Beautiful Python Code

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(6...

Cryptocurrency price prediction is the task of forecasting the future prices of a digital currency based on historical data and various factors. There are various methods and libraries in Python that can perform this task, such as machine learning, time series analysis, AutoTS, etc. One example is using a machine learning algorithm to predict the future prices of Dogecoin, a popular cryptocurrency . Here is a code snippet that shows how to use it: # Importing libraries import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split # Reading data from csv file data = pd.read_csv("DOGE-USD.csv") # Selecting features and target X = data[["Open", "High", "Low", "Volume"]] y = data["Close"] # Splitting data into train and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Creating and fitting a linear regression model m...