📈 Stock-Trend-Predictor-using-ML

🌟 Stock Price Predictor: Advanced LSTM Model with RNN

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🔍 Project Overview

This repository presents a state-of-the-art stock price prediction model, capable of forecasting trends for any stock available on Yahoo Finance. With Long Short-Term Memory (LSTM) neural networks at its core, this project embodies a powerful and sophisticated approach to time series forecasting in the fast-paced world of stock markets.

Model Highlights:

• Data Source: Historical data (20 years) via Yahoo Finance
• Model Architecture: Multi-layered LSTM with RNNs, enhanced by dropout for better generalization
• Performance: Achieved a Mean Absolute Percentage Error (MAPE) of 7.49% and R-squared of 0.9317

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🛠️ Key Features and Technical Highlights

🔹 Robust Data Acquisition

• Comprehensive historical stock data retrieval using yfinance

🔹 Advanced Preprocessing

• Implements moving averages, technical indicators, and normalization techniques for clean, reliable data

🔹 Sophisticated LSTM Architecture

• Multi-layer LSTM structure with dropout layers to enhance prediction accuracy and reduce overfitting

🔹 Adaptive Learning

• Learning rate scheduling and early stopping mechanisms for improved training efficiency and stability

🔹 Comprehensive Visualization

• Interactive data visualization tools for easier interpretation of historical data and prediction results

🔹 Streamlit Integration

• User-friendly Streamlit app enables easy interaction and prediction for any chosen stock

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📊 Performance Metrics

Metric	Value
Mean Absolute Error (MAE)	0.0416
Root Mean Square Error (RMSE)	0.0482
Mean Absolute Percentage Error (MAPE)	7.49%
R-squared	0.9317

These metrics showcase the model’s remarkable accuracy in predicting stock trends, establishing it as a valuable tool for financial analysis and potential use in algorithmic trading strategies.

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💡 Potential Applications and Adaptability

While initially optimized for Google stock, this model’s versatile design makes it highly adaptable to various stocks and financial instruments. It serves as an invaluable resource for:

• Financial Analysts: Enhances data-driven insights in financial analysis
• Algorithmic Traders: Foundation for building sophisticated trading strategies
• Data Scientists: Practical, hands-on application of advanced time series forecasting

The project’s modular design supports easy adjustment to new datasets, broadening its potential across diverse financial markets.

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📘 Educational Value and Ethical Considerations

This project is intended as an educational resource, illustrating the application of deep learning in financial forecasting. It offers a hands-on introduction to TensorFlow, Keras, and advanced time series analysis, promoting responsible and well-informed use of machine learning in finance.

Disclaimer: Stock market predictions are inherently uncertain. While the model demonstrates high accuracy, it should not be the sole basis for investment decisions and must be used as a supplement to a comprehensive financial toolkit.

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🚀 Get Started

🔗 Installation

1. Clone the repository

   git clone https://github.com/username/Stock-Trend-Predictor.git

2. Install dependencies

   pip install -r requirements.txt

3. Run the Streamlit app

   streamlit run app.py

📂 File Structure

• data/ - Historical stock data
• models/ - LSTM model architecture and weights
• app.py - Main Streamlit app for interactive predictions
• README.md - Project documentation

📌 Requirements

• Python 3.x
• TensorFlow, Keras, yfinance, Streamlit, matplotlib, pandas, numpy

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🏷️ License

This project is licensed under the MIT License - see the LICENSE file for details.

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Happy Predicting! 🎉