Clustering

Importing libraries and packages

# Mathematical operations and data manipulation
import pandas as pd

# Plotting
import matplotlib.pyplot as plt

# Modelling
from sklearn.cluster import KMeans

# Warnings
import warnings

warnings.filterwarnings("ignore")

%matplotlib inline

Set paths

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset

# load data
dataset = pd.read_csv(f"{data_path}/online_shoppers_intention.csv")
dataset.head().T

	0	1	2	3	4
Administrative	0	0	0	0	0
Administrative_Duration	0.0	0.0	0.0	0.0	0.0
Informational	0	0	0	0	0
Informational_Duration	0.0	0.0	0.0	0.0	0.0
ProductRelated	1	2	1	2	10
ProductRelated_Duration	0.0	64.0	0.0	2.666667	627.5
BounceRates	0.2	0.0	0.2	0.05	0.02
ExitRates	0.2	0.1	0.2	0.14	0.05
PageValues	0.0	0.0	0.0	0.0	0.0
SpecialDay	0.0	0.0	0.0	0.0	0.0
Month	Feb	Feb	Feb	Feb	Feb
OperatingSystems	1	2	4	3	3
Browser	1	2	1	2	3
Region	1	1	9	2	1
TrafficType	1	2	3	4	4
VisitorType	Returning_Visitor	Returning_Visitor	Returning_Visitor	Returning_Visitor	Returning_Visitor
Weekend	False	False	False	False	True
Revenue	False	False	False	False	False

Exploring dataset

# Printing dimensionality of the data, columns, types and missing values
print(f"Data dimension: {dataset.shape}")
for col in dataset.columns:
    print(
        f"Column: {col:35} | "
        f"type: {str(dataset[col].dtype):7} | "
        f"missing values: {dataset[col].isna().sum():3d}"
    )

Data dimension: (12330, 18)
Column: Administrative                      | type: int64   | missing values:   0
Column: Administrative_Duration             | type: float64 | missing values:   0
Column: Informational                       | type: int64   | missing values:   0
Column: Informational_Duration              | type: float64 | missing values:   0
Column: ProductRelated                      | type: int64   | missing values:   0
Column: ProductRelated_Duration             | type: float64 | missing values:   0
Column: BounceRates                         | type: float64 | missing values:   0
Column: ExitRates                           | type: float64 | missing values:   0
Column: PageValues                          | type: float64 | missing values:   0
Column: SpecialDay                          | type: float64 | missing values:   0
Column: Month                               | type: object  | missing values:   0
Column: OperatingSystems                    | type: int64   | missing values:   0
Column: Browser                             | type: int64   | missing values:   0
Column: Region                              | type: int64   | missing values:   0
Column: TrafficType                         | type: int64   | missing values:   0
Column: VisitorType                         | type: object  | missing values:   0
Column: Weekend                             | type: bool    | missing values:   0
Column: Revenue                             | type: bool    | missing values:   0

# Computing statistics on numerical features
dataset.describe().T

	count	mean	std	min	25%	50%	75%	max
Administrative	12330.0	2.315166	3.321784	0.0	0.000000	1.000000	4.000000	27.000000
Administrative_Duration	12330.0	80.818611	176.779107	0.0	0.000000	7.500000	93.256250	3398.750000
Informational	12330.0	0.503569	1.270156	0.0	0.000000	0.000000	0.000000	24.000000
Informational_Duration	12330.0	34.472398	140.749294	0.0	0.000000	0.000000	0.000000	2549.375000
ProductRelated	12330.0	31.731468	44.475503	0.0	7.000000	18.000000	38.000000	705.000000
ProductRelated_Duration	12330.0	1194.746220	1913.669288	0.0	184.137500	598.936905	1464.157214	63973.522230
BounceRates	12330.0	0.022191	0.048488	0.0	0.000000	0.003112	0.016813	0.200000
ExitRates	12330.0	0.043073	0.048597	0.0	0.014286	0.025156	0.050000	0.200000
PageValues	12330.0	5.889258	18.568437	0.0	0.000000	0.000000	0.000000	361.763742
SpecialDay	12330.0	0.061427	0.198917	0.0	0.000000	0.000000	0.000000	1.000000
OperatingSystems	12330.0	2.124006	0.911325	1.0	2.000000	2.000000	3.000000	8.000000
Browser	12330.0	2.357097	1.717277	1.0	2.000000	2.000000	2.000000	13.000000
Region	12330.0	3.147364	2.401591	1.0	1.000000	3.000000	4.000000	9.000000
TrafficType	12330.0	4.069586	4.025169	1.0	2.000000	2.000000	4.000000	20.000000

Clustering

K-means Clustering for Informational Duration versus Bounce Rate

x = dataset.iloc[:, [3, 6]].values

wcss = []
for i in range(1, 11):
    km = KMeans(
        n_clusters=i,
        init="k-means++",
        max_iter=300,
        n_init=10,
        random_state=0,
        algorithm="elkan",
        tol=0.001,
    )
    km.fit(x)
    labels = km.labels_
    wcss.append(km.inertia_)

plt.rcParams["figure.figsize"] = (15, 7)
plt.plot(range(1, 11), wcss)
plt.grid()
plt.tight_layout()
plt.title("The Elbow Method", fontsize=20)
plt.xlabel("No. of Clusters")
plt.ylabel("wcss")
plt.show()

k=2 is the optimum value for clustering.

km = KMeans(
    n_clusters=2, init="k-means++", max_iter=300, n_init=10, random_state=0
)
y_means = km.fit_predict(x)

plt.scatter(
    x[y_means == 0, 0],
    x[y_means == 0, 1],
    s=100,
    c="pink",
    label="Un-interested Customers",
)
plt.scatter(
    x[y_means == 1, 0],
    x[y_means == 1, 1],
    s=100,
    c="yellow",
    label="Target Customers",
)
plt.scatter(
    km.cluster_centers_[:, 0],
    km.cluster_centers_[:, 1],
    s=50,
    c="blue",
    label="centeroid",
)

plt.title("Informational Duration vs Bounce Rates", fontsize=20)
plt.grid()
plt.xlabel("Informational Duration")
plt.ylabel("Bounce Rates")
plt.legend()
plt.show()

Target customers spend around 850-900 seconds on average on the Information page.

K-means Clustering for Informational Duration versus Exit Rate

x = dataset.iloc[:, [4, 7]].values

wcss = []
for i in range(1, 11):
    km = KMeans(
        n_clusters=i,
        init="k-means++",
        max_iter=300,
        n_init=10,
        random_state=0,
        algorithm="elkan",
        tol=0.001,
    )
    km.fit(x)
    labels = km.labels_
    wcss.append(km.inertia_)

plt.rcParams["figure.figsize"] = (15, 7)
plt.plot(range(1, 11), wcss)
plt.grid()
plt.tight_layout()
plt.title("The Elbow Method", fontsize=20)
plt.xlabel("No. of Clusters")
plt.ylabel("wcss")
plt.show()

km = KMeans(
    n_clusters=2, init="k-means++", max_iter=300, n_init=10, random_state=0
)
y_means = km.fit_predict(x)

plt.scatter(
    x[y_means == 0, 0],
    x[y_means == 0, 1],
    s=100,
    c="pink",
    label="Un-interested Customers",
)
plt.scatter(
    x[y_means == 1, 0],
    x[y_means == 1, 1],
    s=100,
    c="yellow",
    label="Target Customers",
)
plt.scatter(
    km.cluster_centers_[:, 0],
    km.cluster_centers_[:, 1],
    s=50,
    c="blue",
    label="centeroid",
)

plt.title("Informational Duration vs Exit Rates", fontsize=20)
plt.grid()
plt.xlabel("Informational Duration")
plt.ylabel("Exit Rates")
plt.legend()
plt.show()

K-means Clustering for Administrative Duration versus Bounce Rate and Administrative Duration versus Exit Rate

# Administrative duration vs Bounce Rate
x = dataset.iloc[:, [1, 6]].values
x.shape

(12330, 2)

wcss = []
for i in range(1, 11):
    km = KMeans(
        n_clusters=i,
        init="k-means++",
        max_iter=300,
        n_init=10,
        random_state=0,
        algorithm="elkan",
        tol=0.001,
    )
    km.fit(x)
    labels = km.labels_
    wcss.append(km.inertia_)

plt.rcParams["figure.figsize"] = (15, 7)
plt.plot(range(1, 11), wcss)
plt.grid()
plt.tight_layout()
plt.title("The Elbow Method", fontsize=20)
plt.xlabel("No. of Clusters")
plt.ylabel("wcss")
plt.show()

km = KMeans(
    n_clusters=2, init="k-means++", max_iter=300, n_init=10, random_state=0
)
y_means = km.fit_predict(x)

plt.scatter(
    x[y_means == 0, 0],
    x[y_means == 0, 1],
    s=100,
    c="pink",
    label="Un-interested Customers",
)
plt.scatter(
    x[y_means == 1, 0],
    x[y_means == 1, 1],
    s=100,
    c="cyan",
    label="Target Customers",
)
plt.scatter(
    km.cluster_centers_[:, 0],
    km.cluster_centers_[:, 1],
    s=50,
    c="blue",
    label="centeroid",
)

plt.title("Administrative Duration vs Bounce Rate", fontsize=20)
plt.grid()
plt.xlabel("Administrative Duration")
plt.ylabel("Bounce Rates")
plt.legend()
plt.show()

# Administrative duration vs Exit Rate
x = dataset.iloc[:, [1, 7]].values

wcss = []
for i in range(1, 11):
    km = KMeans(
        n_clusters=i,
        init="k-means++",
        max_iter=300,
        n_init=10,
        random_state=0,
        algorithm="elkan",
        tol=0.001,
    )
    km.fit(x)
    labels = km.labels_
    wcss.append(km.inertia_)

plt.rcParams["figure.figsize"] = (15, 7)
plt.plot(range(1, 11), wcss)
plt.grid()
plt.tight_layout()
plt.title("The Elbow Method", fontsize=20)
plt.xlabel("No. of Clusters")
plt.ylabel("wcss")
plt.show()

km = KMeans(
    n_clusters=2, init="k-means++", max_iter=300, n_init=10, random_state=0
)
y_means = km.fit_predict(x)

plt.scatter(
    x[y_means == 0, 0],
    x[y_means == 0, 1],
    s=100,
    c="pink",
    label="Un-interested Customers",
)
plt.scatter(
    x[y_means == 1, 0],
    x[y_means == 1, 1],
    s=100,
    c="yellow",
    label="Target Customers",
)
plt.scatter(
    km.cluster_centers_[:, 0],
    km.cluster_centers_[:, 1],
    s=50,
    c="blue",
    label="centeroid",
)

plt.title("Administrative Duration vs Exit Rates", fontsize=20)
plt.grid()
plt.xlabel("Administrative Duration")
plt.ylabel("Exit Rates")
plt.legend()
plt.show()

Uninterested customers spend less time in administrative pages compared with the target customers, who spend around 750 seconds on the administrative page before exiting.

• The conversion rates of new visitors are high compared to those of returning customers.

• While the number of returning customers to the website is high, the conversion rate is low compared to that of new customers.

• Pages with a high page value have a lower bounce rate. We should be talking with our tech team to find ways to improve the page value of the web pages.