Please answer using the below Default template taking the expected output into consideration. The previous answers to this question returned the wrong sample proportion

Default Template

# Import necessary modules

age = int(input())

# Read csv file titanic.csv into data frame

# Take the subset of the data where Embarked = "S" south = # Code to take subset

# Find total number in subset n1 = # Code to count number in subset

# Find number in subset where Age > age x1 = # Code to count number where Age > age

# Calculate proportion p1 = # Code to calculate proportion print(f'Sample proportion is {p1:.6f}')

# Calculate standard error stderr = # Code to calculate standard error

# Find 95% confidence interval conf_int = # Code to find confidence interval print(f'({conf_int[0]:.6f}, {conf_int[1]:.6f})')

# Find proportion for full data n2 = # Code to count number in data set x2 = # Code to count number in full data set where Age > age p2 = # Code to calculate proportion

# Determine if the actual proportion falls within the 95% confidence interval if conf_int[0]

5.11 LAB: Confidence intervals for population proportions - Load the titanic dataset. - Find the proportion of passengers who embarked from Southampton whose age is greater than the user defined age. - Calculate the 95% confidence interval for the sample proportion. - Find proportion of all passengers whose age is greater than the user defined age. - Report whether the actual proportion is within the sample confidence interval. When the input is: 50 the output is: Sample proportion is 0.069876 (0.050186,0.089565) Actual proportion, 0.071829, is within the 958 confidence interval