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lab 2 is done

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Табеев Александр 2024-11-01 20:30:19 +04:00
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README.md
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# AIM_PIbd-31_Tabeev_A.P
# Табеев Александр Павлович
# Вариант 3
# https://clck.yandex.ru/redir/nWO_r1F33ck?data=NnBZTWRhdFZKOHRaTENSMFc4S0VQUGMtSXk0bDRzNnVpakFkYjNNRE5ZNFRuVk4yRGpQaHlFSHNPNVpscDY1RGxPdmF0UFlUU3V4cmpoNDBvcE5vQVAxUzRxUzFpU1YzejluV1ozdUpoS1ZMeWdjcHktYS1IT2diWFhTLWpZcVhkdzF4a25GUGRYZGtSQ3RsclBRV1RiTWJsdFlyNVFIV1MyVHp0NDJTY21Z&b64e=2&sign=0e97a68a5fb67b83ca9ea592b182bbae&keyno=17

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lab_1/lab1.ipynb
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},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [
{

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lab_2/lab2.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузка датасетов и вывод информации"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8036 entries, 0 to 8035\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Date 8036 non-null object \n",
" 1 Open 8036 non-null float64\n",
" 2 High 8036 non-null float64\n",
" 3 Low 8036 non-null float64\n",
" 4 Close 8036 non-null float64\n",
" 5 Adj Close 8036 non-null float64\n",
" 6 Volume 8036 non-null int64 \n",
"dtypes: float64(5), int64(1), object(1)\n",
"memory usage: 439.6+ KB\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 896 entries, 0 to 895\n",
"Data columns (total 5 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 Store ID 896 non-null int64\n",
" 1 Store_Area 896 non-null int64\n",
" 2 Items_Available 896 non-null int64\n",
" 3 Daily_Customer_Count 896 non-null int64\n",
" 4 Store_Sales 896 non-null int64\n",
"dtypes: int64(5)\n",
"memory usage: 35.1 KB\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 8 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 gender 1000 non-null object\n",
" 1 race/ethnicity 1000 non-null object\n",
" 2 parental level of education 1000 non-null object\n",
" 3 lunch 1000 non-null object\n",
" 4 test preparation course 1000 non-null object\n",
" 5 math score 1000 non-null int64 \n",
" 6 reading score 1000 non-null int64 \n",
" 7 writing score 1000 non-null int64 \n",
"dtypes: int64(3), object(5)\n",
"memory usage: 62.6+ KB\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"datasets_lab2/coffee.csv\")\n",
"df2 = pd.read_csv(\"datasets_lab2/Stores.csv\")\n",
"df3 = pd.read_csv(\"datasets_lab2/StudentsPerformance.csv\")\n",
"df.info()\n",
"df2.info()\n",
"df3.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Проблемная область"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Первый датасет coffee.csv позволяет спрогнозировать будущие показатели акций кофейни Starbucks. Второй датасет Stores.csv - магазины. Третий датасет StudentsPerformance.csv - успеваемость студентов на экзамене"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Анализ набора данных"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Объекты: \n",
"1. Акции\n",
"2. Магазины\n",
"3. Успеваемость студентов\n",
"Атрибуты: \n",
"1. Дата; начальная цена за день; максимальная цена; минимальная цена; цена на момент закрытия продаж; скорректированая цена на момент закрытия; объем торговли акций за день.\n",
"2. Идентификатор магазина; физическая площадь; кол-во доступных товаров; количество покупателей, посетивших магазины в среднем за месяц; продажив магазинах (в долларах США).\n",
"3. Пол, раса/этническая принадлежность, уровень образования родителей, обед, курс подготовки к тестированию, оценка по математике, оценка по чтению, оценка по письму."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Бизнес цели"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Анализ показателей акций Starbucks — прогнозирование будущей стоимости акций для улучшения финансовых решений, предотвращения резких убытков и повышения прибыли.\n",
"2. Оценка эффективности магазинов — понимание факторов, влияющих на успешность магазинов, включая выручку и посещаемость, что поможет принимать решения о возможном расширении сети или оптимизации текущей стратегии размещения.\n",
"3. Анализ образовательной успеваемости студентов — выявление факторов, влияющих на академические успехи для разработки программ поддержки студентов."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Примеры целей технического проекта. Что поступает на вход, что является целевым признаком."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"На входе будут переданы следующие датасеты, целевые признаки для каждого:\n",
"1. coffee.csv — максимальная цена акций за день.\n",
"2. Stores.csv — выручка магазина.\n",
"3. StudentsPerformance.csv — общий средний балл (среднее значение по математике, чтению и письму)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Проблемы набора данных и их решения"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Устаревшие данные: Важно проверять актуальность данных, особенно для финансовых временных рядов и данных о магазинах. Для этого старые записи могут быть удалены или обновлены, если доступны более свежие данные.\n",
"2. Выбросы: Необходимо выявить аномалии, такие как резкие изменения в ценах акций или посещаемости, и принять решение об их удалении или сглаживании, исходя из размера выборки."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Качество набора данных"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Наборы данных содержат достаточно примеров и признаков для обучения модели. Учтены различные ситуации проблемной области. Данные соответствуют данным, которые будут подаваться в производственной среде. Все метки согласованы."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Поиск аномалий"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Open High Low Close Adj Close \\\n",
"count 8036.000000 8036.000000 8036.000000 8036.000000 8036.000000 \n",
"mean 30.054280 30.351487 29.751322 30.058857 26.674025 \n",
"std 33.615577 33.906613 33.314569 33.615911 31.728090 \n",
"min 0.328125 0.347656 0.320313 0.335938 0.260703 \n",
"25% 4.392031 4.531250 4.304922 4.399610 3.414300 \n",
"50% 13.325000 13.493750 13.150000 13.330000 10.352452 \n",
"75% 55.250000 55.722501 54.852499 55.267499 47.464829 \n",
"max 126.080002 126.320000 124.809998 126.059998 118.010414 \n",
"\n",
" Volume \n",
"count 8.036000e+03 \n",
"mean 1.470459e+07 \n",
"std 1.340021e+07 \n",
"min 1.504000e+06 \n",
"25% 7.817750e+06 \n",
"50% 1.169815e+07 \n",
"75% 1.778795e+07 \n",
"max 5.855088e+08 \n",
" Store ID Store_Area Items_Available Daily_Customer_Count \\\n",
"count 896.000000 896.000000 896.000000 896.000000 \n",
"mean 448.500000 1485.409598 1782.035714 786.350446 \n",
"std 258.797218 250.237011 299.872053 265.389281 \n",
"min 1.000000 775.000000 932.000000 10.000000 \n",
"25% 224.750000 1316.750000 1575.500000 600.000000 \n",
"50% 448.500000 1477.000000 1773.500000 780.000000 \n",
"75% 672.250000 1653.500000 1982.750000 970.000000 \n",
"max 896.000000 2229.000000 2667.000000 1560.000000 \n",
"\n",
" Store_Sales \n",
"count 896.000000 \n",
"mean 59351.305804 \n",
"std 17190.741895 \n",
"min 14920.000000 \n",
"25% 46530.000000 \n",
"50% 58605.000000 \n",
"75% 71872.500000 \n",
"max 116320.000000 \n",
" math score reading score writing score\n",
"count 1000.00000 1000.000000 1000.000000\n",
"mean 66.08900 69.169000 68.054000\n",
"std 15.16308 14.600192 15.195657\n",
"min 0.00000 17.000000 10.000000\n",
"25% 57.00000 59.000000 57.750000\n",
"50% 66.00000 70.000000 69.000000\n",
"75% 77.00000 79.000000 79.000000\n",
"max 100.00000 100.000000 100.000000\n"
]
}
],
"source": [
"print(df.describe())\n",
"print(df2.describe())\n",
"print(df3.describe())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"При просмотре вывода не было замечено аномалий в столбцах датасетов."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Проблема пропущенных данных"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Первый датасет coffee.csv\n",
"Второй датасет Stores.csv\n",
"Третий датасет StudentsPerformance.csv\n"
]
}
],
"source": [
"print(\"Первый датасет coffee.csv\")\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}\")\n",
"print(\"Второй датасет Stores.csv\")\n",
"for i in df2.columns:\n",
" null_rate = df2[i].isnull().sum() / len(df2)*100\n",
" if null_rate > 0:\n",
" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")\n",
"print(\"Третий датасет StudentsPerformance.csv\")\n",
"for i in df3.columns:\n",
" null_rate = df3[i].isnull().sum() / len(df3)*100\n",
" if null_rate > 0:\n",
" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Во всех трех датасетах пустых значений не найдено, по наполненности датасеты удовлетворительны. Но можно добавить побольше столбцов в датасет магазинов."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В первом датасете coffee.csv удалим строки, где год ниже 2000."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Date Open High Low Close Adj Close \\\n",
"1899 2000-01-03 2.984375 3.085938 2.906250 3.082031 2.391797 \n",
"1900 2000-01-04 3.007813 3.109375 2.968750 2.984375 2.316012 \n",
"1901 2000-01-05 2.992188 3.078125 2.960938 3.023438 2.346326 \n",
"1902 2000-01-06 3.000000 3.203125 3.000000 3.132813 2.431207 \n",
"1903 2000-01-07 3.093750 3.125000 3.031250 3.117188 2.419082 \n",
"... ... ... ... ... ... ... \n",
"8031 2024-05-17 75.269997 78.000000 74.919998 77.849998 77.849998 \n",
"8032 2024-05-20 77.680000 78.320000 76.709999 77.540001 77.540001 \n",
"8033 2024-05-21 77.559998 78.220001 77.500000 77.720001 77.720001 \n",
"8034 2024-05-22 77.699997 81.019997 77.440002 80.720001 80.720001 \n",
"8035 2024-05-23 80.099998 80.699997 79.169998 79.260002 79.260002 \n",
"\n",
" Volume \n",
"1899 24232000 \n",
"1900 21564800 \n",
"1901 28206400 \n",
"1902 30825600 \n",
"1903 26044800 \n",
"... ... \n",
"8031 14436500 \n",
"8032 11183800 \n",
"8033 8916600 \n",
"8034 22063400 \n",
"8035 4651418 \n",
"\n",
"[6137 rows x 7 columns]\n"
]
}
],
"source": [
"df_filtered = df[df['Date'] > '2000-01-01']\n",
"print(df_filtered)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Во втором датасете Stores.csv всем магазинам поставим среднее значение площади."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Store ID Store_Area Items_Available Daily_Customer_Count Store_Sales\n",
"0 1 1500.0 1961 530 66490\n",
"1 2 1500.0 1752 210 39820\n",
"2 3 1500.0 1609 720 54010\n",
"3 4 1500.0 1748 620 53730\n",
"4 5 1500.0 2111 450 46620\n",
".. ... ... ... ... ...\n",
"891 892 1500.0 1910 1080 66390\n",
"892 893 1500.0 1663 850 82080\n",
"893 894 1500.0 1436 1060 76440\n",
"894 895 1500.0 1560 770 96610\n",
"895 896 1500.0 1429 1110 54340\n",
"\n",
"[896 rows x 5 columns]\n"
]
}
],
"source": [
"store_area_mean = df2['Store_Area'].mean()\n",
"df2['Store_Area'] = store_area_mean\n",
"print(df2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В третьем датасете StudentsPerformance.csv всем студентам сделаем ланч стандартным."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" gender race/ethnicity parental level of education lunch \\\n",
"0 female group B bachelor's degree standard \n",
"1 female group C some college standard \n",
"2 female group B master's degree standard \n",
"3 male group A associate's degree standard \n",
"4 male group C some college standard \n",
".. ... ... ... ... \n",
"995 female group E master's degree standard \n",
"996 male group C high school standard \n",
"997 female group C high school standard \n",
"998 female group D some college standard \n",
"999 female group D some college standard \n",
"\n",
" test preparation course math score reading score writing score \n",
"0 none 72 72 74 \n",
"1 completed 69 90 88 \n",
"2 none 90 95 93 \n",
"3 none 47 57 44 \n",
"4 none 76 78 75 \n",
".. ... ... ... ... \n",
"995 completed 88 99 95 \n",
"996 none 62 55 55 \n",
"997 completed 59 71 65 \n",
"998 completed 68 78 77 \n",
"999 none 77 86 86 \n",
"\n",
"[1000 rows x 8 columns]\n"
]
}
],
"source": [
"df3['lunch'] = 'standard'\n",
"print(df3)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"def split_stratified_into_train_val_test(\n",
" df_input,\n",
" stratify_colname=\"y\",\n",
" frac_train=0.6,\n",
" frac_val=0.15,\n",
" frac_test=0.25,\n",
" random_state=None,\n",
"):\n",
" if frac_train + frac_val + frac_test != 1.0:\n",
" raise ValueError(\n",
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
" % (frac_train, frac_val, frac_test)\n",
" )\n",
"\n",
" if stratify_colname not in df_input.columns:\n",
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
"\n",
" X = df_input # 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": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train df: (4821, 7), Validation df: (1607, 7), Test df: (1608, 7)\n",
"Train df2: (537, 5), Validation df2: (179, 5), Test df2: (180, 5)\n",
"Train df3: (600, 8), Validation df3: (200, 8), Test df3: (200, 8)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"train_df, temp_df = train_test_split(df, test_size=0.4, random_state=42)\n",
"val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42) \n",
"\n",
"train_df2, temp_df2 = train_test_split(df2, test_size=0.4, random_state=42)\n",
"val_df2, test_df2 = train_test_split(temp_df2, test_size=0.5, random_state=42)\n",
"\n",
"train_df3, temp_df3 = train_test_split(df3, test_size=0.4, random_state=42)\n",
"val_df3, test_df3 = train_test_split(temp_df3, test_size=0.5, random_state=42)\n",
"print(f\"Train df: {train_df.shape}, Validation df: {val_df.shape}, Test df: {test_df.shape}\")\n",
"print(f\"Train df2: {train_df2.shape}, Validation df2: {val_df2.shape}, Test df2: {test_df2.shape}\")\n",
"print(f\"Train df3: {train_df3.shape}, Validation df3: {val_df3.shape}, Test df3: {test_df3.shape}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Было сделано разбиение на три выборки: 60%, 20% и 20% при помощи библиотеки scikit-learn и функции train_test_split. На взгляд сбалансированные"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Приращение методами выборки с избытком (oversampling) и выборки с недостатком (undersampling)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Выборка после oversampling (df):\n",
"High_category\n",
"Low 4835\n",
"Medium 4835\n",
"High 4835\n",
"Luxury 4835\n",
"Name: count, dtype: int64\n",
"Выборка после undersampling (df):\n",
"High_category\n",
"Low 321\n",
"Medium 321\n",
"High 321\n",
"Luxury 321\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"from imblearn.over_sampling import RandomOverSampler\n",
"from imblearn.under_sampling import RandomUnderSampler\n",
"\n",
"df = pd.read_csv(\"datasets_lab2/coffee.csv\")\n",
"df['High_category'] = pd.cut(df['High'], bins=[0.1, 20, 60, 105, float('inf')],\n",
" labels=['Low', 'Medium', 'High', 'Luxury'])\n",
"\n",
"y = df['High_category']\n",
"X = df.drop(columns=['High', 'High_category'])\n",
"\n",
"oversampler = RandomOverSampler(random_state=42)\n",
"X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
"\n",
"undersampler = RandomUnderSampler(random_state=42)\n",
"X_resampled_under, y_resampled_under = undersampler.fit_resample(X, y)\n",
"\n",
"print(\"Выборка после oversampling (df):\")\n",
"print(pd.Series(y_resampled).value_counts())\n",
"\n",
"print(\"Выборка после undersampling (df):\")\n",
"print(pd.Series(y_resampled_under).value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Выборка после oversampling (df2):\n",
"Sales_category\n",
"Low 598\n",
"Medium 598\n",
"High 598\n",
"Luxury 0\n",
"Name: count, dtype: int64\n",
"Выборка после undersampling (df2):\n",
"Sales_category\n",
"Low 7\n",
"Medium 7\n",
"High 7\n",
"Luxury 0\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"df2 = pd.read_csv(\"datasets_lab2/Stores.csv\")\n",
"\n",
"df2['Sales_category'] = pd.cut(df2['Store_Sales'], bins=[0, 50000, 100000, 200000, float('inf')],\n",
" labels=['Low', 'Medium', 'High', 'Luxury'])\n",
"\n",
"y2 = df2['Sales_category']\n",
"X2 = df2.drop(columns=['Store_Sales', 'Sales_category'])\n",
"\n",
"oversampler2 = RandomOverSampler(random_state=42)\n",
"X_resampled_2, y_resampled_2 = oversampler2.fit_resample(X2, y2)\n",
"\n",
"undersampler2 = RandomUnderSampler(random_state=42)\n",
"X_resampled_2_under, y_resampled_2_under = undersampler2.fit_resample(X2, y2)\n",
"\n",
"print(\"Выборка после oversampling (df2):\")\n",
"print(pd.Series(y_resampled_2).value_counts())\n",
"\n",
"print(\"Выборка после undersampling (df2):\")\n",
"print(pd.Series(y_resampled_2_under).value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Выборка после oversampling (df3):\n",
"reading_score_category\n",
"Low 903\n",
"Medium 903\n",
"High 903\n",
"Luxury 0\n",
"Name: count, dtype: int64\n",
"Выборка после undersampling (df3):\n",
"reading_score_category\n",
"Low 1\n",
"Medium 1\n",
"High 1\n",
"Luxury 0\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"df3 = pd.read_csv(\"datasets_lab2/StudentsPerformance.csv\")\n",
"\n",
"df3['reading_score_category'] = pd.cut(df3['reading score'], bins=[0, 20, 50, 100, float('inf')],\n",
" labels=['Low', 'Medium', 'High', 'Luxury'])\n",
"\n",
"y3 = df3['reading_score_category']\n",
"X3 = df3.drop(columns=['reading score', 'reading_score_category'])\n",
"\n",
"oversampler3 = RandomOverSampler(random_state=42)\n",
"X_resampled_3, y_resampled_3 = oversampler3.fit_resample(X3, y3)\n",
"\n",
"undersampler3 = RandomUnderSampler(random_state=42)\n",
"X_resampled_3_under, y_resampled_3_under = undersampler3.fit_resample(X3, y3)\n",
"\n",
"print(\"Выборка после oversampling (df3):\")\n",
"print(pd.Series(y_resampled_3).value_counts())\n",
"\n",
"print(\"Выборка после undersampling (df3):\")\n",
"print(pd.Series(y_resampled_3_under).value_counts())"
]
}
],
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