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

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"## Balkabağı Çeşitleri ve Renk\n",
"\n",
"Gerekli kütüphaneleri ve veri setini yükleyin. Verileri, verilerin bir alt kümesini içeren bir veri çerçevesine dönüştürün:\n",
"\n",
"Renk ve çeşit arasındaki ilişkiye bir göz atalım\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",
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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",
"[5 rows x 26 columns]"
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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**Feragatname**: \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
]
}
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