import pandas as pd import numpy| as np data =1 '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).