From the course: Complete Guide to Generative AI for Data Analysis and Data Science

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Handling missing data

Handling missing data

From the course: Complete Guide to Generative AI for Data Analysis and Data Science

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Handling missing data

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- [Instructor] When we work with real-world data, we often have to address issues around data quality, like missing values. So what I've done is I've made a copy of the synthetic dataset that we used to build a simple classifier, and I've removed some of the values. I've just gone through and just randomly deleted some values that we can see here. Some values are missing, not too many, but just enough to make it interesting from a model-building perspective. We have enough missing values, we're going to see what the impact is and how we can address that. So I've opened up a CoLab notebook and I've uploaded that missing data value. I call it missing Synthetic_Iris_Like_Data. I've uploaded that dataset into my CoLab environment and I've modified the model we built in the previous video to point to the missing synthetic data. So now, if you recall, when we first ran the model, we got around like an 87% accuracy, so it went really well. So let's see what happens when we have missing…

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