Prior to beginning work on this assignment, review the following:

• Read Chapters 11, 12, 13, and 14 in your textbook, Business Analytics: Communicating with Numbers, 2e.
• Review the infographic "Data Mining - Overview".

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

this assignment, 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.

In your paper,

• 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.

The Data Mining Best Practices paper

Data Mining - Overview In days of simpler, limited data, Traditional Data Analysis Tools were sufficient. JUTOTOTOUTTOTOUTTOTOT101010 011001111001 Then entered our superheroes - 110100110101007 110% 40101010011010 001101010011014 2 013 Q011001111001 Artificial Intelligence 1110100110101001 - /0110- 412 MACHINE LEARNING 1010 and Machine Learning, 100110 ARTIFICIAL 0011001111001 INTELLIGENCE Giving us the gift of complex, nuanced, and 111010TVTV TVV 11 10101010011010 gigantic volumes of data. 4 Move over, Traditional Data Analysis... Welcome Data MiningData Mining Algorithms Processes Supervised Data Mining Unsupervised Data Mining CRISP-DM SEMMA, KDD Ideal for data exploration, Ideal for predictive models The most commonly used Some other data dimension reduction, process framework mining processes pattern recognition Identified target variable Target variable not identified