The document summarizes a term project analyzing product data scraped from The Body Shop website. It describes scraping 326 products, collecting 12 data points per product, and analyzing the dataset using Pandas. It identifies popular products and ingredients, visualizes the dataset to show category and sub-category trends, and explores correlations between variables like reviews and ratings. The project encountered challenges with website changes and complex HTML, but was able to mine useful insights from the scraped Body Shop data.

2. CIS5357-
COMPUTING
FOR DATA
ANALYTICS
Term Project
2019
The Body Shop
By – Aditi & Pooja

3. Introduction
– The Body Shop International Limited, trading as The Body
Shop, is a cosmetics, skin care and perfume company which
is a subsidiary of Brazilian company Natura & Co.
– In this presentation, we’ll show how we performed web
scraping using Python 3 and the BeautifulSoup library on the
Body Shop website.
– We’ll be scraping all products information from
the https://www.thebodyshop.com/en-us/ , and then
analyzing them using the Pandas library.

4. DATA
COLLECTION
of Body Shop
Dataset

5. Popular products
 Body butters (including Moringa, Satsuma, Strawberry, Olive, Shea, Mango and Coconut)
 Body products such as body scrub, body butter and bath lilies
 Cosmetics (including mascara, lipstick, lip gloss, eye shadow and cotton rounds)
 Full skin care ranges (including Tea tree, Vitamin C, Vitamin E, Aloe vera and Seaweed)
 Men's skin care (Including maca root and white musk)
 Hair care (including their famous Banana shampoo and Banana conditioner)
 Fragrances (Women's and Men's)
 Bath products including shower gels and solid soaps

6.  We parsed through all main
categories and scraped their
products information.
 We were able to fetch about
326 products from the website.
Web Scraping

8. Columns in our dataset
 Item Number
 Main Category
 Sub-Category
 Product Name
 Main Ingredient
 Reviews Count
 Ratings out of 5
 Sizes Available
 Sizes
 Prices
 Short Description
 Long Description
328 Rows, 12 Columns

9. Snapshot of our dataset after
web scraping
Head rows

10. Challenges
 External sites can change without warning.
 Due to holiday season, the website was changing frequently and
that broke scrapers often
 Confusing and difficult to dig HTML tags
 Difficulty in scraping ‘Reviews Count’ & ‘Ratings’
 ‘Main Ingredient’ might just not scrap successfully sometimes and
result in huge number of Nas
 Nested sizes and prices columns difficult to mine or explode

11. DATA VISUALIZATION of
Body Shop Dataset

12. What are the most
popular Main
Ingredients ?
 There 31 unique Main
Ingredients
 This bar plot shows the top 5
popular Main Ingredients are:
 Aloe Vera
 Shea
 Marula
 Organic Alcohol
 Honey

13. Total products
in each Main
Category ?
Products in BODY category are
highest in number with 36.89% of all
products.

14. What are the most popular
Sub-Categories ?
 This horizontal bar plot shows top 5 sub-
categories are:
 Lotions & Creams
 Hand Creams
 Body Butter
 Body Wash
 Lip Balm

15. What are the top 10
highest reviewed
products?
What are the
top 25 highest
rated products?

16. Data Mining
 Let us check how many products
are Vegan or Vegetarian?
 Created a function to data mine
from “Long Descriptions” texts
 Created a new column called
“Cruelty-Free” to show if the
product is Vegan or Vegetarian
Pie Chart showing percent of
products in cruelty-free categories.

17. Correlation Matrix between
Reviews_Count, Ratings_out_of_5 &
Sizes_Available

18. Pairplot or
Correlogram
 Using Seaborn
 Shows scatter plot between
each numerical variable
 Here, there is no linear trend
visible

19. Data Slicing and Dicing
Exploding nested columns
 The Sizes column was a string containing all sizes
 Created a function to clean it and convert it to list
 Exploded new list column into separate rows
 Grouped the rows for each Main category by three sizes – Large, Medium &
Small
 Visualized grouped data

20. Grouped data

21. Visualizing grouped sizes
 Two examples of two categories
 Body and Makeup
 Small sized products are popular in makeup
 While Body sells more medium and some large sized
products

22. Thank you