from matplotlib import pyplot as plt
data = pd.read_csv("student-mat.csv", sep="；")
data = data[["G1"/span>, "G2"/span>, "G3"/span>, "學習時間", "失敗", "缺席"]]
predict = "G3"。
X = np.array(data.drop([預測], 1)
y = np.array(data[predict])
x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.1)
linear = linear_model.LinearRegression()
linear.fit(x_train, y_train)
準確性 = linear.score(x_test, y_test)
print(accuracy*100)
print('Coefficient:
', linear.coef_)
print('Intercept:
', linear.intercept_)
predictions = linear.predict(x_test)
for x in range(len(predictions)) 。
print(predictions[x], x_test[x], y_test[x])
uj5u.com熱心網友回復：
例子（與上面的評論類似）：
import seaborn as sns
sns.regplot(x=y_test, y=predictions, ci=None, color="r"/span>)
uj5u.com熱心網友回復：
你可以使用下面的代碼，用matplotlib繪制折線圖：
a = linear.coef_
b = linear.intercept_
plt.plot(x_train, a*x_train b)
或者另一個可能的解決方案是 :
m = linear.coef_
b = linear.intercept_
regression_line = [(m*x) b for x in x_train]
import matplotlib.pyplot as plt
from matplotlib importstyle
style.use('ggplot')
plt.scatter(x_train,y_train,color='#003F72')
plt.plot(x_train, regression_line)
plt.show()
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