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Analysis of Financial Transactions using Machine Learning

Wamai Egesa, Adam LU (2016) In LU-CS-EX 2016-05 EDA920 20151
Department of Computer Science
Abstract
Many people want to know the socio-ecological impact of the goods they purchase. In this thesis, we describe a system that computes the socio-ecological impact of those goods by analyzing uncategorized financial transactions. The computation is made possible by extending a system that can computate socio-ecological impact from categorized transactions. The extension further includes visualizations on the system’s web GUI using AngularJS and extension of the system’s Node.js API.

To compute the socio-ecological impact the report describes a categorization service. To connect the service to the core system a RabbitMQ message queue was used. The service trained supervised machine learning models using Apache Spark’s machine learning... (More)
Many people want to know the socio-ecological impact of the goods they purchase. In this thesis, we describe a system that computes the socio-ecological impact of those goods by analyzing uncategorized financial transactions. The computation is made possible by extending a system that can computate socio-ecological impact from categorized transactions. The extension further includes visualizations on the system’s web GUI using AngularJS and extension of the system’s Node.js API.

To compute the socio-ecological impact the report describes a categorization service. To connect the service to the core system a RabbitMQ message queue was used. The service trained supervised machine learning models using Apache Spark’s machine learning library (MLlib) on a dataset containing about 2.4 million categorized transactions. This achieved a categorization accuracy of 82.9%.

The main focus for future work is to increase accuracy by using named-entity recognition and splitting up the categorization into two steps using multiple categorizers. (Less)
Popular Abstract (Swedish)
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author
Wamai Egesa, Adam LU
supervisor
organization
alternative title
An Application to Compute the Socio-ecological Impact of Consumer Spending
course
EDA920 20151
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
machine learning, apache spark, mllib, mcc, financial transactions
publication/series
LU-CS-EX 2016-05
report number
LU-CS-EX 2016-05
ISSN
1650-2884
language
English
id
8871594
date added to LUP
2016-04-29 13:56:50
date last changed
2018-01-01 04:09:12
@misc{8871594,
  abstract     = {Many people want to know the socio-ecological impact of the goods they purchase. In this thesis, we describe a system that computes the socio-ecological impact of those goods by analyzing uncategorized financial transactions. The computation is made possible by extending a system that can computate socio-ecological impact from categorized transactions. The extension further includes visualizations on the system’s web GUI using AngularJS and extension of the system’s Node.js API.

To compute the socio-ecological impact the report describes a categorization service. To connect the service to the core system a RabbitMQ message queue was used. The service trained supervised machine learning models using Apache Spark’s machine learning library (MLlib) on a dataset containing about 2.4 million categorized transactions. This achieved a categorization accuracy of 82.9%.

The main focus for future work is to increase accuracy by using named-entity recognition and splitting up the categorization into two steps using multiple categorizers.},
  author       = {Wamai Egesa, Adam},
  issn         = {1650-2884},
  keyword      = {machine learning,apache spark,mllib,mcc,financial transactions},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-CS-EX 2016-05},
  title        = {Analysis of Financial Transactions using Machine Learning},
  year         = {2016},
}