🛒 Retail Sales Analytics Dashboard (2022–2023)

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📌 Project Overview

A full end-to-end Business & Sales Analytics project that simulates a real Egyptian retail company with 2,000 transactions over 2 years. The goal is to extract actionable insights from sales data using Python, SQL-style queries, and interactive visualizations.

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🎯 Business Questions Answered

#	Question
1	Which product category generates the most revenue?
2	What is the monthly revenue trend over 2 years?
3	Which region has the highest sales volume?
4	Who is the top-performing sales representative?
5	How does discount level affect average order value?
6	What is the most popular payment method?
7	What is the quarterly revenue trend per category?
8	How does customer satisfaction correlate with order value?

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📊 Dashboard Preview

Overview Dashboard

Deep-Dive Analysis

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📁 Project Structure

Retail-Sales-Analytics/
│
├── 📄 sales_data.csv            # Raw dataset (2,000 transactions)
├── 🐍 generate_data.py          # Data generation script
├── 🐍 analysis.py               # Full EDA + SQL-style analysis
├── 📊 dashboard_overview.png    # Main KPI dashboard
├── 📊 deep_dive_analysis.png    # Correlation & trend analysis
├── 📋 analysis_summary.xlsx     # Excel report (3 sheets)
└── 📖 README.md

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📦 Dataset Description

Column	Type	Description
Order_ID	String	Unique order identifier
Date	Date	Transaction date (2022–2023)
Category	String	Product category (5 categories)
Region	String	Egyptian city
Sales_Rep	String	Representative name
Quantity	Integer	Units sold
Unit_Price	Float	Price per unit ($)
Discount	Float	Discount applied (0–20%)
Total_Sales	Float	Net revenue after discount
Payment_Method	String	Payment type
Customer_Age	Integer	Customer age
Customer_Satisfaction	Integer	Rating 1–5

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🔍 Key Findings

• 💰 Electronics is the highest-revenue category (~30% of total sales)
• 📈 Revenue shows a positive growth trend in Q4 2023
• 🗺️ Cairo leads in total regional revenue
• 👤 Nour Ibrahim is the top sales representative ($2.06M)
• 💳 Credit Card is the most preferred payment method
• 🏷️ Orders with 10–15% discount show the highest average order value
• 😊 Customer satisfaction score doesn't strongly predict order size — suggesting pricing is needs-driven

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🛠️ Technologies Used

pandas       # Data manipulation & SQL-style queries
numpy        # Numerical operations
matplotlib   # Custom charts & dashboards
seaborn      # Statistical visualizations
openpyxl     # Excel report export

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▶️ How to Run

# 1. Clone the repository
git clone https://github.com/eng-heba-tech/Retail-Sales-Analytics
cd Retail-Sales-Analytics

# 2. Install dependencies
pip install pandas numpy matplotlib seaborn openpyxl

# 3. Generate dataset
python generate_data.py

# 4. Run full analysis
python analysis.py

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📈 SQL-Style Queries Performed

-- Monthly Revenue Growth
SELECT Month, SUM(Total_Sales) AS Revenue,
       LAG(Revenue) OVER (ORDER BY Month) AS Prev_Month,
       ((Revenue - Prev_Month) / Prev_Month * 100) AS Growth_Pct
FROM sales_data GROUP BY Month;

-- Top Sales Representatives
SELECT Sales_Rep,
       SUM(Total_Sales) AS Total_Revenue,
       AVG(Total_Sales) AS Avg_Order,
       COUNT(Order_ID)  AS Total_Orders
FROM sales_data GROUP BY Sales_Rep ORDER BY Total_Revenue DESC;

-- Category Performance by Region
SELECT Category, Region, SUM(Total_Sales)
FROM sales_data GROUP BY Category, Region;

(Implemented using pandas groupby — equivalent to SQL)

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👩‍💻 Author

Eng. Heba | Data Analyst & Engineer

• 🐙 GitHub: @eng-heba-tech
• 📧 eng.heba.tech@gmail.com
• 🌍 Egypt

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