Modeling Data in the Tidyverse Course Project.Consumer Complaint Database (CFPB) Predictions
giovanni barrero ortiz
4/10/2021
Objective
The goal of project is to predict the consumer complaint category. This is the “Product” variable in the training data set.May use any of the other variables to predict with. We should create a report describing how we built the model, how we used cross validation, what we think the expected out of sample error is, and why we made the choices we did. We will also use the prediction model to predict 20 different test cases.
In this project, the consumer complains data from the Consumer Complaint Database (CFPB) https://www.consumerfinance.gov/data-research/consumer-complaints/. would be used. The CFPB is an independent agency of the United States government that promotes transparency and protects consumers by providing information needed to make decisions when choosing financial institutions including banking institutions, lenders, mortgage services, credit unions, securities firms, foreclosure services, and debt collectors. One of the purposes of the agency is to receive and process complaints and questions about consumer financial products and services.
When a complaint is submitted by a consumer, the CFPB has to determine which category the complaint falls in (e.g. “Mortgage”, “Student loan”, etc). In this project, the goal would be to build a classification algorithm to classify consumer complaints into one of four categories: “Credit card or prepaid card”, “Mortgage”, “Student loan”, or “Vehicle loan or lease”. The datasets can be downloaded from the course website:
Exploratory Data Analysis
Load required library
options(warn=-1)
library(tidyverse) # for data frame manipulation## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
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## x recipes::step() masks stats::step()library(tm) # for text manipulation## Loading required package: NLP##
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library(dplyr)
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## vidf <- read.csv("data_complaints.csv" ,header= TRUE, stringsAsFactors = F)create a bar chart that shows most frequently complained product
colcount.product = length(unique(df$Product))
getPalette = colorRampPalette(brewer.pal(8, "Set2"))
ggplot(df, aes(x= Product)) + geom_bar(aes(fill = factor(Product))) + theme(axis.text.x = element_blank()) +
scale_fill_manual(values = getPalette(colcount.product))
create a consultant chart that shows most frequently complained product.
ggplot(df, aes(x= Product)) + geom_bar(aes(fill = factor(Product))) +
scale_fill_manual(values = getPalette(colcount.product)) + coord_polar() +
theme(axis.title.x = element_blank(), axis.title.y = element_blank(),
axis.text.y = element_blank(), axis.text.x = element_text(size = 12),
axis.ticks.x = element_blank(), axis.ticks.y = element_blank())
Data Import
df<-read_csv("data_complaints_train.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## Product = col_character(),
## `Consumer complaint narrative` = col_character(),
## Company = col_character(),
## State = col_character(),
## `ZIP code` = col_character(),
## `Submitted via` = col_character()
## )Data Inspection
glimpse(df)## Rows: 90,975
## Columns: 6
## $ Product <chr> "Credit card or prepaid card", "Mortgag~
## $ `Consumer complaint narrative` <chr> "I initially in writing to Chase Bank i~
## $ Company <chr> "JPMORGAN CHASE & CO.", "Ditech Financi~
## $ State <chr> "CT", "GA", "IN", "MI", "MI", "FL", "WA~
## $ `ZIP code` <chr> "064XX", "None", "463XX", "490XX", "480~
## $ `Submitted via` <chr> "Web", "Web", "Web", "Web", "Web", "Web~A few things about the data :
- The outcome variable is called Product .
- there are 5 variables which can be used as predictors, the variable seems to provide the most relevant information for making predictions on the product category.
- Thus, we only select the above two variables for further processing.
# Rename the complaint column and only select the two variables
df<-df %>%
rename(Complain=`Consumer complaint narrative`) %>% select(Product, Complain)Data Pre-processing
# Check if there are any categories
unique(df$Product)## [1] "Credit card or prepaid card" "Mortgage"
## [3] "Student loan" "Vehicle loan or lease"# Check the first few rows of Complain
head(df$Complain)## [1] "I initially in writing to Chase Bank in late XXXX about a charge that was unauthorized on XXXX from XXXX in the amount of {$790.00} after many letters and complaints they still did not do anything yet in XXXX they closed my original account and reissued a new account and sent me a letter indicating they moved this charge to fraud and it would be immediately credited however they have failed to do that and almost 8 months later it is still on my statement and I'm getting charged interest. Even after this I still use my card and PAY for my charges and have never been late however I will not pay for a charge I didn't make and refuse and Chase refuses to do anything even after many attempts and letters they write stating so. I feel this is last step before I begin a case in XXXX XXXX XXXX XXXX against them"
## [2] "My ex husband and myself had a mobile home ( Home Mortgage ) with XXXX XXXX back in XX/XX/XXXX. I went through a horrible divorce as my husband was using drugs and failing to pay the bills which led to my having to divorce him. The Mortgage was in both of our names. We only had the Mortgage for one year and they got the trailer bc of being unable to pay debt. I was not working at the time as I had just given birth to my first child in XX/XX/XXXX ( she is now XXXX of age ) as this happened years ago. \n\nNow after 21 years, XXXX XXXX XXXX XXXX filled a XXXX XXXX to the IRS and the IRS alerted me which caused me an adverse affect on my IRS taxes. \nI received a l letter from the IRS XX/XX/XXXX which stated that due to DITECH Mortgage sending a XXXX with taxable income of {$17000.00}, this could cause me to have to owe the IRS {$2900.00}. IRS Told me to contact DITECH Financial to dispute this and request that DITECH Mortgage rescind the XXXX in order ( so it is not XXXX, XXXX taxable income ). \n\nI contacted DITECH at XXXX spoke with a \" XXXX '' and I TOLD HER THAT I did not want to be recorded but she did it anyways. \nI told her that I never settled on an amount of XXXX XXXX of a debt from a mortgage that I had back in XX/XX/XXXX with DITECH nor did I ever receive a XXXX from them. I asked them to rescind the XXXX ( taxable income of {$17000.00} ) as this is a debt from \" 20 Years ago '' in which I should not be taxed for taxable income of XXXX, XXXX on a debt from 20 years ago. However, XXXX refused to rescind ( reverse ) the XXXX. \nNOTE, I have suffered with my credit from this debt for over ten years and it was removed from my debt bc it has been over the 7 to 10 year period that the debt is lawfully removed from my credit. They can't go back 20 years later AND IN XX/XX/XXXX send the IRS and myself a XXXX form to tax me {$17000.00} for a debt from over 20 years ago that WAS charged off my credit report back in the year XX/XX/XXXX!!! this is very unfair and not allowed. \nALSO< I disputing the fact that they kept putting this on and off my credit for over 20 years now and it has negatively affected my credit for way over the 7 year period. \nPOINT BEING< IF THEY HAD PLANS OF TAXING ME AT {$17000.00} then they should have taxed me 20 yrs ago ( BEFORE THE 7 TO 10 YEAR PERIOD PASSED WHICH IS NOW WAY OVER ). If they were going to send a XXXX taxable income, they should have done this YEARS AGO NOT IN XX/XX/XXXX. I AM REQUESTING A COMPLAINT SUBMITTED AS THEY HAVE UNFAIRLY AFFECTED MY CREDIT PAST THE 20 YEAR PERIOD AND IT IS VIOLATING MY RIGHTS TO BE TAXED FOR SOMETHING IN XX/XX/XXXX ON A DEBT FROM THE YEAR XX/XX/XXXX"
## [3] "I was a student at XXXX XXXX from XX/XX/XXXX-XX/XX/XXXX. I accumulated XXXX of debt in student loans. This school flat out lied to me. I was under the assumption it would have been less than half of that since I had a lot of credits that transferred, not to mention this school promised me job placement, which they did not! They told me I would be making a lot more than what I do. They gave me false information. The only thing this school did for me is put me in tons of debt and lied to me. I am a single mother of XXXX and was so excited to get my degree. Until I graduated and realize how much they lied to me. The school is now shut down. Can I get these loans forgiven due to being lied to and given false information that basically pressured me into enrolling. If I would have known that my student loans would have been this high. I would have just stayed at the XXXX college where I did my prerequisites. I feel like I fool that XXXX had done this to me."
## [4] "It has come to my attention the CITI group is actively attempting to interfere with rights guaranteed by the Constitution ( 2nd Amendment ) by manipulating or denying service to certain entities or transactions that are protected by the Constitution. I do not expect CITI group, or any other financial institution to be controlling the nation 's activities through such unpatriotic and un-American behavior. I do expect CITI group to tend to the business of their business and stay out of the business of social tyranny."
## [5] "This banks new firearm policies run counter to laws and regulations passed by Congress, and they infringe and discriminate against an individuals Second Amendment rights. Such policies, should not be endorsed by our federal government, which instead should do business with companies that respect all of our constitutional rights, including the Second Amendment. Our federal government should take all necessary steps to review and terminate its contract with Citibank unless they rescind their guidelines."
## [6] "I only use my walmart store card to keep from having it cancelled. My sons a college student had his card cancelled for non use. I made a small purchase in XX/XX/XXXX and paid it off. Made another small purchase in XX/XX/XXXX and thought i paid it off online via wlamart.com but that seemed not to work i didn't receive a statment after that probably the mail courier.i have an apartment style mailbox system where i live. Nevertheless. So i thought all was good until credit sesame showed a past due report.i was out of town at the time. so i immediately searched walmarts payment site and paid the account in full. I also noticed that my {$400.00} account had been reduced to a mere {$110.00} just {$9.00} short of my now due payment which included aprox. {$60.00} in past due fees. So it appears that i maxed out the account by a $ 30- {$40.00} purchase. My credit dropped 47 score points from XXXX down to XXXX. From excellent to a fair rating."We see that
only has 4 categories. We can make it into a factor. However for
Complain , it is a long narrative which is much more complicated and thus we have to clean it up. We will carry out the following clean-up process:
- Drop any rows with NA in the column.
- Remove any strings such as “XX”, “XXX” and “XXXX” which were used to mask private information and dates.
- Convert all letters to lower case.
- Remove any numbers.
- Remove all punctuation.
- Remove escape characters and white spaces: and .
- Remove stop words e.g. “I”, “My”, “which”, “won’t” etc..
df_clean<-df %>% filter(Complain!=is.na(.)) %>%
mutate(Complain=gsub("[XX]+", "", Complain)) %>%
mutate(Complain=str_to_lower(Complain)) %>%
mutate(Complain=gsub("[0-9]", "", Complain)) %>%
mutate(Complain=removePunctuation(Complain)) %>%
mutate(Complain=gsub("\n", "", Complain)) %>%
mutate(Complain=gsub("\t", "", Complain)) %>%
mutate(Complain=stripWhitespace(Complain))
complains<-Corpus(VectorSource(df_clean$Complain)) %>% tm_map(removeWords, stopwords())We will now create a document term matrix using the tm package where each complaint will be a “document” occupying a row, each “term” will be a column name, and the counts of each term per document will be the values.
dtm<-DocumentTermMatrix(complains)
inspect(dtm)## <<DocumentTermMatrix (documents: 90975, terms: 81653)>>
## Non-/sparse entries: 7048327/7421333348
## Sparsity : 100%
## Maximal term length: 2229
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs account bank card credit loan mortgage payment payments received told
## 19650 4 34 0 3 49 10 6 7 12 0
## 25455 79 2 0 11 80 57 29 4 3 0
## 27671 2 1 0 1 4 4 8 5 1 0
## 33614 2 32 2 6 1 12 1 0 3 0
## 64243 0 0 0 0 26 15 1 10 4 2
## 68621 9 0 0 10 56 0 16 9 10 8
## 70389 1 0 0 3 2 26 0 3 0 0
## 73540 8 1 0 6 33 61 7 37 3 0
## 77518 0 0 0 0 103 0 45 1 0 2
## 84551 36 0 0 8 68 60 22 16 0 0We note that there are 90975 documents and 81653 terms - that’s huge! We shall try to reduce our data set initially by keeping only the relevant terms for exploratory analysis. Here, we define relevant terms as terms which appear 1000 times or more.
# Find terms which appear 1000 times or more
freqTerms<-findFreqTerms(dtm, lowfreq=1000)
# Limit DTM to only contain terms that appear 1000 times or more. dtm<-DocumentTermMatrix(complains, list(dictionary=freqTerms))
inspect(dtm)## <<DocumentTermMatrix (documents: 90975, terms: 81653)>>
## Non-/sparse entries: 7048327/7421333348
## Sparsity : 100%
## Maximal term length: 2229
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs account bank card credit loan mortgage payment payments received told
## 19650 4 34 0 3 49 10 6 7 12 0
## 25455 79 2 0 11 80 57 29 4 3 0
## 27671 2 1 0 1 4 4 8 5 1 0
## 33614 2 32 2 6 1 12 1 0 3 0
## 64243 0 0 0 0 26 15 1 10 4 2
## 68621 9 0 0 10 56 0 16 9 10 8
## 70389 1 0 0 3 2 26 0 3 0 0
## 73540 8 1 0 6 33 61 7 37 3 0
## 77518 0 0 0 0 103 0 45 1 0 2
## 84551 36 0 0 8 68 60 22 16 0 0We have reduced the number of terms to 1552, but the sparsity has dropped marginally to 96%. Let’s further simplify by removing sparse terms which have at least 95% of sparse elements (i.e, terms only occurring 0 times in a document) which can help to reduce the matrix without losing significant relations inherent to the matrix.
dtm<-removeSparseTerms(dtm, 0.95)
inspect(dtm)## <<DocumentTermMatrix (documents: 90975, terms: 341)>>
## Non-/sparse entries: 3548325/27474150
## Sparsity : 89%
## Maximal term length: 14
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs account bank card credit loan mortgage payment payments received told
## 11002 1 5 0 1 68 42 4 4 10 1
## 11343 4 0 1 9 30 6 7 1 2 57
## 25455 79 2 0 11 80 57 29 4 3 0
## 37990 12 6 0 11 41 6 6 4 29 9
## 56802 18 7 0 0 6 20 17 17 21 8
## 60769 0 0 6 5 43 6 7 4 27 18
## 64243 0 0 0 0 26 15 1 10 4 2
## 68621 9 0 0 10 56 0 16 9 10 8
## 70174 8 1 0 7 41 5 59 32 5 9
## 84551 36 0 0 8 68 60 22 16 0 0We have reduced the number of terms to 341, and the sparsity is now 89%. Following which, lets now carry out some simple exploratory analysis on the dataset. We can start by finding out the top 150 terms.
# Convert to Matrix for further processing
dtm_mat<-as.matrix(dtm)
# Get the total number of counts for each term and show the top 150 terms
sumTerm<-colSums(dtm_mat) %>%
.[order(-.)]
head(sumTerm, 150)## payment credit account loan card
## 125209 123231 117566 116300 93671
## told mortgage payments received bank
## 76344 73598 73240 67255 63024
## time called one pay company
## 62293 61445 52298 51811 51569
## back due get will made
## 50662 49787 48903 48615 48526
## never said can call amount
## 47774 45900 45510 44637 44617
## paid information sent interest now
## 44542 42565 42548 42043 40050
## letter also late balance home
## 39799 39453 39253 38122 36074
## since days make month phone
## 35532 34912 34029 33346 33049
## asked still money months new
## 32690 31371 31006 29746 29102
## even service email number customer
## 28717 28081 27621 27604 26921
## years date help loans check
## 26660 26045 25725 25575 25024
## just statement first charge another
## 24582 23808 22992 22745 22681
## request report chase charges fees
## 22511 22450 22237 22001 21947
## times stated contacted day insurance
## 21883 21656 21563 21552 21435
## issue two complaint see fee
## 21151 21099 20936 20663 20596
## need process escrow know like
## 20144 19818 19747 19565 19369
## monthly property closed documents spoke
## 19358 19242 19003 18996 18875
## year modification please applied dispute
## 18740 18528 18504 18354 18296
## requested representative contact take car
## 18258 18064 18009 17853 17835
## want going financial without send
## 17745 17662 17501 17237 17233
## last got however receive rate
## 17183 17168 17066 16982 16894
## several department fraud went able
## 16876 16811 16792 16675 16662
## full charged online work didnt
## 16627 16573 16342 15917 15843
## paying provided dont every informed
## 15769 15652 15634 15593 15522
## business application well tried name
## 15295 15063 14718 14641 14520
## provide stating put address refund
## 14519 14308 14307 14138 14011
## response trying case later bill
## 13969 13927 13613 13587 13498
## calls mail needed purchase nothing
## 13459 13452 13419 13373 13225
## today past way done current
## 13079 13001 12862 12792 12715
## debt funds used closing took
## 12707 12701 12637 12577 1255130 Common Terms
#Sum count of each term across all documents
cumulativeAllTerms<-colSums(as.matrix(dtm_mat))
#Sort in descending order and take top 30 terms
Top30<-head(cumulativeAllTerms[order(-cumulativeAllTerms)], 150)
#Convert to data frame
Top30<-data.frame(term=names(Top30), count=Top30)
Top30<-Top30[order(-Top30$count),]
#Plot
barplot(rev(Top30$count), horiz = T, names.arg = Top30$term, las=2, col="blue", main="Most Frequ
ent 150 Terms")
It appears that there are several other frequent words in the top 150 which may not add value to the data set e.g. “told”, “called”, “back”, “get”, “will” etc. Let’s remove them to make the data cleaner.
extraStopWords<-c("told", "called", "back", "get", "will", "never", "said", "can", "call", "now", "also",
"even", "just", "like", "please", "take", "want", "going", "without", "got", "however",
"went", "able", "didnt", "dont", "put", "later", "way", "done", "needed", "today", "used", "took")
complains<-tm_map(complains, removeWords, extraStopWords)Now, we shall recreate the DTM, but with all the additional filters.
# Create a new DTM with all previous filters.
dtm<-DocumentTermMatrix(complains, list(dictionary=freqTerms))
inspect(dtm)## <<DocumentTermMatrix (documents: 90975, terms: 1552)>>
## Non-/sparse entries: 5138413/136054787
## Sparsity : 96%
## Maximal term length: 15
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs account bank card credit loan mortgage payment payments received time
## 11002 1 5 0 1 68 42 4 4 10 10
## 19650 4 34 0 3 49 10 6 7 12 5
## 25436 67 6 0 9 86 61 25 3 5 2
## 25455 79 2 0 11 80 57 29 4 3 3
## 60769 0 0 6 5 43 6 7 4 27 9
## 64243 0 0 0 0 26 15 1 10 4 20
## 68621 9 0 0 10 56 0 16 9 10 15
## 73540 8 1 0 6 33 61 7 37 3 2
## 84551 36 0 0 8 68 60 22 16 0 8
## 9878 2 6 0 0 16 16 13 1 13 4dtm<-removeSparseTerms(dtm, 0.95)
inspect(dtm)## <<DocumentTermMatrix (documents: 90975, terms: 308)>>
## Non-/sparse entries: 2977757/25042543
## Sparsity : 89%
## Maximal term length: 14
## Weighting : term frequency (tf)
## Sample :
## Terms
## Docs account bank card credit loan mortgage payment payments received time
## 11002 1 5 0 1 68 42 4 4 10 10
## 15879 2 73 0 0 19 12 2 6 38 6
## 19650 4 34 0 3 49 10 6 7 12 5
## 25436 67 6 0 9 86 61 25 3 5 2
## 25455 79 2 0 11 80 57 29 4 3 3
## 56802 18 7 0 0 6 20 17 17 21 12
## 60769 0 0 6 5 43 6 7 4 27 9
## 64243 0 0 0 0 26 15 1 10 4 20
## 68621 9 0 0 10 56 0 16 9 10 15
## 84551 36 0 0 8 68 60 22 16 0 8We have reduced the number of terms to 308.
Building a ML model for predicting product category
Create Train and Test Sets
As we have briefly processed the “train” data set as dtm previously, we would now need to split it into training (75%) and test (25%) sets. Let’s start by creating the training data set.
# Convert Product to a factor
df_clean<-df_clean %>% mutate(Product=factor(Product))
# Convert dtm_mat into a data frame and add the outcome Product column.
dtm_mat<-as.matrix(dtm)
dtm_train<-dtm_mat %>% as.matrix() %>%
as.data.frame() %>% bind_cols(Product=df_clean$Product) %>% select(Product, everything())
# Create the training data set
set.seed(1234)
split_dtm<-initial_split(dtm_train, strata=Product, prop=3/4)
training_dtm<-training(split_dtm)count(training_dtm, Product)## Product n
## 1 Credit card or prepaid card 28721
## 2 Mortgage 23218
## 3 Student loan 9364
## 4 Vehicle loan or lease 6930We shall do the same for the testing data set.
testing_dtm<-testing(split_dtm) count(testing_dtm, Product)## Product n
## 1 Credit card or prepaid card 9573
## 2 Mortgage 7739
## 3 Student loan 3121
## 4 Vehicle loan or lease 2309Create cross validation folds.
Before moving on, we can also create cross validation folds with rsample . We will carry out a 10-fold cross-validation.
vfold_dtm<-vfold_cv(data=training_dtm, v=10)
vfold_dtm## # 10-fold cross-validation
## # A tibble: 10 x 2
## splits id
## <list> <chr>
## 1 <split [61409/6824]> Fold01
## 2 <split [61409/6824]> Fold02
## 3 <split [61409/6824]> Fold03
## 4 <split [61410/6823]> Fold04
## 5 <split [61410/6823]> Fold05
## 6 <split [61410/6823]> Fold06
## 7 <split [61410/6823]> Fold07
## 8 <split [61410/6823]> Fold08
## 9 <split [61410/6823]> Fold09
## 10 <split [61410/6823]> Fold10pull(vfold_dtm, splits)## [[1]]
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## <61410/6823/68233># Check first fold
first_fold<-vfold_dtm$splits[[1]]
# Training set of this fold
head(as.data.frame(first_fold, data="analysis"))## Product account almost amount anything bank card case
## 1 Credit card or prepaid card 2 1 1 2 1 1 1
## 2 Mortgage 0 0 1 0 0 0 0
## 3 Student loan 0 0 0 0 0 0 0
## 4 Credit card or prepaid card 0 0 0 0 0 0 0
## 5 Credit card or prepaid card 0 0 0 0 0 0 0
## 6 Credit card or prepaid card 3 0 0 0 0 2 0
## charge charged charges chase closed feel fraud getting immediately interest
## 1 3 1 1 2 1 1 1 1 1 1
## 2 0 1 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 1 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 1 0
## last late letter make many months new original pay sent statement stating
## 1 1 2 1 1 2 1 1 1 2 1 1 1
## 2 0 0 1 0 0 0 0 0 2 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 1 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## still use yet ago asked cant complaint contact contacted credit debt dispute
## 1 3 1 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 6 1 1 1 1 1 6 9 1
## 3 0 0 0 0 0 0 0 0 0 0 2 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 2 0 0 0 0 0 0 0 2 0 0
## due ever fact financial first form given happened home income mortgage note
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 1 1 1 1 1 1 1 1 2 5 6 1
## 3 1 0 0 0 0 0 1 0 0 0 0 0
## 4 0 0 0 1 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 3 0 0 0 0 0 0 0 0 0 0 0
## one order owe past period point receive received refused removed report
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 1 1 1 1 4 1 1 1 1 2 1
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 2 0 0 1 0 0 0 0
## request requesting send something spoke stated submitted time unable using
## 1 0 0 0 0 0 0 0 0 0 0
## 2 1 1 2 1 1 1 1 1 1 1
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 1 0 0
## working year years gave information loans making much since student
## 1 0 0 0 0 0 0 0 0 0 0
## 2 1 7 9 0 0 0 0 0 0 0
## 3 0 0 0 1 2 3 1 1 1 3
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 1
## transferred business service federal including instead review second another
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 1 0 0 0 0 0 0 0 0
## 4 0 3 1 0 0 0 0 0 0
## 5 0 1 0 2 1 1 1 2 0
## 6 0 0 0 0 0 0 0 0 1
## fees full good keep made mail online paid payment purchase score system
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 1 1 1 1 2 1 1 3 2 3 1 1
## thought via work answer applied ask check fee help need previous problem
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 2 1 1 0 0 0 0 0 0 0 0 0
## provided representative response started three calls company days end hours
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## payments phone agreement asking loan office paperwork paying regarding see
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## statements understand wanted advised around balance find money monthly rep
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## reporting returned website week assistance complete department documents
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## every issue may modification month number process stop funds give know matter
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## provide say thank times transaction believe currently next program services
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## several address calling claim filed informed let someone status continue
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## either everything found people taken based cfpb rate refund requested resolve
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## total tried upon additional change changed day notified wrong close offered
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## opened bill car hold reason agent date fraudulent insurance property
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## servicing already correct current dated following foreclosure law part place
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## required came house lender name try application apply recently plan situation
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## always customer long multiple saying terms weeks attached available denied
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## email error set well closing sold tell escrow attorney ive resolved reported
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## state states file must though two different longer receiving sure accounts
## 1 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## consumer documentation nothing per return cards finally line notice signed
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## explained proof approved offer supervisor trying wasnt agreed copy issues
## 1 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0
## prior within right history show transfer look purchased care left person owed
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## manager speak showing
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0# Test set of this fold
head(as.data.frame(first_fold, data="assessment"))## Product account almost amount anything bank card case
## 43 Credit card or prepaid card 0 0 0 0 0 0 0
## 50 Credit card or prepaid card 1 0 2 0 8 1 0
## 63 Mortgage 0 0 0 0 0 0 0
## 88 Student loan 0 0 1 1 0 0 0
## 92 Credit card or prepaid card 0 0 2 1 0 6 0
## 104 Credit card or prepaid card 1 0 1 0 0 0 0
## charge charged charges chase closed feel fraud getting immediately interest
## 43 0 0 0 0 0 0 0 0 0 0
## 50 0 1 2 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 2 0
## 88 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 1 0 0
## last late letter make many months new original pay sent statement stating
## 43 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 1 0 0 0 0 0 0 1 0 2
## 63 0 0 0 0 0 0 0 0 0 2 1 0
## 88 0 0 0 1 0 1 0 0 1 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0
## 104 0 0 1 0 0 0 1 0 2 1 2 0
## still use yet ago asked cant complaint contact contacted credit debt
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 3 0 0 2 0
## 63 1 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 3 0 0 1 0 0
## 92 0 0 0 0 0 0 0 0 1 3 0
## 104 1 0 0 0 0 0 2 0 0 0 0
## dispute due ever fact financial first form given happened home income
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 1 0 1 1 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 1
## 88 0 1 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 1 0 0 0 0 0
## mortgage note one order owe past period point receive received refused
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 1 0 0 0 2 0
## 92 0 0 0 0 0 0 0 0 0 1 0
## 104 0 0 0 0 2 0 0 1 0 1 2
## removed report request requesting send something spoke stated submitted
## 43 0 0 0 0 0 0 0 0 0
## 50 0 0 0 1 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 1 0 1 0
## 104 0 0 0 0 1 0 0 0 0
## time unable using working year years gave information loans making much
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 1 0 0 0 0 0 0 0 0 0 1
## 63 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 1 0 0 1 0 0
## 92 0 0 0 0 1 0 0 0 0 0 0
## 104 0 0 1 0 0 0 0 0 0 0 1
## since student transferred business service federal including instead review
## 43 0 0 0 0 0 0 0 0 0
## 50 1 0 0 0 0 0 0 1 0
## 63 0 0 0 0 0 0 0 0 1
## 88 0 0 0 0 0 0 0 0 0
## 92 0 0 0 1 3 0 0 1 0
## 104 0 0 0 0 0 0 0 0 0
## second another fees full good keep made mail online paid payment purchase
## 43 2 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 1 1 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0 5 0
## 92 0 0 0 0 0 0 0 0 0 1 0 1
## 104 1 0 0 0 0 0 0 1 0 0 0 0
## score system thought via work answer applied ask check fee help need
## 43 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 1 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0 1 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0 0 0
## previous problem provided representative response started three calls
## 43 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 1 0
## 88 0 0 0 0 0 0 0 1
## 92 0 0 0 1 0 0 0 0
## 104 0 0 0 0 0 0 0 0
## company days end hours payments phone agreement asking loan office
## 43 0 0 0 0 0 0 0 0 0 0
## 50 2 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 3 0 0 0
## 104 1 0 0 0 1 0 0 0 0 0
## paperwork paying regarding see statements understand wanted advised around
## 43 0 0 0 0 0 0 0 0 0
## 50 0 1 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0
## 88 0 0 1 0 0 0 0 0 0
## 92 0 2 0 0 2 0 0 0 0
## 104 0 2 0 0 0 0 0 0 0
## balance find money monthly rep reporting returned website week assistance
## 43 0 0 0 0 0 0 0 0 0 0
## 50 0 1 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 1 0 0 0 0 0 0
## 92 0 0 0 1 2 0 0 0 0 0
## 104 1 0 1 0 0 0 0 0 0 0
## complete department documents every issue may modification month number
## 43 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0
## 63 0 0 2 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0
## process stop funds give know matter provide say thank times transaction
## 43 0 0 0 0 1 0 0 0 0 0 0
## 50 0 0 0 1 0 0 0 1 0 0 0
## 63 0 0 0 0 0 0 0 0 0 2 0
## 88 0 0 0 0 0 0 0 0 0 1 0
## 92 0 0 0 0 0 1 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 2 0 0
## believe currently next program services several address calling claim filed
## 43 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 1 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0
## 104 1 0 0 0 0 0 3 0 0 0
## informed let someone status continue either everything found people taken
## 43 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 1 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0
## based cfpb rate refund requested resolve total tried upon additional change
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 1 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0 0
## 92 1 0 0 0 0 0 1 0 0 0 0
## 104 0 3 0 0 0 0 0 0 0 0 0
## changed day notified wrong close offered opened bill car hold reason agent
## 43 0 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 4 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0 0 0
## date fraudulent insurance property servicing already correct current dated
## 43 0 0 0 0 0 0 0 0 0
## 50 0 1 0 0 0 0 0 0 0
## 63 0 0 0 0 1 0 0 0 0
## 88 0 0 0 0 0 0 0 1 0
## 92 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 1
## following foreclosure law part place required came house lender name try
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 2 0 0 0 0 0 0 0
## 63 1 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 1 1 0 0
## 92 0 0 0 0 0 1 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0 0
## application apply recently plan situation always customer long multiple
## 43 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 2
## 88 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 1 0 0
## 104 0 0 0 0 0 0 0 0 0
## saying terms weeks attached available denied email error set well closing
## 43 0 0 0 0 0 0 0 0 0 1 0
## 50 0 0 0 0 0 2 0 0 0 0 0
## 63 0 0 1 4 0 0 1 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0 0
## 92 0 1 0 0 0 0 0 0 0 0 0
## 104 1 0 0 0 0 0 2 0 0 0 1
## sold tell escrow attorney ive resolved reported state states file must
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 3 0
## 88 0 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0 0
## though two different longer receiving sure accounts consumer documentation
## 43 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0
## 63 0 2 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0
## nothing per return cards finally line notice signed explained proof
## 43 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0 0 0 0
## 63 1 0 0 0 0 0 0 0 0 0
## 88 0 0 1 0 0 0 0 0 1 0
## 92 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0
## approved offer supervisor trying wasnt agreed copy issues prior within
## 43 0 0 0 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 1 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0
## 88 0 0 0 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0 0 0 0
## right history show transfer look purchased care left person owed manager
## 43 0 0 0 0 0 0 0 0 0 0 0
## 50 2 0 0 0 0 0 0 0 0 0 0
## 63 0 0 0 0 0 0 0 0 0 0 0
## 88 1 0 0 0 0 0 0 0 0 0 0
## 92 0 0 1 0 0 0 0 0 0 0 0
## 104 1 0 0 0 0 0 0 0 1 1 0
## speak showing
## 43 0 0
## 50 0 0
## 63 0 0
## 88 0 0
## 92 0 0
## 104 0 0Creating ML workflow
We can now create a recipe, model, and workflow for the ML model. We will use a classification decision tree model ( rpart engine) as our variable is categorical.
# Create a recipe
dtm_recipe<-training_dtm %>% recipe(Product~.)
# Create a model
dtm_model<-decision_tree() %>% set_mode("classification") %>% set_engine("rpart")
dtm_model## Decision Tree Model Specification (classification)
##
## Computational engine: rpart# Create a workflow
dtm_workflow<-workflow() %>% add_recipe(dtm_recipe) %>% add_model(dtm_model)
dtm_workflow## == Workflow ====================================================================
## Preprocessor: Recipe
## Model: decision_tree()
##
## -- Preprocessor ----------------------------------------------------------------
## 0 Recipe Steps
##
## -- Model -----------------------------------------------------------------------
## Decision Tree Model Specification (classification)
##
## Computational engine: rpartFit and assess model performance
Now that the setup is done, we shall attempt to fit the workflow.
# Fit without cross validation
dtm_fit<-fit(dtm_workflow, data=training_dtm)
dtm_workflow_fit<-dtm_fit %>%
pull_workflow_fit()
dtm_workflow_fit## parsnip model object
##
## Fit time: 35.6s
## n= 68233
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 68233 39512 Credit card or prepaid card (0.420925359 0.340275233 0.137235648 0.101563759)
## 2) mortgage< 0.5 51003 22686 Credit card or prepaid card (0.555202635 0.131443248 0.179734525 0.133619591)
## 4) card>=0.5 20773 604 Credit card or prepaid card (0.970923795 0.005680451 0.011842295 0.011553459) *
## 5) card< 0.5 30230 21309 Student loan (0.269533576 0.217863050 0.295104201 0.217499173)
## 10) student< 0.5 25944 17819 Credit card or prepaid card (0.313174530 0.253122109 0.181236509 0.252466852)
## 20) car< 0.5 22648 14640 Credit card or prepaid card (0.353585306 0.287398446 0.204874603 0.154141646)
## 40) loan< 0.5 13944 6353 Credit card or prepaid card (0.544391853 0.193703385 0.130593804 0.131310958)
## 80) loans< 0.5 13204 5629 Credit card or prepaid card (0.573689791 0.196455620 0.093683732 0.136170857)
## 160) escrow< 0.5 12746 5176 Credit card or prepaid card (0.593911815 0.168052722 0.097050055 0.140985407) *
## 161) escrow>=0.5 458 6 Mortgage (0.010917031 0.986899563 0.000000000 0.002183406) *
## 81) loans>=0.5 740 156 Student loan (0.021621622 0.144594595 0.789189189 0.044594595) *
## 41) loan>=0.5 8704 4896 Mortgage (0.047909007 0.437500000 0.323874081 0.190716912)
## 82) home>=0.5 1726 188 Mortgage (0.011587486 0.891077636 0.044032445 0.053302433) *
## 83) home< 0.5 6978 4235 Student loan (0.056893093 0.325308111 0.393092577 0.224706220)
## 166) loans< 0.5 5547 3426 Mortgage (0.070127997 0.382368848 0.281954209 0.265548945) *
## 167) loans>=0.5 1431 252 Student loan (0.005590496 0.104122991 0.823899371 0.066387142) *
## 21) car>=0.5 3296 237 Vehicle loan or lease (0.035497573 0.017597087 0.018810680 0.928094660) *
## 11) student>=0.5 4286 67 Student loan (0.005366309 0.004433038 0.984367709 0.005832944) *
## 3) mortgage>=0.5 17230 716 Mortgage (0.023447475 0.958444573 0.011433546 0.006674405) *library(vip)
dtm_workflow_fit$fit$variable.importance## mortgage card loan student home escrow
## 13022.539833 8121.493156 3328.579838 3172.341466 2619.200304 2359.487061
## car credit property loans modification foreclosure
## 2323.668037 2206.991232 1900.096642 1809.184200 1594.478808 1580.100621
## charges cards use servicing application federal
## 953.951923 948.478429 938.313367 143.065321 121.498758 26.002335
## program years closing payments situation house
## 20.499412 18.439810 13.282531 8.881964 7.754960 7.590018
## close wanted plan financial company getting
## 6.072014 5.639971 5.277753 4.934975 2.819985 2.114989
## insurance
## 1.003036dtm_workflow_fit%>%vip(num_features = 10)
Here, we can see that :
mortgage , card and loan
were the most important words for predicting Product. Following this, we can perform some predictions.
predict_Product<-predict(dtm_workflow_fit, new_data=training_dtm)
accuracy(training_dtm, truth=Product, estimate=predict_Product$.pred_class)## # A tibble: 1 x 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 accuracy multiclass 0.841# Breakdown of Product
count(training_dtm, Product)## Product n
## 1 Credit card or prepaid card 28721
## 2 Mortgage 23218
## 3 Student loan 9364
## 4 Vehicle loan or lease 6930# Breakdown of Predicted Product
count(predict_Product, .pred_class)## # A tibble: 4 x 2
## .pred_class n
## * <fct> <int>
## 1 Credit card or prepaid card 33519
## 2 Mortgage 24961
## 3 Student loan 6457
## 4 Vehicle loan or lease 3296# Show which rows were predicted correctly
predicted_and_truth<-bind_cols(training_dtm, predicted_Product=pull(predict_Product,
.pred_class)) %>% select(predicted_Product, everything())The accuracy is about 84.1%. Seems pretty alright on the first go. We also note that extra “Credit card or prepaid card” and “Mortgage” was predicted, while “Student loan” and “Vehicle loan or lease” was under predicted. Additionally, we can also see exactly which rows resulted in incorrect predictions. Lets now try to fit the model to our cross validation folds.
set.seed(122)
resample_fit<-fit_resamples(dtm_workflow, vfold_dtm)
collect_metrics(resample_fit)## # A tibble: 2 x 6
## .metric .estimator mean n std_err .config
## <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 accuracy multiclass 0.841 10 0.00128 Preprocessor1_Model1
## 2 roc_auc hand_till 0.929 10 0.000905 Preprocessor1_Model1the model accuracy stays the same.
Tuning the model
Now, lets try to improve the model by tuning a hyperparameter.
Create tuned model
dtm_model_tune<-decision_tree(cost_complexity=tune(), tree_depth=tune()) %>% set_mode("classification") %>%
set_engine("rpart")
tree_grid<-grid_regular(cost_complexity(), tree_depth(), levels=3)Create workflow
dtm_workflow_tune<-workflow() %>% add_recipe(dtm_recipe) %>% add_model(dtm_model_tune)
dtm_workflow_tune## == Workflow ====================================================================
## Preprocessor: Recipe
## Model: decision_tree()
##
## -- Preprocessor ----------------------------------------------------------------
## 0 Recipe Steps
##
## -- Model -----------------------------------------------------------------------
## Decision Tree Model Specification (classification)
##
## Main Arguments:
## cost_complexity = tune()
## tree_depth = tune()
##
## Computational engine: rpartUse multiple cores for faster run time.
##library(parallel)
doParallel::registerDoParallel(cores=1)Perform tuning
set.seed(123)
model_resample_fit<-tune_grid(dtm_workflow_tune,
resamples=vfold_dtm,
grid=tree_grid,
metrics=metric_set(accuracy, roc_auc))Assess Performance
show_best(model_resample_fit, metric="accuracy")## # A tibble: 5 x 8
## cost_complexity tree_depth .metric .estimator mean n std_err .config
## <dbl> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 0.0000000001 15 accuracy multiclass 0.881 10 6.00e-4 Preprocess~
## 2 0.00000316 15 accuracy multiclass 0.881 10 6.00e-4 Preprocess~
## 3 0.0000000001 8 accuracy multiclass 0.854 10 1.03e-3 Preprocess~
## 4 0.00000316 8 accuracy multiclass 0.854 10 1.03e-3 Preprocess~
## 5 0.0000000001 1 accuracy multiclass 0.657 10 1.50e-3 Preprocess~The highest accuracy achieved has gone up to 88.1%. Let’s stop the model building here and just
update dtm_workflow_tune with the values chosen by select_best() .
# Specify min_n value
tuned_values<-select_best(model_resample_fit,"accuracy")
# Finalize model/workflow
dtm_workflow_tuned<-dtm_workflow_tune %>%
finalize_workflow(tuned_values)Final Model Performance Evaluation
We shall fit the final model on the full training set using the last_fit() function.
# Fit final model on the full training set
overall_fit<-last_fit(dtm_workflow_tuned, split_dtm)
collect_metrics(overall_fit)## # A tibble: 2 x 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 accuracy multiclass 0.881 Preprocessor1_Model1
## 2 roc_auc hand_till 0.962 Preprocessor1_Model1The accuracy is approximately 88.1%. Let’s take a look at the predicted values for the test set.
test_predictions<-collect_predictions(overall_fit)
head(test_predictions)## # A tibble: 6 x 9
## id `.pred_Credit c~ .pred_Mortgage `.pred_Student ~ `.pred_Vehicle ~ .row
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 train~ 0.745 0.0755 0.0566 0.123 10
## 2 train~ 0.000902 0.996 0.00205 0.00148 11
## 3 train~ 0.00429 0.00238 0.991 0.00214 13
## 4 train~ 0.994 0.00150 0.00181 0.00253 18
## 5 train~ 0.0412 0.0105 0.0167 0.932 20
## 6 train~ 0.000902 0.996 0.00205 0.00148 23
## # ... with 3 more variables: .pred_class <fct>, Product <fct>, .config <chr>visualize the predictions.
ggplot(test_predictions, aes(x=Product, fill=.pred_class)) +
geom_bar(position="fill", color="blue") +
scale_fill_brewer(palette="Set1") + labs(x="Actual Outcome Values",
y="Proportion", fill="Predicted Outcome") +
theme_bw() +
theme(axis.text.x=element_text(angle=45, hjust=1, vjust=1))
It appears that our model does relatively well on all outcomes except for “Vehicle loan or lease”.
Predicting outcomes for the the provided test data
Let’s start by loading the data and doing some pre-processing.
# Load and clean up the data
df_test<-read_csv("data_complaints_test.csv") %>%
rename(Complain=`Consumer complaint narrative`) %>%
select(Complain) %>%
mutate(Complain=gsub("[XX]+", "", Complain)) %>%
mutate(Complain=str_to_lower(Complain)) %>%
mutate(Complain=gsub("[0-9]", "", Complain)) %>%
mutate(Complain=removePunctuation(Complain)) %>%
mutate(Complain=gsub("\n", "", Complain)) %>%
mutate(Complain=gsub("\t", "", Complain)) %>%
mutate(Complain=stripWhitespace(Complain))##
## -- Column specification --------------------------------------------------------
## cols(
## problem_id = col_double(),
## `Consumer complaint narrative` = col_character(),
## Company = col_character(),
## State = col_character(),
## `ZIP code` = col_character(),
## `Submitted via` = col_character()
## )# Make corpus
test_complains<-Corpus(VectorSource(df_test$Complain)) %>%
tm_map(removeWords, stopwords())
## Warning in tm_map.SimpleCorpus(., removeWords, stopwords()): transformation ## drops documents
# Make DTM
dtm_test<-DocumentTermMatrix(test_complains, list(dictionary=freqTerms))
dtm_test_mat<-dtm_test %>%
as.matrix() %>%
as.data.frame()
head(dtm_test_mat)## also contacting credit deferred due file inform late loan missed multiple
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## 6 0 0 0 0 0 0Make predictions!
final_model<-fit(dtm_workflow_tuned, dtm_train)
predict(final_model, new_data=dtm_test_mat)## # A tibble: 20 x 1
## .pred_class
## <fct>
## 1 Vehicle loan or lease
## 2 Vehicle loan or lease
## 3 Student loan
## 4 Mortgage
## 5 Student loan
## 6 Credit card or prepaid card
## 7 Credit card or prepaid card
## 8 Credit card or prepaid card
## 9 Student loan
## 10 Vehicle loan or lease
## 11 Student loan
## 12 Mortgage
## 13 Credit card or prepaid card
## 14 Credit card or prepaid card
## 15 Mortgage
## 16 Credit card or prepaid card
## 17 Mortgage
## 18 Student loan
## 19 Vehicle loan or lease
## 20 MortgagecONCLUSION
THE most frequently complained product are:
* credit acrd or prepaid card
* mortgage
* studen loan
* vehicle loan or leasethe most frequently complained product are:
* credit acrd or prepaid card
* mortgage
* studen loan
* vehicle loan or leasethe most important words for predicting Product are:
mortgage , card , loanthe accuracy of predictions was : 84,1%
with tuning the highes accuracy achieved has gone up to 88,1%
It appears that our model does relatively well on all outcomes except for “Vehicle loan or lease”.
the predicted outcomes was: