Predictive Analytics - Allstate Claims Severity

Posted on Jul 27, 2017


The Allstate claims severity challenge was a Kaggle competition that was open in 2016. The task at hand was to perform predictive analytics to find claim severity. A training dataset containing 116 categorical variables and 14 continuous variables was provided. Each row represented a claim. Along with the predictor variables, there was also the actual loss on that claim.

The training dataset contains 188,318 claim records. The prediction task was to find the loss for each of the 126546 claims in the test data set.

The scoring was done based on accuracy of submissions that were made on claim severity of a separate test file that was provided. This file did not have the loss column.


My Workflow


  1. Load Train Data
  2. Data Visualization
  3. Data Preprocessing
  4. Model Selection
    1. Feature Selection
    2. Model Training
    3. Model Validation
    4. Execution of model on new data
  5. Ensembling of models
  6. Extreme Gradient Boosting


  1. Load Test Data
  2. Data Preprocessing
  3. Model Execution on Test data
  4. Creation and submission of the prediction

Loading of data and visualizations


Loss Distribution in Train File

We see that the loss itself is heavily skewed to the left. This has an implication when it comes to model training. I found that by using logarithmic functions on top of loss, the distribution was made to be much more normalized and this helped in getting better predictions.

Log of Loss - Allstate Train Data

Log of Loss - Allstate Train Data

To further visualize all the fields, a for loop was used to create charts on each of the variables.

carvars = paste("cat", 1:116, sep="")
for( catvar in 1:11){
   catvar <- paste("cat", catvar, sep="")
   p <- ggplot(allstate, aes_string("logloss", fill=catvar)) + geom_histogram(binwidth = 1) 

Loading the train data

Partitioning of the data

We are going to take 80% of the train data and use it for our model training. The remaining 20% of the data will be used to determine how well our model worked.

# define an 80%/20% train/test split of the dataset
trainIndex <- createDataPartition(allstate$id, p=split, list=FALSE)
data_train <- allstate[ trainIndex,]
data_test <- allstate[-trainIndex,]


Feature Selection

To optimize our models, we will need to take a subset of the features available. I chose to run the rpart model on the entire dataset to come up with the fields that we need.

catfactors <- paste("cat", 1:116, sep="")
contfactors <-paste("cont", 1:14, sep="")
formula = reformulate(termlabels = c(catfactors,contfactors), response = 'logloss')
modelFit <- train( formula,data=allstate, method="rpart" )  

cat80D 100.00

cat80B 99.75

cat12B 78.30

cat79D 75.14

cat79B 59.11

cat10B 18.35

cat1B 18.27

cat81D 16.09

cat81B 14.15


Execution of various models

The following models were tried and the best scores were obtained by the XgBoost model.

  1. Linear Regression
  2. Rpart
  3. Xgboost


catfactors <- c(“cat80”,“cat12”,“cat79”, “cat10”, “cat1”, “cat81”)

formula = reformulate(termlabels = c(catfactors,contfactors), response = ‘logloss’)

ControlParamteres <- trainControl(method = “cv”, number = 10, savePredictions = TRUE, classProbs = TRUE, verboseIter = TRUE )

parametersGrid <- expand.grid(nrounds=100, lambda=.5, alpha=.5, eta = 0.1 )

model.xgboost <- train(formula, data = data_train,method = “xgbLinear”, trControl = ControlParamteres, tuneGrid=parametersGrid)warnings() summary(model.xgboost)

Validating the model

# Validating our model
x_test <- data_test[c(catfactors,contfactors)]
y_test <- data_test[,"logloss"]
predictions <- predict(model.xgboost, x_test) 
#Computing RMSE and R2
caret::RMSE(pred = predictions, obs = y_test)
caret::R2(pred = predictions, obs = y_test)


Predictions on the test file

out_test <-testFile 
id <-out_test$id 
logloss <- predict(model.xgboost, out_test) 
loss <- exp(logloss) 


Creating the submission file

options(scipen=999) # Removing the exponential format  
write.csv(file=“./submit.csv”,out_file,row.names = FALSE) 

Challenges faced

The data is highly anonymized and this prevented any kind of feature engineering on top of the data provided. Using Domain knowledge to fine tune our models was almost impossible.

The large set of features slowed down the models by a huge extend. In the interest of creating a model that is of practical use, I had to select the features to use. The lack of domain information led to possibility of errors while eliminating features for training.


I was able to learn how to apply various models. Caret as a package makes adoption of various models a breeze. I was also able to start using RMarkdown as a documenting tool while developing models.



About Author


Smitha Mathew

Technology Enthusiast, with attention to detail, having global exposure. She is a self-motivated problem solver with experience analyzing data and deriving meaningful statistical information. Her goal is to be able to make a positive difference in peoples lives...
View all posts by Smitha Mathew >

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