sklearn实现葡萄牙银行机构营销案例
时间: 2023-07-22 18:58:15 浏览: 128
要使用scikit-learn(sklearn)来实现葡萄牙银行机构的营销案例,您可以按照以下步骤进行操作:
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:
- 将分类变量进行编码:
```python
label_encoder = LabelEncoder()
data["job"] = label_encoder.fit_transform(data["job"])
data["marital"] = label_encoder.fit_transform(data["marital"])
data["education"] = label_encoder.fit_transform(data["education"])
data["default"] = label_encoder.fit_transform(data["default"])
data["housing"] = label_encoder.fit_transform(data["housing"])
data["loan"] = label_encoder.fit_transform(data["loan"])
data["contact"] = label_encoder.fit_transform(data["contact"])
data["month"] = label_encoder.fit_transform(data["month"])
data["poutcome"] = label_encoder.fit_transform(data["poutcome"])
data["y"] = label_encoder.fit_transform(data["y"])
```
- 分割特征和目标变量:
```python
X = data.drop("y", axis=1)
y = data["y"]
```
- 划分训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
4. 构建模型并训练:
```python
model = RandomForestClassifier()
model.fit(X_train, y_train)
```
5. 预测:
```python
y_pred = model.predict(X_test)
```
6. 评估模型性能:
```python
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
这样,您就可以使用scikit-learn来实现葡萄牙银行机构的营销案例了。请确保您已经准备好了相关的数据集,并将其命名为"bank.csv"。
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