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<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="static/css/index.css">
<title>Stock Price Prediction</title>
</head>
<body>
<h1>Stock Close Price Prediction</h1>
<p>This project implements an LSTM (Long Short-Term Memory) model for stock price prediction, deployed using Flask.</p>
<p>
The LSTM model is pretrained on four major stock tickers: <strong>Amazon (AMZN), Microsoft (MSFT), Google (GOOG),</strong> and <strong>Apple (AAPL)</strong>.
For these stocks, the prediction process is optimized and ready to use. However, the app is designed to handle predictions for other stock tickers as well.
When a new stock ticker is selected, the app dynamically trains a new LSTM model for that stock. Please note that this process will take some time as the model needs to be trained first.
</p>
<p>
<li>
This project demonstrates the integration of <strong>batch learning</strong> (pretraining on select stock tickers) and <strong>online learning</strong> (dynamic training on user-requested stock tickers).
</li>
<li>
A streamlit web app is also deployed - <a href="https://stock-prediction-web-app-y8rdn3fupbxijb2thvzxb6.streamlit.app/">Stock-Prediction-Web-App</a>
</li>
</p>
<a href="app.py/home">Take A Demo</a>
</body>
</html>