BTC.py

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import seaborn as sns

# Load the data
df = pd.read_csv('BTC-USD.csv')

# Convert 'Date' to datetime
df['Date'] = pd.to_datetime(df['Date'])

# Set 'Date' as the index
df.set_index('Date', inplace=True)

# Feature Engineering: Create additional time-based features
df['Year'] = df.index.year
df['Month'] = df.index.month
df['Day'] = df.index.day
df['DayOfWeek'] = df.index.dayofweek

# Define features and target
X = df[['Open', 'High', 'Low', 'Volume', 'Year', 'Month', 'Day', 'DayOfWeek']]
y = df['Close']

# Split the data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)

# Standardize the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Initialize and train the model
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)

# Make predictions
y_pred = model.predict(X_test_scaled)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
accuracy = r2 * 100

print(f'Mean Squared Error: {mse}')
print(f'Mean Absolute Error: {mae}')
print(f'R-squared: {r2}')
print(f'Accuracy: {accuracy}%')

# Plot the actual vs predicted values
plt.figure(figsize=(14, 7))
plt.plot(y_test.index, y_test, label='Actual')
plt.plot(y_test.index, y_pred, label='Predicted')
plt.xlabel('Date')
plt.ylabel('Close Price')
plt.title('Actual vs Predicted Close Price')
plt.legend()
plt.show()