Baselight

House Price Prediction Treated Dataset

House sale prices for King County between May 2014 and May 2015

@kaggle.aravinii_house_price_prediction_treated_dataset

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About this Dataset

House Price Prediction Treated Dataset

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Same dataset as "House Sales in King County, USA", but with treated content and with a split version (train-test) allowing direct use in machine learning models.

We have 14 columns in the dataset, as it follows:

  • date: Date of the home sale
  • price: Price of each home sold
  • bedrooms: Number of bedrooms
  • bathrooms: Number of bathrooms
  • living_in_m2: Square meters of the apartments interior living space
  • nice_view: A flag that indicates the view's quality of a property
  • perfect_condition: A flag that indicates the maximum index of the apartment condition
  • grade: An index from 1 to 5, where 1 falls short of quality level and 5 have a high quality level of construction and design
  • has_basement: A flag indicating whether or not a property has a basement
  • renovated: A flag if the property was renovated
  • has_lavatory: Check for the presence of these incomplete/secondary bathrooms (bathtub, sink, toilet)
  • single_floor: A flag indicating whether the property had only one floor
  • month: The month of the home sale
  • quartile_zone: A quartile distribution index of the most expensive zip codes, where 1 means less expansive and 4 most expansive.

Tables

Df Test

@kaggle.aravinii_house_price_prediction_treated_dataset.df_test
  • 64.17 KB
  • 6700 rows
  • 14 columns
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CREATE TABLE df_test (
  "date" TIMESTAMP,
  "price" DOUBLE,
  "bedrooms" BIGINT,
  "grade" BIGINT,
  "has_basement" BOOLEAN,
  "living_in_m2" DOUBLE,
  "renovated" BOOLEAN,
  "nice_view" BOOLEAN,
  "perfect_condition" BOOLEAN,
  "real_bathrooms" BIGINT,
  "has_lavatory" BOOLEAN,
  "single_floor" BOOLEAN,
  "month" BIGINT,
  "quartile_zone" BIGINT
);

Df Train

@kaggle.aravinii_house_price_prediction_treated_dataset.df_train
  • 113.49 KB
  • 13603 rows
  • 14 columns
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CREATE TABLE df_train (
  "date" TIMESTAMP,
  "price" DOUBLE,
  "bedrooms" BIGINT,
  "grade" BIGINT,
  "has_basement" BOOLEAN,
  "living_in_m2" DOUBLE,
  "renovated" BOOLEAN,
  "nice_view" BOOLEAN,
  "perfect_condition" BOOLEAN,
  "real_bathrooms" BIGINT,
  "has_lavatory" BOOLEAN,
  "single_floor" BOOLEAN,
  "month" BIGINT,
  "quartile_zone" BIGINT
);

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