From the course: Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
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Exploratory data analysis (EDA)
From the course: Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
Exploratory data analysis (EDA)
- [Instructor] In this video, we will delve into exploratory data analysis. In the previous chapter, we did an initial data check and some preliminary exploratory data analysis. Our preliminary EDA examined our telecom dataset to understand its structure, content, and potential issues. This preliminary EDA allowed us to then pre-process the data and address some issues. EDA can also guide the creation of new features from existing data or the transformation of existing features to improve model performance. By understanding the data better, you can gain insights into why a model make certain predictions, aiding in interpreting its behavior. Recall that we're using Python with its very helpful libraries like Pandas, NumPy, Matplotlib, and scikit-Learn. So what is the value of data pre-processing to EDA? Well, the payment method was initially a categorical feature, with three values: credit card, bank withdrawal,…
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