Maximize Business Potential by Scraping Nykaa, Purplle, and Zeptonow Data for Beauty & Makeup Products - PowerPoint PPT Presentation

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Maximize Business Potential by Scraping Nykaa, Purplle, and Zeptonow Data for Beauty & Makeup Products

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Scraping data from Nykaa, Purplle, and Zeptonow offers insights for market analysis, competitive intelligence, pricing strategies, and inventory management. – PowerPoint PPT presentation

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Title: Maximize Business Potential by Scraping Nykaa, Purplle, and Zeptonow Data for Beauty & Makeup Products


1
What Are the Key Steps in Scraping Product Data
from Amazon India?
This project utilizes e-commerce data scraping
techniques employing Selenium and BeautifulSoup
to extract specific product details. Focused on
showcasing a single product type, it retrieves
information on Name, Price, Rating, Number of
reviews, and the product's URL. The adaptable
code allows customization for diverse websites.
Post-extraction, the data is compiled into a
.csv file, facilitating user utilization for
model shortlisting or analytics. The project
centers on DELL Laptops, employing Pandas,
Matplotlib, and Seaborn for dataset analysis
within a Jupyter Notebook environment. Essential
package installations include Selenium and bs4,
while browser-specific drivers, like
msedgedriver.exe for Microsoft Edge, enable
access to website data. Begin the coding process
for the Amazon data scraping function by
following these steps
2
About EBay Price Tracker
An eBay price tracker is a specialized tool or
software designed to monitor and analyze product
prices on the eBay e-commerce platform. These
trackers are essential for individual shoppers
and online sellers, providing real-time and
historical data on pricing dynamics. For sellers,
eBay price trackers offer competitive analysis
capabilities, helping them compare their product
prices to those of competitors and adjust their
pricing strategies accordingly. Price trend
analysis enables informed decisions on when to
modify prices to maximize profit, taking
advantage of supply and demand fluctuations.
These tools also support campaign planning by
allowing sellers to align marketing efforts with
price trends. Furthermore, eBay price trackers
aid in inventory management, helping users
identify products that are competitively priced
and in demand. Overall, eBay price trackers
offer valuable insights and market intelligence,
ensuring users can navigate the dynamic eBay
marketplace with a data-driven approach
Import Packages
To scrape Amazon data, import the required
packages for the project. Ensure inclusion of
essential libraries.
Web Driver
Define the execution path of the downloaded
driver, such as "location/msedgedriver.exe," to
enable its usage. This specification ensures the
browser launches automatically with an empty
page.
3
Generate Search Item URL
To search, combine the URL with the item's name.
Utilize the search_term variable, representing
the item name, and create a function to insert
this name into the URL dynamically. By using an
e-commerce data scraper, this method ensures
seamless searching for the specified item.
Replace Spaces In Search Term
Substitute spaces with "" in the search_term
variable. In URLs, replace the spaces, and
multi-word inputs are connected using this
symbol. This adjustment ensures the proper
formation of the search term for URL
compatibility.
Now, proceed to open the generated URL in the
browser. This action is essential for initiating
the Amazon data scraping process and navigating
to the specific search results page.
4
Extract Data
Retrieve all HTML code from the Page Source.
Although manual extraction from the site's page
source is possible through right-clicking and
selecting "View page source," this process is
inefficient. Instead, utilize BeautifulSoup to
automate the extraction of HTML code,
streamlining the data retrieval process.
Extract Relevant Data
Focus solely on the results pertinent to the
search_term. After analyzing the page source,
identify the suitable tag for extraction lt div
data-component-type"s-search-result" gt. Retrieve
all data associated with this tag to gather the
relevant information for the specified search
term.
Iterative Data Extraction
The provided code extracts e-commerce data solely
from the first page. To extend this
functionality across multiple pages, incorporate
a loop in subsequent code segments. The length
of the data_extracted variable corresponds to the
number of products on the initial page. Be
mindful that some products may lack pricing,
rating, or review information, posing potential
errors that lie in later code sections.
5
Data Prototype
Establish a foundational understanding of the
tags essential for extracting specific product
information. Create a prototype as a reference,
outlining the tags for the extraction process.
This prototype serves as a guide for identifying
and retrieving relevant data about each product
on the webpage.
Extract Record Function
Our e-commerce data scraping services help refine
the extraction by creating an extract_record()
function. This function focuses on retrieving
specific details, such as price and ratings,
essential for forming conclusions about each
product. This optimization ensures that only the
necessary information is extracted from the HTML
code, streamlining the data analysis process.
6
Implement error handling within the
extract_record() function to accommodate cases
where variables, such as price or reviews, might
not have assigned values. It ensures the
robustness of the code, preventing potential
errors when specific product details are
unavailable.
Error Handling
Utilize a loop to iterate over each product,
retrieving the data into the records list. This
list will eventually become a compilation of
tuples, each representing the details of a
specific laptop. This structured approach allows
for organized product information storage for
further analysis or export.
7
Implement error handling within the
extract_record() function to accommodate cases
where variables, such as price or reviews, might
not have assigned values. It ensures the
robustness of the code, preventing potential
errors when specific product details are
unavailable.
Intel Core i7-12650H (10-Core, 24MB, up to 4.70
GHz) // Memory Storage 16 GB, 2 x 8 GB, DDR5,
4800 MHz, dual-channel 512GB SSD
Navigate Through Pages
Utilize the page query in the URL, such as
https//www.amazon.in/gp/browse.html?node13754240
31ref_nav_em_sbc_mobcomp_lapt ops_0_2_8_15, to
navigate through pages. Concatenate each query
with the URL using "" to access different pages
sequentially. This method systematically explores
multiple pages to obtain comprehensive data on
the searched item.
8
Combined Code
Upon executing the preceding function, the query
will resemble the following format
https//www.amazon.in/s?klaptops
refnb_sb_noss_2page. In this structure, any
page number can be passed as a placeholder
within the "" to navigate through various pages
in the search results.
The consolidated code incorporates the functions
and assignments in the required order. Copy and
run this code on your system, provided you have
the necessary packages installed, to initiate
the web scraping process efficiently.
9
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10
Next Step Analysis Of DELL Laptops On Amazon
India
The driverFunction() function will generate an
"amazon_scrape_data.csv" file, serving as a
valuable resource for product selection and
future analysis. This CSV file consolidates the
extracted data, offering a convenient format for
users to explore, evaluate, and utilize the
scraped information.
With the established data scraping mechanism, we
can now delve into the analysis and visual
representation of DELL Laptops on Amazon India.
Let's explore critical insights, trends, and
patterns within the extracted data, providing a
comprehensive view for informed decision- making
and strategic planning.
Sample Laptop Information Brand Dell Model Name
G15-5520 Screen Size 15.6 Colour Dark Shadow
Grey Hard Disk Size 512 GB CPU Model Core i7 RAM
Memory Installed Size 16 GB Operating System
Windows 11
11
Special Feature Backlit Keyboard Graphics Card
Description This laptop's name encompasses
essential details such as screen size, processor,
colour options, hard disk size, and
specifications related to graphics, operating
system, RAM, and storage.
It's imperative to gain a preliminary
understanding of the collected data. It involves
extracting key insights, patterns, and trends
from our gathered information. This initial
analysis will lay the foundation for more
in-depth exploration and strategic
decision-making based on the available data.
Filtering Unwanted Data
It's crucial to eliminate laptops from other
companies, inadvertently included due to
sponsorships or advertisements. Implement a
meticulous process to exclude these entries and
remove any other extraneous or unwanted data,
ensuring the dataset remains focused and relevant
to our analysis.
12
Cleaning The Dataset
Before delving deeper into the dataset, the
initial step involves the removal of laptops not
associated with DELL. This cleaning process
ensures that only relevant data from DELL,
excluding other companies, is retained for
subsequent analysis.
To enhance accuracy, eliminate duplicate data
entries present in the dataset. This step ensures
that each laptop's information is unique,
preventing redundancy and providing a more
precise representation of the collected data.
Observing that Price, Ratings, and Review_Count
are currently in string format, we plan to
modify them later. Before this adjustment,
checking for null values within these variables
is essential to ensure data integrity and
completeness. print(Number of Null values in
each column\n)
Addressing the absence of ratings in 24 laptops,
a value of 0 will be added to indicate no
rating. Additionally, the data type for the
Ratings column will be modified to float,
enhancing data consistency and facilitating
further analysis
13
Now, remove all null values
Creating Processor Column
After the removal of null rows, it's imperative
to adjust the index values. Ensuring the index
correctly aligns with the modified dataset is
crucial for streamlined data access and analysis.
This correction facilitates a more organized and
accurate representation of the data.
14
A new column specifies the processor name for
each laptop. This addition provides a detailed
breakdown of the processor information,
facilitating more comprehensive analysis and
insights into the dataset.
Ensure the processor column is available to the
dataset by thoroughly checking. This step
confirms the inclusion of the new column and
validates its presence in the dataset for further
analysis.
Since some laptops may not specify the processor,
implement a solution to handle these instances
of missing processor information. It ensures that
the dataset remains comprehensive and accurate,
accounting for variations in the availability of
specific details.
15
Removing Laptops with Missing Processor
Information Identify and exclude laptops from
the dataset that do not provide any information
regarding the processor name. It ensures that
the dataset only includes entries with relevant
processor details, contributing to the accuracy
and relevance of the analysis.
Determine the current number of laptops remaining
in the dataset after implementing the necessary
cleaning and filtering procedures. This count
provides valuable insight into the dataset's
size and completeness, paving the way for
subsequent analyses.
Transform the "Price" column into numerical
format using Price Intelligence for a more
standardized and analytically helpful
representation. This conversion enables efficient
numerical operations and facilitates meaningful
analysis of the pricing information in the
dataset. Pricing
Visualization Utilize a barplot to visually
represent the distribution of laptops with Intel
and AMD processors. This graphical
representation provides a clear overview of the
processor types present in the dataset,
facilitating a quick and informative analysis.
16
Explore the distribution of laptops based on
their ratings and prices. This analysis aims to
unveil patterns and trends, offering insights
into the relationship between a laptop's rating
and its corresponding price. The graphical
representation, likely a scatter plot or similar
visualization, will provide a comprehensive
overview of these two crucial factors, aiding in
strategic decision-making and product evaluation.
17
Analyzing the price distribution reveals that the
of laptops, 63.7, falls into the mid to high
price range, exceeding Rs. 70,000. Notably, there
are laptops priced at most Rs. 50,000 in the
dataset. This information provides insights into
the prevailing price brackets of the available
laptops, guiding potential customers and
influencing purchasing decisions. Develop a
versatile function that allows users to input a
specific price range and receive a list of
laptops falling within that range. This
functionality enhances user engagement, providing
a tailored approach to explore laptops based on
individual budget preferences.
The returned list
Explore the dataset to identify the most
expensive laptops based on the "Price" attribute.
This information is crucial for users seeking
high-end options and contributes to a
comprehensive understanding of the price
distribution within the available laptops.
18
Cheapest One
Ratings Highest Rated
Least
19
Most Reviewed
Ratings Highest Rated
Least reviewed
Conclusion By leveraging the provided code to
extract a .csv file from Amazon India, users can
create a DataFrame for visualization or specific
data analysis. Additional modifications can
cater to different product categories. The
insights gained in this project show that most
MSI laptops fall within the medium to high price
range and predominantly feature Intel
processors. Notably, 50 of laptops need ratings
or reviews. The least expensive laptop is
Rs.53,990 (3.3 stars, 7 reviews), while the most
expensive is Rs.2,99,999 (0 stars, 0 reviews).
The top-reviewed model is the MSI Bravo 15 Ryzen
7 4800H, priced at Rs75,990, with a rating of
4.2 stars and 53 reviews. Product Data Scrape is
committed to ethical standards across all facets,
spanning Competitor Price Monitoring Services to
Mobile Apps Data Scraping. Our global footprint
ensures unparalleled and transparent services,
catering to a broad spectrum of client
requirements.
20
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