This project analyzes Diwali sales data to understand customer purchasing behavior, identify high-value segments, and generate actionable business insights using Python.
- Analyze customer demographics and buying patterns
- Identify top-performing customer segments
- Discover high-revenue product categories
- Provide business recommendations to improve sales
- Python (Pandas, NumPy)
- Data Visualization (Matplotlib, Seaborn)
- Jupyter Notebook
Before Cleaning:
- Missing values in key columns
- Incorrect data types
- Irrelevant columns present
After Cleaning:
- Removed null values
- Converted data types (Amount → numeric)
- Dropped unnecessary columns
- Standardized categorical data
- 👩 Female customers contribute more to total sales
- 🎯 Age group 26–35 is the highest spending segment
- 💍 Married customers spend more than unmarried
- 🌍 Certain states drive majority of revenue
- 💼 IT, Healthcare & Aviation professionals are top buyers
- 🛍️ Top categories: Food, Clothing, Electronics
- High-value customers = Married females (Age 26–35)
- Strong relationship between age and spending behavior
- Occupation significantly impacts purchasing power
- Regional clusters show consistent high demand
- Target females (26–35) with personalized campaigns
- Focus marketing on high-performing states
- Offer festive discounts for married customers
- Promote top categories with combo deals
- Use occupation-based targeting for premium products
- Introduce loyalty programs for repeat customers
This project demonstrates end-to-end data analysis including data cleaning, visualization, and business insight generation. It reflects practical skills required for a Data Analyst role.
Karan Aspiring Data Analyst