training deep leaning models target "depositpredictors set(new_df columnsset([target]print("new_df shape:",new_df shapenew_df[predictorshead(out[]new_df shape( we have now defined list of predictors that contain all independent predictor column namesand target that contains our -- deposit column name the new_df dataframe has all categorical columns processed as onehot encoded forms by the get_dummies function in pandas the preceding output for listing - limits the view of columns to the first fewwe can see that contact is now transformed as contact_unknowncontact_cellularetc the dataset now has only numeric columns finallybefore designing our neural networkwe would need to convert all the columns to float datatype and split into training and validation datasetsand then convert to pytorch tensors listing - prepares the dataset for training and validation listing - preparing the dataset for training and validation #convert all datatypes within pandas dataframe to float #(compatibility with pytorch tensorsnew_df new_df astype(np float #split dataset into train/test [ :