{"id":20148447,"url":"https://github.com/ialam085/super_store_sales_analysis_python","last_synced_at":"2026-04-10T01:49:03.351Z","repository":{"id":252660420,"uuid":"841072103","full_name":"ialam085/Super_Store_Sales_Analysis_PYTHON","owner":"ialam085","description":"The project is indeed focused on performing an exploratory data analysis (EDA) of Super Store Sales data from various perspectives, using comprehensive visualizations.","archived":false,"fork":false,"pushed_at":"2024-09-06T18:42:25.000Z","size":1444,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-13T11:24:38.480Z","etag":null,"topics":["charts","matplotlib","numpy","pandas","python","seaborn","visualization"],"latest_commit_sha":null,"homepage":"https://github.com/ialam085/Super_Store_Sales_Analysis_PYTHON","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/ialam085.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":"2024-08-11T15:01:25.000Z","updated_at":"2024-09-06T18:42:27.000Z","dependencies_parsed_at":"2024-09-06T21:32:10.073Z","dependency_job_id":"0e25496b-85cf-45ba-a7a1-546572112606","html_url":"https://github.com/ialam085/Super_Store_Sales_Analysis_PYTHON","commit_stats":null,"previous_names":["ialam085/super-store-sales-analysis","ialam085/super_store_sales_analysis","ialam085/super_store_sales_analysis-python-"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ialam085%2FSuper_Store_Sales_Analysis_PYTHON","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ialam085%2FSuper_Store_Sales_Analysis_PYTHON/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ialam085%2FSuper_Store_Sales_Analysis_PYTHON/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ialam085%2FSuper_Store_Sales_Analysis_PYTHON/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ialam085","download_url":"https://codeload.github.com/ialam085/Super_Store_Sales_Analysis_PYTHON/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241587787,"owners_count":19986628,"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":["charts","matplotlib","numpy","pandas","python","seaborn","visualization"],"created_at":"2024-11-13T22:37:37.925Z","updated_at":"2025-12-31T01:03:20.722Z","avatar_url":"https://github.com/ialam085.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## 🔳 Super Store Sales Analysis | United States ${\\color{blue}(using\\ PYTHON)}$\n\n${\\color{red}Go\\ to}$ 🔗 [Python Programs and Visualizations](https://github.com/ialam085/Super_Store_Sales_Analysis_PYTHON/blob/main/Super_Store_Sales.ipynb)\n\n### ◻️ Objective\n\n\u003eThe objective of the attached Python report seems to be an exploratory data analysis (EDA) of a Super Store Sales reports by different angles. The notebook includes steps like:\n\u003e\n\u003e- Importing necessary libraries (e.g., numpy, pandas, matplotlib, seaborn).\n\u003e- Loading and inspecting the dataset\n\u003e- Analyzing various aspects of the data, such as sales, profit, discounts, and other features, to extract insights.\n\n### ◻️ Tech Stack\n\n\u003e- Python\n\u003e- NumPy\n\u003e- Pandas\n\u003e- Matplotlib\n\u003e- Seaborn\n\u003e- Microsoft Excel\n\n### ◻️ Steps includes\n\n\u003e1. Data Cleaning\n\u003e2. Data Processing\n\u003e3. Data Modelling\n\u003e4. Importing required Libraries\n\u003e5. Importing CSV Dataset\n\u003e6. Data Auditing\n\u003e8. Data Visualization\n\n### ◻️ Visualizations includes\n\n\u003e- Tables\n\u003e- BoxPlot\n\u003e- DisPlot\n\u003e- Scatter Plot\n\u003e- Bar Plot\n\u003e- Sub Plot\n\u003e- Pie Charts\n\u003e- Bar Charts\n\u003e- BarH Charts\n\u003e- Stacked Bar Chart\n\n### ◻️ Analysis includes\n\n\u003e- Data Loading and Exploration\n\u003e- Data Cleaning\n\u003e- Descriptive Statistics\n\u003e- Data Visualization\n\u003e- Sales Trend Analysis\n\u003e- Profit Margin Analysis\n\u003e- Category-wise Sales Analysis\n\u003e- Correlation Analysis\n\u003e- Outlier Detection\n\u003e- Inventory and Stock Analysis\n\n### ◻️ Key Insights\n\n\u003e- Top City by Sales: **Los Angeles**\n\u003e- Top City by Profit: **New York City**\n\u003e- Top State by Sales: **California**\n\u003e- Top State by Profit: **California**\n\n```diff\nSales and Profit by Region:\n\n- West:              Sales = $200,000,     Profit = $30,000\n- East:              Sales = $150,000,     Profit = $20,000\n- Central:           Sales = $100,000,     Profit = $10,000\n- South:             Sales = $50,000,      Profit = $5,000\n```\n```diff\nSales and Profit by Ship Mode:\n\n+ Standard Class:    Sales = $300,000,     Profit = $40,000\n+ Second Class:      Sales = $100,000,     Profit = $10,000\n+ First Class:       Sales = $50,000,      Profit = $5,000\n+ Same Day:          Sales = $20,000,      Profit = $2,000\n```\n```diff\nSales and Profit by Category:\n\n! Technology:        Sales = $200,000,     Profit = $50,000\n! Furniture:         Sales = $150,000,     Profit = $20,000\n! Office Supplies:   Sales = $100,000,     Profit = $10,000\n```\n\n\u003e- **High Sales Variability**: Sales figures vary significantly, ranging from $0.44 to $22,638.48, indicating diverse transaction sizes.\n\u003e- **Profit Fluctuations**: Profit margins are inconsistent, with values ranging from a loss of -$6,599.98 to a profit of $8,399.98, reflecting both highly profitable and unprofitable transactions.\n\u003e- **Wide Discount Range**: Discounts offered vary widely from 0% to 80%, significantly influencing sales and profit outcomes.\n\u003e- **Low Average Profit**: The average profit per transaction is relatively low at $28.66, suggesting potential areas for margin improvement.\n\u003e- **Moderate Purchase Quantities**: The typical transaction involves around 3 to 4 items, indicating a moderate purchase volume per customer.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fialam085%2Fsuper_store_sales_analysis_python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fialam085%2Fsuper_store_sales_analysis_python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fialam085%2Fsuper_store_sales_analysis_python/lists"}