Retail Sales Analytics — 7,000+ transactions across 5 datasets

## 📌 Project Overview

A full end-to-end sales analytics project built with **Power Query** and **Power BI**, covering data cleaning, modeling, DAX measures, and interactive dashboards across 5 real-world datasets.

---

## 📂 Datasets Used

| File | Rows | Description |

|------|------|-------------|

| Product-Sales-Region.xlsx | 1,500 | Regional sales transactions |

| Online-Store-Orders.xlsx | 1,200 | E-commerce orders |

| Retail-Store-Transactions.xlsx | 2,000 | In-store transactions |

| Customer-Purchase-History.xlsx | 1,800 | Customer purchase records |

| Inventory-Tracking.xlsx | 500 | Stock and supplier data |

**Total: 7,000+ rows of sales data**

---

## 🔧 Tools & Skills

- **Power Query** — Data cleaning, transformation, custom columns, Merge Queries

- **Power BI** — Data modeling, DAX measures, interactive dashboards

- **DAX** — SUMX, DIVIDE, FILTER, COUNTROWS, ADDCOLUMNS, CALENDAR

---

## 🧹 Data Cleaning (Power Query)

- Changed column data types (Date, Whole Number, Decimal)

- Applied Text.Trim to remove hidden spaces from all text columns

- Replaced null values with meaningful labels:

- `Promotion` → "No Promotion"

- `CouponCode` → "No Coupon"

- Added custom calculated columns:

- `Delivery Days` = DeliveryDate - OrderDate

- `Discount Label` = Low / Medium / High / No Discount

- `Revenue Band` = Small / Medium / Large

- `Rating Label` = Positive / Neutral / Negative

- `Stock Status` = Out of Stock / Low Stock / Moderate / Well Stocked

- `Inventory Value` = QuantityInStock × UnitCost

- Merged **Product Sales** with **Inventory Tracking** via Product key