Pima-Diabetes-Prediction

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Pima-Diabetes-Prediction

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This project implements a complete machine learning pipeline to predict diabetes risk using the Pima Indians dataset. It covers data preprocessing, model building, validation, optimization, and deployment via a FastAPI-based API.

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# Pime Diabetes Prediction

## Overview

This project implements a complete machine learning pipeline to predict diabetes risk using the Pima Indians dataset. It covers data preprocessing, model building, validation, optimization, and deployment via a FastAPI-based API.

**Content:**
- [Scripts](#scripts)
- [Notebooks](#notebooks)
- [Deployment](#deployment)

**Online Project**  
For more details and updates about this project, visit [this website](https://haroldeustaquio.com/projects/pima-diabetes-prediction).

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## Scripts

**data_preprocessing.py**  
- Loads and transforms raw data (replacing zero values with NaN for key features).  
- Generates 8 preprocessing combinations (applying/removing outliers, balancing, scaling).  
- Saves processed CSV files with descriptive names.

**pipeline.py**  
- Configures preprocessing using simple and iterative imputations via a ColumnTransformer.  
- Integrates a pre-trained classifier into a unified pipeline.  
- Trains the model using processed data and saves the pipeline.

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## Notebooks

- **01_data_preprocessing.ipynb:** Data import, cleaning, exploration, and normalization.
- **02_data_model_validation.ipynb:** Splitting data, model evaluation with metrics, and comparison.
- **03_model_optimization.ipynb:** Hyperparameter tuning and robust model validation.

Diagrams illustrate the ML pipeline architecture and best model performance.

---

## Deployment

- **API Overview:**  
  A FastAPI-based service for diabetes prediction.
  
- **Key Endpoints:**  
  - **GET `/`**: Health check.  
  - **POST `/predict`**: Returns a binary prediction.  
  - **POST `/predict_proba`**: Returns class probability scores.
  
- **Deployment Methods:**  
  Includes instructions for local Docker deployment and scalable deployment on AWS Elastic Beanstalk.

---

## Contributing

We welcome contributions! If you have ideas, improvements, or fixes, please feel free to fork the repository and submit pull requests. Your involvement helps make this project even better.

Pima-Diabetes-Prediction

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