Skip months of data cleaning. Our pre-validated Parquet datasets are ready for your ML pipelines, feature engineering, and model training.
Pre-labeled business datasets with clear target variables. Risk scores, classification labels, regression targets included.
Daily/monthly time series data spanning 5+ years. Perfect for ARIMA, Prophet, LSTM models. No gaps.
Weather × Economy, Health × Climate correlations. Unique multi-source datasets unavailable elsewhere.
Pre-computed features reduce prep time by 80%. Rolling averages, lags, normalized scores ready to use.
# Load Tiger Data in Python
import polars as pl
# Direct load from Parquet
df = pl.read_parquet("tiger-data-france_real_estate.parquet")
# 1.17M rows ready for ML
print(df.shape) # (1168684, 38)
# Your model in minutes, not months
X = df.select(["surface", "code_postal", "type_local"])
y = df["valeur_fonciere"]Use code LAUNCH40 • Ends Sunday • No hidden fees
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