Retail-Store-Location-Scraper

Using web scraping methods (such requests and BeautifulSoup) to collect information from a website, such as store names, addresses, opening and closing times, coordinates (latitude and longitude), and phone numbers.

Introduction

In this assignment, the goal was to scrape the store locations, store name, address, timings, coordinates, and phone number of my favourite retail brand in India. I chose a popular retail brand Café Coffee day and used Python libraries like BeautifulSoup , requests and json to scrape the website and extract the required information. The extracted data was saved to a CSV file and uploaded to a GitHub repository along with the code. This report outlines the approach, challenges faced, and solutions implemented during the web scraping process.

Approach

The first step was to identify the website of the retail brand where the location data can be found (cafecoffeeday.com/store-locator) and understand its HTML structure. I inspected the website's source code and found that the store locator was a dynamic element that required JavaScript to load. After analysing the website, I found that the relevant data was present in index 11 of script elements that contained JSON data. Firstly, I import the necessary modules, including requests for sending HTTP requests, JSON for handling JSON data, BeautifulSoup for parsing the content and converting html content for reading and interpreting and finally used pandas for converting JSON into DataFrame that later can help in storing data in csv format and also help in data pre-processing if needed. I used Request library in python to send a request to the website's URL and retrieve the JSON data. I used the built-in Python JSON module to parse the JSON data and extract the required information such as store name, address, timings, coordinates, and phone number.

Challenges

One of the main challenges faced while working with this code was to extract the required data from the JSON response. As the JSON response was a nested structure, it required careful understanding and analysis to extract the relevant data. We had to identify the keys and values that corresponded to the information we needed. Another challenge was to check the reliability of locations coordinates whether they are working or not and next was to check if I have the required information or not and also to remove unnecessary columns which don’t have any use case.

Solution

To overcome the challenge of extracting the required data from the JSON response, we carefully analysed the structure of the JSON data and used the appropriate keys to extract the information we needed. We also used the built-in Python JSON module to simplify the parsing process. To check the reliability of location coordinates if they are working I used folium library to plot the locations and markers in the map (FastMarkerCluster in folium)

To clean the data, we removed the irrelevant columns from the CSV file, ensuring that only the required information was present change the data types for some column like time column and coordinates and make csv ready for the end use case for further analysing like Exploratory data analysis, predictive analytics etc. which can help in taking important business decisions and build strategy. Use case can be If company wants to open a new store where it should be , which stores are working great and which are not can be easily understand with map visualisation because it opens door for think with a new perspective like it can tell if the location have a relationship with store performance.

Conclusion

In conclusion, web scraping is a powerful technique that can be used to extract valuable information from websites. However, it can also be challenging, especially when dealing with dynamic elements and nested JSON structures. By understanding the website's HTML structure, using appropriate tools and libraries, and ensuring the extracted data is accurate and reliable, we can successfully extract the required information from a website. The data is 100% reliable and extracted from the café coffee day official website and it can be useful for various business use cases.