Your model predictions are off due to data quality issues. How can you still meet your client's expectations?
When data science models fail to deliver accurate predictions, it's often a sign that there's a problem with the data quality. As a data scientist, your reputation hinges on the reliability of your models. But what happens when you're faced with data quality issues that compromise your model's performance? You can still meet your client's expectations by taking proactive steps to address the root causes and improve your model's predictions.
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Zindzi GriffinGraduate Student @Carnegie Mellon University | Spelman College Alumna | Seeking Full Time Opportunities
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Shuvojit DasMachine Learning Engineer | Generative AI & Data Science Enthusiast | Transforming Data into Actionable Insights |…
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Anshul GuptaSenior Associate - DS at Axtria | Panjab University