2. IBM Data Science Certification: Sample Question and Solution
Question: In an IBM Data Science project, you are given a dataset with missing values in multiple columns. How would you handle these missing values to ensure the accuracy of your model?
Solution: Handling missing values is a critical step in preparing data for machine learning models. Here are some common methods to address missing data:
- Remove Rows with Missing Values: If the dataset is large and the number of rows with missing values is small, you can remove those rows without significantly affecting the model's performance.
- Impute Missing Values: If removing rows isn't feasible, you can impute missing values using statistical techniques such as:
- Mean/Median Imputation: Replace missing values with the mean or median of the column.
- Mode Imputation: For categorical data, replace missing values with the mode (most frequent value).
- Advanced Imputation Methods: Use more advanced techniques such as K-Nearest Neighbors (KNN) imputation or regression imputation to fill in missing values based on the relationship between other variables.
