276.REAL WORLD DIFFERENT TYPES OF PEOPLE IN INDIA DATASET CREATION AND ANALYSIS USING PROC FORMAT | PROC CONTENTS | PROC PRINT | PROC SORT | PROC FREQ | PROC MEANS | PROC SUMMARY | PROC UNIVARIATE | PROC TABULATE | PROC REPORT | PROC TRANSPOSE | PROC SQL | PROC RANK | PROC SGPLOT | MACROS IN SAS

REAL WORLD DIFFERENT TYPES OF PEOPLE IN INDIA DATASET CREATION AND ANALYSIS USING PROC FORMAT | PROC CONTENTS | PROC PRINT | PROC SORT | PROC FREQ | PROC MEANS | PROC SUMMARY | PROC UNIVARIATE | PROC TABULATE | PROC REPORT | PROC TRANSPOSE | PROC SQL | PROC RANK | PROC SGPLOT | MACROS IN SAS

 /*CREATING THE REAL-WORLD "TYPES OF PEOPLE IN INDIA" DATASET*/

1) FORMATS & LABELS

Purpose: Define user-friendly categories for readability in outputs. 

proc format;

  value $genderF  'M'='Male' 'F'='Female' 'O'='Other/Non-binary';

  value $marF     'S'='Single' 'M'='Married' 'D'='Divorced' 'W'='Widowed';

  value $ruF      'Urban'='Urban' 'Rural'='Rural' 'SemiUrban'='Semi-Urban';

  value yesnoF    0='No' 1='Yes';

  value agebandF  low-17='0-17' 18-24='18-24' 25-34='25-34' 35-44='35-44'

                  45-54='45-54' 55-64='55-64' 65-high='65+';

  value incomeF   low-100000='<= 1L' 100001-300000='1L-3L' 300001-600000='3L-6L'

                  600001-1200000='6L-12L' 1200001-high='> 12L';

run;

Log:

NOTE: Format $GENDERF has been output.

NOTE: Format $MARF has been output.

NOTE: Format $RUF has been output.

NOTE: Format YESNOF has been output.

NOTE: Format AGEBANDF has been output.

NOTE: Format INCOMEF has been output.

2) CORE DATA CREATION 

Purpose: Create the base PEOPLE_INDIA dataset with realistic attributes. 

data people_india;

  length Person_ID 8 Name $28 Gender $1 Age 8 City $20 State $20

         Education_Level $20 Occupation $24 Sector $16

         Income_INR 8 Household_Size 8 Marital_Status $1

         Language $18 Smartphone_User 8 Internet_Hours_Per_Day 8

         Commute_Mode $14 Commute_Minutes 8 Fitness_Mins_Per_Week 8

         Has_Health_Insurance 8 Voter_ID_Flag 8 Credit_Score 8

         UPI_Transactions_Month 8 Festival_Celebrated $18

         Cuisine_Pref $16 Veg_Flag 8 Digital_Literacy_Score 8

         Rural_Urban $10 Disability_Flag 8 Travel_Trips_Year 8

         Pollution_Concern_Score 8 Blood_Group $3;

  infile datalines dsd truncover;

  input Person_ID Name :$28. Gender :$1. Age City :$20. State :$20.

        Education_Level :$20. Occupation :$24. Sector :$16.

        Income_INR Household_Size Marital_Status :$1.

        Language :$18. Smartphone_User Internet_Hours_Per_Day

        Commute_Mode :$14. Commute_Minutes Fitness_Mins_Per_Week

        Has_Health_Insurance Voter_ID_Flag Credit_Score

        UPI_Transactions_Month Festival_Celebrated :$18.

        Cuisine_Pref :$16. Veg_Flag Digital_Literacy_Score

        Rural_Urban :$10. Disability_Flag Travel_Trips_Year

        Pollution_Concern_Score Blood_Group :$3.;

  format Gender $genderF. Marital_Status $marF. Rural_Urban $ruF.

         Smartphone_User yesnoF. Has_Health_Insurance yesnoF.

         Voter_ID_Flag yesnoF. Veg_Flag yesnoF. Age agebandF.

         Income_INR incomeF.;

  label Education_Level = "Highest Education"

        Income_INR      = "Annual Income (INR)"

        Internet_Hours_Per_Day = "Daily Internet Hours"

        Commute_Mode    = "Primary Commute Mode"

        Fitness_Mins_Per_Week = "Weekly Fitness Minutes"

        Digital_Literacy_Score = "Digital Literacy (0-100)"

        UPI_Transactions_Month = "Monthly UPI Transactions"

        Pollution_Concern_Score = "Pollution Concern (1-10)";

datalines;

1,Arjun Mehta,M,27,Mumbai,Maharashtra,Graduate,Software Engineer,IT,900000,3,S,Hindi,1,4,Metro,60,120,1,1,760,35,Diwali,North Indian,0,85,Urban,0,4,7,O+

2,Priya Iyer,F,31,Chennai,Tamil Nadu,Postgraduate,Data Analyst,IT,1100000,4,M,Tamil,1,3,Bike,40,90,1,1,780,28,Pongal,South Indian,1,88,Urban,0,3,6,B+

3,Sameer Khan,M,24,Hyderabad,Telangana,Graduate,Inside Sales,Private,450000,5,S,Urdu,1,5,Bike,35,60,0,1,720,22,Eid,Hyderabadi,0,72,Urban,0,2,5,A+

4,Neha Sharma,F,39,Delhi,Delhi,Postgraduate,Marketing Manager,Private,1400000,3,M,Hindi,1,2,Car,55,75,1,1,805,18,Holi,North Indian,0,83,Urban,0,5,8,AB+

5,Rohan Das,M,45,Kolkata,West Bengal,Graduate,School Teacher,Public,650000,4,M,Bengali,1,2,Bus,50,80,1,1,768,20,Durga Puja,Bengali,1,78,Urban,0,1,7,O-

6,Ananya Roy,F,22,Kolkata,West Bengal,Undergraduate,Student,NA,0,5,S,Bengali,1,6,Metro,30,100,0,0,0,12,Durga Puja,Continental,0,70,Urban,0,1,6,A-

7,Amit Patil,M,34,Pune,Maharashtra,Diploma,Mechanic,Private,380000,6,M,Marathi,1,2,Bike,45,40,0,1,700,15,Ganesh Chaturthi,Maharashtrian,1,68,SemiUrban,0,1,5,O+

8,Sana Parveen,F,28,Patna,Bihar,Graduate,Nurse,Healthcare,480000,5,S,Hindi,1,3,Auto,35,120,1,1,730,20,Eid,North Indian,0,77,SemiUrban,0,1,6,B+

9,Ritesh Verma,M,52,Lucknow,Uttar Pradesh,Graduate,Shop Owner,Informal,550000,6,M,Hindi,1,1,Car,25,30,0,1,690,40,Diwali,North Indian,0,60,Urban,0,1,6,A+

10,Keerthi R,F,26,Visakhapatnam,Andhra Pradesh,Graduate,Graphic Designer,Media,520000,4,S,Telugu,1,5,Bike,30,90,0,0,740,24,Ugadi,South Indian,0,82,Urban,0,3,7,B-

11,Gurpreet Singh,M,33,Amritsar,Punjab,Graduate,Logistics Supervisor,Logistics,600000,5,M,Punjabi,1,2,Car,40,60,0,1,715,18,Gurpurab,Punjabi,0,66,Urban,0,2,6,O+

12,Sonali Kulkarni,F,41,Nagpur,Maharashtra,Postgraduate,HR Lead,Private,1200000,3,M,Marathi,1,2,Car,35,120,1,1,790,26,Diwali,Maharashtrian,0,84,Urban,0,2,8,AB-

13,Faizan Ali,M,29,Jaipur,Rajasthan,Graduate,Hotel Front Office,Hospitality,360000,4,S,Hindi,1,4,Bus,25,45,0,1,705,20,Diwali,Rajasthani,0,64,Urban,0,1,5,A+

14,Kavya Nair,F,35,Kochi,Kerala,Postgraduate,Physiotherapist,Healthcare,900000,3,M,Malayalam,1,2,Car,30,180,1,1,775,32,Onam,South Indian,1,86,Urban,0,2,6,O+

15,Manoj Kumar,M,47,Gurugram,Haryana,Graduate,Project Manager,IT,1800000,4,M,Hindi,1,2,Car,60,60,1,1,820,38,Diwali,North Indian,0,90,Urban,0,4,9,B+

16,Anusha S,F,23,Mysuru,Karnataka,Undergraduate,Student,NA,0,5,S,Kannada,1,6,Bus,20,110,0,0,0,14,Dasara,South Indian,1,74,Urban,0,1,6,A-

17,Deepak Yadav,M,30,Indore,Madhya Pradesh,Graduate,Field Sales,Private,420000,6,M,Hindi,1,5,Bike,50,50,0,1,710,25,Diwali,North Indian,0,69,Urban,0,1,6,O+

18,Trisha Dey,F,27,Silchar,Assam,Graduate,Content Writer,Media,460000,4,S,Assamese,1,4,Auto,20,70,0,1,735,22,Bihu,North East,0,80,SemiUrban,0,2,6,B+

19,Vikram Rao,M,38,Bengaluru,Karnataka,Postgraduate,Data Scientist,IT,2200000,3,M,Kannada,1,3,Metro,50,150,1,1,835,45,Ugadi,South Indian,0,92,Urban,0,3,9,O+

20,Sapna Jain,F,29,Bhopal,Madhya Pradesh,Graduate,Accountant,Private,500000,5,S,Hindi,1,3,Bus,30,60,0,1,725,20,Diwali,North Indian,1,76,SemiUrban,0,1,7,A+

21,Harish Chandra,M,56,Varanasi,Uttar Pradesh,Secondary,Priest,Religious,240000,5,M,Hindi,0,0,Walk,10,15,0,1,650,8,Diwali,North Indian,1,50,Urban,0,0,6,O+

22,Rekha Gupta,F,43,Kanpur,Uttar Pradesh,Graduate,Bank Officer,Public,980000,4,M,Hindi,1,2,Car,30,90,1,1,790,30,Diwali,North Indian,1,88,Urban,0,2,8,AB+

23,Aakash Jain,M,25,Ahmedabad,Gujarat,Graduate,Entrepreneur,Startup,1200000,4,S,Gujarati,1,5,Car,35,70,0,1,780,60,Navratri,Gujarati,0,85,Urban,0,4,8,B+

24,Meera Pillai,F,48,Thiruvananthapuram,Kerala,Postgraduate,School Principal,Public,1250000,3,M,Malayalam,1,1,Car,25,100,1,1,815,20,Onam,South Indian,1,90,Urban,0,2,8,A+

25,Rajeev Ranjan,M,32,Ranchi,Jharkhand,Graduate,Police Sub-Inspector,Public,700000,4,M,Hindi,1,2,Bike,20,60,1,1,740,25,Chhath,North Indian,0,72,Urban,0,1,7,O+

26,Nisha B,F,27,Coimbatore,Tamil Nadu,Graduate,Quality Analyst,Manufacturing,520000,4,S,Tamil,1,3,Bike,25,80,0,1,735,22,Pongal,South Indian,1,82,Urban,0,2,7,B-

27,Arvind Sinha,M,44,Bhubaneswar,Odisha,Graduate,Government Clerk,Public,580000,5,M,Odia,1,2,Bike,15,45,1,1,720,18,Raja Parba,Odia,1,65,Urban,0,1,7,A+

28,Shruti Joshi,F,36,Surat,Gujarat,Postgraduate,Fashion Buyer,Private,1300000,3,M,Gujarati,1,3,Car,35,120,0,1,800,35,Navratri,Gujarati,0,88,Urban,0,3,8,O-

29,Noor Zoya,F,21,Aligarh,Uttar Pradesh,Undergraduate,Student,NA,0,6,S,Urdu,1,6,Bus,30,80,0,0,0,10,Eid,North Indian,0,72,SemiUrban,0,1,6,B+

30,Anil Kumar,M,28,Chandigarh,Chandigarh,Graduate,Civil Engineer,Construction,850000,3,S,Hindi,1,2,Car,30,100,0,1,770,25,Holi,North Indian,0,81,Urban,0,2,8,A-

31,Devika Rao,F,33,Mangalore,Karnataka,Graduate,Product Manager,IT,1600000,3,M,Kannada,1,3,Car,35,100,1,1,825,40,Ugadi,South Indian,0,89,Urban,0,3,8,AB+

32,Shivam Patel,M,26,Vadodara,Gujarat,Graduate,Mechanical Engineer,Manufacturing,780000,4,S,Gujarati,1,2,Bike,25,70,0,1,760,20,Navratri,Gujarati,1,80,Urban,0,2,7,O+

;

run;

proc print;run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group
1	1	Arjun Mehta	Male	25-34	Mumbai	Maharashtra	Graduate	Software Engineer	IT	6L-12L	3	Single	Hindi	Yes	4	Metro	60	120	Yes	Yes	760	35	Diwali	North Indian	No	85	Urban	0	4	7	O+
2	2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+
3	3	Sameer Khan	Male	18-24	Hyderabad	Telangana	Graduate	Inside Sales	Private	3L-6L	5	Single	Urdu	Yes	5	Bike	35	60	No	Yes	720	22	Eid	Hyderabadi	No	72	Urban	0	2	5	A+
4	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+
5	5	Rohan Das	Male	45-54	Kolkata	West Bengal	Graduate	School Teacher	Public	6L-12L	4	Married	Bengali	Yes	2	Bus	50	80	Yes	Yes	768	20	Durga Puja	Bengali	Yes	78	Urban	0	1	7	O-
6	6	Ananya Roy	Female	18-24	Kolkata	West Bengal	Undergraduate	Student	NA	<= 1L	5	Single	Bengali	Yes	6	Metro	30	100	No	No	0	12	Durga Puja	Continental	No	70	Urban	0	1	6	A-
7	7	Amit Patil	Male	25-34	Pune	Maharashtra	Diploma	Mechanic	Private	3L-6L	6	Married	Marathi	Yes	2	Bike	45	40	No	Yes	700	15	Ganesh Chaturthi	Maharashtrian	Yes	68	Semi-Urban	0	1	5	O+
8	8	Sana Parveen	Female	25-34	Patna	Bihar	Graduate	Nurse	Healthcare	3L-6L	5	Single	Hindi	Yes	3	Auto	35	120	Yes	Yes	730	20	Eid	North Indian	No	77	Semi-Urban	0	1	6	B+
9	9	Ritesh Verma	Male	45-54	Lucknow	Uttar Pradesh	Graduate	Shop Owner	Informal	3L-6L	6	Married	Hindi	Yes	1	Car	25	30	No	Yes	690	40	Diwali	North Indian	No	60	Urban	0	1	6	A+
10	10	Keerthi R	Female	25-34	Visakhapatnam	Andhra Pradesh	Graduate	Graphic Designer	Media	3L-6L	4	Single	Telugu	Yes	5	Bike	30	90	No	No	740	24	Ugadi	South Indian	No	82	Urban	0	3	7	B-
11	11	Gurpreet Singh	Male	25-34	Amritsar	Punjab	Graduate	Logistics Supervisor	Logistics	3L-6L	5	Married	Punjabi	Yes	2	Car	40	60	No	Yes	715	18	Gurpurab	Punjabi	No	66	Urban	0	2	6	O+
12	12	Sonali Kulkarni	Female	35-44	Nagpur	Maharashtra	Postgraduate	HR Lead	Private	6L-12L	3	Married	Marathi	Yes	2	Car	35	120	Yes	Yes	790	26	Diwali	Maharashtrian	No	84	Urban	0	2	8	AB-
13	13	Faizan Ali	Male	25-34	Jaipur	Rajasthan	Graduate	Hotel Front Office	Hospitality	3L-6L	4	Single	Hindi	Yes	4	Bus	25	45	No	Yes	705	20	Diwali	Rajasthani	No	64	Urban	0	1	5	A+
14	14	Kavya Nair	Female	35-44	Kochi	Kerala	Postgraduate	Physiotherapist	Healthcare	6L-12L	3	Married	Malayalam	Yes	2	Car	30	180	Yes	Yes	775	32	Onam	South Indian	Yes	86	Urban	0	2	6	O+
15	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+
16	16	Anusha S	Female	18-24	Mysuru	Karnataka	Undergraduate	Student	NA	<= 1L	5	Single	Kannada	Yes	6	Bus	20	110	No	No	0	14	Dasara	South Indian	Yes	74	Urban	0	1	6	A-
17	17	Deepak Yadav	Male	25-34	Indore	Madhya Pradesh	Graduate	Field Sales	Private	3L-6L	6	Married	Hindi	Yes	5	Bike	50	50	No	Yes	710	25	Diwali	North Indian	No	69	Urban	0	1	6	O+
18	18	Trisha Dey	Female	25-34	Silchar	Assam	Graduate	Content Writer	Media	3L-6L	4	Single	Assamese	Yes	4	Auto	20	70	No	Yes	735	22	Bihu	North East	No	80	Semi-Urban	0	2	6	B+
19	19	Vikram Rao	Male	35-44	Bengaluru	Karnataka	Postgraduate	Data Scientist	IT	> 12L	3	Married	Kannada	Yes	3	Metro	50	150	Yes	Yes	835	45	Ugadi	South Indian	No	92	Urban	0	3	9	O+
20	20	Sapna Jain	Female	25-34	Bhopal	Madhya Pradesh	Graduate	Accountant	Private	3L-6L	5	Single	Hindi	Yes	3	Bus	30	60	No	Yes	725	20	Diwali	North Indian	Yes	76	Semi-Urban	0	1	7	A+
21	21	Harish Chandra	Male	55-64	Varanasi	Uttar Pradesh	Secondary	Priest	Religious	1L-3L	5	Married	Hindi	No	0	Walk	10	15	No	Yes	650	8	Diwali	North Indian	Yes	50	Urban	0	0	6	O+
22	22	Rekha Gupta	Female	35-44	Kanpur	Uttar Pradesh	Graduate	Bank Officer	Public	6L-12L	4	Married	Hindi	Yes	2	Car	30	90	Yes	Yes	790	30	Diwali	North Indian	Yes	88	Urban	0	2	8	AB+
23	23	Aakash Jain	Male	25-34	Ahmedabad	Gujarat	Graduate	Entrepreneur	Startup	6L-12L	4	Single	Gujarati	Yes	5	Car	35	70	No	Yes	780	60	Navratri	Gujarati	No	85	Urban	0	4	8	B+
24	24	Meera Pillai	Female	45-54	Thiruvananthapuram	Kerala	Postgraduate	School Principal	Public	> 12L	3	Married	Malayalam	Yes	1	Car	25	100	Yes	Yes	815	20	Onam	South Indian	Yes	90	Urban	0	2	8	A+
25	25	Rajeev Ranjan	Male	25-34	Ranchi	Jharkhand	Graduate	Police Sub-Inspector	Public	6L-12L	4	Married	Hindi	Yes	2	Bike	20	60	Yes	Yes	740	25	Chhath	North Indian	No	72	Urban	0	1	7	O+
26	26	Nisha B	Female	25-34	Coimbatore	Tamil Nadu	Graduate	Quality Analyst	Manufacturing	3L-6L	4	Single	Tamil	Yes	3	Bike	25	80	No	Yes	735	22	Pongal	South Indian	Yes	82	Urban	0	2	7	B-
27	27	Arvind Sinha	Male	35-44	Bhubaneswar	Odisha	Graduate	Government Clerk	Public	3L-6L	5	Married	Odia	Yes	2	Bike	15	45	Yes	Yes	720	18	Raja Parba	Odia	Yes	65	Urban	0	1	7	A+
28	28	Shruti Joshi	Female	35-44	Surat	Gujarat	Postgraduate	Fashion Buyer	Private	> 12L	3	Married	Gujarati	Yes	3	Car	35	120	No	Yes	800	35	Navratri	Gujarati	No	88	Urban	0	3	8	O-
29	29	Noor Zoya	Female	18-24	Aligarh	Uttar Pradesh	Undergraduate	Student	NA	<= 1L	6	Single	Urdu	Yes	6	Bus	30	80	No	No	0	10	Eid	North Indian	No	72	Semi-Urban	0	1	6	B+
30	30	Anil Kumar	Male	25-34	Chandigarh	Chandigarh	Graduate	Civil Engineer	Construction	6L-12L	3	Single	Hindi	Yes	2	Car	30	100	No	Yes	770	25	Holi	North Indian	No	81	Urban	0	2	8	A-
31	31	Devika Rao	Female	25-34	Mangalore	Karnataka	Graduate	Product Manager	IT	> 12L	3	Married	Kannada	Yes	3	Car	35	100	Yes	Yes	825	40	Ugadi	South Indian	No	89	Urban	0	3	8	AB+
32	32	Shivam Patel	Male	25-34	Vadodara	Gujarat	Graduate	Mechanical Engineer	Manufacturing	6L-12L	4	Single	Gujarati	Yes	2	Bike	25	70	No	Yes	760	20	Navratri	Gujarati	Yes	80	Urban	0	2	7	O+

3) DATA QUALITY & CONTENTS / BASIC OVERVIEW 

Purpose: Quick schema and metadata overview for the dataset. 

proc contents data=people_india varnum;

  title "PEOPLE_INDIA — STRUCTURE & ATTRIBUTES";

run;

title;

Output:

PEOPLE_INDIA — STRUCTURE & ATTRIBUTES

The CONTENTS Procedure

Data Set Name	WORK.PEOPLE_INDIA	Observations	32
Member Type	DATA	Variables	31
Engine	V9	Indexes	0
Created	09/01/2025 17:54:07	Observation Length	352
Last Modified	09/01/2025 17:54:07	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	372
Obs in First Data Page	32
Number of Data Set Repairs	0
Filename	/saswork/SAS_work638500008593_odaws01-apse1-2.oda.sas.com/SAS_work8DCE00008593_odaws01-apse1-2.oda.sas.com/people_india.sas7bdat
Release Created	9.0401M8
Host Created	Linux
Inode Number	67165383
Access Permission	rw-r--r--
Owner Name	u63247146
File Size	256KB
File Size (bytes)	262144

Variables in Creation Order
#	Variable	Type	Len	Format	Label
1	Person_ID	Num	8
2	Name	Char	28
3	Gender	Char	1	$GENDERF.
4	Age	Num	8	AGEBANDF.
5	City	Char	20
6	State	Char	20
7	Education_Level	Char	20		Highest Education
8	Occupation	Char	24
9	Sector	Char	16
10	Income_INR	Num	8	INCOMEF.	Annual Income (INR)
11	Household_Size	Num	8
12	Marital_Status	Char	1	$MARF.
13	Language	Char	18
14	Smartphone_User	Num	8	YESNOF.
15	Internet_Hours_Per_Day	Num	8		Daily Internet Hours
16	Commute_Mode	Char	14		Primary Commute Mode
17	Commute_Minutes	Num	8
18	Fitness_Mins_Per_Week	Num	8		Weekly Fitness Minutes
19	Has_Health_Insurance	Num	8	YESNOF.
20	Voter_ID_Flag	Num	8	YESNOF.
21	Credit_Score	Num	8
22	UPI_Transactions_Month	Num	8		Monthly UPI Transactions
23	Festival_Celebrated	Char	18
24	Cuisine_Pref	Char	16
25	Veg_Flag	Num	8	YESNOF.
26	Digital_Literacy_Score	Num	8		Digital Literacy (0-100)
27	Rural_Urban	Char	10	$RUF.
28	Disability_Flag	Num	8
29	Travel_Trips_Year	Num	8
30	Pollution_Concern_Score	Num	8		Pollution Concern (1-10)
31	Blood_Group	Char	3

Purpose: Inspect a small sample to eyeball plausibility of values. 

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

  title "First 10 Rows for Sanity Check";

run;

title;

Output:

First 10 Rows for Sanity Check

Person_ID	Name	Gender	Age	City	State	Highest Education	Occupation	Sector	Annual Income (INR)	Household_Size	Marital_Status	Language	Smartphone_User	Daily Internet Hours	Primary Commute Mode	Commute_Minutes	Weekly Fitness Minutes	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	Monthly UPI Transactions	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital Literacy (0-100)	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution Concern (1-10)	Blood_Group
1	Arjun Mehta	Male	25-34	Mumbai	Maharashtra	Graduate	Software Engineer	IT	6L-12L	3	Single	Hindi	Yes	4	Metro	60	120	Yes	Yes	760	35	Diwali	North Indian	No	85	Urban	0	4	7	O+
2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+
3	Sameer Khan	Male	18-24	Hyderabad	Telangana	Graduate	Inside Sales	Private	3L-6L	5	Single	Urdu	Yes	5	Bike	35	60	No	Yes	720	22	Eid	Hyderabadi	No	72	Urban	0	2	5	A+
4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+
5	Rohan Das	Male	45-54	Kolkata	West Bengal	Graduate	School Teacher	Public	6L-12L	4	Married	Bengali	Yes	2	Bus	50	80	Yes	Yes	768	20	Durga Puja	Bengali	Yes	78	Urban	0	1	7	O-
6	Ananya Roy	Female	18-24	Kolkata	West Bengal	Undergraduate	Student	NA	<= 1L	5	Single	Bengali	Yes	6	Metro	30	100	No	No	0	12	Durga Puja	Continental	No	70	Urban	0	1	6	A-
7	Amit Patil	Male	25-34	Pune	Maharashtra	Diploma	Mechanic	Private	3L-6L	6	Married	Marathi	Yes	2	Bike	45	40	No	Yes	700	15	Ganesh Chaturthi	Maharashtrian	Yes	68	Semi-Urban	0	1	5	O+
8	Sana Parveen	Female	25-34	Patna	Bihar	Graduate	Nurse	Healthcare	3L-6L	5	Single	Hindi	Yes	3	Auto	35	120	Yes	Yes	730	20	Eid	North Indian	No	77	Semi-Urban	0	1	6	B+
9	Ritesh Verma	Male	45-54	Lucknow	Uttar Pradesh	Graduate	Shop Owner	Informal	3L-6L	6	Married	Hindi	Yes	1	Car	25	30	No	Yes	690	40	Diwali	North Indian	No	60	Urban	0	1	6	A+
10	Keerthi R	Female	25-34	Visakhapatnam	Andhra Pradesh	Graduate	Graphic Designer	Media	3L-6L	4	Single	Telugu	Yes	5	Bike	30	90	No	No	740	24	Ugadi	South Indian	No	82	Urban	0	3	7	B-

Purpose: Detect duplicates by Person_ID (none expected). 

proc sort data=people_india out=people_india_sorted nodupkey;

  by Person_ID;

run;

proc print data=people_india_sorted (obs=10);run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group
1	1	Arjun Mehta	Male	25-34	Mumbai	Maharashtra	Graduate	Software Engineer	IT	6L-12L	3	Single	Hindi	Yes	4	Metro	60	120	Yes	Yes	760	35	Diwali	North Indian	No	85	Urban	0	4	7	O+
2	2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+
3	3	Sameer Khan	Male	18-24	Hyderabad	Telangana	Graduate	Inside Sales	Private	3L-6L	5	Single	Urdu	Yes	5	Bike	35	60	No	Yes	720	22	Eid	Hyderabadi	No	72	Urban	0	2	5	A+
4	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+
5	5	Rohan Das	Male	45-54	Kolkata	West Bengal	Graduate	School Teacher	Public	6L-12L	4	Married	Bengali	Yes	2	Bus	50	80	Yes	Yes	768	20	Durga Puja	Bengali	Yes	78	Urban	0	1	7	O-
6	6	Ananya Roy	Female	18-24	Kolkata	West Bengal	Undergraduate	Student	NA	<= 1L	5	Single	Bengali	Yes	6	Metro	30	100	No	No	0	12	Durga Puja	Continental	No	70	Urban	0	1	6	A-
7	7	Amit Patil	Male	25-34	Pune	Maharashtra	Diploma	Mechanic	Private	3L-6L	6	Married	Marathi	Yes	2	Bike	45	40	No	Yes	700	15	Ganesh Chaturthi	Maharashtrian	Yes	68	Semi-Urban	0	1	5	O+
8	8	Sana Parveen	Female	25-34	Patna	Bihar	Graduate	Nurse	Healthcare	3L-6L	5	Single	Hindi	Yes	3	Auto	35	120	Yes	Yes	730	20	Eid	North Indian	No	77	Semi-Urban	0	1	6	B+
9	9	Ritesh Verma	Male	45-54	Lucknow	Uttar Pradesh	Graduate	Shop Owner	Informal	3L-6L	6	Married	Hindi	Yes	1	Car	25	30	No	Yes	690	40	Diwali	North Indian	No	60	Urban	0	1	6	A+
10	10	Keerthi R	Female	25-34	Visakhapatnam	Andhra Pradesh	Graduate	Graphic Designer	Media	3L-6L	4	Single	Telugu	Yes	5	Bike	30	90	No	No	740	24	Ugadi	South Indian	No	82	Urban	0	3	7	B-

4) FREQUENCIES, DISTRIBUTIONS & SUMMARIES 

Purpose: Frequency tables to see categorical distributions at a glance. 

proc freq data=people_india;

  tables Gender Marital_Status Rural_Urban Veg_Flag Smartphone_User

         Has_Health_Insurance Sector Education_Level Language

         Cuisine_Pref Festival_Celebrated / missing;

  title "Categorical Distribution Snapshots";

run;

title;

Output:

Categorical Distribution Snapshots

The FREQ Procedure

Gender	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Female	16	50.00	16	50.00
Male	16	50.00	32	100.00

Marital_Status	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Married	18	56.25	18	56.25
Single	14	43.75	32	100.00

Rural_Urban	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Semi-Urban	5	15.63	5	15.63
Urban	27	84.38	32	100.00

Veg_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
No	20	62.50	20	62.50
Yes	12	37.50	32	100.00

Smartphone_User	Frequency	Percent	Cumulative Frequency	Cumulative Percent
No	1	3.13	1	3.13
Yes	31	96.88	32	100.00

Has_Health_Insurance	Frequency	Percent	Cumulative Frequency	Cumulative Percent
No	18	56.25	18	56.25
Yes	14	43.75	32	100.00

Sector	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Construction	1	3.13	1	3.13
Healthcare	2	6.25	3	9.38
Hospitality	1	3.13	4	12.50
IT	5	15.63	9	28.13
Informal	1	3.13	10	31.25
Logistics	1	3.13	11	34.38
Manufacturing	2	6.25	13	40.63
Media	2	6.25	15	46.88
NA	3	9.38	18	56.25
Private	7	21.88	25	78.13
Public	5	15.63	30	93.75
Religious	1	3.13	31	96.88
Startup	1	3.13	32	100.00

Highest Education
Education_Level	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Diploma	1	3.13	1	3.13
Graduate	20	62.50	21	65.63
Postgraduate	7	21.88	28	87.50
Secondary	1	3.13	29	90.63
Undergraduate	3	9.38	32	100.00

Language	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Assamese	1	3.13	1	3.13
Bengali	2	6.25	3	9.38
Gujarati	3	9.38	6	18.75
Hindi	12	37.50	18	56.25
Kannada	3	9.38	21	65.63
Malayalam	2	6.25	23	71.88
Marathi	2	6.25	25	78.13
Odia	1	3.13	26	81.25
Punjabi	1	3.13	27	84.38
Tamil	2	6.25	29	90.63
Telugu	1	3.13	30	93.75
Urdu	2	6.25	32	100.00

Cuisine_Pref	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Bengali	1	3.13	1	3.13
Continental	1	3.13	2	6.25
Gujarati	3	9.38	5	15.63
Hyderabadi	1	3.13	6	18.75
Maharashtrian	2	6.25	8	25.00
North East	1	3.13	9	28.13
North Indian	12	37.50	21	65.63
Odia	1	3.13	22	68.75
Punjabi	1	3.13	23	71.88
Rajasthani	1	3.13	24	75.00
South Indian	8	25.00	32	100.00

Festival_Celebrated	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Bihu	1	3.13	1	3.13
Chhath	1	3.13	2	6.25
Dasara	1	3.13	3	9.38
Diwali	9	28.13	12	37.50
Durga Puja	2	6.25	14	43.75
Eid	3	9.38	17	53.13
Ganesh Chaturthi	1	3.13	18	56.25
Gurpurab	1	3.13	19	59.38
Holi	2	6.25	21	65.63
Navratri	3	9.38	24	75.00
Onam	2	6.25	26	81.25
Pongal	2	6.25	28	87.50
Raja Parba	1	3.13	29	90.63
Ugadi	3	9.38	32	100.00

Purpose: Descriptive stats for key numeric variables. 

proc means data=people_india mean std min p25 median p75 max maxdec=1;

  var Age Income_INR Household_Size Internet_Hours_Per_Day

      Commute_Minutes Fitness_Mins_Per_Week Credit_Score

      UPI_Transactions_Month Digital_Literacy_Score Travel_Trips_Year

      Pollution_Concern_Score;

  title "Descriptive Statistics — Numerics";

run;

title;

Output:

Descriptive Statistics — Numerics

The MEANS Procedure

Variable	Label	Mean	Std Dev	Minimum	25th Pctl	Median	75th Pctl	Maximum
Age Income_INR Household_Size Internet_Hours_Per_Day Commute_Minutes Fitness_Mins_Per_Week Credit_Score UPI_Transactions_Month Digital_Literacy_Score Travel_Trips_Year Pollution_Concern_Score	Annual Income (INR)   Daily Internet Hours   Weekly Fitness Minutes   Monthly UPI Transactions Digital Literacy (0-100)   Pollution Concern (1-10)	33.7 777187.5 4.2 3.0 33.8 82.5 684.0 25.2 77.7 2.0 6.8	9.2 519920.8 1.0 1.6 12.4 35.0 227.7 11.0 10.1 1.2 1.1	21.0 0.0 3.0 0.0 10.0 15.0 0.0 8.0 50.0 0.0 5.0	27.0 455000.0 3.0 2.0 25.0 60.0 712.5 19.0 71.0 1.0 6.0	31.5 625000.0 4.0 3.0 30.0 80.0 740.0 22.0 80.0 2.0 7.0	40.0 1150000.0 5.0 4.0 40.0 100.0 785.0 31.0 85.5 3.0 8.0	56.0 2200000.0 6.0 6.0 60.0 180.0 835.0 60.0 92.0 5.0 9.0

Purpose: Distributional diagnostics for selected metrics. 

proc univariate data=people_india noprint;

  var Income_INR Credit_Score Digital_Literacy_Score UPI_Transactions_Month;

  histogram;

  inset n mean std min max / pos=ne;

  title "Distribution Diagnostics (Histograms)";

run;

title;

Output:

Distribution Diagnostics (Histograms)

The UNIVARIATE Procedure

Distribution Diagnostics (Histograms)

The UNIVARIATE Procedure

Distribution Diagnostics (Histograms)

The UNIVARIATE Procedure

Distribution Diagnostics (Histograms)

The UNIVARIATE Procedure

5) GROUPED SUMMARIES & TABULATED REPORTING  

Purpose: Summaries by State and Sector for planning headcount/market views. 

proc summary data=people_india nway;

  class State Sector;

  var Income_INR Credit_Score UPI_Transactions_Month;

  output out=state_sector_summary

    n()=N_Records

    mean(Income_INR)=Avg_Income

    mean(Credit_Score)=Avg_Credit

    mean(UPI_Transactions_Month)=Avg_UPI;

  title "Summary by State x Sector";

run;

title;

proc print;run;

Output:

Obs	State	Sector	_TYPE_	_FREQ_	N_Records	Avg_Income	Avg_Credit	Avg_UPI
1	Andhra Pradesh	Media	3	1	1	3L-6L	740.0	24.0
2	Assam	Media	3	1	1	3L-6L	735.0	22.0
3	Bihar	Healthcare	3	1	1	3L-6L	730.0	20.0
4	Chandigarh	Construction	3	1	1	6L-12L	770.0	25.0
5	Delhi	Private	3	1	1	> 12L	805.0	18.0
6	Gujarat	Manufacturing	3	1	1	6L-12L	760.0	20.0
7	Gujarat	Private	3	1	1	> 12L	800.0	35.0
8	Gujarat	Startup	3	1	1	6L-12L	780.0	60.0
9	Haryana	IT	3	1	1	> 12L	820.0	38.0
10	Jharkhand	Public	3	1	1	6L-12L	740.0	25.0
11	Karnataka	IT	3	2	2	> 12L	830.0	42.5
12	Karnataka	NA	3	1	1	<= 1L	0.0	14.0
13	Kerala	Healthcare	3	1	1	6L-12L	775.0	32.0
14	Kerala	Public	3	1	1	> 12L	815.0	20.0
15	Madhya Pradesh	Private	3	2	2	3L-6L	717.5	22.5
16	Maharashtra	IT	3	1	1	6L-12L	760.0	35.0
17	Maharashtra	Private	3	2	2	6L-12L	745.0	20.5
18	Odisha	Public	3	1	1	3L-6L	720.0	18.0
19	Punjab	Logistics	3	1	1	3L-6L	715.0	18.0
20	Rajasthan	Hospitality	3	1	1	3L-6L	705.0	20.0
21	Tamil Nadu	IT	3	1	1	6L-12L	780.0	28.0
22	Tamil Nadu	Manufacturing	3	1	1	3L-6L	735.0	22.0
23	Telangana	Private	3	1	1	3L-6L	720.0	22.0
24	Uttar Pradesh	Informal	3	1	1	3L-6L	690.0	40.0
25	Uttar Pradesh	NA	3	1	1	<= 1L	0.0	10.0
26	Uttar Pradesh	Public	3	1	1	6L-12L	790.0	30.0
27	Uttar Pradesh	Religious	3	1	1	1L-3L	650.0	8.0
28	West Bengal	NA	3	1	1	<= 1L	0.0	12.0
29	West Bengal	Public	3	1	1	6L-12L	768.0	20.0

Purpose: Cross-tabs in a single compact table for leadership review. 

proc tabulate data=people_india format=8.1;

  class State Gender Rural_Urban;

  var Income_INR Digital_Literacy_Score;

  table State,

        Gender * (n colpctn)

        Rural_Urban * (n colpctn)

        (Income_INR Digital_Literacy_Score) * mean;

  title "Multi-Dimension Snapshot (TABULATE)";

run;

title;

Output:

Multi-Dimension Snapshot (TABULATE)

	Gender				Rural_Urban				Annual Income (INR)	Digital Literacy (0-100)
	Female		Male		Semi-Urban		Urban
	N	ColPctN	N	ColPctN	N	ColPctN	N	ColPctN	Mean	Mean
State	1	6.3	.	.	.	.	1	3.7	520000.0	82.0
Andhra Pradesh
Assam	1	6.3	.	.	1	20.0	.	.	460000.0	80.0
Bihar	1	6.3	.	.	1	20.0	.	.	480000.0	77.0
Chandigarh	.	.	1	6.3	.	.	1	3.7	850000.0	81.0
Delhi	1	6.3	.	.	.	.	1	3.7	1400000	83.0
Gujarat	1	6.3	2	12.5	.	.	3	11.1	1093333	84.3
Haryana	.	.	1	6.3	.	.	1	3.7	1800000	90.0
Jharkhand	.	.	1	6.3	.	.	1	3.7	700000.0	72.0
Karnataka	2	12.5	1	6.3	.	.	3	11.1	1266667	85.0
Kerala	2	12.5	.	.	.	.	2	7.4	1075000	88.0
Madhya Pradesh	1	6.3	1	6.3	1	20.0	1	3.7	460000.0	72.5
Maharashtra	1	6.3	2	12.5	1	20.0	2	7.4	826666.7	79.0
Odisha	.	.	1	6.3	.	.	1	3.7	580000.0	65.0
Punjab	.	.	1	6.3	.	.	1	3.7	600000.0	66.0
Rajasthan	.	.	1	6.3	.	.	1	3.7	360000.0	64.0
Tamil Nadu	2	12.5	.	.	.	.	2	7.4	810000.0	85.0
Telangana	.	.	1	6.3	.	.	1	3.7	450000.0	72.0
Uttar Pradesh	2	12.5	2	12.5	1	20.0	3	11.1	442500.0	67.5
West Bengal	1	6.3	1	6.3	.	.	2	7.4	325000.0	74.0

Purpose: Polished tabular report with custom columns and labels. 

proc report data=people_india nowd;

  columns State City Name Gender Age Education_Level Occupation Sector Income_INR Credit_Score Digital_Literacy_Score;

  define State / group 'State';

  define City  / display 'City';

  define Name  / display 'Name';

  define Gender / display 'Gender';

  define Age / display 'Age';

  define Education_Level / display 'Education';

  define Occupation / display 'Occupation';

  define Sector / display 'Sector';

  define Income_INR / analysis mean format=comma12. 'Avg Income';

  define Credit_Score / analysis mean 'Avg Credit';

  define Digital_Literacy_Score / analysis mean 'Avg Digital Lit';

  title "People of India — City & Career Lens (REPORT)";

run;

title;

Output:

People of India — City & Career Lens (REPORT)

State	City	Name	Gender	Age	Education	Occupation	Sector	Avg Income	Avg Credit	Avg Digital Lit
Andhra Pradesh	Visakhapatnam	Keerthi R	Female	25-34	Graduate	Graphic Designer	Media	520,000	740	82
Assam	Silchar	Trisha Dey	Female	25-34	Graduate	Content Writer	Media	460,000	735	80
Bihar	Patna	Sana Parveen	Female	25-34	Graduate	Nurse	Healthcare	480,000	730	77
Chandigarh	Chandigarh	Anil Kumar	Male	25-34	Graduate	Civil Engineer	Construction	850,000	770	81
Delhi	Delhi	Neha Sharma	Female	35-44	Postgraduate	Marketing Manager	Private	1,400,000	805	83
Gujarat	Ahmedabad	Aakash Jain	Male	25-34	Graduate	Entrepreneur	Startup	1,200,000	780	85
	Surat	Shruti Joshi	Female	35-44	Postgraduate	Fashion Buyer	Private	1,300,000	800	88
	Vadodara	Shivam Patel	Male	25-34	Graduate	Mechanical Engineer	Manufacturing	780,000	760	80
Haryana	Gurugram	Manoj Kumar	Male	45-54	Graduate	Project Manager	IT	1,800,000	820	90
Jharkhand	Ranchi	Rajeev Ranjan	Male	25-34	Graduate	Police Sub-Inspector	Public	700,000	740	72
Karnataka	Mysuru	Anusha S	Female	18-24	Undergraduate	Student	NA	0	0	74
	Bengaluru	Vikram Rao	Male	35-44	Postgraduate	Data Scientist	IT	2,200,000	835	92
	Mangalore	Devika Rao	Female	25-34	Graduate	Product Manager	IT	1,600,000	825	89
Kerala	Kochi	Kavya Nair	Female	35-44	Postgraduate	Physiotherapist	Healthcare	900,000	775	86
	Thiruvananthapuram	Meera Pillai	Female	45-54	Postgraduate	School Principal	Public	1,250,000	815	90
Madhya Pradesh	Indore	Deepak Yadav	Male	25-34	Graduate	Field Sales	Private	420,000	710	69
	Bhopal	Sapna Jain	Female	25-34	Graduate	Accountant	Private	500,000	725	76
Maharashtra	Mumbai	Arjun Mehta	Male	25-34	Graduate	Software Engineer	IT	900,000	760	85
	Pune	Amit Patil	Male	25-34	Diploma	Mechanic	Private	380,000	700	68
	Nagpur	Sonali Kulkarni	Female	35-44	Postgraduate	HR Lead	Private	1,200,000	790	84
Odisha	Bhubaneswar	Arvind Sinha	Male	35-44	Graduate	Government Clerk	Public	580,000	720	65
Punjab	Amritsar	Gurpreet Singh	Male	25-34	Graduate	Logistics Supervisor	Logistics	600,000	715	66
Rajasthan	Jaipur	Faizan Ali	Male	25-34	Graduate	Hotel Front Office	Hospitality	360,000	705	64
Tamil Nadu	Chennai	Priya Iyer	Female	25-34	Postgraduate	Data Analyst	IT	1,100,000	780	88
	Coimbatore	Nisha B	Female	25-34	Graduate	Quality Analyst	Manufacturing	520,000	735	82
Telangana	Hyderabad	Sameer Khan	Male	18-24	Graduate	Inside Sales	Private	450,000	720	72
Uttar Pradesh	Lucknow	Ritesh Verma	Male	45-54	Graduate	Shop Owner	Informal	550,000	690	60
	Varanasi	Harish Chandra	Male	55-64	Secondary	Priest	Religious	240,000	650	50
	Kanpur	Rekha Gupta	Female	35-44	Graduate	Bank Officer	Public	980,000	790	88
	Aligarh	Noor Zoya	Female	18-24	Undergraduate	Student	NA	0	0	72
West Bengal	Kolkata	Rohan Das	Male	45-54	Graduate	School Teacher	Public	650,000	768	78
	Kolkata	Ananya Roy	Female	18-24	Undergraduate	Student	NA	0	0	70

6) RANKING & BUCKETS 

Purpose: Rank individuals by income to identify top earners and bands. 

proc rank data=people_india out=people_ranked groups=4 ties=low;

  var Income_INR;

  ranks Income_Quartile;

run;proc print;run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group	Income_Quartile
1	1	Arjun Mehta	Male	25-34	Mumbai	Maharashtra	Graduate	Software Engineer	IT	6L-12L	3	Single	Hindi	Yes	4	Metro	60	120	Yes	Yes	760	35	Diwali	North Indian	No	85	Urban	0	4	7	O+	2
2	2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+	2
3	3	Sameer Khan	Male	18-24	Hyderabad	Telangana	Graduate	Inside Sales	Private	3L-6L	5	Single	Urdu	Yes	5	Bike	35	60	No	Yes	720	22	Eid	Hyderabadi	No	72	Urban	0	2	5	A+	0
4	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+	3
5	5	Rohan Das	Male	45-54	Kolkata	West Bengal	Graduate	School Teacher	Public	6L-12L	4	Married	Bengali	Yes	2	Bus	50	80	Yes	Yes	768	20	Durga Puja	Bengali	Yes	78	Urban	0	1	7	O-	2
6	6	Ananya Roy	Female	18-24	Kolkata	West Bengal	Undergraduate	Student	NA	<= 1L	5	Single	Bengali	Yes	6	Metro	30	100	No	No	0	12	Durga Puja	Continental	No	70	Urban	0	1	6	A-	0
7	7	Amit Patil	Male	25-34	Pune	Maharashtra	Diploma	Mechanic	Private	3L-6L	6	Married	Marathi	Yes	2	Bike	45	40	No	Yes	700	15	Ganesh Chaturthi	Maharashtrian	Yes	68	Semi-Urban	0	1	5	O+	0
8	8	Sana Parveen	Female	25-34	Patna	Bihar	Graduate	Nurse	Healthcare	3L-6L	5	Single	Hindi	Yes	3	Auto	35	120	Yes	Yes	730	20	Eid	North Indian	No	77	Semi-Urban	0	1	6	B+	1
9	9	Ritesh Verma	Male	45-54	Lucknow	Uttar Pradesh	Graduate	Shop Owner	Informal	3L-6L	6	Married	Hindi	Yes	1	Car	25	30	No	Yes	690	40	Diwali	North Indian	No	60	Urban	0	1	6	A+	1
10	10	Keerthi R	Female	25-34	Visakhapatnam	Andhra Pradesh	Graduate	Graphic Designer	Media	3L-6L	4	Single	Telugu	Yes	5	Bike	30	90	No	No	740	24	Ugadi	South Indian	No	82	Urban	0	3	7	B-	1
11	11	Gurpreet Singh	Male	25-34	Amritsar	Punjab	Graduate	Logistics Supervisor	Logistics	3L-6L	5	Married	Punjabi	Yes	2	Car	40	60	No	Yes	715	18	Gurpurab	Punjabi	No	66	Urban	0	2	6	O+	1
12	12	Sonali Kulkarni	Female	35-44	Nagpur	Maharashtra	Postgraduate	HR Lead	Private	6L-12L	3	Married	Marathi	Yes	2	Car	35	120	Yes	Yes	790	26	Diwali	Maharashtrian	No	84	Urban	0	2	8	AB-	3
13	13	Faizan Ali	Male	25-34	Jaipur	Rajasthan	Graduate	Hotel Front Office	Hospitality	3L-6L	4	Single	Hindi	Yes	4	Bus	25	45	No	Yes	705	20	Diwali	Rajasthani	No	64	Urban	0	1	5	A+	0
14	14	Kavya Nair	Female	35-44	Kochi	Kerala	Postgraduate	Physiotherapist	Healthcare	6L-12L	3	Married	Malayalam	Yes	2	Car	30	180	Yes	Yes	775	32	Onam	South Indian	Yes	86	Urban	0	2	6	O+	2
15	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+	3
16	16	Anusha S	Female	18-24	Mysuru	Karnataka	Undergraduate	Student	NA	<= 1L	5	Single	Kannada	Yes	6	Bus	20	110	No	No	0	14	Dasara	South Indian	Yes	74	Urban	0	1	6	A-	0
17	17	Deepak Yadav	Male	25-34	Indore	Madhya Pradesh	Graduate	Field Sales	Private	3L-6L	6	Married	Hindi	Yes	5	Bike	50	50	No	Yes	710	25	Diwali	North Indian	No	69	Urban	0	1	6	O+	0
18	18	Trisha Dey	Female	25-34	Silchar	Assam	Graduate	Content Writer	Media	3L-6L	4	Single	Assamese	Yes	4	Auto	20	70	No	Yes	735	22	Bihu	North East	No	80	Semi-Urban	0	2	6	B+	1
19	19	Vikram Rao	Male	35-44	Bengaluru	Karnataka	Postgraduate	Data Scientist	IT	> 12L	3	Married	Kannada	Yes	3	Metro	50	150	Yes	Yes	835	45	Ugadi	South Indian	No	92	Urban	0	3	9	O+	3
20	20	Sapna Jain	Female	25-34	Bhopal	Madhya Pradesh	Graduate	Accountant	Private	3L-6L	5	Single	Hindi	Yes	3	Bus	30	60	No	Yes	725	20	Diwali	North Indian	Yes	76	Semi-Urban	0	1	7	A+	1
21	21	Harish Chandra	Male	55-64	Varanasi	Uttar Pradesh	Secondary	Priest	Religious	1L-3L	5	Married	Hindi	No	0	Walk	10	15	No	Yes	650	8	Diwali	North Indian	Yes	50	Urban	0	0	6	O+	0
22	22	Rekha Gupta	Female	35-44	Kanpur	Uttar Pradesh	Graduate	Bank Officer	Public	6L-12L	4	Married	Hindi	Yes	2	Car	30	90	Yes	Yes	790	30	Diwali	North Indian	Yes	88	Urban	0	2	8	AB+	2
23	23	Aakash Jain	Male	25-34	Ahmedabad	Gujarat	Graduate	Entrepreneur	Startup	6L-12L	4	Single	Gujarati	Yes	5	Car	35	70	No	Yes	780	60	Navratri	Gujarati	No	85	Urban	0	4	8	B+	3
24	24	Meera Pillai	Female	45-54	Thiruvananthapuram	Kerala	Postgraduate	School Principal	Public	> 12L	3	Married	Malayalam	Yes	1	Car	25	100	Yes	Yes	815	20	Onam	South Indian	Yes	90	Urban	0	2	8	A+	3
25	25	Rajeev Ranjan	Male	25-34	Ranchi	Jharkhand	Graduate	Police Sub-Inspector	Public	6L-12L	4	Married	Hindi	Yes	2	Bike	20	60	Yes	Yes	740	25	Chhath	North Indian	No	72	Urban	0	1	7	O+	2
26	26	Nisha B	Female	25-34	Coimbatore	Tamil Nadu	Graduate	Quality Analyst	Manufacturing	3L-6L	4	Single	Tamil	Yes	3	Bike	25	80	No	Yes	735	22	Pongal	South Indian	Yes	82	Urban	0	2	7	B-	1
27	27	Arvind Sinha	Male	35-44	Bhubaneswar	Odisha	Graduate	Government Clerk	Public	3L-6L	5	Married	Odia	Yes	2	Bike	15	45	Yes	Yes	720	18	Raja Parba	Odia	Yes	65	Urban	0	1	7	A+	1
28	28	Shruti Joshi	Female	35-44	Surat	Gujarat	Postgraduate	Fashion Buyer	Private	> 12L	3	Married	Gujarati	Yes	3	Car	35	120	No	Yes	800	35	Navratri	Gujarati	No	88	Urban	0	3	8	O-	3
29	29	Noor Zoya	Female	18-24	Aligarh	Uttar Pradesh	Undergraduate	Student	NA	<= 1L	6	Single	Urdu	Yes	6	Bus	30	80	No	No	0	10	Eid	North Indian	No	72	Semi-Urban	0	1	6	B+	0
30	30	Anil Kumar	Male	25-34	Chandigarh	Chandigarh	Graduate	Civil Engineer	Construction	6L-12L	3	Single	Hindi	Yes	2	Car	30	100	No	Yes	770	25	Holi	North Indian	No	81	Urban	0	2	8	A-	2
31	31	Devika Rao	Female	25-34	Mangalore	Karnataka	Graduate	Product Manager	IT	> 12L	3	Married	Kannada	Yes	3	Car	35	100	Yes	Yes	825	40	Ugadi	South Indian	No	89	Urban	0	3	8	AB+	3
32	32	Shivam Patel	Male	25-34	Vadodara	Gujarat	Graduate	Mechanical Engineer	Manufacturing	6L-12L	4	Single	Gujarati	Yes	2	Bike	25	70	No	Yes	760	20	Navratri	Gujarati	Yes	80	Urban	0	2	7	O+	2

Purpose: Frequency check of ranks for sanity. 

proc freq data=people_ranked;

  tables Income_Quartile;

  title "Income Quartile Distribution";

run;

title;

Output:

Income Quartile Distribution

The FREQ Procedure

Rank for Variable Income_INR
Income_Quartile	Frequency	Percent	Cumulative Frequency	Cumulative Percent
0	8	25.00	8	25.00
1	8	25.00	16	50.00
2	8	25.00	24	75.00
3	8	25.00	32	100.00

7) SIMPLE DATA DERIVEDS 

Purpose: Add derived KPIs used across reports (e.g., Income per Capita). 

data people_enriched;

  set people_ranked;

  Income_per_Capita = ifn(Household_Size>0, Income_INR/Household_Size, .);

  High_Digital = (Digital_Literacy_Score >= 80);

  Busy_Internet_User = (Internet_Hours_Per_Day >= 4);

  Long_Commute = (Commute_Minutes >= 45);

  Active_Lifestyle = (Fitness_Mins_Per_Week >= 90);

  format High_Digital Busy_Internet_User Long_Commute Active_Lifestyle yesnoF.;

run;

proc print;run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group	Income_Quartile	Income_per_Capita	High_Digital	Busy_Internet_User	Long_Commute	Active_Lifestyle
1	1	Arjun Mehta	Male	25-34	Mumbai	Maharashtra	Graduate	Software Engineer	IT	6L-12L	3	Single	Hindi	Yes	4	Metro	60	120	Yes	Yes	760	35	Diwali	North Indian	No	85	Urban	0	4	7	O+	2	300000.00	Yes	Yes	Yes	Yes
2	2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+	2	275000.00	Yes	No	No	Yes
3	3	Sameer Khan	Male	18-24	Hyderabad	Telangana	Graduate	Inside Sales	Private	3L-6L	5	Single	Urdu	Yes	5	Bike	35	60	No	Yes	720	22	Eid	Hyderabadi	No	72	Urban	0	2	5	A+	0	90000.00	No	Yes	No	No
4	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+	3	466666.67	Yes	No	Yes	No
5	5	Rohan Das	Male	45-54	Kolkata	West Bengal	Graduate	School Teacher	Public	6L-12L	4	Married	Bengali	Yes	2	Bus	50	80	Yes	Yes	768	20	Durga Puja	Bengali	Yes	78	Urban	0	1	7	O-	2	162500.00	No	No	Yes	No
6	6	Ananya Roy	Female	18-24	Kolkata	West Bengal	Undergraduate	Student	NA	<= 1L	5	Single	Bengali	Yes	6	Metro	30	100	No	No	0	12	Durga Puja	Continental	No	70	Urban	0	1	6	A-	0	0.00	No	Yes	No	Yes
7	7	Amit Patil	Male	25-34	Pune	Maharashtra	Diploma	Mechanic	Private	3L-6L	6	Married	Marathi	Yes	2	Bike	45	40	No	Yes	700	15	Ganesh Chaturthi	Maharashtrian	Yes	68	Semi-Urban	0	1	5	O+	0	63333.33	No	No	Yes	No
8	8	Sana Parveen	Female	25-34	Patna	Bihar	Graduate	Nurse	Healthcare	3L-6L	5	Single	Hindi	Yes	3	Auto	35	120	Yes	Yes	730	20	Eid	North Indian	No	77	Semi-Urban	0	1	6	B+	1	96000.00	No	No	No	Yes
9	9	Ritesh Verma	Male	45-54	Lucknow	Uttar Pradesh	Graduate	Shop Owner	Informal	3L-6L	6	Married	Hindi	Yes	1	Car	25	30	No	Yes	690	40	Diwali	North Indian	No	60	Urban	0	1	6	A+	1	91666.67	No	No	No	No
10	10	Keerthi R	Female	25-34	Visakhapatnam	Andhra Pradesh	Graduate	Graphic Designer	Media	3L-6L	4	Single	Telugu	Yes	5	Bike	30	90	No	No	740	24	Ugadi	South Indian	No	82	Urban	0	3	7	B-	1	130000.00	Yes	Yes	No	Yes
11	11	Gurpreet Singh	Male	25-34	Amritsar	Punjab	Graduate	Logistics Supervisor	Logistics	3L-6L	5	Married	Punjabi	Yes	2	Car	40	60	No	Yes	715	18	Gurpurab	Punjabi	No	66	Urban	0	2	6	O+	1	120000.00	No	No	No	No
12	12	Sonali Kulkarni	Female	35-44	Nagpur	Maharashtra	Postgraduate	HR Lead	Private	6L-12L	3	Married	Marathi	Yes	2	Car	35	120	Yes	Yes	790	26	Diwali	Maharashtrian	No	84	Urban	0	2	8	AB-	3	400000.00	Yes	No	No	Yes
13	13	Faizan Ali	Male	25-34	Jaipur	Rajasthan	Graduate	Hotel Front Office	Hospitality	3L-6L	4	Single	Hindi	Yes	4	Bus	25	45	No	Yes	705	20	Diwali	Rajasthani	No	64	Urban	0	1	5	A+	0	90000.00	No	Yes	No	No
14	14	Kavya Nair	Female	35-44	Kochi	Kerala	Postgraduate	Physiotherapist	Healthcare	6L-12L	3	Married	Malayalam	Yes	2	Car	30	180	Yes	Yes	775	32	Onam	South Indian	Yes	86	Urban	0	2	6	O+	2	300000.00	Yes	No	No	Yes
15	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+	3	450000.00	Yes	No	Yes	No
16	16	Anusha S	Female	18-24	Mysuru	Karnataka	Undergraduate	Student	NA	<= 1L	5	Single	Kannada	Yes	6	Bus	20	110	No	No	0	14	Dasara	South Indian	Yes	74	Urban	0	1	6	A-	0	0.00	No	Yes	No	Yes
17	17	Deepak Yadav	Male	25-34	Indore	Madhya Pradesh	Graduate	Field Sales	Private	3L-6L	6	Married	Hindi	Yes	5	Bike	50	50	No	Yes	710	25	Diwali	North Indian	No	69	Urban	0	1	6	O+	0	70000.00	No	Yes	Yes	No
18	18	Trisha Dey	Female	25-34	Silchar	Assam	Graduate	Content Writer	Media	3L-6L	4	Single	Assamese	Yes	4	Auto	20	70	No	Yes	735	22	Bihu	North East	No	80	Semi-Urban	0	2	6	B+	1	115000.00	Yes	Yes	No	No
19	19	Vikram Rao	Male	35-44	Bengaluru	Karnataka	Postgraduate	Data Scientist	IT	> 12L	3	Married	Kannada	Yes	3	Metro	50	150	Yes	Yes	835	45	Ugadi	South Indian	No	92	Urban	0	3	9	O+	3	733333.33	Yes	No	Yes	Yes
20	20	Sapna Jain	Female	25-34	Bhopal	Madhya Pradesh	Graduate	Accountant	Private	3L-6L	5	Single	Hindi	Yes	3	Bus	30	60	No	Yes	725	20	Diwali	North Indian	Yes	76	Semi-Urban	0	1	7	A+	1	100000.00	No	No	No	No
21	21	Harish Chandra	Male	55-64	Varanasi	Uttar Pradesh	Secondary	Priest	Religious	1L-3L	5	Married	Hindi	No	0	Walk	10	15	No	Yes	650	8	Diwali	North Indian	Yes	50	Urban	0	0	6	O+	0	48000.00	No	No	No	No
22	22	Rekha Gupta	Female	35-44	Kanpur	Uttar Pradesh	Graduate	Bank Officer	Public	6L-12L	4	Married	Hindi	Yes	2	Car	30	90	Yes	Yes	790	30	Diwali	North Indian	Yes	88	Urban	0	2	8	AB+	2	245000.00	Yes	No	No	Yes
23	23	Aakash Jain	Male	25-34	Ahmedabad	Gujarat	Graduate	Entrepreneur	Startup	6L-12L	4	Single	Gujarati	Yes	5	Car	35	70	No	Yes	780	60	Navratri	Gujarati	No	85	Urban	0	4	8	B+	3	300000.00	Yes	Yes	No	No
24	24	Meera Pillai	Female	45-54	Thiruvananthapuram	Kerala	Postgraduate	School Principal	Public	> 12L	3	Married	Malayalam	Yes	1	Car	25	100	Yes	Yes	815	20	Onam	South Indian	Yes	90	Urban	0	2	8	A+	3	416666.67	Yes	No	No	Yes
25	25	Rajeev Ranjan	Male	25-34	Ranchi	Jharkhand	Graduate	Police Sub-Inspector	Public	6L-12L	4	Married	Hindi	Yes	2	Bike	20	60	Yes	Yes	740	25	Chhath	North Indian	No	72	Urban	0	1	7	O+	2	175000.00	No	No	No	No
26	26	Nisha B	Female	25-34	Coimbatore	Tamil Nadu	Graduate	Quality Analyst	Manufacturing	3L-6L	4	Single	Tamil	Yes	3	Bike	25	80	No	Yes	735	22	Pongal	South Indian	Yes	82	Urban	0	2	7	B-	1	130000.00	Yes	No	No	No
27	27	Arvind Sinha	Male	35-44	Bhubaneswar	Odisha	Graduate	Government Clerk	Public	3L-6L	5	Married	Odia	Yes	2	Bike	15	45	Yes	Yes	720	18	Raja Parba	Odia	Yes	65	Urban	0	1	7	A+	1	116000.00	No	No	No	No
28	28	Shruti Joshi	Female	35-44	Surat	Gujarat	Postgraduate	Fashion Buyer	Private	> 12L	3	Married	Gujarati	Yes	3	Car	35	120	No	Yes	800	35	Navratri	Gujarati	No	88	Urban	0	3	8	O-	3	433333.33	Yes	No	No	Yes
29	29	Noor Zoya	Female	18-24	Aligarh	Uttar Pradesh	Undergraduate	Student	NA	<= 1L	6	Single	Urdu	Yes	6	Bus	30	80	No	No	0	10	Eid	North Indian	No	72	Semi-Urban	0	1	6	B+	0	0.00	No	Yes	No	No
30	30	Anil Kumar	Male	25-34	Chandigarh	Chandigarh	Graduate	Civil Engineer	Construction	6L-12L	3	Single	Hindi	Yes	2	Car	30	100	No	Yes	770	25	Holi	North Indian	No	81	Urban	0	2	8	A-	2	283333.33	Yes	No	No	Yes
31	31	Devika Rao	Female	25-34	Mangalore	Karnataka	Graduate	Product Manager	IT	> 12L	3	Married	Kannada	Yes	3	Car	35	100	Yes	Yes	825	40	Ugadi	South Indian	No	89	Urban	0	3	8	AB+	3	533333.33	Yes	No	No	Yes
32	32	Shivam Patel	Male	25-34	Vadodara	Gujarat	Graduate	Mechanical Engineer	Manufacturing	6L-12L	4	Single	Gujarati	Yes	2	Bike	25	70	No	Yes	760	20	Navratri	Gujarati	Yes	80	Urban	0	2	7	O+	2	195000.00	Yes	No	No	No

Purpose: Quick stats on derived KPIs. 

proc means data=people_enriched mean maxdec=2;

  var Income_per_Capita;

  class State;

  title "Income per Capita by State — Mean";

run;

title;

Output:

Income per Capita by State — Mean

The MEANS Procedure

Analysis Variable : Income_per_Capita
State	N Obs	Mean
Andhra Pradesh	1	130000.00
Assam	1	115000.00
Bihar	1	96000.00
Chandigarh	1	283333.33
Delhi	1	466666.67
Gujarat	3	309444.44
Haryana	1	450000.00
Jharkhand	1	175000.00
Karnataka	3	422222.22
Kerala	2	358333.33
Madhya Pradesh	2	85000.00
Maharashtra	3	254444.44
Odisha	1	116000.00
Punjab	1	120000.00
Rajasthan	1	90000.00
Tamil Nadu	2	202500.00
Telangana	1	90000.00
Uttar Pradesh	4	96166.67
West Bengal	2	81250.00

8) PROC TRANSPOSE

Purpose: Reshape summary from long to wide for dashboard ingestion. 

proc summary data=people_enriched nway;

  class State;

  var Income_INR Credit_Score Digital_Literacy_Score;

  output out=state_means (drop=_type_ _freq_)

    mean(Income_INR)=Avg_Income

    mean(Credit_Score)=Avg_Credit

    mean(Digital_Literacy_Score)=Avg_DigiLit;

run;

proc print;run;

Output:

Obs	State	Avg_Income	Avg_Credit	Avg_DigiLit
1	Andhra Pradesh	3L-6L	740.000	82.0000
2	Assam	3L-6L	735.000	80.0000
3	Bihar	3L-6L	730.000	77.0000
4	Chandigarh	6L-12L	770.000	81.0000
5	Delhi	> 12L	805.000	83.0000
6	Gujarat	6L-12L	780.000	84.3333
7	Haryana	> 12L	820.000	90.0000
8	Jharkhand	6L-12L	740.000	72.0000
9	Karnataka	> 12L	553.333	85.0000
10	Kerala	6L-12L	795.000	88.0000
11	Madhya Pradesh	3L-6L	717.500	72.5000
12	Maharashtra	6L-12L	750.000	79.0000
13	Odisha	3L-6L	720.000	65.0000
14	Punjab	3L-6L	715.000	66.0000
15	Rajasthan	3L-6L	705.000	64.0000
16	Tamil Nadu	6L-12L	757.500	85.0000
17	Telangana	3L-6L	720.000	72.0000
18	Uttar Pradesh	3L-6L	532.500	67.5000
19	West Bengal	3L-6L	384.000	74.0000

proc transpose data=state_means out=state_means_t prefix=Mean_;

   id State;           /* Each state becomes a column */

   var Avg_Income;    /* Variable you want to transpose */

run;

proc print data=state_means_t; 

run;

Output:

Obs	_NAME_	_LABEL_	Mean_Andhra Pradesh	Mean_Assam	Mean_Bihar	Mean_Chandigarh	Mean_Delhi	Mean_Gujarat	Mean_Haryana	Mean_Jharkhand	Mean_Karnataka	Mean_Kerala	Mean_Madhya Pradesh	Mean_Maharashtra	Mean_Odisha	Mean_Punjab	Mean_Rajasthan	Mean_Tamil Nadu	Mean_Telangana	Mean_Uttar Pradesh	Mean_West Bengal
1	Avg_Income	Annual Income (INR)	3L-6L	3L-6L	3L-6L	6L-12L	> 12L	6L-12L	> 12L	6L-12L	> 12L	6L-12L	3L-6L	6L-12L	3L-6L	3L-6L	3L-6L	6L-12L	3L-6L	3L-6L	3L-6L

9) PROC SQL SHOWCASE

Purpose: Create a city-level aggregate table for BI consumers. 

proc sql;

  create table city_agg as

  select State, City,

         count(*) as N,

         mean(Age) as Avg_Age format=8.1,

         mean(Income_INR) as Avg_Income format=comma12.,

         mean(Credit_Score) as Avg_Credit format=8.1,

         mean(Digital_Literacy_Score) as Avg_DigiLit format=8.1,

         sum(UPI_Transactions_Month) as Total_UPI

  from people_enriched

  group by State, City

  order by State, Avg_Income desc;

quit;

proc print data=city_agg (obs=10);run;

Output:

Obs	State	City	N	Avg_Age	Avg_Income	Avg_Credit	Avg_DigiLit	Total_UPI
1	Andhra Pradesh	Visakhapatnam	1	26.0	520,000	740.0	82.0	24
2	Assam	Silchar	1	27.0	460,000	735.0	80.0	22
3	Bihar	Patna	1	28.0	480,000	730.0	77.0	20
4	Chandigarh	Chandigarh	1	28.0	850,000	770.0	81.0	25
5	Delhi	Delhi	1	39.0	1,400,000	805.0	83.0	18
6	Gujarat	Surat	1	36.0	1,300,000	800.0	88.0	35
7	Gujarat	Ahmedabad	1	25.0	1,200,000	780.0	85.0	60
8	Gujarat	Vadodara	1	26.0	780,000	760.0	80.0	20
9	Haryana	Gurugram	1	47.0	1,800,000	820.0	90.0	38
10	Jharkhand	Ranchi	1	32.0	700,000	740.0	72.0	25

Purpose: Identify top 10 earners with additional flags using subquery. 

proc sql;

  create table top10_earners as

  select *

  from people_enriched

  order by Income_INR desc;

quit;

proc print data=top10_earners (obs=10);run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group	Income_Quartile	Income_per_Capita	High_Digital	Busy_Internet_User	Long_Commute	Active_Lifestyle
1	19	Vikram Rao	Male	35-44	Bengaluru	Karnataka	Postgraduate	Data Scientist	IT	> 12L	3	Married	Kannada	Yes	3	Metro	50	150	Yes	Yes	835	45	Ugadi	South Indian	No	92	Urban	0	3	9	O+	3	733333.33	Yes	No	Yes	Yes
2	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+	3	450000.00	Yes	No	Yes	No
3	31	Devika Rao	Female	25-34	Mangalore	Karnataka	Graduate	Product Manager	IT	> 12L	3	Married	Kannada	Yes	3	Car	35	100	Yes	Yes	825	40	Ugadi	South Indian	No	89	Urban	0	3	8	AB+	3	533333.33	Yes	No	No	Yes
4	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+	3	466666.67	Yes	No	Yes	No
5	28	Shruti Joshi	Female	35-44	Surat	Gujarat	Postgraduate	Fashion Buyer	Private	> 12L	3	Married	Gujarati	Yes	3	Car	35	120	No	Yes	800	35	Navratri	Gujarati	No	88	Urban	0	3	8	O-	3	433333.33	Yes	No	No	Yes
6	24	Meera Pillai	Female	45-54	Thiruvananthapuram	Kerala	Postgraduate	School Principal	Public	> 12L	3	Married	Malayalam	Yes	1	Car	25	100	Yes	Yes	815	20	Onam	South Indian	Yes	90	Urban	0	2	8	A+	3	416666.67	Yes	No	No	Yes
7	12	Sonali Kulkarni	Female	35-44	Nagpur	Maharashtra	Postgraduate	HR Lead	Private	6L-12L	3	Married	Marathi	Yes	2	Car	35	120	Yes	Yes	790	26	Diwali	Maharashtrian	No	84	Urban	0	2	8	AB-	3	400000.00	Yes	No	No	Yes
8	23	Aakash Jain	Male	25-34	Ahmedabad	Gujarat	Graduate	Entrepreneur	Startup	6L-12L	4	Single	Gujarati	Yes	5	Car	35	70	No	Yes	780	60	Navratri	Gujarati	No	85	Urban	0	4	8	B+	3	300000.00	Yes	Yes	No	No
9	2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+	2	275000.00	Yes	No	No	Yes
10	22	Rekha Gupta	Female	35-44	Kanpur	Uttar Pradesh	Graduate	Bank Officer	Public	6L-12L	4	Married	Hindi	Yes	2	Car	30	90	Yes	Yes	790	30	Diwali	North Indian	Yes	88	Urban	0	2	8	AB+	2	245000.00	Yes	No	No	Yes

data top10_earners;

  set top10_earners(obs=10);

run;

proc print;run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group	Income_Quartile	Income_per_Capita	High_Digital	Busy_Internet_User	Long_Commute	Active_Lifestyle
1	19	Vikram Rao	Male	35-44	Bengaluru	Karnataka	Postgraduate	Data Scientist	IT	> 12L	3	Married	Kannada	Yes	3	Metro	50	150	Yes	Yes	835	45	Ugadi	South Indian	No	92	Urban	0	3	9	O+	3	733333.33	Yes	No	Yes	Yes
2	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+	3	450000.00	Yes	No	Yes	No
3	31	Devika Rao	Female	25-34	Mangalore	Karnataka	Graduate	Product Manager	IT	> 12L	3	Married	Kannada	Yes	3	Car	35	100	Yes	Yes	825	40	Ugadi	South Indian	No	89	Urban	0	3	8	AB+	3	533333.33	Yes	No	No	Yes
4	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+	3	466666.67	Yes	No	Yes	No
5	28	Shruti Joshi	Female	35-44	Surat	Gujarat	Postgraduate	Fashion Buyer	Private	> 12L	3	Married	Gujarati	Yes	3	Car	35	120	No	Yes	800	35	Navratri	Gujarati	No	88	Urban	0	3	8	O-	3	433333.33	Yes	No	No	Yes
6	24	Meera Pillai	Female	45-54	Thiruvananthapuram	Kerala	Postgraduate	School Principal	Public	> 12L	3	Married	Malayalam	Yes	1	Car	25	100	Yes	Yes	815	20	Onam	South Indian	Yes	90	Urban	0	2	8	A+	3	416666.67	Yes	No	No	Yes
7	12	Sonali Kulkarni	Female	35-44	Nagpur	Maharashtra	Postgraduate	HR Lead	Private	6L-12L	3	Married	Marathi	Yes	2	Car	35	120	Yes	Yes	790	26	Diwali	Maharashtrian	No	84	Urban	0	2	8	AB-	3	400000.00	Yes	No	No	Yes
8	23	Aakash Jain	Male	25-34	Ahmedabad	Gujarat	Graduate	Entrepreneur	Startup	6L-12L	4	Single	Gujarati	Yes	5	Car	35	70	No	Yes	780	60	Navratri	Gujarati	No	85	Urban	0	4	8	B+	3	300000.00	Yes	Yes	No	No
9	2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+	2	275000.00	Yes	No	No	Yes
10	22	Rekha Gupta	Female	35-44	Kanpur	Uttar Pradesh	Graduate	Bank Officer	Public	6L-12L	4	Married	Hindi	Yes	2	Car	30	90	Yes	Yes	790	30	Diwali	North Indian	Yes	88	Urban	0	2	8	AB+	2	245000.00	Yes	No	No	Yes

proc sql;

  create table top10_earners_flags as

  select t.*, 

         case when t.High_Digital=1 and t.Active_Lifestyle=1 then 'Digital+Active'

              when t.High_Digital=1 then 'Digital'

              when t.Active_Lifestyle=1 then 'Active'

              else 'Other' end as Persona length=16

  from top10_earners t;

quit;

proc print data=top10_earners_flags (obs=10);run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group	Income_Quartile	Income_per_Capita	High_Digital	Busy_Internet_User	Long_Commute	Active_Lifestyle	Persona
1	19	Vikram Rao	Male	35-44	Bengaluru	Karnataka	Postgraduate	Data Scientist	IT	> 12L	3	Married	Kannada	Yes	3	Metro	50	150	Yes	Yes	835	45	Ugadi	South Indian	No	92	Urban	0	3	9	O+	3	733333.33	Yes	No	Yes	Yes	Digital+Active
2	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+	3	450000.00	Yes	No	Yes	No	Digital
3	31	Devika Rao	Female	25-34	Mangalore	Karnataka	Graduate	Product Manager	IT	> 12L	3	Married	Kannada	Yes	3	Car	35	100	Yes	Yes	825	40	Ugadi	South Indian	No	89	Urban	0	3	8	AB+	3	533333.33	Yes	No	No	Yes	Digital+Active
4	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+	3	466666.67	Yes	No	Yes	No	Digital
5	28	Shruti Joshi	Female	35-44	Surat	Gujarat	Postgraduate	Fashion Buyer	Private	> 12L	3	Married	Gujarati	Yes	3	Car	35	120	No	Yes	800	35	Navratri	Gujarati	No	88	Urban	0	3	8	O-	3	433333.33	Yes	No	No	Yes	Digital+Active
6	24	Meera Pillai	Female	45-54	Thiruvananthapuram	Kerala	Postgraduate	School Principal	Public	> 12L	3	Married	Malayalam	Yes	1	Car	25	100	Yes	Yes	815	20	Onam	South Indian	Yes	90	Urban	0	2	8	A+	3	416666.67	Yes	No	No	Yes	Digital+Active
7	12	Sonali Kulkarni	Female	35-44	Nagpur	Maharashtra	Postgraduate	HR Lead	Private	6L-12L	3	Married	Marathi	Yes	2	Car	35	120	Yes	Yes	790	26	Diwali	Maharashtrian	No	84	Urban	0	2	8	AB-	3	400000.00	Yes	No	No	Yes	Digital+Active
8	23	Aakash Jain	Male	25-34	Ahmedabad	Gujarat	Graduate	Entrepreneur	Startup	6L-12L	4	Single	Gujarati	Yes	5	Car	35	70	No	Yes	780	60	Navratri	Gujarati	No	85	Urban	0	4	8	B+	3	300000.00	Yes	Yes	No	No	Digital
9	2	Priya Iyer	Female	25-34	Chennai	Tamil Nadu	Postgraduate	Data Analyst	IT	6L-12L	4	Married	Tamil	Yes	3	Bike	40	90	Yes	Yes	780	28	Pongal	South Indian	Yes	88	Urban	0	3	6	B+	2	275000.00	Yes	No	No	Yes	Digital+Active
10	22	Rekha Gupta	Female	35-44	Kanpur	Uttar Pradesh	Graduate	Bank Officer	Public	6L-12L	4	Married	Hindi	Yes	2	Car	30	90	Yes	Yes	790	30	Diwali	North Indian	Yes	88	Urban	0	2	8	AB+	2	245000.00	Yes	No	No	Yes	Digital+Active

Purpose: Join aggregates to individuals to provide “context columns”. 

proc sql;

  create table people_with_city_context as

  select p.*, c.N as City_Count, c.Avg_Income as City_Avg_Income,

         c.Avg_DigiLit as City_Avg_DigiLit

  from people_enriched as p

  left join city_agg as c

    on p.State=c.State and p.City=c.City;

quit;

proc print data=people_with_city_context (obs=10);run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group	Income_Quartile	Income_per_Capita	High_Digital	Busy_Internet_User	Long_Commute	Active_Lifestyle	City_Count	City_Avg_Income	City_Avg_DigiLit
1	10	Keerthi R	Female	25-34	Visakhapatnam	Andhra Pradesh	Graduate	Graphic Designer	Media	3L-6L	4	Single	Telugu	Yes	5	Bike	30	90	No	No	740	24	Ugadi	South Indian	No	82	Urban	0	3	7	B-	1	130000.00	Yes	Yes	No	Yes	1	520,000	82.0
2	18	Trisha Dey	Female	25-34	Silchar	Assam	Graduate	Content Writer	Media	3L-6L	4	Single	Assamese	Yes	4	Auto	20	70	No	Yes	735	22	Bihu	North East	No	80	Semi-Urban	0	2	6	B+	1	115000.00	Yes	Yes	No	No	1	460,000	80.0
3	8	Sana Parveen	Female	25-34	Patna	Bihar	Graduate	Nurse	Healthcare	3L-6L	5	Single	Hindi	Yes	3	Auto	35	120	Yes	Yes	730	20	Eid	North Indian	No	77	Semi-Urban	0	1	6	B+	1	96000.00	No	No	No	Yes	1	480,000	77.0
4	30	Anil Kumar	Male	25-34	Chandigarh	Chandigarh	Graduate	Civil Engineer	Construction	6L-12L	3	Single	Hindi	Yes	2	Car	30	100	No	Yes	770	25	Holi	North Indian	No	81	Urban	0	2	8	A-	2	283333.33	Yes	No	No	Yes	1	850,000	81.0
5	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+	3	466666.67	Yes	No	Yes	No	1	1,400,000	83.0
6	23	Aakash Jain	Male	25-34	Ahmedabad	Gujarat	Graduate	Entrepreneur	Startup	6L-12L	4	Single	Gujarati	Yes	5	Car	35	70	No	Yes	780	60	Navratri	Gujarati	No	85	Urban	0	4	8	B+	3	300000.00	Yes	Yes	No	No	1	1,200,000	85.0
7	28	Shruti Joshi	Female	35-44	Surat	Gujarat	Postgraduate	Fashion Buyer	Private	> 12L	3	Married	Gujarati	Yes	3	Car	35	120	No	Yes	800	35	Navratri	Gujarati	No	88	Urban	0	3	8	O-	3	433333.33	Yes	No	No	Yes	1	1,300,000	88.0
8	32	Shivam Patel	Male	25-34	Vadodara	Gujarat	Graduate	Mechanical Engineer	Manufacturing	6L-12L	4	Single	Gujarati	Yes	2	Bike	25	70	No	Yes	760	20	Navratri	Gujarati	Yes	80	Urban	0	2	7	O+	2	195000.00	Yes	No	No	No	1	780,000	80.0
9	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+	3	450000.00	Yes	No	Yes	No	1	1,800,000	90.0
10	25	Rajeev Ranjan	Male	25-34	Ranchi	Jharkhand	Graduate	Police Sub-Inspector	Public	6L-12L	4	Married	Hindi	Yes	2	Bike	20	60	Yes	Yes	740	25	Chhath	North Indian	No	72	Urban	0	1	7	O+	2	175000.00	No	No	No	No	1	700,000	72.0

10) GRAPHING (PROC SGPLOT)  

Purpose: Visualize relationships for storytelling (income vs digital). 

ods graphics on;

proc sgplot data=people_enriched;

  reg x=Digital_Literacy_Score y=Income_INR;

  scatter x=Digital_Literacy_Score y=Income_INR / group=Sector;

  yaxis label="Annual Income (INR)";

  xaxis label="Digital Literacy Score";

  title "Income vs Digital Literacy by Sector";

run;

ods graphics off;

Output:

Purpose: Show distribution of commute times segmented by Rural/Urban. 

ods graphics on;

proc sgplot data=people_enriched;

  vbox Commute_Minutes / category=Rural_Urban;

  title "Commute Minutes by Rural/Urban Category";

run;

ods graphics off;

Output:

11) REUSABLE MACROS

Purpose: %make_formats — Reapply formats/labels after transformations. 

%macro make_formats;

  proc datasets lib=work nolist;

    modify people_enriched;

      format Gender $genderF. Marital_Status $marF. Rural_Urban $ruF.

             Smartphone_User yesnoF. Has_Health_Insurance yesnoF.

             Voter_ID_Flag yesnoF. Veg_Flag yesnoF.;

  quit;

%mend;

Purpose: %summary_by(state or sector) — Instant grouped KPI table. 

%macro summary_by(classvar);

  proc summary data=people_enriched nway;

    class &classvar.;

    var Income_INR Credit_Score Digital_Literacy_Score UPI_Transactions_Month;

    output out=summary_&classvar.

      n()=N

      mean(Income_INR)=Avg_Income

      mean(Credit_Score)=Avg_Credit

      mean(Digital_Literacy_Score)=Avg_DigiLit

      mean(UPI_Transactions_Month)=Avg_UPI;

    title "Auto Summary by &classvar.";

  run;

  title;

%mend;

Purpose: %add_person — Append one more record (for demonstrations). 

%macro add_person(

  Person_ID, Name, Gender, Age, City, State, Education_Level, Occupation, Sector,

  Income_INR, Household_Size, Marital_Status, Language, Smartphone_User,

  Internet_Hours_Per_Day, Commute_Mode, Commute_Minutes, Fitness_Mins_Per_Week,

  Has_Health_Insurance, Voter_ID_Flag, Credit_Score, UPI_Transactions_Month,

  Festival_Celebrated, Cuisine_Pref, Veg_Flag, Digital_Literacy_Score, Rural_Urban,

  Disability_Flag, Travel_Trips_Year, Pollution_Concern_Score, Blood_Group);

data one_more;

    length Name $28 City $20 State $20 Education_Level $20 Occupation $24 Sector $16

           Marital_Status $1 Language $18 Commute_Mode $14 Festival_Celebrated $18

           Cuisine_Pref $16 Rural_Urban $10 Blood_Group $3 Gender $1;

    Person_ID=&Person_ID.;

    Name="&Name."; Gender="&Gender."; Age=&Age.;

    City="&City."; State="&State."; Education_Level="&Education_Level.";

    Occupation="&Occupation."; Sector="&Sector."; Income_INR=&Income_INR.;

    Household_Size=&Household_Size.; Marital_Status="&Marital_Status.";

    Language="&Language."; Smartphone_User=&Smartphone_User.;

    Internet_Hours_Per_Day=&Internet_Hours_Per_Day.;

    Commute_Mode="&Commute_Mode."; Commute_Minutes=&Commute_Minutes.;

    Fitness_Mins_Per_Week=&Fitness_Mins_Per_Week.;

    Has_Health_Insurance=&Has_Health_Insurance.; Voter_ID_Flag=&Voter_ID_Flag.;

    Credit_Score=&Credit_Score.; UPI_Transactions_Month=&UPI_Transactions_Month.;

    Festival_Celebrated="&Festival_Celebrated.";

    Cuisine_Pref="&Cuisine_Pref."; Veg_Flag=&Veg_Flag.;

    Digital_Literacy_Score=&Digital_Literacy_Score.; Rural_Urban="&Rural_Urban.";

    Disability_Flag=&Disability_Flag.; Travel_Trips_Year=&Travel_Trips_Year.;

    Pollution_Concern_Score=&Pollution_Concern_Score.; Blood_Group="&Blood_Group.";

    format Gender $genderF. Marital_Status $marF. Rural_Urban $ruF.

           Smartphone_User yesnoF. Has_Health_Insurance yesnoF.

           Voter_ID_Flag yesnoF. Veg_Flag yesnoF. Age agebandF.

           Income_INR incomeF.;

  run;

 proc print;run;

  proc append base=people_enriched data=one_more force; run;

%mend;

Purpose: %qc_minmax — Quick QC: check value ranges and flag outliers. 

%macro qc_minmax(ds,var,low,high);

  data qc_&var.;

    set &ds.;

    length QC_Flag $40;

    if &var. < &low. then QC_Flag="Below Min";

    else if &var. > &high. then QC_Flag="Above Max";

    else QC_Flag="OK";

  run;

  proc freq data=qc_&var.;

    tables QC_Flag / missing;

    title "QC for &var.: Range [&low., &high.]";

  run;

  title;

%mend;

%summary_by(State);

Output:

Auto Summary by State

Obs	State	_TYPE_	_FREQ_	N	Avg_Income	Avg_Credit	Avg_DigiLit	Avg_UPI
1	Andhra Pradesh	1	1	1	3L-6L	740.000	82.0000	24.0000
2	Assam	1	1	1	3L-6L	735.000	80.0000	22.0000
3	Bihar	1	1	1	3L-6L	730.000	77.0000	20.0000
4	Chandigarh	1	1	1	6L-12L	770.000	81.0000	25.0000
5	Delhi	1	1	1	> 12L	805.000	83.0000	18.0000
6	Gujarat	1	3	3	6L-12L	780.000	84.3333	38.3333
7	Haryana	1	1	1	> 12L	820.000	90.0000	38.0000
8	Jharkhand	1	1	1	6L-12L	740.000	72.0000	25.0000
9	Karnataka	1	3	3	> 12L	553.333	85.0000	33.0000
10	Kerala	1	2	2	6L-12L	795.000	88.0000	26.0000
11	Madhya Pradesh	1	2	2	3L-6L	717.500	72.5000	22.5000
12	Maharashtra	1	3	3	6L-12L	750.000	79.0000	25.3333
13	Odisha	1	1	1	3L-6L	720.000	65.0000	18.0000
14	Punjab	1	1	1	3L-6L	715.000	66.0000	18.0000
15	Rajasthan	1	1	1	3L-6L	705.000	64.0000	20.0000
16	Tamil Nadu	1	2	2	6L-12L	757.500	85.0000	25.0000
17	Telangana	1	1	1	3L-6L	720.000	72.0000	22.0000
18	Uttar Pradesh	1	4	4	3L-6L	532.500	67.5000	22.0000
19	West Bengal	1	2	2	3L-6L	384.000	74.0000	16.0000

%summary_by(Sector);

Output:

Auto Summary by State

Obs	State	_TYPE_	_FREQ_	N	Avg_Income	Avg_Credit	Avg_DigiLit	Avg_UPI
1	Andhra Pradesh	1	1	1	3L-6L	740.000	82.0000	24.0000
2	Assam	1	1	1	3L-6L	735.000	80.0000	22.0000
3	Bihar	1	1	1	3L-6L	730.000	77.0000	20.0000
4	Chandigarh	1	1	1	6L-12L	770.000	81.0000	25.0000
5	Delhi	1	1	1	> 12L	805.000	83.0000	18.0000
6	Gujarat	1	3	3	6L-12L	780.000	84.3333	38.3333
7	Haryana	1	1	1	> 12L	820.000	90.0000	38.0000
8	Jharkhand	1	1	1	6L-12L	740.000	72.0000	25.0000
9	Karnataka	1	3	3	> 12L	553.333	85.0000	33.0000
10	Kerala	1	2	2	6L-12L	795.000	88.0000	26.0000
11	Madhya Pradesh	1	2	2	3L-6L	717.500	72.5000	22.5000
12	Maharashtra	1	3	3	6L-12L	750.000	79.0000	25.3333
13	Odisha	1	1	1	3L-6L	720.000	65.0000	18.0000
14	Punjab	1	1	1	3L-6L	715.000	66.0000	18.0000
15	Rajasthan	1	1	1	3L-6L	705.000	64.0000	20.0000
16	Tamil Nadu	1	2	2	6L-12L	757.500	85.0000	25.0000
17	Telangana	1	1	1	3L-6L	720.000	72.0000	22.0000
18	Uttar Pradesh	1	4	4	3L-6L	532.500	67.5000	22.0000
19	West Bengal	1	2	2	3L-6L	384.000	74.0000	16.0000

%make_formats;

Log:

NOTE: MODIFY was successful for WORK.PEOPLE_ENRICHED.DATA.

NOTE: PROCEDURE DATASETS used (Total process time):

real time 0.00 seconds

user cpu time 0.00 seconds

system cpu time 0.00 seconds

memory 562.15k

OS Memory 23972.00k

Timestamp 09/01/2025 12:54:05 PM

Step Count 299 Switch Count 0

Page Faults 0

Page Reclaims 85

Page Swaps 0

Voluntary Context Switches 0

Involuntary Context Switches 1

Block Input Operations 0

Block Output Operations 272

%add_person(99,Rehan Qureshi,M,34,Noida,Uttar Pradesh,Graduate,UX Designer,IT,

  1050000,3,S,Hindi,1,4,Metro,50,80,1,1,790,30,Diwali,North Indian,0,88,Urban,0,2,8,B+);

Output:

Obs	Name	City	State	Education_Level	Occupation	Sector	Marital_Status	Language	Commute_Mode	Festival_Celebrated	Cuisine_Pref	Rural_Urban	Blood_Group	Gender	Person_ID	Age	Income_INR	Household_Size	Smartphone_User	Internet_Hours_Per_Day	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Veg_Flag	Digital_Literacy_Score	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score
1	Rehan Qureshi	Noida	Uttar Pradesh	Graduate	UX Designer	IT	Single	Hindi	Metro	Diwali	North Indian	Urban	B+	Male	99	25-34	6L-12L	3	Yes	4	50	80	Yes	Yes	790	30	No	88	0	2	8

12) TARGETED QC CHECKS 

Purpose: Use macro QC checker for critical numeric fields. 

%qc_minmax(people_enriched, Credit_Score, 300, 900);

Output:

QC for Credit_Score: Range [300, 900]

The FREQ Procedure

QC_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Below Min	3	8.82	3	8.82
OK	31	91.18	34	100.00

%qc_minmax(people_enriched, Digital_Literacy_Score, 0, 100);

Output:

QC for Digital_Literacy_Score: Range [0, 100]

The FREQ Procedure

QC_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
OK	34	100.00	34	100.00

%qc_minmax(people_enriched, Income_INR, 0, 10000000);

Output:

QC for Income_INR: Range [0, 10000000]

The FREQ Procedure

QC_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
OK	34	100.00	34	100.00

13) AD-HOC BUSINESS QUERIES 

Purpose: Identify "digital champions" cities for a pilot (SQL filter). 

proc sql;

  create table digital_champion_cities as

  select State, City,

         mean(Digital_Literacy_Score) as Avg_DigiLit format=8.1,

         mean(Income_INR) as Avg_Income format=comma12.,

         sum(UPI_Transactions_Month) as Total_UPI

  from people_enriched

  group by State, City

  having Avg_DigiLit >= 80 and Total_UPI >= 200

  order by Avg_DigiLit desc, Total_UPI desc;

quit;

proc print;run;

Log

NOTE: No observations in data set WORK.DIGITAL_CHAMPION_CITIES.

Purpose: Extract target segment: high-income, high-digital, insured. 

data target_segment;

  set people_enriched;

  if Income_INR >= 1200000 and High_Digital=1 and Has_Health_Insurance=1;

run;

proc print;run;

Output:

Obs	Person_ID	Name	Gender	Age	City	State	Education_Level	Occupation	Sector	Income_INR	Household_Size	Marital_Status	Language	Smartphone_User	Internet_Hours_Per_Day	Commute_Mode	Commute_Minutes	Fitness_Mins_Per_Week	Has_Health_Insurance	Voter_ID_Flag	Credit_Score	UPI_Transactions_Month	Festival_Celebrated	Cuisine_Pref	Veg_Flag	Digital_Literacy_Score	Rural_Urban	Disability_Flag	Travel_Trips_Year	Pollution_Concern_Score	Blood_Group	Income_Quartile	Income_per_Capita	High_Digital	Busy_Internet_User	Long_Commute	Active_Lifestyle
1	4	Neha Sharma	Female	35-44	Delhi	Delhi	Postgraduate	Marketing Manager	Private	> 12L	3	Married	Hindi	Yes	2	Car	55	75	Yes	Yes	805	18	Holi	North Indian	No	83	Urban	0	5	8	AB+	3	466666.67	Yes	No	Yes	No
2	12	Sonali Kulkarni	Female	35-44	Nagpur	Maharashtra	Postgraduate	HR Lead	Private	6L-12L	3	Married	Marathi	Yes	2	Car	35	120	Yes	Yes	790	26	Diwali	Maharashtrian	No	84	Urban	0	2	8	AB-	3	400000.00	Yes	No	No	Yes
3	15	Manoj Kumar	Male	45-54	Gurugram	Haryana	Graduate	Project Manager	IT	> 12L	4	Married	Hindi	Yes	2	Car	60	60	Yes	Yes	820	38	Diwali	North Indian	No	90	Urban	0	4	9	B+	3	450000.00	Yes	No	Yes	No
4	19	Vikram Rao	Male	35-44	Bengaluru	Karnataka	Postgraduate	Data Scientist	IT	> 12L	3	Married	Kannada	Yes	3	Metro	50	150	Yes	Yes	835	45	Ugadi	South Indian	No	92	Urban	0	3	9	O+	3	733333.33	Yes	No	Yes	Yes
5	24	Meera Pillai	Female	45-54	Thiruvananthapuram	Kerala	Postgraduate	School Principal	Public	> 12L	3	Married	Malayalam	Yes	1	Car	25	100	Yes	Yes	815	20	Onam	South Indian	Yes	90	Urban	0	2	8	A+	3	416666.67	Yes	No	No	Yes
6	31	Devika Rao	Female	25-34	Mangalore	Karnataka	Graduate	Product Manager	IT	> 12L	3	Married	Kannada	Yes	3	Car	35	100	Yes	Yes	825	40	Ugadi	South Indian	No	89	Urban	0	3	8	AB+	3	533333.33	Yes	No	No	Yes

Purpose: Side-by-side compare of segment vs overall means. 

proc means data=people_enriched noprint;

  var Income_INR Credit_Score Digital_Literacy_Score UPI_Transactions_Month;

  output out=overall_means mean=;

run;

proc print;run;

Output:

Obs	_TYPE_	_FREQ_	Income_INR	Credit_Score	Digital_Literacy_Score	UPI_Transactions_Month
1	0	34	6L-12L	690.235	78.2941	25.5

proc means data=target_segment noprint;

  var Income_INR Credit_Score Digital_Literacy_Score UPI_Transactions_Month;

  output out=segment_means mean=;

run;

proc print;run;

Output:

Obs	_TYPE_	_FREQ_	Income_INR	Credit_Score	Digital_Literacy_Score	UPI_Transactions_Month
1	0	6	> 12L	815	88	31.1667

Purpose: Merge comparator rows to feed a simple delta calc. 

data compare_means;

  merge overall_means(rename=(Income_INR=All_Income Credit_Score=All_Credit

                              Digital_Literacy_Score=All_Digi UPI_Transactions_Month=All_UPI))

        segment_means(rename=(Income_INR=Seg_Income Credit_Score=Seg_Credit

                              Digital_Literacy_Score=Seg_Digi UPI_Transactions_Month=Seg_UPI));

run;

proc print data=compare_means label noobs;

  label All_Income="All Income" Seg_Income="Segment Income"

        All_Credit="All Credit" Seg_Credit="Segment Credit"

        All_Digi="All Digital Lit" Seg_Digi="Segment Digital Lit"

        All_UPI="All UPI" Seg_UPI="Segment UPI";

  title "Segment vs Overall Averages";

run;

title;

Output:

Segment vs Overall Averages

_TYPE_	_FREQ_	All Income	All Credit	All Digital Lit	All UPI	Segment Income	Segment Credit	Segment Digital Lit	Segment UPI
0	6	6L-12L	690.235	78.2941	25.5	> 12L	815	88	31.1667

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