Back to ProjectsEnd‑to‑end pipeline: data prep, model training, real‑time endpoint deployment, inference, and evaluation (confusion matrix, accuracy). Automated use of SageMaker built‑in XGBoost container with configurable hyper‑parameters. Seamless data handling with S3 storage and IAM role‑based security. Clean-up routine to terminate endpoints and delete S3 objects to avoid extra costs.
Jupyter Notebook
Bank-Marketing-Campaign-Prediction-using-AWS
What the project does**
System Overview
What the project does
Builds, trains, deploys, and evaluates an XGBoost model on AWS SageMaker to predict whether a bank customer will subscribe to a term deposit based on historical marketing data.
Key features
Tech stack
AWS SageMaker, S3, IAM, SageMaker SDK, XGBoost, Python (pandas, NumPy).
Use case
Enables banks or financial analysts to quickly score prospective customers for term‑deposit campaigns, improving targeting efficiency and conversion rates.
Architecture Details
This system integrates multiple components for a seamless automation flow. Structural interpretation based on project focus:
Backend Infrastructure
Core execution layer for robust data processing and API handling.
AI / Logic Core
Intelligent decisioning via models or logical workflow rules.
Tech Stack
Jupyter NotebookIntegrationAutomationAPIs
Key Capabilities
- ▹ Custom workflow execution
- ▹ Data transformation and routing
- ▹ Extensible architecture