The Ames Housing Dataset is a well-known dataset in the field of machine learning and data analysis. It contains various features and attributes of residential homes in Ames, Iowa, USA. The dataset is often used for regression tasks, particularly for predicting housing prices.

Here are some key details about the Ames Housing Dataset:

• Number of Instances: The dataset consists of 2,930 instances or observations.
• Number of Features: There are 79 different features or variables that describe various aspects of the residential properties.
• Target Variable: The target variable in the dataset is the "SalePrice," representing the sale price of the houses.
• Data Types: The features include both numerical and categorical variables, covering a wide range of aspects such as lot size, number of rooms, location, construction, and more.

The Ames Housing Dataset is widely used in the machine learning community for tasks such as regression modeling, feature engineering, and predictive analytics related to housing prices. It serves as a valuable resource for developing and testing machine learning algorithms and techniques in the real estate domain.

Dataset

This dataset was created by Pawan_Tiwari670

Released under Other (specified in description)

Contents

Context

The Ames Housing dataset is a great alternative to the popular but older Boston Housing dataset.

Content

The Ames Housing dataset contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa with the goal of predicting the selling price.

Acknowledgements

The Ames Housing dataset was compiled by Dean De Cock in 2011, for use in data science education.

Inspiration

The Default task for this dataset is Regression.

Dataset

This dataset was created by Zahra Amini

Contents

Dataset

This dataset was created by Mehdi

Released under CC0: Public Domain

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Dataset

This dataset was created by Hamza Jabbar Khan

Contents

Dataset

This dataset was created by KIROSG

Released under CC0: Public Domain

Contents

(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?

This dataset of residential homes in Ames, Iowa contains 79 explanatory variables. A contest on Kaggle You can access the dataset and the competition page of the project from the link below. The dataset belongs to a kaggle competition so there are two different csv files, train and test. In the test dataset, house prices are left blank and this you are expected to estimate the values.

https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview/evaluation

Total Observations 1460 Numeric Variable 38 Categorical Variable 43

SalePrice - the selling price of the property in dollars. This is the target variable you are trying to estimate.

MSSubClass: The building class MSZoning: The general zoning classification LotFrontage: Linear feet of street connected to property LotArea: Lot size in square feet Street: Type of road access Alley: Type of alley access LotShape: General shape of property LandContour: Flatness of the property Utilities: Type of utilities available LotConfig: Lot configuration LandSlope: Slope of property Neighborhood: Physical locations within Ames city limits Condition1: Proximity to main road or railroad Condition2: Proximity to main road or railroad (if a second is present) BldgType: Type of dwelling HouseStyle: Style of dwelling OverallQual: Overall material and finish quality OverallCond: Overall condition rating YearBuilt: Original construction date YearRemodAdd: Remodel date RoofStyle: Type of roof RoofMatl: Roof material Exterior1st: Exterior covering on house Exterior2nd: Exterior covering on house (if more than one material) MasVnrType: Masonry veneer type MasVnrArea: Masonry veneer area in square feet ExterQual: Exterior material quality ExterCond: Present condition of the material on the exterior Foundation: Type of foundation BsmtQual: Height of the basement BsmtCond: General condition of the basement BsmtExposure: Walkout or garden level basement walls BsmtFinType1: Quality of basement finished area BsmtFinSF1: Type 1 finished square feet BsmtFinType2: Quality of second finished area (if present) BsmtFinSF2: Type 2 finished square feet BsmtUnfSF: Unfinished square feet of basement area TotalBsmtSF: Total square feet of basement area Heating: Type of heating HeatingQC: Heating quality and condition CentralAir: Central air conditioning Electrical: Electrical system 1stFlrSF: First Floor square feet 2ndFlrSF: Second floor square feet LowQualFinSF: Low quality finished square feet (all floors) GrLivArea: Above grade (ground) living area square feet BsmtFullBath: Basement full bathrooms BsmtHalfBath: Basement half bathrooms FullBath: Full bathrooms above grade HalfBath: Half baths above grade Bedroom: Number of bedrooms above basement level Kitchen: Number of kitchens KitchenQual: Kitchen quality TotRmsAbvGrd: Total rooms above grade (does not include bathrooms) Functional: Home functionality rating Fireplaces: Number of fireplaces FireplaceQu: Fireplace quality GarageType: Garage location GarageYrBlt: Year garage was built GarageFinish: Interior finish of the garage GarageCarsv: Size of garage in car capacity GarageArea: Size of garage in square feet GarageQual: Garage quality GarageCond: Garage condition PavedDrive: Paved driveway WoodDeckSF: Wood deck area in square feet OpenPorchSF: Open porch area in square feet EnclosedPorch: Enclosed porch area in square feet 3SsnPorch: Three season porch area in square feet ScreenPorch: Screen porch area in square feet PoolArea: Pool area in square feet PoolQC: Pool quality Fence: Fence quality MiscFeature: Miscellaneous feature not covered in other categories MiscVal: $Value of miscellaneous feature MoSold: Month Sold YrSold: Year Sold SaleType: Type of sale SaleCondition: Condition of sale