You can access my notebook from the link : https://www.kaggle.com/code/cansusary/e-commerce-analytics .
This project dives deep into an e-commerce dataset to uncover valuable insights about sales, customer behavior, and performance metrics. By employing exploratory data analysis (EDA), data visualization, and key performance indicator (KPI) calculations, this analysis provides a comprehensive understanding of e-commerce operations.
Objective: To analyze e-commerce data and derive actionable insights.
Key Goals:
-Identify top-performing products and revenue trends.
-Understand customer behavior and lifetime value.
-Evaluate sales distribution across different regions and times.
This dataset contains key features, including transaction details, customer information, and product descriptions: InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country.
Key steps taken in this analysis include:
- Loading and Understanding the Dataset: Ensured a clear understanding of data structures and types.
- Handling Missing Values: Removed and imputed missing data for a clean dataset.
- Data Formatting: Standardized column formats for consistency.
- Negative Quantity Filtering: Removed invalid transactions to improve accuracy.
Revenue Calculation: Revenue = Quantity × UnitPrice.
Order Date Extraction: Extracted year, month, day, and hour from InvoiceDate.
💰 Total Revenue: Aggregated revenue across all transactions.
📆 Monthly Revenue Growth: Tracked growth trends over time.
🎁 Best-Selling Products: Identified high-performing items.
🛒 Average Order Value (AOV): Revenue divided by the total number of orders.
👤 Customer Lifetime Value (CLV): Estimated the total revenue generated by customers.
I am passionate about learning and growing through collaboration and would love to hear your thoughts! Feel free to comment, ask questions, or provide feedback on this project. 🌟