{"id":24616381,"url":"https://github.com/ranish-shrestha/sales_data_analysis","last_synced_at":"2026-02-18T15:02:12.147Z","repository":{"id":261530344,"uuid":"884582939","full_name":"Ranish-Shrestha/Sales_Data_Analysis","owner":"Ranish-Shrestha","description":"Sales data analysis using Python.","archived":false,"fork":false,"pushed_at":"2024-11-07T03:02:57.000Z","size":569,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-24T22:47:32.828Z","etag":null,"topics":["data-analysis-python","python-project"],"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/Ranish-Shrestha.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":"2024-11-07T02:33:13.000Z","updated_at":"2024-11-13T14:45:49.000Z","dependencies_parsed_at":"2024-11-07T03:39:22.775Z","dependency_job_id":null,"html_url":"https://github.com/Ranish-Shrestha/Sales_Data_Analysis","commit_stats":null,"previous_names":["ranish-shrestha/sales_data_analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ranish-Shrestha%2FSales_Data_Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ranish-Shrestha%2FSales_Data_Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ranish-Shrestha%2FSales_Data_Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ranish-Shrestha%2FSales_Data_Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ranish-Shrestha","download_url":"https://codeload.github.com/Ranish-Shrestha/Sales_Data_Analysis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244300580,"owners_count":20430795,"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":["data-analysis-python","python-project"],"created_at":"2025-01-24T22:47:40.844Z","updated_at":"2025-10-12T11:43:44.837Z","avatar_url":"https://github.com/Ranish-Shrestha.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Sales Data Analysis\n\nThis project is an exploratory analysis of Diwali sales data, using Python libraries such as Pandas, Matplotlib, and Seaborn to analyze and visualize trends based on various factors like gender.\n\n### Project Overview\n\nThis analysis aims to identify sales patterns during the Diwali season, with a particular focus on understanding how different demographics contribute to overall sales. Through data cleaning, transformation, and visualization, the project provides insights into consumer behavior by Gender, Age, State, Marital Status, Occupation, and Product Category.\n\n### Requirements\n\nInstall following Python libraries to run the code:\n\n- `numpy`\n- `pandas`\n- `matplotlib`\n- `seaborn`\n\nTo install these dependencies, use the following command:\n\n```sh\npip install numpy pandas matplotlib seaborn\n```\n\n### Usage\n\nTo run the code, load the `Diwali Sales Data.csv` file into the same directory as your script, then execute the script in a Jupyter notebook or your preferred Python environment.\n\n### Conclusion\n\nThis analysis provides insights into consumer behavior based on gender, age, states and occupation during the Diwali season. It can guide marketing and sales strategies by highlighting which demographics contribute more significantly to sales.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Franish-shrestha%2Fsales_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Franish-shrestha%2Fsales_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Franish-shrestha%2Fsales_data_analysis/lists"}