banking sector analysis of izmir province:a graphical data-mining analysis (r software applications)
TRANSCRIPT
BANKING SECTOR ANALYSIS OF IZMIR PROVINCE:A GRAPHICAL DATA-MINING ANALYSIS
(R SOFTWARE APPLICATIONS)
FATMA ÇINAR MBA, CAPITAL MARKET BOARD OF TURKEY
Assoc. Prof. Dr. C. COŞKUN KÜÇÜKÖZMEN İZMİR UNIVERSITY OF ECONOMICS
3 4 . O p e r a t i on s
R e s e a r c h & I nd u s t r i a l
E ng ın e e r ı n g N a t i on a l
C on g r e s s 2 5 - 2 7 J u n e
2 0 1 4
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Real Time Interactive Data Management for the Effect and Response Analysis
Technique; Lattice and ggplot2 Graphical Packages of R Software
İzmir Dataset Graphical
Datamining Analysis
Purpose of the study
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Yöneticilerin, IT dünyasının karmaşık kavram ve süreçlerinden arındıran, interaktif ve yalın görseller aracılığı ile veri bankalarının karmaşıklığını yok eden bir yaklaşımla stratejik karar verme pozisyonuna ulaştırabilmektir.
• Recent advances in computing and accompanied with the development of analytical tools it is now possible to use state of the art techniques at a very rapid pace.
• Increased use of data analysis techniques and the emergence of gigantic data led to the birth of a new discipline called data science.
• Financial institutions in general and banks in particular need to process the data in order to have prior and valuable information about the safety and soundness of their institutions.
Purpose of the study
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Yöneticilerin, IT dünyasının karmaşık kavram ve süreçlerinden arındıran, interaktif ve yalın görseller aracılığı ile veri bankalarının karmaşıklığını yok eden bir yaklaşımla stratejik karar verme pozisyonuna ulaştırabilmektir.
• We propose a new data-mining system via graphical representations.
• To achieve this object we use R software and its advanced graphical packages for graphical inquiries.
• Thus, all data in a single pool of resources can be managed and integrated with the reports consolidated while summary reports which provide spontaneous pictures and graphics are provided to be displayed instantly.
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• We generously exploit the richness of the data information pools and the power of our analytics to bring our new approach into reality against traditional analysis.
• To test our model we use regions/province specific bank data which belong to the third largest as well as commercial city of Turkey, İzmir.
Purpose of the study
Agenda Dataset: Financial Transactions of İzmir and Similar Cities of Turkey. (BRSA)*
Dataset: 15 cities and 6 account period Graphical Datamining Analysis
Period: 2008-2013 Accounts
Dataset are factorizet according to city and year factors.
Graphical Datamining applied on this factorizet data.
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*BRSA: Banking Regulations and Supervisison Agency
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summary(Dataset)
ILKOD SEHIR SYIL NYIL AY
Min. : 1.00 ADANA : 22 Y2008:60 Min. :2008 Min. : 3.000
1st Qu.:16.00 ANKARA : 22 Y2009:60 1st Qu.:2009 1st Qu.: 3.000
Median :33.00 ANTALYA : 22 Y2010:60 Median :2010 Median : 6.000
Mean :29.67 BURSA : 22 Y2011:60 Mean :2010 Mean : 7.227
3rd Qu.:42.00 DENİZLİ : 22 Y2012:60 3rd Qu.:2012 3rd Qu.: 9.000
Max. :55.00 GAZİANTEP: 22 Y2013:30 Max. :2013 Max. :12.000
(Other) :198
SDONEM NDONEM TOPNAKDIKREDI NAKDIKREDI
D200803: 15 Min. :200803 Min. : 2301180 Min. : 2249452
D200806: 15 1st Qu.:200906 1st Qu.: 5006994 1st Qu.: 4775867
D200809: 15 Median :201011 Median : 9001388 Median : 8623443
D200812: 15 Mean :201035 Mean : 14542560 Mean : 14003463
D200903: 15 3rd Qu.:201203 3rd Qu.: 15756949 3rd Qu.: 15263775
D200906: 15 Max. :201306 Max. :113564461 Max. :110692193
(Other):240
TAKIPALACAK GNAKDIKREDI TASIT KONUT
Min. : 39600 Min. : 215400 Min. : 34377 Min. : 313429
1st Qu.: 251686 1st Qu.: 971274 1st Qu.: 70138 1st Qu.: 625852
Median : 339949 Median : 1923710 Median :106403 Median : 944120
Mean : 539097 Mean : 4654118 Mean :168232 Mean : 1740547
3rd Qu.: 599559 3rd Qu.: 2933005 3rd Qu.:213790 3rd Qu.: 1812319
Max. :2872268 Max. :62782383 Max. :789062 Max. :13037891
KMH DIGERTUKETICI KREDIKARTI TAKIPTASIT
Min. : 13457 Min. : 329107 Min. : 2929 Min. : 1454
1st Qu.: 37365 1st Qu.: 728700 1st Qu.: 503812 1st Qu.: 3398
Median : 56261 Median : 1215660 Median : 828166 Median : 5354
Mean : 88532 Mean : 1815939 Mean :1197707 Mean : 8673
Summary of the dataset
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• We do factor-based analysis for the begining with the simplest graphical form of the histogram.
• Histograms of a single numeric value by one factor are the starting point of the factor based graphical analysis
Histogram graph of the lattice
packakge for 1 numerical 1
factor values
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Distribution of Total
Cash Credits According
the City Factor
Histogram (lattice)
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Distribution of Total
Cash Credits According
the City Factor
Histogram (lattice)
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Default Credits
According to the City
Factor Histogram
(lattice)
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Default Credits
According to the Year
Factor Histogramı
(lattice)
Dotplot graph of the lattice
packakge for 1 numerical
many factor values
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• Dotplot of Lattice R software package for one but many factors
• This we are able to analyze all the factors that affect a single numeric variable many factor data
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Default Credits
Percents According to the City and Year Factors DotPlot Graph (lattice)
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Total Cash Credits
According to the City and Year Factors DotPlot Graph (lattice)
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Default Credits
According to the City and Year
Factors DotPlot Graph (lattice)
Scatterplot graph of the
lattice packakge for 2
numerical 1 factor values
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• When it comes to analyze of the two numerical values against a factor, scaateerplot graph of the lattice packakge is useful
• This graph type also gives us the anomalies of the data which is quite useful for risk analyses
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Total Cash Credits Vs.
Default Credits
ScatterPlot Graph
(lattice)
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Total Cash Credits Vs.
Default Credits
According to the City
Factors ScatterPlot
Graph (lattice)
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Total Cash Credits Vs.
Default Credits
According to the Year
Factors ScatterPlot
Graph (lattice)
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Default Credits Vs.
Default Energy
ScatterPlot Graph (lattice)
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Default Credits Vs.
Default Energy
According to the City
Factor ScatterPlo
t Graph (lattice)
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Default Credits Vs.
Default Energy
According to the Year
Factor ScatterPlot
Graph (lattice)
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Default Credits Vs.
Default Vehicles
ScatterPlot Graph
(lattice)
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Default Credits Vs.
Default Vehicles
According to the City
Factor ScatterPlo
t Graph (lattice)
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Default Credits Vs.
Default Mortgage According to the Year
Factor ScatterPlo
t Graph (lattice)
XYPLOT graph of the lattice
packakge for 2 numerical and
many factor values
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When it comes to analyse of the relation of 2 values under the effect of many factors XYPLOT graph of lattice package is very useful.
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Default Credits Vs.
Default Energy
According to the Year
and City Factors XYPlot
Graph (lattice)
Wednesday, December 24, 2014
Default
Credits Vs. Default
Mortgage According to the Year
and City Factors XYPlot Graph
(lattice)
Ggplot2 packakge for 3 variables under
many factor effects.
Beautiful graphs in matrix form
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• Lattice package are not capable of to analyse of 3 numerical values under the effect of many factors.• In this case we use ggplot2 package of R
software.• This quite intelligent esthetical packakge
provide us beatiful graphics with comprehensible information.
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Default Credits Vs. Default
Energy According to
the Total Cash Credits and City
Factors BaloonGraph
(ggplot2)
Wednesday, December 24, 2014
Default Credits Vs. Default
Energy According to
the Total Cash Credits and
City and Years Factors
BaloonGraph (ggplot2)
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Default Credits Vs. Default Mortgage
According to the Total Cash
Credits and City and Years
Factors BaloonGraph
(ggplot2)
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Total Cash Credits Vs.
Default Credits
According to the Year and City Factors MatrixPlot
Graph (ggplot2)
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Energy Vs. Default Energy
According to the City and Year Factors MatrixPlot
Graph (ggplot2)
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Total Cash Credits Vs.
Default Credits
According to the Year and City Factors MatrixPlot Graph with Free Scales
(ggplot2)
Dataset: 15 cities and 6 account period Graphical Datamining Analysis
Period: 2008-2013 Accounts
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Dataset are factorizet according to city and year factors. Graphical Datamining applied on this factorized data.
We analyset the behaviour of the default credits according to the factors of the city and year with the support of the R Softwre packages of lattice and ggplot2 with a new approach of Graphical Datamining.
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http://www.ieu.edu.tr/tr
http://www.coskunkucukozmen.com
http://www.spk.gov.tr/
http://www.riskonomi.com
@fatma_cinar_ftm
@ckucukozmen
@RiskLab Turkey
@datanalitik
@Riskonometri
@Riskonomi
tr.linkedin.com/in/fatmacinar/
tr.linkedin.com/in/coskunkucukozmen
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