📈 Stock Price Prediction Using TensorFlow

🔮 A deep learning model built with TensorFlow to predict short-term stock prices. This project uses historical stock price data and applies machine learning techniques to forecast future stock movements.

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🌟 Overview

Predicting stock prices is a challenging task due to market volatility. This project leverages deep learning and hyperparameter tuning to build a robust stock prediction model. We use Keras Tuner to optimize neural network architecture for improved accuracy.

📌 Key Highlights:

• Uses historical stock data 📊
• Applies feature scaling for better model performance 🔄
• Implements a deep learning model with optimized layers 🤖
• Leverages Keras Tuner for hyperparameter tuning 🎯
• Provides visualizations to compare predicted vs. actual stock prices 📉

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🛠 Installation

To get started, follow these steps:

1️⃣ Clone the Repository

git clone https://github.com/phoneix116/stock-price-prediction.git
cd stock-price-prediction

2️⃣ Install Dependencies

Make sure you have Python installed, then run:

pip install -r requirements.txt

3️⃣ Run the Model

To train the model and make predictions, execute:

open the jupyter notebook

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📂 Dataset

This model uses Tata Global Beverages Limited stock data. The dataset includes the following features:

Column	Description
Date	Trading Date
Open	Opening Price
High	Highest Price
Low	Lowest Price
Close	Closing Price

Ensure your dataset follows a similar structure before training the model.

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🏗 Model Architecture

🔍 The deep learning model consists of:

✅ Input Layer → Takes Open, High, Low, Close values
🔄 Hidden Layers → Fully connected dense layers (32-512 units)
🎛 Activation Function → ReLU for hidden layers, linear for output
⚙️ Optimizer → Adam (learning rate tuned with Keras Tuner)
🎯 Loss Function → Mean Squared Error (MSE)

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🔥 Training Process

The model is trained using a split dataset strategy:

• Training Set: 80% of historical data
• Validation Set: 10% for tuning
• Test Set: 10% for final evaluation

📌 Optimization Features:
✅ Keras Tuner to select the best layer configurations
✅ EarlyStopping to prevent overfitting
✅ MinMaxScaler for feature normalization

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📊 Results & Visualization

Once trained, the model predicts next-day closing prices based on historical trends.

📉 Predicted vs Actual Stock Prices:

The model provides reasonable accuracy for short-term forecasting. Further improvements can be made by experimenting with:

• More advanced neural network architectures (LSTM, GRU)
• Larger datasets with different stocks
• Incorporating external market indicators

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🚀 Hyperparameter Tuning with Keras Tuner

Keras Tuner is used to optimize:

• Number of neurons per layer
• Number of hidden layers
• Learning rate of optimizer

The best configuration is selected dynamically based on validation performance.

📌 To run the tuning process separately, execute:

This process is integrated into the Jupyter Notebook

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🏆 How to Contribute

🚀 Want to improve the model? Follow these steps:

1️⃣ Fork the Repository
2️⃣ Create a New Branch

git checkout -b feature-xyz

3️⃣ Commit Your Changes

git commit -m "Added feature xyz"

4️⃣ Push to GitHub

git push origin feature-xyz

5️⃣ Open a Pull Request 🎉

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📝 License

📜 MIT License – Feel free to use and modify!

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🎯 Let's Predict the Market! 🚀

If you found this project useful, please ⭐ Star the repository! 😊

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