Dataset in the test: M$1\_$Ques$1\_1.$csv and M$1\_$Ques$1\_1$b$.$csv

Orange project: Admin#$\_$Assignment.ows

All the answers, including your observation and conclusion, please write on the text field and

paste it on the project.

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 AI model to predict potential failures

and take preventive action before a machine stops working.

To train the AI 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.

You have compiled the data collected into a CSV called $$M$1\_$Quest$1\_1.$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 $$M$1\_$Quest$1\_1$b$.$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.

IMPORTANT:

Do write down all the decisions and steps taken for each of the $4$ steps above by text fields

in the project.