线性回归python实现

线性回归是一种常见的机器学习算法，用于预测一个数值型变量的值。下面是用Python实现线性回归的基本步骤：

1. 导入必要的库

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

2. 读取数据

data = pd.read_csv("data.csv")
X = data.iloc[:, 0]
Y = data.iloc[:, 1]

3. 绘制散点图

plt.scatter(X, Y)
plt.show()

4. 初始化参数

m = 0
c = 0
L = 0.0001
epochs = 1000

5. 定义损失函数

def compute_cost(m, c, X, Y):
    n = len(X)
    total_cost = sum([val ** 2 for val in (Y - (m * X + c))])
    return total_cost / n

6. 定义梯度下降函数

def gradient_descent(X, Y, m, c, L, epochs):
    n = len(X)
    for i in range(epochs):
        Y_pred = m * X + c
        D_m = (-2 / n) * sum(X * (Y - Y_pred))
        D_c = (-2 / n) * sum(Y - Y_pred)
        m = m - L * D_m
        c = c - L * D_c
    return m, c

7. 调用梯度下降函数并输出结果

m, c = gradient_descent(X, Y, m, c, L, epochs)
print("Slope = ", m, "Intercept = ", c)
print("Cost = ", compute_cost(m, c, X, Y))

以上就是用Python实现线性回归的基本步骤。