Set up an analytical program.
Apply data structures, objects, and classes by utilizing the build-in learning resources.
Utilize scripting and programming languages to read and write data files used in Data Science.
Utilize core programming fundamentals to achieve desired analytical outcomes.
Employ advanced data structures.
Utilize advanced programming fundamentals.
After doing some marketing, your organization was able to procure a potential customer who is interested in examining different sales scenarios to find the business model that is most profitable.
You have been provided a set of sales data which has characterized clothing stores with a number of attributes along with associated sales. Through the application of decision tree analysis, you are asked to actually build a model that will read in the data and after loading into a sophisticated data structure (data frames or associative array). By splitting the data into two sets (over and under the median sales), you will then apply a decision-tree analysis in order to find out which attributes contribute to either high or low sales.
Taking the Rossman data set from Kaggle, you will use either the python or R programming language to read in the associated data set. Next, you are to load the data into either an associative array or frame-based representation to make it suitable to analysis.
Next, you are to apply the Python or R libraries which may include, but not be limited to, the R (CART) module or the associated Python (scikit learn).
Perform the analysis and output the file containing only the limited feature set.
Note: you will have only a single submission which will be your source code in a plain text file and output generated, and it will be implemented in your preference of either the Python or R programming language.
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