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<!DOCTYPE html>
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<title>P6: Exploring Prosper Loan Dataset</title>
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<body>
<h1>P6: Exploring Prosper Loan Dataset</h1>
<p>Author: Ying Wu</p>
<h1>Introduction</h1>
<p>Prosper is a company that offers a marketplace for peer to peer lending.
Individuals can request loans and Prosper will calculate a score value
indicating the risk of the loan the individual is asking for and then
add that loan into their system. Investors can choose the type of loans
that they would want to get exposure to allowing them to trade off risk
for return.</p>
<p>My goal is to pick out an interesting facet of this loan data and explore
it further using a d3.js visualization.</p>
<h1>Exploration</h1>
<p>Looking through the variable definitions, I was most intrigued by the categories.
I exploring the relationship between these categories and the rest of the
variables available in the Prosper loan data.</p>
<pre><code class="r">library(data.table)
library(ggplot2)
# dt = fread("prosperLoanData.csv") # everything is character
df = read.table("prosperLoanData.csv", header=T, sep=",")
dt = as.data.table(df)
rm(df)
dt$ListingNumber = as.factor(dt$ListingNumber)
dt$ListingCreationDate = as.POSIXct(dt$ListingCreationDate)
dt$Term = as.factor(dt$Term)
dt$ClosedDate = as.Date(dt$ClosedDate)
dt$ListingCategory..numeric. = factor(dt$ListingCategory..numeric.)
levels(dt$ListingCategory..numeric.) = c(NA, "Debt Consolidation", "Home Improvement", "Business", "Personal Loan",
"Student Use", "Auto", "Other", "Baby&Adoption", "Boat", "Cosmetic Procedure",
"Engagement Ring", "Green Loans", "Household Expenses", "Large Purchases",
"Medical/Dental", "Motorcycle", "RV", "Taxes", "Vacation", "Wedding Loans")
dt$DateCreditPulled = as.POSIXct(dt$DateCreditPulled)
dt$FirstRecordedCreditLine = as.Date(dt$FirstRecordedCreditLine)
dt$LoanOriginationDate = as.Date(dt$LoanOriginationDate)
dt$IncomeRange = ordered(dt$IncomeRange, levels(dt$IncomeRange)[c(8, 1, 3, 4, 5, 6, 2, 7)])
summary(dt)
</code></pre>
<pre><code>## ListingKey ListingNumber
## 17A93590655669644DB4C06: 6 951186 : 6
## 349D3587495831350F0F648: 4 882888 : 4
## 47C1359638497431975670B: 4 892845 : 4
## 8474358854651984137201C: 4 1056749: 4
## DE8535960513435199406CE: 4 1057901: 4
## 04C13599434217079754AEE: 3 875616 : 3
## (Other) :113912 (Other):113912
## ListingCreationDate CreditGrade Term
## Min. :2005-11-09 20:44:28 :84984 12: 1614
## 1st Qu.:2008-09-19 10:02:14 C : 5649 36:87778
## Median :2012-06-16 12:37:19 D : 5153 60:24545
## Mean :2011-07-09 08:30:35 B : 4389
## 3rd Qu.:2013-09-09 19:40:48 AA : 3509
## Max. :2014-03-10 12:20:53 HR : 3508
## (Other): 6745
## LoanStatus ClosedDate BorrowerAPR
## Current :56576 Min. :2005-11-25 Min. :0.00653
## Completed :38074 1st Qu.:2009-07-14 1st Qu.:0.15629
## Chargedoff :11992 Median :2011-04-05 Median :0.20976
## Defaulted : 5018 Mean :2011-03-07 Mean :0.21883
## Past Due (1-15 days) : 806 3rd Qu.:2013-01-30 3rd Qu.:0.28381
## Past Due (31-60 days): 363 Max. :2014-03-10 Max. :0.51229
## (Other) : 1108 NA's :58848 NA's :25
## BorrowerRate LenderYield EstimatedEffectiveYield
## Min. :0.0000 Min. :-0.0100 Min. :-0.183
## 1st Qu.:0.1340 1st Qu.: 0.1242 1st Qu.: 0.116
## Median :0.1840 Median : 0.1730 Median : 0.162
## Mean :0.1928 Mean : 0.1827 Mean : 0.169
## 3rd Qu.:0.2500 3rd Qu.: 0.2400 3rd Qu.: 0.224
## Max. :0.4975 Max. : 0.4925 Max. : 0.320
## NA's :29084
## EstimatedLoss EstimatedReturn ProsperRating..numeric.
## Min. :0.005 Min. :-0.183 Min. :1.000
## 1st Qu.:0.042 1st Qu.: 0.074 1st Qu.:3.000
## Median :0.072 Median : 0.092 Median :4.000
## Mean :0.080 Mean : 0.096 Mean :4.072
## 3rd Qu.:0.112 3rd Qu.: 0.117 3rd Qu.:5.000
## Max. :0.366 Max. : 0.284 Max. :7.000
## NA's :29084 NA's :29084 NA's :29084
## ProsperRating..Alpha. ProsperScore ListingCategory..numeric.
## :29084 Min. : 1.00 Debt Consolidation:58308
## C :18345 1st Qu.: 4.00 Other :10494
## B :15581 Median : 6.00 Home Improvement : 7433
## A :14551 Mean : 5.95 Business : 7189
## D :14274 3rd Qu.: 8.00 Auto : 2572
## E : 9795 Max. :11.00 (Other) :10976
## (Other):12307 NA's :29084 NA's :16965
## BorrowerState Occupation EmploymentStatus
## CA :14717 Other :28617 Employed :67322
## TX : 6842 Professional :13628 Full-time :26355
## NY : 6729 Computer Programmer : 4478 Self-employed: 6134
## FL : 6720 Executive : 4311 Not available: 5347
## IL : 5921 Teacher : 3759 Other : 3806
## : 5515 Administrative Assistant: 3688 : 2255
## (Other):67493 (Other) :55456 (Other) : 2718
## EmploymentStatusDuration IsBorrowerHomeowner CurrentlyInGroup
## Min. : 0.00 False:56459 False:101218
## 1st Qu.: 26.00 True :57478 True : 12719
## Median : 67.00
## Mean : 96.07
## 3rd Qu.:137.00
## Max. :755.00
## NA's :7625
## GroupKey DateCreditPulled
## :100596 Min. :2005-11-09 00:30:04
## 783C3371218786870A73D20: 1140 1st Qu.:2008-09-16 22:25:27
## 3D4D3366260257624AB272D: 916 Median :2012-06-17 07:52:34
## 6A3B336601725506917317E: 698 Mean :2011-07-09 15:51:53
## FEF83377364176536637E50: 611 3rd Qu.:2013-09-11 14:30:24
## C9643379247860156A00EC0: 342 Max. :2014-03-10 12:20:56
## (Other) : 9634
## CreditScoreRangeLower CreditScoreRangeUpper FirstRecordedCreditLine
## Min. : 0.0 Min. : 19.0 Min. :1947-08-24
## 1st Qu.:660.0 1st Qu.:679.0 1st Qu.:1990-06-01
## Median :680.0 Median :699.0 Median :1995-11-01
## Mean :685.6 Mean :704.6 Mean :1994-11-17
## 3rd Qu.:720.0 3rd Qu.:739.0 3rd Qu.:2000-03-14
## Max. :880.0 Max. :899.0 Max. :2012-12-22
## NA's :591 NA's :591 NA's :697
## CurrentCreditLines OpenCreditLines TotalCreditLinespast7years
## Min. : 0.00 Min. : 0.00 Min. : 2.00
## 1st Qu.: 7.00 1st Qu.: 6.00 1st Qu.: 17.00
## Median :10.00 Median : 9.00 Median : 25.00
## Mean :10.32 Mean : 9.26 Mean : 26.75
## 3rd Qu.:13.00 3rd Qu.:12.00 3rd Qu.: 35.00
## Max. :59.00 Max. :54.00 Max. :136.00
## NA's :7604 NA's :7604 NA's :697
## OpenRevolvingAccounts OpenRevolvingMonthlyPayment InquiriesLast6Months
## Min. : 0.00 Min. : 0.0 Min. : 0.000
## 1st Qu.: 4.00 1st Qu.: 114.0 1st Qu.: 0.000
## Median : 6.00 Median : 271.0 Median : 1.000
## Mean : 6.97 Mean : 398.3 Mean : 1.435
## 3rd Qu.: 9.00 3rd Qu.: 525.0 3rd Qu.: 2.000
## Max. :51.00 Max. :14985.0 Max. :105.000
## NA's :697
## TotalInquiries CurrentDelinquencies AmountDelinquent
## Min. : 0.000 Min. : 0.0000 Min. : 0.0
## 1st Qu.: 2.000 1st Qu.: 0.0000 1st Qu.: 0.0
## Median : 4.000 Median : 0.0000 Median : 0.0
## Mean : 5.584 Mean : 0.5921 Mean : 984.5
## 3rd Qu.: 7.000 3rd Qu.: 0.0000 3rd Qu.: 0.0
## Max. :379.000 Max. :83.0000 Max. :463881.0
## NA's :1159 NA's :697 NA's :7622
## DelinquenciesLast7Years PublicRecordsLast10Years
## Min. : 0.000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.0000
## Mean : 4.155 Mean : 0.3126
## 3rd Qu.: 3.000 3rd Qu.: 0.0000
## Max. :99.000 Max. :38.0000
## NA's :990 NA's :697
## PublicRecordsLast12Months RevolvingCreditBalance BankcardUtilization
## Min. : 0.000 Min. : 0 Min. :0.000
## 1st Qu.: 0.000 1st Qu.: 3121 1st Qu.:0.310
## Median : 0.000 Median : 8549 Median :0.600
## Mean : 0.015 Mean : 17599 Mean :0.561
## 3rd Qu.: 0.000 3rd Qu.: 19521 3rd Qu.:0.840
## Max. :20.000 Max. :1435667 Max. :5.950
## NA's :7604 NA's :7604 NA's :7604
## AvailableBankcardCredit TotalTrades
## Min. : 0 Min. : 0.00
## 1st Qu.: 880 1st Qu.: 15.00
## Median : 4100 Median : 22.00
## Mean : 11210 Mean : 23.23
## 3rd Qu.: 13180 3rd Qu.: 30.00
## Max. :646285 Max. :126.00
## NA's :7544 NA's :7544
## TradesNeverDelinquent..percentage. TradesOpenedLast6Months
## Min. :0.000 Min. : 0.000
## 1st Qu.:0.820 1st Qu.: 0.000
## Median :0.940 Median : 0.000
## Mean :0.886 Mean : 0.802
## 3rd Qu.:1.000 3rd Qu.: 1.000
## Max. :1.000 Max. :20.000
## NA's :7544 NA's :7544
## DebtToIncomeRatio IncomeRange IncomeVerifiable
## Min. : 0.000 $25,000-49,999:32192 False: 8669
## 1st Qu.: 0.140 $50,000-74,999:31050 True :105268
## Median : 0.220 $100,000+ :17337
## Mean : 0.276 $75,000-99,999:16916
## 3rd Qu.: 0.320 Not displayed : 7741
## Max. :10.010 $1-24,999 : 7274
## NA's :8554 (Other) : 1427
## StatedMonthlyIncome LoanKey TotalProsperLoans
## Min. : 0 CB1B37030986463208432A1: 6 Min. :0.00
## 1st Qu.: 3200 2DEE3698211017519D7333F: 4 1st Qu.:1.00
## Median : 4667 9F4B37043517554537C364C: 4 Median :1.00
## Mean : 5608 D895370150591392337ED6D: 4 Mean :1.42
## 3rd Qu.: 6825 E6FB37073953690388BC56D: 4 3rd Qu.:2.00
## Max. :1750003 0D8F37036734373301ED419: 3 Max. :8.00
## (Other) :113912 NA's :91852
## TotalProsperPaymentsBilled OnTimeProsperPayments
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 9.00 1st Qu.: 9.00
## Median : 16.00 Median : 15.00
## Mean : 22.93 Mean : 22.27
## 3rd Qu.: 33.00 3rd Qu.: 32.00
## Max. :141.00 Max. :141.00
## NA's :91852 NA's :91852
## ProsperPaymentsLessThanOneMonthLate ProsperPaymentsOneMonthPlusLate
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.00
## Mean : 0.61 Mean : 0.05
## 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :42.00 Max. :21.00
## NA's :91852 NA's :91852
## ProsperPrincipalBorrowed ProsperPrincipalOutstanding
## Min. : 0 Min. : 0
## 1st Qu.: 3500 1st Qu.: 0
## Median : 6000 Median : 1627
## Mean : 8472 Mean : 2930
## 3rd Qu.:11000 3rd Qu.: 4127
## Max. :72499 Max. :23451
## NA's :91852 NA's :91852
## ScorexChangeAtTimeOfListing LoanCurrentDaysDelinquent
## Min. :-209.00 Min. : 0.0
## 1st Qu.: -35.00 1st Qu.: 0.0
## Median : -3.00 Median : 0.0
## Mean : -3.22 Mean : 152.8
## 3rd Qu.: 25.00 3rd Qu.: 0.0
## Max. : 286.00 Max. :2704.0
## NA's :95009
## LoanFirstDefaultedCycleNumber LoanMonthsSinceOrigination LoanNumber
## Min. : 0.00 Min. : 0.0 Min. : 1
## 1st Qu.: 9.00 1st Qu.: 6.0 1st Qu.: 37332
## Median :14.00 Median : 21.0 Median : 68599
## Mean :16.27 Mean : 31.9 Mean : 69444
## 3rd Qu.:22.00 3rd Qu.: 65.0 3rd Qu.:101901
## Max. :44.00 Max. :100.0 Max. :136486
## NA's :96985
## LoanOriginalAmount LoanOriginationDate LoanOriginationQuarter
## Min. : 1000 Min. :2005-11-15 Q4 2013:14450
## 1st Qu.: 4000 1st Qu.:2008-10-02 Q1 2014:12172
## Median : 6500 Median :2012-06-26 Q3 2013: 9180
## Mean : 8337 Mean :2011-07-21 Q2 2013: 7099
## 3rd Qu.:12000 3rd Qu.:2013-09-18 Q3 2012: 5632
## Max. :35000 Max. :2014-03-12 Q2 2012: 5061
## (Other):60343
## MemberKey MonthlyLoanPayment LP_CustomerPayments
## 63CA34120866140639431C9: 9 Min. : 0.0 Min. : -2.35
## 16083364744933457E57FB9: 8 1st Qu.: 131.6 1st Qu.: 1005.76
## 3A2F3380477699707C81385: 8 Median : 217.7 Median : 2583.83
## 4D9C3403302047712AD0CDD: 8 Mean : 272.5 Mean : 4183.08
## 739C338135235294782AE75: 8 3rd Qu.: 371.6 3rd Qu.: 5548.40
## 7E1733653050264822FAA3D: 8 Max. :2251.5 Max. :40702.39
## (Other) :113888
## LP_CustomerPrincipalPayments LP_InterestandFees LP_ServiceFees
## Min. : 0.0 Min. : -2.35 Min. :-664.87
## 1st Qu.: 500.9 1st Qu.: 274.87 1st Qu.: -73.18
## Median : 1587.5 Median : 700.84 Median : -34.44
## Mean : 3105.5 Mean : 1077.54 Mean : -54.73
## 3rd Qu.: 4000.0 3rd Qu.: 1458.54 3rd Qu.: -13.92
## Max. :35000.0 Max. :15617.03 Max. : 32.06
##
## LP_CollectionFees LP_GrossPrincipalLoss LP_NetPrincipalLoss
## Min. :-9274.75 Min. : -94.2 Min. : -954.5
## 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.00 Median : 0.0 Median : 0.0
## Mean : -14.24 Mean : 700.4 Mean : 681.4
## 3rd Qu.: 0.00 3rd Qu.: 0.0 3rd Qu.: 0.0
## Max. : 0.00 Max. :25000.0 Max. :25000.0
##
## LP_NonPrincipalRecoverypayments PercentFunded Recommendations
## Min. : 0.00 Min. :0.7000 Min. : 0.00000
## 1st Qu.: 0.00 1st Qu.:1.0000 1st Qu.: 0.00000
## Median : 0.00 Median :1.0000 Median : 0.00000
## Mean : 25.14 Mean :0.9986 Mean : 0.04803
## 3rd Qu.: 0.00 3rd Qu.:1.0000 3rd Qu.: 0.00000
## Max. :21117.90 Max. :1.0125 Max. :39.00000
##
## InvestmentFromFriendsCount InvestmentFromFriendsAmount Investors
## Min. : 0.00000 Min. : 0.00 Min. : 1.00
## 1st Qu.: 0.00000 1st Qu.: 0.00 1st Qu.: 2.00
## Median : 0.00000 Median : 0.00 Median : 44.00
## Mean : 0.02346 Mean : 16.55 Mean : 80.48
## 3rd Qu.: 0.00000 3rd Qu.: 0.00 3rd Qu.: 115.00
## Max. :33.00000 Max. :25000.00 Max. :1189.00
##
</code></pre>
<p>Some data summaries and reading online reveals that Prosper changed their API in
the latter half of 2009. In order to have the most consistant dataset, I exclude
data from before this period. I believe the most interesting comparison with
ListingCategory is the IncomeRange of the inviduals asking for the loan</p>
<pre><code class="r">table(dt[ListingCreationDate > "2009-07-01"]$ListingCategory..numeric., dt[ListingCreationDate > "2009-07-01"]$IncomeRange)
</code></pre>
<pre><code>##
## Not employed $0 $1-24,999 $25,000-49,999
## Debt Consolidation 247 14 2339 14817
## Home Improvement 29 3 262 1682
## Business 87 16 321 1481
## Personal Loan 0 0 0 0
## Student Use 19 1 77 90
## Auto 24 0 272 885
## Other 140 10 857 2953
## Baby&Adoption 1 0 5 54
## Boat 1 0 6 17
## Cosmetic Procedure 1 0 9 36
## Engagement Ring 4 0 7 56
## Green Loans 2 0 5 15
## Household Expenses 56 0 166 641
## Large Purchases 6 0 56 236
## Medical/Dental 16 1 92 484
## Motorcycle 0 0 38 94
## RV 0 0 6 11
## Taxes 4 0 36 163
## Vacation 8 0 59 239
## Wedding Loans 3 0 41 213
##
## $50,000-74,999 $75,000-99,999 $100,000+ Not displayed
## Debt Consolidation 16721 9417 9625 0
## Home Improvement 1989 1229 1607 0
## Business 1417 885 1091 0
## Personal Loan 0 0 0 0
## Student Use 55 17 15 0
## Auto 604 258 194 0
## Other 2545 1406 1307 0
## Baby&Adoption 59 28 52 0
## Boat 28 17 16 0
## Cosmetic Procedure 28 13 4 0
## Engagement Ring 65 35 50 0
## Green Loans 17 10 10 0
## Household Expenses 556 304 273 0
## Large Purchases 247 136 195 0
## Medical/Dental 441 248 240 0
## Motorcycle 99 32 41 0
## RV 18 7 10 0
## Taxes 250 180 252 0
## Vacation 227 141 94 0
## Wedding Loans 255 134 125 0
</code></pre>
<pre><code class="r">ggplot(dt[ListingCreationDate > "2009-07-01"], aes(ListingCategory..numeric., fill = IncomeRange)) + geom_bar() + coord_flip()
</code></pre>
<p><img 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" alt="plot of chunk summary1"/></p>
<pre><code class="r">setnames(dt, "ListingCategory..numeric.", "ListingCategory")
</code></pre>
<p>merge income ranges and summarize</p>
<pre><code class="r">tmp = dt[ListingCreationDate > "2009-07-01", list(Total = .N), by = list(ListingCategory, IncomeRange)]
out = dcast(tmp, ListingCategory ~ IncomeRange, value.var = "Total", fill = 0)
out$LowIncome = out$"Not employed" + out$"$0" + out$"$1-24,999"
out$MiddleIncome = out$"$25,000-49,999" + out$"$50,000-74,999"
out$HighIncome = out$"$75,000-99,999" + out$"$100,000+"
out[, Total := LowIncome + MiddleIncome + HighIncome]
out[, LowIncomePct := round(LowIncome / Total, 3)]
out[, MiddleIncomePct := round(MiddleIncome / Total, 3)]
out[, HighIncomePct := 1 - MiddleIncomePct - LowIncomePct] # so all sum to 1
out = out[ListingCategory != "Other"] # remove NA and "Other" since uninformative
setorder(out, HighIncomePct)
out$ListingCategory = factor(out$ListingCategory, levels = as.character(out$ListingCategory)) # reorder
ggplot(melt(out, id.vars = "ListingCategory", measure.vars = c("LowIncomePct", "MiddleIncomePct", "HighIncomePct")),
aes(ListingCategory, value, fill = variable)) + geom_bar(stat = 'identity') + coord_flip()
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk summary2"/></p>
<p>write out the data</p>
<pre><code class="r"># cleanup
out = out[, .(ListingCategory, LowIncomePct, MiddleIncomePct, HighIncomePct, Total)]
setnames(out, c("ListingCategory", "LowIncome <$25k", "MiddleIncome", "HighIncome >$75k", "Total"))
write.csv(out, file = "data.csv", row.names = FALSE)
</code></pre>
<h1>Summary</h1>
<p>Thinking from a perspective of an affliate advertiser, my goal is to identify
target audiences that might be interested in lending using Prosper based off
historical loan data. To achieve this, I identified two interesting variables
in the Prosper dataset: ListingCategory and IncomeRange. The former provides
good sites to place ads for Prosper whereas the latter can be used to filter
the customers to target these advertisements for.</p>
<p>Based on the data, I found that the following class of individuals
would be a good target for Prosper ads:</p>
<ul>
<li>Low income students</li>
<li>Middle income looking for cosmetic procedures</li>
<li>High income in months before taxes being due</li>
</ul>
<p>These three categories are specific to each income category. Sorting by
high income: Taxes have the highest percentage while Cosmetic Procedure
and Student Use are the lowest. Sorting by middle income: Cosmetic Procedure
had the highest percentage while Student Use and Taxes were the lowest.
Sorting by low income: Student Use by far had the highest percentage
with Taxes near the bottom and Cosmetic Procedure in the middle.</p>
<h1>Design</h1>
<p>I used a proportional stacked bar chart to show the number of loans for a
given category and IncomeRange. The IncomeRange is encoded using different
colors in a gradient to show the ordinal nature of this variable.</p>
<p>For interactive component, I added in a mouseover tooltip on the Y-axis to
allow the user to see how many elements there were total. In addition, another
mouseover showed the percentage of every income class.</p>
<p>After collecting feedback on the initial version, I realized that I needed a
better mechanism to emphasize the differences between the income classes. I added
a dropdown menu to sort by different income classes to emphasize the differences
between income classes. Additionally, I bolded the categories of interest and
I sorted by Low income by default since “Student Use” had the most striking
example of percentage overrepresentation by income class.</p>
<h1>Feedback</h1>
<p>I asked the following questions and wrote down the respones below:</p>
<ul>
<li>What do you notice in the visualization?</li>
<li>What questions do you have about the data?</li>
<li>What relationships do you notice?</li>
<li>What do you think is the main takeaway from this visualization?</li>
<li>Is there something you don’t understand in the graphic?</li>
</ul>
<h2>Feedback 1 - Lynn</h2>
<p>Poor students are one of the highest proportions.</p>
<p>Could you sort by the other income classes too (low and middle)?</p>
<p>Middle income seems to dominate Cosmetic Procedure and transportation related
(Auto and Motorcycle).</p>
<p>Poor students would be a good target for Prosper advertisements.</p>
<p>I understand the graphic.</p>
<h2>Feedback 2 - Jamie</h2>
<p>Middle income has the largest % of loans</p>
<p>Could you also show the dollar amount of each loan?</p>
<p>Low income have disporportionately high student loans</p>
<p>I understand the graphic.</p>
<h2>Feedback 3 - Jo</h2>
<p>The difference between what poor people ask for loans on and what rich people
ask for loans on is quite big.</p>
<p>Could you sort by low income instead of high?</p>
<p>Why students getting loans from Prosper rather than regular student loans.</p>
<p>Low income is more likely to use prosper for student loans. Middle income for
status symbol items like beauty, and cars. High income for home improvement or taxes.</p>
<p>I understand the graphic. </p>
<h1>Resources</h1>
<p><a href="https://developers.prosper.com/docs/investor/loans-api/">https://developers.prosper.com/docs/investor/loans-api/</a></p>
<p><a href="http://square.github.io/intro-to-d3/">http://square.github.io/intro-to-d3/</a></p>
<p><a href="https://github.com/d3/d3/wiki/">https://github.com/d3/d3/wiki/</a></p>
<p><a href="https://bl.ocks.org/mbostock/3886208">https://bl.ocks.org/mbostock/3886208</a> </p>
<p><a href="https://github.com/d3/d3/blob/master/API.md">https://github.com/d3/d3/blob/master/API.md</a></p>
<p>various stackoverflow and plnkr/jsfiddle/bl.ocks examples</p>
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