Articles about Machine Learning

Pecas: Machine Learning Problem Shaping and Algorithm Selection

In our previous article, Machine Learning Aided Time Tracking Review: A Business Case we introduced the business case behind Pecas, an internal tool designed to help us analyse and classify time tracking entries as valid or invalid.

This series will walk through the process of shaping the original problem as a machine learning problem and building the Pecas machine learning model and the Slackbot that makes its connection with Slack.

In this first article, we’ll talk through shaping the problem as a machine learning problem and gathering the data available to analyse and process.

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Machine Learning Aided Time Tracking Review: A Business Case

As an agency, our business model revolves around time. Our client activities rely on a dedicated number of hours per week worked on a project, and our internal activities follow the same pattern. As such, time tracking is a vital part of our work. Ensuring time is tracked correctly, and time entries meet a minimum quality standard, allows us to be more data-driven in our decisions, provide detailed invoices to our clients and better manage our own projects and initiatives.

Despite being a core activity, we had been having several issues with it not being completed or not being completed properly. A report we ran at the end of 2022 showed our time tracking issues were actually quite severe. We lost approximately one million dollars in 2022 due to time tracking issues that led to decisions made on poor data. It was imperative that we solved the problem.

To help with this issue, we created an evolution of our Pecas project. We turned Pecas into a machine learning powered application capable of alerting users of issues in their time entries. In this article, we’ll talk though the business case behind it and expected benefits to our company.

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