https://github.com/girish119628/bigbasket-price-comparison
Web scraping BigBasket products, cleaning and storing data in MySQL, and visualizing insights with a Power BI dashboard.
https://github.com/girish119628/bigbasket-price-comparison
Last synced: 4 months ago
JSON representation
Web scraping BigBasket products, cleaning and storing data in MySQL, and visualizing insights with a Power BI dashboard.
- Host: GitHub
- URL: https://github.com/girish119628/bigbasket-price-comparison
- Owner: girish119628
- Created: 2025-02-08T07:25:54.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-08T08:22:27.000Z (4 months ago)
- Last Synced: 2025-02-08T08:32:22.104Z (4 months ago)
- Language: Python
- Size: 222 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# BigBasket Grocery Price Comparison
Web scraping BigBasket products, cleaning and storing data in MySQL, and visualizing insights with a Power BI dashboard.
# Project Stages
**Stage 1: Scraping and Storing Raw Data**
* Scraped grocery product details (Product Name, Price, Discount, and Category) from BigBasket.
* Stored the raw data in bigbasket.csv for further processing.
**Stage 2: Data Preprocessing and Storing in MySQL**
* Cleaned and preprocessed the raw data (handling duplicates, missing values, and formatting).
* Stored the cleaned data in bigbasket_cln.csv and directly inserted it into the MySQL database using Python and pymysql connector.
**Stage 3: Running Queries for Data Analysis**
* Executed SQL queries on MySQL to filter and analyze grocery pricing trends.
* Extracted insights based on price variations, discount patterns, and category-wise comparisons.
**Stage 4: Data Visualization in Power BI**
* Retrieved processed data from MySQL into Power BI.
* Built interactive dashboards to compare grocery prices, analyze affordability, and visualize trends effectively.
# Technologies Used
* Python (for Web Scraping & Data Cleaning)
* MySQL (for Data Storage & Querying)
* Power BI (for Data Visualization)
* Pandas, Selenium, pymysql (for data handling and database connection)
# Project Outcome
* Successfully extracted grocery product data.
* Stored and managed data efficiently in MySQL.
* Built an insightful Power BI dashboard for comparative analysis.