Bitcoin-Price-Prediction-Using-RNN-LSTM

This notebook demonstrates the prediction of the bitcoin price by the neural network model. We are using long short term memory (LSTM)

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes

Prerequisites

you need to install all the necessary libraries n order to run the project sklearn tensorflow pandas matplotlib

Installing

pip install sklearn
pip install tensorflow
pip install pandas
pip install matplotlib

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we will be going through a four step process to predict cryptocurrency prices:

1. Getting real-time crptocurrency data(bitcoin).
2. Prepare data for training and testing.
3. Predict the price of crptocurrency using LSTM neural network (deep learning).
4. Visualize the prediction results.

1. Getting real-time crptocurrency data(bitcoin)

You can collect the current data for Bitcoin from Yahoo Finance

2. Prepare data for training and testing.

You can preprocess the data before dividing it into traning and testing

data_training = data[data['Date']< '2020-01-01'].copy()
data_training

data_test = data[data['Date']> '2020-01-01'].copy()
data_test

3. Predict the price of crptocurrency using LSTM neural network (deep learning)

regressor = Sequential()
regressor.add(LSTM(units = 60, activation = 'relu', return_sequences = True, input_shape = (X_train.shape[1], 5)))
regressor.add(Dropout(0.2))
regressor.compile(optimizer = 'adam', loss='mean_absolute_error')
regressor.fit(X_train, Y_train, epochs = 20, batch_size =50)

Y_pred = regressor.predict(X_test)
Y_pred, Y_test

4. Visualize the prediction results.

plt.figure(figsize=(14,5))
plt.plot(Y_test, color = 'red', label = 'Real Bitcoin Price')
plt.plot(Y_pred, color = 'green', label = 'Predicted Bitcoin Price')
plt.title('Bitcoin Price Prediction using RNN-LSTM')
plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()