Dataset
paris-housing-prices
An analytical project examining the Paris Housing Dataset to uncover which property attributes most strongly affect asking prices. It involves cleaning, visualizing, and statistically modeling the records to answer targeted research questions.
Data preview
Dataset card
Paris Housing Prices is a small tabular dataset hosted on Hugging Face by publisher MAY199 that records residential property attributes in Paris for housing-price analysis. It contains roughly 10,000 rows in CSV format with an unstated license.
What this dataset contains
The dataset is a single tabular file of Paris residential property records sized between 10,000 and 100,000 rows, distributed as a train split of about 10,000 rows. Each row describes one property and includes numeric fields covering physical characteristics such as squareMeters, numberOfRooms, floors, basement, attic, and garage, binary attributes like hasYard, hasPool, isNewBuilt, hasStormProtector, hasStorageRoom, and hasGuestRoom, and identifiers or descriptors including cityCode, cityPartRange, numPrevOwners, and made. A price column is the target variable for analysis.
Structure and fields
All seventeen columns are numeric: squareMeters, numberOfRooms, hasYard, hasPool, floors, cityCode, cityPartRange, numPrevOwners, made, isNewBuilt, hasStormProtector, basement, attic, garage, hasStorageRoom, hasGuestRoom, and price. Sixteen columns are int64 and only price is float64. The schema is flat with no nested fields, and the file is delivered as CSV compatible with pandas, polars, and the Hugging Face datasets library.
How analysts use it
The accompanying notebook illustrates exploratory analysis such as filtering by z-score, computing correlation matrices among columns like squareMeters, garage, floors, and hasPool against price, and plotting price distributions. For investment purposes, analysts could prototype regression models that estimate price from physical attributes and cityPartRange, or study how categorical features such as hasPool or hasYard influence valuations across cityCode segments. It is best treated as a teaching or benchmarking corpus rather than a production-grade market data feed.
Documentation & references 1
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