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  Flow Chart

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  Performance of best trained model

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  Implementation

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  Fraudulent Transaction

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  Legit Transaction

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  Dataset Preciew

Project Reflection

Inspiration:

The inspiration for this project stemmed from the critical need to enhance the security infrastructure surrounding credit card transactions. The prevalence of fraud in electronic transactions necessitated a comprehensive solution leveraging advanced machine learning techniques. The alarming class imbalance within the dataset fueled the motivation to design a fraud detection system that could discern subtle patterns indicative of fraudulent activities.

Learning Experience:

The project provided an invaluable learning experience across various domains. Handling imbalanced datasets demanded a deep dive into under-sampling methodologies, prompting a nuanced understanding of class distribution rectification. The exploration of diverse machine learning algorithms, including Logistic Regression, Isolation Forest, and Local Outlier Factor, enriched my knowledge of anomaly detection. The venture into ensemble modeling and the application of boosting techniques broadened my understanding of model combination strategies, with AdaBoost standing out as a particularly enlightening facet.

Project Development:

The development journey was executed predominantly in Python, leveraging Pandas and NumPy for intricate data manipulations. The strategic amalgamation of diverse algorithms and the orchestration of an ensemble model comprising six different classifiers demanded meticulous coding within the dynamic contours of Jupyter Notebooks. The integration of a real-world payment processor, Stripe, added a practical dimension to the project, allowing for the simulation of transaction scenarios and testing the model's efficacy in a realistic environment.

Challenges Faced:

The primary challenge revolved around handling the class imbalance inherent in credit card transaction datasets. Crafting effective under-sampling techniques and balancing model performance across diverse algorithms posed intricate challenges. Integrating a real-world payment processor introduced complexities related to transaction simulation and model testing. Iterative refinement and fine-tuning were imperative to navigate these challenges successfully.

Built With

• amazon-web-services
• c++
• django
• html
• javascript
• jupyter-notebook
• kaggle
• python
• pytorch
• streamlit
• xgboost