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Prediction Cryptocurrencies using Hybridization Machine Learning

UMalaya Team: Mohamad Nazrin Bin Napiah,Nur Baiti Afini Normadi,Sabrina Binti Kamal,Nur Hidayah Binti Mohd Rosli,Prasanta A/L Sathasivam,Yee Xun Wei

In this paper, this study attempts to predict the twenty cryptocurrency price by taking into consideration various parameters that affect the trading or investment market. For the first level of this study aim to construct the forecasting model to predict the future values of cryptocurrencies and the live model of decision making for trading is deploy. This body of this project were follow the CRISP data mining methodology in supporting to process the models. The data source is from the Academia Datathon 2018. The data set consists of various attributes related to the various coin, price and time, recorded daily in 2018. For the second level, by using the best model from level one which has been compared the accuracy and performance, will be used to construct Artificial Intelligence bot for decision making of trading or investment. By focusing on twenty major cryptocurrencies, each with the large market size and price, this study attempts to predict the forecasting price based on time series method such as min, max and mean price values.

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5 thoughts on “Prediction Cryptocurrencies using Hybridization Machine Learning

  1. 0
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    I can judge only from crypto related side so in general I like how the solution is structured and every step is explained. Even I am not data scientist I managed to understand a bit because of the good explanation. The test will show if you have managed to do the job but besides that great work. Keep the good work and good luck!

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    Very nice structure of the paper, with all the results explained in details and all the required test supplied. Well Done. I can’t find the source code for prediction. I found only the matlab code for data manipulation

  3. 0
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    Very good article! Very well-written, good job 🙂

    It would be nice to compare your results with a baseline (predicting the previous point, or average of the previous points or something similar). Also, your idea to use accumulated measures for the past 1 hour is interesting. Did you compare it with just giving the previous points as features to the regression?

    It would be nice to share your code as well.

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