{"id":16966335,"url":"https://github.com/alvinmurimi/jumia","last_synced_at":"2026-05-09T10:58:04.622Z","repository":{"id":211093619,"uuid":"719670613","full_name":"alvinmurimi/jumia","owner":"alvinmurimi","description":"Data analysis on smartphones listed on jumia.co.ke","archived":false,"fork":false,"pushed_at":"2023-12-31T16:54:46.000Z","size":3017,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-26T12:41:20.936Z","etag":null,"topics":["beautifulsoup4","jumia","matplotlib","numpy","pandas","scraping","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/alvinmurimi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-11-16T16:53:26.000Z","updated_at":"2024-10-18T11:18:46.000Z","dependencies_parsed_at":"2023-12-09T20:23:05.848Z","dependency_job_id":"7d7dfcbb-f5f9-4cfb-8f24-b55bb8f1516f","html_url":"https://github.com/alvinmurimi/jumia","commit_stats":null,"previous_names":["alvinmurimi/jumia"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvinmurimi%2Fjumia","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvinmurimi%2Fjumia/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvinmurimi%2Fjumia/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alvinmurimi%2Fjumia/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alvinmurimi","download_url":"https://codeload.github.com/alvinmurimi/jumia/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244841299,"owners_count":20519376,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["beautifulsoup4","jumia","matplotlib","numpy","pandas","scraping","seaborn"],"created_at":"2024-10-14T00:05:29.088Z","updated_at":"2026-05-09T10:57:59.564Z","avatar_url":"https://github.com/alvinmurimi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Analysis on [Jumia](https://jumia.co.ke)\n\n## Overview\n\nThis 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.\n\n## Files\n\n- **[analysis.ipynb](analysis.ipynb)**: Jupyter Notebook containing the code and visualizations for the data analysis.\n- **[smartphones.csv](smartphones.csv)**: CSV file containing the dataset used for the analysis.\n\n## Analysis Highlights\n\n- **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.\n\n- **Visualizations**: Various visualizations, including histograms, scatter plots, and radar charts, are utilized to present a clear understanding of the data.\n\n- **Brand Performance**: The analysis delves into the performance of different smartphone brands, highlighting key metrics such as average ratings, pricing and so forth.\n\n\n## Dependencies\n\nThe analysis is built using Python and Jupyter Notebook, relying on the libraries below:\n\n- **BeautifulSoup (bs4)**: A library for web scraping and parsing HTML or XML documents.\n- **pandas**: A data manipulation and analysis library, used for handling and processing tabular data.\n- **numpy**: A library for numerical operations in Python, essential for efficient data handling.\n- **matplotlib**: A 2D plotting library for creating static, animated, and interactive visualizations.\n- **seaborn**: A data visualization library based on matplotlib, providing additional functionality and improved aesthetics.\n- **textblob**: A library for processing textual data, including sentiment analysis using the Naive Bayes classifier.\n\nTo install these dependencies along with Jupyter, you can use the following command:\n\n```bash\npip install jupyter bs4 pandas numpy matplotlib seaborn\n```\n## Usage\n\n```bash\ngit clone https://github.com/alvinmurimi/jumia.git\ncd jumia\njupyter notebook analysis.ipynb\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falvinmurimi%2Fjumia","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falvinmurimi%2Fjumia","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falvinmurimi%2Fjumia/lists"}