README.md

Data Cleaning Project: Nashville Housing Data

This project focuses on cleaning and standardizing a real-world dataset on Nashville housing transactions. The key steps include standardizing date formats, filling in missing values, breaking out addresses into individual components, standardizing categorical data, removing duplicates, and dropping unused columns.

Skills used: Joins, CTEs, Substring, Update, Row_Number, Case, Self-Join, SQL Functions

Data Source: Nashville Housing data set was downloaded from Alex The Analyst's github account. You can find the dataset here: Nashville_Housing_Data.xlsx

View our data:

SELECT *
FROM PortfolioProject..Nashville_Housing

PART 1: Standardize the Date

Create a column called SaleDateConverted where we will add our standardized date

ALTER TABLE Nashville_Housing
ADD SaleDateConverted Date

Populate the new column with our standardized data

UPDATE Nashville_Housing
SET SaleDateConverted = CONVERT(DATE, SaleDate)

View the results

SELECT SaleDateConverted, SaleDate
FROM PortfolioProject..Nashville_Housing

PART 2: Populate Property Address Data

View the "PropertyAddress" column

SELECT PropertyAddress
FROM PortfolioProject..Nashville_Housing
--WHERE PropertyAddress IS NULL
ORDER BY ParcelID

The null "PropertyAddress" values are supposed have the same "ParcelID" as some other non-Null values We will use a self-join to see this and populate the NULL values later

SELECT a.ParcelID, a.PropertyAddress, b.ParcelID, b.PropertyAddress, ISNULL(a.PropertyAddress, b.PropertyAddress)
FROM PortfolioProject..Nashville_Housing a
JOIN PortfolioProject..Nashville_Housing b
	ON a.ParcelID = b.ParcelID
	AND a.[UniqueID ] <> b.[UniqueID ]
WHERE a.PropertyAddress IS NULL

Populate the NULL values

UPDATE a
SET PropertyAddress = ISNULL(a.PropertyAddress, b.PropertyAddress)
FROM PortfolioProject..Nashville_Housing a
JOIN PortfolioProject..Nashville_Housing b
	ON a.ParcelID = b.ParcelID
	AND a.[UniqueID ] <> b.[UniqueID ]

PART 3: Breaking Out Address Into Individual Columns (Address, City, State)

View the data

SELECT PropertyAddress
FROM PortfolioProject..Nashville_Housing
--WHERE PropertyAddress IS NULL
--ORDER BY ParcelID

Use "SUBSTRING" to split the "PropertyAddress" and view the results.

NOTE: No changes are made to the data. This is only for viewing. The changes are made in the next code.

SELECT PropertyAddress,
SUBSTRING(PropertyAddress, 1, CHARINDEX(',', PropertyAddress) - 1) AS Address,
SUBSTRING(PropertyAddress, CHARINDEX(',', PropertyAddress) + 1, LEN(PropertyAddress)) AS Address_2
FROM PortfolioProject..Nashville_Housing

Create new column "PropertySplitAddress" and populate with the split data

ALTER TABLE Nashville_Housing
ADD PropertySplitAddress nvarchar(255)

UPDATE Nashville_Housing
SET PropertySplitAddress = SUBSTRING(PropertyAddress, 1, CHARINDEX(',', PropertyAddress) - 1)

Create new column "PropertySplitCity" and populate with the split data

ALTER TABLE Nashville_Housing
ADD PropertySplitCity nvarchar(255)

UPDATE Nashville_Housing
SET PropertySplitCity = SUBSTRING(PropertyAddress, CHARINDEX(',', PropertyAddress) + 1, LEN(PropertyAddress))

View the results

SELECT *
FROM PortfolioProject..Nashville_Housing

Next, we are going to do the same for the "OwnerAddress" column.

SELECT OwnerAddress
FROM PortfolioProject..Nashville_Housing

Use "PARSENAME" to split the "OwnerAddress" and view the results.

NOTE: No changes are made to the data. This is only for viewing. The changes are made in the next code.

SELECT OwnerAddress,
PARSENAME(REPLACE(OwnerAddress, ',', '.'), 3) AS OwnerSplitAddress,
PARSENAME(REPLACE(OwnerAddress, ',', '.'), 2) AS OwnerSplitCity,
PARSENAME(REPLACE(OwnerAddress, ',', '.'), 1) AS OwnerSplitState
FROM PortfolioProject..Nashville_Housing

Create Owner Address Column and populate it with the results:

ALTER TABLE Nashville_Housing
ADD OwnerSplitAddress nvarchar(255)

UPDATE Nashville_Housing
SET OwnerSplitAddress = PARSENAME(REPLACE(OwnerAddress, ',', '.'), 3)

Create Owner City Column and populate it with the results:

ALTER TABLE Nashville_Housing
ADD OwnerSplitCity nvarchar(255)

UPDATE Nashville_Housing
SET OwnerSplitCity = PARSENAME(REPLACE(OwnerAddress, ',', '.'), 2)

Create Owner State Column and populate it with the results:

ALTER TABLE Nashville_Housing
ADD OwnerSplitState nvarchar(255)

UPDATE Nashville_Housing
SET OwnerSplitState = PARSENAME(REPLACE(OwnerAddress, ',', '.'), 1)

View the results

SELECT *
FROM PortfolioProject..Nashville_Housing

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

View the number of different Yes and No in the column We can see that there is both "Y" and "Yes". There is also both "N" and "No". This is what we must change.

SELECT DISTINCT(SoldAsVacant), COUNT(SoldAsVacant)
FROM PortfolioProject..Nashville_Housing
GROUP BY SoldAsVacant
ORDER BY 2

Use "CASE" to see how the changes would occur

SELECT SoldAsVacant,
CASE
	WHEN SoldAsVacant = 'Y' THEN 'Yes'
	WHEN SoldAsVacant = 'N' THEN 'No'
	ELSE SoldAsVacant
END
FROM PortfolioProject..Nashville_Housing

Apply the changes

UPDATE Nashville_Housing
SET SoldAsVacant = 
CASE
	WHEN SoldAsVacant = 'Y' THEN 'Yes'
	WHEN SoldAsVacant = 'N' THEN 'No'
	ELSE SoldAsVacant
END

PART 5: Remove duplicates

We will use "ROW_NUMBER" to do this

WITH RowNumCTE AS (
SELECT *,
	ROW_NUMBER() OVER(
	PARTITION BY ParcelID,
					PropertyAddress,
					SalePrice,
					SaleDate,
					LegalReference
					ORDER BY
					UniqueID
					) row_num
FROM PortfolioProject..Nashville_Housing
)
DELETE
FROM RowNumCTE
WHERE row_num > 1
--ORDER BY PropertyAddress

View the results

SELECT *
FROM PortfolioProject..Nashville_Housing

PART 6: Delete Unused Columns

View the data

SELECT *
FROM PortfolioProject..Nashville_Housing

Drop OwnerAddress, TaxDistrict, PropertyAddress because we already created columns with their split data

ALTER TABLE PortfolioProject..Nashville_Housing
DROP COLUMN OwnerAddress, TaxDistrict, PropertyAddress

We also dropped SaleDate because we created a standardized date column for it earlier

ALTER TABLE PortfolioProject..Nashville_Housing
DROP COLUMN SaleDate

Thank You!