Learn about feature selection in mobile phone price prediction models, including key steps and considerations for refining datasets to enhance model accuracy.
Table of Contents
Question
Your company created a new mobile phone. You need to define a price range (0 – low cost to 3 – very high cost) for the phone. You collected technical and sales data for the phones on the market. Now you are ready to train your model. Here is your train dataset:
What column will you discard from the final dataset during feature selection?
Answer
Color
Explanation
Data pre-processing involves various techniques, like feature selection, normalization or feature engineering, etc.
During feature selection, we identify features that would help us with label prediction. And we discard the rest. In our dataset, the Color feature wouldn’t correlate with the label due to the constant value of “black.” We can safely remove this feature from the final dataset.
All other options should be included in the training dataset.
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