📈 Stock Prediction
🔮 Predicting Stock Prices Using Machine Learning & Deep Learning Techniques
✨ Overview
This project is designed to analyze historical stock market data and predict future stock prices. By leveraging Python, ML/DL models, and visualization tools, we aim to uncover trends and insights for better financial decision-making.
📂 Project Structure
📦 Stock-Prediction
┣ 📜 README.md # Project documentation
┣ 📜 requirements.txt # Dependencies
┣ 📜 app.py # Streamlit demo app
┣ 📓 stock_prediction.ipynb # Jupyter Notebook for analysis
┣ 📂 data/ # Dataset folder
┣ 📂 models/ # Saved trained models
┣ 📂 results/ # Graphs & predictions
┣ 📂 src/ # Source code (utils, preprocessing, models)
⚙️ Installation
1️⃣ Clone This Repository
git clone https://github.com/your-username/Stock-Prediction.git
cd Stock-Prediction
2️⃣ Create and Activate a Virtual Environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Linux/Mac
venv\Scripts\activate # On Windows
3️⃣ Install Dependencies pip install -r requirements.txt
4️⃣ Run Jupyter Notebook jupyter notebook
5️⃣ Launch the Demo App 🚀 streamlit run app.py
📊 Dataset
📌 Source: Yahoo Finance / Kaggle Stock Dataset 📌 Features Used:
🟢 Open
🔴 High
🔵 Low
⚫ Close
📉 Volume
🧠 Models Implemented 🤖 Machine Learning
Linear Regression
Random Forest
XGBoost
🧮 Deep Learning
LSTM (Long Short-Term Memory)
GRU (Gated Recurrent Units)
📏 Evaluation Metrics
RMSE (Root Mean Squared Error)
MAE (Mean Absolute Error)
R² Score
📈 Results & Visualizations
✅ Actual vs. Predicted Stock Prices Plotted ✅ Model Performance Comparison Charts ✅ Insights on the Most Accurate Algorithms
🎯 Future Enhancements
✨ Add Real-Time Stock Prediction Using APIs ✨ Integrate Sentiment Analysis from Financial News ✨ Explore Transformer-Based Deep Learning Models
🤝 Contributing
💡 Pull Requests Are Welcome! 📢 For major changes, please open an issue first to discuss what you would like to change.
🔖 Relevant Tags
stock-market-forecasting
financial-time-series
predictive-modeling
supervised-learning
regression-analysis
neural-networks (if DL is used)
quantitative-finance
algorithmic-trading
feature-engineering