{"id":26787088,"url":"https://github.com/kshitiz1302/pizza-sales-report","last_synced_at":"2026-05-18T03:10:52.266Z","repository":{"id":272062044,"uuid":"915409142","full_name":"kshitiz1302/Pizza-Sales-Report","owner":"kshitiz1302","description":"The report provides insights into pizza sales trends for 2015, focusing on peak periods, customer preferences for large pizzas, and the best-performing menu items.","archived":false,"fork":false,"pushed_at":"2025-01-17T21:56:49.000Z","size":5542,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-04T19:39:16.751Z","etag":null,"topics":["data-cleaning","data-management","data-manipulation","data-modeling","data-storytelling","data-visualization","dax","dax-expression","dax-query","mysql","mysql-database","mysqlworkbench","powerbi","powerbi-dashboards","powerbi-desktop","powerbi-report","powerbi-visuals","sql","sql-server"],"latest_commit_sha":null,"homepage":"","language":null,"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/kshitiz1302.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,"zenodo":null}},"created_at":"2025-01-11T19:15:06.000Z","updated_at":"2025-01-17T21:56:50.000Z","dependencies_parsed_at":"2025-01-11T20:25:02.466Z","dependency_job_id":"9aebc4e9-5e3f-4c35-b678-231158712cbd","html_url":"https://github.com/kshitiz1302/Pizza-Sales-Report","commit_stats":null,"previous_names":["kshitiz1302/pizza-sales-report"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kshitiz1302/Pizza-Sales-Report","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kshitiz1302%2FPizza-Sales-Report","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kshitiz1302%2FPizza-Sales-Report/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kshitiz1302%2FPizza-Sales-Report/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kshitiz1302%2FPizza-Sales-Report/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kshitiz1302","download_url":"https://codeload.github.com/kshitiz1302/Pizza-Sales-Report/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kshitiz1302%2FPizza-Sales-Report/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33163440,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-17T22:39:12.733Z","status":"online","status_checked_at":"2026-05-18T02:00:06.436Z","response_time":71,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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-cleaning","data-management","data-manipulation","data-modeling","data-storytelling","data-visualization","dax","dax-expression","dax-query","mysql","mysql-database","mysqlworkbench","powerbi","powerbi-dashboards","powerbi-desktop","powerbi-report","powerbi-visuals","sql","sql-server"],"created_at":"2025-03-29T12:18:15.848Z","updated_at":"2026-05-18T03:10:52.242Z","avatar_url":"https://github.com/kshitiz1302.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Pizza Sales Report Dashboard\n\n\n## Problem Statement\n\nThis report analyzes pizza sales data from January to December 2015, generating $817.86K in total revenue from 21,350 orders and 49,574 pizzas sold. \n\nIt highlights key trends such as peak sales on Friday and Saturday evenings, large pizzas dominating with 45.89% of sales, and the Thai Chicken Pizza leading in revenue at $43K.\n\nThe findings aim to guide strategic decisions for enhancing performance and customer satisfaction.\n\n\n### Steps followed \n\n- Step 1 : Prepare a CSV file for the dataset and create tables in the SQL.\n- Step 2 : Import the CSV file to SQL.\n- Step 3 : Perform some Data-Modeling in SQL through MYSQL and then import the dataset from the SQL Server into Power BI by establishing a direct connection of servers.\n- Step 4 : Open the power query editor. In the view tab under the Data preview section, check the \"column distribution,\" \"column quality,\" and \"column profile\" options.\n- Step 5 :  It was observed that in none of the columns errors \u0026 empty values were present \n- Step 6 : In the report view, under the view tab, theme was selected.. \n- Step 7 : Visual filters (Slicers) were added for three fields named \"Pizza Size\", \"Order Date\" \u0026 \"Pizza Category\".\n- Step 9 : Four card new visuals were added to the canvas, representing Total Revenue, Total Orders, Total Pizza Sold, Average Order value \u0026 Average Pizza Per Order. \n           Using visual level filter from the filters pane, basic filtering was used \u0026 null values were unselected for consideration into average calculation.\n           \n           Although, by default, while calculating average, blank values are ignored.\n \n- Step 10 : Calculated column was created to extract order day from calender.\n\nfor creating new column following DAX expression was written;\n       \n        order day = UPPER(LEFT(pizza_sales[Day Name],3))\n        \nSnap of new calculated column,\n\n![Image](https://github.com/user-attachments/assets/d8789399-0465-4ba2-aad4-99a854a76492)\n\n\n- Step 11 : Another calculated column was created to extract order month from calender.\n\nfor creating new column following DAX expression was written;\n\n        order month = UPPER(LEFT(pizza_sales[Month Name],3))\n\nSnap of new calculated column,\n\n![Image](https://github.com/user-attachments/assets/12eb8394-8546-497c-97b8-0524703b98db)\n        \n- Step 12: New measure was created to find total revenue.\n\nFollowing DAX expression was written for the same,\n        \n        Total Revenue = SUM(pizza_sales[total_price])       \nA card visual was used to represent total revenue.\n\n![Image](https://github.com/user-attachments/assets/e069e411-3b4a-40b4-85d7-50520bd58d5b)\n        \n - Step 13 : New measure was created to find  total orders,\n \n Following DAX expression was written to find total order placed,\n \n        Total Orders = DISTINCTCOUNT(pizza_sales[order_id])\n \n A card visual was used to represent this value.\n \n Snap of total orders placed by customers\n \n![Image](https://github.com/user-attachments/assets/50d82d36-6f1d-4f03-984e-19436d3561a8)\n \n - Step 14 : New measure was created to calculate total pizza sold in whole year.\n \n Following DAX expression was written to find pizza sold\n \n         Total Pizza Sold = SUM(pizza_sales[quantity])\n    \n A card visual was used to represent this total pizza sold.\n \n  Snap of total pizza sold \n \n \n![Image](https://github.com/user-attachments/assets/f4ea2d7e-cbe5-43c1-bc83-3e0a8ae90a4d)\n \n - Step 15 : New measure was created to calculate average order value in whole year.\n\n  Following DAX expression was written to find this,\n \n         Avg Order Value = [Total Revenue]/[Total Orders]\n\nA card visual was used to represent average order value.\n \n  Snap of this measure\n\n  ![Image](https://github.com/user-attachments/assets/96eddfa9-31ee-40c1-8930-d5497a02b16e) \n\n   - Step 16 : New measure was created to calculate average pizza per order.\n\n   Following DAX expression was written to find this,\n \n        Avg Pizzas per Order = [Total Pizza Sold]/[Total Orders]\n\nA card visual was used to represent average pizza per order.\n \n  Snap of this measure\n\n  ![Image](https://github.com/user-attachments/assets/2516f7dc-0ca0-4168-a1ff-cf61d6d43b01)\n \n \n\n\n# Snapshot of Dashboard (Power BI Service)\n\n![Image](https://github.com/user-attachments/assets/78ad00e2-bde2-4c27-b389-8413215c9107)\n\n\n \n # Report Snapshot (Power BI DESKTOP)\n\n \n![Image](https://github.com/user-attachments/assets/0f1522c3-241b-47b4-8033-54a0df611bc8)\n\n\n# Insights\n\nA double page report was created on Power BI Desktop\n\nFollowing inferences can be drawn from the dashboard;\n\n### [1] Total Number of Orders = 21350\n\n   Total revenue was $817.86K.\n\n   Total Pizza sold 49,570.\n\n   The average order value was $38.31.\n\n   2.32 pizzas sold per order.\n\n\n\n ### [2] Some other insights\n \n ### Pizza Category\n \n 1.1) Classic pizzas contributed the most sales 26.91% (14,888 units).\n \n 1.2) 25.46 % revenue produced by Supreme pizzas (11,987 units).\n \n 1.3) 23.96 % revenue produced by Chicken pizzas (11,050 units).\n\n 1.4) 23.68 % revenue produced by Veggie pizzas (11,649 units).\n\n\n \n         thus, maximum Classic Pizzas are at top in both sales and units sold category.\n \n ### Pizza Size\n \n 2.1) 45.89 % revenue produced by Larged sized pizzas (18,956 units).\n \n 2.2) 30.49 % revenue produced by Medium sized pizzas (15,635 units).\n \n 2.3) 21.77 % revenue produced by Regular sized pizzas (14,403 units).\n\n 2.4) 1.72% revenue produced by X-Large sized pizzas (552 units).\n\n 2.5) 0.12% revenue produced by XX-Large sized pizzas(28 units).\n \n         thus, Larged Sized Pizzas top the chart.         \n### Pizza Name\n\n3.1) The Thai Chicken Pizza generated the highest revenue ($43K), while the Classic Deluxe Pizza had the most orders (2,329).\n\n3.2) The Brie Carre Pizza had the lowest revenue ($12K) and the fewest orders (480).\\\n\n\n\n### Other\nFriday and Saturday evenings had the highest sales, with July and January being the peak months.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkshitiz1302%2Fpizza-sales-report","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkshitiz1302%2Fpizza-sales-report","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkshitiz1302%2Fpizza-sales-report/lists"}