不使用sklearn 运用python对Advertising数据(广告投入与销售额数据)进行线性回归

好的，针对您的问题，我可以给出以下代码实现：

首先，我们需要导入必要的库：

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

接着，我们读入数据集：

df = pd.read_csv("Advertising.csv")

然后，我们需要将数据分为特征和标签，即广告投入和销售额：

X = df.iloc[:, 1:4].values
y = df.iloc[:, 4].values

接下来，我们需要将数据集分为训练集和测试集：

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

然后，我们需要对特征进行标准化处理：

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

接着，我们可以使用线性回归模型对数据进行拟合：

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

最后，我们可以进行预测并计算误差：

y_pred = regressor.predict(X_test)
from sklearn.metrics import mean_squared_error
print("Mean squared error: ", mean_squared_error(y_test, y_pred))

完整代码如下：

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

df = pd.read_csv("Advertising.csv")
X = df.iloc[:, 1:4].values
y = df.iloc[:, 4].values

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

y_pred = regressor.predict(X_test)

from sklearn.metrics import mean_squared_error
print("Mean squared error: ", mean_squared_error(y_test, y_pred))

希望这可以帮到您！