A counseling system to predict the study path for freshmen

A counseling system to predict the study path for freshmen

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.

Bibliographische Detailangaben
Hauptverfasser:	Usha, Rowshni Tasneem, Parvez, Shiny Raisa, Sejuti, Fariha Sazid, Hossain, Maisha
Weitere Verfasser:	Majumdar, Mahbub
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	BRAC University 2019
Schlagworte:	Machine learning Prediction Decision tree Random forest Ranking algorithm Major selection AHP
Online Zugang:	http://hdl.handle.net/10361/12276

Ähnliche Einträge

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

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

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

Machine learning for stress prediction
von: Salehin, Sherajus, et al.
Veröffentlicht: (2023)

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

Heart disease prediction using machine learning algorithms
von: Ali, Musfiq, et al.
Veröffentlicht: (2019)

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

Predicting depression in Bangladeshi undergraduates using machine learning
von: Choudhury, Ahnaf Atef, et al.
Veröffentlicht: (2019)

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

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

Machine learning as an indicator for breast cancer prediction
von: Shadman, Tahsin Mohammed, et al.
Veröffentlicht: (2019)

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

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

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

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

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

Prediction of coronary heart diseases using supervised machine learning algorithms
von: Salam, Nazia Binte, et al.
Veröffentlicht: (2023)

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

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

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

Bank loan prediction using machine learning
von: Mahottam, Parthosarothi, et al.
Veröffentlicht: (2023)

Machine learning based data processing and latency reduction in the internet of things for agriculture
von: Rahman, Sajeed Ur, et al.
Veröffentlicht: (2019)

Predicting climate induced floods using machine learning
von: Saleh, Chowdhury Nafis, et al.
Veröffentlicht: (2022)

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

A machine learning approach on classifying orthopedic patients based on their biomechanical features
von: Hasan, Kamrul, et al.
Veröffentlicht: (2018)

An advanced hospital rating system using machine learning and natural language processing
von: Tamjid, Ali Asgar, et al.
Veröffentlicht: (2023)

Observing mega cities to grow from space and predicting its growth using machine learning techniques
von: Chakma, Mitesh, et al.
Veröffentlicht: (2018)

Comparative analysis between machine learning algorithms in efficiency of Coronary Heart Disease (CHD) prediction
von: Oishi, Fayza Rezwana, et al.
Veröffentlicht: (2020)

Crop prediction based on geographical and climatic data using machine learning and deep learning
von: Alif, Al Amin, et al.
Veröffentlicht: (2019)

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

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

Application of machine learning techniques on the context of livestock analysis
von: Islam, Md. Shajedul, et al.
Veröffentlicht: (2018)

Health monitoring IoT device with risk prediction using cloud computing and machine learning
von: Das, Anindya, et al.
Veröffentlicht: (2021)

Early grade prediction using profile data
von: Iqbal, Sumaiya, et al.
Veröffentlicht: (2021)

Comparison of machine learning techniques to predict cardiovascular disease
von: Jaber, Mir Mohammad, et al.
Veröffentlicht: (2019)

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

Predictive location generation using machine learning approach
von: Hasan, S.A.M Khaled, et al.
Veröffentlicht: (2018)

Empowering mobile network planning through deep learning: a path to democratization
von: Nabi, Syed Tauhidun
Veröffentlicht: (2024)

Predicting the length of stay in a hospital for a particular disease using machine learning algorithms
von: Imam, Saif
Veröffentlicht: (2021)

Public vehicle ETA system using machine learning
von: Kabir, Md. Miraz, et al.
Veröffentlicht: (2018)