Gold Price Prediction Project

This project predicts the daily GLD price using market features.

It includes:

• A model training script
• A FastAPI backend for predictions
• A Streamlit web app for user input and results

What This Project Does

The model learns from historical data and predicts GLD price from these inputs:

• SPX
• USO
• SLV
• EUR/USD

Project Structure

• src/train_model.py trains the model and saves files
• src/gold_model.py contains shared ML logic (load data, train, save, load, predict)
• src/api.py provides REST API endpoints
• src/app.py provides Streamlit UI
• data/gld_price_data.csv is the dataset
• artifacts/ stores generated model and metrics

Easy Flow

1. Run training once.
2. Training creates model and metrics files in artifacts/.
3. Start FastAPI backend (model is loaded there).
4. Streamlit sends user input to FastAPI /predict.
5. Streamlit shows predicted GLD price from API response.

Output Files After Training

When training finishes, these files are created:

• artifacts/gold_price_model.joblib (trained model)
• artifacts/metrics.json (R2, MAE, RMSE)

Setup

Use your virtual environment and install dependencies:

pip install -r requirements.txt

Run Commands

Train model:

python src/train_model.py

Run FastAPI server:

python -m uvicorn src.api:app --reload

Run Streamlit app:

streamlit run app.py

Optional (if backend is not local):

$env:BACKEND_URL="http://127.0.0.1:8000"

API Usage

Base URL after running API:

• http://127.0.0.1:8000

Health route:

• GET /

Prediction route:

• POST /predict

Example request body:

{
  "spx": 1447.16,
  "uso": 78.47,
  "slv": 15.18,
  "eurUsd": 1.471692
}

Example response:

{
  "predicted_gld_price": 84.98
}

Notes

• If Streamlit cannot predict, ensure FastAPI is running and BACKEND_URL is correct.
• This project is for learning and experimentation.
• It is not financial advice.

Get This Repository

Clone from GitHub:

git clone https://github.com/saimtec/Gold-Price-Prediction.git
cd Gold-Price-Prediction

Install dependencies:

pip install -r requirements.txt