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@@ -41,4 +41,37 @@ configs:
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  path: data/original-*
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  - split: augmented
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  path: data/augmented-*
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  path: data/original-*
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  - split: augmented
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  path: data/augmented-*
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+ license: mit
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+ pretty_name: NFL 2024 Receiving Yard Leaders
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  ---
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+
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+ ## Dataset Description
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+ This dataset contains statistics for the **NFL 2024 receiving yard leaders**, with both original measurements and augmented samples. Each row corresponds to a player, with the target variable being **TD (touchdowns scored)**.
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+
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+ - **Original split:** 30 manually collected samples with real player names.
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+ - **Augmented split:** 300 synthetic samples with the player name replaced by placeholder values like `Synthetic_Player_1`, `Synthetic_Player_2`, etc., created using data augmentation techniques.
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+
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+ ## Features
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+ - `player` (string): Player name, or placeholder for augmented data
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+ - `Yds` (int32): Total receiving yards
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+ - `TD` (int32): Total touchdowns scored (target variable)
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+ - `20+` (int32): Receptions of 20+ yards
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+ - `40+` (int32): Receptions of 40+ yards
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+ - `LNG` (int32): Longest reception
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+ - `Rec` (int32): Total receptions
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+ - `1st` (int32): First down receptions
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+ - `1st%` (float32): First down reception percentage
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+ - `Rec FUM` (int32): Fumbles on receptions
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+ - `Rec YAC/R` (float32): Yards after catch per reception
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+ - `Tgts` (int32): Total targets
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+
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+ ## Intended Use
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+ This dataset is suitable for **regression or supervised learning tasks**, such as predicting touchdowns based on receiving stats. It is also intended for **educational purposes**, including exploring feature importance, synthetic data generation, and supervised learning workflows.
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+
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+ ## Limitations
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+ - Original split is small (30 samples) and represents only top-performing players.
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+ - Augmented split uses synthetic player names and may not fully represent real-world distributions.
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+
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+ ## Citation
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+ If you use this dataset, please cite:
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+ "Collected NFL 2024 Receiving Yard Leaders Dataset, augmented for educational purposes in a machine learning coursework project."