Don't Waste Data! An Experiment with Machine Learning

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • CPython

    The Python programming language

    Once we had determined the shape of the data and the features we should focus on, we set out to create a model. (There is a wealth of ML tools available across programming languages like Python and Julia.) We chose scikit-learn, one of the most popular ML libraries around, and plugged the data into a random forest regression. (Say what? Here’s a quick and dirty guide to random forest regression.) As input, we used the ZIP codes of the print partner and the destination of the mailpiece. Our output target was the metric we had calculated during pre-processing: the difference in days between the earliest and latest USPS events recorded for each mailpiece (the mailpiece's time in transit).

  • scikit-learn

    scikit-learn: machine learning in Python

    Once we had determined the shape of the data and the features we should focus on, we set out to create a model. (There is a wealth of ML tools available across programming languages like Python and Julia.) We chose scikit-learn, one of the most popular ML libraries around, and plugged the data into a random forest regression. (Say what? Here’s a quick and dirty guide to random forest regression.) As input, we used the ZIP codes of the print partner and the destination of the mailpiece. Our output target was the metric we had calculated during pre-processing: the difference in days between the earliest and latest USPS events recorded for each mailpiece (the mailpiece's time in transit).

  • SonarLint

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  • julia

    The Julia Programming Language

    Once we had determined the shape of the data and the features we should focus on, we set out to create a model. (There is a wealth of ML tools available across programming languages like Python and Julia.) We chose scikit-learn, one of the most popular ML libraries around, and plugged the data into a random forest regression. (Say what? Here’s a quick and dirty guide to random forest regression.) As input, we used the ZIP codes of the print partner and the destination of the mailpiece. Our output target was the metric we had calculated during pre-processing: the difference in days between the earliest and latest USPS events recorded for each mailpiece (the mailpiece's time in transit).

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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