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SQL Data Cleaning.

Introduction

This SQL data cleaning project employed queries to standardise the date format, fill NULL values in the PropertyAddress column, split the Address column, alter 'Y' and 'N' to 'Yes' and 'No' in a field, remove duplicates based on specified fields, and delete unrequired columns. It was necessary to do self-joins, update values, add new columns,  apply conditional updates, and make use of Common Table Expressions. Overall, the procedure worked effectively.

In Summary:

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  1. Standardise Date Format

  2. Populate Property Address data

  3. Breaking out Address into Individual Columns (Address, City, State)

  4. Change Y and N to Yes and No in "Sold as Vacant" field

  5. Removing Duplicates

  6. Ordering data

  7. Deleting Unused Columns

Step by Step:

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Data
Raw Nashville Housing Data - Excel file: 19 columns, 772639 rows. uncleaned.
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Data
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Full commented code

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