100%

Housing Prices Dataset

✓ Published0🌍 Public

Last edited Sep 4, 2025

Created on Sep 4, 2025

This example loads the Housing Prices Dataset from a CSV file and displays the raw data as JSON in a pre element. The code uses D3 v7 functions such as `d3.select`, `d3.csvParse`, and the `join` operation to manage the data loading and DOM rendering. It demonstrates a straightforward approach to fetching, parsing, and visualizing tabular data, with the fetched CSV transformed into an array of objects for display.

AI-generated description

Dataset 3: Housing Prices Dataset

Source:
Housing Prices Dataset

This dataset contains housing price records used for prediction and regression tasks. It includes a variety of features such as the size of the house, number of rooms, year built, and more, making it useful for exploratory data analysis, visualization, and machine learning projects.

Attributes and Types

Id — Quantitative, nominal (unique identifier)
MSSubClass — Categorical, nominal (type of dwelling)
MSZoning — Categorical, nominal (general zoning classification)
LotArea — Quantitative, ratio (lot size in square feet)
Street — Categorical, nominal (type of road access)
LotShape — Categorical, ordinal (general shape of property)
YearBuilt — Quantitative, interval (original construction date)
OverallQual — Quantitative, ordinal (overall material and finish quality)
OverallCond — Quantitative, ordinal (overall condition rating)
GrLivArea — Quantitative, ratio (above ground living area in square feet)
FullBath — Quantitative, ratio (full bathrooms above grade)
BedroomAbvGr — Quantitative, ratio (number of bedrooms above grade)
GarageCars — Quantitative, ratio (size of garage in car capacity)
GarageArea — Quantitative, ratio (garage size in square feet)
SalePrice — Quantitative, ratio (target variable: sale price in USD)

MIT Licensed

Similar vizzes

Loading thumbnail…

Loading House sales dataset

This example loads and parses a CSV dataset of U.S. housing sales, displaying a summary of the file size, row count, and column count. It first uses `d3.csv` to parse the remote data, then calculates the number of rows and columns from the parsed objects. A separate `fetch` request retrieves the raw file as a blob to compute its size in kilobytes. The summary updates in the DOM, with an error message shown if loading fails. The data is sourced from a GitHub gist hosted by the author.

Ssowmyakancharla01@gmail.com

73% match

Loading thumbnail…

world-happiness-data

This example loads the Iris dataset from a CSV file and displays it as raw JSON text in a `<pre>` element. The visualization uses d3.csvParse to convert the CSV string into an array of objects, then coerces numeric columns to numbers. It demonstrates a state management pattern with d3-selection’s select and join methods, where data is asynchronously fetched and rendered after a loading state. The code shows only text output, not a graphical chart.

Ccsabavath@wpi.edu

73% match

Loading thumbnail…

covid19-data

This example displays the raw COVID-19 dataset as a formatted JSON preview, loading the CSV file via `fetch` and parsing it with `d3.csvParse`. The visualization shows the first rows of data, including date, region, confirmed cases, deaths, and recoveries, placing each field on a separate line for inspection. The code uses `d3.select` to manage a container element and `d3.selectAll` with a `join` operation to update the displayed content whenever the state changes, initially setting the data to a `'LOADING'` placeholder before fetching the file.

Ccsabavath@wpi.edu

73% match

Loading thumbnail…

Boston Housing data

This visualization displays summary statistics for the Boston Housing dataset, reporting the total row count, column count, and file size in kilobytes. It also prints the first ten rows of the data as formatted JSON for inspection. The code uses D3 v5’s `csv` function to load the remote CSV file, `csvFormat` to calculate data size, and `select` to append `<pre>` elements to the page body. The dataset originates from Kaggle’s Boston Housing competition.

Ddielaenge

72% match

Loading thumbnail…

Housing Values At Boston Suburbs

This visualization displays summary statistics for the Boston Housing dataset, showing the file size in kilobytes, the number of rows, and the number of columns. It loads a CSV file from a Gist URL using the `d3.csv` API, formats the data with `d3.csvFormat`, and presents the results in a large pre-formatted text element. The example demonstrates basic data loading and parsing with D3.js v5, sourced from the Boston Housing Data repository by selva86.

Yyuzhang21

72% match

Loading thumbnail…

Housing Values At Boston Suburbs

Yyuzhang21

72% match

Loading thumbnail…

Boston Housing data

This example displays a plain-text summary of the Boston Housing dataset, reporting the row count, column count, and file size in kilobytes, followed by the first ten rows of data as formatted JSON on a black terminal-style background. It uses the D3 library’s `csv` and `csvFormat` functions to fetch and measure the remote CSV file, while `select` appends the text elements to the page. The data source is the Kaggle-hosted Boston housing dataset, which includes features like crime rate and nitrogen oxides concentration.

Ddielaenge

72% match

Loading thumbnail…

3 datasets 02

This visualization displays house price data from the House Prices dataset, showing the first record in a JSON-formatted pre tag. It answers a question about distinguishing houses by state and city by parsing quantitative attributes into numbers. The code imports data from CSV using a native import statement, converts all numeric fields with the unary plus operator, and exports both the main container function and the parsed data. The main function uses template literals to render a single data object as readable JSON text with a 30-pixel font size.

JJingxuan Yang

72% match