360.TRANSPORT ROUTES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC REG | PROC SGPLOT | MACROS | DATE FUNCTIONS (MDY-INTNX-INTCK) | APPEND | MERGE | SET | TRANSPOSE | PROC COPY

TRANSPORT ROUTES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC REG | PROC SGPLOT | MACROS | DATE FUNCTIONS (MDY-INTNX-INTCK) | APPEND | MERGE | SET | TRANSPOSE | PROC COPY

options nocenter;

1.TRANSPORT ROUTES DATASET CREATION

data transport_routes;

    length Route_Name $25 City $15;

    format Start_Date Analysis_Date date9.;

    Analysis_Date = '01JAN2026'd;

    input Route_Name $ City $ Stops Distance Travelers Revenue Start_Date :date9.;

    Route_Age_Years = intck('year', Start_Date, Analysis_Date);

    datalines;

BlueLine_Metro     Hyderabad 22 29.5 185000 750000 15MAR2015

RedLine_Metro      Delhi     30 34.2 240000 980000 10JAN2012

GreenBus_Corridor  Pune      18 21.0 95000  320000 01JUL2018

CityBus_Route12    Chennai   25 27.8 110000 410000 05MAY2016

Suburban_Train1    Mumbai    40 45.0 310000 1250000 12DEC2008

Airport_Express    Delhi     10 22.0 70000  520000 20FEB2011

Metro_Orange       Jaipur    14 16.5 52000  210000 01APR2019

BusRapid_A1        Ahmedabad 20 24.0 88000  360000 18AUG2017

RingRail_Outer     Kolkata   35 39.0 145000 670000 25NOV2014

CityLink_Bus9      Kochi     15 19.2 47000  180000 11JAN2020

Metro_Purple       Bengaluru 28 32.4 195000 820000 03SEP2013

HillRail_Shuttle   Shimla    8  9.5  18000  95000  01JAN2021

;

run;

proc print data=transport_routes;

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years
1	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11
2	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14
3	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8
4	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10
5	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18
6	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15
7	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7
8	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9
9	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12
10	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6
11	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13
12	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5

2.DATA VALIDATION – PROC CONTENTS & PRINT

proc contents data=transport_routes;

run;

OUTPUT:

The CONTENTS Procedure

Data Set Name	WORK.TRANSPORT_ROUTES	Observations	12
Member Type	DATA	Variables	9
Engine	V9	Indexes	0
Created	01/05/2026 07:20:26	Observation Length	96
Last Modified	01/05/2026 07:20:26	Deleted Observations	0
Protection		Compressed	NO
Data Set Type		Sorted	NO
Label
Data Representation	SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LINUX_IA64
Encoding	utf-8 Unicode (UTF-8)

Engine/Host Dependent Information
Data Set Page Size	131072
Number of Data Set Pages	1
First Data Page	1
Max Obs per Page	1363
Obs in First Data Page	12
Number of Data Set Repairs	0
Filename	/saswork/SAS_work7ECA00012603_odaws01-apse1-2.oda.sas.com/SAS_workA2B100012603_odaws01-apse1-2.oda.sas.com/transport_routes.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	67108910
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Alphabetic List of Variables and Attributes
#	Variable	Type	Len	Format
4	Analysis_Date	Num	8	DATE9.
2	City	Char	15
6	Distance	Num	8
8	Revenue	Num	8
9	Route_Age_Years	Num	8
1	Route_Name	Char	25
3	Start_Date	Num	8	DATE9.
5	Stops	Num	8
7	Travelers	Num	8

proc print data=transport_routes(obs=5);

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years
1	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11
2	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14
3	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8
4	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10
5	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18

3.DESCRIPTIVE STATISTICS – PROC MEANS

proc means data=transport_routes mean min max sum;

    var Stops Distance Travelers Revenue Route_Age_Years;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Sum
Stops Distance Travelers Revenue Route_Age_Years	22.0833333 26.6750000 129583.33 547083.33 10.6666667	8.0000000 9.5000000 18000.00 95000.00 5.0000000	40.0000000 45.0000000 310000.00 1250000.00 18.0000000	265.0000000 320.1000000 1555000.00 6565000.00 128.0000000

4.SQL-BASED ANALYSIS – PROC SQL

proc sql;

    create table city_summary as

    select City,

           count(*) as Total_Routes,

           sum(Travelers) as Total_Travelers,

           sum(Revenue) as Total_Revenue

    from transport_routes

    group by City;

quit;

proc print data=city_summary;

run;

OUTPUT:

Obs	City	Total_Routes	Total_Travelers	Total_Revenue
1	Ahmedabad	1	88000	360000
2	Bengaluru	1	195000	820000
3	Chennai	1	110000	410000
4	Delhi	2	310000	1500000
5	Hyderabad	1	185000	750000
6	Jaipur	1	52000	210000
7	Kochi	1	47000	180000
8	Kolkata	1	145000	670000
9	Mumbai	1	310000	1250000
10	Pune	1	95000	320000
11	Shimla	1	18000	95000

5.MACRO FOR ROUTE PERFORMANCE RATING

%macro route_rating(inset=, outset=);

data &outset;

    set &inset;

    if Revenue >= 800000 then Performance = "Excellent";

    else if Revenue >= 400000 then Performance = "Good";

    else if Revenue >= 200000 then Performance = "Average";

    else Performance = "Low";

run;

proc print data=&outset;

run;

%mend;

%route_rating(inset=transport_routes, outset=routes_rated);

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years	Performance
1	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11	Good
2	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14	Excellent
3	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8	Average
4	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10	Good
5	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18	Excellent
6	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15	Good
7	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7	Average
8	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9	Average
9	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12	Good
10	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6	Low
11	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13	Excellent
12	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5	Low

6.TIME-BASED ANALYSIS – INTNX AND MDY

data route_dates;

    set transport_routes;

    Next_Review_Date = intnx('month', Analysis_Date, 6, 'same');

    Financial_Year_Start = mdy(4,1,year(Analysis_Date));

    format Next_Review_Date Financial_Year_Start date9.;

run;

proc print data=route_dates;

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years	Next_Review_Date	Financial_Year_Start
1	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11	01JUL2026	01APR2026
2	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14	01JUL2026	01APR2026
3	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8	01JUL2026	01APR2026
4	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10	01JUL2026	01APR2026
5	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18	01JUL2026	01APR2026
6	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15	01JUL2026	01APR2026
7	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7	01JUL2026	01APR2026
8	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9	01JUL2026	01APR2026
9	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12	01JUL2026	01APR2026
10	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6	01JUL2026	01APR2026
11	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13	01JUL2026	01APR2026
12	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5	01JUL2026	01APR2026

7.REGRESSION ANALYSIS – PROC REG

proc reg data=transport_routes;

    model Revenue = Travelers Distance Stops;

run;

quit;

OUTPUT:

The REG Procedure

Model: MODEL1

Dependent Variable: Revenue

Number of Observations Read	12
Number of Observations Used	12

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	3	1.352004E12	4.506682E11	188.58	<.0001
Error	8	19118420960	2389802620
Corrected Total	11	1.371123E12

Root MSE	48886	R-Square	0.9861
Dependent Mean	547083	Adj R-Sq	0.9808
Coeff Var	8.93568

Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > |t|
Intercept	1	-98376	55344	-1.78	0.1134
Travelers	1	3.45438	0.39871	8.66	<.0001
Distance	1	25282	6082.00392	4.16	0.0032
Stops	1	-21581	5425.32108	-3.98	0.0041

The REG Procedure

Model: MODEL1

Dependent Variable: Revenue

8.DATA VISUALIZATION – PROC SGPLOT

proc sgplot data=transport_routes;

    scatter x=Travelers y=Revenue;

    reg x=Travelers y=Revenue;

    title "Revenue vs Travelers Across Transport Routes";

run;

OUTPUT:

9.APPEND OPERATION – ADDING NEW ROUTES

data new_routes;

    length Route_Name $25 City $15;

    format Start_Date date9.;

    input Route_Name $ City $ Stops Distance Travelers Revenue Start_Date :date9.;

    datalines;

Metro_Silver     Noida 18 21.5 65000 280000 01JAN2022

Bus_CrossTown    Indore 14 17.2 42000 160000 10FEB2023

;

run;

proc print data=new_routes;

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Stops	Distance	Travelers	Revenue
1	Metro_Silver	Noida	01JAN2022	18	21.5	65000	280000
2	Bus_CrossTown	Indore	10FEB2023	14	17.2	42000	160000

proc append base=transport_routes

            data=new_routes force;

run;

proc print data=transport_routes;

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years
1	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11
2	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14
3	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8
4	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10
5	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18
6	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15
7	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7
8	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9
9	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12
10	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6
11	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13
12	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5
13	Metro_Silver	Noida	01JAN2022	.	18	21.5	65000	280000	.
14	Bus_CrossTown	Indore	10FEB2023	.	14	17.2	42000	160000	.

10.MERGE OPERATION – ADDING CITY ZONES

data city_zones;

    input City $ Zone $;

    datalines;

Hyderabad South

Delhi North

Mumbai West

Bengaluru South

;

run;

proc print data=city_zones;

run;

OUTPUT:

Obs	City	Zone
1	Hyderaba	South
2	Delhi	North
3	Mumbai	West
4	Bengalur	South

proc sort data=transport_routes; by City; run;

proc print data=transport_routes;

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years
1	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9
2	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13
3	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10
4	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14
5	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15
6	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11
7	Bus_CrossTown	Indore	10FEB2023	.	14	17.2	42000	160000	.
8	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7
9	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6
10	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12
11	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18
12	Metro_Silver	Noida	01JAN2022	.	18	21.5	65000	280000	.
13	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8
14	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5

proc sort data=city_zones; by City; run;

proc print data=city_zones;

run;

OUTPUT:

Obs	City	Zone
1	Bengalur	South
2	Delhi	North
3	Hyderaba	South
4	Mumbai	West

data routes_merged;

    merge transport_routes(in=a) city_zones(in=b);

    by City;

    if a;

run;

proc print data=routes_merged;

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years	Zone
1	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9
2	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13
3	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10
4	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14	North
5	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15	North
6	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11
7	Bus_CrossTown	Indore	10FEB2023	.	14	17.2	42000	160000	.
8	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7
9	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6
10	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12
11	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18	West
12	Metro_Silver	Noida	01JAN2022	.	18	21.5	65000	280000	.
13	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8
14	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5

11.SET STATEMENT – STACKING DATA

data all_routes;

    set transport_routes 

        routes_rated;

run;

proc print data=all_routes;

run;

OUTPUT:

Obs	Route_Name	City	Start_Date	Analysis_Date	Stops	Distance	Travelers	Revenue	Route_Age_Years	Performance
1	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9
2	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13
3	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10
4	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14
5	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15
6	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11
7	Bus_CrossTown	Indore	10FEB2023	.	14	17.2	42000	160000	.
8	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7
9	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6
10	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12
11	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18
12	Metro_Silver	Noida	01JAN2022	.	18	21.5	65000	280000	.
13	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8
14	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5
15	BlueLine_Metro	Hyderabad	15MAR2015	01JAN2026	22	29.5	185000	750000	11	Good
16	RedLine_Metro	Delhi	10JAN2012	01JAN2026	30	34.2	240000	980000	14	Excellent
17	GreenBus_Corridor	Pune	01JUL2018	01JAN2026	18	21.0	95000	320000	8	Average
18	CityBus_Route12	Chennai	05MAY2016	01JAN2026	25	27.8	110000	410000	10	Good
19	Suburban_Train1	Mumbai	12DEC2008	01JAN2026	40	45.0	310000	1250000	18	Excellent
20	Airport_Express	Delhi	20FEB2011	01JAN2026	10	22.0	70000	520000	15	Good
21	Metro_Orange	Jaipur	01APR2019	01JAN2026	14	16.5	52000	210000	7	Average
22	BusRapid_A1	Ahmedabad	18AUG2017	01JAN2026	20	24.0	88000	360000	9	Average
23	RingRail_Outer	Kolkata	25NOV2014	01JAN2026	35	39.0	145000	670000	12	Good
24	CityLink_Bus9	Kochi	11JAN2020	01JAN2026	15	19.2	47000	180000	6	Low
25	Metro_Purple	Bengaluru	03SEP2013	01JAN2026	28	32.4	195000	820000	13	Excellent
26	HillRail_Shuttle	Shimla	01JAN2021	01JAN2026	8	9.5	18000	95000	5	Low

12.TRANSPOSE – ROUTE METRICS FORMAT CHANGE

proc transpose data=transport_routes out=routes_transposed;

    var Travelers Revenue;

    by Route_Name NotSorted;

run;

proc print data=routes_transposed;

run;

OUTPUT:

Obs	Route_Name	_NAME_	COL1
1	BusRapid_A1	Travelers	88000
2	BusRapid_A1	Revenue	360000
3	Metro_Purple	Travelers	195000
4	Metro_Purple	Revenue	820000
5	CityBus_Route12	Travelers	110000
6	CityBus_Route12	Revenue	410000
7	RedLine_Metro	Travelers	240000
8	RedLine_Metro	Revenue	980000
9	Airport_Express	Travelers	70000
10	Airport_Express	Revenue	520000
11	BlueLine_Metro	Travelers	185000
12	BlueLine_Metro	Revenue	750000
13	Bus_CrossTown	Travelers	42000
14	Bus_CrossTown	Revenue	160000
15	Metro_Orange	Travelers	52000
16	Metro_Orange	Revenue	210000
17	CityLink_Bus9	Travelers	47000
18	CityLink_Bus9	Revenue	180000
19	RingRail_Outer	Travelers	145000
20	RingRail_Outer	Revenue	670000
21	Suburban_Train1	Travelers	310000
22	Suburban_Train1	Revenue	1250000
23	Metro_Silver	Travelers	65000
24	Metro_Silver	Revenue	280000
25	GreenBus_Corridor	Travelers	95000
26	GreenBus_Corridor	Revenue	320000
27	HillRail_Shuttle	Travelers	18000
28	HillRail_Shuttle	Revenue	95000

13.PROC COPY – DATA MIGRATION

libname backup "/folders/myfolders/backup";

proc copy in=work out=backup;

    select transport_routes;

run;

**Try The Above Code And Comment It

To Visit My Previous Intermediate Students Performance Dataset:Click Here

To Visit My Previous Namkeen Data Creation:Click Here

To Visit My Previous Hyd_Mall_Family Dataset:Click Here

To Visit My Previous Home Energy Consumption Dataset:Click Here

Follow Us On : 

 

--->FOLLOW OUR BLOG FOR MORE INFORMATION.

--->PLEASE DO COMMENTS AND SHARE OUR BLOG.