Python XRP Price Prediction
XRP is a cryptocurrency that powers the Ripple network, which is a system for cross-border payments. Predicting XRP price can be done using Python and machine learning techniques, such as regression or neural networks. Here is an example of a code snippet that uses a neural network to predict XRP price based on historical data:
# Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
# Load data
df = pd.read_csv('XRP-USD.csv')
df = df.dropna()
df = df[['Close']]
# Scale data
scaler = MinMaxScaler(feature_range=(0,1))
df = scaler.fit_transform(df)
# Split data into train and test sets
train_size = int(len(df) * 0.8)
test_size = len(df) - train_size
train_data, test_data = df[0:train_size,:], df[train_size:len(df),:]
# Create x_train and y_train data sets
x_train = []
y_train = []
for i in range(60,len(train_data)):
x_train.append(train_data[i-60:i,0])
y_train.append(train_data[i,0])
x_train,y_train = np.array(x_train),np.array(y_train)
# Reshape x_train for LSTM model
x_train = np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))
# Create LSTM model
model = Sequential()
model.add(LSTM(units=50,return_sequences=True,input_shape=(x_train.shape[1],1)))
model.add(LSTM(units=50))
model.add(Dense(25))
model.add(Dense(1))
# Compile and fit model
model.compile(optimizer='adam',loss='mean_squared_error')
model.fit(x_train,y_train,batch_size=32,epochs=100)
# Create x_test and y_test data sets
x_test = []
y_test = test_data[60:len(test_data),:]
for i in range(60,len(test_data)):
x_test.append(test_data[i-60:i,0])
x_test = np.array(x_test)
# Reshape x_test for LSTM model
x_test = np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))
# Get model predictions
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
# Plot predictions vs actual prices
plt.figure(figsize=(16,8))
plt.title('XRP Price Prediction')
plt.xlabel('Date',fontsize=18)
plt.ylabel('Close Price USD ($)',fontsize=18)
plt.plot(y_test)
plt.plot(predictions)
plt.legend(['Actual','Predicted'],loc='lower right')
plt.show()
If you have any questions about this code, you can drop a line in comment.
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