https://github.com/alihassanml/bitcoin-prediction
https://github.com/alihassanml/bitcoin-prediction
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/alihassanml/bitcoin-prediction
- Owner: alihassanml
- Created: 2024-05-16T16:43:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-16T16:49:49.000Z (over 1 year ago)
- Last Synced: 2025-02-21T11:26:50.566Z (8 months ago)
- Language: Jupyter Notebook
- Size: 1.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
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---
# Bitcoin Price Prediction with ARIMA Model
## Overview
This repository contains code for training a machine learning model to predict Bitcoin prices using the ARIMA (AutoRegressive Integrated Moving Average) model. The ARIMA model is implemented using the `statsmodels` library in Python.
### Libraries Used:
- **Pandas**: For data manipulation and analysis.
- **NumPy**: For numerical computing and array operations.
- **Matplotlib**: For data visualization.
- **Seaborn**: For statistical data visualization.
- **Statsmodels**: For time series analysis and ARIMA modeling.
- **Plotly Express**: For interactive data visualization.
### Methodology:
1. **Data Collection**: Gather historical Bitcoin price data from reliable sources.
2. **Data Preprocessing**:
- Handle missing values and outliers.
- Convert non-stationary data into stationary data using various methods like shifting, logarithmic transformation, square root transformation, and cube root transformation.
3. **Exploratory Data Analysis (EDA)**: Analyze the distribution of Bitcoin prices, identify trends, seasonality, and correlations with other variables.
4. **Model Building**:
- Utilize ARIMA (AutoRegressive Integrated Moving Average) model for time series forecasting.
- Evaluate model performance using appropriate metrics.
5. **Model Tuning and Optimization**: Fine-tune the model parameters to improve prediction accuracy.
6. **Prediction**: Forecast future Bitcoin prices based on the trained model.
7. **Evaluation**: Assess the accuracy of predictions and adjust the model as necessary.
## Requirements
To run the code in this repository, you need:
- Python 3.x
- `statsmodels` library
You can install the required library using pip:
```
pip install statsmodels
```
## Usage
1. Clone this repository:
```
https://github.com/alihassanml/Bitcoin-price-prediction.git
```
2. Navigate to the cloned directory:
```
cd bitcoin-price-prediction
```
3. Run the script `bitcoin_prediction.py` to train the ARIMA model and make predictions:
```
python bitcoin_prediction.py
```
## ARIMA Model
ARIMA (AutoRegressive Integrated Moving Average) is a popular time series forecasting model. It combines autoregression (AR), differencing (I), and moving average (MA) components. The model is trained on historical Bitcoin price data to predict future prices.
The ARIMA model is implemented using the `statsmodels` library, specifically the `ARIMA` class from `statsmodels.api`.
## Preprocessing
To prepare the data for training the ARIMA model, various preprocessing techniques can be applied to make the data stationary. These techniques include:
- Shifting method
- Log transformation
- Square root transformation
- Cube root transformation
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or create a pull request.
## License
This project is licensed under the MIT License - see the [(alihassanml)] file for details.
---