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ML-For-Beginners/translations/ja/2-Regression/4-Logistic/notebook.ipynb

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"## かぼちゃの種類と色\n",
"\n",
"必要なライブラリとデータセットを読み込みます。データをサブセットを含むデータフレームに変換します：\n",
"\n",
"色と種類の関係を見てみましょう\n"
]
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" <th>Crop</th>\n",
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" City Name Type Package Variety Sub Variety Grade Date \\\n",
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"2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n",
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"4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n",
"\n",
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"2 160.0 160.0 160.0 ... NaN NaN NaN \n",
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"0 NaN NaN NaN E NaN NaN NaN \n",
"1 NaN NaN NaN E NaN NaN NaN \n",
"2 NaN NaN NaN N NaN NaN NaN \n",
"3 NaN NaN NaN N NaN NaN NaN \n",
"4 NaN NaN NaN N NaN NaN NaN \n",
"\n",
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"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
"\n",
"full_pumpkins.head()\n"
]
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"\n---\n\n**免責事項**: \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があります。元の言語で記載された原文が正式な情報源と見なされるべきです。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤認について、当社は一切の責任を負いません。\n"
]
}
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