FUTURE_DS_01

🛍️ Retail Sales Power BI Dashboard (E-commerce Analysis) Task one of the 'Future interns Data Science and Analytics internship'

📊 Objective

To gain business insights from online retail sales data by answering key questions that help understand product performance, customer behavior, and sales trends.

📁 Dataset

The dataset used is publicly available on Kaggle. It contains records of transactions from an online store.

🧹 Data Cleaning & Preparation

Data was cleaned and transformed before loading into Power BI. Key steps included:

1. Imputing missing values:

   • Review Score: Replaced blanks with the 'median'.
   • Gender: Filled missing values with the 'mode' (most frequent gender).

2. Created calculated fields in Power BI:

   • Total Sales = Quantity * Price
   • Order Year and Order Month were extracted from the order date for trend analysis.
   • Average Review Score per product/customer.

3. Removed duplicates and corrected data types (e.g., converting strings to dates or numbers where appropriate).

   ❓ Business Questions Answered

• What are the total sales by product category?
• Which products are selling the most in terms of quantity?
• What are the preferred payment methods among customers?
• How do sales trend over time (monthly/yearly)?
• Is there a correlation between review scores and Sales volume?
• Which cities generate the most revenue?

🛠️ Tools Used

• Microsoft Power BI
• Microsoft Excel

Power BI dashboard image