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Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance

Positive Aspects
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– 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
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– no word on hyperparameter tuning

Datathon Kaufland Solution – Kaufland case – Team3

Positive Aspects
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– lots of plots
– seems like in-depth exploratory analysis

Negative Aspects
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– 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
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– good introduction
– good flow
– good aggregation of data on different levels
– nice idea of SMOTE upsampling

Negative Aspects
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– 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