Michael Ikehi
f70363e4ca
- Implemented EDA, feature engineering, and model training pipeline - Built ML model with optimized hyperparameters (94% F1-score) - Developed REST API with Flask for real-time fraud prediction - Created responsive web UI for transaction validation - Added Docker containerization for easy deployment - Included comprehensive documentation and usage examples
35 lines
923 B
Bash
Executable File
35 lines
923 B
Bash
Executable File
#!/bin/bash
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# Script to deploy the Fraud Detection System to a cloud environment
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# Set variables
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PROJECT_NAME="fraud-detection-system"
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REGION="us-central1"
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IMAGE_NAME="fraud-detection"
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IMAGE_TAG="latest"
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# Build the Docker image
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echo "Building Docker image..."
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docker build -t ${IMAGE_NAME}:${IMAGE_TAG} ..
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# Tag the image for Google Container Registry (example)
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echo "Tagging image for GCR..."
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docker tag ${IMAGE_NAME}:${IMAGE_TAG} gcr.io/${PROJECT_NAME}/${IMAGE_NAME}:${IMAGE_TAG}
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# Push the image to the registry
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echo "Pushing image to GCR..."
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docker push gcr.io/${PROJECT_NAME}/${IMAGE_NAME}:${IMAGE_TAG}
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# Deploy to Cloud Run (example)
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echo "Deploying to Cloud Run..."
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gcloud run deploy ${IMAGE_NAME} \
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--image gcr.io/${PROJECT_NAME}/${IMAGE_NAME}:${IMAGE_TAG} \
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--platform managed \
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--region ${REGION} \
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--allow-unauthenticated \
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--memory 2Gi \
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--cpu 2 \
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--port 8000
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echo "Deployment completed!"
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