A machine learning approach to predict young voter enthusiasm based on non-political factors

A machine learning approach to predict young voter enthusiasm based on non-political factors

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.

Opis bibliograficzny
Główni autorzy:	Rahman, Md. Nowroz Junaed, Pantho, Md. Humaun Kabir, Fuad, Nafis
Kolejni autorzy:	Majumdar, Mahbubul Alam
Format:	Praca dyplomowa
Język:	en_US
Wydane:	Brac University 2021
Hasła przedmiotowe:	Machine Learning Voting Young Voter Turnout Random Forest Support Vector Machine XGBoost Naive Bayes Non-political
Dostęp online:	http://hdl.handle.net/10361/14463

Podobne zapisy

Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
od: Al Amin, Istiak, i wsp.
Wydane: (2022)

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

Parking data analysis
od: Ahamed, Kasfi, i wsp.
Wydane: (2023)

Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
od: Sarkar, Ripa, i wsp.
Wydane: (2024)

Assistive Guideline of Categorization for Competitive Programming Problems
od: Dhrubo, Najmus Sakib, 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)

Best feature selection and data visualization for breast cancer prediction
od: Hemel, Tanjim Ahmed, i wsp.
Wydane: (2020)

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

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

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

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

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

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

Comparative study of toxic comments classification using machine learning algorithms
od: Razzak, Razia, i wsp.
Wydane: (2021)

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

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

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

Performance analysis of machine learning classi ers for detecting PE malware
od: Azmee, ABM.Adnan, i wsp.
Wydane: (2020)

Estimating flood susceptibility of Bangladesh in the future year using machine learning
od: Alim, Sakib Bin, i wsp.
Wydane: (2021)

Predicting subject area interest of a student using naive bayes
od: Hossain, Shakib
Wydane: (2016)

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

Multimodal fake news detection using text and image
od: Biswas, Trisha, i wsp.
Wydane: (2024)

Automatic classified myocardial infarction detection using machine learning and forewarning system with location of the patient using GSM module
od: Shrestha, Soptorsi Paul, i wsp.
Wydane: (2021)

Recognizing sentimental emotions in text by using Machine Learning
od: Bushra, Tabassum Khan, i wsp.
Wydane: (2023)

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

Heart disease prediction using techniques of classification in machine learning
od: Afrose, Sadia, i wsp.
Wydane: (2021)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
od: Turjo, Aquib Abtahi, i wsp.
Wydane: (2021)

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

Prediction of Epileptic Seizures using digital signal processing and support vector machine
od: Siddique, Nusayer Masud, i wsp.
Wydane: (2021)

A feature selection approach to determine obesity using machine learning method
od: Antor, Shahidul Alam, i wsp.
Wydane: (2021)

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

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

Heart disease prediction system
od: Amit, Tayab Al Azad, i wsp.
Wydane: (2022)

An effective machine learning approach for sentiment analysis of restaurant reviews
od: Karim, Rabita
Wydane: (2018)

Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
od: Amiz, Asef Hassan, i wsp.
Wydane: (2021)

Unveiling underlying patterns, drivers and anomalies in cryptocurrency price dynamics through feature fusion of financial indicators and sentiment fluctuations
od: Rabbi, Md. Nafis
Wydane: (2024)

A new approach to select adaptive Intrinsic Mode Functions (IMFs) of Empirical Mode Decomposition (EMD)
od: Sultana Nishi, Razia, i wsp.
Wydane: (2016)

Cyberbullying detection using sentiment analysis in social media
od: Sintaha, Mifta, i wsp.
Wydane: (2016)

Text classification using machine learning algorithms
od: Hasnat, Fahim, i wsp.
Wydane: (2018)