Big Data

   · The deliverable should accommodate the afterward components: (1) All-embracing Goals/Research Hypothesis (20 %) 1-3 assay questions to navigate/direct all your project. · You may adjournment this breadth until (1) you abstraction all antecedent assignment and (2) you do some assay and accept the dataset/project (2) (Previous/Related Contributions) (40 %) As best of the called projects use accessible datasets, no agnosticism there are altered attempts/projects to assay those datasets. 30 % of this deliverable is in your all-embracing appraisal of antecedent abstracts assay efforts. This accomplishment should include: · Evaluating absolute antecedent codes that they accept (e.g. in Kernels and altercation sections) or any alternative refence. Make abiding you try those codes and appearance their results · In accession to the code, abridge best accordant abstract or efforts to assay the aforementioned dataset you accept picked.  · For the few who best their own datasets, you are still assured to do your abstract assay in this breadth on what is best accordant to your data/idea/area and abridge those best accordant contributions. (3) A allegory abstraction (40 %) Compare after-effects in your own work/project with after-effects from antecedent or alternative contributions (data and assay allegory not abstract review) The aberration amid breadth 3 and breadth 2 is that breadth 2 focuses on code/data assay begin in sources such as Kaggle, github, etc. while breadth 3 focuses on assay affidavit that not all-important advised the aforementioned dataset, but the aforementioned focus area