Bitcoin prediction
- 0 Collaborators
By using Intel sklearn extension, and using RNN algorithm. This project will predict the future bitcoin prediction. ...learn more
Project status: Under Development
Intel Technologies
DevCloud,
oneAPI,
Intel Deep Link
Overview / Usage
Bitcoin price prediction is a challenging task due to its extreme volatility and complex market dynamics. It involves analyzing historical data, market sentiment, and fundamental factors to make forecasts. Various methods, including technical analysis, machine learning models, and sentiment analysis, are employed. Predictions should be approached with caution, as the cryptocurrency market is highly speculative, and unexpected events can lead to sudden price fluctuations. Diversification and risk management are crucial for anyone considering Bitcoin investments.
Methodology / Approach
**1.Data Collection: **Gather historical Bitcoin price data from reliable sources like crypto currency exchanges and financial data providers.
**2.Data preprocessing: **Clean and preprocess the data and also includes handling missing values, normalizing the data and possibly removing outliers.
**3.Time Series Split: **Split the data into training, validation and test sets, considering the temporal nature of time series data.
**4.Model Selection and Architecture: ** Build an RNN-based model for time series prediction. You can use libraries like TensorFlow or PyTorch.
**5.Training: ** Train the RNN model using the training data and validate its performance using the validation set and also Implement early stopping to prevent overfitting.
**6.Evalution: ** Evaluate the model's performance using appropriate metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).
**7.Prediction and visualization: ** Once the model is trained and validated, you can use it to make future Bitcoin price predictions and Visualize the model's predictions alongside actual Bitcoin price data to assess its accuracy.
Technologies Used
- Intel OneDAL toolkit, in this i use Data Preprocessing feature scaling.
- Keras build and train deep learning models and also analyzing historical data and making future price forecasts.
- Using Tensorflow AI toolkit i trained the model.
- Using matplotlib I made an visual representation for my predicted trained model.