주식데이터 학습 방법
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- 모델링 { 데이터 모델링 가이드 }
1 주식데이터 학습[편집]
1.1 주식 데이터 학습 방법[편집]
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
# Load the data
df = pd.read_csv('stock_data.csv')
# Split the data into input and target variables
X = df[['volume', 'fluctuation_rate']]
y = df['price']
# Split the data into training and testing sets
train_size = int(len(X) * 0.8)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]
# Create a linear regression model
model = LinearRegression()
# Train the model on the training data
model.fit(X_train, y_train)
# Make predictions on the testing data
y_pred = model.predict(X_test)
# Evaluate the performance of the model
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)
print(f'Mean Squared Error: {mse:.2f}')
print(f'Root Mean Squared Error: {rmse:.2f}')
print(f'R-squared: {r2:.2f}')
# Make a prediction for a new input
new_input = [[1000000, 0.02]]
new_output = model.predict(new_input)
print(f'Predicted price for new input: {new_output[0]:.2f}')