A C++ implementation of the Medical Insurance Costs Project from Codecademy's Data Science Career Path.
- Import your dataset Import insurance.csv into your Python file and inspect the contents.
- Look over your dataset Download a zip file here with the necessary datasets and an empty Jupyter Notebook where you can write your code. Open insurance.csv and take a look at the file. Take note of how information is organized. How will this affect how you analyze the data in Python? Is there anything of particular interest to you in the dataset that you want to investigate? Think about these things before you jump into analyzing it.
- Scoping Your Project. Now that you have looked over your dataset, plan out what you want to analyze. What is it that you want to find out about this dataset? Based on the way information is organized, certain inspections may be easier to perform than others. As you map out the process, consider the scope of your analysis as well. Properly scoping your project will greatly benefit you; scoping creates structure while requiring you to think through your entire project before you begin. You should start by stating the goals for your project, then gathering the data, and considering the analytical steps required. A proper project scope can be a great road map for your project, but keep in mind that some down-stream tasks may become dead ends which will require adjustment to the scope.
- Save the features of your dataset (the columns) from insurance.csv by storing them in variables that can be used for analysis. As you consider what types of variables to use and how many you plan to create, think ahead about the parameters you wish to investigate and how your organization will impact this analysis.
- Build out analysis functions or class methods. You now have everything you need to begin your analysis. You have organized the information from insurance.csv and have spent some time thinking about what it is you would like to investigate. Now is the time to build out how you perform these investigations. Use the Python fundamentals you have learned so far to accomplish these tasks. There are many different ways you can achieve these analyses. In our hint, we will provide some ideas for how you can use Python to analyze data.