https://github.com/alvinmurimi/jumia

Data analysis on smartphones listed on jumia.co.ke
https://github.com/alvinmurimi/jumia

beautifulsoup4 jumia matplotlib numpy pandas scraping seaborn

Last synced: about 1 month ago
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Data analysis on smartphones listed on jumia.co.ke

• Host: GitHub
• URL: https://github.com/alvinmurimi/jumia
• Owner: alvinmurimi
• Created: 2023-11-16T16:53:26.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-31T16:54:46.000Z (11 months ago)
• Last Synced: 2023-12-31T17:32:13.601Z (11 months ago)
• Topics: beautifulsoup4, jumia, matplotlib, numpy, pandas, scraping, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.88 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data Analysis on [Jumia](https://jumia.co.ke)

## Overview

This repository contains a Jupyter Notebook (`analysis.ipynb`) and a dataset (`smartphones.csv`) for a comprehensive data analysis on [Jumia](https://jumia.co.ke), 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](analysis.ipynb)**: Jupyter Notebook containing the code and visualizations for the data analysis.

- **[smartphones.csv](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:

```bash

pip install jupyter bs4 pandas numpy matplotlib seaborn

```

## Usage

```bash

git clone https://github.com/alvinmurimi/jumia.git

cd jumia

jupyter notebook analysis.ipynb