As an engineer in a manufacturing plant producing IC chips, you are responsible for machine maintenance in your department. Ensuring that machines run smoothly without disruption is a key part of your role.
Currently, the machines operate continuously and only stop when a failure occurs. When a machine fails, it requires repairs or replacement of parts, which can take several days and severely impact production output. To reduce the frequency of machine failures, you have proposed using an Al model to predict potential failures and take preventive action before a machine stops working.
To train the Al model for this predictive maintenance, you have collected all available data on every machine on your production floor. You have found that failure of different type of machine occur with different combination of measured data.
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You have compiled the data collected into a CSV called “M1_Quest1_1.csv”, please use the Orange Data Mining app to work on the followings:
1. Analysis the dataset and preprocess the data.
2. Identify the correct features and target from the dataset.
3. Using any 3 models (must include the best model) for training and compare their performance. You have to include these 3 models in your submission.
4. Select the model to be used and save the model.
After you have trained the model and save the model, you then collect real time data and save it to another CSV called “M1_Quest1_1b.csv”. Load your model and inference this data collected and identify which machine ID will fail by pasting the machine ID through a text field in the project.
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