This report has the objective of analysing datasets from the Lending Club. There are two datasets called ‘LC_Accept.csv’ and ‘LC-Decline.csv’, they have information about the services of lending money online by matching individual lenders and borrowers.
Beyond that, the ‘LC_Accept.csv’ contains the accepted services, on the other hand, the ‘LC-Decline.csv’ contain the decline services.
## [1] "Accept Dataset"## 'data.frame': 71695 obs. of 7 variables:
## $ Date : int 201501 201501 201501 201501 201501 201501 201501 201501 201501 201501 ...
## $ State : chr "SD" "TX" "CO" "WA" ...
## $ Purpose : chr "Debt_Consolidation" "Credit_Card" "Debt_Consolidation" "Debt_Consolidation" ...
## $ Amount_Requested : int 18000 21000 9600 23000 22475 15850 24000 24800 18000 15000 ...
## $ Risk_Score : int 700 690 665 745 715 710 660 665 680 705 ...
## $ Debt_Income_Ratio: num 0.219 0.225 0.122 0.148 0.178 ...
## $ Employment_Length: int 4 11 7 1 8 1 11 6 9 4 ...
## NULL## [1] "Decline Dataset"## 'data.frame': 287169 obs. of 7 variables:
## $ Date : int 201501 201501 201501 201501 201501 201501 201501 201501 201501 201501 ...
## $ State : chr "IL" "WA" "FL" "NY" ...
## $ Purpose : chr "Debt_Consolidation" "Credit_Card" "Credit_Card" "Purchase" ...
## $ Amount_Requested : int 20000 35000 5000 15000 10500 5000 2000 5000 13000 35000 ...
## $ Risk_Score : int 514 671 661 617 527 602 657 645 720 708 ...
## $ Debt_Income_Ratio: num 0.1562 0.0635 0.4876 0.1664 0.1515 ...
## $ Employment_Length: int 0 0 0 0 0 0 0 0 0 0 ...
## NULL## [1] "Group by Date"## [1] "Accept Grouped"## Group.1 x
## 1 2015-01-15 25517150
## 2 2015-02-15 17541050
## 3 2015-03-15 18664625
## 4 2015-04-15 25743000
## 5 2015-05-15 23018050
## 6 2015-06-15 20639300
## 7 2015-07-15 32804500
## 8 2015-08-15 26356725
## 9 2015-09-15 21206725
## 10 2015-10-15 35768650
## 11 2015-11-15 26991625
## 12 2015-12-15 31317625
## 13 2016-01-15 24935475
## 14 2016-02-15 29344100
## 15 2016-03-15 45028425
## 16 2016-04-15 25828525
## 17 2016-05-15 20174300
## 18 2016-06-15 23405425
## 19 2016-07-15 23140325
## 20 2016-08-15 24583375
## 21 2016-09-15 19355250
## 22 2016-10-15 21410875
## 23 2016-11-15 22990725
## 24 2016-12-15 24435925
## 25 2017-01-15 22019875
## 26 2017-02-15 19334325
## 27 2017-03-15 25999150
## 28 2017-04-15 20616250
## 29 2017-05-15 26107275
## 30 2017-06-15 25548250
## 31 2017-07-15 26902050
## 32 2017-08-15 29407025
## 33 2017-09-15 27749125
## 34 2017-10-15 27031750
## 35 2017-11-15 29939650
## 36 2017-12-15 27485400
## 37 2018-01-15 27479350
## 38 2018-02-15 24473400
## 39 2018-03-15 28974050
## 40 2018-04-15 32183775
## 41 2018-05-15 34049975
## 42 2018-06-15 30503075## [1] "Decline Grouped"## Group.1 x
## 1 2015-01-15 124829400
## 2 2015-02-15 92951725
## 3 2015-03-15 21827775
## 4 2015-04-15 6713750
## 5 2015-05-15 5329425
## 6 2015-06-15 6791500
## 7 2015-07-15 11697400
## 8 2015-08-15 9356975
## 9 2015-09-15 5884375
## 10 2015-10-15 14752255
## 11 2015-11-15 18251100
## 12 2015-12-15 22616050
## 13 2016-01-15 33732150
## 14 2016-02-15 51245850
## 15 2016-03-15 59921625
## 16 2016-04-15 48420575
## 17 2016-05-15 15563300
## 18 2016-06-15 9318075
## 19 2016-07-15 17674550
## 20 2016-08-15 27234700
## 21 2016-09-15 57120775
## 22 2016-10-15 105852975
## 23 2016-11-15 136059675
## 24 2016-12-15 121367050
## 25 2017-01-15 133772000
## 26 2017-02-15 119076450
## 27 2017-03-15 138840925
## 28 2017-04-15 123821125
## 29 2017-05-15 154172600
## 30 2017-06-15 138475350
## 31 2017-07-15 166736175
## 32 2017-08-15 188308875
## 33 2017-09-15 219165775
## 34 2017-10-15 295649325
## 35 2017-11-15 362381575
## 36 2017-12-15 322945175
## 37 2018-01-15 225254425
## 38 2018-02-15 11477200
## 39 2018-03-15 12151925
## 40 2018-04-15 12209625
## 41 2018-05-15 13216250
## 42 2018-06-15 7249200The first task of the report is to analyse the acceptance rates of loan applications over the months. The formula used to calculate the acceptance rate was rate = accept/ (accept + decline).
It was created a line plot to illustrate how the acceptance rate behaves through time, the graph show the period from January 2015 until June 2018.
Observing the graph from Task 1 it is possible to conclude that, the most important periods to increase the acceptance of the lending money services provided were during the December 2017 to February 2018 and from January 2015 until April 2015.
The second task consists of creating a side-by-side bar chart of loan purposes grouped by the status of accept and decline. The datasets have a total of five loan purposes.
The graph created in Task 2 shows that the purpose Debt Consolidation was the one with more number of accept, in the other hand, this was the same purpose with the higher number of decline.