7-3 Milestone 3

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Southern New Hampshire University *

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520

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Business

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Feb 20, 2024

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4

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MILESTONE THREE 1 DAT 520 DECISION METHODS & MODELING January 25, 2024 7-3 Final Project Milestone 3
MILESTONE THREE 2 A. Summarize My research question “What in game activities most likely contribute to winning in the NFL?” has proven difficult for me to get a solid decision tree model formed. This question calls for a bottom-up modeling style, meaning I need to use a decision tree chart for my model. I have been continuously struggling to get a chart to even establish for me. My target variable is set as ‘Outcome’ from the NFL game logs and this represents whether the selected team Won or Lost. Input variables have included: Touchdown (TD) passes, rushing TD, pass yards, rush yards, and receiving yards. I used several different combinations of these input variables to manipulate the system enough to create the decision chart, these attempts remained unsuccessful. I initially thought the data set I was using may be too large and that was the reasoning for my constant error with the decision tree, so I tried minimizing my data set and input
MILESTONE THREE 3 variables. I was able to get a small chart created this way, so I made some progress, however I need to continue to adjust and add more variables to see if I am able to establish a larger and more accurate chart. The image shown below is the model I ended up with this week. B. Evaluate The current results clearly are not very reasonable with the given input variables. The chart shows that the team with the most passing completions wins the game 59% of the time. While the result seems accurate and I can agree that pass completions and touchdowns contribute to winning, I cannot get fully reasonable and accurate results without other input variables in the chart. C. Leverage Tools I plan to use to determine the accuracy of my decision model include PowerBI, NFL Stats, and the game logs provided for data sets. As stated above, the current model is
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MILESTONE THREE 4 accurate yet does not give enough input variables to conclude feasible research for the question at hand. D. Identify As it stands, my model is currently missing several elements since I am still not able to get an accurate chart loaded. I am continuing to mess with the system and input variables. Changing the target variable would not be beneficial, since the main goal here is to determine what activities lead to winning, Win or Lose would be the variable. E. Outline Common errors associated with creating a bottom-up decision tree model include: Insufficient data, Overfitting, Incorrect assumptions, Lack of validation, and disregarding model interpretation. To avoid insufficient data, we need to ensure data sets are relevant & accurate. To avoid overfitting the data set needs to be simplified to keep the model from becoming overly complex. It is essential to split the data into training and test sets to evaluate the model’s performance and accuracy.