14. import pandas as pdfrom sklearn import preprocessingdata = {"gender": list('FMMF')}df = pd.DataFrame(data)one_hot = preprocessing.OneHotEncoder(sparse_output=False,drop='if_binary')new_df = pd.DataFrame(one_hot.fit_transform(df), columns= one_hot.get_feature_names_out())new_dfWhat is the output of the code above? Gender_M01.010.020.031.0 Gender_FGender_M00.01.011.00.021.00.030.01.0 Gender_FGender_M01.00.010.01.020.01.031.00.0  Gender_M00.011.021.030.0

10. import pandas as pdfrom sklearn.preprocessing import train_test_splitdf = pd.read_csv('insurance_claims.csv')xtrain, xtest, ytrain, ytest = train_test_split(df.drop("is_claim", axis=1), df.is_claim, test_size=0.3, random_state=42)Which of the following is true about the code above?The code reads a csv file named insurance_claims. It splits the data into train and test sets. The test split contains 30% of the data. The random state makes sure that the data is split at random to remove inherent order which may be in the data. When the code is run multiple times it produces the diffent splits since `train_test_split` with the parameter `random_state` splits data at random.None of the given answersThe code reads a csv file names insurance claims. The `train_test_split` function will give an error since the second position argument `df.is_claim` is referencing a column that has been drop on the first position argument `df.drop("is_claim", axis=1)The code reads a csv file named insurance_claims. It splits the data into train and test sets. The train split contains 70% of the data. The random state makes sure that when the code is run multiple times it produces the same identical splits since `train_test_split` splits data at random.

What is the output of the following Python code:Codeimport numpy as npimport pandas as pddata={'name':['Alice','Bob','Ben'], 'order':[1,3,2], 'sector':['Sales','Finance','Marketing'] }df=pd.DataFrame(data)print(df)

Lewis Terman classified gender based on the MF test.Group of answer choicesTrueFalse

Assume, you have defined a data frame which has 2 columns.import numpy as npdf = pd.DataFrame({'Id':[1,2,3,4],'val':[2,5,np.nan,6]})Which of the following will be the output of the below print statement?print df.val == np.nan0    False1    False2    False3    False0    False1    False2    True3    False0    True1    True2    True3    TrueNone of these

, you are given a list of items in a DataFrame as below.D = [‘A’,’B’,’C’,’D’,’E’,’AA’,’AB’]Now, you want to apply label encoding on this list for importing and transforming, using LabelEncoder.from sklearn.preprocessing import LabelEncoderle = LabelEncoder()What will be the output of the print statement below ?print le.fit_transform(D)array([0, 2, 3, 4, 5, 6, 1])array([0, 3, 4, 5, 6, 1, 2])array([0, 2, 3, 4, 5, 1, 6])Any of the above