python 人工智能 实例

# 导入必要的库
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

# 创建模拟数据
# 假设我们有一些房屋的面积和对应的房价
areas = np.array([500, 600, 700, 800, 900, 1000, 1100, 1200]).reshape(-1, 1)
prices = np.array([200000, 250000, 300000, 350000, 400000, 450000, 500000, 550000])

# 将数据分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(areas, prices, test_size=0.2, random_state=42)

# 创建线性回归模型
model = LinearRegression()

# 训练模型
model.fit(X_train, y_train)

# 使用测试集进行预测
predictions = model.predict(X_test)

# 计算模型的均方误差
mse = mean_squared_error(y_test, predictions)
print(f"模型的均方误差为: {mse}")

# 输出模型的系数和截距
print(f"模型的系数为: {model.coef_}")
print(f"模型的截距为: {model.intercept_}")