As we all know, Machine Learning is ruling the world nowadays. You name it to predict, Machine Learning is there for your predictions. Whether it is Breast Cancer predictions or online grocery recommendation Machine Learning is everywhere and is used by many small to big-sized companies. However, the predictions are not 100% accurate. Depending on which algorithms you have applied, the accuracy varies. Bitcoin Price prediction using Machine Learning can be 67-70% accurate.
The objective of this machine learning project is to predict the price of Bitcoins. After the popularity of Bitcoins, it is classified as assets.
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Hardware and Software Specifications:
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How To Install Jupyter Notebook?
If you want to download Jupyter using Anaconda, You have to install python and Jupyter using Anaconda Distribution. Head to Anaconda 2019 Windows Installer and you can find download links there. This installation includes Jupyter Notebook.
Collect Data Set:
To train the model to predict the Bitcoin prices we need to have training data. Collect the data. The data might have some gaps.
Read the CSV files. The code is given below.
data = read.csv(“../input/data.csv”)
Remove the rows and columns that you will not use or which are not required.
Now, split the dataset into two parts. One becomes a training data set and another is test data set. Here we have taken 80% of the data set for training the model and the rest of the 20% are for test set.
n_train_rows = int(dataset.shape*.8)-1
train = dataset.iloc[:n_train_rows, :]
test = dataset.iloc[n_train_rows:, :]
We are using LSTM algorithm here, so we need to organize our data in blocks. Our data is organized in 1 minute intervals. So we will be using 50 blocks to predict.
Now prepare the training data.
x_train = 
y_train = 
for i in range(steps, training_set_scaled.shape-steps):
x_train, y_train = np.array(x_train), np.array(y_train)
That is it. You have successfully predicted the price of Bitcoin. However, using this algorithm will give you a 70% accurate prediction.
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