Datasets:
Tasks:
Tabular Regression
Modalities:
Tabular
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
File size: 2,322 Bytes
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---
dataset_info:
features:
- name: MedInc
dtype: float64
- name: HouseAge
dtype: float64
- name: AveRooms
dtype: float64
- name: AveBedrms
dtype: float64
- name: Population
dtype: float64
- name: AveOccup
dtype: float64
- name: Latitude
dtype: float64
- name: Longitude
dtype: float64
- name: MedHouseVal
dtype: float64
splits:
- name: train
num_bytes: 1198080
num_examples: 16640
- name: validation
num_bytes: 144000
num_examples: 2000
- name: test
num_bytes: 144000
num_examples: 2000
download_size: 1056079
dataset_size: 1486080
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: mit
task_categories:
- tabular-regression
language:
- en
size_categories:
- 10K<n<100K
pretty_name: California Housing
---
# California Housing
## About
🏠 The California Housing dataset, first appearing in "Sparse spatial autoregressions" (1997)
## Description
This is an (unofficial) Hugging Face version of the California Housing dataset from the S&P Letters paper "Sparse spatial autoregressions" (1997). It can also be found in [StatLib](https://lib.stat.cmu.edu/datasets/) and [Luis Torgo's page](https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html). A modified version of it, used in "Hands-On Machine learning with Scikit-Learn and TensorFlow", with 9 differenfeatures and missing values, also circulates online.
The California Housing dataset comes from the California 1990 Census. It contains 20640 samples, each of which corresponds to a geographical block and the people living therein. Specifically, it contains the following 8 features:
1) MedInc: Median income of the people living in the block
2) HouseAge: Median age of the houses in a block
3) AveRooms: Average rooms of houses in a block
4) AveBedrms: Average bedrooms of houses in a block
5) Population: Number of people living in a block
6) AveOccup: Average number of people under the same roof
7) Latitude: Geographical latitude
8) Longitude: Geographical longitude
The target variable is the median house value (MedHouseVal).
## Usage
import datasets
dataset = datasets.load_dataset("gvlassis/california_housing") |