pricingautomobilesprice-predictionregressionucipricing

Automobile Price Prediction

Free

Open dataset

Sample structure: 100 / 100
5 download links issued
Seller: DataBazaar
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Category
Pricing
Records
159 rows
Format
PARQUET
Update Frequency
Not documented
Collection Method
uploaded
PII
No flagged field names; not a privacy audit
File Size
~0.02 MB
Download links issued
5

Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License
Public
Source / creator
openml: 207
Collection method
uploaded
Coverage start
Not documented
Coverage end
Not documented
Data last updated
Not documented
Update schedule
Not documented

Source documentation ↗

OpenML labels the source "Public" but does not identify a specific reuse license; public access alone does not establish redistribution rights.

Sample structure score: 100 / 100

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

Assessed 10 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.

CheckPointsEvidence
Populated cells50 / 50160 of 160 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated.
Consistent value types30 / 30160 of 160 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth.
Consistent record shape20 / 2010 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys.
Field-level findings and improvements

Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.

FieldMissing cellsMost common typeOther populated types
symboling0 / 10number0 / 10
normalized-losses0 / 10number0 / 10
wheel-base0 / 10number0 / 10
length0 / 10number0 / 10
width0 / 10number0 / 10
height0 / 10number0 / 10
curb-weight0 / 10number0 / 10
engine-size0 / 10number0 / 10
bore0 / 10number0 / 10
stroke0 / 10number0 / 10
compression-ratio0 / 10number0 / 10
horsepower0 / 10number0 / 10
peak-rpm0 / 10number0 / 10
city-mpg0 / 10number0 / 10
highway-mpg0 / 10number0 / 10
class0 / 10number0 / 10
How the score is calculated, its limitations, and how to correct an assessment →

About this data

Classic regression benchmark with 15 engineering and design attributes including wheelbase, dimensions, engine size, horsepower, and fuel economy, with price as the prediction target.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example
python3 retrieve-dataset.py c7f85f8e-c758-413c-be5f-b80a22110769 --output dataset.bin
Full supplier documentation
159 automobiles described by 15 engineering and design attributes — wheelbase, dimensions, engine size, horsepower, fuel economy, and more — with price as the prediction target. A classic UCI regression benchmark.

Schema

NameTypeDescription
symbolingDOUBLE
normalized-lossesDOUBLE
wheel-baseDOUBLE
lengthDOUBLE
widthDOUBLE
heightDOUBLE
curb-weightDOUBLE
engine-sizeDOUBLE
boreDOUBLE
strokeDOUBLE
compression-ratioDOUBLE
horsepowerUTINYINT
peak-rpmDOUBLE
city-mpgUTINYINT
highway-mpgUTINYINT
classDOUBLE

Sample Data

Preview a sample of the data before downloading.

Public sample only. Sign in to retrieve the full dataset, including free datasets.

For AI Agents

Via MCP Server
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "Automobile Price Prediction" })
// Found: c7f85f8e-c758-413c-be5f-b80a22110769
get_download_url({ dataset_id: "c7f85f8e-c758-413c-be5f-b80a22110769" })  // free — sign in with MCP OAuth first
Via REST API
# Free dataset — sign in or use your account API key:
curl https://api.databazaar.io/datasets/c7f85f8e-c758-413c-be5f-b80a22110769/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"
Automobile Price Prediction — Free Dataset | DataBazaar