Computer science report writing and data analysis project

Produce a report capturing a critically comparison of the performance of different data analytics methods applied to at least 3 related datasets appropriate to the Fintech domain. The over-arching focus of the project is to develop an critical understanding of methods with respect to their ability to reveal insights from data sets, but aligned to their performance and application limitation within the Fintech domain. The application of each method should be applied in order to answer a specific (small-scale) research question aligned to the overall goal(s) of the project. It is also expected that the application of each method is accompanied by an appropriately sized lit review documenting pertinent and contemporary approaches in the literature that can both inform the application of a method as well as justify its potential merit(s). Projects will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Code and data sets are to also be submitted with the paper. Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. The reproducibility factor will play an important role in the assessment of each submission. • Key details, requirements, and definitions Data Requirements Each dataset should be for predictive analytics tasks, i.e. it should have a meaningful easily identity able response variable. Each dataset should also be suitably large (at least 20000 rows, and at least 10 columns). An example dataset meeting these requirements is the Adult dataset available here: [login to view URL] Deliverables There are THREE deliverables for this project: 1) a pdf report (defined below) 2) a .zip containing all code, and the datasets used. Number of methods : in total, you should apply and critically evaluate at least FOUR methods of machine or statistical learning for this project to facilitate your discussion, i.e. you apply each method to more than one dataset in order to better understand its performance, and provide a basis for comparison. Notions of performance : the discussion of performance should be orientated around multiple notions of performance. It is not sufficient to discuss only accuracy or R2 for the methods applied. Other possibilities include, but are not limited to: Cohen's Kappa, RSME