Rule based classification A hotel s customers dataset

Overview

Rule-based-classification-A-hotel-s-customers-dataset-

Aim: Categorize new customers by segment and predict how much revenue they can generate

This real-world customer dataset with 31 variables describes 83,590 instances (customers) from a hotel in Lisbon, Portugal.

Content

The data comprehends three full years of customer personal, behavioral, demographic, and geographical information.

İnformation about the variables used from the study

LodgingRevenue:Total amount spent on lodging expenses by the customer (in Euros). This value includes room, crib, and other related lodging expenses

OtherRevenue: Total amount spent on other expenses by the customer (in Euros). This value includes food, beverage, spa, and other expenses

DocIDHash:SHA2-256 hash-string of the identification document number the customer provided at check-in (passport number, national ID card number, or other)

Nationality:Country of origin. Categories are represented in the ISO 3155-3:2013

DistributionChannel:Distribution channel usually used by the customer to make bookings at the hotel

Owner
Şebnem
Şebnem
Deep Learning Training Scripts With Python

Deep Learning Training Scripts DNN Frameworks Caffe PyTorch Tensorflow CNN Models VGG ResNet DenseNet Inception Language Modeling GatedCNN-LM Attentio

Multicore Computing Research Lab 16 Dec 15, 2022
TrTr: Visual Tracking with Transformer

TrTr: Visual Tracking with Transformer We propose a novel tracker network based on a powerful attention mechanism called Transformer encoder-decoder a

趙 漠居(Zhao, Moju) 66 Dec 27, 2022
Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images

Keras-ICNet [paper] Keras implementation of Real-Time Semantic Segmentation on High-Resolution Images. Training in progress! Requisites Python 3.6.3 K

Aitor Ruano 87 Dec 16, 2022
Gesture recognition on Event Data

Event based Gesture Recognition Gesture recognition on Event Data usually involv

2 Feb 14, 2022
Code for Massive-scale Decoding for Text Generation using Lattices

Massive-scale Decoding for Text Generation using Lattices Jiacheng Xu, Greg Durrett TL;DR: a new search algorithm to construct lattices encoding many

Jiacheng Xu 37 Dec 18, 2022
This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.

optimaladj: A library for computing optimal adjustment sets in causal graphical models This package implements the algorithms introduced in Smucler, S

Facundo Sapienza 6 Aug 04, 2022
Reference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Diffusion Probabilistic Models This repository provides a reference implementation of the method described in the paper: Deep Unsupervised Learning us

Jascha Sohl-Dickstein 238 Jan 02, 2023
object recognition with machine learning on Respberry pi

Respberrypi_object-recognition object recognition with machine learning on Respberry pi line.py 建立一支與樹梅派連線的 linebot 使用此 linebot 遠端控制樹梅派拍照 config.ini l

1 Dec 11, 2021
Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch

Triangle Multiplicative Module - Pytorch Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or c

Phil Wang 22 Oct 28, 2022
Official Pytorch implementation for video neural representation (NeRV)

NeRV: Neural Representations for Videos (NeurIPS 2021) Project Page | Paper | UVG Data Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav S

hao 214 Dec 28, 2022
Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

Debiasing Item-to-Item Recommendations With Small Annotated Datasets This is the code for our RecSys '20 paper. Other materials can be found here: Ful

Microsoft 34 Aug 10, 2022
DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations

DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations This repository contains the data, scripts and baseline co

Alexa 51 Dec 17, 2022
Invert and perturb GAN images for test-time ensembling

GAN Ensembling Project Page | Paper | Bibtex Ensembling with Deep Generative Views. Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhan

Lucy Chai 93 Dec 08, 2022
Differentiable Factor Graph Optimization for Learning Smoothers @ IROS 2021

Differentiable Factor Graph Optimization for Learning Smoothers Overview Status Setup Datasets Training Evaluation Acknowledgements Overview Code rele

Brent Yi 60 Nov 14, 2022
[AAAI2021] The source code for our paper 《Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion》.

DSM The source code for paper Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion Project Website; Datasets li

Jinpeng Wang 114 Oct 16, 2022
Deep Latent Force Models

Deep Latent Force Models This repository contains a PyTorch implementation of the deep latent force model (DLFM), presented in the paper, Compositiona

Tom McDonald 5 Oct 26, 2022
Analyzes your GitHub Profile and presents you with a report on how likely you are to become the next MLH Fellow!

Fellowship Prediction GitHub Profile Comparative Analysis Tool Built with BentoML Table of Contents: Features Disclaimer Technologies Used Contributin

Damir Temir 51 Dec 29, 2022
An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise

45 Dec 08, 2022
Job Assignment System by Real-time Emotion Detection

Emotion-Detection Job Assignment System by Real-time Emotion Detection Emotion is the essential role of facial expression and it could provide a lot o

1 Feb 08, 2022
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

3k Jan 08, 2023