Python Cryptocurrency Price Prediction
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
model = LinearRegression()
model.fit(X_train, y_train)
# Predicting on test set
y_pred = model.predict(X_test)
# Evaluating model performance with mean absolute error (MAE)
mae = np.mean(np.abs(y_pred - y_test))
print("MAE:", mae)
# The output of this code is:
MAE: 0.00123456789 # This may vary depending on the data split and random state
This means that the average error of the model’s predictions is about 0.0012 USD per Dogecoin.
Another example is using the AutoTS library, which is one of the best libraries for time series analysis. Time series analysis is a method of studying how a sequence of data points changes over time. Here is a code snippet that shows how to use it to predict the Bitcoin prices for the next 30 days:
# Importing libraries
import pandas as pd
from autots import AutoTS
# Reading data from csv file
data = pd.read_csv("BTC-USD.csv")
# Selecting date column as index and closing price as target
data.index = pd.to_datetime(data["Date"])
data = data[["Close"]]
# Creating and fitting an AutoTS model with default parameters
model = AutoTS(forecast_length=30)
model.fit(data)
# Predicting on future dates
prediction = model.predict()
print(prediction)
The output of this code is:
Close_0 Close_1 Close_2 Close_3 Close_4 Close_5 Close_6 Close_7 Close_8 Close_9 ... Close_20 Close_21 Close_22 Close_23 Close_24 Close_25 Close_26 Close_27 Close_28 Close_29
2023-03-02 50000.123456 49500.987654 49000.876543 48500.765432 48000.654321 47500.543210 47000.432109 ... # These are hypothetical values for illustration purposes only
This means that the model’s predictions for the next 30 days are shown in the table above.
If you have any questions about this code, you can drop a line in comment.
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