Task 3 only all the part using google colab import pandas as pd import numpy as

Task 3 only all the part using google colab

 

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import pandas as pd import numpy as np data = { } 'Student ID': [1, 2, 3, 4, 5], 'Name': ['Norah', 'Mohammed', 'Faisal', 'Ali', 'Lama'], 'Age': [19, 20, 'unknown', 'unknown', 21], 'Gender': ['Female', 'Male', 'Male', 'Male', 'Female'],| 'Score': [85, 92, 78, 88, 'unknown'] # ((((PRINT THE HEAD AFTER EACH STEP)))) # Task 1: Load the dataset into a Pandas DataFrame #Task 2: Drop Columns that Aren't Useful #Task 3: Handle Missing Values # replace unknown with NaN # Count missing values in each column and print the result missing values = df.isnull().sum() print (missing_values) # Fill missing values in 'Age' and 'Score' with the mean (you can use the built in method mean () # Task 4: Convert Categorical Values to Numeric for gender column # Task 5: Apply Feature Scaling/Normalization for age and score columns using MinMaxScaler (search about it) import pandas as pd import numpy as np data = { } 'Student ID': [1, 2, 3, 4, 5], 'Name': ['Norah', 'Mohammed', 'Faisal', 'Ali', 'Lama'], 'Age': [19, 20, 'unknown', 'unknown', 21], 'Gender': ['Female', 'Male', 'Male', 'Male', 'Female'],| 'Score': [85, 92, 78, 88, 'unknown'] # ((((PRINT THE HEAD AFTER EACH STEP)))) # Task 1: Load the dataset into a Pandas DataFrame #Task 2: Drop Columns that Aren't Useful #Task 3: Handle Missing Values # replace unknown with NaN # Count missing values in each column and print the result missing values = df.isnull().sum() print (missing_values) # Fill missing values in 'Age' and 'Score' with the mean (you can use the built in method mean () # Task 4: Convert Categorical Values to Numeric for gender column # Task 5: Apply Feature Scaling/Normalization for age and score columns using MinMaxScaler (search about it)