《Python 逻辑回归实战：预测用户流失率（附代码）》（663）
# 📊 Python逻辑回归实战：预测用户流失率（附代码）💻
在数据科学领域，逻辑回归（Logistic Regression）是一种经典的分类算法，常用于预测二分类问题，如用户是否会流失（Churn Prediction）。本文将带你用Python实现一个简单的逻辑回归模型，预测用户流失率！🚀
## 📌 数据集准备
我们使用经典的电信用户流失数据集（如`Telco-Customer-Churn`），包含用户特征如：
- **Contract**（合约类型）📝
- **MonthlyCharges**（月消费）💰
- **Tenure**（在网时长）⏳
- **InternetService**（网络服务）🌐
## 🔧 代码实现
1️⃣ **数据预处理**
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
# 读取数据
data = pd.read_csv("Telco-Customer-Churn.csv")
# 处理分类变量
le = LabelEncoder()
data["Churn"] = le.fit_transform(data["Churn"]) # 流失=1，未流失=0
# 特征选择与标准化
X = data[["Tenure", "MonthlyCharges"]]
y = data["Churn"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
```
2️⃣ **训练逻辑回归模型**
```python
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
model = LogisticRegression()
model.fit(X_train, y_train)
# 预测
y_pred = model.predict(X_test)
print("准确率:", accuracy_score(y_test, y_pred)) # 📈
print("混淆矩阵:\
", confusion_matrix(y_test, y_pred)) # 📊
```
3️⃣ **可视化结果**
```python
import matplotlib.pyplot as plt
import seaborn as sns
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt="d")
plt.title("Confusion Matrix")
plt.show()
```
## 🎯 模型优化
- 尝试更多特征（如`Contract`、`InternetService`）
- 调整正则化参数`C`防止过拟合
- 使用`GridSearchCV`进行超参数调优
## 📝 结论
逻辑回归简单高效，适合初步探索分类问题。通过特征工程和调参，可以进一步提升模型性能！🎉
**完整代码可在GitHub获取**：[链接]
#Python #机器学习 #逻辑回归 #数据分析 #用户流失预测 🚀
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