https://github.com/rachakondaganesh/superstore-sales-data-analysis-project
Analyzed retail sales data to uncover key business insights using Python and Power BI. Explored patterns in profit, sales, and customer segments across regions. Built interactive dashboards to visualize trends, identify top-performing categories, and highlight areas for improvement in shipping and discount strategies.
https://github.com/rachakondaganesh/superstore-sales-data-analysis-project
dashboard exploratory-data-analysis matplotlib-pyplot numpy pandas powerbi seaborn
Last synced: about 1 month ago
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Analyzed retail sales data to uncover key business insights using Python and Power BI. Explored patterns in profit, sales, and customer segments across regions. Built interactive dashboards to visualize trends, identify top-performing categories, and highlight areas for improvement in shipping and discount strategies.
- Host: GitHub
- URL: https://github.com/rachakondaganesh/superstore-sales-data-analysis-project
- Owner: Rachakondaganesh
- Created: 2025-10-08T10:39:05.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-10-08T10:55:39.000Z (about 1 month ago)
- Last Synced: 2025-10-08T12:31:34.698Z (about 1 month ago)
- Topics: dashboard, exploratory-data-analysis, matplotlib-pyplot, numpy, pandas, powerbi, seaborn
- Language: Python
- Homepage:
- Size: 1.68 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🏪 Superstore Sales Data Analysis Project
This project performs **Exploratory Data Analysis (EDA)** on Superstore Sales data using **Python** and presents key business insights using a **Power BI Dashboard**.
It aims to identify trends, patterns, and key performance drivers that help improve sales strategies and profitability.
---
## 📘 Overview
The **Superstore Sales dataset** contains transactional data from a retail store, including sales, profit, customer segments, and regional information.
The analysis focuses on:
* Understanding **sales and profit** across regions, categories, and segments
* Identifying **top-performing products and sub-categories**
* Exploring **profitability trends** and **customer behavior**
* Building an **interactive Power BI Dashboard** for dynamic exploration
---
## 🧠 Key Insights from Python Analysis
* ✅ **No missing or duplicate values** were found after cleaning
* 💰 **Top-performing categories** and **sub-categories** identified through sales data
* 🌍 **Regional and segment-based profit analysis** revealed performance gaps
* 🧾 **Top 10 best-selling products and sub-categories** were visualized
* 📊 Clear patterns found in **profit vs. sales** across customer segments and states
Visualizations include:
* Region-wise **Sales** and **Profit**
* Segment-wise **Sales** and **Profit**
* State-wise **Profit Distribution**
* Top-selling **Products** and **Sub-Categories**
---
## ⚙️ Tools & Technologies
| Tool | Purpose |
| ------------------------------ | ------------------------------------- |
| **Python (Pandas, NumPy)** | Data cleaning, preprocessing |
| **Matplotlib, Seaborn** | Data visualization and trend analysis |
| **Power BI** | Interactive dashboard visualization |
| **Excel / CSV Dataset** | Source data file |
| **PyCharm** | Development environment |
---
## 📊 Power BI Dashboard
The **Power BI dashboard** provides a clear visual summary of the findings with interactive filters and visuals for better decision-making.
### Key Dashboard Features:
* 📈 **Regional Profit & Sales Analysis**
* 🏷️ **Category and Sub-Category Sales**
* 🧍♂️ **Customer Segment Performance**
* ⏳ **Yearly & Monthly Sales Trends**
* 💡 **Top Performing Products Overview**
The dashboard allows slicing and drilling down data across various dimensions like region, product category, and customer segment.
---
## 📂 Project Structure
```
Superstore-Sales-Data-Analysis/
│
├── Superstore_Project.py # Python script for EDA and visualization
├── Superstore_Excel.xlsx # Dataset used for analysis
├── Superstore_PowerBI_Dashboard.pbix # Power BI dashboard file
├── README.md # Project documentation
└── images/ # (Optional) dashboard or chart screenshots
```
---
## 🚀 How to Run
### 🔹 Run the Python Analysis
1. Install dependencies:
```bash
pip install pandas numpy matplotlib seaborn
```
2. Run the script:
```bash
python Superstore_Project.py
```
3. Visual outputs (bar plots, scatter plots, etc.) will appear.
---
### 🔹 View the Power BI Dashboard
1. Open `Superstore_PowerBI_Dashboard.pbix` in **Power BI Desktop**
2. Explore different pages and filters for insights by:
* Region
* Segment
* Category
* Time period
---
## 📈 Future Enhancements
* Incorporate **forecasting** models for sales prediction
* Perform **customer segmentation** using clustering algorithms
* Add **interactive dashboards** in Streamlit or Dash
* Integrate **real-time sales data** from APIs
---
## 👤 Author
**Rachakonda Ganesh**
📧 [[rachakondaganesh60@gmail.com](mailto:rachakondaganesh60@gmail.com)]
🔗 [GitHub Profile](https://github.com/Rachakondaganesh)
🔗 [LinkedIn Profile](https://www.linkedin.com/in/rachakonda-ganesh-2782452a8)
---
## 🏁 Conclusion
This project delivers a **complete data analytics workflow** — from data cleaning and exploration in Python to visualization and storytelling in Power BI.
It demonstrates how data-driven insights can guide **business strategy**, improve **profitability**, and enhance **decision-making** in retail operations.