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Data Cleaning

Lesson 1: Data Cleaning

Data cleaning is the process of preparing raw data so it can be used reliably for analysis. Real-world sales datasets often contain incomplete, inconsistent, or poorly formatted data that can lead to incorrect results if not handled properly.

In this project, data cleaning is required because the sales dataset includes missing address details, unknown states, and incomplete location information. Cleaning ensures that the dataset remains consistent and usable for grouping, aggregation, and visualization.

  • Improving Data Quality: Removes inconsistencies that could distort analysis results.
  • Preventing Analysis Errors: Ensures functions like grouping and plotting work correctly.
  • Preparing Data for Analysis: Makes the dataset structured and reliable for further steps.

Data cleaning is always the first step before any meaningful analysis.