My specific instruction to ChatGPT was ‘write a python code for churn prediction’. it didn’t do too badly at all.  It spurned out the following code and appended a meaningful  explanation to it. Let me let you to go throw it.

Here is an example of a Python code for churn prediction using a Random Forest Classifier:

This code loads a dataset called ‘churn_data.csv’ using pandas, splits the data into features and target (the column ‘churn’), and then splits the data into training and test sets. It then creates a Random Forest Classifier model with 100 trees, trains the model on the training data, makes predictions on the test set, and calculates the accuracy of the predictions.

Please note that this is just an example and the accuracy can vary depending on the data and the parameters used.

My thoughts on the above

I didn’t know what to make of this so I ask ChatGPT the following

“what are my thoughts on ChatGPT generating a python churn code?”

Finally, my human thought on this is that the future is pregnant and exciting.  It thus behooves actors in this sector such as myself to craft a symbiotic relationship with the likes of ChatGPT and leverage their respective strengths within the problem domains, It will definitely speed up some of the projects I’m seized with.