🧹 Superstore Sales Data Cleaning & Preprocessing

This project performs data cleaning and transformation on the Superstore Sales dataset to make it ready for interactive dashboarding and analytics.

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📝 Project Description

The Superstore dataset, often used for sales analytics, contains inconsistencies such as:

• Extra spaces in column names
• Missing postal codes
• Incorrect date formats
• Duplicate entries

This Python script processes the raw data into a clean, consistent, and analysis-ready format suitable for BI tools like Tableau or Power BI.

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💡 Features

• ✅ Standardized column names (snake_case)
• 🗓️ Converted date columns into datetime format
• 🧱 Filled missing postal codes
• 🧹 Removed duplicate records
• 📆 Created a new month_year column for time-based analysis
• 💾 Exported final cleaned dataset for visualization

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🛠️ Skills & Tools Used

Language	Libraries	Concepts Applied
Python	pandas	Data Cleaning & Preprocessing
	datetime	Time Series Handling
		Missing Value Imputation
		Feature Engineering (month_year)

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📁 File Structure

📦 Superstore-Sales-Cleaning
├── superstore_sales_dataset.csv          # Raw dataset (input)
├── superstore_sales_cleaned_final.csv    # Cleaned, dashboard-ready output
├── clean_superstore.py                   # Python script for cleaning
└── README.md                             # Project documentation