Analysis of Financial Transactions using Machine Learning
(2016) In LU-CS-EX 2016-05 EDA920 20151Department 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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Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8871594
- author
- Wamai Egesa, Adam LU
- supervisor
- organization
- alternative title
- An Application to Compute the Socio-ecological Impact of Consumer Spending
- course
- EDA920 20151
- year
- 2016
- 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}}, language = {{eng}}, note = {{Student Paper}}, series = {{LU-CS-EX 2016-05}}, title = {{Analysis of Financial Transactions using Machine Learning}}, year = {{2016}}, }