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.

Xehetasun bibliografikoak
Egile Nagusiak:	Kabir, Tasmia, Nishat, Tahnin, Tory, Saria Bulbul
Beste egile batzuk:	Ashraf, Faisal Bin
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2021
Gaiak:	Random forest Decision tree Support vector machine Confusion matrix Outlier Machine learning Credit card fraud
Sarrera elektronikoa:	http://hdl.handle.net/10361/15784

Antzeko izenburuak

Detecting online recruitment fraud by using machine learning
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Fraud detection in E-commerce using natural language processing
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Detection of alzheimer's disease using deep learning
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Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
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Parking data analysis
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Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
nork: Hussain, Nadib, et al.
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Amazon product reviews sentiment analysis using supervised learning algorithms
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Comparative analysis and implementation of credit risk prediction through distinct machine learning models
nork: Turjo, Aquib Abtahi, et al.
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An introduction to support vector machines : and other kernel-based learning methods /
nork: Cristianini, Nello
Argitaratua: (2000)

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nork: Usha, Rowshni Tasneem, et al.
Argitaratua: (2019)

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

A machine learning approach to detect depression and anxiety using supervised learning
nork: Ullas, Md Tahmidur Rahman, et al.
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Cardiovascular disease prediction model using Machine Learning Algorithm
nork: Arefin Mirdha, MD Shamsul
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Heart disease prediction system
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Argitaratua: (2022)

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

Fighting depression: psychological approaches among Bangladeshi university students
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Argitaratua: (2022)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
nork: Rahee, Arnob, et al.
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Analysis of the impact of online education using EEG signals and machine learning algorithms
nork: Hoque, Ehsanul, et al.
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Predicting stock market movement using sentiment analysis of twitter feed
nork: Chakraborty, Pranjal, et al.
Argitaratua: (2017)

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

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

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

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

Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
nork: Niloy, Ahashan Habib, et al.
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Finding suitable locations for live campaigns using different machine learning techniques
nork: Qudar, Mohiuddin Md. Abdul, et al.
Argitaratua: (2018)

Diagnosis of cervical cancer using effective hybrid model in Bangladesh
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Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
nork: Sarkar, Ripa, et al.
Argitaratua: (2024)

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

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

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

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
nork: Farabe, Abdullah Al, et al.
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Detection of mind wandering using EEG signals
nork: Tasika, Nadia Jebin, et al.
Argitaratua: (2020)

Performance analysis of machine learning algorithms for Malware classification
nork: Bushra, Raisa Hasan, et al.
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Effectiveness of data mining in predicting heart diseases
nork: Afrin, Shahria, et al.
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Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
nork: Ahmed, Md. Sabbir, et al.
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Prediction of Epileptic Seizures using digital signal processing and support vector machine
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A feature selection approach to determine obesity using machine learning method
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