Integrate Basecamp Classic with Monkey Learn

Basecamp Classic?

Basecamp Classic is a project management software developed by 37signals in 1999. It allows users to create projects, add to-do lists, add files and add tasks to be done in a single screen.

Monkey Learn?

Monkey Learn is a machine learning platform that allows users to train their own machine learning models in a simple way. It provides a cplection of machine learning models for text classification, text extraction, image recognition and much more. It also provides the possibility to track the performance of their models in an easy way.

Integration of Basecamp Classic and Monkey Learn

In this section, I will show how to integrate Basecamp Classic with Monkey Learn in order to classify documents based on tags. In this example, I will use a chat conversation between me and one of my cpleagues to classify it into two tags. chat and support.

First, we need to create a model in Monkey Learn for this task. After logging in, we can click Create a Model in the top menu. Then we can choose a model type (in this case, it would be Chat Support.

Now we can choose some tags that our model should know (in this example, I chose chat and support. and feed some data samples (in this case, I used the last text conversation between me and my cpleague. After that, we can press Next Step.

After pressing Next Step, we'll see the validation stage. We can choose if we want to validate our model or not (in this case, we'll choose yes. and press next step. Then we'll see the fplowing interface:

We can see all of the tags used during the training process, as well as information about how they were classified after training. At the bottom of the interface, we can check the confusion matrix that shows how the model has been classified correctly or incorrectly. The numbers inside each box represents the percentage of correct classifications. If the number is high, then the model has been classified correctly. After choosing which tags you want your model to know about (in this example, chat and support), you can press Train Model at the bottom right side of the interface. It will ask you if you want to keep your model private or public, so you can decide if you want to keep your model private for internal purposes or make it public for external purposes.

After pressing next step, you will see something like this:

It shows you all of your models that you have created previously, as well as instructions for using them. It's important to note that you can configure different types of models depending on what you want to do with them. After selecting Chat Support as your model, you'll see something like this:

You can now copy/paste the HTTP endpoint for this model into Basecamp Classic after clicking Connect. You'll see an example of this at the bottom of your interface. You can now press Download Model Package at the bottom right corner of your interface. This will download a package called basecamp_chat_support_model_package_v1.zip. Inside that package, there will be two files with .pbapp extension. You need to import one of those files into Basecamp Classic in order for it to work with your Basecamp Classic account. You can either import "chat_support_model_pb2-1.0-basecamp_chat_support_model-v1.0.pbapp" or "chat_support_model_pb2-1.0-basecamp_chat_support_model-v1.0-prod-no-locs-v1.0.pbapp". The difference between those two files is that the second one has optimizations for production use (it's faster and uses less memory. But if you don't care about performance and memory usage, you can import the first one as well because it's easier to debug errors when testing models during development stages. After importing those files into Basecamp Classic, you'll see something like this:

If you click on "Import", you'll see an example like this:

And it should appear like this:

Now you can try creating tasks and tagging them using these tags. chat and support. After doing so, you can go back to your Basecamp Classic project and click on "Tasks" in the left sidebar and select "All Tasks" from the drop down menu at the top:

After clicking on "All Tasks", you'll see an example like this:

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