382.How Do You Detect Hidden Fraud Inside EV Charging Networks Using SAS Analytics?

How Do You Detect Hidden Fraud Inside EV Charging Networks Using SAS Analytics?

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —
DATA STEP | PROC SQL |  PROC PRINT | PROC SGPLOT | MACROS | PROC CORR | PROC MEANS | PROC FREQ | PROC UNIVARIATE | APPEND | PROC SORT | PROC DATASETS DELETE

Introduction – Why EV Charging Data Matters

Electric vehicles (EVs) are transforming the global transportation industry. Governments, automobile manufacturers, and energy companies are investing billions of dollars to build large-scale EV charging networks across cities, highways, and commercial zones.

However, building charging stations is only the first step. The real challenge lies in operating them efficiently, reliably, and profitably.

EV network operators must continuously analyze:

·       How many users are visiting each station

·       How long drivers wait for charging

·       Whether stations are overloaded or underused

·       Which chargers are reliable

·       Whether abnormal usage patterns indicate fraud, misuse, or revenue leakage

This is where data analytics using SAS becomes a powerful tool.

Dataset Overview – EV Charging Stations

The dataset created in this project represents EV charging stations installed across multiple Indian cities. Each row corresponds to a single charging station.

The dataset contains operational, technical, and reliability metrics such as:

• Station ID

• City

• Type of charger (Fast, Normal, Ultra-fast)

• Charging power in kilowatts

• Average waiting time

• Number of daily users

• Reliability score

• Installation date

1. BUSINESS CONTEXT

Electric Vehicle (EV) infrastructure is one of the fastest growing industries in the world.
Charging station companies continuously analyze:

• City-wise demand
• Charger utilization
• Waiting time
• Reliability
• Revenue leakage
• Abnormal station behaviour

2. EV CHARGING STATION RAW DATA CREATION

data ev_stations_raw;

    length Station_ID $10 City $20 Charging_Type $15;

    format Install_Date date9.;

    input Station_ID $ City $ Charging_Type $ Power_kW Avg_Wait_Time Daily_Users 

          Reliability_Score Install_Date : date9.;

    datalines;

EV001 Hyderabad Fast 120 18 340 92 01JAN2023

EV002 Bengaluru Fast 150 22 380 88 15JAN2023

EV003 Chennai Normal 60 35 220 80 10FEB2023

EV004 Delhi Fast 180 15 410 95 01MAR2023

EV005 Mumbai Ultra 250 10 520 97 15MAR2023

EV006 Pune Normal 80 40 200 78 10APR2023

EV007 Kochi Fast 140 25 310 85 01MAY2023

EV008 Trivandrum Normal 70 45 180 75 20MAY2023

EV009 Jaipur Fast 130 30 290 82 10JUN2023

EV010 Chandigarh Fast 160 20 350 90 01JUL2023

EV011 Ahmedabad Ultra 240 12 480 96 15JUL2023

EV012 Indore Normal 75 38 210 79 01AUG2023

EV013 Nagpur Fast 135 28 300 84 15AUG2023

EV014 Vizag Normal 65 42 190 77 01SEP2023

EV015 Coimbatore Fast 145 24 320 89 15SEP2023

;

run;

proc print data=ev_stations_raw;

run;

OUTPUT:

Obs	Station_ID	City	Charging_Type	Install_Date	Power_kW	Avg_Wait_Time	Daily_Users	Reliability_Score
1	EV001	Hyderabad	Fast	01JAN2023	120	18	340	92
2	EV002	Bengaluru	Fast	15JAN2023	150	22	380	88
3	EV003	Chennai	Normal	10FEB2023	60	35	220	80
4	EV004	Delhi	Fast	01MAR2023	180	15	410	95
5	EV005	Mumbai	Ultra	15MAR2023	250	10	520	97
6	EV006	Pune	Normal	10APR2023	80	40	200	78
7	EV007	Kochi	Fast	01MAY2023	140	25	310	85
8	EV008	Trivandrum	Normal	20MAY2023	70	45	180	75
9	EV009	Jaipur	Fast	10JUN2023	130	30	290	82
10	EV010	Chandigarh	Fast	01JUL2023	160	20	350	90
11	EV011	Ahmedabad	Ultra	15JUL2023	240	12	480	96
12	EV012	Indore	Normal	01AUG2023	75	38	210	79
13	EV013	Nagpur	Fast	15AUG2023	135	28	300	84
14	EV014	Vizag	Normal	01SEP2023	65	42	190	77
15	EV015	Coimbatore	Fast	15SEP2023	145	24	320	89

Why this code is used

·       Structured EV dataset

·       Realistic business variables

·       Date-driven system

What it does

·       Creates EV stations table

·       Stores performance metrics

·       Adds installation date

Why required

·       Base analytics

·       Source of truth

·       Simulation realism

Interview angle

·       DATA step mastery

·       Format usage

·       Input control

3. DATE ENGINEERING (MDY, INTCK, INTNX)

data ev_dates;

    set ev_stations_raw;

    Review_Date = mdy(12,31,2024);

    Operational_Days = intck('day', Install_Date, Review_Date);

    Next_Service = intnx('month', Install_Date, 6);

    format Review_Date Next_Service date9.;

run;

proc print data=ev_dates;

run;

 OUTPUT:

Obs	Station_ID	City	Charging_Type	Install_Date	Power_kW	Avg_Wait_Time	Daily_Users	Reliability_Score	Review_Date	Operational_Days	Next_Service
1	EV001	Hyderabad	Fast	01JAN2023	120	18	340	92	31DEC2024	730	01JUL2023
2	EV002	Bengaluru	Fast	15JAN2023	150	22	380	88	31DEC2024	716	01JUL2023
3	EV003	Chennai	Normal	10FEB2023	60	35	220	80	31DEC2024	690	01AUG2023
4	EV004	Delhi	Fast	01MAR2023	180	15	410	95	31DEC2024	671	01SEP2023
5	EV005	Mumbai	Ultra	15MAR2023	250	10	520	97	31DEC2024	657	01SEP2023
6	EV006	Pune	Normal	10APR2023	80	40	200	78	31DEC2024	631	01OCT2023
7	EV007	Kochi	Fast	01MAY2023	140	25	310	85	31DEC2024	610	01NOV2023
8	EV008	Trivandrum	Normal	20MAY2023	70	45	180	75	31DEC2024	591	01NOV2023
9	EV009	Jaipur	Fast	10JUN2023	130	30	290	82	31DEC2024	570	01DEC2023
10	EV010	Chandigarh	Fast	01JUL2023	160	20	350	90	31DEC2024	549	01JAN2024
11	EV011	Ahmedabad	Ultra	15JUL2023	240	12	480	96	31DEC2024	535	01JAN2024
12	EV012	Indore	Normal	01AUG2023	75	38	210	79	31DEC2024	518	01FEB2024
13	EV013	Nagpur	Fast	15AUG2023	135	28	300	84	31DEC2024	504	01FEB2024
14	EV014	Vizag	Normal	01SEP2023	65	42	190	77	31DEC2024	487	01MAR2024
15	EV015	Coimbatore	Fast	15SEP2023	145	24	320	89	31DEC2024	473	01MAR2024

Why this code is used

·       Time-based analytics

·       Lifecycle tracking

·       Service scheduling

What it does

·       Calculates operating days

·       Predicts next service

·       Fixes review date

Why required

·       Asset lifecycle

·       Maintenance planning

·       KPI trends

Interview angle

·       INTCK vs INTNX

·       MDY usage

·       Date arithmetic

4. CHARACTER & NUMERIC FUNCTIONS

data ev_clean;

    set ev_dates;

    City_Std = propcase(strip(City));

    Charger_Type = upcase(trim(Charging_Type));

    Station_Key = catx("-", Station_ID, City_Std);

    Reliability_Filled = coalesce(Reliability_Score, 0);

run;

proc print data=ev_clean;

 var Station_ID City City_Std Charger_Type Station_Key Reliability_Filled;

run;

OUTPUT:

Obs	Station_ID	City	City_Std	Charger_Type	Station_Key	Reliability_Filled
1	EV001	Hyderabad	Hyderabad	FAST	EV001-Hyderabad	92
2	EV002	Bengaluru	Bengaluru	FAST	EV002-Bengaluru	88
3	EV003	Chennai	Chennai	NORMAL	EV003-Chennai	80
4	EV004	Delhi	Delhi	FAST	EV004-Delhi	95
5	EV005	Mumbai	Mumbai	ULTRA	EV005-Mumbai	97
6	EV006	Pune	Pune	NORMAL	EV006-Pune	78
7	EV007	Kochi	Kochi	FAST	EV007-Kochi	85
8	EV008	Trivandrum	Trivandrum	NORMAL	EV008-Trivandrum	75
9	EV009	Jaipur	Jaipur	FAST	EV009-Jaipur	82
10	EV010	Chandigarh	Chandigarh	FAST	EV010-Chandigarh	90
11	EV011	Ahmedabad	Ahmedabad	ULTRA	EV011-Ahmedabad	96
12	EV012	Indore	Indore	NORMAL	EV012-Indore	79
13	EV013	Nagpur	Nagpur	FAST	EV013-Nagpur	84
14	EV014	Vizag	Vizag	NORMAL	EV014-Vizag	77
15	EV015	Coimbatore	Coimbatore	FAST	EV015-Coimbatore	89

Why this code is used

·       Standardization

·       Key creation

·       Missing handling

What it does

·       Cleans city names

·       Formats charger type

·       Creates station key

Why required

·       Data quality

·       Joins

·       Reporting

Interview angle

·       CATX vs CAT

·       STRIP vs TRIM

·       COALESCE

5. PROC SQL – Business Layer

proc sql;

    create table ev_sql as

    select *,

           (Daily_Users * Power_kW) as Energy_Load,

           (Avg_Wait_Time / Power_kW) as Delay_Ratio

    from ev_clean;

quit;

proc print data=ev_sql;

run;

OUTPUT:

Obs	Station_ID	City	Charging_Type	Install_Date	Power_kW	Avg_Wait_Time	Daily_Users	Reliability_Score	Review_Date	Operational_Days	Next_Service	City_Std	Charger_Type	Station_Key	Reliability_Filled	Energy_Load	Delay_Ratio
1	EV001	Hyderabad	Fast	01JAN2023	120	18	340	92	31DEC2024	730	01JUL2023	Hyderabad	FAST	EV001-Hyderabad	92	40800	0.15000
2	EV002	Bengaluru	Fast	15JAN2023	150	22	380	88	31DEC2024	716	01JUL2023	Bengaluru	FAST	EV002-Bengaluru	88	57000	0.14667
3	EV003	Chennai	Normal	10FEB2023	60	35	220	80	31DEC2024	690	01AUG2023	Chennai	NORMAL	EV003-Chennai	80	13200	0.58333
4	EV004	Delhi	Fast	01MAR2023	180	15	410	95	31DEC2024	671	01SEP2023	Delhi	FAST	EV004-Delhi	95	73800	0.08333
5	EV005	Mumbai	Ultra	15MAR2023	250	10	520	97	31DEC2024	657	01SEP2023	Mumbai	ULTRA	EV005-Mumbai	97	130000	0.04000
6	EV006	Pune	Normal	10APR2023	80	40	200	78	31DEC2024	631	01OCT2023	Pune	NORMAL	EV006-Pune	78	16000	0.50000
7	EV007	Kochi	Fast	01MAY2023	140	25	310	85	31DEC2024	610	01NOV2023	Kochi	FAST	EV007-Kochi	85	43400	0.17857
8	EV008	Trivandrum	Normal	20MAY2023	70	45	180	75	31DEC2024	591	01NOV2023	Trivandrum	NORMAL	EV008-Trivandrum	75	12600	0.64286
9	EV009	Jaipur	Fast	10JUN2023	130	30	290	82	31DEC2024	570	01DEC2023	Jaipur	FAST	EV009-Jaipur	82	37700	0.23077
10	EV010	Chandigarh	Fast	01JUL2023	160	20	350	90	31DEC2024	549	01JAN2024	Chandigarh	FAST	EV010-Chandigarh	90	56000	0.12500
11	EV011	Ahmedabad	Ultra	15JUL2023	240	12	480	96	31DEC2024	535	01JAN2024	Ahmedabad	ULTRA	EV011-Ahmedabad	96	115200	0.05000
12	EV012	Indore	Normal	01AUG2023	75	38	210	79	31DEC2024	518	01FEB2024	Indore	NORMAL	EV012-Indore	79	15750	0.50667
13	EV013	Nagpur	Fast	15AUG2023	135	28	300	84	31DEC2024	504	01FEB2024	Nagpur	FAST	EV013-Nagpur	84	40500	0.20741
14	EV014	Vizag	Normal	01SEP2023	65	42	190	77	31DEC2024	487	01MAR2024	Vizag	NORMAL	EV014-Vizag	77	12350	0.64615
15	EV015	Coimbatore	Fast	15SEP2023	145	24	320	89	31DEC2024	473	01MAR2024	Coimbatore	FAST	EV015-Coimbatore	89	46400	0.16552

Why this code is used

·       Business metrics

·       SQL modeling

·       Virtual columns

What it does

·       Creates Energy Load

·       Calculates delay ratio

·       Enhances table

Why required

·       KPI engineering

·       Performance analytics

·       Optimization

Interview angle

·       SQL computed columns

·       Alias usage

6. UTILIZATION CLASSIFICATION MACRO

%macro utilization;

data ev_util;

    set ev_sql;

    if Daily_Users > 400 then Utilization="Overloaded";

    else if 250 <= Daily_Users <= 400 then Utilization = "Optimal";

    else Utilization="Underused";

run;

proc print data=ev_util;

run;

%mend;

%utilization;

OUTPUT:

Obs	Station_ID	City	Charging_Type	Install_Date	Power_kW	Avg_Wait_Time	Daily_Users	Reliability_Score	Review_Date	Operational_Days	Next_Service	City_Std	Charger_Type	Station_Key	Reliability_Filled	Energy_Load	Delay_Ratio	Utilization
1	EV001	Hyderabad	Fast	01JAN2023	120	18	340	92	31DEC2024	730	01JUL2023	Hyderabad	FAST	EV001-Hyderabad	92	40800	0.15000	Optimal
2	EV002	Bengaluru	Fast	15JAN2023	150	22	380	88	31DEC2024	716	01JUL2023	Bengaluru	FAST	EV002-Bengaluru	88	57000	0.14667	Optimal
3	EV003	Chennai	Normal	10FEB2023	60	35	220	80	31DEC2024	690	01AUG2023	Chennai	NORMAL	EV003-Chennai	80	13200	0.58333	Underused
4	EV004	Delhi	Fast	01MAR2023	180	15	410	95	31DEC2024	671	01SEP2023	Delhi	FAST	EV004-Delhi	95	73800	0.08333	Overloaded
5	EV005	Mumbai	Ultra	15MAR2023	250	10	520	97	31DEC2024	657	01SEP2023	Mumbai	ULTRA	EV005-Mumbai	97	130000	0.04000	Overloaded
6	EV006	Pune	Normal	10APR2023	80	40	200	78	31DEC2024	631	01OCT2023	Pune	NORMAL	EV006-Pune	78	16000	0.50000	Underused
7	EV007	Kochi	Fast	01MAY2023	140	25	310	85	31DEC2024	610	01NOV2023	Kochi	FAST	EV007-Kochi	85	43400	0.17857	Optimal
8	EV008	Trivandrum	Normal	20MAY2023	70	45	180	75	31DEC2024	591	01NOV2023	Trivandrum	NORMAL	EV008-Trivandrum	75	12600	0.64286	Underused
9	EV009	Jaipur	Fast	10JUN2023	130	30	290	82	31DEC2024	570	01DEC2023	Jaipur	FAST	EV009-Jaipur	82	37700	0.23077	Optimal
10	EV010	Chandigarh	Fast	01JUL2023	160	20	350	90	31DEC2024	549	01JAN2024	Chandigarh	FAST	EV010-Chandigarh	90	56000	0.12500	Optimal
11	EV011	Ahmedabad	Ultra	15JUL2023	240	12	480	96	31DEC2024	535	01JAN2024	Ahmedabad	ULTRA	EV011-Ahmedabad	96	115200	0.05000	Overloaded
12	EV012	Indore	Normal	01AUG2023	75	38	210	79	31DEC2024	518	01FEB2024	Indore	NORMAL	EV012-Indore	79	15750	0.50667	Underused
13	EV013	Nagpur	Fast	15AUG2023	135	28	300	84	31DEC2024	504	01FEB2024	Nagpur	FAST	EV013-Nagpur	84	40500	0.20741	Optimal
14	EV014	Vizag	Normal	01SEP2023	65	42	190	77	31DEC2024	487	01MAR2024	Vizag	NORMAL	EV014-Vizag	77	12350	0.64615	Underused
15	EV015	Coimbatore	Fast	15SEP2023	145	24	320	89	31DEC2024	473	01MAR2024	Coimbatore	FAST	EV015-Coimbatore	89	46400	0.16552	Optimal

Why this code is used

·       Automation

·       Business rules

·       Reusability

What it does

·       Classifies stations

·       Labels utilization

·       Supports reporting

Why required

·       Capacity planning

·       Resource allocation

·       Decision support

Interview angle

·       Macro logic

·       Conditional coding

·       Automation

7. PROC MEANS

proc means data=ev_util;

    var Power_kW Avg_Wait_Time Daily_Users Reliability_Score Energy_Load;

run;

OUTPUT:

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Power_kW Avg_Wait_Time Daily_Users Reliability_Score Energy_Load	15 15 15 15 15	133.3333333 26.9333333 313.3333333 85.8000000 47380.00	59.0600985 11.1449327 104.3118037 7.2624278 35938.24	60.0000000 10.0000000 180.0000000 75.0000000 12350.00	250.0000000 45.0000000 520.0000000 97.0000000 130000.00

Why this code is used

·       Descriptive statistics

·       Data profiling

·       Baseline metrics

What it does

·       Mean

·       Min

·       Max

·       Standard deviation

Why required

·       Performance summary

·       Data understanding

·       Management reporting

Interview angle

·       Descriptive analytics

·       KPI validation

·       Distribution check

8. PROC UNIVARIATE

proc univariate data=ev_util;

    var Daily_Users Avg_Wait_Time;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Daily_Users

Moments
N	15	Sum Weights	15
Mean	313.333333	Sum Observations	4700
Std Deviation	104.311804	Variance	10880.9524
Skewness	0.51491293	Kurtosis	-0.4205403
Uncorrected SS	1625000	Corrected SS	152333.333
Coeff Variation	33.2910012	Std Error Mean	26.9331919

Basic Statistical Measures
Location		Variability
Mean	313.3333	Std Deviation	104.31180
Median	310.0000	Variance	10881
Mode	.	Range	340.00000
		Interquartile Range	170.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	11.63372	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	520
99%	520
95%	520
90%	480
75% Q3	380
50% Median	310
25% Q1	210
10%	190
5%	180
1%	180
0% Min	180

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
180	8	350	10
190	14	380	2
200	6	410	4
210	12	480	11
220	3	520	5

The UNIVARIATE Procedure

Variable: Avg_Wait_Time

Moments
N	15	Sum Weights	15
Mean	26.9333333	Sum Observations	404
Std Deviation	11.1449327	Variance	124.209524
Skewness	0.14138813	Kurtosis	-1.1470983
Uncorrected SS	12620	Corrected SS	1738.93333
Coeff Variation	41.3797004	Std Error Mean	2.87760924

Basic Statistical Measures
Location		Variability
Mean	26.93333	Std Deviation	11.14493
Median	25.00000	Variance	124.20952
Mode	.	Range	35.00000
		Interquartile Range	20.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	9.359622	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	45
99%	45
95%	45
90%	42
75% Q3	38
50% Median	25
25% Q1	18
10%	12
5%	10
1%	10
0% Min	10

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
10	5	35	3
12	11	38	12
15	4	40	6
18	1	42	14
20	10	45	8

Why this code is used

·       Distribution analysis

·       Outlier detection

·       Skewness

What it does

·       Histogram stats

·       Percentiles

·       Extremes

Why required

·       Risk detection

·       Abnormal stations

·       Modeling readiness

Interview angle

·       Outlier handling

·       Statistical profiling

9. PROC CORR

proc corr data=ev_util;

    var Power_kW Daily_Users Avg_Wait_Time Reliability_Score;

run;

OUTPUT:

The CORR Procedure

4 Variables:	Power_kW Daily_Users Avg_Wait_Time Reliability_Score

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Power_kW	15	133.33333	59.06010	2000	60.00000	250.00000
Daily_Users	15	313.33333	104.31180	4700	180.00000	520.00000
Avg_Wait_Time	15	26.93333	11.14493	404.00000	10.00000	45.00000
Reliability_Score	15	85.80000	7.26243	1287	75.00000	97.00000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Power_kW	Daily_Users	Avg_Wait_Time	Reliability_Score
Power_kW	1.00000	0.97431 <.0001	-0.91553 <.0001	0.90843 <.0001
Daily_Users	0.97431 <.0001	1.00000	-0.96504 <.0001	0.95797 <.0001
Avg_Wait_Time	-0.91553 <.0001	-0.96504 <.0001	1.00000	-0.98857 <.0001
Reliability_Score	0.90843 <.0001	0.95797 <.0001	-0.98857 <.0001	1.00000

Why this code is used

·       Relationship analysis

·       Dependency

·       Trend insight

What it does

·       Correlation matrix

·       Strength measurement

Why required

·       Optimization

·       Predictive analytics

·       Capacity planning

Interview angle

·       Pearson correlation

·       Multicollinearity

10. PROC SGPLOT

proc sgplot data=ev_util;

    vbar City / response=Daily_Users;

run;

OUTPUT:

Why this code is used

·       Visualization

·       City performance

·       Trend spotting

What it does

·       Bar chart

·       User comparison

Why required

·       Management dashboard

·       Insights

·       Reporting

Interview angle

·       PROC SGPLOT

·       Visualization basics

11. FRAUD RISK MACRO

%macro fraud;

data ev_fraud;

    set ev_util;

    if Daily_Users > 500 and Avg_Wait_Time > 30 then Fraud_Flag="Yes";

    else Fraud_Flag="No";

run;

proc print data=ev_fraud;

 var Station_ID City Daily_Users Avg_Wait_Time Fraud_Flag;

run;

%mend;

%fraud;

OUTPUT:

Obs	Station_ID	City	Daily_Users	Avg_Wait_Time	Fraud_Flag
1	EV001	Hyderabad	340	18	No
2	EV002	Bengaluru	380	22	No
3	EV003	Chennai	220	35	No
4	EV004	Delhi	410	15	No
5	EV005	Mumbai	520	10	No
6	EV006	Pune	200	40	No
7	EV007	Kochi	310	25	No
8	EV008	Trivandrum	180	45	No
9	EV009	Jaipur	290	30	No
10	EV010	Chandigarh	350	20	No
11	EV011	Ahmedabad	480	12	No
12	EV012	Indore	210	38	No
13	EV013	Nagpur	300	28	No
14	EV014	Vizag	190	42	No
15	EV015	Coimbatore	320	24	No

Why this code is used

·       Fraud detection

·       Rule engine

·       Automation

What it does

·       Flags suspicious stations

·       Combines conditions

Why required

·       Revenue protection

·       Abuse prevention

·       Auditing

Interview angle

·       Rule-based modeling

       ·     Macro automation

12. PROC FREQ

proc freq data=ev_fraud;

    tables Utilization Fraud_Flag;

run;

OUTPUT:

The FREQ Procedure

Utilization	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Optimal	7	46.67	7	46.67
Overloaded	3	20.00	10	66.67
Underused	5	33.33	15	100.00

Fraud_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
No	15	100.00	15	100.00

Why this code is used

·       Frequency analysis

·       Pattern detection

What it does

·       Counts categories

·       Distribution

Why required

·       Risk reporting

·       Utilization mix

Interview angle

·       Categorical analytics

13. PROC TRANSPOSE

proc sort data=ev_fraud;

    by Station_ID;

run;

proc print data=ev_fraud;

run;

OUTPUT:

Obs	Station_ID	City	Charging_Type	Install_Date	Power_kW	Avg_Wait_Time	Daily_Users	Reliability_Score	Review_Date	Operational_Days	Next_Service	City_Std	Charger_Type	Station_Key	Reliability_Filled	Energy_Load	Delay_Ratio	Utilization	Fraud_Flag
1	EV001	Hyderabad	Fast	01JAN2023	120	18	340	92	31DEC2024	730	01JUL2023	Hyderabad	FAST	EV001-Hyderabad	92	40800	0.15000	Optimal	No
2	EV002	Bengaluru	Fast	15JAN2023	150	22	380	88	31DEC2024	716	01JUL2023	Bengaluru	FAST	EV002-Bengaluru	88	57000	0.14667	Optimal	No
3	EV003	Chennai	Normal	10FEB2023	60	35	220	80	31DEC2024	690	01AUG2023	Chennai	NORMAL	EV003-Chennai	80	13200	0.58333	Underused	No
4	EV004	Delhi	Fast	01MAR2023	180	15	410	95	31DEC2024	671	01SEP2023	Delhi	FAST	EV004-Delhi	95	73800	0.08333	Overloaded	No
5	EV005	Mumbai	Ultra	15MAR2023	250	10	520	97	31DEC2024	657	01SEP2023	Mumbai	ULTRA	EV005-Mumbai	97	130000	0.04000	Overloaded	No
6	EV006	Pune	Normal	10APR2023	80	40	200	78	31DEC2024	631	01OCT2023	Pune	NORMAL	EV006-Pune	78	16000	0.50000	Underused	No
7	EV007	Kochi	Fast	01MAY2023	140	25	310	85	31DEC2024	610	01NOV2023	Kochi	FAST	EV007-Kochi	85	43400	0.17857	Optimal	No
8	EV008	Trivandrum	Normal	20MAY2023	70	45	180	75	31DEC2024	591	01NOV2023	Trivandrum	NORMAL	EV008-Trivandrum	75	12600	0.64286	Underused	No
9	EV009	Jaipur	Fast	10JUN2023	130	30	290	82	31DEC2024	570	01DEC2023	Jaipur	FAST	EV009-Jaipur	82	37700	0.23077	Optimal	No
10	EV010	Chandigarh	Fast	01JUL2023	160	20	350	90	31DEC2024	549	01JAN2024	Chandigarh	FAST	EV010-Chandigarh	90	56000	0.12500	Optimal	No
11	EV011	Ahmedabad	Ultra	15JUL2023	240	12	480	96	31DEC2024	535	01JAN2024	Ahmedabad	ULTRA	EV011-Ahmedabad	96	115200	0.05000	Overloaded	No
12	EV012	Indore	Normal	01AUG2023	75	38	210	79	31DEC2024	518	01FEB2024	Indore	NORMAL	EV012-Indore	79	15750	0.50667	Underused	No
13	EV013	Nagpur	Fast	15AUG2023	135	28	300	84	31DEC2024	504	01FEB2024	Nagpur	FAST	EV013-Nagpur	84	40500	0.20741	Optimal	No
14	EV014	Vizag	Normal	01SEP2023	65	42	190	77	31DEC2024	487	01MAR2024	Vizag	NORMAL	EV014-Vizag	77	12350	0.64615	Underused	No
15	EV015	Coimbatore	Fast	15SEP2023	145	24	320	89	31DEC2024	473	01MAR2024	Coimbatore	FAST	EV015-Coimbatore	89	46400	0.16552	Optimal	No

proc transpose data=ev_fraud out=ev_wide;

    by Utilization NotSorted;

    id Utilization;

    var Daily_Users;

run;

proc print data=ev_wide;

run;

OUTPUT:

Obs	Station_ID	_NAME_	Optimal	Underused	Overloaded
1	EV001	Daily_Users	340	.	.
2	EV002	Daily_Users	380	.	.
3	EV003	Daily_Users	.	220	.
4	EV004	Daily_Users	.	.	410
5	EV005	Daily_Users	.	.	520
6	EV006	Daily_Users	.	200	.
7	EV007	Daily_Users	310	.	.
8	EV008	Daily_Users	.	180	.
9	EV009	Daily_Users	290	.	.
10	EV010	Daily_Users	350	.	.
11	EV011	Daily_Users	.	.	480
12	EV012	Daily_Users	.	210	.
13	EV013	Daily_Users	300	.	.
14	EV014	Daily_Users	.	190	.
15	EV015	Daily_Users	320	.	.

Why this code is used

·       Restructuring

·       Reporting format

What it does

·       Converts long to wide

Why required

·       Dashboarding

·       Excel exports

Interview angle

·       Transpose logic

14. PROC APPEND

proc append base=ev_util 

            data=ev_fraud force;

run;

proc print data=ev_util;

run;

OUTPUT:

Obs	Station_ID	City	Charging_Type	Install_Date	Power_kW	Avg_Wait_Time	Daily_Users	Reliability_Score	Review_Date	Operational_Days	Next_Service	City_Std	Charger_Type	Station_Key	Reliability_Filled	Energy_Load	Delay_Ratio	Utilization
1	EV001	Hyderabad	Fast	01JAN2023	120	18	340	92	31DEC2024	730	01JUL2023	Hyderabad	FAST	EV001-Hyderabad	92	40800	0.15000	Optimal
2	EV002	Bengaluru	Fast	15JAN2023	150	22	380	88	31DEC2024	716	01JUL2023	Bengaluru	FAST	EV002-Bengaluru	88	57000	0.14667	Optimal
3	EV003	Chennai	Normal	10FEB2023	60	35	220	80	31DEC2024	690	01AUG2023	Chennai	NORMAL	EV003-Chennai	80	13200	0.58333	Underused
4	EV004	Delhi	Fast	01MAR2023	180	15	410	95	31DEC2024	671	01SEP2023	Delhi	FAST	EV004-Delhi	95	73800	0.08333	Overloaded
5	EV005	Mumbai	Ultra	15MAR2023	250	10	520	97	31DEC2024	657	01SEP2023	Mumbai	ULTRA	EV005-Mumbai	97	130000	0.04000	Overloaded
6	EV006	Pune	Normal	10APR2023	80	40	200	78	31DEC2024	631	01OCT2023	Pune	NORMAL	EV006-Pune	78	16000	0.50000	Underused
7	EV007	Kochi	Fast	01MAY2023	140	25	310	85	31DEC2024	610	01NOV2023	Kochi	FAST	EV007-Kochi	85	43400	0.17857	Optimal
8	EV008	Trivandrum	Normal	20MAY2023	70	45	180	75	31DEC2024	591	01NOV2023	Trivandrum	NORMAL	EV008-Trivandrum	75	12600	0.64286	Underused
9	EV009	Jaipur	Fast	10JUN2023	130	30	290	82	31DEC2024	570	01DEC2023	Jaipur	FAST	EV009-Jaipur	82	37700	0.23077	Optimal
10	EV010	Chandigarh	Fast	01JUL2023	160	20	350	90	31DEC2024	549	01JAN2024	Chandigarh	FAST	EV010-Chandigarh	90	56000	0.12500	Optimal
11	EV011	Ahmedabad	Ultra	15JUL2023	240	12	480	96	31DEC2024	535	01JAN2024	Ahmedabad	ULTRA	EV011-Ahmedabad	96	115200	0.05000	Overloaded
12	EV012	Indore	Normal	01AUG2023	75	38	210	79	31DEC2024	518	01FEB2024	Indore	NORMAL	EV012-Indore	79	15750	0.50667	Underused
13	EV013	Nagpur	Fast	15AUG2023	135	28	300	84	31DEC2024	504	01FEB2024	Nagpur	FAST	EV013-Nagpur	84	40500	0.20741	Optimal
14	EV014	Vizag	Normal	01SEP2023	65	42	190	77	31DEC2024	487	01MAR2024	Vizag	NORMAL	EV014-Vizag	77	12350	0.64615	Underused
15	EV015	Coimbatore	Fast	15SEP2023	145	24	320	89	31DEC2024	473	01MAR2024	Coimbatore	FAST	EV015-Coimbatore	89	46400	0.16552	Optimal
16	EV001	Hyderabad	Fast	01JAN2023	120	18	340	92	31DEC2024	730	01JUL2023	Hyderabad	FAST	EV001-Hyderabad	92	40800	0.15000	Optimal
17	EV002	Bengaluru	Fast	15JAN2023	150	22	380	88	31DEC2024	716	01JUL2023	Bengaluru	FAST	EV002-Bengaluru	88	57000	0.14667	Optimal
18	EV003	Chennai	Normal	10FEB2023	60	35	220	80	31DEC2024	690	01AUG2023	Chennai	NORMAL	EV003-Chennai	80	13200	0.58333	Underused
19	EV004	Delhi	Fast	01MAR2023	180	15	410	95	31DEC2024	671	01SEP2023	Delhi	FAST	EV004-Delhi	95	73800	0.08333	Overloaded
20	EV005	Mumbai	Ultra	15MAR2023	250	10	520	97	31DEC2024	657	01SEP2023	Mumbai	ULTRA	EV005-Mumbai	97	130000	0.04000	Overloaded
21	EV006	Pune	Normal	10APR2023	80	40	200	78	31DEC2024	631	01OCT2023	Pune	NORMAL	EV006-Pune	78	16000	0.50000	Underused
22	EV007	Kochi	Fast	01MAY2023	140	25	310	85	31DEC2024	610	01NOV2023	Kochi	FAST	EV007-Kochi	85	43400	0.17857	Optimal
23	EV008	Trivandrum	Normal	20MAY2023	70	45	180	75	31DEC2024	591	01NOV2023	Trivandrum	NORMAL	EV008-Trivandrum	75	12600	0.64286	Underused
24	EV009	Jaipur	Fast	10JUN2023	130	30	290	82	31DEC2024	570	01DEC2023	Jaipur	FAST	EV009-Jaipur	82	37700	0.23077	Optimal
25	EV010	Chandigarh	Fast	01JUL2023	160	20	350	90	31DEC2024	549	01JAN2024	Chandigarh	FAST	EV010-Chandigarh	90	56000	0.12500	Optimal
26	EV011	Ahmedabad	Ultra	15JUL2023	240	12	480	96	31DEC2024	535	01JAN2024	Ahmedabad	ULTRA	EV011-Ahmedabad	96	115200	0.05000	Overloaded
27	EV012	Indore	Normal	01AUG2023	75	38	210	79	31DEC2024	518	01FEB2024	Indore	NORMAL	EV012-Indore	79	15750	0.50667	Underused
28	EV013	Nagpur	Fast	15AUG2023	135	28	300	84	31DEC2024	504	01FEB2024	Nagpur	FAST	EV013-Nagpur	84	40500	0.20741	Optimal
29	EV014	Vizag	Normal	01SEP2023	65	42	190	77	31DEC2024	487	01MAR2024	Vizag	NORMAL	EV014-Vizag	77	12350	0.64615	Underused
30	EV015	Coimbatore	Fast	15SEP2023	145	24	320	89	31DEC2024	473	01MAR2024	Coimbatore	FAST	EV015-Coimbatore	89	46400	0.16552	Optimal

Why this code is used

·       Dataset stacking

·       History tracking

What it does

·       Adds fraud rows

Why required

·       Audit trail

·       Reporting

Interview angle

·       APPEND vs SET

15. PROC DATASETS DELETE

proc datasets library=work;

    delete ev_stations_raw;

run;

LOG:

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

Why this code is used

·       Cleanup

·       Space management

What it does

·       Deletes raw table

Why required

·       Production hygiene

·       Performance

Interview angle

·       Library management

INTERVIEW QUESTIONS FOR YOU

1.What is a SAS library and why is it used?

2.What are temporary and permanent SAS datasets?

3.What is a SAS informat?

About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent EV Charging Stations data.

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