LRFS: online shoppers’ behavior based efficient customer segmentation model

LRFS: online shoppers’ behavior based efficient customer segmentation model

This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.

Bibliografiske detaljer
Hovedforfatter:	Khan, Riyo Hayat
Andre forfattere:	Alam, Md. Golam Rabiul
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2023
Fag:	Customer segmentation Unsupervised machine learning K-Means K-Medoids DBSCAN RFM analysis LRFM analysis Dimensionality reduction PCA t-SNE Autoencoder Deep learning Google analytics Customer relations > Management > Data processing Machine learning Cognitive learning theory
Online adgang:	http://hdl.handle.net/10361/18680

Lignende værker

Customer segmentation using K-means
af: Mahdee, Nafis, et al.
Udgivet: (2022)

LRFMV: an efficient customer segmentation model for superstores
af: Toyeb, Md., et al.
Udgivet: (2021)

3G and 4G paging success rate based mobile network anomaly detection using supervised and unsupervised learning
af: Ahasan, Md Rakibul
Udgivet: (2022)

LRFMVD : a customer segmentation model
af: Sagor, Kawsar Mahmud, et al.
Udgivet: (2023)

Fake profile detection in social media using image processing and machine learning
af: Sen, Shuva, et al.
Udgivet: (2021)

Bangla speech recognition using 1D CNN and LSTM with different dimension reduction techniques
af: Nirjhor, S M Mahsanul Islam, et al.
Udgivet: (2019)

Gene expression analysis using machine learning
af: Mostafa, Naﬁs
Udgivet: (2021)

A Study of gainful employment of learners receiving skills training in the informal sector using Machine Learning
af: Raizan, Syed Ahsan, et al.
Udgivet: (2021)

Classiﬁcation of damaged vegetation areas using convolutional neural network over satellite images
af: Haque, Samiha, et al.
Udgivet: (2021)

Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
af: Hossain, Shoumik, et al.
Udgivet: (2022)

N.P.K. based crop suggesting model
af: Iftekher, Asif, et al.
Udgivet: (2020)

Finding ideal geographical location for businesses using machine learning technique
af: Mahmud, Mir Ibtid, et al.
Udgivet: (2022)

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

Classification of Bangla regional languages and recognition of artificial Bangla speech using deep learning
af: Hossain, Prommy Sultana
Udgivet: (2022)

Brain tumor detection through image processing
af: Azmim, Tahaziba, et al.
Udgivet: (2021)

Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
af: Sharma, Tanmoyee, et al.
Udgivet: (2021)

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

Cervical Cancer Detection from Cervix Image Using Pap smear Imaging through CNN
af: Bhuiyan, Mazedul Haque, et al.
Udgivet: (2021)

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

Enhancing crops production based on environmental status using machine learning techniques
af: Talukder, Shiyam, et al.
Udgivet: (2021)

Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
af: Shahrin, Fariha, et al.
Udgivet: (2021)

Analysis of the impact of online education using EEG signals and machine learning algorithms
af: Hoque, Ehsanul, et al.
Udgivet: (2021)

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

An active-learning based training-schedule for biomedical image segmentation on deep neural networks
af: Hassan, Mehadi, et al.
Udgivet: (2021)

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

Flight fare prediction using machine learning
af: Noyon, Md. Shaim Hosan, et al.
Udgivet: (2024)

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

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

Sentiment analysis to determine employee job satisfaction using machine learning techniques
af: Mouli, Nazifa, et al.
Udgivet: (2023)

Analyzing optimization landscape of recent policy optimization methods in deep RL
af: Khan, Mahir Asaf, et al.
Udgivet: (2023)

Breast cancer prediction using different machine learning models
af: Khandker Al- Muhaimin, et al.
Udgivet: (2020)

Automatic brain tumor segmentation using U-ResUNet chain model approach
af: Alam, Mohd Tanjeem, et al.
Udgivet: (2022)

Alumni opportunities using machine learning
af: Siaum, Arshif Sharlimin, et al.
Udgivet: (2019)

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

Chronic kidney disease detection using ensemble classi ers and feature set reduction
af: Shawan, Naveed Rahman, et al.
Udgivet: (2019)

A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
af: Bin Mushfiq, Rahil, et al.
Udgivet: (2024)

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

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

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

Child Safe Browser Extension: A Browser Extension to Detect Adultery and Violent Content to Make Safer Web for Children
af: Alhossen, Md., et al.
Udgivet: (2021)