Python 机器学习指南：从零基础到大师级，你的 AI 梦想从此起航

# 注释# 变量x = 5y = "Hello, world!"# 数据类型print(type(x))# <class>print(type(y))# <class># 控制结构if x &gt; 0:print("x is positive.")else:print("x is not positive.")# 函数def my_function(x):return x * 2print(my_function(5))# 10</class></class>

# 导入必要的库import numpy as npimport pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegression# 加载数据data = pd.read_csv("data.csv")# 划分训练集和测试集X = data.drop("target", axis=1)# 特征数据y = data["target"]# 标签数据X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)# 训练模型model = LinearRegression()model.fit(X_train, y_train)# 评估模型score = model.score(X_test, y_test)print("准确率：", score)# 预测predictions = model.predict(X_test)

# 导入必要的库from sklearn.linear_model import LinearRegressionfrom sklearn.linear_model import LoGISticRegressionfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.svm import SVCfrom sklearn.ensemble import RandomForestClassifier# 加载数据data = pd.read_csv("data.csv")# 划分训练集和测试集X = data.drop("target", axis=1)# 特征数据y = data["target"]# 标签数据X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)# 训练模型models = [LinearRegression(),LogisticRegression(),DecisionTreeClassifier(),SVC(),RandomForestClassifier()]for model in models:model.fit(X_train, y_train)# 评估模型score = model.score(X_test, y_test)print(model.__class__.__name__, "准确率：", score)

# 导入必要的库import Tensorflow as tf# 定义神经网络模型model = tf.keras.Sequential([tf.keras.layers.Dense(100, activation="relu"),tf.keras.layers.Dense(10, activation="softmax")])# 编译模型model.compile(optimizer="adam", loss="sparse_cateGorical_crossentropy", metrics=["accuracy"])# 训练模型model.fit(X_train, y_train, epochs=10)# 评估模型score = model.evaluate(X_test, y_test)print("准确率：", score[1])# 预测predictions = model.predict(X_test)