📊Sales Data Project

A hands-on project demonstrating a complete ETL (Extract, Transform, Load) pipeline for sales data using Python and pandas.
The repository is designed for practicing data engineering workflows, Git collaboration, and real-world data cleaning tasks.

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🎯 Project Objectives

• Build a reproducible ETL pipeline for messy sales data.
• Practice using Git and GitHub for collaborative workflows (branching, merging, resolving conflicts).
• Showcase data engineering skills with professional project documentation.

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📂 Project Structure

graph TD
A[etl-sales-data] --> B[data]
  B --> B1[raw_sales.csv]
  A --> C[etl]
  C --> C1[etl_pipeline.py]
  A --> D[README.md]

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⚙️ Tools & Technologies

• Python 3 → programming language for ETL logic
• pandas → data manipulation and cleaning
• CSV → raw data source and output format
• Git → version control system for collaboration
• GitHub → remote repository for hosting and teamwork

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🛠️ ETL Workflow

1. Extract

• Load raw sales data from data/raw_sales.csv.
• The dataset includes fields such as:
  • Transaction ID
  • Date
  • Product & Category
  • Quantity & Price (with missing values)
  • Customer ID
  • Region

2. Transform

• Standardize column names (lowercase, underscores instead of spaces).
• Handle missing values:
  • Replace missing Price values with mean of the total value.
• Add derived fields:
  • total_sales = quantity * price
• Ensure clean, consistent output ready for analysis.

3. Load

• Save the cleaned dataset as data/clean_sales.csv.
• Future extension: load into SQLite or PostgreSQL for analytics.

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🚀 Getting Started

Prerequisites

• Python 3.8 or later
• Git installed
• pandas library installed

1. Clone the Repository

git clone https://github.com/DevMuzee/etl-sales-data.git
cd etl-sales-data

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ETL Flow (Mermaid Diagram)

graph TD
  A[Raw CSV Data] --> B[Extract]
  B --> C[Transform]
  C -->|Clean column names| C1[Standardize schema]
  C -->|Handle missing values| C2[Fill NA in Price]
  C -->|Create derived columns| C3[total_sales = qty * price]
  C --> D[Load]
  D --> E[Clean CSV / Database]

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📌 Future Enhancements

• Add unit tests for transformations
• Load cleaned data into SQLite/PostgreSQL
• Schedule pipeline runs using Apache Airflow or Prefect
• Extend project with data visualization (Matplotlib/Seaborn)
• Integrate CI/CD pipelines for automated testing

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📜 License

This project is for educational purposes only. The dataset provided is synthetic and does not represent real sales data.

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###🙌 Acknowledgements

Inspired by real-world data engineering pipelines Designed as a learning project for Git collaboration and ETL practice

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