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The data repository for the PLOS ONE paper titled "Ensemble Machine Learning and Forecasting Can Achieve 99% Uptime for Rural Handpumps"

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daterdots/Machine-Learning-Handpumps

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Data Dictionary

site_barcode

Type: id

Description: unique identifier for each pump installation

local_date

Type: id

Description: date at location of pump installation

nevents

Type: feature

Description: number of events recorded

naive_failure

Type: feature

Description: indicator number of events < 10

nevents_overall_cluster_percent_log

Type: feature

Description: log-fold difference in number of events relative to expected defined using k-means clustering

nevents_overall_simple_percent_log

Type: feature

Description: log-fold difference in number of events relative to expected defined as mean

nevents_wd_cluster_percent_log

Type: feature

Description: log-fold difference in number of events relative to expected defined using k-means clustering per day of week

nevents_wd_simple_percent_log

Type: feature

Description: log-fold difference in number of events relative to expected defined as mean per day of week

duration

Type: feature

Description: total duration of all pumping events

pf_overall_stl_seasonal_percent_log

Type: feature

Description: log-fold difference in pump function relative to stl regression with weekly seasonality

pf_wd_simple_percent_log

Type: feature

Description: log-fold difference in pump function relative to expected defined as mean per day of week

flow_overall_stl_seasonal_percent_log

Type: feature

Description: log-fold difference in flow rate relative to stl regression with weekly seasonality

nevents_overall_stl_seasonal_percent_log

Type: feature

Description: log-fold difference in number of events relative to stl regression with weekly seasonality

fail_next_week

Type: outcome

Description: indicator that the pump will fail in the next seven days (including current date)

pred_fail_next_week_SL

Type: prediction

Description: predicted probability of forecasted failure using SuperLearner (cross-validated)

currently_failed

Type: outcome

Description: indicator that the pump is currently failed

pred_currently_failed_SL

Type: prediction

Description: predicted probability of currently failure using SuperLearner (cross-validated)

pred_fail_next_week_SL

Type: prediction

Description: predicted probability of forecasted failure using GLM (cross-validated)

pred_currently_failed_SL

Type: prediction

Description: predicted probability of currently failure using GLM (cross-validated)

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The data repository for the PLOS ONE paper titled "Ensemble Machine Learning and Forecasting Can Achieve 99% Uptime for Rural Handpumps"

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