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[FEATURE] Adding Analysis of a sales Dataset #229
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👋 Thanks for opening this issue! We appreciate your contribution. Please make sure you’ve provided all the necessary details and screenshots, and don't forget to follow our Guidelines and Code of Conduct. Happy coding! 🚀 |
Ensure the issue is not similar or previously being worked on.Thanks for your time |
@Mayureshd-18 please assign this to me |
@jvedsaqib please assign this to me |
why no one is responding |
i wan to work on this |
✅ This issue has been successfully closed. Thank you for your contribution and helping us improve the project! If you have any more ideas or run into other issues, feel free to open a new one. Happy coding! 🚀 |
Is this a unique feature?
Is your feature request related to a problem/unavailable functionality? Please describe.
Currently, the project lacks functionality for analyzing sales datasets, which is a key feature for deriving meaningful insights such as sales trends, best-selling products, revenue breakdowns, and customer preferences.
I'm often frustrated when I cannot visualize or analyze sales data to gain actionable insights directly from the application.
Proposed Solution
I propose adding a module for Sales Dataset Analysis that includes:
Data Cleaning: Handle null values, duplicates, and ensure data consistency.
Exploratory Data Analysis (EDA): Summarize the dataset with descriptive statistics and uncover patterns like:
Total sales over time.
Revenue breakdown by product, category, or region.
Customer segmentation (e.g., based on purchase history).
Data Visualization: Use libraries like Matplotlib or Plotly to create:
Line graphs for sales trends over time.
Bar charts for top-selling products or regions.
Pie charts for revenue distribution.
Interactive Reports: Generate easy-to-understand summaries and visualizations that can be exported as images or PDFs.
Screenshots
Screen.Recording.2025-01-09.171409.1.mp4
Do you want to work on this issue?
Yes
If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)
To implement this feature, I would:
Dataset Preparation: Add a sample sales dataset to the repository for testing and demonstration purposes.
Script Development:
Use Pandas for data cleaning and summarization.
Use Matplotlib or Seaborn for creating visualizations.
Integration: Create a standalone module or notebook for the sales analysis and link it with the existing application.
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