https://github.com/shudhanshusaurabh001/super_market-data-analysis-using-python
This project focuses on analyzing supermarket sales data using Python. The goal is to extract meaningful insights from the dataset, such as sales trends, customer purchasing behavior, and product performance.
https://github.com/shudhanshusaurabh001/super_market-data-analysis-using-python
analysis csv data insights matplotlib numpy pandas project python seaborn
Last synced: 3 months ago
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This project focuses on analyzing supermarket sales data using Python. The goal is to extract meaningful insights from the dataset, such as sales trends, customer purchasing behavior, and product performance.
- Host: GitHub
- URL: https://github.com/shudhanshusaurabh001/super_market-data-analysis-using-python
- Owner: ShudhanshuSaurabh001
- Created: 2025-03-08T16:42:01.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-08T16:49:03.000Z (over 1 year ago)
- Last Synced: 2025-03-08T17:29:01.918Z (over 1 year ago)
- Topics: analysis, csv, data, insights, matplotlib, numpy, pandas, project, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Super_Market-Data-Analysis-Using-Python
## Project Overview
This project focuses on analyzing supermarket sales data using Python. The goal is to extract meaningful insights from the dataset, such as sales trends, customer purchasing behavior, and product performance.
### Dataset
### The dataset used for this analysis contains transaction details from a supermarket, including:
- Invoice ID
- Branch
- City
- Customer type
- Gender
- Product line
- Unit price
- Quantity
- Tax and total
- Date and time of purchase
- Payment method
- Customer rating
## Technologies Used
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter Notebook
## Key Analysis Performed
- Data Cleaning
- Handled missing values and incorrect data types.
- Processed date and time columns for better analysis.
- Exploratory Data Analysis (EDA)
- Identified sales trends over time.
- Analyzed customer demographics and behavior.
- Evaluated the popularity of different product lines.
## Visualization
- Created bar charts, histograms, and box plots to represent key insights.
- Used heatmaps to analyze correlations within the dataset.
## Summary
This project analyzes supermarket sales data using Python to uncover insights about sales trends, customer behavior, and product performance. The dataset includes transaction details such as invoice IDs, product lines, customer demographics, payment methods, and purchase timestamps.