Please, according to the attached instruction write the 2 papers about:

Obesity Statistics in the United States

Obesity, a common and costly health issue that increases risk for heart disease, type 2 diabetes, and cancer, affects more than one-third of adults and 17 percent of youth in the United States. By the numbers, 78 million adults and 12 million children are obesefigures many regard as an epidemic. Adults are considered obese when they are about 35 pounds overweight. In 2013, obesity rates among American adults remained high. No state has an obesity rate below 21 percent and rates have risen in six statesAlaska, Delaware, Idaho, New Jersey, Tennessee, and Wyoming. In two states obesity rates now exceed 35 percent for the first time and 20 states have obesity rates at or above 30 percent.

Please follow these instructions:

Research Question, please choose a data set from the curated list of sources or submit your proposal for a different data source than those listed. Then write a data analysis research question, which should be framed as a discrete set of choices to be analyzed, perhaps using decision trees or one of the other algorithms you've seen in the course or in your own research.

These two initial pieces of the final project are important to determine as early and accurately as possible, to guide the next phases of your research. But this is, of course, an iterative process and the research question can get fine-tuned as you go on to the subsequent milestones.

Deliverable

For milestone 1, please ensure you have the following sections at a minimum:

• Research Question: start with a business problem and convert it to a research problem statement
• Dataset description and details: describe the dataset, summarize its statistics, and include links to its source/provenance
• Reasoning and Hypothesis: explain what you expect to find and why

Tips

In regards to formulating a research question, it helps to start by first stating the business problem. E.g., What factors are related to employee churn? Can we predict future terminations?

Then, convert that business problem into a research problem statement by making it precise and quantitative and something you can use with a machine learning model. E.g., is there a correlation between age, length of service, and business unit with the terminated status of an employee?

In general, the research problem statement should be precise and quantitative and is often expressed as some independent variable(s) (the predictor(s)) having a presumed correlation with some dependent variable (the response variable or class label).

Once you have a precise, quantifiable research question, you can see if it matches one or more of these questions that machine learning usually answers: