[Group4] Bankrutcy Prediciton

組員

• 鄭宇傑, 108703014
• 賴冠瑜, 108703019
• 張瀚文, 108304003
• 江宗樺, 108703029
• 田詠恩, 108703030

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

• ShinyApp
• Rscript 使用

  Rscript pcaRpart.R (max depth) (threshold)

檔案架構及其相關資訊

docs

• Google Slide for Presentation

data

• 來源
• 格式
• 預處理
  • 資料分析
  • PCA
  • Normalize

code

• method we use

  • decision tree

  Rscript pcaRpart.R (max depth) (threshold)

  

  • random forest

  Rscript pcaRandomForest.R (tree number)

  

  • logistic regression

  Rscript pcaGlm.R (threshold)

  

  • cnn

• Null model predict all 1

• data split

  

• 使用 SMOTE 製作額外的 traning data

  

results

• 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 （詳細請到code資料夾）

  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)

  

• random forest （詳細請到code資料夾）

  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"]]) 
  
  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)

  

• logistic regression （詳細請到code資料夾）

  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)])

  

• cnn （詳細請到code資料夾）

  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

  

• 較有挑戰性的部分

  • 訓練資料中不平均（大多都沒有倒閉）
  • tuning of hyperparameters
  • testing of different epoch
  • choose between precision and recall

References

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