Would you be able to write a 4-5 page paper in APA format? Paper must answer the

Would you be able to write a 4-5 page paper in APA format? Paper must answer the following questions

• Discuss the industry standards for data mining best practices.
• Identify pitfalls in data mining, including practices that should be avoided.
• Provide an example of a company that has successfully practiced data mining to forecast the market.
  • Explain the company’s forecasting model.
  • Describe how they deployed these data mining practices, the insights they gleaned, and the outcomes they achieved.
• Provide an example of a company that experienced a failure in data mining that led to an incorrect market forecast.
  • Explain the company’s forecasting model.
  • What pitfalls did the organization fall into?
  • Explain which data mining best practice(s) they could have implemented instead to avoid this failure.
• • Read Chapters 11, 12, 13, and 14 in your textbook, Business Analytics: Communicating with Numbers, 2e.
  • Review the Data Mining – Overview infographic below.

  

  Long description

  The image comprises three frames that show top to bottom progression where the top image shows a man looking at upward arrows. These indicate traditional data analysis tools such as vertical bar graphs. In days of limited data, these were sufficient. The middle frame shows two characters dressed as superheroes calling themselves “artificial intelligence” and “machine learning” and standing in front of a binary wall indicating digital data. They do superhuman tasks like analyzing complex, nuanced, and gigantic volumes of data and making intelligent predictions. And the lowermost frame shows the two characters looking at upward arrows and a digital wall with random numbers in the background. This indicates that the two have successfully performed data analytics with their superior capabilities using techniques such as data mining.

  

  Long description

  The image comprises an org-chart like tree that bifurcates data mining into algorithms and processes.Within algorithms you have supervised and unsupervised data mining.Supervised data mining is ideal for data exploration, dimension reduction, and pattern recognition; however, it is used when the target variable is not identified.Unsupervised data mining is ideal for predictive models and the target variable is identifiable as compared to supervised data mining.Processes bifurcate into CRISP-DM and SEMMA, KDD. CRISP-DM is the most commonly used process framework, whereas some other processes use SEMMA, KDD.

  
  

  In this assignment, you will analyze current data mining practices and evaluate the pros and cons of data mining. You will research an example of a company that has successfully practiced data mining to forecast the market and a company that could not leverage data mining effectively to forecast the market.