Bitcoin-Price-Prediction

📌 Project Overview

This project utilizes a Long Short-Term Memory (LSTM) neural network to predict Bitcoin prices based on historical data. The model processes time-series data to forecast future prices, providing insights into cryptocurrency trends.

🚀 Features

• Data Analysis & Preprocessing: Load and preprocess historical Bitcoin price data.
• LSTM Model: Train an LSTM network to predict future Bitcoin prices.
• Interactive Prediction: Use the Streamlit app to input data and receive predictions.

📂 Project Structure

• cryptocurrency_data_analysis_and_prediction.ipynb: Jupyter Notebook containing data analysis, preprocessing, model training, and evaluation.
• app.py: Streamlit application for interactive Bitcoin price prediction.
• lstm_crypto_model.pth: Saved PyTorch model weights for the trained LSTM.

📊 Dataset

• The dataset consists of historical Bitcoin prices.
• Data includes attributes like date, open, high, low, close prices, and volume.
• Preprocessing includes normalization and converting data into a format suitable for LSTM.

🛠️ Technologies

• Programming Languages: Python
• Libraries : PyTorch, NumPy, Pandas, Matplotlib, Scikit-learn, Streamlit
• Deep Learning Framework : LSTM Neural Network

➡️ Flow Diagram

🚀 How to Run the Project

1. Clone the Repository

     git clone https://github.com/Avinashrkrk/Bitcoin-Price-Prediction.git

2. Navigate to the folder

     cd Bitcoin-Price-Prediction

3. Install Dependencies

     pip install -r requirements.txt

4. Running the Jupyter Notebook: Open and execute cryptocurrency_data_analysis_and_prediction.ipynb to explore data analysis, model training, and evaluation steps.
5. Running the Streamlit App
   • Ensure lstm_crypto_model.pth is in the project directory.
   • Run the Streamlit application:

       streamlit run app.py

   • Access the app in your browser at http://localhost:8501

Dashboard

Model Performance Metrics

Price Predictions

📊 Results & Insights

• The LSTM model successfully learns Bitcoin price trends and predicts future values.
• Model performance can be further improved with additional features, hyperparameter tuning, and more historical data.
• The interactive Streamlit dashboard provides real-time visualization of predicted prices.

📌 Future Enhancements

• Integrate real-time Bitcoin price data for live predictions.
• Experiment with other deep learning architectures (e.g., GRU, Transformer models).
• Deploy as a web application with Flask or FastAPI.