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.

Bibliografski detalji
Glavni autor:	Khan, Riyo Hayat
Daljnji autori:	Alam, Md. Golam Rabiul
Format:	Disertacija
Jezik:	English
Izdano:	Brac University 2023
Teme:	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 pristup:	http://hdl.handle.net/10361/18680

Similar Items

Customer segmentation using K-means
od: Mahdee, Nafis, i dr.
Izdano: (2022)

LRFMV: an efficient customer segmentation model for superstores
od: Toyeb, Md., i dr.
Izdano: (2021)

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

LRFMVD : a customer segmentation model
od: Sagor, Kawsar Mahmud, i dr.
Izdano: (2023)

Fake profile detection in social media using image processing and machine learning
od: Sen, Shuva, i dr.
Izdano: (2021)

Bangla speech recognition using 1D CNN and LSTM with different dimension reduction techniques
od: Nirjhor, S M Mahsanul Islam, i dr.
Izdano: (2019)

Gene expression analysis using machine learning
od: Mostafa, Naﬁs
Izdano: (2021)

A Study of gainful employment of learners receiving skills training in the informal sector using Machine Learning
od: Raizan, Syed Ahsan, i dr.
Izdano: (2021)

Classiﬁcation of damaged vegetation areas using convolutional neural network over satellite images
od: Haque, Samiha, i dr.
Izdano: (2021)

Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
od: Hossain, Shoumik, i dr.
Izdano: (2022)

N.P.K. based crop suggesting model
od: Iftekher, Asif, i dr.
Izdano: (2020)

Finding ideal geographical location for businesses using machine learning technique
od: Mahmud, Mir Ibtid, i dr.
Izdano: (2022)

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

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

Brain tumor detection through image processing
od: Azmim, Tahaziba, i dr.
Izdano: (2021)

Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
od: Sharma, Tanmoyee, i dr.
Izdano: (2021)

Prediction of coronary heart diseases using supervised machine learning algorithms
od: Salam, Nazia Binte, i dr.
Izdano: (2023)

Cervical Cancer Detection from Cervix Image Using Pap smear Imaging through CNN
od: Bhuiyan, Mazedul Haque, i dr.
Izdano: (2021)

Heart disease prediction using machine learning algorithms
od: Ali, Musfiq, i dr.
Izdano: (2019)

Enhancing crops production based on environmental status using machine learning techniques
od: Talukder, Shiyam, i dr.
Izdano: (2021)

Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning
od: Shahrin, Fariha, i dr.
Izdano: (2021)

Analysis of the impact of online education using EEG signals and machine learning algorithms
od: Hoque, Ehsanul, i dr.
Izdano: (2021)

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

An active-learning based training-schedule for biomedical image segmentation on deep neural networks
od: Hassan, Mehadi, i dr.
Izdano: (2021)

Public vehicle ETA system using machine learning
od: Kabir, Md. Miraz, i dr.
Izdano: (2018)

Flight fare prediction using machine learning
od: Noyon, Md. Shaim Hosan, i dr.
Izdano: (2024)

Predicting depression in Bangladeshi undergraduates using machine learning
od: Choudhury, Ahnaf Atef, i dr.
Izdano: (2019)

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

Sentiment analysis to determine employee job satisfaction using machine learning techniques
od: Mouli, Nazifa, i dr.
Izdano: (2023)

Analyzing optimization landscape of recent policy optimization methods in deep RL
od: Khan, Mahir Asaf, i dr.
Izdano: (2023)

Breast cancer prediction using different machine learning models
od: Khandker Al- Muhaimin, i dr.
Izdano: (2020)

Automatic brain tumor segmentation using U-ResUNet chain model approach
od: Alam, Mohd Tanjeem, i dr.
Izdano: (2022)

Alumni opportunities using machine learning
od: Siaum, Arshif Sharlimin, i dr.
Izdano: (2019)

Machine learning as an indicator for breast cancer prediction
od: Shadman, Tahsin Mohammed, i dr.
Izdano: (2019)

Chronic kidney disease detection using ensemble classi ers and feature set reduction
od: Shawan, Naveed Rahman, i dr.
Izdano: (2019)

A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
od: Bin Mushfiq, Rahil, i dr.
Izdano: (2024)

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

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 dr.
Izdano: (2024)

Observing mega cities to grow from space and predicting its growth using machine learning techniques
od: Chakma, Mitesh, i dr.
Izdano: (2018)

Child Safe Browser Extension: A Browser Extension to Detect Adultery and Violent Content to Make Safer Web for Children
od: Alhossen, Md., i dr.
Izdano: (2021)