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Please submit your Milestone Two: Introduction and Exploratory Data Analysis (ED
Please submit your Milestone Two: Introduction and Exploratory Data Analysis (EDA) assignment as a Word Document here.
Here is my Research Topic:
Predicting Bank Failures in USA
Here is my Research Question:
Which U.S. state is most likely to experience the greatest number of bank failures in the future, and what are the main factors that contribute to this likelihood?
Here is my Dataset
Dataset Name: FDIC Failed Bank List
FDIC Failed Bank List Dataset
Download the FDIC Failed Bank List (CSV)
Here is my chosen ML Method:
Classification machine learning method
Introduction and Exploratory Data Analysis (EDA)
In this milestone, you will write the introduction to your final paper. Specifically, the introduction should explain and discuss your research, including the final research question, dataset, and the chosen machine learning model. It should give the appropriate context of the analysis for your reader, and explicitly state your final research question as well as your hypothesis and the machine learning model you’ve selected. A good introduction not only gives background material on the machine learning method and dataset, but it also dives into how data analysis principles specifically apply to the situation: why did you decide on that particular machine learning model for this specific dataset and research question?
In particular, now that you have selected your dataset and formulated a research question, you should explore and characterize the dataset by doing an Exploratory Data Analysis. This data appraisal will inform your introduction, which will provide the context for your data analysis. Finally, it should consider the metrics you will utilize to evaluate the results of your analysis.
Deliverable
For milestone 2, please ensure you have the following REQUIRED sections ONLY:
Introduction: Describe the purpose, type, intended populations, and uses of the analysis report to establish an appropriate context for the data analysis plan. Appraise the data within the context of the problem to be solved and industry standards. How will you use the data? For example, expound upon the limitations of the data set in the context of your needs.
Please do especially ensure you clearly state your final, possibly revised, research question in a separate sub-section
Exploratory Data Analysis: Characterize the data set. For example, what is the purpose such data are generally used for? You should do the data preparation for analysis and compute and present the summary statistics and the Exploratory Data Analysis (EDA) for the dataset.
Measurable Metrics and ML Methods/Utilities: Explain the ML methods/utilities that you will be using and how the data supports that choice. The utilities, in this case, are the different machine learning algorithms you are considering, along with any particular software implementations you are thinking of using for it. You should also think of measurable metrics to evaluate the results of your analysis, as well.
I’ve attached the sample “template-milestone_2” and the previously submitted Milestone-2 where I was partially graded. Please take a look at the template and previously submitted doc and re-write the entire ask.
Instructor’s Comments:
Please ensure your RQ has the explicit independent and dependent variables and, if possible, also mentions the ML approach. In addition. Your EDA is lacking the actual EDA and the metrics should be for the specific chosen ML algorithms only.
Please make sure that the revised document include all the above as stated by professor. I am also attaching the Milestones that I’ve submitted after this (Milestone3 and 4) for your reference.
Please answer/re-write Milestone-2 without any overlapping from Milestones 3 and 4.
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