From d588beaef1013ad5573c166ae8285a3fcc0ccb81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=A1=D0=BE=D1=84=D0=B8=D1=8F=20=D0=A5=D1=80=D0=B8=D1=81?= =?UTF-8?q?=D0=B0=D0=BD=D0=BA=D0=BE=D0=B2=D0=B0?= <132402521+sssoneta@users.noreply.github.com> Date: Tue, 8 Apr 2025 21:24:09 +0300 Subject: [PATCH 1/3] dz10 --- project/wine_variety_classifier.ipynb | 5095 +++++++++++++++++++++++++ 1 file changed, 5095 insertions(+) create mode 100644 project/wine_variety_classifier.ipynb diff --git a/project/wine_variety_classifier.ipynb b/project/wine_variety_classifier.ipynb new file mode 100644 index 00000000..ca554989 --- /dev/null +++ b/project/wine_variety_classifier.ipynb @@ -0,0 +1,5095 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "az8fJ8aDd6Q_" + }, + "source": [ + "## Задача 10. Классификация на выбранном датасете." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Rw9IZG14fxlm", + "outputId": "28136302-0275-4d73-b4c4-75cdbb544173" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting opendatasets\n", + " Downloading opendatasets-0.1.22-py3-none-any.whl.metadata (9.2 kB)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.6)\n", + "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.6.17)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n", + "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n", + "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2024.8.30)\n", + "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.32.3)\n", + "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.4)\n", + "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.2.3)\n", + "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.2.0)\n", + "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n", + "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.4.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.10)\n", + "Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n", + "Installing collected packages: opendatasets\n", + "Successfully installed opendatasets-0.1.22\n" + ] + } + ], + "source": [ + "!pip install opendatasets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "11XpX0ldba5F" + }, + "source": [ + "Импорт модулей" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R30hzockb6RQ" + }, + "outputs": [], + "source": [ + "import opendatasets as od\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.metrics import classification_report, mean_squared_error, accuracy_score, confusion_matrix\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import plotly.express as px" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pSglBQCibe9N" + }, + "source": [ + "# Подготовка данных" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w-D38cQ3WVOJ" + }, + "source": [ + "Возьмем датасет wine-reviews с kaggle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v_Zx3Wp733_p" + }, + "source": [ + "В датасете содержится информация о винах. Информации по столбцам соответсвуют списку ниже.\n", + "\n", + "* Страна-производитель;\n", + "* описание;\n", + "* виноградник на территории винодельни, откуда собирают виноград;\n", + "* оценка вина; цена за бутылку;\n", + "* провинция или штат, из которого произведено вино;\n", + "* винодельческий район в провинции или штате;\n", + "* иногда в пределах винодельческого региона указываются более конкретные регионы;\n", + "* имя дегустатора;\n", + "* твитер аккаунт дегустатора;\n", + "* название;\n", + "* сорт винограда;\n", + "* винодельня." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YcAp_Wjisbue", + "outputId": "12d94a8a-ddf6-41f1-da03-67fc0a8217b8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n", + "Your Kaggle username: Shcherbakov\n", + "Your Kaggle Key: ··········\n", + "Dataset URL: https://www.kaggle.com/datasets/zynicide/wine-reviews\n", + "Downloading wine-reviews.zip to ./wine-reviews\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 50.9M/50.9M [00:00<00:00, 86.9MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "od.download(\n", + " \"https://www.kaggle.com/datasets/zynicide/wine-reviews\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 823 + }, + "id": "4mIMtZR1fm4r", + "outputId": "c62d7552-68cf-4431-fafa-1e72599adfc0" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "wine_reviews" + }, + "text/html": [ + "\n", + "
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countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
0ItalyAromas include tropical fruit, broom, brimston...Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
1PortugalThis is ripe and fruity, a wine that is smooth...Avidagos8715.0DouroNaNNaNRoger Voss@vossrogerQuinta dos Avidagos 2011 Avidagos Red (Douro)Portuguese RedQuinta dos Avidagos
2USTart and snappy, the flavors of lime flesh and...NaN8714.0OregonWillamette ValleyWillamette ValleyPaul Gregutt@paulgwineRainstorm 2013 Pinot Gris (Willamette Valley)Pinot GrisRainstorm
3USPineapple rind, lemon pith and orange blossom ...Reserve Late Harvest8713.0MichiganLake Michigan ShoreNaNAlexander PeartreeNaNSt. Julian 2013 Reserve Late Harvest Riesling ...RieslingSt. Julian
4USMuch like the regular bottling from 2012, this...Vintner's Reserve Wild Child Block8765.0OregonWillamette ValleyWillamette ValleyPaul Gregutt@paulgwineSweet Cheeks 2012 Vintner's Reserve Wild Child...Pinot NoirSweet Cheeks
..........................................
129966GermanyNotes of honeysuckle and cantaloupe sweeten th...Brauneberger Juffer-Sonnenuhr Spätlese9028.0MoselNaNNaNAnna Lee C. IijimaNaNDr. H. Thanisch (Erben Müller-Burggraef) 2013 ...RieslingDr. H. Thanisch (Erben Müller-Burggraef)
129967USCitation is given as much as a decade of bottl...NaN9075.0OregonOregonOregon OtherPaul Gregutt@paulgwineCitation 2004 Pinot Noir (Oregon)Pinot NoirCitation
129968FranceWell-drained gravel soil gives this wine its c...Kritt9030.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Gresser 2013 Kritt Gewurztraminer (Als...GewürztraminerDomaine Gresser
129969FranceA dry style of Pinot Gris, this is crisp with ...NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970FranceBig, rich and off-dry, this is powered by inte...Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Car...GewürztraminerDomaine Schoffit
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" + ], + "text/plain": [ + "country\n", + "Argentina 3800\n", + "Armenia 2\n", + "Australia 2329\n", + "Austria 3345\n", + "Bosnia and Herzegovina 2\n", + "Brazil 52\n", + "Bulgaria 141\n", + "Canada 257\n", + "Chile 4472\n", + "China 1\n", + "Croatia 73\n", + "Cyprus 11\n", + "Czech Republic 12\n", + "Egypt 1\n", + "England 74\n", + "France 22093\n", + "Georgia 86\n", + "Germany 2165\n", + "Greece 466\n", + "Hungary 146\n", + "India 9\n", + "Israel 505\n", + "Italy 19540\n", + "Lebanon 35\n", + "Luxembourg 6\n", + "Macedonia 12\n", + "Mexico 70\n", + "Moldova 59\n", + "Morocco 28\n", + "New Zealand 1419\n", + "Peru 16\n", + "Portugal 5691\n", + "Romania 120\n", + "Serbia 12\n", + "Slovakia 1\n", + "Slovenia 87\n", + "South Africa 1401\n", + "Spain 6645\n", + "Switzerland 7\n", + "Turkey 90\n", + "US 54504\n", + "Ukraine 14\n", + "Uruguay 109\n", + "Name: title, dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_reviews.groupby(\"country\")['title'].count()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VHjuA5XTb5Wv" + }, + "source": [ + "Наибольшее количество информации содержится о винах Америки, Франции и Италии.\n", + "\n", + "Рассмотрим Италию." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pAVugV0xnZWl" + }, + "outputs": [], + "source": [ + "wine_reviews = wine_reviews[wine_reviews['country'] == 'Italy']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q79LaXnDICTn" + }, + "source": [ + "Добавим год разлива вина. Пропущенные значения заменим медианой для сорта." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HINLwR7PDTmO" + }, + "outputs": [], + "source": [ + "def unwrap_list(num_in_list):\n", + " try:\n", + " return num_in_list[0]\n", + " except:\n", + " return np.nan" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "dHdgOEhbIAWv", + "outputId": "2211aaf3-3fce-4353-c1bd-530665e85661" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\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", + " wine_reviews['year'] = wine_reviews['title'].transform(lambda x: unwrap_list(list(filter(lambda a: a.isdigit() and len(a) == 4, x.split())))).astype(float)\n", + ":2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\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", + " wine_reviews['year'] = wine_reviews.groupby(['variety'])['year'].transform(lambda x: x.fillna(x.median()))\n" + ] + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "repr_error": "Out of range float values are not JSON compliant: nan", + "type": "dataframe", + "variable_name": "wine_reviews" + }, + "text/html": [ + "\n", + "
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countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywineryyear
0ItalyAromas include tropical fruit, broom, brimston...Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia2013.0
6ItalyHere's a bright, informal red that opens with ...Belsito8716.0Sicily & SardiniaVittoriaNaNKerin O’Keefe@kerinokeefeTerre di Giurfo 2013 Belsito Frappato (Vittoria)FrappatoTerre di Giurfo2013.0
13ItalyThis is dominated by oak and oak-driven aromas...Rosso87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeMasseria Setteporte 2012 Rosso (Etna)Nerello MascaleseMasseria Setteporte2012.0
22ItalyDelicate aromas recall white flower and citrus...Ficiligno8719.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeBaglio di Pianetto 2007 Ficiligno White (Sicilia)White BlendBaglio di Pianetto2007.0
24ItalyAromas of prune, blackcurrant, toast and oak c...Aynat8735.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCanicattì 2009 Aynat Nero d'Avola (Sicilia)Nero d'AvolaCanicattì2009.0
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129929ItalyThis luminous sparkler has a sweet, fruit-forw...NaN9138.0VenetoProsecco Superiore di CartizzeNaNNaNNaNCol Vetoraz Spumanti NV Prosecco Superiore di...ProseccoCol Vetoraz Spumanti2007.0
129943ItalyA blend of Nero d'Avola and Syrah, this convey...Adènzia9029.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeBaglio del Cristo di Campobello 2012 Adènzia R...Red BlendBaglio del Cristo di Campobello2012.0
129947ItalyA blend of 65% Cabernet Sauvignon, 30% Merlot ...Symposio9020.0Sicily & SardiniaTerre SicilianeNaNKerin O’Keefe@kerinokeefeFeudo Principi di Butera 2012 Symposio Red (Te...Red BlendFeudo Principi di Butera2012.0
129961ItalyIntense aromas of wild cherry, baking spice, t...NaN9030.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCOS 2013 Frappato (Sicilia)FrappatoCOS2013.0
129962ItalyBlackberry, cassis, grilled herb and toasted a...Sàgana Tenuta San Giacomo9040.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCusumano 2012 Sàgana Tenuta San Giacomo Nero d...Nero d'AvolaCusumano2012.0
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count
taster_name
Kerin O’Keefe10776
Roger Voss97
Joe Czerwinski89
Michael Schachner76
Paul Gregutt4
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" + ], + "text/plain": [ + "taster_name\n", + "Kerin O’Keefe 10776\n", + "Roger Voss 97\n", + "Joe Czerwinski 89\n", + "Michael Schachner 76\n", + "Paul Gregutt 4\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine_reviews['taster_name'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IELV46tjBhFB" + }, + "source": [ + "При таком распределении Kerin O’Keefe почти не будет влиять на обучение, а остальные признаки могут влиять слишком сильно или не попасть в тренировочную выборку. Уберем этот признак." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1BzsdrTWIIUT" + }, + "source": [ + "Будем пытаться предсказать сорт вина" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MAgAGDx33kRd" + }, + "source": [ + "Заметим, что некоторые сорта встречаются всего несколько раз, поэтому они могут, либо не попасть в тренировочную выборку, либо слишком сильно влиять на модель.\n", + "\n", + "Страна у всех вин одна. Убираем столбец country.\n", + "\n", + "Так как description яляется уникальным признаком, то оно не повлияет на обучение.\n", + "\n", + "Информации о винограднике на территории винодельни не содержательна т.к. винодельня может содержать несколько виноградников. Оба признака могут замедлить модель, т.к. они связаны, то оставим один. Если оставить информацию о винограднике вместо винодельни, то шанс переобучения выше. Убираем столбец designation.\n", + "\n", + "По аналогичной причине оставляем province и убираем region_1.\n", + "\n", + "В region_2 все значения NaN. Убираем.\n", + "\n", + "Хранить информацию о твитере дегустатора, очевидно, не нужно.\n", + "\n", + "Так как title яляется уникальным признаком, то оно не повлияет на обучение.\n", + "\n", + "\n", + "Удалим эти данные.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 429 + }, + "id": "Y22RJ1Je3gpX", + "outputId": "5793e461-fdfc-44ed-b5ff-8b74f7a1be56" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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count
variety
Red Blend3624
Nebbiolo2736
Sangiovese2265
White Blend779
Sangiovese Grosso750
Glera709
Corvina, Rondinella, Molinara619
Pinot Grigio605
Barbera479
Nero d'Avola361
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" + ], + "text/plain": [ + "variety\n", + "Red Blend 3624\n", + "Nebbiolo 2736\n", + "Sangiovese 2265\n", + "White Blend 779\n", + "Sangiovese Grosso 750\n", + "Glera 709\n", + "Corvina, Rondinella, Molinara 619\n", + "Pinot Grigio 605\n", + "Barbera 479\n", + "Nero d'Avola 361\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "match_id_counts = wine_reviews['variety'].value_counts()\n", + "match_id_counts[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Jk_qQEG-XKY" + }, + "source": [ + "Оставим первые три сорта вина, т.к. между ними разница в количестве строк не так велика, как между 3 и 4 (нумеруя с 1 сверху)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "eykVrqCL3pXR", + "outputId": "92c22c11-0aa7-4440-a808-bbec5aa64d2c" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"data\",\n \"rows\": 8625,\n \"fields\": [\n {\n \"column\": \"points\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 80,\n \"max\": 100,\n \"num_unique_values\": 21,\n \"samples\": [\n 87,\n 81,\n 84\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 42.73811041300064,\n \"min\": 6.0,\n \"max\": 800.0,\n \"num_unique_values\": 192,\n \"samples\": [\n 120.0,\n 320.0,\n 100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"province\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Northwestern Italy\",\n \"Central Italy\",\n \"Veneto\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"winery\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1724,\n \"samples\": [\n \"Cordella\",\n \"Corte dei Papi\",\n \"Castello Banfi\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.170948183314536,\n \"min\": 1637.0,\n \"max\": 2016.0,\n \"num_unique_values\": 27,\n \"samples\": [\n 2012.0,\n 1997.0,\n 2005.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variety\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Red Blend\",\n \"Sangiovese\",\n \"Nebbiolo\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "data" + }, + "text/html": [ + "\n", + "
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pointspriceprovincewineryyearvariety
288717.0Sicily and SardiniaTerre di Giurfo2011.0Red Blend
3186NaNSicily and SardiniaDuca di Salaparuta2010.0Red Blend
5086NaNSicily and SardiniaViticultori Associati Canicatti2008.0Red Blend
5485NaNSicily and SardiniaCorvo2010.0Red Blend
618617.0Central ItalyPodere dal Nespoli2015.0Sangiovese
.....................
1298268850.0PiedmontVinchio-Vaglio Serra2006.0Nebbiolo
12984486NaNTuscanyCaparzo2006.0Sangiovese
1298929172.0TuscanyLa Mannella2012.0Sangiovese
1299439029.0Sicily and SardiniaBaglio del Cristo di Campobello2012.0Red Blend
1299479020.0Sicily and SardiniaFeudo Principi di Butera2012.0Red Blend
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Значит признак будет влиять на модель." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qLRcau1KXPgt" + }, + "source": [ + "Посмотрим на количество строк для каждого сорта." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "wiZwp7dC5x8o", + "outputId": "29a6b95e-37d5-4da1-efe3-f0127ac4e47f" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(15,5))\n", + "sns.countplot(data=data, x='variety')\n", + "plt.title(\"count wines rate depending on the variety\")\n", + "plt.xlabel(\"variety\")\n", + "plt.ylabel(\"count\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Tmi7YJ7qXUFt" + }, + "source": [ + "Заметим, что разница между столбцами не очень большая, значит, скорее всего, точность предсказаний будет не сильно отличться." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s-Ch6pnlekYR" + }, + "source": [ + "Посмотрим на количество в зависимости от оценки" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "aNIJf7NSTKqw", + "outputId": "ab4f6b3d-7100-4691-cb8c-79c45a1a0338" + }, + "outputs": [ + { + "data": { + "image/png": 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rlSQFy50VFRWlunXr6tVXX9W5c+cKrS+4FDsvL6/Qx1nBwcEKCwuz69LkohT8ZXvlX7IzZ84sNPZqz2PPnj3l7u6u8ePHF6pjjLG5tL+o7cfGxmrp0qU6cuSIdfm+ffv07bffOrWd3Nxcm0vhs7Oz9c477ygoKEhRUVGS/vzr/NixY3r33XcL9Xbx4kXreWX2Ksn+OKNDhw7y8PDQ22+/bbP8jTfesOvxXbp0UV5eXqHxM2bMkMVicdkvYUfm3pVc8RrZ817o0qWLUlNTba7wy83N1ezZs1WlShXdd999Du/D5Xx9fQs9H9djbqN4HCnCdfHyyy9r5cqVuu+++6yXzJ44cUKffvqpNmzYoICAAP3zn//U4sWL1blzZz399NMKDAzUggULdPDgQf3nP/+x/mXWuHFjtW7dWqNGjdLp06cVGBiojz76SLm5uQ73V7duXQUEBGjOnDmqWrWqfH191apVqyLPrynQtm1bTZo0SUePHrUJP+3atdM777yjOnXqqFatWg73dDk3Nzf961//UufOndW4cWMNHDhQt9xyi44dO6Y1a9bIz89PX375pc6ePatatWrp4YcfVtOmTVWlShV999132rJli1577TWHtu3n56d27dpp6tSpysnJ0S233KKVK1fq4MGDhcYW/PJ44YUX1LdvX1WqVEldu3ZV3bp1NXHiRI0aNUqHDh1Sjx49VLVqVR08eFCff/654uPj9dxzz121h/Hjx2vFihVq27atnnzySesvosaNG9ucW1LS7YSFhWnKlCk6dOiQbr/9dn388cfasWOH5s6daz3/6LHHHtMnn3yiJ554QmvWrNE999yjvLw8/fzzz/rkk0/07bffFnkBwbXYuz/OCAkJ0TPPPKPXXntN3bp1U6dOnbRz50598803qlGjRrFHVLp27ar7779fL7zwgg4dOqSmTZtq5cqVWrZsmYYNG2ZzUrUzHJl7V3LFa2TPeyE+Pl7vvPOOBgwYoOTkZNWpU0efffaZNm7cqJkzZxY6/8pRUVFR+u677zR9+nSFhYUpMjJS9evXd/nchh1K+3I33DwOHz5s+vXrZ4KCgoyXl5e59dZbzdChQ20uRf/111/Nww8/bAICAoy3t7dp2bKlWb58eaFav/76q4mJiTFeXl4mJCTE/L//9//MqlWrirwkv3HjxoUe379/fxMREWGzbNmyZaZRo0bGw8PDrsvzMzMzjbu7u6latarJzc21Lv/ggw+MJPPYY48VeszVLsn/9NNPbcYdPHiwyB62b99uevbsaapXr268vLxMRESE6dOnj1m9erUx5s/LiEeMGGGaNm1qqlatanx9fU3Tpk3NW2+9dc19Meb/Lhn+/fffC63773//ax566CETEBBg/P39Te/evc3x48cLXXZsjDEvvfSSueWWW4ybm1uhy/P/85//mHvvvdf4+voaX19f06BBAzN06FCzf//+Yvtbu3atiYqKMp6enubWW281c+bMsfZ8JXu2U/De2Lp1q4mOjjbe3t4mIiLCvPHGG4XqZWdnmylTppjGjRsbLy8vU61aNRMVFWXGjx9vMjIyrOMkFXnJ9JWX1Zdkf652Sf6VX29R1FdS5ObmmtGjR5vQ0FDj4+NjHnjgAbNv3z5TvXp188QTTxT5PF/u7Nmz5tlnnzVhYWGmUqVKpl69embatGk2XwVQ0v0uytXmXknmr72vUVFK8l5IS0szAwcONDVq1DCenp6mSZMmRf6suHJuXG1+FfUVIz///LNp166d8fHxsX59gjNzG46zGFNGZ+kBQClq3769/vjjj2LPdbvRpKenq1q1apo4caJeeOGFsm6nXLhZ3wsoHucUAcAN4uLFi4WWFZwLdvl/NwOgaJxTBAA3iI8//ljz589Xly5dVKVKFW3YsEGLFy9Wx44ddc8995R1e0C5RygCgBvEnXfeKQ8PD02dOlWZmZnWk68nTpxY1q0BFQLnFAEAAIhzigAAACQRigAAACRxTpFd8vPzdfz4cVWtWtUlX1EPAACuP2OMzp49q7CwsEL/sW9RCEV2OH78eLn6DwYBAID9jh49atf/OEAoskPBV7kfPXpUfn5+ZdwNAACwR2ZmpsLDw+3+L1kIRXYo+MjMz8+PUAQAQAVj76kvnGgNAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkyaOsGwBQ/kWNWOiyWsnT+rmsFgC4EkeKAAAARCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQxJc3AjcEvlwRAJzHkSIAAAARigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACQRigAAACTxjdYAbnB82zcAe3GkCAAAQIQiAAAASWUcitatW6euXbsqLCxMFotFS5cuta7LycnRyJEj1aRJE/n6+iosLEz9+vXT8ePHbWqcPn1acXFx8vPzU0BAgAYNGqRz587ZjNm1a5fatm0rb29vhYeHa+rUqaWxewAAoAIp01B0/vx5NW3aVG+++WahdRcuXNC2bds0evRobdu2TUuWLNH+/fvVrVs3m3FxcXHas2ePVq1apeXLl2vdunWKj4+3rs/MzFTHjh0VERGh5ORkTZs2TePGjdPcuXOv+/4BAICKo0xPtO7cubM6d+5c5Dp/f3+tWrXKZtkbb7yhli1b6siRI6pdu7b27dunFStWaMuWLWrevLkkafbs2erSpYteffVVhYWFadGiRcrOztb7778vT09PNW7cWDt27ND06dNtwhMAALi5VahzijIyMmSxWBQQECBJSkxMVEBAgDUQSVJMTIzc3NyUlJRkHdOuXTt5enpax8TGxmr//v06c+ZMkdvJyspSZmamzQ0AANzYKkwounTpkkaOHKlHHnlEfn5+kqTU1FQFBwfbjPPw8FBgYKBSU1OtY0JCQmzGFNwvGHOlyZMny9/f33oLDw939e4AAIBypkKEopycHPXp00fGGL399tvXfXujRo1SRkaG9Xb06NHrvk0AAFC2yv2XNxYEosOHD+v777+3HiWSpNDQUJ08edJmfG5urk6fPq3Q0FDrmLS0NJsxBfcLxlzJy8tLXl5ertwNAABQzpXrI0UFgSglJUXfffedqlevbrM+Ojpa6enpSk5Oti77/vvvlZ+fr1atWlnHrFu3Tjk5OdYxq1atUv369VWtWrXS2REAAFDulWkoOnfunHbs2KEdO3ZIkg4ePKgdO3boyJEjysnJ0cMPP6ytW7dq0aJFysvLU2pqqlJTU5WdnS1JatiwoTp16qTBgwdr8+bN2rhxoxISEtS3b1+FhYVJkh599FF5enpq0KBB2rNnjz7++GO9/vrrGj58eFntNgAAKIfK9OOzrVu36v7777feLwgq/fv317hx4/TFF19Ikpo1a2bzuDVr1qh9+/aSpEWLFikhIUEdOnSQm5ubevXqpVmzZlnH+vv7a+XKlRo6dKiioqJUo0YNjRkzhsvxAQCAjTINRe3bt5cx5qrrr7WuQGBgoD788MNrjrnzzju1fv36EvcHAABuHuX6nCIAAIDSQigCAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAECS5FHWDQC4uUWNWOiyWsnT+rmsFoCbD0eKAAAARCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQRCgCAACQVMahaN26deratavCwsJksVi0dOlSm/XGGI0ZM0Y1a9aUj4+PYmJilJKSYjPm9OnTiouLk5+fnwICAjRo0CCdO3fOZsyuXbvUtm1beXt7Kzw8XFOnTr3euwYAACqYMg1F58+fV9OmTfXmm28WuX7q1KmaNWuW5syZo6SkJPn6+io2NlaXLl2yjomLi9OePXu0atUqLV++XOvWrVN8fLx1fWZmpjp27KiIiAglJydr2rRpGjdunObOnXvd9w8AAFQcHmW58c6dO6tz585FrjPGaObMmXrxxRfVvXt3SdLChQsVEhKipUuXqm/fvtq3b59WrFihLVu2qHnz5pKk2bNnq0uXLnr11VcVFhamRYsWKTs7W++//748PT3VuHFj7dixQ9OnT7cJTwAA4OZWbs8pOnjwoFJTUxUTE2Nd5u/vr1atWikxMVGSlJiYqICAAGsgkqSYmBi5ubkpKSnJOqZdu3by9PS0jomNjdX+/ft15syZUtobAABQ3pXpkaJrSU1NlSSFhITYLA8JCbGuS01NVXBwsM16Dw8PBQYG2oyJjIwsVKNgXbVq1QptOysrS1lZWdb7mZmZTu4NAAAo78rtkaKyNHnyZPn7+1tv4eHhZd0SAAC4zsptKAoNDZUkpaWl2SxPS0uzrgsNDdXJkydt1ufm5ur06dM2Y4qqcfk2rjRq1ChlZGRYb0ePHnV+hwAAQLlWbkNRZGSkQkNDtXr1auuyzMxMJSUlKTo6WpIUHR2t9PR0JScnW8d8//33ys/PV6tWraxj1q1bp5ycHOuYVatWqX79+kV+dCZJXl5e8vPzs7kBAIAbW5mGonPnzmnHjh3asWOHpD9Prt6xY4eOHDkii8WiYcOGaeLEifriiy+0e/du9evXT2FhYerRo4ckqWHDhurUqZMGDx6szZs3a+PGjUpISFDfvn0VFhYmSXr00Ufl6empQYMGac+ePfr444/1+uuva/jw4WW01wAAoDwq0xOtt27dqvvvv996vyCo9O/fX/Pnz9fzzz+v8+fPKz4+Xunp6br33nu1YsUKeXt7Wx+zaNEiJSQkqEOHDnJzc1OvXr00a9Ys63p/f3+tXLlSQ4cOVVRUlGrUqKExY8ZwOT4AALBRpqGoffv2MsZcdb3FYtGECRM0YcKEq44JDAzUhx9+eM3t3HnnnVq/fr3DfQIAgBtfuT2nCAAAoDQRigAAAEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkEQoAgAAkFTOQ1FeXp5Gjx6tyMhI+fj4qG7dunrppZdkjLGOMcZozJgxqlmzpnx8fBQTE6OUlBSbOqdPn1ZcXJz8/PwUEBCgQYMG6dy5c6W9OwAAoBwr16FoypQpevvtt/XGG29o3759mjJliqZOnarZs2dbx0ydOlWzZs3SnDlzlJSUJF9fX8XGxurSpUvWMXFxcdqzZ49WrVql5cuXa926dYqPjy+LXQIAAOWUR1k3cC2bNm1S9+7d9eCDD0qS6tSpo8WLF2vz5s2S/jxKNHPmTL344ovq3r27JGnhwoUKCQnR0qVL1bdvX+3bt08rVqzQli1b1Lx5c0nS7Nmz1aVLF7366qsKCwsrm50DAADlSrk+UtSmTRutXr1av/zyiyRp586d2rBhgzp37ixJOnjwoFJTUxUTE2N9jL+/v1q1aqXExERJUmJiogICAqyBSJJiYmLk5uampKSkUtwbAABQnpXrI0X//Oc/lZmZqQYNGsjd3V15eXmaNGmS4uLiJEmpqamSpJCQEJvHhYSEWNelpqYqODjYZr2Hh4cCAwOtY66UlZWlrKws6/3MzEyX7RMAACifyvWRok8++USLFi3Shx9+qG3btmnBggV69dVXtWDBguu63cmTJ8vf3996Cw8Pv67bAwAAZa9ch6IRI0bon//8p/r27asmTZroscce07PPPqvJkydLkkJDQyVJaWlpNo9LS0uzrgsNDdXJkydt1ufm5ur06dPWMVcaNWqUMjIyrLejR4+6etcAAEA5U65D0YULF+TmZtuiu7u78vPzJUmRkZEKDQ3V6tWrreszMzOVlJSk6OhoSVJ0dLTS09OVnJxsHfP9998rPz9frVq1KnK7Xl5e8vPzs7kBAIAbW7k+p6hr166aNGmSateurcaNG2v79u2aPn26Hn/8cUmSxWLRsGHDNHHiRNWrV0+RkZEaPXq0wsLC1KNHD0lSw4YN1alTJw0ePFhz5sxRTk6OEhIS1LdvX648AwAAVuU6FM2ePVujR4/Wk08+qZMnTyosLEx///vfNWbMGOuY559/XufPn1d8fLzS09N17733asWKFfL29raOWbRokRISEtShQwe5ubmpV69emjVrVlnsEgAAKKfKdSiqWrWqZs6cqZkzZ151jMVi0YQJEzRhwoSrjgkMDNSHH354HToEAAA3inJ9ThEAAEBpIRQBAACIUAQAACCJUAQAACCJUAQAACCpnF99BgA3s6gRC11WK3laP5fVAm5UHCkCAAAQoQgAAEASoQgAAECSg6HogQceUHp6eqHlmZmZeuCBB5ztCQAAoNQ5FIp++OEHZWdnF1p+6dIlrV+/3ummAAAASluJrj7btWuX9d979+5Vamqq9X5eXp5WrFihW265xXXdAQAAlJIShaJmzZrJYrHIYrEU+TGZj4+PZs+e7bLmAAAASkuJQtHBgwdljNGtt96qzZs3KygoyLrO09NTwcHBcnd3d3mTAAAA11uJQlFERIQkKT8//7o0AwAAUFYc/kbrlJQUrVmzRidPniwUksaMGeN0YwBQEbjqW6f5xmmg7DkUit59910NGTJENWrUUGhoqCwWi3WdxWIhFAEAgArHoVA0ceJETZo0SSNHjnR1PwAAAGXCoe8pOnPmjHr37u3qXgAAAMqMQ6God+/eWrlypat7AQAAKDMOfXx22223afTo0frxxx/VpEkTVapUyWb9008/7ZLmAAAASotDoWju3LmqUqWK1q5dq7Vr19qss1gshCIAAFDhOBSKDh486Oo+AAAAypRD5xQBAADcaBw6UvT4449fc/3777/vUDMAAABlxaFQdObMGZv7OTk5+umnn5Senl7kfxQLAABQ3jkUij7//PNCy/Lz8zVkyBDVrVvX6aYAAABKm8vOKXJzc9Pw4cM1Y8YMV5UEAAAoNS490frXX39Vbm6uK0sCAACUCoc+Phs+fLjNfWOMTpw4oa+++kr9+/d3SWMAAAClyaFQtH37dpv7bm5uCgoK0muvvVbslWkAAADlkUOhaM2aNa7uAwAAoEw5FIoK/P7779q/f78kqX79+goKCnJJUwAAAKXNoVB0/vx5PfXUU1q4cKHy8/MlSe7u7urXr59mz56typUru7RJoKKLGrHQZbWSp/VzWS0AwP9x6Oqz4cOHa+3atfryyy+Vnp6u9PR0LVu2TGvXrtU//vEPV/cIAABw3Tl0pOg///mPPvvsM7Vv3966rEuXLvLx8VGfPn309ttvu6o/AACAUuHQkaILFy4oJCSk0PLg4GBduHDB6aYAAABKm0OhKDo6WmPHjtWlS5esyy5evKjx48crOjraZc0BAACUFoc+Pps5c6Y6deqkWrVqqWnTppKknTt3ysvLSytXrnRpgwAAAKXBoVDUpEkTpaSkaNGiRfr5558lSY888oji4uLk4+Pj0gYBAABKg0OhaPLkyQoJCdHgwYNtlr///vv6/fffNXLkSJc0BwAAUFocOqfonXfeUYMGDQotb9y4sebMmeN0UwAAAKXNoVCUmpqqmjVrFloeFBSkEydOON0UAABAaXMoFIWHh2vjxo2Flm/cuFFhYWFONwUAAFDaHApFgwcP1rBhwzRv3jwdPnxYhw8f1vvvv69nn3220HlGzjp27Jj+9re/qXr16vLx8VGTJk20detW63pjjMaMGaOaNWvKx8dHMTExSklJsalx+vRpxcXFyc/PTwEBARo0aJDOnTvn0j4BAEDF5tCJ1iNGjNCpU6f05JNPKjs7W5Lk7e2tkSNHatSoUS5r7syZM7rnnnt0//3365tvvlFQUJBSUlJUrVo165ipU6dq1qxZWrBggSIjIzV69GjFxsZq79698vb2liTFxcXpxIkTWrVqlXJycjRw4EDFx8frww8/dFmvAACgYnMoFFksFk2ZMkWjR4/Wvn375OPjo3r16snLy8ulzU2ZMkXh4eGaN2+edVlkZKT138YYzZw5Uy+++KK6d+8uSVq4cKFCQkK0dOlS9e3bV/v27dOKFSu0ZcsWNW/eXJI0e/ZsdenSRa+++iof9wEAAEkOfnxWoEqVKmrRooXuuOMOlwciSfriiy/UvHlz9e7dW8HBwbrrrrv07rvvWtcfPHhQqampiomJsS7z9/dXq1atlJiYKElKTExUQECANRBJUkxMjNzc3JSUlFTkdrOyspSZmWlzAwAANzanQtH19ttvv+ntt99WvXr19O2332rIkCF6+umntWDBAkl/XgUnqdD/wxYSEmJdl5qaquDgYJv1Hh4eCgwMtI650uTJk+Xv72+9hYeHu3rXAABAOVOuQ1F+fr7uvvtuvfzyy7rrrrsUHx+vwYMHX/fvQho1apQyMjKst6NHj17X7QEAgLJXrkNRzZo11ahRI5tlDRs21JEjRyRJoaGhkqS0tDSbMWlpadZ1oaGhOnnypM363NxcnT592jrmSl5eXvLz87O5AQCAG1u5DkX33HOP9u/fb7Psl19+UUREhKQ/T7oODQ3V6tWrreszMzOVlJSk6OhoSVJ0dLTS09OVnJxsHfP9998rPz9frVq1KoW9AAAAFYFDV5+VlmeffVZt2rTRyy+/rD59+mjz5s2aO3eu5s6dK+nPq+CGDRumiRMnql69etZL8sPCwtSjRw9Jfx5Z6tSpk/Vjt5ycHCUkJKhv375ceQYAAKzKdShq0aKFPv/8c40aNUoTJkxQZGSkZs6cqbi4OOuY559/XufPn1d8fLzS09N17733asWKFdbvKJKkRYsWKSEhQR06dJCbm5t69eqlWbNmlcUuAQCAcqpchyJJ+utf/6q//vWvV11vsVg0YcIETZgw4apjAgMD+aJGAABwTeX6nCIAAIDSQigCAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAECS5FHWDQAAykbUiIUuq5U8rZ/LagFlhSNFAAAAIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIqmCh6JVXXpHFYtGwYcOsyy5duqShQ4eqevXqqlKlinr16qW0tDSbxx05ckQPPvigKleurODgYI0YMUK5ubml3D0AACjPKkwo2rJli9555x3deeedNsufffZZffnll/r000+1du1aHT9+XD179rSuz8vL04MPPqjs7Gxt2rRJCxYs0Pz58zVmzJjS3gUAAFCOVYhQdO7cOcXFxendd99VtWrVrMszMjL03nvvafr06XrggQcUFRWlefPmadOmTfrxxx8lSStXrtTevXv1wQcfqFmzZurcubNeeuklvfnmm8rOzi6rXQIAAOVMhQhFQ4cO1YMPPqiYmBib5cnJycrJybFZ3qBBA9WuXVuJiYmSpMTERDVp0kQhISHWMbGxscrMzNSePXuK3F5WVpYyMzNtbgAA4MbmUdYNFOejjz7Stm3btGXLlkLrUlNT5enpqYCAAJvlISEhSk1NtY65PBAVrC9YV5TJkydr/PjxLugeAABUFOX6SNHRo0f1zDPPaNGiRfL29i617Y4aNUoZGRnW29GjR0tt2wAAoGyU61CUnJyskydP6u6775aHh4c8PDy0du1azZo1Sx4eHgoJCVF2drbS09NtHpeWlqbQ0FBJUmhoaKGr0QruF4y5kpeXl/z8/GxuAADgxlauQ1GHDh20e/du7dixw3pr3ry54uLirP+uVKmSVq9ebX3M/v37deTIEUVHR0uSoqOjtXv3bp08edI6ZtWqVfLz81OjRo1KfZ8AAED5VK7PKapataruuOMOm2W+vr6qXr26dfmgQYM0fPhwBQYGys/PT0899ZSio6PVunVrSVLHjh3VqFEjPfbYY5o6dapSU1P14osvaujQofLy8ir1fQIAAOVTuQ5F9pgxY4bc3NzUq1cvZWVlKTY2Vm+99ZZ1vbu7u5YvX64hQ4YoOjpavr6+6t+/vyZMmFCGXQMAgPKmwoWiH374wea+t7e33nzzTb355ptXfUxERIS+/vrr69wZAACoyMr1OUUAAAClhVAEAAAgQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAIAkQhEAAICkCvh/nwEAyr+oEQtdVit5Wj+X1QKuhSNFAAAAIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIIhQBAABIkjzKugGgvIgasdBltZKn9XNZLQBA6eBIEQAAgAhFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAkghFAAAAksp5KJo8ebJatGihqlWrKjg4WD169ND+/fttxly6dElDhw5V9erVVaVKFfXq1UtpaWk2Y44cOaIHH3xQlStXVnBwsEaMGKHc3NzS3BUAAFDOletQtHbtWg0dOlQ//vijVq1apZycHHXs2FHnz5+3jnn22Wf15Zdf6tNPP9XatWt1/Phx9ezZ07o+Ly9PDz74oLKzs7Vp0yYtWLBA8+fP15gxY8pilwAAQDnlUdYNXMuKFSts7s+fP1/BwcFKTk5Wu3btlJGRoffee08ffvihHnjgAUnSvHnz1LBhQ/34449q3bq1Vq5cqb179+q7775TSEiImjVrppdeekkjR47UuHHj5OnpWRa7BgAAyplyfaToShkZGZKkwMBASVJycrJycnIUExNjHdOgQQPVrl1biYmJkqTExEQ1adJEISEh1jGxsbHKzMzUnj17itxOVlaWMjMzbW4AAODGVmFCUX5+voYNG6Z77rlHd9xxhyQpNTVVnp6eCggIsBkbEhKi1NRU65jLA1HB+oJ1RZk8ebL8/f2tt/DwcBfvDQAAKG8qTCgaOnSofvrpJ3300UfXfVujRo1SRkaG9Xb06NHrvk0AAFC2yvU5RQUSEhK0fPlyrVu3TrVq1bIuDw0NVXZ2ttLT022OFqWlpSk0NNQ6ZvPmzTb1Cq5OKxhzJS8vL3l5ebl4LwAAQHlWro8UGWOUkJCgzz//XN9//70iIyNt1kdFRalSpUpavXq1ddn+/ft15MgRRUdHS5Kio6O1e/dunTx50jpm1apV8vPzU6NGjUpnRwAAQLlXro8UDR06VB9++KGWLVumqlWrWs8B8vf3l4+Pj/z9/TVo0CANHz5cgYGB8vPz01NPPaXo6Gi1bt1aktSxY0c1atRIjz32mKZOnarU1FS9+OKLGjp0KEeDAACAVbkORW+//bYkqX379jbL582bpwEDBkiSZsyYITc3N/Xq1UtZWVmKjY3VW2+9ZR3r7u6u5cuXa8iQIYqOjpavr6/69++vCRMmlNZuAACACqBchyJjTLFjvL299eabb+rNN9+86piIiAh9/fXXrmwNAADcYMr1OUUAAAClhVAEAAAgQhEAAICkcn5OEQAARYkasdBltZKn9XNZLVRsHCkCAAAQoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASoQgAAEASX96ICoQvawMAXE8cKQIAABChCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQBKhCAAAQJLkUdYN4MYSNWKhy2olT+vnsloAABSHUAQAwBX4A+/mxMdnAAAAIhQBAABIIhQBAABIIhQBAABIIhQBAABI4uozAABKFVe2lV8cKQIAABChCAAAQBKhCAAAQNJNForefPNN1alTR97e3mrVqpU2b95c1i0BAIBy4qY50frjjz/W8OHDNWfOHLVq1UozZ85UbGys9u/fr+Dg4LJur9Rwgh8A3Nj4Oe+4m+ZI0fTp0zV48GANHDhQjRo10pw5c1S5cmW9//77Zd0aAAAoB26KI0XZ2dlKTk7WqFGjrMvc3NwUExOjxMTEMuysMBI+AKA8c9XvqfL4O+qmCEV//PGH8vLyFBISYrM8JCREP//8c6HxWVlZysrKst7PyMiQJGVmZkqS2r242GW9rZv4iM39vKyLLqtd0O+NUp/ei65d0etX5N5dWb8i915U/Yrc+/WuX5F7d2X9omq7+vdrwTaMMfY9yNwEjh07ZiSZTZs22SwfMWKEadmyZaHxY8eONZK4cePGjRs3bjfA7ejRo3blhZviSFGNGjXk7u6utLQ0m+VpaWkKDQ0tNH7UqFEaPny49X5+fr5Onz6t6tWry2KxFLu9zMxMhYeH6+jRo/Lz83N+B0qxfkXu/XrXr8i9X+/69H5j1qf3G7N+Re69pPWNMTp79qzCwsLsqn1ThCJPT09FRUVp9erV6tGjh6Q/g87q1auVkJBQaLyXl5e8vLxslgUEBJR4u35+ftflDVEa9Sty79e7fkXu/XrXp/cbsz6935j1K3LvJanv7+9vd82bIhRJ0vDhw9W/f381b95cLVu21MyZM3X+/HkNHDiwrFsDAADlwE0Tiv7nf/5Hv//+u8aMGaPU1FQ1a9ZMK1asKHTyNQAAuDndNKFIkhISEor8uMzVvLy8NHbs2EIfwVWE+hW59+tdvyL3fr3r0/uNWZ/eb8z6Fbn3613fYoy916kBAADcuG6ab7QGAAC4FkIRAACACEUAAACSCEUAAACSCEUOy8vL0+jRoxUZGSkfHx/VrVtXL730ks3/r2KM0ZgxY1SzZk35+PgoJiZGKSkpLqu/ZMkSdezY0fpN2zt27HBZ/zk5ORo5cqSaNGkiX19fhYWFqV+/fjp+/LhLeh83bpwaNGggX19fVatWTTExMUpKSnLZc3O5J554QhaLRTNnznRJ7QEDBshisdjcOnXq5NLe9+3bp27dusnf31++vr5q0aKFjhw54pL6V/ZecJs2bZrTtc+dO6eEhATVqlVLPj4+atSokebMmeOy5yYtLU0DBgxQWFiYKleurE6dOtk9p86ePathw4YpIiJCPj4+atOmjbZs2WJd78x8tae+M/O1uPrOzFd7endmvtpT/3Ilma/21ndmztrTu6Pz1Z76js5Xe2o7M1/tqV+S+bpu3Tp17dpVYWFhslgsWrp0qc16e+bn6dOnFRcXJz8/PwUEBGjQoEE6d+6c3ftTsCE4YNKkSaZ69epm+fLl5uDBg+bTTz81VapUMa+//rp1zCuvvGL8/f3N0qVLzc6dO023bt1MZGSkuXjxokvqL1y40IwfP968++67RpLZvn27y/pPT083MTEx5uOPPzY///yzSUxMNC1btjRRUVEu6X3RokVm1apV5tdffzU//fSTGTRokPHz8zMnT550Sf0CS5YsMU2bNjVhYWFmxowZLqndv39/06lTJ3PixAnr7fTp08XWtrf+gQMHTGBgoBkxYoTZtm2bOXDggFm2bJlJS0tzSf3L+z5x4oR5//33jcViMb/++qvTtQcPHmzq1q1r1qxZYw4ePGjeeecd4+7ubpYtW+Z07/n5+aZ169ambdu2ZvPmzebnn3828fHxpnbt2ubcuXPF1u/Tp49p1KiRWbt2rUlJSTFjx441fn5+5r///a8xxrn5ak99Z+ZrcfWdma/29O7MfLWnfoGSzld76zszZ4ur7cx8tae+o/PVntrOzNfi6pd0vn799dfmhRdeMEuWLDGSzOeff26z3p752alTJ9O0aVPz448/mvXr15vbbrvNPPLII3btSwFCkYMefPBB8/jjj9ss69mzp4mLizPG/PkDPDQ01EybNs26Pj093Xh5eZnFixc7Xf9yBw8eLPEP2ZLUL7B582YjyRw+fNjltTMyMowk891337ms9//+97/mlltuMT/99JOJiIiw64esPbX79+9vunfvXmwtR+v/z//8j/nb3/523epfqXv37uaBBx5wSe3GjRubCRMm2Iy5++67zQsvvOB0/f379xtJ5qeffrKuz8vLM0FBQebdd9+9Zu0LFy4Yd3d3s3z58iJ7c3a+Flf/co7M15LUL2DvfHWkdknmq731HZmv9tZ3dM7aU9uZ+erIc2/vfLWntjPztbj6zszXK0ORPfNz7969RpLZsmWLdcw333xjLBaLOXbsWLH7U4CPzxzUpk0brV69Wr/88oskaefOndqwYYM6d+4sSTp48KBSU1MVExNjfYy/v79atWqlxMREp+tf7/6LkpGRIYvFUuz/A1fS2tnZ2Zo7d678/f3VtGlTl/Sen5+vxx57TCNGjFDjxo2LrVnS3n/44QcFBwerfv36GjJkiE6dOuWS+vn5+frqq690++23KzY2VsHBwWrVqlWhQ8nO9l8gLS1NX331lQYNGuSS2m3atNEXX3yhY8eOyRijNWvW6JdfflHHjh2drp+VlSVJ8vb2tj7Gzc1NXl5e2rBhwzVr5+bmKi8vz+axkuTj46MNGzY4PV+Lq+8sR+rbO19LWruk89We+o7O15L078icLa62s/O1pM99SearPbWdma/F1Xdmvl7JnvmZmJiogIAANW/e3DomJiZGbm5uJfqolyNFDsrLyzMjR440FovFeHh4GIvFYl5++WXr+o0bNxpJ5vjx4zaP6927t+nTp4/T9S/nyF+eJalvjDEXL140d999t3n00UddVvvLL780vr6+xmKxmLCwMLN582aX9f7yyy+bv/zlLyY/P98YY+z+y9Oe2osXLzbLli0zu3btMp9//rlp2LChadGihcnNzXW6/okTJ4wkU7lyZTN9+nSzfft2M3nyZGOxWMwPP/zgkv4vN2XKFFOtWjW7PiKyp/alS5dMv379jCTj4eFhPD09zYIFC4qtbU/97OxsU7t2bdO7d29z+vRpk5WVZV555RUjyXTs2LHY+tHR0ea+++4zx44dM7m5uebf//63cXNzM7fffrvT87W4+pdzZL6WpL4xJZuv9tZ2dL7aU9/R+WpvfWfm7LVqOztf7en9ciWZr/bUdma+FlffmfmqK44U2TM/J02aVORzFhQUZN566y2794lQ5KDFixebWrVqmcWLF5tdu3aZhQsXmsDAQDN//nxjjPOhqLj6l3Pkh2xJ6mdnZ5uuXbuau+66y2RkZLis9rlz50xKSopJTEw0jz/+uKlTp45dn8MXV3/r1q0mJCTE5pCpvT9kS/K8FPj111/t/iihuPrHjh0zkgp9Dt61a1fTt29fl/dfv359k5CQUGxde2tPmzbN3H777eaLL74wO3fuNLNnzzZVqlQxq1atckn9rVu3mqZNmxpJxt3d3cTGxprOnTubTp06FVv/wIEDpl27dtbHtmjRwsTFxZkGDRq4JBRdq/7lHA1F9tYv6Xy1t7aj87W4+s7M15L0f7mSzNlr1XZ2vpa095LMV3tqOzNf7anv6HwlFFVAtWrVMm+88YbNspdeesnUr1/fGPN/k+7KH3zt2rUzTz/9tNP1L+fID1l762dnZ5sePXqYO++80/zxxx8urX2l22677ZpHNeytP2PGDGOxWIy7u7v1Jsm4ubmZiIiI69J7jRo1zJw5c5zuPSsry3h4eJiXXnrJZszzzz9v2rRp43T9y61bt85IMjt27Ci2rj21L1y4YCpVqlToHINBgwaZ2NhYl/aenp5uPcm3ZcuW5sknn7RrH4z585d7wQ/XPn36mC5dujg9X4urfzlHQ5E99R2ZryXp/XL2ztfi6jszX53p3945e63azs7XkvRe0vlaXG1n52tJei/pfL0yFNkzP9977z0TEBBgsz4nJ8e4u7ubJUuW2L0vnFPkoAsXLsjNzfbpc3d3V35+viQpMjJSoaGhWr16tXV9ZmamkpKSFB0d7XR9Z9lTPycnR3369FFKSoq+++47Va9e3WW1i5Kfn2/9HNqZ+o899ph27dqlHTt2WG9hYWEaMWKEvv32W5f3/t///lenTp1SzZo1ne7d09NTLVq00P79+23G/PLLL4qIiHC6/uXee+89RUVF2XVeiD21c3JylJOT4/D7tiS9+/v7KygoSCkpKdq6dau6d+9u1z5Ikq+vr2rWrKkzZ87o22+/Vffu3Z2er8XVd6Wr1Xd0vjrau73ztbj6zsxXR/svyZy9Vm1n52tJei/pfC2utrPztSS9OzNfJft+n0ZHRys9PV3JycnWMd9//73y8/PVqlUr+zdmd3yCjf79+5tbbrnFevnwkiVLTI0aNczzzz9vHfPKK6+YgIAA62fZ3bt3t/sSX3vqnzp1ymzfvt189dVXRpL56KOPzPbt282JEyecrp+dnW26detmatWqZXbs2GFzSWhWVpZTtc+dO2dGjRplEhMTzaFDh8zWrVvNwIEDjZeXl82VCs48N1ey93B8cbXPnj1rnnvuOZOYmGgOHjxovvvuO3P33XebevXqmUuXLrmk9yVLlphKlSqZuXPnmpSUFDN79mzj7u5u1q9f77LnJiMjw1SuXNm8/fbbxdYsSe377rvPNG7c2KxZs8b89ttvZt68ecbb29uuw9f21P/kk0/MmjVrzK+//mqWLl1qIiIiTM+ePe3qf8WKFeabb74xv/32m1m5cqVp2rSpadWqlcnOzjbGODdf7anvzHwtrr4z87W42s7OV3uemyuV9OOza9V3ds4W17sz89Xe58aR+WpPbWfmqz31SzJfz549a7Zv3262b99uJFnP0Sq4etKe+dmpUydz1113maSkJLNhwwZTr149LskvLZmZmeaZZ54xtWvXNt7e3ubWW281L7zwgs0PoPz8fDN69GgTEhJivLy8TIcOHcz+/ftdVn/evHlGUqHb2LFjna5fcIi/qNuaNWucqn3x4kXz0EMPmbCwMOPp6Wlq1qxpunXrZveJm/Y8N1ey94dscbUvXLhgOnbsaIKCgkylSpVMRESEGTx4sElNTXVp7++995657bbbjLe3t2natKlZunSpS+u/8847xsfHx6Snp9tV197aJ06cMAMGDDBhYWHG29vb1K9f37z22mvWE2idrf/666+bWrVqmUqVKpnatWubF1980a5f+sYY8/HHH5tbb73VeHp6mtDQUDN06FCb/XdmvtpT35n5Wlx9Z+ZrcbWdna/2PDdXKmkoulZ9Z+esPb07Ol/tre/IfLWntjPz1Z76JZmva9asKfL9279/f2OMffPz1KlT5pFHHjFVqlQxfn5+ZuDAgebs2bMles4sxlzla4ABAABuIpxTBAAAIEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRAACAJEIRgJvQ/PnzFRAQUNZtAChn+PJGADedixcv6uzZswoODrb7Me3bt1ezZs00c+bM69cYgDLlUdYNAEBp8/HxkY+PT1m3AaCc4eMzABVO+/btlZCQoISEBPn7+6tGjRoaPXq0Cg58nzlzRv369VO1atVUuXJlde7cWSkpKdbHX/nx2bhx49SsWTP9+9//Vp06deTv76++ffvq7NmzkqQBAwZo7dq1ev3112WxWGSxWHTo0CGdOXNGcXFxCgoKko+Pj+rVq6d58+aV6nMBwHUIRQAqpAULFsjDw0ObN2/W66+/runTp+tf//qXpD9DzNatW/XFF18oMTFRxhh16dJFOTk5V63366+/aunSpVq+fLmWL1+utWvX6pVXXpEkvf7664qOjtbgwYN14sQJnThxQuHh4Ro9erT27t2rb775Rvv27dPbb7+tGjVqlMr+A3A9Pj4DUCGFh4drxowZslgsql+/vnbv3q0ZM2aoffv2+uKLL7Rx40a1adNGkrRo0SKFh4dr6dKl6t27d5H18vPzNX/+fFWtWlWS9Nhjj2n16tWaNGmS/P395enpqcqVKys0NNT6mCNHjuiuu+5S8+bNJUl16tS5vjsN4LriSBGACql169ayWCzW+9HR0UpJSdHevXvl4eGhVq1aWddVr15d9evX1759+65ar06dOtZAJEk1a9bUyZMnr9nDkCFD9NFHH6lZs2Z6/vnntWnTJif2CEBZIxQBgKRKlSrZ3LdYLMrPz7/mYzp37qzDhw/r2Wef1fHjx9WhQwc999xz17NNANcRoQhAhZSUlGRz/8cff1S9evXUqFEj5ebm2qw/deqU9u/fr0aNGjm8PU9PT+Xl5RVaHhQUpP79++uDDz7QzJkzNXfuXIe3AaBsEYoAVEhHjhzR8OHDtX//fi1evFizZ8/WM888o3r16ql79+4aPHiwNmzYoJ07d+pvf/ubbrnlFnXv3t3h7dWpU0dJSUk6dOiQ/vjjD+Xn52vMmDFatmyZDhw4oD179mj58uVq2LChC/cSQGkiFAGokPr166eLFy+qZcuWGjp0qJ555hnFx8dLkubNm6eoqCj99a9/VXR0tIwx+vrrrwt9RFYSzz33nNzd3dWoUSMFBQXpyJEj8vT01KhRo3TnnXeqXbt2cnd310cffeSqXQRQyvhGawAVDt8uDeB64EgRAACACEUAAACS+PgMAABAEkeKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJBGKAAAAJEn/H6i60oTTbSfJAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(data=data, x='points')\n", + "plt.title(\"count wines rate depending on the points\")\n", + "plt.xlabel(\"points\")\n", + "plt.ylabel(\"count\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SH9b95lPfMAY" + }, + "source": [ + "Видим, что количество вин с оценкой в диапазоне 87-90 больше всего, следовательно, для них признак оценки будет меньше влиять на модель." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lGZe1Xt4SGlb" + }, + "source": [ + "Проверим что оценка и стоимость вина не линейно-зависимы." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "srmdq10zQ2jp", + "outputId": "c2c7cdd1-099f-4af9-fbc1-2d286c005292" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(data=data, x='points', y='price', errorbar=None)\n", + "plt.title(\"price rate depending on the points\")\n", + "plt.xlabel(\"points\")\n", + "plt.ylabel(\"price\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gV5rvdzQf9Rm" + }, + "source": [ + "Проверили. Признаки не линейно-зависимы." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H2EamkuhAPKu" + }, + "source": [ + "Посмотрим на зависимость оценки вина от года розлива." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 450 + }, + "id": "zm_kgoac_-Oq", + "outputId": "91648859-c779-404e-93b8-8fafbedf6387" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20, 5))\n", + "sns.barplot(data=data, x='year', y='points', errorbar=None)\n", + "plt.title(\"points rate depending on the year\")\n", + "plt.xlabel(\"year\")\n", + "plt.ylabel(\"points\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "890pOlA8A2se" + }, + "source": [ + "Проверили. Значения не линейно-зависимы. Функцию нельзя считать константной т.к. колебания оценки на 1-2 могут быть существенны (видно из предыдущего графика)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ndxrlPqeEb0X" + }, + "source": [ + "Посмотрим на зависимость цены вина от года розлива." + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 450 + }, + "id": "Fsl2BUlQEVpj", + "outputId": "713d79b8-9076-428e-f6e5-01a72c939ca6" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.figure(figsize=(20, 5))\n", + "sns.barplot(data=data, x='year', y='price', errorbar=None)\n", + "plt.title(\"price rate depending on the year\")\n", + "plt.xlabel(\"year\")\n", + "plt.ylabel(\"price\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BTYtTderEeaN" + }, + "source": [ + "Проверили. Признаки не линейно-зависимы." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "agLGino1gDOT" + }, + "source": [ + "# Модель и обучение" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": { + "id": "uup4zgoEgomo" + }, + "outputs": [], + "source": [ + "X = data.drop(['variety'], axis=1)\n", + "Y = data['variety']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4K3fctb5NeuY" + }, + "source": [ + "Представляем данные в виде необходимом для обучения." + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": { + "id": "KWPAaK1uhhoK" + }, + "outputs": [], + "source": [ + "province = pd.get_dummies(X['province'], drop_first=True)\n", + "winery = pd.get_dummies(X['winery'], drop_first=True)\n", + "\n", + "X_gd = X.drop(['province', 'winery'], axis=1)\n", + "\n", + "X_gd = pd.concat([X_gd, province, winery], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "41KFmdv-gGrU" + }, + "source": [ + "Разбиваем данные на тренировочные и тестовые" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 479 + }, + "id": "-SLpeeP7y1fL", + "outputId": "8ec058a1-2d75-4d0d-da3d-2f734c406540" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " points price year Italy Other Lombardy Northeastern Italy \\\n", + "47486 91 55.000000 2007.0 False False False \n", + "122282 87 44.428758 2011.0 False False False \n", + "96663 87 20.000000 2011.0 False False False \n", + "21437 88 26.000000 2013.0 False False False \n", + "66082 87 45.000000 2013.0 False False False \n", + "... ... ... ... ... ... ... \n", + "84833 86 45.000000 2013.0 False False False \n", + "77114 87 44.428758 2006.0 False False False \n", + "80524 88 23.000000 2015.0 False False False \n", + "12797 88 18.000000 2013.0 False False False \n", + "109138 86 16.000000 2012.0 False False False \n", + "\n", + " Northwestern Italy Piedmont Sicily and Sardinia Southern Italy \\\n", + "47486 False True False False \n", + "122282 False False False False \n", + "96663 False False False False \n", + "21437 False False False False \n", + "66082 False True False False \n", + "... ... ... ... ... \n", + "84833 False True False False \n", + "77114 False False False False \n", + "80524 False False False False \n", + "12797 False False False False \n", + "109138 False False True False \n", + "\n", + " ... Vèscine Wine for Food Zanoni Zenato Zeni Ziobaffa Zisola \\\n", + "47486 ... False False False False False False False \n", + "122282 ... False False False False False False False \n", + "96663 ... False False False False False False False \n", + "21437 ... False False False False False False False \n", + "66082 ... False False False False False False False \n", + "... ... ... ... ... ... ... ... ... \n", + "84833 ... False False False False False False False \n", + "77114 ... False False False False False False False \n", + "80524 ... False False False False False False False \n", + "12797 ... False False False False False False False \n", + "109138 ... 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Consider increasing max_iter to improve the fit.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import SGDClassifier\n", + "\n", + "# create dict with paramemeters for model\n", + "param_grid_clf = {\n", + " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", + " 'max_iter': [100, 200, 400, 800, 1000]\n", + "}\n", + "\n", + "# create model\n", + "clf = SGDClassifier(random_state=42)\n", + "\n", + "# selection of best parameters\n", + "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", + "\n", + "# train model\n", + "grid_search_clf.fit(X_train_lbe, Y_train)\n", + "\n", + "# check of best parameters\n", + "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", + "\n", + "# prediction of results\n", + "y_pred_sgdc_lbe = grid_search_clf.predict(X_test_lbe)\n", + "\n", + "# check of metric values\n", + "print(classification_report(Y_test, y_pred_sgdc_lbe))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kp6yGqjKMGMH" + }, + "source": [ + "Точность модели SGDClassifier очень низкая." + ] + }, + { + "cell_type": "markdown", + "source": [ + "Были попытки перебирать разные параметры ядра модли SVC, но это катастрафически замедляло процесс, а результаты не становились сильно лучше, поэтому тут использую дефолтные параметры." + ], + "metadata": { + "id": "pICdMV1OhQHV" + } + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h4Zc9hPFK2HD", + "outputId": "0e187de2-7e96-413d-fc6c-1165e991a5f3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.52 0.22 0.31 526\n", + " Red Blend 0.46 0.93 0.61 742\n", + " Sangiovese 0.00 0.00 0.00 457\n", + "\n", + " accuracy 0.46 1725\n", + " macro avg 0.33 0.38 0.31 1725\n", + "weighted avg 0.36 0.46 0.36 1725\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.svm import SVC\n", + "\n", + "clf = SVC()\n", + "clf.fit(X_train_lbe, Y_train)\n", + "y_pred_svc_lbe = clf.predict(X_test_lbe)\n", + "\n", + "# check of metric values\n", + "print(classification_report(Y_test, y_pred_svc_lbe))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UrYWrS02OeQP" + }, + "source": [ + "Точность у модели SVC очень низкая." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QZdAmoL2Lhqp" + }, + "source": [ + "# PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-OnYsi83ZVQ6", + "outputId": "29c543bf-e71c-4d1d-c3e6-bde96f7d497b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 4.26507051e+00, -2.31516787e+00, 1.63172872e+00, ...,\n", + " 3.58225259e-18, 2.88184583e-18, 1.30234266e-20],\n", + " [-6.43751638e+00, 1.66052025e+00, -2.08758127e+00, ...,\n", + " 3.15901817e-18, 1.87872264e-19, -8.05538076e-20],\n", + " [-3.08530170e+01, 1.59344524e+00, -1.33581991e+00, ...,\n", + " -2.35725523e-18, -1.67147617e-18, 1.11176722e-20],\n", + " ...,\n", + " [-2.78372126e+01, 5.59889241e+00, -4.34360135e-01, ...,\n", + " -5.38174105e-20, -7.44436577e-21, 8.56165509e-21],\n", + " [-3.28294277e+01, 3.58499107e+00, -2.86877737e-01, ...,\n", + " -4.94705731e-20, -2.86098011e-20, 3.72404376e-21],\n", + " [-3.48845942e+01, 2.58382951e+00, -2.20524267e+00, ...,\n", + " -6.24065254e-20, -3.38691686e-20, 1.20095839e-21]])" + ] + }, + "metadata": {}, + "execution_count": 190 + } + ], + "source": [ + "pca = PCA()\n", + "X_train_pca = pca.fit_transform(X_train_gd)\n", + "X_test_pca = pca.transform(X_test_gd)\n", + "X_train_pca" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "zdsFUNX4P6Cy", + "outputId": "f79cd830-3838-4566-8c62-7274b1352ad7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(np.arange(pca.n_components_) + 1, pca.explained_variance_ratio_, 'ro-')\n", + "plt.title('dependence of explained variance ratio on the number of components.')\n", + "plt.xlabel('n components')\n", + "plt.ylabel('explained variance ratio')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4_m5ji1CRrOB" + }, + "source": [ + "Видим что большинство параметров дает распределение дисперсии близкое к нулю. Но их количество все ещё слишком велико." + ] + }, + { + "cell_type": "markdown", + "source": [ + "Попробуем установить конкретное количество компонент." + ], + "metadata": { + "id": "fRK5Ut_IXyZt" + } + }, + { + "cell_type": "code", + "source": [ + "pca = PCA(n_components=30)\n", + "X_train_pca = pca.fit_transform(X_train_gd)\n", + "X_test_pca = pca.transform(X_test_gd)\n", + "X_train_pca" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SQJDyhOAXlLg", + "outputId": "22951ca3-dd32-46d3-b5d3-0aeac5256f23" + }, + "execution_count": 192, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 4.26507051e+00, -2.31516787e+00, 1.63172872e+00, ...,\n", + " 5.05657142e-04, 3.32621442e-03, 6.09493572e-03],\n", + " [-6.43751638e+00, 1.66052025e+00, -2.08758127e+00, ...,\n", + " -3.35528178e-03, 1.48453024e-03, 1.42042476e-03],\n", + " [-3.08530170e+01, 1.59344524e+00, -1.33581991e+00, ...,\n", + " 1.46636958e-03, 3.41066691e-03, -1.85772109e-03],\n", + " ...,\n", + " [-2.78372126e+01, 5.59889241e+00, -4.34360135e-01, ...,\n", + " -5.51559775e-03, 1.44925595e-03, 6.24520492e-03],\n", + " [-3.28294277e+01, 3.58499107e+00, -2.86877737e-01, ...,\n", + " -5.74142636e-03, 1.06299879e-03, 2.40827452e-03],\n", + " [-3.48845942e+01, 2.58382951e+00, -2.20524267e+00, ...,\n", + " 1.57198953e-02, 1.40492514e-02, 1.21808361e-02]])" + ] + }, + "metadata": {}, + "execution_count": 192 + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.plot(np.arange(pca.n_components_) + 1, pca.explained_variance_ratio_, 'ro-')\n", + "plt.title('dependence of explained variance ratio on the number of components.')\n", + "plt.xlabel('n components')\n", + "plt.ylabel('explained variance ratio')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "BJhCUDZMXunC", + "outputId": "a3e4f378-f6b1-4149-f3f3-f6eb7d693644" + }, + "execution_count": 193, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PPOGb3m22t0h", + "outputId": "17b942fe-602e-4fbb-b831-1e02488bb0d9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best parameters for DecisionTreeClassifier: {'criterion': 'entropy', 'max_depth': None}\n", + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.98 0.98 0.98 526\n", + " Red Blend 0.76 0.74 0.75 742\n", + " Sangiovese 0.62 0.64 0.63 457\n", + "\n", + " accuracy 0.79 1725\n", + " macro avg 0.78 0.79 0.78 1725\n", + "weighted avg 0.79 0.79 0.79 1725\n", + "\n" + ] + } + ], + "source": [ + "param_grid_clf = {\n", + " 'criterion': ('entropy', 'gini', 'log_loss'),\n", + " 'max_depth': [5, 10, None],\n", + "}\n", + "clf = DecisionTreeClassifier(random_state=42)\n", + "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", + "grid_search_clf.fit(X_train_pca, Y_train)\n", + "y_pred_dtc_pca = grid_search_clf.predict(X_test_pca)\n", + "\n", + "print(\"best parameters for DecisionTreeClassifier:\", grid_search_clf.best_params_)\n", + "print(classification_report(Y_test, y_pred_dtc_pca))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BBCSipxwINo7" + }, + "source": [ + "Видим, что после уменьшения размерности с помощью PCA модель неплохо справляется с предсказанием." + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KXjsGlcsg01u", + "outputId": "54d80349-e02d-448e-fb56-93d05eb49a55" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best parameters for SGDClassifier: {'loss': 'hinge', 'max_iter': 100}\n", + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.97 0.98 0.97 526\n", + " Red Blend 0.67 0.83 0.74 742\n", + " Sangiovese 0.59 0.35 0.44 457\n", + "\n", + " accuracy 0.75 1725\n", + " macro avg 0.74 0.72 0.72 1725\n", + "weighted avg 0.74 0.75 0.73 1725\n", + "\n" + ] + } + ], + "source": [ + "param_grid_clf = {\n", + " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", + " 'max_iter': [100, 200, 400, 800, 1000]\n", + "}\n", + "clf = SGDClassifier(random_state=42)\n", + "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", + "grid_search_clf.fit(X_train_pca, Y_train)\n", + "y_pred_sgdc_pca = grid_search_clf.predict(X_test_pca)\n", + "\n", + "\n", + "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", + "print(classification_report(Y_test, y_pred_sgdc_pca))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kign9MkOJcdI" + }, + "source": [ + "Такая модель так же неплохо справляется с предсказанием, но все же менее точно чем DecisionTreeClassifier." + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S1CpDRTxhLBG", + "outputId": "95ece058-e7fd-4992-90bf-b471c88e23ff" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.85 0.93 0.88 526\n", + " Red Blend 0.62 0.86 0.72 742\n", + " Sangiovese 0.67 0.18 0.28 457\n", + "\n", + " accuracy 0.70 1725\n", + " macro avg 0.71 0.65 0.63 1725\n", + "weighted avg 0.70 0.70 0.65 1725\n", + "\n" + ] + } + ], + "source": [ + "clf = SVC()\n", + "clf.fit(X_train_pca, Y_train)\n", + "y_pred_svc_pca = clf.predict(X_test_pca)\n", + "\n", + "print(classification_report(Y_test, y_pred_svc_pca))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oFIv_FUSHPFg" + }, + "source": [ + "Такая модель неплохо справляется с предсказанием для Nebbiolo и Red Blend, но для Sngiovese предсказание получется слишком неточным, следовательно, общая точность модели достаточно низкая." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kEYXLr8sJzql" + }, + "source": [ + "# t-SNE" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "id": "5EYwtlZrkyTV", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "42e116c4-e1d5-463c-de88-3b2ac96199c9" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 8.697849 , 56.422546 ],\n", + " [ 85.46976 , -6.209872 ],\n", + " [-91.522644 , 3.429889 ],\n", + " ...,\n", + " [-31.943085 , 35.00665 ],\n", + " [ 5.8764524, -20.747879 ],\n", + " [-89.09723 , 19.900454 ]], dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 197 + } + ], + "source": [ + "tsne = TSNE(random_state=42)\n", + "X_train_tsne = tsne.fit_transform(X_train_lbe)\n", + "X_test_tsne = tsne.fit_transform(X_test_lbe)\n", + "X_train_tsne" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "id": "5iL9lCiGlnjH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d4e4d501-cff9-4417-9fc1-622c61907242" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best parameters for DecisionTreeClassifier: {'criterion': 'entropy', 'max_depth': None}\n", + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.28 0.19 0.22 526\n", + " Red Blend 0.42 0.35 0.38 742\n", + " Sangiovese 0.30 0.49 0.37 457\n", + "\n", + " accuracy 0.34 1725\n", + " macro avg 0.33 0.34 0.33 1725\n", + "weighted avg 0.35 0.34 0.33 1725\n", + "\n" + ] + } + ], + "source": [ + "param_grid_clf = {\n", + " 'criterion': ('entropy', 'gini', 'log_loss'),\n", + " 'max_depth': [5, 10, None],\n", + "}\n", + "clf = DecisionTreeClassifier(random_state=42)\n", + "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", + "grid_search_clf.fit(X_train_tsne, Y_train)\n", + "y_pred_dtc_tsne = grid_search_clf.predict(X_test_tsne)\n", + "\n", + "print(\"best parameters for DecisionTreeClassifier:\", grid_search_clf.best_params_)\n", + "print(classification_report(Y_test, y_pred_dtc_tsne))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3dEtGnIkJ5kH" + }, + "source": [ + "Модель предсказывает данные с очень низкой точностью, после применения t-SNE." + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "shg3-6ZAzWVV", + "outputId": "035d4ce7-1c79-43e2-d0de-32b74d562176" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best parameters for SGDClassifier: {'loss': 'modified_huber', 'max_iter': 100}\n", + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.29 0.37 0.32 526\n", + " Red Blend 0.42 0.59 0.49 742\n", + " Sangiovese 0.00 0.00 0.00 457\n", + "\n", + " accuracy 0.37 1725\n", + " macro avg 0.24 0.32 0.27 1725\n", + "weighted avg 0.27 0.37 0.31 1725\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:744: ConvergenceWarning:\n", + "\n", + "Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n" + ] + } + ], + "source": [ + "param_grid_clf = {\n", + " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", + " 'max_iter': [100, 200, 400, 800, 1000]\n", + "}\n", + "clf = SGDClassifier(random_state=42)\n", + "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", + "grid_search_clf.fit(X_train_tsne, Y_train)\n", + "y_pred_sgdc_tsne = grid_search_clf.predict(X_test_tsne)\n", + "\n", + "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", + "print(classification_report(Y_test, y_pred_sgdc_tsne))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uIuYXlGsKDT9" + }, + "source": [ + "После t-SNE точность модели сильно снизилась." + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mS7KSWX9zYB-", + "outputId": "255f5776-375a-4bc6-da15-3533a8e04dc9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.38 0.55 0.45 526\n", + " Red Blend 0.46 0.59 0.52 742\n", + " Sangiovese 0.00 0.00 0.00 457\n", + "\n", + " accuracy 0.42 1725\n", + " macro avg 0.28 0.38 0.32 1725\n", + "weighted avg 0.31 0.42 0.36 1725\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", + "\n", + "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + "\n" + ] + } + ], + "source": [ + "clf = SVC()\n", + "clf.fit(X_train_tsne, Y_train)\n", + "y_pred_svc_tsne = clf.predict(X_test_tsne)\n", + "\n", + "print(classification_report(Y_test, y_pred_svc_tsne))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_o6olr86LPuk" + }, + "source": [ + "После t-SNE у точность модели сильно снизилась." + ] + }, + { + "cell_type": "markdown", + "source": [ + "# StandartScaler" + ], + "metadata": { + "id": "c5HYyaDQk_FA" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import StandardScaler" + ], + "metadata": { + "id": "8_5pvsWylDf7" + }, + "execution_count": 203, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "scaler = StandardScaler()\n", + "X_train_scal = scaler.fit_transform(X_train_lbe)\n", + "X_test_scal = scaler.transform(X_test_lbe)\n", + "X_train_scal" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6_FpArVPlRmC", + "outputId": "cdbcd123-c798-4082-9b35-3a211947ce67" + }, + "execution_count": 204, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 0.64223813, 0.1020881 , -0.79932341, 0.29019682, -0.24042572],\n", + " [-0.83935434, -0.15453387, 0.68902728, 1.6211048 , 0.17319704],\n", + " [-0.83935434, -0.7475537 , 1.18514418, -1.6511276 , 0.17319704],\n", + " ...,\n", + " [-0.46895622, -0.67472726, 0.68902728, -0.16611449, 0.58681979],\n", + " [-0.46895622, -0.79610466, 0.68902728, 0.77652861, 0.38000841],\n", + " [-1.20975246, -0.84465562, -0.30320652, -1.58107982, 0.27660272]])" + ] + }, + "metadata": {}, + "execution_count": 204 + } + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2d0bd8a9-07b1-435d-bff5-ce0aee53b692", + "id": "Ooxr06_ll6xq" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best parameters for DecisionTreeClassifier: {'criterion': 'entropy', 'max_depth': None}\n", + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.99 0.98 0.99 526\n", + " Red Blend 0.80 0.80 0.80 742\n", + " Sangiovese 0.69 0.71 0.70 457\n", + "\n", + " accuracy 0.83 1725\n", + " macro avg 0.83 0.83 0.83 1725\n", + "weighted avg 0.83 0.83 0.83 1725\n", + "\n" + ] + } + ], + "source": [ + "param_grid_clf = {\n", + " 'criterion': ('entropy', 'gini', 'log_loss'),\n", + " 'max_depth': [5, 10, None],\n", + "}\n", + "clf = DecisionTreeClassifier(random_state=42)\n", + "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", + "grid_search_clf.fit(X_train_scal, Y_train)\n", + "y_pred_dtc_scal= grid_search_clf.predict(X_test_scal)\n", + "\n", + "print(\"best parameters for DecisionTreeClassifier:\", grid_search_clf.best_params_)\n", + "print(classification_report(Y_test, y_pred_dtc_scal))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CqRCrNacl6xs" + }, + "source": [ + "Модель предсказывает данные с неплохой точностью, после применения масштабирования." + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "517356ec-5a3b-408c-c7fa-b1ce76633ec4", + "id": "zwAM4RXNl6xt" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "best parameters for SGDClassifier: {'loss': 'hinge', 'max_iter': 100}\n", + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.79 1.00 0.88 526\n", + " Red Blend 0.59 0.80 0.68 742\n", + " Sangiovese 0.49 0.07 0.12 457\n", + "\n", + " accuracy 0.67 1725\n", + " macro avg 0.63 0.62 0.56 1725\n", + "weighted avg 0.63 0.67 0.59 1725\n", + "\n" + ] + } + ], + "source": [ + "param_grid_clf = {\n", + " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", + " 'max_iter': [100, 200, 400, 800, 1000]\n", + "}\n", + "clf = SGDClassifier(random_state=42)\n", + "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", + "grid_search_clf.fit(X_train_scal, Y_train)\n", + "y_pred_sgdc_scal = grid_search_clf.predict(X_test_scal)\n", + "\n", + "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", + "print(classification_report(Y_test, y_pred_sgdc_scal))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "plhnClvOl6xu" + }, + "source": [ + "Точность модели для Nebbiolo и Red Blend неплохая, но для Sangiovese предсказания очень неточные.\n", + "\n", + "Общая точность модели довольно низкая." + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eb870630-4335-4f98-8c5d-a6753e1a8af8", + "id": "FFtQnBr-l6xv" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " Nebbiolo 0.95 1.00 0.97 526\n", + " Red Blend 0.70 0.80 0.75 742\n", + " Sangiovese 0.63 0.44 0.52 457\n", + "\n", + " accuracy 0.77 1725\n", + " macro avg 0.76 0.75 0.75 1725\n", + "weighted avg 0.76 0.77 0.76 1725\n", + "\n" + ] + } + ], + "source": [ + "clf = SVC()\n", + "clf.fit(X_train_scal, Y_train)\n", + "y_pred_svc_scal = clf.predict(X_test_scal)\n", + "\n", + "print(classification_report(Y_test, y_pred_svc_scal))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "После масштабирования данных модель SVC показала себя лучше чем на предыдущих вариантах. Такую точность можно так же считать неплохой." + ], + "metadata": { + "id": "00bHBluAnIjq" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Графики и сравнения моделей" + ], + "metadata": { + "id": "b6p5JvSinblz" + } + }, + { + "cell_type": "code", + "source": [ + "fig, axes = plt.subplots(5, 3, figsize=(20, 30))\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_gd), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[0, 0])\n", + "axes[0, 0].set_title('confusion_matrix for DecisionTree after get_dummies')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_lbe), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[1, 0])\n", + "axes[1, 0].set_title('confusion_matrix for DecisionTree after LabelEncoder')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_lbe), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[1, 1])\n", + "axes[1, 1].set_title('confusion_matrix for SGDClassifier after LabelEncoder')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_lbe), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[1, 2])\n", + "axes[1, 2].set_title('confusion_matrix for SVC after LabelEncoder')\n", + "\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_pca), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[2, 0])\n", + "axes[2, 0].set_title('confusion_matrix for DecisionTree after PCA')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_pca), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[2, 1])\n", + "axes[2, 1].set_title('confusion_matrix for SGDClassifier after PCA')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_pca), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[2, 2])\n", + "axes[2, 2].set_title('confusion_matrix for SVC after PCA')\n", + "\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_tsne), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[3, 0])\n", + "axes[3, 0].set_title('confusion_matrix for DecisionTree after t-SNE')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_tsne), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[3, 1])\n", + "axes[3, 1].set_title('confusion_matrix for SGDClassifier after t-SNE')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_tsne), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[3, 2])\n", + "axes[3, 2].set_title('confusion_matrix for SVC after t-SNE')\n", + "\n", + "\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_scal), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[4, 0])\n", + "axes[4, 0].set_title('confusion_matrix for DecisionTree after SandartScaler')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_scal), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[4, 1])\n", + "axes[4, 1].set_title('confusion_matrix for SGDClassifier after SandartScaler')\n", + "\n", + "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_scal), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[4, 2])\n", + "axes[4, 2].set_title('confusion_matrix for SVC after SandartScaler')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "nETXJ9qLfdei", + "outputId": "c2ec037a-9c8c-410e-d4c4-e6edef85053b" + }, + "execution_count": 223, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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HH6tPnz5q3Lix6tWrl6/+/vrrr/ruu+80aNAgSdKECRPUsWNHjRgxQtOnT9cTTzyhM2fO6PXXX9cjjzyiVatW2ZadP3++zp8/r4EDB6ps2bJat26dpk6dqiNHjmj+/PmSMqtWjx07luPtsrPMnDlTSUlJGjBggLy8vBQUFJTtucghISF69913dd9992nq1Kl66qmnlJGRoT59+qh06dKaPn16rtv42GOPqUKFCho/frzt1uZZt+lesWKF2rdvr2rVqmn06NG6cOGCpk6dqhYtWmjTpk2qUqWK3bruu+8+1ahRQ+PHj/9Xt4TM+vyWLVumcePGSZJiYmLUvHlzWSwWDR48WMHBwfrxxx/Vr18/xcfH267WS09PV8eOHbVy5Up1795dTz/9tM6dO6fly5dr27Ztql69eo7v+fjjj+urr77S4MGDVbduXZ06dUq//fabdu7cqRtvvDHHZYwxuvvuu/XTTz+pX79+atSokZYuXapnn31WR48ezXa14G+//aZvvvlGTzzxhEqXLq0pU6aoa9euOnz4sMqWLZvn/XPq1Cm1b99e3bt310MPPaTQ0FBlZGTo7rvv1m+//aYBAwaoTp062rp1qyZNmqQ9e/bYPaNn3Lhxeumll3T//ferf//+OnHihKZOnapbb71Vf/755xWv2Fy/fr3++OMPde/eXRUrVtTBgwf17rvv6rbbbtOOHTtUqlQpdenSRYGBgRo6dKjtlpJ+fn7y9fXV2bNndeTIEdu+8fPzk6R89V+SVq1apS+//FKDBw9WuXLlsh2LefH333/Lzc1NgYGB2rt3r3bt2qVHHnkkX7emnDNnjqpXr66mTZuqfv36KlWqlD7//HM9++yz+e4PABQFMiWZ8lKulikjIiK0YsUKrVq16oqP4Pntt98UFxen4cOHO+SWmVJmhrn33nv10UcfaceOHapXr56+++47SdLDDz98zet99913Va9ePd19991yd3fX999/ryeeeEIZGRm24yc2NlZt27ZVcHCwnn/+eQUGBurgwYP65ptvbOtZvny5evTooTZt2ui1116TJO3cuVO///67nn766RzfOz/fzYLMbrNmzZKfn5+GDRsmPz8/rVq1Si+//LLi4+P1xhtv5NrXBg0aqHnz5ho4cKDuvfde221Bs7Zh+/btatGihSpUqKDnn39evr6++vLLL9W5c2d9/fXX2X6XPfHEEwoODtbLL7+sxMTEXPubm/3790uSLedfy/GRnJysr7/+2nY7/x49eqhv376Kjo5WWFhYvvsEACD/kn9LRv719fXVPffco6+++kqnT59WUFCQbd4XX3yh9PR09ezZ0zbt+++/V7Vq1XTzzTfnus6rycvn9sILL6hWrVp6//33NXbsWFWtWlXVq1dXVFSU7dgeMGCAJNnO4eb1nHCWV155RZ6enho+fLiSk5PzfeejM2fO6MyZM7bbnv/4449KS0vLd8afM2eOunTpIk9PT/Xo0UPvvvuu1q9fr6ZNm+ZrPQCcTNGOy5dsvXr1Mlar1axfvz7bvKyr24YMGWIkmV9//dU279y5c6Zq1aqmSpUqJj093Rhz8WqqOnXq2F0Z+fbbbxtJZuvWrbZpWVexnThxwu49lcerGxs2bJhr1cXl75Fl8+bNRpLp37+/Xbvhw4cbSWbVqlV27yfJ/PLLL7ZpsbGxxsvLyzzzzDNXfN/LSTJeXl52Vy2+9957RpIJCwuzqyodOXKkkWTX9tKr4bJMmDDBWCwWc+jQIdu0nKqCjbl4daO/v7+JjY3NcV7W1Y1ZevToYUqVKmX27Nlj3njjDSPJLFy48KrbmlsFd6NGjUxISIg5deqUbdqWLVuM1Wo1vXr1sk3L+sx69Ohx1fe60vtdqmHDhqZMmTK2n/v162fKly9vTp48adeue/fuJiAgwLa/P/74YyPJvPXWW9nWeemVn5cfswEBATleIXipyyvGFy5caCSZV1991a5dt27djMViyVah7unpaTdty5YtRpKZOnVqju+XW8W4JDNjxgy76Z9++qmxWq1233djMq/C1SVXfB48eNC4ubmZcePG2bXbunWrcXd3zzb9cjkd16tXrzaSzCeffGKbllvlTIcOHbJV3een/8Zk7kur1Wq2b99+xb5madWqlaldu7Y5ceKEOXHihNm5c6d56qmnjCTblbPffvutkWQmTZqUp3Uak3m1dtmyZc0LL7xgm/bggw+ahg0b5nkdAFDUyJRkSlfOlNu2bTM+Pj5GkmnUqJF5+umnzcKFC01iYqJdu6xj8PJtSEtLs+WDrNelefFKFePGGDNp0iQjyVZxfO+99xpJV6xKuVROVV05fd7t2rUz1apVs/28YMECIynH722Wp59+2vj7+1+xQv7yivFL+3T5d/PyivGCzG457YPHHnvMlCpVyq66Lae+njhxItffI23atDENGjSwW0dGRoa5+eab7Sp4sirGW7Zsmac7DGR9j8aMGWNOnDhhoqOjzc8//2xuuOEGI8l8/fXXxpj8Hx/GGPPVV18ZSbaqw/j4eOPt7Z2vzAoAsEf+Jf+WhPxrjDE//PCDkWTee+89u+nNmzc3FSpUsB3HZ8+eNZLMPffck6c+5Cavn1tW1rr8O+jr62t3zGfJ6znhrM+iWrVqOfYlJ5JMv379zIkTJ0xsbKxZu3atadOmjZFkJk6caIwxZujQoUaS+fPPP/O0TmOM2bBhg5Fkli9fbozJ/N1SsWJF210/Abiu3B/MgAKVkZGhhQsXqlOnTmrSpEm2+VnPy1u8eLFuuukmtWzZ0jbPz89PAwYM0MGDB7Vjxw675fr27Wt3BdUtt9wiKbOq0lECAwO1fft27d27N8/LLF68WJI0bNgwu+lZV81f/nyTunXr2vouScHBwapVq9Y1bUebNm3sruBr1qyZJKlr1652VaVZ0y99Dx8fH9u/ExMTdfLkSd18880yxujPP//Mcx+6du2q4ODgPLV95513FBAQoG7duumll17Sww8/rHvuuSfP73Wp48ePa/PmzerTp4/dVYXXX3+97rjjDtvncqnHH3/8mt4rJ35+fjp37pykzMrsr7/+Wp06dZIxRidPnrS92rVrp7Nnz2rTpk2SpK+//lrlypXTk08+mW2dlz5L8nKBgYFau3atjh07luc+Ll68WG5ubnrqqafspj/zzDMyxujHH3+0mx4VFWVXsX799dfL398/38eml5eX+vbtazdt/vz5qlOnjmrXrm23f7KuHv3pp58kSd98840yMjJ0//3327ULCwtTjRo1bO1yc+lxnZqaqlOnTum6665TYGCg7TO4Fnntf5ZWrVqpbt26eV7/rl27FBwcrODgYNWpU0dTp05Vhw4d9PHHH0uS4uPjJSlf1eI//vijTp06pR49etim9ejRQ1u2bNH27dvzvB4AKCpkykxkyuxcJVPWq1dPmzdv1kMPPaSDBw/q7bffVufOnRUaGqoPPvjA1i7r//msO9Vk2bp1qy0fZL1OnTqV523LWl9WZr2WPHG5Sz/vs2fP6uTJk2rVqpX+/vtvnT17VtLF5zEuWrRIqampOa4nMDBQiYmJWr58+TX35UoKMrtdug/OnTunkydP6pZbbtH58+e1a9eua+rv6dOntWrVKt1///22dZ48eVKnTp1Su3bttHfvXh09etRumUcffTRfdxgYNWqUgoODFRYWpttuu0379+/Xa6+9Zqtcv5bjY86cOWrSpImtYql06dLq0KGD5syZk+d1AAAuIv9mIv9mV9zyryTbHYbmzp1rm3bgwAGtWbNGPXr0sD132xEZVnLc53ap/JwTztK7d2+7vlzNRx99pODgYIWEhKhZs2b6/fffNWzYMFsl+rVmuNDQULVu3VpS5u+WBx54QPPmzVN6enqe1wPA+TAwXkROnDih+Ph41a9f/4rtDh06pFq1amWbXqdOHdv8S1WuXNnu5zJlykjKvH2Io4wdO1ZxcXGqWbOmGjRooGeffVZ//fXXFZc5dOiQrFar7WRAlrCwMAUGBl51O6TMbbmW7bh8XQEBAZKkSpUq5Tj90vc4fPiwLQD5+fkpODhYrVq1kiTbSa28qFq1ap7bBgUFacqUKfrrr78UEBCgKVOm5HnZy2Xt19yOoZMnT2a7pWB++no1CQkJtsBx4sQJxcXF6f3338928jJrgDg2NlZS5i0La9WqJXf3/D3t4fXXX9e2bdtUqVIl3XTTTRo9evRVg/+hQ4cUHh6eLRjl9TsmXduxWaFChWy3Adq7d6+2b9+ebf/UrFlT0sX9s3fvXhljVKNGjWxtd+7caWuXmwsXLujll19WpUqV5OXlpXLlyik4OFhxcXH5Oq4vl9f+Z8nvsValShUtX75cK1as0G+//abo6GgtWrRI5cqVkyT5+/tLunhiOy8+++wzVa1aVV5eXtq3b5/27dun6tWrq1SpUpysBOASyJSZyJTZuVKmrFmzpj799FOdPHlSf/31l8aPHy93d3cNGDBAK1askHTxJFZCQoLdstddd52WL1+u5cuXX9Ptz7PWl7X+a8kTl/v9998VFRUlX19fBQYGKjg4WP/3f/8n6eLn3apVK3Xt2lVjxoxRuXLldM8992jmzJlKTk62reeJJ55QzZo11b59e1WsWFGPPPKIlixZcs39ulxBZrft27fr3nvvVUBAgPz9/RUcHKyHHnpIUv6O+Uvt27dPxhi99NJL2fo8atSof91nSRowYICWL1+ulStXauPGjYqNjdWIESNs8/N7fMTFxWnx4sVq1aqVLWvu27dPLVq00IYNG7Rnz5589Q8AQP7NQv7NrrjlX0lyd3fXAw88oF9//dV2AWDWIPmlt1F3RIaVHPe5XSo/54Sz5DfD3XPPPbZzhmvXrtXJkyc1ceJE24UD+d0/6enpmjdvnlq3bq0DBw7YMlyzZs0UExOjlStX5qt/AJwLzxgvZnK7Gt78i2dFX34F1K233qr9+/fr22+/1bJly/Thhx9q0qRJmjFjhvr373/FdV2p2vdSjtyO3NZ1tfdIT0/XHXf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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "labels = ['get_dummies', 'LabelEncoder', 'PCA', 't-SNE', 'SandartScaler']\n", + "y_preds_dtc = [y_pred_dtc_gd, y_pred_dtc_lbe, y_pred_dtc_pca, y_pred_dtc_tsne, y_pred_dtc_scal]\n", + "accuracy_dct = list(map(lambda x: accuracy_score(Y_test, x), y_preds_dtc))\n", + "\n", + "y_preds_sgdc = [ y_pred_sgdc_lbe, y_pred_sgdc_pca, y_pred_sgdc_tsne, y_pred_sgdc_scal]\n", + "accuracy_sgdc = list(map(lambda x: accuracy_score(Y_test, x), y_preds_sgdc))\n", + "\n", + "y_preds_svc = [ y_pred_svc_lbe, y_pred_svc_pca, y_pred_svc_tsne, y_pred_svc_scal]\n", + "accuracy_svc = list(map(lambda x: accuracy_score(Y_test, x), y_preds_svc))\n", + "\n", + "\n", + "plt.plot(labels, accuracy_dct)\n", + "plt.title('accuracy score for DecisionTree.')\n", + "plt.show()\n", + "\n", + "\n", + "plt.plot(labels[1:], accuracy_sgdc)\n", + "plt.title('accuracy score for SGDClassifier.')\n", + "plt.show()\n", + "\n", + "plt.plot(labels[1:], accuracy_svc)\n", + "plt.title('accuracy score for SVC.')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "fV3XMnjYsjFS", + "outputId": "afefdeb9-c18f-476f-e1d1-523d4c52f2be" + }, + "execution_count": 249, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OAA--Zd8McII" + }, + "source": [ + "# Вывод\n", + "\n", + "В ходе работы были соединины в один ноутбук методы уменьшения размерности данных, масштабирования данных, перебора гиперпараметров для моделей, изменения представления данных и т.д.\n", + "\n", + "С решением задачи классификации на выбранном датасате лучше всего справилась модель решающего дерева, с использованием get_dummies для представления данных. Однако модели после применения PCA и LabelEncoder + StandartScaler, показали результаты чуть меньшей точности, но с большой скоростью обучения." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 333e4328265c6ee0e77eee6b38fceaf75ec86c24 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=A1=D0=BE=D1=84=D0=B8=D1=8F=20=D0=A5=D1=80=D0=B8=D1=81?= =?UTF-8?q?=D0=B0=D0=BD=D0=BA=D0=BE=D0=B2=D0=B0?= <132402521+sssoneta@users.noreply.github.com> Date: Sat, 26 Apr 2025 02:54:01 +0300 Subject: [PATCH 2/3] dz10 --- project/15_fake_news_detection.csv | 1001 +++++ project/15_fake_news_detection.ipynb | 1982 ++++++++++ project/cleaned_fake_news.csv | 19 + project/processed_news_data.csv | 1001 +++++ project/wine_variety_classifier.ipynb | 5095 ------------------------- 5 files changed, 4003 insertions(+), 5095 deletions(-) create mode 100644 project/15_fake_news_detection.csv create mode 100644 project/15_fake_news_detection.ipynb create mode 100644 project/cleaned_fake_news.csv create mode 100644 project/processed_news_data.csv delete mode 100644 project/wine_variety_classifier.ipynb diff --git a/project/15_fake_news_detection.csv b/project/15_fake_news_detection.csv new file mode 100644 index 00000000..5ed5d5a2 --- /dev/null +++ b/project/15_fake_news_detection.csv @@ -0,0 +1,1001 @@ +title,text,label +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake diff --git a/project/15_fake_news_detection.ipynb b/project/15_fake_news_detection.ipynb new file mode 100644 index 00000000..f9bf462e --- /dev/null +++ b/project/15_fake_news_detection.ipynb @@ -0,0 +1,1982 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bdf9f4ff-423b-425b-ad11-5d50b3e83bf9", + "metadata": {}, + "source": [ + "# Задача 10. Сравнение методов классификации" + ] + }, + { + "cell_type": "markdown", + "id": "8993b83b-bc9f-4219-89f6-c1a24523b256", + "metadata": {}, + "source": [ + "Импорт необходимых библиотек" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "33754548-d468-4d0b-afd9-71217dd0e2ec", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.metrics import accuracy_score, classification_report" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7d299573-e193-4c0c-b8bf-e31437aba2a3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "8f22f44e-53f2-4fec-964e-51b1d96414a5", + "metadata": {}, + "source": [ + "Настройка отображения" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f5162aca-4568-4dce-aae0-5b29291d41cd", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "plt.style.use('ggplot')\n", + "pd.set_option('display.max_colwidth', 200)\n", + "pd.set_option('display.max_rows', 100)" + ] + }, + { + "cell_type": "markdown", + "id": "f9530f62-9133-48e6-b6de-6de156ac23fb", + "metadata": {}, + "source": [ + "## 1. Загрузка данных" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7efc8f03-b42a-4fa7-a306-75431f93a748", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Данные успешно загружены\n" + ] + } + ], + "source": [ + "try:\n", + " df = pd.read_csv('15_fake_news_detection.csv')\n", + " print(\"Данные успешно загружены\")\n", + "except FileNotFoundError:\n", + " print(\"Файл не найден. Проверьте путь к файлу.\")\n", + " exit()" + ] + }, + { + "cell_type": "markdown", + "id": "9c226f84-f65b-48a5-b37c-92967fd472f8", + "metadata": {}, + "source": [ + "## 2. Первичный осмотр данных" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f5c70ad2-5459-4f02-8cda-cbe890afe6df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Первичный осмотр данных ===\n", + "\n", + "Первые 5 строк данных:\n" + ] + }, + { + "data": { + "text/html": [ + "
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titletextlabel
0Government Announces New Education ReformsThe education ministry has proposed reforms to modernize the curriculum.real
1Economy Shows Signs of RecoveryThe new study conducted by international researchers shows walking improves heart health.real
2Aliens Land in Central ParkSources claim that extraterrestrial beings were seen stepping out of a spaceship.fake
3Aliens Land in Central ParkThe celebrity stated that secret documents reveal shocking information about the president.fake
4New Study Reveals Health Benefits of WalkingThe education ministry has proposed reforms to modernize the curriculum.real
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" + ], + "text/plain": [ + " title \\\n", + "0 Government Announces New Education Reforms \n", + "1 Economy Shows Signs of Recovery \n", + "2 Aliens Land in Central Park \n", + "3 Aliens Land in Central Park \n", + "4 New Study Reveals Health Benefits of Walking \n", + "\n", + " text \\\n", + "0 The education ministry has proposed reforms to modernize the curriculum. \n", + "1 The new study conducted by international researchers shows walking improves heart health. \n", + "2 Sources claim that extraterrestrial beings were seen stepping out of a spaceship. \n", + "3 The celebrity stated that secret documents reveal shocking information about the president. \n", + "4 The education ministry has proposed reforms to modernize the curriculum. \n", + "\n", + " label \n", + "0 real \n", + "1 real \n", + "2 fake \n", + "3 fake \n", + "4 real " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Информация о структуре данных:\n", + "\n", + "RangeIndex: 1000 entries, 0 to 999\n", + "Data columns (total 3 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 title 1000 non-null object\n", + " 1 text 1000 non-null object\n", + " 2 label 1000 non-null object\n", + "dtypes: object(3)\n", + "memory usage: 23.6+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "None" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Основные статистики:\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " title \\\n", + "count 1000 \n", + "unique 6 \n", + "top Economy Shows Signs of Recovery \n", + "freq 213 \n", + "\n", + " text \\\n", + "count 1000 \n", + "unique 6 \n", + "top Recent economic data shows that the GDP is steadily growing over the last two quarters. \n", + "freq 183 \n", + "\n", + " label \n", + "count 1000 \n", + "unique 2 \n", + "top real \n", + "freq 532 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n=== Первичный осмотр данных ===\")\n", + "print(\"\\nПервые 5 строк данных:\")\n", + "display(df.head())\n", + "\n", + "print(\"\\nИнформация о структуре данных:\")\n", + "display(df.info())\n", + "\n", + "print(\"\\nОсновные статистики:\")\n", + "display(df.describe(include='object'))" + ] + }, + { + "cell_type": "markdown", + "id": "4018d0dd-67ca-47a5-a4ae-070ab82fdc45", + "metadata": {}, + "source": [ + "## 3. Анализ распределения меток" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a286581e-2b2b-4ba8-93f7-438844634fa5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Анализ распределения меток ===\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Количество записей по категориям:\n" + ] + }, + { + "data": { + "text/plain": [ + "label\n", + "real 532\n", + "fake 468\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n=== Анализ распределения меток ===\")\n", + "plt.figure(figsize=(8, 5))\n", + "\n", + "# Исправленная версия с учетом нового синтаксиса seaborn\n", + "sns.countplot(data=df, x='label', hue='label', palette='viridis', legend=False)\n", + "\n", + "plt.title('Распределение реальных и фейковых новостей')\n", + "plt.xlabel('Категория новости')\n", + "plt.ylabel('Количество записей')\n", + "plt.show()\n", + "\n", + "label_counts = df['label'].value_counts()\n", + "print(\"\\nКоличество записей по категориям:\")\n", + "display(label_counts)" + ] + }, + { + "cell_type": "markdown", + "id": "36d16d8a-705a-49d2-94cc-e91eb9d88e56", + "metadata": {}, + "source": [ + "## 4. Проверка на пропущенные значения" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "eeb0e8eb-04f1-4c95-8a46-889c74cb0a9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Проверка на пропущенные значения ===\n" + ] + }, + { + "data": { + "text/plain": [ + "title 0\n", + "text 0\n", + "label 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Пропущенных значений не обнаружено.\n" + ] + } + ], + "source": [ + "print(\"\\n=== Проверка на пропущенные значения ===\")\n", + "missing_values = df.isnull().sum()\n", + "display(missing_values)\n", + "\n", + "if missing_values.sum() > 0:\n", + " print(\"\\nОбнаружены пропущенные значения. Заполняем пустыми строками.\")\n", + " df.fillna('', inplace=True)\n", + "else:\n", + " print(\"\\nПропущенных значений не обнаружено.\")" + ] + }, + { + "cell_type": "markdown", + "id": "6edcf3a2-f622-4b27-a8cc-384160266db5", + "metadata": {}, + "source": [ + "## 5. Анализ длины текстов" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8419c8a8-8d94-4815-970d-05575ffb6757", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Анализ длины текстов ===\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Статистика длины текста:\n" + ] + }, + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
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" + ], + "text/plain": [ + " count mean std min 25% 50% 75% max\n", + "label \n", + "fake 9.0 13.333333 2.000000 12.0 12.0 12.0 16.0 16.0\n", + "real 9.0 12.333333 2.179449 10.0 10.0 12.0 15.0 15.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n=== Анализ длины текстов ===\")\n", + "\n", + "# Создаем копию DataFrame для безопасного изменения\n", + "df_analysis = df_clean.copy()\n", + "\n", + "# Правильное создание нового столбца\n", + "df_analysis.loc[:, 'text_length'] = df_analysis['text'].apply(lambda x: len(str(x).split()))\n", + "\n", + "# Построение boxplot с учетом новых требований seaborn\n", + "plt.figure(figsize=(12, 6))\n", + "sns.boxplot(\n", + " data=df_analysis, \n", + " x='label', \n", + " y='text_length', \n", + " hue='label', # Добавлено для нового синтаксиса\n", + " palette='Set2',\n", + " legend=False # Отключаем легенду, так как она избыточна\n", + ")\n", + "plt.title('Распределение длины текста по категориям')\n", + "plt.ylabel('Количество слов')\n", + "plt.xlabel('Категория')\n", + "plt.show()\n", + "\n", + "# Статистика длины текста\n", + "print(\"\\nСтатистика длины текста:\")\n", + "display(df_analysis.groupby('label')['text_length'].describe())" + ] + }, + { + "cell_type": "markdown", + "id": "bb5484fd-c283-4580-bfbc-7173c96ea60f", + "metadata": {}, + "source": [ + "## 6. Анализ заголовков" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f14db05a-e19d-41a8-b4dc-aa94cbae5b04", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== АНАЛИЗ ЗАГОЛОВКОВ ===\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " count mean std min 25% 50% 75% max\n", + "label \n", + "fake 9.0 5.666667 1.0 5.0 5.0 5.0 7.0 7.0\n", + "real 9.0 5.666667 1.0 5.0 5.0 5.0 7.0 7.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Топ-10 самых частых заголовков:\n" + ] + }, + { + "data": { + "text/plain": [ + "title\n", + "Government Announces New Education Reforms 3\n", + "Economy Shows Signs of Recovery 3\n", + "Aliens Land in Central Park 3\n", + "New Study Reveals Health Benefits of Walking 3\n", + "Cure for Aging Discovered in Remote Village 3\n", + "Celebrity Reveals Secret Government Plans 3\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n=== АНАЛИЗ ЗАГОЛОВКОВ ===\")\n", + "\n", + "# Создаем копию DataFrame для безопасного изменения\n", + "df_titles = df_clean.copy()\n", + "\n", + "# Правильное создание нового столбца с длиной заголовка\n", + "df_titles.loc[:, 'title_length'] = df_titles['title'].apply(lambda x: len(str(x).split()))\n", + "\n", + "# Построение boxplot с учетом новых требований seaborn\n", + "plt.figure(figsize=(12, 6))\n", + "sns.boxplot(\n", + " data=df_titles,\n", + " x='label',\n", + " y='title_length',\n", + " hue='label', # Добавлено согласно новому синтаксису\n", + " palette='pastel',\n", + " legend=False # Отключаем избыточную легенду\n", + ")\n", + "plt.title('Распределение длины заголовков по категориям')\n", + "plt.ylabel('Количество слов')\n", + "plt.xlabel('Категория')\n", + "plt.show()\n", + "\n", + "# Статистика длины заголовков\n", + "print(\"\\nСтатистика длины заголовков:\")\n", + "display(df_titles.groupby('label')['title_length'].describe())\n", + "\n", + "# Топ-10 самых частых заголовков\n", + "print(\"\\nТоп-10 самых частых заголовков:\")\n", + "display(df_titles['title'].value_counts().head(10))" + ] + }, + { + "cell_type": "markdown", + "id": "b545b4df-dd17-4b5c-b040-5ce4cfbecf12", + "metadata": {}, + "source": [ + "## 7. Сохранение результатов" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "59a91576-c50d-4dce-88c9-b323322e70bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== Сохранение результатов ===\n", + "Очищенные данные сохранены в файл 'cleaned_fake_news.csv'\n", + "\n", + "Анализ завершен!\n" + ] + } + ], + "source": [ + "print(\"\\n=== Сохранение результатов ===\")\n", + "df_clean.to_csv('cleaned_fake_news.csv', index=False)\n", + "print(\"Очищенные данные сохранены в файл 'cleaned_fake_news.csv'\")\n", + "\n", + "print(\"\\nАнализ завершен!\")" + ] + }, + { + "cell_type": "markdown", + "id": "c85a8264-9c55-40a1-a0cf-a904b5cec409", + "metadata": {}, + "source": [ + "## Разведочный анализ данных (EDA) новостей" + ] + }, + { + "cell_type": "markdown", + "id": "e592d5aa-a4f2-42ab-89a8-876e4c606fd3", + "metadata": {}, + "source": [ + "## 1. Распределение классов" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "722d54c5-112a-4bd8-8020-a199958f9b86", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,5))\n", + "sns.countplot(data=df, x='label', hue='label', palette='viridis', legend=False)\n", + "plt.title('Распределение новостей по категориям')\n", + "plt.xlabel('Тип новости')\n", + "plt.ylabel('Количество')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6af559c9-0f52-4cf5-b75a-bb03d125d651", + "metadata": {}, + "source": [ + "## 2. Анализ длины текстов" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "88f6bf44-4b75-4e5c-b284-735bf54e382b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " count mean std min 25% 50% 75% max\n", + "label \n", + "fake 468.0 13.282051 1.868692 12.0 12.0 12.0 16.0 16.0\n", + "real 532.0 12.370301 2.071259 10.0 10.0 12.0 15.0 15.0\n" + ] + } + ], + "source": [ + "df['text_length'] = df['text'].apply(lambda x: len(str(x).split()))\n", + "plt.figure(figsize=(12,6))\n", + "sns.boxplot(data=df, x='label', y='text_length', hue='label', palette='Set2', legend=False)\n", + "plt.title('Распределение длины текста')\n", + "plt.ylabel('Количество слов')\n", + "plt.xlabel('Тип новости')\n", + "plt.show()\n", + "\n", + "print(df.groupby('label')['text_length'].describe())" + ] + }, + { + "cell_type": "markdown", + "id": "69955e19-d745-42b9-b967-e2614206ec33", + "metadata": {}, + "source": [ + "## 3. Анализ заголовков" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3c48ac92-fe9c-4d49-a39f-9dee77c6b681", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Топ-10 заголовков real:\n", + "title\n", + "Economy Shows Signs of Recovery 213\n", + "Government Announces New Education Reforms 175\n", + "New Study Reveals Health Benefits of Walking 144\n", + "Name: count, dtype: int64\n", + "\n", + "Топ-10 заголовков fake:\n", + "title\n", + "Celebrity Reveals Secret Government Plans 162\n", + "Cure for Aging Discovered in Remote Village 157\n", + "Aliens Land in Central Park 149\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "df['title_length'] = df['title'].apply(lambda x: len(str(x).split()))\n", + "plt.figure(figsize=(12,6))\n", + "sns.boxplot(data=df, x='label', y='title_length', hue='label', palette='pastel', legend=False)\n", + "plt.title('Распределение длины заголовков')\n", + "plt.ylabel('Количество слов')\n", + "plt.xlabel('Тип новости')\n", + "plt.show()\n", + "\n", + "print(\"Топ-10 заголовков real:\")\n", + "print(df[df['label']=='real']['title'].value_counts().head(10))\n", + "print(\"\\nТоп-10 заголовков fake:\")\n", + "print(df[df['label']=='fake']['title'].value_counts().head(10))" + ] + }, + { + "cell_type": "markdown", + "id": "7f605c1b-5ce0-4dc7-b35e-747511ef5ac6", + "metadata": {}, + "source": [ + "## 4. Анализ ключевых слов" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3af80523-186c-410f-b39b-afeeacc346ee", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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AAFwWMz8AAAAAAIBLI/wAAAAAAAAujfADAAAAAAC4NMIPAAAAAADg0gg/AAAAAACASyP8cJIpU6bonXfeKXD7hIQERUdH6+LFiyV+LQAAAAAAXAmfur0HxMTEyDAMx3ZsbKzCwsLUr18/5xUFAAAAAMAdQvjhwnJyciRJ3t7eTq4EAAAAAADnIfy4DTk5OVq+fLnWrVunM2fOqFy5cmrXrp2efvpppaena9asWdq1a5dMJpNq166tfv36KSAg4IZ9LVu2TOvWrdO5c+cUHBysrl27qmnTprna7du3T/PmzdPJkycVFhamF154QaGhoZKkDRs2aObMmRo2bJjmzp2rkydP6oMPPtCiRYt08eJFvfbaa5oyZYr27t2rvXv3atWqVZKkyZMna9y4cWrXrp2efPJJx7WSk5P12muv6YMPPlBgYGAJPUUAAAAAAEoW4cdtmDdvnr777jv17dtXEREROnfunE6cOKGsrCyNHz9eNWvW1Jtvvimz2azFixfr7bff1rvvviuLJe9jX7p0qb7//nv99a9/VVBQkBITE/Xhhx/K19dXderUcbSbPXu2YmJi5Ofnp3nz5ikuLk6TJk1y9JmZmally5Zp0KBBKlu2rMqVK5frOjExMTp58qSqVKmi7t27S5J8fX3VunVrbdiwIVf4sX79etWuXZvgAwAAAABQqrHgaRFdvnxZq1ev1nPPPadWrVopMDBQERERevTRR7VlyxYZhqFBgwYpNDRUlStX1pAhQ5Senq6EhIQ8fdntdi1ZskSDBw9WgwYNVKlSJbVq1UotWrTQ2rVrc7V95plnFBkZqdDQUA0bNkznz5/X9u3bHcezs7M1YMAA1apVS8HBwbJarbnO9/b2lsVikdVqlZ+fn/z8/GQ2m9WqVSulpKTo4MGDkqSsrCz98MMPat269Q2fgd1u16VLlxw/ly9fvp1HCgAAAABAiWDmRxGdOHFCdrtd9erVy3Ps6NGjSk1NVZ8+fXLtt9vtOnXqVJ72qampyszM1Lhx43Ltz8rKUnh4eK59NWvWdPzax8dHwcHBOnHihGOfxWJR1apVC30/FSpUUKNGjfTf//5X1atX188//6ysrCw9/PDDNzxnyZIlio+Pd2yHh4crLi6u0NcGAAAAAKAkEX4UkYeHxw2P2Ww2VatWTS+++GKeY76+vvm2l6RRo0apQoUKuY7l94rMreoymUyFOueaNm3aaPLkyerXr5/Wr1+vhx9+OM/Mket16dJFHTt2dGwX9boAAAAAAJQkwo8iCgwMlIeHh3bv3q1HH30017Hw8HBt2bJFvr6+BfrSSuXKleXu7q709PRc63vkZ//+/apYsaIk6cKFCzp58qRCQkIKVbvFYnF8CeZ6jRo1ktVq1bfffqudO3dq7NixN+3H3d1d7u7uhbo2AAAAAAB3GuFHEXl4eKhz586aM2eOLBaLatWqpYyMDB0/flwtWrTQ8uXLNXHiREVHR+u+++7T6dOntW3bNnXu3Fn33Xdfrr68vLzUqVMnffHFF8rJyVFERIQuXbqkffv2ycvLS61atXK0/eqrrxwLmS5YsEBly5ZV48aNC1W7v7+/Dhw4oLS0NHl6esrHx0dms9mx9se8efMUGBiY6xUbAAAAAABKK8KP29C1a1e5ublp4cKF+v3331W+fHm1a9dOVqtVY8eO1Zw5c/Tuu+/KZrOpQoUKqlu3rry8vPLtq3v37vL19dXSpUt16tQplSlTRuHh4erSpUuuds8++6xmzpzp+NTtyJEjC/1qTKdOnTRlyhS98sorunLliiZPnuz4BG+bNm20ZMmSmy50CgAAAABAaWIyDMNwdhG4eyQmJurNN9/URx99JD8/vyL1Mf/HRTr9R3rxFgYAgIvrVrezs0u4Z1mtVmVmZjq7DJQijBkUBuOlZLm7u8vf3/+W7Zj5AUlXv0STkZGhRYsW6eGHHy5y8AEAAAAAwN3G7OwCcHfYvHmzhgwZoosXL+q5555zdjkAAAAAABQbZn5AktSqVatcC6sCAAAAAOAqmPkBAAAAAABcGuEHAAAAAABwaYQfAAAAAADApbHmB4pd2+qtZLfbnV0GSgE++4XCYLygMBgvAADgesz8AAAAAAAALo3wAwAAAAAAuDTCDwAAAAAA4NIIPwAAAAAAgEsj/AAAAAAAAC6Nr72g2H2d+F+lnj/t7DIAAChVYh542tklAADgspj5AQAAAAAAXBrhBwAAAAAAcGmEHwAAAAAAwKURfgAAAAAAAJdG+AEAAAAAAFwa4QcAAAAAAHBphB+4oYULF2rEiBHOLgMAAAAAgNtC+HGXmjJlit55551Cn0dgAQAAAABAboQfAAAAAADApVmcXcC9buvWrVq0aJFSU1NltVoVHh6usLAwbdy4UZIUHR0tSRozZoz+8pe/aM6cOdqxY4fOnDkjPz8/NW/eXN26dZPFYtGGDRsUHx+f67whQ4aoVatWunjxombPnq0dO3YoKytL1apVU9++fRUWFuaoZenSpVq5cqUyMzP18MMPy9fX984+DAAAAAAASgDhhxOdPXtWkyZNUq9evdS4cWPZbDYlJiYqKipK6enpunz5soYMGSJJ8vHxkSR5eXlpyJAhKl++vI4dO6Zp06bJy8tLnTt31iOPPKJjx45p586d+uc//ylJ8vb2liS999578vDw0Ouvvy5vb2+tXbtW48aN06RJk+Tj46MtW7Zo0aJFGjBggCIiIrRp0yatXr1aAQEBznk4AAAAAAAUE8IPJzp79qyys7PVpEkT+fv7S5JCQ0MlSR4eHrLb7fLz88t1TteuXR2/DggIUEpKirZs2aLOnTvLw8NDnp6eMpvNuc5LSkrSwYMH9dlnn8nd3V2S1KdPH+3YsUNbt25V27ZttWrVKrVu3Vpt2rSRJPXo0UO7d+/WlStXbli/3W6X3W53bJtMJnl5ed3WMwEAAAAAoLgRfjhRWFiY6tWrp+HDh6t+/fqKjIxU06ZNHbM88rNlyxatXr1aqampstlsysnJuWXgkJycLJvNpv79++faf+XKFaWmpkqSTpw4oXbt2uU6XqNGDSUkJNyw3yVLljhes5Gk8PBwxcXF3bQWAAAAAADuNMIPJzKbzRo9erT27dunXbt2ac2aNVqwYIHefvvtfNvv379fH3zwgaKjo1W/fn15e3tr8+bNWrFixU2vY7PZVL58ecXGxuY5du21mKLo0qWLOnbs6Ng2mUxF7gsAAAAAgJJC+OFkJpNJERERioiIULdu3TRkyBBt375dFotFOTk5udru27dP/v7+evrppx370tPTc7XJ77xq1arp3LlzMpvNN1zDIyQkRAcOHFBUVJRj34EDB25au7u7u+M1GgAAAAAA7lZ86taJDhw4oMWLF+vQoUNKT0/Xtm3blJGRoZCQEPn7++vYsWNKSUlRRkaGsrKyFBQUpPT0dG3evFmpqalatWqVtm/fnqvPgIAApaWlKTk5WRkZGbLb7apXr55q1qypiRMnaufOnUpLS9O+ffs0f/58HTp0SJLUoUMHrV+/XuvXr1dKSooWLlyo48ePO+OxAAAAAABQrJj54UReXl5KTEzUqlWrdPnyZVWsWFF9+vRRw4YNdf/992vv3r36+9//LpvNpjFjxujBBx/UE088oenTp8tut6tRo0bq2rWrFi1a5OizSZMm2rZtm8aOHauLFy86PnU7atQozZ8/X1OnTlVGRob8/PxUu3ZtlStXTpL0yCOPKDU1VXPmzJHdbleTJk3Url077dy501mPBwAAAACAYmEyDMNwdhFwLZ9v+lKp5087uwwAAEqVmAeevnUjlAir1arMzExnl4FShDGDwmC8lCx3d3fH11NvhtdeAAAAAACASyP8AAAAAAAALo3wAwAAAAAAuDTCDwAAAAAA4NIIPwAAAAAAgEsj/AAAAAAAAC7N4uwC4HqerN1Gdrvd2WWgFOCzXygMxgsKg/ECAACux8wPAAAAAADg0gg/AAAAAACASyP8AAAAAAAALo3wAwAAAAAAuDTCDwAAAAAA4NL42guK3YKd65Ry7rSzywAAoFT52yPdnF0CAAAui5kfAAAAAADApRF+AAAAAAAAl0b4AQAAAAAAXBrhBwAAAAAAcGmEHwAAAAAAwKURfgAAAAAAAJdG+PEnsbGxmjlzprPLAAAAAAAAxYTw4x6VkJCg6OhoXbx40dmlAAAAAABQogg/AAAAAACAS7M4uwBnstls+uyzz7Rt2zZ5eXmpU6dOuY5fuHBBM2fO1M8//yy73a46deooJiZGQUFBjjZJSUlasGCBDh48KHd3d1WvXl0vvfSSfHx8NHToUHXo0EFPPPGEo/2IESP00EMPKTo6WpIUHR2tv/71r/r555+1Z88e+fv7a/DgwfL19dXHH3+sQ4cOqWrVqho2bJgCAwMd/ezYsUPx8fE6fvy4ypcvr6ioKD399NNyc3Nz9PvCCy/ol19+0c6dO1WhQgX16dNHDz74oNLS0jR27FhJUkxMjCQpKipKQ4cO1datW7Vo0SKlpqbKarUqPDxcI0aMkKenZ8n8JgAAAAAAUMLu6fBjzpw52rt3r1577TWVK1dO8+bN05EjRxQWFiZJmjp1qk6ePKnXXntNXl5emjt3riZMmKD33ntPFotFycnJGjdunFq3bq1+/frJzc1NCQkJysnJKVQdX331lfr06aM+ffpo7ty5mjRpkipVqqSnnnpKFStW1EcffaTp06fr9ddflyQlJiZq8uTJiomJUe3atXXq1ClNmzZNkvTMM884+o2Pj1evXr3Uu3dvrV69Wh988IGmTp2qihUr6tVXX9W///1vvf/++/L29paHh4fOnj2rSZMmqVevXmrcuLFsNpsSExOL52EDAAAAAOAk9+xrLzabTf/973/Vu3dv1atXT6GhoRo2bJiys7MlSSdPntRPP/2kQYMGqXbt2goLC9OLL76o33//XTt27JAkLVu2TNWqVdPAgQMVFhamKlWq6LHHHpOvr2+hamnVqpUeeeQRBQcHq3Pnzjp9+rS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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_top_words(text_series, title, n=20):\n", + " # Инициализация векторизатора с ограничением на стоп-слова\n", + " vec = CountVectorizer(stop_words='english', max_features=n)\n", + " \n", + " # Подгонка и преобразование текста\n", + " bag_of_words = vec.fit_transform(text_series)\n", + " sum_words = bag_of_words.sum(axis=0) \n", + " \n", + " # Создание списка частот слов\n", + " words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n", + " words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)\n", + " \n", + " # Создание DataFrame для визуализации\n", + " words_df = pd.DataFrame(words_freq[:n], columns=['word', 'count'])\n", + " \n", + " # Визуализация с актуальным синтаксисом seaborn\n", + " plt.figure(figsize=(12, 8))\n", + " sns.barplot(\n", + " data=words_df,\n", + " y='word',\n", + " x='count',\n", + " hue='word', # Добавлен hue для нового синтаксиса\n", + " palette='crest',\n", + " legend=False # Отключаем легенду\n", + " )\n", + " plt.title(title, fontsize=14, pad=20)\n", + " plt.xlabel('Частота встречаемости', fontsize=12)\n", + " plt.ylabel('Топ слова', fontsize=12)\n", + " plt.xticks(fontsize=10)\n", + " plt.yticks(fontsize=10)\n", + " plt.grid(axis='x', alpha=0.3)\n", + " plt.show()\n", + "\n", + "# Применение функции к данным\n", + "plot_top_words(df[df['label']=='real']['text'], 'Топ-20 слов в реальных новостях')\n", + "plot_top_words(df[df['label']=='fake']['text'], 'Топ-20 слов в фейковых новостях')" + ] + }, + { + "cell_type": "markdown", + "id": "7dcb23fd-90b0-4f0e-82a0-cf746fa76e92", + "metadata": {}, + "source": [ + "## 5. Корреляция признаков" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "1d40069a-b78a-40ac-87a8-ad4a0cc72766", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== АНАЛИЗ КОРРЕЛЯЦИИ ПРИЗНАКОВ ===\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " text_length title_length label_num\n", + "text_length 1.000000 -0.027829 -0.224248\n", + "title_length -0.027829 1.000000 -0.070484\n", + "label_num -0.224248 -0.070484 1.000000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"\\n=== АНАЛИЗ КОРРЕЛЯЦИИ ПРИЗНАКОВ ===\")\n", + "\n", + "# Создаем копию DataFrame для безопасного изменения\n", + "numeric_df = df[['text_length', 'title_length']].copy()\n", + "\n", + "# Правильное создание нового столбца\n", + "numeric_df.loc[:, 'label_num'] = df['label'].map({'real': 1, 'fake': 0})\n", + "\n", + "# Вычисляем матрицу корреляции\n", + "corr_matrix = numeric_df.corr()\n", + "\n", + "# Визуализация тепловой карты\n", + "plt.figure(figsize=(10, 8))\n", + "sns.heatmap(\n", + " corr_matrix,\n", + " annot=True,\n", + " cmap='coolwarm',\n", + " center=0,\n", + " fmt=\".2f\",\n", + " linewidths=.5,\n", + " annot_kws={\"size\": 12}\n", + ")\n", + "\n", + "# Настройка отображения\n", + "plt.title('Матрица корреляции признаков', fontsize=14, pad=20)\n", + "plt.xticks(fontsize=10, rotation=45)\n", + "plt.yticks(fontsize=10, rotation=0)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Вывод численных значений корреляции\n", + "print(\"\\nЧисленные значения корреляции:\")\n", + "display(corr_matrix)" + ] + }, + { + "cell_type": "markdown", + "id": "2b95b797-59ac-4ca2-90da-f5536cb0eaad", + "metadata": {}, + "source": [ + "## 1. Создаем копию данных для обработки" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "68e0b53e-c974-4b70-8454-b9ef26984ecd", + "metadata": {}, + "outputs": [], + "source": [ + "df_processed = df.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "20847895-9d1d-40f3-b21c-de251dc459cf", + "metadata": {}, + "source": [ + "## 2. Преобразование категориального признака" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "b0116eff-b140-4c7b-abba-1487125b6efe", + "metadata": {}, + "outputs": [], + "source": [ + "df_processed['label_num'] = df_processed['label'].map({'real': 1, 'fake': 0})" + ] + }, + { + "cell_type": "markdown", + "id": "7e5e081f-57ad-43c8-bf29-cc22b15bf07b", + "metadata": {}, + "source": [ + "## 3. Создание новых признаков из текста" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f759a1b6-366b-4339-8a89-6ced2bdd1489", + "metadata": {}, + "outputs": [], + "source": [ + "import re\n", + "\n", + "def extract_text_features(text):\n", + " text = str(text)\n", + " features = {\n", + " 'num_words': len(text.split()),\n", + " 'num_chars': len(text),\n", + " 'num_upper': sum(1 for c in text if c.isupper()),\n", + " 'num_excl': text.count('!'),\n", + " 'num_quest': text.count('?'),\n", + " 'num_quotes': text.count('\"') + text.count(\"'\"),\n", + " 'num_digits': sum(c.isdigit() for c in text),\n", + " 'num_links': len(re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\(\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text))\n", + " }\n", + " return features\n", + "\n", + "# Применяем функцию к тексту и заголовкам\n", + "text_features = pd.DataFrame(df_processed['text'].apply(extract_text_features).tolist()) # Исправлено toList() на tolist()\n", + "title_features = pd.DataFrame(df_processed['title'].apply(extract_text_features).tolist())\n", + "\n", + "# Добавляем префиксы к названиям столбцов\n", + "text_features = text_features.add_prefix('text_')\n", + "title_features = title_features.add_prefix('title_')\n", + "\n", + "# Объединяем с основным DataFrame\n", + "df_processed = pd.concat([df_processed, text_features, title_features], axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "76517662-85e7-428d-83d5-dcf9f65d4450", + "metadata": {}, + "source": [ + "## 4. Анализ тональности" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a2c611f9-b014-4d8e-9c05-9e283c186abd", + "metadata": {}, + "outputs": [], + "source": [ + "def get_sentiment(text):\n", + " analysis = TextBlob(str(text))\n", + " return analysis.sentiment.polarity, analysis.sentiment.subjectivity\n", + "\n", + "df_processed[['text_polarity', 'text_subjectivity']] = df_processed['text'].apply(get_sentiment).apply(pd.Series)\n", + "df_processed[['title_polarity', 'title_subjectivity']] = df_processed['title'].apply(get_sentiment).apply(pd.Series)" + ] + }, + { + "cell_type": "markdown", + "id": "aa4b1b84-c3d0-4744-953d-0f4a8ebf220c", + "metadata": {}, + "source": [ + "## 5. Удаление исходных текстовых признаков" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7a6f9b20-1380-4da2-bcfe-0807a382c38e", + "metadata": {}, + "outputs": [], + "source": [ + "df_processed.drop(['title', 'text', 'label'], axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "19f129bf-d534-4a48-a49c-d73d5eb8414c", + "metadata": {}, + "source": [ + "## 6. Проверка результата" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "592f0c83-85ce-4790-ad07-d7c768381936", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Преобразованные данные:\n", + "Всего признаков: 23\n", + " text_length title_length label_num text_num_words text_num_chars \\\n", + "0 10 5 1 10 72 \n", + "1 12 5 1 12 89 \n", + "2 12 5 0 12 81 \n", + "3 12 5 0 12 91 \n", + "4 10 7 1 10 72 \n", + "\n", + " text_num_upper text_num_excl text_num_quest text_num_quotes \\\n", + "0 1 0 0 0 \n", + "1 1 0 0 0 \n", + "2 1 0 0 0 \n", + "3 1 0 0 0 \n", + "4 1 0 0 0 \n", + "\n", + " text_num_digits ... title_num_upper title_num_excl title_num_quest \\\n", + "0 0 ... 5 0 0 \n", + "1 0 ... 4 0 0 \n", + "2 0 ... 4 0 0 \n", + "3 0 ... 4 0 0 \n", + "4 0 ... 6 0 0 \n", + "\n", + " title_num_quotes title_num_digits title_num_links text_polarity \\\n", + "0 0 0 0 0.000000 \n", + "1 0 0 0 0.068182 \n", + "2 0 0 0 0.000000 \n", + "3 0 0 0 -0.700000 \n", + "4 0 0 0 0.000000 \n", + "\n", + " text_subjectivity title_polarity title_subjectivity \n", + "0 0.000000 0.136364 0.454545 \n", + "1 0.227273 0.000000 0.000000 \n", + "2 0.000000 0.000000 0.250000 \n", + "3 0.850000 0.000000 0.250000 \n", + "4 0.000000 0.136364 0.454545 \n", + "\n", + "[5 rows x 23 columns]\n" + ] + } + ], + "source": [ + "print(\"Преобразованные данные:\")\n", + "print(f\"Всего признаков: {len(df_processed.columns)}\")\n", + "print(df_processed.head())" + ] + }, + { + "cell_type": "markdown", + "id": "a798f58f-b6d1-4825-a78e-786af058068f", + "metadata": {}, + "source": [ + "## Сохранение обработанных данных" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "11a3590f-d3a1-4864-bb4a-2f3e250e91b5", + "metadata": {}, + "outputs": [], + "source": [ + "df_processed.to_csv('processed_news_data.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "946611ad-1d66-45f0-8b2c-5fdd6cc7781b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Настройка логистической регрессии...\n", + "Настройка SVM...\n", + "Настройка Random Forest...\n", + "Настройка Gradient Boosting...\n", + "\n", + "Результаты на тестовой выборке:\n", + "\n", + "LogisticRegression:\n", + "Accuracy: 1.0000\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 113\n", + " 1 1.00 1.00 1.00 87\n", + "\n", + " accuracy 1.00 200\n", + " macro avg 1.00 1.00 1.00 200\n", + "weighted avg 1.00 1.00 1.00 200\n", + "\n", + "\n", + "SVM:\n", + "Accuracy: 1.0000\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 113\n", + " 1 1.00 1.00 1.00 87\n", + "\n", + " accuracy 1.00 200\n", + " macro avg 1.00 1.00 1.00 200\n", + "weighted avg 1.00 1.00 1.00 200\n", + "\n", + "\n", + "RandomForest:\n", + "Accuracy: 1.0000\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 113\n", + " 1 1.00 1.00 1.00 87\n", + "\n", + " accuracy 1.00 200\n", + " macro avg 1.00 1.00 1.00 200\n", + "weighted avg 1.00 1.00 1.00 200\n", + "\n", + "\n", + "GradientBoosting:\n", + "Accuracy: 1.0000\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 113\n", + " 1 1.00 1.00 1.00 87\n", + "\n", + " accuracy 1.00 200\n", + " macro avg 1.00 1.00 1.00 200\n", + "weighted avg 1.00 1.00 1.00 200\n", + "\n" + ] + } + ], + "source": [ + "# 1. Загрузка и подготовка данных\n", + "data = pd.read_csv('15_fake_news_detection.csv')\n", + "X = data['text']\n", + "y = data['label'].map({'real': 0, 'fake': 1})\n", + "\n", + "# 2. Векторизация текста\n", + "tfidf = TfidfVectorizer(max_features=5000, stop_words='english')\n", + "X_tfidf = tfidf.fit_transform(X)\n", + "\n", + "# 3. Разделение данных\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_tfidf, y, test_size=0.2, random_state=42\n", + ")\n", + "\n", + "# 4. Масштабирование данных\n", + "scaler = StandardScaler(with_mean=False)\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "best_models = {}\n", + "\n", + "# 5. Логистическая регрессия\n", + "print(\"Настройка логистической регрессии...\")\n", + "lr_param_grid = {'C': [0.01, 0.1, 1, 10], 'penalty': ['l1', 'l2'], 'solver': ['liblinear']}\n", + "lr_grid = GridSearchCV(LogisticRegression(random_state=42), lr_param_grid, cv=5, n_jobs=-1)\n", + "lr_grid.fit(X_train_scaled, y_train)\n", + "best_models['LogisticRegression'] = lr_grid.best_estimator_\n", + "\n", + "# 6. SVM\n", + "print(\"Настройка SVM...\")\n", + "svm_param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf'], 'gamma': ['scale', 'auto']}\n", + "svm_grid = GridSearchCV(SVC(random_state=42, probability=True), svm_param_grid, cv=5, n_jobs=-1)\n", + "svm_grid.fit(X_train_scaled, y_train)\n", + "best_models['SVM'] = svm_grid.best_estimator_\n", + "\n", + "# 7. Random Forest\n", + "print(\"Настройка Random Forest...\")\n", + "rf_param_grid = {'n_estimators': [100, 200], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5]}\n", + "rf_grid = GridSearchCV(RandomForestClassifier(random_state=42), rf_param_grid, cv=5, n_jobs=-1)\n", + "rf_grid.fit(X_train, y_train)\n", + "best_models['RandomForest'] = rf_grid.best_estimator_\n", + "\n", + "# 8. Gradient Boosting (вместо XGBoost)\n", + "print(\"Настройка Gradient Boosting...\")\n", + "gb_param_grid = {'n_estimators': [100, 200], 'learning_rate': [0.01, 0.1], 'max_depth': [3, 5]}\n", + "gb_grid = GridSearchCV(GradientBoostingClassifier(random_state=42), gb_param_grid, cv=5, n_jobs=-1)\n", + "gb_grid.fit(X_train, y_train)\n", + "best_models['GradientBoosting'] = gb_grid.best_estimator_\n", + "\n", + "# 9. Оценка моделей\n", + "print(\"\\nРезультаты на тестовой выборке:\")\n", + "for name, model in best_models.items():\n", + " if name in ['LogisticRegression', 'SVM']:\n", + " y_pred = model.predict(X_test_scaled)\n", + " else:\n", + " y_pred = model.predict(X_test)\n", + " \n", + " print(f\"\\n{name}:\")\n", + " print(f\"Accuracy: {accuracy_score(y_test, y_pred):.4f}\")\n", + " print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "34c9e8a0-26aa-4fe2-a8b4-3786a1a1287f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ModelAccuracyPrecision (Fake)Recall (Fake)F1-Score (Fake)
0LogisticRegression1.01.01.01.0
1RandomForest1.01.01.01.0
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" + ], + "text/plain": [ + " Model Accuracy Precision (Fake) Recall (Fake) \\\n", + "0 LogisticRegression 1.0 1.0 1.0 \n", + "1 RandomForest 1.0 1.0 1.0 \n", + "\n", + " F1-Score (Fake) \n", + "0 1.0 \n", + "1 1.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.svm import SVC\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.metrics import (accuracy_score, \n", + " classification_report, \n", + " confusion_matrix)\n", + "\n", + "# 1. Загрузка и подготовка данных\n", + "data = pd.read_csv('15_fake_news_detection.csv')\n", + "X = data['text']\n", + "y = data['label'].map({'real': 0, 'fake': 1})\n", + "\n", + "# 2. Векторизация текста\n", + "tfidf = TfidfVectorizer(max_features=5000, stop_words='english')\n", + "X_tfidf = tfidf.fit_transform(X)\n", + "\n", + "# 3. Разделение данных\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X_tfidf, y, test_size=0.2, random_state=42\n", + ")\n", + "\n", + "# 4. Масштабирование данных\n", + "scaler = StandardScaler(with_mean=False)\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Обучение моделей (пример для 2 моделей для краткости)\n", + "models = {\n", + " 'LogisticRegression': LogisticRegression(random_state=42),\n", + " 'RandomForest': RandomForestClassifier(random_state=42)\n", + "}\n", + "\n", + "for name, model in models.items():\n", + " if name == 'LogisticRegression':\n", + " model.fit(X_train_scaled, y_train)\n", + " else:\n", + " model.fit(X_train, y_train)\n", + "\n", + "# Функция для построения confusion matrix\n", + "def plot_confusion_matrix(y_true, y_pred, model_name, ax):\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax,\n", + " xticklabels=['Real', 'Fake'], \n", + " yticklabels=['Real', 'Fake'])\n", + " ax.set_title(f'{model_name}\\nAccuracy: {accuracy_score(y_true, y_pred):.2f}')\n", + " ax.set_xlabel('Predicted')\n", + " ax.set_ylabel('Actual')\n", + "\n", + "# Создаем фигуру для графиков\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# Оценка моделей и построение матриц ошибок\n", + "for i, (name, model) in enumerate(models.items()):\n", + " # Предсказание на тестовых данных\n", + " if name == 'LogisticRegression':\n", + " y_pred = model.predict(X_test_scaled)\n", + " else:\n", + " y_pred = model.predict(X_test)\n", + " \n", + " # Построение confusion matrix\n", + " plot_confusion_matrix(y_test, y_pred, name, axes[i])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Сравнительная таблица метрик\n", + "results = []\n", + "for name, model in models.items():\n", + " if name == 'LogisticRegression':\n", + " y_pred = model.predict(X_test_scaled)\n", + " else:\n", + " y_pred = model.predict(X_test)\n", + " \n", + " report = classification_report(y_test, y_pred, output_dict=True)\n", + " results.append({\n", + " 'Model': name,\n", + " 'Accuracy': report['accuracy'],\n", + " 'Precision (Fake)': report['1']['precision'],\n", + " 'Recall (Fake)': report['1']['recall'],\n", + " 'F1-Score (Fake)': report['1']['f1-score']\n", + " })\n", + "\n", + "results_df = pd.DataFrame(results)\n", + "print(\"\\nСравнительная таблица метрик:\")\n", + "display(results_df)" + ] + }, + { + "cell_type": "markdown", + "id": "b3b449e7-ac3a-4709-90bd-2c1d1f7388a6", + "metadata": {}, + "source": [ + "## 1. Реализация логистической регрессии" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "82e949ab-97c8-4068-978b-d8e48156b004", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.base import BaseEstimator, ClassifierMixin\n", + "\n", + "class MyLogisticRegression(BaseEstimator, ClassifierMixin):\n", + " def __init__(self, learning_rate=0.01, max_iter=1000, tol=1e-4, C=1.0, penalty='l2'):\n", + " self.learning_rate = learning_rate\n", + " self.max_iter = max_iter\n", + " self.tol = tol\n", + " self.C = C # Параметр регуляризации\n", + " self.penalty = penalty # 'l2' или 'l1'\n", + " self.weights = None\n", + " self.bias = None\n", + " \n", + " def _sigmoid(self, z):\n", + " return 1 / (1 + np.exp(-z))\n", + " \n", + " def _loss(self, h, y):\n", + " # Функция потерь с регуляризацией\n", + " m = y.shape[0]\n", + " loss = (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()\n", + " \n", + " if self.penalty == 'l2':\n", + " reg = (self.C / (2 * m)) * np.sum(self.weights**2)\n", + " elif self.penalty == 'l1':\n", + " reg = (self.C / m) * np.sum(np.abs(self.weights))\n", + " else:\n", + " reg = 0\n", + " \n", + " return loss + reg\n", + " \n", + " def fit(self, X, y):\n", + " # Инициализация параметров\n", + " m, n = X.shape\n", + " self.weights = np.zeros(n)\n", + " self.bias = 0\n", + " \n", + " # Градиентный спуск\n", + " for i in range(self.max_iter):\n", + " # Прямое распространение\n", + " z = np.dot(X, self.weights) + self.bias\n", + " h = self._sigmoid(z)\n", + " \n", + " # Обратное распространение\n", + " dw = (1/m) * np.dot(X.T, (h - y))\n", + " db = (1/m) * np.sum(h - y)\n", + " \n", + " # Регуляризация\n", + " if self.penalty == 'l2':\n", + " dw += (self.C / m) * self.weights\n", + " elif self.penalty == 'l1':\n", + " dw += (self.C / m) * np.sign(self.weights)\n", + " \n", + " # Обновление параметров\n", + " self.weights -= self.learning_rate * dw\n", + " self.bias -= self.learning_rate * db\n", + " \n", + " # Проверка сходимости\n", + " if i % 100 == 0:\n", + " loss = self._loss(h, y)\n", + " if hasattr(self, 'prev_loss') and np.abs(self.prev_loss - loss) < self.tol:\n", + " break\n", + " self.prev_loss = loss\n", + " \n", + " def predict_proba(self, X):\n", + " return self._sigmoid(np.dot(X, self.weights) + self.bias)\n", + " \n", + " def predict(self, X, threshold=0.5):\n", + " return (self.predict_proba(X) >= threshold).astype(int)" + ] + }, + { + "cell_type": "markdown", + "id": "841e2b68-ea29-440e-870f-c0eec84603e0", + "metadata": {}, + "source": [ + "## 2. Обучение и оценка модели" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "b9b5a815-9c0a-4fec-9d9c-7dcbe3d6a609", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 1.0\n", + "\n", + "Confusion Matrix:\n", + "[[113 0]\n", + " [ 0 87]]\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 113\n", + " 1 1.00 1.00 1.00 87\n", + "\n", + " accuracy 1.00 200\n", + " macro avg 1.00 1.00 1.00 200\n", + "weighted avg 1.00 1.00 1.00 200\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Масштабирование данных\n", + "scaler = StandardScaler(with_mean=False)\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Создание и обучение модели\n", + "my_lr = MyLogisticRegression(learning_rate=0.1, max_iter=5000, C=1.0, penalty='l2')\n", + "my_lr.fit(X_train_scaled.toarray(), y_train) # .toarray() для разреженных матриц\n", + "\n", + "# Предсказания\n", + "y_pred = my_lr.predict(X_test_scaled.toarray())\n", + "\n", + "# Оценка качества\n", + "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n", + "print(\"\\nConfusion Matrix:\")\n", + "print(confusion_matrix(y_test, y_pred))\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "id": "0a70fbb0-a055-4e5e-9da9-832febe013b6", + "metadata": {}, + "source": [ + "## 3. Подбор гиперпараметров" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fe3cce8-7dfa-4539-b806-1d102860b34c", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "# Параметры для подбора\n", + "param_grid = {\n", + " 'learning_rate': [0.01, 0.1, 1],\n", + " 'C': [0.1, 1, 10],\n", + " 'penalty': ['l1', 'l2'],\n", + " 'max_iter': [1000, 5000]\n", + "}\n", + "\n", + "# Сеточный поиск\n", + "grid_search = GridSearchCV(MyLogisticRegression(), param_grid, cv=5, scoring='accuracy')\n", + "grid_search.fit(X_train_scaled.toarray(), y_train)\n", + "\n", + "# Лучшие параметры\n", + "print(\"Best parameters:\", grid_search.best_params_)\n", + "best_my_lr = grid_search.best_estimator_\n", + "\n", + "# Оценка лучшей модели\n", + "y_pred_best = best_my_lr.predict(X_test_scaled.toarray())\n", + "print(\"\\nBest model accuracy:\", accuracy_score(y_test, y_pred_best))" + ] + }, + { + "cell_type": "markdown", + "id": "ace461f0-5ca7-4201-a5ed-f2e08bea333b", + "metadata": {}, + "source": [ + "## 4. Сравнение с sklearn LogisticRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "5d2f00f0-d9e2-449a-ad08-854da1805629", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "My Logistic Regression Accuracy: 1.0\n", + "Sklearn Logistic Regression Accuracy: 1.0\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "# Обучение sklearn версии\n", + "sk_lr = LogisticRegression(penalty='l2', C=1.0, solver='liblinear', max_iter=5000)\n", + "sk_lr.fit(X_train_scaled, y_train)\n", + "y_pred_sk = sk_lr.predict(X_test_scaled)\n", + "\n", + "# Сравнение метрик\n", + "print(\"\\nMy Logistic Regression Accuracy:\", accuracy_score(y_test, y_pred))\n", + "print(\"Sklearn Logistic Regression Accuracy:\", accuracy_score(y_test, y_pred_sk))" + ] + }, + { + "cell_type": "markdown", + "id": "15a97285-d758-46e0-993b-2c5e5b13bfcc", + "metadata": {}, + "source": [ + "## 5. Визуализация результатов" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "b295294b-a589-4180-bfde-5a89dc473995", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# Confusion matrix для моей реализации\n", + "cm_my = confusion_matrix(y_test, y_pred)\n", + "sns.heatmap(cm_my, annot=True, fmt='d', cmap='Blues', ax=ax1,\n", + " xticklabels=['Real', 'Fake'], yticklabels=['Real', 'Fake'])\n", + "ax1.set_title('My Logistic Regression')\n", + "\n", + "# Confusion matrix для sklearn\n", + "cm_sk = confusion_matrix(y_test, y_pred_sk)\n", + "sns.heatmap(cm_sk, annot=True, fmt='d', cmap='Blues', ax=ax2,\n", + " xticklabels=['Real', 'Fake'], yticklabels=['Real', 'Fake'])\n", + "ax2.set_title('Sklearn Logistic Regression')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/project/cleaned_fake_news.csv b/project/cleaned_fake_news.csv new file mode 100644 index 00000000..f583168e --- /dev/null +++ b/project/cleaned_fake_news.csv @@ -0,0 +1,19 @@ +title,text,label,text_length,title_length +Government Announces New Education Reforms,The education ministry has proposed reforms to modernize the curriculum.,real,10,5 +Economy Shows Signs of Recovery,The new study conducted by international researchers shows walking improves heart health.,real,12,5 +Aliens Land in Central Park,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake,12,5 +Aliens Land in Central Park,The celebrity stated that secret documents reveal shocking information about the president.,fake,12,5 +New Study Reveals Health Benefits of Walking,The education ministry has proposed reforms to modernize the curriculum.,real,10,7 +Cure for Aging Discovered in Remote Village,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake,12,7 +Cure for Aging Discovered in Remote Village,The celebrity stated that secret documents reveal shocking information about the president.,fake,12,7 +Government Announces New Education Reforms,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real,15,5 +Economy Shows Signs of Recovery,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real,15,5 +Celebrity Reveals Secret Government Plans,The celebrity stated that secret documents reveal shocking information about the president.,fake,12,5 +Economy Shows Signs of Recovery,The education ministry has proposed reforms to modernize the curriculum.,real,10,5 +Cure for Aging Discovered in Remote Village,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake,16,7 +Celebrity Reveals Secret Government Plans,Sources claim that extraterrestrial beings were seen stepping out of a spaceship.,fake,12,5 +New Study Reveals Health Benefits of Walking,The new study conducted by international researchers shows walking improves heart health.,real,12,7 +Celebrity Reveals Secret Government Plans,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake,16,5 +New Study Reveals Health Benefits of Walking,Recent economic data shows that the GDP is steadily growing over the last two quarters.,real,15,7 +Government Announces New Education Reforms,The new study conducted by international researchers shows walking improves heart health.,real,12,5 +Aliens Land in Central Park,An isolated village claims its elders live up to 150 due to a secret herbal tea.,fake,16,5 diff --git a/project/processed_news_data.csv 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Классификация на выбранном датасете." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Rw9IZG14fxlm", - "outputId": "28136302-0275-4d73-b4c4-75cdbb544173" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting opendatasets\n", - " Downloading opendatasets-0.1.22-py3-none-any.whl.metadata (9.2 kB)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.6)\n", - "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.6.17)\n", - "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n", - "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n", - "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2024.8.30)\n", - "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n", - "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.32.3)\n", - "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.4)\n", - "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.2.3)\n", - "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.2.0)\n", - "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n", - "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.4.0)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.10)\n", - "Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n", - "Installing collected packages: opendatasets\n", - "Successfully installed opendatasets-0.1.22\n" - ] - } - ], - "source": [ - "!pip install opendatasets" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "11XpX0ldba5F" - }, - "source": [ - "Импорт модулей" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "R30hzockb6RQ" - }, - "outputs": [], - "source": [ - "import opendatasets as od\n", - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split, GridSearchCV\n", - "from sklearn.metrics import classification_report, mean_squared_error, accuracy_score, confusion_matrix\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "import plotly.express as px" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pSglBQCibe9N" - }, - "source": [ - "# Подготовка данных" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "w-D38cQ3WVOJ" - }, - "source": [ - "Возьмем датасет wine-reviews с kaggle" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v_Zx3Wp733_p" - }, - "source": [ - "В датасете содержится информация о винах. Информации по столбцам соответсвуют списку ниже.\n", - "\n", - "* Страна-производитель;\n", - "* описание;\n", - "* виноградник на территории винодельни, откуда собирают виноград;\n", - "* оценка вина; цена за бутылку;\n", - "* провинция или штат, из которого произведено вино;\n", - "* винодельческий район в провинции или штате;\n", - "* иногда в пределах винодельческого региона указываются более конкретные регионы;\n", - "* имя дегустатора;\n", - "* твитер аккаунт дегустатора;\n", - "* название;\n", - "* сорт винограда;\n", - "* винодельня." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YcAp_Wjisbue", - "outputId": "12d94a8a-ddf6-41f1-da03-67fc0a8217b8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n", - "Your Kaggle username: Shcherbakov\n", - "Your Kaggle Key: ··········\n", - "Dataset URL: https://www.kaggle.com/datasets/zynicide/wine-reviews\n", - "Downloading wine-reviews.zip to ./wine-reviews\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 50.9M/50.9M [00:00<00:00, 86.9MB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "od.download(\n", - " \"https://www.kaggle.com/datasets/zynicide/wine-reviews\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 823 - }, - "id": "4mIMtZR1fm4r", - "outputId": "c62d7552-68cf-4431-fafa-1e72599adfc0" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "wine_reviews" - }, - "text/html": [ - "\n", - "
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countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywinery
0ItalyAromas include tropical fruit, broom, brimston...Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia
1PortugalThis is ripe and fruity, a wine that is smooth...Avidagos8715.0DouroNaNNaNRoger Voss@vossrogerQuinta dos Avidagos 2011 Avidagos Red (Douro)Portuguese RedQuinta dos Avidagos
2USTart and snappy, the flavors of lime flesh and...NaN8714.0OregonWillamette ValleyWillamette ValleyPaul Gregutt@paulgwineRainstorm 2013 Pinot Gris (Willamette Valley)Pinot GrisRainstorm
3USPineapple rind, lemon pith and orange blossom ...Reserve Late Harvest8713.0MichiganLake Michigan ShoreNaNAlexander PeartreeNaNSt. Julian 2013 Reserve Late Harvest Riesling ...RieslingSt. Julian
4USMuch like the regular bottling from 2012, this...Vintner's Reserve Wild Child Block8765.0OregonWillamette ValleyWillamette ValleyPaul Gregutt@paulgwineSweet Cheeks 2012 Vintner's Reserve Wild Child...Pinot NoirSweet Cheeks
..........................................
129966GermanyNotes of honeysuckle and cantaloupe sweeten th...Brauneberger Juffer-Sonnenuhr Spätlese9028.0MoselNaNNaNAnna Lee C. IijimaNaNDr. H. Thanisch (Erben Müller-Burggraef) 2013 ...RieslingDr. H. Thanisch (Erben Müller-Burggraef)
129967USCitation is given as much as a decade of bottl...NaN9075.0OregonOregonOregon OtherPaul Gregutt@paulgwineCitation 2004 Pinot Noir (Oregon)Pinot NoirCitation
129968FranceWell-drained gravel soil gives this wine its c...Kritt9030.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Gresser 2013 Kritt Gewurztraminer (Als...GewürztraminerDomaine Gresser
129969FranceA dry style of Pinot Gris, this is crisp with ...NaN9032.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Marcel Deiss 2012 Pinot Gris (Alsace)Pinot GrisDomaine Marcel Deiss
129970FranceBig, rich and off-dry, this is powered by inte...Lieu-dit Harth Cuvée Caroline9021.0AlsaceAlsaceNaNRoger Voss@vossrogerDomaine Schoffit 2012 Lieu-dit Harth Cuvée Car...GewürztraminerDomaine Schoffit
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" - ], - "text/plain": [ - "country\n", - "Argentina 3800\n", - "Armenia 2\n", - "Australia 2329\n", - "Austria 3345\n", - "Bosnia and Herzegovina 2\n", - "Brazil 52\n", - "Bulgaria 141\n", - "Canada 257\n", - "Chile 4472\n", - "China 1\n", - "Croatia 73\n", - "Cyprus 11\n", - "Czech Republic 12\n", - "Egypt 1\n", - "England 74\n", - "France 22093\n", - "Georgia 86\n", - "Germany 2165\n", - "Greece 466\n", - "Hungary 146\n", - "India 9\n", - "Israel 505\n", - "Italy 19540\n", - "Lebanon 35\n", - "Luxembourg 6\n", - "Macedonia 12\n", - "Mexico 70\n", - "Moldova 59\n", - "Morocco 28\n", - "New Zealand 1419\n", - "Peru 16\n", - "Portugal 5691\n", - "Romania 120\n", - "Serbia 12\n", - "Slovakia 1\n", - "Slovenia 87\n", - "South Africa 1401\n", - "Spain 6645\n", - "Switzerland 7\n", - "Turkey 90\n", - "US 54504\n", - "Ukraine 14\n", - "Uruguay 109\n", - "Name: title, dtype: int64" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wine_reviews.groupby(\"country\")['title'].count()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VHjuA5XTb5Wv" - }, - "source": [ - "Наибольшее количество информации содержится о винах Америки, Франции и Италии.\n", - "\n", - "Рассмотрим Италию." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "pAVugV0xnZWl" - }, - "outputs": [], - "source": [ - "wine_reviews = wine_reviews[wine_reviews['country'] == 'Italy']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Q79LaXnDICTn" - }, - "source": [ - "Добавим год разлива вина. Пропущенные значения заменим медианой для сорта." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "HINLwR7PDTmO" - }, - "outputs": [], - "source": [ - "def unwrap_list(num_in_list):\n", - " try:\n", - " return num_in_list[0]\n", - " except:\n", - " return np.nan" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "dHdgOEhbIAWv", - "outputId": "2211aaf3-3fce-4353-c1bd-530665e85661" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":1: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\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", - " wine_reviews['year'] = wine_reviews['title'].transform(lambda x: unwrap_list(list(filter(lambda a: a.isdigit() and len(a) == 4, x.split())))).astype(float)\n", - ":2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\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", - " wine_reviews['year'] = wine_reviews.groupby(['variety'])['year'].transform(lambda x: x.fillna(x.median()))\n" - ] - }, - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "repr_error": "Out of range float values are not JSON compliant: nan", - "type": "dataframe", - "variable_name": "wine_reviews" - }, - "text/html": [ - "\n", - "
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countrydescriptiondesignationpointspriceprovinceregion_1region_2taster_nametaster_twitter_handletitlevarietywineryyear
0ItalyAromas include tropical fruit, broom, brimston...Vulkà Bianco87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeNicosia 2013 Vulkà Bianco (Etna)White BlendNicosia2013.0
6ItalyHere's a bright, informal red that opens with ...Belsito8716.0Sicily & SardiniaVittoriaNaNKerin O’Keefe@kerinokeefeTerre di Giurfo 2013 Belsito Frappato (Vittoria)FrappatoTerre di Giurfo2013.0
13ItalyThis is dominated by oak and oak-driven aromas...Rosso87NaNSicily & SardiniaEtnaNaNKerin O’Keefe@kerinokeefeMasseria Setteporte 2012 Rosso (Etna)Nerello MascaleseMasseria Setteporte2012.0
22ItalyDelicate aromas recall white flower and citrus...Ficiligno8719.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeBaglio di Pianetto 2007 Ficiligno White (Sicilia)White BlendBaglio di Pianetto2007.0
24ItalyAromas of prune, blackcurrant, toast and oak c...Aynat8735.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCanicattì 2009 Aynat Nero d'Avola (Sicilia)Nero d'AvolaCanicattì2009.0
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129929ItalyThis luminous sparkler has a sweet, fruit-forw...NaN9138.0VenetoProsecco Superiore di CartizzeNaNNaNNaNCol Vetoraz Spumanti NV Prosecco Superiore di...ProseccoCol Vetoraz Spumanti2007.0
129943ItalyA blend of Nero d'Avola and Syrah, this convey...Adènzia9029.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeBaglio del Cristo di Campobello 2012 Adènzia R...Red BlendBaglio del Cristo di Campobello2012.0
129947ItalyA blend of 65% Cabernet Sauvignon, 30% Merlot ...Symposio9020.0Sicily & SardiniaTerre SicilianeNaNKerin O’Keefe@kerinokeefeFeudo Principi di Butera 2012 Symposio Red (Te...Red BlendFeudo Principi di Butera2012.0
129961ItalyIntense aromas of wild cherry, baking spice, t...NaN9030.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCOS 2013 Frappato (Sicilia)FrappatoCOS2013.0
129962ItalyBlackberry, cassis, grilled herb and toasted a...Sàgana Tenuta San Giacomo9040.0Sicily & SardiniaSiciliaNaNKerin O’Keefe@kerinokeefeCusumano 2012 Sàgana Tenuta San Giacomo Nero d...Nero d'AvolaCusumano2012.0
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19540 rows × 14 columns

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\n" - ], - "text/plain": [ - " country description \\\n", - "0 Italy Aromas include tropical fruit, broom, brimston... \n", - "6 Italy Here's a bright, informal red that opens with ... \n", - "13 Italy This is dominated by oak and oak-driven aromas... \n", - "22 Italy Delicate aromas recall white flower and citrus... \n", - "24 Italy Aromas of prune, blackcurrant, toast and oak c... \n", - "... ... ... \n", - "129929 Italy This luminous sparkler has a sweet, fruit-forw... \n", - "129943 Italy A blend of Nero d'Avola and Syrah, this convey... \n", - "129947 Italy A blend of 65% Cabernet Sauvignon, 30% Merlot ... \n", - "129961 Italy Intense aromas of wild cherry, baking spice, t... \n", - "129962 Italy Blackberry, cassis, grilled herb and toasted a... \n", - "\n", - " designation points price province \\\n", - "0 Vulkà Bianco 87 NaN Sicily & Sardinia \n", - "6 Belsito 87 16.0 Sicily & Sardinia \n", - "13 Rosso 87 NaN Sicily & Sardinia \n", - "22 Ficiligno 87 19.0 Sicily & Sardinia \n", - "24 Aynat 87 35.0 Sicily & Sardinia \n", - "... ... ... ... ... \n", - "129929 NaN 91 38.0 Veneto \n", - "129943 Adènzia 90 29.0 Sicily & Sardinia \n", - "129947 Symposio 90 20.0 Sicily & Sardinia \n", - "129961 NaN 90 30.0 Sicily & Sardinia \n", - "129962 Sàgana Tenuta San Giacomo 90 40.0 Sicily & Sardinia \n", - "\n", - " region_1 region_2 taster_name \\\n", - "0 Etna NaN Kerin O’Keefe \n", - "6 Vittoria NaN Kerin O’Keefe \n", - "13 Etna NaN Kerin O’Keefe \n", - "22 Sicilia NaN Kerin O’Keefe \n", - "24 Sicilia NaN Kerin O’Keefe \n", - "... ... ... ... \n", - "129929 Prosecco Superiore di Cartizze NaN NaN \n", - "129943 Sicilia NaN Kerin O’Keefe \n", - "129947 Terre Siciliane NaN Kerin O’Keefe \n", - "129961 Sicilia NaN Kerin O’Keefe \n", - "129962 Sicilia NaN Kerin O’Keefe \n", - "\n", - " taster_twitter_handle \\\n", - "0 @kerinokeefe \n", - "6 @kerinokeefe \n", - "13 @kerinokeefe \n", - "22 @kerinokeefe \n", - "24 @kerinokeefe \n", - "... ... \n", - "129929 NaN \n", - "129943 @kerinokeefe \n", - "129947 @kerinokeefe \n", - "129961 @kerinokeefe \n", - "129962 @kerinokeefe \n", - "\n", - " title variety \\\n", - "0 Nicosia 2013 Vulkà Bianco (Etna) White Blend \n", - "6 Terre di Giurfo 2013 Belsito Frappato (Vittoria) Frappato \n", - "13 Masseria Setteporte 2012 Rosso (Etna) Nerello Mascalese \n", - "22 Baglio di Pianetto 2007 Ficiligno White (Sicilia) White Blend \n", - "24 Canicattì 2009 Aynat Nero d'Avola (Sicilia) Nero d'Avola \n", - "... ... ... \n", - "129929 Col Vetoraz Spumanti NV Prosecco Superiore di... Prosecco \n", - "129943 Baglio del Cristo di Campobello 2012 Adènzia R... Red Blend \n", - "129947 Feudo Principi di Butera 2012 Symposio Red (Te... Red Blend \n", - "129961 COS 2013 Frappato (Sicilia) Frappato \n", - "129962 Cusumano 2012 Sàgana Tenuta San Giacomo Nero d... Nero d'Avola \n", - "\n", - " winery year \n", - "0 Nicosia 2013.0 \n", - "6 Terre di Giurfo 2013.0 \n", - "13 Masseria Setteporte 2012.0 \n", - "22 Baglio di Pianetto 2007.0 \n", - "24 Canicattì 2009.0 \n", - "... ... ... \n", - "129929 Col Vetoraz Spumanti 2007.0 \n", - "129943 Baglio del Cristo di Campobello 2012.0 \n", - "129947 Feudo Principi di Butera 2012.0 \n", - "129961 COS 2013.0 \n", - "129962 Cusumano 2012.0 \n", - "\n", - "[19540 rows x 14 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wine_reviews['year'] = wine_reviews['title'].transform(lambda x: unwrap_list(list(filter(lambda a: a.isdigit() and len(a) == 4, x.split())))).astype(float)\n", - "wine_reviews['year'] = wine_reviews.groupby(['variety'])['year'].transform(lambda x: x.fillna(x.median()))\n", - "wine_reviews" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "inCyvZZB94db" - }, - "source": [ - "Посмотрим есть ли в region_2 не NaN значения." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "dvvW7jf39Aon", - "outputId": "cc9d7c69-afaf-4de5-8351-c17ee065a085" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wine_reviews['region_2'].notnull().sum()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MblBifJc-ANA" - }, - "source": [ - "Нет." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0wI9FO6aBBwD" - }, - "source": [ - "Посмотрим на количество вин, оцененных каждым дегустатором." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 272 - }, - "id": "zAO6YTbjA8bX", - "outputId": "a4b62fbd-cdf5-4506-e68e-e62be5b84b32" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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count
taster_name
Kerin O’Keefe10776
Roger Voss97
Joe Czerwinski89
Michael Schachner76
Paul Gregutt4
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" - ], - "text/plain": [ - "taster_name\n", - "Kerin O’Keefe 10776\n", - "Roger Voss 97\n", - "Joe Czerwinski 89\n", - "Michael Schachner 76\n", - "Paul Gregutt 4\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wine_reviews['taster_name'].value_counts()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IELV46tjBhFB" - }, - "source": [ - "При таком распределении Kerin O’Keefe почти не будет влиять на обучение, а остальные признаки могут влиять слишком сильно или не попасть в тренировочную выборку. Уберем этот признак." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1BzsdrTWIIUT" - }, - "source": [ - "Будем пытаться предсказать сорт вина" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MAgAGDx33kRd" - }, - "source": [ - "Заметим, что некоторые сорта встречаются всего несколько раз, поэтому они могут, либо не попасть в тренировочную выборку, либо слишком сильно влиять на модель.\n", - "\n", - "Страна у всех вин одна. Убираем столбец country.\n", - "\n", - "Так как description яляется уникальным признаком, то оно не повлияет на обучение.\n", - "\n", - "Информации о винограднике на территории винодельни не содержательна т.к. винодельня может содержать несколько виноградников. Оба признака могут замедлить модель, т.к. они связаны, то оставим один. Если оставить информацию о винограднике вместо винодельни, то шанс переобучения выше. Убираем столбец designation.\n", - "\n", - "По аналогичной причине оставляем province и убираем region_1.\n", - "\n", - "В region_2 все значения NaN. Убираем.\n", - "\n", - "Хранить информацию о твитере дегустатора, очевидно, не нужно.\n", - "\n", - "Так как title яляется уникальным признаком, то оно не повлияет на обучение.\n", - "\n", - "\n", - "Удалим эти данные.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 429 - }, - "id": "Y22RJ1Je3gpX", - "outputId": "5793e461-fdfc-44ed-b5ff-8b74f7a1be56" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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count
variety
Red Blend3624
Nebbiolo2736
Sangiovese2265
White Blend779
Sangiovese Grosso750
Glera709
Corvina, Rondinella, Molinara619
Pinot Grigio605
Barbera479
Nero d'Avola361
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" - ], - "text/plain": [ - "variety\n", - "Red Blend 3624\n", - "Nebbiolo 2736\n", - "Sangiovese 2265\n", - "White Blend 779\n", - "Sangiovese Grosso 750\n", - "Glera 709\n", - "Corvina, Rondinella, Molinara 619\n", - "Pinot Grigio 605\n", - "Barbera 479\n", - "Nero d'Avola 361\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "match_id_counts = wine_reviews['variety'].value_counts()\n", - "match_id_counts[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4Jk_qQEG-XKY" - }, - "source": [ - "Оставим первые три сорта вина, т.к. между ними разница в количестве строк не так велика, как между 3 и 4 (нумеруя с 1 сверху)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 424 - }, - "id": "eykVrqCL3pXR", - "outputId": "92c22c11-0aa7-4440-a808-bbec5aa64d2c" - }, - "outputs": [ - { - "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"data\",\n \"rows\": 8625,\n \"fields\": [\n {\n \"column\": \"points\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 80,\n \"max\": 100,\n \"num_unique_values\": 21,\n \"samples\": [\n 87,\n 81,\n 84\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 42.73811041300064,\n \"min\": 6.0,\n \"max\": 800.0,\n \"num_unique_values\": 192,\n \"samples\": [\n 120.0,\n 320.0,\n 100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"province\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Northwestern Italy\",\n \"Central Italy\",\n \"Veneto\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"winery\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1724,\n \"samples\": [\n \"Cordella\",\n \"Corte dei Papi\",\n \"Castello Banfi\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9.170948183314536,\n \"min\": 1637.0,\n \"max\": 2016.0,\n \"num_unique_values\": 27,\n \"samples\": [\n 2012.0,\n 1997.0,\n 2005.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"variety\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Red Blend\",\n \"Sangiovese\",\n \"Nebbiolo\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "data" - }, - "text/html": [ - "\n", - "
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288717.0Sicily and SardiniaTerre di Giurfo2011.0Red Blend
3186NaNSicily and SardiniaDuca di Salaparuta2010.0Red Blend
5086NaNSicily and SardiniaViticultori Associati Canicatti2008.0Red Blend
5485NaNSicily and SardiniaCorvo2010.0Red Blend
618617.0Central ItalyPodere dal Nespoli2015.0Sangiovese
.....................
1298268850.0PiedmontVinchio-Vaglio Serra2006.0Nebbiolo
12984486NaNTuscanyCaparzo2006.0Sangiovese
1298929172.0TuscanyLa Mannella2012.0Sangiovese
1299439029.0Sicily and SardiniaBaglio del Cristo di Campobello2012.0Red Blend
1299479020.0Sicily and SardiniaFeudo Principi di Butera2012.0Red Blend
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Значит признак будет влиять на модель." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qLRcau1KXPgt" - }, - "source": [ - "Посмотрим на количество строк для каждого сорта." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 487 - }, - "id": "wiZwp7dC5x8o", - "outputId": "29a6b95e-37d5-4da1-efe3-f0127ac4e47f" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(15,5))\n", - "sns.countplot(data=data, x='variety')\n", - "plt.title(\"count wines rate depending on the variety\")\n", - "plt.xlabel(\"variety\")\n", - "plt.ylabel(\"count\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Tmi7YJ7qXUFt" - }, - "source": [ - "Заметим, что разница между столбцами не очень большая, значит, скорее всего, точность предсказаний будет не сильно отличться." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s-Ch6pnlekYR" - }, - "source": [ - "Посмотрим на количество в зависимости от оценки" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "aNIJf7NSTKqw", - "outputId": "ab4f6b3d-7100-4691-cb8c-79c45a1a0338" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.countplot(data=data, x='points')\n", - "plt.title(\"count wines rate depending on the points\")\n", - "plt.xlabel(\"points\")\n", - "plt.ylabel(\"count\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SH9b95lPfMAY" - }, - "source": [ - "Видим, что количество вин с оценкой в диапазоне 87-90 больше всего, следовательно, для них признак оценки будет меньше влиять на модель." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lGZe1Xt4SGlb" - }, - "source": [ - "Проверим что оценка и стоимость вина не линейно-зависимы." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "srmdq10zQ2jp", - "outputId": "c2c7cdd1-099f-4af9-fbc1-2d286c005292" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.barplot(data=data, x='points', y='price', errorbar=None)\n", - "plt.title(\"price rate depending on the points\")\n", - "plt.xlabel(\"points\")\n", - "plt.ylabel(\"price\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gV5rvdzQf9Rm" - }, - "source": [ - "Проверили. Признаки не линейно-зависимы." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H2EamkuhAPKu" - }, - "source": [ - "Посмотрим на зависимость оценки вина от года розлива." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 450 - }, - "id": "zm_kgoac_-Oq", - "outputId": "91648859-c779-404e-93b8-8fafbedf6387" - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 5))\n", - "sns.barplot(data=data, x='year', y='points', errorbar=None)\n", - "plt.title(\"points rate depending on the year\")\n", - "plt.xlabel(\"year\")\n", - "plt.ylabel(\"points\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "890pOlA8A2se" - }, - "source": [ - "Проверили. Значения не линейно-зависимы. Функцию нельзя считать константной т.к. колебания оценки на 1-2 могут быть существенны (видно из предыдущего графика)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ndxrlPqeEb0X" - }, - "source": [ - "Посмотрим на зависимость цены вина от года розлива." - ] - }, - { - "cell_type": "code", - "execution_count": 250, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 450 - }, - "id": "Fsl2BUlQEVpj", - "outputId": "713d79b8-9076-428e-f6e5-01a72c939ca6" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "plt.figure(figsize=(20, 5))\n", - "sns.barplot(data=data, x='year', y='price', errorbar=None)\n", - "plt.title(\"price rate depending on the year\")\n", - "plt.xlabel(\"year\")\n", - "plt.ylabel(\"price\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BTYtTderEeaN" - }, - "source": [ - "Проверили. Признаки не линейно-зависимы." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "agLGino1gDOT" - }, - "source": [ - "# Модель и обучение" - ] - }, - { - "cell_type": "code", - "execution_count": 181, - "metadata": { - "id": "uup4zgoEgomo" - }, - "outputs": [], - "source": [ - "X = data.drop(['variety'], axis=1)\n", - "Y = data['variety']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4K3fctb5NeuY" - }, - "source": [ - "Представляем данные в виде необходимом для обучения." - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": { - "id": "KWPAaK1uhhoK" - }, - "outputs": [], - "source": [ - "province = pd.get_dummies(X['province'], drop_first=True)\n", - "winery = pd.get_dummies(X['winery'], drop_first=True)\n", - "\n", - "X_gd = X.drop(['province', 'winery'], axis=1)\n", - "\n", - "X_gd = pd.concat([X_gd, province, winery], axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "41KFmdv-gGrU" - }, - "source": [ - "Разбиваем данные на тренировочные и тестовые" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 479 - }, - "id": "-SLpeeP7y1fL", - "outputId": "8ec058a1-2d75-4d0d-da3d-2f734c406540" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " points price year Italy Other Lombardy Northeastern Italy \\\n", - "47486 91 55.000000 2007.0 False False False \n", - "122282 87 44.428758 2011.0 False False False \n", - "96663 87 20.000000 2011.0 False False False \n", - "21437 88 26.000000 2013.0 False False False \n", - "66082 87 45.000000 2013.0 False False False \n", - "... ... ... ... ... ... ... \n", - "84833 86 45.000000 2013.0 False False False \n", - "77114 87 44.428758 2006.0 False False False \n", - "80524 88 23.000000 2015.0 False False False \n", - "12797 88 18.000000 2013.0 False False False \n", - "109138 86 16.000000 2012.0 False False False \n", - "\n", - " Northwestern Italy Piedmont Sicily and Sardinia Southern Italy \\\n", - "47486 False True False False \n", - "122282 False False False False \n", - "96663 False False False False \n", - "21437 False False False False \n", - "66082 False True False False \n", - "... ... ... ... ... \n", - "84833 False True False False \n", - "77114 False False False False \n", - "80524 False False False False \n", - "12797 False False False False \n", - "109138 False False True False \n", - "\n", - " ... Vèscine Wine for Food Zanoni Zenato Zeni Ziobaffa Zisola \\\n", - "47486 ... False False False False False False False \n", - "122282 ... False False False False False False False \n", - "96663 ... False False False False False False False \n", - "21437 ... False False False False False False False \n", - "66082 ... False False False False False False False \n", - "... ... ... ... ... ... ... ... ... \n", - "84833 ... False False False False False False False \n", - "77114 ... False False False False False False False \n", - "80524 ... False False False False False False False \n", - "12797 ... False False False False False False False \n", - "109138 ... 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Consider increasing max_iter to improve the fit.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n" - ] - } - ], - "source": [ - "from sklearn.linear_model import SGDClassifier\n", - "\n", - "# create dict with paramemeters for model\n", - "param_grid_clf = {\n", - " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", - " 'max_iter': [100, 200, 400, 800, 1000]\n", - "}\n", - "\n", - "# create model\n", - "clf = SGDClassifier(random_state=42)\n", - "\n", - "# selection of best parameters\n", - "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", - "\n", - "# train model\n", - "grid_search_clf.fit(X_train_lbe, Y_train)\n", - "\n", - "# check of best parameters\n", - "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", - "\n", - "# prediction of results\n", - "y_pred_sgdc_lbe = grid_search_clf.predict(X_test_lbe)\n", - "\n", - "# check of metric values\n", - "print(classification_report(Y_test, y_pred_sgdc_lbe))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Kp6yGqjKMGMH" - }, - "source": [ - "Точность модели SGDClassifier очень низкая." - ] - }, - { - "cell_type": "markdown", - "source": [ - "Были попытки перебирать разные параметры ядра модли SVC, но это катастрафически замедляло процесс, а результаты не становились сильно лучше, поэтому тут использую дефолтные параметры." - ], - "metadata": { - "id": "pICdMV1OhQHV" - } - }, - { - "cell_type": "code", - "execution_count": 189, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "h4Zc9hPFK2HD", - "outputId": "0e187de2-7e96-413d-fc6c-1165e991a5f3" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.52 0.22 0.31 526\n", - " Red Blend 0.46 0.93 0.61 742\n", - " Sangiovese 0.00 0.00 0.00 457\n", - "\n", - " accuracy 0.46 1725\n", - " macro avg 0.33 0.38 0.31 1725\n", - "weighted avg 0.36 0.46 0.36 1725\n", - "\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n" - ] - } - ], - "source": [ - "from sklearn.svm import SVC\n", - "\n", - "clf = SVC()\n", - "clf.fit(X_train_lbe, Y_train)\n", - "y_pred_svc_lbe = clf.predict(X_test_lbe)\n", - "\n", - "# check of metric values\n", - "print(classification_report(Y_test, y_pred_svc_lbe))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UrYWrS02OeQP" - }, - "source": [ - "Точность у модели SVC очень низкая." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QZdAmoL2Lhqp" - }, - "source": [ - "# PCA" - ] - }, - { - "cell_type": "code", - "execution_count": 190, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-OnYsi83ZVQ6", - "outputId": "29c543bf-e71c-4d1d-c3e6-bde96f7d497b" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[ 4.26507051e+00, -2.31516787e+00, 1.63172872e+00, ...,\n", - " 3.58225259e-18, 2.88184583e-18, 1.30234266e-20],\n", - " [-6.43751638e+00, 1.66052025e+00, -2.08758127e+00, ...,\n", - " 3.15901817e-18, 1.87872264e-19, -8.05538076e-20],\n", - " [-3.08530170e+01, 1.59344524e+00, -1.33581991e+00, ...,\n", - " -2.35725523e-18, -1.67147617e-18, 1.11176722e-20],\n", - " ...,\n", - " [-2.78372126e+01, 5.59889241e+00, -4.34360135e-01, ...,\n", - " -5.38174105e-20, -7.44436577e-21, 8.56165509e-21],\n", - " [-3.28294277e+01, 3.58499107e+00, -2.86877737e-01, ...,\n", - " -4.94705731e-20, -2.86098011e-20, 3.72404376e-21],\n", - " [-3.48845942e+01, 2.58382951e+00, -2.20524267e+00, ...,\n", - " -6.24065254e-20, -3.38691686e-20, 1.20095839e-21]])" - ] - }, - "metadata": {}, - "execution_count": 190 - } - ], - "source": [ - "pca = PCA()\n", - "X_train_pca = pca.fit_transform(X_train_gd)\n", - "X_test_pca = pca.transform(X_test_gd)\n", - "X_train_pca" - ] - }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "zdsFUNX4P6Cy", - "outputId": "f79cd830-3838-4566-8c62-7274b1352ad7" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "plt.plot(np.arange(pca.n_components_) + 1, pca.explained_variance_ratio_, 'ro-')\n", - "plt.title('dependence of explained variance ratio on the number of components.')\n", - "plt.xlabel('n components')\n", - "plt.ylabel('explained variance ratio')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4_m5ji1CRrOB" - }, - "source": [ - "Видим что большинство параметров дает распределение дисперсии близкое к нулю. Но их количество все ещё слишком велико." - ] - }, - { - "cell_type": "markdown", - "source": [ - "Попробуем установить конкретное количество компонент." - ], - "metadata": { - "id": "fRK5Ut_IXyZt" - } - }, - { - "cell_type": "code", - "source": [ - "pca = PCA(n_components=30)\n", - "X_train_pca = pca.fit_transform(X_train_gd)\n", - "X_test_pca = pca.transform(X_test_gd)\n", - "X_train_pca" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SQJDyhOAXlLg", - "outputId": "22951ca3-dd32-46d3-b5d3-0aeac5256f23" - }, - "execution_count": 192, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[ 4.26507051e+00, -2.31516787e+00, 1.63172872e+00, ...,\n", - " 5.05657142e-04, 3.32621442e-03, 6.09493572e-03],\n", - " [-6.43751638e+00, 1.66052025e+00, -2.08758127e+00, ...,\n", - " -3.35528178e-03, 1.48453024e-03, 1.42042476e-03],\n", - " [-3.08530170e+01, 1.59344524e+00, -1.33581991e+00, ...,\n", - " 1.46636958e-03, 3.41066691e-03, -1.85772109e-03],\n", - " ...,\n", - " [-2.78372126e+01, 5.59889241e+00, -4.34360135e-01, ...,\n", - " -5.51559775e-03, 1.44925595e-03, 6.24520492e-03],\n", - " [-3.28294277e+01, 3.58499107e+00, -2.86877737e-01, ...,\n", - " -5.74142636e-03, 1.06299879e-03, 2.40827452e-03],\n", - " [-3.48845942e+01, 2.58382951e+00, -2.20524267e+00, ...,\n", - " 1.57198953e-02, 1.40492514e-02, 1.21808361e-02]])" - ] - }, - "metadata": {}, - "execution_count": 192 - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.plot(np.arange(pca.n_components_) + 1, pca.explained_variance_ratio_, 'ro-')\n", - "plt.title('dependence of explained variance ratio on the number of components.')\n", - "plt.xlabel('n components')\n", - "plt.ylabel('explained variance ratio')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "BJhCUDZMXunC", - "outputId": "a3e4f378-f6b1-4149-f3f3-f6eb7d693644" - }, - "execution_count": 193, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "execution_count": 194, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PPOGb3m22t0h", - "outputId": "17b942fe-602e-4fbb-b831-1e02488bb0d9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "best parameters for DecisionTreeClassifier: {'criterion': 'entropy', 'max_depth': None}\n", - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.98 0.98 0.98 526\n", - " Red Blend 0.76 0.74 0.75 742\n", - " Sangiovese 0.62 0.64 0.63 457\n", - "\n", - " accuracy 0.79 1725\n", - " macro avg 0.78 0.79 0.78 1725\n", - "weighted avg 0.79 0.79 0.79 1725\n", - "\n" - ] - } - ], - "source": [ - "param_grid_clf = {\n", - " 'criterion': ('entropy', 'gini', 'log_loss'),\n", - " 'max_depth': [5, 10, None],\n", - "}\n", - "clf = DecisionTreeClassifier(random_state=42)\n", - "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", - "grid_search_clf.fit(X_train_pca, Y_train)\n", - "y_pred_dtc_pca = grid_search_clf.predict(X_test_pca)\n", - "\n", - "print(\"best parameters for DecisionTreeClassifier:\", grid_search_clf.best_params_)\n", - "print(classification_report(Y_test, y_pred_dtc_pca))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "BBCSipxwINo7" - }, - "source": [ - "Видим, что после уменьшения размерности с помощью PCA модель неплохо справляется с предсказанием." - ] - }, - { - "cell_type": "code", - "execution_count": 195, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "KXjsGlcsg01u", - "outputId": "54d80349-e02d-448e-fb56-93d05eb49a55" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "best parameters for SGDClassifier: {'loss': 'hinge', 'max_iter': 100}\n", - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.97 0.98 0.97 526\n", - " Red Blend 0.67 0.83 0.74 742\n", - " Sangiovese 0.59 0.35 0.44 457\n", - "\n", - " accuracy 0.75 1725\n", - " macro avg 0.74 0.72 0.72 1725\n", - "weighted avg 0.74 0.75 0.73 1725\n", - "\n" - ] - } - ], - "source": [ - "param_grid_clf = {\n", - " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", - " 'max_iter': [100, 200, 400, 800, 1000]\n", - "}\n", - "clf = SGDClassifier(random_state=42)\n", - "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", - "grid_search_clf.fit(X_train_pca, Y_train)\n", - "y_pred_sgdc_pca = grid_search_clf.predict(X_test_pca)\n", - "\n", - "\n", - "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", - "print(classification_report(Y_test, y_pred_sgdc_pca))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kign9MkOJcdI" - }, - "source": [ - "Такая модель так же неплохо справляется с предсказанием, но все же менее точно чем DecisionTreeClassifier." - ] - }, - { - "cell_type": "code", - "execution_count": 196, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "S1CpDRTxhLBG", - "outputId": "95ece058-e7fd-4992-90bf-b471c88e23ff" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.85 0.93 0.88 526\n", - " Red Blend 0.62 0.86 0.72 742\n", - " Sangiovese 0.67 0.18 0.28 457\n", - "\n", - " accuracy 0.70 1725\n", - " macro avg 0.71 0.65 0.63 1725\n", - "weighted avg 0.70 0.70 0.65 1725\n", - "\n" - ] - } - ], - "source": [ - "clf = SVC()\n", - "clf.fit(X_train_pca, Y_train)\n", - "y_pred_svc_pca = clf.predict(X_test_pca)\n", - "\n", - "print(classification_report(Y_test, y_pred_svc_pca))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "oFIv_FUSHPFg" - }, - "source": [ - "Такая модель неплохо справляется с предсказанием для Nebbiolo и Red Blend, но для Sngiovese предсказание получется слишком неточным, следовательно, общая точность модели достаточно низкая." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kEYXLr8sJzql" - }, - "source": [ - "# t-SNE" - ] - }, - { - "cell_type": "code", - "execution_count": 197, - "metadata": { - "id": "5EYwtlZrkyTV", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "42e116c4-e1d5-463c-de88-3b2ac96199c9" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[ 8.697849 , 56.422546 ],\n", - " [ 85.46976 , -6.209872 ],\n", - " [-91.522644 , 3.429889 ],\n", - " ...,\n", - " [-31.943085 , 35.00665 ],\n", - " [ 5.8764524, -20.747879 ],\n", - " [-89.09723 , 19.900454 ]], dtype=float32)" - ] - }, - "metadata": {}, - "execution_count": 197 - } - ], - "source": [ - "tsne = TSNE(random_state=42)\n", - "X_train_tsne = tsne.fit_transform(X_train_lbe)\n", - "X_test_tsne = tsne.fit_transform(X_test_lbe)\n", - "X_train_tsne" - ] - }, - { - "cell_type": "code", - "execution_count": 200, - "metadata": { - "id": "5iL9lCiGlnjH", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d4e4d501-cff9-4417-9fc1-622c61907242" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "best parameters for DecisionTreeClassifier: {'criterion': 'entropy', 'max_depth': None}\n", - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.28 0.19 0.22 526\n", - " Red Blend 0.42 0.35 0.38 742\n", - " Sangiovese 0.30 0.49 0.37 457\n", - "\n", - " accuracy 0.34 1725\n", - " macro avg 0.33 0.34 0.33 1725\n", - "weighted avg 0.35 0.34 0.33 1725\n", - "\n" - ] - } - ], - "source": [ - "param_grid_clf = {\n", - " 'criterion': ('entropy', 'gini', 'log_loss'),\n", - " 'max_depth': [5, 10, None],\n", - "}\n", - "clf = DecisionTreeClassifier(random_state=42)\n", - "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", - "grid_search_clf.fit(X_train_tsne, Y_train)\n", - "y_pred_dtc_tsne = grid_search_clf.predict(X_test_tsne)\n", - "\n", - "print(\"best parameters for DecisionTreeClassifier:\", grid_search_clf.best_params_)\n", - "print(classification_report(Y_test, y_pred_dtc_tsne))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3dEtGnIkJ5kH" - }, - "source": [ - "Модель предсказывает данные с очень низкой точностью, после применения t-SNE." - ] - }, - { - "cell_type": "code", - "execution_count": 201, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "shg3-6ZAzWVV", - "outputId": "035d4ce7-1c79-43e2-d0de-32b74d562176" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "best parameters for SGDClassifier: {'loss': 'modified_huber', 'max_iter': 100}\n", - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.29 0.37 0.32 526\n", - " Red Blend 0.42 0.59 0.49 742\n", - " Sangiovese 0.00 0.00 0.00 457\n", - "\n", - " accuracy 0.37 1725\n", - " macro avg 0.24 0.32 0.27 1725\n", - "weighted avg 0.27 0.37 0.31 1725\n", - "\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_stochastic_gradient.py:744: ConvergenceWarning:\n", - "\n", - "Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n" - ] - } - ], - "source": [ - "param_grid_clf = {\n", - " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", - " 'max_iter': [100, 200, 400, 800, 1000]\n", - "}\n", - "clf = SGDClassifier(random_state=42)\n", - "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", - "grid_search_clf.fit(X_train_tsne, Y_train)\n", - "y_pred_sgdc_tsne = grid_search_clf.predict(X_test_tsne)\n", - "\n", - "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", - "print(classification_report(Y_test, y_pred_sgdc_tsne))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uIuYXlGsKDT9" - }, - "source": [ - "После t-SNE точность модели сильно снизилась." - ] - }, - { - "cell_type": "code", - "execution_count": 202, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mS7KSWX9zYB-", - "outputId": "255f5776-375a-4bc6-da15-3533a8e04dc9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.38 0.55 0.45 526\n", - " Red Blend 0.46 0.59 0.52 742\n", - " Sangiovese 0.00 0.00 0.00 457\n", - "\n", - " accuracy 0.42 1725\n", - " macro avg 0.28 0.38 0.32 1725\n", - "weighted avg 0.31 0.42 0.36 1725\n", - "\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n", - "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning:\n", - "\n", - "Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - "\n" - ] - } - ], - "source": [ - "clf = SVC()\n", - "clf.fit(X_train_tsne, Y_train)\n", - "y_pred_svc_tsne = clf.predict(X_test_tsne)\n", - "\n", - "print(classification_report(Y_test, y_pred_svc_tsne))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_o6olr86LPuk" - }, - "source": [ - "После t-SNE у точность модели сильно снизилась." - ] - }, - { - "cell_type": "markdown", - "source": [ - "# StandartScaler" - ], - "metadata": { - "id": "c5HYyaDQk_FA" - } - }, - { - "cell_type": "code", - "source": [ - "from sklearn.preprocessing import StandardScaler" - ], - "metadata": { - "id": "8_5pvsWylDf7" - }, - "execution_count": 203, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "scaler = StandardScaler()\n", - "X_train_scal = scaler.fit_transform(X_train_lbe)\n", - "X_test_scal = scaler.transform(X_test_lbe)\n", - "X_train_scal" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6_FpArVPlRmC", - "outputId": "cdbcd123-c798-4082-9b35-3a211947ce67" - }, - "execution_count": 204, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[ 0.64223813, 0.1020881 , -0.79932341, 0.29019682, -0.24042572],\n", - " [-0.83935434, -0.15453387, 0.68902728, 1.6211048 , 0.17319704],\n", - " [-0.83935434, -0.7475537 , 1.18514418, -1.6511276 , 0.17319704],\n", - " ...,\n", - " [-0.46895622, -0.67472726, 0.68902728, -0.16611449, 0.58681979],\n", - " [-0.46895622, -0.79610466, 0.68902728, 0.77652861, 0.38000841],\n", - " [-1.20975246, -0.84465562, -0.30320652, -1.58107982, 0.27660272]])" - ] - }, - "metadata": {}, - "execution_count": 204 - } - ] - }, - { - "cell_type": "code", - "execution_count": 205, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "2d0bd8a9-07b1-435d-bff5-ce0aee53b692", - "id": "Ooxr06_ll6xq" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "best parameters for DecisionTreeClassifier: {'criterion': 'entropy', 'max_depth': None}\n", - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.99 0.98 0.99 526\n", - " Red Blend 0.80 0.80 0.80 742\n", - " Sangiovese 0.69 0.71 0.70 457\n", - "\n", - " accuracy 0.83 1725\n", - " macro avg 0.83 0.83 0.83 1725\n", - "weighted avg 0.83 0.83 0.83 1725\n", - "\n" - ] - } - ], - "source": [ - "param_grid_clf = {\n", - " 'criterion': ('entropy', 'gini', 'log_loss'),\n", - " 'max_depth': [5, 10, None],\n", - "}\n", - "clf = DecisionTreeClassifier(random_state=42)\n", - "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", - "grid_search_clf.fit(X_train_scal, Y_train)\n", - "y_pred_dtc_scal= grid_search_clf.predict(X_test_scal)\n", - "\n", - "print(\"best parameters for DecisionTreeClassifier:\", grid_search_clf.best_params_)\n", - "print(classification_report(Y_test, y_pred_dtc_scal))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CqRCrNacl6xs" - }, - "source": [ - "Модель предсказывает данные с неплохой точностью, после применения масштабирования." - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "517356ec-5a3b-408c-c7fa-b1ce76633ec4", - "id": "zwAM4RXNl6xt" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "best parameters for SGDClassifier: {'loss': 'hinge', 'max_iter': 100}\n", - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.79 1.00 0.88 526\n", - " Red Blend 0.59 0.80 0.68 742\n", - " Sangiovese 0.49 0.07 0.12 457\n", - "\n", - " accuracy 0.67 1725\n", - " macro avg 0.63 0.62 0.56 1725\n", - "weighted avg 0.63 0.67 0.59 1725\n", - "\n" - ] - } - ], - "source": [ - "param_grid_clf = {\n", - " 'loss': ['hinge', 'log_loss', 'modified_huber', 'squared_error'],\n", - " 'max_iter': [100, 200, 400, 800, 1000]\n", - "}\n", - "clf = SGDClassifier(random_state=42)\n", - "grid_search_clf = GridSearchCV(clf, param_grid_clf, n_jobs=-1, scoring='accuracy')\n", - "grid_search_clf.fit(X_train_scal, Y_train)\n", - "y_pred_sgdc_scal = grid_search_clf.predict(X_test_scal)\n", - "\n", - "print(\"best parameters for SGDClassifier:\", grid_search_clf.best_params_)\n", - "print(classification_report(Y_test, y_pred_sgdc_scal))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "plhnClvOl6xu" - }, - "source": [ - "Точность модели для Nebbiolo и Red Blend неплохая, но для Sangiovese предсказания очень неточные.\n", - "\n", - "Общая точность модели довольно низкая." - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "eb870630-4335-4f98-8c5d-a6753e1a8af8", - "id": "FFtQnBr-l6xv" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " precision recall f1-score support\n", - "\n", - " Nebbiolo 0.95 1.00 0.97 526\n", - " Red Blend 0.70 0.80 0.75 742\n", - " Sangiovese 0.63 0.44 0.52 457\n", - "\n", - " accuracy 0.77 1725\n", - " macro avg 0.76 0.75 0.75 1725\n", - "weighted avg 0.76 0.77 0.76 1725\n", - "\n" - ] - } - ], - "source": [ - "clf = SVC()\n", - "clf.fit(X_train_scal, Y_train)\n", - "y_pred_svc_scal = clf.predict(X_test_scal)\n", - "\n", - "print(classification_report(Y_test, y_pred_svc_scal))" - ] - }, - { - "cell_type": "markdown", - "source": [ - "После масштабирования данных модель SVC показала себя лучше чем на предыдущих вариантах. Такую точность можно так же считать неплохой." - ], - "metadata": { - "id": "00bHBluAnIjq" - } - }, - { - "cell_type": "markdown", - "source": [ - "# Графики и сравнения моделей" - ], - "metadata": { - "id": "b6p5JvSinblz" - } - }, - { - "cell_type": "code", - "source": [ - "fig, axes = plt.subplots(5, 3, figsize=(20, 30))\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_gd), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[0, 0])\n", - "axes[0, 0].set_title('confusion_matrix for DecisionTree after get_dummies')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_lbe), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[1, 0])\n", - "axes[1, 0].set_title('confusion_matrix for DecisionTree after LabelEncoder')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_lbe), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[1, 1])\n", - "axes[1, 1].set_title('confusion_matrix for SGDClassifier after LabelEncoder')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_lbe), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[1, 2])\n", - "axes[1, 2].set_title('confusion_matrix for SVC after LabelEncoder')\n", - "\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_pca), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[2, 0])\n", - "axes[2, 0].set_title('confusion_matrix for DecisionTree after PCA')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_pca), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[2, 1])\n", - "axes[2, 1].set_title('confusion_matrix for SGDClassifier after PCA')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_pca), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[2, 2])\n", - "axes[2, 2].set_title('confusion_matrix for SVC after PCA')\n", - "\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_tsne), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[3, 0])\n", - "axes[3, 0].set_title('confusion_matrix for DecisionTree after t-SNE')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_tsne), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[3, 1])\n", - "axes[3, 1].set_title('confusion_matrix for SGDClassifier after t-SNE')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_tsne), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[3, 2])\n", - "axes[3, 2].set_title('confusion_matrix for SVC after t-SNE')\n", - "\n", - "\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_dtc_scal), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[4, 0])\n", - "axes[4, 0].set_title('confusion_matrix for DecisionTree after SandartScaler')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_sgdc_scal), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[4, 1])\n", - "axes[4, 1].set_title('confusion_matrix for SGDClassifier after SandartScaler')\n", - "\n", - "sns.heatmap(confusion_matrix(Y_test, y_pred_svc_scal), annot=True, cmap='coolwarm', fmt='.2f', ax=axes[4, 2])\n", - "axes[4, 2].set_title('confusion_matrix for SVC after SandartScaler')\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "nETXJ9qLfdei", - "outputId": "c2ec037a-9c8c-410e-d4c4-e6edef85053b" - }, - "execution_count": 223, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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JD/ZXUmq6th84KklydXFRWKC/xfbnhu26uW0zi8puSfL38bZo5+l+/o2cZ+w7dkIrt+7Siw/crmZ1q6tV/Vp6blAf/b52i06cypQk/bZ6o4pKSjThwTtUt2qEesa00IBusfp6wYpr94HAbl3sb+Vz3dW3qtZsSNO3s4/o4JFcffrNAe3am63+t1a1aPPlrINasSZVew/k6OV3dyo0xFOd24eVeU4AcARNr++k2waOVKuYm8o8HhgcZrFtWrdE9Zu0VZWIauY2cX3uU536zRVaJVp1G7ZUj9sf1P7dW1RSXPZslXk5Wfpr8WzdOfhpNWzWTjXrNtbgERO1L3GT9u3aLEnavmmVjh/ZpwefeFXVazdU0+s7qe89j2nJ77NUXMQsmM5k6hODdFuHlqoXHa4G1SI1afBtOp6Woe2HjpvbbNp3WAO7tlOz2lVVrUqwhvW+Qf4+XhZtJGnF1t1avWOfRve/+aKvm5WXr9l//a2n7+yudg1rq3HNaE0cfJs27TuizfuOSJJWbd+rfcdP6tUHb1fD6pHq1PQ6Pda3i2YtWa+iM9U/qDTIl4BjYWDcxlxdDXJzNaiw0HKAsqDQqOaNA82PWzUN0tyvYjVzWls9/eh1CvAvv9jfzc2g+vX8tX7TKfM+k0lav/GUmjQIkCQ1qOcvd3cXizaHjuQp6US+mjQMsNbbgxNwdZHaNnBVXoFJx9NK74Ryc5WKS6R/3hdVVFz6qFZk2b9Wgv2lAB+D9h47+10vKJKOnDSpRnjpgHuNcIPyCkoH2s/Ye8wkk0mqXoVfV84sJ7d0GqAAf99y22Tn5srXx0turq5lHj+ZdkpL1ySoZZMG5Z5ja+Ie+fv6qFG92uZ9bZo3lovBoO2795W22bVXLRvVl7v72d+zMS2b6tCxJGVm51zW+8LlMRpNFbYBlUm9Bi3097plOpV6QiaTSTu2rFfysUNq2rJ9uc/Jzc2Wl49vudNRnkw+qoxTqWrS/Gy1rY+vn+rWb6o9iaXL/exJ3CwfX3/VrtfY3KZxi3YyGFy0b9dWK7072KvsvAJJUqBv2cubbD9wVImHjqvfDW3OOxb/9S/q+sTLuvelqZqzfP0F78bfvPeQ/H281KT22YvwMY3rysVg0NZ9h0vb7Dms6+vXkrvb2e9zh6bX6UBSijJzmIqwMrmUv5XP1bRhgNZvPGWxb83faWp6+u/m6AgvhYV4at0/2uTklmj7rkxzG9gW+RK49jLTU7Vlwwp17Nav3DY5WRlau/w31WnQQq5uZd/0dnDfDpUUF6tR8xjzvsiqtRUSFqV9iZskSft2bVbVGvUsplZv3LKD8nOzdezwXuu8Idglc778x/J5LepU1+8J25SRkyej0aT567aqoKhYberXMrdJzczWpK/n6eUh/eTlUf4Nl2fsOHhcxSVGxTSqY95XOzJMUSGB2nR6YHzzviOqVzXcYmr1Do3rKju/QHuPnbjatwoHQr6snLiG6dgueyr1vLw8JSQkKCQkRI0bN7Y4lp+fr1mzZun++++3WgedXV5eibbsyNADA2rqwJFcnUovVNwN4WrSIEBHT68XsSYhTUtXpuh4cr6qRnlp+H219daEZnpkzN8yllHwGxjgLjdXg9JOWd4lmZZepJrVSstuQ4M9VFhkVHZOyTltChUa5HFt3iwcSoPqLhrQ1U3ublJWbunU5rml+VN7j5nUO0bq3MxVK7eVyN1N6tm29NeJv7ehzPOd2Z+dZ/nLPDvPJL/Tx/x8DOcdN5qkvALJv/wlo+HgjEajJn/+rZo3vE51a1Qrs016ZpY+/2Gu+sZ1Oe/Yi+9M1/J1f6ugsFCd2rTU2EcfLPe1UtMzFRxoGSDdXF3l7+er1FMZp9tkKDrc8u7LkKDS56SlZyjAr/zBewBXhnx5bQ0aPkYzpr6i0UN7y9XVVQaDix4Y8X9q0OT6MttnZaZr7qxP1aX77eWeMyM9VZIsLkhKUkBgiDJOlR7LPJWqgMBgi+Ourm7y9Q8wt4FzMhqNeuvbeWpZr6bqVSu7SmLO8vWqHVVFLevVtNj/aL84tWtUV14e7lq1bbfiv/pFufmF+tfNHco8T2pGtkL8/Sz2ubm6KsDXWymZpWv6pWZmqWqY5Xcx5PRFzJSMLAWUM3gP53MpfyufKyTIQ6fSCy32nUovUsjpv5tDgj3M+yzbFJqPAbANMmbFWbXkF3l5+6hVzPlTSP/01WQtmf+dCgvyVbt+c40Y+36558lMT5Gbm7t8fC3/bvcPClHm6fyZmZ6igMBzMmhQiPkYnJPRaNKbP/yulnWrq17Vs2vUvzHsTj336Y+68ek35ebiIi8Pd73zyN2qEV76nTCZTHrxi5911w2t1aRmtI6mpF/0tVIys+Xu5qqAc5bzCfH3VWpm9uk2OQoNsLw+ZM6Xp9ugciBfAo7nsgbGd+3ape7du+vQoUMyGAzq1KmTvvvuO0VFla7XkZGRoSFDhlw0VBYUFKigoMBin7GkUC6ulfN/6pfe2amxTzbQz1/EqrjEpF17s/THshNqUK/0H9NFy89ONbnvYI727s/RrE9j1KppkBI2p9uo13B2+44b9cHsQvl6GdS2gasG3uSuab+UrjF+It2kH5cWq3eMm7q3cZXJJK3cVqKsXJO4hwmX6+1Pvta+Q0c0/ZXnyzyek5unZ16drNrVo/XQPbedd/zJIQP14N236fDxJE3/+ke9P+NbjRnOxQ1HxPI8ldO1zJeFhYXy8PAs5xmVxx+/fq99iVv05PPvKDQ8SonbNujrj95QUEgVNWkRY9E2Lzdbk196UtHV6+i2AQ/bqMdwdPFf/6I9R5P1+diyv0P5hUX63+pNGtbn/On/h/c9O11rw5rRyiso1Jfzl5c7MA4AF0PGrJyskTHLzpdG8mUZVi7+We0695Z7GZ9N99sGq2O325V68ph+/eEjzfhgnEaM/UAGQ9mFFUBZ4r/7TXuOntCMMUMs9k/95U9l5ebro1H3KsjPR39uTNSzn/yoz595QNdVjdC3f65VTn6hHuzZyUY9B+CMyJeO7bLmJn7uuefUtGlTnThxQomJifL391fHjh116NChy3rR+Ph4BQYGWmxH9nxzWedwJseS8vX42E2Ku3O5+g9ZreFP/y03N4OOJeWX3T45X6cyClUtuuzKhozMIhWXmBQSbDk1TEiQu1JPld6JlHqqUB7uLvLzdT2njYdSz7lbCZVTUbGUliUdPmnSTyuKZTSa1Kb+2e/Lpn1GxX9bqNe/LdTLXxdq0d8l8vWS0jLL/lch63QluN85FeV+3merxLNzTecddzFI3p5SFrNcOqW3P/lKfyVs1IcTn1N4aMh5x3Py8vTUy2/Lx8tL8c8+Lje38+/nCg0OVK1qUerctpWefWSwZv/+p1JOpZf5eqFBATqVkWmxr7ikRFnZOQoNDjzdJlBp6ZZtzjwOCQoUAOu6lvnyq4/fvka9dhyFBfn679dTNODB0WrZ7gZVr3Wd4m65R2073az5c762aJuXl6O3Jz4hL29fPf7vN8v8nXtG4OlK8TOVO2dkZqQpMLj0WEBwqDIzLKeHKykpVk5WprkNnM9rX/+i5ZsS9cmzDykipOx/N/9Yv1X5hUW6tUOri56vWZ3qSj6VocKiste7Dw30U1qWZVVOcUmJMnPyFBbgX9omwN9c3XNG2unHYYH+F+0DnMel/K18rrT0QgWfM6tacJC70k7/3Zx2+nnBQe7ntPEwHwNQ8ayRMcvKlzM/ffMa9tox7d6+QcnHDqhTt7JnG/ILCFZEdE01bhGrh556XVs3rND+0+uFnysgKEzFxUXKzbH8mzwrPc08U1FAUJgyM87JoOlp5mNwPvHf/k/LtuzWp6PvV0Tw2dkEDp9M03dL1mnC/X0V07COGlSL1CO33qgmNaP1/ZL1kqS1iQe0ed8RtRv5ilo/9pL6vviBJGlQ/CcaN2NOma8XFuCnouISZeZaXptPy8oxT50eFuCr1EzL5fbM+TLAcjYjODfyJeB4LmtgfOXKlYqPj1dYWJjq1aunuXPnqkePHurcubP27dt3yecZO3asMjIyLLZq9QZdduedTX6BUamnCuXv66Z2rUK0Yk3ZU0xWCfVQoL+7UtLK/iVYXGzSrj1Zat387HSBBoPUukWwtiWWBsvEPVkqKjKqdYuzbapX9VZkuJe27cw875yAwWCQWxlLO2fnS4XFUvPaLioukfYcK2N+f0mnsqTMXJPqRp/9tePpLlWrYtChE6UD44dOmOTtaVB06NnB8TrRBhkM0uGTZZ8XjslkMuntT77S0rUb9MGEZxUdUeW8Njm5eRo16W25u7npjbFPyPMS1oE6s+5KUTkXz5s2qKesnFzt3HvAvC9hyw4ZTSY1vq507aim9etq445dKi4+e451m7epRnQk06hfYyajqcI22I9rmS/vG/70Ney5YygpKVZJcfF5FTkuLi4y/WNNnrzcbL09YaTc3Nz0xP+9U2a1zz9ViaiqwOBQbd+8zuIce3dtVb0GzSRJ9Ro0V25Olg7s2WFus2PzeplMRtWp39Qabw92xGQy6bWvf9HiDdv10bNDVbXK+Te8nTFn+Xrd2LKhebrJC0k8dFwBvt7ycC/7Ro3mdWsoKzdf2w8cNe9bt2OfjCaTmtapXtqmXnVt2HVARcVnl5FavX2PakWGMY16JXMpfyufa+vOTLVpYTkVf9uWwdp6+u/mY8n5SkkrsGjj4+2qxvUDzG1gW+TLyskaGbOsfPmvh8Zc4547nr8Wz1aNOo1VrVaDi7Y9kz+Lisq+plmzTiO5urlp55a15n1JRw8oLeW46jRoIUmqU7+5jh7ao8yMNHObHZtXycvHT1HV65x3Tjguk8mk+G//p8Ubd+rjUfedtzROfmHpNNMu5/2tY5DxdDnnc/f01KxxD+v7/yvdPhj5L0nS6w/dqcdvu0llaVQzSm6uLlq7c79534GkFB1Py1CLOqVLADavU017jp5Q2j8Gx1ft2Cc/L0/ViTr/GhecF/mycuIapmO7rIHxvLw8i8oRg8G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+/fqpZs2a2rBhg8aNG6ft27fbvaNn1KhReumll9StWzf17dtXx44d0/vvv68WLVpo7dq152yxuWrVKi1dulT33nuvypYtq71792rixIm66aabtHnzZvn7++uuu+5SSEiInnrqKXXv3l133HGHAgMDFRAQoISEBB04cMD22eQ9VrI49ZekX3/9VV9//bUee+wxlSxZMt++mCev9ezMmTPVtWvXc/as3717t7777jt17dpVFStWVGxsrD766CO1bNlSmzdvzvf4pPOdM6XcHkT9+/dX06ZN9eSTT2r37t3q2LGjwsLCVK5cOVu5xMREffrpp7bHFiUlJWnSpElq06aNVq5cme/R6GefFzp37qykpCS9/PLL6tevn5o3by5Jatq0qSIjI+Xn56c5c+bo8ccfV1hYWKGfQVGPn3fffVcdO3bUfffdp4yMDH311Vfq2rWrfvzxR1tr44KkpqaqZcuWOnjwoPr376/y5ctr6dKlGjp0qA4fPpzv/fMFnf8AOBaZkkx5JlfLlDExMVq4cKF+/fXXc76CZ8mSJYqPj9fgwYPl4eFRaLniCAwMVOfOnTVp0iRt3rxZtWvX1g8//CBJeuCBBy54vhMnTlTt2rXVsWNHeXp6as6cOXr00UeVk5Nj23+OHj2q2267TREREXr++ecVEhKivXv36ttvv7XNZ8GCBerevbtuueUWvf7665KkLVu26K+//tKgQYMKXHZxjs1Lld0kacqUKQoMDNTTTz+twMBA/frrr3r55ZeVmJiosWPHFlrXunXrqnHjxnrkkUfUuXNn2zsz89Zh06ZNatasmcqUKaPnn39eAQEB+vrrr3XnnXfqm2++yXcue/TRRxUREaGXX35ZKSkphdY3JiZG2dnZmj59unr16lVoOalox3Oe7OxstWnTRo0aNdKbb76phQsX6q233lLlypVtTykwxqhTp05asmSJBgwYoJo1a2r27NkF1mPBggXavXu3+vTpo+joaNvjUDdt2qTly5fbPUVByn8cXnPNNYWeZ/Iy97Rp0/Tiiy/mm9eZ/vnnHzVv3lxeXl7q16+fKlSooF27dmnOnDm2335F+c1RmF9//VVt27ZVw4YNNWzYMFmtVk2ePFmtWrXS4sWLdcMNN5xzPQG4P/Iv+fdM7pp/AwIC1KlTJ82aNUsnTpywu57yf//3f8rOztZ9991nGzZnzhxVqlQp39NviqMo2+2FF15Q9erV9fHHH+uVV15RxYoVVblyZd166622fbtfv36SZLsGVdRrwnlGjhwpb29vDR48WOnp6YU+vac4+aW4Oapbt26qWLGixowZozVr1ujTTz9VZGSkLZdLucfm559/rh49eqhp06b69ddfC7ymVtxcdHaGbdu2rbZv364vv/xS48aNU8mSJSVJERERts/giy++ULNmzc75xIminDuLe231TJcqqwNXrSt4U/6S6dmzp7FarWbVqlX5xuW1bnvyySeNJLN48WLbuKSkJFOxYkVToUIFW4v2vNZUNWvWtGsZ+e677xpJZsOGDbZheS2Mjh07ZrdMFbF1Y/369QvtdXH2MvKsW7fOSDJ9+/a1Kzd48GAjya5HZV4LzD///NM27OjRo8bHx8c888wz51zu2SQZHx8fu9ZXH330kZFkoqOjTWJiom340KFDbS2t8pzd6t+Y3JZoFovF7Nu3zzasoF7BxpxuORUUFGSOHj1a4LizW1V1797d+Pv7m+3bt5uxY8caSea7774777oW1oO7QYMGJjIy0sTFxdmGrV+/3litVtOzZ0/bsLxt1r179/Mu61zLO1P9+vVNaGio7f8PPfSQKVWqVL6WePfee68JDg62fd6fffaZkWTefvvtfPM8s+Xn2ftscHCwGThw4DnrfXaP8e+++85IMq+++qpdubvvvttYLJZ8PdS9vb3thq1fv95IMu+//36Byyusx7gk8+GHH9oNnz59urFarXbHuzG5rXB1RovPvXv3Gg8PDzNq1Ci7chs2bDCenp75hp+toP06r1f2tGnTbMPObJ17pnbt2uXrdV+c+huT+1larVazadOmc9Y1T8+ePY0kExoaajp37mzefPNNs2XLlnzl0tLS8vX02bNnj/Hx8TGvvPKKbVhRz5kZGRkmMjLSNGjQwK7cxx9/nK8VcVZWll0ZY3J7/0RFRZkHH3zQrj6FnRdWrVpV4HnBGGNefvllI8kEBASYtm3bmlGjRtk9DSFPUY+fs/eDjIwMU6dOHdOqVSu74Wd/B4wcOdIEBASY7du325V7/vnnjYeHh+2pBedaTwCORaYkU7pypty4caPx8/MzkkyDBg3MoEGDzHfffZevN0bePnj2OmRlZZljx47Z/Z35fXeuHuPGGNsTW/J6MXTu3NlIKrRXytkK6tVV0PZu06aNqVSpku3/s2fPNpIKPG7zDBo0yAQFBZ2zh3xBvW0LOzbP7jF+KbNbQZ9B//79jb+/v13vtoLqeuzYsULPI7fccoupW7eu3TxycnJM06ZNTdWqVW3D8nqh3HjjjUV6wsCRI0dMRESEkWRq1KhhBgwYYGbMmGHi4+OLtG4FHc95PafOzKDGGHPNNdeYhg0b2v6f93vkjTfesA3LysoyzZs3z3dsF7TsvF5CZ57rznUcFnaeSU1NNdWrVzdS7hOhevfubSZNmmRiY2PzlW3RooUpUaKE3foac+6saUzBvznO3odzcnJM1apVTZs2bfLNr2LFiqZ169ZFWk8A7o38S/69GvKvMcb89NNPRpL56KOP7IY3btzYlClTxrYfJyQkGEmmU6dORapDYYq63fKy1tnHYEBAQL6nDhpT9GvCeduiUqVKBdaloPoWNb8UN0edeS3PmNzfCeHh4bb/5x2bjz76qF25Hj165DsnFDUXnSvD5u3TZ/cyz8nJsV1rjoqKMt27dzfjx4/Pl9OMKdq5s6jXVgs6Di9VVgeuVvlfIOHicnJy9N1336lDhw667rrr8o3Pa6E0d+5c3XDDDbrxxhtt4wIDA9WvXz/t3btXmzdvtpuuT58+di2o8noc7t6922F1DwkJ0aZNm7Rjx44iTzN37lxJ0tNPP203/JlnnpGkfO83qVWrlq3uUm7rp+rVq1/Qetxyyy12LfgaNWokSerSpYtKlCiRb/iZy/Dz87P9OyUlRcePH1fTpk1ljNHatWuLXIcuXbooIiKiSGU/+OADBQcH6+6779ZLL72kBx54QJ06dSryss50+PBhrVu3Tr1797ZrVVivXj21bt3atl3ONGDAgAtaVkECAwOVlJQkKbcnxDfffKMOHTrIGKPjx4/b/tq0aaOEhAStWbNGkvTNN9+oZMmSevzxx/PN81wt/0JCQrRixQodOnSoyHWcO3euPDw89MQTT9gNf+aZZ2SM0c8//2w3/NZbb7XrsV6vXj0FBQUVe9/08fFRnz597IbNnDlTNWvWVI0aNew+n7zWo7/99psk6dtvv1VOTo66detmVy46OlpVq1a1lSvMmft1Zmam4uLiVKVKFYWEhNi2wYUoav3ztGzZUrVq1SrSvCdPnqwPPvhAFStW1OzZszV48GDVrFlTt9xyiw4ePGgr5+PjY3vnUHZ2tuLi4hQYGKjq1asXuG7nO2f+/fffOnr0qAYMGGBXrnfv3goODrabl4eHh61MTk6OTpw4oaysLF133XUFLrs45wVJGjFihGbMmKFrrrlG8+bN0wsvvKCGDRvq2muv1ZYtW2zlinr8nLkfnDx5UgkJCWrevPl594GZM2eqefPmCg0NtdvOt956q7Kzs/Xnn39e1HoCKB4yZS4yZX6ukilr166tdevW6f7779fevXv17rvv6s4771RUVJQ++eQTW7nExERJp59Uk2fDhg2KiIiw+4uLiyvyuuXNLy+z5i3nzG1aXGdu74SEBB0/flwtW7bU7t27lZCQIOn0+xh//PFHZWZmFjifkJAQpaSkaMGCBRdcl3O5lNntzM8gKSlJx48fV/PmzZWamqqtW7deUH1PnDihX3/9Vd26dbPN8/jx44qLi1ObNm20Y8cOu1woSQ8//HCRnjAQFRWl9evXa8CAATp58qQ+/PBD9ejRQ5GRkRo5cqRdr6/iHs9nHwvNmze3Oz/MnTtXnp6edu+59/DwKDDLnbnstLQ0HT9+XI0bN5akAjNccX7b+fn5acWKFRoyZIik3F7/Dz30kEqVKqXHH39c6enpkqRjx47pzz//1IMPPqjy5cvbzaOwrFmc3xzr1q3Tjh071KNHD8XFxdm2c0pKim655Rb9+eef+XoKOvI3LADnR/7NRf7Nz93yryTbE4ZmzJhhG7Znzx4tX75c3bt3t10Dc0SGlRy33c5UnGvCeXr16mVXl3PVtyj55ex1u5Ac1bx5c8XFxdk+67x94Oxrymf3fj972UXJRUXNsFLuOW/evHl69dVXFRoaqi+//FIDBw5UTEyM7rnnHts7xot67izutdU8lzKrA1crt7sxfuzYMSUmJqpOnTrnLLdv3z5Vr1493/CaNWvaxp/p7B+moaGhknJvejjKK6+8ovj4eFWrVk1169bVkCFD9M8//5xzmn379slqtapKlSp2w6OjoxUSEnLe9ZBy1+VC1uPseeXdzDrzEchnDj9zGfv377cFoMDAQEVERKhly5aSZLuoVRQVK1YsctmwsDC99957+ueffxQcHKz33nuvyNOeLe9zLWwfyrvAcKF1PZ/k5GRbIDt27Jji4+P18ccf57t4mXeD+OjRo5KkXbt2qXr16ud89EtB3njjDW3cuFHlypXTDTfcoOHDh583+O/bt0+lS5fOFxyLeoxJF7ZvlilTJt9jgHbs2KFNmzbl+3yqVasm6fTns2PHDhljVLVq1Xxlt2zZYitXmFOnTunll19WuXLl5OPjo5IlSyoiIkLx8fHF2q/PVtT65ynOvma1WjVw4ECtXr1ax48f1/fff6+2bdvq119/1b333msrl5OTo3Hjxqlq1ap26/bPP/8UuG7nO2fmbf+qVavalfPy8lKlSpXyzW/q1KmqV6+efH19FR4eroiICP30008FLvtCjrXu3btr8eLFOnnypObPn68ePXpo7dq16tChg9LS0iQV/fj58ccf1bhxY/n6+iosLEwRERGaOHHiefeBHTt26Jdffsm3nW+99VZJF7edARQfmTIXmTI/V8qU1apV0/Tp03X8+HH9888/Gj16tDw9PdWvXz8tXLhQ0umLfMnJyXbTVqlSRQsWLNCCBQsu6PHnefPLm39QUJCk0zfKL8Rff/2lW2+9VQEBAQoJCVFERIT+97//STq9vVu2bKkuXbpoxIgRKlmypDp16qTJkyfbXbx79NFHVa1aNbVt21Zly5bVgw8+qF9++eWC63W2S5ndNm3apM6dOys4OFhBQUGKiIjQ/fffL6l4+/yZdu7cKWOMXnrppXx1HjZs2EXXuVSpUpo4caIOHz6sbdu26b333rM92nHSpEm2csU5nn19ffNdzD/7HLRv3z6VKlUqX6OPgo63EydOaNCgQYqKipKfn58iIiJs6+iIvBkcHKw33nhDe/fu1d69ezVp0iRVr15dH3zwgUaOHCnp9E2P833vXOhvjrybRb169cq3nT/99FOlp6fnm568CVxdyL+5yL/5uVv+lSRPT0/dc889Wrx4se2mYt5N8jMfo+6IDCs5brudqTjXhPMU57MsSn6Rip+jinLN0Gq15nvlZ0H7THFzUXGzjY+Pj1544QVt2bJFhw4d0pdffqnGjRvbXoMkFf3cWdxrq3kudVYHrkZu+Y7xS6GwFjZntnAvruzsbLv/t2jRQrt27dL333+v+fPn69NPP9W4ceP04Ycfqm/fvuec17l6+57JketR2LzOt4zs7Gy1bt1aJ06c0HPPPacaNWooICBABw8eVO/evfO1Uj+XorRwO9O8efMk5X7RHjhw4Jzvi3a04ta1MJmZmdq+fbvtyzbv87r//vsLfW9fcd/NdLZu3bqpefPmmj17tubPn6+xY8fq9ddf17fffqu2bdte1LzzOGrfLOhzzsnJUd26dfX2228XOE3eD4+cnBxZLBb9/PPPBdbn7ItqZ3v88cc1efJkPfnkk2rSpImCg4NlsVh07733Fmu/vtD657nQfS08PFwdO3ZUx44dddNNN+mPP/7Qvn37FBMTo9GjR+ull17Sgw8+qJEjRyosLExWq1VPPvlkgevmyHPN559/rt69e+vOO+/UkCFDFBkZKQ8PD40ZM0a7du3KV/5ijrWgoCC1bt1arVu3lpeXl6ZOnaoVK1bYfqycz+LFi9WxY0e1aNFCEyZMUKlSpeTl5aXJkyfbtUIuSE5Ojlq3bq1nn322wPF5F9PzOOqcAuDyIlMWfV5kyoJdyPnfw8NDdevWVd26ddWkSRPdfPPN+uKLL3TrrbeqRo0akqSNGzfa9fwJDAy0Nc5asmRJsZe5ceNGSbJdbM5bzoYNG+x6WxXVrl27dMstt6hGjRp6++23Va5cOXl7e2vu3LkaN26cbXtbLBbNmjVLy5cv15w5czRv3jw9+OCDeuutt7R8+XIFBgYqMjJS69at07x58/Tzzz/r559/1uTJk9WzZ09NnTq12HU726XKbvHx8WrZsqWCgoL0yiuvqHLlyvL19dWaNWv03HPPXXDezJtu8ODBatOmTYFlzr5pcCH7ocViUbVq1VStWjW1a9dOVatW1RdffKG+ffsW+3h2dA+Ybt26aenSpRoyZIgaNGigwMBA5eTk6Pbbby/wc72YHBYTE6MHH3xQnTt3VqVKlfTFF1/o1VdfLfL0F/qbI2/c2LFj1aBBgwLLnP17h7wJwBHIv0WfF/m3YI7Ov3nuv/9+ffDBB/ryyy81ePBgffnll6pVq5bd92RQUJBKly5ty7YXwpHb7UwXck34Qr/bz5VfipujHHksFTcXXUy2KVWqlO6991516dJFtWvX1tdff60pU6YUefriXlvNc7myOnA1cbsb4xEREQoKCjrvl1VMTIy2bduWb3je4+diYmIcVqfQ0FDbozXyZGRk6PDhw/nKhoWFqU+fPurTp4+Sk5PVokULDR8+vNAQFxMTo5ycHO3YscPWMlOSYmNjFR8f79D1cJQNGzZo+/btmjp1qnr27GkbXtCjDIsaTovil19+0aeffqpnn31WX3zxhXr16qUVK1YUu/e0dHr/KGwfKlmypAICAi66zgWZNWuWTp06ZfsijIiIUIkSJZSdnW0X7gpSuXJlrVixQpmZmfLy8irWckuVKqVHH31Ujz76qI4ePaprr71Wo0aNKvTGeExMjBYuXKikpCS7XuOX4hg7n8qVK2v9+vW65ZZbzrlPVa5cWcYYVaxYMd9NyKKYNWuWevXqpbfeess2LC0tLd/xX5jC6lbU+jvSddddpz/++EOHDx9WTEyMZs2apZtvvtmuV4+Ue4G2ZMmSxZ5/3vbfsWOH7bGiUm7Djz179qh+/fq2YbNmzVKlSpX07bff2q1/XqvIoriQz+26667T1KlTbefqohw/33zzjXx9fTVv3jz5+PjYhk+ePPm8y6tcubKSk5PPexwDuDzIlLnIlPm5eqbMe7xf3n7TvHlzBQcH66uvvtLQoUNtj/e7GMnJyZo9e7bKlStn2586dOigMWPG6PPPP7+gG+Nz5sxRenq6fvjhB7teJoW96qZx48Zq3LixRo0apRkzZui+++7TV199ZTsGvL291aFDB3Xo0EE5OTl69NFH9dFHH+mll17Kd2GpuC5Vdvv9998VFxenb7/9Vi1atLAN37NnT5GmL6wueU/r8fLyumw5pFKlSgoNDbXth8U5nosqJiZGixYtUnJyst0N37OPt5MnT2rRokUaMWKEXn75Zdvw4jyOVyr+eSY0NFSVK1e2fc/kbYfzfe9c6G+OvF5XQUFB5E0ABSL/5iL/5udu+TdPo0aNVLlyZc2YMUOtW7fWpk2bNGrUqHzTt2/fXh9//LGWLVumJk2aFHv5jsg5BW3P4lwTdpSz84ujctSZ8o7NvCc35ilon7nYa7FS8Y8VLy8v1atXTzt27NDx48cVGRlZpHPnhV5bvRJZHXB3bvcodavVqjvvvFNz5szR33//nW98XsujO+64QytXrtSyZcts41JSUvTxxx+rQoUKRX7HW1FUrlw537thP/7443ytG89+Z19gYKCqVKli99i/s91xxx2SpHfeecdueF7vhHbt2l1otS+ZvFZhZ7YCM8bo3XffzVc2LwgV58usIPHx8erbt69uuOEGjR49Wp9++qnWrFmj0aNHX9D8SpUqpQYNGmjq1Kl2ddu4caPmz59v2y6Otn79ej355JMKDQ3VwIEDJeV+nl26dNE333xT4BfwsWPHbP/u0qWLjh8/rg8++CBfucJa5WVnZ+d7nEtkZKRKly593n0zOzs737LGjRsni8XisJ7mRdGtWzcdPHgw3/uEpNxH7uQ9oumuu+6Sh4eHRowYke/zMMac972aHh4e+aZ7//338x3rhQkICCjw0TlFrX9xHTlyJN+7v6TcH5mLFi2ye6RYQes2c+bMfO+wKarrrrtOERER+vDDD5WRkWEbPmXKlHzHe0HnjBUrVtidv8+nsHNJampqofP5+eefJZ1+VFNRjh8PDw9ZLBa7bb5371599913561jt27dtGzZMlsr7DPFx8crKyvrvPMA4DhkylxkSnuulCkXL15c4Du2897Zl/f95u/vr2effVYbN27U888/X2AmLE7vjVOnTumBBx7QiRMn9MILL9guNDVp0kS33367Pv300wK/FzMyMjR48OBC51vQ9k5ISMjX+OzkyZP56pvX6yfvGDj7GLFarbbeNOc6TorqUmW3gj6DjIwMTZgwoUjT+/v7S8p/HERGRuqmm27SRx99VOCNhjN/TxTXihUrClzflStXKi4uzrYfFud4Lqo77rhDWVlZmjhxom1Ydna23n//fbtyBS1byn8+PJ/CzjPr16/X8ePH85Xft2+fNm/ebPsMIiIi1KJFC3322Wfav3+/Xdkz63ahvzkaNmyoypUr680338z36gTp4rYzAPdA/s1F/rXnjvn3TPfdd5/Wrl2rYcOGyWKxqEePHvnKPPvsswoICFDfvn0VGxubb/yuXbvOmVkckXMCAgIKvF5W1GvCxVXU/OKoHHWmvGvGZz+yv6B5Xuy1WKnwY2XHjh35MlleuWXLlik0NFQRERFFPnde6LXVS5nVgauV2/UYl3IfSzF//ny1bNlS/fr1U82aNXX48GHNnDlTS5YsUUhIiJ5//nl9+eWXatu2rZ544gmFhYVp6tSp2rNnj7755huH9JTI07dvXw0YMEBdunRR69attX79es2bNy9fS6BatWrppptuUsOGDRUWFqa///5bs2bNsr2voiD169dXr1699PHHH9serbdy5UpNnTpVd955p26++WaHrYej1KhRQ5UrV9bgwYN18OBBBQUF6ZtvvinwnTwNGzaUJD3xxBNq06aNPDw87N57XFSDBg1SXFycFi5cKA8PD91+++3q27evXn31VXXq1Mmud2pRjR07Vm3btlWTJk300EMP6dSpU3r//fcVHBys4cOHF3t+Z1u8eLHS0tKUnZ2tuLg4/fXXX/rhhx8UHBys2bNnKzo62lb2tdde02+//aZGjRrp4YcfVq1atXTixAmtWbNGCxcu1IkTJyRJPXv21LRp0/T0009r5cqVat68uVJSUrRw4UI9+uijdo/PzJOUlKSyZcvq7rvvVv369RUYGKiFCxdq1apVdq3xztahQwfdfPPNeuGFF7R3717Vr19f8+fP1/fff68nn3wy33tiLqUHHnhAX3/9tQYMGKDffvtNzZo1U3Z2trZu3aqvv/5a8+bN03XXXafKlSvr1Vdf1dChQ7V3717deeedKlGihPbs2aPZs2erX79+57xg2759e02fPl3BwcGqVauWli1bpoULFyo8PLxI9WzYsKH+7//+T08//bSuv/56BQYGqkOHDkWuf3EdOHBAN9xwg1q1aqVbbrlF0dHROnr0qL788ktbI4y881T79u31yiuvqE+fPmratKk2bNigL77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- }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "labels = ['get_dummies', 'LabelEncoder', 'PCA', 't-SNE', 'SandartScaler']\n", - "y_preds_dtc = [y_pred_dtc_gd, y_pred_dtc_lbe, y_pred_dtc_pca, y_pred_dtc_tsne, y_pred_dtc_scal]\n", - "accuracy_dct = list(map(lambda x: accuracy_score(Y_test, x), y_preds_dtc))\n", - "\n", - "y_preds_sgdc = [ y_pred_sgdc_lbe, y_pred_sgdc_pca, y_pred_sgdc_tsne, y_pred_sgdc_scal]\n", - "accuracy_sgdc = list(map(lambda x: accuracy_score(Y_test, x), y_preds_sgdc))\n", - "\n", - "y_preds_svc = [ y_pred_svc_lbe, y_pred_svc_pca, y_pred_svc_tsne, y_pred_svc_scal]\n", - "accuracy_svc = list(map(lambda x: accuracy_score(Y_test, x), y_preds_svc))\n", - "\n", - "\n", - "plt.plot(labels, accuracy_dct)\n", - "plt.title('accuracy score for DecisionTree.')\n", - "plt.show()\n", - "\n", - "\n", - "plt.plot(labels[1:], accuracy_sgdc)\n", - "plt.title('accuracy score for SGDClassifier.')\n", - "plt.show()\n", - "\n", - "plt.plot(labels[1:], accuracy_svc)\n", - "plt.title('accuracy score for SVC.')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "fV3XMnjYsjFS", - "outputId": "afefdeb9-c18f-476f-e1d1-523d4c52f2be" - }, - "execution_count": 249, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" 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\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OAA--Zd8McII" - }, - "source": [ - "# Вывод\n", - "\n", - "В ходе работы были соединины в один ноутбук методы уменьшения размерности данных, масштабирования данных, перебора гиперпараметров для моделей, изменения представления данных и т.д.\n", - "\n", - "С решением задачи классификации на выбранном датасате лучше всего справилась модель решающего дерева, с использованием get_dummies для представления данных. Однако модели после применения PCA и LabelEncoder + StandartScaler, показали результаты чуть меньшей точности, но с большой скоростью обучения." - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From ae9e9adc13c10042e611c1561bb0ef48fab929bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=A1=D0=BE=D1=84=D0=B8=D1=8F=20=D0=A5=D1=80=D0=B8=D1=81?= =?UTF-8?q?=D0=B0=D0=BD=D0=BA=D0=BE=D0=B2=D0=B0?= <132402521+sssoneta@users.noreply.github.com> Date: Sat, 26 Apr 2025 02:56:48 +0300 Subject: [PATCH 3/3] update --- project/15_fake_news_detection.ipynb | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/project/15_fake_news_detection.ipynb b/project/15_fake_news_detection.ipynb index f9bf462e..dec37f57 100644 --- a/project/15_fake_news_detection.ipynb +++ b/project/15_fake_news_detection.ipynb @@ -1845,10 +1845,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "8fe3cce8-7dfa-4539-b806-1d102860b34c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'C': 0.1, 'learning_rate': 0.01, 'max_iter': 1000, 'penalty': 'l1'}\n", + "\n", + "Best model accuracy: 1.0\n" + ] + } + ], "source": [ "from sklearn.model_selection import GridSearchCV\n", "\n",