Description
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Project Notebook
Please provide the following regarding the project notebook (Note: Python 3.7+/sklearn 0.22.2+/ONNX 1.6+):
A Scikit-Learn based Pipeline for training with the provided data in csv format – model research/ t development.
A ONNX based Model for testing with a runtime session in onnx format – model deployment/edge inference.
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Project Report
Please provide the following regarding the project report (Note: Can be inline with the Project Notebook ):
Abstract – Research summary, ndings, and next steps.
Overview – Problem statement, relevant literature, proposed methodology. Data Processing – Pipeline details, data issues, assumptions/adjustments. Data Analysis – Summary statistics, visualization, feature extraction.
Model Training – Feature engineering, evaluation metrics, model selection. Model Validation – Testing results, performance criteria, biases/risks.
Conclusion – Positive/Negative results, recommendations, caveats/cautions. Data Sources – Links, downloads, access information.
Source Code – Listings, documentation, dependencies (open-source).
Bibliography – Reference citations (Chicago style – AMS/AIP or ACM/IEEE).
Prof. Panchal: CS 422 – Data Mining Spring 2020:
Wed. 6:25PM-9:05PM Section 04
