In a case study, which method would be best for handling missing values in a dat

Practice Questions

Q1
In a case study, which method would be best for handling missing values in a dataset?
  1. Drop the rows with missing values
  2. Impute missing values with the mean
  3. Use a neural network to predict missing values
  4. All of the above

Questions & Step-by-Step Solutions

In a case study, which method would be best for handling missing values in a dataset?
  • Step 1: Identify the missing values in your dataset.
  • Step 2: Determine the amount of missing data (is it a small percentage or a large percentage?).
  • Step 3: Consider the context of your data (what type of data is it and how important are the missing values?).
  • Step 4: Choose a method to handle the missing values, such as removing them, filling them with averages, or using more complex techniques like interpolation.
  • Step 5: Apply the chosen method and check how it affects your dataset.
No concepts available.
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