How to Use Deep Learning and Predict Bitcoin Price? – Guide 2022

Bitcoin is a popular majorly-invested cryptocurrency, and many people are initiating to invest their money. Due to price fluctuation, its value rises to billions. It becomes difficult for the trader to trade his Bitcoin and get profits. But there is one thing you can do to invest profitably and make money.

The method is machine learning, but it is a popular strategy to predict the prices of stock market. Now, everyone wants to know whether it works for predicting the cost of digital currencies. Undoubtedly, you can use a deep learning method for predicting the cost of virtual assets. But the process is pretty restrictive because the fluctuation in price happens for many reasons.

It can be competition, economic situations, politics, security problems, etc. It is hard to understand the volatile nature of digital currencies. Compared to the stock market, the crypto industry is way more unpredictable.

Process of the Price Prediction of Digital Currencies

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The whole process is done in four steps:

  1. Collect complete data related to real-time digital currencies.
  2. Prepare the gathered data for further process of training and then testing.
  3. By operating neural network of LSTM, one can predict the cost of BTC.
  4. After that, you can visualize the predicted results to proceed further.

What are the Challenges That One Can Face in the Dataset?

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The initial challenge that one can face by using deep learning is regarding the data. The dataset consists of all the information regarding the cryptocurrency. It involves different price values, like low, closing, high, opening, volume, etc. You need to consider every aspect of the information before predicting the value. You must know the meaning of these terms to understand the numbers carefully. Let us know more about those features:

  1. Closing: This is the price of closing of the cryptocurrency on a particular day.
  2. High: In a day, it is the cryptocurrency’s highest cost, and it does not fluctuate above the shown number.
  3. Opening: It is the opening market cost of the digital currency on a particular day.
  4. Low: In a day, it is the virtual currency’s lowest cost, and it does not fluctuate below the shown number.
  5. Volume: The currency volume is traded for the entire day.

Code Use

You can use readymade codes in your system for analyzing the information and getting it ready for testing along with training. You can load all the premade libraries as well as dependencies as per your requirements. There is a method, known as to_datetime(), that helps convert the objects of date and time in the Python coding language.

It is easy to operate this function and get data regarding any cryptocurrency’s date and time string. You will get all the data in a tabular form where all the features are listed, and the price fluctuations are also mentioned. Now, you have to divide the datasets for training and testing.

The table with 80% of data is for training, and 20% is for testing. When it comes to real-time projects, you can divide more parts like 60% to training and leave 20% and 20% for validation and testing. After that, you can analyze the price fluctuation in a graph of previous years. It is crucial to determine the pattern to predict the price you expect from the dataset.

Sometimes, it is quite challenging to generalize things, but a little focus can help you do what you want. You can also use various readymade functions for normalizing various values. In ML, the technique of normalization helps in preparing the data.

In this method, you can manipulate the dataset numeric columns that you can get in the standard scale without any further distortion. You have to work on the function for the data preparation, and you can directly switch to the LSTM neural network.

Understand the LSTM Process

Source: medium.datadriveninvestor.com

If you need to determine every LSTM layer, then you must know how to operate gates. It helps in taking the previous data to another one. The process goes further, and the data goes from various gates. There are plenty of functions attached to these gates, and the data goes by the activation programs to pass completely through cells of LSTM.

The network remembers all the crucial details and forgets the incorrect ones in this process. You can use the sequential stacking model over all the available layers. In the neural network, 20% of dropout layer is there, and the rest is known as the dense layer. The linear activation function separates these layers. You can operate different parameters to handle the information accordingly.

You can also label the functions you are applying in the code. If you want to determine the basic evaluation metric, then you need to find the MAE. It is easy to interpret the MAE instead of the RMSE. Even if you have a non-technical audience, the mean error will work better.

About MAE

With the help of this factor, you can calculate the average magnitude of the error by using a particular prediction set. There is no need to consider the direction. There will be the same weight of all the differences.

This average is for the given test sample and evaluates basic differences between the predicted as well as actual observations. If you find the good value of MAE, it means that you are predicting the cryptocurrency price correctly.

Final Thoughts

Source: towardsdatascience.com

It is quite complicated to understand the neural network for predicting the value of digital currencies. The initial step is to get the data, analyze it, predict the price and see the results. If you want to check the price of various digital assets, then you can visit https://bwcevent.com/.

You can code and operate multiple parameters to play with the data and predict the real values of digital currencies. It is important to have enough information about the process and know how to code well. You should know how to collect and handle the data correctly. Deep learning can be the best process for predicting crypto values like stocks.

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