sklearn实现葡萄牙银行机构营销案例
时间: 2023-07-22 09:58:15 浏览: 123
要使用scikit-learn(sklearn)实现葡萄牙银行机构营销案例,可以按照以下步骤进行:
1. 导入必要的库和模块:
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
```
2. 加载数据集:
```python
data = pd.read_csv('bank.csv', sep=';')
```
3. 数据预处理和特征工程:
```python
# 删除不需要的列
data = data.drop(['duration', 'contact', 'month', 'day_of_week', 'pdays', 'previous', 'poutcome'], axis=1)
# 将分类变量进行编码
le = LabelEncoder()
data['job'] = le.fit_transform(data['job'])
data['marital'] = le.fit_transform(data['marital'])
data['education'] = le.fit_transform(data['education'])
data['default'] = le.fit_transform(data['default'])
data['housing'] = le.fit_transform(data['housing'])
data['loan'] = le.fit_transform(data['loan'])
data['y'] = le.fit_transform(data['y'])
```
4. 划分数据集为训练集和测试集:
```python
X = data.drop('y', axis=1)
y = data['y']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
5. 构建模型并进行训练:
```python
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
```
6. 预测并评估模型:
```python
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("准确率:", accuracy)
```
以上是一个简单的使用scikit-learn实现葡萄牙银行机构营销案例的步骤,具体根据你的数据集和需求进行相应的调整。