Python, pandas, sklearn

Working as an undergraduate analyst, I developed numerous tools in Python to ingest, process, and visualise/output information.

Writing in Python, I used pandas and scitkit-learn to identify trends in planned vs actual drill locations. This used a k-nearest neighbours classifier to match the datasets, then calculating variance between the two. This then output a scatter plot file for the user.

The final solution was packaged as an executable using PyInstaller to deploy on client sites. The output scatter plots were generated using matplotlib and seaborn libraries.

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