What kind of patterns can be mined in data mining? (2024)

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
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from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
Y = boston.target
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
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model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
plt.scatter(y_test, y_pred)
plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], linestyle='--', color='red', linewidth=2) # Regression line
plt.xlabel("True Values")
plt.ylabel("Predicted Values")
plt.title("True vs. Predicted Values")
plt.savefig('./output/plot.png')
plt.show()