I considered the draft law of e-checks, analysed the banking industry and created a data science product which provides personalised experience for banking customers. This was possible after the developing an intellectual algorithm for the most relevant banking offers selection, based on the methods of collaborative filtering. These algorithms are implemented for the scalable software platform Apache Spark in Scala and R. The experiments aimed at assessing the quality and scalability of the implemented algorithms were conducted on the Microsoft Azure cloud service.
The repository includes:
- Installation notes for environment deployment (install_env.txt)
- Source code for creating collaborative filtering (item_based_filtering.R and item_user_based_filtering.R)
- Source code to compare recommendations (comparison_recomendations.R)
- Source code for creating content-based recommendations (DecisionTrees.scala and RandomForests.scala)
- Initial datasets to make analysis (bank.csv and Data.csv)
- Initial datasets to make analysis (bank.csv and Data.csv)
- Results (Report.Rmd) and action plan + marketing research (diploma.docx)
recommenderlabggplot2Spark MLlib
For reproducing the results, download repository and run files separatelly file.
Copyright (c) 2018 Ekaterina Lyapina. Contact me for commercial use (or rather any use that is not academic research) (email: aegorfk at gmail.com). Free for research use, as long as proper attribution is given and this copyright notice is retained.