https://github.com/ankitrai259/diwali-sales-analysis-using-python

Diwali Sales Data Analysis Using Python Libraries
https://github.com/ankitrai259/diwali-sales-analysis-using-python

jupyter-notebook matplotlib-pyplot pandas python seaborn

Last synced: 18 days ago
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Diwali Sales Data Analysis Using Python Libraries

• Host: GitHub
• URL: https://github.com/ankitrai259/diwali-sales-analysis-using-python
• Owner: AnkitRai259
• Created: 2024-10-21T16:45:21.000Z (29 days ago)
• Default Branch: main
• Last Pushed: 2024-10-21T17:25:21.000Z (29 days ago)
• Last Synced: 2024-10-31T23:08:02.564Z (18 days ago)
• Topics: jupyter-notebook, matplotlib-pyplot, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 639 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Diwali Sales Data Analysis Using Python

## Overview

This project involves analyzing the Diwali sales dataset to gain insights into customer behavior and sales performance during the festive season. The dataset includes various attributes related to products and customers, allowing for a comprehensive exploratory data analysis (EDA)

## Dataset

The dataset used for this project is sourced from Rishabh Mishra's GitHub repository. It contains the following columns:

* Product ID: Unique identifier for each product

* Product Category: Category of the product (e.g., Electronics, Clothing, etc.)

* Customer Name: Name of the customer

* Customer Occupation: Occupation of the customer

* Customer Marital Status: Marital status of the customer (e.g., Single, Married)

* State: State where the customer resides

* Region: Region of the customer

* Gender: Gender of the customer

* Amount: Total amount spent by the customer

* Quantity Ordered: Quantity of the product ordered

## Libraries Used

This project utilizes the following libraries:

* NumPy: For numerical operations and data manipulation

* Pandas: For data manipulation and analysis

* Matplotlib: For creating static, interactive, and animated visualizations

* Seaborn: For statistical data visualization and enhanced graphics

## Project Structure

The project is organized as follows:

Diwali-Sales-Analysis-Using-Python/

│

├── Diwali_Sales_Data_Analysis.ipynb    # Jupyter Notebook containing the analysis

├── Diwali Sales Data.csv               # Diwali sales dataset    

└── README.md                           # Project README file

## Steps Performed

1. Data Cleaning:

    * Loaded the dataset and examined its structure.

    * Handled missing values and removed duplicates.

    * Converted data types as necessary for accurate analysis.

  

2. Exploratory Data Analysis (EDA):

    * Analyzed customer demographics and sales trends.

    * Visualized relationships between product categories and sales amounts.

    * Explored customer purchasing behavior based on occupation and marital status

## Conclusion

This project provides valuable insights into Diwali sales, helping businesses understand customer preferences and optimize their strategies for future sales events.

Feel free to explore and modify the analysis to suit your needs!