{ "cells": [ { "cell_type": "markdown", "metadata": { "vscode": { "languageId": "plaintext" } }, "source": [ "#### Загрузка набора данных" ] }, { "cell_type": "code", "execution_count": 1, "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": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import math\n", "\n", "\n", "from pandas import DataFrame\n", "from sklearn.preprocessing import StandardScaler\n", "from imblearn.over_sampling import RandomOverSampler\n", "from sklearn.preprocessing import OneHotEncoder\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", "\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", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Анализ датасета и очистка данных" ] }, { "cell_type": "code", "execution_count": 2, "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": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 3, "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": 3, "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": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([186005, 192000, 200000, ..., 140607, 307325, 186923])" ] }, "execution_count": 4, "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": 5, "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": 5, "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": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 6, 4, 8, 1, 12, 3, 2, 16, 5, 7, 9, 10, 14])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Cylinders\"] = df[\"Cylinders\"].astype(\"int64\")\n", "df[\"Cylinders\"].unique()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['04-May', '02-Mar', '>5'], dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Doors\"].unique()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Четырехдверный', 'Двухдверный', 'Многодверный'], dtype=object)" ] }, "execution_count": 8, "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": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1, 3, 6, ..., 627220, 872946, 26307500])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_df = df.sort_values(by=\"Price\")\n", "sorted_df[\"Price\"].unique()" ] }, { "cell_type": "code", "execution_count": 10, "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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 500, 549, 600, ..., 627220, 872946, 26307500])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_df = df.sort_values(by=\"Price\")\n", "sorted_df[\"Price\"].unique()" ] }, { "cell_type": "code", "execution_count": 12, "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": 12, "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": 13, "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": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Очистка дубликатов и пропущенных значений" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "np.int64(2773)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.duplicated().sum()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_27528\\3006716147.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)" ] }, { "cell_type": "code", "execution_count": 16, "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": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Очистка выбросов" ] }, { "cell_type": "code", "execution_count": 17, "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": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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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": 19, "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": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] }, "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": [ "#### Разбиение на выборки" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размеры выборок:\n", "Обучающая выборка: 8817 записей\n", "Category\n", "Sedan 3954\n", "Jeep 2263\n", "Hatchback 1554\n", "Minivan 312\n", "Coupe 251\n", "Universal 180\n", "Microbus 143\n", "Goods wagon 120\n", "Pickup 22\n", "Cabriolet 16\n", "Limousine 2\n", "Name: count, dtype: int64\n", "Тестовая выборка: 3780 записей\n", "Category\n", "Sedan 1692\n", "Jeep 990\n", "Hatchback 636\n", "Minivan 151\n", "Coupe 117\n", "Universal 82\n", "Goods wagon 52\n", "Microbus 46\n", "Pickup 8\n", "Cabriolet 5\n", "Limousine 1\n", "Name: count, dtype: int64\n" ] } ], "source": [ "X = df\n", "y = df[\"Category\"]\n", "\n", "train_df, test_df, y_train, y_test = train_test_split(\n", " X, y, 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(train_df.Category.value_counts())\n", "print(f\"Тестовая выборка: {test_df.shape[0]} записей\")\n", "print(test_df.Category.value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Oversampling" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размеры выборок:\n", "Обучающая выборка: 43494 записей\n", "Category\n", "Sedan 3954\n", "Jeep 3954\n", "Universal 3954\n", "Hatchback 3954\n", "Coupe 3954\n", "Goods wagon 3954\n", "Minivan 3954\n", "Microbus 3954\n", "Pickup 3954\n", "Limousine 3954\n", "Cabriolet 3954\n", "Name: count, dtype: int64\n", "Тестовая выборка: 18612 записей\n", "Category\n", "Hatchback 1692\n", "Sedan 1692\n", "Universal 1692\n", "Jeep 1692\n", "Coupe 1692\n", "Minivan 1692\n", "Goods wagon 1692\n", "Microbus 1692\n", "Pickup 1692\n", "Cabriolet 1692\n", "Limousine 1692\n", "Name: count, dtype: int64\n" ] } ], "source": [ "def oversample(df):\n", " X = df.drop(\"Category\", axis=1)\n", " y = df[\"Category\"]\n", "\n", " oversampler = RandomOverSampler(random_state=42)\n", " X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", "\n", " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", " return resampled_df\n", "\n", "\n", "train_df_overs = oversample(train_df)\n", "test_df_overs = oversample(test_df)\n", "\n", "print(\"Размеры выборок:\")\n", "print(f\"Обучающая выборка: {train_df_overs.shape[0]} записей\")\n", "print(train_df_overs.Category.value_counts())\n", "print(f\"Тестовая выборка: {test_df_overs.shape[0]} записей\")\n", "print(test_df_overs.Category.value_counts())" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "price_y_train = train_df[\"Price\"]\n", "price_y_test = test_df[\"Price\"]\n", "train_df = train_df.drop(columns=[\"Price\"])\n", "test_df = test_df.drop(columns=[\"Price\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Ручной синтез признаков." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def age_create(df): \n", " df[\"Age\"] = 2020 - df[\"Prod. year\"]\n", " df = df.drop(columns=[\"Prod. year\"])\n", " return df\n", "\n", "train_df = age_create(train_df)\n", "test_df = age_create(test_df)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", " 17, 18, 19, 20, 21])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_df = train_df.sort_values(by=\"Age\")\n", "sorted_df[\"Age\"].unique()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(4, 30))\n", "plt.subplot(6, 1, i)\n", "train_df.boxplot(column=\"Age\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Унитарное кодирование категориальных признаков" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Levy int64\n", "Manufacturer object\n", "Model object\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", "Age int64\n", "dtype: object" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.dtypes" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., ..., 0., 1., 0.],\n", " [0., 0., 0., ..., 0., 1., 1.],\n", " [0., 0., 0., ..., 0., 1., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 1., 0.],\n", " [0., 0., 0., ..., 0., 1., 0.],\n", " [1., 0., 0., ..., 0., 1., 0.]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_features_for_encoding = [\n", " \"Leather interior\",\n", " \"Category\",\n", " \"Fuel type\",\n", " \"Gear box type\",\n", " \"Drive wheels\",\n", " \"Doors\",\n", " \"Wheel\",\n", "]\n", "\n", "def onehot_encode(df):\n", " encoder = OneHotEncoder(sparse_output=False, drop=\"first\")\n", " temp = encoder.fit_transform(df[categorical_features_for_encoding])\n", " df = df.drop(columns=categorical_features_for_encoding)\n", " return temp\n", "\n", "\n", "temp = onehot_encode(train_df)\n", "temp\n", "# onehot_encode(test_df)\n", "\n", "# train_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Масштабирование признаков" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Levy int64\n", "Manufacturer object\n", "Model object\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", "Age int64\n", "dtype: object" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.dtypes" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LevyManufacturerModelCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsAge
15146-1.192982TOYOTAPriusSedanNoPetrol-0.479341-1.531744-0.403213AutomaticFrontЧетырехдверныйLeft wheelWhite-0.683755-1.946936
14145-1.192982TOYOTARactisSedanNoPetrol-0.887855-0.130245-0.403213TiptronicFrontЧетырехдверныйRight-hand driveBrown-1.1902170.879266
89430.081576HYUNDAIElantraSedanNoPetrol-0.479341-0.651122-0.403213TiptronicFrontЧетырехдверныйLeft wheelBeige0.835631-0.642535
17889-1.192982SUBARUForester L.L.BEANJeepYesCNG0.4738581.263152-0.403213Automatic4x4ЧетырехдверныйLeft wheelGreen-0.4305241.531466
95151.703146HYUNDAIH1UniversalYesDiesel0.473858-0.739330-0.403213AutomaticFrontЧетырехдверныйLeft wheelGrey-0.683755-1.512135
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18201-1.192982AUDIA4SedanYesPetrol0.3376870.3180181.538421Manual4x4ЧетырехдверныйLeft wheelGrey-0.6837550.879266
74360.476602KIAAvellaSedanYesPetrol-0.206998-0.011145-0.403213AutomaticFrontЧетырехдверныйLeft wheelSilver1.342092-0.859935
77280.646834TOYOTAPriusHatchbackNoHybrid-0.8878551.155137-0.403213VariatorFrontЧетырехдверныйLeft wheelSilver0.3291690.661866
1136-1.192982FORDFiestaSedanNoPetrol-0.751684-0.697325-0.403213AutomaticFrontЧетырехдверныйLeft wheelSilver0.329169-1.077335
106400.446048KIAOptima EXSedanYesPetrol0.337687-0.4650937.363324TiptronicFrontЧетырехдверныйLeft wheelSilver0.329169-0.425135
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8817 rows × 16 columns

\n", "
" ], "text/plain": [ " Levy Manufacturer Model Category Leather interior \\\n", "15146 -1.192982 TOYOTA Prius Sedan No \n", "14145 -1.192982 TOYOTA Ractis Sedan No \n", "8943 0.081576 HYUNDAI Elantra Sedan No \n", "17889 -1.192982 SUBARU Forester L.L.BEAN Jeep Yes \n", "9515 1.703146 HYUNDAI H1 Universal Yes \n", "... ... ... ... ... ... \n", "18201 -1.192982 AUDI A4 Sedan Yes \n", "7436 0.476602 KIA Avella Sedan Yes \n", "7728 0.646834 TOYOTA Prius Hatchback No \n", "1136 -1.192982 FORD Fiesta Sedan No \n", "10640 0.446048 KIA Optima EX Sedan Yes \n", "\n", " Fuel type Engine volume Mileage Cylinders Gear box type \\\n", "15146 Petrol -0.479341 -1.531744 -0.403213 Automatic \n", "14145 Petrol -0.887855 -0.130245 -0.403213 Tiptronic \n", "8943 Petrol -0.479341 -0.651122 -0.403213 Tiptronic \n", "17889 CNG 0.473858 1.263152 -0.403213 Automatic \n", "9515 Diesel 0.473858 -0.739330 -0.403213 Automatic \n", "... ... ... ... ... ... \n", "18201 Petrol 0.337687 0.318018 1.538421 Manual \n", "7436 Petrol -0.206998 -0.011145 -0.403213 Automatic \n", "7728 Hybrid -0.887855 1.155137 -0.403213 Variator \n", "1136 Petrol -0.751684 -0.697325 -0.403213 Automatic \n", "10640 Petrol 0.337687 -0.465093 7.363324 Tiptronic \n", "\n", " Drive wheels Doors Wheel Color Airbags \\\n", "15146 Front Четырехдверный Left wheel White -0.683755 \n", "14145 Front Четырехдверный Right-hand drive Brown -1.190217 \n", "8943 Front Четырехдверный Left wheel Beige 0.835631 \n", "17889 4x4 Четырехдверный Left wheel Green -0.430524 \n", "9515 Front Четырехдверный Left wheel Grey -0.683755 \n", "... ... ... ... ... ... \n", "18201 4x4 Четырехдверный Left wheel Grey -0.683755 \n", "7436 Front Четырехдверный Left wheel Silver 1.342092 \n", "7728 Front Четырехдверный Left wheel Silver 0.329169 \n", "1136 Front Четырехдверный Left wheel Silver 0.329169 \n", "10640 Front Четырехдверный Left wheel Silver 0.329169 \n", "\n", " Age \n", "15146 -1.946936 \n", "14145 0.879266 \n", "8943 -0.642535 \n", "17889 1.531466 \n", "9515 -1.512135 \n", "... ... \n", "18201 0.879266 \n", "7436 -0.859935 \n", "7728 0.661866 \n", "1136 -1.077335 \n", "10640 -0.425135 \n", "\n", "[8817 rows x 16 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler = StandardScaler()\n", "\n", "numeric_features_for_stardartization = [\n", " \"Levy\",\n", " \"Engine volume\",\n", " \"Mileage\",\n", " \"Cylinders\",\n", " \"Airbags\",\n", " \"Age\",\n", "]\n", "\n", "train_df[numeric_features_for_stardartization] = scaler.fit_transform(\n", " train_df[numeric_features_for_stardartization]\n", ")\n", "test_df[numeric_features_for_stardartization] = scaler.transform(\n", " test_df[numeric_features_for_stardartization]\n", ")\n", "\n", "train_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Числовое кодирование категориальных признаков" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Levy float64\n", "Manufacturer object\n", "Model object\n", "Category object\n", "Leather interior object\n", "Fuel type object\n", "Engine volume float64\n", "Mileage float64\n", "Cylinders float64\n", "Gear box type object\n", "Drive wheels object\n", "Doors object\n", "Wheel object\n", "Color object\n", "Airbags float64\n", "Age float64\n", "dtype: object" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.dtypes" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LevyManufacturerModelCategoryLeather interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsAge
15146-1.19298247798905-0.479341-1.531744-0.403213012014-0.683755-1.946936
14145-1.19298247864905-0.887855-0.130245-0.40321321213-1.1902170.879266
89430.08157617420905-0.479341-0.651122-0.403213212000.835631-0.642535
17889-1.192982454884100.4738581.263152-0.40321300206-0.4305241.531466
95151.7031461754910110.473858-0.739330-0.40321301207-0.683755-1.512135
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18201-1.19298221219150.3376870.3180181.53842110207-0.6837550.879266
74360.47660222178915-0.206998-0.011145-0.4032130120121.342092-0.859935
77280.64683447798302-0.8878551.155137-0.4032133120120.3291690.661866
1136-1.19298213474905-0.751684-0.697325-0.4032130120120.329169-1.077335
106400.446048227439150.337687-0.4650937.3633242120120.329169-0.425135
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8817 rows × 16 columns

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" ], "text/plain": [ " Levy Manufacturer Model Category Leather interior Fuel type \\\n", "15146 -1.192982 47 798 9 0 5 \n", "14145 -1.192982 47 864 9 0 5 \n", "8943 0.081576 17 420 9 0 5 \n", "17889 -1.192982 45 488 4 1 0 \n", "9515 1.703146 17 549 10 1 1 \n", "... ... ... ... ... ... ... \n", "18201 -1.192982 2 121 9 1 5 \n", "7436 0.476602 22 178 9 1 5 \n", "7728 0.646834 47 798 3 0 2 \n", "1136 -1.192982 13 474 9 0 5 \n", "10640 0.446048 22 743 9 1 5 \n", "\n", " Engine volume Mileage Cylinders Gear box type Drive wheels Doors \\\n", "15146 -0.479341 -1.531744 -0.403213 0 1 2 \n", "14145 -0.887855 -0.130245 -0.403213 2 1 2 \n", "8943 -0.479341 -0.651122 -0.403213 2 1 2 \n", "17889 0.473858 1.263152 -0.403213 0 0 2 \n", "9515 0.473858 -0.739330 -0.403213 0 1 2 \n", "... ... ... ... ... ... ... \n", "18201 0.337687 0.318018 1.538421 1 0 2 \n", "7436 -0.206998 -0.011145 -0.403213 0 1 2 \n", "7728 -0.887855 1.155137 -0.403213 3 1 2 \n", "1136 -0.751684 -0.697325 -0.403213 0 1 2 \n", "10640 0.337687 -0.465093 7.363324 2 1 2 \n", "\n", " Wheel Color Airbags Age \n", "15146 0 14 -0.683755 -1.946936 \n", "14145 1 3 -1.190217 0.879266 \n", "8943 0 0 0.835631 -0.642535 \n", "17889 0 6 -0.430524 1.531466 \n", "9515 0 7 -0.683755 -1.512135 \n", "... ... ... ... ... \n", "18201 0 7 -0.683755 0.879266 \n", "7436 0 12 1.342092 -0.859935 \n", "7728 0 12 0.329169 0.661866 \n", "1136 0 12 0.329169 -1.077335 \n", "10640 0 12 0.329169 -0.425135 \n", "\n", "[8817 rows x 16 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_features_for_encoding = [\n", " \"Model\",\n", " \"Manufacturer\",\n", " \"Color\",\n", " \"Leather interior\",\n", " \"Category\",\n", " \"Fuel type\",\n", " \"Gear box type\",\n", " \"Drive wheels\",\n", " \"Doors\",\n", " \"Wheel\",\n", "]\n", "def label_encode(df):\n", " encoder = LabelEncoder()\n", " for column in df.columns:\n", " if column in categorical_features_for_encoding:\n", " df[column] = encoder.fit_transform(df[column])\n", "\n", "label_encode(train_df)\n", "label_encode(test_df)\n", "\n", "train_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Формирование набора моделей" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Levy float64\n", "Manufacturer int64\n", "Model int64\n", "Category int64\n", "Leather interior int64\n", "Fuel type int64\n", "Engine volume float64\n", "Mileage float64\n", "Cylinders float64\n", "Gear box type int64\n", "Drive wheels int64\n", "Doors int64\n", "Wheel int64\n", "Color int64\n", "Airbags float64\n", "Age float64\n", "dtype: object" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.dtypes" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Levy float64\n", "Manufacturer int64\n", "Model int64\n", "Category int64\n", "Leather interior int64\n", "Fuel type int64\n", "Engine volume float64\n", "Mileage float64\n", "Cylinders float64\n", "Gear box type int64\n", "Drive wheels int64\n", "Doors int64\n", "Wheel int64\n", "Color int64\n", "Airbags float64\n", "Age float64\n", "dtype: object" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.dtypes" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "random_state = 9\n", "\n", "set_config(transform_output=\"pandas\")\n", "\n", "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": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: linear\n", "Model: linear_poly\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 PolynomialFeatures 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 PolynomialFeatures was fitted without feature names\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Model: linear_interact\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 PolynomialFeatures 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 PolynomialFeatures 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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "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 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": 37, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 RMSE_trainRMSE_testRMAE_testR2_test
random_forest6319.8401516857.36653070.1719600.604144
decision_tree6865.8061157608.47191173.7505770.512676
linear_poly7462.2720047912.70909476.6730240.472924
linear_interact7853.4590948132.73130477.8171160.443204
ridge8971.3944019047.30901483.3530340.310932
linear8971.3731409047.47241783.3605400.310908
knn7044.27326612172.93280697.028762-0.247422
\n" ], "text/plain": [ "" ] }, "execution_count": 37, "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": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 2 }