📌 Project Overview

This Power BI project analyzes BlinkIt’s sales data to uncover insights into item trends, outlet performance, customer ratings, and sales distribution. The goal was to transform raw retail data into a dynamic, interactive dashboard that supports data-driven decisions.

🧰 Tools & Technologies Used

• Power BI Desktop
• Power Query Editor (for data cleaning and transformation)
• DAX (for calculated KPIs and measures)
• Data Profiling (to assess and improve data quality)
• Visualizations (pie charts, bar charts, funnel charts, line charts)

📦 Dataset Information

The dataset consists of retail-level item and outlet data with the following columns:

Item Fat Content, Item Identifier, Item Type, Outlet Establishment Year, Outlet Identifier

Outlet Location Tier, Outlet Size, Outlet Type, Item Visibility, Item Weight, Sales, Rating

🧹 Data Preparation

Using Power Query Editor, I performed the following data-cleaning steps:

Replaced inconsistent text values (e.g., standardizing fat content categories)

Renamed columns for clarity

Fixed data types (e.g., numbers, dates, text)

Checked data quality using Power BI's "Column quality" and "Column distribution" features

Filtered unnecessary or null values for improved accuracy