No detailed data science question will be asked they will ask very basic questions and rest is your luck. because you will not be able to know what and why did not like. the question such as how did you remove null value /outlier treatment/ training process.
1) Mostly on random forest (since it was my project) 2) how would you avoid overfitting 3) What would be your approach if the data is not trainable. 4) Metric 5) Why do you want to join Target
Normal python questions and questions related to my projects.
bias vs variance tradeoff
Resume based
In depth about model performance metrics of supervised machine learning models.
Talk about one of your previous projects in MLOPS that went into production.
SQL and Python pandas database transformation. Walk through a classification problem and caveats (how would you measure performance and feature importance)
Quel algo tu utilises pour faire de la feature selection ?
Can linear regression predict negative values if they were no negative values in the training data
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