https://github.com/codeofrahul/diwali_sales_analysis_with_python
In this project i have performed end to end analysis on Diwali sales data using python libraries pandas, matplotlib, seaborn.
https://github.com/codeofrahul/diwali_sales_analysis_with_python
dataanalysis datavisualization diwali-sales-analysis matplotlib pandas python seaborn
Last synced: 7 months ago
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In this project i have performed end to end analysis on Diwali sales data using python libraries pandas, matplotlib, seaborn.
- Host: GitHub
- URL: https://github.com/codeofrahul/diwali_sales_analysis_with_python
- Owner: CodeofRahul
- Created: 2024-07-30T05:07:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-30T06:42:38.000Z (over 1 year ago)
- Last Synced: 2025-03-25T00:45:28.762Z (10 months ago)
- Topics: dataanalysis, datavisualization, diwali-sales-analysis, matplotlib, pandas, python, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 482 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Diwali_Sales_Analysis_With_Python
## **Problem Statement**
How can we leverage Diwali sales data to understand customer behavior, optimize product offerings, and enhance marketing strategies to maximize sales and customer satisfaction
during the Dewali Season?
## **Context**
Diwali is a major festival in India, presents a significant opportunity for retailers to boost sales through targeted marketing and promotions. The dataset contains sales data
during the Diwali season, including customer demographics, product details, and transaction information. By leveraging this data, the company aims to enhace its marketing
strategies, optimize product offerings, and improve overall sales performance during this peak shopping period.
## **Objective:**
The primary objective is to analyze the Diwali sales data to gain actionable insights that can help in understanding customer behavior, identifying keys sales drivers, and
optimizing marketing strategies to maximize sales and customer satisfaction.
## **Steps:**
- Data Gathering
- Data Injection
- Data Cleaning and Preparation
- Exploratory Data Analysis (EDA) using pandas, matplotlib and seaborn libraries
- Customer Demographic Analysis
- Product Performance Analysis
- Sales Trends and Pattern Analysis
- Customer Behavior Analysis
- Deriving Actionable Insights
- Visualization
## **Project Learnings**
- Improved Customer experience by identifying potential customers across different states, occupation, gender, and age groups
- Improved sales by identifying most selling product categories and products, which can help to plan inventory and meet the demands