Smartphone Market Analysis (Jumia Web Scraping Project) (May 2025)

Tools: Python (BeautifulSoup, Pandas, Matplotlib, Seaborn, Plotly)

Process:

Web Scraping: Collected smartphone data (brand, model, storage, RAM, display size, network type, and price) directly from Jumia’s e-commerce

website.

Data Cleaning: removed irrelevant items like product link handled missing values, standardized text formats, and extracted structured fields (e.g.,

Display size, RAM, Storage, Network type).

Feature Engineering: created new variables such as Screen Size, brand name ,RAM, Storage ,Dual Sim ,Network, mobile version

Exploratory Data Analysis (EDA): analyzed brand distribution, pricing trends, storage vs. RAM combinations, and 4G vs. 5G adoption.

Visualization: built interactive and static charts (bar plots, histograms, scatter plots, heatmaps, choropleth maps) to uncover patterns and market

insights.

Key Insights:

Examined the distribution of smartphone models across brands to identify which companies dominate the market and which focus on niche

segments.

Explored the relationship between display size and pricing to understand how screen dimensions influence market value.

Analyzed internal storage capacity to assess its impact on average smartphone pricing.

Investigated price range distributions to determine where the highest concentration of models is positioned.

Compared 4G vs. 5G support to highlight network adoption trends in the market.

Evaluated how average smartphone prices vary across different RAM sizes.