Repository includes code and document related to project for MLOps Zoomcamp course https://github.com/DataTalksClub/mlops-zoomcamp
- Attrition on credit cards has become a major worry for the banking industry. Given that the expense of obtaining new consumers is higher than the expense of keeping existing customers, it significantly affects profitability.
- In this work, a selection of supervised Machine Learning models to identify which customers want to cancel their credit cards is evaluated.
- Attrition prediction models are important to reduce the cancellation of credit card products and analyze which customers have a greater tendency to cancel.
- This work was carried out through a strategic segmentation focusing on the client to generate campaigns according to the profile of each one, to understand and anticipate their behavior. Likewise, productive resources were focused on high-value groups, as it is cheaper to retain a client than attract a new one.
Predicting customers who are likely to drop off credit card. to work on customer retaintion strategy like running compaigns.
Credit Card Churn Prediction used for this project.
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/web-service: build flask api + docker pakaging + deployment api through docker
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/lambda-terraform: build lambda function + docker pakaging + terraform LaC + Mlflow
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/monitoring : build batch monitoring service with evidently
note : please check my "feature" branch for below change https://github.com/vermadev54/MlopsZoomcampCourceProject/tree/feature
- /allconcept : build streaming api with (unit test + code lint + LaC + cicd )
- Model training
python3 train.py --data_path
- data preprocessing/training/mlflow experiment tracking /prefect Workflow orchestration
python3 preprocessing_data.py
- Model registery
python3 registry_model.py --data_path ./output
- Prefect deployment with scheduled crons
prefect deployment create prefect_deploy.py
mlflow server -h 0.0.0.0 -p 5000 --backend-store-uri postgresql://mlflow:FdXoiuOCyQvyiDL0Gftk@mlflow-database.ciuzmsnp32jg.us-east-1.rds.amazonaws.com:5432/mlflow_db --default-artifact-root s3://jai-mlops-zoomcamp-tfstate
prefect config unset PREFECT_ORION_UI_API_URL
prefect config set PREFECT_ORION_UI_API_URL="http://54.146.195.209:4200/api"
prefect orion start --host 0.0.0.0