Datasets:
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README.md
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The main purpose is to collect the large data into one place for easy training and evaluation.
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## Dataset size
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| Dataset | Train split | Test split |
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|---------|-------------|------------|
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| quality | 809 533 | 100 000 |
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The main purpose is to collect the large data into one place for easy training and evaluation.
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## Data Preparation and Transformation
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### Quality Score Normalization
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The dataset was enhanced with additional columns, and quality scores (n = 909 533) were normalized using Ordered Quantile normalization through the `bestNormalize` package in R. This transformation mapped original values to a standardized normal distribution, resulting in a new variable, `transformed_quality`, included in both training and test datasets to enhance statistical modeling capabilities.
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### Readability Score Calculation
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U.S. reading grade levels were transformed using the Box-Cox method (`bestNormalize` package) with λ = 0.8766912. A custom function standardized the results and inverted the scale to generate 'readability' scores, where higher values indicate easier readability.
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The standardized Box-Cox transformation was applied to 919 663 non-missing observations, yielding the following statistics:
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- λ (lambda) = 0.8766912
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- Mean (before standardization) = 7.908629
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- Standard deviation (before standardization) = 3.339119
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These transformations improved the dataset's suitability for subsequent statistical analyses.
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## Dataset size
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The full datasets were shuffled and randomly split into `train` and `test` splits.
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| Dataset | Train split | Test split |
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|---------|-------------|------------|
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| quality | 809 533 | 100 000 |
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