KAGGLE COMPETITION: ALLSTATE CLAIMS SEVERITY

Presenter: Yuwei(Ruby) Jiao

16-11-29 CS 846 Software Engineering for Big Data 1

Outline • Background • Problem • Goals • Approach • Expectation •  Future Work

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Background • Kaggle:

•  a platform for predictive modeling and analytics competitions

•  companies and researchers post their data •  statisticians and data miners from all over the world

compete to produce the best models • Allstate:

•  the second largest personal lines insurer in the United States

•  is currently developing automated methods of predicting the cost, and hence severity of claims

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Problem • Data

•  train.csv (188318 x 132) •  test.csv (125546 x 131)

• Attributes •  ID 1 •  Categorical 116 •  Continuous 14 •  Loss 1

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Goals • Explore raw data

•  Data Statistics •  Data Visualization •  Data Transformation •  Data Interaction •  Data Preparation

• Evaluation, prediction and analysis •  Explore different machine learning models and algorithms

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Approach •  Language:

•  Python 3.0

•  Library:

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Approach --- Data Statistics

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skew

Approach --- Data Visualization

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Approach --- Data Visualization

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Approach --- Data Transformation

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Approach --- Data Interaction

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Approach --- Data Preparation • Divide into dataset into train and validation set • Convert categorical attributes to binary vector with one-

hot encoding •  Determining the state has a low and constant cost •  Changing the state has the constant cost •  Easy to design and modify •  Easy to detect illegal states •  Takes advantage of an FPGA's abundant flip-flops

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Expectation • Make prediction:

•  XGBoost

• Current ranking: •  50%

• Expectation ranking: •  30%?

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Future Work •  Feature engineering

•  Use domain knowledge of the data to create features •  Make machine learning algorithms work

• Evaluation, prediction and analysis •  Linear Regression (Linear algo) •  LASSO Linear Regression (Linear algo) •  KNN (non-linear algo) •  SVM (Non-linear algo) •  Random Forest (Bagging) •  AdaBoost (Boosting)

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Thank you!

Q + A?