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A predictive web application that collects user responses of credit health questions, predicts their credit remedy needs using ML model (and simple rule based ), and securely saves the data (name, email, answers, and prediction) directly into a Google Spreadsheet for further tracking and analysis, Built for deployment on AWS EC2.

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enigmao/Course_Predictor_using_ML

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Credit Health Check - Flask + Google Sheets

This project is a Flask web application that collects responses to 20 credit health questions, predicts a risk category, and stores submissions directly in a Google Spreadsheet.


🚀 Features

  • Flask-based questionnaire with Yes/No questions.
  • Collects Name, Email, and answers.
  • Predicts a remedy status (Preventive, Remedy Needed, Urgent, Invalid).
  • Saves responses directly with date, name, email, answers, prediction to a Google Sheet.

🛠 Requirements

Install dependencies from requirements.txt:

pip install -r requirements.txt

▶️ Running the App

Local development

python Application.py

Visit: http://127.0.0.1:5000

Production

gunicorn -w 4 -b 0.0.0.0:5000 Application:app

📊 Data Saved in Google Sheets

Date Name Email Q1 Q2 ... Q20 Prediction
2025-08-03 Your Name you@example.com Yes No ... Yes Urgent

📂 Project Structure

.
├── Application.py     # Flask backend
├── index.html         # Questionnaire form
├── response.html      # Result page
├── requirements.txt   # Dependencies
└── README.md          # Project guide

About

A predictive web application that collects user responses of credit health questions, predicts their credit remedy needs using ML model (and simple rule based ), and securely saves the data (name, email, answers, and prediction) directly into a Google Spreadsheet for further tracking and analysis, Built for deployment on AWS EC2.

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