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Data Mining in Industrial Processes: Evaluation of different machine learning models for product quality prediction. Evaluated model types are Random Forest, Naive Gaussian Bayes, Logistic Regression, K Nearest Neighbour and Support Vector Machine. Comparision of non time based state based approach with time series based approach. Final result i…

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AndreasBraun5/Data-Mining-in-Industrial-Processes-Evaluation-of-different-machine-learning-models-for-Prediction

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Data Mining in Industrial Processes: Evaluation of different machine learning models for product quality prediction

####################################################################################### LinkedIn: https://www.linkedin.com/in/andreas-braun-6796ba12a/. #######################################################################################

Project Strucutre

Data Science as a Software, extension of (https://github.com/drivendata/cookiecutter-data-science)

Some Frameworks:

Preprocessing, Pandas: https://github.com/pandas-dev/pandas
Machine Learning, Scikit-learn: https://github.com/scikit-learn/scikit-learn
Auto Machine Learning, TPOT: https://github.com/EpistasisLab/tpot
Time Series Feature Extraction, TSFRESH: https://github.com/blue-yonder/tsfresh

Final Result

Data Processing Pipeline

Data Type Portability

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Data Mining in Industrial Processes: Evaluation of different machine learning models for product quality prediction. Evaluated model types are Random Forest, Naive Gaussian Bayes, Logistic Regression, K Nearest Neighbour and Support Vector Machine. Comparision of non time based state based approach with time series based approach. Final result i…

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