547 lines
98 KiB
Plaintext
547 lines
98 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
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" 'price', 'x', 'y', 'z'],\n",
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" dtype='object')\n",
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"Зашумленные столбцы: []\n",
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"Смещение: Unnamed: 0 0.000000\n",
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"carat 1.116705\n",
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"cut -0.717161\n",
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"color -0.189454\n",
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"clarity 0.551503\n",
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"depth -0.082187\n",
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"table 0.796836\n",
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"price 1.618476\n",
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"x 0.378685\n",
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"y 2.434233\n",
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"z 1.522481\n",
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"dtype: float64\n",
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"Сильно смещенные столбцы: ['carat', 'price', 'y', 'z']\n",
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"Выбросы в столбце 'Unnamed: 0':\n",
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"Series([], Name: Unnamed: 0, dtype: int64)\n",
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"\n",
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"Выбросы в столбце 'carat':\n",
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"12246 2.06\n",
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"13002 2.14\n",
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"13118 2.15\n",
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"13757 2.22\n",
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"13991 2.01\n",
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" ... \n",
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"27741 2.15\n",
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"27742 2.04\n",
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"27744 2.29\n",
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"27746 2.07\n",
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"27749 2.29\n",
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"Name: carat, Length: 1889, dtype: float64\n",
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"\n",
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"Выбросы в столбце 'cut':\n",
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"Series([], Name: cut, dtype: int64)\n",
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"\n",
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"Выбросы в столбце 'color':\n",
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"Series([], Name: color, dtype: int64)\n",
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"\n",
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"Выбросы в столбце 'clarity':\n",
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"Series([], Name: clarity, dtype: int64)\n",
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"\n",
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"Выбросы в столбце 'depth':\n",
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"2 56.9\n",
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"8 65.1\n",
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"24 58.1\n",
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"35 58.2\n",
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"42 65.2\n",
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" ... \n",
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"53882 65.4\n",
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"53886 58.0\n",
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"53890 57.9\n",
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"53895 57.8\n",
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"53927 58.1\n",
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"Name: depth, Length: 2545, dtype: float64\n",
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"\n",
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"Выбросы в столбце 'table':\n",
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"2 65.0\n",
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"91 69.0\n",
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"145 64.0\n",
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"219 64.0\n",
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"227 67.0\n",
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" ... \n",
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"53695 65.0\n",
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"53697 65.0\n",
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"53756 64.0\n",
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"53757 64.0\n",
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"53785 65.0\n",
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"Name: table, Length: 605, dtype: float64\n",
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"\n",
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"Выбросы в столбце 'price':\n",
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"23820 11886\n",
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"23821 11886\n",
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"23822 11888\n",
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"23823 11888\n",
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"23824 11888\n",
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" ... \n",
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"27745 18803\n",
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"27746 18804\n",
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"27747 18806\n",
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"27748 18818\n",
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"27749 18823\n",
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"Name: price, Length: 3540, dtype: int64\n",
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"\n",
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"Выбросы в столбце 'x':\n",
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"11182 0.00\n",
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"11963 0.00\n",
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"15951 0.00\n",
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"22741 9.54\n",
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"22831 9.38\n",
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"23644 9.53\n",
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"24131 9.44\n",
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"24297 9.49\n",
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"24328 9.65\n",
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"24520 0.00\n",
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"24816 9.42\n",
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"25460 9.44\n",
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"25850 9.32\n",
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"25998 10.14\n",
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"25999 10.02\n",
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"26243 0.00\n",
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"26431 9.42\n",
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"26444 10.01\n",
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"26534 9.86\n",
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"26932 9.30\n",
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"27130 10.00\n",
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"27415 10.74\n",
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"27429 0.00\n",
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"27514 9.36\n",
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"27630 10.23\n",
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"27638 9.51\n",
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"27649 9.44\n",
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"27679 9.66\n",
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"27684 9.35\n",
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"27685 9.41\n",
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"49556 0.00\n",
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"49557 0.00\n",
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"Name: x, dtype: float64\n",
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"\n",
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"Выбросы в столбце 'y':\n",
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"11963 0.00\n",
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"15951 0.00\n",
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"22741 9.38\n",
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"22831 9.31\n",
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"23644 9.48\n",
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"24067 58.90\n",
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"24131 9.40\n",
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"24297 9.42\n",
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"24328 9.59\n",
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"24520 0.00\n",
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"25460 9.37\n",
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"25998 10.10\n",
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"25999 9.94\n",
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"26243 0.00\n",
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"26431 9.34\n",
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"26444 9.94\n",
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"26534 9.81\n",
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"27130 9.85\n",
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"27415 10.54\n",
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"27429 0.00\n",
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"27514 9.31\n",
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"27630 10.16\n",
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"27638 9.46\n",
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"27649 9.38\n",
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"27679 9.63\n",
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"27685 9.32\n",
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"49189 31.80\n",
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"49556 0.00\n",
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"49557 0.00\n",
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"Name: y, dtype: float64\n",
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"\n",
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"Выбросы в столбце 'z':\n",
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"2207 0.00\n",
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"2314 0.00\n",
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"4791 0.00\n",
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"5471 0.00\n",
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"10167 0.00\n",
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"11182 0.00\n",
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"11963 0.00\n",
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"13601 0.00\n",
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"14635 1.07\n",
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"15951 0.00\n",
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"16283 5.77\n",
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"17196 5.76\n",
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"19346 5.97\n",
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"21758 5.98\n",
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"22540 5.91\n",
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"23539 5.79\n",
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"23644 6.38\n",
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"24067 8.06\n",
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"24131 5.85\n",
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"24297 5.92\n",
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"24328 6.03\n",
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"24394 0.00\n",
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"24520 0.00\n",
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"25998 6.17\n",
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"25999 6.24\n",
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"26100 5.75\n",
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"26123 0.00\n",
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"26194 6.16\n",
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"26243 0.00\n",
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"26431 6.27\n",
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"26444 6.31\n",
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"26534 6.13\n",
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"26744 5.86\n",
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"27112 0.00\n",
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"27415 6.98\n",
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"27429 0.00\n",
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"27503 0.00\n",
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"27515 5.90\n",
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"27516 5.90\n",
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"27517 5.77\n",
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"27518 5.77\n",
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"27630 6.72\n",
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"27679 6.03\n",
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"49556 0.00\n",
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"49557 0.00\n",
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"51506 0.00\n",
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"Name: z, dtype: float64\n",
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"\n"
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]
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},
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{
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"data": {
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|
||
"text/plain": [
|
||
"<Figure size 1200x800 with 11 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Просачивание данных: Высокая корреляция (0.92) между столбцами 'carat' и 'price'\n",
|
||
"Просачивание данных: Высокая корреляция (0.98) между столбцами 'carat' и 'x'\n",
|
||
"Просачивание данных: Высокая корреляция (0.95) между столбцами 'carat' и 'y'\n",
|
||
"Просачивание данных: Высокая корреляция (0.95) между столбцами 'carat' и 'z'\n",
|
||
"Просачивание данных: Высокая корреляция (0.92) между столбцами 'price' и 'carat'\n",
|
||
"Просачивание данных: Высокая корреляция (0.98) между столбцами 'x' и 'carat'\n",
|
||
"Просачивание данных: Высокая корреляция (0.97) между столбцами 'x' и 'y'\n",
|
||
"Просачивание данных: Высокая корреляция (0.97) между столбцами 'x' и 'z'\n",
|
||
"Просачивание данных: Высокая корреляция (0.95) между столбцами 'y' и 'carat'\n",
|
||
"Просачивание данных: Высокая корреляция (0.97) между столбцами 'y' и 'x'\n",
|
||
"Просачивание данных: Высокая корреляция (0.95) между столбцами 'y' и 'z'\n",
|
||
"Просачивание данных: Высокая корреляция (0.95) между столбцами 'z' и 'carat'\n",
|
||
"Просачивание данных: Высокая корреляция (0.97) между столбцами 'z' и 'x'\n",
|
||
"Просачивание данных: Высокая корреляция (0.95) между столбцами 'z' и 'y'\n",
|
||
"Index(['carat', 'price', 'cut'], dtype='object')\n",
|
||
"Обучающая выборка: (32365, 3)\n",
|
||
"carat\n",
|
||
"0.30 1568\n",
|
||
"0.31 1370\n",
|
||
"1.01 1357\n",
|
||
"0.70 1197\n",
|
||
"0.32 1097\n",
|
||
" ... \n",
|
||
"2.71 1\n",
|
||
"2.67 1\n",
|
||
"2.60 1\n",
|
||
"3.02 1\n",
|
||
"3.65 1\n",
|
||
"Name: count, Length: 265, dtype: int64\n",
|
||
"Контрольная выборка: (10789, 3)\n",
|
||
"carat\n",
|
||
"0.30 509\n",
|
||
"0.31 458\n",
|
||
"1.01 451\n",
|
||
"0.32 401\n",
|
||
"0.70 380\n",
|
||
" ... \n",
|
||
"2.61 1\n",
|
||
"2.43 1\n",
|
||
"1.99 1\n",
|
||
"2.55 1\n",
|
||
"1.98 1\n",
|
||
"Name: count, Length: 231, dtype: int64\n",
|
||
"Тестовая выборка: (10789, 3)\n",
|
||
"carat\n",
|
||
"0.30 527\n",
|
||
"1.01 434\n",
|
||
"0.31 421\n",
|
||
"0.70 405\n",
|
||
"0.32 342\n",
|
||
" ... \n",
|
||
"2.19 1\n",
|
||
"2.72 1\n",
|
||
"2.32 1\n",
|
||
"2.43 1\n",
|
||
"2.68 1\n",
|
||
"Name: count, Length: 235, dtype: int64\n",
|
||
"Обучающая выборка после oversampling: (63947, 3)\n",
|
||
"carat\n",
|
||
"1.010000 1921\n",
|
||
"0.700000 1741\n",
|
||
"0.300000 1686\n",
|
||
"1.000000 1439\n",
|
||
"0.310000 1422\n",
|
||
" ... \n",
|
||
"1.532017 1\n",
|
||
"1.990214 1\n",
|
||
"1.565413 1\n",
|
||
"0.954826 1\n",
|
||
"2.042019 1\n",
|
||
"Name: count, Length: 27956, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd\n",
|
||
"from sklearn.feature_selection import mutual_info_regression\n",
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"from imblearn.over_sampling import ADASYN\n",
|
||
"from imblearn.under_sampling import RandomUnderSampler\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"\n",
|
||
"df = pd.read_csv(\"data/Diamonds.csv\")\n",
|
||
"print(df.columns)\n",
|
||
"\n",
|
||
"noisy_features = []\n",
|
||
"for col in df.columns:\n",
|
||
" if df[col].isnull().sum() / len(df) > 0.1: # Если более 10% пропусков\n",
|
||
" noisy_features.append(col)\n",
|
||
"print(f\"Зашумленные столбцы: {noisy_features}\")\n",
|
||
"\n",
|
||
"cut_mapping = {'Fair': 0, 'Good': 1, 'Very Good': 2, 'Premium': 3, 'Ideal': 4}\n",
|
||
"df['cut'] = df['cut'].map(cut_mapping)\n",
|
||
"\n",
|
||
"color_mapping = {'J': 0, 'I': 1, 'H': 2, 'G': 3, 'F': 4, 'E': 5, 'D': 6} \n",
|
||
"df['color'] = df['color'].map(color_mapping)\n",
|
||
"\n",
|
||
"clarity_mapping = {'I1': 0, 'SI2': 1, 'SI1': 2, 'VS2': 3, 'VS1': 4, 'VVS2': 5, 'VVS1': 6, 'IF': 7} \n",
|
||
"df['clarity'] = df['clarity'].map(clarity_mapping)\n",
|
||
"\n",
|
||
"skewness = df.skew()\n",
|
||
"print(f\"Смещение: {skewness}\")\n",
|
||
"\n",
|
||
"skewed_features = skewness[abs(skewness) > 1].index.tolist()\n",
|
||
"print(f\"Сильно смещенные столбцы: {skewed_features}\")\n",
|
||
"\n",
|
||
"for col in df.select_dtypes(include=['number']).columns:\n",
|
||
" if col == 'id':\n",
|
||
" continue\n",
|
||
" Q1 = df[col].quantile(0.25)\n",
|
||
" Q3 = df[col].quantile(0.75)\n",
|
||
" IQR = Q3 - Q1\n",
|
||
" lower_bound = Q1 - 1.5 * IQR\n",
|
||
" upper_bound = Q3 + 1.5 * IQR\n",
|
||
" outliers = df[col][(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
|
||
" print(f\"Выбросы в столбце '{col}':\\n{outliers}\\n\")\n",
|
||
"\n",
|
||
"numeric_cols = df.select_dtypes(include=['number']).columns\n",
|
||
"numeric_cols = [col for col in numeric_cols if col != 'id']\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12, 8))\n",
|
||
"\n",
|
||
"for i, col in enumerate(numeric_cols, 1):\n",
|
||
" plt.subplot(len(numeric_cols) // 3 + 1, 3, i) \n",
|
||
" sns.boxplot(data=df, x=col)\n",
|
||
" plt.title(f'Boxplot for {col}')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"if len(df.columns) >= 2:\n",
|
||
" for col1 in df.columns:\n",
|
||
" for col2 in df.columns:\n",
|
||
" if col1 != col2:\n",
|
||
" correlation = df[col1].corr(df[col2])\n",
|
||
" if abs(correlation) > 0.9:\n",
|
||
" print(f\"Просачивание данных: Высокая корреляция ({correlation:.2f}) между столбцами '{col1}' и '{col2}'\")\n",
|
||
"\n",
|
||
"df['log_carat'] = np.log(df['carat'] + 1)\n",
|
||
"\n",
|
||
"df['cut'] = df['cut'].fillna('unknown')\n",
|
||
"\n",
|
||
"df['carat'] = df['carat'].fillna(df['carat'].mean())\n",
|
||
"\n",
|
||
"def split_stratified_into_train_val_test(\n",
|
||
" df_input,\n",
|
||
" stratify_colname=\"y\",\n",
|
||
" frac_train=0.6,\n",
|
||
" frac_val=0.15,\n",
|
||
" frac_test=0.25,\n",
|
||
" random_state=None,\n",
|
||
"):\n",
|
||
" if frac_train + frac_val + frac_test != 1.0:\n",
|
||
" raise ValueError(\n",
|
||
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
|
||
" % (frac_train, frac_val, frac_test)\n",
|
||
" )\n",
|
||
"\n",
|
||
" if stratify_colname not in df_input.columns:\n",
|
||
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
|
||
"\n",
|
||
" X = df_input \n",
|
||
" y = df_input[\n",
|
||
" [stratify_colname]\n",
|
||
" ] \n",
|
||
"\n",
|
||
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
|
||
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
|
||
" )\n",
|
||
"\n",
|
||
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
|
||
" df_val, df_test, y_val, y_test = train_test_split(\n",
|
||
" df_temp,\n",
|
||
" y_temp,\n",
|
||
" stratify=y_temp,\n",
|
||
" test_size=relative_frac_test,\n",
|
||
" random_state=random_state,\n",
|
||
" )\n",
|
||
"\n",
|
||
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
|
||
"\n",
|
||
" return df_train, df_val, df_test\n",
|
||
"\n",
|
||
"data = df[[\"carat\", \"price\", \"cut\"]].copy()\n",
|
||
"\n",
|
||
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
||
" data, stratify_colname=\"cut\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
|
||
")\n",
|
||
" \n",
|
||
"print(df_train.columns) \n",
|
||
" \n",
|
||
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
||
"print(df_train.carat.value_counts()) \n",
|
||
"\n",
|
||
"print(\"Контрольная выборка: \", df_val.shape)\n",
|
||
"print(df_val.carat.value_counts())\n",
|
||
"\n",
|
||
"print(\"Тестовая выборка: \", df_test.shape)\n",
|
||
"print(df_test.carat.value_counts())\n",
|
||
"\n",
|
||
"ada = ADASYN()\n",
|
||
"\n",
|
||
"X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"cut\"])\n",
|
||
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
|
||
"\n",
|
||
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
|
||
"print(df_train_adasyn.carat.value_counts())\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Датасет 1. Цены бриллиантов**\n",
|
||
"1. **carat**: Вес бриллианта в каратах\n",
|
||
"2. **cut**: Качество огранки.\n",
|
||
"3. **color**: Цвет бриллианта\n",
|
||
"4. **clarity**: Чистота бриллианта\n",
|
||
"5. **depth**: Процент глубины бриллианта\n",
|
||
"6. **table**: Процент ширины бриллианта\n",
|
||
"7. **price**: Цена бриллианта в долларах США\n",
|
||
"8. **x**: Длина бриллианта в миллиметрах\n",
|
||
"9. **y**: Ширина бриллианта в миллиметрах\n",
|
||
"10. **z**: Глубина бриллианта в миллиметрах\n",
|
||
"\n",
|
||
"**Объект наблюдения**: Каждый объект представляет собой отдельный бриллиант.\\\n",
|
||
"**Связи между объектами**: Внутри одного объекта есть взаимосвязь между характеристиками и его ценой. Например, вес, цвет, чистота и огранка могут влиять на цену.\\\n",
|
||
"**Бизнес-цель**: Оптимизация продаж бриллиантов, оценка цен в зависимости от характеристик.\\\n",
|
||
"**Эффект для бизнеса**: Более точная оценка стоимости бриллиантов может помочь ювелирам предлагать конкурентоспособные цены и максимизировать прибыль.\\\n",
|
||
"**Техническая цель**: Построение модели машинного обучения для прогнозирования цены бриллианта на основе его характеристик.\\\n",
|
||
"* **Вход**: Характеристики бриллианта (вес, огранка, цвет, чистота, размеры).\\\n",
|
||
"* **Целевой признак**: Цена бриллианта. \n",
|
||
"\n",
|
||
"**Информативность**: Высокая. Набор данных содержит важные характеристики бриллиантов, которые влияют на их цену: карат, огранка, цвет, чистота, размеры.\\\n",
|
||
"**Степень покрытия**: Высокая. В наборе данных представлено 53 940 бриллиантов, что является достаточно большим объемом для анализа. \\\n",
|
||
"**Соответствие реальным данным**: Высокая. Характеристики бриллиантов в наборе данных соответствуют реальным характеристикам бриллиантов, определяемым геммологами. \\\n",
|
||
"**Согласованность меток**: Высокая. В данном наборе данных нет проблем с несогласованностью меток, так как все данные соответствуют описанию в заголовках столбцов. \n",
|
||
"\n",
|
||
"**Датасет 2. Цены акций Starbucks**\n",
|
||
"1. **Date**: Дата торгов\n",
|
||
"2. **Open**: Цена открытия торгов\n",
|
||
"3. **High**: Максимальная цена акции за день\n",
|
||
"4. **Low**: Минимальная цена акции за день\n",
|
||
"5. **Close**: Цена закрытия торгов в данный день\n",
|
||
"6. **Adj Close**: Скоректированная цена закрытия.\n",
|
||
"7. **Volume**: Объем торгов акциями в данный день.\n",
|
||
"\n",
|
||
"**Объект наблюдения**: Объектом наблюдения является торговый день на рынке акций компании Starbucks.\\\n",
|
||
"**Связи между объектами**: Временная связь между днями торгов. Важна динамика изменений цен и объемов торгов в зависимости от времени.\\\n",
|
||
"**Бизнес-цель**: Прогнозирование цен акций для управления портфелем акций.\\\n",
|
||
"**Эффект для бизнеса**: Прогнозирование позволит трейдерам принимать более информированные решения, оптимизировать инвестиции и минимизировать риски.\\\n",
|
||
"**Техническая цель**: Прогнозирование цены закрытия акций на основе временных рядов.\\\n",
|
||
"* **Вход**: Временные ряды с историческими данными по ценам открытия, закрытия, объёмам.\\\n",
|
||
"* **Целевой признак**: Цена закрытия на следующий день.\n",
|
||
"\n",
|
||
"**Датасет 3. Цены на золото**\n",
|
||
"1. **Date**: Дата\n",
|
||
"2. **Open**: Цена открытия торгов\n",
|
||
"3. **High**: Максимальная цена за день\n",
|
||
"4. **Low**: Минимальная цена за день\n",
|
||
"5. **Close**: Цена закрытия торгов\n",
|
||
"6. **Adjusted Close**: Скоректированная цена закрытия\n",
|
||
"7. **Volume**: Объем торгов за день\n",
|
||
"\n",
|
||
"**Дополнительные столбцы (факторы, влияющие на цену золота):**\n",
|
||
"\n",
|
||
"8. **SP_open**, **SP_high**, **SP_low**, **SP_close**, **SP_Ajclose**, **SP_volume**: Данные индекса S&P 500.\n",
|
||
"9. **DJ_open**, **DJ_high**, **DJ_low**, **DJ_close**, **DJ_Ajclose**, **DJ_volume**: Данные индекса Dow Jones.\n",
|
||
"10. **EG_open**, **EG_high**, **EG_low**, **EG_close**, **EG_Ajclose**, **EG_volume**: Данные компании Eldorado Gold Corporation (EGO).\n",
|
||
"11. **EU_Price**, **EU_open**, **EU_high**, **EU_low**, **EU_Trend**: Курс валютной пары EUR/USD.\n",
|
||
"12. **OF_Price**, **OF_Open**, **OF_High**, **OF_Low**, **OF_Volume**, **OF_Trend**: Цена фьючерсов на нефть Brent.\n",
|
||
"13. **OS_Price**, **OS_Open**, **OS_High**, **OS_Low**, **OS_Trend**: Цена нефти WTI.\n",
|
||
"14. **SF_Price**, **SF_Open**, **SF_High**, **SF_Low**, **SF_Volume, SF_Trend**: Цена фьючерсов на серебро.\n",
|
||
"15. **USB_Price**, **USB_Open**, **USB_High**, **USB_Low**, **USB_Trend**: Ставка по облигациям США.\n",
|
||
"16. **PLT_Price**, **PLT_Open**, **PLT_High**, **PLT_Low**, **PLT_Trend**: Цена платины.\n",
|
||
"17. **PLD_Price**, **PLD_Open**, **PLD_High**, **PLD_Low**, **PLD_Trend**: Цена палладия.\n",
|
||
"18. **RHO_PRICE**: Цена родия.\n",
|
||
"19. **USDI_Price**, **USDI_Open**, **USDI_High**, **USDI_Low**, **USDI_Volume**, **USDI_Trend**: Индекс доллара США.\n",
|
||
"20. **GDX_Open**, **GDX_High**, **GDX_Low**, **GDX_Close**, **GDX_Adj Close**, **GDX_Volume**: Данные ETF на золотые шахты.\n",
|
||
"21. **USO_Open**, **USO_High**, **USO_Low**, **USO_Close**, **USO_Adj Close**, **USO_Volume**: Данные ETF на нефть USO.\n",
|
||
"\n",
|
||
"**Объект наблюдения**: Объектом наблюдения является торговый день для цены золота с дополнительными факторами влияния.\\\n",
|
||
"**Связи между объектами**: Взаимосвязь между движением цен на золото и другими экономическими показателями и активами (например, нефть, фондовые индексы). Золото часто коррелирует с другими активами в периоды нестабильности.\\\n",
|
||
"**Бизнес-цель**: Управление инвестициями в золото и связанные активы (нефть, индексы).\\\n",
|
||
"**Эффект для бизнеса**: Правильное прогнозирование цен на золото и связанных активов может помочь инвесторам защитить капитал.\\\n",
|
||
"**Техническая цель**: Построение модели для анализа взаимосвязи между ценами на золото и дополнительными факторами (нефть, фондовые индексы, валютные курсы).\n",
|
||
"* **Вход**: Данные по ценам на золото и дополнительным факторам (нефть, индексы, валюты).\\\n",
|
||
"* **Целевой признак**: Цена закрытия золота."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|