GitHub - AnilSener/Axa-Insurance-Telematics-Kaggle: I developed this case study only in 7 days with Pyspark (Spark 1.6.0) SQL & MLlib. I used Databricks cluster and AWS. %90 AUC is achieved (without involving Trip Matching-Repeated Trips feature) with Random Forest. Many ensembles with RF, GBT and Logistic Regression and outlier elimination could be used to improve this result. There are two versions of my code (test and full execution). Since AWS costs have exceeded my budget I sopped to train my model(s) all dataset for full dataset execution. There is also a ppt that presents my outputs in test execution. Full Data Execution code is more production ready and slightly different version. I had to use Databricks Table Caching to TRAIN and TEST data tables to obtain acceptable performance in production ready version.

AnilSener / Axa-Insurance-Telematics-Kaggle Public

I developed this case study only in 7 days with Pyspark (Spark 1.6.0) SQL & MLlib. I used Databricks cluster and AWS. %90 AUC is achieved (without involving Trip Matching-Repeated Trips feature) with Random Forest. Many ensembles with RF, GBT and Logistic Regression and outlier elimination could be used to improve this result. There are two vers…

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Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
.gitattributes		.gitattributes
.gitignore		.gitignore
Axa Insurance Telematics.pptx		Axa Insurance Telematics.pptx
DATASUBSET.zip		DATASUBSET.zip
full_dataset_execution.ipynb		full_dataset_execution.ipynb
test_env.ipynb		test_env.ipynb