https://github.com/saisathvik07/e-commerce-sales-analysis-using-sql-and-powerbi
This repository provides an extensive examination of Amazon Sales Data utilizing SQL
https://github.com/saisathvik07/e-commerce-sales-analysis-using-sql-and-powerbi
analytics data-science data-visualization mysql-database powerbi
Last synced: over 1 year ago
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This repository provides an extensive examination of Amazon Sales Data utilizing SQL
- Host: GitHub
- URL: https://github.com/saisathvik07/e-commerce-sales-analysis-using-sql-and-powerbi
- Owner: Saisathvik07
- Created: 2025-01-23T11:02:34.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-01T13:46:51.000Z (over 1 year ago)
- Last Synced: 2025-02-01T14:33:13.158Z (over 1 year ago)
- Topics: analytics, data-science, data-visualization, mysql-database, powerbi
- Homepage:
- Size: 195 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# E-commerce-sales-analysis-using-SQL-and-PowerBI
# Goal Of The Project
The 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.
# Dataset Summary
Dataset Overview
This 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.
# Key Features
Volume: 1000 records
Fields: 17 distinct fields including:
1.Invoice ID: Unique identifier for each sale.
2.Branch: Designated branch (A, B, C).
3.City: Location of sale (Naypyitaw, Yangon, Mandalay).
4.Customer Type: Classification of customer.
5.Gender: Customer gender.
6.Product Line: Category of product sold.
7.Unit Price: Price per unit.
8.Quantity: Number of units sold.
9.VAT: Value-added tax.
10.Total: Total sales amount.
11.Date: Date of transaction.
12.Time: Time of transaction.
13.Payment Method: Mode of payment.
14.COGS: Cost of goods sold.
15.Gross Margin Percentage: Profit margin percentage.
16.Gross Income: Total gross income.
17.Rating: Customer rating of the transaction.
# Actions Taken on Dataset
Data Collection: Gathered 1,000 sales records from branches A, B, and C across Naypyitaw, Yangon, and Mandalay for the first quarter of 2019.
Data Cleaning: Conducted a thorough cleansing to remove duplicates, handle missing values, and ensure consistency in data entries.
Data Transformation: Converted date and time fields into standardized formats and calculated additional metrics such as VAT and total sales.
Exploratory Data Analysis (EDA):Analyzed sales trends across different cities and branches.
Evaluated customer demographics and product performance.
Visualized the distribution of unit prices, quantities, and ratings.
Summary Statistics: Generated descriptive statistics to provide insights into sales performance, customer behavior, and product popularity.
Data Visualization: Created various charts and graphs to represent key findings, such as sales distribution, customer ratings, and profit margins.
Reporting: Compiled a comprehensive report summarizing the key insights, findings, and visual representations from the dataset.
# Questions Answered
What is the count of distinct cities in the dataset?
For each branch, what is the corresponding city?
What is the count of distinct product lines in the dataset?
Which payment method occurs most frequently?
Which product line has the highest sales?
How much revenue is generated each month?
In which month did the cost of goods sold reach its peak?
Which product line generated the highest revenue?
In which city was the highest revenue recorded?
Which product line incurred the highest Value Added Tax?