Data Analysis on Jumia

Overview

This repository contains a Jupyter Notebook (analysis.ipynb) and a dataset (smartphones.csv) for a comprehensive data analysis on Jumia, focusing on smartphone products. The analysis aims to uncover insights into various aspects of the smartphone offerings, including pricing, product features, customer reviews, and brand performance.

Files

• analysis.ipynb: Jupyter Notebook containing the code and visualizations for the data analysis.
• smartphones.csv: CSV file containing the dataset used for the analysis.

Analysis Highlights

• Data Exploration: The Jupyter Notebook explores the dataset, providing insights into key features such as pricing, RAM and ROM, battery, display sizes, and customer reviews.

• Visualizations: Various visualizations, including histograms, scatter plots, and radar charts, are utilized to present a clear understanding of the data.

• Brand Performance: The analysis delves into the performance of different smartphone brands, highlighting key metrics such as average ratings, pricing and so forth.

Dependencies

The analysis is built using Python and Jupyter Notebook, relying on the libraries below:

• BeautifulSoup (bs4): A library for web scraping and parsing HTML or XML documents.
• pandas: A data manipulation and analysis library, used for handling and processing tabular data.
• numpy: A library for numerical operations in Python, essential for efficient data handling.
• matplotlib: A 2D plotting library for creating static, animated, and interactive visualizations.
• seaborn: A data visualization library based on matplotlib, providing additional functionality and improved aesthetics.
• textblob: A library for processing textual data, including sentiment analysis using the Naive Bayes classifier.

To install these dependencies along with Jupyter, you can use the following command:

pip install jupyter bs4 pandas numpy matplotlib seaborn

Usage

git clone https://github.com/alvinmurimi/jumia.git
cd jumia
jupyter notebook analysis.ipynb