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**Gürdal Ertek ertekg@sabanciuniv.edu**

Analytical Benchmarking Meets Data Mining: The SmartDEA Framework, SmartDEA Software, and Case Studies for Industry Gürdal Ertek Invited Seminar at A*Star SIMTECH, Singapore, August 2, 2013, Friday

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Istanbul, Turkey Singapore

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**Young, high-profile private University Outskirts of Istanbul, Turkey **

First students accepted in 1999

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**Established by the Sabanci Foundation**

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Sabancı Group

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**Sabancı Family: Sakıp Sabancı, Güler Sabancı, 200+**

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**~3000 undergrad & ~500 grad students**

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**Highest research income per faculty member among Turkish universities**

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**Young, high-profile private University**

Established by the Sabanci Foundation Sabancı Group Sabancı Family: Sakıp Sabancı, Güler Sabancı, 200+ First students accepted in 1999 ~3000 undergrad & ~500 grad students Highest research income per faculty member

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Dr. Gürdal Ertek Assistant Professor at Sabancı University, Istanbul, Turkey, since 2002 Ph.D. from School of Industrial and Systems Georgia Institute of Technology, Atlanta, GA, USA Research areas include warehousing & material handling data visualization & data mining

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Analytical Benchmarking Meets Data Mining: The SmartDEA Framework, SmartDEA Software, and Case Studies for Industry Gürdal Ertek Invited Seminar at A*Star SIMTECH, Singapore, August 2, 2013, Friday

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**Motivation Analytical Benchmarking**

application of mathematics and computation based methods for benchmarking a group of entities aims at developing objective and automated methods of benchmarking. Overwhelming majority of literature focuses on developing new benchmarking methodologies An important aspect forgotten: post-analysis of the benchmarking results

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**Motivation Data Mining Overwhelming majority of literature focuses on**

growing field of computer science aims at discovering the hidden patterns and coming up with actionable insights. Overwhelming majority of literature focuses on developing more efficient and effective computational algorithms. Important aspects not drawing deserved attention: the quest for practical actionable knowledge data mining can be used for post-analysis of results of other methodologies & algorithms

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**This Seminar Goals Case study applications**

SmartDEA Solver framework for integrating analytical benchmarking with data mining How DEA results should be structured Meaningful interpretation of DEA results Case study applications Automotive Wind energy Apparel retail

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Research Questions How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? (SmartDEA) How can DEA & information visualization be used together? (Case Study 1) Which visualization techniques are appropriate for analyzing DEA results? (Case Study 2) How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results? (Case Study 3)

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**Presentation Contents**

Background on Data Envelopment Analysis (DEA) SmartDEA framework Case Studies Automotive Wind Energy Apparel Retail

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Background

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Sample DEA Analysis

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**Data Envelopment Analysis (DEA)**

Entities = DMUs (n DMUs) Comparison of DMUs Inputs and outputs (m inputs, s outputs) Results Efficiency score between 0 and 1 Reference sets Projections

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Basic DEA Models Maximize the ratio : 𝑉𝑖𝑟𝑡𝑢𝑎𝑙 𝑖𝑛𝑝𝑢𝑡 𝑉𝑖𝑟𝑡𝑢𝑎𝑙 𝑜𝑢𝑡𝑝𝑢𝑡 for each DMU0

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Basic DEA Models CRR-Input model CRR-Output model

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Basic DEA Models BCC-Input model BCC-Output model

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Basic DEA Models

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**Alp Eren Akcay, Gürdal Ertek, Gulcin Buyukozkan**

Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA Framework Alp Eren Akcay, Gürdal Ertek, Gulcin Buyukozkan Gurdal Ertek

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Research Questions How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? (SmartDEA) How can DEA & information visualization be used together? Which visualization techniques are appropriate for analyzing DEA results? How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results?

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Goal To build a framework for making analytical benchmarking and performance evaluations To design and develop a convenient DEA software, SmartDEA

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**Contribution To develop a general framework**

To help DEA analysts to generate important and interesting insights systematically To integrate the results for information visualization techniques

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Framework Integration of DEA results with data mining and information visualization

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Proposed framework integrates data mining and information visualization with DEA, generates clean data for mining (data auditing at the DEA modeling stage), allows the incorporation of “other data” into the process, can accommodate multiple DEA models within same analysis.

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Notation

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**SmartDEA: the developed software**

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**Modeling Process C# language Results in file format of MS Excel**

Imported data requires a certain format

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**Modeling Process 1- Importing Excel File:**

Data requires a certain format

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Modeling Process 2- Selecting the spreadsheet:

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Modeling Process 3- Constructing the model:

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Modeling Process 4-Selecting the DEA Model:

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Modeling Process 5- Solving and generating the solution file:

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**Dr. Gürdal Ertek, Tuna Çaprak**

Case Study 1: Integrating DEA with Information Visualization for Benchmarking Dealers in the Automotive Industry Dr. Gürdal Ertek, Tuna Çaprak

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Research Questions How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? How can DEA & information visualization be used together? (Case Study 1) Which visualization techniques are appropriate for analyzing DEA results? How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results?

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**A New Approach for Benchmarking and Managing TOFAŞ Dealers**

Tuna Çaprak Leaders for Industry Program ’07-’08, Sabancı University Gürdal Ertek, Ph.D. Faculty of Engineering and Natural Sciences, Sabancı University

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**A New Approach for Benchmarking and Managing TOFAŞ Dealers**

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**Data Envelopment Analysis (DEA)**

Benchmark Independent Decision Making Units (DMUs) Consider Multidimensional Input / Output Relations Express Efficiency with a Single Score Between 0 and 1

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**Reveal Hidden Structures Identify Patterns Derive Actionable Insights**

Information Visualization (InfoViz) Reveal Hidden Structures Identify Patterns Derive Actionable Insights

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**Develop Competitive Strategies**

Information Visualization (InfoViz) Reveal Hidden Structures Identify Patterns Derive Actionable Insights Develop Competitive Strategies

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**Model 1: Measuring “Efficiency”**

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**Model 1: Measuring “Efficiency”**

I N P U T S O U T P U T Dealer Expenses DMU Dealer Spare Parts Area Revenue (Total) No of Employees

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**Model 2: Measuring “Efficiency for TOFAŞ”**

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**Model 2: Measuring “Efficiency for TOFAŞ”**

I N P U T S O U T P U T Dealer Expenses DMU Dealer Spare Parts Area Amount Purchased from TOFAŞ (YTL) No of Employees

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**Other Data of Interest on DMUs**

Share of TOFAŞ IsRentEstimated Cities No of Services

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**ANALYSIS and DISCUSSIONS**

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**Visualization of results**

Miner 3D

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**Visualization of results**

Omniscope

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**Incorporation of City Growths Technical Report and Paper**

Future Work Further Data Analysis Incorporation of City Growths Technical Report and Paper

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Special Thanks to … Prof. Muhittin Oral Sinan Südütemiz Hasan Erdoğan

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**Dr. Gürdal Ertek, Murat Mustafa Tunç Ece Kurtaraner, Doğancan Kebude**

Case Study 2: Insights into the Efficiencies of On-Shore Wind Turbines: A Data-Centric Analysis Dr. Gürdal Ertek, Murat Mustafa Tunç Ece Kurtaraner, Doğancan Kebude

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Research Questions How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? How can DEA & information visualization be used together? Which visualization techniques are appropriate for analyzing DEA results? (Case Study 2) How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results?

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**Outline Wind Turbines Our Study Methodology : Analysis and Results**

Data Envelopment Analysis (DEA) Visual Data Analysis Hypothesis Testing Analysis and Results Insights Brief outline of my presentattion

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Wind Turbines Mechatronic devices that convert wind energy into electrical energy via mechanical energy. Features: Diameter Air dynamics Tower height Controlling devices Location (On-shore / Off-shore)

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**Importance of Wind Turbines**

Green Energy Worldwide installed wind power capacity In 1990: 2,160 MW In 2011: 238,351 MW (Global Wind Energy Council) 16% of Europe’s electricity by (The European Wind Energy Association) İmportance of green energies is known worldwide İncreased around 120 times Wind power can generate the 16% of europes electricity by 2020

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**Wind Energy in Turkey 40 GW wind energy potential in next 20 years**

Image Source:

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Wind Energy in Turkey MİLRES: 500kW wind turbine to be designed and made in Turkey, In 2013 output of 500 kW In 2015 output of 2 MW Largest budget civilian R&D project in the history of the Turkish Republic

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Our Study Technical data of wind turbines are collected and analysed by following methodologies: Data Envelopment Analysis (DEA) Visual Data Analysis Hypothesis Testing Aim: Decision of the efficient wind turbines Understanding of how to make an unefficient turbine efficient by referencing the efficient ones Benchmarking of commercial wind turbines visually and statistically. Bunun gramatiğini kontrol et

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**Literature First example of: Use of DEA and visualization together:**

Benchmarking of commercial wind turbines Visualisation as a directed graph of reference sets in DEA results Use of DEA and visualization together: Ertek et al. (2007) “Benchmarking the Turkish apparel retail industry”. Ulus et al. (2006) “Financial benchmarking of transportation companies in the New York Stock Exchange (NYSE)”.

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**Methodologies Data Envelopment Analysis**

Efficiency comparision of Decision Making Units (DMU) according to Inputs (lower) Outputs (higher) For each DMU Efficiency score (between 0 and 1) Reference sets Projections

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**Methodologies Visual Data Analysis**

To distinguish different patterns in data and achieve new and useful insights. (Keim, 2002) Orange Canvas (software) Scatter plot Miner 3d (software) Surface plot

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**Top 10 companies in worldwide market share**

Database Top 10 companies in worldwide market share Vestas (Denmark) Sinovel (China) Goldwind (China) Gamesa (Spain) Enercon (Germany) GE (USA) Suzlon (India) Guodian (China) Siemens (Germany) Ming Yang (China)

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**DEA Model Model A : 74 on-shore wind turbine models**

Model B : 32 on-shore wind turbine models (low-wind) Inputs: - Diameter (m) - Nominal wind speed (m/s) Outputs: - Nominal Output (V) Other features: Cut-in wind speed (low/medium/high) Company We especially focused on on shore and low wind turbine models, because of milres project, that models are more suitable for turkey

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**DEA Model “BCC Output Oriented” Smart DEA Solver software**

Developed in Sabancı University Reads data from MS Excel and generate results Visual analysis with Orange Canvas and Miner3D using efficiency scores

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Analysis and Results

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**1 - Efficiency vs Companies**

Enercon and GE tend to have higher efficiency scores

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**2 - Efficiency vs Nominal Output**

A – efficiency is not significantly depend on nominal output, there are turbines that have efficiency score of 1 but with different nominal outputs B – there is a cluster of turbines with 1500 kw output that have higher efficiency score

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**3 - Efficiency vs Cut-in Wind Speed**

Cut in wind speed : min. Wind speed that turbine can start to operate Threre are more observations for cut in speed of 3, 3.5 and 4 m/s

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**4 - Efficiency vs Diameter**

A - the turbine with the min diametwer is efficient B - and the turbine with the second min diameter has a high efficiency score C – there is a cluster of turbines with diameter between 70 and 85 meters

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5 - Reference Analysis Which efficient turbine models should inefficient ones take as references? X axis: Efficient turbine model that should taken as reference Y axis: DMU name Size of circle: Weight of reference

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**A – this line shows the turbines with efficiency score of 1**

B – some turbines like no 49 should primarly take as example only one model C – some turbines like no 66 have balanced distribution of reference set

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**6 - Reference sets for Model B with yEd software**

This is for model b (low wind cut in wind speeds) Dmu and 49 are the ones that take as a reference at most

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7 - Projection Analysis At which percentage should the models change their inputs and outputs to become efficient? X-axis : Percentage change Y-axis : Efficiency Colors: Inputs and outputs

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**A - Some of the models should both decrease inputs and increase outputs to become efficient**

B - For most of the models it’s enough to increase outputs

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**8 - Miner 3D Surface Plot Analysis**

Shows the interaction between nominal wind speed, nominal output and diameter This regions parameters result in low efficincy scores so should be avoided

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**9 - Miner 3D Surface Plot Analysis**

İnteraction between diameter cut in wind speed and nominal output For high cut in wind speed, the region a and c should be avoided, diameter and nominal output should be aranged like in region b for higher efficiency scores

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**Insights Efficiency according to companies:**

Enercon and GE are the most efficient companies The efficiencies of turbines of Goldwind, Ming Yang, Mitsubishi and Siemens are under 60% Efficiency according to nominal output: Lower or higher values of nominal output is not effect efficiency But, outputs around 1.5 MW have higher efficiencies This insights can help manaers in investment decisions and design engineers

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**Insights Efficiency according to cut-in wind speed:**

2 and 2.5 m/s have lower; 3, 3.5 and 4 m/s have higer number of models 3 m/s and over have higher efficiency scores compared to 2 and 2.5 m/s Efficiency according to diameter: Model with the smallest diameter is the most efficient turbine Efficiency score of models with diameter between 70m and 85m are higher than expected

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**Insights Reference analysis: Projection analysis:**

DMUs 15, 20, 27, 61, 81 are the ones that taken as a reference at most Projection analysis: Some of the models should both decrease inputs and increase outputs to become efficient For most of the models it’s enough to increase outputs Miner 3D surface plot analysis: Input and outputs parameters of the models in light colored regions are ideal for higher efficiency

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**Hypothesis Testing Kruskal – Wallis Test confirmed that:**

Efficiency scores and cut-in wind speed is significantly different depending on the companies.

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References Cooper, W. W., Seiford, L. M., Tone, K. (2006), Introduction to Data Envelopment Analysis and its Uses, Springer, New York. Ertek, G., Can, M.A., Ulus, F. (2007) “Benchmarking the Turkish apparel retail industry through data envelopment analysis (DEA) and data visualization”. In: EUROMA th International Annual EurOMA Conference: Managing Operations in an Expanding, Ankara, Turkey Keim, D. A. (2002), “Information visualization and data mining,” IEEE Transactions on Visualization and Computer Graphics, Vol.8, No.1, pp. 1-8. Ulus, Firdevs and Köse, Özlem and Ertek, Gürdal and Şen, Simay (2006) “Financial benchmarking of transportation companies in the New York Stock Exchange (NYSE) through data envolopment analaysis (DEA) and Visulation”. In: 4th International Logistics and Supply Chain Congress, İzmir, Turkey, İzmir Weill, L. (2004), “Measuring cost efficiency in European banking: a comparison of frontier techniques,” Journal of Productivity Analysis, Vol.21, No.2, pp

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**Q&A Dr. Gürdal Ertek (ertekg@sabanciuniv.edu)**

Murat Mustafa Tunç Ece Kurtaraner Doğancan Kebude

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**IDETC/CIE 2012 Gurdal Ertek, Murat Mustafa Tunc ertekg@sabanciuniv.edu**

Case Study 3: Re-Mining Association Mining Results Through Visualization, Data Envelopment Analysis, and Decision Trees Gurdal Ertek, Murat Mustafa Tunc

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Research Questions How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? How can DEA & information visualization be used together? Which visualization techniques are appropriate for analyzing DEA results? How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results? (Case Study 3)

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**Book Chapter Published in**

‘‘Computational Intelligence Applications in Industrial Engineering’’ A book edited by Prof. Cengiz Kahraman Published by Atlantis & Springer

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**Outline Introduction Literature Methodology Case Study Conclusion**

Data Analysis Data Visualization Data Envelopment Analysis Decision Trees Classification Conclusion

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Introduction How the results of association mining analysis further analyzed using Data visualization Data Envelopment Analysis (DEA) Decision Trees Visual Re-Mining of an item considering both Positive assocations Negative associations

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**Association Mining Inputs: Outputs: Primary metrics:**

Transaction data that contains a subset of items Outputs: List of item-set that appear together frequently Primary metrics: Support is the percentage of transactions that the items appear in Confidence is the conditional probability that item B appearing in transaction given that item A readily appears

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Association Mining A classical application is market basket analysis

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**Graph Visualization Refers to the drawing of graphs, that consists**

Nodes Arcs Special algorithms In order to obtain actionable insights

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Re-mining Mining of a newly formed data constructed upon the results of data mining process The goal is to obtain new insights that couldn’t have been discovered otherwise, and to characterize, describe, and explain the results of the original data mining process

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**Data Envelopment Analysis**

Benchmark a group of entities through efficient scores Entities are called Decision Making Units (DMUs) Efficiency score increases, if DMU generates higher output using same input, or DMU uses less input for the same output

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**Graph Metrics Degree shows the number of connections**

Betweenness centrality represents total number of shortest paths Closeness centrality shows the distance between the node and every other node Eigenvector centrality shows the distance between the node and every other “special” node

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Graph Metrics Page rank is the value that increases if node is closely related with “special” nodes Clustering coefficient represents the tendency of aggregation for several nodes

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Decision Trees Main goal: To identify the nodes that differs considerably from its root node Each node is split (branced) according to a criterion Our study uses ID3 algorithm Branches are created in Orange software

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Classification Dataset is divided into two groups, namely learning dataset and test dataset Classification algorithms are called learners Naive Bayes k-Nearest Neighbor (kNN) C4.5 Support Vector Machines (SVM) Decision Trees The prediction success of each learner is measured through classification accuracy (CA)

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**Methodology Perform positive association mining**

Find negatively association item pairs from 1. Compute the percentage of positive associations Construct two association graphs, (1) shows only positive assoc., (2) shows only negative Compute graph metrics for each node

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**Methodology 6. Construct the dataset for re-mining**

Apply grid layout for graphs, then visually analyze them. Construct a DEA model, to combine the insights and to find the most important items Construct a classification model and decision trees Apply multiple learners and evaluate classification accuracy

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**Case Study Based on real company data in apperal retail industry**

Merchandise group in men clothes line 2007 season

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**Case Study Company headquartered in Istanbul 300+ stores in Turkey**

30+ stores in more than 10 countries

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Case Study As of Nov. 2010, the U.S. retail industry exceeded $377.5 billion

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**Data Analysis Step 1: Positive association mining**

Min. support value : 100 Result: 3930 frequent item pairs involving 538 items Step 2: Negative association mining Result: 2433 item pairs involving 537 items Step 3: Percentage of positive associations of each item

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Data Analysis Step 3: Percentage of positive associations of each item

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Data Analysis Step 4: Positive and negative association graphs

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Data Analysis Step 5: Graph metrics were computed using NodeXL add-in for MS Excel Step 6: Dataset formed for re-mining Each row is item involding positive association Columns include unique item number support count (SupC) StartWeek EndWeek LifeTime MaxPrice MinPrice PriceDiff MerchSubGroup Category PercOfPositiveAssoc Graph Metrics

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**Data Visualization Step 7: Grid layout applied for visualization**

Color denotes PercOfPositiveAssoc Lighter items are mostly negative associated Darker items are mostly positive associated

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Data Visualization

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**Data Visualization Second graph:**

IDETC/CIE 2012 Data Visualization Second graph: Node size represents end-of-season sales prices (MinPrice) Larger nodes denote higher MinPrice (more typically high-priced items) Smaller nodes denote lower MinPrice

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Data Visualization

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**Data Visualization Third graph:**

IDETC/CIE 2012 Data Visualization Third graph: Node shape represents category We want to answer if the items have a particular category type Upper left region Darker nodes Larger nodes

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Data Visualization

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**Data Envelopment Analysis (DEA)**

To analytically integrate the insights found in visualizations above Input: Uniform for each item Output: Support Count (SupC) PercOfPositiveAssoc MinPrice Output oriented BCC model

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**Data Envelopment Analysis**

INPUT INPUT OUTPUT OUPUT Item Eff1* Eff2** Input_Auxiliary Input_LifeTime PercOfPositiveAssoc SupC MinPrice 059 Yes 1 16 91.67 4157 19.99 087 094 106 26 11 32 92.31 75.00 30.00 8947 4647 346933 14.90 41.57 9.25 169 No 7 4464 34.90 289 8 87.50 4317 23.06 412 13 88.89 2658 438 10 4999 513 4 80.00 5115 13.80

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**Conclusions Our methodology combines**

Association mining Graph theory Classification Data Envelopment Analysis Re-mining Positive associations are related to graph metric values and item’s attributes

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References A. Demiriz, G. Ertek, T. Atan and U. Kula, Re-mining item associations: Methodology and a case study in apparel retailing, Decision Support Systems, 52(1), pp (2011). J.R. Quinlan,Induction of decision trees, Machine Learning, 1(1), pp (1986). Orange. E.Alpaydin, Introduction to Machine Learning,The MIT Press(2010). A. Demiriz, G. Ertek, T. Atan and U. Kula, Re-mining item associations: Methodology and a case study in apparel retailing, Decision Support Systems, 52(1), pp (2011). E.M.Bonsignore, C. Dunne, D.Rotman, M. Smith, T. Capone, D.L. Hansen andB. Shneiderman, First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL,inInternational Symposium on Social Intelligence and Networking (2009). R. Agrawal, T. Imielinski and A.N. Swami, Mining association rules between sets of items in large databases,in SIGMOD Conference,P. Buneman and S.Jajodia, (Eds) (1993).

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**References NodeXL. http://nodexl.codeplex.com/.**

A.E. Akcay, G. Ertek and G. Buyukozkan, Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework, Expert Systems with Applications (2012). R.D. Banker, A. Charnesand W.W. Cooper, Some models for estimating technical and scale inefficiencies in data envelopment analysis,Management Science. 30(9), pp. 1078–1092. (1984). G. Ertek and A. Demiriz, A framework for visualizing association mining results, Lecture Notes in Computer Science (LNCS), 4263, pp (2006) G. Ertek, M. Kaya, C.Kefeli, O. Onurand K. Uzer, Scoring and Predicting Risk Preferences,in Behavior Computing: Modeling, Analysis, Mining and Decision, Cao, L., Yu, P. S. (Eds), Springer(2012). C. Borgeltand R. Kruse, Graphical models: methods for data analysis and mining, Wiley (2002). E.N. Cinicioglu, G. Ertek, D. Demirerand H.E. Yoruk,A framework for automated association mining over multiple databases, in Innovations in Intelligent Systems and Applications (INISTA), International Symposium, IEEE, (2011).

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References A. Savasere, E. Omiecinski and S. Navathe, Mining for strong negative associations in a large database of customer transactions, in Data Engineering, Proceedings., 14th International Conference, IEEE (1998). P.N. Tan, V. Kumar and H.Kuno, in Western Users of SAS Software Conference (2001). I. Herman, G. Melanconand M.S. Marshall, Graph visualization and navigation in information visualization: A survey, Visualization and Comp. Graphics, 6 (2000) M. Van Kreveld and B. Speckmann, Graph Drawing,Lecture Notes in Computer Science (LNCS), 7034 (2012). R. Spence, Information Visualization, ACM Press (2001). H. Ltifi, B. Ayed, A.M. Alimiand S. Lepreux,Survey of information visualization techniques for exploitation in KDD, in Int. Conf. Comp. Sys.and App.(2009). C. Chen, Information Visualization, Wiley Interdisciplinary Reviews: Computational Statistics, 2 (2010). W.W. Cooper, L.M. Seiford and K. Tone, Introduction to Data Envelopment Analysis and Its Uses: With DEA Solver Software and References,Springer (2006). S. Gattoufi, M. Oral and A. Reisman, Data envelopment analysis literature: A bibliography update ( ), Journal of Socio-Econ. Planning Sci., 38, pp (2004).

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**Gürdal Ertek ertekg@sabanciuniv.edu**

Analytical Benchmarking Meets Data Mining: The SmartDEA Framework, SmartDEA Software, and Case Studies for Industry Gürdal Ertek Invited Seminar at A*Star SIMTECH, Singapore, August 2, 2013, Friday

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Research Questions How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? (SmartDEA) How can DEA & information visualization be used together? (Case Study 1, Automative) Which visualization techniques are appropriate for analyzing DEA results? (Case Study 2, Wind) How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results? (Case Study 3, Apparel Retail)

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Questions?

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**Thank you 感谢 Terima Kasih நன்றி Teşekkürler :-)**

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