Main Objective:
To implement a non-linear NLP (Natural Language Processing) pipeline capable of categorizing unstructured customer feedback without prior task-specific training.
Model Output :
Text: "I think the subscription has become too expensive for what it is." Prediction: Churn Risk (Confidence: 0.58).
Text: "I love my plan, the network is great everywhere!" Prediction: Support Request (Confidence: 0.36).
Text: "My contract is expiring soon and I'm looking at the competition." Prediction: Support Request (Confidence: 0.52).
Key Takeaways :
For the first result (0.58): > "The model identified a pricing pain point. In natural language processing, 'expensive' is a strong predictor for customer attrition, hence the 'Churn Risk' label.
For the second result (0.36): > "This is a false positive due to a low confidence score. Because the model didn't have a 'Positive' or 'Satisfied' category to choose from, it defaulted to 'Support Request' with very low certainty. We call this a forced choice.
For the third result (0.52): > "The model correctly sensed an intent to switch. Mentioning 'competition' and 'contract expiration' triggers a high probability for a retention-related support ticket."
馃幆 The detailed methodology and results can be accessed through this link:
馃憠click here now! : https://www.geeksforgeeks.org/
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