This repository contains all the necessary files for performing data analysis using Excel. The primary file in this repository is Dashboard_Excel.xlsx, where an Excel dashboard has been created to analyze an e-commerce dataset.
The Dashboard_Excel.xlsx file provides a comprehensive Excel dashboard that allows for in-depth analysis of an e-commerce dataset. The dashboard includes various charts, pivot tables, and conditional formatting to help visualize and understand the data effectively.
Dashboard_Excel.xlsx: This is the main file containing the Excel dashboard for analyzing the e-commerce dataset.
Object Create an Excel dashboard using pivot tables and charts to provide insights into the e-commerce data. The dashboard should allow users to analyze sales performance, customer demographics, product categories, and order statuses.
1.index: A unique identifier for each row.
2.Order ID: Unique identifier for each order.
3.Cust ID: Unique identifier for each customer.
4.Gender: Gender of the customer.
5.Transformed_Gender: Transformed Gender (potentially cleaned or standardized).
6.Age: Age of the customer.
7.Date: Date of the order.
8.Status: Status of the order (e.g., Delivered, Pending).
9.Channel: Sales channel through which the order was placed (e.g., Myntra, Ajio, Amazon).
10.SKU: Stock Keeping Unit, a unique identifier for each product.
11.Category: Product category (e.g., kurta, Set).
12.Size: Size of the product.
13.Qty: Quantity of the product ordered.
14.Transformed Quantity: Transformed Qty (Potentially cleaned or standardized )
15.currency: Currency of the transaction (e.g., INR).
16.Amount: Total amount of the order.
17.ship-city: City where the order was shipped.
18.ship-postal-code: Postal code of the shipping address.
19.ship-country: Country where the order was shipped.
20.B2B: Indicates if the order is a business-to-business transaction (True/False).
- Data Visualization: Includes various charts such as bar charts, line charts, and pie charts to visualize the e-commerce data.
- Pivot Tables: Utilizes pivot tables to summarize and analyze data.
- Conditional Formatting: Highlights key data points to make the analysis more intuitive.
- Interactive Elements: Includes slicers and filters to interact with the data and customize the analysis.
To get started with the analysis, follow these steps:
- Clone the repository to your local machine using:
git clone https://github.com/yourusername/Data-Analysis-using-Excel.git
2.Open Dashboard_Excel.xlsx in Microsoft Excel.
Open the Dashboard_Excel.xlsx file in Excel to explore the e-commerce dataset using the pre-built dashboard. You can interact with various elements like charts, pivot tables, and slicers to gain insights from the data.
Contributions are welcome! If you have any improvements or suggestions, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more details.