This project entailed the meticulous cleaning of a dataset detailing the Nashville housing market. The source of the data was an .xlsx file which was subsequently imported into SSMS and housed in a table named 'NashvilleHousing'.

• Review of Raw Data: Started with a thorough examination of the raw data contained within the 'NashvilleHousing' table.

• 'SaleDate' Column: Utilized the CONVERT function to format dates, removing the time component.
• 'PropertyAddress' Column: Employed the ISNULL function to fill null entries using values from rows sharing the same Parcel ID.
• Column Division - 'PropertyAddress': Split into two new columns to separate Address and City using the SUBSTRING function.
• Column Division - 'OwnerAddress': Split into three new columns for Address, City, and State using the PARSENAME function.
• 'SoldAsVacant' Column: Transformed 'Y' and 'N' to 'Yes' and 'No' with a CASE statement.
• Removing Duplicates: Implemented the ROW_NUMBER function within a CTE to identify and remove duplicates.
• Useless Columns Removal: Deleted obsolete columns post-manipulation and those lacking valuable information, such as 'TaxDistrict'.

• Final Check: Conducted a last review to ensure the integrity and cleanliness of the updated table.

The original dataset comprising 56,477 rows was condensed to a clean, analysis-ready table of 56,373 rows. The cleaning process resulted in the elimination of merely 104 rows, which constitutes roughly 0.002% of the total data, ensuring a dataset primed for future analysis.

I applied a variety of SQL techniques, such as Aggregate Functions, Joins, Window Functions, CTEs, and Views to refine the dataset.

This project followed a step-by-step tutorial: SQL Tutorial

• SQL/SSMS
• Excel

1. Download the dataset.
2. Load the data into SSMS.
3. Execute the cleaning operations.