---
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
---

# 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")