Project Context

In this project, I used MySQL to clean Nashville Housing data and improve its quality for data exploration and analysis.

The dataset for Nashville housing from 2013 to 2016 is available here.

This project follows four basic steps for producing reliable data through data cleaning, and they are as follows:

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All the queries written below are compiled on GitHub

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Groundwork

Before I started this data cleaning project, I needed to have a better understanding of data cleaning and lay the groundwork for the steps ahead. Here’s what I learned:

• What is the purpose of data cleaning?
• Why is removing unwanted observations so important?
• Why is duplicate data a problem?
• How important is it to use the correct type of data?
• Why is it important to correct misspellings?
• Why is it important to handle missing data?

How to Perform Data Cleaning

Step 1: Remove Duplicate or Irrelevant Observations

01. Inspecting a table for unwanted columns