>>> import pandas as pd >>> df = pd.read_csv('dataset.csv') >>> print(df) | | FlightID | Airline | FlightNumber | Origin | Destination | DelayMinutes | DelayReason | Cancelled | Diverted | AircraftType | TailNumber | Distance | |---|---|---|---|---|---|---|---|---|---|---|---|---| | 0 | 1 | United | 4558 | ORD | MIA | 8 | Weather | True | False | Boeing 737 | N71066 | 1031 | | 1 | 2 | Delta | 8021 | LAX | MIA | 2 | Air Traffic Control | True | True | Airbus A320 | N22657 | 1006 | | 2 | 3 | Southwest | 7520 | DFW | SFO | 14 | Weather | True | True | Boeing 737 | N95611 | 2980 | | 3 | 4 | Delta | 2046 | ORD | BOS | -10 | NaN | False | False | Boeing 777 | N90029 | 1408 | | 4 | 5 | Delta | 6049 | LAX | SEA | 24 | Air Traffic Control | False | True | Boeing 737 | N27417 | 2298 | | 5 | 6 | Southwest | 6311 | LAX | JFK | -5 | NaN | False | False | Boeing 777 | N92652 | 1386 | | 6 | 7 | Southwest | 4188 | ORD | JFK | 7 | Weather | False | True | Boeing 777 | N25382 | 1674 | | 7 | 8 | Southwest | 3179 | DFW | MIA | 24 | Weather | False | True | Boeing 777 | N65410 | 2931 | | 8 | 9 | American Airlines | 3613 | ORD | MIA | 2 | Weather | False | False | Airbus A320 | N45570 | 2337 | | 9 | 10 | Southwest | 2186 | LAX | BOS | 25 | Maintenance | True | False | Boeing 777 | N15724 | 605 | | 10 | 11 | American Airlines | 9317 | JFK | MIA | -6 | NaN | True | True | Airbus A320 | N54905 | 2848 | | 11 | 12 | American Airlines | 5251 | DFW | BOS | 30 | Weather | False | False | Airbus A320 | N9…

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

>>> import pandas as pd >>> df = pd.read_csv('dataset.csv') >>> print(df) | | FlightID | Airline | FlightNumber | Origin | Destination | DelayMinutes | DelayReason | Cancelled | Diverted | AircraftType | TailNumber | Distance | |---|---|---|---|---|---|---|---|---|---|---|---|---| | 0 | 1 | United | 4558 | ORD | MIA | 8 | Weather | True | False | Boeing 737 | N71066 | 1031 | | 1 | 2 | Delta | 8021 | LAX | MIA | 2 | Air Traffic Control | True | True | Airbus A320 | N22657 | 1006 | | 2 | 3 | Southwest | 7520 | DFW | SFO | 14 | Weather | True | True | Boeing 737 | N95611 | 2980 | | 3 | 4 | Delta | 2046 | ORD | BOS | -10 | NaN | False | False | Boeing 777 | N90029 | 1408 | | 4 | 5 | Delta | 6049 | LAX | SEA | 24 | Air Traffic Control | False | True | Boeing 737 | N27417 | 2298 | | 5 | 6 | Southwest | 6311 | LAX | JFK | -5 | NaN | False | False | Boeing 777 | N92652 | 1386 | | 6 | 7 | Southwest | 4188 | ORD | JFK | 7 | Weather | False | True | Boeing 777 | N25382 | 1674 | | 7 | 8 | Southwest | 3179 | DFW | MIA | 24 | Weather | False | True | Boeing 777 | N65410 | 2931 | | 8 | 9 | American Airlines | 3613 | ORD | MIA | 2 | Weather | False | False | Airbus A320 | N45570 | 2337 | | 9 | 10 | Southwest | 2186 | LAX | BOS | 25 | Maintenance | True | False | Boeing 777 | N15724 | 605 | | 10 | 11 | American Airlines | 9317 | JFK | MIA | -6 | NaN | True | True | Airbus A320 | N54905 | 2848 | | 11 | 12 | American Airlines | 5251 | DFW | BOS | 30 | Weather | False | False | Airbus A320 | N9…

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

>>> import pandas as pd >>> df = pd.read_csv('dataset.csv') >>> print(df) | | FlightID | Airline | FlightNumber | Origin | Destination | DelayMinutes | DelayReason | Cancelled | Diverted | AircraftType | TailNumber | Distance | |---|---|---|---|---|---|---|---|---|---|---|---|---| | 0 | 1 | United | 4558 | ORD | MIA | 8 | Weather | True | False | Boeing 737 | N71066 | 1031 | | 1 | 2 | Delta | 8021 | LAX | MIA | 2 | Air Traffic Control | True | True | Airbus A320 | N22657 | 1006 | | 2 | 3 | Southwest | 7520 | DFW | SFO | 14 | Weather | True | True | Boeing 737 | N95611 | 2980 | | 3 | 4 | Delta | 2046 | ORD | BOS | -10 | NaN | False | False | Boeing 777 | N90029 | 1408 | | 4 | 5 | Delta | 6049 | LAX | SEA | 24 | Air Traffic Control | False | True | Boeing 737 | N27417 | 2298 | | 5 | 6 | Southwest | 6311 | LAX | JFK | -5 | NaN | False | False | Boeing 777 | N92652 | 1386 | | 6 | 7 | Southwest | 4188 | ORD | JFK | 7 | Weather | False | True | Boeing 777 | N25382 | 1674 | | 7 | 8 | Southwest | 3179 | DFW | MIA | 24 | Weather | False | True | Boeing 777 | N65410 | 2931 | | 8 | 9 | American Airlines | 3613 | ORD | MIA | 2 | Weather | False | False | Airbus A320 | N45570 | 2337 | | 9 | 10 | Southwest | 2186 | LAX | BOS | 25 | Maintenance | True | False | Boeing 777 | N15724 | 605 | | 10 | 11 | American Airlines | 9317 | JFK | MIA | -6 | NaN | True | True | Airbus A320 | N54905 | 2848 | | 11 | 12 | American Airlines | 5251 | DFW | BOS | 30 | Weather | False | False | Airbus A320 | N9…

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

MLP Quiz 1: 27 October 2024 (September 2024 term)

Question 1

+2 marksOne correct option

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

	FlightID	Airline	FlightNumber	Origin	Destination	DelayMinutes	DelayReason	Cancelled	Diverted	AircraftType	TailNumber	Distance
0	1	United	4558	ORD	MIA	8	Weather	True	False	Boeing 737	N71066	1031
1	2	Delta	8021	LAX	MIA	2	Air Traffic Control	True	True	Airbus A320	N22657	1006
2	3	Southwest	7520	DFW	SFO	14	Weather	True	True	Boeing 737	N95611	2980
3	4	Delta	2046	ORD	BOS	-10	NaN	False	False	Boeing 777	N90029	1408
4	5	Delta	6049	LAX	SEA	24	Air Traffic Control	False	True	Boeing 737	N27417	2298
5	6	Southwest	6311	LAX	JFK	-5	NaN	False	False	Boeing 777	N92652	1386
6	7	Southwest	4188	ORD	JFK	7	Weather	False	True	Boeing 777	N25382	1674
7	8	Southwest	3179	DFW	MIA	24	Weather	False	True	Boeing 777	N65410	2931
8	9	American Airlines	3613	ORD	MIA	2	Weather	False	False	Airbus A320	N45570	2337
9	10	Southwest	2186	LAX	BOS	25	Maintenance	True	False	Boeing 777	N15724	605
10	11	American Airlines	9317	JFK	MIA	-6	NaN	True	True	Airbus A320	N54905	2848
11	12	American Airlines	5251	DFW	BOS	30	Weather	False	False	Airbus A320	N94983	131

Based on the above data, answer the given subquestions.

Which option will give the total count of canceled flights in the dataset?

Question 2

+2 marksOne correct option

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

	FlightID	Airline	FlightNumber	Origin	Destination	DelayMinutes	DelayReason	Cancelled	Diverted	AircraftType	TailNumber	Distance
0	1	United	4558	ORD	MIA	8	Weather	True	False	Boeing 737	N71066	1031
1	2	Delta	8021	LAX	MIA	2	Air Traffic Control	True	True	Airbus A320	N22657	1006
2	3	Southwest	7520	DFW	SFO	14	Weather	True	True	Boeing 737	N95611	2980
3	4	Delta	2046	ORD	BOS	-10	NaN	False	False	Boeing 777	N90029	1408
4	5	Delta	6049	LAX	SEA	24	Air Traffic Control	False	True	Boeing 737	N27417	2298
5	6	Southwest	6311	LAX	JFK	-5	NaN	False	False	Boeing 777	N92652	1386
6	7	Southwest	4188	ORD	JFK	7	Weather	False	True	Boeing 777	N25382	1674
7	8	Southwest	3179	DFW	MIA	24	Weather	False	True	Boeing 777	N65410	2931
8	9	American Airlines	3613	ORD	MIA	2	Weather	False	False	Airbus A320	N45570	2337
9	10	Southwest	2186	LAX	BOS	25	Maintenance	True	False	Boeing 777	N15724	605
10	11	American Airlines	9317	JFK	MIA	-6	NaN	True	True	Airbus A320	N54905	2848
11	12	American Airlines	5251	DFW	BOS	30	Weather	False	False	Airbus A320	N94983	131

Based on the above data, answer the given subquestions.

Which of the following code snippets will add a new column ’OnTime’ that shows True if the flight was not delayed, else False?

Question 3

+3 marksOne or more correct options

python

1>>> import pandas as pd
2>>> df = pd.read_csv('dataset.csv')
3>>> print(df)

	FlightID	Airline	FlightNumber	Origin	Destination	DelayMinutes	DelayReason	Cancelled	Diverted	AircraftType	TailNumber	Distance
0	1	United	4558	ORD	MIA	8	Weather	True	False	Boeing 737	N71066	1031
1	2	Delta	8021	LAX	MIA	2	Air Traffic Control	True	True	Airbus A320	N22657	1006
2	3	Southwest	7520	DFW	SFO	14	Weather	True	True	Boeing 737	N95611	2980
3	4	Delta	2046	ORD	BOS	-10	NaN	False	False	Boeing 777	N90029	1408
4	5	Delta	6049	LAX	SEA	24	Air Traffic Control	False	True	Boeing 737	N27417	2298
5	6	Southwest	6311	LAX	JFK	-5	NaN	False	False	Boeing 777	N92652	1386
6	7	Southwest	4188	ORD	JFK	7	Weather	False	True	Boeing 777	N25382	1674
7	8	Southwest	3179	DFW	MIA	24	Weather	False	True	Boeing 777	N65410	2931
8	9	American Airlines	3613	ORD	MIA	2	Weather	False	False	Airbus A320	N45570	2337
9	10	Southwest	2186	LAX	BOS	25	Maintenance	True	False	Boeing 777	N15724	605
10	11	American Airlines	9317	JFK	MIA	-6	NaN	True	True	Airbus A320	N54905	2848
11	12	American Airlines	5251	DFW	BOS	30	Weather	False	False	Airbus A320	N94983	131

Based on the above data, answer the given subquestions.

How can you filter the flights that have a delay due to ’Weather’?

Select all that apply.

19 more questions in this paper

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More on the MLP Quiz 1 27 Oct 2024 paper

The IIT Madras BS Machine Learning Practice (MLP) Quiz 1 paper sat on 27 Oct 2024, in the September 2024 term: 22 questions for 50 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

Feature	MLP Quiz 1 27 Oct 2024 at a glance
Term	September 2024 term
Subject	Machine Learning Practice
Course code	BSCS2008
Questions	22
Marks	50
Duration	120 min
MCQ	16
MSQ	6
Official paper	IIT M DIPLOMA AN EXAM QDD2 27 Oct 2024
Negative marking	No negative marking.
Updated	25 Sept 2026

MLP Quiz 1: 27 October 2024 (September 2024 term)

Question 1

Question 2

Question 3

19 more questions in this paper

More on the MLP Quiz 1 27 Oct 2024 paper

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