https://github.com/vvipjain/super-mart-sales-analysis
Super Mart Sales Analysis
https://github.com/vvipjain/super-mart-sales-analysis
pandas pandas-library pandas-python plotly plotly-express plotly-python python
Last synced: 17 days ago
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Super Mart Sales Analysis
- Host: GitHub
- URL: https://github.com/vvipjain/super-mart-sales-analysis
- Owner: VVipJain
- Created: 2024-08-05T09:49:32.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-05T10:43:57.000Z (about 1 year ago)
- Last Synced: 2025-02-10T05:14:15.080Z (9 months ago)
- Topics: pandas, pandas-library, pandas-python, plotly, plotly-express, plotly-python, python
- Language: Jupyter Notebook
- Homepage:
- Size: 623 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Super-Mart-Sales-Analysis
This repository contains an in-depth analysis of sales data from a supermart, utilizing Python, Pandas, and Plotly for data manipulation and visualization. The project aims to uncover insights into sales performance, customer behavior, and product trends.
INTRODUCTION :-
In this project, we perform a comprehensive analysis of sales data from a supermart. By leveraging the powerful data manipulation capabilities of Pandas and the interactive visualization features of Plotly, we aim to provide valuable insights into various aspects of the supermart's operations. This project is ideal for data analysts, business managers, and anyone interested in retail analytics.
DATASET :-
The dataset used in this analysis contains detailed information about sales transactions, including product details, sales amounts, customer demographics, and more. The data is stored in a CSV file named 10000 Sales Records.csv.
ANALYSIS :-
The analysis is divided into several sections:
Loading and Cleaning Data: Importing the dataset and performing initial cleaning operations such as handling missing values and converting data types.
Data Exploration: Exploring the dataset to understand its structure, including summary statistics and unique values.
Sales Performance: Analyzing sales performance across different branches, product lines, and customer types.
Customer Behavior: Understanding customer demographics, preferences, and purchasing patterns.
Product Trends: Identifying top-selling products, seasonal trends, and product performance over time.
VISUALISATION :-
We use Plotly to create interactive visualizations. Some of the key visualizations include:
Bar Charts: Margins analysis region wise from the revenue and cost.
Pie Charts: Distribution of sales by category and item wise profit analysis.
Line Charts: Trends in sales and profit over a month.
