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Product Segmentation Case Study | Case Study Assignment Help

Context When you think of sneakers for a trip, the importance of good footwear cannot be discarded, and the obvious brands that come to mind are Adidas and Nike. Adidas vs Nike is a constant debate as the two giants in the apparel market, with a large market cap and market share, battle it out to come on top. As a newly hired Data Scientist in a market research company, you have been given the task of extracting insights from the data of men's and women's shoes, and grouping products together to identify similarities and differences between the product range of these renowned brands.

Objective To perform an exploratory data analysis and cluster the products based on various factors

Key Questions

  • Which variables are most important for clustering?

  • How each cluster is different from the others?

  • What are the business recommendations?

Data Description The dataset consists of 3268 products from Nike and Adidas with features of information including their ratings, discount, sales price, listed price, product name, and the number of reviews.

  • Product Name: Name of the product

  • Product ID: ID of the product

  • Listing Price: Listed price of the product

  • Sale Price: Sale price of the product

  • Discount: Percentage of discount on the product

  • Brand: Brand of the product

  • Rating: Rating of the product

  • Reviews: Number of reviews for the product

Let's start coding!

Importing necessary libraries

# this will help in making the Python code more structured automatically (good coding practice)
%load_ext nb_black

# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

# to scale the data using z-score
from sklearn.preprocessing import StandardScaler

# to compute distances
from scipy.spatial.distance import pdist

# to perform hierarchical clustering, compute cophenetic correlation, and create dendrograms
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet
# loading the dataset
data = pd.read_csv("product.csv")

Check shape of dataset


# viewing a random sample of the dataset
data.sample(n=10, random_state=1)


# copying the data to another variable to avoid any changes to original data
df = data.copy()
# fixing column names
df.columns = [c.replace(" ", "_") for c in df.columns]
# let's look at the structure of the data


We won't need Product_ID for analysis, so let's drop this column.

df.drop("Product_ID", axis=1, inplace=True)
# let's check for duplicate observations
  • There are 117 duplicate observations. We will remove them from the data.

df = df[(~df.duplicated())].copy()

Let's take a look at the summary of the data




  • 0 in the listing price indicates missing values.

  • The average listing price is 7046.

  • The average sale price is 5983.

  • The average discount is 28%.

  • The average rating is 3.3.

  • The average number of reviews is 42.

# let's check how many products have listing price 0
(df.Listing_Price == 0).sum()
# let's check the products which have listing price 0
df[(df.Listing_Price == 0)]


df[(df.Listing_Price == 0)].describe()


  • There are 336 observations that have missing values in the listing price column

  • We see that the discount for the products with listing price 0 is 0.

  • So, we will replace the listing price with the corresponding sale price for those observations.

df.loc[(df.Listing_Price == 0), ["Listing_Price"]] = df.loc[
    (df.Listing_Price == 0), ["Sale_Price"]



# checking missing values


Exploratory Data Analysis

# function to plot a boxplot and a histogram along the same scale.
def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (12,7))
    kde: whether to the show density curve (default False)
    bins: number of bins for histogram (default None)
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
    )  # creating the 2 subplots
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a star will indicate the mean value of the column
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter"
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram
# selecting numerical columns
num_col = df.select_dtypes(include=np.number).columns.tolist()

for item in num_col:
    histogram_boxplot(df, item)



  • Listing price and sale price have right-skewed distributions with upper outliers, which indicates the presence of very expensive products.

  • The maximum discount given is 60%.

  • Rating is left-skewed and most of the ratings are between 2.5 and 4.5.

  • The number of reviews is between 1 and 100, with an outlier value above 200.

fig, axes = plt.subplots(3, 2, figsize=(20, 15))
fig.suptitle("CDF plot of numerical variables", fontsize=20)
counter = 0
for ii in range(3):
    sns.ecdfplot(ax=axes[ii][0], x=df[num_col[counter]])
    counter = counter + 1
    if counter != 5:
        sns.ecdfplot(ax=axes[ii][1], x=df[num_col[counter]])
        counter = counter + 1




  • 90% of the products have listing prices less than 15000.

  • 95% of the product have a sale price of less than 15000.

  • 80% of the products have at least 50% discount or less than 50%.

  • 50% off the products have a rating of 3.5 or less than 3.5.

  • Almost all products have 100 or fewer reviews.

# function to create labeled barplots
def labeled_barplot(data, feature, perc=False, n=None):
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(

    for p in ax.patches:
        if perc:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

            (x, y),
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage  # show the plot

# let's explore discounts further
labeled_barplot(df, "Discount", perc=True)


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