training deep leaning models so farwe have explored toy datasets crafted using pythonand thus we barely explored the idea of data processing and data engineering that is essential before building deep learning models this would hold true for all forms of data visualization--tabularimagestextaudio/video/speechetc in this exercisewe will look at few basic data processing steps although extensive data processing is beyond the scope of this bookthe objective here is to give you an idea of the kind of processing that might be required for real-life use cases let' get started before downloading the aforementioned datasetyou first need to register and create an account at www kaggle com in listing - we import the essential python packages for our exercise listing - importing the required libraries #import required libraries import torch nn as nn import torch as tch import numpy as nppandas as pd from sklearn metrics import confusion_matrixaccuracy_score from sklearn metrics import precision_scorerecall_score,roc_ curveaucroc_auc_score from sklearn model_selection import train_test_split from sklearn utils import shuffle import matplotlib pyplot as plt sklearn is machine learning library within python that provides comprehensive list of algorithmsmetricsdata processing toolsand other utility functions we use the metrics module within sklearn for handy functions that help in computing model performance--metrics such as precisionrecallaccuracyetc similarlypandas is great python package that provides comprehensive means to processmanipulateand explore tabular dataframes we will use pandas to read and explore the dataset for our exerciseas well as leverage few functions within pandas to tailor the