超市商场客户细分项目

news2024/10/6 20:19:41

注意：本文引用自专业人工智能社区Venus AI

项目背景

在竞争激烈的商业环境中，了解和满足客户的需求是任何成功商场的关键要素。为了更好地理解我们的客户并制定有针对性的营销策略，我们拥有一家超市商场，并通过会员卡收集了客户的基本数据。其中，我们尤其关注了一项叫做”消费分数”的指标，这是根据我们定义的一些参数，如客户行为和购买数据，为每位客户分配的分数。

项目目标

本项目的主要目标包括：

通过K均值聚类算法对客户进行细分，以识别不同的客户群体。
确定目标客户，即哪些客户更容易受到吸引。
提供有关不同客户群体的详细信息，以便为市场团队制定有针对性的营销策略。

通过这个项目，我们希望能够更好地了解我们的客户，从而为我们的商场提供更智能、个性化和有效的市场战略。我们将通过数据分析和机器学习算法的应用来实现这一目标，并将在未来分享我们的研究结果和见解。

数据集描述

我们的数据集包含了以下关键属性：

客户ID
年龄
性别
年收入
消费分数

我们可以通过对这些属性进行分析和建模，深入了解客户群体的特征，以及哪些客户更容易被吸引和满足。

模型选择与依赖库

算法：我们的关键算法是K均值聚类（Kmeans Clustering），它可以将客户分成不同的群体，每个群体具有相似的特征。这将有助于我们理解不同群体的需求和购买行为，从而更好地满足他们的期望。

Libraries（依赖库）：为了完成这个项目，我们将利用以下Python库和机器学习算法：

Pandas：用于数据加载和处理，帮助我们清洗和准备数据。
Matplotlib和Seaborn：用于数据可视化，以便更好地理解客户数据的分布和关联关系。
Scikit-learn：这是一个强大的机器学习库，我们将使用其中的K均值聚类算法来对客户进行细分。

代码实现

导入模块

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

加载数据集

df = pd.read_csv('Mall_Customers.csv')
df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40

# 统计信息
df.describe()

	CustomerID	Age	Annual Income (k$)	Spending Score (1-100)
count	200.000000	200.000000	200.000000	200.000000
mean	100.500000	38.850000	60.560000	50.200000
std	57.879185	13.969007	26.264721	25.823522
min	1.000000	18.000000	15.000000	1.000000
25%	50.750000	28.750000	41.500000	34.750000
50%	100.500000	36.000000	61.500000	50.000000
75%	150.250000	49.000000	78.000000	73.000000
max	200.000000	70.000000	137.000000	99.000000

# 数据类型信息
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 5 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   CustomerID              200 non-null    int64 
 1   Gender                  200 non-null    object
 2   Age                     200 non-null    int64 
 3   Annual Income (k$)      200 non-null    int64 
 4   Spending Score (1-100)  200 non-null    int64 
dtypes: int64(4), object(1)
memory usage: 7.9+ KB

探索性数据分析

gender_counts = df['Gender'].value_counts()
sns.barplot(x=gender_counts.index, y=gender_counts.values)
plt.xlabel('Gender')
plt.ylabel('Count')
plt.title('Gender Distribution')
plt.show()

sns.displot(df['Age'], kde=True)  # kde=True添加核密度估计曲线
plt.xlabel('Age')
plt.ylabel('Density')
plt.title('Age Distribution')
plt.show()


sns.displot(df['Annual Income (k$)'], kde=True)  # kde=True添加核密度估计曲线
plt.xlabel('Annual Income (k$)')
plt.ylabel('Density')
plt.title('Annual Income (k$) Distribution')
plt.show()

sns.displot(df['Spending Score (1-100)'], kde=True)  # kde=True添加核密度估计曲线
plt.xlabel('Spending Score (1-100)')
plt.ylabel('Density')
plt.title('Spending Score (1-100) Distribution')
plt.show()

聚类

df.head()

	CustomerID	Gender	Age	Annual Income (k$)	Spending Score (1-100)
0	1	Male	19	15	39
1	2	Male	21	15	81
2	3	Female	20	16	6
3	4	Female	23	16	77
4	5	Female	31	17	40

# 基于 2 个特征的聚类
df1 = df[['Annual Income (k$)', 'Spending Score (1-100)']]
df1.head()

	Annual Income (k$)	Spending Score (1-100)
0	15	39
1	15	81
2	16	6
3	16	77
4	17	40

# 散点图
sns.scatterplot(x='Annual Income (k$)', y='Spending Score (1-100)', data=df1)
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.title('Scatter Plot of Annual Income vs. Spending Score')
plt.show()

import warnings 
warnings.filterwarnings("ignore", category=UserWarning) 
from sklearn.cluster import KMeans

errors = []
for i in range(1, 11): 
    kmeans = KMeans(n_clusters=i, random_state=0,n_init=10, max_iter=300 )
    kmeans.fit(df1)
    errors.append(kmeans.inertia_)

# 绘制肘部法的结果
plt.figure(figsize=(13,6))
plt.plot(range(1,11), errors)
plt.plot(range(1,11), errors, linewidth=3, color='red', marker='8')
plt.xlabel('No. of clusters')
plt.ylabel('WCSS')
plt.xticks(np.arange(1,11,1))
plt.show()

km = KMeans(n_clusters=5, n_init=10)
km.fit(df1)

y = km.predict(df1)
df1.loc[:, 'Label'] = y

df1.head()

C:\Users\1\AppData\Local\Temp\ipykernel_21088\2186447201.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df1.loc[:, 'Label'] = y

	Annual Income (k$)	Spending Score (1-100)	Label
0	15	39	0
1	15	81	3
2	16	6	0
3	16	77	3
4	17	40	0

sns.scatterplot(x='Annual Income (k$)', y='Spending Score (1-100)', data=df1, hue='Label', s=50, palette=['red', 'green', 'brown', 'blue', 'orange'])

<Axes: xlabel='Annual Income (k$)', ylabel='Spending Score (1-100)'>

# 基于 3 个特征的聚类
df2 = df[['Annual Income (k$)', 'Spending Score (1-100)', 'Age']]
df2.head()

	Annual Income (k$)	Spending Score (1-100)	Age
0	15	39	19
1	15	81	21
2	16	6	20
3	16	77	23
4	17	40	31

import warnings 
warnings.filterwarnings("ignore", category=UserWarning) 
from sklearn.cluster import KMeans

errors = []
for i in range(1, 11): 
    kmeans = KMeans(n_clusters=i, random_state=0,n_init=10, max_iter=300 )
    kmeans.fit(df2)
    errors.append(kmeans.inertia_)

# 绘制肘部法的结果
plt.figure(figsize=(13,6))
plt.plot(range(1,11), errors)
plt.plot(range(1,11), errors, linewidth=3, color='red', marker='8')
plt.xlabel('No. of clusters')
plt.ylabel('WCSS')
plt.xticks(np.arange(1,11,1))
plt.show()

km = KMeans(n_clusters=5, n_init=10)
km.fit(df2)

y = km.predict(df2)
df2.loc[:, 'Label'] = y

df2.head()

C:\Users\1\AppData\Local\Temp\ipykernel_21088\4218386864.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df2.loc[:, 'Label'] = y

	Annual Income (k$)	Spending Score (1-100)	Age	Label
0	15	39	19	4
1	15	81	21	0
2	16	6	20	4
3	16	77	23	0
4	17	40	31	4

# 3d 散点图
fig = plt.figure(figsize=(20,15))
ax = fig.add_subplot(111, projection='3d')

ax.scatter(df2['Age'][df2['Label']==0], df2['Annual Income (k$)'][df2['Label']==0], df2['Spending Score (1-100)'][df2['Label']==0], c='red', s=50)
ax.scatter(df2['Age'][df2['Label']==1], df2['Annual Income (k$)'][df2['Label']==1], df2['Spending Score (1-100)'][df2['Label']==1], c='green', s=50)
ax.scatter(df2['Age'][df2['Label']==2], df2['Annual Income (k$)'][df2['Label']==2], df2['Spending Score (1-100)'][df2['Label']==2], c='blue', s=50)
ax.scatter(df2['Age'][df2['Label']==3], df2['Annual Income (k$)'][df2['Label']==3], df2['Spending Score (1-100)'][df2['Label']==3], c='brown', s=50)
ax.scatter(df2['Age'][df2['Label']==4], df2['Annual Income (k$)'][df2['Label']==4], df2['Spending Score (1-100)'][df2['Label']==4], c='orange', s=50)
ax.view_init(30, 190)
ax.set_xlabel('Age')
ax.set_ylabel('Annual Income')
ax.set_zlabel('Spending Score')
plt.show()