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lab3
PIbd-33_Dyakonov_R_R_MAI/lab3.ipynb

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2024-11-08 17:42:04 +04:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Бизнес цели:\n",
"1. Оптимизация ценовой стратегии: анализ факторов, влияющих на стоимость недвижимости, чтобы помочь продавцам устанавливать конкурентоспособные цены и увеличивать прибыль.\n",
"2. Улучшение инвестиционных решений: предоставление аналитики для инвесторов, чтобы они могли определить наиболее выгодные районы и типы недвижимости для вложений."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Цели технического проекта:\n",
"1. Создание модели машинного обучения для прогнозирования стоимости недвижимости на основе таких характеристик, как площадь дома, количество спален и ванных комнат, расположение, возраст дома, наличие бассейна и других факторов.\n",
"2. Разработка системы, которая анализирует волатильность цен (показатель изменчивости цены актива за определённый период времени) на недвижимость в разных районах, учитывая исторические данные о продажах, сезонные колебания и демографические изменения, чтобы выявить наиболее стабильные и перспективные зоны для инвестиций."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id date price bedrooms bathrooms \\\n",
"0 7129300520 20141013T000000 221900.0 3 1.00 \n",
"1 6414100192 20141209T000000 538000.0 3 2.25 \n",
"2 5631500400 20150225T000000 180000.0 2 1.00 \n",
"3 2487200875 20141209T000000 604000.0 4 3.00 \n",
"4 1954400510 20150218T000000 510000.0 3 2.00 \n",
"... ... ... ... ... ... \n",
"21608 263000018 20140521T000000 360000.0 3 2.50 \n",
"21609 6600060120 20150223T000000 400000.0 4 2.50 \n",
"21610 1523300141 20140623T000000 402101.0 2 0.75 \n",
"21611 291310100 20150116T000000 400000.0 3 2.50 \n",
"21612 1523300157 20141015T000000 325000.0 2 0.75 \n",
"\n",
" sqft_living sqft_lot floors waterfront view ... grade \\\n",
"0 1180 5650 1.0 0 0 ... 7 \n",
"1 2570 7242 2.0 0 0 ... 7 \n",
"2 770 10000 1.0 0 0 ... 6 \n",
"3 1960 5000 1.0 0 0 ... 7 \n",
"4 1680 8080 1.0 0 0 ... 8 \n",
"... ... ... ... ... ... ... ... \n",
"21608 1530 1131 3.0 0 0 ... 8 \n",
"21609 2310 5813 2.0 0 0 ... 8 \n",
"21610 1020 1350 2.0 0 0 ... 7 \n",
"21611 1600 2388 2.0 0 0 ... 8 \n",
"21612 1020 1076 2.0 0 0 ... 7 \n",
"\n",
" sqft_above sqft_basement yr_built yr_renovated zipcode lat \\\n",
"0 1180 0 1955 0 98178 47.5112 \n",
"1 2170 400 1951 1991 98125 47.7210 \n",
"2 770 0 1933 0 98028 47.7379 \n",
"3 1050 910 1965 0 98136 47.5208 \n",
"4 1680 0 1987 0 98074 47.6168 \n",
"... ... ... ... ... ... ... \n",
"21608 1530 0 2009 0 98103 47.6993 \n",
"21609 2310 0 2014 0 98146 47.5107 \n",
"21610 1020 0 2009 0 98144 47.5944 \n",
"21611 1600 0 2004 0 98027 47.5345 \n",
"21612 1020 0 2008 0 98144 47.5941 \n",
"\n",
" long sqft_living15 sqft_lot15 \n",
"0 -122.257 1340 5650 \n",
"1 -122.319 1690 7639 \n",
"2 -122.233 2720 8062 \n",
"3 -122.393 1360 5000 \n",
"4 -122.045 1800 7503 \n",
"... ... ... ... \n",
"21608 -122.346 1530 1509 \n",
"21609 -122.362 1830 7200 \n",
"21610 -122.299 1020 2007 \n",
"21611 -122.069 1410 1287 \n",
"21612 -122.299 1020 1357 \n",
"\n",
"[21613 rows x 21 columns]\n",
"0 16356\n",
"1 16413\n",
"2 16491\n",
"3 16413\n",
"4 16484\n",
" ... \n",
"21608 16211\n",
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"21611 16451\n",
"21612 16358\n",
"Name: date_numeric, Length: 21613, dtype: int64\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from imblearn.under_sampling import RandomUnderSampler\n",
"\n",
"df = pd.read_csv(\"data/kc_house_data.csv\")\n",
"print(df)\n",
"\n",
"# Преобразование даты продажи в числовой формат (кол-во дней с 01.01.1970)\n",
"df['date'] = pd.to_datetime(df['date'])\n",
"df['date_numeric'] = (df['date'] - pd.Timestamp('1970-01-01')).dt.days\n",
"print(df['date_numeric'])\n"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
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"[21613 rows x 41 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"encoder = OneHotEncoder(sparse_output=False, drop=\"first\")\n",
"\n",
"encoded_values = encoder.fit_transform(df[[\"bathrooms\", \"bedrooms\"]])\n",
"\n",
"encoded_columns = encoder.get_feature_names_out([\"bathrooms\", \"bedrooms\"])\n",
"\n",
"encoded_values_df = pd.DataFrame(encoded_values, columns=encoded_columns)\n",
"\n",
"encoded_values_df"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
" 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
" 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
" 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'date_numeric',\n",
" 'price_binned'],\n",
" dtype='object')\n",
"Обучающая выборка: (12967, 23)\n",
"price\n",
"550000.0 105\n",
"350000.0 104\n",
"450000.0 99\n",
"400000.0 96\n",
"325000.0 94\n",
" ... \n",
"887250.0 1\n",
"123000.0 1\n",
"638700.0 1\n",
"502500.0 1\n",
"793000.0 1\n",
"Name: count, Length: 2982, dtype: int64\n",
"Валидационная выборка: (4322, 23)\n",
"price\n",
"350000.0 39\n",
"450000.0 37\n",
"425000.0 34\n",
"325000.0 33\n",
"575000.0 33\n",
" ..\n",
"500007.0 1\n",
"129888.0 1\n",
"546800.0 1\n",
"378750.0 1\n",
"436500.0 1\n",
"Name: count, Length: 1526, dtype: int64\n",
"Тестовая выборка: (4323, 23)\n",
"price\n",
"450000.0 36\n",
"375000.0 36\n",
"300000.0 34\n",
"500000.0 31\n",
"425000.0 30\n",
" ..\n",
"196700.0 1\n",
"482975.0 1\n",
"2238890.0 1\n",
"694000.0 1\n",
"1770000.0 1\n",
"Name: count, Length: 1565, dtype: int64\n",
"Обучающая выборка по категориям: price_binned\n",
"High 4377\n",
"Low 4335\n",
"Medium 4255\n",
"Name: count, dtype: int64\n",
"Валидационная выборка по категориям: price_binned\n",
"High 1459\n",
"Low 1445\n",
"Medium 1418\n",
"Name: count, dtype: int64\n",
"Тестовая выборка по категориям: price_binned\n",
"High 1460\n",
"Low 1445\n",
"Medium 1418\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Функция для разбиение на выборки\n",
"def split_stratified_into_train_val_test(\n",
" df_input,\n",
" stratify_colname=\"y\",\n",
" frac_train=0.6,\n",
" frac_val=0.2,\n",
" frac_test=0.2,\n",
" random_state=None,\n",
"):\n",
" if frac_train + frac_val + frac_test != 1.0:\n",
" raise ValueError(\n",
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
" % (frac_train, frac_val, frac_test)\n",
" )\n",
"\n",
" if stratify_colname not in df_input.columns:\n",
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
"\n",
" X = df_input\n",
" y = df_input[[stratify_colname]]\n",
"\n",
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
" )\n",
"\n",
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
" df_val, df_test, y_val, y_test = train_test_split(\n",
" df_temp,\n",
" y_temp,\n",
" stratify=y_temp,\n",
" test_size=relative_frac_test,\n",
" random_state=random_state,\n",
" )\n",
"\n",
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
"\n",
" return df_train, df_val, df_test\n",
"\n",
"# Определение бинов для цены\n",
"bins = [\n",
" df[\"price\"].min(),\n",
" df[\"price\"].quantile(0.33),\n",
" df[\"price\"].quantile(0.66),\n",
" df[\"price\"].max(),\n",
"]\n",
"labels = [\"Low\", \"Medium\", \"High\"]\n",
"df[\"price_binned\"] = pd.cut(df[\"price\"], bins=bins, labels=labels)\n",
"df = df.dropna()\n",
"\n",
"# Стратифицированное (для сохранения пропорций)\n",
"# разбиение на обучающую, валидационную и тестовую выборки\n",
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
" df, stratify_colname=\"price_binned\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
")\n",
"\n",
"print(df_train.columns)\n",
"\n",
"print(\"Обучающая выборка: \", df_train.shape)\n",
"print(df_train[\"price\"].value_counts())\n",
"\n",
"print(\"Валидационная выборка: \", df_val.shape)\n",
"print(df_val[\"price\"].value_counts())\n",
"\n",
"print(\"Тестовая выборка: \", df_test.shape)\n",
"print(df_test[\"price\"].value_counts())\n",
"\n",
"print(\"Обучающая выборка по категориям: \", df_train[\"price_binned\"].value_counts())\n",
"print(\"Валидационная выборка по категориям: \", df_val[\"price_binned\"].value_counts())\n",
"print(\"Тестовая выборка по категориям: \", df_test[\"price_binned\"].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обучающая выборка после undersampling: (12765, 23)\n",
"price_binned\n",
"Low 4255\n",
"Medium 4255\n",
"High 4255\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"rus = RandomUnderSampler(random_state=42)\n",
"X_resampled, y_resampled = rus.fit_resample(df_train, df_train[\"price_binned\"])\n",
"\n",
"# Создание датафрейма для результирующей выборки\n",
"df_train_rus = pd.DataFrame(X_resampled)\n",
"\n",
"# Добавление целевой переменной в новый датафрейм\n",
"df_train_rus[\"price_binned\"] = y_resampled\n",
"\n",
"print(\"Обучающая выборка после undersampling: \", df_train_rus.shape)\n",
"print(df_train_rus[\"price_binned\"].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id date price bedrooms bathrooms sqft_living \\\n",
"921 4046500320 2015-01-20 342000.0 3 1.75 1660 \n",
"16637 6802200190 2015-01-21 222500.0 3 2.00 1450 \n",
"20955 8138870530 2014-05-05 419190.0 2 2.50 1590 \n",
"14806 4370700065 2015-05-04 907500.0 3 2.25 2850 \n",
"20000 2767601311 2014-10-24 445000.0 3 2.50 1260 \n",
"... ... ... ... ... ... ... \n",
"2640 4045800030 2015-05-11 739000.0 3 2.25 2220 \n",
"16590 8635750950 2014-06-07 568500.0 4 2.50 2460 \n",
"20011 9396700028 2014-07-22 358000.0 2 2.50 1278 \n",
"9296 625049310 2015-03-11 587750.0 2 1.00 890 \n",
"15823 5088500170 2014-10-27 435000.0 3 2.50 2530 \n",
"\n",
" sqft_lot floors waterfront view ... zipcode lat long \\\n",
"921 16275 2.0 0 0 ... 98014 47.6903 -121.915 \n",
"16637 9044 2.0 0 0 ... 98022 47.1955 -121.987 \n",
"20955 1426 2.0 0 0 ... 98029 47.5441 -122.013 \n",
"14806 6281 2.0 0 2 ... 98115 47.6911 -122.326 \n",
"20000 1102 3.0 0 0 ... 98107 47.6750 -122.387 \n",
"... ... ... ... ... ... ... ... ... \n",
"2640 10530 1.0 0 0 ... 98052 47.6383 -122.098 \n",
"16590 4200 2.0 0 0 ... 98074 47.6041 -122.020 \n",
"20011 987 2.0 0 0 ... 98136 47.5532 -122.381 \n",
"9296 4730 1.0 0 0 ... 98103 47.6876 -122.341 \n",
"15823 16102 2.0 0 0 ... 98038 47.3710 -122.055 \n",
"\n",
" sqft_living15 sqft_lot15 date_numeric price_binned_Low \\\n",
"921 1520 16275 16455 True \n",
"16637 1450 9044 16456 True \n",
"20955 1590 1426 16195 False \n",
"14806 1680 7006 16559 False \n",
"20000 1320 2500 16367 False \n",
"... ... ... ... ... \n",
"2640 2500 10014 16566 False \n",
"16590 2460 4200 16228 False \n",
"20011 1220 1287 16273 True \n",
"9296 1330 5904 16505 False \n",
"15823 2370 14957 16370 False \n",
"\n",
" price_binned_Medium price_binned_High Area_binned \n",
"921 False False 1 \n",
"16637 False False 1 \n",
"20955 True False 1 \n",
"14806 False True 3 \n",
"20000 True False 0 \n",
"... ... ... ... \n",
"2640 False True 2 \n",
"16590 False True 2 \n",
"20011 False False 0 \n",
"9296 False True 0 \n",
"15823 True False 2 \n",
"\n",
"[12967 rows x 26 columns]\n"
]
}
],
"source": [
"# Преобразование категориального столбца price_binned в дамми-переменные\n",
"# Добавляем новые столбцы (Low, Medium, High) и выставляем 1 или 0 в зависмоитси от цены\n",
"df_train = pd.get_dummies(df_train, columns=[\"price_binned\"])\n",
"\n",
"# Binning площади в 4 категории на основе квартилей\n",
"# Новый столбец с со значением 0, если оно подходит первому квартилю (категории значений) и тд\n",
"df_train[\"Area_binned\"] = pd.qcut(df_train[\"sqft_living\"], q=4, labels=False)\n",
"\n",
"# Вывод обновленного датафрейма\n",
"print(df_train)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id date price bedrooms bathrooms sqft_living \\\n",
"921 4046500320 2015-01-20 -0.540558 3 1.75 -0.461829 \n",
"16637 6802200190 2015-01-21 -0.863431 3 2.00 -0.693145 \n",
"20955 8138870530 2014-05-05 -0.332001 2 2.50 -0.538934 \n",
"14806 4370700065 2015-05-04 0.987347 3 2.25 0.848963 \n",
"20000 2767601311 2014-10-24 -0.262266 3 2.50 -0.902431 \n",
"... ... ... ... ... ... ... \n",
"2640 4045800030 2015-05-11 0.532083 3 2.25 0.155014 \n",
"16590 8635750950 2014-06-07 0.071415 4 2.50 0.419376 \n",
"20011 9396700028 2014-07-22 -0.497328 2 2.50 -0.882604 \n",
"9296 625049310 2015-03-11 0.123426 2 1.00 -1.309988 \n",
"15823 5088500170 2014-10-27 -0.289284 3 2.50 0.496481 \n",
"\n",
" sqft_lot floors waterfront view ... lat long \\\n",
"921 0.051429 0.942938 -0.087265 0 ... 47.6903 -121.915 \n",
"16637 -0.157341 0.942938 -0.087265 0 ... 47.1955 -121.987 \n",
"20955 -0.377285 0.942938 -0.087265 0 ... 47.5441 -122.013 \n",
"14806 -0.237113 0.942938 -0.087265 2 ... 47.6911 -122.326 \n",
"20000 -0.386639 2.802570 -0.087265 0 ... 47.6750 -122.387 \n",
"... ... ... ... ... ... ... ... \n",
"2640 -0.114438 -0.916694 -0.087265 0 ... 47.6383 -122.098 \n",
"16590 -0.297195 0.942938 -0.087265 0 ... 47.6041 -122.020 \n",
"20011 -0.389959 0.942938 -0.087265 0 ... 47.5532 -122.381 \n",
"9296 -0.281893 -0.916694 -0.087265 0 ... 47.6876 -122.341 \n",
"15823 0.046434 0.942938 -0.087265 0 ... 47.3710 -122.055 \n",
"\n",
" sqft_living15 sqft_lot15 date_numeric price_binned_Low \\\n",
"921 1520 16275 16455 True \n",
"16637 1450 9044 16456 True \n",
"20955 1590 1426 16195 False \n",
"14806 1680 7006 16559 False \n",
"20000 1320 2500 16367 False \n",
"... ... ... ... ... \n",
"2640 2500 10014 16566 False \n",
"16590 2460 4200 16228 False \n",
"20011 1220 1287 16273 True \n",
"9296 1330 5904 16505 False \n",
"15823 2370 14957 16370 False \n",
"\n",
" price_binned_Medium price_binned_High Area_binned Volatility \n",
"921 False False 1 -0.513258 \n",
"16637 False False 1 -0.535804 \n",
"20955 True False 1 -0.161650 \n",
"14806 False True 3 1.086076 \n",
"20000 True False 0 -0.515792 \n",
"... ... ... ... ... \n",
"2640 False True 2 0.269452 \n",
"16590 False True 2 0.716571 \n",
"20011 False False 0 -0.492645 \n",
"9296 False True 0 -1.028095 \n",
"15823 True False 2 0.450047 \n",
"\n",
"[12967 rows x 27 columns]\n"
]
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Нормализация значений для указанных столбцов\n",
"# чтобы значения разных столбов в среднем были 0, а стандартное отклонение - 1\n",
"scaler = StandardScaler()\n",
"df_train[[\"price\", \"sqft_living\", \"sqft_lot\", \"floors\", \"waterfront\"]] = (\n",
" scaler.fit_transform(\n",
" df_train[[\"price\", \"sqft_living\", \"sqft_lot\", \"floors\", \"waterfront\"]]\n",
" )\n",
")\n",
"\n",
"# Расчет волатильности (разница между площадью дома и площадью участка)\n",
"df_train[\"Volatility\"] = (\n",
" df_train[\"sqft_living\"] - df_train[\"sqft_lot\"]\n",
") \n",
"\n",
"# Вывод обновленного датафрейма\n",
"print(df_train)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\TEMP_UNIVERSITY\\mai\\.venv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"c:\\TEMP_UNIVERSITY\\mai\\.venv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
" pd.to_datetime(\n",
"c:\\TEMP_UNIVERSITY\\mai\\.venv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[<Feature: price>, <Feature: bedrooms>, <Feature: bathrooms>, <Feature: sqft_living>, <Feature: sqft_lot>, <Feature: floors>, <Feature: waterfront>, <Feature: view>, <Feature: condition>, <Feature: grade>, <Feature: sqft_above>, <Feature: sqft_basement>, <Feature: yr_built>, <Feature: yr_renovated>, <Feature: zipcode>, <Feature: lat>, <Feature: long>, <Feature: sqft_living15>, <Feature: sqft_lot15>, <Feature: date_numeric>, <Feature: price_binned>, <Feature: DAY(date)>, <Feature: MONTH(date)>, <Feature: WEEKDAY(date)>, <Feature: YEAR(date)>]\n"
]
}
],
"source": [
"import featuretools as ft\n",
"\n",
"# Убеждаемся, что столбец 'date' в формате datetime\n",
"df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
"\n",
"# Установка индекса на столбец 'id' (предполагается, что 'id' уникален)\n",
"df.set_index(\"id\", inplace=True)\n",
"\n",
"# Создание EntitySet для объединения разных датасетов для удобного использования\n",
"es = ft.EntitySet(id=\"house_sales\")\n",
"es = es.add_dataframe(\n",
" dataframe_name=\"house_data\",\n",
" dataframe=df,\n",
" index=\"id\", # уникальный идентификатор\n",
")\n",
"\n",
"# Генерация признаков\n",
"feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name=\"house_data\")\n",
"\n",
"# Показать определение признаков\n",
"print(feature_defs)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id int64\n",
"bedrooms int64\n",
"bathrooms float64\n",
"sqft_living float64\n",
"sqft_lot float64\n",
"floors float64\n",
"waterfront float64\n",
"view int64\n",
"condition int64\n",
"grade int64\n",
"sqft_above int64\n",
"sqft_basement int64\n",
"yr_built int64\n",
"yr_renovated int64\n",
"zipcode int64\n",
"lat float64\n",
"long float64\n",
"sqft_living15 int64\n",
"sqft_lot15 int64\n",
"date_numeric int64\n",
"price_binned_Low bool\n",
"price_binned_Medium bool\n",
"price_binned_High bool\n",
"Area_binned int64\n",
"Volatility float64\n",
"dtype: object\n",
"MAE: 539145.7717358439\n",
"MSE: 430064692545.0895\n",
"RMSE: 655793.1781782191\n"
]
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
"\n",
"# Копирование датафрейма для регрессионного анализа\n",
"df_train_regression = df_train.copy()\n",
"\n",
"# Определение признаков и целевой переменной\n",
"X_train = df_train_regression.drop(\n",
" [\"price\", \"date\"], axis=1\n",
")\n",
"y_train = df_train_regression[\"price\"] # Целевая переменная\n",
"X_test = df_test.drop(\n",
" [\"price\", \"date\"], axis=1\n",
")\n",
"y_test = df_test[\"price\"] # Целевая переменная\n",
"\n",
"# Преобразование категориальных признаков в дамми-переменные\n",
"# (создание столбцов значений со значениями 0 или 1, если это булевой столбец)\n",
"X_train_encoded = pd.get_dummies(X_train, drop_first=True)\n",
"X_test_encoded = pd.get_dummies(X_test, drop_first=True)\n",
"\n",
"# Устранение различий в количестве столбцов между обучающей и тестовой выборками\n",
"X_test_encoded = X_test_encoded.reindex(columns=X_train_encoded.columns, fill_value=0)\n",
"\n",
"# Проверка типов данных\n",
"print(X_train_encoded.dtypes)\n",
"\n",
"# Обучение модели линейной регрессии (поиск зависимостей между признаками)\n",
"model = LinearRegression()\n",
"model.fit(X_train_encoded, y_train)\n",
"\n",
"# Предсказание цены на тестовой выборке\n",
"y_pred = model.predict(X_test_encoded)\n",
"\n",
"# Оценка качества модели\n",
"mae = mean_absolute_error(y_test, y_pred)\n",
"mse = mean_squared_error(y_test, y_pred)\n",
"rmse = mse**0.5 # Корень из MSE для RMSE\n",
"\n",
"print(\"MAE:\", mae)\n",
"print(\"MSE:\", mse)\n",
"print(\"RMSE:\", rmse)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Время, затраченное на обучение модели: 0.08102583885192871.\n",
"Время, затраченное на предсказание: 0.003012418746948242\n"
]
}
],
"source": [
"# Оценка скорости вычисления\n",
"import time\n",
"\n",
"start_time = time.time()\n",
"model.fit(X_train_encoded, y_train)\n",
"training_time = time.time() - start_time\n",
"\n",
"start_time = time.time()\n",
"predictions = model.predict(X_test_encoded)\n",
"prediction_time = time.time() - start_time\n",
"\n",
"print(\n",
" f\"Время, затраченное на обучение модели: {training_time}.\\nВремя, затраченное на предсказание: {prediction_time}\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Оценка корреляции\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"corr_matrix = df_train_regression.corr()\n",
"sns.heatmap(corr_matrix, annot=False)\n",
"plt.show()"
]
}
],
"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
}
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