100+ datasets found
  1. House Price Prediction Dataset & Code

    • kaggle.com
    Updated Sep 19, 2023
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    Tushar Paul (2023). House Price Prediction Dataset & Code [Dataset]. http://doi.org/10.34740/kaggle/ds/3757184
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 19, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Tushar Paul
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    House price prediction dataset

    This dataset comprises housing data for various metropolitan cities of India. It includes: - Collection of prices of new and resale houses - The amenities provided for each house

    This housing dataset is useful for a range of stakeholders, including real estate agents, property developers, buyers, renters, and researchers interested in analyzing housing markets and trends in metropolitan cities across India. It can be used for market analysis, price prediction, property recommendations, and various other real estate-related tasks.

    Shape of dataset : (6207, 40)

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2F75861c40e86a4d2d10c044be79542436%2FCapture.JPG?generation=1704918894425981&alt=media" alt="">

    Github Link : https://github.com/TusharPaul01/House-Price-Prediction

    For more such dataset & code check : https://www.kaggle.com/tusharpaul2001

  2. Housing Price Dataset

    • kaggle.com
    Updated Jul 25, 2022
    + more versions
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    Meenakshi Sajan (2022). Housing Price Dataset [Dataset]. https://www.kaggle.com/datasets/meenakshisajan/housing-price-dataset/versions/1
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 25, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Meenakshi Sajan
    Description

    Dataset

    This dataset was created by Meenakshi Sajan

    Contents

  3. House price dataset

    • kaggle.com
    Updated May 26, 2025
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    Mohamed Jamyl (2025). House price dataset [Dataset]. https://www.kaggle.com/datasets/mohamedjamyl/house-price-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 26, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Mohamed Jamyl
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Dataset

    This dataset was created by Mohamed Jamyl

    Released under Apache 2.0

    Contents

  4. House Price Prediction Dataset : InsuranceHub- USA

    • kaggle.com
    Updated Aug 2, 2020
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    Bs004 (2020). House Price Prediction Dataset : InsuranceHub- USA [Dataset]. https://www.kaggle.com/datasets/bharatsahu/house-price-prediction-dataset-insurancehub-usa
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 2, 2020
    Dataset provided by
    Kaggle
    Authors
    Bs004
    Area covered
    United States
    Description

    Context

    Insurance companies collect multiple features of a House and select which houses can be insured and what amount they can charge the Premium from them. So here I have collected data from multiple insurance companies in USA where features with house prices are given

    Content

    This data set has many property details from address to their location co ordinates nad many other features, use them to predict the House price

    Inspiration

    Multiple regression datasets have been published every one unique in their own way, Use of location coordinates and some other co-ordinates are new here.

  5. Housing Prices Dataset

    • kaggle.com
    Updated Jun 30, 2025
    + more versions
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    abdo elsayed (2025). Housing Prices Dataset [Dataset]. https://www.kaggle.com/datasets/oxcolaa/housing-prices-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 30, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    abdo elsayed
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Dataset

    This dataset was created by abdo elsayed

    Released under Apache 2.0

    Contents

  6. House Price Prediction

    • kaggle.com
    Updated Apr 6, 2025
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    EL-Hussein salah (2025). House Price Prediction [Dataset]. https://www.kaggle.com/datasets/logiic/house-price-prediction
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 6, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    EL-Hussein salah
    License

    http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.htmlhttp://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html

    Description

    Dataset

    This dataset was created by EL-Hussein salah

    Released under GPL 2

    Contents

  7. Australia Real Estate Dataset

    • kaggle.com
    Updated Nov 25, 2023
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    smmmmmmmmmmmm (2023). Australia Real Estate Dataset [Dataset]. https://www.kaggle.com/datasets/smmmmmmmmmmmm/australia-real-estate-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 25, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    smmmmmmmmmmmm
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Australia
    Description

    The dataset "aus_real_estate.csv" encapsulates comprehensive real estate information pertaining to Australia, showcasing diverse attributes essential for property assessment and market analysis. This dataset, comprising 5000 entries across 10 distinct columns, offers a detailed portrayal of various residential properties in cities across Australia.

    The dataset encompasses crucial factors influencing property valuation and purchase decisions. The 'Price' column represents the property's cost, spanning a range between $100,000 and $2,000,000. Attributes such as 'Bedrooms' and 'Bathrooms' highlight the accommodation specifics, varying from one to five bedrooms and one to three bathrooms, respectively. 'SqFt' denotes the square footage of the properties, varying between 800 and 4000 square feet, elucidating their size and spatial dimensions.

    The 'City' column encompasses major Australian urban centers, including Sydney, Melbourne, Brisbane, Perth, and Adelaide, delineating the geographical distribution of the properties. 'State' further categorizes the locations into New South Wales (NSW), Victoria (VIC), Queensland (QLD), Western Australia (WA), and South Australia (SA).

    The dataset encapsulates temporal information through the 'Year_Built' attribute, spanning from 1950 to 2023, providing insights into the age and vintage of the properties. Moreover, property types are delineated within the 'Type' column, encompassing variations such as 'Apartment,' 'House,' and 'Townhouse.' The binary 'Garage' column signifies the presence (1) or absence (0) of a garage, while 'Lot_Area' provides an understanding of the land area, ranging from 1000 to 10,000 square feet.

    This dataset offers a comprehensive outlook into the Australian real estate landscape, facilitating multifaceted analyses encompassing property valuation, market trends, and regional preferences. Its diverse attributes make it a valuable resource for researchers, analysts, and stakeholders within the real estate domain, enabling robust investigations and informed decision-making processes regarding property investments and market dynamics in Australia.

  8. Regression Techniques on House Prices

    • kaggle.com
    Updated Sep 27, 2022
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    Aneela Abdullah (2022). Regression Techniques on House Prices [Dataset]. https://www.kaggle.com/datasets/aneelaabdullah/regression-techniques-on-house-prices
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 27, 2022
    Dataset provided by
    Kaggle
    Authors
    Aneela Abdullah
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    About Dataset

    Edit Context: This data gives predicted sales prices of the houses.

    Content: There are only 2 variables which gives house property ID and predicted variable is in last Sales price of the house.

    Acknowledgements: Please compare all the variable with respect to sales price and try to create different model, come up with the solution for sales price predictions of the house.

    Technique Used: Data Cleansing Handling Categorical Features Concatenation XGBoost Regressor

  9. House Price Predication

    • kaggle.com
    Updated May 7, 2024
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    Sheema Zain (2024). House Price Predication [Dataset]. https://www.kaggle.com/datasets/sheemazain/house-price-predication
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 7, 2024
    Dataset provided by
    Kaggle
    Authors
    Sheema Zain
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    House price prediction Predicting house prices is a common task in data science and machine learning. Here's a high-level overview of how you might approach it:

    Data Collection: Gather a dataset containing features of houses (e.g., size, number of bedrooms, location, amenities) and their corresponding prices. Websites like Zillow, Kaggle, or government housing datasets are good sources.

    Data Preprocessing: Clean the data by handling missing values, encoding categorical variables, and scaling numerical features if necessary. This step ensures that the data is in a suitable format for training a model. Feature Selection/Engineering: Choose relevant features that are likely to influence house prices. You may also create new features based on domain knowledge or data analysis.

    Model Selection: Select a regression model suitable for predicting continuous target variables like house prices. Common choices include Linear Regression, Decision Trees, Random Forests, Gradient Boosting, and Neural Networks.

    Model Training: Split your dataset into training and testing sets to train and evaluate the performance of your model. You can further split the training set for validation purposes or use cross-validation techniques.

    Model Evaluation: Assess the performance of your model using appropriate evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).

    Hyperparameter Tuning: Fine-tune your model's hyperparameters to improve its performance. Techniques like grid search or random search can be employed for this purpose.

    Deployment: Once satisfied with your model's performance, deploy it to make predictions on new data. This could be as simple as saving the trained model and creating an interface for users to input house features.

  10. Synthetic House Price Prediction Datasets

    • kaggle.com
    Updated Jul 26, 2025
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    D.Madhan raj (2025). Synthetic House Price Prediction Datasets [Dataset]. http://doi.org/10.34740/kaggle/dsv/12582291
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 26, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    D.Madhan raj
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    The Synthetic House Price Prediction Datasets is a publicly available Kaggle dataset created by D.Madhan Raj for machine learning experiments. It features a single CSV file containing synthetic data on house attributes such as bedrooms, bathrooms, square footage, house age, location rating, and estimated prices in USD. Designed for regression tasks, the dataset allows users to practice predictive modeling without the constraints of real-world data privacy. It's licensed under Apache 2.0 and includes around 3,203 data rows, making it a handy resource for learning, prototyping, and fine-tuning models learning

  11. house-price-predictions

    • kaggle.com
    Updated Apr 22, 2020
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    Khaja Syed (2020). house-price-predictions [Dataset]. https://www.kaggle.com/datasets/khajasyedml/housepricepredictions/metadata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 22, 2020
    Dataset provided by
    Kaggle
    Authors
    Khaja Syed
    Description

    (https://www.kaggle.com/c/house-prices-advanced-regression-techniques) About this Dataset Start here if... You have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition.

    Competition Description

    Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

    With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

    Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting Acknowledgments The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.

    Context

    There's a story behind every dataset and here's your opportunity to share yours.

    Content

    What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

    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?

  12. House Price Dataset

    • kaggle.com
    Updated Sep 12, 2022
    + more versions
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    Dream37 (2022). House Price Dataset [Dataset]. https://www.kaggle.com/dream37/house-price-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 12, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Dream37
    Description

    Dataset

    This dataset was created by Dream37

    Contents

  13. House Price Dataset with Other Information

    • kaggle.com
    Updated Aug 17, 2021
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    Esrat Maria (2021). House Price Dataset with Other Information [Dataset]. https://www.kaggle.com/esratmaria/house-price-dataset-with-other-information/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 17, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Esrat Maria
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    Context

    A very simple dataset to predict house prices with great accuracy.

    Content

    This dataset contains 21 columns that help determine the price of the house and how these components have an effect on house price.

  14. Crisis 2008-2009 Housing Data

    • kaggle.com
    zip
    Updated Aug 31, 2019
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    Ievgen Iosifov (2019). Crisis 2008-2009 Housing Data [Dataset]. https://www.kaggle.com/datasets/eiosifov/crisis-20082009-housing-data
    Explore at:
    zip(1727 bytes)Available download formats
    Dataset updated
    Aug 31, 2019
    Authors
    Ievgen Iosifov
    Description

    Context

    Data augmentation for housing prices

    Content

    US Housing Data for 2008-2009 (pre crisis and crisis year) to predict housing prices more accurate

    Inspiration

    Housing price prediction competition on Kaggle

  15. Mumbai House Price Data (70k Entries)

    • kaggle.com
    Updated Oct 18, 2024
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    kevinnadar22 (2024). Mumbai House Price Data (70k Entries) [Dataset]. https://www.kaggle.com/datasets/kevinnadar22/mumbai-house-price-data-70k-entries
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 18, 2024
    Dataset provided by
    Kaggle
    Authors
    kevinnadar22
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Area covered
    Mumbai
    Description

    Mumbai House Price Dataset (70k+ Entries)

    Dataset Overview

    This dataset provides detailed information on housing prices in Mumbai, India. It includes over 70,000 entries and is ideal for analyzing various factors affecting real estate prices in the city. The dataset captures key aspects of residential properties such as price, area, property type, and more, enabling detailed insights into the real estate market trends.

    Note: This data is based on the year 2024

    Sources

    This dataset has been scraped from makaan.com using Python and Requests library

    Potential Use Cases

    • Real Estate Market Analysis: Understanding property price trends across different localities and neighborhoods in Mumbai.
    • Price Prediction Models: Building machine learning models to predict housing prices based on features like area, property type, and location.

    Data Quality

    All columns in this dataset are fully populated with non-null values

  16. House Price Dataset

    • kaggle.com
    Updated Apr 1, 2024
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    Hosam Mhmd Ali (2024). House Price Dataset [Dataset]. https://www.kaggle.com/datasets/hosammhmdali/house-price-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 1, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Hosam Mhmd Ali
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    About this file add_comment Add Suggestion The California housing dataset contains information on various socio-economic features of block groups in California. Each row in the dataset represents a single block group, and there are 20,640 observations, each with 10 attributes.The Features are as follows: 1.Longitude: The longitude of the center of each block group in California. 2.Latitude: The latitude of the center of each block group in California. 3.Housing Median Age: The median age of the housing units in each block group. 4.Total Rooms: The total number of rooms in the housing units in each block group. 5.Total Bedrooms: The total number of bedrooms in the housing units in each block group. 6.Population: The total population of the block group. 7.Households: The total number of households in the block group. 8.Median Income: The median income of the block group. 9.Median House Value: The median value of the housing units in the block group. 10.Ocean Proximity: The proximity of the block group to the ocean or other bodies of water. Table

  17. House Price Prediction Dataset

    • kaggle.com
    Updated Dec 12, 2024
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    johnkagglereg (2024). House Price Prediction Dataset [Dataset]. https://www.kaggle.com/datasets/johnkagglereg/house-price-prediction-dataset/suggestions
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 12, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    johnkagglereg
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Dataset

    This dataset was created by johnkagglereg

    Released under Apache 2.0

    Contents

  18. house price dataset for linear regression

    • kaggle.com
    Updated Sep 7, 2024
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    Ashwitha chandrasekar (2024). house price dataset for linear regression [Dataset]. https://www.kaggle.com/datasets/ashwithachandrasekar/house-price-dataset-for-linear-regression
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 7, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ashwitha chandrasekar
    Description

    Dataset

    This dataset was created by Ashwitha chandrasekar

    Contents

  19. House-Price-Prediction

    • kaggle.com
    Updated Feb 10, 2022
    + more versions
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    Sreekanth Maila (2022). House-Price-Prediction [Dataset]. https://www.kaggle.com/sreekanthmaila/house-price-prediction/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 10, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sreekanth Maila
    Description

    Dataset

    This dataset was created by Sreekanth Maila

    Contents

  20. Advance House Price dataset

    • kaggle.com
    Updated Jul 31, 2020
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    Ghanender Pahuja (2020). Advance House Price dataset [Dataset]. https://www.kaggle.com/ghanender/advance-house-price-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 31, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Ghanender Pahuja
    Description

    Dataset

    This dataset was created by Ghanender Pahuja

    Contents

Share
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Tushar Paul (2023). House Price Prediction Dataset & Code [Dataset]. http://doi.org/10.34740/kaggle/ds/3757184
Organization logo

House Price Prediction Dataset & Code

Predicting price of house in metropolitan cities (Dataset & Code)

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Sep 19, 2023
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Tushar Paul
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

House price prediction dataset

This dataset comprises housing data for various metropolitan cities of India. It includes: - Collection of prices of new and resale houses - The amenities provided for each house

This housing dataset is useful for a range of stakeholders, including real estate agents, property developers, buyers, renters, and researchers interested in analyzing housing markets and trends in metropolitan cities across India. It can be used for market analysis, price prediction, property recommendations, and various other real estate-related tasks.

Shape of dataset : (6207, 40)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11965067%2F75861c40e86a4d2d10c044be79542436%2FCapture.JPG?generation=1704918894425981&alt=media" alt="">

Github Link : https://github.com/TusharPaul01/House-Price-Prediction

For more such dataset & code check : https://www.kaggle.com/tusharpaul2001

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