{
    "data_id": "43432",
    "name": "Chess-Position--Chess-Moves",
    "exact_name": "Chess-Position--Chess-Moves",
    "version": 1,
    "version_label": "v1.0",
    "description": "Context\nThe objective of this dataset is to create a chess engine through machine learning. In this first part we will first predict the pieces to be moved depending on the position of the chessboard\nThis is inspired by this research (https:\/\/pdfs.semanticscholar.org\/28a9\/fff7208256de548c273e96487d750137c31d.pdf) but by comparing several approaches and having the best performance\nThe data used by this competition is a processed version of the dataset https:\/\/www.kaggle.com\/milesh1\/35-million-chess-games which\nContent\nThe players are represented by the winning player and the losing player.\nThe first 64 columns represent the 64 squares of the chessboard and each value corresponds to the piece which is on this square\nthe unique values are as follows\n\nWR: Rook of wining player\nWB: Bishop of wining player\nWN: Knight of wining player\nWQ: Queen of wining player\nWK: King of wining player\nWP: Pawn of wining player\nLR : Rook of losing player\nLB : Bishop of losing player\nLN : Knight of losing player\nLQ : Queen of losing player\nLK : King of losing player\nLP : Pawn of losing player\n\nThe next turn is the winner's turn so the output is the algebrical value of the box containing the piece to move A1 to H8 (64 classes)",
    "format": "arff",
    "uploader": "Dustin Carrion",
    "uploader_id": 30123,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 13:20:08",
    "update_comment": null,
    "last_update": "2022-03-23 13:20:08",
    "licence": "GPL 2",
    "status": "active",
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            "Chess-Position--Chess-Moves",
            "Context The objective of this dataset is to create a chess engine through machine learning. In this first part we will first predict the pieces to be moved depending on the position of the chessboard This is inspired by this research (https:\/\/pdfs.semanticscholar.org\/28a9\/fff7208256de548c273e96487d750137c31d.pdf) but by comparing several approaches and having the best performance The data used by this competition is a processed version of the dataset https:\/\/www.kaggle.com\/milesh1\/35-million-che "
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        "weight": 5
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    "qualities": {
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        "NumberOfNumericFeatures": 0,
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        "Dimensionality": 2.5068815798519647e-5,
        "PercentageOfNumericFeatures": 0,
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            "uploader": "38960",
            "tag": "Computer Systems"
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