Credit card fraud detection using machine learning techniques

Credit card fraud detection using machine learning techniques

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Detalhes bibliográficos
Principais autores:	Kabir, Tasmia, Nishat, Tahnin, Tory, Saria Bulbul
Outros Autores:	Ashraf, Faisal Bin
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2021
Assuntos:	Random forest Decision tree Support vector machine Confusion matrix Outlier Machine learning Credit card fraud
Acesso em linha:	http://hdl.handle.net/10361/15784

Registros relacionados

Detecting online recruitment fraud by using machine learning
por: Ghosh, Gitanjali, et al.
Publicado em: (2021)

Fraud detection in E-commerce using natural language processing
por: Kabir, Iftekhar, et al.
Publicado em: (2023)

Machine learning-based approach on predicting online shopping addiction using EEG signals
por: Nawer, Nafisa, et al.
Publicado em: (2023)

A machine learning approach to predict young voter enthusiasm based on non-political factors
por: Rahman, Md. Nowroz Junaed, et al.
Publicado em: (2021)

A hybrid rumor detection model derived from a comparative study of supervised approaches
por: Aothoi, Mehzabin Sadat, et al.
Publicado em: (2023)

Detection of alzheimer's disease using deep learning
por: Hasan, Mahmudul, et al.
Publicado em: (2021)

Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
por: Al Amin, Istiak, et al.
Publicado em: (2022)

Parking data analysis
por: Ahamed, Kasfi, et al.
Publicado em: (2023)

Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
por: Hussain, Nadib, et al.
Publicado em: (2018)

Amazon product reviews sentiment analysis using supervised learning algorithms
por: Hawladar, Mohibullah, et al.
Publicado em: (2021)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
por: Turjo, Aquib Abtahi, et al.
Publicado em: (2021)

An introduction to support vector machines : and other kernel-based learning methods /
por: Cristianini, Nello
Publicado em: (2000)

A counseling system to predict the study path for freshmen
por: Usha, Rowshni Tasneem, et al.
Publicado em: (2019)

Performance analysis of machine learning classi ers for detecting PE malware
por: Azmee, ABM.Adnan, et al.
Publicado em: (2020)

A machine learning approach to detect depression and anxiety using supervised learning
por: Ullas, Md Tahmidur Rahman, et al.
Publicado em: (2019)

Cardiovascular disease prediction model using Machine Learning Algorithm
por: Arefin Mirdha, MD Shamsul
Publicado em: (2023)

Heart disease prediction system
por: Amit, Tayab Al Azad, et al.
Publicado em: (2022)

Cyberbullying detection using sentiment analysis in social media
por: Sintaha, Mifta, et al.
Publicado em: (2016)

Fighting depression: psychological approaches among Bangladeshi university students
por: Sen, Sanjib Kumar, et al.
Publicado em: (2022)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
por: Rahee, Arnob, et al.
Publicado em: (2021)

Analysis of the impact of online education using EEG signals and machine learning algorithms
por: Hoque, Ehsanul, et al.
Publicado em: (2021)

Predicting stock market movement using sentiment analysis of twitter feed
por: Chakraborty, Pranjal, et al.
Publicado em: (2017)

Early detection of chronic kidney disease using machine learning
por: Abrar, Tahmid, et al.
Publicado em: (2019)

Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
por: Amiz, Asef Hassan, et al.
Publicado em: (2021)

Pattern recognition with Quantum Support Vector Machine(QSVM) on near term quantum processors.
por: Ahmed, Sajjad
Publicado em: (2019)

Modelling and forecasting energy demand of Bangladesh using AI based algorithms
por: Mahmud, Booshra Nazifa, et al.
Publicado em: (2019)

Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
por: Niloy, Ahashan Habib, et al.
Publicado em: (2021)

Finding suitable locations for live campaigns using different machine learning techniques
por: Qudar, Mohiuddin Md. Abdul, et al.
Publicado em: (2018)

Diagnosis of cervical cancer using effective hybrid model in Bangladesh
por: Saad-Ul-Kabir, Syed, et al.
Publicado em: (2021)

Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
por: Sarkar, Ripa, et al.
Publicado em: (2024)

A new approach to select adaptive Intrinsic Mode Functions (IMFs) of Empirical Mode Decomposition (EMD)
por: Sultana Nishi, Razia, et al.
Publicado em: (2016)

Where will you setup your business next?: a machine learning approach to suggest ideal geographical location for new restaurant establishment
por: Shihab, Ibne Farabi, et al.
Publicado em: (2018)

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
por: Tasnim, Anika, et al.
Publicado em: (2022)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
por: Farabe, Abdullah Al, et al.
Publicado em: (2021)

Detection of mind wandering using EEG signals
por: Tasika, Nadia Jebin, et al.
Publicado em: (2020)

Performance analysis of machine learning algorithms for Malware classification
por: Bushra, Raisa Hasan, et al.
Publicado em: (2023)

Effectiveness of data mining in predicting heart diseases
por: Afrin, Shahria, et al.
Publicado em: (2018)

Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
por: Ahmed, Md. Sabbir, et al.
Publicado em: (2021)

Prediction of Epileptic Seizures using digital signal processing and support vector machine
por: Siddique, Nusayer Masud, et al.
Publicado em: (2021)

A feature selection approach to determine obesity using machine learning method
por: Antor, Shahidul Alam, et al.
Publicado em: (2021)