From Flipkart to Dashboard: A Mobile Market Analysis Using Python and Power BI

This project was exactly that — calm, fulfilling, and proudly built from scratch.

Introduction

As an aspiring data analyst, I wanted to work on something real-world, relatable, and end-to-end.

So, I chose Flipkart — India’s go-to online shopping platform — and decided to:

• Scrape live data of smartphone listings
• Clean and transform that data using Power BI
• And finally, build an interactive dashboard that tells a business story

This case study walks you through how I did it — step by step.

— — —

Project Objective

To turn raw smartphone listings from Flipkart into an insightful and interactive Power BI dashboard that answers questions like:

• Which brand leads in revenue?
• What price category dominates the market?
• Who offers budget vs flagship phones?
• What are the top specs being listed?

— — —

Tools & Technologies Used

I used Python with requests and BeautifulSoup to scrape mobile listing data from Flipkart. The data was structured and exported to CSV using pandas. In Power BI, I performed data cleaning and modeling. Using Power Query, I split combined specifications (like RAM and ROM), removed symbols (₹, GB, mAh), and handled missing values. Finally, I used DAX to create KPIs, calculated columns, and apply Top-N logic for dynamic visual insights.

— — —

TO CHALIYE SURU KARTE HAI……

First Step: Scraping Flipkart with Python

I used Python with the requests and BeautifulSoup libraries to scrape multiple pages of smartphone listings from Flipkart.

Captured data for each product included:

• Brand
• Name
• Price
• Ratings
• Specifications (RAM, ROM, Battery, Processor)

I then saved the data into a .csv file using pandas.

— — —

Second Step: Cleaning Data in Power BI

• Separated composite specs into individual columns (e.g., “8 GB RAM | 128 GB ROM”).
• Removed unwanted symbols like “GB”, “mAh”, “₹”.
• Created custom column Price_Category: Budget, Mid-Range and Flagship

— — —

Final Step: Building the Dashboard

Designed a clean, dark-themed, interactive dashboard with modern cards, slicers, and visualizations.

• KPI Cards (Brands, Revenue, Avg. Price, Units Sold)
• Top 10 Brands by Revenue
• Top 10 Models Listed by Brand
• Top 5 Brands by Units Sold
• Price Distribution by Category
• Slicers for: RAM, ROM, Battery, Processor, Brand, Price_Category

— — —

Key Insights

• Motorola leads in revenue and total units listed
• Mid-Range phones dominate the listings (approx. 56%)
• Google has the highest average phone price (~₹45,000)
• Redmi is the most affordable brand (~₹11,000 average)
• Flagship phones are rarely listed (only ~2.1% of total)
• CMF, realme, and vivo dominate the Budget and Mid-Range segments.

— — —

Dashboard Preview

POWER BI DASHBOARD

— — —

What I Learned

• Structured web scraping with Python and BeautifulSoup
• Cleaning messy e-commerce specs in Power Query
• Creating meaningful KPIs and visuals using DAX
• Designing an interactive dashboard with filters
• Applying real-world business thinking in analytics

— — —

GitHub Repo

https://github.com/Tiwarishivansh07/FLIPKART_MOBILE_ANALYSIS.git

— — —

About Me

I’m Shivansh Tiwari, a data enthusiast exploring real-world analytics using Python, Power BI, and SQL. If you liked this case study, let’s connect www.linkedin.com/in/shivanshtiwari1

— — —

If you found this helpful or inspiring, feel free to leave a comment or give it a clap..