Alex, a data scientist, starts their day with coffee and emails before diving into a project to update a customer churn prediction model. They clean and analyze data using Pandas and Matplotlib, then train a CatBoost model. After validating the model, Alex deploys it via a Flask API and containerizes it with Docker for consistent performance. They participate in team meetings, review feedback, and explore Streamlit for interactive dashboards. By day's end, Alex has advanced their projects and planned to continue learning ...
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Alex, a data scientist, starts their day with coffee and emails before diving into a project to update a customer churn prediction model. They clean and analyze data using Pandas and Matplotlib, then train a CatBoost model. After validating the model, Alex deploys it via a Flask API and containerizes it with Docker for consistent performance. They participate in team meetings, review feedback, and explore Streamlit for interactive dashboards. By day's end, Alex has advanced their projects and planned to continue learning about Streamlit and Deep Learning, blending coding, collaboration, and ongoing development.
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