[Recommended]Describe the benefits of using the tool you have chosen (Python, R, or SAS) for extracting data in this scenario.

In this task, you will use Python, SAS, or R to analyze data for a telecommunications company (see “Customer Data” web link) and create a…

In this task, you will use Python, SAS, or R to analyze data for a telecommunications company (see “Customer Data” web link) and create a data mining report in a word processor (e.g., Microsoft Word). You will create visual representations throughout the submission to show each step of your work and to visually represent the findings of your data analysis.
 
You are an analyst for a telecommunications company that is concerned about the number of customers leaving their landline business for cable competitors. The company needs to know which customers are leaving and attempt to mitigate continued customer loss. You have been asked to analyze customer data to identify why customers are leaving and potential indicators to explain why those customers are leaving so the company can make an informed plan to mitigate further loss.
 
I: Tool Selection Execute data extraction from the “Customer Data” web link using data mining software (Python, R, or SAS). Provide a screen shot of the code you have written and its successful application with a copy of all the extracted data.
 

Describe the benefits of using      the tool you have chosen (Python, R, or SAS) for extracting data in this      scenario.
Define the objectives or goals      of the data analysis. Ensure that your objectives or goals are reasonable      within the scope of the scenario and are represented in the available      data.
Select a descriptive      method and a nondescriptive method (i.e., predictive,      classification, or probabilistic techniques) you will use to analyze the      data, and explain how the methods you have selected are appropriate for      the objectives or goals you have defined.

 
II: Data Exploration and Preparation Clean the data you have extracted and save as .xls or .xlsx format for submission. Be sure to address all necessary formatting, converting, and missing data.
 

Describe the target variable in      the data and indicate the specific type of data the target variable is      using, including examples that support your claims.
Describe an independent      predictor variable in the data and indicate the specific type of data      being described. Use examples from the data set that support your claims.
Propose the goal in      manipulation of the data and define your data preparation aims.
Define the statistical identity      of the data, including the essential criteria and phenomenon to be      predicted.
Explain the steps used to clean      the data and how you addressed any anomalies or missing data.

 
III: Data Analysis For each of the following steps, be sure to clearly indicate each step within your data sheet with a screen shot and annotations in your final submission. All algorithms used need to be clearly identified in the screen shot and submission.
 

Identify the distribution of      variables using univariate statistics from your cleaned and prepared data.      Represent your findings visually as part of your submission.
Identify the distribution of      variables using bivariate statistics from your cleaned and prepared data.      Represent your findings visually as part of your submission.
Apply an analytic method and an      evaluative method. Annotate the data showing both methods and your      findings.
Justify the methods you have      chosen to analyze your data. Be sure to include details about how the      methods you have chosen better represents your findings than other      methods.
Justify the methods you have      chosen to visually present your data. Be sure to include details about how      the presentation methods you chose better represents your findings than      other presentation methods.
 

 
IV: Data Summary Summarize the findings of your data evaluation. Provide the final findings dataset, including evaluation measures.

Explain how your data shows      that it was discriminating or not and whether the phenomenon you wanted to      detect was present in your findings. Provide specific examples from the      data to support your claims. 
Describe the methods you used      for detecting interactions and for selecting the most important predictor      variables. Include the specific interactions you detected and the most      important predictor variables that you found.
Acknowledge sources, using      in-text citations and references, for content that is quoted, paraphrased,      or summarized.

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