Retail Data Store Cleaning Project

Goals

• Practice real-world data cleaning techniques on a retail dataset.
• Handle missing values using appropriate strategies rather than blindly dropping data.
• Standardize column names, categories, and date formats for consistency.
• Produce a clean CSV ready for analysis or visualization.

Process

• Explored dataset structure using df.info(), df.isna().sum() and df.describe()
• Standardized column names — stripped whitespace, lowercased, replaced spaces with underscores.
• Handled nulls per column based on context — filled, calculated, or dropped where justified.
• Cleaned and standardized the category column formatting

Insights

• Nearly 5% of transactions had no recoverable quantity or spending data — dropping them was justified and had minimal impact on the dataset
• The item column encoded category information in its naming pattern (Item_[number]_[category]), which revealed a way to validate the category column
• discount_applied nulls were meaningful — they represented transactions with no discount, not truly missing data. Context matters when handling nulls
• Standardizing text columns early (before any grouping or analysis) prevents silent bugs where "Food" and "food" are treated as different categories