[clustering] Mall_Customers

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')

df  = pd.read_csv('./Mall_Customers/Mall_Customers.csv')
print(df.shape)
df.head(3)

df = df.rename(columns = {"Annual Income (k$)": "income", 
                          "Spending Score (1-100)":"score", "Gender":"gender", "Age":"age"})

sns.pairplot(df, hue='gender')

df.drop('CustomerID',axis=1,inplace=True)

plt.figure(figsize=(14,6))
sns.scatterplot(data=df, x='income',y='score',hue='gender')

for col in ['age','income','score']:
    fig, ax= plt.subplots(figsize=(5,3))
    sns.boxplot(data=df, x='gender', y=col,ax=ax)
    ax.set_title(f"{col}")
    plt.show()

X = df.drop(columns='gender',axis=1)

from sklearn.cluster import KMeans

clusters = []

for i in range(1,11):
    km = KMeans(n_clusters=i).fit(X)
    clusters.append(km.inertia_)
    
fig, ax = plt.subplots(figsize=(12,8))
sns.lineplot(x=list(range(1,11)), y=clusters, ax=ax)
ax.set_title("Searching for Elbow")
ax.set_xlabel("Clusters")
ax.set_ylabel("Inertia")

ax.annotate("Possible Elbow Point", xy=(3,140000), xytext=(3,50000), xycoords='data', arrop)

km3 = KMeans(n_clusters=3).fit(X)

X['labels'] = km3.labels_
plt.figure(figsize=(12,8))
sns.scatterplot(X['income'],X['score'], hue=X['labels'], palette=sns.color_palette("hls",3))
sns.scatterplot(km3.cluster_centers_[:,1],km3.cluster_centers_[:,2],color='orange',s=100)
plt.title("KMeans with 3 Clusters")
plt.show()

 

km5 = KMeans(n_clusters=5).fit(X)

X['labels'] = km5.labels_
plt.figure(figsize=(12,8))
sns.scatterplot(X['income'],X['score'], hue=X['labels'], palette=sns.color_palette("hls",5))
sns.scatterplot(km5.cluster_centers_[:,1],km5.cluster_centers_[:,2],color='orange',s=100)
plt.title("KMeans with 5 Clusters")
plt.show()

fig = plt.figure(figsize=(20,6))
ax = fig.add_subplot(121)
sns.swarmplot(x='labels',y='income', data=X, ax=ax)
ax.set_title("Labels according to income")

ax = fig.add_subplot(122)
sns.swarmplot(x='labels',y='score', data=X, ax=ax)
ax.set_title("Labels according to score")

# Agglomerative

from sklearn.cluster import AgglomerativeClustering
agglom = AgglomerativeClustering(n_clusters=5, linkage='average').fit(X)

X['labels'] = agglom.labels_

plt.figure(figsize=(12,8))
sns.scatterplot(data=X, x='income', y='score', hue='labels', 
                palette=sns.color_palette("hls",len(np.unique(X.labels))))

# Dendrogram

import scipy.cluster.hierarchy as sch

fig = plt.figure(figsize=(20,6))
ax = fig.add_subplot(111)
dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'),ax=ax)
ax.set_title('Dendrogam', fontsize = 20)
ax.set_xlabel('Customers')
ax.set_ylabel('Ecuclidean Distance')
plt.show()