Mini Project Presentation Template-1.ppt

Mini Project Presentation Template-1.ppt

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  DEPARTMENT OF COMPUTERSCIENCE AND ENGINEERING MINI PROJECT PHASE PRESENTATION COURSE CODE: 21CSMP67 PROJECT TITLE CREDIT CARD FRAUD DETECTION 7/17/2024 1 Guide Name: Name: Shivanand Hiremath Designation: Students Name: Mr.Pruthviraj Ganachari (2BU21CS095) Ms.Sachin Ravalukedari (2BU21CS113) Mr.Sambhaji Kunnurkar (2BU21CS118) Accredited by NBA VISVESVARAYA TECHNOLOGICAL UNIVERSITY - BELAGAVI
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  OUTLINE • Introduction • LiteratureSurvey • Problem Statement • Objectives • Requirement Specification • Proposed Architecture diagram • Design Modules • Design Diagrams 7/17/2024 2
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  • Design Diagrams:Work Flow Diagrams (DFD Level 1 and Level 2, Class Diagrams, Activity Diagrams, Use-case diagrams, Database diagrams, Circuit diagrams) • Results • Outcome Snapshot • Conclusion and Future Enhancement • References • Work contribution 100% of implementation Suggestions given by panel members and guide, should be incorporated in mega exhibition. 7/17/2024
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  INTRODUCTION Credit card fraudis a significant and growing problem in the financial sector, causing substantial financial losses for both consumers and financial institutions. With the increasing use of credit cards for online and offline transactions, the need for effective fraud detection methods has become more critical than ever. This project aims to develop a machine learning model to accurately detect fraudulent credit card transactions using logistic regression. 7/17/2024 4
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  LITERATURE SURVEY>>>>>>> MIN20 REFERENCES S.NO AUTHORS /YEAR TITLE OBSERVATIONS 1 Bolton & Hand, 2002 Statistical Fraud Detection: A Review  Traditional statistical methods like rule-based systems are often insufficient due to their inability to adapt to evolving fraud patterns. The study suggests incorporating machine learning techniques to enhance detection capabilities. 2 Phua et al., 2010 A Comprehensive Survey of Data Mining-based Fraud Detection Research  Machine learning algorithms, particularly supervised learning methods like logistic regression, are effective in detecting fraud due to their ability to learn from labeled data and identify patterns indicative of fraudulent behavior. 3 Ngai et al., 2011 The Application of Data Mining Techniques in Financial Fraud Detection  Logistic regression is identified as a widely used technique due to its simplicity, interpretability, and effectiveness in binary classification problems such as fraud detection. 4 Dal Pozzolo et al., 2018 Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy  The authors emphasize the importance of handling imbalanced datasets in fraud detection and propose undersampling and synthetic data generation techniques to improve model performance. Logistic regression, when combined with these techniques, shows promising results in detecting fraudulent transactions. 7/17/2024 5
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  CONTD… S.NO AUTHORS/ YEAR TITLE OBSERVATIONS 5Bhandari, 2020 Machine Learning for Credit Card Fraud Detection  The author demonstrates the effectiveness of logistic regression in detecting fraud and provides insights into data preprocessing, feature engineering, and model evaluation. The article highlights the importance of using appropriate metrics, such as precision, recall, and F1-score, to assess model performance in the context of imbalanced datasets. 6 Bhattacharyya et al., 2011 Data Mining for Credit Card Fraud: A Comparative Study  Logistic regression performed well in terms of interpretability and accuracy, but combining multiple techniques (ensemble methods) often yields better performance. The study highlights the importance of feature selection and engineering in improving model accuracy. 7 Duman & Ozcelik, 2011 Credit Card Fraud Detection Using Bayesian and Neural Networks  While neural networks can capture complex patterns, logistic regression offers a balance between performance and computational efficiency. The study also emphasizes the importance of updating models regularly to adapt to new fraud patterns. 8 Zareapoor & Seeja, 2015 A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective  The study discusses the strengths and weaknesses of logistic regression compared to other machine learning algorithms and highlights the role of feature selection, data preprocessing, and model tuning in enhancing detection capabilities 9 Carcillo et al., 2019 Credit Card Fraud Detection with Machine Learning Algorithms  Logistic regression, when combined with feature engineering and proper handling of imbalanced data, performs competitively. The study also explores the use of time-series analysis to capture temporal patterns in fraud detection 10 Whitrow et al., 2009 Real-Time Credit Card Fraud Detection: An Adaptive Approach  The authors highlight the challenges of real-time detection and propose a framework that adapts to changing fraud patterns. Logistic regression is noted for its speed and efficiency, making it suitable for real-time applications. 7/17/2024 6
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  PROBLEM STATEMENT The CreditCard Frayd Detection problem includes modeling Past Credit Card Transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify wheather a new transaction if fraudlent or not. 7/17/2024
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  OBJECTIVES • Develop aLogistic Regression Model: Create a logistic regression model to accurately classify credit card transactions as fraudulent or legitimate. • Real-time Fraud Detection: Design and implement a system capable of processing transaction data in real-time to provide immediate fraud detection, thereby preventing fraudulent transactions from being completed. • User-friendly Interface: Develop an intuitive and user-friendly interface using Streamlit that allows users to input transaction data and receive real-time predictions about the legitimacy of the transaction. • Scalability and Efficiency: Ensure that the system is scalable and efficient, capable of handling large volumes of transaction data without significant delays in processing and prediction. 7/17/2024
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  REQUIREMENT SPECIFICATION • Hardware: 1.Processor: • A multi-core processor (e.g., Intel Core i5/i7 or AMD Ryzen 5/7) to handle the computational load of training and running machine learning models. 2. Memory (RAM): • At least 8 GB of RAM for development purposes. For larger datasets and more complex models, 16 GB or more is recommended. 3. Storage: • A Solid-State Drive (SSD) with at least 256 GB of storage for faster data read/write operations. More storage may be needed depending on the size of the datasets and models • Software: 7/17/2024
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  • Software Requirements: 1.Operating System: • Windows 10/11, macOS, or a popular Linux distribution (e.g., Ubuntu). 2. IDE/Code Editor: • An Integrated Development Environment (IDE) or code editor such as PyCharm, VS Code, or Jupyter Notebook for writing and debugging code. 3. Python: • Python 3.7 or higher. The project is based on Python, so an up-to-date Python installation is necessary. 7/17/2024 10
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  PROPOSED ARCHITECTURE DIAGRAM 7/17/2024
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  RESULT The results ofthis credit card fraud detection project demonstrate the effectiveness of using logistic regression, along with appropriate data preprocessing and balancing techniques, to accurately identify fraudulent transactions. Below are the key outcomes and metrics from the project: • Model Performance Metrics: 1. Accuracy: The model achieved an accuracy of 99.3%, indicating that it correctly classified the majority of transactions as either fraudulent or legitimate. 2. Precision: The precision of the model was 90%, meaning that 90% of the transactions flagged as fraudulent were actually fraudulent. High precision is crucial in reducing false positives, which can cause inconvenience to legitimate customers. 3. Recall (Sensitivity): The recall was 87%, signifying that the model correctly identified 87% of the actual fraudulent transactions. High recall is essential to minimize the number of fraudulent transactions that go undetected. 7/17/2024
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  OUTCOME SNAPSHOT 7/17/2024
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  CONCLUSION In this project,we developed a credit card fraud detection system using logistic regression, leveraging various machine learning techniques and best practices to address the challenges posed by fraudulent transactions. • Key Achievements: 1. Effective Logistic Regression Model: The logistic regression model demonstrated a balance between simplicity, interpretability, and accuracy. By implementing techniques to handle data imbalance, such as undersampling and oversampling, the model achieved significant improvements in detecting fraudulent transactions. 2. Real-time Detection: The system was designed to process and analyze transaction data in real-time, providing immediate fraud detection and thereby preventing unauthorized transactions from being completed. 3. User-friendly Interface: Using Streamlit, we developed an intuitive interface that allows users to input transaction data and receive real-time predictions, making the system accessible to non-technical users. 7/17/2024
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  FUTURE ENHANCEMENTS To furtherenhance the effectiveness and efficiency of the credit card fraud detection system, several future enhancements can be considered: • Integration with Real-world Systems: • Explore opportunities to integrate the fraud detection system with real-world banking and financial systems, providing a practical and deployable solution for financial institutions. • Scalability and Efficiency: • Optimize the system for scalability and efficiency to handle large volumes of transaction data without compromising on speed or accuracy. Consider distributed computing and parallel processing techniques. 7/17/2024 16
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  REFERENCES • Datasets andDocumentation: • Credit Card Fraud Detection Dataset" by Andrea Dal Pozzolo. Available at: Kaggle • Understanding Logistic Regression" by Jason Brownlee. Available at: Machine Learning Mastery • Python Libraries Documentation: • scikit-learn Documentation • pandas Documentation • numpy Documentation • Streamlit Documentation 7/17/2024 17
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  7/17/2024 18 THANK YOU