在数据挖掘与机器学习中写出以下代码：加载数据并查看数据，转换数据的属性编码，创建分类器，实现分类预测

1. 加载数据并查看数据

import pandas as pd

# 加载数据
data = pd.read_csv('data.csv')

# 查看前5行数据
print(data.head())

# 查看数据信息
print(data.info())

2. 转换数据的属性编码

from sklearn.preprocessing import LabelEncoder

# 将标签列进行编码
label_encoder = LabelEncoder()
data['label'] = label_encoder.fit_transform(data['label'])

# 将属性列进行one-hot编码
data = pd.get_dummies(data, columns=['attribute'])

3. 创建分类器

from sklearn.tree import DecisionTreeClassifier

# 创建决策树分类器
clf = DecisionTreeClassifier()

4. 实现分类预测

# 分离特征和标签
X = data.drop('label', axis=1)
y = data['label']

# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

# 训练分类器
clf.fit(X_train, y_train)

# 预测测试集
y_pred = clf.predict(X_test)

# 计算分类器准确率
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("分类器准确率为：", accuracy)