Overview
This project provides an analysis of sales data for Diwali. It includes visualizations and insights to understand sales patterns, customer behavior, and product performance during the Diwali season.
Features
-Data visualization of sales trends
-Analysis of top-selling products
-Customer segmentation insights
-Comparison of sales across different regions
Technologies Used
Programming Languages: Python
Libraries: Pandas, NumPy, Matplotlib, Seaborn
Tools: Jupyter Notebook, Excel
Installation
Clone the repository:
git clone "https://github.com/RishabhRaj43/Diwali-Sales-Analysis.git"
Install dependencies:
pip install numpy pandas matplotlib seaborn
Run the analysis :
Open the Jupyter Notebook (Diwali_Sales_Analysis.ipynb) and execute the cells to generate the analysis and visualizations.
Data Description :
Diwali Sales Data.csv: Contains sales transactions with fields such as User id, Customer name, Product id, Gender, Age and etc.
Analysis Results :
-Sales Trends: Visualizations of sales over time.
-Gender: Most of the buyers are females and even purchasing power of females are greater than men.
-Top-Selling Products: Most of the sold products are from Food, Clothing and Electronics category.
-Customer Segmentation: Most of the buyers are working in IT, Healthcare and Aviation sector.
-Regional Sales Comparison: Most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively.
Conclusion :
Married women age group 26-35 yrs from UP, Maharastra and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category