E-commerce Mobile Phones Market Analysis (Amazon & Jumia)

Overview

This project explores the Egyptian e-commerce mobile phone market by scraping product data from Amazon and Jumia, cleaning and preprocessing the dataset, and analyzing it with Power BI.

The goal is to uncover market trends, brand positioning, and pricing strategies to demonstrate skills in Python, Excel, and Power BI.

Data Workflow

Data Collection

Used Python (BeautifulSoup, Requests) to scrape product listings.

the code is here scraping_and_cleaning.ipynb

and here scraping_and_cleaning.ipynb

Extracted: product name, brand, price, storage/RAM, and platform (Amazon or Jumia).

Data Cleaning & Preprocessing

Cleaned with Pandas (removed duplicates, handled missing values, standardized brand names).

Exported to Excel for quick validation.

Final dataset loaded into Power BI.

Data Modeling & Measures

Built relationships between product attributes.

Created multiple DAX measures (average price, count of phones, platform share, price vs spec KPIs).

Dashboard Pages

The Power BI dashboard contains 4 interactive pages:

Market Overview – KPIs, total listings, price distribution.

Brand Analysis – Market share, avg. price per brand, segment comparisons.

Platform Comparison – Amazon vs Jumia differences in pricing and availability.

Specs vs Price Analysis – How features (RAM, storage) affect pricing.

Key Insights

Amazon vs Jumia: Amazon lists more premium models; Jumia offers more budget options.

Brand Analysis: Apple dominates high-end; Samsung/Xiaomi strong in mid-range; Infinix targets budget buyers.

Specs vs Price: Strong correlation between specs (RAM, storage) and price, with brand-based exceptions.

? Skills Demonstrated

Python – Web scraping with BeautifulSoup, data cleaning with Pandas.

Excel – Data validation and exploration.

Power BI – Data modeling, DAX measures, dashboard design.

Business Analysis – Market research, competitor comparison, actionable insights.