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https://github.com/ayushverma135/bitcoin-price-prediction-using-deep-learning

Utilizing deep learning techniques to predict Bitcoin price movements based on historical data and relevant market indicators.
https://github.com/ayushverma135/bitcoin-price-prediction-using-deep-learning

artificial-neural-networks bitcoin-prediction bitcoin-price-prediction btc-prediction cnn-model deep-learning machine-learning

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Utilizing deep learning techniques to predict Bitcoin price movements based on historical data and relevant market indicators.

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README 

        
# Bitcoin-Price-Prediction-using-Deep-Learning

Predicting the future price of Bitcoin (BTC) or any cryptocurrency is highly speculative and subject to a wide range of factors, including market sentiment, regulatory developments, technological advancements, macroeconomic trends, and more. Here are a few points to consider:



- __Volatility:__ Bitcoin is known for its extreme price volatility. Prices can fluctuate significantly in short periods, making accurate predictions challenging.



- __Market Sentiment:__ Investor sentiment plays a crucial role. Positive news (like institutional adoption or regulatory clarity) tends to drive prices up, while negative news (like regulatory crackdowns or security breaches) can lead to sharp declines.



- __Technological Developments:__ Upgrades to the Bitcoin network, such as improvements in scalability (like the Lightning Network) or changes in mining technology, can influence price movements.



- __Macroeconomic Factors:__ Bitcoin is often seen as a hedge against inflation and currency devaluation. Economic events, such as changes in interest rates or geopolitical tensions, can impact its price.



__Regulatory Environment:__ Government regulations and policies regarding cryptocurrencies can have a significant impact on their adoption and, consequently, their value.



Given these complexities, making a precise prediction for Bitcoin's price is not feasible. It's important to approach any predictions with caution and consider a diverse range of viewpoints and analysis from financial experts and analysts.



![](https://cdn-images-1.medium.com/v2/resize:fit:550/1*pO5X2c28F1ysJhwnmPsy3Q.gif)



## Overview

This repository contains a deep learning model for predicting Bitcoin (BTC) prices based on historical data. The model utilizes advanced neural network techniques to analyze past price movements and other relevant indicators to forecast future prices.



The goal of this project is to develop a machine learning model that can forecast the future prices of Bitcoin by leveraging deep learning techniques. The model is trained on historical Bitcoin price data and potentially other relevant features such as trading volume and macroeconomic indicators.



## Key Features



- **Data Preprocessing**: Includes steps for cleaning and preparing historical data for model training.

- **Neural Network Model**: Uses neural networks to learn patterns in Bitcoin price movements over time.

- **Evaluation Metrics**: Measures model performance using metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE).

- **Prediction**: Provides functionality to make predictions on unseen data and visualize results.



## Installation

### Prerequisites

  - Python 3.x

  - Jupyter Notebook

### Steps

  - Clone the repository:

    

        git clone https://github.com/Ayushverma135/Bitcoin-Price-Prediction-using-Deep-Learning.git

        cd Bitcoin-Price-Prediction-using-Deep-Learning

  - Install dependencies:

    

        pip install -r requirements.txt

  - Launch Jupyter Notebook:

  - Open and run the __bitcoin_prediction.ipynb__ notebook.



## Usage

- __Training the Model__

Open the Jupyter Notebook bitcoin_price_prediction.ipynb and run the cells in sequence to train the neural network model on Bitcoin price data.



- __Making Predictions__

Within the same Jupyter Notebook, follow the steps provided to make predictions using the trained model.



- __Evaluation__

The notebook includes cells for evaluating the model's performance on test data using various evaluation metrics. Run these cells to see the model's performance.



## Contributing

Contributions to improve the project are welcome! Here are a few ways you can contribute:



- Implement additional neural network architectures for comparison.

- Enhance data preprocessing techniques.

- Optimize hyperparameters and improve model accuracy.

- Add visualization tools for better understanding of predictions.

  

If you find any issues or have suggestions, please open an issue or create a pull request.



## Contact

For questions or discussions, feel free to reach out:



Email: [email protected]