目录
一、Scikit-Learn
- 场景：机器学习建模和评估，分类、回归、聚类、模型评估
- 简单示例：
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
X = df[['feature1', 'feature2']]
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
二、TensorFlow and Keras
- 场景：深度学习，图像识别、自然语言处理、时间序列预测
- 简单示例：
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential([
Dense(128, activation='relu', input_shape=(X_train.shape[1],)),
Dense(64, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10)
三、BeautifulSoup and Requests
- 场景：网页数据抓取，从网页获取数据进行分析
- 简单示例：
import requests
from bs4 import BeautifulSoup
response = requests.get('https://example.com')
soup = BeautifulSoup(response.content, 'html.parser')
titles = soup.find_all('h1')
