A data analysis project exploring customer behavior during Diwali shopping using Python and data visualization tools. The project aims to uncover insights from customer demographics, location-based trends, spending habits, and product preferences.
Dataset Name: Diwali Sales Data
Key Columns:
- Gender: Male/Female customers
- Age Group: Customer age distribution
- State: Location of customers
- Marital Status: Married/Unmarried customers
- Occupation: Buyer professions
- Product Category: Type of products purchased
- Amount: Total amount spent
| Category | Tools |
|---|---|
| Programming Language | Python |
| Libraries Used | Pandas, NumPy, Matplotlib, Seaborn |
| Visualization Tools | Seaborn, Matplotlib |
| Platform | Google Colab / Jupyter Notebook |
- Majority of buyers are aged 26-35 years
- Women contribute significantly to high-value purchases
- Highest number of orders come from:
- Uttar Pradesh
- Maharashtra
- Karnataka
- Unmarried women tend to spend more on shopping
- Top buying professions: IT, Healthcare, and Aviation
- Most sold product categories:
- Clothing & Apparel
- Food
- Electronics & Gadgets
Data Preprocessing
- Handled missing values
- Converted data types
- Created new features (feature engineering)
Exploratory Data Analysis (EDA)
- Analyzed customer demographics
- Identified spending patterns and top buyer segments
Visualization
- Used Seaborn and Matplotlib
- Created insightful plots: bar charts, count plots, and heatmaps
Conclusions & Business Insights
- Derived actionable insights to assist business decision-making during festive sales
This project highlights how Diwali shopping behavior varies across demographics, locations, and product preferences. The analysis can help businesses optimize marketing campaigns, product inventory, and target audiences more effectively during festive seasons.
- Dataset used for educational and analytical purposes
- Inspired by real-world retail analytics scenarios