From 0187fc34fa61a373ccf4ec460a6783e47bbe36b2 Mon Sep 17 00:00:00 2001 From: "github-classroom[bot]" <66690702+github-classroom[bot]@users.noreply.github.com> Date: Thu, 16 Dec 2021 07:18:59 +0000 Subject: [PATCH 01/39] Setting up GitHub Classroom Feedback From 83d97187052963d69dac39d6fa2e3edc382359a8 Mon Sep 17 00:00:00 2001 From: YJack Date: Mon, 3 Jan 2022 22:51:41 +0800 Subject: [PATCH 02/39] Bankrutcy Prediciton ================== Context The data were collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange. --- data/data.csv | 6820 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 6820 insertions(+) create mode 100644 data/data.csv diff --git a/data/data.csv b/data/data.csv new file mode 100644 index 0000000..166432b --- /dev/null +++ b/data/data.csv @@ -0,0 +1,6820 @@ +Bankrupt?, ROA(C) before interest and depreciation before interest, ROA(A) before interest and % after tax, ROA(B) before interest and depreciation after tax, Operating Gross Margin, Realized Sales Gross Margin, Operating Profit Rate, Pre-tax net Interest Rate, After-tax net Interest Rate, Non-industry income and expenditure/revenue, Continuous interest rate (after tax), Operating Expense Rate, Research and development expense rate, Cash flow rate, Interest-bearing debt interest rate, Tax rate (A), Net Value Per Share (B), Net Value Per Share (A), Net Value Per Share (C), Persistent EPS in the Last Four Seasons, Cash Flow Per Share, Revenue Per Share (Yuan ¥), Operating Profit Per Share (Yuan ¥), Per Share Net profit before tax (Yuan ¥), Realized Sales Gross Profit Growth Rate, Operating Profit Growth Rate, After-tax Net Profit Growth Rate, Regular Net Profit Growth Rate, Continuous Net Profit Growth Rate, Total Asset Growth Rate, Net Value Growth Rate, Total Asset Return Growth Rate Ratio, Cash Reinvestment %, Current Ratio, Quick Ratio, Interest Expense Ratio, Total debt/Total net worth, Debt ratio %, Net worth/Assets, Long-term fund suitability ratio (A), Borrowing dependency, Contingent liabilities/Net worth, Operating profit/Paid-in capital, Net profit before tax/Paid-in capital, Inventory and accounts receivable/Net value, Total Asset Turnover, Accounts Receivable Turnover, Average Collection Days, Inventory Turnover Rate (times), Fixed Assets Turnover Frequency, Net Worth Turnover Rate (times), Revenue per person, Operating profit per person, Allocation rate per person, Working Capital to Total Assets, Quick Assets/Total Assets, Current Assets/Total Assets, Cash/Total Assets, Quick Assets/Current Liability, Cash/Current Liability, Current Liability to Assets, Operating Funds to Liability, Inventory/Working Capital, Inventory/Current Liability, Current Liabilities/Liability, Working Capital/Equity, Current Liabilities/Equity, Long-term Liability to Current Assets, Retained Earnings to Total Assets, Total income/Total expense, Total expense/Assets, Current Asset Turnover Rate, Quick Asset Turnover Rate, Working capitcal Turnover Rate, Cash Turnover Rate, Cash Flow to Sales, Fixed Assets to Assets, Current Liability to Liability, Current Liability to Equity, Equity to Long-term Liability, Cash Flow to Total Assets, Cash Flow to Liability, CFO to Assets, Cash Flow to Equity, Current Liability to Current Assets, Liability-Assets Flag, Net Income to Total Assets, Total assets to GNP price, No-credit Interval, Gross Profit to Sales, Net Income to Stockholder's Equity, Liability to Equity, Degree of Financial Leverage (DFL), Interest Coverage Ratio (Interest expense to EBIT), Net Income Flag, Equity to Liability 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From eb8809628c02babcd19a57f96e32c8cb0e59e5bb Mon Sep 17 00:00:00 2001 From: YJack Date: Tue, 4 Jan 2022 21:51:55 +0800 Subject: [PATCH 03/39] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E7=B5=84=E5=93=A1?= =?UTF-8?q?=E5=90=8D=E7=A8=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index f6732f4..56b9dcc 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,11 @@ -# [GroupID] Title of your final project +# [GroupID] Bankrutcy Prediciton ### Groups -* name, student ID1 -* name, student ID2 -* name, student ID3 -* ... - +* 鄭宇傑, 108703014 +* 賴冠瑜, 108703019 +* 張瀚文, 108304003 +* 江宗樺, 108703029 +* 田詠恩, 108703030 ### Goal A breif introduction about your project, i.e., what is your goal? From 8122c79b6a401994f57d0b329a2c4fd49d48807f Mon Sep 17 00:00:00 2001 From: YJack Date: Tue, 4 Jan 2022 21:53:02 +0800 Subject: [PATCH 04/39] =?UTF-8?q?=E5=88=AA=E9=99=A4=20empty?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- data/empty | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 data/empty diff --git a/data/empty b/data/empty deleted file mode 100644 index e69de29..0000000 From 7d2a0ed2102f32b399057273ede9b98ec4255d5f Mon Sep 17 00:00:00 2001 From: YJack Date: Thu, 6 Jan 2022 01:05:33 +0800 Subject: [PATCH 05/39] Correlation variance explained PCA --- code/shinyUI/app.R | 54 + code/shinyUI/data/data.csv | 6820 +++++++++++++++++ .../shinyapps.io/yjack0000/shinyui.dcf | 11 + 3 files changed, 6885 insertions(+) create mode 100644 code/shinyUI/app.R create mode 100644 code/shinyUI/data/data.csv create mode 100644 code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf diff --git a/code/shinyUI/app.R b/code/shinyUI/app.R new file mode 100644 index 0000000..497a4fd --- /dev/null +++ b/code/shinyUI/app.R @@ -0,0 +1,54 @@ +library(shiny) +library(ggplot2) +library(GGally) +library(corrplot) +#library(ggpubr) + +data = read.csv(file = 'data/data.csv' , header = T) + +#remove the column with only one unique numbers +which(apply(data, 2, var)==0) +data<-subset(data,select=-Net.Income.Flag) + +ui <- fluidPage( + plotOutput("plot"), + plotOutput("plot2"), + plotOutput("plot3") +) + +server <- function(input, output) { + output$plot <- renderPlot({ + cor.matrix <- as.matrix(cor(data)) + correlation <- sort(cor.matrix[2:95],decreasing = T) + + data1 <- data[,-95] + correlationMatrix <- cor(data1[,1:94]) + + hist1 = ggplot(data, aes(Bankrupt.,)) + geom_bar(fill = c("Green","Red")) + theme_bw() + ggtitle("Survive(0) VS Bankruptcy(1)") + theme(plot.title = element_text(hjust = 0.5)) + xlab("Survive(0) Bankruptcy(1)") + corr1 = ggcorr(data[,1:20],size=2,hjust=1)+geom_point()+scale_alpha_manual()+guides() + corr2 = corrplot(corr=correlationMatrix, tl.cex=0.1) + }) + + output$plot2 <- renderPlot({ + #### Pca visualization + + data.pca.scale <- prcomp(data[,2:95], center=T ,scale = TRUE) + #biplot(data.pca.scale, cex = 0.5) + + # Extract the variance explained + tot.var <- sum(data.pca.scale$sdev^2) + var.explained <- data.frame(pc = seq(1:94), var.explained = data.pca.scale$sdev^2/tot.var ) + ggplot(var.explained, aes(pc, var.explained)) + geom_bar(stat = "identity") + ggtitle("Bankruptcy") + }) + + output$plot3 <- renderPlot({ + #### Pca visualization + + data.pca.scale <- prcomp(data[,2:95], center=T ,scale = TRUE) + data.df <- data.frame(PC1 = data.pca.scale$x[,1], PC2 = data.pca.scale$x[,2], PC3 = data.pca.scale$x[,3], labels = as.factor(data$Bankrupt.)) + ggplot(data.df, aes(PC1, PC2, col = labels)) + geom_point() + }) +} + + +shinyApp(ui = ui, server = server) \ No newline at end of file diff --git a/code/shinyUI/data/data.csv b/code/shinyUI/data/data.csv new file mode 100644 index 0000000..166432b --- /dev/null +++ b/code/shinyUI/data/data.csv @@ -0,0 +1,6820 @@ +Bankrupt?, ROA(C) before interest and depreciation before interest, ROA(A) before interest and % after tax, ROA(B) before interest and depreciation after tax, Operating Gross Margin, Realized Sales Gross Margin, Operating Profit Rate, Pre-tax net Interest Rate, After-tax net Interest Rate, Non-industry income and expenditure/revenue, Continuous interest rate (after tax), Operating Expense Rate, Research and development expense rate, Cash flow rate, Interest-bearing debt interest rate, Tax rate (A), Net Value Per Share (B), Net Value Per Share (A), Net Value Per Share (C), Persistent EPS in the Last Four Seasons, Cash Flow Per Share, Revenue Per Share (Yuan ¥), Operating Profit Per Share (Yuan ¥), Per Share Net profit before tax (Yuan ¥), Realized Sales Gross Profit Growth Rate, Operating Profit Growth Rate, After-tax Net Profit Growth Rate, Regular Net Profit Growth Rate, Continuous Net Profit Growth Rate, Total Asset Growth Rate, Net Value Growth Rate, Total Asset Return Growth Rate Ratio, Cash Reinvestment %, Current Ratio, Quick Ratio, Interest Expense Ratio, Total debt/Total net worth, Debt ratio %, Net worth/Assets, Long-term fund suitability ratio (A), Borrowing dependency, Contingent liabilities/Net worth, Operating profit/Paid-in capital, Net profit before tax/Paid-in capital, Inventory and accounts receivable/Net value, Total Asset Turnover, Accounts Receivable Turnover, Average Collection Days, Inventory Turnover Rate (times), Fixed Assets Turnover Frequency, Net Worth Turnover Rate (times), Revenue per person, Operating profit per person, Allocation rate per person, Working Capital to Total Assets, Quick Assets/Total Assets, Current Assets/Total Assets, Cash/Total Assets, Quick Assets/Current Liability, Cash/Current Liability, Current Liability to Assets, Operating Funds to Liability, Inventory/Working Capital, Inventory/Current Liability, Current Liabilities/Liability, Working Capital/Equity, Current Liabilities/Equity, Long-term Liability to Current Assets, Retained Earnings to Total Assets, Total income/Total expense, Total expense/Assets, Current Asset Turnover Rate, Quick Asset Turnover Rate, Working capitcal Turnover Rate, Cash Turnover Rate, Cash Flow to Sales, Fixed Assets to Assets, Current Liability to Liability, Current Liability to Equity, Equity to Long-term Liability, Cash Flow to Total Assets, Cash Flow to Liability, CFO to Assets, Cash Flow to Equity, Current Liability to Current Assets, Liability-Assets Flag, Net Income to Total Assets, Total assets to GNP price, No-credit Interval, Gross Profit to Sales, Net Income to Stockholder's Equity, Liability to Equity, Degree of Financial Leverage (DFL), Interest Coverage Ratio (Interest expense to EBIT), Net Income Flag, Equity to Liability 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diff --git a/code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf b/code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf new file mode 100644 index 0000000..13a5b61 --- /dev/null +++ b/code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf @@ -0,0 +1,11 @@ +name: shinyui +title: +username: +account: yjack0000 +server: shinyapps.io +hostUrl: https://api.shinyapps.io/v1 +appId: 5397561 +bundleId: 5437016 +url: https://yjack0000.shinyapps.io/shinyui/ +when: 1641397789.57417 +lastSyncTime: 1641397789.57418 From d6ae5676d44553b10368489701b9c2acc91249bf Mon Sep 17 00:00:00 2001 From: Mark Lai Date: Thu, 6 Jan 2022 14:53:22 +0800 Subject: [PATCH 06/39] =?UTF-8?q?=E5=8A=A0=20gitignore=20=E5=92=8C=20decis?= =?UTF-8?q?ion=5Ftree?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 1 + code/models/decisoin_tree.R | 68 +++++++++++++++++++++++++++++++++++++ 2 files changed, 69 insertions(+) create mode 100644 .gitignore create mode 100644 code/models/decisoin_tree.R diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e43b0f9 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.DS_Store diff --git a/code/models/decisoin_tree.R b/code/models/decisoin_tree.R new file mode 100644 index 0000000..350a407 --- /dev/null +++ b/code/models/decisoin_tree.R @@ -0,0 +1,68 @@ +#install.packages("rpart" ,repos='http://cran.us.r-project.org', force=FALSE) +library('rpart') +library('dplyr') + +print("reading file")####### +data <- read.csv("data.csv") + +data <- data[sample(1:nrow(data)), ] + +spec = c(train = .6, test = .2, validate = .2) + +g = sample(cut( + seq(nrow(data)), + nrow(data)*cumsum(c(0,spec)), + labels = names(spec) +)) + +res = split(data, g) +nrow(res[["train"]]) +nrow(res[["validate"]]) +nrow(res[["test"]]) +names(res[["test"]]) + +model<- rpart( Bankrupt. ~ ., + data=res[["train"]], + control=rpart.control(maxdepth=20), + method="class") + + +trainframe <- data.frame(truth=res[["train"]]$Bankrupt., + pred=predict(model, type="class")) + +TP.train<-nrow(filter(trainframe, truth == pred , truth == 1,)) +TN.train<-nrow(filter(trainframe, truth == pred , truth == 0,)) +FP.train<-nrow(filter(trainframe, truth != pred , truth == 0,)) +FN.train<-nrow(filter(trainframe, truth != pred , truth == 1,)) +accuracy.train<-(TP.train+TN.train)/nrow(trainframe) +fallback.train<-(TP.train)/(TP.train+FN.train) +precision.train<-(TP.train)/(TP.train+FP.train) +NegativePrecision.train<-(TN.train)/(TN.train+FN.train) +print(accuracy.train) +print(fallback.train) +print(precision.train) +print(NegativePrecision.train) + + + + +#write.csv(trainframe, file = "./trainPredict.csv", quote = F, row.names = F) +validframe <- data.frame(truth=res[["validate"]]$Bankrupt., + pred=predict(model, newdata=res[["validate"]], type="class")) + +validframe + +TP.valid<-nrow(filter(validframe, truth == pred , truth == 1,)) +TN.valid<-nrow(filter(validframe, truth == pred , truth == 0,)) +FP.valid<-nrow(filter(validframe, truth != pred , truth == 0,)) +FN.valid<-nrow(filter(validframe, truth != pred , truth == 1,)) +accuracy.valid<-(TP.valid+TN.valid)/nrow(validframe) +fallback.valid<-(TP.valid)/(TP.valid+FN.valid) +precision.valid<-(TP.valid)/(TP.valid+FP.valid) +NegativePrecision.valid<-(TN.valid)/(TN.valid+FN.valid) +print(accuracy.valid) +print(fallback.valid) +print(precision.valid) +print(NegativePrecision.valid) + + From 3246e71745145fff2af41161fc63df682de69452 Mon Sep 17 00:00:00 2001 From: YJack Date: Thu, 6 Jan 2022 15:28:34 +0800 Subject: [PATCH 07/39] =?UTF-8?q?=E5=8A=A0=E5=85=A5=20panel?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/shinyUI/app.R | 64 +++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 60 insertions(+), 4 deletions(-) diff --git a/code/shinyUI/app.R b/code/shinyUI/app.R index 497a4fd..32a556b 100644 --- a/code/shinyUI/app.R +++ b/code/shinyUI/app.R @@ -11,9 +11,54 @@ which(apply(data, 2, var)==0) data<-subset(data,select=-Net.Income.Flag) ui <- fluidPage( - plotOutput("plot"), - plotOutput("plot2"), - plotOutput("plot3") + navbarPage("NCAA Swimming", + tabPanel("Correlation Analysis", fluid = TRUE, icon = icon("globe-americas"), + sidebarLayout( + sidebarPanel( + selectInput(inputId = "EventFinder", + label = "Select Analysis", + choices = c("Correlation Analysis", "PC Analysis"), + selected = "50 Free", + width = "220px" + ), + ), + mainPanel( + plotOutput("plot"), + ) + ) + ), + tabPanel("PC Analysis", fluid = TRUE, icon = icon("globe-americas"), + sidebarLayout( + sidebarPanel( + selectInput(inputId = "EventFinder", + label = "Select Analysis", + choices = c("Correlation Analysis", "PC Analysis"), + selected = "50 Free", + width = "220px" + ), + ), + mainPanel( + plotOutput("plot2"), + plotOutput("plot3"), + ) + ) + ), + tabPanel("Survive&Bankruptcy", fluid = TRUE, icon = icon("globe-americas"), + sidebarLayout( + sidebarPanel( + selectInput(inputId = "EventFinder", + label = "Select Analysis", + choices = c("Correlation Analysis", "PC Analysis"), + selected = "50 Free", + width = "220px" + ), + ), + mainPanel( + plotOutput("survive_bankrupt") + ) + ) + ) + ) ) server <- function(input, output) { @@ -27,6 +72,7 @@ server <- function(input, output) { hist1 = ggplot(data, aes(Bankrupt.,)) + geom_bar(fill = c("Green","Red")) + theme_bw() + ggtitle("Survive(0) VS Bankruptcy(1)") + theme(plot.title = element_text(hjust = 0.5)) + xlab("Survive(0) Bankruptcy(1)") corr1 = ggcorr(data[,1:20],size=2,hjust=1)+geom_point()+scale_alpha_manual()+guides() corr2 = corrplot(corr=correlationMatrix, tl.cex=0.1) + # This plot shows the correlation of each features.The deeper the color the more correlation for the features. }) output$plot2 <- renderPlot({ @@ -39,15 +85,25 @@ server <- function(input, output) { tot.var <- sum(data.pca.scale$sdev^2) var.explained <- data.frame(pc = seq(1:94), var.explained = data.pca.scale$sdev^2/tot.var ) ggplot(var.explained, aes(pc, var.explained)) + geom_bar(stat = "identity") + ggtitle("Bankruptcy") + # shows how many variances explained by each PCs. From the first plot we can see around 40 PCs explained more than 90% of variances. }) output$plot3 <- renderPlot({ #### Pca visualization - data.pca.scale <- prcomp(data[,2:95], center=T ,scale = TRUE) data.df <- data.frame(PC1 = data.pca.scale$x[,1], PC2 = data.pca.scale$x[,2], PC3 = data.pca.scale$x[,3], labels = as.factor(data$Bankrupt.)) ggplot(data.df, aes(PC1, PC2, col = labels)) + geom_point() }) + + output$survive_bankrupt <- renderPlot({ + ggplot(data, aes(Bankrupt.,)) + geom_bar(fill=c("Green","Red")) + theme_bw()+ggtitle("Survive(0) VS Bankruptcy(1)")+theme(plot.title = element_text(hjust = 0.5))+xlab("Survive(0) Bankruptcy(1)") + cor.matrix<-as.matrix(cor(data)) + correlation<-sort(cor.matrix[2:95],decreasing = T) + #### number of classes,220 bankruptcy vs 6599 survive companies + + print(c((paste0("Bankrupt:",nrow(data[data$Bankrupt.=="1",]))),(paste0("Surivive:",nrow(data[data$Bankrupt.=="0",]))))) + print("number of classes,220 bankruptcy vs 6599 survive companies") + }) } From cb65deec5ab3035c69fdbd60ad35bd6be6f65272 Mon Sep 17 00:00:00 2001 From: bryant-nn Date: Thu, 6 Jan 2022 18:01:06 +0800 Subject: [PATCH 08/39] upload pca with decision tree --- code/models/pcaRpart.R | 67 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 code/models/pcaRpart.R diff --git a/code/models/pcaRpart.R b/code/models/pcaRpart.R new file mode 100644 index 0000000..4d5ce05 --- /dev/null +++ b/code/models/pcaRpart.R @@ -0,0 +1,67 @@ +library('rpart') +library('dplyr') + + + +data <- read.csv("data.csv") + +data <- data[sample(1:nrow(data)), ] + +spec = c(train = .6, test = .2, validate = .2) + +g = sample(cut( + seq(nrow(data)), + nrow(data)*cumsum(c(0,spec)), + labels = names(spec) +)) + +res = split(data, g) +nrow(res[["train"]]) +nrow(res[["validate"]]) +nrow(res[["test"]]) +names(res[["test"]]) + + +#train pca +in_d <- res[["train"]] +in_d = in_d[,!colnames(in_d) %in% c('Net.Income.Flag','Bankrupt.')] +pca <- prcomp(in_d, center=TRUE, scale=TRUE) + +#--- watch pc +std_dev <- pca$sdev +pr_var <- std_dev^2 +prop_varex <- pr_var/sum(pr_var) +prop_varex +plot(prop_varex, type = 'lines') + +#--- built train data with Bankrupt and top 40 component +train.data <- data.frame(Bankrupt. = res[["train"]]$Bankrupt., pca$x) +train.data <- train.data[,1:41] +model <- rpart(Bankrupt. ~ .,data = train.data, method = "anova") + +#--- built validation data with Bankrupt and top 40 component +val.data <- predict(pca, newdata = res[["validate"]]) +val.data <- as.data.frame(val.data) +val.data <- val.data[,1:40] + +val <- data.frame(truth = res[["validate"]]$Bankrupt., + prediction = predict(model, val.data)) +val <- mutate(val, result = ifelse(prediction > 0.5, 1, 0)) + +# confusion matrix of validation +cm <- table(val[,c(1,3)]) + +TP <- cm[2,2] +TN <- cm[1,1] +FP <- cm[1,2] +FN <- cm[2,1] + +accuracy <- (TP+TN)/(TP+FP+FN+TN) +fallback <- TP/(TP+FN) +precision <- TP/(TP+FP) +NegativePrecision <- TN/(TN+FN) + +print( accuracy ) +print( fallback ) +print( precision ) +print( NegativePrecision ) From aa822a44c65d7920fa1eb4e9b8f0756f1bca8e4b Mon Sep 17 00:00:00 2001 From: YJack Date: Wed, 12 Jan 2022 22:29:16 +0800 Subject: [PATCH 09/39] README v1.0 --- README.md | 25 +++++---- data/DataContext.md | 110 ++++++++++++++++++++++++++++++++++++++++ docs/GoogleSlideLink.md | 1 + docs/empty | 0 4 files changed, 123 insertions(+), 13 deletions(-) create mode 100644 data/DataContext.md create mode 100644 docs/GoogleSlideLink.md delete mode 100644 docs/empty diff --git a/README.md b/README.md index 56b9dcc..aa4af2a 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,15 @@ -# [GroupID] Bankrutcy Prediciton +# [Group4] Bankrutcy Prediciton -### Groups +### 組員 * 鄭宇傑, 108703014 * 賴冠瑜, 108703019 * 張瀚文, 108304003 * 江宗樺, 108703029 * 田詠恩, 108703030 -### Goal -A breif introduction about your project, i.e., what is your goal? - +### 目標 +95% 以上的資料中的公司都沒有破產(Bankruptcy == 0) +所以全部猜 1 就可以有超級高的 Accuracy +我們將目標設定成要盡可能增加 recall 。嘗試預測出更多可能會倒的公司去對他們做關切 或提早做應對措施，並去檢視可能面臨的問題，是這次專題的主要目標。 ### Demo You should provide an example commend to reproduce your result ```R @@ -16,13 +17,10 @@ Rscript code/your_script.R --input data/training --output results/performance.ts ``` * any on-line visualization -## Folder organization and its related information +## 檔案架構及其相關資訊 ### docs -* Your presentation, 1101_datascience_FP_.ppt/pptx/pdf, by **Jan. 13** -* Any related document for the final project - * papers - * software user guide +* [Google Slide for Presentation](https://docs.google.com/presentation/d/1TWPNksUenzi-DsquO6Yv7WBCVPvZE-HgyjMmvAcAH3U/edit#slide=id.g10d591fe8d9_0_169) ### data @@ -37,6 +35,7 @@ Rscript code/your_script.R --input data/training --output results/performance.ts * Which method do you use? * What is a null model for comparison? * How do your perform evaluation? ie. cross-validation, or addtional indepedent data set +使用 train, validation, test split，並且使用 SMOTE 製作額外的 traning data ### results @@ -46,6 +45,6 @@ Rscript code/your_script.R --input data/training --output results/performance.ts * What is the challenge part of your project? ## References -* Code/implementation which you include/reference (__You should indicate in your presentation if you use code for others. Otherwise, cheating will result in 0 score for final project.__) -* Packages you use -* Related publications +https://www.kaggle.com/jerryfang5/bankrutcy-prediciton-by-r/notebook +https://www.kaggle.com/seongwonr/bankruptcy-prediction-with-smote +https://colab.research.google.com/drive/12wXAyrbX8Ji5J6CNAEIQwtDOaxy8BCIO?usp=sharing diff --git a/data/DataContext.md b/data/DataContext.md new file mode 100644 index 0000000..c2faa21 --- /dev/null +++ b/data/DataContext.md @@ -0,0 +1,110 @@ +## Context +The data were collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange. + +## Attribute Information +Version 2: Updated column names and description to make the data easier to understand (Y = Output feature, X = Input features) + +## Y - Bankrupt?: Class label +X1 - ROA(C) before interest and depreciation before interest: Return On Total Assets(C) +X2 - ROA(A) before interest and % after tax: Return On Total Assets(A) +X3 - ROA(B) before interest and depreciation after tax: Return On Total Assets(B) +X4 - Operating Gross Margin: Gross Profit/Net Sales +X5 - Realized Sales Gross Margin: Realized Gross Profit/Net Sales +X6 - Operating Profit Rate: Operating Income/Net Sales +X7 - Pre-tax net Interest Rate: Pre-Tax Income/Net Sales +X8 - After-tax net Interest Rate: Net Income/Net Sales +X9 - Non-industry income and expenditure/revenue: Net Non-operating Income Ratio +X10 - Continuous interest rate (after tax): Net Income-Exclude Disposal Gain or Loss/Net Sales +X11 - Operating Expense Rate: Operating Expenses/Net Sales +X12 - Research and development expense rate: (Research and Development Expenses)/Net Sales +X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities +X14 - Interest-bearing debt interest rate: Interest-bearing Debt/Equity +X15 - Tax rate (A): Effective Tax Rate +X16 - Net Value Per Share (B): Book Value Per Share(B) +X17 - Net Value Per Share (A): Book Value Per Share(A) +X18 - Net Value Per Share (C): Book Value Per Share(C) +X19 - Persistent EPS in the Last Four Seasons: EPS-Net Income +X20 - Cash Flow Per Share +X21 - Revenue Per Share (Yuan ¥): Sales Per Share +X22 - Operating Profit Per Share (Yuan ¥): Operating Income Per Share +X23 - Per Share Net profit before tax (Yuan ¥): Pretax Income Per Share +X24 - Realized Sales Gross Profit Growth Rate +X25 - Operating Profit Growth Rate: Operating Income Growth +X26 - After-tax Net Profit Growth Rate: Net Income Growth +X27 - Regular Net Profit Growth Rate: Continuing Operating Income after Tax Growth +X28 - Continuous Net Profit Growth Rate: Net Income-Excluding Disposal Gain or Loss Growth +X29 - Total Asset Growth Rate: Total Asset Growth +X30 - Net Value Growth Rate: Total Equity Growth +X31 - Total Asset Return Growth Rate Ratio: Return on Total Asset Growth +X32 - Cash Reinvestment %: Cash Reinvestment Ratio +X33 - Current Ratio +X34 - Quick Ratio: Acid Test +X35 - Interest Expense Ratio: Interest Expenses/Total Revenue +X36 - Total debt/Total net worth: Total Liability/Equity Ratio +X37 - Debt ratio %: Liability/Total Assets +X38 - Net worth/Assets: Equity/Total Assets +X39 - Long-term fund suitability ratio (A): (Long-term Liability+Equity)/Fixed Assets +X40 - Borrowing dependency: Cost of Interest-bearing Debt +X41 - Contingent liabilities/Net worth: Contingent Liability/Equity +X42 - Operating profit/Paid-in capital: Operating Income/Capital +X43 - Net profit before tax/Paid-in capital: Pretax Income/Capital +X44 - Inventory and accounts receivable/Net value: (Inventory+Accounts Receivables)/Equity +X45 - Total Asset Turnover +X46 - Accounts Receivable Turnover +X47 - Average Collection Days: Days Receivable Outstanding +X48 - Inventory Turnover Rate (times) +X49 - Fixed Assets Turnover Frequency +X50 - Net Worth Turnover Rate (times): Equity Turnover +X51 - Revenue per person: Sales Per Employee +X52 - Operating profit per person: Operation Income Per Employee +X53 - Allocation rate per person: Fixed Assets Per Employee +X54 - Working Capital to Total Assets +X55 - Quick Assets/Total Assets +X56 - Current Assets/Total Assets +X57 - Cash/Total Assets +X58 - Quick Assets/Current Liability +X59 - Cash/Current Liability +X60 - Current Liability to Assets +X61 - Operating Funds to Liability +X62 - Inventory/Working Capital +X63 - Inventory/Current Liability +X64 - Current Liabilities/Liability +X65 - Working Capital/Equity +X66 - Current Liabilities/Equity +X67 - Long-term Liability to Current Assets +X68 - Retained Earnings to Total Assets +X69 - Total income/Total expense +X70 - Total expense/Assets +X71 - Current Asset Turnover Rate: Current Assets to Sales +X72 - Quick Asset Turnover Rate: Quick Assets to Sales +X73 - Working capitcal Turnover Rate: Working Capital to Sales +X74 - Cash Turnover Rate: Cash to Sales +X75 - Cash Flow to Sales +X76 - Fixed Assets to Assets +X77 - Current Liability to Liability +X78 - Current Liability to Equity +X79 - Equity to Long-term Liability +X80 - Cash Flow to Total Assets +X81 - Cash Flow to Liability +X82 - CFO to Assets +X83 - Cash Flow to Equity +X84 - Current Liability to Current Assets +X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise +X86 - Net Income to Total Assets +X87 - Total assets to GNP price +X88 - No-credit Interval +X89 - Gross Profit to Sales +X90 - Net Income to Stockholder's Equity +X91 - Liability to Equity +X92 - Degree of Financial Leverage (DFL) +X93 - Interest Coverage Ratio (Interest expense to EBIT) +X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise +X95 - Equity to Liability + +## Source +Deron Liang and Chih-Fong Tsai, deronliang '@' gmail.com; cftsai '@' mgt.ncu.edu.tw, National Central University, Taiwan +The data was obtained from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Taiwanese+Bankruptcy+Prediction + +## Relevant Papers +Liang, D., Lu, C.-C., Tsai, C.-F., and Shih, G.-A. (2016) Financial Ratios and Corporate Governance Indicators in Bankruptcy Prediction: A Comprehensive Study. European Journal of Operational Research, vol. 252, no. 2, pp. 561-572. +https://www.sciencedirect.com/science/article/pii/S0377221716000412 \ No newline at end of file diff --git a/docs/GoogleSlideLink.md b/docs/GoogleSlideLink.md new file mode 100644 index 0000000..277a319 --- /dev/null +++ b/docs/GoogleSlideLink.md @@ -0,0 +1 @@ +* [Google Slide for Presentation](https://docs.google.com/presentation/d/1TWPNksUenzi-DsquO6Yv7WBCVPvZE-HgyjMmvAcAH3U/edit#slide=id.g10d591fe8d9_0_169) diff --git a/docs/empty b/docs/empty deleted file mode 100644 index e69de29..0000000 From cd44583c6eee0ea70ac8df3437aa554645fe2872 Mon Sep 17 00:00:00 2001 From: YJack Date: Wed, 12 Jan 2022 22:29:25 +0800 Subject: [PATCH 10/39] =?UTF-8?q?=E6=B2=92=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- code/empty | 0 code/models/pcaRpart.R | 3 - code/shinyUI/app.R | 106 ++++++++++++------ .../shinyapps.io/yjack0000/shinyui.dcf | 6 +- 4 files changed, 77 insertions(+), 38 deletions(-) delete mode 100644 code/empty diff --git a/code/empty b/code/empty deleted file mode 100644 index e69de29..0000000 diff --git a/code/models/pcaRpart.R b/code/models/pcaRpart.R index 4d5ce05..550f7d8 100644 --- a/code/models/pcaRpart.R +++ b/code/models/pcaRpart.R @@ -1,8 +1,6 @@ library('rpart') library('dplyr') - - data <- read.csv("data.csv") data <- data[sample(1:nrow(data)), ] @@ -21,7 +19,6 @@ nrow(res[["validate"]]) nrow(res[["test"]]) names(res[["test"]]) - #train pca in_d <- res[["train"]] in_d = in_d[,!colnames(in_d) %in% c('Net.Income.Flag','Bankrupt.')] diff --git a/code/shinyUI/app.R b/code/shinyUI/app.R index 32a556b..2268f60 100644 --- a/code/shinyUI/app.R +++ b/code/shinyUI/app.R @@ -11,52 +11,80 @@ which(apply(data, 2, var)==0) data<-subset(data,select=-Net.Income.Flag) ui <- fluidPage( - navbarPage("NCAA Swimming", + navbarPage("Bankrutcy Prediciton", + tabPanel("All data", fluid = TRUE, icon = icon("globe-americas"), + #dataTableOutput(outputId="dataTable") + div(dataTableOutput("dataTable"), style = "font-size: 75%; width: 100%") + ), tabPanel("Correlation Analysis", fluid = TRUE, icon = icon("globe-americas"), sidebarLayout( sidebarPanel( - selectInput(inputId = "EventFinder", - label = "Select Analysis", - choices = c("Correlation Analysis", "PC Analysis"), - selected = "50 Free", - width = "220px" - ), + numericInput( + inputId = "num1", + label = "from", + value = 0, + min = 0, + max = 96, + step = NA, + width = NULL + ), + numericInput( + inputId = "num2", + label = "to", + value = 94, + min = 1, + max = 94, + step = NA, + width = NULL + ) ), mainPanel( - plotOutput("plot"), + plotOutput("plot", width = "100%"), ) ) ), + tabPanel("Variable explain", fluid = TRUE, icon = icon("globe-americas"), + plotOutput("plot2") + ), tabPanel("PC Analysis", fluid = TRUE, icon = icon("globe-americas"), sidebarLayout( sidebarPanel( - selectInput(inputId = "EventFinder", - label = "Select Analysis", - choices = c("Correlation Analysis", "PC Analysis"), - selected = "50 Free", + selectInput(inputId = "PC_x", + label = "Select PC", + choices = c("PC1", "PC2", "PC3"), + selected = "PC1", + width = "220px" + ), + selectInput(inputId = "PC_y", + label = "Select PC", + choices = c("PC1", "PC2", "PC3"), + selected = "PC2", width = "220px" ), ), mainPanel( - plotOutput("plot2"), plotOutput("plot3"), ) ) ), - tabPanel("Survive&Bankruptcy", fluid = TRUE, icon = icon("globe-americas"), + tabPanel("Scatter plot", fluid = TRUE, icon = icon("globe-americas"), sidebarLayout( sidebarPanel( - selectInput(inputId = "EventFinder", - label = "Select Analysis", - choices = c("Correlation Analysis", "PC Analysis"), - selected = "50 Free", - width = "220px" - ), + selectInput( + inputId = "scat_x", + label = "x", + choices = c(colnames(data)), + selected = colnames(data), + ), ), mainPanel( - plotOutput("survive_bankrupt") + plotOutput("scat_plot"), + #plotOutput("box_plot", width = "100%"), ) ) + ), + tabPanel("Survive&Bankruptcy", fluid = TRUE, icon = icon("globe-americas"), + plotOutput("survive_bankrupt") ) ) ) @@ -67,13 +95,17 @@ server <- function(input, output) { correlation <- sort(cor.matrix[2:95],decreasing = T) data1 <- data[,-95] - correlationMatrix <- cor(data1[,1:94]) + correlationMatrix <- cor(data1[,input$num1:input$num2]) hist1 = ggplot(data, aes(Bankrupt.,)) + geom_bar(fill = c("Green","Red")) + theme_bw() + ggtitle("Survive(0) VS Bankruptcy(1)") + theme(plot.title = element_text(hjust = 0.5)) + xlab("Survive(0) Bankruptcy(1)") corr1 = ggcorr(data[,1:20],size=2,hjust=1)+geom_point()+scale_alpha_manual()+guides() corr2 = corrplot(corr=correlationMatrix, tl.cex=0.1) # This plot shows the correlation of each features.The deeper the color the more correlation for the features. - }) + + #hist1 + #corr1 + corr2 + }, height = 700) output$plot2 <- renderPlot({ #### Pca visualization @@ -92,19 +124,29 @@ server <- function(input, output) { #### Pca visualization data.pca.scale <- prcomp(data[,2:95], center=T ,scale = TRUE) data.df <- data.frame(PC1 = data.pca.scale$x[,1], PC2 = data.pca.scale$x[,2], PC3 = data.pca.scale$x[,3], labels = as.factor(data$Bankrupt.)) - ggplot(data.df, aes(PC1, PC2, col = labels)) + geom_point() - }) + if((input$PC_x == "PC1" && input$PC_y == "PC2") || (input$PC_x == "PC2" && input$PC_y == "PC1")) + ggplot(data.df, aes(PC1, PC2, col = labels)) + geom_point() + else if((input$PC_x == "PC2" && input$PC_y == "PC3") || (input$PC_x == "PC3" && input$PC_y == "PC2")) + ggplot(data.df, aes(PC2, PC3, col = labels)) + geom_point() + else if((input$PC_x == "PC1" && input$PC_y == "PC3") || (input$PC_x == "PC3" && input$PC_y == "PC1")) + ggplot(data.df, aes(PC1, PC3, col = labels)) + geom_point() + }, height = 700) output$survive_bankrupt <- renderPlot({ - ggplot(data, aes(Bankrupt.,)) + geom_bar(fill=c("Green","Red")) + theme_bw()+ggtitle("Survive(0) VS Bankruptcy(1)")+theme(plot.title = element_text(hjust = 0.5))+xlab("Survive(0) Bankruptcy(1)") - cor.matrix<-as.matrix(cor(data)) - correlation<-sort(cor.matrix[2:95],decreasing = T) #### number of classes,220 bankruptcy vs 6599 survive companies + hist1 = ggplot(data, aes(Bankrupt.,)) + geom_bar(fill = c("Green","Red")) + theme_bw() + ggtitle("Survive(0) VS Bankruptcy(1)") + theme(plot.title = element_text(hjust = 0.5)) + xlab("Survive(0) Bankruptcy(1)") + hist1 + }, height = 700) - print(c((paste0("Bankrupt:",nrow(data[data$Bankrupt.=="1",]))),(paste0("Surivive:",nrow(data[data$Bankrupt.=="0",]))))) - print("number of classes,220 bankruptcy vs 6599 survive companies") - }) -} + output$dataTable <- renderDataTable(data) + output$scat_plot <- renderPlot({ + ggplot(data, aes_string(x = input$scat_x, y = 'Bankrupt.')) + + geom_point(aes(col = data$'Net Income Flag'), size=3) + + scale_color_discrete(name = 'Net Income Flag') + + geom_smooth(aes(group = data$'Net Income Flag', color = data$'Net Income Flag'), method='lm') + }, height=500) + +} shinyApp(ui = ui, server = server) \ No newline at end of file diff --git a/code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf b/code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf index 13a5b61..36860fd 100644 --- a/code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf +++ b/code/shinyUI/rsconnect/shinyapps.io/yjack0000/shinyui.dcf @@ -5,7 +5,7 @@ account: yjack0000 server: shinyapps.io hostUrl: https://api.shinyapps.io/v1 appId: 5397561 -bundleId: 5437016 +bundleId: 5460655 url: https://yjack0000.shinyapps.io/shinyui/ -when: 1641397789.57417 -lastSyncTime: 1641397789.57418 +when: 1641995434.58701 +lastSyncTime: 1641995434.58702 From 350d5fcaffbbc02a4e0c733bbb3772051f84f091 Mon Sep 17 00:00:00 2001 From: YJack Date: Wed, 12 Jan 2022 22:31:23 +0800 Subject: [PATCH 11/39] =?UTF-8?q?=E6=96=B0=E5=A2=9E=20shiny?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index aa4af2a..714ab74 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,7 @@ 所以全部猜 1 就可以有超級高的 Accuracy 我們將目標設定成要盡可能增加 recall 。嘗試預測出更多可能會倒的公司去對他們做關切 或提早做應對措施，並去檢視可能面臨的問題，是這次專題的主要目標。 ### Demo +* [ShinyApp]](https://yjack0000.shinyapps.io/shinyui/?_ga=2.142920117.1862022445.1641973117-1531152518.1641397296) You should provide an example commend to reproduce your result ```R Rscript code/your_script.R --input data/training --output results/performance.tsv From 8e7bdf6256a27baee64af1d675f245fe31bb1b7e Mon Sep 17 00:00:00 2001 From: YJack Date: Wed, 12 Jan 2022 22:32:06 +0800 Subject: [PATCH 12/39] sihny --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 714ab74..ce2f891 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ 所以全部猜 1 就可以有超級高的 Accuracy 我們將目標設定成要盡可能增加 recall 。嘗試預測出更多可能會倒的公司去對他們做關切 或提早做應對措施，並去檢視可能面臨的問題，是這次專題的主要目標。 ### Demo -* [ShinyApp]](https://yjack0000.shinyapps.io/shinyui/?_ga=2.142920117.1862022445.1641973117-1531152518.1641397296) +* [ShinyApp](https://yjack0000.shinyapps.io/shinyui/?_ga=2.142920117.1862022445.1641973117-1531152518.1641397296) You should provide an example commend to reproduce your result ```R Rscript code/your_script.R --input data/training --output results/performance.tsv From 80a91355ed352acdb7cd240cd742afedae8a7b87 Mon Sep 17 00:00:00 2001 From: YJack Date: Wed, 12 Jan 2022 22:35:39 +0800 Subject: [PATCH 13/39] =?UTF-8?q?=E6=94=B9=E6=88=90=20=EF=BC=B2=EF=BC=A5?= =?UTF-8?q?=EF=BC=A1=EF=BC=A4=EF=BC=AD=EF=BC=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- data/{DataContext.md => README.md} | 0 docs/{GoogleSlideLink.md => README.md} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename data/{DataContext.md => README.md} (100%) rename docs/{GoogleSlideLink.md => README.md} (100%) diff --git a/data/DataContext.md b/data/README.md similarity index 100% rename from data/DataContext.md rename to data/README.md diff --git a/docs/GoogleSlideLink.md b/docs/README.md similarity index 100% rename from docs/GoogleSlideLink.md rename to docs/README.md From 464ca3db78d86c48f80aa2da9bc28f9d8cc73f01 Mon Sep 17 00:00:00 2001 From: YJack Date: Wed, 12 Jan 2022 22:36:21 +0800 Subject: [PATCH 14/39] README. --- README.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ce2f891..e1dee84 100644 --- a/README.md +++ b/README.md @@ -12,11 +12,10 @@ 我們將目標設定成要盡可能增加 recall 。嘗試預測出更多可能會倒的公司去對他們做關切 或提早做應對措施，並去檢視可能面臨的問題，是這次專題的主要目標。 ### Demo * [ShinyApp](https://yjack0000.shinyapps.io/shinyui/?_ga=2.142920117.1862022445.1641973117-1531152518.1641397296) -You should provide an example commend to reproduce your result +* Rscript 使用 ```R Rscript code/your_script.R --input data/training --output results/performance.tsv ``` -* any on-line visualization ## 檔案架構及其相關資訊 @@ -25,9 +24,9 @@ Rscript code/your_script.R --input data/training --output results/performance.ts ### data -* Source -* Input format -* Any preprocessing? +* [來源](https://www.kaggle.com/fedesoriano/company-bankruptcy-prediction) +* [格式](https://github.com/1101-datascience/finalproject_group4/tree/main/data) +* 預處理 * Handle missing data * Scale value From 962e45ec125e6d7fbe17c5a8aeff485a023fb306 Mon Sep 17 00:00:00 2001 From: YJack Date: Wed, 12 Jan 2022 22:39:19 +0800 Subject: [PATCH 15/39] =?UTF-8?q?=E6=89=93=E5=A5=BD=E6=A0=BC=E5=BC=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- data/README.md | 193 +++++++++++++++++++++++++------------------------ 1 file changed, 97 insertions(+), 96 deletions(-) diff --git a/data/README.md b/data/README.md index c2faa21..13231ac 100644 --- a/data/README.md +++ b/data/README.md @@ -4,102 +4,103 @@ The data were collected from the Taiwan Economic Journal for the years 1999 to 2 ## Attribute Information Version 2: Updated column names and description to make the data easier to understand (Y = Output feature, X = Input features) -## Y - Bankrupt?: Class label -X1 - ROA(C) before interest and depreciation before interest: Return On Total Assets(C) -X2 - ROA(A) before interest and % after tax: Return On Total Assets(A) -X3 - ROA(B) before interest and depreciation after tax: Return On Total Assets(B) -X4 - Operating Gross Margin: Gross Profit/Net Sales -X5 - Realized Sales Gross Margin: Realized Gross Profit/Net Sales -X6 - Operating Profit Rate: Operating Income/Net Sales -X7 - Pre-tax net Interest Rate: Pre-Tax Income/Net Sales -X8 - After-tax net Interest Rate: Net Income/Net Sales -X9 - Non-industry income and expenditure/revenue: Net Non-operating Income Ratio -X10 - Continuous interest rate (after tax): Net Income-Exclude Disposal Gain or Loss/Net Sales -X11 - Operating Expense Rate: Operating Expenses/Net Sales -X12 - Research and development expense rate: (Research and Development Expenses)/Net Sales -X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities -X14 - Interest-bearing debt interest rate: Interest-bearing Debt/Equity -X15 - Tax rate (A): Effective Tax Rate -X16 - Net Value Per Share (B): Book Value Per Share(B) -X17 - Net Value Per Share (A): Book Value Per Share(A) -X18 - Net Value Per Share (C): Book Value Per Share(C) -X19 - Persistent EPS in the Last Four Seasons: EPS-Net Income -X20 - Cash Flow Per Share -X21 - Revenue Per Share (Yuan ¥): Sales Per Share -X22 - Operating Profit Per Share (Yuan ¥): Operating Income Per Share -X23 - Per Share Net profit before tax (Yuan ¥): Pretax Income Per Share -X24 - Realized Sales Gross Profit Growth Rate -X25 - Operating Profit Growth Rate: Operating Income Growth -X26 - After-tax Net Profit Growth Rate: Net Income Growth -X27 - Regular Net Profit Growth Rate: Continuing Operating Income after Tax Growth -X28 - Continuous Net Profit Growth Rate: Net Income-Excluding Disposal Gain or Loss Growth -X29 - Total Asset Growth Rate: Total Asset Growth -X30 - Net Value Growth Rate: Total Equity Growth -X31 - Total Asset Return Growth Rate Ratio: Return on Total Asset Growth -X32 - Cash Reinvestment %: Cash Reinvestment Ratio -X33 - Current Ratio -X34 - Quick Ratio: Acid Test -X35 - Interest Expense Ratio: Interest Expenses/Total Revenue -X36 - Total debt/Total net worth: Total Liability/Equity Ratio -X37 - Debt ratio %: Liability/Total Assets -X38 - Net worth/Assets: Equity/Total Assets -X39 - Long-term fund suitability ratio (A): (Long-term Liability+Equity)/Fixed Assets -X40 - Borrowing dependency: Cost of Interest-bearing Debt -X41 - Contingent liabilities/Net worth: Contingent Liability/Equity -X42 - Operating profit/Paid-in capital: Operating Income/Capital -X43 - Net profit before tax/Paid-in capital: Pretax Income/Capital -X44 - Inventory and accounts receivable/Net value: (Inventory+Accounts Receivables)/Equity -X45 - Total Asset Turnover -X46 - Accounts Receivable Turnover -X47 - Average Collection Days: Days Receivable Outstanding -X48 - Inventory Turnover Rate (times) -X49 - Fixed Assets Turnover Frequency -X50 - Net Worth Turnover Rate (times): Equity Turnover -X51 - Revenue per person: Sales Per Employee -X52 - Operating profit per person: Operation Income Per Employee -X53 - Allocation rate per person: Fixed Assets Per Employee -X54 - Working Capital to Total Assets -X55 - Quick Assets/Total Assets -X56 - Current Assets/Total Assets -X57 - Cash/Total Assets -X58 - Quick Assets/Current Liability -X59 - Cash/Current Liability -X60 - Current Liability to Assets -X61 - Operating Funds to Liability -X62 - Inventory/Working Capital -X63 - Inventory/Current Liability -X64 - Current Liabilities/Liability -X65 - Working Capital/Equity -X66 - Current Liabilities/Equity -X67 - Long-term Liability to Current Assets -X68 - Retained Earnings to Total Assets -X69 - Total income/Total expense -X70 - Total expense/Assets -X71 - Current Asset Turnover Rate: Current Assets to Sales -X72 - Quick Asset Turnover Rate: Quick Assets to Sales -X73 - Working capitcal Turnover Rate: Working Capital to Sales -X74 - Cash Turnover Rate: Cash to Sales -X75 - Cash Flow to Sales -X76 - Fixed Assets to Assets -X77 - Current Liability to Liability -X78 - Current Liability to Equity -X79 - Equity to Long-term Liability -X80 - Cash Flow to Total Assets -X81 - Cash Flow to Liability -X82 - CFO to Assets -X83 - Cash Flow to Equity -X84 - Current Liability to Current Assets -X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise -X86 - Net Income to Total Assets -X87 - Total assets to GNP price -X88 - No-credit Interval -X89 - Gross Profit to Sales -X90 - Net Income to Stockholder's Equity -X91 - Liability to Equity -X92 - Degree of Financial Leverage (DFL) -X93 - Interest Coverage Ratio (Interest expense to EBIT) -X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise -X95 - Equity to Liability +## Y - Bankrupt?: +### Class label +* X1 - ROA(C) before interest and depreciation before interest: Return On Total Assets(C) +* X2 - ROA(A) before interest and % after tax: Return On Total Assets(A) +* X3 - ROA(B) before interest and depreciation after tax: Return On Total Assets(B) +* X4 - Operating Gross Margin: Gross Profit/Net Sales +* X5 - Realized Sales Gross Margin: Realized Gross Profit/Net Sales +* X6 - Operating Profit Rate: Operating Income/Net Sales +* X7 - Pre-tax net Interest Rate: Pre-Tax Income/Net Sales +* X8 - After-tax net Interest Rate: Net Income/Net Sales +* X9 - Non-industry income and expenditure/revenue: Net Non-operating Income Ratio +* X10 - Continuous interest rate (after tax): Net Income-Exclude Disposal Gain or Loss/Net Sales +* X11 - Operating Expense Rate: Operating Expenses/Net Sales +* X12 - Research and development expense rate: (Research and Development Expenses)/Net Sales +* X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities +* X14 - Interest-bearing debt interest rate: Interest-bearing Debt/Equity +* X15 - Tax rate (A): Effective Tax Rate +* X16 - Net Value Per Share (B): Book Value Per Share(B) +* *X17 - Net Value Per Share (A): Book Value Per Share(A) +* X18 - Net Value Per Share (C): Book Value Per Share(C) +* X19 - Persistent EPS in the Last Four Seasons: EPS-Net Income +* X20 - Cash Flow Per Share +* X21 - Revenue Per Share (Yuan ¥): Sales Per Share +* X22 - Operating Profit Per Share (Yuan ¥): Operating Income Per Share +* X23 - Per Share Net profit before tax (Yuan ¥): Pretax Income Per Share +* X24 - Realized Sales Gross Profit Growth Rate +* X25 - Operating Profit Growth Rate: Operating Income Growth +* X26 - After-tax Net Profit Growth Rate: Net Income Growth +* X27 - Regular Net Profit Growth Rate: Continuing Operating Income after Tax Growth +* X28 - Continuous Net Profit Growth Rate: Net Income-Excluding Disposal Gain or Loss Growth +* X29 - Total Asset Growth Rate: Total Asset Growth +* X30 - Net Value Growth Rate: Total Equity Growth +* X31 - Total Asset Return Growth Rate Ratio: Return on Total Asset Growth +* X32 - Cash Reinvestment %: Cash Reinvestment Ratio +* X33 - Current Ratio +* X34 - Quick Ratio: Acid Test +* X35 - Interest Expense Ratio: Interest Expenses/Total Revenue +* X36 - Total debt/Total net worth: Total Liability/Equity Ratio +* X37 - Debt ratio %: Liability/Total Assets +* X38 - Net worth/Assets: Equity/Total Assets +* X39 - Long-term fund suitability ratio (A): (Long-term Liability+Equity)/Fixed Assets +* X40 - Borrowing dependency: Cost of Interest-bearing Debt +* X41 - Contingent liabilities/Net worth: Contingent Liability/Equity +* X42 - Operating profit/Paid-in capital: Operating Income/Capital +* X43 - Net profit before tax/Paid-in capital: Pretax Income/Capital +* X44 - Inventory and accounts receivable/Net value: (Inventory+Accounts Receivables)/Equity +* X45 - Total Asset Turnover +* X46 - Accounts Receivable Turnover +* X47 - Average Collection Days: Days Receivable Outstanding +* X48 - Inventory Turnover Rate (times) +* X49 - Fixed Assets Turnover Frequency +* X50 - Net Worth Turnover Rate (times): Equity Turnover +* X51 - Revenue per person: Sales Per Employee +* X52 - Operating profit per person: Operation Income Per Employee +* X53 - Allocation rate per person: Fixed Assets Per Employee +* X54 - Working Capital to Total Assets +* X55 - Quick Assets/Total Assets +* X56 - Current Assets/Total Assets +* X57 - Cash/Total Assets +* X58 - Quick Assets/Current Liability +* X59 - Cash/Current Liability +* X60 - Current Liability to Assets +* X61 - Operating Funds to Liability +* X62 - Inventory/Working Capital +* X63 - Inventory/Current Liability +* X64 - Current Liabilities/Liability +* X65 - Working Capital/Equity +* X66 - Current Liabilities/Equity +* X67 - Long-term Liability to Current Assets +* X68 - Retained Earnings to Total Assets +* X69 - Total income/Total expense +* X70 - Total expense/Assets +* X71 - Current Asset Turnover Rate: Current Assets to Sales +* X72 - Quick Asset Turnover Rate: Quick Assets to Sales +* X73 - Working capitcal Turnover Rate: Working Capital to Sales +* X74 - Cash Turnover Rate: Cash to Sales +* X75 - Cash Flow to Sales +* X76 - Fixed Assets to Assets +* X77 - Current Liability to Liability +* X78 - Current Liability to Equity +* X79 - Equity to Long-term Liability +* X80 - Cash Flow to Total Assets +* X81 - Cash Flow to Liability +* X82 - CFO to Assets +* X83 - Cash Flow to Equity +* X84 - Current Liability to Current Assets +* X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise +* X86 - Net Income to Total Assets +* X87 - Total assets to GNP price +* X88 - No-credit Interval +* X89 - Gross Profit to Sales +* X90 - Net Income to Stockholder's Equity +* X91 - Liability to Equity +* X92 - Degree of Financial Leverage (DFL) +* X93 - Interest Coverage Ratio (Interest expense to EBIT) +* X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise +* X95 - Equity to Liability ## Source Deron Liang and Chih-Fong Tsai, deronliang '@' gmail.com; cftsai '@' mgt.ncu.edu.tw, National Central University, Taiwan From 93dc5cbc01dd878a7fc85ab5bf338b42774c2135 Mon Sep 17 00:00:00 2001 From: YJack0000 <49984479+YJack0000@users.noreply.github.com> Date: Wed, 12 Jan 2022 22:40:42 +0800 Subject: [PATCH 16/39] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index e1dee84..29745d1 100644 --- a/README.md +++ b/README.md @@ -29,12 +29,15 @@ Rscript code/your_script.R --input data/training --output results/performance.ts * 預處理 * Handle missing data * Scale value + * PCA? + * Normalize? ### code * Which method do you use? * What is a null model for comparison? * How do your perform evaluation? ie. cross-validation, or addtional indepedent data set + 使用 train, validation, test split，並且使用 SMOTE 製作額外的 traning data ### results From 46183c2940303a80dbbabd58f4b0a07eb563a2d3 Mon Sep 17 00:00:00 2001 From: YJack0000 <49984479+YJack0000@users.noreply.github.com> Date: Thu, 13 Jan 2022 00:43:27 +0800 Subject: [PATCH 17/39] Update README.md --- README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 29745d1..9c22856 100644 --- a/README.md +++ b/README.md @@ -27,10 +27,9 @@ Rscript code/your_script.R --input data/training --output results/performance.ts * [來源](https://www.kaggle.com/fedesoriano/company-bankruptcy-prediction) * [格式](https://github.com/1101-datascience/finalproject_group4/tree/main/data) * 預處理 - * Handle missing data - * Scale value - * PCA? - * Normalize? + * 資料分析 + * PCA + * Normalize ### code From ffb6ed2f2713163d97727d34f8db8fd4fd4104b7 Mon Sep 17 00:00:00 2001 From: Mark Lai Date: Thu, 13 Jan 2022 01:23:29 +0800 Subject: [PATCH 18/39] epoch graph --- graph/epoch1-10.png | Bin 0 -> 28859 bytes graph/epoch10-150.png | Bin 0 -> 50174 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 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R-square * Is your improvement significant? -* What is the challenge part of your project? +* 較有挑戰性的部分 + * 訓練資料中不平均（大多都沒有倒閉） ## References https://www.kaggle.com/jerryfang5/bankrutcy-prediciton-by-r/notebook diff --git a/corr1.png b/corr1.png new file mode 100644 index 0000000000000000000000000000000000000000..592f84865580adbd660171f125fbb45d5c0e6976 GIT binary patch literal 174079 zcmeFZWmwc*+dc{i#-M;m+@ipsA|-=#hlG^W(BOcGbjQ#kD1s;0l~t;x+N_o4#&d6 zO#v^C>sP@yA`(rZSXeiWOvJ<#rNzXcindmUCguiMSW+Lu)URnM|00Xi4hatK!nqwx z>~crkj@?yp&Dju}<<3a=!Og!Dgd^K(#`;Cn;Kwe@OGWSJ@+?>VMS(+UFeG+}TLv#Gna-K+So)c=? z@Zj4cU;fkcC++9qWA_ge2+f8vL`|G>9(Zbe*B(}WKoSYDiQo}r)KW6@bCqy27{?Z2 z5OsF)hEsFS{2D6oc#B=g9)IsXcH!-&#F>~3Dbn~j8pxc;O?7-exv!lceKn|Oe&A?I z#mE>H+sov%l50iK7!<3VXy)11=Qj>sadyCwH8>AYtXCRt;ehyTN)stV%bpO?K7W>j zT^Ra=A10d0j#B&?NYVGDRiAWITl78?kuM&@sy^P`o-U0r51Tymr@H3FicKHk)2D}x z2i6Ae1)&0KA}3GDb5x;*v^Pg?{|tF}&2AdsTkLD4ekb0{d!DdZ9cd5699x+Dmp@Gz zJ2rOD8j7bA`v-jBg%H; 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code/models/pcaGlm.R create mode 100644 code/models/pcaRandomForest.R diff --git a/code/models/pcaGlm.R b/code/models/pcaGlm.R new file mode 100644 index 0000000..af06798 --- /dev/null +++ b/code/models/pcaGlm.R @@ -0,0 +1,94 @@ +library('dplyr') + +data <- read.csv("../../data/data.csv") + +#input +args = commandArgs(trailingOnly=TRUE) +ts <- args[1] + + +accuracy <- 0 +recall <- 0 +precision <- 0 +NegativePrecision <- 0 +max <- 0 + + +i <- 1 +while(i < 10){ + data <- data[sample(1:nrow(data)), ] + + spec = c(train = .8, validate = .2) + + g = sample(cut( + seq(nrow(data)), + nrow(data)*cumsum(c(0,spec)), + labels = names(spec) + )) + + res = split(data, g) + + #train pca + in_d <- res[["train"]] + in_d = in_d[,!colnames(in_d) %in% c('Net.Income.Flag','Bankrupt.')] + pca <- prcomp(in_d, center=TRUE, scale=TRUE) + + #--- built train data with Bankrupt and top 40 component + train.data <- data.frame(Bankrupt. = res[["train"]]$Bankrupt., pca$x) + + train.data <- train.data[,1:41] + + # model using glm + model <- glm(formula = Bankrupt. ~ . , + family = binomial(link='probit'), + epsilon = 1e-14, + data = train.data) + + # val data + val.data <- predict(pca, newdata = res[["validate"]]) + val.data <- as.data.frame(val.data) + val.data <- val.data[,1:40] + + + # data frame of val + val <- data.frame(truth = res[["validate"]]$Bankrupt., + prediction = predict(model, val.data)) + val <- mutate(val, pred = ifelse(prediction > ts, 1, 0)) + + # confusion matrix of validation + cm <- table(val[,c(1,3)]) + + + TP <- cm[2,2] + TN <- cm[1,1] + FP <- cm[1,2] + FN <- cm[2,1] + + r <- TP/(TP+FN) + + if(r > max){ + max <- r + accuracy <- (TP+TN)/(TP+FP+FN+TN) + recall <- TP/(TP+FN) + precision <- TP/(TP+FP) + NegativePrecision <- TN/(TN+FN) + + final_cm <- data.frame(cm) + } + i = i + 1 +} + +result <- c("accuracy" = accuracy, + "recall" = recall, + "precision" = precision, + "NegativePrecision" = NegativePrecision) +res <- as.matrix(result) +cm <- data.frame(final_cm) + +dir.create("LogisticRegression", recursive = TRUE ,showWarnings = FALSE) +setwd("LogisticRegression") + +write.csv(cm, file = "output.csv", quote = F, row.names = F) +write.table(res, file = "output.csv", sep=",", quote = F, append=TRUE, col.names=FALSE, row.names = TRUE) + + diff --git a/code/models/pcaRandomForest.R b/code/models/pcaRandomForest.R new file mode 100644 index 0000000..e73bd11 --- /dev/null +++ b/code/models/pcaRandomForest.R @@ -0,0 +1,104 @@ +library("randomForest") +library('rpart') +library('dplyr') + +#input +args = commandArgs(trailingOnly=TRUE) +tree <- args[1] + +# load data +data <- read.csv("../../data/data.csv") + +accuracy <- 0 +recall <- 0 +precision <- 0 +NegativePrecision <- 0 +max <- 0 + + +i <- 1 +while(i < 10){ + data <- data[sample(1:nrow(data)), ] + + # shuffle data + spec = c(train = .8, validate = .2) + + g = sample(cut( + seq(nrow(data)), + nrow(data)*cumsum(c(0,spec)), + labels = names(spec) + )) + + # final data + res = split(data, g) + + + + + #train pca + in_d <- res[["train"]] + in_d = in_d[,!colnames(in_d) %in% c('Net.Income.Flag','Bankrupt.')] + pca <- prcomp(in_d, center=TRUE, scale=TRUE) + + #--- watch pc + std_dev <- pca$sdev + pr_var <- std_dev^2 + prop_varex <- pr_var/sum(pr_var) + #plot(prop_varex, type = 'lines') + + + #--- built train data with Bankrupt and top 40 component + train.data <- data.frame(Bankrupt. = res[["train"]]$Bankrupt., pca$x) + train.data <- train.data[,1:41] + train.data.var <- colnames(train.data[,2:40]) + + #--- built val data with Bankrupt and top 40 component + val.data <- predict(pca, newdata = res[["validate"]]) + val.data <- as.data.frame(val.data) + val.data <- val.data[,1:40] + + # build random forest model + model <- randomForest(x = train.data[,train.data.var], y = as.factor(train.data$Bankrupt.), + ntree = as.integer(tree), importance = T) + + val <- data.frame(truth = res[["validate"]]$Bankrupt., + prediction = predict(model, val.data)) + #val <- mutate(val, result = ifelse(prediction > 0.5, 1, 0)) + + # confusion matrix of validation + cm <- table(val) + + + TP <- cm[2,2] + TN <- cm[1,1] + FP <- cm[1,2] + FN <- cm[2,1] + + + r <- TP/(TP+FN) + + if(r > max){ + max <- r + accuracy <- (TP+TN)/(TP+FP+FN+TN) + recall <- TP/(TP+FN) + precision <- TP/(TP+FP) + NegativePrecision <- TN/(TN+FN) + final_cm <- data.frame(cm) + } + i = i +1 +} + +result <- c("accuracy" = accuracy, + "recall" = recall, + "precision" = precision, + "NegativePrecision" = NegativePrecision) +res <- as.matrix(result) + + +dir.create("RandomForest", recursive = TRUE ,showWarnings = FALSE) +setwd("RandomForest") + +write.csv(final_cm, file = "output.csv", quote = F, row.names = F) +write.table(res, file = "output.csv", sep=",", quote = F, append=TRUE, col.names=FALSE, row.names = TRUE) + + diff --git a/code/models/pcaRpart.R b/code/models/pcaRpart.R index 550f7d8..2dda77e 100644 --- a/code/models/pcaRpart.R +++ b/code/models/pcaRpart.R @@ -1,64 +1,101 @@ library('rpart') library('dplyr') -data <- read.csv("data.csv") - -data <- data[sample(1:nrow(data)), ] - -spec = c(train = .6, test = .2, validate = .2) - -g = sample(cut( - seq(nrow(data)), - nrow(data)*cumsum(c(0,spec)), - labels = names(spec) -)) - -res = split(data, g) -nrow(res[["train"]]) -nrow(res[["validate"]]) -nrow(res[["test"]]) -names(res[["test"]]) - -#train pca -in_d <- res[["train"]] -in_d = in_d[,!colnames(in_d) %in% c('Net.Income.Flag','Bankrupt.')] -pca <- prcomp(in_d, center=TRUE, scale=TRUE) - -#--- watch pc -std_dev <- pca$sdev -pr_var <- std_dev^2 -prop_varex <- pr_var/sum(pr_var) -prop_varex -plot(prop_varex, type = 'lines') - -#--- built train data with Bankrupt and top 40 component -train.data <- data.frame(Bankrupt. = res[["train"]]$Bankrupt., pca$x) -train.data <- train.data[,1:41] -model <- rpart(Bankrupt. ~ .,data = train.data, method = "anova") - -#--- built validation data with Bankrupt and top 40 component -val.data <- predict(pca, newdata = res[["validate"]]) -val.data <- as.data.frame(val.data) -val.data <- val.data[,1:40] - -val <- data.frame(truth = res[["validate"]]$Bankrupt., - prediction = predict(model, val.data)) -val <- mutate(val, result = ifelse(prediction > 0.5, 1, 0)) - -# confusion matrix of validation -cm <- table(val[,c(1,3)]) - -TP <- cm[2,2] -TN <- cm[1,1] -FP <- cm[1,2] -FN <- cm[2,1] - -accuracy <- (TP+TN)/(TP+FP+FN+TN) -fallback <- TP/(TP+FN) -precision <- TP/(TP+FP) -NegativePrecision <- TN/(TN+FN) - -print( accuracy ) -print( fallback ) -print( precision ) -print( NegativePrecision ) + +# load data +data <- read.csv("../../data/data.csv") + +#input +args = commandArgs(trailingOnly=TRUE) +depth <- args[1] +threshold <- args[2] + + + +accuracy <- 0 +recall <- 0 +precision <- 0 +NegativePrecision <- 0 +max <- 0 + + +i <- 1 +while(i < 10){ + # shuffle data + data <- data[sample(1:nrow(data)), ] + spec = c(train = .8, validate = .2) + + + + g = sample(cut( + seq(nrow(data)), + nrow(data)*cumsum(c(0,spec)), + labels = names(spec) + )) + + res = split(data, g) + + + + + #train pca + in_d <- res[["train"]] + in_d = in_d[,!colnames(in_d) %in% c('Net.Income.Flag','Bankrupt.')] + pca <- prcomp(in_d, center=TRUE, scale=TRUE) + + #--- watch pc + std_dev <- pca$sdev + pr_var <- std_dev^2 + prop_varex <- pr_var/sum(pr_var) + #plot(prop_varex, type = 'lines') + + #--- built train data with Bankrupt and top 40 component + train.data <- data.frame(Bankrupt. = res[["train"]]$Bankrupt., pca$x) + + train.data <- train.data[,1:41] + model <- rpart(Bankrupt. ~ .,data = train.data, method = "anova", control=rpart.control(maxdepth= depth),) + + + val.data <- predict(pca, newdata = res[["validate"]]) + val.data <- as.data.frame(val.data) + val.data <- val.data[,1:40] + + val <- data.frame(truth = res[["validate"]]$Bankrupt., + prediction = predict(model, val.data)) + val <- mutate(val, pred = ifelse(prediction > threshold, 1, 0)) + + # confusion matrix of validation + cm <- table(val[,c(1,3)]) + + + TP <- cm[2,2] + TN <- cm[1,1] + FP <- cm[1,2] + FN <- cm[2,1] + r <- TP/(TP+FN) + + if(r > max){ + max <- r + accuracy <- (TP+TN)/(TP+FP+FN+TN) + recall <- TP/(TP+FN) + precision <- TP/(TP+FP) + NegativePrecision <- TN/(TN+FN) + + final_cm <- data.frame(cm) + } + i = i + 1 +} + +result <- c("accuracy" = accuracy, + "recall" = recall, + "precision" = precision, + "NegativePrecision" = NegativePrecision) +res <- as.matrix(result) + +dir.create("DecisionTree", recursive = TRUE ,showWarnings = FALSE) +setwd("DecisionTree") + +write.csv(final_cm, file = "output.csv", quote = F, row.names = F) +write.table(res, file = "output.csv", sep=",", quote = F, append=TRUE, col.names=FALSE, row.names = TRUE) + + From 0191279f7693582a1380fce375b4dabdb43b3cd2 Mon Sep 17 00:00:00 2001 From: Mark Lai Date: Thu, 13 Jan 2022 02:53:48 +0800 Subject: [PATCH 22/39] add image --- graph/data_split.png | Bin 0 -> 1804 bytes graph/decision_tree.png | Bin 0 -> 29373 bytes graph/logistic_regression.jpeg | Bin 0 -> 65946 bytes graph/original.png | 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z000000000003<;|761SM00000002Oe{}TWJ|Nj*t|K!1=mURFC002ovPDHLkV1gTk B&+`BP literal 0 HcmV?d00001 From 0ad53aa8d7f6a0f8ed304305d0383a89d1b36b42 Mon Sep 17 00:00:00 2001 From: Mark Lai Date: Thu, 13 Jan 2022 02:54:09 +0800 Subject: [PATCH 23/39] readme --- README.md | 32 +++++++++++++++++++++++++++----- 1 file changed, 27 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 9c22856..3008b6e 100644 --- a/README.md +++ b/README.md @@ -33,18 +33,40 @@ Rscript code/your_script.R --input data/training --output results/performance.ts ### code -* Which method do you use? -* What is a null model for comparison? -* How do your perform evaluation? ie. cross-validation, or addtional indepedent data set +* method we use + * decision tree -使用 train, validation, test split，並且使用 SMOTE 製作額外的 traning data + ![desision tree](./graph/decision_tree.png) + * random forest + + ![random forest](./graph/random_forest.png) + * logistic regression + + ![logistic regression](./graph/logistic_regression.jpeg) + * cnn + + +* Null model predict all 1 +* data split + + ![data split](./graph/data_split.png) + + +* 使用 SMOTE 製作額外的 traning data + + ![smote](./graph/original.png) + ![smot](./graph/oversample.png) ### results * Which metric do you use * precision, recall, R-square * Is your improvement significant? -* What is the challenge part of your project? +* challenge part of project + * unbalance of data make prediction more difficult + * tuning of hyperparameters + * testing of different epoch + * choose between precision and recall ## References https://www.kaggle.com/jerryfang5/bankrutcy-prediciton-by-r/notebook From 59e6dc97626757a55184d61be72e66d5b47bb752 Mon Sep 17 00:00:00 2001 From: bryant-nn Date: Thu, 13 Jan 2022 03:01:45 +0800 Subject: [PATCH 24/39] load plcture with decision tree, random forest, lr --- graph/DecisionTree-ConfusionMatrix.png | Bin 0 -> 10299 bytes graph/DecisionTree-PCA-ConfusionMatrix.png | Bin 0 -> 10035 bytes 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text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 131 ++++++++++++++++++++++++++++++++-- graph/cnn-ConfusionMatrix.png | Bin 0 -> 4510 bytes 2 files changed, 127 insertions(+), 4 deletions(-) create mode 100644 graph/cnn-ConfusionMatrix.png diff --git a/README.md b/README.md index 6f4907b..92cef2c 100644 --- a/README.md +++ b/README.md @@ -35,12 +35,21 @@ Rscript code/your_script.R --input data/training --output results/performance.ts * method we use * decision tree + ```console + + ``` ![desision tree](./graph/decision_tree.png) * random forest - + ```console + + ``` + ![random forest](./graph/random_forest.png) * logistic regression + ```console + + ``` ![logistic regression](./graph/logistic_regression.jpeg) * cnn @@ -59,9 +68,123 @@ Rscript code/your_script.R --input data/training --output results/performance.ts ### results -* Which metric do you use - * precision, recall, R-square -* Is your improvement significant? +* use precision accuracy and recall to evaluate model +* improvement + 1. 用pca 分析並使用前40個主成份 + 2. 利用k-fold validation的方法 篩選出recall最高的model + 3. 對於threshold、max depth等超參數進行調整 + +* decision tree + ```r + model<- rpart( Bankrupt. ~ ., + data=res[["train"]], + control=rpart.control(maxdepth=20), + method="class") + + + trainframe <- data.frame(truth=res[["train"]]$Bankrupt., + pred=predict(model, type="class")) + + TP.train<-nrow(filter(trainframe, truth == pred , truth == 1,)) + TN.train<-nrow(filter(trainframe, truth == pred , truth == 0,)) + FP.train<-nrow(filter(trainframe, truth != pred , truth == 0,)) + FN.train<-nrow(filter(trainframe, truth != pred , truth == 1,)) + accuracy.train<-(TP.train+TN.train)/nrow(trainframe) + fallback.train<-(TP.train)/(TP.train+FN.train) + precision.train<-(TP.train)/(TP.train+FP.train) + NegativePrecision.train<-(TN.train)/(TN.train+FN.train) + ``` + + ![decision tree](./graph/DecisionTree-ConfusionMatrix.png) ![decision tree](./graph/DecisionTree-PCA-ConfusionMatrix.png) + +* random forest + + ```r + g = sample(cut( + seq(nrow(data)), + nrow(data)*cumsum(c(0,spec)), + labels = names(spec) + )) + + # final data + res = split(data, g) + + + + + #train pca + in_d <- res[["train"]] + in_d = in_d[,!colnames(in_d) %in% c('Net.Income.Flag','Bankrupt.')] + pca <- prcomp(in_d, center=TRUE, scale=TRUE) + + #--- watch pc + std_dev <- pca$sdev + pr_var <- std_dev^2 + prop_varex <- pr_var/sum(pr_var) + #plot(prop_varex, type = 'lines') + + + #--- built train data with Bankrupt and top 40 component + train.data <- data.frame(Bankrupt. = res[["train"]]$Bankrupt., pca$x) + train.data <- train.data[,1:41] + train.data.var <- colnames(train.data[,2:40]) + + #--- built val data with Bankrupt and top 40 component + val.data <- predict(pca, newdata = res[["validate"]]) + ``` + + ![random forest](./graph/RandomForest-ConfusionMatrix.png) ![random forest](./graph/RandomForest-PCA-ConfusionMatrix.png) + +* logistic regression + ```r + model <- glm(formula = Bankrupt. ~ . , + family = binomial(link='probit'), + epsilon = 1e-14, + data = train.data) + + # val data + val.data <- predict(pca, newdata = res[["validate"]]) + val.data <- as.data.frame(val.data) + val.data <- val.data[,1:40] + + + # data frame of val + val <- data.frame(truth = res[["validate"]]$Bankrupt., + prediction = predict(model, val.data)) + val <- mutate(val, pred = ifelse(prediction > ts, 1, 0)) + + # confusion matrix of validation + cm <- table(val[,c(1,3)]) + ``` + + ![logistic regression](./graph/LogisticRegression-ConfusionMatrix.png) ![logistic regression](./graph/LogisticRegression-PCA-ConfusionMatrix.png) +* cnn + ```python + class BinaryClassification(nn.Module): + def __init__(self): + super(BinaryClassification, self).__init__() + # Number of input features is 12. + self.layer_1 = nn.Linear(95, 200) + self.layer_2 = nn.Linear(200, 64) + self.layer_out = nn.Linear(64, 1) + + self.relu = nn.ReLU() + self.dropout = nn.Dropout(p=0.5) + self.batchnorm1 = nn.BatchNorm1d(200) + self.batchnorm2 = nn.BatchNorm1d(64) + + def forward(self, inputs): + x = self.relu(self.layer_1(inputs)) + x = self.batchnorm1(x) + x = self.relu(self.layer_2(x)) + x = self.batchnorm2(x) + x = self.dropout(x) + x = self.layer_out(x) + + return x + ``` + + ![cnn](./graph/cnn-ConfusionMatrix.png) * 較有挑戰性的部分 * 訓練資料中不平均（大多都沒有倒閉） diff --git a/graph/cnn-ConfusionMatrix.png b/graph/cnn-ConfusionMatrix.png new file mode 100644 index 0000000000000000000000000000000000000000..971057611bfe6f54fe6ac15da0b59d70a3d56e2e GIT binary patch literal 4510 zcmZWs2Q-{pv?faQ7NW}-k*l{c#OP)wj2HwnY8b{S8FjP?qJ$tKg(!*M1yLhfM33IP z1W}@dXi*|cyve=y-Fw%2|62P$`#<~p_CDu3Yp?a6hlcvvjC4G7WMpKF2pyOaX)Gog zn1-74Rtx79BO@b+J42y{2q+X_NN{&>cEOR6iNsX@d&IWDggNanN_B9dwDod##9-16&(BvqsHuIC z)DcaS=jZ3|&(6=AdP7z|+hMNnFp>eU*a-U0V`50UqH$&j2PBe=BrX{Z8F{2L870Xh zCm9dP$jB(-BFHY1MmCbcGB5lSEzYF)#|QrgY8pck2-0YbCE##wUQX`bk~}Ws5b!6& z8?Op9LmC30?gShFEF~``15~2}001fkdk2US?8e`8(w8dG$=lllA}#Ib=O^U{l5!_F 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z9fke6Pn&?R=93J9z{u4YLv$fRZz&(ZN4=c>?dz!CQGd#Vd})5_Zl65cFzT6G#&vvk zUxya>dK0B0t|(IgYv_OWsC?PtpXz9NK>uAo3hsOHCR6PsLYyX2vu-tub|O=-?TDQ7 z%Q{p!K=WHO>jzdHlf;4b=!eJrjsvM5`lk1$%xz=|5_@_%VS2i6AOqlU97;_>KFtK)jBmHY5*zL!JFinNStAI~0Vs1_lPB z&0(h>)ST9`--xxwRwI3=AP%Ei5py}(e!f}@Jdgdye||01R9)r0EbSE8a@u+T;ivw( z^XRmpmcSGi9(EoFf3bf?T=}EmYyZs@W`5-=sNqgBpWr1mGbJti8HNZ#DX4VpXPN6z zGx<~_wxFZ?%g5_DlX1tTk# literal 0 HcmV?d00001 From 9877e83edc9cc901541306ae80f8ac1898f5e51a Mon Sep 17 00:00:00 2001 From: Mark Lai Date: Thu, 13 Jan 2022 03:56:39 +0800 Subject: [PATCH 26/39] =?UTF-8?q?=E5=AE=8C=E6=88=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 6 +++--- code/models/bank-pytorch.ipynb | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) create mode 100644 code/models/bank-pytorch.ipynb diff --git a/README.md b/README.md index 92cef2c..785a4df 100644 --- a/README.md +++ b/README.md @@ -36,19 +36,19 @@ Rscript code/your_script.R --input data/training --output results/performance.ts * method we use * decision tree ```console - + Rscript pcaRpart.R (max depth) (threshold) ``` ![desision tree](./graph/decision_tree.png) * random forest ```console - + Rscript pcaRandomForest.R (tree number) ``` ![random forest](./graph/random_forest.png) * logistic regression ```console - + Rscript pcaGlm.R (threshold) ``` ![logistic regression](./graph/logistic_regression.jpeg) diff --git a/code/models/bank-pytorch.ipynb b/code/models/bank-pytorch.ipynb new file mode 100644 index 0000000..0387a28 --- /dev/null +++ b/code/models/bank-pytorch.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom sklearn.preprocessing import StandardScaler \nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-01-12T16:19:31.424199Z","iopub.execute_input":"2022-01-12T16:19:31.424873Z","iopub.status.idle":"2022-01-12T16:19:31.433006Z","shell.execute_reply.started":"2022-01-12T16:19:31.424834Z","shell.execute_reply":"2022-01-12T16:19:31.432271Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_X = pd.read_csv('/kaggle/input/oversampling/df_X.csv')\ndf_y = pd.read_csv('/kaggle/input/oversampling/df_y.csv')\nX_test = pd.read_csv('/kaggle/input/oversampling/df_X_test.csv')\ny_test = pd.read_csv('/kaggle/input/oversampling/df_y_test.csv')\n\n\n\n","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:19:31.752219Z","iopub.execute_input":"2022-01-12T16:19:31.753004Z","iopub.status.idle":"2022-01-12T16:19:32.018337Z","shell.execute_reply.started":"2022-01-12T16:19:31.752955Z","shell.execute_reply":"2022-01-12T16:19:32.017625Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# sns.countplot(x = 'Bankrupt?', data=df)\n","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:19:32.256145Z","iopub.execute_input":"2022-01-12T16:19:32.256401Z","iopub.status.idle":"2022-01-12T16:19:32.259989Z","shell.execute_reply.started":"2022-01-12T16:19:32.256371Z","shell.execute_reply":"2022-01-12T16:19:32.259314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = df_X.iloc[:, 0:]\ny = df_y.iloc[:, 0]","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:19:32.477878Z","iopub.execute_input":"2022-01-12T16:19:32.478258Z","iopub.status.idle":"2022-01-12T16:19:32.484849Z","shell.execute_reply.started":"2022-01-12T16:19:32.478227Z","shell.execute_reply":"2022-01-12T16:19:32.484073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=1)\n\n# X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:19:32.71705Z","iopub.execute_input":"2022-01-12T16:19:32.717679Z","iopub.status.idle":"2022-01-12T16:19:32.730217Z","shell.execute_reply.started":"2022-01-12T16:19:32.717634Z","shell.execute_reply":"2022-01-12T16:19:32.729599Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"scaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\nX_val = scaler.transform(X_val)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:19:32.953412Z","iopub.execute_input":"2022-01-12T16:19:32.953685Z","iopub.status.idle":"2022-01-12T16:19:32.981688Z","shell.execute_reply.started":"2022-01-12T16:19:32.953653Z","shell.execute_reply":"2022-01-12T16:19:32.980969Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.decomposition import PCA\n \n# Let's say, components = 2\npca = PCA(n_components = 40)\npca.fit(X_train)\nX_train = pca.transform(X_train)\nX_test = pca.transform(X_test)\nX_val = pca.transform(X_val)","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:19:33.198192Z","iopub.execute_input":"2022-01-12T16:19:33.198977Z","iopub.status.idle":"2022-01-12T16:19:33.294829Z","shell.execute_reply.started":"2022-01-12T16:19:33.198939Z","shell.execute_reply":"2022-01-12T16:19:33.293849Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"EPOCHS = 1\nBATCH_SIZE = 64\nLEARNING_RATE = 0.001","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:32.312025Z","iopub.execute_input":"2022-01-12T16:28:32.312616Z","iopub.status.idle":"2022-01-12T16:28:32.316621Z","shell.execute_reply.started":"2022-01-12T16:28:32.312562Z","shell.execute_reply":"2022-01-12T16:28:32.315556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## train data\nclass TrainData(Dataset):\n \n def __init__(self, X_data, y_data):\n self.X_data = X_data\n self.y_data = y_data\n \n def __getitem__(self, index):\n return self.X_data[index], self.y_data[index]\n \n def __len__ (self):\n return len(self.X_data)\n\n\ntrain_data = TrainData(torch.FloatTensor(X_train), \n torch.FloatTensor(y_train))\n## test data \nclass TestData(Dataset):\n \n def __init__(self, X_data):\n self.X_data = X_data\n \n def __getitem__(self, index):\n return self.X_data[index]\n \n def __len__ (self):\n return len(self.X_data)\n \nvalid_data = TestData(torch.FloatTensor(X_val))\ntest_data = TestData(torch.FloatTensor(X_test))\n\n \n","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:32.564982Z","iopub.execute_input":"2022-01-12T16:28:32.565227Z","iopub.status.idle":"2022-01-12T16:28:32.576617Z","shell.execute_reply.started":"2022-01-12T16:28:32.5652Z","shell.execute_reply":"2022-01-12T16:28:32.575721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_loader = DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)\nvalid_loader = DataLoader(dataset=valid_data, batch_size=1)\ntest_loader = DataLoader(dataset=test_data, batch_size=1)","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:32.814936Z","iopub.execute_input":"2022-01-12T16:28:32.815676Z","iopub.status.idle":"2022-01-12T16:28:32.821518Z","shell.execute_reply.started":"2022-01-12T16:28:32.81564Z","shell.execute_reply":"2022-01-12T16:28:32.820793Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class BinaryClassification(nn.Module):\n def __init__(self):\n super(BinaryClassification, self).__init__()\n # Number of input features is 12.\n self.layer_1 = nn.Linear(40, 64) \n self.layer_2 = nn.Linear(64, 64)\n self.layer_out = nn.Linear(64, 1) \n \n self.relu = nn.ReLU()\n self.dropout = nn.Dropout(p=0.5)\n self.batchnorm1 = nn.BatchNorm1d(64)\n self.batchnorm2 = nn.BatchNorm1d(64)\n \n def forward(self, inputs):\n x = self.relu(self.layer_1(inputs))\n x = self.batchnorm1(x)\n x = self.relu(self.layer_2(x))\n x = self.batchnorm2(x)\n x = self.dropout(x)\n x = self.layer_out(x)\n \n return x","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:33.035155Z","iopub.execute_input":"2022-01-12T16:28:33.035676Z","iopub.status.idle":"2022-01-12T16:28:33.043402Z","shell.execute_reply.started":"2022-01-12T16:28:33.035642Z","shell.execute_reply":"2022-01-12T16:28:33.042368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nprint(device)","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:33.292994Z","iopub.execute_input":"2022-01-12T16:28:33.293244Z","iopub.status.idle":"2022-01-12T16:28:33.298481Z","shell.execute_reply.started":"2022-01-12T16:28:33.293214Z","shell.execute_reply":"2022-01-12T16:28:33.297797Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = BinaryClassification()\nmodel.to(device)\nprint(model)\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:33.533782Z","iopub.execute_input":"2022-01-12T16:28:33.534295Z","iopub.status.idle":"2022-01-12T16:28:33.542308Z","shell.execute_reply.started":"2022-01-12T16:28:33.534261Z","shell.execute_reply":"2022-01-12T16:28:33.541609Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def binary_acc(y_pred, y_test):\n y_pred_tag = torch.round(torch.sigmoid(y_pred))\n\n correct_results_sum = (y_pred_tag == y_test).sum().float()\n acc = correct_results_sum/y_test.shape[0]\n acc = torch.round(acc * 100)\n \n return acc","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:33.779813Z","iopub.execute_input":"2022-01-12T16:28:33.780268Z","iopub.status.idle":"2022-01-12T16:28:33.786595Z","shell.execute_reply.started":"2022-01-12T16:28:33.780235Z","shell.execute_reply":"2022-01-12T16:28:33.785738Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.train()\nfor e in range(1, EPOCHS+1):\n epoch_loss = 0\n epoch_acc = 0\n for X_batch, y_batch in train_loader:\n X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n optimizer.zero_grad()\n \n y_pred = model(X_batch)\n \n loss = criterion(y_pred, y_batch.unsqueeze(1))\n acc = binary_acc(y_pred, y_batch.unsqueeze(1))\n \n loss.backward()\n optimizer.step()\n \n epoch_loss += loss.item()\n epoch_acc += acc.item()\n \n\n print(f'Epoch {e+0:03}: | Loss: {epoch_loss/len(train_loader):.5f} | Acc: {epoch_acc/len(train_loader):.3f}')","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:34.023386Z","iopub.execute_input":"2022-01-12T16:28:34.024073Z","iopub.status.idle":"2022-01-12T16:28:34.454651Z","shell.execute_reply.started":"2022-01-12T16:28:34.024033Z","shell.execute_reply":"2022-01-12T16:28:34.453719Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"z_pred_list = []\nmodel.eval()\nwith torch.no_grad():\n for X_batch in valid_loader:\n X_batch = X_batch.to(device)\n z_test_pred = model(X_batch)\n z_test_pred = torch.sigmoid(z_test_pred)\n z_pred_tag = torch.round(z_test_pred)\n z_pred_list.append(z_pred_tag.cpu().numpy())\n\nz_pred_list = [a.squeeze().tolist() for a in z_pred_list]","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:34.456303Z","iopub.execute_input":"2022-01-12T16:28:34.461716Z","iopub.status.idle":"2022-01-12T16:28:35.229555Z","shell.execute_reply.started":"2022-01-12T16:28:34.461666Z","shell.execute_reply":"2022-01-12T16:28:35.228859Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_val, z_pred_list)","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:35.231749Z","iopub.execute_input":"2022-01-12T16:28:35.231979Z","iopub.status.idle":"2022-01-12T16:28:35.249319Z","shell.execute_reply.started":"2022-01-12T16:28:35.231944Z","shell.execute_reply":"2022-01-12T16:28:35.248589Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(y_val, z_pred_list))","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:35.250593Z","iopub.execute_input":"2022-01-12T16:28:35.250901Z","iopub.status.idle":"2022-01-12T16:28:35.266864Z","shell.execute_reply.started":"2022-01-12T16:28:35.250866Z","shell.execute_reply":"2022-01-12T16:28:35.266181Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_list = []\nmodel.eval()\nwith torch.no_grad():\n for X_batch in test_loader:\n X_batch = X_batch.to(device)\n y_test_pred = model(X_batch)\n y_test_pred = torch.sigmoid(y_test_pred)\n y_pred_tag = torch.round(y_test_pred)\n y_pred_list.append(y_pred_tag.cpu().numpy())\n\ny_pred_list = [a.squeeze().tolist() for a in y_pred_list]","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:36.933545Z","iopub.execute_input":"2022-01-12T16:28:36.933879Z","iopub.status.idle":"2022-01-12T16:28:37.696858Z","shell.execute_reply.started":"2022-01-12T16:28:36.933845Z","shell.execute_reply":"2022-01-12T16:28:37.696149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(y_test, y_pred_list)","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:37.723825Z","iopub.execute_input":"2022-01-12T16:28:37.724035Z","iopub.status.idle":"2022-01-12T16:28:37.742059Z","shell.execute_reply.started":"2022-01-12T16:28:37.724009Z","shell.execute_reply":"2022-01-12T16:28:37.741295Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(y_test, y_pred_list))","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:28:38.1273Z","iopub.execute_input":"2022-01-12T16:28:38.127542Z","iopub.status.idle":"2022-01-12T16:28:38.142341Z","shell.execute_reply.started":"2022-01-12T16:28:38.127514Z","shell.execute_reply":"2022-01-12T16:28:38.141642Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"EPOCHS","metadata":{"execution":{"iopub.status.busy":"2022-01-12T16:25:06.392866Z","iopub.execute_input":"2022-01-12T16:25:06.393109Z","iopub.status.idle":"2022-01-12T16:25:06.398264Z","shell.execute_reply.started":"2022-01-12T16:25:06.393077Z","shell.execute_reply":"2022-01-12T16:25:06.397557Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file From 2109dc1a6cb4b8a237bdfd6400ebf9d44dfe4b58 Mon Sep 17 00:00:00 2001 From: Mark Lai Date: Thu, 13 Jan 2022 04:00:36 +0800 Subject: [PATCH 27/39] =?UTF-8?q?=E5=8A=A0=20random=20forest=20code?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index 785a4df..a15741f 100644 --- a/README.md +++ b/README.md @@ -131,6 +131,16 @@ Rscript code/your_script.R --input data/training --output results/performance.ts #--- built val data with Bankrupt and top 40 component val.data <- predict(pca, newdata = res[["validate"]]) + + model <- randomForest(x = train.data[,train.data.var], y = as.factor(train.data$Bankrupt.), + ntree = as.integer(tree), importance = T) + + val <- data.frame(truth = res[["validate"]]$Bankrupt., + prediction = predict(model, val.data)) + #val <- mutate(val, result = ifelse(prediction > 0.5, 1, 0)) + + # confusion matrix of validation + cm <- table(val) ``` ![random forest](./graph/RandomForest-ConfusionMatrix.png) ![random forest](./graph/RandomForest-PCA-ConfusionMatrix.png) From 627d08612419c444a924738f7b25bc142cadc548 Mon Sep 17 00:00:00 2001 From: Mark Lai Date: Thu, 13 Jan 2022 04:03:59 +0800 Subject: [PATCH 28/39] add link --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index a15741f..0f51a8f 100644 --- a/README.md +++ b/README.md @@ -74,7 +74,7 @@ Rscript code/your_script.R --input data/training --output results/performance.ts 2. 利用k-fold validation的方法 篩選出recall最高的model 3. 對於threshold、max depth等超參數進行調整 -* decision tree +* decision tree （詳細請到code資料夾） ```r model<- rpart( Bankrupt. ~ ., data=res[["train"]], @@ -97,7 +97,7 @@ Rscript code/your_script.R --input data/training --output results/performance.ts ![decision tree](./graph/DecisionTree-ConfusionMatrix.png) ![decision tree](./graph/DecisionTree-PCA-ConfusionMatrix.png) -* random forest +* random forest （詳細請到code資料夾） ```r g = sample(cut( @@ -145,7 +145,7 @@ Rscript code/your_script.R --input data/training --output results/performance.ts ![random forest](./graph/RandomForest-ConfusionMatrix.png) ![random forest](./graph/RandomForest-PCA-ConfusionMatrix.png) -* logistic regression +* logistic regression （詳細請到code資料夾） ```r model <- glm(formula = Bankrupt. ~ . , family = binomial(link='probit'), @@ -168,7 +168,7 @@ Rscript code/your_script.R --input data/training --output results/performance.ts ``` ![logistic regression](./graph/LogisticRegression-ConfusionMatrix.png) ![logistic regression](./graph/LogisticRegression-PCA-ConfusionMatrix.png) -* cnn +* cnn （詳細請到code資料夾） ```python class BinaryClassification(nn.Module): def __init__(self): @@ -207,3 +207,4 @@ Rscript code/your_script.R --input data/training --output results/performance.ts https://www.kaggle.com/jerryfang5/bankrutcy-prediciton-by-r/notebook https://www.kaggle.com/seongwonr/bankruptcy-prediction-with-smote https://colab.research.google.com/drive/12wXAyrbX8Ji5J6CNAEIQwtDOaxy8BCIO?usp=sharing +https://towardsdatascience.com/deep-learning-using-pytorch-for-tabular-data-c68017d8b480 From 4717158b411321a39008f821744f6d94ef8d4f6e Mon Sep 17 00:00:00 2001 From: YJack Date: Thu, 13 Jan 2022 04:10:55 +0800 Subject: [PATCH 29/39] last version --- README.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index a15741f..83af830 100644 --- a/README.md +++ b/README.md @@ -13,9 +13,9 @@ ### Demo * [ShinyApp](https://yjack0000.shinyapps.io/shinyui/?_ga=2.142920117.1862022445.1641973117-1531152518.1641397296) * Rscript 使用 -```R -Rscript code/your_script.R --input data/training --output results/performance.tsv -``` +```console + Rscript pcaRpart.R (max depth) (threshold) + ``` ## 檔案架構及其相關資訊 @@ -28,7 +28,10 @@ Rscript code/your_script.R --input data/training --output results/performance.ts * [格式](https://github.com/1101-datascience/finalproject_group4/tree/main/data) * 預處理 * 資料分析 + ![Correltion](./graph/corr2.png) * PCA + ![PCA](./graph/PC.png) + ![Variable Explained](./graph/var_explain.png) * Normalize ### code @@ -39,26 +42,26 @@ Rscript code/your_script.R --input data/training --output results/performance.ts Rscript pcaRpart.R (max depth) (threshold) ``` - ![desision tree](./graph/decision_tree.png) + ![Desision Tree](./graph/decision_tree.png) * random forest ```console Rscript pcaRandomForest.R (tree number) ``` - ![random forest](./graph/random_forest.png) + ![Random Forest](./graph/random_forest.png) * logistic regression ```console Rscript pcaGlm.R (threshold) ``` - ![logistic regression](./graph/logistic_regression.jpeg) + ![Logistic Regression](./graph/logistic_regression.jpeg) * cnn * Null model predict all 1 * data split - ![data split](./graph/data_split.png) + ![Data Split](./graph/data_split.png) * 使用 SMOTE 製作額外的 traning data From d85e1bbb38075f17f949941f9e39573c9babbb69 Mon Sep 17 00:00:00 2001 From: CTHua Date: Thu, 13 Jan 2022 05:23:42 +0800 Subject: [PATCH 30/39] [What]Finish slides and add some image --- docs/slides.md | 220 +++++++++++++++++++++++++++++++ graph/DecisionTree-PCA.png | Bin 0 -> 57161 bytes graph/DecisionTree.png | Bin 0 -> 57511 bytes graph/LogisticRegression-PCA.png | Bin 0 -> 66950 bytes graph/LogisticRegression.png | Bin 0 -> 68920 bytes graph/RandomForest-PCA.png | Bin 0 -> 63337 bytes graph/RandomForest.png | Bin 0 -> 58320 bytes graph/kaggleDescriptionPage.png | Bin 0 -> 443703 bytes graph/pcaLeft.png | Bin 0 -> 68425 bytes graph/pcaRight.png | Bin 0 -> 41484 bytes graph/shinyAppQRcode.png | Bin 0 -> 2533 bytes 11 files changed, 220 insertions(+) create mode 100644 docs/slides.md create mode 100644 graph/DecisionTree-PCA.png create mode 100644 graph/DecisionTree.png create mode 100644 graph/LogisticRegression-PCA.png create mode 100644 graph/LogisticRegression.png create mode 100644 graph/RandomForest-PCA.png create mode 100644 graph/RandomForest.png create mode 100644 graph/kaggleDescriptionPage.png create mode 100644 graph/pcaLeft.png create mode 100644 graph/pcaRight.png create mode 100644 graph/shinyAppQRcode.png diff --git a/docs/slides.md b/docs/slides.md new file mode 100644 index 0000000..536febe --- /dev/null +++ b/docs/slides.md @@ -0,0 +1,220 @@ +--- +marp: true +title: 1101-DataScience-Final-Presentation-Group4 +date: 2022-01-13 +paginate: true +--- + + + +# Company Bankrutcy Prediciton + +Data Science Final Presentation Group 4 + +### 2022-01-13 + +--- + +# Group Members + +Group4： +108703014 鄭宇傑 +108703019 賴冠瑜 +108703029 江宗樺 +108703030 田詠恩 +108304003 張瀚文 + +--- + +# Outline + +--- + +# 資料介紹 + +--- + +# 資料介紹 + +1999 年至 2009 年數據來自自台灣經濟日報的統計。公司破產之定義為台灣證券交易所的業務規則而定。 +![](../graph/kaggleDescriptionPage.png) + +--- + +# 屬性資訊 + +- X2 - ROA(A) before interest and % after tax: Return On Total Assets(A) +- X11 - Operating Expense Rate: Operating Expenses/Net Sales +- X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities +- X33 - Current Ratio +- X92 - Degree of Financial Leverage (DFL) + +- X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise +- X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise + +--- + +# 資料分析 + +--- + +# Shiny App + +## ![center width:500px](../graph/shinyAppQRcode.png) + +--- + +# 相關係數 + +該圖顯示了每個特徵對於對方的相關性。圖中 x 及 y 軸為資料的屬性，格子中的顏色越深，代表著兩屬性之間的相關性越高。 +![width:600px bg right](../graph/corr2.png) + +--- + +# 相關係數 + +該圖顯示了每個特徵對於對方的相關性。圖中 x 及 y 軸為資料的屬性，格子中的顏色越深，代表著兩屬性之間的相關性越高。 +![width:600px bg right](../graph/corr1.png) + +--- + +# 主成份分析 + +從第一主成份(PC1)到第二主成份(PC2)可以發現沒有明顯可以分辨破產與否的成分。 +![width:600px bg right](../graph/PC.png) + +--- + +# 資料解釋度 + +我們可以看到大約 40 個主成份就解釋了超過 90%的差異。 +![](../graph/var_explain.png) + +--- + +# 公司破產分佈 + +![](../graph/SvB.png) + +--- + +# 模型評估標準 + +- 95% 以上的資料中的公司都沒有破產(Bankruptcy == 0) + 所以全部猜 1 就可以有超級高的 Accuracy (NULL model) + +- 我們將目標設定成要盡可能增加 recall 。 + 嘗試預測出更多可能會倒的公司去對他們做關切或提早做應對措施，並去檢視可能面臨的問題，是這次專題的主要目標。 + +--- + +# Models + +--- + +# pca 分析 -> 取前 40 個 + +![bg right width:500px](../graph/pcaRight.png) +![right width:500px](../graph/pcaLeft.png) + +--- + +# model #1: rpart decision tree + +| decision tree | decision tree with pca | +| ------------------------------------------------------ | --------------------------------------------------------- | +| ![ h:300px](../graph/DecisionTree-ConfusionMatrix.png) | ![h:300px](../graph/DecisionTree-PCA-ConfusionMatrix.png) | + +--- + +# model #1: rpart decision tree + +| decision tree | decision tree with pca | +| ----------------------------------------- | ------------------------------------- | +| ![h:300px](../graph/DecisionTree-PCA.png) | ![h:300px](../graph/DecisionTree.png) | + +--- + +# model #2: random forest + +| random forest | decision tree with pca | +| ------------------------------------------------------ | --------------------------------------------------------- | +| ![ h:300px](../graph/RandomForest-ConfusionMatrix.png) | ![h:300px](../graph/RandomForest-PCA-ConfusionMatrix.png) | + +--- + +# model #2: random forest + +| random forest | decision tree with pca | +| ------------------------------------- | ----------------------------------------- | +| ![h:300px](../graph/RandomForest.png) | ![h:300px](../graph/RandomForest-PCA.png) | + +--- + +# model #3: logistic regression + +| logistic regression | logistic regression with pca | +| ------------------------------------------------------------ | --------------------------------------------------------------- | +| ![ h:300px](../graph/LogisticRegression-ConfusionMatrix.png) | ![h:300px](../graph/LogisticRegression-PCA-ConfusionMatrix.png) | + +--- + +# model #3: logistic regression + +| logistic regression | logistic regression with pca | +| ------------------------------------------- | ----------------------------------------------- | +| ![h:300px](../graph/LogisticRegression.png) | ![h:300px](../graph/LogisticRegression-PCA.png) | + +--- + +# SMOTE 生成資料 + +--- + +# Pytorch + +--- + +# epoch 次數比較 + +| epoch 1-10 | epoch 10-150 | +| ---------------------------------- | ------------------------------------ | +| ![h:300px](../graph/epoch1-10.png) | ![h:300px](../graph/epoch10-150.png) | + +--- + +# 總結 + +| recall | 原始資料 | 透過 PCA 降維處理 | +| -------------------------- | -------- | ----------------- | +| Decision Tree | | | +| Random Forest | | | +| Logistic Regression | | | +| Convolution Neural Network | | | + +--- + +# 問題與討論 + +- 資料面? + - 資料的收集 + - 資料的真實性 +- 模型面? + - 資料數量 + - 對未來預測的時效性 + +--- + +# Reference + +https://www.kaggle.com/jerryfang5/bankrutcy-prediciton-by-r/notebook +https://www.kaggle.com/seongwonr/bankruptcy-prediction-with-smote +https://colab.research.google.com/drive/12wXAyrbX8Ji5J6CNAEIQwtDOaxy8BCIO?usp=sharing diff --git a/graph/DecisionTree-PCA.png b/graph/DecisionTree-PCA.png new file mode 100644 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zco#LOn-mMcwYnf*ZT;CRulmPX&5uy+qQ@QxtvWizr{^7qUo#x(LrT`?9~4*p8&v&V Am;e9( literal 0 HcmV?d00001 From e4556f33c8c83166d7301f8fe8fcba44efce4aab Mon Sep 17 00:00:00 2001 From: CTHua Date: Thu, 13 Jan 2022 05:24:43 +0800 Subject: [PATCH 31/39] [What]Export MARP to PDF --- docs/README.md | 225 +++++++++++++++++++++++++++++++++++++++++++++++- docs/slides.pdf | Bin 0 -> 1905444 bytes 2 files changed, 224 insertions(+), 1 deletion(-) create mode 100644 docs/slides.pdf diff --git a/docs/README.md b/docs/README.md index 277a319..a7c1cf7 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1 +1,224 @@ -* [Google Slide for Presentation](https://docs.google.com/presentation/d/1TWPNksUenzi-DsquO6Yv7WBCVPvZE-HgyjMmvAcAH3U/edit#slide=id.g10d591fe8d9_0_169) +--- +marp: true +title: 1101-DataScience-Final-Presentation-Group4 +date: 2022-01-13 +paginate: true +--- + + + +# Company Bankrutcy Prediciton + +Data Science Final Presentation Group 4 + +### 2022-01-13 + +--- + +# Group Members + +Group4： +108703014 鄭宇傑 +108703019 賴冠瑜 +108703029 江宗樺 +108703030 田詠恩 +108304003 張瀚文 + +--- + +# Outline + +--- + +# 資料介紹 + +--- + +# 資料介紹 + +1999 年至 2009 年數據來自自台灣經濟日報的統計。公司破產之定義為台灣證券交易所的業務規則而定。 +![](../graph/kaggleDescriptionPage.png) + +--- + +# 屬性資訊 + +- X2 - ROA(A) before interest and % after tax: Return On Total Assets(A) +- X11 - Operating Expense Rate: Operating Expenses/Net Sales +- X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities +- X33 - Current Ratio +- X92 - Degree of Financial Leverage (DFL) + +- X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise +- X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise + +--- + +# 資料分析 + +--- + +# Shiny App + +## ![center width:500px](../graph/shinyAppQRcode.png) + +--- + +# 相關係數 + +該圖顯示了每個特徵對於對方的相關性。圖中 x 及 y 軸為資料的屬性，格子中的顏色越深，代表著兩屬性之間的相關性越高。 +![width:600px bg right](../graph/corr2.png) + +--- + +# 相關係數 + +該圖顯示了每個特徵對於對方的相關性。圖中 x 及 y 軸為資料的屬性，格子中的顏色越深，代表著兩屬性之間的相關性越高。 +![width:600px bg right](../graph/corr1.png) + +--- + +# 主成份分析 + +從第一主成份(PC1)到第二主成份(PC2)可以發現沒有明顯可以分辨破產與否的成分。 +![width:600px bg right](../graph/PC.png) + +--- + +# 資料解釋度 + +我們可以看到大約 40 個主成份就解釋了超過 90%的差異。 +![](../graph/var_explain.png) + +--- + +# 公司破產分佈 + +![](../graph/SvB.png) + +--- + +# 模型評估標準 + +- 95% 以上的資料中的公司都沒有破產(Bankruptcy == 0) + 所以全部猜 1 就可以有超級高的 Accuracy (NULL model) + +- 我們將目標設定成要盡可能增加 recall 。 + 嘗試預測出更多可能會倒的公司去對他們做關切或提早做應對措施，並去檢視可能面臨的問題，是這次專題的主要目標。 + +--- + +# Models + +--- + +# pca 分析 -> 取前 40 個 + +![bg right width:500px](../graph/pcaRight.png) +![right width:500px](../graph/pcaLeft.png) + +--- + +# model #1: rpart decision tree + +| decision tree | decision tree with pca | +| ------------------------------------------------------ | --------------------------------------------------------- | +| ![ h:300px](../graph/DecisionTree-ConfusionMatrix.png) | ![h:300px](../graph/DecisionTree-PCA-ConfusionMatrix.png) | + +--- + +# model #1: rpart decision tree + +| decision tree | decision tree with pca | +| ----------------------------------------- | ------------------------------------- | +| ![h:300px](../graph/DecisionTree-PCA.png) | ![h:300px](../graph/DecisionTree.png) | + +--- + +# model #2: random forest + +| random forest | decision tree with pca | +| ------------------------------------------------------ | --------------------------------------------------------- | +| ![ h:300px](../graph/RandomForest-ConfusionMatrix.png) | ![h:300px](../graph/RandomForest-PCA-ConfusionMatrix.png) | + +--- + +# model #2: random forest + +| random forest | decision tree with pca | +| ------------------------------------- | ----------------------------------------- | +| ![h:300px](../graph/RandomForest.png) | ![h:300px](../graph/RandomForest-PCA.png) | + +--- + +# model #3: logistic regression + +| logistic regression | logistic regression with pca | +| ------------------------------------------------------------ | --------------------------------------------------------------- | +| ![ h:300px](../graph/LogisticRegression-ConfusionMatrix.png) | ![h:300px](../graph/LogisticRegression-PCA-ConfusionMatrix.png) | + +--- + +# model #3: logistic regression + +| logistic regression | logistic regression with pca | +| ------------------------------------------- | ----------------------------------------------- | +| ![h:300px](../graph/LogisticRegression.png) | ![h:300px](../graph/LogisticRegression-PCA.png) | + +--- + +# SMOTE 生成資料 + +--- + +# Pytorch + +--- + +# epoch 次數比較 + +| epoch 1-10 | epoch 10-150 | +| ---------------------------------- | ------------------------------------ | +| ![h:300px](../graph/epoch1-10.png) | ![h:300px](../graph/epoch10-150.png) | + +--- + +# 總結 + +| recall | 原始資料 | 透過 PCA 降維處理 | +| -------------------------- | -------- | ----------------- | +| Decision Tree | | | +| Random Forest | | | +| Logistic Regression | | | +| Convolution Neural Network | | | + +--- + +# 問題與討論 + +- 資料面? + - 資料的收集 + - 資料的真實性 +- 模型面? + - 資料數量 + - 對未來預測的時效性 + +--- + +# Reference + +https://www.kaggle.com/jerryfang5/bankrutcy-prediciton-by-r/notebook +https://www.kaggle.com/seongwonr/bankruptcy-prediction-with-smote +https://colab.research.google.com/drive/12wXAyrbX8Ji5J6CNAEIQwtDOaxy8BCIO?usp=sharing + +--- + +- [Google Slide for Presentation](https://docs.google.com/presentation/d/1TWPNksUenzi-DsquO6Yv7WBCVPvZE-HgyjMmvAcAH3U/edit#slide=id.g10d591fe8d9_0_169) diff --git a/docs/slides.pdf b/docs/slides.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6f38d08bf79c50ba3adbae68475c42034b18ee70 GIT binary patch literal 1905444 zcma&Nbx@meyDeI*#R`Q`+$Fe6a4Qxd6nBcd1lOX)0u=Y)QnW=&arfe`L5jNAMlhzjO{o!972R$0oGmM*I#@1=Cr#|JXPDIdE}lN@&9mxbE!e*fA{P$$Y#wH<4-(FH(u4AD#Q$@{9?T?qcyke=tQL{ zV4$HHr^EHTP!>I(Q`;t6Pm(CcKFB}mu(!k0cY5lyr4;@mn^>^~i0OgUn)vlmzz3NCTZ5@zW}w^tXW zzoK}jVkd#~KK6ltZ2~@mu zaWGqYVGd^Cb=)@xwP9QNPD8rds?5we^m?XUFwsQMsaF_Q2E^OKb}1yi?iJ<(XVTD2z%*`HDBvD=L0 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1101-DataScience-Final-Presentation-Group4 -date: 2022-01-13 -paginate: true ---- - - - # Company Bankrutcy Prediciton Data Science Final Presentation Group 4 @@ -34,14 +17,6 @@ Group4： --- -# Outline - ---- - -# 資料介紹 - ---- - # 資料介紹 1999 年至 2009 年數據來自自台灣經濟日報的統計。公司破產之定義為台灣證券交易所的業務規則而定。 @@ -64,11 +39,9 @@ Group4： # 資料分析 ---- - -# Shiny App +## Shiny App -## ![center width:500px](../graph/shinyAppQRcode.png) +![center width:500px](../graph/shinyAppQRcode.png) --- @@ -132,14 +105,7 @@ Group4： | decision tree | decision tree with pca | | ------------------------------------------------------ | --------------------------------------------------------- | | ![ h:300px](../graph/DecisionTree-ConfusionMatrix.png) | ![h:300px](../graph/DecisionTree-PCA-ConfusionMatrix.png) | - ---- - -# model #1: rpart decision tree - -| decision tree | decision tree with pca | -| ----------------------------------------- | ------------------------------------- | -| ![h:300px](../graph/DecisionTree-PCA.png) | ![h:300px](../graph/DecisionTree.png) | +| ![h:300px](../graph/DecisionTree-PCA.png) | ![h:300px](../graph/DecisionTree.png) | --- @@ -148,14 +114,7 @@ Group4： | random forest | decision tree with pca | | ------------------------------------------------------ | --------------------------------------------------------- | | ![ h:300px](../graph/RandomForest-ConfusionMatrix.png) | ![h:300px](../graph/RandomForest-PCA-ConfusionMatrix.png) | - ---- - -# model #2: random forest - -| random forest | decision tree with pca | -| ------------------------------------- | ----------------------------------------- | -| ![h:300px](../graph/RandomForest.png) | ![h:300px](../graph/RandomForest-PCA.png) | +| ![h:300px](../graph/RandomForest.png) | ![h:300px](../graph/RandomForest-PCA.png) | --- @@ -164,14 +123,7 @@ Group4： | logistic regression | logistic regression with pca | | ------------------------------------------------------------ | --------------------------------------------------------------- | | ![ h:300px](../graph/LogisticRegression-ConfusionMatrix.png) | ![h:300px](../graph/LogisticRegression-PCA-ConfusionMatrix.png) | - ---- - -# model #3: logistic regression - -| logistic regression | logistic regression with pca | -| ------------------------------------------- | ----------------------------------------------- | -| ![h:300px](../graph/LogisticRegression.png) | ![h:300px](../graph/LogisticRegression-PCA.png) | +| ![h:300px](../graph/LogisticRegression.png) | ![h:300px](../graph/LogisticRegression-PCA.png) | --- diff --git a/docs/slides.md b/docs/slides.md index 536febe..631521b 100644 --- a/docs/slides.md +++ b/docs/slides.md @@ -34,10 +34,6 @@ Group4： --- -# Outline - ---- - # 資料介紹 --- diff --git a/docs/slides.pdf b/docs/slides.pdf index 6f38d08bf79c50ba3adbae68475c42034b18ee70..b4736cef39a7d8ed441bead07c232ff23a132293 100644 GIT binary patch delta 54963 zcmaI7Wl$weyDW;kyE_c-?hNkkKDfKf!WrCIID@;pySqDsy9^G4UEcTGJI?*$+_({) 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a/docs/README.md +++ b/docs/README.md @@ -43,43 +43,31 @@ Group4： ![center width:500px](../graph/shinyAppQRcode.png) ---- - -# 相關係數 +## 相關係數 該圖顯示了每個特徵對於對方的相關性。圖中 x 及 y 軸為資料的屬性，格子中的顏色越深，代表著兩屬性之間的相關性越高。 ![width:600px bg right](../graph/corr2.png) ---- - -# 相關係數 +## 相關係數 該圖顯示了每個特徵對於對方的相關性。圖中 x 及 y 軸為資料的屬性，格子中的顏色越深，代表著兩屬性之間的相關性越高。 ![width:600px bg right](../graph/corr1.png) ---- - -# 主成份分析 +## 主成份分析 從第一主成份(PC1)到第二主成份(PC2)可以發現沒有明顯可以分辨破產與否的成分。 ![width:600px bg right](../graph/PC.png) ---- - -# 資料解釋度 +## 資料解釋度 我們可以看到大約 40 個主成份就解釋了超過 90%的差異。 ![](../graph/var_explain.png) ---- - -# 公司破產分佈 +## 公司破產分佈 ![](../graph/SvB.png) ---- - -# 模型評估標準 +## 模型評估標準 - 95% 以上的資料中的公司都沒有破產(Bankruptcy == 0) 所以全部猜 1 就可以有超級高的 Accuracy (NULL model) @@ -91,34 +79,26 @@ Group4： # Models ---- - -# pca 分析 -> 取前 40 個 +## pca 分析 -> 取前 40 個 ![bg right width:500px](../graph/pcaRight.png) ![right width:500px](../graph/pcaLeft.png) ---- - -# model #1: rpart decision tree +## model #1: rpart decision tree | decision tree | decision tree with pca | | ------------------------------------------------------ | --------------------------------------------------------- | | ![ h:300px](../graph/DecisionTree-ConfusionMatrix.png) | ![h:300px](../graph/DecisionTree-PCA-ConfusionMatrix.png) | | ![h:300px](../graph/DecisionTree-PCA.png) | ![h:300px](../graph/DecisionTree.png) | ---- - -# model #2: random forest +## model #2: random forest | random forest | decision tree with pca | | ------------------------------------------------------ | --------------------------------------------------------- | | ![ h:300px](../graph/RandomForest-ConfusionMatrix.png) | ![h:300px](../graph/RandomForest-PCA-ConfusionMatrix.png) | | ![h:300px](../graph/RandomForest.png) | ![h:300px](../graph/RandomForest-PCA.png) | ---- - -# model #3: logistic regression +## model #3: logistic regression | logistic regression | logistic regression with pca | | ------------------------------------------------------------ | --------------------------------------------------------------- | @@ -129,13 +109,9 @@ Group4： # SMOTE 生成資料 ---- - # Pytorch ---- - -# epoch 次數比較 +## epoch 次數比較 | epoch 1-10 | epoch 10-150 | | ---------------------------------- | ------------------------------------ | From 21aaf1a5333e319711906c2bbacd0eeef6f8175a Mon Sep 17 00:00:00 2001 From: CTHua Date: Thu, 13 Jan 2022 08:38:16 +0800 Subject: [PATCH 34/39] [What]Fix conclusion --- docs/README.md | 8 ++++---- docs/slides.md | 8 ++++---- docs/slides.pdf | Bin 1904939 -> 1905801 bytes 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/README.md b/docs/README.md index 3a1a6bd..956bd8e 100644 --- a/docs/README.md +++ b/docs/README.md @@ -123,10 +123,10 @@ Group4： | recall | 原始資料 | 透過 PCA 降維處理 | | -------------------------- | -------- | ----------------- | -| Decision Tree | | | -| Random Forest | | | -| Logistic Regression | | | -| Convolution Neural Network | | | +| Decision Tree | 0.15 | 0.26 | +| Random Forest | 0.36 | 0.15 | +| Logistic Regression | 0.32 | 0.63 | +| Convolution Neural Network | 0.78 | Yes | --- diff --git a/docs/slides.md b/docs/slides.md index 631521b..5489026 100644 --- a/docs/slides.md +++ b/docs/slides.md @@ -191,10 +191,10 @@ Group4： | recall | 原始資料 | 透過 PCA 降維處理 | | -------------------------- | -------- | ----------------- | -| Decision Tree | | | -| Random Forest | | | -| Logistic Regression | | | -| Convolution Neural Network | | | +| Decision Tree | 0.15 | 0.26 | +| Random Forest | 0.36 | 0.15 | +| Logistic Regression | 0.32 | 0.63 | +| Convolution Neural Network | 0.78 | Yes | --- diff --git a/docs/slides.pdf b/docs/slides.pdf index b4736cef39a7d8ed441bead07c232ff23a132293..f02f117579b247dc24cc7f859f0284d5d921c936 100644 GIT binary patch delta 32758 zcmZ6ycRbf$^f=z@W$&51p3j$=J)`WbP$7!!O=!r7WW|e;5?SFYDOUbWKMGqCJCVgbxWIIN?(46CA zU;NeBZfyZ<5}bEdroJxCeUMB4i%Y@0uA=H3U7Qg4^`@#yU?bIl1}uCFhQxo+OXMulazs&hPDKQndGk@K7bN_!`1eP$@F 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----------------------------------------- | ------------------------------------- | From 3f840be7c3484d3cc65d2b408c2d358e4a74dceb Mon Sep 17 00:00:00 2001 From: CTHua Date: Thu, 13 Jan 2022 09:21:27 +0800 Subject: [PATCH 36/39] [What]Update conclusion --- docs/README.md | 2 +- docs/slides.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/README.md b/docs/README.md index 956bd8e..fe4a82c 100644 --- a/docs/README.md +++ b/docs/README.md @@ -126,7 +126,7 @@ Group4： | Decision Tree | 0.15 | 0.26 | | Random Forest | 0.36 | 0.15 | | Logistic Regression | 0.32 | 0.63 | -| Convolution Neural Network | 0.78 | Yes | +| Convolution Neural Network | 0.68 | 0.78 | --- diff --git a/docs/slides.md b/docs/slides.md index de39a63..92c69bb 100644 --- a/docs/slides.md +++ b/docs/slides.md @@ -194,7 +194,7 @@ Group4： | Decision Tree | 0.15 | 0.26 | | Random Forest | 0.36 | 0.15 | | Logistic Regression | 0.32 | 0.63 | -| Convolution Neural Network | 0.78 | Yes | +| Convolution 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