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sanaablog1 · 5 months ago

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Assignment 4:

K-means Cluster Analysis in the banking system

Introduction:

A personal equity plan (PEP) was an investment plan introduced in the United Kingdom that encouraged people over the age of 18 to invest in British companies. Participants could invest in shares, authorized unit trusts, or investment trusts and receive both income and capital gains free of tax. The PEP was designed to encourage investment by individuals. Banks engage in data analysis related to Personal Equity Plans (PEPs) for various reasons. They use it to assess the risk associated with these investment plans. By examining historical performance, market trends, and individual investor behavior, banks can make informed decisions about offering PEPs to their clients.

In general, banks analyze PEP-related data to make informed investment decisions, comply with regulations, and tailor their offerings to customer needs. The goal is to provide equitable opportunities for investors while managing risks effectively.

SAS Code

proc import out=mylib.mydata datafile='/home/u63879373/bank1.csv' dbms=CSV replace;

proc print data=mylib.mydata;

run;

/********************************************************************

DATA MANAGEMENT

*********************************************************************/

data new_clust; set mylib.mydata;

* create a unique identifier to merge cluster assignment variable with

the main data set;

idnum=_n_;

keep idnum age sex region income married children car save_act current_act mortgage pep;

* delete observations with missing data;

if cmiss(of _all_) then delete;

run;

ods graphics on;

* Split data randomly into test and training data;

proc surveyselect data=new_clust out=traintest seed = 123

samprate=0.7 method=srs outall;

run;

data clus_train;

set traintest;

if selected=1;

run;

data clus_test;

set traintest;

if selected=0;

run;

* standardize the clustering variables to have a mean of 0 and standard deviation of 1;

proc standard data=clus_train out=clustvar mean=0 std=1;

var age sex region income married children car save_act current_act mortgage;

run;

%macro kmean(K);

proc fastclus data=clustvar out=outdata&K. outstat=cluststat&K. maxclusters= &K. maxiter=300;

var age sex region income married children car save_act current_act mortgage;

run;

%mend;

%kmean(1);

%kmean(2);

%kmean(3);

%kmean(4);

%kmean(5);

%kmean(6);

%kmean(7);

%kmean(8);

%kmean(9);

* extract r-square values from each cluster solution and then merge them to plot elbow curve;

data clus1;

set cluststat1;

nclust=1;

if _type_='RSQ';

keep nclust over_all;

run;

data clus2;

set cluststat2;

nclust=2;

if _type_='RSQ';

keep nclust over_all;

run;

data clus3;

set cluststat3;

nclust=3;

if _type_='RSQ';

keep nclust over_all;

run;

data clus4;

set cluststat4;

nclust=4;

if _type_='RSQ';

keep nclust over_all;

run;

data clus5;

set cluststat5;

nclust=5;

if _type_='RSQ';

keep nclust over_all;

run;

data clus6;

set cluststat6;

nclust=6;

if _type_='RSQ';

keep nclust over_all;

run;

data clus7;

set cluststat7;

nclust=7;

if _type_='RSQ';

keep nclust over_all;

run;

data clus8;

set cluststat8;

nclust=8;

if _type_='RSQ';

keep nclust over_all;

run;

data clus9;

set cluststat9;

nclust=9;

if _type_='RSQ';

keep nclust over_all;

run;

data clusrsquare;

set clus1 clus2 clus3 clus4 clus5 clus6 clus7 clus8 clus9;

run;

* plot elbow curve using r-square values;

symbol1 color=blue interpol=join;

proc gplot data=clusrsquare;

plot over_all*nclust;

run;

*****************************************************************************

Number of clusters suggested by the elbow curve

*****************************************************************************

*the proposed numbers are: 2,5,7, and 8

* plot clusters for 5 cluster solution;

proc candisc data=outdata5 out=clustcan;

class cluster;

var age sex region income married children car save_act current_act mortgage;

run;

proc sgplot data=clustcan;

scatter y=can2 x=can1 / group=cluster;

run;

* validate clusters on PEP; The categorial target variable

* first merge clustering variable and assignment data with PEP data;

data pep_data;

set clus_train;

keep idnum pep;

run;

proc sort data=outdata5;

by idnum;

run;

proc sort data=pep_data;

by idnum;

run;

data merged;

merge outdata5 pep_data;

by idnum;

run;

proc sort data=merged;

by cluster;

run;

proc means data=merged;

var pep;

by cluster;

run;

proc anova data=merged;

class cluster;

model pep = cluster;

means cluster/tukey;

run;

Dataset

The dataset I used in this assignment contains information about customers in a bank. The Data analysis used will help the bank take know the important features that can affect the PEP of a client from the following features: age, sex, region, income, married, children, car, save_act, current_act and the mortgage.

Id: a unique identification number,

age: age of customer in years (numeric),

income: income of customer (numeric)

sex: 0 for MALE / 1 for FEMALE

married: is the customer married (1 for YES/ 0 for NO)

children: number of children (numeric)

car: does the customer own a car (1 for YES/ 0 for NO)

save_acct: does the customer have a saving account (1 for YES/ 0 for NO)

current_acct: does the customer have a current account (1 for YES/ 0 for NO)

mortgage: does the customer have a mortgage (1 for YES/ 0 for NO)

Figure1: dataset

K-means Clustering Algorithm

Cluster analysis is an unsupervised learning method used to group or cluster observations into subsets based on the similarity of responses on multiple variables. Observations that have similar response patterns are grouped together to form clusters. The goal is to partition the observations in a data set into a smaller set of clusters and each observation belongs to only one cluster.

Cluster analysis will be used in this assignment to develop individual banks’ customers profiles to target the ones that will benefit from the pep from those who shouldn’t.

With cluster analysis, we want customers within clusters to be more similar to each other than they are to observations in other clusters.

K-means Cluster Analysis

I used the libname statement to call my dataset. All the data is numerical (continues or logical depending on the feature description).

We'll first create a dataset that includes only my clustering variables and the PEP variable which will be used to externally validate the clusters. Then, we will assign each observation a unique identifier so that we can merge the cluster assignment variable back with the main dataset later on.

data new_clust; set mylib.mydata;

idnum=_n_; * create a unique identifier to merge cluster assignment variable with the main data set;

keep age sex region income married children car save_act current_act mortgage pep;

The SURVEYSELECT procedure

ODS graphics is turned on for the SAS to plot graphics. The data set is randomly split into a training data set consisting of 70% of the total observations in the data set, and a test data set consisting of the other 30% of the observations. Data equals, specifies the name of my managed dataset, called new_clust as seen in the figure below.

Figure 2

Statistics for variables

proc surveyselect data=new_clust out=traintest seed = 123

samprate=0.7 method=srs outall;

run;

method = srs specifies that the data are to be split using simple random sampling.

out=traintest to include both the training and test observations in a single output data set that has a new variable called selected.

The selected variable is 1 when an observation belongs to the training data set and 0 when an observation belongs to the test data set.

In cluster analysis, variables with large values contribute more to the distance calculations. Variables measured on different scales should be standardized prior to clustering. So, that the solution is not driven by variables measured on larger scales. We use the following code to standardize the clustering variables to have a mean of zero and a standard deviation of one.

proc standard data=clus_train out=clustvar mean=0 std=1;

var age sex region income married children car save_act current_act mortgage; run;

Figure 3

The Elbow curve

%macro kmean(K); indicates that the code is part of a Sass macro called knean and the K in parenthesis indicates that the macro will run the procedure code for number of different values of K. the output is then printed and the output data sets for K from 1 to 11 clusters is created.

Figure4

To view the different R-squared values for each of the k equals 1 to 11, the elbow plot is drawn as shown in the figure 5. We start with the K equals 1 with an R-squared=0 zero because there's no clustering yet. 2 cluster solution accounts for about 13% of the variance. The R-square value increases as more clusters are specified. We are looking for the bend in the elbow that shows where the R-square value might be leveling off. From the graph we can notice that there is a bend at 2 clusters, 5 clusters, 7 clusters, and 10 clusters. To help us figure out which solutions is best, we should further examine the results for the 2, 5, 7, and 10 cluster solutions.

Figure 5

The Canonical discriminate analysis

We should further examine the results for the 2, 5, 7, and 10 cluster solutions to see whether the clusters overlap or the patterns of means on the clustering variables are unique and meaningful and whether there are significant differences between the clusters on our external validation variable, PEP. We will interpret the result for the 5 cluster solution.

Since we have 10 variables, we will not be able to plot a scatter chart to see whether or not the clusters overlap with each other in terms of their location in the 10-dimensional space. For this, we have to use the canonical discriminate analysis which is a data reduction technique that creates a smaller number of variables that are linear combinations of the 10 clustering variables. Usually, the majority of the variants in the clustering variable will be accounted for by the first couple of canonical variables and those are the variables we can plot.

Figure 6

Results in Figure 6 show that the 10 variables are now reduced to 4 canonical variables that can be used to visualize the location of the clusters in a two or three dimensional space as shown in Figure 7.

Figure 7

What this shows is that the observations in cluster 5 is little more spread out, indicating less correlation among the observations and higher within cluster variance. Clusters 2 and 3 are relatively distinct with the exception that some of the observations are closer to each other indicating some overlap with these clusters. The same thing applies to cluster 2 and cluster 4. However, cluster 1 is all over the place. There is some indication of a cluster but the observations are spread out more than the other clusters. This means that the within cluster variance is high as there is less correlation between the observations in this cluster, so we don't really know what's going to happen with that cluster. So, the best cluster solution may have fewer than 5 clusters.

Cluster means table

We will consider for the rest that k=5. We will take a look at the cluster means table to examine the patterns of means on the clustering variables for each cluster which was shown in Figure8.

Figure 8

The means on the clustering variables show that compared to the other clusters, customers in cluster 2 and 5 have relatively good income, a current and saving accounts with no mortgage while customers in cluster 1 and 3 they have a low income, married and less number of children. Cluster 4 includes customers with a low income, low saving account and a mortgage.

Fit criteria for pep

The last part of this analysis will show how the clusters differ in PEP. We first have to extract the PEP variable from the training data set, then sort both data sets by the unique identifier, ID num which we will use to link the data sets and finally merge it with the data set that includes the cluster assignment variable.

The graph in Figure 9 below shows the mean PEP by cluster. As it was described in the mean table before, customers in cluster 1 and 3 they have a low income, married and less number of children so these customers will be certainly accorded a PEP with 60% while customers in cluster 2 and 5 who have relatively good income, a current and saving accounts with no mortgage will be accorded also a PEP with 40%. However, customers in cluster 4 with a low income, low saving account and a mortgage will be accorded a pep with 25% as this is logic as they already have mortgage.

Figure 9

The tukey test

The anova procedure is used to test whether there are significant differences between clusters and PEP as follows:

proc anova data=merged;

class cluster;

model pep = cluster;

means cluster/tukey;

run;

The tukey test shows that the clusters differed significantly in mean PEP as shown in figure 10, with the exception of clusters 2 and 3, which did not differ significantly from each other.

Figure 10

Conclusion

Kmeans is Cluster analysis is an unsupervised learning method used to group or cluster observations into subsets based on the similarity of responses on multiple variables. It is a useful machine learning method that can be applied in any field. However, k-means cluster analysis will have to give us the correct number of clusters and figuring out the correct number clusters that represent the true number of clusters in the population is pretty subjective. Also, if results can change depending on the location of the observations that are randomly chosen as initial centroids. It also assumes that the underlying clusters in the population are spherical, distinct, and are of approximately equal size. As a result, tends to identify clusters with these characteristics. It won't work as well if clusters are elongated or not equal in size.

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emkanews7 · 1 year ago

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निष्कर्ष

जैसा कि आपने लिस्ट के माध्यम से जाना कि कौन सा bank सबए ज्यादा ब्याज देता fd पर, small finance bank सबसे ज्यादा ब्याज देती हैं Fd पर, अगर कोई और प्रश्न हैं तो हमें नीचे कमेंट मे जरूर बताये. Read the full article

#BestBankForFixedDeposit #BestBankForFixedDeposit2023

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thebluesource · 3 years ago

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Now. Blue. Listen to me. Go to a store called Bank and buy all their bags. With the paper in them

Thats……….

…actually not a bad idea

I do need to change currencies

#blue #source #thebluesource #gv #ask #anon #anonymous #steamverse #bank1 #s2 #season 2

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neurosciencenews · 6 years ago

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Groundbreaking genetic discovery shows why Lupus develops Rare gene variants BLK and BANK1 are present in a substantial percentage of people with Lupus. The genetic variants suppress 1RF5 and type-1 1FN in B cells, causing dysfunction in the immune cells.

#genetics #SLE #systemic lupus erythematosus #lupus #autoimmune disease #neurology #neuroscience #science #health #medicine

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manpowergroup · 2 years ago

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https://rightmanagement.co.in/case-studies/bank1

Integrating an effective career transition strategy into an International banking organisation’s outplacement strategy

#RightManagement Case Study

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pdfsayar · 2 years ago

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Solomon Organic Chemistry 10th

10 sonuç Boyut Önizleme İndirme Organic Chemistry 10th Edition Solomons Test Bank1. Credit for the first synthesis of an organic compound from an inorganic precursor is usually given to: A) Berzelius B) Arrhenius C) Kekule D) Wohler E) Lewis Ans: D Topic: Atomic Orbitals, …Kaynak: https://testallbank.com/sample/organic-chemistry-10th-edition-solomons-test-bank.pdf KB Önizle İndir Solomon…

View On WordPress

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me3dia · 3 years ago

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I got the following spam email today:

Crime tips from Eric Rapaport

The following is who I am in relation to law enforcement:

Yes, I am formerly commander and director Rapaport of the CIA. The Central Intelligence Agency fired me so that they could get away with bank frauding me for money I made writing small parts of microsofts product line and for money I made writing various movies, without being prosecuted. The agency also stole 4-5 congressional Medals of Honor from me.

I initiated the Aldrich Ames investigation.

CMH #1 - Colorado cannibal war

CMH #2 - Arizona cannibal war

CMH #3 – Somalia

CMH #4 – The China incident

CMH #5 - All other over sees ops. Iraq, Yugoslavia, Pakistan, Fiji, Panama, Ect……

Here are your crime tips:

I read that Steven Seagal was accused of multiple counts of rape and it is being investigated by Beverly Hills PD.

In 1996? I got Steven Seagal off of rape charges at his request in Aspen for 1 million to the victim.

I suspected that he might kill his victim and take the money back after he paid so I made sure that her family and CIA Director Tom Roberts had the evidence on the money.

This is true (what I wrote above this line did happen).

Did the following happen?

Did Steven Seagal kill his victim and her family and take the money back with or without CIA director Tom Roberts help?

The following is what Steven Seagal did to me (I have also been a screen writer for most of my life.)

I wrote Hard to Kill, Marked for Death and The Patriot 1996 - 400k paid - Steven Seagal has refused to turn over any evidence of the payment. The money was deposited into Nationwide bank uptown Blvd. in Albuquerque which is now Bank of America and the bank will not admit that the check ever existed. This makes Steven Segal and Bank of America guilty of conspiracy to commit bank fraud, conspiracy to commit bank robbery.

I also wrote parts of Above the Law and small amounts of Steven Seagal movies not listed above.

Next Subject:

The only connections between the Seagal case and this case would be Aspen PD and bank fraud.

A very long time ago I wrote, produced and directed a decent amount of Oh God with John Denver.

A very short amount of time before John Denver supposedly died in a plane crash Mr. Denver paid me either 700K or 1 million (I don’t remember which figure). I either deposited the money in Bank1 which was at Broadway and Canyon in Boulder or I might have deposited the check at Nationwide bank uptown Blvd. in Albuquerque which is now Bank of America. No one will turn in evidence of the payment. Therefore, Bank of America or Bank 1 is guilty of conspiracy in the murder of John Denver. Both of these banks are guilty of conspiracy to commit bank robbery and conspiracy to commit bank fraud.

www.TheftCase.com

Janna Rapaport, Philip Marchiondo and mass murder CIA agents had my guns stolen and then tortured John Denver to death at his house by Aspen.

By the time I recovered some guns in order to make the rescue John Denver was dead and the murders Janna Rapaport and Philip Marchiondo were gone.

Janna Rapaport and Philip Marchiondo and their thugs had forced John Denver’s family to eat pieces of their husband and father. After the thugs were dead John Denver’s family was walking outside their house in a catatonic state and were unable to speak.

John Denver’s money was probably then divided up between different mass murder CIA agents and possibly Bill Clinton.

Janna Rapaport and Philip Marchiondo divided up my bank account that John Denver helped me created from the money I received for writing and producing most of Oh-God.

This also means that John Denver was then unavailable to help me recover the stolen bank account.

To this day, no one will turn in evidence on my stolen bank accounts.

To this day, Janna Rapaport and Philip Marchiondo are still walking around free.

Next Subject

I recently started emailing the following. This looks like good material to email to news organizations.

Because I am formerly the head of internal affairs for the United States, the mass murder part of the CIA which the CIA calls Black Flag, these people kept confessing to me about the number of people that they were killing. (1985 to 2019).

The leaders of Black Flag are of course whatever X US presidents and current US president are still alive.

Arizona 5 million US citizens killed by the US government slightly South of Show Low Arizona off of highway 60. (in addition, when I was the head of IA for the US, Southern Arizona was packed full of mass murder cannibal police, mass murder cannibal sheriffs and mass murder cannibal highway patrols. I don’t know what the current state of the problem is.)

Colorado 23 million US citizens killed by the US government between Wellington Colorado and Nun Colorado

- 23 was the Black Flag confession to me and the number might be 28 million.

(in addition, when I was the head of IA for the US, Northern Colorado was packed full of mass murder cannibal police, mass murder cannibal sheriffs and mass murder cannibal highway patrols. I don’t know what the current state of the problem is.)

Kansas Millions of US citizens were killed by the US government west of Kansas City and I do not know the numbers.

Minnesota 25 million US citizens killed by the US government.

South Carolina. 30 million US citizens killed by the US government. While I was involved in fighting on civilian’s behalf in all of these states, I don’t remember exactly where in South Carolina this happened.

Utah 5 million US citizens killed by the US government south of I70 in Utah by the Colorado, Utah border.

West Virginia 50 million US citizens killed by the US government in West Virginia in one of the world’s largest caves.

2.5 million US citizens were sent to China for the Chinese to torture to death.

50,000 US citizens were sent to the Philippians for the Philippians to torture to death.

5 + 23 + 25 + 30 + 5 + 50 + 2.5 + .05 = 140.55 million total US citizens killed by the US government according to Black Flag’s confessions to me. The real number is reportedly 188 million.

Only recently (2010 and 2019) I found 2 different large-scale kidnappings committed by US feds and Las Vegas PD in the Cosmopolitan Hotel in Las Vegas NV. Reportedly US president Bill Clinton and Black Flag murdered these people for 1,000 condominiums. (If you find a vehicle or vehicles with kidnap victims inside, it is important that the vehicles tires are shot out or punctured).

Kansas City and St. Louis were attacked repeatedly. While I was involved in fighting on civilian’s behalf, I don’t know the death tolls.

I paid prince Charles and Mary Elisabeth $300.00 to take care of the evidence on a 600K savings account of mine in 1998 or 1997. The bank account and evidence on this bank account has since then been stolen.

It is a safe assumption that I will never see the evidence. This money of mine was probably the last of the United States Internal Affairs budget.

Former US president Ronald Reagan once told me “The Chinese just paid me 300 million Yuan to get things started and I have some Indian rupees coming”.

The demographics for the US look like there have been a rise in Asian Immigrants, when in fact if you visit some parts of the United States one can see and hear that some parts of the United States are now almost 100% recent Immigrants.

Thank you

Eric Rapaport

Bizarre, and obviously the delusions of someone mentally ill. It turns out, though, that he's been at this since at least 2007, and possibly by email going back to the mid-'90s. It's unclear exactly what he's hoping to achieve with it all, except possibly to clear his name and seek imagined revenge on Steven Seagal.

#Eric Rapaport #theft case #spam email #who killed john denver

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