A staggering 99% of a massive dataset consisting of over 1 million rows and only 100 instances of product unavailability has been analyzed to predict when products will become unavailable the next day. The target variable reflects the probability of this event occurring, making it an imbalanced dataset. To tackle this challenge, various machine learning models such as Logistic Regression, Random Forest, XGBoost, Naive Bayes, and K-Nearest Neighbors (KNN) have been employed.
To ensure accurate predictions, features on different scales require normalization or standardization to prevent model bias. The absence of missing values simplifies the analysis process. Data has been split by product, with each product’s full series either in the training set or test set, to avoid leakage. Evaluating the models’ performance using the Receiver Operating Characteristic-Area Under the Curve (ROC AUC) metric is crucial due to the class imbalance.
By monitoring sudden spikes in attributes or values above the 90th percentile, anomalies can be flagged, providing valuable insights into product unavailability.
Scott Manley
@DJSnM
·
May 18 Reading the paper behind this video and they actually expected the nozzle to fail, the carbon-carbon nozzle extension had a preexisting crack. But it wasn’t expected to cause any issues upstream.
The chamber failure was the ‘surprise’, and it failed at a layer where the 3D print
AvuxTF
@AvuxTF
·
Apr 27 So you’re telling me that Hasbro shipped out their biggest HasLab yet with a critical design flaw that means it’s ripping itself apart internally?
Great job guys!
Northlandfox
@northlandfox
·
Jul 1 Maybe they can fix the labeling and seal on that and everything else. I thought it was just torn and poorly placed, but it seems the label instead of being on the side of the jar is under the product label, which then tears when container is opened.
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