The purpose of this neural network is to predict whether a college is selective or not based on attributes.
The current model is using:
- 75th percentile ACT scores
- Admittance rate percentages
- Total enrollment
- Total out-of-state price
- Percent of enrollment that is non-white
- Historically black university status (dummy variable)
- Percent of total enrollment that is women
These are useful predictors for whether a college is selective. I define a 'selective' university as one that has an acceptance rate of less than 50%.
Evaluating kurtosis:
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Mesokurtic: Data follows a normal distribution
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Leptokurtic: Heavy tails on either side, indicating large outliers. Looks like Top-Thrill Dragster.
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Playtkurtic: Flat tails indicate that there aren't many outliers.
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A kurtosis value greater than +1 indicates the graph is very peaked. Leptokurtic.
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A kurtosis value less than -1 indicates the graph is relatively flat. Playtkurtic.
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A kurtosis value of 0 indicates that the graph follows a normal distribution. Mesokurtic.
Evaluating skewness:
- A negative value indicates the tail is on the left side of the distribution.
- A positive value indicates the tail is on the right side of the distribution.
- A value of zero indicates that there is no skewness in the distribution; it's perfectly symmetrical.
Thank you Venelin (https://curiousily.com/posts/build-your-first-neural-network-with-pytorch/) and StatQuest (https://www.youtube.com/watch?v=FHdlXe1bSe4) for creating fantastic guides to PyTorch.