Kaggle: Allstate Insurance

Frederick Cheung
Yisong Tao
Jordan Waters
, and
Posted on Dec 4, 2016

Background

For this project our dataset was selected for us. Since it was a Kaggle competition our primary goal in mind was simply to obtain as high of a score as possible on the leaderboard. Our secondary goal was both to exercise and to demonstrate our knowledge of machine learning and apply it to our given dataset.

The main takeaway

Although the data has been anonymized and features obscured, through feature selection we have determined that all the features are needed to help improve our score, none can be eliminated. Additionally, an ensemble method of XGBoost and Neural Networks seems to offer the best results for the competition (our best result placed us in the top 7% on the Kaggle Leaderboard).

Our process

EDA:

For the purpose of cutting down our learning curve regarding the dataset we made extensive use of the forums to read on what methods of EDA people had used to gain basic insights into the dataset. We settled on reproducing specific insights.

newplot Cat116 was shown to be states. Upon comparing the medians of each of the states we drew the conclusion that state from which the claim originated had little effect on the loss column.
train_cont7_14 The continuous variables in the dataset do not show a strong correlation with the log(loss)

Note: we took the log of the loss column for the purpose of attempting to find a linear relationship

 

Feature Selection

mcatrain We chose to use MCA, a similar technique to PCA but for use with categorical variables. Even with running 100 MCAs none of them showed an Eigenvalue <1
ridge  From the cross-validation plot, we can find the best lambda value that minimizes the mean absolute error of the ridge regression model. the faster a coefficient shrinks the zero, the less important it is.

Tools

We made use of AWS Cloud Computing services in our project to overcome the logistical difficulty of lacking enough computing power to run our more complicated models. Our initial trials on our laptops and on our school server crashed every time we attempted to run XGBoost so utilized an AWS based RStudio instance and used our experience from that to run our Neural Net on an Ubuntu instance. For both of these models we ran our script on a subset from the data and ensured it worked before doing the actual run on the On-Demand Instance on AWS.

Further work

Through a combination of utilizing prior knowledge from other Kaggler's EDA and their experience seeing which models worked best we were able to settle on an ensemble of XGBoost and Neural Nets. However, further tuning of parameters in each of these models and in combination with each other is necessary to better our score. There is a methodical approach toward tackling Data Science problems and we hope to use our knowledge gained from trying various models and difficulties in workflow to help us work more effectively and efficiently in future projects.

About Authors

Frederick Cheung

Frederick Cheung

Hi my name is Fred. Although my educational background is an M.S. in Medical Science, my professional experience is with Small Business management, operations and sustainable business practices. I’ve recently completed a Data Science program working with languages...
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Yisong Tao

Yisong Tao

Yisong Tao graduated from Columbia University with a PhD degree in Chemistry, after which he worked as a research associate at Albert Einstein College of Medicine for 6 years. He’s shown good judgement in developing projects, ability to...
View all posts by Yisong Tao >
Jordan Waters

Jordan Waters

After receiving as B.S. in microbiology from Auburn University, I was hired by the Centers for Disease Control in Atlanta, Ga. There, I designed DNA-based detection and diagnostic tools to decrease public health costs and response times ....
View all posts by Jordan Waters >

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