| marcosan93/Price-Forecaster |
69 |
|
0 |
0 |
over 4 years ago |
0 |
|
2 |
|
Jupyter Notebook |
| Forecasting the future prices of BTC and More using Machine and Deep Learning Models |
| cawfree/crypto-lstm-3 |
8 |
|
0 |
0 |
about 6 years ago |
0 |
|
0 |
|
JavaScript |
| 💵 Using word2vec to predict trends in cryptocurrency. |
| Schlam/LSTM-time-series-forecasting |
6 |
|
0 |
0 |
about 5 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Predicting the behavior of $BTC-USD by training a memory-based neural net on historical data |
| SanjoShaju/Cryptocurrency-Analysis-Python |
6 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
|
HTML |
| Time series forecasting using RNN, Twitter Sentiment Analysis and Turtle Trading Strategy applied on Cryptocurrency |
| komal98/BitPredict |
5 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
|
R |
| This project is concerned with predicting the price of Bitcoin using machine learning. The goal is to ascertain with what accuracy can the direction of Bitcoin price in USD can be predicted. The task is achieved with varying degrees of success through the implementation of Bayesian Regression.The popular ARIMA model for time series forecasting is implemented as a comparison to Holt’s Forecasting Model, exponential triple smoothing and Bayesian Regression. It can clearly be seen that Bayesian regression gives the best forecasting model for bitcoin price prediction. It has a very low Mean accuracy prediction error of 7.82 which is way lower compared to other models. The model is run only for 10 iterations or Markov chains and increasing this number would further improve the model accuracy. The implementation is done in R. |
| leehanchung/btc_dash |
5 |
|
0 |
0 |
over 4 years ago |
0 |
|
3 |
gpl-3.0 |
Jupyter Notebook |
| Bitcoin Price Prediction Modeling and Dashboard |
| pcann9/Predict_Bitcoin_Using_Reddit_Sentiment |
5 |
|
0 |
0 |
over 7 years ago |
0 |
|
0 |
mit |
Jupyter Notebook |
| Sentiment analysis of Reddit comments to predict bitcoin price movement |
