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.

Opis bibliograficzny
Główni autorzy:	Turjo, Aquib Abtahi, Karim, S.M. Mynul, Biswas, Tausif Hossain, Rahman, Yeaminur, Dewan, Ifroim
Kolejni autorzy:	Hossain, Muhammad Iqbal
Format:	Praca dyplomowa
Język:	English
Wydane:	Brac University 2021
Hasła przedmiotowe:	Credit Risk Loan Machine Learning Regression Model Gradient Boosting Deep Learning Neural Networks Support Vector Random Forest
Dostęp online:	http://hdl.handle.net/10361/15189

Podobne zapisy

Prediction of bipolar disorder from mental episodes using machine learning approach
od: Tasmeem, Sumaiya, i wsp.
Wydane: (2023)

Prediction of earthquakes: a step towards predicting the unpredictable
od: Chowdhury, Maheen, i wsp.
Wydane: (2023)

Building a credit scoring model to assign a reference score based on credit transaction and relevant profile data
od: Islam, Saqib Al, i wsp.
Wydane: (2020)

Speech emotion detection using supervised, unsupervised and feature selection algorithms
od: Rifat, Abu Nuraiya Mahfuza Yesmin, i wsp.
Wydane: (2019)

Detecting online recruitment fraud by using machine learning
od: Ghosh, Gitanjali, i wsp.
Wydane: (2021)

A hybrid rumor detection model derived from a comparative study of supervised approaches
od: Aothoi, Mehzabin Sadat, i wsp.
Wydane: (2023)

Fighting depression: psychological approaches among Bangladeshi university students
od: Sen, Sanjib Kumar, i wsp.
Wydane: (2022)

Stock market prediction using ensemble learning
od: Rabbani, Rakin Bin, i wsp.
Wydane: (2021)

Application of CNN based architectures in detection of distracted drivers
od: Arafin, Irfana, i wsp.
Wydane: (2023)

A machine learning approach to detect depression and anxiety using supervised learning
od: Ullas, Md Tahmidur Rahman, i wsp.
Wydane: (2019)

Aspect based opinion mining on restaurant reviews
od: Tonmoy, Sazid Hasan, i wsp.
Wydane: (2024)

Rating detection by reviews using ML and NLP towards mobile phone recommendation
od: Sarker, Md Wafi, i wsp.
Wydane: (2023)

A machine learning approach to predict young voter enthusiasm based on non-political factors
od: Rahman, Md. Nowroz Junaed, i wsp.
Wydane: (2021)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
od: Rahee, Arnob, i wsp.
Wydane: (2021)

Prediction and detection in change of cognitive load for VIP’s by a machine learning approach
od: Rahman, Fahim, i wsp.
Wydane: (2021)

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
od: Tasnim, Anika, i wsp.
Wydane: (2022)

Credit card fraud detection using machine learning techniques
od: Kabir, Tasmia, i wsp.
Wydane: (2021)

Diagnosis of cervical cancer using effective hybrid model in Bangladesh
od: Saad-Ul-Kabir, Syed, i wsp.
Wydane: (2021)

Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
od: Toufique, S.M, i wsp.
Wydane: (2024)

Predicting the outcome of purchasing a hotel package to assist policymakers using machine learning
od: Islam, Zahedul, i wsp.
Wydane: (2023)

Predicting crime using deep learning
od: Shihab, Muhammad Nafees, i wsp.
Wydane: (2024)

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

Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
od: Bhattacharjee, Ruposri, i wsp.
Wydane: (2024)

Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
od: Hasan, M. M. Kamrul, i wsp.
Wydane: (2022)

A counseling system to predict the study path for freshmen
od: Usha, Rowshni Tasneem, i wsp.
Wydane: (2019)

Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh
od: Biswas, Sunanda, i wsp.
Wydane: (2022)

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

Financial factors analysis for acquisition premium and anticipation using extreme gradient boosting and deep recurrent neural network
od: Rayhan, Mohammad, i wsp.
Wydane: (2020)

Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
od: Ahmed, Md. Sabbir, i wsp.
Wydane: (2021)

Early detection of chronic kidney disease using machine learning
od: Abrar, Tahmid, i wsp.
Wydane: (2019)

A modern technique to detect potholes by Computer Vision and Deep Learning
od: Saif, Muntasir Mahmud, i wsp.
Wydane: (2023)

Performance analysis of machine learning algorithms for Malware classification
od: Bushra, Raisa Hasan, i wsp.
Wydane: (2023)

Deep learning approaches for Bengali cyberbullying cetection on social media: a comparative study of BiLSTM, BiGRU and BERT models
od: Fahim, Kaji Mehedi Hasan, i wsp.
Wydane: (2024)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
od: Alam, Auninda, i wsp.
Wydane: (2021)

Corn leaf disease detection using deep convolution neural network
od: Rabbi, Rawhatur, i wsp.
Wydane: (2023)

Exploring machine learning techniques for symptom-based detection of livestock diseases
od: Niloy, Mahir Ahmed, i wsp.
Wydane: (2024)

Machine learning-based approach on predicting online shopping addiction using EEG signals
od: Nawer, Nafisa, i wsp.
Wydane: (2023)

Weather forecasting using Deep Learning
od: Fahad, Sheikh Abdul, i wsp.
Wydane: (2023)

An interpretable transformer based approach to classify Malaria from blood cell images
od: Islam, Mehafuza, i wsp.
Wydane: (2023)

Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
od: Hussain, Nadib, i wsp.
Wydane: (2018)