# This is an introductory study of Machine Learning study of Financial data
# We use an enhanced FINTURK data with NUTS and SEGE regional information
# Machine Learning techniques require very big computing power and memory of computers.
# PCA analysis and Dimentional Reduction techniques represents a suitable starting point to tackle with these questions
# This PPT representation covers the runnable R program of Duplex PCA Analysis and Clustering on FINTURK Data Credits and Defaults and resulting graphs.
Loans In Light of the New Support System The Financial Map: A Graphical Data-...
DUBLEX PCA FOR LOANS & DEFAULTS of FINTURK DATASET with R SoftWare
1. #DUBLEX PCA FOR LOANS & DEFAULTS
of FINTURK DATASET with R
Institutional Aspects of
Banking & Finance
4-5 November 2016, Kadir Has University, Turkey
Doç. Dr. Kutlu MERİH
2. INTRODUCTION
# This is an introductory study of Machine Learning study of
Financial data
# We use an enhanced FINTURK data with NUTS and SEGE
regional information
# Machine Learning techniques require very big computing
power and memory of computers.
# PCA analysis and Dimentional Reduction techniques
represents a suitable starting point to tackle with these
questions
# This PPT representation covers the runnable R program of
Duplex PCA Analysis and Clustering on FINTURK Data
Credits and Defaults and resulting graphs.
3. # R paketleri ve data yükleme
library(FactoMineR) # PCA analizi icin
library(ggthemes) # orijinal temalar icin
library(factoextra) # görsellestirme icin
library(plyr) # revalue icin
library(car) # matris scatterplot icin
library(ggcorrplot) # korealsyon matrisleri icin
library(readxl) # excel dosyalarını okumak icin
library(gridExtra) # duble grafik göstermek icin
rm(list = ls()) # hafızadan object temizlemek için
tma<-theme_igray() # orijinal tema secimi
8. 10.40.40.50.40.30.50.40.30.50.40.40.50.6
0.410.70.80.70.80.70.70.70.80.70.80.80.8
0.40.710.90.80.90.70.70.80.90.80.80.80.8
0.50.80.910.90.90.90.80.90.90.80.90.90.9
0.40.70.80.910.90.80.80.90.90.80.80.90.9
0.30.80.90.90.910.80.80.90.90.80.90.90.8
0.50.70.70.90.80.81 10.80.80.80.90.90.9
0.40.70.70.80.80.81 10.80.80.80.80.90.9
0.30.70.80.90.90.90.80.810.90.910.90.9
0.50.80.90.90.90.90.80.80.910.90.90.90.9
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0.40.80.80.90.80.90.90.810.91 10.90.9
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0.60.80.80.90.90.80.90.90.90.90.90.91 1
AGRICULTURELOANS
TOURISMLOANS
TEXTILELOANS
WHOSESALELOANS
MINERALSLOANS
FINANCIALLOANS
BUILDINGLOANS
ENERGYLOANS
MARITIMELOANS
FOODLOANS
AUTOLOANS
CARDSLOANS
MORTGAGELOANS
CONSUMERLOANS
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-1.0
-0.5
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1.0
Corr
Loans Korelasyon Nümerik
1 0.20.40.30.30.40.30.30.30.20.30.30.30.3
0.2 1 0.30.30.40.40.40.40.40.30.30.40.40.3
0.40.3 1 0.70.70.70.60.80.70.50.30.40.40.4
0.30.30.7 1 0.80.80.80.80.70.50.50.60.50.6
0.30.40.70.8 1 0.90.90.70.70.60.60.80.70.8
0.40.40.70.80.9 1 0.90.70.70.70.70.80.90.8
0.30.40.60.80.90.9 1 0.70.70.60.70.80.70.8
0.30.40.80.80.70.70.7 1 0.80.30.30.40.30.3
0.30.40.70.70.70.70.70.8 1 0.40.40.50.40.5
0.20.30.50.50.60.70.60.30.4 1 0.60.70.70.6
0.30.30.30.50.60.70.70.30.40.6 1 0.80.80.7
0.30.40.40.60.80.80.80.40.50.70.8 1 0.90.9
0.30.40.40.50.70.90.70.30.40.70.80.9 1 0.9
0.30.30.40.60.80.80.80.30.50.60.70.90.9 1
DEFENERGY
DEFAGRICULTURE
DEFFINANCIAL
DEFMARITIME
DEFTEXTILE
DEFCARDS
DEFAUTO
DEFMORTGAGE
DEFFOOD
DEFTOURISM
DEFBUILDING
DEFWHOLESALE
DEFCONSUMER
DEFMINERALS
D
EFENER
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Y
D
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EFFIN
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IAL
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AG
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ER
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-1.0
-0.5
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Corr
Defaults Korelasyon Nümerik