https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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
This dataset was created by Meenakshi Sajan
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
This dataset was created by Mohamed Jamyl
Released under Apache 2.0
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
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
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.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
This dataset was created by abdo elsayed
Released under Apache 2.0
http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.htmlhttp://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html
This dataset was created by EL-Hussein salah
Released under GPL 2
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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
(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.
There's a story behind every dataset and here's your opportunity to share yours.
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.
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.
Your data will be in front of the world's largest data science community. What questions do you want to see answered?
This dataset was created by Dream37
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
A very simple dataset to predict house prices with great accuracy.
This dataset contains 21 columns that help determine the price of the house and how these components have an effect on house price.
Data augmentation for housing prices
US Housing Data for 2008-2009 (pre crisis and crisis year) to predict housing prices more accurate
Housing price prediction competition on Kaggle
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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
This dataset has been scraped from makaan.com using Python and Requests library
All columns in this dataset are fully populated with non-null values
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
This dataset was created by johnkagglereg
Released under Apache 2.0
This dataset was created by Ashwitha chandrasekar
This dataset was created by Sreekanth Maila
This dataset was created by Ghanender Pahuja
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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