training deep leaning models check for missing values identify strategies to treat missing values drop records (if the number of missing records < %impute records with approaches similar to outliers nextlet' explore the different datatypes within the dataset deep learning models only understand numbers pytorchmore specificallyonly handles -bit floating-point numbers we would need to transform our dataset into suitable form that can be ready to use with pytorch listing - explores the distribution of distinct datatypes listing - distributing the distinct datatypes #check the distinct datatypes within the dataset df dtypes value_counts(out[]int object dtypeint we have six object (stringdatatype-based columnswhich we would need to convert into numeric flags before building models we would convert categorical columns into one-hot encoded forms where each category value is represented as binary flag before doing thathoweverlet' manually convert columns that have yes/no binary categories into single column and leverage pandas-based function to convert the remainder set of categorical columns automatically listing - demonstrates extracting categorical columns from the dataset