Country of origin? | Austria |
---|---|
For how many years have you been experimenting with data? | 4 |
Popular articles by sist
Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance
Datathon Kaufland Solution – Predictive Maintenance Based on Sensor Data for Forklifts
Datathon Ontotext Mentors’ Guidelines – Text Mining Classification
Datathon Kaufland Solution – Team Total Kaputt! – Why da faQ the machine broke down?
The Kaufland Case [Global Datathon 2018] – Guidelines by Simon Stiebellehner
Datathon Kaufland Solution – Kaufland case – Team3
Datathon Kaufland Mentors’ Guidelines – On Predictive Maintenance
Datathon Sofia Air Mentors’ Guidelines – On IOT Prediction
Datathon Telenor Mentors’ Guidelines – On TelCo predictions
Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction
Popular comments by sist
Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance
Positive Aspects
—————————-
– very good introduction
– excellent descriptions & explanations
– good flow & project plan
– decent data description
– comparisons on machine and sensor level
– reasonable modeling approaches
– evaluation of different approaches
– suggestion of combination of methods
Negative Aspects
—————————-
– no word on hyperparameter tuning
Datathon Kaufland Solution – Team Total Kaputt! – Why da faQ the machine broke down?
Positive Aspects
—————————
– good analysis
– laid out ideas
Negative Aspects
————————-
– no modeling
– plot interpretations and explanations missing
– no finding/definition of proxy variable for downtimes
Datathon Kaufland Solution – Kaufland case – Team3
Positive Aspects
————————–
– lots of plots
– seems like in-depth exploratory analysis
Negative Aspects
—————————–
– no verbal explanations
– very hard to follow
– no laid out structure or plan
Datathon Kaufland Solution – Predictive Maintenance Based on Sensor Data for Forklifts
Positive Aspects
—————————
– good introduction
– good flow
– good aggregation of data on different levels
– nice idea of SMOTE upsampling
Negative Aspects
—————————
– layout of structure and “plan” missing
– no cross validation
– exploratory analysis of sensors missing
– often explanations missing, which makes it hard to follow through
– not quite clear to me which variables are used in classification method
– no variable importance analysis
– assumption of using stale machines as label is not quite solid as it could have been a planned routine maintenance/check, too