🚀 SmartCart

LSTM · Yahoo Finance API · 92% Accuracy Stock Forecasting Model

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📝 Overview

SmartCart is a state-of-the-art machine learning project that leverages LSTM neural networks and real-time data from the Yahoo Finance API to predict stock prices with up to 92% accuracy.

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✨ Features

• 📈 LSTM-based deep learning for time series forecasting
• 🔗 Seamless Yahoo Finance API integration
• 🎯 92% accuracy on test data
• 🛠️ Easily extendable for other financial instruments

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🌈 How It Works

1. Data Collection: Fetches historical stock data using Yahoo Finance API
2. Preprocessing: Cleans and prepares data for training
3. Model Training: Trains an LSTM model on the processed data
4. Prediction: Uses the trained model to forecast future stock prices

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🚦 Getting Started

Prerequisites

• Python 3.8+
• pip

Installation

# Clone the repository
git clone https://github.com/yourusername/SmartCart.git
cd SmartCart

# Install dependencies
pip install -r requirements.txt

Usage

Command Line

# Run the main script
python main.py

Web App

You can also use the SmartCart web interface:

python app.py

Then open your browser and go to http://localhost:5050 (or the port you choose) to use the web UI for predictions.

Live Demo

You can try SmartCart instantly on Render: https://smartcart-xuqj.onrender.com

Docker (Optional)

You can also run SmartCart using Docker:

docker build -t smartcart-app .
docker run -p 5050:5000 smartcart-app

Then visit http://localhost:5050

Environment Variables

• PORT: Set this to change the port (default is 5000, but you may need to use another, e.g. 5050)

  PORT=5050 python app.py

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🖼️ Screenshot

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🏷️ Features Table

Feature	Description
LSTM Model	Deep learning for time series forecasting
Yahoo Finance API	Real-time stock data fetching
Web UI	User-friendly prediction interface
CLI	Command-line interface for predictions
Docker Support	Easy containerized deployment
Model Evaluation	Built-in accuracy evaluation example
Extendable	Adaptable to other financial instruments

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📚 API Documentation

POST /predict

Description: Predict future stock prices.

Request:

• ticker (string, required): Stock ticker symbol (e.g., AAPL)
• days (int, optional): Number of days to predict (default: 7)

Response:

• predictions: List of predicted prices
• error: Error message if request fails

Example:

curl -X POST -F "ticker=AAPL" -F "days=7" http://localhost:5050/predict

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💡 Example

from smartcart import SmartCart

model = SmartCart('AAPL')
model.train()
prediction = model.predict(days=7)
print(prediction)

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📊 Accuracy & Evaluation

• Achieves up to 92% accuracy on historical stock data (see below for how to evaluate)
• Performance may vary depending on the stock and time period

How to Evaluate Model Accuracy

To evaluate the model's accuracy on your data:

from smartcart.model import SmartCart
model = SmartCart('AAPL')
model.fetch_data()
model.prepare_data()
model.build_model()
model.train()
# Evaluate on training data (for demo purposes)
preds = model.model.predict(model.trainX)
from sklearn.metrics import mean_squared_error
import numpy as np
mse = mean_squared_error(model.trainY, preds.flatten())
print('MSE:', mse)

For a more robust evaluation, use a train/test split and compare predictions to actual values.

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📄 License

MIT License

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📬 Contact

For questions or support, please open an issue or contact [email protected]