🪔 Diwali Sales Data Analysis This project is an Exploratory Data Analysis (EDA) of Diwali sales data to understand customer behavior, preferences, and key market trends. The insights gained can help in targeted marketing and strategic decision-making for future campaigns.
📌 Project Overview The dataset includes customer demographic details and their purchasing behavior during the Diwali festival season. The analysis focuses on identifying:
Top-performing age groups and genders
Sales distribution by state and zone
Popular product categories
Occupations with the highest spending
Marital status trends
Top-selling products
📁 Dataset Description The dataset contains the following columns:
Column Name Description User_ID Unique ID of the customer Cust_name Name of the customer Product_ID ID of the product purchased Gender Gender of the customer Age Group Age group category Age Age of the customer Marital_Status 0 = Single, 1 = Married State State of residence Zone Geographical zone (e.g., North, South) Occupation Profession of the customer Product_Category Product category purchased Orders Number of items ordered Amount Total amount spent 🧼 Data Cleaning Removed null and unnecessary columns (Status, unnamed1)
Converted Amount column to integer for accurate aggregation
Removed rows with missing values
📊 Exploratory Data Analysis 📍 Gender-wise Sales Majority of buyers are female
Female customers also have higher purchasing power
📍 Age Group Analysis 26-35 age group is the most active
Followed by 36-45 and 18-25 age brackets
📍 State-wise Sales Uttar Pradesh, Maharashtra, and Karnataka are top-performing states in both orders and revenue
📍 Marital Status Married women dominate sales, indicating strong buying power in that demographic
📍 Occupation-wise Trends Most purchases come from IT, Healthcare, and Aviation sectors
📍 Product Categories Highest sales from:
Food
Footwear
Electronics & Gadgets
📍 Top-Selling Products Identified 10 most frequently ordered products
📌 Conclusion Target Audience Insight Married women aged 26-35, living in Uttar Pradesh, Maharashtra, or Karnataka, working in IT, Healthcare, or Aviation, are most likely to purchase during Diwali — primarily Food, Clothing, and Electronics.
🛠️ Tools & Libraries Used Python (Pandas, NumPy)
Data Visualization: Seaborn, Matplotlib
Jupyter Notebook / Colab
📎 How to Run Clone the repository:
bash Copy Edit git clone https://github.com/your-username/diwali-sales-analysis.git Install the required packages:
bash Copy Edit pip install pandas matplotlib seaborn Open the Jupyter Notebook:
bash Copy Edit jupyter notebook Diwali_Sales_Analysis.ipynb 📌 Future Scope Integrate time-based analysis if timestamps are added
Predictive modeling to forecast Diwali sales
Build a dashboard using Plotly Dash or Power BI