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New mobile apps to keep an eye on
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What new social media mobile apps are available in 2023?
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Use new social media apps as marketing funnels
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Try out Twitter Spaces or Clubhouse on iPhone
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What app are you currently experimenting on?
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In the world of predictive AI, data quality is essential. One common challenge that data scientists and AI engineers face is dealing with incomplete data in their datasets and models.
The most common places where incomplete data may occur are blank entries within fields in your datasets. These blank fields can significantly impact model performance if not handled correctly. Today, we'll explore two distinct scenarios for managing empty values and discuss why understanding the context of your data is crucial for building accurate predictive models.
Case 1: Redefine the Blank Field as Zero
In some situations, a null value actually represents a meaningful zero. Let's consider a lead conversion prediction model that takes into account factors like page views and form submissions on a company's website.
Example: Imagine you're building a model to predict which leads are most likely to convert based on their engagement with your website. Your dataset includes fields for:
- Number of page views
- Number of form submissions
For a new lead who hasn't interacted with your site yet, these fields may contain blank values. However, treating these blanks as truly "unknown" would be a mistake. In this context, null actually means the lead has zero page views and zero form submissions.
Why it matters: By treating these blanks as zeros, your model can learn that leads with no interaction are less likely to convert compared to those with some level of engagement. This distinction is crucial for accurate predictions and for understanding the customer journey.
Case 2: Randomize the Empty Data Field When Building your Model
In other scenarios, a blank value represents genuinely missing or unknown information that shouldn't be interpreted as zero.
Example: Consider a field in your dataset that represents the industry a lead belongs to. This information might sometimes be missing due to various reasons:
- The lead didn't provide this information
- There was an error in data collection
- The industry doesn't fit neatly into predefined categories
In this case, treating the blank value as zero doesn't make sense. "Zero industry" is not a meaningful concept, and attempting to force it into your model could lead to skewed results.
Why it matters:
By instructing your model to ignore or handle these blanks separately, you prevent it from drawing false conclusions based on non-existent relationships. This approach maintains the integrity of your predictions and ensures that the model focuses on genuinely informative features.
Forwrd's latest feature launch:
With our latest feature launch, Forwrd.AI has just simplified your model building and approach to empty fields within datasets and blank values. Now you can define your approach to empty fields based on your marketing and revenue strategy within the Forwrd platform AND…. based on the specific use case, business logic and field.
Forwrd continues to be your predictive superhero 🦸 🤖 🦾 in simplifying data science workflows and bringing predictive AI to GTM teams worldwide.
