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Vehicle Insurance ML Pipeline
End-to-end predictive classification pipeline with modular MLOps architecture.
01 · The Problem & Hypothesis
Why this needed to be built
Assessing vehicle insurance claim propensities requires handling severe class imbalances, multi-modal features, and repeatable retraining pipelines.
The Core Idea
Build a modular, reproducible ML pipeline implementing structured feature engineering, hyperparameter tuning, and containerized artifact tracking.
02 · Technical Implementation
How the system was architected
Engineered as an exploratory MLOps project to master modular software design in machine learning. Features automated data validation, custom transformers, and structured prediction endpoints.
Python
Scikit-learn
Pandas
Flask
Docker
03 · Key Takeaways Earned
Takeaway 01
“Modular code organization separates data engineering concerns from model evaluation.”