🛒 Retail Orders Data Analysis Project

🎯 Project Title

Retail Orders Analysis and Data Pipeline with SQL Integration

📋 Project Description

This project involves analyzing retail orders data sourced from Kaggle. The data was downloaded as a ZIP file through the Kaggle API, extracted using code in Jupyter Notebook, then cleaned, transformed, and loaded into Microsoft SQL Server. The goal of this project is to demonstrate data wrangling, transformation, and visualization skills while maintaining a robust data pipeline workflow.

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🚀 Project Workflow

🔹 Data Extraction

• The dataset was imported from Kaggle using the Kaggle API.
• The dataset was downloaded as a ZIP file, extracted using Python code in Jupyter Notebook.
• The data was loaded directly into Jupyter Notebook for further processing.

🔹 Data Cleaning and Transformation

• The data was examined for missing values, duplicates, and incorrect data types.
• Unnecessary columns were removed, and the data was formatted for improved readability.

🔹 Data Storage

• The cleaned and transformed data was exported to Microsoft SQL Server.
• SQL Server was connected via Jupyter Notebook to facilitate seamless data migration.

🔹 Data Analysis and Insights

📊 Key SQL Analysis Questions

🔸 Top 10 revenue-generating categories and subcategories 🔸 Top 5 highest-selling products in each region 🔸 Month-over-month growth comparison for 2022 and 2023 sales 🔸 Identifying the month with the highest sales for each category 🔸 Category with the highest growth percentage in profit in 2023 compared to 2022 🔸 Subcategory with the highest growth percentage in profit in 2023 compared to 2022 🔸 Top 5 states with the highest sales

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📂 Deployment on GitHub

• The project includes:
  • 📒 Jupyter Notebook containing code for data cleaning, transformation, and SQL integration.
  • 📁 The original dataset for reference.
  • 📝 A detailed README file (this document).

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⚙️ Requirements

• Python Libraries:
• pandas
• pyodbc (for SQL Server connection)
• Microsoft SQL Server (with database setup for data storage)
• Jupyter Notebook

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📈 Key Insights

✅ Identified peak sales periods and trends.
✅ Recognized top-selling products and customer segments.
✅ Provided actionable insights for improving business decisions.

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🔮 Future Enhancements

• ⚙️ Automate the data pipeline for real-time updates.
• 📊 Develop dashboards for improved visualization.
• 🧠 Expand analysis to predict sales trends using machine learning.

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🤝 Contribution

Contributions are welcome! Feel free to submit issues or pull requests to enhance this project.

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📧 Contact

For questions or collaboration, reach out via rakeshmeka.work@gmail.com.