Hi all,

I am an intern who is currently working on the project "Use machine
learning for predictive maintenance", which is a use case of IOT. I have
been working with the "Turbofan Engine Degradation Simulation Data Set"
published by NASA as my core data set. The goal of this experiment is to
predict the remaining useful lifetime of turbofan engines based on their
sensor readings. I have built both classification and regression models for
this scenario.

Link to the data set.
https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/#turbofan.

The algorithms that I have used and their test results are,
*Random Forest Classification* [Classification]
Result :
https://docs.google.com/a/wso2.com/document/d/1EiPbEA5cq4823qTB5TAq80ItywzQwPdo8dn9iSQig4s/edit?usp=sharing

*Random Forest Regression* [Regression]
Result :
https://docs.google.com/a/wso2.com/document/d/1HKW84QyJyAwK6WchRpGwmXVwDRdTEIAmdD1Bz5hVxjs/edit?usp=sharing

*H2O Deep Learning* [Regression]
Result :
https://docs.google.com/a/wso2.com/document/d/1SncvDlVsxsSfUIj11rAwWQWWU6uWW41pPSJ92iHuV-c/edit?usp=sharing

*These results are only valid for following data sets,*
Training   : train_FD001.txt
Testing    : test_FD001.txt
Validating : RUL_FD001.txt

If you have any suggestions for improvements, please let me know. For
further information, contact through the email.

Best Regards,
-- 
Roshan Alwis
*Trainee Software Engineer*
*WSO2*
m: 0715894672
a: No 3/56, Aluthgama Rd, Elpitiya.
e: rosh...@wso2.com
<https://web.facebook.com/alwisroshan>   <https://twitter.com/rm_alwis>
<https://lk.linkedin.com/in/roshanalwis>
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