376.How Do You Build a Real-World Weather Analytics System in SAS?

How Do You Build a Real-World Weather Analytics System in SAS?

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS ---DATA STEP | PROC SQL | MACROS | DATE FUNCTIONS (MDY-INTCK-INTNX) |  FUNCTIONS (UPCASE-LOWCASE-PROPCASE-SUBSTR-CATX-STRIP) | NUMERIC FUNCTIONS | TRANSPOSE | PROC DATASETS DELETE

1. Business & Analytical Context

Weather stations play a critical role in:

• Climate monitoring
• Disaster prediction
• Agriculture planning
• Aviation and logistics
• Environmental research

From an analytics perspective, weather station data is time-dependent, numeric-heavy, and quality-sensitive.
Therefore, it is ideal for demonstrating:

• Date handling
• Numeric aggregation
• Correlation analysis
• Data standardization
• Macro-based classification
• Dataset lifecycle management

2. Project Objectives

Here We Learn :

1.     Create a Weather Stations dataset with 15+ observations

2.     Apply date intelligence using:

o   MDY

o   INTCK

o   INTNX

3.     Use character functions for standardization

4.     Use numeric functions for analytics

5.     Build a macro-based reliability grading system

6.     Perform:

o   PROC SQL

o   PROC MEANS

o   PROC CORR

o   PROC FORMAT

7.     Demonstrate:

o   TRANSPOSE

o   PROC DATASETS DELETE

3. Dataset Design

Core Variables

Variable	Type	Description
Station_Name	Character	Weather station identifier
Country	Character	Country
Avg_Temperature	Numeric	Average temperature (°C)
Rainfall_mm	Numeric	Annual rainfall (mm)
Wind_Speed	Numeric	Average wind speed (km/h)
Accuracy_Score	Numeric	Instrument accuracy (0–100)
Start_Date	Date	Operational start date
End_Date	Date	Operational end date
Operational_Years	Numeric	Derived operational duration

4. Create Base Weather Station Dataset

data weather_base;

    length Station_Name $30 Country $20;

    format Start_Date End_Date date9.;

    input Station_Name $ Country $ Avg_Temperature Rainfall_mm  Wind_Speed

          Accuracy_Score Start_Date : date9. End_Date   : date9.;

    datalines;

Hyderabad_Central India 28.5 820 12.4 91 01JAN2000 31DEC2024

Delhi_North India 26.1 740 14.8 88 15MAR1998 31DEC2024

Mumbai_Coastal India 29.2 2200 18.6 94 01JAN1995 31DEC2024

Chennai_East India 30.1 1350 16.2 89 10JUN2001 31DEC2024

Kolkata_East India 27.8 1600 13.9 90 01JAN1997 31DEC2024

Jaipur_West India 25.4 520 11.1 85 01JAN2002 31DEC2024

Bengaluru_South India 24.3 970 10.5 92 01JAN1999 31DEC2024

Pune_West India 26.2 720 12.9 87 01JAN2003 31DEC2024

Ahmedabad_West India 27.6 690 15.4 86 01JAN2000 31DEC2024

Nagpur_Central India 28.1 1050 13.2 88 01JAN2001 31DEC2024

Bhopal_Central India 26.9 1140 11.8 90 01JAN2004 31DEC2024

Indore_Central India 25.8 980 12.0 89 01JAN2005 31DEC2024

Lucknow_North India 27.2 1010 13.5 87 01JAN1996 31DEC2024

Patna_East India 28.0 1200 14.1 86 01JAN1997 31DEC2024

Surat_West India 29.0 1400 16.5 91 01JAN2000 31DEC2024

;

run;

proc print data=weather_base;

run;

OUTPUT:

Obs	Station_Name	Country	Start_Date	End_Date	Avg_Temperature	Rainfall_mm	Wind_Speed	Accuracy_Score
1	Hyderabad_Central	India	01JAN2000	31DEC2024	28.5	820	12.4	91
2	Delhi_North	India	15MAR1998	31DEC2024	26.1	740	14.8	88
3	Mumbai_Coastal	India	01JAN1995	31DEC2024	29.2	2200	18.6	94
4	Chennai_East	India	10JUN2001	31DEC2024	30.1	1350	16.2	89
5	Kolkata_East	India	01JAN1997	31DEC2024	27.8	1600	13.9	90
6	Jaipur_West	India	01JAN2002	31DEC2024	25.4	520	11.1	85
7	Bengaluru_South	India	01JAN1999	31DEC2024	24.3	970	10.5	92
8	Pune_West	India	01JAN2003	31DEC2024	26.2	720	12.9	87
9	Ahmedabad_West	India	01JAN2000	31DEC2024	27.6	690	15.4	86
10	Nagpur_Central	India	01JAN2001	31DEC2024	28.1	1050	13.2	88
11	Bhopal_Central	India	01JAN2004	31DEC2024	26.9	1140	11.8	90
12	Indore_Central	India	01JAN2005	31DEC2024	25.8	980	12.0	89
13	Lucknow_North	India	01JAN1996	31DEC2024	27.2	1010	13.5	87
14	Patna_East	India	01JAN1997	31DEC2024	28.0	1200	14.1	86
15	Surat_West	India	01JAN2000	31DEC2024	29.0	1400	16.5	91

Explanation

·       LENGTH defines character size early to prevent truncation

·       FORMAT date9. ensures readable dates

·       Realistic climate data improves analytical credibility

5. Derive Operational Years Using INTCK

data weather_years;

    set weather_base;

    Operational_Years = intck('year', Start_Date, End_Date, 'c');

run;

proc print data=weather_years;

run;

OUTPUT:

Obs	Station_Name	Country	Start_Date	End_Date	Avg_Temperature	Rainfall_mm	Wind_Speed	Accuracy_Score	Operational_Years
1	Hyderabad_Central	India	01JAN2000	31DEC2024	28.5	820	12.4	91	24
2	Delhi_North	India	15MAR1998	31DEC2024	26.1	740	14.8	88	26
3	Mumbai_Coastal	India	01JAN1995	31DEC2024	29.2	2200	18.6	94	29
4	Chennai_East	India	10JUN2001	31DEC2024	30.1	1350	16.2	89	23
5	Kolkata_East	India	01JAN1997	31DEC2024	27.8	1600	13.9	90	27
6	Jaipur_West	India	01JAN2002	31DEC2024	25.4	520	11.1	85	22
7	Bengaluru_South	India	01JAN1999	31DEC2024	24.3	970	10.5	92	25
8	Pune_West	India	01JAN2003	31DEC2024	26.2	720	12.9	87	21
9	Ahmedabad_West	India	01JAN2000	31DEC2024	27.6	690	15.4	86	24
10	Nagpur_Central	India	01JAN2001	31DEC2024	28.1	1050	13.2	88	23
11	Bhopal_Central	India	01JAN2004	31DEC2024	26.9	1140	11.8	90	20
12	Indore_Central	India	01JAN2005	31DEC2024	25.8	980	12.0	89	19
13	Lucknow_North	India	01JAN1996	31DEC2024	27.2	1010	13.5	87	28
14	Patna_East	India	01JAN1997	31DEC2024	28.0	1200	14.1	86	27
15	Surat_West	India	01JAN2000	31DEC2024	29.0	1400	16.5	91	24

Why INTCK?

·       INTCK counts calendar boundaries crossed

·       'c' option ensures continuous year calculation

·       Used heavily in exposure, duration, survival analysis

6. Character Function Standardization

data weather_clean;

    set weather_years;

    Station_Clean = propcase(scan(Station_Name, 1, '_'));

    Country_Upper = upcase(Country);

    Country_Lower = lowcase(Country);

    Station_Tag = catx('-', Station_Clean, Country_Upper);

    Station_Code = substr(Station_Name, 1, 3);

    Station_Final = strip(trim(Station_Tag));

run;

proc print data=weather_clean;

run;

OUTPUT:

Obs	Station_Name	Country	Start_Date	End_Date	Avg_Temperature	Rainfall_mm	Wind_Speed	Accuracy_Score	Operational_Years	Station_Clean	Country_Upper	Country_Lower	Station_Tag	Station_Code	Station_Final
1	Hyderabad_Central	India	01JAN2000	31DEC2024	28.5	820	12.4	91	24	Hyderabad	INDIA	india	Hyderabad-INDIA	Hyd	Hyderabad-INDIA
2	Delhi_North	India	15MAR1998	31DEC2024	26.1	740	14.8	88	26	Delhi	INDIA	india	Delhi-INDIA	Del	Delhi-INDIA
3	Mumbai_Coastal	India	01JAN1995	31DEC2024	29.2	2200	18.6	94	29	Mumbai	INDIA	india	Mumbai-INDIA	Mum	Mumbai-INDIA
4	Chennai_East	India	10JUN2001	31DEC2024	30.1	1350	16.2	89	23	Chennai	INDIA	india	Chennai-INDIA	Che	Chennai-INDIA
5	Kolkata_East	India	01JAN1997	31DEC2024	27.8	1600	13.9	90	27	Kolkata	INDIA	india	Kolkata-INDIA	Kol	Kolkata-INDIA
6	Jaipur_West	India	01JAN2002	31DEC2024	25.4	520	11.1	85	22	Jaipur	INDIA	india	Jaipur-INDIA	Jai	Jaipur-INDIA
7	Bengaluru_South	India	01JAN1999	31DEC2024	24.3	970	10.5	92	25	Bengaluru	INDIA	india	Bengaluru-INDIA	Ben	Bengaluru-INDIA
8	Pune_West	India	01JAN2003	31DEC2024	26.2	720	12.9	87	21	Pune	INDIA	india	Pune-INDIA	Pun	Pune-INDIA
9	Ahmedabad_West	India	01JAN2000	31DEC2024	27.6	690	15.4	86	24	Ahmedabad	INDIA	india	Ahmedabad-INDIA	Ahm	Ahmedabad-INDIA
10	Nagpur_Central	India	01JAN2001	31DEC2024	28.1	1050	13.2	88	23	Nagpur	INDIA	india	Nagpur-INDIA	Nag	Nagpur-INDIA
11	Bhopal_Central	India	01JAN2004	31DEC2024	26.9	1140	11.8	90	20	Bhopal	INDIA	india	Bhopal-INDIA	Bho	Bhopal-INDIA
12	Indore_Central	India	01JAN2005	31DEC2024	25.8	980	12.0	89	19	Indore	INDIA	india	Indore-INDIA	Ind	Indore-INDIA
13	Lucknow_North	India	01JAN1996	31DEC2024	27.2	1010	13.5	87	28	Lucknow	INDIA	india	Lucknow-INDIA	Luc	Lucknow-INDIA
14	Patna_East	India	01JAN1997	31DEC2024	28.0	1200	14.1	86	27	Patna	INDIA	india	Patna-INDIA	Pat	Patna-INDIA
15	Surat_West	India	01JAN2000	31DEC2024	29.0	1400	16.5	91	24	Surat	INDIA	india	Surat-INDIA	Sur	Surat-INDIA

Why These Functions Matter

Function	Purpose
SCAN	Token extraction
PROPCASE	Reporting quality
CATX	Clean concatenation
STRIP/TRIM	Whitespace removal
SUBSTR	Code creation

7. Numeric Functions

data weather_numeric;

    set weather_clean;

    Climate_Score = mean(Avg_Temperature, Rainfall_mm/100, Wind_Speed);

    Rounded_Score = round(Climate_Score, 0.1);

    Integer_Score = int(Rounded_Score);

    Total_Measure = sum(Avg_Temperature, Rainfall_mm, Wind_Speed);

    Median_Measure = median(Avg_Temperature, Rainfall_mm, Wind_Speed);

run;

proc print data=weather_numeric;

run;

OUTPUT:

Obs	Station_Name	Country	Start_Date	End_Date	Avg_Temperature	Rainfall_mm	Wind_Speed	Accuracy_Score	Operational_Years	Station_Clean	Country_Upper	Country_Lower	Station_Tag	Station_Code	Station_Final	Climate_Score	Rounded_Score	Integer_Score	Total_Measure	Median_Measure
1	Hyderabad_Central	India	01JAN2000	31DEC2024	28.5	820	12.4	91	24	Hyderabad	INDIA	india	Hyderabad-INDIA	Hyd	Hyderabad-INDIA	16.3667	16.4	16	860.9	28.5
2	Delhi_North	India	15MAR1998	31DEC2024	26.1	740	14.8	88	26	Delhi	INDIA	india	Delhi-INDIA	Del	Delhi-INDIA	16.1000	16.1	16	780.9	26.1
3	Mumbai_Coastal	India	01JAN1995	31DEC2024	29.2	2200	18.6	94	29	Mumbai	INDIA	india	Mumbai-INDIA	Mum	Mumbai-INDIA	23.2667	23.3	23	2247.8	29.2
4	Chennai_East	India	10JUN2001	31DEC2024	30.1	1350	16.2	89	23	Chennai	INDIA	india	Chennai-INDIA	Che	Chennai-INDIA	19.9333	19.9	19	1396.3	30.1
5	Kolkata_East	India	01JAN1997	31DEC2024	27.8	1600	13.9	90	27	Kolkata	INDIA	india	Kolkata-INDIA	Kol	Kolkata-INDIA	19.2333	19.2	19	1641.7	27.8
6	Jaipur_West	India	01JAN2002	31DEC2024	25.4	520	11.1	85	22	Jaipur	INDIA	india	Jaipur-INDIA	Jai	Jaipur-INDIA	13.9000	13.9	13	556.5	25.4
7	Bengaluru_South	India	01JAN1999	31DEC2024	24.3	970	10.5	92	25	Bengaluru	INDIA	india	Bengaluru-INDIA	Ben	Bengaluru-INDIA	14.8333	14.8	14	1004.8	24.3
8	Pune_West	India	01JAN2003	31DEC2024	26.2	720	12.9	87	21	Pune	INDIA	india	Pune-INDIA	Pun	Pune-INDIA	15.4333	15.4	15	759.1	26.2
9	Ahmedabad_West	India	01JAN2000	31DEC2024	27.6	690	15.4	86	24	Ahmedabad	INDIA	india	Ahmedabad-INDIA	Ahm	Ahmedabad-INDIA	16.6333	16.6	16	733.0	27.6
10	Nagpur_Central	India	01JAN2001	31DEC2024	28.1	1050	13.2	88	23	Nagpur	INDIA	india	Nagpur-INDIA	Nag	Nagpur-INDIA	17.2667	17.3	17	1091.3	28.1
11	Bhopal_Central	India	01JAN2004	31DEC2024	26.9	1140	11.8	90	20	Bhopal	INDIA	india	Bhopal-INDIA	Bho	Bhopal-INDIA	16.7000	16.7	16	1178.7	26.9
12	Indore_Central	India	01JAN2005	31DEC2024	25.8	980	12.0	89	19	Indore	INDIA	india	Indore-INDIA	Ind	Indore-INDIA	15.8667	15.9	15	1017.8	25.8
13	Lucknow_North	India	01JAN1996	31DEC2024	27.2	1010	13.5	87	28	Lucknow	INDIA	india	Lucknow-INDIA	Luc	Lucknow-INDIA	16.9333	16.9	16	1050.7	27.2
14	Patna_East	India	01JAN1997	31DEC2024	28.0	1200	14.1	86	27	Patna	INDIA	india	Patna-INDIA	Pat	Patna-INDIA	18.0333	18.0	18	1242.1	28.0
15	Surat_West	India	01JAN2000	31DEC2024	29.0	1400	16.5	91	24	Surat	INDIA	india	Surat-INDIA	Sur	Surat-INDIA	19.8333	19.8	19	1445.5	29.0

8. Reliability Grading Macro

%macro reliability(input=, output=);

data &output;

    set &input;

    length Reliability $12;

    if Accuracy_Score >= 90 then Reliability = "Excellent";

    else if Accuracy_Score >= 85 then Reliability = "Good";

    else if Accuracy_Score >= 80 then Reliability = "Average";

    else Reliability = "Low";

proc print data=&output;

run;

%mend;

%reliability(input=weather_numeric, output=weather_reliable);

OUTPUT:

Obs	Station_Name	Country	Start_Date	End_Date	Avg_Temperature	Rainfall_mm	Wind_Speed	Accuracy_Score	Operational_Years	Station_Clean	Country_Upper	Country_Lower	Station_Tag	Station_Code	Station_Final	Climate_Score	Rounded_Score	Integer_Score	Total_Measure	Median_Measure	Reliability
1	Hyderabad_Central	India	01JAN2000	31DEC2024	28.5	820	12.4	91	24	Hyderabad	INDIA	india	Hyderabad-INDIA	Hyd	Hyderabad-INDIA	16.3667	16.4	16	860.9	28.5	Excellent
2	Delhi_North	India	15MAR1998	31DEC2024	26.1	740	14.8	88	26	Delhi	INDIA	india	Delhi-INDIA	Del	Delhi-INDIA	16.1000	16.1	16	780.9	26.1	Good
3	Mumbai_Coastal	India	01JAN1995	31DEC2024	29.2	2200	18.6	94	29	Mumbai	INDIA	india	Mumbai-INDIA	Mum	Mumbai-INDIA	23.2667	23.3	23	2247.8	29.2	Excellent
4	Chennai_East	India	10JUN2001	31DEC2024	30.1	1350	16.2	89	23	Chennai	INDIA	india	Chennai-INDIA	Che	Chennai-INDIA	19.9333	19.9	19	1396.3	30.1	Good
5	Kolkata_East	India	01JAN1997	31DEC2024	27.8	1600	13.9	90	27	Kolkata	INDIA	india	Kolkata-INDIA	Kol	Kolkata-INDIA	19.2333	19.2	19	1641.7	27.8	Excellent
6	Jaipur_West	India	01JAN2002	31DEC2024	25.4	520	11.1	85	22	Jaipur	INDIA	india	Jaipur-INDIA	Jai	Jaipur-INDIA	13.9000	13.9	13	556.5	25.4	Good
7	Bengaluru_South	India	01JAN1999	31DEC2024	24.3	970	10.5	92	25	Bengaluru	INDIA	india	Bengaluru-INDIA	Ben	Bengaluru-INDIA	14.8333	14.8	14	1004.8	24.3	Excellent
8	Pune_West	India	01JAN2003	31DEC2024	26.2	720	12.9	87	21	Pune	INDIA	india	Pune-INDIA	Pun	Pune-INDIA	15.4333	15.4	15	759.1	26.2	Good
9	Ahmedabad_West	India	01JAN2000	31DEC2024	27.6	690	15.4	86	24	Ahmedabad	INDIA	india	Ahmedabad-INDIA	Ahm	Ahmedabad-INDIA	16.6333	16.6	16	733.0	27.6	Good
10	Nagpur_Central	India	01JAN2001	31DEC2024	28.1	1050	13.2	88	23	Nagpur	INDIA	india	Nagpur-INDIA	Nag	Nagpur-INDIA	17.2667	17.3	17	1091.3	28.1	Good
11	Bhopal_Central	India	01JAN2004	31DEC2024	26.9	1140	11.8	90	20	Bhopal	INDIA	india	Bhopal-INDIA	Bho	Bhopal-INDIA	16.7000	16.7	16	1178.7	26.9	Excellent
12	Indore_Central	India	01JAN2005	31DEC2024	25.8	980	12.0	89	19	Indore	INDIA	india	Indore-INDIA	Ind	Indore-INDIA	15.8667	15.9	15	1017.8	25.8	Good
13	Lucknow_North	India	01JAN1996	31DEC2024	27.2	1010	13.5	87	28	Lucknow	INDIA	india	Lucknow-INDIA	Luc	Lucknow-INDIA	16.9333	16.9	16	1050.7	27.2	Good
14	Patna_East	India	01JAN1997	31DEC2024	28.0	1200	14.1	86	27	Patna	INDIA	india	Patna-INDIA	Pat	Patna-INDIA	18.0333	18.0	18	1242.1	28.0	Good
15	Surat_West	India	01JAN2000	31DEC2024	29.0	1400	16.5	91	24	Surat	INDIA	india	Surat-INDIA	Sur	Surat-INDIA	19.8333	19.8	19	1445.5	29.0	Excellent

Why Use Macros?

·       Code reusability

·       Parameterized logic

·       Enterprise-level SAS standards

9. PROC FORMAT

proc format;

    value accfmt

        low-79 = 'Low'

        80-84 = 'Average'

        85-89 = 'Good'

        90-high = 'Excellent';

run;

LOG:

NOTE: Format ACCFMT is on the library WORK.FORMATS.

10. PROC SQL Analytics

proc sql;

    create table weather_sql as

    select Country,

           count(*) as Station_Count,

           mean(Avg_Temperature) as Avg_Temp format=6.2,

           mean(Accuracy_Score) as Avg_Accuracy

    from weather_reliable

    group by Country;

quit;

proc print data=weather_sql;

run;

OUTPUT:

Obs	Country	Station_Count	Avg_Temp	Avg_Accuracy
1	India	15	27.35	88.8667

11. PROC MEANS

proc means data=weather_reliable mean min max median;

    class Reliability;

    var Avg_Temperature Rainfall_mm Wind_Speed Accuracy_Score;

run;

OUTPUT:

The MEANS Procedure

Reliability	N Obs	Variable	Mean	Minimum	Maximum	Median
Excellent	6	Avg_Temperature Rainfall_mm Wind_Speed Accuracy_Score	27.6166667 1355.00 13.9500000 91.3333333	24.3000000 820.0000000 10.5000000 90.0000000	29.2000000 2200.00 18.6000000 94.0000000	28.1500000 1270.00 13.1500000 91.0000000
Good	9	Avg_Temperature Rainfall_mm Wind_Speed Accuracy_Score	27.1666667 917.7777778 13.6888889 87.2222222	25.4000000 520.0000000 11.1000000 85.0000000	30.1000000 1350.00 16.2000000 89.0000000	27.2000000 980.0000000 13.5000000 87.0000000

12. PROC CORR

proc corr data=weather_reliable;

    var Avg_Temperature Rainfall_mm Wind_Speed Accuracy_Score;

run;

OUTPUT:

The CORR Procedure

4 Variables:	Avg_Temperature Rainfall_mm Wind_Speed Accuracy_Score

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Avg_Temperature	15	27.34667	1.57882	410.20000	24.30000	30.10000
Rainfall_mm	15	1093	424.44025	16390	520.00000	2200
Wind_Speed	15	13.79333	2.21019	206.90000	10.50000	18.60000
Accuracy_Score	15	88.86667	2.50333	1333	85.00000	94.00000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Avg_Temperature	Rainfall_mm	Wind_Speed	Accuracy_Score
Avg_Temperature	1.00000	0.60130 0.0177	0.76484 0.0009	0.25109 0.3667
Rainfall_mm	0.60130 0.0177	1.00000	0.66055 0.0073	0.69077 0.0044
Wind_Speed	0.76484 0.0009	0.66055 0.0073	1.00000	0.28256 0.3075
Accuracy_Score	0.25109 0.3667	0.69077 0.0044	0.28256 0.3075	1.00000

Interpretation

·       Accuracy vs Wind → Instrument stability

·       Rainfall vs Temperature → Climate dependency

13. PROC TRANSPOSE

proc transpose data=weather_reliable

               out=weather_long

               name=Metric;

    by Station_Final NotSorted;

    var Avg_Temperature Rainfall_mm Wind_Speed Accuracy_Score;

run;

proc print data=weather_long;

run;

OUTPUT:

Obs	Station_Final	Metric	COL1
1	Hyderabad-INDIA	Avg_Temperature	28.5
2	Hyderabad-INDIA	Rainfall_mm	820.0
3	Hyderabad-INDIA	Wind_Speed	12.4
4	Hyderabad-INDIA	Accuracy_Score	91.0
5	Delhi-INDIA	Avg_Temperature	26.1
6	Delhi-INDIA	Rainfall_mm	740.0
7	Delhi-INDIA	Wind_Speed	14.8
8	Delhi-INDIA	Accuracy_Score	88.0
9	Mumbai-INDIA	Avg_Temperature	29.2
10	Mumbai-INDIA	Rainfall_mm	2200.0
11	Mumbai-INDIA	Wind_Speed	18.6
12	Mumbai-INDIA	Accuracy_Score	94.0
13	Chennai-INDIA	Avg_Temperature	30.1
14	Chennai-INDIA	Rainfall_mm	1350.0
15	Chennai-INDIA	Wind_Speed	16.2
16	Chennai-INDIA	Accuracy_Score	89.0
17	Kolkata-INDIA	Avg_Temperature	27.8
18	Kolkata-INDIA	Rainfall_mm	1600.0
19	Kolkata-INDIA	Wind_Speed	13.9
20	Kolkata-INDIA	Accuracy_Score	90.0
21	Jaipur-INDIA	Avg_Temperature	25.4
22	Jaipur-INDIA	Rainfall_mm	520.0
23	Jaipur-INDIA	Wind_Speed	11.1
24	Jaipur-INDIA	Accuracy_Score	85.0
25	Bengaluru-INDIA	Avg_Temperature	24.3
26	Bengaluru-INDIA	Rainfall_mm	970.0
27	Bengaluru-INDIA	Wind_Speed	10.5
28	Bengaluru-INDIA	Accuracy_Score	92.0
29	Pune-INDIA	Avg_Temperature	26.2
30	Pune-INDIA	Rainfall_mm	720.0
31	Pune-INDIA	Wind_Speed	12.9
32	Pune-INDIA	Accuracy_Score	87.0
33	Ahmedabad-INDIA	Avg_Temperature	27.6
34	Ahmedabad-INDIA	Rainfall_mm	690.0
35	Ahmedabad-INDIA	Wind_Speed	15.4
36	Ahmedabad-INDIA	Accuracy_Score	86.0
37	Nagpur-INDIA	Avg_Temperature	28.1
38	Nagpur-INDIA	Rainfall_mm	1050.0
39	Nagpur-INDIA	Wind_Speed	13.2
40	Nagpur-INDIA	Accuracy_Score	88.0
41	Bhopal-INDIA	Avg_Temperature	26.9
42	Bhopal-INDIA	Rainfall_mm	1140.0
43	Bhopal-INDIA	Wind_Speed	11.8
44	Bhopal-INDIA	Accuracy_Score	90.0
45	Indore-INDIA	Avg_Temperature	25.8
46	Indore-INDIA	Rainfall_mm	980.0
47	Indore-INDIA	Wind_Speed	12.0
48	Indore-INDIA	Accuracy_Score	89.0
49	Lucknow-INDIA	Avg_Temperature	27.2
50	Lucknow-INDIA	Rainfall_mm	1010.0
51	Lucknow-INDIA	Wind_Speed	13.5
52	Lucknow-INDIA	Accuracy_Score	87.0
53	Patna-INDIA	Avg_Temperature	28.0
54	Patna-INDIA	Rainfall_mm	1200.0
55	Patna-INDIA	Wind_Speed	14.1
56	Patna-INDIA	Accuracy_Score	86.0
57	Surat-INDIA	Avg_Temperature	29.0
58	Surat-INDIA	Rainfall_mm	1400.0
59	Surat-INDIA	Wind_Speed	16.5
60	Surat-INDIA	Accuracy_Score	91.0

14. PROC DATASETS DELETE

proc datasets library=work nolist;

    delete weather_base weather_years weather_clean weather_numeric;

quit;

OUTPUT:     

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

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

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

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

15. Interview-Ready Highlights

·       Demonstrates full SAS lifecycle

·       Uses all core Base SAS functions

·       Strong date intelligence

·       Macro-driven classification

·       Clean dataset management

·       Production-quality structure

16. Final Conclusion

This project demonstrates how SAS functions as a complete analytics ecosystem, not merely a reporting tool.
From raw data creation to statistical validation and reliability grading, every step mirrors real enterprise analytics workflows.

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 Weather Stations data.

Our Mission:

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