Overview

This SQL project focuses on exploring the Nashville housing dataset to clean and prepare the data for analysis. The dataset contains information about housing sales in Nashville, including property addresses, sale dates, and sale prices.

Link to the project 👉 Nashville Housing Data Exploration Project

picture_data_covid

Picture of Tanner Boriack in Unsplash

Objectives

  • Standardize date formats for consistency.
  • Populate missing property address data.
  • Split address and owner address columns into individual components.
  • Convert ‘Y’ and ‘N’ values in the ‘SoldAsVacant’ field to ‘Yes’ and ‘No’ for clarity.
  • Remove duplicate records to ensure data integrity.
  • Delete unused columns to streamline the dataset.

Methodology

1. Standardizing Date Format

  • Identified and converted the ‘SaleDate’ column to a standardized date format.

2. Populating Missing Property Address Data

  • Identified and populated missing property address values based on ParcelID.

3. Splitting Address Columns

  • Split the ‘PropertyAddress’ column into ‘Address’ and ‘City’ columns.
  • Split the ‘OwnerAddress’ column into ‘Address’, ‘City’, and ‘State’ columns.

4. Updating ‘SoldAsVacant’ Field

  • Replaced ‘Y’ with ‘Yes’ and ‘N’ with ‘No’ in the ‘SoldAsVacant’ field for clarity.

5. Removing Duplicate Records

  • Identified and removed duplicate records based on selected criteria.

6. Deleting Unused Columns

  • Removed unused columns such as ‘OwnerAddress’, ‘TaxDistrict’, ‘PropertyAddress’, and ‘SaleDate’ to streamline the dataset.

Key Findings

  • Standardized date formats for consistency across the dataset.
  • Populated missing property address data based on ParcelID.
  • Split address columns into individual components for better analysis.
  • Updated ‘SoldAsVacant’ field for clarity.
  • Ensured data integrity by removing duplicate records.
  • Streamlined the dataset by deleting unused columns.

Conclusion

This project successfully cleaned and prepared the Nashville housing dataset for further analysis. By standardizing date formats, populating missing data, splitting address columns, updating field values, removing duplicates, and deleting unused columns, the dataset is now ready for in-depth analysis and insights.

Colophon

thanks Alex for his tutorials and explanations (Alex The Analyst)