Handling Null values is vital for data professionals, especially when dealing with sparse numerical datasets. Today, Khun Jan Kuliga Kitsachoke, a Data Scientist from SCB TechX, will share five practical techniques for you here:
- Mean/Median/Mode Imputation: This involves replacing Null values with the mean, median, or mode of the entire dataset. This technique is commonly applied to columns with closely distributed values.
- Forward Fill / Backward Fill: Null values are replaced with the value preceding or following them. This method is preferred for time series data with minimal fluctuations and continuous trends.
3. Interpolation: Null values are replaced with values calculated based on adjacent data points, considering the preceding or following values. Interpolation is favored when there’s a discernible trend or pattern among data points in a column.
4. Replace with Constant Values: Null values are replaced with predetermined constant values, such as 0. This approach is suitable when null values signify “no data available,” for instance, when there are zero website visitors on a particular day.
5. Replace with Random Values: Null values are replaced with any random value. This method is useful when aiming for data diversity.
These techniques offer flexibility, but the choice depends on thorough analysis for optimal data utilization.
Lastly, SCB TechX is ready to provide any organization with professional advice, technology solutions, and comprehensive Data Platform services through TechX Data Platform.
If you are interested, please feel free to contact us at contact@scbtechx.io
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