{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Загрузка данных в DataFrame" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 19237 entries, 45654403 to 45813273\n", "Data columns (total 17 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Price 19237 non-null int64 \n", " 1 Levy 13418 non-null object \n", " 2 Manufacturer 19237 non-null object \n", " 3 Model 19237 non-null object \n", " 4 Prod_year 19237 non-null int64 \n", " 5 Category 19237 non-null object \n", " 6 Leather_interior 19237 non-null int64 \n", " 7 Fuel type 19237 non-null object \n", " 8 Engine volume 19237 non-null object \n", " 9 Mileage 19237 non-null object \n", " 10 Cylinders 19237 non-null float64\n", " 11 Gear box type 19237 non-null object \n", " 12 Drive wheels 19237 non-null object \n", " 13 Doors 19237 non-null object \n", " 14 Wheel 19237 non-null object \n", " 15 Color 19237 non-null object \n", " 16 Airbags 19237 non-null int64 \n", "dtypes: float64(1), int64(4), object(12)\n", "memory usage: 2.6+ MB\n", "(19237, 17)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\1\\AppData\\Local\\Temp\\ipykernel_25288\\68381857.py:5: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " df[\"Leather_interior\"] = df[\"Leather_interior\"].replace({\"Yes\": 1, \"No\": 0})\n" ] }, { "data": { "text/html": [ "
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PriceLevyManufacturerModelProd_yearCategoryLeather_interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbags
ID
45654403133281399LEXUSRX 4502010Jeep1Hybrid3.5186005 km6.0Automatic4x404-MayLeft wheelSilver12
44731507166211018CHEVROLETEquinox2011Jeep0Petrol3192000 km6.0Tiptronic4x404-MayLeft wheelBlack8
457744198467NoneHONDAFIT2006Hatchback0Petrol1.3200000 km4.0VariatorFront04-MayRight-hand driveBlack2
457691853607862FORDEscape2011Jeep1Hybrid2.5168966 km4.0Automatic4x404-MayLeft wheelWhite0
4580926311726446HONDAFIT2014Hatchback1Petrol1.391901 km4.0AutomaticFront04-MayLeft wheelSilver4
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" ], "text/plain": [ " Price Levy Manufacturer Model Prod_year Category \\\n", "ID \n", "45654403 13328 1399 LEXUS RX 450 2010 Jeep \n", "44731507 16621 1018 CHEVROLET Equinox 2011 Jeep \n", "45774419 8467 None HONDA FIT 2006 Hatchback \n", "45769185 3607 862 FORD Escape 2011 Jeep \n", "45809263 11726 446 HONDA FIT 2014 Hatchback \n", "\n", " Leather_interior Fuel type Engine volume Mileage Cylinders \\\n", "ID \n", "45654403 1 Hybrid 3.5 186005 km 6.0 \n", "44731507 0 Petrol 3 192000 km 6.0 \n", "45774419 0 Petrol 1.3 200000 km 4.0 \n", "45769185 1 Hybrid 2.5 168966 km 4.0 \n", "45809263 1 Petrol 1.3 91901 km 4.0 \n", "\n", " Gear box type Drive wheels Doors Wheel Color Airbags \n", "ID \n", "45654403 Automatic 4x4 04-May Left wheel Silver 12 \n", "44731507 Tiptronic 4x4 04-May Left wheel Black 8 \n", "45774419 Variator Front 04-May Right-hand drive Black 2 \n", "45769185 Automatic 4x4 04-May Left wheel White 0 \n", "45809263 Automatic Front 04-May Left wheel Silver 4 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy import nan\n", "import pandas as pd\n", "\n", "df = pd.read_csv(\"data/car_price_prediction.csv\", index_col=\"ID\")\n", "df[\"Leather_interior\"] = df[\"Leather_interior\"].replace({\"Yes\": 1, \"No\": 0})\n", "df[\"Levy\"] = df[\"Levy\"].replace({\"-\": None})\n", "\n", "df.info()\n", "print(df.shape)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Получение сведений о пропущенных данных" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Типы пропущенных данных:\n", "- None - представление пустых данных в Python\n", "- NaN - представление пустых данных в Pandas\n", "- '' - пустая строка" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Price 0\n", "Levy 5819\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\n", "\n", "Price False\n", "Levy True\n", "Manufacturer False\n", "Model False\n", "Prod_year False\n", "Category False\n", "Leather_interior False\n", "Fuel type False\n", "Engine volume False\n", "Mileage False\n", "Cylinders False\n", "Gear box type False\n", "Drive wheels False\n", "Doors False\n", "Wheel False\n", "Color False\n", "Airbags False\n", "dtype: bool\n", "\n", "Levy процент пустых значений: %30.25\n" ] } ], "source": [ "# Количество пустых значений признаков\n", "print(df.isnull().sum())\n", "\n", "print()\n", "\n", "# Есть ли пустые значения признаков\n", "print(df.isnull().any())\n", "\n", "print()\n", "\n", "# Процент пустых значений признаков\n", "for i in df.columns:\n", " null_rate = df[i].isnull().sum() / len(df) * 100\n", " if null_rate > 0:\n", " print(f\"{i} процент пустых значений: %{null_rate:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Заполнение пропущенных данных\n", "\n", "https://pythonmldaily.com/posts/pandas-dataframes-search-drop-empty-values\n", "\n", "https://scales.arabpsychology.com/stats/how-to-fill-nan-values-with-median-in-pandas/" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(19237, 17)\n", "Price False\n", "Levy False\n", "Manufacturer False\n", "Model False\n", "Prod_year False\n", "Category False\n", "Leather_interior False\n", "Fuel type False\n", "Engine volume False\n", "Mileage False\n", "Cylinders False\n", "Gear box type False\n", "Drive wheels False\n", "Doors False\n", "Wheel False\n", "Color False\n", "Airbags False\n", "dtype: bool\n" ] }, { "data": { "text/html": [ "
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PriceLevyManufacturerModelProd_yearCategoryLeather_interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsLevyFillNALevyFillMedian
ID
457983558467NoneMERCEDES-BENZCLK 2001999Coupe1CNG2.0 Turbo300000 km4.0ManualRear02-MarLeft wheelSilver50642.0
4577885615681831HYUNDAISonata2011Sedan1Petrol2.4161600 km4.0TiptronicFront04-MayLeft wheelRed8831831
4580499726108836HYUNDAITucson2010Jeep1Diesel2116365 km4.0AutomaticFront04-MayLeft wheelGrey4836836
4579352653311288CHEVROLETCaptiva2007Jeep1Diesel251258 km4.0AutomaticFront04-MayLeft wheelBlack412881288
45813273470753HYUNDAISonata2012Sedan1Hybrid2.4186923 km4.0AutomaticFront04-MayLeft wheelWhite12753753
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" ], "text/plain": [ " Price Levy Manufacturer Model Prod_year Category \\\n", "ID \n", "45798355 8467 None MERCEDES-BENZ CLK 200 1999 Coupe \n", "45778856 15681 831 HYUNDAI Sonata 2011 Sedan \n", "45804997 26108 836 HYUNDAI Tucson 2010 Jeep \n", "45793526 5331 1288 CHEVROLET Captiva 2007 Jeep \n", "45813273 470 753 HYUNDAI Sonata 2012 Sedan \n", "\n", " Leather_interior Fuel type Engine volume Mileage Cylinders \\\n", "ID \n", "45798355 1 CNG 2.0 Turbo 300000 km 4.0 \n", "45778856 1 Petrol 2.4 161600 km 4.0 \n", "45804997 1 Diesel 2 116365 km 4.0 \n", "45793526 1 Diesel 2 51258 km 4.0 \n", "45813273 1 Hybrid 2.4 186923 km 4.0 \n", "\n", " Gear box type Drive wheels Doors Wheel Color Airbags \\\n", "ID \n", "45798355 Manual Rear 02-Mar Left wheel Silver 5 \n", "45778856 Tiptronic Front 04-May Left wheel Red 8 \n", "45804997 Automatic Front 04-May Left wheel Grey 4 \n", "45793526 Automatic Front 04-May Left wheel Black 4 \n", "45813273 Automatic Front 04-May Left wheel White 12 \n", "\n", " LevyFillNA LevyFillMedian \n", "ID \n", "45798355 0 642.0 \n", "45778856 831 831 \n", "45804997 836 836 \n", "45793526 1288 1288 \n", "45813273 753 753 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fillna_df = df.fillna(0)\n", "\n", "print(fillna_df.shape)\n", "\n", "print(fillna_df.isnull().any())\n", "\n", "# Замена пустых данных на 0\n", "df[\"LevyFillNA\"] = df[\"Levy\"].fillna(0)\n", "\n", "# Замена пустых данных на медиану\n", "df[\"LevyFillMedian\"] = df[\"Levy\"].fillna(df[\"LevyFillNA\"].median())\n", "\n", "df.tail()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PriceLevyManufacturerModelProd. yearCategoryLeather_interiorFuel typeEngine volumeMileageCylindersGear box typeDrive wheelsDoorsWheelColorAirbagsLevyFillNALevyFillMedianLevyCopy
ID
457983558467NoneMERCEDES-BENZCLK 2001999Coupe1CNG2.0 Turbo300000 km4.0ManualRear02-MarLeft wheelSilver50642.00
4577885615681831HYUNDAISonata2011Sedan1Petrol2.4161600 km4.0TiptronicFront04-MayLeft wheelRed8831831831
4580499726108836HYUNDAITucson2010Jeep1Diesel2116365 km4.0AutomaticFront04-MayLeft wheelGrey4836836836
4579352653311288CHEVROLETCaptiva2007Jeep1Diesel251258 km4.0AutomaticFront04-MayLeft wheelBlack4128812881288
45813273470753HYUNDAISonata2012Sedan1Hybrid2.4186923 km4.0AutomaticFront04-MayLeft wheelWhite12753753753
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" ], "text/plain": [ " Price Levy Manufacturer Model Prod. year Category \\\n", "ID \n", "45798355 8467 None MERCEDES-BENZ CLK 200 1999 Coupe \n", "45778856 15681 831 HYUNDAI Sonata 2011 Sedan \n", "45804997 26108 836 HYUNDAI Tucson 2010 Jeep \n", "45793526 5331 1288 CHEVROLET Captiva 2007 Jeep \n", "45813273 470 753 HYUNDAI Sonata 2012 Sedan \n", "\n", " Leather_interior Fuel type Engine volume Mileage Cylinders \\\n", "ID \n", "45798355 1 CNG 2.0 Turbo 300000 km 4.0 \n", "45778856 1 Petrol 2.4 161600 km 4.0 \n", "45804997 1 Diesel 2 116365 km 4.0 \n", "45793526 1 Diesel 2 51258 km 4.0 \n", "45813273 1 Hybrid 2.4 186923 km 4.0 \n", "\n", " Gear box type Drive wheels Doors Wheel Color Airbags \\\n", "ID \n", "45798355 Manual Rear 02-Mar Left wheel Silver 5 \n", "45778856 Tiptronic Front 04-May Left wheel Red 8 \n", "45804997 Automatic Front 04-May Left wheel Grey 4 \n", "45793526 Automatic Front 04-May Left wheel Black 4 \n", "45813273 Automatic Front 04-May Left wheel White 12 \n", "\n", " LevyFillNA LevyFillMedian LevyCopy \n", "ID \n", "45798355 0 642.0 0 \n", "45778856 831 831 831 \n", "45804997 836 836 836 \n", "45793526 1288 1288 1288 \n", "45813273 753 753 753 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"LevyCopy\"] = df[\"Levy\"]\n", "\n", "# Замена данных сразу в DataFrame без копирования\n", "df.fillna({\"LevyCopy\": 0}, inplace=True)\n", "\n", "df.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Удаление наблюдений с пропусками" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(13418, 20)\n", "Price False\n", "Levy False\n", "Manufacturer False\n", "Model False\n", "Prod. year False\n", "Category False\n", "Leather_interior False\n", "Fuel type False\n", "Engine volume False\n", "Mileage False\n", "Cylinders False\n", "Gear box type False\n", "Drive wheels False\n", "Doors False\n", "Wheel False\n", "Color False\n", "Airbags False\n", "dtype: bool\n" ] } ], "source": [ "dropna_df = df.dropna()\n", "\n", "print(dropna_df.shape)\n", "\n", "print(fillna_df.isnull().any())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Создание выборок данных\n", "\n", "Библиотека scikit-learn\n", "\n", "https://scikit-learn.org/stable/index.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Функция для создания выборок\n", "from sklearn.model_selection import train_test_split\n", "\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", " \"\"\"\n", " Splits a Pandas dataframe into three subsets (train, val, and test)\n", " following fractional ratios provided by the user, where each subset is\n", " stratified by the values in a specific column (that is, each subset has\n", " the same relative frequency of the values in the column). It performs this\n", " splitting by running train_test_split() twice.\n", "\n", " Parameters\n", " ----------\n", " df_input : Pandas dataframe\n", " Input dataframe to be split.\n", " stratify_colname : str\n", " The name of the column that will be used for stratification. Usually\n", " this column would be for the label.\n", " frac_train : float\n", " frac_val : float\n", " frac_test : float\n", " The ratios with which the dataframe will be split into train, val, and\n", " test data. The values should be expressed as float fractions and should\n", " sum to 1.0.\n", " random_state : int, None, or RandomStateInstance\n", " Value to be passed to train_test_split().\n", "\n", " Returns\n", " -------\n", " df_train, df_val, df_test :\n", " Dataframes containing the three splits.\n", " \"\"\"\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 # Contains all columns.\n", " y = df_input[\n", " [stratify_colname]\n", " ] # Dataframe of just the column on which to stratify.\n", "\n", " # Split original dataframe into train and temp dataframes.\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", " # Split the temp dataframe into val and test dataframes.\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" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Leather_interior\n", "1 13954\n", "0 5283\n", "Name: count, dtype: int64\n", "Обучающая выборка: (11542, 3)\n", "Leather_interior\n", "1 8372\n", "0 3170\n", "Name: count, dtype: int64\n", "Контрольная выборка: (3847, 3)\n", "Leather_interior\n", "1 2791\n", "0 1056\n", "Name: count, dtype: int64\n", "Тестовая выборка: (3848, 3)\n", "Leather_interior\n", "1 2791\n", "0 1057\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# Вывод распределения количества наблюдений по меткам (классам)\n", "print(df.Leather_interior.value_counts())\n", "\n", "data = df[[\"Leather_interior\", \"Price\", \"Prod_year\"]].copy()\n", "\n", "df_train, df_val, df_test = split_stratified_into_train_val_test(\n", " data,\n", " stratify_colname=\"Leather_interior\",\n", " frac_train=0.60,\n", " frac_val=0.20,\n", " frac_test=0.20,\n", ")\n", "\n", "print(\"Обучающая выборка: \", df_train.shape)\n", "print(df_train.Leather_interior.value_counts())\n", "\n", "print(\"Контрольная выборка: \", df_val.shape)\n", "print(df_val.Leather_interior.value_counts())\n", "\n", "print(\"Тестовая выборка: \", df_test.shape)\n", "print(df_test.Leather_interior.value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Выборка с избытком (oversampling)\n", "\n", "https://www.blog.trainindata.com/oversampling-techniques-for-imbalanced-data/\n", "\n", "https://datacrayon.com/machine-learning/class-imbalance-and-oversampling/\n", "\n", "Выборка с недостатком (undersampling)\n", "\n", "https://machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/\n", "\n", "Библиотека imbalanced-learn\n", "\n", "https://imbalanced-learn.org/stable/" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Обучающая выборка: (11542, 3)\n", "Leather_interior\n", "1 8372\n", "0 3170\n", "Name: count, dtype: int64\n", "Обучающая выборка после oversampling: (16453, 3)\n", "Leather_interior\n", "1 8372\n", "0 8081\n", "Name: count, dtype: int64\n" ] }, { "data": { "text/html": [ "
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Leather_interiorPriceProd_year
01226212011
11358502006
21203852012
3145472009
41194162013
............
164480313612015
164490313612015
164500313612015
164510111722009
164520111332006
\n", "

16453 rows × 3 columns

\n", "
" ], "text/plain": [ " Leather_interior Price Prod_year\n", "0 1 22621 2011\n", "1 1 35850 2006\n", "2 1 20385 2012\n", "3 1 4547 2009\n", "4 1 19416 2013\n", "... ... ... ...\n", "16448 0 31361 2015\n", "16449 0 31361 2015\n", "16450 0 31361 2015\n", "16451 0 11172 2009\n", "16452 0 11133 2006\n", "\n", "[16453 rows x 3 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from imblearn.over_sampling import ADASYN\n", "\n", "ada = ADASYN()\n", "\n", "print(\"Обучающая выборка: \", df_train.shape)\n", "print(df_train.Leather_interior.value_counts())\n", "\n", "X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"Leather_interior\"]) # type: ignore\n", "df_train_adasyn = pd.DataFrame(X_resampled)\n", "\n", "print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n", "print(df_train_adasyn.Leather_interior.value_counts())\n", "\n", "df_train_adasyn" ] } ], "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 }