Diwali Sales Data Analytics is a Python-based data analysis project focused on understanding customer purchasing behavior during the festive season. Using Data Cleaning and Exploratory Data Analysis (EDA) techniques, the project uncovers actionable insights that help retailers optimize marketing strategies, inventory planning, and customer targeting.
During major festivals like Diwali, retailers experience a massive surge in sales. To maximize revenue and improve customer satisfaction, businesses need answers to key questions:
- Who are the primary customers?
- Which regions generate the most revenue?
- Which product categories perform best across demographics?
This project analyzes customer demographics, geography, and occupation-linked buying behavior to answer these questions.
- Customer Segmentation: Identify high-value customer segments for targeted marketing
- Profitability Analysis: Determine the most profitable product categories
- Geographical Mapping: Analyze sales distribution across Indian states
- Inventory Optimization: Align inventory with age-group and gender preferences
- Strategic Planning: Provide insights for future festive-season campaigns
- File Name:
Diwali Sales Data.csv - Total Records: 11,251 (raw data)
- Industry: Retail & E-commerce
- Data Type: Structured CSV
The dataset contains:
- Demographics (Age Group, Gender, Marital Status)
- Geography (State, Zone)
- Transactional data (Product ID, Category, Amount, Orders)
| Column Name | Description |
|---|---|
| User_ID | Unique identifier for each customer |
| Cust_name | Customer name |
| Product_ID | Unique product identifier |
| Gender | Gender of the customer (M/F) |
| Age Group | Categorized age ranges |
| State | Indian state of the customer |
| Occupation | Professional background |
| Product_Category | Category of the purchased product |
| Orders | Number of items ordered |
1. Gender Distribution
Analysis: Order count and total spending by gender
Insight: Females dominate both purchase volume and total spending
2. Age Group Analysis
Analysis: Customer segmentation by age groups
Insight: The 26–35 age group is the most active, with females leading purchases
3. State-wise Performance
Analysis: Top 10 states by total revenue and orders
Insight:
Uttar Pradesh
Maharashtra
Karnataka are the highest revenue-contributing states
4. Occupational Buying Power
Analysis: Spending patterns based on profession
Insight: Highest purchases come from customers working in:
IT Sector
Healthcare
Aviation
5. Top Product Categories
Analysis: Ranking categories by total sales value
Insight: The most popular categories are:
Food
Clothing
Electronics
The ideal target customer persona for Diwali sales optimization is:
Married women aged 26–35 years
Residing in Uttar Pradesh, Maharashtra, or Karnataka
Employed in IT, Healthcare, or Aviation
Highly inclined to purchase Food, Clothing, and Electronics
These insights can significantly improve festive marketing strategies and inventory planning.
Rakesh Kumar Mistri Aspiring Data Analyst
🔗 GitHub: https://github.com/rakeshkumarmistri010413-collab
💼 LinkedIn: https://www.linkedin.com/in/rakesh-kumar-mistri-07ab15334/
📧 Email: rakeshkumarmistri010413@gmail.com