Don't Waste Data! An Experiment with Machine Learning

• CPython

  1,314 59,531 10.0 Python

  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

  81 58,046 9.9 Python

  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).

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• julia

  350 44,510 10.0 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).

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