Diabetic Prediction ML with API

This project aims to create a Machine Learning model to predict the likelihood of diabetes and expose the model via an API.

Steps

1. Data Collection: Gather relevant data for diabetic prediction.
2. Data Preprocessing: Clean and preprocess the data.
3. Model Training: Build and train the ML model.
4. Model Evaluation: Evaluate the model's performance.
5. API Development: Develop an API to expose the trained model.
6. Deployment: Deploy the API to a server or cloud service.

Requirements

• Python 3.x
• Flask
• Scikit-learn
• Pandas
• Numpy

Setup

1. Clone the repository.
2. Install the required packages:

   pip install -r requirements.txt

3. Run the API:

   python app.py

API Endpoints

• POST /predict: Predict the likelihood of diabetes based on input data.

Example Request

curl -X POST -H "Content-Type: application/json" -d '{
  "gender": "Female",
  "age": 45,
  "hypertension": 0,
  "heart_disease": 0,
  "smoking_history": "never",
  "bmi": 28.5,
  "HbA1c_level": 5.5,
  "blood_glucose_level": 130
}' http://localhost:5000/predict