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.

Bibliografiske detaljer
Main Authors:	Kabir, Tasmia, Nishat, Tahnin, Tory, Saria Bulbul
Andre forfattere:	Ashraf, Faisal Bin
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2021
Fag:	Random forest Decision tree Support vector machine Confusion matrix Outlier Machine learning Credit card fraud
Online adgang:	http://hdl.handle.net/10361/15784

Lignende værker

Detecting online recruitment fraud by using machine learning
af: Ghosh, Gitanjali, et al.
Udgivet: (2021)

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

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

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

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

Detection of alzheimer's disease using deep learning
af: Hasan, Mahmudul, et al.
Udgivet: (2021)

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

Parking data analysis
af: Ahamed, Kasfi, et al.
Udgivet: (2023)

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

Amazon product reviews sentiment analysis using supervised learning algorithms
af: Hawladar, Mohibullah, et al.
Udgivet: (2021)

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

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

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

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

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

Cardiovascular disease prediction model using Machine Learning Algorithm
af: Arefin Mirdha, MD Shamsul
Udgivet: (2023)

Heart disease prediction system
af: Amit, Tayab Al Azad, et al.
Udgivet: (2022)

Cyberbullying detection using sentiment analysis in social media
af: Sintaha, Mifta, et al.
Udgivet: (2016)

Fighting depression: psychological approaches among Bangladeshi university students
af: Sen, Sanjib Kumar, et al.
Udgivet: (2022)

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

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

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

Early detection of chronic kidney disease using machine learning
af: Abrar, Tahmid, et al.
Udgivet: (2019)

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

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

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

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

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

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

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

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

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

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

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

Detection of mind wandering using EEG signals
af: Tasika, Nadia Jebin, et al.
Udgivet: (2020)

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

Effectiveness of data mining in predicting heart diseases
af: Afrin, Shahria, et al.
Udgivet: (2018)

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

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

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