Exploratory analysis of Diwali retail sales: who buys, which categories move, and which states lead. Work is in a Jupyter notebook with pandas, matplotlib, and seaborn.
- Which customer segments spend the most?
- Which product categories lead volume?
- How do gender, age group, state, occupation, and marital status line up with sales?
- Highest-intent segment in this dataset: married women aged 26–35 in Uttar Pradesh, Maharashtra, and Karnataka, often in IT, Healthcare, and Aviation.
- Top categories: Food, Clothing, Electronics.
- Those three states also dominate total sales.
| File | Role |
|---|---|
Sales_Analysis.ipynb |
Full EDA notebook |
Sales Data.csv |
Source dataset |
notes.md |
Step-by-step pandas workflow |
requirements.txt |
Python packages |
git clone https://github.com/RajaWasim100/Data-Analytics-with-Python-.git
cd Data-Analytics-with-Python-
pip install -r requirements.txt
jupyter notebook Sales_Analysis.ipynbAnalysis steps: load → drop unused columns / nulls → cast Amount → group and plot by demographic and product dimensions → summarize.
Raja Wasim — github.com/RajaWasim100