Sökning: "Customer Clustering"

Visar resultat 1 - 5 av 61 uppsatser innehållade orden Customer Clustering.

1. 1. Exploring Automated Early Problem Identification Based on Diagnostic Trouble Codes

   Master-uppsats, Institutionen för tillämpad informationsteknologi

   Författare :Mathias Forsman; Yihan Yang; [2024-03-05]
   Nyckelord :Automotive Industry; Early Problem Identification; Diagnostic Trouble Code; Case Study; Laboratory Experiment ; Machine Learning; Linear Regression; K-means Clustering;

   Sammanfattning : In the current automotive industry, problem identification is a reactive process. It starts when the customer experiences a vehicle problem and goes to the workshop. Subsequently, all the problem-related data will be collected from the workshop and forwarded to the vehicle manufacturer. LÄS MER

3. 2. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Luca Colasanti; [2023]
   Nyckelord :Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

   Sammanfattning : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. LÄS MER

4. 3. Unsupervised Clustering of Behavior Data From a Parking Application : A Heuristic and Deep Learning Approach

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

   Författare :Edvard Magnell; Joakim Nordling; [2023]
   Nyckelord :ML; Machine learning; clustering; unsupervised learning; deep learning; autoencoder; AI; artificial intelligence;

   Sammanfattning : This report aims to present a project in the field of unsupervised clustering on human behavior in a parking application. With increasing opportunities to collect and store data, the demands to utilize the data in meaningful ways also increase. LÄS MER

5. 4. Do estate-level characteristics generate unsafety? : Examining neighborhood and estate characteristics influence on perceived residential safety in Gothenburg

   Master-uppsats, Stockholms universitet/Statsvetenskapliga institutionen

   Författare :Madeleine Frisk Garcia; [2023]
   Nyckelord :Perceived safety; fixed effects; estate level effects; customer satisfaction surveys;

   Sammanfattning : Do estate and neighborhood characteristics influence perceptions of safety? Using data from a survey of residents living in municipal housing in Gothenburg, this paper argues that the spatial and social characteristics of a neighborhood vastly outpace the role of its socioeconomic and demographic composition, when it comes to accounting for the perceived safety of its residents. The dataset consists of survey data on residents’ perception of safety from 2013-2014 and 2016-2021 in Gothenburg linked with sociodemographic data at an estate level. LÄS MER

7. 5. Evaluation of Machine Learning techniques for Master Data Management

   Magister-uppsats, Högskolan i Skövde/Institutionen för informationsteknologi

   Författare :Fatime Toçi; [2023]
   Nyckelord :Master Data Management; Machine Learning; data quality; data duplicates;

   Sammanfattning : In organisations, duplicate customer master data present a recurring problem. Duplicate records can result in errors, complication, and inefficiency since they frequently result from dissimilar systems or inadequate data integration. LÄS MER