by the model.
• False Negative (FN): 26 - The number of actual positive cases incorrectly identified as negative by the model.

XGBoost

• Accuracy (Doğruluk): %91.76
• Precision (Kesinlik): %93.08
• Recall (Duyarlılık): %84.62
• F1 Score: %88.64
• ROC AUC: %98.41

Confusion Matrix:

[[224   9]
 [ 22 121]]

Model dosyası:

models/xgboost.pkl

Explanations:

1. Accuracy: The ratio of correctly predicted instances to the total instances.
2. Precision: The ratio of true positive predictions to the total predicted positives. It measures the accuracy of positive predictions.
3. Recall: The ratio of true positive predictions to the actual positives. It measures the model's ability to identify positive instances.
4. F1 Score: The harmonic mean of precision and recall. It provides a balance between precision and recall.
5. ROC AUC: The area under the ROC curve. It summarizes the model's ability to distinguish between classes.

Confusion Matrix:

• True Positive (TP): 121 - The number of actual positive cases correctly identified by the model.
• True Negative (TN): 224 - The number of actual negative cases correctly identified by the model.
• False Positive (FP): 9 - The number of actual negative cases incorrectly identified as positive by the model.
• False Negative (FN): 22 - The number of actual positive cases incorrectly identified as negative by the model.

📈 Results

Model performance reports and evaluation metrics are saved and displayed in the comprehensive_model_report.py script output.

💡 Future Work

• Implement more advanced deep learning models (e.g., Neural Networks, LSTM).
• Perform hyperparameter tuning to optimize model performance.
• Explore feature selection techniques to improve model accuracy.
• Integrate additional health datasets for broader analysis.

🤝 Contributing

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

Whether it's improving the documentation, adding new features, or fixing bugs, your contributions are highly appreciated. Let's make this project better together! 🚀

How to Contribute:

1. Fork the Repository: Click on the 'Fork' button at the top right corner of this page to create a copy of this repository in your GitHub account.

2. Clone the Forked Repository:

   git clone https://github.com/your-username/Diabetes_Health_Prediction_and_Analysis.git

3. Create a New Branch:

   git checkout -b feature/your-feature-name

4. Make Your Changes: Implement your feature, bug fix, or improvement.

5. Commit Your Changes:

   git commit -m "Add your commit message here"

6. Push to Your Forked Repository:

   git push origin feature/your-feature-name

7. Open a Pull Request: Go to the original repository on GitHub and click on the 'New Pull Request' button. Compare changes from your forked repository and submit the pull request.

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Thank you for your contributions! Together, we can build a more robust and efficient Diabetes Health Prediction and Analysis tool. 🌟

📄 License

This project is licensed under the MIT License.

📬 Contact

If you have any questions or suggestions, feel free to open an issue or contact me directly. I am always open to feedback and would love to hear from you!

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How to Reach Me:

• Email: piinartp@gmail.com
• GitHub Issues: Open an Issue
• LinkedIn: Your LinkedIn Profile

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Thank you for your interest in the Diabetes Health Prediction and Analysis project! Your feedback and suggestions are invaluable in making this project better and more useful for everyone. 🌟

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⭐️ Don't forget to give this project a star if you found it useful! ⭐️