{"id":24696776,"url":"https://github.com/saisathvik07/e-commerce-sales-analysis-using-sql-and-powerbi","last_synced_at":"2025-03-22T03:24:33.090Z","repository":{"id":273861718,"uuid":"921110734","full_name":"Saisathvik07/E-commerce-sales-analysis-using-SQL-and-PowerBI","owner":"Saisathvik07","description":"This repository provides an extensive examination of Amazon Sales Data utilizing SQL","archived":false,"fork":false,"pushed_at":"2025-02-01T13:46:51.000Z","size":200,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-01T14:33:13.158Z","etag":null,"topics":["analytics","data-science","data-visualization","mysql-database","powerbi"],"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/Saisathvik07.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":"2025-01-23T11:02:34.000Z","updated_at":"2025-02-01T13:46:55.000Z","dependencies_parsed_at":null,"dependency_job_id":"9ef070fe-46fc-4fcb-b962-96a4a9e6819d","html_url":"https://github.com/Saisathvik07/E-commerce-sales-analysis-using-SQL-and-PowerBI","commit_stats":null,"previous_names":["saisathvik07/e-commerce-sales-analysis-using-sql-and-powerbi"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Saisathvik07%2FE-commerce-sales-analysis-using-SQL-and-PowerBI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Saisathvik07%2FE-commerce-sales-analysis-using-SQL-and-PowerBI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Saisathvik07%2FE-commerce-sales-analysis-using-SQL-and-PowerBI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Saisathvik07%2FE-commerce-sales-analysis-using-SQL-and-PowerBI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Saisathvik07","download_url":"https://codeload.github.com/Saisathvik07/E-commerce-sales-analysis-using-SQL-and-PowerBI/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244901144,"owners_count":20528869,"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":["analytics","data-science","data-visualization","mysql-database","powerbi"],"created_at":"2025-01-27T02:04:26.651Z","updated_at":"2025-03-22T03:24:33.084Z","avatar_url":"https://github.com/Saisathvik07.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# E-commerce-sales-analysis-using-SQL-and-PowerBI\n# Goal Of The Project\n\nThe primary objective of this project is to explore Amazon's sales data to extract insights and analyze the factors influencing sales across various cities and their respective branches using SQL. It focuses on identifying sales trends, understanding customer behavior, and evaluating product performance. Through the use of SQL queries, the project aims to derive meaningful patterns and valuable statistics from intricate datasets.\n\n# Dataset Summary \n\nDataset Overview\nThis dataset encompasses sales records from three major cities in Myanmar: Naypyitaw, Yangon, and Mandalay, along with their respective branches A, B, and C. The sales data pertains to the first quarter of 2019.\n\n# Key Features\nVolume: 1000 records\n\nFields: 17 distinct fields including:\n\n1.Invoice ID: Unique identifier for each sale.\n\n2.Branch: Designated branch (A, B, C).\n\n3.City: Location of sale (Naypyitaw, Yangon, Mandalay).\n\n4.Customer Type: Classification of customer.\n\n5.Gender: Customer gender.\n\n6.Product Line: Category of product sold.\n\n7.Unit Price: Price per unit.\n\n8.Quantity: Number of units sold.\n\n9.VAT: Value-added tax.\n\n10.Total: Total sales amount.\n\n11.Date: Date of transaction.\n\n12.Time: Time of transaction.\n\n13.Payment Method: Mode of payment.\n\n14.COGS: Cost of goods sold.\n\n15.Gross Margin Percentage: Profit margin percentage.\n\n16.Gross Income: Total gross income.\n\n17.Rating: Customer rating of the transaction. \n\n# Actions Taken on Dataset\n\nData Collection: Gathered 1,000 sales records from branches A, B, and C across Naypyitaw, Yangon, and Mandalay for the first quarter of 2019.\n\nData Cleaning: Conducted a thorough cleansing to remove duplicates, handle missing values, and ensure consistency in data entries.\n\nData Transformation: Converted date and time fields into standardized formats and calculated additional metrics such as VAT and total sales.\n\nExploratory Data Analysis (EDA):Analyzed sales trends across different cities and branches.\n\nEvaluated customer demographics and product performance.\n\nVisualized the distribution of unit prices, quantities, and ratings.\n\nSummary Statistics: Generated descriptive statistics to provide insights into sales performance, customer behavior, and product popularity.\n\nData Visualization: Created various charts and graphs to represent key findings, such as sales distribution, customer ratings, and profit margins.\n\nReporting: Compiled a comprehensive report summarizing the key insights, findings, and visual representations from the dataset.\n\n# Questions Answered \n\nWhat is the count of distinct cities in the dataset?\n\nFor each branch, what is the corresponding city?\n\nWhat is the count of distinct product lines in the dataset?\n\nWhich payment method occurs most frequently?\n\nWhich product line has the highest sales?\n\nHow much revenue is generated each month?\n\nIn which month did the cost of goods sold reach its peak?\n\nWhich product line generated the highest revenue?\n\nIn which city was the highest revenue recorded?\n\nWhich product line incurred the highest Value Added Tax?\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaisathvik07%2Fe-commerce-sales-analysis-using-sql-and-powerbi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaisathvik07%2Fe-commerce-sales-analysis-using-sql-and-powerbi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaisathvik07%2Fe-commerce-sales-analysis-using-sql-and-powerbi/lists"}