I am modeling the popular German credit data using, Decision Trees, Support Vector Machines as standalone classifiers, the homogeneous ensemble of SVMs and random forest and, finally, the ensemble of Support Vector Machines and Decision Tree. The motivation is to establish that heterogeneous ensembles are better predictors than the homogeneous ensemble.
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My core competency lies in development and implementation of financial models based on academic literature. Programming Skills: Python, R, SQL Packages Stay tuned, I'm still working on this proposal.
Recently finished a project which did something similar Relevant Skills and Experience Machine learning, R, Python Stay tuned, I'm still working on this proposal.