PatZhang0214/business-risk-prediction-dataset

Yelp Business Economic Indicators Dataset

⚠️ Important Version Notice (Please Read)

Version 3 (v3) should be used instead of v1 or v2.

• v1 (~1k rows) and v2 (~10k rows) use county-level economic indicators from 2023
• v3 (~17k rows) uses economic indicators from 2018, which better aligns with:
  • the temporal coverage of the Yelp Open Dataset
  • business survival modeling
  • avoidance of temporal leakage

Earlier versions are retained for reproducibility only and are not recommended for modeling.

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Dataset Overview

This dataset combines business-level information from the Yelp Open Dataset with county-level economic indicators sourced from U.S. government datasets.

The dataset is designed for predictive modeling, particularly tasks such as:

• Predicting whether a business will remain open or close
• Studying business survival and risk
• Analyzing interactions between local economic conditions and business outcomes

Each row corresponds to a single Yelp business.

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What’s New in v3

Version 3 adds substantial feature improvements over v1 and v2:

• ✔ Expanded dataset size (~17k rows)
• ✔ Corrected economic data timing (2018 instead of 2023)
• ✔ Yelp category multi-hot encoded features
• ✔ Business longevity features derived from Yelp check-ins
• ✔ Improved engagement signals

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Features (v3)

Business Engagement & Quality

Column	Description
rating_x_reviews	Yelp rating multiplied by log(review_count + 1)
review_count	Total number of Yelp reviews
num_categories	Number of Yelp categories assigned

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Business Longevity (Derived from Check-ins)

Column	Description
years_in_business	Years between first and last Yelp check-in (observed lifespan)
num_checkins	Total number of Yelp check-ins
has_checkin	1 if any check-in exists, else 0

Note:
These features estimate observed Yelp activity duration, not true opening date. They are intended for relative comparison across businesses, not causal inference.

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Target Variable

Column	Description
is_open	1 if business is open, 0 if closed

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Location

Column	Description
latitude	Business latitude
longitude	Business longitude
fips	County FIPS code

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County-Level Economic Indicators (2018)

Column	Description
pcpi	Per capita personal income (USD)
poverty_rate	Poverty rate (%)
median_household_income	Median household income (USD)
unemployment_rate	Unemployment rate (%)
avg_weekly_wages	Average weekly wages (USD)

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Yelp Category Indicators

Multi-hot encoded binary features indicating whether a business belongs to a given category.

Examples:

• cat_Restaurants
• cat_Food
• cat_Automotive
• cat_Bars
• cat_Health & Medical
• cat_Shopping
• …

Only the most frequent categories are included to limit sparsity.

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Normalization Note

Some earlier versions of this dataset were normalized for convenience.
Normalization is not required and not recommended for tree-based models such as:

• XGBoost
• LightGBM

Version 3 is intended to be used as-is with raw feature values.

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Sources

Yelp

• Yelp Open Dataset
  https://www.yelp.com/dataset
  Business attributes, categories, reviews, and check-ins.

Economic Indicators

• BLS – Quarterly Census of Employment and Wages (QCEW)
  https://www.bls.gov/cew/
  Average weekly wages.

• FRED – Federal Reserve Economic Data
  https://fred.stlouisfed.org/
  Per capita personal income (PCPI).

• U.S. Census Bureau – ACS / SAIPE
  https://www.census.gov/programs-surveys/saipe.html
  Poverty rates, median household income, unemployment rates.

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Usage Notes

• Missing values may exist for some businesses (e.g., no check-ins)
• No feature normalization is required for tree-based models
• Designed for tabular ML, feature interaction modeling, and exploratory analysis

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Example Usage

import pandas as pd
from datasets import load_dataset

# Load from Hugging Face
dataset = load_dataset("your-username/yelp-business-economic-indicators")
df = dataset["train"].to_pandas()

print(df.head())

License

This dataset is released under the CC BY 4.0 License.

Yelp Dataset Terms: https://www.yelp.com/dataset/terms

Economic data sources are U.S. government public-domain datasets.