研究汽车销量与生产总值、汽油价格相关性及预测模型建立
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor
加载数据集并拆分训练数据集和测试数据集
# 加载数据集
data = pd.read_csv('/data.csv')
x = data.iloc[:, 3:5]
y = data.iloc[:,2]
# 拆分训练数据集和测试数据集
X_train, X_test, y_train, y_test = train_test_split(x, y,test_size = 0.25, random_state = 1)
数据集进行处理并拟合
# 数据归一化
standardScaler = StandardScaler()
standardScaler.fit(X_train)
X_train_standard = standardScaler.transform(X_train)
X_test_standard = standardScaler.transform(X_test)
# 实例化 SGDRegressor
sgd = SGDRegressor(max_iter=1000, tol=1e-5)
# 对训练数据集进行拟合
sgd.fit(X_train_standard, y_train)
print('coefficients(b1,b2...):',sgd.coef_)
print('intercept(b0):',sgd.intercept_)
预测数据并评判
# 预测数据
y_pred = sgd.predict(X_test)
print(y_pred)
# 对测试数据集进行评分
print('模型评分：', sgd.score(X_test_standard, y_test))
