《使用Python进行数据分析的精细化技术》|开发技术分享-西西软件园




```python
#import the necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#read the data into a pandas dataframe
df = pd.read_csv("data.csv")

#explore the data
df.head()
df.describe()

#visualize the data
plt.scatter(df['x'], df['y'])
plt.show()

#create a linear regression model
from sklearn.linear_model import LinearRegression
model = LinearRegression()

#split the data into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(df[['x']], df['y'], test_size=0.2)

#fit the model to the training data
model.fit(X_train, y_train)

#predict the testing data
predictions = model.predict(X_test)

#evaluate the model
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, predictions)
print("Mean Squared Error:", mse)

#visualize the results
plt.scatter(X_test, y_test)
plt.plot(X_test, predictions, color='red')
plt.show()
```

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