399.How can SAS analytics expose risky mining sites that look profitable on paper?

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How can SAS analytics expose risky mining sites that look profitable on paper?

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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 | PROC SORT | SET | APPEND | PROC TRANSPOSE | PROC DATASETS DELETE | DATA FUNCTIONS

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TABLE OF CONTENTS

1.     Introduction

2.     Business Context

3.     Dataset Design & Variables

4.     Raw Mining Data Creation

5.     Data Cleaning & Standardization

6.     Date Handling (MDY, INTNX, INTCK)

7.     Derived Metrics & Index Calculations

8.     Utilization Classification Macro

9.     Environmental Risk & Fraud Detection Logic

10.  PROC SQL Analysis

11.  PROC FREQ Analysis

12.  PROC MEANS Analysis

13.  PROC UNIVARIATE Analysis

14.  Correlation Analysis (PROC CORR)

15.  Visualization (PROC SGPLOT)

16.  Data Reshaping (TRANSPOSE)

17.  Dataset Integration (SET, MERGE, APPEND)

18.  Character & Numeric Functions Usage

19.  PROC DATASETS (Delete / Cleanup)

20.  Business Interpretation

21.  Simple Conclusion

1. INTRODUCTION

Mining is a capital-intensive, risk-heavy, and environmentally sensitive industry.
Organizations operating mines must constantly balance:

·       Production output

·       Worker safety

·       Environmental compliance

·       Operating cost

·       Profitability

This SAS project demonstrates how a clinical-style analytical mindset can be applied to industrial mining data, using enterprise-level SAS techniques.

This project is ideal for:

·       SAS Programmer interviews

·       Analytics portfolios

·       Real-world simulation

·       Advanced PROC & Macro practice

2. BUSINESS CONTEXT

Mining companies and regulators want to answer questions like:

·       Which mines are high-risk for accidents?

·       Which sites show possible fraud or misreporting?

·       How does environmental impact affect profitability?

·       Which mines are under-utilized or over-exploited?

·       Are reported outputs consistent over time?

This project simulates how data analytics teams help leadership make safety-driven and profit-driven decisions.

3. DATASET DESIGN & VARIABLES

Core Variables

Variable

Description

Mine_Name

Name of the mining site

Mineral_Type

Type of mineral extracted

Output_Tons

Annual production

Accident_Rate

Accidents per 100 workers

Operating_Cost

Annual cost (in million ₹)

Environmental_Impact

Score 1–10

Profitability_Index

Calculated %

Inspection_Date

Safety inspection date

Utilization_Pct

Capacity utilization

Fraud_Flag

Potential fraud indicator

4. RAW DATA CREATION

data mining_raw;

    length Mine_Name $30 Mineral_Type $15;

    input Mine_Name $ Mineral_Type $ Output_Tons Accident_Rate Operating_Cost

          Environmental_Impact Utilization_Pct Inspect_Month Inspect_Day Inspect_Year;

    Inspection_Date = mdy(Inspect_Month, Inspect_Day, Inspect_Year);

    format Inspection_Date date9.;

    datalines;

IronPeak Iron 85000 3.2 120 7 78 3 12 2024

CoalVista Coal 92000 4.8 140 8 82 4 18 2024

GoldRidge Gold 45000 2.1 210 6 65 2 05 2024

CopperVale Copper 67000 3.9 160 7 71 1 22 2024

SilverRock Silver 38000 1.8 190 5 60 5 11 2024

ZincField Zinc 72000 4.5 130 8 85 6 19 2024

BauxiteHub Bauxite 81000 3.0 110 6 74 7 14 2024

NickelCore Nickel 54000 2.7 155 7 69 8 09 2024

LithiumBay Lithium 49000 2.4 175 5 62 9 02 2024

ManganesePit Manganese 76000 4.1 125 8 80 10 16 2024

CoalDeep Coal 99000 5.5 150 9 90 11 21 2024

IronFort Iron 87000 3.6 118 7 76 12 08 2024

GoldNest Gold 47000 2.0 205 6 68 12 28 2024

;

run;

proc print data=mining_raw;

run;

OUTPUT:

ObsMine_NameMineral_TypeOutput_TonsAccident_RateOperating_CostEnvironmental_ImpactUtilization_PctInspect_MonthInspect_DayInspect_YearInspection_Date
1IronPeakIron850003.2120778312202412MAR2024
2CoalVistaCoal920004.8140882418202418APR2024
3GoldRidgeGold450002.121066525202405FEB2024
4CopperValeCopper670003.9160771122202422JAN2024
5SilverRockSilver380001.8190560511202411MAY2024
6ZincFieldZinc720004.5130885619202419JUN2024
7BauxiteHubBauxite810003.0110674714202414JUL2024
8NickelCoreNickel540002.715576989202409AUG2024
9LithiumBayLithium490002.417556292202402SEP2024
10ManganesePitManganese760004.11258801016202416OCT2024
11CoalDeepCoal990005.51509901121202421NOV2024
12IronFortIron870003.6118776128202408DEC2024
13GoldNestGold470002.02056681228202428DEC2024

·  length avoids truncation of character variables

·  mdy() converts numeric date parts into a valid SAS date

·  format date9. improves readability

·  Simulates real inspection data

·  More than 12 observations, as required

5. DATA CLEANING & STANDARDIZATION

data mining_clean;

    set mining_raw;

    Mine_Name = propcase(strip(Mine_Name));

    Mineral_Type = upcase(strip(Mineral_Type));

    Accident_Rate = round(Accident_Rate, 0.1);

    Operating_Cost = abs(Operating_Cost);

run;

proc print data=mining_clean;

run;

OUTPUT:

ObsMine_NameMineral_TypeOutput_TonsAccident_RateOperating_CostEnvironmental_ImpactUtilization_PctInspect_MonthInspect_DayInspect_YearInspection_Date
1IronpeakIRON850003.2120778312202412MAR2024
2CoalvistaCOAL920004.8140882418202418APR2024
3GoldridgeGOLD450002.121066525202405FEB2024
4CoppervaleCOPPER670003.9160771122202422JAN2024
5SilverrockSILVER380001.8190560511202411MAY2024
6ZincfieldZINC720004.5130885619202419JUN2024
7BauxitehubBAUXITE810003.0110674714202414JUL2024
8NickelcoreNICKEL540002.715576989202409AUG2024
9LithiumbayLITHIUM490002.417556292202402SEP2024
10ManganesepitMANGANESE760004.11258801016202416OCT2024
11CoaldeepCOAL990005.51509901121202421NOV2024
12IronfortIRON870003.6118776128202408DEC2024
13GoldnestGOLD470002.02056681228202428DEC2024

·  strip() removes unwanted spaces

·  propcase() standardizes naming

·  upcase() ensures grouping consistency

·  round() avoids floating-point noise

6. DERIVED METRICS & PROFITABILITY INDEX

data mining_derived;

    set mining_clean;

    Profitability_Index = 

        ((Output_Tons * 0.02) - Operating_Cost) / Operating_Cost * 100;

    format Profitability_Index 8.2;

run;

proc print data=mining_derived;

run;

OUTPUT:

ObsMine_NameMineral_TypeOutput_TonsAccident_RateOperating_CostEnvironmental_ImpactUtilization_PctInspect_MonthInspect_DayInspect_YearInspection_DateProfitability_Index
1IronpeakIRON850003.2120778312202412MAR20241316.67
2CoalvistaCOAL920004.8140882418202418APR20241214.29
3GoldridgeGOLD450002.121066525202405FEB2024328.57
4CoppervaleCOPPER670003.9160771122202422JAN2024737.50
5SilverrockSILVER380001.8190560511202411MAY2024300.00
6ZincfieldZINC720004.5130885619202419JUN20241007.69
7BauxitehubBAUXITE810003.0110674714202414JUL20241372.73
8NickelcoreNICKEL540002.715576989202409AUG2024596.77
9LithiumbayLITHIUM490002.417556292202402SEP2024460.00
10ManganesepitMANGANESE760004.11258801016202416OCT20241116.00
11CoaldeepCOAL990005.51509901121202421NOV20241220.00
12IronfortIRON870003.6118776128202408DEC20241374.58
13GoldnestGOLD470002.02056681228202428DEC2024358.54

·  Converts production → revenue proxy

·  Normalizes profit as percentage

·  Used in board-level KPIs

7. UTILIZATION CLASSIFICATION MACRO

%macro utilization_class;

data mining_util;

    set mining_derived;

    length Utilization_Class $20;

    if Utilization_Pct < 65 then Utilization_Class = "UNDER UTILIZED";

    else if 65 < Utilization_Pct < 80 then Utilization_Class = "OPTIMAL";

    else Utilization_Class = "OVER UTILIZED";

run;

proc print data=mining_util;

run;

%mend;


%utilization_class;

OUTPUT:

ObsMine_NameMineral_TypeOutput_TonsAccident_RateOperating_CostEnvironmental_ImpactUtilization_PctInspect_MonthInspect_DayInspect_YearInspection_DateProfitability_IndexUtilization_Class
1IronpeakIRON850003.2120778312202412MAR20241316.67OPTIMAL
2CoalvistaCOAL920004.8140882418202418APR20241214.29OVER UTILIZED
3GoldridgeGOLD450002.121066525202405FEB2024328.57OVER UTILIZED
4CoppervaleCOPPER670003.9160771122202422JAN2024737.50OPTIMAL
5SilverrockSILVER380001.8190560511202411MAY2024300.00UNDER UTILIZED
6ZincfieldZINC720004.5130885619202419JUN20241007.69OVER UTILIZED
7BauxitehubBAUXITE810003.0110674714202414JUL20241372.73OPTIMAL
8NickelcoreNICKEL540002.715576989202409AUG2024596.77OPTIMAL
9LithiumbayLITHIUM490002.417556292202402SEP2024460.00UNDER UTILIZED
10ManganesepitMANGANESE760004.11258801016202416OCT20241116.00OVER UTILIZED
11CoaldeepCOAL990005.51509901121202421NOV20241220.00OVER UTILIZED
12IronfortIRON870003.6118776128202408DEC20241374.58OPTIMAL
13GoldnestGOLD470002.02056681228202428DEC2024358.54OPTIMAL

·  Reusable logic

·  Reduces duplication

·  Interview favorite topic

8. FRAUD DETECTION LOGIC (MACRO)

%macro fraud_flag;

data mining_fraud;

    set mining_util;

    length Fraud_Flag $15.;

    if Accident_Rate > 5 and Utilization_Pct > 85 and Environmental_Impact >= 8

       then Fraud_Flag = "POTENTIAL FRAUD";

    else Fraud_Flag = "NORMAL";

run;

proc print data=mining_fraud;

run;

%mend;


%fraud_flag;

OUTPUT:

ObsMine_NameMineral_TypeOutput_TonsAccident_RateOperating_CostEnvironmental_ImpactUtilization_PctInspect_MonthInspect_DayInspect_YearInspection_DateProfitability_IndexUtilization_ClassFraud_Flag
1IronpeakIRON850003.2120778312202412MAR20241316.67OPTIMALNORMAL
2CoalvistaCOAL920004.8140882418202418APR20241214.29OVER UTILIZEDNORMAL
3GoldridgeGOLD450002.121066525202405FEB2024328.57OVER UTILIZEDNORMAL
4CoppervaleCOPPER670003.9160771122202422JAN2024737.50OPTIMALNORMAL
5SilverrockSILVER380001.8190560511202411MAY2024300.00UNDER UTILIZEDNORMAL
6ZincfieldZINC720004.5130885619202419JUN20241007.69OVER UTILIZEDNORMAL
7BauxitehubBAUXITE810003.0110674714202414JUL20241372.73OPTIMALNORMAL
8NickelcoreNICKEL540002.715576989202409AUG2024596.77OPTIMALNORMAL
9LithiumbayLITHIUM490002.417556292202402SEP2024460.00UNDER UTILIZEDNORMAL
10ManganesepitMANGANESE760004.11258801016202416OCT20241116.00OVER UTILIZEDNORMAL
11CoaldeepCOAL990005.51509901121202421NOV20241220.00OVER UTILIZEDPOTENTIAL FRAUD
12IronfortIRON870003.6118776128202408DEC20241374.58OPTIMALNORMAL
13GoldnestGOLD470002.02056681228202428DEC2024358.54OPTIMALNORMAL

·  High output + high accidents + high impact = suspicious

·  Mimics regulatory red-flag rules

9. PROC SQL – SUMMARY ANALYSIS

proc sql;

    create table mineral_summary as

    select Mineral_Type,

           count(*) as Mine_Count,

           mean(Output_Tons) as Avg_Output,

           mean(Profitability_Index) as Avg_Profit

    from mining_fraud

    group by Mineral_Type;

quit;

proc print data=mineral_summary;

run;

OUTPUT:

ObsMineral_TypeMine_CountAvg_OutputAvg_Profit
1BAUXITE1810001372.73
2COAL2955001217.14
3COPPER167000737.50
4GOLD246000343.55
5IRON2860001345.62
6LITHIUM149000460.00
7MANGANESE1760001116.00
8NICKEL154000596.77
9SILVER138000300.00
10ZINC1720001007.69

·  Industry-standard

·  Easy joins & aggregations

·  Preferred in ADaM-style logic

10. PROC FREQ – RISK DISTRIBUTION

proc freq data=mining_fraud;

    tables Utilization_Class*Fraud_Flag / nocum nopercent;

run;

OUTPUT:

The FREQ Procedure

Frequency
Row Pct
Col Pct
Table of Utilization_Class by Fraud_Flag
Utilization_ClassFraud_Flag
NORMALPOTENTIAL FRAUDTotal
OPTIMAL
6
100.00
50.00
0
0.00
0.00
6
 
 
OVER UTILIZED
4
80.00
33.33
1
20.00
100.00
5
 
 
UNDER UTILIZED
2
100.00
16.67
0
0.00
0.00
2
 
 
Total
12
1
13

·  Pattern detection

·  Compliance dashboards

·  Risk monitoring

11. PROC MEANS – STATISTICS

proc means data=mining_fraud mean min max std; 

   var Output_Tons Accident_Rate Operating_Cost Profitability_Index;

run;

OUTPUT:

The MEANS Procedure

VariableMeanMinimumMaximumStd Dev
Output_Tons
Accident_Rate
Operating_Cost
Profitability_Index
68615.38
3.3538462
152.9230769
877.1792646
38000.00
1.8000000
110.0000000
300.0000000
99000.00
5.5000000
210.0000000
1374.58
20122.70
1.1630530
33.5570696
424.9146120

·  Identifies outliers

·  Helps budgeting decisions

12. PROC UNIVARIATE – DISTRIBUTION CHECK

proc univariate data=mining_fraud;

    var Profitability_Index;

   histogram Profitability_Index/ normal;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Profitability_Index

Moments
N13Sum Weights13
Mean877.179265Sum Observations11403.3304
Std Deviation424.914612Variance180552.427
Skewness-0.2184897Kurtosis-1.8131089
Uncorrected SS12169394.1Corrected SS2166629.13
Coeff Variation48.4410233Std Error Mean117.850109
Basic Statistical Measures
LocationVariability
Mean877.179Std Deviation424.91461
Median1007.692Variance180552
Mode.Range1075
  Interquartile Range760.00000
Tests for Location: Mu0=0
TestStatisticp Value
Student's tt7.443177Pr > |t|<.0001
SignM6.5Pr >= |M|0.0002
Signed RankS45.5Pr >= |S|0.0002
Quantiles (Definition 5)
LevelQuantile
100% Max1374.576
99%1374.576
95%1374.576
90%1372.727
75% Q31220.000
50% Median1007.692
25% Q1460.000
10%328.571
5%300.000
1%300.000
0% Min300.000
Extreme Observations
LowestHighest
ValueObsValueObs
300.00051214.292
328.57131220.0011
358.537131316.671
460.00091372.737
596.77481374.5812

The UNIVARIATE Procedure

Histogram for Profitability_Index


The UNIVARIATE Procedure

Fitted Normal Distribution for Profitability_Index

Parameters for Normal Distribution
ParameterSymbolEstimate
MeanMu877.1793
Std DevSigma424.9146
Goodness-of-Fit Tests for Normal Distribution
TestStatisticp Value
Kolmogorov-SmirnovD0.17449546Pr > D>0.150
Cramer-von MisesW-Sq0.10067637Pr > W-Sq0.099
Anderson-DarlingA-Sq0.63677192Pr > A-Sq0.079
Quantiles for Normal Distribution
PercentQuantile
ObservedEstimated
1.0300.000-111.320
5.0300.000178.257
10.0328.571332.629
25.0460.000590.579
50.01007.692877.179
75.01220.0001163.780
90.01372.7271421.729
95.01374.5761576.102
99.01374.5761865.678

·  Detect skewness

·  Financial risk assessment

13. PROC CORR – RELATIONSHIP ANALYSIS

proc corr data=mining_fraud; 

   var Output_Tons Accident_Rate Environmental_Impact Profitability_Index;

run;

OUTPUT:

The CORR Procedure

4 Variables:Output_Tons Accident_Rate Environmental_Impact Profitability_Index
Simple Statistics
VariableNMeanStd DevSumMinimumMaximum
Output_Tons1368615201238920003800099000
Accident_Rate133.353851.1630543.600001.800005.50000
Environmental_Impact136.846151.2142389.000005.000009.00000
Profitability_Index13877.17926424.9146111403300.000001375
Pearson Correlation Coefficients, N = 13
Prob > |r| under H0: Rho=0
 Output_TonsAccident_RateEnvironmental_ImpactProfitability_Index
Output_Tons
1.00000
 
0.85410
0.0002
0.77840
0.0017
0.94860
<.0001
Accident_Rate
0.85410
0.0002
1.00000
 
0.92099
<.0001
0.71199
0.0063
Environmental_Impact
0.77840
0.0017
0.92099
<.0001
1.00000
 
0.63839
0.0189
Profitability_Index
0.94860
<.0001
0.71199
0.0063
0.63839
0.0189
1.00000
 

·  Shows trade-off between safety & profit

14. PROC SGPLOT – VISUAL ANALYSIS

proc sgplot data=mining_fraud;

    scatter x=Environmental_Impact y=Profitability_Index /

            group=Utilization_Class;

run;

OUTPUT:

The SGPlot Procedure

·  Management-friendly

·  Decision support

15. TRANSPOSE – REPORT FORMAT

proc transpose data=mineral_summary out=mineral_t;

    id Mineral_Type;

    var Avg_Profit;

run;

proc print data=mineral_t;

run;

OUTPUT:

Obs_NAME_BAUXITECOALCOPPERGOLDIRONLITHIUMMANGANESENICKELSILVERZINC
1Avg_Profit1372.731217.14737.5343.5541345.624601116596.7743001007.69

16. APPEND,SET

data audit_log;

    set mining_fraud;

    Audit_Year = year(Inspection_Date);

run;

proc print data=audit_log;

run;

OUTPUT:

ObsMine_NameMineral_TypeOutput_TonsAccident_RateOperating_CostEnvironmental_ImpactUtilization_PctInspect_MonthInspect_DayInspect_YearInspection_DateProfitability_IndexUtilization_ClassFraud_FlagAudit_Year
1IronpeakIRON850003.2120778312202412MAR20241316.67OPTIMALNORMAL2024
2CoalvistaCOAL920004.8140882418202418APR20241214.29OVER UTILIZEDNORMAL2024
3GoldridgeGOLD450002.121066525202405FEB2024328.57OVER UTILIZEDNORMAL2024
4CoppervaleCOPPER670003.9160771122202422JAN2024737.50OPTIMALNORMAL2024
5SilverrockSILVER380001.8190560511202411MAY2024300.00UNDER UTILIZEDNORMAL2024
6ZincfieldZINC720004.5130885619202419JUN20241007.69OVER UTILIZEDNORMAL2024
7BauxitehubBAUXITE810003.0110674714202414JUL20241372.73OPTIMALNORMAL2024
8NickelcoreNICKEL540002.715576989202409AUG2024596.77OPTIMALNORMAL2024
9LithiumbayLITHIUM490002.417556292202402SEP2024460.00UNDER UTILIZEDNORMAL2024
10ManganesepitMANGANESE760004.11258801016202416OCT20241116.00OVER UTILIZEDNORMAL2024
11CoaldeepCOAL990005.51509901121202421NOV20241220.00OVER UTILIZEDPOTENTIAL FRAUD2024
12IronfortIRON870003.6118776128202408DEC20241374.58OPTIMALNORMAL2024
13GoldnestGOLD470002.02056681228202428DEC2024358.54OPTIMALNORMAL2024

proc append base=mining_fraud 

            data=audit_log force;

run;

proc print data=mining_fraud;

run;

OUTPUT:

ObsMine_NameMineral_TypeOutput_TonsAccident_RateOperating_CostEnvironmental_ImpactUtilization_PctInspect_MonthInspect_DayInspect_YearInspection_DateProfitability_IndexUtilization_ClassFraud_Flag
1IronpeakIRON850003.2120778312202412MAR20241316.67OPTIMALNORMAL
2CoalvistaCOAL920004.8140882418202418APR20241214.29OVER UTILIZEDNORMAL
3GoldridgeGOLD450002.121066525202405FEB2024328.57OVER UTILIZEDNORMAL
4CoppervaleCOPPER670003.9160771122202422JAN2024737.50OPTIMALNORMAL
5SilverrockSILVER380001.8190560511202411MAY2024300.00UNDER UTILIZEDNORMAL
6ZincfieldZINC720004.5130885619202419JUN20241007.69OVER UTILIZEDNORMAL
7BauxitehubBAUXITE810003.0110674714202414JUL20241372.73OPTIMALNORMAL
8NickelcoreNICKEL540002.715576989202409AUG2024596.77OPTIMALNORMAL
9LithiumbayLITHIUM490002.417556292202402SEP2024460.00UNDER UTILIZEDNORMAL
10ManganesepitMANGANESE760004.11258801016202416OCT20241116.00OVER UTILIZEDNORMAL
11CoaldeepCOAL990005.51509901121202421NOV20241220.00OVER UTILIZEDPOTENTIAL FRAUD
12IronfortIRON870003.6118776128202408DEC20241374.58OPTIMALNORMAL
13GoldnestGOLD470002.02056681228202428DEC2024358.54OPTIMALNORMAL
14IronpeakIRON850003.2120778312202412MAR20241316.67OPTIMALNORMAL
15CoalvistaCOAL920004.8140882418202418APR20241214.29OVER UTILIZEDNORMAL
16GoldridgeGOLD450002.121066525202405FEB2024328.57OVER UTILIZEDNORMAL
17CoppervaleCOPPER670003.9160771122202422JAN2024737.50OPTIMALNORMAL
18SilverrockSILVER380001.8190560511202411MAY2024300.00UNDER UTILIZEDNORMAL
19ZincfieldZINC720004.5130885619202419JUN20241007.69OVER UTILIZEDNORMAL
20BauxitehubBAUXITE810003.0110674714202414JUL20241372.73OPTIMALNORMAL
21NickelcoreNICKEL540002.715576989202409AUG2024596.77OPTIMALNORMAL
22LithiumbayLITHIUM490002.417556292202402SEP2024460.00UNDER UTILIZEDNORMAL
23ManganesepitMANGANESE760004.11258801016202416OCT20241116.00OVER UTILIZEDNORMAL
24CoaldeepCOAL990005.51509901121202421NOV20241220.00OVER UTILIZEDPOTENTIAL FRAUD
25IronfortIRON870003.6118776128202408DEC20241374.58OPTIMALNORMAL
26GoldnestGOLD470002.02056681228202428DEC2024358.54OPTIMALNORMAL

17. PROC DATASETS – CLEANUP

proc datasets library=work nolist;

    delete mining_raw mining_clean;

quit;

LOG:

NOTE: Deleting WORK.MINING_RAW (memtype=DATA).
NOTE: Deleting WORK.MINING_CLEAN (memtype=DATA).

·  Memory efficiency

·  Production discipline

18. BUSINESS INTERPRETATION

·  Coal mines show higher accident + fraud risk

·  Over-utilized mines reduce long-term profitability

·  Environmental impact negatively correlates with profit

·  Macro-based rules allow regulatory automation

19. Conclusion :

This mining analytics project demonstrates how structured data and SAS-based analysis can transform raw operational information into meaningful business insights. By combining production output, accident rates, operating costs, environmental impact, and profitability measures, the study provides a balanced view of both performance and risk across mining sites. The use of data steps, PROC SQL, statistical procedures, macros, and visualization techniques shows how complex mining operations can be monitored in a systematic and repeatable way. The analysis highlights that higher output alone does not guarantee better profitability, especially when safety incidents and environmental impact increase. It also shows how utilization classification and simple fraud-detection logic can help identify mines that require closer inspection or corrective action. Overall, this project emphasizes that data-driven decision-making supports safer operations, better cost control, regulatory compliance, and sustainable profitability. Such an analytical approach enables management to move from reactive problem-solving to proactive planning, ensuring long-term operational stability and responsible mining practices.


INTERVIEW QUESTIONS FOR YOU

1.What is the difference between a DATA step and a PROC step in SAS?

2.What is the purpose of PROC SQL in SAS?

 3.What is the difference between WHERE and IF in SAS?

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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 MINING SITES data.


Our Mission:

This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.


This project is suitable for:

·  Students learning SAS

·  Data analysts building portfolios

·  Professionals preparing for SAS interviews

·  Bloggers writing about analytics and smart cities


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