Dataset

This dataset was created by Mohammed_Basheer

Contents

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 Nikhil Pathrikar

Contents

Dataset

This dataset was created by Kunaal Naik

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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 ---

Dataset

This dataset was created by Agoer

Released under CC0: Public Domain

Contents

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 Masayu Anandita

Released under Data files © Original Authors

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Dataset

This dataset was created by Jyoti kumar Rout

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 Benjamin Nnabo

Contents

Dataset

This dataset was created by Nilay Chauhan

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 Muhammad Jiyad Khan

Contents

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

Analysis of ‘Delhi House Price Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/neelkamal692/delhi-house-price-prediction on 28 January 2022.

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

Content

This is not a comprehensive list, some of the attributes i left intentionally and some just couldn't extract. Dataset consists of 12 columns and 1259 rows. 6 of the features are numerical valued and rest are categorical. code for extracting Data is available at my Github account.

Acknowledgements

The Data has been extracted from MagicBricks (a website, provides common platform to property buyer and seller ).

Inspiration

I have done property price prediction on Boston Dataset, so i was wondering, if i can do it for Delhi properties too.

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

Dataset

This dataset was created by Mahmoud Abdrabo

Released under Other (specified in description)

Contents

Dataset

This dataset was created by malvika chauhan

Contents