259.🚗 Which Vehicles Truly Perform Better on Indian Roads? – A Real-World SAS Analytics Project

🚗 Which Vehicles Truly Perform Better on Indian Roads? – A Real-World SAS Analytics Project

/*CREATING A DATASET OF REAL-WORLD VEHICLES COMPARISON IN INDIA*/

options nodate nonumber nocenter; 

1) Create dataset: vehicles

data vehicles;

length VehicleID 8 Vehicle_Type $25 Manufacturer $25 Region $15 Fuel_Type $12 Owner_Type $12 Emission_Std $8;

format Price_INR comma12. Mileage_kmpl 8.2 Running_km comma12. Year_Mfg 4.;

input VehicleID Vehicle_Type $ Manufacturer $ Year_Mfg Price_INR Mileage_kmpl Running_km Fuel_Type $ Seating Owner_Type $ Region $ Emission_Std $ Battery_kWh;

datalines;

1 Car Maruti 2018 550000 18.5 42000 Petrol 5 Individual North BSIV .

2 Motorcycle Bajaj 2020 75000 65.0 12000 Petrol 2 Individual West BSIV .

3 Scooter TVS 2019 62000 45.0 8000 Petrol 2 Individual South BSIV .

4 Truck Tata 2015 1500000 6.5 120000 Diesel 2 Commercial North BSIII .

5 Bus AshokLeyland 2014 3500000 3.2 250000 Diesel 40 Commercial South BSIII .

6 Auto_Rickshaw Bajaj 2017 65000 35.0 60000 CNG 3 Commercial East BSIV .

7 Electric_Car Tata 2021 1400000 0 15000 Electric 5 Individual West BSVI 22

8 E_Bike Ather 2022 170000 0 5000 Electric 2 Individual South BSVI 3.7

9 Van Maruti_Env 2016 850000 14.0 60000 Diesel 8 Commercial West BSIV .

10 Tractor Mahindra 2013 800000 0 90000 Diesel 2 Agricultural East BSIV .

11 Bicycle Hero 2020 4000 0 300 Individual 1 Individual North . .

12 Luxury_Car BMW 2019 4200000 12.0 25000 Petrol 5 Individual South BSIV .

13 Delivery_Van Tata 2018 720000 11.0 80000 Diesel 3 Commercial West BSIV .

14 Scooter_Hybrid Hero 2020 85000 55.0 9000 Petrol 2 Individual East BSIV .

15 Electric_Scooter Ola 2022 90000 0 3000 Electric 2 Individual North BSVI 2.5

16 Pickup Mahindra 2017 650000 12.5 40000 Diesel 3 Commercial South BSIV .

17 CNG_Car Maruti_CNG 2016 780000 22.0 50000 CNG 5 Individual West BSIV .

18 School_Bus Tata 2015 1800000 3.8 180000 Diesel 45 Commercial North BSIII .

19 Cargo_Truck Volvo 2018 6000000 5.8 100000 Diesel 2 Commercial East BSIV .

20 E_Rickshaw Kinetic 2021 42000 0 15000 Electric 3 Commercial South BSVI 1.5

21 Electric_Bicycle Yulu 2022 12000 0 2000 Electric 1 Individual West BSVI 0.5

22 SUV Hyundai 2020 1800000 14.0 22000 Petrol 7 Individual North BSIV .

;

run;

proc print;run;

Output:

Obs	VehicleID	Vehicle_Type	Manufacturer	Region	Fuel_Type	Owner_Type	Emission_Std	Price_INR	Mileage_kmpl	Running_km	Year_Mfg	Seating	Battery_kWh
1	1	Car	Maruti	North	Petrol	Individual	BSIV	550,000	18.50	42,000	2018	5	.
2	2	Motorcycle	Bajaj	West	Petrol	Individual	BSIV	75,000	65.00	12,000	2020	2	.
3	3	Scooter	TVS	South	Petrol	Individual	BSIV	62,000	45.00	8,000	2019	2	.
4	4	Truck	Tata	North	Diesel	Commercial	BSIII	1,500,000	6.50	120,000	2015	2	.
5	5	Bus	AshokLeyland	South	Diesel	Commercial	BSIII	3,500,000	3.20	250,000	2014	40	.
6	6	Auto_Rickshaw	Bajaj	East	CNG	Commercial	BSIV	65,000	35.00	60,000	2017	3	.
7	7	Electric_Car	Tata	West	Electric	Individual	BSVI	1,400,000	0.00	15,000	2021	5	22.0
8	8	E_Bike	Ather	South	Electric	Individual	BSVI	170,000	0.00	5,000	2022	2	3.7
9	9	Van	Maruti_Env	West	Diesel	Commercial	BSIV	850,000	14.00	60,000	2016	8	.
10	10	Tractor	Mahindra	East	Diesel	Agricultural	BSIV	800,000	0.00	90,000	2013	2	.
11	11	Bicycle	Hero	North	Individual	Individual		4,000	0.00	300	2020	1	.
12	12	Luxury_Car	BMW	South	Petrol	Individual	BSIV	4,200,000	12.00	25,000	2019	5	.
13	13	Delivery_Van	Tata	West	Diesel	Commercial	BSIV	720,000	11.00	80,000	2018	3	.
14	14	Scooter_Hybrid	Hero	East	Petrol	Individual	BSIV	85,000	55.00	9,000	2020	2	.
15	15	Electric_Scooter	Ola	North	Electric	Individual	BSVI	90,000	0.00	3,000	2022	2	2.5
16	16	Pickup	Mahindra	South	Diesel	Commercial	BSIV	650,000	12.50	40,000	2017	3	.
17	17	CNG_Car	Maruti_CNG	West	CNG	Individual	BSIV	780,000	22.00	50,000	2016	5	.
18	18	School_Bus	Tata	North	Diesel	Commercial	BSIII	1,800,000	3.80	180,000	2015	45	.
19	19	Cargo_Truck	Volvo	East	Diesel	Commercial	BSIV	6,000,000	5.80	100,000	2018	2	.
20	20	E_Rickshaw	Kinetic	South	Electric	Commercial	BSVI	42,000	0.00	15,000	2021	3	1.5
21	21	Electric_Bicycle	Yulu	West	Electric	Individual	BSVI	12,000	0.00	2,000	2022	1	0.5
22	22	SUV	Hyundai	North	Petrol	Individual	BSIV	1,800,000	14.00	22,000	2020	7	.

2) Format variables for reporting

proc format;

value $vtfmt

'Car'='Car'

'Motorcycle'='Motorcycle'

'Scooter'='Scooter'

'Truck'='Truck'

'Bus'='Bus'

'Auto_Rickshaw'='Auto Rickshaw'

'Electric_Car'='Electric Car'

'E_Bike'='E-Bike'

'Van'='Van'

'Tractor'='Tractor'

'Bicycle'='Bicycle'

'Luxury_Car'='Luxury Car'

'Delivery_Van'='Delivery Van'

'Scooter_Hybrid'='Scooter Hybrid'

'Electric_Scooter'='Electric Scooter'

'Pickup'='Pickup'

'CNG_Car'='CNG Car'

'School_Bus'='School Bus'

'Cargo_Truck'='Cargo Truck'

'E_Rickshaw'='E-Rickshaw'

'Electric_Bicycle'='Electric Bicycle'

'SUV'='SUV';

run;

3) Quick look

proc print data=vehicles(obs=10) label noobs;

title "SAMPLE: VEHICLES (FIRST 10)";

run;

title;

Output:

SAMPLE: VEHICLES (FIRST 10)

VehicleID	Vehicle_Type	Manufacturer	Region	Fuel_Type	Owner_Type	Emission_Std	Price_INR	Mileage_kmpl	Running_km	Year_Mfg	Seating	Battery_kWh
1	Car	Maruti	North	Petrol	Individual	BSIV	550,000	18.50	42,000	2018	5	.
2	Motorcycle	Bajaj	West	Petrol	Individual	BSIV	75,000	65.00	12,000	2020	2	.
3	Scooter	TVS	South	Petrol	Individual	BSIV	62,000	45.00	8,000	2019	2	.
4	Truck	Tata	North	Diesel	Commercial	BSIII	1,500,000	6.50	120,000	2015	2	.
5	Bus	AshokLeyland	South	Diesel	Commercial	BSIII	3,500,000	3.20	250,000	2014	40	.
6	Auto_Rickshaw	Bajaj	East	CNG	Commercial	BSIV	65,000	35.00	60,000	2017	3	.
7	Electric_Car	Tata	West	Electric	Individual	BSVI	1,400,000	0.00	15,000	2021	5	22.0
8	E_Bike	Ather	South	Electric	Individual	BSVI	170,000	0.00	5,000	2022	2	3.7
9	Van	Maruti_Env	West	Diesel	Commercial	BSIV	850,000	14.00	60,000	2016	8	.
10	Tractor	Mahindra	East	Diesel	Agricultural	BSIV	800,000	0.00	90,000	2013	2	.

4) Summary statistics (continuous vars)

proc means data=vehicles n mean median min max std q1 q3;

var Price_INR Mileage_kmpl Running_km Battery_kWh;

class Vehicle_Type;

title "SUMMARY STATISTICS BY VEHICLE TYPE";

run;

title;

Output:

SUMMARY STATISTICS BY VEHICLE TYPE

The MEANS Procedure

Vehicle_Type	N Obs	Variable	N	Mean	Median	Minimum	Maximum	Std Dev	Lower Quartile	Upper Quartile
Auto_Rickshaw	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	65000.00 35.0000000 60000.00 .	65000.00 35.0000000 60000.00 .	65000.00 35.0000000 60000.00 .	65000.00 35.0000000 60000.00 .	. . . .	65000.00 35.0000000 60000.00 .	65000.00 35.0000000 60000.00 .
Bicycle	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	4000.00 0 300.0000000 .	4000.00 0 300.0000000 .	4000.00 0 300.0000000 .	4000.00 0 300.0000000 .	. . . .	4000.00 0 300.0000000 .	4000.00 0 300.0000000 .
Bus	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	3500000.00 3.2000000 250000.00 .	3500000.00 3.2000000 250000.00 .	3500000.00 3.2000000 250000.00 .	3500000.00 3.2000000 250000.00 .	. . . .	3500000.00 3.2000000 250000.00 .	3500000.00 3.2000000 250000.00 .
CNG_Car	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	780000.00 22.0000000 50000.00 .	780000.00 22.0000000 50000.00 .	780000.00 22.0000000 50000.00 .	780000.00 22.0000000 50000.00 .	. . . .	780000.00 22.0000000 50000.00 .	780000.00 22.0000000 50000.00 .
Car	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	550000.00 18.5000000 42000.00 .	550000.00 18.5000000 42000.00 .	550000.00 18.5000000 42000.00 .	550000.00 18.5000000 42000.00 .	. . . .	550000.00 18.5000000 42000.00 .	550000.00 18.5000000 42000.00 .
Cargo_Truck	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	6000000.00 5.8000000 100000.00 .	6000000.00 5.8000000 100000.00 .	6000000.00 5.8000000 100000.00 .	6000000.00 5.8000000 100000.00 .	. . . .	6000000.00 5.8000000 100000.00 .	6000000.00 5.8000000 100000.00 .
Delivery_Van	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	720000.00 11.0000000 80000.00 .	720000.00 11.0000000 80000.00 .	720000.00 11.0000000 80000.00 .	720000.00 11.0000000 80000.00 .	. . . .	720000.00 11.0000000 80000.00 .	720000.00 11.0000000 80000.00 .
E_Bike	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 1	170000.00 0 5000.00 3.7000000	170000.00 0 5000.00 3.7000000	170000.00 0 5000.00 3.7000000	170000.00 0 5000.00 3.7000000	. . . .	170000.00 0 5000.00 3.7000000	170000.00 0 5000.00 3.7000000
E_Rickshaw	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 1	42000.00 0 15000.00 1.5000000	42000.00 0 15000.00 1.5000000	42000.00 0 15000.00 1.5000000	42000.00 0 15000.00 1.5000000	. . . .	42000.00 0 15000.00 1.5000000	42000.00 0 15000.00 1.5000000
Electric_Bicycle	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 1	12000.00 0 2000.00 0.5000000	12000.00 0 2000.00 0.5000000	12000.00 0 2000.00 0.5000000	12000.00 0 2000.00 0.5000000	. . . .	12000.00 0 2000.00 0.5000000	12000.00 0 2000.00 0.5000000
Electric_Car	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 1	1400000.00 0 15000.00 22.0000000	1400000.00 0 15000.00 22.0000000	1400000.00 0 15000.00 22.0000000	1400000.00 0 15000.00 22.0000000	. . . .	1400000.00 0 15000.00 22.0000000	1400000.00 0 15000.00 22.0000000
Electric_Scooter	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 1	90000.00 0 3000.00 2.5000000	90000.00 0 3000.00 2.5000000	90000.00 0 3000.00 2.5000000	90000.00 0 3000.00 2.5000000	. . . .	90000.00 0 3000.00 2.5000000	90000.00 0 3000.00 2.5000000
Luxury_Car	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	4200000.00 12.0000000 25000.00 .	4200000.00 12.0000000 25000.00 .	4200000.00 12.0000000 25000.00 .	4200000.00 12.0000000 25000.00 .	. . . .	4200000.00 12.0000000 25000.00 .	4200000.00 12.0000000 25000.00 .
Motorcycle	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	75000.00 65.0000000 12000.00 .	75000.00 65.0000000 12000.00 .	75000.00 65.0000000 12000.00 .	75000.00 65.0000000 12000.00 .	. . . .	75000.00 65.0000000 12000.00 .	75000.00 65.0000000 12000.00 .
Pickup	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	650000.00 12.5000000 40000.00 .	650000.00 12.5000000 40000.00 .	650000.00 12.5000000 40000.00 .	650000.00 12.5000000 40000.00 .	. . . .	650000.00 12.5000000 40000.00 .	650000.00 12.5000000 40000.00 .
SUV	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	1800000.00 14.0000000 22000.00 .	1800000.00 14.0000000 22000.00 .	1800000.00 14.0000000 22000.00 .	1800000.00 14.0000000 22000.00 .	. . . .	1800000.00 14.0000000 22000.00 .	1800000.00 14.0000000 22000.00 .
School_Bus	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	1800000.00 3.8000000 180000.00 .	1800000.00 3.8000000 180000.00 .	1800000.00 3.8000000 180000.00 .	1800000.00 3.8000000 180000.00 .	. . . .	1800000.00 3.8000000 180000.00 .	1800000.00 3.8000000 180000.00 .
Scooter	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	62000.00 45.0000000 8000.00 .	62000.00 45.0000000 8000.00 .	62000.00 45.0000000 8000.00 .	62000.00 45.0000000 8000.00 .	. . . .	62000.00 45.0000000 8000.00 .	62000.00 45.0000000 8000.00 .
Scooter_Hybrid	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	85000.00 55.0000000 9000.00 .	85000.00 55.0000000 9000.00 .	85000.00 55.0000000 9000.00 .	85000.00 55.0000000 9000.00 .	. . . .	85000.00 55.0000000 9000.00 .	85000.00 55.0000000 9000.00 .
Tractor	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	800000.00 0 90000.00 .	800000.00 0 90000.00 .	800000.00 0 90000.00 .	800000.00 0 90000.00 .	. . . .	800000.00 0 90000.00 .	800000.00 0 90000.00 .
Truck	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	1500000.00 6.5000000 120000.00 .	1500000.00 6.5000000 120000.00 .	1500000.00 6.5000000 120000.00 .	1500000.00 6.5000000 120000.00 .	. . . .	1500000.00 6.5000000 120000.00 .	1500000.00 6.5000000 120000.00 .
Van	1	Price_INR Mileage_kmpl Running_km Battery_kWh	1 1 1 0	850000.00 14.0000000 60000.00 .	850000.00 14.0000000 60000.00 .	850000.00 14.0000000 60000.00 .	850000.00 14.0000000 60000.00 .	. . . .	850000.00 14.0000000 60000.00 .	850000.00 14.0000000 60000.00 .

5) Frequency / distribution (categorical vars)

proc freq data=vehicles;

tables Vehicle_Type Fuel_Type Region Owner_Type Emission_Std / nocum nopercent;

title "FREQUENCIES: CATEGORICAL VARIABLES";

run;

title;

Output:

FREQUENCIES: CATEGORICAL VARIABLES

The FREQ Procedure

Vehicle_Type	Frequency
Auto_Rickshaw	1
Bicycle	1
Bus	1
CNG_Car	1
Car	1
Cargo_Truck	1
Delivery_Van	1
E_Bike	1
E_Rickshaw	1
Electric_Bicycle	1
Electric_Car	1
Electric_Scooter	1
Luxury_Car	1
Motorcycle	1
Pickup	1
SUV	1
School_Bus	1
Scooter	1
Scooter_Hybrid	1
Tractor	1
Truck	1
Van	1

Fuel_Type	Frequency
CNG	2
Diesel	8
Electric	5
Individual	1
Petrol	6

Region	Frequency
East	4
North	6
South	6
West	6

Owner_Type	Frequency
Agricultural	1
Commercial	9
Individual	12

Emission_Std	Frequency
Frequency Missing = 1
BSIII	3
BSIV	13
BSVI	5

6) PROC SQL: Aggregations

proc sql;

create table agg_by_fuel as

select Fuel_Type, count(*) as N, mean(Price_INR) as Avg_Price format=comma12., mean(Mileage_kmpl) as Avg_Mileage format=8.2

from vehicles

group by Fuel_Type

order by Avg_Price desc;

quit;

proc print data=agg_by_fuel noobs; 

    title "AGGREGATE BY FUEL TYPE"; 

run; 

title;

Output:

AGGREGATE BY FUEL TYPE

Fuel_Type	N	Avg_Price	Avg_Mileage
Diesel	8	1,977,500	7.10
Petrol	6	1,128,667	34.92
CNG	2	422,500	28.50
Electric	5	342,800	0.00
Individual	1	4,000	0.00

7) PROC TRANSPOSE: Price by Year (wide)

proc sort data=vehicles out=_vsorted; by Year_Mfg; run;

proc transpose data=_vsorted out=price_by_year prefix=Price_;

by Year_Mfg notsorted;

id Vehicle_Type;

var Price_INR;

run;

proc print data=price_by_year(obs=10) noobs; 

   title "TRANSPOSED: PRICE BY YEAR (WIDE)";

run; 

title;

Output:

TRANSPOSED: PRICE BY YEAR (WIDE)

Year_Mfg	_NAME_	Price_Tractor	Price_Bus	Price_Truck	Price_School_Bus	Price_Van	Price_CNG_Car	Price_Auto_Rickshaw	Price_Pickup	Price_Car	Price_Delivery_Van	Price_Cargo_Truck	Price_Scooter	Price_Luxury_Car	Price_Motorcycle	Price_Bicycle	Price_Scooter_Hybrid	Price_SUV	Price_Electric_Car	Price_E_Rickshaw	Price_E_Bike	Price_Electric_Scooter	Price_Electric_Bicycle
2013	Price_INR	800,000	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
2014	Price_INR	.	3,500,000	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
2015	Price_INR	.	.	1,500,000	1,800,000	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
2016	Price_INR	.	.	.	.	850,000	780,000	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
2017	Price_INR	.	.	.	.	.	.	65,000	650,000	.	.	.	.	.	.	.	.	.	.	.	.	.	.
2018	Price_INR	.	.	.	.	.	.	.	.	550,000	720,000	6,000,000	.	.	.	.	.	.	.	.	.	.	.
2019	Price_INR	.	.	.	.	.	.	.	.	.	.	.	62,000	4,200,000	.	.	.	.	.	.	.	.	.
2020	Price_INR	.	.	.	.	.	.	.	.	.	.	.	.	.	75,000	4,000	85,000	1,800,000	.	.	.	.	.
2021	Price_INR	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1,400,000	42,000	.	.	.
2022	Price_INR	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	170,000	90,000	12,000

8) Macro: generate region-wise summary tables

%macro region_report(reg=);

%let reg_trim=%sysfunc(strip(&reg));

proc sql;

create table region_&reg_trim as

select Vehicle_Type, count(*) as Count, mean(Price_INR) as Avg_Price format=comma12., mean(Mileage_kmpl) as Avg_Mileage format=8.2

from vehicles

where upcase(Region)=upcase("&reg_trim")

group by Vehicle_Type;

quit;

proc report data=region_&reg_trim nowd headline;

column Vehicle_Type Count Avg_Price Avg_Mileage;

define Vehicle_Type / group;

define Count / "COUNT";

define Avg_Price / "AVG PRICE (INR)";

define Avg_Mileage / "AVG MILEAGE";

title "REGION REPORT: &reg_trim";

run;

title;

%mend region_report;

%region_report(reg=North);

Output:

REGION REPORT: North

Vehicle_Type	COUNT	AVG PRICE (INR)	AVG MILEAGE
Bicycle	1	4,000	0.00
Car	1	550,000	18.50
Electric_Scooter	1	90,000	0.00
SUV	1	1,800,000	14.00
School_Bus	1	1,800,000	3.80
Truck	1	1,500,000	6.50

%region_report(reg=South);

Output:

REGION REPORT: South

Vehicle_Type	COUNT	AVG PRICE (INR)	AVG MILEAGE
Bus	1	3,500,000	3.20
E_Bike	1	170,000	0.00
E_Rickshaw	1	42,000	0.00
Luxury_Car	1	4,200,000	12.00
Pickup	1	650,000	12.50
Scooter	1	62,000	45.00

%region_report(reg=West);

Output:

REGION REPORT: West

Vehicle_Type	COUNT	AVG PRICE (INR)	AVG MILEAGE
CNG_Car	1	780,000	22.00
Delivery_Van	1	720,000	11.00
Electric_Bicycle	1	12,000	0.00
Electric_Car	1	1,400,000	0.00
Motorcycle	1	75,000	65.00
Van	1	850,000	14.00

9) PROC SGPLOT: visualize price vs mileage (scatter)

proc sgplot data=vehicles;

scatter x=Mileage_kmpl y=Price_INR / group=Fuel_Type markerattrs=(symbol=circlefilled) jitter;

xaxis label="Mileage (kmpl)";

yaxis label="Price (INR)";

title "PRICE vs MILEAGE (COLOURED BY FUEL TYPE)";

run;

title;

Output:

10) PROC SQL: Create a derived table for inexpensive vehicles under 1 Lakh

proc sql;

create table under_1lakh as

select VehicleID, Vehicle_Type, Manufacturer, Price_INR, Fuel_Type, Region

from vehicles

where Price_INR <= 100000

order by Price_INR;

quit;

proc print data=under_1lakh noobs; 

   title "VEHICLES UNDER INR 1,00,000"; 

run;

title;

Output:

VEHICLES UNDER INR 1,00,000

VehicleID	Vehicle_Type	Manufacturer	Price_INR	Fuel_Type	Region
11	Bicycle	Hero	4,000	Individual	North
21	Electric_Bicycle	Yulu	12,000	Electric	West
20	E_Rickshaw	Kinetic	42,000	Electric	South
3	Scooter	TVS	62,000	Petrol	South
6	Auto_Rickshaw	Bajaj	65,000	CNG	East
2	Motorcycle	Bajaj	75,000	Petrol	West
14	Scooter_Hybrid	Hero	85,000	Petrol	East
15	Electric_Scooter	Ola	90,000	Electric	North

11) Cleanup intermediate

proc datasets lib=work nolist;

delete _vsorted;

quit;

Log:

NOTE: Deleting WORK._VSORTED (memtype=DATA).

NOTE: PROCEDURE DATASETS used (Total process time):

real time 0.00 seconds

user cpu time 0.01 seconds

system cpu time 0.00 seconds

memory 493.25k

OS Memory 22180.00k

Timestamp 08/12/2025 12:59:16 PM

Step Count 137 Switch Count 2

Page Faults 0

Page Reclaims 53

Page Swaps 0

Voluntary Context Switches 11

Involuntary Context Switches 0

Block Input Operations 0

Block Output Operations 8

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