This project performs an in-depth Exploratory Data Analysis (EDA) on a retail sales dataset. By analyzing over 11,000+ rows of transaction data, the goal is to identify the "Power Spender" demographics to help a retail business optimize its marketing budget and inventory management.
After cleaning and analyzing the data, the following high-value consumer segments were identified:
- The Primary Buyer: Married women aged 26-35 years are the most significant contributors to total revenue.
- Geographic Hotspots: The top 3 states by sales volume and order count are Uttar Pradesh, Maharashtra, and Karnataka.
- High-Value Occupations: Customers working in the IT Sector, Healthcare, and Aviation show the highest purchasing power.
- Product Strategy: High demand is concentrated in the Food, Clothing, and Electronics categories.
The analysis explores several dimensions of the data:
- Demographic Analysis: Sales distribution by Gender, Age Group, and Marital Status.
- State-wise Performance: Total orders and revenue mapped by Indian states.
- Professional Insights: Correlation between Occupation and total spend.
- Inventory Focus: Identification of the Top 10 most sold Product IDs.
- Language: Python
- Libraries: Pandas (Data Wrangling), NumPy (Numerical Analysis), Matplotlib & Seaborn (Data Visualization).
├── Sales_Data_Analysis.ipynb # Main Jupyter Notebook with code & charts
├── Sales Data.csv # Raw dataset (Ensure this is uploaded)
└── README.md # Project documentation