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SQL-Data-Cleaning

Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset

Ques : What is Data Cleaning? Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.

This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...

Steps to do Data Cleaning on this Dataset using SQL Queries :

  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.

Nashville Housing Dataset in Excel

In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns

Loaded the Excel data to SQL Server

Showing all the data using SQL Query

Standardize Date Format

Altering the table and updating new SaleDate Values in the column

Populate Property Address

Breaking Out property address

Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)

Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)

Changed Y and N to Yes and No in "Sold as Vacant" field

Remove Duplication

Using windows functions

CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.

Deleting Unused Columns

Results after cleaning the data using SQL Queries

New Dataset in Excel after cleaning the data in SQL Server

The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'

In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.

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Nashville Housing Dataset Cleaning | Excel, SQL Server

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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SQL-Data-Cleaning
Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset
Ques : What is Data Cleaning?
Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...
Steps to do Data Cleaning on this Dataset using SQL Queries :
  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.
Nashville Housing Dataset in Excel
In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns
Loaded the Excel data to SQL Server
Showing all the data using SQL Query
Standardize Date Format
Altering the table and updating new SaleDate Values in the column
Populate Property Address
Breaking Out property address
Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)
Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)
Changed Y and N to Yes and No in "Sold as Vacant" field
Remove Duplication
Using windows functions
CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.
Deleting Unused Columns
Results after cleaning the data using SQL Queries
New Dataset in Excel after cleaning the data in SQL Server
The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'
In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.
About
Nashville Housing Dataset Cleaning | Excel, SQL Server
Topics
excelkaggle-datasetsqlserver-2019
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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SQL-Data-Cleaning
Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset
Ques : What is Data Cleaning?
Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...
Steps to do Data Cleaning on this Dataset using SQL Queries :
  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.
Nashville Housing Dataset in Excel
In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns
Loaded the Excel data to SQL Server
Showing all the data using SQL Query
Standardize Date Format
Altering the table and updating new SaleDate Values in the column
Populate Property Address
Breaking Out property address
Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)
Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)
Changed Y and N to Yes and No in "Sold as Vacant" field
Remove Duplication
Using windows functions
CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.
Deleting Unused Columns
Results after cleaning the data using SQL Queries
New Dataset in Excel after cleaning the data in SQL Server
The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'
In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.
About
Nashville Housing Dataset Cleaning | Excel, SQL Server
Topics
excelkaggle-datasetsqlserver-2019
Resources
Readme
Activity
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Watchers
1 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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SQL-Data-Cleaning
Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset
Ques : What is Data Cleaning?
Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...
Steps to do Data Cleaning on this Dataset using SQL Queries :
  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.
Nashville Housing Dataset in Excel
In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns
Loaded the Excel data to SQL Server
Showing all the data using SQL Query
Standardize Date Format
Altering the table and updating new SaleDate Values in the column
Populate Property Address
Breaking Out property address
Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)
Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)
Changed Y and N to Yes and No in "Sold as Vacant" field
Remove Duplication
Using windows functions
CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.
Deleting Unused Columns
Results after cleaning the data using SQL Queries
New Dataset in Excel after cleaning the data in SQL Server
The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'
In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.
About
Nashville Housing Dataset Cleaning | Excel, SQL Server
Topics
excelkaggle-datasetsqlserver-2019
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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SQL-Data-Cleaning
Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset
Ques : What is Data Cleaning?
Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...
Steps to do Data Cleaning on this Dataset using SQL Queries :
  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.
Nashville Housing Dataset in Excel
In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns
Loaded the Excel data to SQL Server
Showing all the data using SQL Query
Standardize Date Format
Altering the table and updating new SaleDate Values in the column
Populate Property Address
Breaking Out property address
Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)
Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)
Changed Y and N to Yes and No in "Sold as Vacant" field
Remove Duplication
Using windows functions
CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.
Deleting Unused Columns
Results after cleaning the data using SQL Queries
New Dataset in Excel after cleaning the data in SQL Server
The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'
In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.
About
Nashville Housing Dataset Cleaning | Excel, SQL Server
Topics
excelkaggle-datasetsqlserver-2019
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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SQL-Data-Cleaning
Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset
Ques : What is Data Cleaning?
Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...
Steps to do Data Cleaning on this Dataset using SQL Queries :
  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.
Nashville Housing Dataset in Excel
In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns
Loaded the Excel data to SQL Server
Showing all the data using SQL Query
Standardize Date Format
Altering the table and updating new SaleDate Values in the column
Populate Property Address
Breaking Out property address
Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)
Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)
Changed Y and N to Yes and No in "Sold as Vacant" field
Remove Duplication
Using windows functions
CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.
Deleting Unused Columns
Results after cleaning the data using SQL Queries
New Dataset in Excel after cleaning the data in SQL Server
The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'
In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.
About
Nashville Housing Dataset Cleaning | Excel, SQL Server
Topics
excelkaggle-datasetsqlserver-2019
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
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Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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SQL-Data-Cleaning
Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset
Ques : What is Data Cleaning?
Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...
Steps to do Data Cleaning on this Dataset using SQL Queries :
  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.
Nashville Housing Dataset in Excel
In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns
Loaded the Excel data to SQL Server
Showing all the data using SQL Query
Standardize Date Format
Altering the table and updating new SaleDate Values in the column
Populate Property Address
Breaking Out property address
Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)
Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)
Changed Y and N to Yes and No in "Sold as Vacant" field
Remove Duplication
Using windows functions
CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.
Deleting Unused Columns
Results after cleaning the data using SQL Queries
New Dataset in Excel after cleaning the data in SQL Server
The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'
In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.
About
Nashville Housing Dataset Cleaning | Excel, SQL Server
Topics
excelkaggle-datasetsqlserver-2019
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
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SQL-Data-Cleaning
Data Cleaning Using MS SSMS and Excel on Nashville Housing Dataset
Ques : What is Data Cleaning?
Ans : Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
This Dataset consists of following columns like 'UniqueID', 'ParceID', 'LandUse', 'PropertyAddress', 'Saledate', 'Saleprice', 'LegalRefernce', 'SoldAsVacant', 'OwnerName', 'OwnerAddress' and much more...
Steps to do Data Cleaning on this Dataset using SQL Queries :
  1. Downloading the Dataset in Excel format on to the local machine from the source.
  2. Loading the Dataset on the MS SSMS using SQL Server 2019 Import and Export Data(32-bit).
  3. Then, configuring the database to get the table in database and running SQL Queries to clean the data.
  4. Getting the new cleaned data from the raw data to get better insights.
Nashville Housing Dataset in Excel
In this project, cleaned the Nashville Housing Excel data in MS SQL Server Management Studio such as Date Format, Property and Owner Address , Changed the SoldAsVacant column from Y to Yes and No to N, Removed Duplication of data using CTE and deleted unused columns
Loaded the Excel data to SQL Server
Showing all the data using SQL Query
Standardize Date Format
Altering the table and updating new SaleDate Values in the column
Populate Property Address
Breaking Out property address
Using self join and Breaking Out Address(Property and Owner) into indiviual columns (Address, City, State)
Breaking Out Owner Address Not using Substring but using ParseName(replace(), LastValue)
Changed Y and N to Yes and No in "Sold as Vacant" field
Remove Duplication
Using windows functions
CTE : common table expression (CTE) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. You can also use a CTE in a CREATE a view, as part of the view’s SELECT query. In addition, as of SQL Server 2008, you can add a CTE to the new MERGE statement.
Deleting Unused Columns
Results after cleaning the data using SQL Queries
New Dataset in Excel after cleaning the data in SQL Server
The New Columns of the dataset are the 'SaleDateConverted', 'SplitAddress', PropertyAddress', 'PropertyCity', 'OwnerSplitAddress', 'OwnerCity', 'OwnerState' and changed the 'SoldAsVanact' all Y to 'Yes' and N to 'NO'
In This project, successfully cleaned the raw data of Nashville Housing Dataset using Excel and SQL Server Managemenet Studio to get the New Excel Sheet.
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Nashville Housing Dataset Cleaning | Excel, SQL Server
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excelkaggle-datasetsqlserver-2019
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