chore: relocated contrib file

Author: lpm0073

docs/contrib/anomaly_detection_creditcard-02.ipynb

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+    "# Anomaly Detection in Azure ML Studio\n",
+    "\n",
+    "# Credit Card Fraud Detection System (Azure ML Pipeline)\n",
+    "\n",
+    "## Executive Summary\n",
+    "\n",
+    "This notebook presents a fraud detection system built using Azure Machine Learning. It processes transaction data, identifies unusual patterns, and flags potential fraud.\n",
+    "\n",
+    "The goal is to detect fraudulent activity while minimizing disruption to legitimate customers. The system focuses on balancing two key risks:\n",
+    "- Flagging valid transactions incorrectly (false positives)\n",
+    "- Missing actual fraud cases\n",
+    "\n",
+    "This notebook explains each step of the process in simple terms, from data preparation to model evaluation and deployment considerations, to support informed business decisions.\n",
+    "\n",
+    "## End-to-End Process Overview\n",
+    "\n",
+    "Raw Transaction Data \n",
+    "\n",
+    "↓  \n",
+    "\n",
+    "Data Cleaning & Preparation  \n",
+    "\n",
+    "↓  \n",
+    "\n",
+    "Feature Processing  \n",
+    "\n",
+    "↓  \n",
+    "\n",
+    "Model Training  \n",
+    "\n",
+    "↓  \n",
+    "\n",
+    "Model Evaluation  \n",
+    "\n",
+    "↓  \n",
+    "\n",
+    "Deployment  \n",
+    "\n",
+    "↓  \n",
+    "\n",
+    "Monitoring & Improvement\n",
+    "\n",
+    "\n",
+    "## Azure ML Components\n",
+    "\n",
+    "\n",
+    "| Component | Purpose |\n",
+    "|----------|--------|\n",
+    "| Dataset | Stores transaction data |\n",
+    "| Compute | Runs training and processing |\n",
+    "| Pipeline | Automates workflow |\n",
+    "| Model | Detects fraud patterns |\n",
+    "| Endpoint | Enables real-time predictions |\n",
+    "| Monitoring | Tracks model performance |\n",
+    "\n",
+    "\n",
+    "## Workflow Overview\n",
+    "\n",
+    "The system follows these steps:\n",
+    "\n",
+    "1. Load transaction data  \n",
+    "2. Clean and prepare the data  \n",
+    "3. Process features  \n",
+    "4. Train the model  \n",
+    "5. Evaluate performance  \n",
+    "6. Prepare for deployment  \n",
+    "\n",
+    "Each step improves accuracy and reduces false alerts.\n",
+    "\n",
+    "\n",
+    "## Feature Processing\n",
+    "\n",
+    "The dataset includes processed features (V1–V28) created using statistical methods.\n",
+    "\n",
+    "These features help detect patterns but do not directly represent real-world transaction details. Because of this, extreme values can strongly influence model decisions.\n",
+    "\n",
+    "Careful handling of these values is important to reduce false positives.\n",
+    "\n",
+    "\n",
+    "## Business Impact\n",
+    "\n",
+    "### False Positives vs Missed Fraud\n",
+    "\n",
+    "- False positives → customer frustration and lost transactions  \n",
+    "- Missed fraud → financial loss and security risk  \n",
+    "\n",
+    "The goal is to balance both.\n",
+    "\n",
+    "\n",
+    "### Risks\n",
+    "\n",
+    "- Model may flag unusual but valid transactions  \n",
+    "- Data changes over time may reduce accuracy  \n",
+    "- Data adjustments may introduce bias if not reviewed  \n",
+    "\n",
+    "\n",
+    "### Recommendations\n",
+    "\n",
+    "- Improve data quality by handling extreme values  \n",
+    "- Monitor model performance continuously  \n",
+    "- Deploy changes gradually  \n",
+    "\n",
+    "\n",
+    "### Stakeholder Communication\n",
+    "\n",
+    "- Share regular updates  \n",
+    "- Clearly explain limitations  \n",
+    "- Set realistic expectations  \n",
+    "\n",
+    "## Conclusion\n",
+    "\n",
+    "This system provides a strong starting point for fraud detection using machine learning.\n",
+    "\n",
+    "While the current model has limitations, especially with false positives, it demonstrates how data-driven approaches can improve fraud detection.\n",
+    "\n",
+    "Ongoing improvements in data quality, model tuning, and monitoring will be key to long-term success.\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
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