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.

Analysis of ‘Boston-Housing-Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/simpleparadox/bostonhousingdataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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 :)

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Lucas Guttensohn

Contents

Analysis of ‘Boston housing dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/altavish/boston-housing-dataset on 30 September 2021.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Zohair ahmed

Contents

Analysis of ‘Boston Housing’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/schirmerchad/bostonhoustingmlnd on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The dataset for this project originates from the UCI Machine Learning Repository. The Boston housing data was collected in 1978 and each of the 506 entries represent aggregated data about 14 features for homes from various suburbs in Boston, Massachusetts.

Acknowledgements

https://github.com/udacity/machine-learning

https://archive.ics.uci.edu/ml/datasets/Housing

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Boston House Prices-Advanced Regression Techniques’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fedesoriano/the-boston-houseprice-data on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Similar Datasets

• Gender Pay Gap Dataset: LINK
• California Housing Prices Data (5 new features!): LINK
• Company Bankruptcy Prediction: LINK

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 12) B: The result of the equation B=1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 13) LSTAT: % lower status of the population

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. LINK

Belsley, David A. & Kuh, Edwin. & Welsch, Roy E. (1980). Regression diagnostics: identifying influential data and sources of collinearity. New York: Wiley LINK

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Sharan Harsoor

Released under Apache 2.0

Contents

Dataset

This dataset was created by Paulo Arruda

Released under Apache 2.0

Contents

Analysis of ‘Boston House-Predict’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/fauzantaufik/boston-housepredict on 14 February 2022.

--- No further description of dataset provided by original source ---

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Nikhil Pathrikar

Contents

Dataset

This dataset was created by Jyoti kumar Rout

Contents

Title: Boston Housing Price Prediction Dataset

Description:

This dataset contains information about housing prices in Boston and is often used for regression analysis and predictive modeling. The dataset is based on the classic Boston Housing dataset, which is frequently used as a benchmark in machine learning.

Attributes:

1. CRIM (Per Capita Crime Rate): The per capita crime rate in the neighborhood.
2. ZN (Proportion of Residential Land Zoned for Large Lots): The proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS (Proportion of Non-Retail Business Acres): The proportion of non-retail business acres per town.
4. CHAS (Charles River Dummy Variable): A binary variable indicating whether the Charles River bounds the tract (1 if bounded, 0 otherwise).
5. NOX (Nitric Oxides Concentration): Nitric oxides concentration (parts per 10 million).
6. RM (Average Number of Rooms per Dwelling): The average number of rooms per dwelling.
7. AGE (Proportion of Owner-Occupied Units Built Prior to 1940): The proportion of owner-occupied units built prior to 1940.
8. DIS (Weighted Distances to Employment Centers): Weighted distances to five Boston employment centers.
9. RAD (Index of Accessibility to Radial Highways): An index representing accessibility to radial highways.
10. TAX (Full-Value Property Tax Rate per $10,000): The full-value property tax rate per $10,000.
11. PTRATIO (Pupil-Teacher Ratio): The pupil-teacher ratio by town.
12. B (1000(Bk - 0.63)^2 where Bk is the Proportion of Black Residents): A measure of the proportion of Black residents adjusted for an offset.
13. LSTAT (Percentage of Lower Status of the Population): The percentage of lower-status residents in the population.
14. MEDV (Median Value of Owner-Occupied Homes): The median value of owner-occupied homes in $1000s (Target Variable).

Objective:

Predict the median value of owner-occupied homes (MEDV) based on various features to gain insights into factors influencing housing prices.

Usage:

This dataset is suitable for regression tasks, machine learning practice, and understanding the dynamics of housing markets.

Citation:

The dataset is derived from the UCI Machine Learning Repository and can be cited as follows:

Harrison Jr., D., & Rubinfeld, D. L. (1978). Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81-102.

Dataset

This dataset was created by Masayu Anandita

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by Benjamin Nnabo

Contents

Analysis of ‘Real Estate DataSet’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arslanali4343/real-estate-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Concerns housing values in suburbs of Boston.

1. Number of Instances: 506

2. Number of Attributes: 13 continuous attributes (including "class" attribute "MEDV"), 1 binary-valued attribute.

3. Attribute Information:

   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 centres
   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 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
   13. LSTAT % lower status of the population
   14. MEDV Median value of owner-occupied homes in $1000's

4. Missing Attribute Values: None.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Agoer

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Srinath Sridharan

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Cuddly Bear

Contents

Dataset

This dataset was created by Nilay Chauhan

Contents