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  Fault Detection -Challenge 1

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  Challenge 2

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  CCMS

EESL Hackathon CCMS

This repository consists of challenges 1 and 2 prototype model for the CCMS .

Challenge 1:

We have tried to come up with a solution by using hashing to identify different LED lamps and then the data has been cleansed and thoroughly visualized to see various correlations between the different attributes in the datasets.Then we have used Decision Tree to predict the faulty LED lamp by building a model using Sci-kit.

Challenge 2:

Here we have build again a model using the famous Support Vector Machine(SVM) algorithm.Different attributes have been evaluated based on their correlation factors and then a model with an accuracy of 92% has been achieved.A permanent link to the notebook can been found at https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6954848893521581/1283534314062248/2558959897262863/latest.html

Built With

• jupyter-notebook-python