Mini Project Presentation Template-1.ppt

Study about products

1. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
MINI PROJECT PHASE PRESENTATION
COURSE CODE: 21CSMP67
PROJECT TITLE
CREDIT CARD FRAUD DETECTION
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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

2. OUTLINE
•Introduction
•Literature Survey
•Problem Statement
•Objectives
•Requirement Specification
•Proposed Architecture diagram
•Design Modules
•Design Diagrams
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3. •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.
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4. INTRODUCTION
Creditcardfraudisasignificantandgrowingprobleminthefinancialsector,
causingsubstantialfinanciallossesforbothconsumersandfinancialinstitutions.Withthe
increasinguseofcreditcardsforonlineandofflinetransactions,theneedforeffectivefraud
detectionmethodshasbecomemorecriticalthanever.Thisprojectaimstodevelopa
machinelearningmodeltoaccuratelydetectfraudulentcreditcardtransactionsusinglogistic
regression.
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5. LITERATURE SURVEY>>>>>>> MIN 20 REFERENCES
S.NOAUTHORS
/YEAR
TITLE OBSERVATIONS
1Bolton &
Hand,2002
StatisticalFraudDetection:A
Review
Traditionalstatisticalmethodslikerule-basedsystemsareofteninsufficientdueto
theirinabilitytoadapttoevolvingfraudpatterns.Thestudysuggestsincorporating
machinelearningtechniquestoenhancedetectioncapabilities.
2Phuaetal.,
2010
AComprehensiveSurveyofData
Mining-basedFraudDetection
Research
Machinelearningalgorithms,particularlysupervisedlearningmethodslikelogistic
regression,areeffectiveindetectingfraudduetotheirabilitytolearnfromlabeled
dataandidentifypatternsindicativeoffraudulentbehavior.
3Ngaietal.,2011TheApplicationofDataMining
TechniquesinFinancialFraud
Detection
Logisticregressionisidentifiedasawidelyusedtechniqueduetoitssimplicity,
interpretability,andeffectivenessinbinaryclassificationproblemssuchasfraud
detection.
4DalPozzoloet
al.,2018
CreditCardFraudDetection:A
RealisticModelingandaNovel
LearningStrategy
Theauthorsemphasizetheimportanceofhandlingimbalanceddatasetsinfraud
detectionandproposeundersamplingandsyntheticdatagenerationtechniquesto
improvemodelperformance.Logisticregression,whencombinedwiththese
techniques,showspromisingresultsindetectingfraudulenttransactions.
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6. CONTD…
S.NOAUTHORS/
YEAR
TITLE OBSERVATIONS
5Bhandari,2020MachineLearningforCreditCard
FraudDetection
Theauthordemonstratestheeffectivenessoflogisticregressionindetectingfraudand
providesinsightsintodatapreprocessing,featureengineering,andmodelevaluation.The
articlehighlightstheimportanceofusingappropriatemetrics,suchasprecision,recall,and
F1-score,toassessmodelperformanceinthecontextofimbalanceddatasets.
6Bhattacharyya
etal.,2011
DataMiningforCreditCard
Fraud:AComparativeStudy
Logisticregressionperformedwellintermsofinterpretabilityandaccuracy,butcombining
multipletechniques(ensemblemethods)oftenyieldsbetterperformance.Thestudy
highlightstheimportanceoffeatureselectionandengineeringinimprovingmodel
accuracy.
7Duman &
Ozcelik,2011
CreditCardFraudDetection
UsingBayesianandNeural
Networks
Whileneuralnetworkscancapturecomplexpatterns,logisticregressionoffersabalance
betweenperformanceandcomputationalefficiency.Thestudyalsoemphasizesthe
importanceofupdatingmodelsregularlytoadapttonewfraudpatterns.
8Zareapoor&
Seeja,2015
ASurveyofCreditCardFraud
DetectionTechniques:Dataand
TechniqueOrientedPerspective
Thestudydiscussesthestrengthsandweaknessesoflogisticregressioncomparedtoother
machinelearningalgorithmsandhighlightstheroleoffeatureselection,data
preprocessing,andmodeltuninginenhancingdetectioncapabilities
9Carcilloetal.,
2019
CreditCardFraudDetectionwith
MachineLearningAlgorithms
Logisticregression,whencombinedwithfeatureengineeringandproperhandlingof
imbalanceddata,performscompetitively.Thestudyalsoexplorestheuseoftime-series
analysistocapturetemporalpatternsinfrauddetection
10Whitrowetal.,
2009
Real-TimeCreditCardFraud
Detection:AnAdaptiveApproach
Theauthorshighlightthechallengesofreal-timedetectionandproposeaframeworkthat
adaptstochangingfraudpatterns.Logisticregressionisnotedforitsspeedandefficiency,
makingitsuitableforreal-timeapplications.
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7. PROBLEM STATEMENT
The Credit Card 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.
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8. OBJECTIVES
•Develop a Logistic 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.
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9. 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:
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10. •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.
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11. PROPOSED ARCHITECTURE DIAGRAM
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12. RESULT
The results of this 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.
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13. OUTCOME SNAPSHOT
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15. 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 undersamplingand
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.
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16. FUTURE ENHANCEMENTS
To further enhance 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.
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17. REFERENCES
•Datasets and Documentation:
•Credit Card Fraud Detection Dataset" by Andrea Dal Pozzolo. Available at: Kaggle
•Understanding Logistic Regression" by Jason Brownlee. Available at: Machine Learning
Mastery
•Python LibrariesDocumentation:
•scikit-learnDocumentation
•pandas Documentation
•numpyDocumentation
•StreamlitDocumentation
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THANK YOU