Built an AI Junior Data Scientist agent using LangChain AgentExecutor, Ollama, and FastAPI on a 10k‑row bank‑churn dataset; automated EDA and baseline modeling (logistic regression) and served results via a REST API with latency and usage metrics, achieving ~0.82 accuracy on held‑out customers.
python data-science machine-learning rest-api scikit-learn eda pandas fintech churn-prediction model-serving customer-analytics explainable-ai fastapi langchain ollama react-agent agentic-ai metrics-logging
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Updated
Jan 3, 2026 - Python