In the given data set, identify the number of missing values by column; fill the missing...

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In the given data set, identify the number of missing values by column; fill the missing values by most frequent number (hint: you need strategy='most frequent'). import pandas as pd from io import stringio csv_data = \ 111 co11, co12 ,2.0 3 10.0, 11.0 1, 3 1, 11 TIE df = pd.read_csv (String10(csv_data)) df 3.) In the given data set, generate the dummy variables for the categorical variables using one-hot encoding. import pandas as pd import numpy as np np.random.seed (123) df = pd.DataFrame (np.random.rand (10, 2), columns = ['col1', 'co12']) df['co13'] = list (*.join(np.random. choice (list('abc')) for in range (len (df)))) - df 4.) Assume col3 is the target, separate the original data set in #3 into 80% training and 20% test. In the given data set, identify the number of missing values by column; fill the missing values by most frequent number (hint: you need strategy='most frequent'). import pandas as pd from io import stringio csv_data = \ 111 co11, co12 ,2.0 3 10.0, 11.0 1, 3 1, 11 TIE df = pd.read_csv (String10(csv_data)) df 3.) In the given data set, generate the dummy variables for the categorical variables using one-hot encoding. import pandas as pd import numpy as np np.random.seed (123) df = pd.DataFrame (np.random.rand (10, 2), columns = ['col1', 'co12']) df['co13'] = list (*.join(np.random. choice (list('abc')) for in range (len (df)))) - df 4.) Assume col3 is the target, separate the original data set in #3 into 80% training and 20% test.

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