Customer churn prediction dataset in a Bank. 10k customer instances. Source

Customer Churn dataset from ECommerce Sales data. Source: https://www.kaggle.com/huzaiftila/customer-churn-prediction-analysis

Source: https://www.kaggle.com/new-york-city/ny-traffic-volume-counts-2012-2013

An instance per hour has been added such as a high traffic flag.

The dataset has 2 objective fields: car counts (numeric) or high traffic (boolean). It allows to predict traffic volume in New York streets segments.

The dataset has been ordered per date ascending

Predictive maintenance dataset for demo purposes. This dataset contains fake data and emulates a predictive maintenance use case in a Factory. The objective class is very unbalanced. Anomaly detectors isolate well the failures

Madrid air pollution data, to predict pollution alerts 1 day in advance. Each row represents a day in central Madrid including features about: NO2 air levels, weather, traffic information with the objective field pollution "YES/NO". The objective field has been defined using Madrid local government pollution alerts definition. Contains data from 2013 to 2017.

Air: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=f3c0f7d512273410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

Weather: https://datosclima.es/Aemet2013/Datosporestacion.html

Traffic: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=33cb30c367e78410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

Issued tickets for every sale between May and August of 2018. Columns: FolioUnique id for every ticket, HoraDatetime of the sale, TotalTicket amount in Mexican Pesos, CajeroPartial name of the cashier. Source: https://www.kaggle.com/agasca/retail-sales/version/1

Contains product category lists from groceries shopping carts. Source: https://github.com/stedy/Machine-Learning-with-R-datasets/blob/master/groceries.csv

Predictive maintenance dataset about oil wells downhole equipment failures using sensor data. Source including more information: https://www.kaggle.com/c/equipfailstest/overview Actual source accessible data: https://raw.githubusercontent.com/geooot/tamudatathon2019/master/equip_failures_training_set.csv Missing values have been either removed or replaced with the mean as a cleaning step providing good anomaly detectors results.

Madrid air pollution data, to predict pollution alerts 4 days in advance. Each row represents a day in central Madrid including features about: NO2 air levels, weather, traffic information with the objective field pollution "YES/NO". The objective field has been defined using Madrid local government pollution alerts definition. Contains data from 2013 to 2017.

Air: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=f3c0f7d512273410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

Weather: https://datosclima.es/Aemet2013/Datosporestacion.html

Traffic: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=33cb30c367e78410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default