Summary
Tabular augmentation is a technique that can improve the accuracy of ML models by increasing the amount of data used. It adds columns or rows to a dataset generated by existing features or data from other sources. It increases the available input data, allowing the model to make more accurate predictions. Tabular augmentation adds new information not currently included in the dataset, increasing the model’s utility. Tabular augmentation is beneficial when used with other ML techniques, such as DL, to improve the accuracy and performance of predictive models.
Pluto downloaded the real-world Bank Fraud and World Series datasets from the Kaggle website. He wrote most of the code in the Python Notebook for visualizing large datasets using various graphs, such as histograms, heatmaps, correlograms, and waffle and joy plots. He did this because understanding the datasets is essential before augmenting them. However, he didn’t write a CNN or RNN model to verify the...
