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sehidesena/churnpilot
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Python
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Help me set up ChurnPilot's backend and frontend locally, including seeding the database with synthetic customer data.
Explain how ChurnPilot's provider agnostic LLMClient lets me switch between Gemini and Azure OpenAI without changing code.
Walk me through training the XGBoost churn model in ChurnPilot and understanding the SHAP explanation it produces.
Show me how to test one of ChurnPilot's agents, like RiskAgent, directly in a Python shell without a live LLM call.
Full explanation on explaingit →
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