Online_Retail_Data_Analysis_Task

Data Analysis Project Project Title: Revenue, Returns & Customer Segmentation Analysis Dataset: Online Retail Dataset (UCI Version on Kaggle) Business Scenario You are a Data Analyst working for a UK-based online retail company. The company wants to increase profitability, reduce revenue leakage from returns, and identify high-value customers. You are required to clean the data using Python and build a professional dashboard using Power BI. Part 1 – Python (Data Cleaning & Feature Engineering) Step 1 – Data Understanding - Check dataset shape - Check data types - Detect null values - Count duplicates - Detect negative quantities - Detect zero prices Step 2 – Data Cleaning - Remove duplicates - Remove rows where Quantity = 0 - Remove rows where UnitPrice = 0 - Handle returns (Invoices starting with C) - Handle missing CustomerID (Drop or classify as Guest with justification) - Convert InvoiceDate to datetime format - Create Year and Month columns Step 3 – Feature Engineering - Create Revenue column (Quantity × UnitPrice) - Create Return Flag column - Calculate Customer Total Revenue using groupby - Perform RFM Analysis (Recency, Frequency, Monetary) - Classify customers into High, Medium, Low, and Lost segments Part 2 – Power BI Dashboard Page 1 – Executive Overview - KPI Cards: Total Revenue, Total Orders, Total Customers, Return Rate - Line Chart: Revenue Trend by Month - Bar Chart: Revenue by Country - Donut Chart: Customer Segmentation Page 2 – Returns & Revenue Leakage - Return Rate by Country - Top Returned Products - Matrix: Customer vs Revenue vs Return - Scatter Plot: Revenue vs Frequency Analytical Questions 1. Which country generates the highest revenue? 2. What is the overall return rate? 3. Are high frequency customers always profitable? 4. Which products have the highest return rate? 5. Identify 10 high-value customers. 6. Identify risky customers. Deliverables - Python Notebook (.ipynb) - Cleaned Dataset (.csv) - Power BI File (.pbix) - 5-slide Presentation with Insights