Different Approaches to Categorical Encoding There are multiple ways of handling Categorical variables. The two most widely used techniques: - Label Encoding - One-Hot Bncoding We will see here the Label and OneHot Encoding impact on prediction accuracy. import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np import numpy. random as nr import math from sklearn import preprocessing import sklearn, model_selection as ms from sklearn import linear_model import sklearn. metrics as sklm \&matplotlib inline import os currentdirectory = os.getcwd() * Loading the data set: credit = pd. read_cso('Credit. csv) print icredit. shape) credit. head() (682,22) Q1: Show the number of rows per credit type (bad_credit 0 or 1 ): Different Approaches to Categorical Encoding There are multiple ways of handling Categorical variables. The two most widely used techniques: - Label Encoding - One-Hot Bncoding We will see here the Label and OneHot Encoding impact on prediction accuracy. import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np import numpy. random as nr import math from sklearn import preprocessing import sklearn, model_selection as ms from sklearn import linear_model import sklearn. metrics as sklm \&matplotlib inline import os currentdirectory = os.getcwd() * Loading the data set: credit = pd. read_cso('Credit. csv) print icredit. shape) credit. head() (682,22) Q1: Show the number of rows per credit type (bad_credit 0 or 1 )