python散点图线性回归

要在Python中制作散点图和进行线性回归，你可以使用matplotlib和sklearn库。以下是一种实现方法：

首先，导入所需的库：
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

然后，准备数据并绘制散点图：
x = np.array([2, 5, 8, 8, 13, 15, 17, 19, 21, 24])
y = np.array([12, 31, 45, 52, 79, 85, 115, 119, 135, 145])
plt.scatter(x, y)
plt.xlabel("battery")
plt.ylabel("KM")
plt.title("散点图")
plt.show()

接下来，建立线性回归模型并进行拟合：
regressor = LinearRegression()
regressor.fit(np.reshape(x, (-1, 1)), np.reshape(y, (-1, 1)))

打印拟合结果(参数)：
print("斜率：", regressor.coef_)
print("截距：", regressor.intercept_)

最后，画出数据和拟合直线的图：
plt.scatter(x, y)
plt.plot(np.reshape(x, (-1, 1)), regressor.predict(np.reshape(x, (-1, 1))))
plt.xlabel("battery")
plt.ylabel("KM")
plt.title("拟合结果")
plt.show()

这样，你就可以在Python中绘制散点图和进行线性回归了。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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