TASK 1

Fraud Detection Machine Learning Project

Problem Definition

Business Problem: Detect fraudulent transactions in banking or payment systems to prevent financial losses and protect customers.

Business Context:

• The dataset is imbalanced: the majority of transactions are normal, while fraudulent transactions are rare.
• Business priority: maximize Recall to catch as many fraud cases as possible, while keeping Precision acceptable to avoid too many false positives.

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Data Collection & Preprocessing

Data Sources:

• Credit card transaction datasets (e.g., Kaggle Credit Card Fraud dataset)
• Logs, customer info, transaction types, timestamps, etc.

Preprocessing Steps:

• Handle missing values
• Scale numerical features (StandardScaler / MinMaxScaler)
• Address class imbalance with resampling techniques (SMOTE / RandomOverSampler)
• Split data into train and test sets

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Model Selection & Implementation

Algorithms Used:

• Random Forest → robust and interpretable

Techniques:

• Threshold tuning based on Precision-Recall tradeoff
• Feature importance analysis

Python Libraries:

• scikit-learn, imbalanced-learn

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Model Evaluation

Metrics:

• ROC-AUC, Precision-Recall AUC (especially important for imbalanced datasets)
• Precision, Recall, F1-score
• Optimal threshold selection according to business priorities

Visualizations:

• ROC curve
• Precision-Recall curve
• Threshold vs Precision/Recall/F1 plot

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Deployment

Deployment Options:

• Jupyter Notebook / Google Colab: for development and testing
• Flask / FastAPI / Streamlit: create an API for real-time fraud detection
• AWS SageMaker / Azure ML: deploy and monitor model in the cloud

Monitoring:

• Track precision and recall on live data
• Retrain or adjust threshold if fraud patterns change

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Conclusion

This fraud detection project follows the end-to-end Machine Learning pipeline:

1. Problem defined ✅
2. Data collected and preprocessed ✅
3. Model selected and trained (Random Forest with threshold tuning) ✅
4. Evaluated using robust metrics (F1, ROC-AUC, Precision-Recall) ✅
5. Deployment plan ready (API/cloud, threshold tuning, monitoring) ✅

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