{"id":18553550,"url":"https://github.com/quantumudit/consumer-goods-sales-analysis","last_synced_at":"2026-04-29T23:31:48.851Z","repository":{"id":128744340,"uuid":"440975544","full_name":"quantumudit/Consumer-Goods-Sales-Analysis","owner":"quantumudit","description":"This project focuses on analyzing and visualizing the consumer goods sales in the United States between 2015-2016 using Python \u0026 Power 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["data-analysis","data-visualization","database","jupyter-notebook","python","sqlite"],"created_at":"2024-11-06T21:17:31.802Z","updated_at":"2026-04-29T23:31:48.834Z","avatar_url":"https://github.com/quantumudit.png","language":"Python","funding_links":["https://www.patreon.com/quantumudit"],"categories":[],"sub_categories":[],"readme":"![Project Logo][project_logo]\n\n---\n\n\u003ch4 align=\"center\"\u003eAnalyzing \u0026 Visualizing Consumer Goods Sales in the United States with \u003ca href=\"https://en.wikipedia.org/wiki/Python_(programming_language)\" target=\"_blank\"\u003ePython\u003c/a\u003e and \u003ca href=\"https://en.wikipedia.org/wiki/Microsoft_Power_BI\" target=\"_blank\"\u003ePower BI\u003c/a\u003e\u003c/h4\u003e\n\n\u003cp align='center'\u003e\n\u003cimg src=\"https://i.ibb.co/KxfMMsP/built-with-love.png\" alt=\"built-with-love\" border=\"0\"\u003e\n\u003cimg src=\"https://i.ibb.co/MBDK1Pk/powered-by-coffee.png\" alt=\"powered-by-coffee\" border=\"0\"\u003e\n\u003cimg src=\"https://i.ibb.co/CtGqhQH/cc-nc-sa.png\" alt=\"cc-nc-sa\" border=\"0\"\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"#overview\"\u003eOverview\u003c/a\u003e •\n  \u003ca href=\"#prerequisites\"\u003ePrerequisites\u003c/a\u003e •\n  \u003ca href=\"#architecture\"\u003eArchitecture\u003c/a\u003e •\n  \u003ca href=\"#demo\"\u003eDemo\u003c/a\u003e •\n  \u003ca href=\"#support\"\u003eSupport\u003c/a\u003e •\n  \u003ca href=\"#license\"\u003eLicense\u003c/a\u003e\n\u003c/p\u003e\n\n## Overview\n\nThis project focuses on analyzing and visualizing the regional sales across the United States in between 2015-2016. The datasets used are completely fictitious and solely made-up just for data analysis case study.\n\nThe repository directory structure is as follows:\n\nConsumer-Goods-Sales-Analysis\u003cbr\u003e\n├─ 01_SOURCE\u003cbr\u003e\n├─ 02_ETL\u003cbr\u003e\n├─ 03_DATA\u003cbr\u003e\n├─ 04_ANALYSIS\u003cbr\u003e\n├─ 05_DASHBOARD\u003cbr\u003e\n├─ 06_RESOURCES\u003cbr\u003e\n\nThe type of content present in the directories is as follows:\n\n**01_SOURCE**\n\nThis directory contains the the received/downloaded raw data that needs to be cleaned and organized to ease out the data analysis and visualization process.\n\n**02_ETL**\n\nThis directory contains the ETL script that takes the raw dataset(s) as input, transforms it and exports an analysis-ready dataset into the _03_DATA_ directory.\n\nIn this project; we've exported the clean datasets in the form of comma separated flat files into the _FLATFILES_ folder and in the form of SQLite database into the _DATABASE_ folder.\n\n**03_DATA**\n\nThis directory contains the data that can be directly used for exploratory data analysis and data visualization purposes.\n\nIn this project; we have two sub-folders under the _03_DATA_ folder that holds the following:\n\n- _FLATFILES_: comma separated flat files\n- _DATABASE_: SQLite Database\n\nBoth folders has the exact same data but in two different format.\n\n**04_ANALYSIS**\n\nThis directory contains the python notebooks that analyzes the clean dataset to generate insights.\n\nFor analyzing the data with Jupyter Notebook; we have used the clean dataset present in the SQLite database.\n\n**05_DASHBOARD**\n\nThis directory contains the markdown file with an embedded Power BI report link that visualizes the data.\n\nThe Power BI dashboard contains slicers, cross-filtering and other advance capabilities that end user can play with to visualize a specific facet of the data or, to get additional insights.\n\n**06_RESOURCES**\n\nThis directory contains images, icons, layouts, etc. that are used in this project.\n\n## Prerequisites\n\nThe major skills that are required as prerequisite to fully understand this project are as follows:\n\n- Basics of Python \u0026 Jupyter Notebook\n- Basics of Power BI\n\nIn order to complete the project, I've used the following applications and libraries\n\n- Python\n- Python libraries mentioned in [requirements.txt][requirements] file\n- Jupyter Notebook\n- Visual Studio Code\n- Microsoft Power BI\n\n\u003e The choice of applications \u0026 their installation might vary based on individual preferences \u0026 system settings.\n\n## Architecture\n\nThe project architecture is quite straight forward and can be explained through the below image:\n\n![Process Architecture][process_workflow]\n\nAs shown in the above workflow; we are first performing necessary cleaning and transformation in the received raw dataset using Python and exporting the clean dataset as comma-separated flat files and also as a SQLite database.\n\nFinally; we leverage the clean \u0026 analysis-ready dataset for exploratory data analysis (EDA) using Jupyter Notebook and creating an insightful report using Power BI.\n\n## Demo\n\nThe interactive Power BI dashboard can be viewed here:\n\n[![Power BI Dashboard][dashboard_image]][dashboard_link]\n\n## Support\n\nIf you have any doubts, queries or, suggestions then, please connect with me in any of the following platforms:\n\n[![Linkedin Badge][linkedinbadge]][linkedin] [![Twitter Badge][twitterbadge]][twitter]\n\nIf you like my work then, you may support me at Patreon:\n\n\u003ca href=\"https://www.patreon.com/quantumudit\" target=\"_blank\"\u003e\n\u003cimg src=\"https://i.ibb.co/94bkJwp/become-a-patreon.png\" alt=\"become-a-patreon\" border=\"0\" width=\"170\" height=\"50\"\u003e\n\u003c/a\u003e\n\n## License\n\n\u003ca href = 'https://creativecommons.org/licenses/by-nc-sa/4.0/' target=\"_blank\"\u003e\n    \u003cimg src=\"https://i.ibb.co/mvmWGkm/by-nc-sa.png\" alt=\"by-nc-sa\" border=\"0\" width=\"88\" height=\"31\"\u003e\n\u003c/a\u003e\n\nThis license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms.\n\n\u003c!-- Image Links --\u003e\n\n[project_logo]: 06_RESOURCES/project_cover_image.png\n[process_workflow]: 06_RESOURCES/process_architecture.png\n[scraping_graphic]: 06_RESOURCES/scraping_graphic.gif\n[dashboard_image]: 06_RESOURCES/dashboard_image.png\n\n\u003c!-- External Links --\u003e\n\n[requirements]: ./requirements.txt\n\n\u003c!-- Profile Links --\u003e\n\n[linkedin]: https://www.linkedin.com/in/uditkumarchatterjee/\n[twitter]: https://twitter.com/quantumudit\n[dashboard_link]: https://app.powerbi.com/view?r=eyJrIjoiNDc2OTgzNDctMzk1ZC00YjcxLWE1YmQtZmU1ODViNDU3ZmQwIiwidCI6IjcwODlkNGIxLTQyMmUtNDYzZi1hNGM3LTViY2FiOTk0MGRiZCJ9\u0026pageName=ReportSection\n\n\u003c!-- Shields Profile Links --\u003e\n\n[linkedinbadge]: https://img.shields.io/badge/-uditkumarchatterjee-0e76a8?style=flat\u0026labelColor=0e76a8\u0026logo=linkedin\u0026logoColor=white\n[twitterbadge]: https://img.shields.io/badge/-@quantumudit-1ca0f1?style=flat\u0026labelColor=1ca0f1\u0026logo=twitter\u0026logoColor=white\u0026link=https://twitter.com/quantumudit\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantumudit%2Fconsumer-goods-sales-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantumudit%2Fconsumer-goods-sales-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantumudit%2Fconsumer-goods-sales-analysis/lists"}