Comparative analysis and implementation of credit risk prediction through distinct machine learning models

Comparative analysis and implementation of credit risk prediction through distinct machine learning models

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

Bibliografiska uppgifter
Huvudupphovsmän:	Turjo, Aquib Abtahi, Karim, S.M. Mynul, Biswas, Tausif Hossain, Rahman, Yeaminur, Dewan, Ifroim
Övriga upphovsmän:	Hossain, Muhammad Iqbal
Materialtyp:	Lärdomsprov
Språk:	English
Publicerad:	Brac University 2021
Ämnen:	Credit Risk Loan Machine Learning Regression Model Gradient Boosting Deep Learning Neural Networks Support Vector Random Forest
Länkar:	http://hdl.handle.net/10361/15189

Liknande verk

Prediction of bipolar disorder from mental episodes using machine learning approach
av: Tasmeem, Sumaiya, et al.
Publicerad: (2023)

Prediction of earthquakes: a step towards predicting the unpredictable
av: Chowdhury, Maheen, et al.
Publicerad: (2023)

Building a credit scoring model to assign a reference score based on credit transaction and relevant profile data
av: Islam, Saqib Al, et al.
Publicerad: (2020)

Speech emotion detection using supervised, unsupervised and feature selection algorithms
av: Rifat, Abu Nuraiya Mahfuza Yesmin, et al.
Publicerad: (2019)

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

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

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

Stock market prediction using ensemble learning
av: Rabbani, Rakin Bin, et al.
Publicerad: (2021)

Application of CNN based architectures in detection of distracted drivers
av: Arafin, Irfana, et al.
Publicerad: (2023)

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

Aspect based opinion mining on restaurant reviews
av: Tonmoy, Sazid Hasan, et al.
Publicerad: (2024)

Rating detection by reviews using ML and NLP towards mobile phone recommendation
av: Sarker, Md Wafi, et al.
Publicerad: (2023)

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

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

Prediction and detection in change of cognitive load for VIP’s by a machine learning approach
av: Rahman, Fahim, et al.
Publicerad: (2021)

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

Credit card fraud detection using machine learning techniques
av: Kabir, Tasmia, et al.
Publicerad: (2021)

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

Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
av: Toufique, S.M, et al.
Publicerad: (2024)

Predicting the outcome of purchasing a hotel package to assist policymakers using machine learning
av: Islam, Zahedul, et al.
Publicerad: (2023)

Predicting crime using deep learning
av: Shihab, Muhammad Nafees, et al.
Publicerad: (2024)

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

Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
av: Bhattacharjee, Ruposri, et al.
Publicerad: (2024)

Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
av: Hasan, M. M. Kamrul, et al.
Publicerad: (2022)

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

Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh
av: Biswas, Sunanda, et al.
Publicerad: (2022)

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

Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network
av: Rayhan, Mohammad, et al.
Publicerad: (2020)

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

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

A modern technique to detect potholes by Computer Vision and Deep Learning
av: Saif, Muntasir Mahmud, et al.
Publicerad: (2023)

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

Deep learning approaches for Bengali cyberbullying cetection on social media: a comparative study of BiLSTM, BiGRU and BERT models
av: Fahim, Kaji Mehedi Hasan, et al.
Publicerad: (2024)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
av: Alam, Auninda, et al.
Publicerad: (2021)

Corn leaf disease detection using deep convolution neural network
av: Rabbi, Rawhatur, et al.
Publicerad: (2023)

Exploring machine learning techniques for symptom-based detection of livestock diseases
av: Niloy, Mahir Ahmed, et al.
Publicerad: (2024)

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

Weather forecasting using Deep Learning
av: Fahad, Sheikh Abdul, et al.
Publicerad: (2023)

An interpretable transformer based approach to classify Malaria from blood cell images
av: Islam, Mehafuza, et al.
Publicerad: (2023)

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