size_categories:10K<n<100K format:parquet modality:tabular library:datasets library:pandas library:mlcroissant library:polars region:us
---
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: bedrooms__ratio
    dtype: float64
  - name: rooms_per_house__ratio
    dtype: float64
  - name: people_per_house__ratio
    dtype: float64
  - name: total_bedrooms
    dtype: float64
  - name: total_rooms
    dtype: float64
  - name: population
    dtype: float64
  - name: households
    dtype: float64
  - name: median_income
    dtype: float64
  - name: Cluster 0 similarity
    dtype: float64
  - name: Cluster 1 similarity
    dtype: float64
  - name: Cluster 2 similarity
    dtype: float64
  - name: Cluster 3 similarity
    dtype: float64
  - name: Cluster 4 similarity
    dtype: float64
  - name: Cluster 5 similarity
    dtype: float64
  - name: Cluster 6 similarity
    dtype: float64
  - name: Cluster 7 similarity
    dtype: float64
  - name: Cluster 8 similarity
    dtype: float64
  - name: Cluster 9 similarity
    dtype: float64
  - name: ocean_proximity_<1H OCEAN
    dtype: float64
  - name: ocean_proximity_INLAND
    dtype: float64
  - name: ocean_proximity_ISLAND
    dtype: float64
  - name: ocean_proximity_NEAR BAY
    dtype: float64
  - name: ocean_proximity_NEAR OCEAN
    dtype: float64
  - name: housing_median_age
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 3302400
    num_examples: 16512
  download_size: 2821107
  dataset_size: 3302400
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---


This dataset was created as part of an assignment for my CS-482 course. 

It was created by following along with the [Hands-on Machine Learning Textbook](https://github.com/ageron/handson-ml3), specifically Jupyter notebook 02.

