大数据分析案例代码

大数据分析在当今社会各个行业中都占据着重要地位，帮助企业更好地理解数据、推断趋势并做出决策。下面为您提供几个大数据分析案例的示例代码，供参考：

案例：通过用户行为数据预测用户购买意向

```python

导入必要的库

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

读取数据

data = pd.read_csv('user_behaviors.csv')

数据预处理

X = data.drop('purchase', axis=1)

y = data['purchase']

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

随机森林分类器建模

rf = RandomForestClassifier()

rf.fit(X_train, y_train)

预测

predictions = rf.predict(X_test)

评估模型准确率

accuracy = accuracy_score(y_test, predictions)

print('模型准确率：', accuracy)

```

案例：利用病人病历数据预测疾病风险

```python

导入必要的库

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import GradientBoostingClassifier

from sklearn.metrics import roc_auc_score

读取数据

data = pd.read_csv('patient_records.csv')

数据预处理

X = data.drop('disease', axis=1)

y = data['disease']

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

梯度提升分类器建模

gb = GradientBoostingClassifier()

gb.fit(X_train, y_train)

预测概率

probabilities = gb.predict_proba(X_test)[:,1]

评估模型AUC值

auc = roc_auc_score(y_test, probabilities)

print('模型AUC值：', auc)

```

案例：信用评分预测

```python

导入必要的库

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import classification_report

读取数据

data = pd.read_csv('credit_score.csv')

数据预处理

X = data.drop('credit_default', axis=1)

y = data['credit_default']

划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

逻辑回归建模

lr = LogisticRegression()

lr.fit(X_train, y_train)

预测

predictions = lr.predict(X_test)

分类报告

report = classification_report(y_test, predictions)

print('分类报告：\n', report)

```

这些是一些大数据分析的简单案例代码，具体的数据和模型选择会根据实际问题和数据集的特点而有所不同。希望以上代码能帮助您更好地理解大数据分析在不同行业的应用。