1. crim: Per capita crime rate by town.
2. zn: Proportion of large residential lots (over 25,000 sq. ft.).
3. indus: Proportion of non-retail business acres per town.
4. Chas: Binary variable indicating if the property is near Charles River (1 for yes, 0 for no).
5. nox: Concentration of nitrogen oxides in the air.
6. rm: Average number of rooms per dwelling.
7. age: Proportion of old owner-occupied units built before 1940.
8. dis: Weighted distances to Boston employment centers.
9. rad: Index of accessibility to radial highways.
10. tax: Property tax rate per $10,000.

These features provide valuable information about the characteristics of neighborhoods that can influence housing prices.

Dataset

This dataset was created by Ankush Kashyap

Released under Apache 2.0

Contents

Housing Values in Suburbs of Boston The medv variable is the target variable.

Data description The Boston data frame has 506 rows and 14 columns.

This data frame contains the following columns:

crim per capita crime rate by town.

zn proportion of residential land zoned for lots over 25,000 sq.ft.

indus proportion of non-retail business acres per town.

chas Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).

nox nitrogen oxides concentration (parts per 10 million).

rm average number of rooms per dwelling.

age proportion of owner-occupied units built prior to 1940.

dis weighted mean of distances to five Boston employment centres.

rad index of accessibility to radial highways.

tax full-value property-tax rate per $10,000.

ptratio pupil-teacher ratio by town.

black 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.

lstat lower status of the population (percent).

medv median value of owner-occupied homes in $1000s.

Source Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.

Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Dataset

This dataset was created by Vishal Kushwaha

Contents

Context

The Boston Housing Dataset

The Boston Housing Dataset is a derived from information collected by the U.S. Census Service concerning housing in the area of Boston MA. The following describes the dataset columns:

Content

The following describes the dataset columns:

CRIM - per capita crime rate by town ZN - proportion of residential land zoned for lots over 25,000 sq.ft. INDUS - proportion of non-retail business acres per town. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise) NOX - nitric oxides concentration (parts per 10 million) RM - average number of rooms per dwelling AGE - proportion of owner-occupied units built prior to 1940 DIS - weighted distances to five Boston employment centres RAD - index of accessibility to radial highways TAX - full-value property-tax rate per $10,000 PTRATIO - pupil-teacher ratio by town B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town LSTAT - % lower status of the population MEDV - Median value of owner-occupied homes in $1000's

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Domain: Real Estate

Difficulty: Easy to Medium

Challenges: 1. Missing value treatment
2. Outlier treatment 3. Understanding which variables drive the price of homes in Boston

Summary: The Boston housing dataset contains 506 observations and 14 variables. The dataset contains missing values.

housing.csv: this dataset is constituted by 506 points in 14 dimensions. Each point represents a house in the Boston area, and the 14 attributes that you find orderly in each column are the following: * CRIM - per capita crime rate by town * ZN - proportion of residential land zoned for lots over 25,000 sq.ft. * INDUS - proportion of non-retail business acres per town. * CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise) * NOX - nitric oxides concentration (parts per 10 million) * RM - average number of rooms per dwelling * AGE - proportion of owner-occupied units built prior to 1940 * DIS - weighted distances to five Boston employment centres * RAD - index of accessibility to radial highways * TAX - full-value property-tax rate per $10,000 * PTRATIO - pupil-teacher ratio by town * B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town * LSTAT - % lower status of the population * MEDV - Median value of owner-occupied homes in $1000's

This dataset is normally associated with 2 regression tasks: predicting NOX (in which the nitrous oxide level is to be predicted); and predicting price MEDV (in which the median value of a home is to be predicted).

This dataset was also pre-processed and scaled.

There are 14 attributes in each case of the dataset. They are:

CRIM - per capita crime rate by town ZN - proportion of residential land zoned for lots over 25,000 sq.ft. INDUS - proportion of non-retail business acres per town. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise) NOX - nitric oxides concentration (parts per 10 million) RM - average number of rooms per dwelling AGE - proportion of owner-occupied units built prior to 1940 DIS - weighted distances to five Boston employment centres RAD - index of accessibility to radial highways TAX - full-value property-tax rate per $10,000 PTRATIO - pupil-teacher ratio by town B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town LSTAT - % lower status of the population MEDV - Median value of owner-occupied homes in $1000's

Context

This is a copy of the original Boston Housing Dataset. As of December 2021, the original link doesn't contain the dataset so I'm uploading it if anyone wants to use it. I'll implement a linear regression model to predict the output 'MEDV' variable using PyTorch (check the companion notebook).

I took the data given in this link and processed it to include the column names as well.

Acknowledgements

https://www.kaggle.com/prasadperera/the-boston-housing-dataset/data

Inspiration

Good luck on your data science career :)

Housing Values in Suburbs of Boston

This is a classic dataset for machine learning practice. I do not own it and the sources of the dataset are mentioned in the metadata and at the end of this description. I first came in contact with the dataset during the Business Analytics course given by the Lund University's School of Economics and Management.

Content

The Boston dataset contain 506 rows and 14 columns.

Source

http://lib.stat.cmu.edu/datasets/boston

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.

Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Dataset

This dataset was created by Srushti Kardile

Contents

Dataset

This dataset was created by Hemant Choudhary

Released under MIT

Contents

Dataset

This dataset was created by Devendrasingh J Jhala

Contents

The Boston Housing dataset, which is often used for regression analysis and predictive modeling tasks, doesn't typically have an official "subtitle." However, it's commonly referred to as the "Boston Housing dataset" or the "Boston Housing Price dataset" due to its focus on housing-related features and its primary target variable being the median value of owner-occupied homes in Boston suburbs.

Column Description

Columns: 1. CRIM: per capita crime rate by town (numeric) 2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft. (numeric) 3. INDUS: proportion of non-retail business acres per town (numeric) 4. CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise) (categorical) 5. NOX: nitric oxides concentration (parts per 10 million) (numeric) 6. RM: average number of rooms per dwelling (numeric) 7. AGE: proportion of owner-occupied units built prior to 1940 (numeric) 8. DIS: weighted distances to five Boston employment centres (numeric) 9. RAD: index of accessibility to radial highways (numeric) 10. TAX: full-value property-tax rate per $10,000 (numeric) 11. PTRATIO: pupil-teacher ratio by town (numeric) 12. B: 1000(Bk - 0.63)^2 where Bk is the proportion of [people of African American descent] by town (numeric) 13. LSTAT: % lower status of the population (numeric) 14. MEDV: Median value of owner-occupied homes in $1000s (target variable) (numeric)

Dataset

This dataset was created by Lucas Guttensohn

Contents

Dataset

This dataset was created by Kushal Sharma

Contents

Dataset

This dataset was created by Lakshmi Kruthika

Released under Apache 2.0

Contents

About Dataset

All the following text is copied directly from the original dataset used: https://www.kaggle.com/datasets/fedesoriano/the-boston-houseprice-data
The only difference is that features 12 and 13 have been removed for simplicity. See original link for a version with those features in place.

Similar Datasets

Gender Pay Gap Dataset: https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset
California Housing Prices Data (5 new features!): https://www.kaggle.com/fedesoriano/california-housing-prices-data-extra-features
Company Bankruptcy Prediction: https://www.kaggle.com/fedesoriano/company-bankruptcy-prediction
Spanish Wine Quality Dataset: https://www.kaggle.com/datasets/fedesoriano/spanish-wine-quality-dataset

Context

The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.

Attribute Information

Input features in order: 1) CRIM: per capita crime rate by town
2) ZN: proportion of residential land zoned for lots over 25,000 sq.ft.
3) INDUS: proportion of non-retail business acres per town
4) CHAS: Charles River dummy variable (1 if tract bounds river; 0 otherwise)
5) NOX: nitric oxides concentration (parts per 10 million) [parts/10M]
6) RM: average number of rooms per dwelling
7) AGE: proportion of owner-occupied units built prior to 1940
8) DIS: weighted distances to five Boston employment centres
9) RAD: index of accessibility to radial highways
10) TAX: full-value property-tax rate per $10,000 [$/10k]
11) PTRATIO: pupil-teacher ratio by town
[Original features 12 and 13 have been deliberately removed from this version of the dataset]

Output variable:
1) MEDV: Median value of owner-occupied homes in $1000's [k$]

Source

StatLib - Carnegie Mellon University

Relevant Papers

Harrison, David & Rubinfeld, Daniel. (1978). Hedonic housing prices and the demand for clean air. Journal of Environmental Economics and Management. 5. 81-102. 10.1016/0095-0696(78)90006-2. https://www.researchgate.net/profile/Daniel-Rubinfeld/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air/links/5c38ce85458515a4c71e3a64/Hedonic-housing-prices-and-the-demand-for-clean-air.pdf

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley https://www.wiley.com/en-us/Regression+Diagnostics%3A+Identifying+Influential+Data+and+Sources+of+Collinearity-p-9780471691174

Boston Housing Dataset

The Boston Housing dataset is a well-known dataset in the field of predictive modeling and statistics. It contains information collected by the U.S. Census Service concerning housing in the area of Boston Mass.

Dataset Overview

• Number of Instances: 506
• Number of Attributes: 14 (including the target variable)

Attributes

The dataset includes the following features:

1. CRIM - Per capita crime rate by town.
2. ZN - Proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS - Proportion of non-retail business acres per town.
4. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise).
5. NOX - Nitric oxides concentration (parts per 10 million).
6. RM - Average number of rooms per dwelling.
7. AGE - Proportion of owner-occupied units built prior to 1940.
8. DIS - Weighted distances to five Boston employment centers.
9. RAD - Index of accessibility to radial highways.
10. TAX - Full-value property tax rate per $10,000.
11. PTRATIO - Pupil-teacher ratio by town.
12. B - ( B ) stands for ( 1000(Bk - 0.63)^2 ) where ( Bk ) is the proportion of Black residents by town.
13. LSTAT - Percentage of lower status of the population.
14. MEDV - Median value of owner-occupied homes in $1000s (target variable).

Use Cases

This dataset can be used for:

• Regression Analysis: To predict the value of homes based on the features provided.
• Exploratory Data Analysis: To analyze the relationships between different variables.
• Machine Learning: As a benchmark dataset for testing regression models.

Citation

Details about the dataset and its original source can be found in the following reference:

• Harrison, D. and Rubinfeld, D. L. (1978). "Hedonic housing prices and the demand for clean air." J. Environ. Economics and Management, 5, 81-102.

Dataset

This dataset was created by Kunaal Naik

Contents