Using Customer Segmentation Data + Unsupervised Learning to Recognize Buyer Patterns

mktg.py

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

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

df.shape

df.describe()

df.dtypes

df.isnull().sum()

df.drop(["CustomerID"],axis=1,inplace=True)

df.head()

plt.figure(1, figsize=(15,6))
n=0
for x in ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']:
    n += 1
    plt.subplot(1, 3, n)
    plt.subplots_adjust(hspace =0.5 , wspace = 0.5)
    sns.distplot(df[x], bins = 20)
    plt.title("Distplot of {}".format(x))
plt.show()

plt.figure(figsize=(15,5))
sns.countplot(y='Gender',data=df)
plt.show()

plt.figure(1,figsize=(15,7))
n=0
for cols in ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']:
    n+=1
    plt.subplot(1, 3, n)
    sns.set(style="whitegrid")
    plt.subplots_adjust(hspace = 0.5, wspace = 0.5)
    sns.violinplot(x=cols, y = 'Gender', data = df)
    plt.ylabel ('Gender' if n == 1 else '')
    plt.title ('Violin Plot')
    plt.show()

age_18_25 = df.Age[(df.Age >= 18) & (df.Age <= 25)]
age_26_35 = df.Age[(df.Age >= 26) & (df.Age <= 35)]
age_36_45 = df.Age[(df.Age >= 36) & (df.Age <= 45)]
age_46_55 = df.Age[(df.Age >= 46) & (df.Age <= 55)]
age_55above = df.Age[df.Age >= 56]

agex = ["18 - 25", "26 - 35", "36 - 45", "46 - 55", "55+"]
agey = [len(age_18_25.values), len(age_26_35.values), len(age_36_45.values), len(age_46_55.values),len(age_55above.values)]

plt.figure(figsize=(15,6))
sns.barplot(x=agex, y=agey, palette="mako")
plt.title("Number of Customer and Ages")
plt.xlabel("Age")
plt.ylabel("Number of Customer")
plt.show()

sns.relplot(x="Annual Income (k$)", y="Spending Score (1-100)", data=df)
print("This data is in a rel plot! We can see relationships between customers who spent percentages of their annual income and what score they've received for their frequency of spending.")
plt.show()

ss_1_20 = df["Spending Score (1-100)"][(df["Spending Score (1-100)"] >=1) & (df["Spending Score (1-100)"] <=20)]
ss_21_40 = df["Spending Score (1-100)"][(df["Spending Score (1-100)"] >=21) & (df["Spending Score (1-100)"] <=40)]
ss_41_60 = df["Spending Score (1-100)"][(df["Spending Score (1-100)"] >=41) & (df["Spending Score (1-100)"] <=60)]
ss_61_80 = df["Spending Score (1-100)"][(df["Spending Score (1-100)"] >=61) & (df["Spending Score (1-100)"] <=80)]
ss_81_100 = df["Spending Score (1-100)"][(df["Spending Score (1-100)"] >=81) & (df["Spending Score (1-100)"] <=100)]

ssx = ["1-20", "21-40", "41-60", "61-80", "81-100"]
ssy = [len(ss_1_20.values), len(ss_21_40.values), len(ss_41_60.values), len(ss_61_80.values), len(ss_81_100.values)]

plt.figure(figsize=(15,6))
sns.barplot(x=ssx, y=ssy, palette="rocket")
plt.title("Spending Scores")
plt.xlabel("Score")
plt.ylabel("Number of Customers Having the Score")
plt.show()

ai0_30 = df["Annual Income (k$)"][(df["Annual Income (k$)"] >=0) & (df["Annual Income (k$)"] <=30)]
ai31_60 = df["Annual Income (k$)"][(df["Annual Income (k$)"] >=31) & (df["Annual Income (k$)"] <=60)]
ai61_90 = df["Annual Income (k$)"][(df["Annual Income (k$)"] >=61) & (df["Annual Income (k$)"] <=90)]
ai91_120 = df["Annual Income (k$)"][(df["Annual Income (k$)"] >=91) & (df["Annual Income (k$)"] <=120)]
ai121_150 = df["Annual Income (k$)"][(df["Annual Income (k$)"] >=121) & (df["Annual Income (k$)"] <=150)]

aix = ["$ 0 - 30,000", "$30,001 - 60,000", "$60,001 - 90,000", "$90,001 - 120,000", "$120,001 - 150,000"]
aiy = [len(ai0_30.values), len(ai31_60.values), len(ai61_90.values), len(ai91_120.values), len(ai121_150.values)]

plt.figure(figsize=(15,6))
sns.barplot(x=aix, y=aiy, palette = "Spectral")
plt.title("Annual Incomes")
plt.xlabel("Income Streams")
plt.ylabel("Number of Customers")
plt.show()

X1=df.loc[:, ["Age", "Spending Score (1-100)"]].values

from sklearn.cluster import KMeans
wcss = []
for k in range(1,11):
    kmeans = KMeans(n_clusters=k, init = "k-means++")
    kmeans.fit(X1)
    wcss.append(kmeans.inertia_)
plt.figure(figsize=(12,6))
plt.grid()
plt.plot(range(1,11), wcss, linewidth=2, color="red", marker="8")
plt.xlabel("K Values")
plt.ylabel("WCSS")
plt.show()

# initialize class object now that k-value is found
kmeans = KMeans(n_clusters=4)

# predict the labels of clusters
label = kmeans.fit_predict(X1)

print(label)

print(kmeans.cluster_centers_)

plt.scatter(X1[:,0], X1[:,1],c=kmeans.labels_, cmap='rainbow')
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], color='black')
plt.title('Clusters of Customers')
plt.xlabel('Age')
plt.ylabel('Spending Score (1-100)')
plt.show()

X2=df.loc[:, ["Annual Income (k$)", "Spending Score (1-100)"]].values

from sklearn.cluster import KMeans
wcss = []
for k in range(1,11):
    kmeans = KMeans(n_clusters=k, init="k-means++")
    kmeans.fit(X2)
    wcss.append(kmeans.inertia_)
plt.figure(figsize=(12,6))
plt.grid()
plt.plot(range(1,11),wcss, linewidth=2, color="red", marker="8")
plt.xlabel("K Value")
plt.ylabel("WCSS")
plt.show()

kmeans = KMeans(n_clusters=5)
label = kmeans.fit_predict(X2)
print(label)

print(kmeans.cluster_centers_)

plt.scatter(X2[:,0], X1[:,1],c=kmeans.labels_, cmap='rainbow')
plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], color='black')
plt.title('Clusters of Customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.show()

X3=df.iloc[:,1:]

wcss = []
for k in range(1,11):
    kmeans = KMeans(n_clusters=k, init="k-means++")
    kmeans.fit(X3)
    wcss.append(kmeans.inertia_)
plt.figure(figsize=(12,6))
plt.grid()
plt.plot(range(1,11),wcss, linewidth=2, color="red", marker="8")
plt.xlabel("K Value")
plt.ylabel("WCSS")
plt.show()

kmeans = KMeans(n_clusters = 6)

label = kmeans.fit_predict(X3)

print(label)

print(kmeans.cluster_centers_)

clusters = kmeans.fit_predict(X3)
df["label"] = clusters

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(df.Age[df.label == 0], df["Annual Income (k$)"][df.label == 0], df["Spending Score (1-100)"][df.label == 0], c='blue')
ax.scatter(df.Age[df.label == 1], df["Annual Income (k$)"][df.label == 1], df["Spending Score (1-100)"][df.label == 1], c='red')
ax.scatter(df.Age[df.label == 2], df["Annual Income (k$)"][df.label == 2], df["Spending Score (1-100)"][df.label == 2], c='black')
ax.scatter(df.Age[df.label == 3], df["Annual Income (k$)"][df.label == 3], df["Spending Score (1-100)"][df.label == 3], c='orange')
ax.scatter(df.Age[df.label == 4], df["Annual Income (k$)"][df.label == 4], df["Spending Score (1-100)"][df.label == 4], c='purple')
ax.view_init(30,185)