{ "cells": [ { "cell_type": "markdown", "metadata": { "vscode": { "languageId": "plaintext" } }, "source": [ "#### Загрузка набора данных" ] }, { "cell_type": "code", "execution_count": 732, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PriceLevyManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbags
0133281399LEXUSRX 4502010JeepYesHybrid3.5186005 km6.0Automatic4x404-MayLeft wheelSilver12
1166211018CHEVROLETEquinox2011JeepNoPetrol3192000 km6.0Tiptronic4x404-MayLeft wheelBlack8
28467-HONDAFIT2006HatchbackNoPetrol1.3200000 km4.0VariatorFront04-MayRight-hand driveBlack2
33607862FORDEscape2011JeepYesHybrid2.5168966 km4.0Automatic4x404-MayLeft wheelWhite0
411726446HONDAFIT2014HatchbackYesPetrol1.391901 km4.0AutomaticFront04-MayLeft wheelSilver4
......................................................
192328467-MERCEDES-BENZCLK 2001999CoupeYesCNG2.0 Turbo300000 km4.0ManualRear02-MarLeft wheelSilver5
1923315681831HYUNDAISonata2011SedanYesPetrol2.4161600 km4.0TiptronicFront04-MayLeft wheelRed8
1923426108836HYUNDAITucson2010JeepYesDiesel2116365 km4.0AutomaticFront04-MayLeft wheelGrey4
1923553311288CHEVROLETCaptiva2007JeepYesDiesel251258 km4.0AutomaticFront04-MayLeft wheelBlack4
19236470753HYUNDAISonata2012SedanYesHybrid2.4186923 km4.0AutomaticFront04-MayLeft wheelWhite12
\n", "

19237 rows × 17 columns

\n", "
" ], "text/plain": [ " Price Levy Manufacturer Model Prod. year Category \\\n", "0 13328 1399 LEXUS RX 450 2010 Jeep \n", "1 16621 1018 CHEVROLET Equinox 2011 Jeep \n", "2 8467 - HONDA FIT 2006 Hatchback \n", "3 3607 862 FORD Escape 2011 Jeep \n", "4 11726 446 HONDA FIT 2014 Hatchback \n", "... ... ... ... ... ... ... \n", "19232 8467 - MERCEDES-BENZ CLK 200 1999 Coupe \n", "19233 15681 831 HYUNDAI Sonata 2011 Sedan \n", "19234 26108 836 HYUNDAI Tucson 2010 Jeep \n", "19235 5331 1288 CHEVROLET Captiva 2007 Jeep \n", "19236 470 753 HYUNDAI Sonata 2012 Sedan \n", "\n", " Leather interior Fuel type Engine volume Mileage Cylinders \\\n", "0 Yes Hybrid 3.5 186005 km 6.0 \n", "1 No Petrol 3 192000 km 6.0 \n", "2 No Petrol 1.3 200000 km 4.0 \n", "3 Yes Hybrid 2.5 168966 km 4.0 \n", "4 Yes Petrol 1.3 91901 km 4.0 \n", "... ... ... ... ... ... \n", "19232 Yes CNG 2.0 Turbo 300000 km 4.0 \n", "19233 Yes Petrol 2.4 161600 km 4.0 \n", "19234 Yes Diesel 2 116365 km 4.0 \n", "19235 Yes Diesel 2 51258 km 4.0 \n", "19236 Yes Hybrid 2.4 186923 km 4.0 \n", "\n", " Gear box type Drive wheels Doors Wheel Color Airbags \n", "0 Automatic 4x4 04-May Left wheel Silver 12 \n", "1 Tiptronic 4x4 04-May Left wheel Black 8 \n", "2 Variator Front 04-May Right-hand drive Black 2 \n", "3 Automatic 4x4 04-May Left wheel White 0 \n", "4 Automatic Front 04-May Left wheel Silver 4 \n", "... ... ... ... ... ... ... \n", "19232 Manual Rear 02-Mar Left wheel Silver 5 \n", "19233 Tiptronic Front 04-May Left wheel Red 8 \n", "19234 Automatic Front 04-May Left wheel Grey 4 \n", "19235 Automatic Front 04-May Left wheel Black 4 \n", "19236 Automatic Front 04-May Left wheel White 12 \n", "\n", "[19237 rows x 17 columns]" ] }, "execution_count": 732, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import math\n", "\n", "from sklearn.preprocessing import StandardScaler \n", "from sklearn.preprocessing import MinMaxScaler\n", "from imblearn.over_sampling import RandomOverSampler\n", "from sklearn.preprocessing import OneHotEncoder\n", "from sklearn.preprocessing import OrdinalEncoder\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn import metrics\n", "from sklearn import linear_model, tree, neighbors, ensemble\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import FunctionTransformer\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.pipeline import Pipeline\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import set_config\n", "from sklearn.pipeline import make_pipeline\n", "\n", "\n", "df = pd.read_csv(\"../data/car_price_prediction.csv\")\n", "\n", "df = df.drop(columns=[\"ID\"])\n", "\n", "random_state = 9\n", "\n", "set_config(transform_output=\"pandas\")\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Анализ датасета и очистка данных" ] }, { "cell_type": "code", "execution_count": 733, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Price int64\n", "Levy object\n", "Manufacturer object\n", "Model object\n", "Prod. year int64\n", "Category object\n", "Leather interior object\n", "Fuel type object\n", "Engine volume object\n", "Mileage object\n", "Cylinders float64\n", "Gear box type object\n", "Drive wheels object\n", "Doors object\n", "Wheel object\n", "Color object\n", "Airbags int64\n", "dtype: object" ] }, "execution_count": 733, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 734, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 3.5, 3. , 1.3, 2.5, 2. , 1.8, 2.4, 4. , 1.6, 3.3, 2.2,\n", " 4.7, 1.5, 4.4, 1.4, 3.6, 2.3, 5.5, 2.8, 3.2, 3.8, 4.6,\n", " 1.2, 5. , 1.7, 2.9, 0.5, 1.9, 2.7, 4.8, 5.3, 0.4, 1.1,\n", " 2.1, 0.7, 5.4, 3.7, 1. , 2.6, 0.8, 0.2, 5.7, 6.7, 6.2,\n", " 3.4, 6.3, 4.3, 4.2, 0. , 20. , 0.3, 5.9, 5.6, 6. , 0.6,\n", " 6.8, 4.5, 7.3, 0.1, 3.1, 6.4, 3.9, 0.9, 5.2, 5.8])" ] }, "execution_count": 734, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Engine volume\"] = df[\"Engine volume\"].str.replace(\"Turbo\", \"\")\n", "df[\"Engine volume\"] = pd.to_numeric(df[\"Engine volume\"])\n", "df[\"Engine volume\"].unique()" ] }, { "cell_type": "code", "execution_count": 735, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([186005, 192000, 200000, ..., 140607, 307325, 186923])" ] }, "execution_count": 735, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Mileage\"] = df[\"Mileage\"].str.replace(\"km\", \"\")\n", "df[\"Mileage\"] = df[\"Mileage\"].astype(\"int64\")\n", "df[\"Mileage\"].unique()" ] }, { "cell_type": "code", "execution_count": 736, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1399, 1018, 0, 862, 446, 891, 761, 751, 394,\n", " 1053, 1055, 1079, 810, 2386, 1850, 531, 586, 1249,\n", " 2455, 583, 1537, 1288, 915, 1750, 707, 1077, 1486,\n", " 1091, 650, 382, 1436, 1194, 503, 1017, 1104, 639,\n", " 629, 919, 781, 530, 640, 765, 777, 779, 934,\n", " 769, 645, 1185, 1324, 830, 1187, 1111, 760, 642,\n", " 1604, 1095, 966, 473, 1138, 1811, 988, 917, 1156,\n", " 687, 11714, 836, 1347, 2866, 1646, 259, 609, 697,\n", " 585, 475, 690, 308, 1823, 1361, 1273, 924, 584,\n", " 2078, 831, 1172, 893, 1872, 1885, 1266, 447, 2148,\n", " 1730, 730, 289, 502, 333, 1325, 247, 879, 1342,\n", " 1327, 1598, 1514, 1058, 738, 1935, 481, 1522, 1282,\n", " 456, 880, 900, 798, 1277, 442, 1051, 790, 1292,\n", " 1047, 528, 1211, 1493, 1793, 574, 930, 1998, 271,\n", " 706, 1481, 1677, 1661, 1286, 1408, 1090, 595, 1451,\n", " 1267, 993, 1714, 878, 641, 749, 1511, 603, 353,\n", " 877, 1236, 1141, 397, 784, 1024, 1357, 1301, 770,\n", " 922, 1438, 753, 607, 1363, 638, 490, 431, 565,\n", " 517, 833, 489, 1760, 986, 1841, 1620, 1360, 474,\n", " 1099, 978, 1624, 1946, 1268, 1307, 696, 649, 666,\n", " 2151, 551, 800, 971, 1323, 2377, 1845, 1083, 694,\n", " 463, 419, 345, 1515, 1505, 2056, 1203, 729, 460,\n", " 1356, 876, 911, 1190, 780, 448, 2410, 1848, 1148,\n", " 834, 1275, 1028, 1197, 724, 890, 1705, 505, 789,\n", " 2959, 518, 461, 1719, 2858, 3156, 2225, 2177, 1968,\n", " 1888, 1308, 2736, 1103, 557, 2195, 843, 1664, 723,\n", " 4508, 562, 501, 2018, 1076, 1202, 3301, 691, 1440,\n", " 1869, 1178, 418, 1820, 1413, 488, 1304, 363, 2108,\n", " 521, 1659, 87, 1411, 1528, 3292, 7058, 1578, 627,\n", " 874, 1996, 1488, 5679, 1234, 5603, 400, 889, 3268,\n", " 875, 949, 2265, 441, 742, 425, 2476, 2971, 614,\n", " 1816, 1375, 1405, 2297, 1062, 1113, 420, 2469, 658,\n", " 1951, 2670, 2578, 1995, 1032, 994, 1011, 2421, 1296,\n", " 155, 494, 426, 1086, 961, 2236, 1829, 764, 1834,\n", " 1054, 617, 1529, 2266, 637, 626, 1832, 1016, 2002,\n", " 1756, 746, 1285, 2690, 1118, 5332, 980, 1807, 970,\n", " 1228, 1195, 1132, 1768, 1384, 1080, 7063, 1817, 1452,\n", " 1975, 1368, 702, 1974, 1781, 1036, 944, 663, 364,\n", " 1539, 1345, 1680, 2209, 741, 1575, 695, 1317, 294,\n", " 1525, 424, 997, 1473, 1552, 2819, 2188, 1668, 3057,\n", " 799, 1502, 2606, 552, 1694, 1759, 1110, 399, 1470,\n", " 1174, 5877, 1474, 1688, 526, 686, 5908, 1107, 2070,\n", " 1468, 1246, 1685, 556, 1533, 1917, 1346, 732, 692,\n", " 579, 421, 362, 3505, 1855, 2711, 1586, 3739, 681,\n", " 1708, 2278, 1701, 722, 1482, 928, 827, 832, 527,\n", " 604, 173, 1341, 3329, 1553, 859, 167, 916, 828,\n", " 2082, 1176, 1108, 975, 3008, 1516, 2269, 1699, 2073,\n", " 1031, 1503, 2364, 1030, 1442, 5666, 2715, 1437, 2067,\n", " 1426, 2908, 1279, 866, 4283, 279, 2658, 3015, 2004,\n", " 1391, 4736, 748, 1466, 644, 683, 2705, 1297, 731,\n", " 1252, 2216, 3141, 3273, 1518, 1723, 1588, 972, 682,\n", " 1094, 668, 175, 967, 402, 3894, 1960, 1599, 2000,\n", " 2084, 1621, 714, 1109, 3989, 873, 1572, 1163, 1991,\n", " 1716, 1673, 2562, 2874, 965, 462, 605, 1948, 1736,\n", " 3518, 2054, 2467, 1681, 1272, 1205, 750, 2156, 2566,\n", " 115, 524, 3184, 676, 1678, 612, 328, 955, 1441,\n", " 1675, 3965, 2909, 623, 822, 867, 3025, 1993, 792,\n", " 636, 4057, 3743, 2337, 2570, 2418, 2472, 3910, 1662,\n", " 2123, 2628, 3208, 2080, 3699, 2913, 864, 2505, 870,\n", " 7536, 1924, 1671, 1064, 1836, 1866, 4741, 841, 1369,\n", " 5681, 3112, 1366, 2223, 1198, 1039, 3811, 3571, 1387,\n", " 1171, 1365, 1531, 1590, 11706, 2308, 4860, 1641, 1045,\n", " 1901])" ] }, "execution_count": 736, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Levy\"] = df[\"Levy\"].replace(\"-\", \"0\")\n", "df[\"Levy\"] = df[\"Levy\"].astype(\"int64\")\n", "df[\"Levy\"].unique()" ] }, { "cell_type": "code", "execution_count": 737, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 6, 4, 8, 1, 12, 3, 2, 16, 5, 7, 9, 10, 14])" ] }, "execution_count": 737, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Cylinders\"] = df[\"Cylinders\"].astype(\"int64\")\n", "df[\"Cylinders\"].unique()" ] }, { "cell_type": "code", "execution_count": 738, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['04-May', '02-Mar', '>5'], dtype=object)" ] }, "execution_count": 738, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Doors\"].unique()" ] }, { "cell_type": "code", "execution_count": 739, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Четырехдверный', 'Двухдверный', 'Многодверный'], dtype=object)" ] }, "execution_count": 739, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Doors\"] = df[\"Doors\"].map(\n", " {\"02-Mar\": \"Двухдверный\", \"04-May\": \"Четырехдверный\", \">5\": \"Многодверный\"}\n", ")\n", "df[\"Doors\"].unique()" ] }, { "cell_type": "code", "execution_count": 740, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 3, 6, ..., 627220, 872946, 26307500])" ] }, "execution_count": 740, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_df = df.sort_values(by=\"Price\")\n", "sorted_df[\"Price\"].unique()" ] }, { "cell_type": "code", "execution_count": 741, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Количество строк до удаления некорректных значений: 19237\n", "Количество строк после удаления некорректных значений: 17574\n" ] } ], "source": [ "print(f\"Количество строк до удаления некорректных значений: {len(df)}\")\n", "df = df[df[\"Price\"] >= 500]\n", "print(f\"Количество строк после удаления некорректных значений: {len(df)}\")" ] }, { "cell_type": "code", "execution_count": 742, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 500, 549, 600, ..., 627220, 872946, 26307500])" ] }, "execution_count": 742, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_df = df.sort_values(by=\"Price\")\n", "sorted_df[\"Price\"].unique()" ] }, { "cell_type": "code", "execution_count": 743, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1943, 1953, 1957, 1964, 1965, 1968, 1973, 1974, 1977, 1978, 1980,\n", " 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991,\n", " 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n", " 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013,\n", " 2014, 2015, 2016, 2017, 2018, 2019, 2020])" ] }, "execution_count": 743, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_df = df.sort_values(by=\"Prod. year\")\n", "sorted_df[\"Prod. year\"].unique()" ] }, { "cell_type": "code", "execution_count": 744, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PriceLevyManufacturerModelProd. yearCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbags
0133281399LEXUSRX 4502010JeepYesHybrid3.51860056Automatic4x4ЧетырехдверныйLeft wheelSilver12
1166211018CHEVROLETEquinox2011JeepNoPetrol3.01920006Tiptronic4x4ЧетырехдверныйLeft wheelBlack8
284670HONDAFIT2006HatchbackNoPetrol1.32000004VariatorFrontЧетырехдверныйRight-hand driveBlack2
33607862FORDEscape2011JeepYesHybrid2.51689664Automatic4x4ЧетырехдверныйLeft wheelWhite0
411726446HONDAFIT2014HatchbackYesPetrol1.3919014AutomaticFrontЧетырехдверныйLeft wheelSilver4
......................................................
1923158021055MERCEDES-BENZE 3502013SedanYesDiesel3.51078006AutomaticRearЧетырехдверныйLeft wheelGrey12
1923284670MERCEDES-BENZCLK 2001999CoupeYesCNG2.03000004ManualRearДвухдверныйLeft wheelSilver5
1923315681831HYUNDAISonata2011SedanYesPetrol2.41616004TiptronicFrontЧетырехдверныйLeft wheelRed8
1923426108836HYUNDAITucson2010JeepYesDiesel2.01163654AutomaticFrontЧетырехдверныйLeft wheelGrey4
1923553311288CHEVROLETCaptiva2007JeepYesDiesel2.0512584AutomaticFrontЧетырехдверныйLeft wheelBlack4
\n", "

17574 rows × 17 columns

\n", "
" ], "text/plain": [ " Price Levy Manufacturer Model Prod. year Category \\\n", "0 13328 1399 LEXUS RX 450 2010 Jeep \n", "1 16621 1018 CHEVROLET Equinox 2011 Jeep \n", "2 8467 0 HONDA FIT 2006 Hatchback \n", "3 3607 862 FORD Escape 2011 Jeep \n", "4 11726 446 HONDA FIT 2014 Hatchback \n", "... ... ... ... ... ... ... \n", "19231 5802 1055 MERCEDES-BENZ E 350 2013 Sedan \n", "19232 8467 0 MERCEDES-BENZ CLK 200 1999 Coupe \n", "19233 15681 831 HYUNDAI Sonata 2011 Sedan \n", "19234 26108 836 HYUNDAI Tucson 2010 Jeep \n", "19235 5331 1288 CHEVROLET Captiva 2007 Jeep \n", "\n", " Leather interior Fuel type Engine volume Mileage Cylinders \\\n", "0 Yes Hybrid 3.5 186005 6 \n", "1 No Petrol 3.0 192000 6 \n", "2 No Petrol 1.3 200000 4 \n", "3 Yes Hybrid 2.5 168966 4 \n", "4 Yes Petrol 1.3 91901 4 \n", "... ... ... ... ... ... \n", "19231 Yes Diesel 3.5 107800 6 \n", "19232 Yes CNG 2.0 300000 4 \n", "19233 Yes Petrol 2.4 161600 4 \n", "19234 Yes Diesel 2.0 116365 4 \n", "19235 Yes Diesel 2.0 51258 4 \n", "\n", " Gear box type Drive wheels Doors Wheel Color \\\n", "0 Automatic 4x4 Четырехдверный Left wheel Silver \n", "1 Tiptronic 4x4 Четырехдверный Left wheel Black \n", "2 Variator Front Четырехдверный Right-hand drive Black \n", "3 Automatic 4x4 Четырехдверный Left wheel White \n", "4 Automatic Front Четырехдверный Left wheel Silver \n", "... ... ... ... ... ... \n", "19231 Automatic Rear Четырехдверный Left wheel Grey \n", "19232 Manual Rear Двухдверный Left wheel Silver \n", "19233 Tiptronic Front Четырехдверный Left wheel Red \n", "19234 Automatic Front Четырехдверный Left wheel Grey \n", "19235 Automatic Front Четырехдверный Left wheel Black \n", "\n", " Airbags \n", "0 12 \n", "1 8 \n", "2 2 \n", "3 0 \n", "4 4 \n", "... ... \n", "19231 12 \n", "19232 5 \n", "19233 8 \n", "19234 4 \n", "19235 4 \n", "\n", "[17574 rows x 17 columns]" ] }, "execution_count": 744, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Очистка дубликатов и пропущенных значений" ] }, { "cell_type": "code", "execution_count": 745, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.int64(2773)" ] }, "execution_count": 745, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.duplicated().sum()" ] }, { "cell_type": "code", "execution_count": 746, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_30576\\1689817098.py:1: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " df.drop_duplicates(inplace=True)\n" ] } ], "source": [ "df.drop_duplicates(inplace=True)\n", "# df.duplicated().sum()" ] }, { "cell_type": "code", "execution_count": 747, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Price 0\n", "Levy 0\n", "Manufacturer 0\n", "Model 0\n", "Prod. year 0\n", "Category 0\n", "Leather interior 0\n", "Fuel type 0\n", "Engine volume 0\n", "Mileage 0\n", "Cylinders 0\n", "Gear box type 0\n", "Drive wheels 0\n", "Doors 0\n", "Wheel 0\n", "Color 0\n", "Airbags 0\n", "dtype: int64" ] }, "execution_count": 747, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Очистка выбросов" ] }, { "cell_type": "code", "execution_count": 748, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Price int64\n", "Levy int64\n", "Manufacturer object\n", "Model object\n", "Prod. year int64\n", "Category object\n", "Leather interior object\n", "Fuel type object\n", "Engine volume float64\n", "Mileage int64\n", "Cylinders int64\n", "Gear box type object\n", "Drive wheels object\n", "Doors object\n", "Wheel object\n", "Color object\n", "Airbags int64\n", "dtype: object" ] }, "execution_count": 748, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 749, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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NpaWlWrFiRQSrQ7TjETzgqkAgoL1796qyslK5ublatGgRM2gMmFBzjZk0cJXdbldWVpba2tqUlZVFQMMIXJMGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ1cxaL/MBEhDYhF/2EuQhpRj0X/YTK+cYioxqL/iBQW/QdCwKL/MB0hjajGov8wHSGNqMai/zAdIY2oxqL/MB0hjajGov8wHUuVIuqx6D9MxiN4wFUs+o/BxKL/QJhY9B8m4po0ABiMkAYAgxHSAGAwQhoADMaNQ+CqlpYW5ebmqr6+XlOnTlVlZaUSExMjXRaiXNgz6f3792vZsmWaOHGibDabysrK+hyzb98+zZ49W7GxsXI6nXr99ddvoFRg4DidTo0ePVo1NTU6c+aMampqNHr0aDmdzkiXhigXdki3tbVpxowZ2rJlS0j9jx8/rvz8fC1atEiHDx/Ws88+q8cff1y///3vwy4WGAhOp1PHjh2TJOXk5Ohf//VflZOTI0k6duwYQY2I+kpfZrHZbNq5c6dcLlevff75n/9Zu3bt6raAzTe/+U2dP39eb7/9dkjH4cssGCgtLS0aPXq0pCsTEIfDEVxP2u/366abbpIknT9/nksf6FfGfJmlpqZGS5Ys6daWk5OjZ599ttcxHR0d6ujoCG77fD5Jkt/vl9/vH5A6EZ1yc3MlXTknHQ5H8Pzy+/1yOBzKzs6Wx+NRbm6uqqurI1kqhplQs2zAQ7qpqUlJSUnd2pKSkuTz+dTe3q5Ro0ZdM6a4uFgbNmy4pn337t2Ki4sbsFoRferr6yVJixYtUkVFRbDd4/FIkhYuXCiPx6P6+vpu+4Gv6uLFiyH1M/LpjqKiIhUWFga3fT6fUlJStHTpUi53oF9NnTpVZ86c0d69e1VYWCi/3y+Px6Ps7Gw5HI7gvZepU6cqLy8vwtViOOm6QtCXAQ/pCRMmqLm5uVtbc3OzEhISepxFS1JsbKxiY2OvaXc4HHI4HANSJ6JTZWWlRo8erd///vfBSxySgpc+umbUlZWVnHvoV6GeTwP+ZZaMjAxVVVV1a/N4PMrIyBjoQwN9SkxM1JQpUyRJN910k/Lz8/WnP/1J+fn5wZuGU6ZM4aYhIibsmXRra6uOHj0a3D5+/LgOHz6sMWPGaPLkySoqKtJnn32mN954Q5L01FNP6aWXXtL3v/99PfbYY9qzZ49+85vfaNeuXf33LoCv4OjRo8HH8DweT3D2LF0J6C+e78BgC3smfeDAAc2aNUuzZs2SJBUWFmrWrFlav369pCs/2HnixIlg/7S0NO3atUsej0czZsxQSUmJXnnlleBzqIAJjh49qvPnzysjI0Njx45VRkaGzp8/T0Aj4lj0H/gCv98ffE6aa9AYSKHmGgssAYDBCGkAMBghDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpIGrAoGAqqurtX//flVXVysQCES6JICQBiTJ7XbL6XQqOztbmzdvVnZ2tpxOp9xud6RLQ5QjpBH13G63CgoKlJ6eLq/Xq+3bt8vr9So9PV0FBQUENSLKZlmWFeki+hLqT58D4QoEAnI6nUpPT1dZWZkCgYAqKiqUl5cnu90ul8uluro61dfXy263R7pcDCOh5hozaUQ1r9erhoYGrVu3TjEx3T8OMTExKioq0vHjx+X1eiNUIaIdIY2o1tjYKEmaPn16j/u72rv6AYONkEZUS05OliTV1dX1uL+rvasfMNgIaUS1zMxMpaamauPGjers7Oy2r7OzU8XFxUpLS1NmZmaEKkS0I6QR1ex2u0pKSlReXi6Xy6Xa2lq1t7ertrZWLpdL5eXl2rRpEzcNETEjIl0AEGkrVqxQaWmp1q5dqwULFgTb09LSVFpaqhUrVkSwOkQ7HsEDrgoEAtq7d68qKyuVm5urRYsWMYPGgAk115hJA1fZ7XZlZWWpra1NWVlZBDSMwDVpADAYIQ0ABiOkAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0ABiMkAYAgxHSAGAwQhoADEZIA4DBCGkAMBghDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghDQAGOyGQnrLli1KTU3VyJEjNXfuXL333nu99n399ddls9m6/Y0cOfKGCwaAaBJ2SL/11lsqLCzU888/r/fff18zZsxQTk6OTp061euYhIQENTY2Bv8++eSTr1Q0AESLEeEO2Lx5s5544gk9+uijkqSXX35Zu3bt0quvvqof/OAHPY6x2WyaMGFCyMfo6OhQR0dHcNvn80mS/H6//H5/uCUDIes6vzjPMNBCPcfCCunLly/r4MGDKioqCrbFxMRoyZIlqqmp6XVca2urbr/9dnV2dmr27NnauHGj7r777l77FxcXa8OGDde07969W3FxceGUDNwQj8cT6RIwzF28eDGkfmGF9JkzZxQIBJSUlNStPSkpSR9//HGPY+688069+uqruueee9TS0qJNmzZp/vz5+tOf/qTbbrutxzFFRUUqLCwMbvt8PqWkpGjp0qVKSEgIp2QgLH6/Xx6PR9nZ2XI4HJEuB8NY1xWCvoR9uSNcGRkZysjICG7Pnz9fX//61/XLX/5SP/rRj3ocExsbq9jY2GvaHQ4HHxwMCs41DLRQz6+wbhyOHTtWdrtdzc3N3dqbm5tDvubscDg0a9YsHT16NJxDA0BUCiukv/a1r2nOnDmqqqoKtnV2dqqqqqrbbPl6AoGAPvzwQyUnJ4dXKQBEobAvdxQWFmr16tW69957df/99+vFF19UW1tb8GmPVatWadKkSSouLpYk/fCHP9S8efPkdDp1/vx5/fSnP9Unn3yixx9/vH/fCQAMQ2GH9MMPP6zTp09r/fr1ampq0syZM/X2228HbyaeOHFCMTH/O0E/d+6cnnjiCTU1NemWW27RnDlz9O6772ratGn99y4AYJiyWZZlRbqIvvh8PiUmJqqlpYWnOzCg/H6/KioqlJeXx41DDKhQc421OwDAYIQ0ABiMkAYAgxHSAGAwQhoADEZIA4DBCGkAMBghDQAGI6QBwGCENAAYjJAGAIMR0gBgMEIaAAxGSAOAwQhpADAYIQ0ABiOkAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAYjpAHAYIQ0ABiMkAauCgQCqq6u1v79+1VdXa1AIBDpkgBCGpAkt9stp9Op7Oxsbd68WdnZ2XI6nXK73ZEuDVGOkEbUc7vdKigoUHp6urxer7Zv3y6v16v09HQVFBQQ1Igom2VZVqSL6IvP51NiYqJaWlqUkJAQ6XIwjAQCATmdTqWnp6usrEyBQEAVFRXKy8uT3W6Xy+VSXV2d6uvrZbfbI10uhpFQc42ZNKKa1+tVQ0OD1q1bp5iY7h+HmJgYFRUV6fjx4/J6vRGqENGOkEZUa2xslCRNnz69x/1d7V39gMFGSCOqJScnS5Lq6up63N/V3tUPGGyENKJaZmamUlNTtXHjRnV2dnbb19nZqeLiYqWlpSkzMzNCFSLaEdKIana7XSUlJSovL5fL5VJtba3a29tVW1srl8ul8vJybdq0iZuGiJgRkS4AiLQVK1aotLRUa9eu1YIFC4LtaWlpKi0t1YoVKyJYHaIdj+ABVwUCAe3du1eVlZXKzc3VokWLmEFjwISaa8ykgavsdruysrLU1tamrKwsAhpG4Jo0ABiMkAYAgxHSAGAwQhoADEZIA1exnjRMREgDYj1pmIuQRtRjPWmYjC+zIKqxnjQihfWkgRCwnjRMR0gjqrGeNExHSCOqsZ40TEdII6p9cT1pv9/f7RE8v9/PetKIOBZYQlTrWk+6oKBAiYmJam9vlyRt3rxZo0aN0qVLl1RaWspNQ0QMM2lAkmVZunTpUre2S5cuaQg8/IRhjkfwENUCgYAmTpyoU6dOKS8vT1OmTNGRI0d055136tixY6qoqND48eN18uRJZtPoV6HmGiGNqFZVVaUlS5borrvu0qVLl9TQ0BDcl5qaqpEjR+rjjz/WO++8o8WLF0euUAw7PCcNhGDfvn2SpCNHjmjatGlavny50tPTtXz5ck2bNk1Hjhzp1g8YbNw4RFTr+oXw0aNHq6KiItj+4YcfSpJuueUWnTt37ppfEgcGCzNpRLUxY8ZIks6dO9fj/q72rn7AYCOkEdVCDV9CGpFCSCOq7dy5s1/7Af3thkJ6y5YtwTvfc+fO1XvvvXfd/jt27NBdd92lkSNHKj09vdu1PyCSvvg0R3/0A/pb2CH91ltvqbCwUM8//7zef/99zZgxQzk5OTp16lSP/d99911961vf0ne/+10dOnRILpcruPwjEGldNwi7/PznP9eaNWv085///Lr9gMES9nPSc+fO1X333aeXXnpJ0pW74ykpKfqnf/on/eAHP7im/8MPP6y2tjaVl5cH2+bNm6eZM2fq5Zdf7vEYHR0d6ujoCG77fD6lpKTozJkzPCcd5c62XVbZhx+r9fOeb/R9WduFFh2tO9Tr/h1bS4L/vunmBLW1+v53Oz5RbRdagtsrn1zb42s4p8/STfGJIdUz9dZk5X79jpD6Ynjz+XwaO3Zsn89Jh/UI3uXLl3Xw4EEVFRUF22JiYrRkyRLV1NT0OKampkaFhYXd2nJyclRWVtbrcYqLi7Vhw4Zr2nfv3q24uLhwSsYwU9Nsk9u3R7HjqkIfNKn3Xc4Nzi+1jP/S9rjgvw7pdz2+xqFzv5NC+z9DHQcXq+mjRUoaFVp/DF8XL14MqV9YIX3mzBkFAgElJSV1a09KStLHH3/c45impqYe+zc1NfV6nKKiom7B3jWTXrp0KTPpKDev7bLSPpyk1s+/EVL/cGbSfemXmfQsZtK4wufz9d1Jhn6ZJTY2VrGxsde0OxwOORyOCFQEUySNduj/Zs4Jb9A//p9ed/3quR+H/DJvrPt/4R0XuI5QsyysG4djx46V3W5Xc3Nzt/bm5mZNmDChxzETJkwIqz8wmEK9JTMElrjBMBVWSH/ta1/TnDlzVFX1v9cDOzs7VVVVpYyMjB7HZGRkdOsvSR6Pp9f+wGDrK4AJaERS2Jc7CgsLtXr1at177726//779eKLL6qtrU2PPvqoJGnVqlWaNGmSiouLJUnPPPOMsrKyVFJSovz8fL355ps6cOCAtm7d2r/vBPgKLMuSzWbrsR2IpLBD+uGHH9bp06e1fv16NTU1aebMmXr77beDNwdPnDjR7VeX58+fr23btulf/uVftG7dOk2dOlVlZWW9/vAnECmWZcnv96uiokJ5eXnc/4ARWE8a+AJCGoOF9aQBYBggpAHAYIQ0ABiMkAYAgxHSAGAwQhoADGbk2h1f1vWUYKgLkgA3yu/36+LFi/L5fDyChwHVlWd9PQU9JEL6woULkqSUlJQIVwIA/evChQtKTOx9FcUh8WWWzs5OnTx5UvHx8T1+dRfoL13L4n766ad8cQoDyrIsXbhwQRMnTuz2Le0vGxIhDQwWvt0K03DjEAAMRkgDgMEIaeALYmNj9fzzz/f4y0BAJHBNGgAMxkwaAAxGSAOAwQhpADAYIQ0ABiOkMSwtXLhQzz77bHA7NTVVL774YsTqAW4UIY0h45FHHpHNZtNTTz11zb6nn35aNptNjzzyiCTJ7XbrRz/60SBXCPQ/QhpDSkpKit588021t7cH2y5duqRt27Zp8uTJwbYxY8YoPj4+EiUC/YqQxpAye/ZspaSkyO12B9vcbrcmT56sWbNmBdu+fLnjy86fP6/HH39c48aNU0JCgv7hH/5BH3zwQXD/sWPH9NBDDykpKUk333yz7rvvPr3zzjvdXqOxsVH5+fkaNWqU0tLStG3btmsuq/R1HKAvhDSGnMcee0yvvfZacPvVV1/Vo48+GtZrrFy5UqdOnVJlZaUOHjyo2bNna/HixTp79qwkqbW1VXl5eaqqqtKhQ4f04IMPatmyZTpx4kTwNVatWqWTJ09q3759+u1vf6utW7fq1KlTYR0H6JMFDBGrV6+2HnroIevUqVNWbGys1dDQYDU0NFgjR460Tp8+bT300EPW6tWrLcuyrKysLOuZZ54Jjr399tutn/3sZ5ZlWZbX67USEhKsS5cudXv9KVOmWL/85S97Pf7dd99t/eIXv7Asy7L+/Oc/W5Ks//7v/w7ur6+vtyR95eMAXzQkFv0HvmjcuHHKz8/X66+/LsuylJ+fr7Fjx4Y8/oMPPlBra6tuvfXWbu3t7e06duyYpCsz6RdeeEG7du1SY2OjPv/8c7W3twdn0keOHNGIESM0e/bs4Hin06lbbrklrOMAfSGkMSQ99thjWrNmjSRpy5YtYY1tbW1VcnKy9u3bd82+0aNHS5K+973vyePxaNOmTXI6nRo1apQKCgp0+fLlfj0O0BdCGkPSgw8+qMuXL8tmsyknJyessbNnz1ZTU5NGjBih1NTUHvv853/+px555BEtX75c0pXAbWhoCO6/88479fnnn+vQoUOaM2eOJOno0aM6d+5cWMcB+sKNQwxJdrtdf/7zn/XRRx/JbreHNXbJkiXKyMiQy+XS7t271dDQoHfffVfPPfecDhw4IEmaOnWq3G63Dh8+rA8++EDf/va31dnZGXyNu+66S0uWLNGTTz6p9957T4cOHdKTTz6pUaNGBX/iLZTjAH0hpDFkJSQk3NBPXNlsNlVUVGjBggV69NFHdccdd+ib3/ymPvnkEyUlJUmSNm/erFtuuUXz58/XsmXLlJOT0+36syS98cYbSkpK0oIFC7R8+XI98cQTio+P18iRI0M+DtAX1pMG+slf//pXpaSk6J133tHixYsjXQ6GCUIauEF79uxRa2ur0tPT1djYqO9///v67LPP9Je//EUOhyPS5WGY4MYhcIP8fr/WrVun//mf/1F8fLzmz5+vX//61wQ0+hUzaQAwGDcOAcBghDQAGIyQBgCDEdIAYDBCGgAMRkgDgMEIaQAwGCENAAb7/8GR/bp5ZnbuAAAAAElFTkSuQmCC", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "numeric_features_with_outliers = [\n", " \"Price\",\n", " \"Levy\",\n", " \"Mileage\",\n", " \"Prod. year\",\n", "]\n", "\n", "i = 1\n", "for col in numeric_features_with_outliers:\n", " plt.figure(figsize=(4, 30))\n", " plt.subplot(6, 1, i)\n", " df.boxplot(column=col)\n", " i += 1" ] }, { "cell_type": "code", "execution_count": 750, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Количество строк до удаления выбросов: 14801\n", "Количество строк после удаления выбросов: 12597\n" ] } ], "source": [ "def remove_outliers(df, column):\n", " Q1 = df[column].quantile(0.25)\n", " Q3 = df[column].quantile(0.75)\n", " IQR = Q3 - Q1\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", " return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", "\n", "print(f\"Количество строк до удаления выбросов: {len(df)}\")\n", "\n", "for column in numeric_features_with_outliers:\n", " df = remove_outliers(df, column)\n", "\n", "print(f\"Количество строк после удаления выбросов: {len(df)}\")" ] }, { "cell_type": "code", "execution_count": 751, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "i = 1\n", "for col in numeric_features_with_outliers:\n", " plt.figure(figsize=(4, 30))\n", " plt.subplot(6, 1, i)\n", " df.boxplot(column=col)\n", " i += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Формирование конвейера для классификации данных" ] }, { "cell_type": "code", "execution_count": 752, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Price int64\n", "Levy int64\n", "Manufacturer object\n", "Model object\n", "Prod. year int64\n", "Category object\n", "Leather interior object\n", "Fuel type object\n", "Engine volume float64\n", "Mileage int64\n", "Cylinders int64\n", "Gear box type object\n", "Drive wheels object\n", "Doors object\n", "Wheel object\n", "Color object\n", "Airbags int64\n", "dtype: object" ] }, "execution_count": 752, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 753, "metadata": {}, "outputs": [], "source": [ "columns_to_drop = [\n", " \"Model\",\n", " \"Manufacturer\",\n", " \"Color\",\n", " \"Doors\",\n", " \"Cylinders\",\n", " \"Mileage\",\n", "]\n", "\n", "num_columns = [\n", " column\n", " for column in df.columns\n", " if column not in columns_to_drop and df[column].dtype != \"object\"\n", "]\n", "\n", "cat_columns = [\n", " column\n", " for column in df.columns\n", " if column not in columns_to_drop and df[column].dtype == \"object\"\n", "]\n", "\n", "cat_cols_for_one_hot_enc = [\n", " \"Leather interior\",\n", " \"Category\",\n", " \"Fuel type\",\n", " \"Gear box type\",\n", " \"Drive wheels\",\n", " # \"Doors\",\n", " \"Wheel\",\n", "]\n", "\n", "cat_cols_for_num_enc = [\n", " # \"Model\",\n", " # \"Manufacturer\",\n", " # \"Color\",\n", "]\n", "\n", "num_imputer = SimpleImputer(strategy=\"median\")\n", "num_scaler = StandardScaler()\n", "preprocessing_num = Pipeline(\n", " [\n", " (\"imputer\", num_imputer),\n", " (\"scaler\", num_scaler),\n", " ]\n", ")\n", "\n", "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n", "cat_one_hot_encoder = OneHotEncoder(\n", " handle_unknown=\"ignore\", sparse_output=False, drop=\"first\"\n", ")\n", "preprocessing_one_hot = Pipeline(\n", " [\n", " (\"imputer\", cat_imputer),\n", " (\"encoder\", cat_one_hot_encoder),\n", " ]\n", ")\n", "\n", "preprocessing_label_enc = Pipeline(\n", " [\n", " (\"imputer\", cat_imputer),\n", " (\"encoder\", OrdinalEncoder()),\n", " ]\n", ")\n", "\n", "features_preprocessing = ColumnTransformer(\n", " verbose_feature_names_out=False,\n", " transformers=[\n", " (\"prepocessing_one_hot\", preprocessing_one_hot, cat_cols_for_one_hot_enc),\n", " (\"prepocessing_label_enc\", preprocessing_label_enc, cat_cols_for_num_enc),\n", " (\"prepocessing_num\", preprocessing_num, num_columns),\n", " ],\n", " remainder=\"passthrough\",\n", ")\n", "\n", "drop_columns = ColumnTransformer(\n", " verbose_feature_names_out=False,\n", " transformers=[\n", " (\"drop_columns\", \"drop\", columns_to_drop),\n", " ],\n", " remainder=\"passthrough\",\n", ")\n", "\n", "pipeline_end = Pipeline(\n", " [\n", " (\"features_preprocessing\", features_preprocessing),\n", " (\"drop_columns\", drop_columns),\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Демонстрация работы конвейера для предобработки данных" ] }, { "cell_type": "code", "execution_count": 754, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Leather interior_Yes Category_Coupe Category_Goods wagon \\\n", "0 1.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 1.0 0.0 0.0 \n", "4 1.0 0.0 0.0 \n", "... ... ... ... \n", "19225 0.0 0.0 1.0 \n", "19226 1.0 0.0 0.0 \n", "19232 1.0 1.0 0.0 \n", "19233 1.0 0.0 0.0 \n", "19234 1.0 0.0 0.0 \n", "\n", " Category_Hatchback Category_Jeep Category_Limousine \\\n", "0 0.0 1.0 0.0 \n", "1 0.0 1.0 0.0 \n", "2 1.0 0.0 0.0 \n", "3 0.0 1.0 0.0 \n", "4 1.0 0.0 0.0 \n", "... ... ... ... \n", "19225 0.0 0.0 0.0 \n", "19226 0.0 0.0 0.0 \n", "19232 0.0 0.0 0.0 \n", "19233 0.0 0.0 0.0 \n", "19234 0.0 1.0 0.0 \n", "\n", " Category_Microbus Category_Minivan Category_Pickup Category_Sedan \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... \n", "19225 0.0 0.0 0.0 0.0 \n", "19226 0.0 0.0 0.0 1.0 \n", "19232 0.0 0.0 0.0 0.0 \n", "19233 0.0 0.0 0.0 1.0 \n", "19234 0.0 0.0 0.0 0.0 \n", "\n", " ... Gear box type_Tiptronic Gear box type_Variator \\\n", "0 ... 0.0 0.0 \n", "1 ... 1.0 0.0 \n", "2 ... 0.0 1.0 \n", "3 ... 0.0 0.0 \n", "4 ... 0.0 0.0 \n", "... ... ... ... \n", "19225 ... 0.0 0.0 \n", "19226 ... 1.0 0.0 \n", "19232 ... 0.0 0.0 \n", "19233 ... 1.0 0.0 \n", "19234 ... 0.0 0.0 \n", "\n", " Drive wheels_Front Drive wheels_Rear Wheel_Right-hand drive \\\n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 1.0 0.0 1.0 \n", "3 0.0 0.0 0.0 \n", "4 1.0 0.0 0.0 \n", "... ... ... ... \n", "19225 1.0 0.0 0.0 \n", "19226 0.0 1.0 1.0 \n", "19232 0.0 1.0 0.0 \n", "19233 1.0 0.0 0.0 \n", "19234 1.0 0.0 0.0 \n", "\n", " Price Levy Prod. year Engine volume Airbags \n", "0 -0.332733 1.851925 -0.224219 1.849443 1.353782 \n", "1 -0.029023 1.024150 -0.007434 1.162057 0.340316 \n", "2 -0.781060 -1.187596 -1.091356 -1.175055 -1.179883 \n", "3 -1.229294 0.685218 -0.007434 0.474671 -1.686616 \n", "4 -0.480485 -0.218599 0.642919 -1.175055 -0.673150 \n", "... ... ... ... ... ... \n", "19225 -0.752100 1.217515 -0.874572 -0.625146 -0.673150 \n", "19226 -0.838888 -1.187596 -1.741709 1.162057 -1.686616 \n", "19232 -0.781060 -1.187596 -2.608847 -0.212715 -0.419784 \n", "19233 -0.115718 0.617867 -0.007434 0.337194 0.340316 \n", "19234 0.845956 0.628730 -0.224219 -0.212715 -0.673150 \n", "\n", "[12597 rows x 28 columns]" ] }, "execution_count": 754, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preprocessing_result = pipeline_end.fit_transform(df)\n", "df = pd.DataFrame(\n", " preprocessing_result,\n", " columns=pipeline_end.get_feature_names_out(),\n", ")\n", "\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Разбиение на выборки\n" ] }, { "cell_type": "code", "execution_count": 755, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размеры выборок:\n", "Обучающая выборка: 8817 записей\n", "Тестовая выборка: 3780 записей\n" ] } ], "source": [ "train_df, test_df = train_test_split(\n", " df, test_size=0.3, random_state=42\n", ")\n", "\n", "print(\"Размеры выборок:\")\n", "print(f\"Обучающая выборка: {train_df.shape[0]} записей\")\n", "print(f\"Тестовая выборка: {test_df.shape[0]} записей\")" ] }, { "cell_type": "code", "execution_count": 756, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Gear box type_Manual Gear box type_Tiptronic \\\n", "15146 ... 0.0 0.0 \n", "14145 ... 0.0 1.0 \n", "8943 ... 0.0 1.0 \n", "17889 ... 0.0 0.0 \n", "9515 ... 0.0 0.0 \n", "... ... ... ... \n", "18201 ... 1.0 0.0 \n", "7436 ... 0.0 0.0 \n", "7728 ... 0.0 0.0 \n", "1136 ... 0.0 0.0 \n", "10640 ... 0.0 1.0 \n", "\n", " Gear box type_Variator Drive wheels_Front Drive wheels_Rear \\\n", "15146 0.0 1.0 0.0 \n", "14145 0.0 1.0 0.0 \n", "8943 0.0 1.0 0.0 \n", "17889 0.0 0.0 0.0 \n", "9515 0.0 1.0 0.0 \n", "... ... ... ... \n", "18201 0.0 0.0 0.0 \n", "7436 0.0 1.0 0.0 \n", "7728 1.0 1.0 0.0 \n", "1136 0.0 1.0 0.0 \n", "10640 0.0 1.0 0.0 \n", "\n", " Wheel_Right-hand drive Levy Prod. year Engine volume Airbags \n", "15146 0.0 -1.187596 1.943625 -0.487669 -0.673150 \n", "14145 1.0 -1.187596 -0.874572 -0.900100 -1.179883 \n", "8943 0.0 0.081225 0.642919 -0.487669 0.847049 \n", "17889 0.0 -1.187596 -1.524925 0.474671 -0.419784 \n", "9515 0.0 1.695495 1.510056 0.474671 -0.673150 \n", "... ... ... ... ... ... \n", "18201 0.0 -1.187596 -0.874572 0.337194 -0.673150 \n", "7436 0.0 0.474472 0.859703 -0.212715 1.353782 \n", "7728 0.0 0.643938 -0.657787 -0.900100 0.340316 \n", "1136 0.0 -1.187596 1.076487 -0.762623 0.340316 \n", "10640 0.0 0.444055 0.426134 0.337194 0.340316 \n", "\n", "[8817 rows x 27 columns]" ] }, "execution_count": 756, "metadata": {}, "output_type": "execute_result" } ], "source": [ "price_y_train = train_df[\"Price\"]\n", "price_y_test = test_df[\"Price\"]\n", "train_df.drop(columns=[\"Price\"], inplace=True)\n", "test_df.drop(columns=[\"Price\"], inplace=True)\n", "\n", "train_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Формирование набора моделей" ] }, { "cell_type": "code", "execution_count": 757, "metadata": {}, "outputs": [], "source": [ "models = {\n", " \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n", " # \"linear_poly\": {\n", " # \"model\": make_pipeline(\n", " # PolynomialFeatures(degree=2),\n", " # linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n", " # )\n", " # },\n", " # \"linear_interact\": {\n", " # \"model\": make_pipeline(\n", " # PolynomialFeatures(interaction_only=True),\n", " # linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n", " # )\n", " # },\n", " \"ridge\": {\"model\": linear_model.RidgeCV()},\n", " \"decision_tree\": {\n", " \"model\": tree.DecisionTreeRegressor(max_depth=7, random_state=random_state)\n", " },\n", " \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n", " \"random_forest\": {\n", " \"model\": ensemble.RandomForestRegressor(\n", " max_depth=7, random_state=random_state, n_jobs=-1\n", " )\n", " },\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Обучение и оценка моделей с помощью различных алгоритмов" ] }, { "cell_type": "code", "execution_count": 758, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: linear\n", "Model: ridge\n", "Model: decision_tree\n", "Model: knn\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but LinearRegression was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RidgeCV was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RidgeCV was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but DecisionTreeRegressor was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but KNeighborsRegressor was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but KNeighborsRegressor was fitted without feature names\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model: random_forest\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n", " warnings.warn(\n", "c:\\Users\\user\\source\\repos\\mai_pi-33_zakharov\\.venv\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n", " warnings.warn(\n" ] } ], "source": [ "for model_name in models.keys():\n", " print(f\"Model: {model_name}\")\n", "\n", " model = models[model_name][\"model\"]\n", "\n", " fitted_model = model.fit(train_df.values, price_y_train.values.ravel())\n", "\n", " y_train_pred = fitted_model.predict(train_df)\n", " y_test_pred = fitted_model.predict(test_df)\n", "\n", " models[model_name][\"fitted\"] = fitted_model\n", " models[model_name][\"train_preds\"] = y_train_pred\n", " models[model_name][\"preds\"] = y_test_pred\n", "\n", " models[model_name][\"RMSE_train\"] = math.sqrt(\n", " metrics.mean_squared_error(price_y_train, y_train_pred)\n", " )\n", " models[model_name][\"RMSE_test\"] = math.sqrt(\n", " metrics.mean_squared_error(price_y_test, y_test_pred)\n", " )\n", " models[model_name][\"RMAE_test\"] = math.sqrt(\n", " metrics.mean_absolute_error(price_y_test, y_test_pred)\n", " )\n", " models[model_name][\"R2_test\"] = metrics.r2_score(price_y_test, y_test_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Вывод результатов оценки" ] }, { "cell_type": "code", "execution_count": 759, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 RMSE_trainRMSE_testRMAE_testR2_test
knn0.5367120.6023770.6436010.640894
random_forest0.6105110.6343210.6755240.601798
decision_tree0.6507040.6834370.7013930.537743
ridge0.7791990.7880520.7763910.385397
linear0.7791980.7880560.7763790.385390
\n" ], "text/plain": [ "" ] }, "execution_count": 759, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg_metrics = pd.DataFrame.from_dict(models, \"index\")[\n", " [\"RMSE_train\", \"RMSE_test\", \"RMAE_test\", \"R2_test\"]\n", "]\n", "reg_metrics.sort_values(by=\"RMSE_test\").style.background_gradient(\n", " cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE_train\", \"RMSE_test\"]\n", ").background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"RMAE_test\", \"R2_test\"])" ] } ], "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": "3.12.8" } }, "nbformat": 4, "nbformat_minor": 2 }