📈 Stock Price Prediction with Flask Web App

This repository provides a full solution for predicting stock prices using an LSTM model. It includes a Jupyter Notebook for training and a Flask Web App for real-time predictions and visualization.

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📊 Project Overview

The project follows these main steps:

• 📥 Importing Libraries and Datasets: Load essential libraries and stock price data.
• 🧮 Pivoting the Data: Organize the data into a suitable format for analysis.
• 📈 Plotting Close Prices: Visualize the closing stock prices over time.
• 📉 Moving Averages: Compute and plot 10-day and 100-day moving averages.
• 🔄 Normalizing Data: Scale data using MinMaxScaler for better LSTM training.
• 🧠 LSTM Model: Train a Long Short-Term Memory (LSTM) neural network.
• 💾 Model Saving: Save the trained model as lstm.pkl using joblib.
• 🌐 Deployment: Build a user-friendly Flask web app to display predictions interactively.

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🔧 Requirements

Install the necessary Python libraries with or simply refer requirements.txt:

pip install numpy pandas matplotlib scikit-learn tensorflow keras flask joblib

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👨‍💻 How to Use This Project

✅ Step 1: Clone the Repository

git clone https://github.com/yourusername/stock-price-predictor.git
cd stock-price-predictor

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✅ Step 2: Train and Save the Model

1. Open the Jupyter Notebook:

   jupyter notebook

2. Run the cells to:
   • Load and process stock data
   • Train the LSTM model
   • Save the trained model as lstm.pkl

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✅ Step 3: Run the Flask Web App

1. Ensure lstm.pkl is present in the same directory as app.py.
2. Run the Flask app:

python app.py

3. Open your browser and go to:
   👉 http://localhost:5000

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🌐 Flask Web App UI

🖼️ User Interface Preview of the LSTM Stock Predictor Web App (Last 30-days Close Price)

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🤝 Contributing

Contributions are welcome! Feel free to fork the repo and submit pull requests for improvements or new features.

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🪪 License

This project is licensed under the MIT License.