python算法实现客户细分knn
时间: 2024-06-20 22:01:57 浏览: 9
在Python中,K-Nearest Neighbors (KNN) 算法是一个常用的方法用于客户细分或市场分割,目的是根据数据集中的相似性将客户分类到不同的群组。以下是KNN算法在Python中的一种常见实现:
1. 导入所需库:
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
```
2. 数据预处理(假设你有一个CSV文件`data.csv`):
```python
data = pd.read_csv('data.csv')
X = data.drop('target_column', axis=1) # 假设目标列是'target_column'
y = data['target_column']
```
3. 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. 实例化并训练KNN模型:
```python
k = 3 # 选择邻居数量,可以根据需要调整
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
```
5. 预测和评估:
```python
y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
```
6. 客户细分(针对新数据):
```python
new_data = ... # 新的观测数据
segment = knn.predict(new_data)
print(f"Customer segment: {segment}")
```