📊 Sales Data Analysis & Visualization

🔹 Overview

This project was developed as part of the Snestron Data Analytics Internship. It focuses on analyzing a synthetic sales dataset using Python for data cleaning and exploratory analysis, and Power BI for interactive visualization. The aim is to simulate a real-world business scenario and deliver actionable insights through data.

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📂 Repository Structure

sales-data-analysis/
│── data/
│   ├── synthetic_sales_data.csv       # Raw dataset
│   ├── clean_sales_data.csv           # Cleaned dataset for Power BI
│── notebooks/
│   ├── sales_analysis.ipynb           # Python analysis & visualizations
│── powerbi/
│   ├── sales_dashboard.pbix           # Power BI dashboard file
│── screenshots/
│   ├── dashboard_overview.png         # Screenshots of Power BI dashboard
│── README.md                          # Project documentation

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⚙️ Tools & Technologies

• Python (Pandas, Matplotlib, Seaborn)
• Jupyter Notebook
• Power BI Desktop
• GitHub (for project submission)

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📊 Key Insights

From the analysis:

• 📈 Monthly sales trends show clear patterns and seasonality.
• 🏆 Electronics category contributes the highest revenue.
• 🌍 Region-wise, North and West performed better than South and East.
• 💡 Top products and customers generate a significant share of total sales.

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📸 Power BI Dashboard

Interactive dashboard built in Power BI with:

• KPI cards (Total Sales, Total Quantity, Avg Order Value)
• Sales trend over time
• Category-wise and region-wise breakdown
• Top 10 products and customers

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🚀 How to Run

1️⃣ Python Analysis

• Open notebooks/sales_analysis.ipynb

• Install dependencies:

  pip install pandas matplotlib seaborn

• Run the notebook to reproduce EDA and plots.

2️⃣ Power BI Dashboard

• Open powerbi/sales_dashboard.pbix in Power BI Desktop.
• Interact with filters and slicers for insights.

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📌 Submission

This repository contains the final deliverable for the internship task. It demonstrates the complete data analytics lifecycle: Data → Cleaning → Analysis → Visualization → Insights → Reporting.