怎么更改上面的代码

如果你想只使用特征变量进行建模，可以参考如下代码：

import numpy as np 
import pylab as pl 
import pandas as pd 
from sklearn.linear_model import Ridge 
from sklearn.metrics import mean_squared_error 
from sklearn.model_selection import train_test_split 

X2=[] 
X3=[] 
X4=[] 
X5=[] 
X6=[] 
X7=[] 

df=pd.read_excel('C:/Users/86147/OneDrive/文档/777.xlsx',header=0,usecols=(3,)) 
X2=df.values.tolist() 
x2=[] 
for i in X2:      
    if X2.index(i)<=2927: #两个单元楼的分隔数           
        x2.append(i)      

df=pd.read_excel('C:/Users/86147/OneDrive/文档/777.xlsx',header=0,usecols=(4,)) 
X3=df.values.tolist() 
x3=[] 
for i in X3:      
    if X3.index(i)<=2927:           
        x3.append(i)      

df=pd.read_excel('C:/Users/86147/OneDrive/文档/777.xlsx',header=0,usecols=(5,)) 
X4=df.values.tolist() 
x4=[] 
for i in X4:      
    if X4.index(i)<=2927:           
        x4.append(i)      

df=pd.read_excel('C:/Users/86147/OneDrive/文档/777.xlsx',header=0,usecols=(6,)) 
X5=df.values.tolist() 
x5=[] 
for i in X5:      
    if X5.index(i)<=2927:           
        x5.append(i)      

df=pd.read_excel('C:/Users/86147/OneDrive/文档/777.xlsx',header=0,usecols=(7,)) 
X6=df.values.tolist() 
x6=[] 
for i in X6:      
    if X6.index(i)<=2927:           
        x6.append(i)      

df=pd.read_excel('C:/Users/86147/OneDrive/文档/777.xlsx',header=0,usecols=(8,)) 
X7=df.values.tolist() 
x7=[] 
for i in X7:      
    if X7.index(i)<=2927:           
        x7.append(i)      

np.random.seed(42) 
q=np.array(X2[:2922]) 
w=np.array(x3[:2922]) 
e=np.array(x4[:2922]) 
r=np.array(x5[:2922])     
t=np.array(x6[:2922]) 
p=np.array(x7[:2922]) 

eps=np.random.normal(0,0.05,152)         
X=np.c_[q,w,e,r,t,p]             

beta=[0.1,0.15,0.2,0.5,0.33,0.45]    
y=np.dot(X,beta)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 

alpha = 0.1  # 设置岭回归的惩罚参数 
ridge = Ridge(alpha=alpha) 
ridge.fit(X_train, y_train) 
y_pred = ridge.predict(X_test) 

mse = mean_squared_error(y_test, y_pred) 
print('MSE:', mse) 

coef = ridge.coef_  # 计算岭回归的系数 
intercept = ridge.intercept_  # 计算岭回归的截距 

print('Coefficients:', coef) 
print('Intercept:', intercept)

这个代码的主要区别在于，只使用了x2到x7这6个特征变量，而没有使用x1变量。在读取Excel文件时也只读取了这6个变量的数据，并且在合并X的时候只使用了这6个变量的数据。