Strategix is an interactive bank campaign intelligence app that predicts which customers are most likely to subscribe to a term deposit. It combines a FastAPI machine-learning backend with a polished React/Vite frontend for lead prioritization, ROI simulation, model explainability, and manager-ready campaign briefs.

• React dashboard with responsive, Apple-inspired UI
• FastAPI backend serving both API endpoints and the production React build
• Random Forest classification model trained from the bank marketing dataset
• Pre-call customer scoring with duration excluded to avoid data leakage
• Ranked lead queue with score filters, priority filters, age/job filters, and CSV export
• ROI simulator for campaign threshold, call cost, revenue, profit, and calls saved
• Model explainability with feature importance and score-band validation
• Live customer profile testing through /api/predict
• Editable manager brief and recommended call-list export

• Frontend: React, Vite, CSS
• Backend: FastAPI, Uvicorn
• Data/ML: pandas, NumPy, scikit-learn
• Dataset: bank-additional-full.csv

Bank-Datathon/
|-- app.py                         # FastAPI backend and ML pipeline
|-- bank-additional-full.csv        # Required dataset
|-- model.pkl                       # Existing model artifact, not required by current app
`-- frontend/
    |-- package.json
    |-- vite.config.js
    |-- index.html
    `-- src/
        |-- App.jsx                 # Main React application
        |-- main.jsx
        `-- styles.css              # UI theme and interactions

Clone the repository and move into the project folder:

git clone <your-repo-url>
cd Bank-Datathon

Install Python dependencies:

pip install fastapi uvicorn pandas numpy scikit-learn pydantic

Install frontend dependencies:

cd frontend
npm install

Build the React frontend:

npm run build

Return to the project root:

cd ..

Run the app:

python -m uvicorn app:app --host 127.0.0.1 --port 8000

Open the website:

http://127.0.0.1:8000

Use two terminals.

Terminal 1, from the project root:

python -m uvicorn app:app --host 127.0.0.1 --port 8000

Terminal 2:

cd frontend
npm run dev

Open:

http://127.0.0.1:5173

Vite proxies /api requests to the FastAPI backend on port 8000.

GET  /api/health
GET  /api/bootstrap
POST /api/predict

Example prediction request:

{
  "profile": {
    "age": 40,
    "job": "admin.",
    "marital": "married",
    "education": "university.degree",
    "default": "no",
    "housing": "yes",
    "loan": "no",
    "contact": "cellular",
    "month": "may",
    "day_of_week": "mon",
    "campaign": 1,
    "pdays": 999,
    "previous": 0,
    "poutcome": "nonexistent",
    "emp.var.rate": 1.1,
    "cons.price.idx": 93.994,
    "cons.conf.idx": -36.4,
    "euribor3m": 4.857,
    "nr.employed": 5191.0
  }
}

• bank-additional-full.csv must be present in the project root.
• The current FastAPI app trains and caches the model at runtime from the CSV.
• duration is intentionally removed from features because it is only known after a customer call.
• frontend/dist/ is generated by npm run build and is served by FastAPI in production mode.

python -m py_compile app.py

cd frontend
npm run build

Invoke-WebRequest -UseBasicParsing http://127.0.0.1:8000/api/health

This project is for educational and hackathon/demo use. Add a license file before publishing if you plan to distribute or reuse it publicly.

• Anuja Sawant Email : sawantanuja16@gmail.com
• Varshith Sai Manchem Email : manchemvarshith123@gmail.com