Data Cleaning in SQL
About this project
This SQL project aims to clean and standardize a table for Nashville Housing! I perform various data cleaning tasks every day in database management, including standardizing date formats, populating missing property address data, breaking out address columns into individual parts, changing column values, and removing duplicate data.
The first step of the project is to standardize the date format. The SQL query converts the date format to a standardized format to ensure that data is uniform and easy to understand.
The second step involves populating missing property address data. The SQL query selects data from the NashvilleHousing table where the property address is null and orders it by ParcelID. The query then joins the table with itself and populates the missing data using the ISNULL function.
The third step involves breaking out the address into individual columns. The SQL query uses the SUBSTRING function to split the PropertyAddress column into two separate columns for Address and City.
The fourth step involves changing the Y and N values in the "Sold as Vacant" field to "Yes" and "No", respectively. The SQL query uses a CASE statement to change the values and ensure that the column values are more easily understood.
The fifth step involves removing duplicate data. The SQL query identifies duplicate data and selects only the first row for each group of duplicates, thus removing the duplicate data.
Finally, the project deletes unused columns to simplify the table and optimize performance.
