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+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### 1) Информация о наборе данных о возрасте",
+   "id": "b0d69d9cbffe7aa9"
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:34.142613Z",
+     "start_time": "2024-12-07T05:04:31.513373Z"
+    }
+   },
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"AgeDataset-V1.csv\")\n",
+    "sampled_df = df.sample(frac=0.4)\n",
+    "print(df.columns)\n",
+    "print(df)"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Id', 'Name', 'Short description', 'Gender', 'Country', 'Occupation',\n",
+      "       'Birth year', 'Death year', 'Manner of death', 'Age of death'],\n",
+      "      dtype='object')\n",
+      "                Id                     Name  \\\n",
+      "0              Q23        George Washington   \n",
+      "1              Q42            Douglas Adams   \n",
+      "2              Q91          Abraham Lincoln   \n",
+      "3             Q254  Wolfgang Amadeus Mozart   \n",
+      "4             Q255     Ludwig van Beethoven   \n",
+      "...            ...                      ...   \n",
+      "1223004  Q77247326    Marie-Fortunée Besson   \n",
+      "1223005  Q77249504              Ron Thorsen   \n",
+      "1223006  Q77249818       Diether Todenhagen   \n",
+      "1223007  Q77253909  Reginald Oswald Pearson   \n",
+      "1223008  Q77254864             Horst Lerche   \n",
+      "\n",
+      "                                         Short description Gender  \\\n",
+      "0           1st president of the United States (1732–1799)   Male   \n",
+      "1                              English writer and humorist   Male   \n",
+      "2          16th president of the United States (1809-1865)   Male   \n",
+      "3                Austrian composer of the Classical period   Male   \n",
+      "4                   German classical and romantic composer   Male   \n",
+      "...                                                    ...    ...   \n",
+      "1223004                            Frans model (1907-1996)    NaN   \n",
+      "1223005        xugador de baloncestu canadianu (1948–2004)    NaN   \n",
+      "1223006  German navy officer and world war II U-boat co...    NaN   \n",
+      "1223007  English artist, working in stained glass, prin...   Male   \n",
+      "1223008                                     German painter   Male   \n",
+      "\n",
+      "                                                   Country  \\\n",
+      "0        United States of America; Kingdom of Great Bri...   \n",
+      "1                                           United Kingdom   \n",
+      "2                                 United States of America   \n",
+      "3          Archduchy of Austria; Archbishopric of Salzburg   \n",
+      "4                       Holy Roman Empire; Austrian Empire   \n",
+      "...                                                    ...   \n",
+      "1223004                                             France   \n",
+      "1223005                   Canada; United States of America   \n",
+      "1223006                                            Germany   \n",
+      "1223007                                     United Kingdom   \n",
+      "1223008                                            Germany   \n",
+      "\n",
+      "                 Occupation  Birth year  Death year Manner of death  \\\n",
+      "0                Politician        1732      1799.0  natural causes   \n",
+      "1                    Artist        1952      2001.0  natural causes   \n",
+      "2                Politician        1809      1865.0        homicide   \n",
+      "3                    Artist        1756      1791.0             NaN   \n",
+      "4                    Artist        1770      1827.0             NaN   \n",
+      "...                     ...         ...         ...             ...   \n",
+      "1223004       Tailor; model        1907      1996.0             NaN   \n",
+      "1223005             Athlete        1948      2004.0             NaN   \n",
+      "1223006  Military personnel        1920      1944.0             NaN   \n",
+      "1223007              Artist        1887      1915.0             NaN   \n",
+      "1223008              Artist        1938      2017.0             NaN   \n",
+      "\n",
+      "         Age of death  \n",
+      "0                67.0  \n",
+      "1                49.0  \n",
+      "2                56.0  \n",
+      "3                35.0  \n",
+      "4                57.0  \n",
+      "...               ...  \n",
+      "1223004          89.0  \n",
+      "1223005          56.0  \n",
+      "1223006          24.0  \n",
+      "1223007          28.0  \n",
+      "1223008          79.0  \n",
+      "\n",
+      "[1223009 rows x 10 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Столбцы на русском:\n",
+    "```Id``` - идентификатор - страна<br/>\n",
+    "```Name```- Полное имя<br/>\n",
+    "```Short description``` - Краткое описание <br/>\n",
+    "```Gender``` - пол<br/>\n",
+    "```Country``` - Страна / исторический регион<br/>\n",
+    "```Occupation``` - Название профессии<br/>\n",
+    "```Birth year``` - Год рождения<br/>\n",
+    "```Death year``` - Год смерти<br/>\n",
+    "```Manner of death``` - Способ смерти<br/>\n",
+    "```Age of death``` - Возраст смерти<br/>"
+   ],
+   "id": "3670f952147eb566"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:34.393967Z",
+     "start_time": "2024-12-07T05:04:34.168224Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df.info()\n",
+    "df.head()"
+   ],
+   "id": "ddb102a236b75928",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1223009 entries, 0 to 1223008\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column             Non-Null Count    Dtype  \n",
+      "---  ------             --------------    -----  \n",
+      " 0   Id                 1223009 non-null  object \n",
+      " 1   Name               1223009 non-null  object \n",
+      " 2   Short description  1155109 non-null  object \n",
+      " 3   Gender             1089363 non-null  object \n",
+      " 4   Country            887500 non-null   object \n",
+      " 5   Occupation         1016095 non-null  object \n",
+      " 6   Birth year         1223009 non-null  int64  \n",
+      " 7   Death year         1223008 non-null  float64\n",
+      " 8   Manner of death    53603 non-null    object \n",
+      " 9   Age of death       1223008 non-null  float64\n",
+      "dtypes: float64(2), int64(1), object(7)\n",
+      "memory usage: 93.3+ MB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "     Id                     Name  \\\n",
+       "0   Q23        George Washington   \n",
+       "1   Q42            Douglas Adams   \n",
+       "2   Q91          Abraham Lincoln   \n",
+       "3  Q254  Wolfgang Amadeus Mozart   \n",
+       "4  Q255     Ludwig van Beethoven   \n",
+       "\n",
+       "                                 Short description Gender  \\\n",
+       "0   1st president of the United States (1732–1799)   Male   \n",
+       "1                      English writer and humorist   Male   \n",
+       "2  16th president of the United States (1809-1865)   Male   \n",
+       "3        Austrian composer of the Classical period   Male   \n",
+       "4           German classical and romantic composer   Male   \n",
+       "\n",
+       "                                             Country  Occupation  Birth year  \\\n",
+       "0  United States of America; Kingdom of Great Bri...  Politician        1732   \n",
+       "1                                     United Kingdom      Artist        1952   \n",
+       "2                           United States of America  Politician        1809   \n",
+       "3    Archduchy of Austria; Archbishopric of Salzburg      Artist        1756   \n",
+       "4                 Holy Roman Empire; Austrian Empire      Artist        1770   \n",
+       "\n",
+       "   Death year Manner of death  Age of death  \n",
+       "0      1799.0  natural causes          67.0  \n",
+       "1      2001.0  natural causes          49.0  \n",
+       "2      1865.0        homicide          56.0  \n",
+       "3      1791.0             NaN          35.0  \n",
+       "4      1827.0             NaN          57.0  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Id</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Short description</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Occupation</th>\n",
+       "      <th>Birth year</th>\n",
+       "      <th>Death year</th>\n",
+       "      <th>Manner of death</th>\n",
+       "      <th>Age of death</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Q23</td>\n",
+       "      <td>George Washington</td>\n",
+       "      <td>1st president of the United States (1732–1799)</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>United States of America; Kingdom of Great Bri...</td>\n",
+       "      <td>Politician</td>\n",
+       "      <td>1732</td>\n",
+       "      <td>1799.0</td>\n",
+       "      <td>natural causes</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q42</td>\n",
+       "      <td>Douglas Adams</td>\n",
+       "      <td>English writer and humorist</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Artist</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>2001.0</td>\n",
+       "      <td>natural causes</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Q91</td>\n",
+       "      <td>Abraham Lincoln</td>\n",
+       "      <td>16th president of the United States (1809-1865)</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>United States of America</td>\n",
+       "      <td>Politician</td>\n",
+       "      <td>1809</td>\n",
+       "      <td>1865.0</td>\n",
+       "      <td>homicide</td>\n",
+       "      <td>56.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Q254</td>\n",
+       "      <td>Wolfgang Amadeus Mozart</td>\n",
+       "      <td>Austrian composer of the Classical period</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Archduchy of Austria; Archbishopric of Salzburg</td>\n",
+       "      <td>Artist</td>\n",
+       "      <td>1756</td>\n",
+       "      <td>1791.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Q255</td>\n",
+       "      <td>Ludwig van Beethoven</td>\n",
+       "      <td>German classical and romantic composer</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Holy Roman Empire; Austrian Empire</td>\n",
+       "      <td>Artist</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>1827.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Объект наблюдения жизнь людей содержит такие данные как дата рождения, дата смерти причина смерти профессия и тп",
+   "id": "481e7e75c47a6235"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:48.557176Z",
+     "start_time": "2024-12-07T05:04:34.437056Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "df1 = df[df['Birth year'] >= 0]\n",
+    "\n",
+    "plt.scatter(df1['Birth year'], df1['Age of death'], c=df1['Age of death'], alpha=0.6)\n",
+    "\n",
+    "plt.title(\"Диаграмма 1\")\n",
+    "plt.ylabel(\"Возраст смерти\")\n",
+    "plt.xlabel(\"Дата рождения\")\n",
+    "plt.grid(visible=True)\n",
+    "\n",
+    "plt.show()"
+   ],
+   "id": "13f9f5235d21fa36",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Анализируем датафрейм при помощи \"ящика с усами\". Есть смещение в сторону больших значений, это можно исправить при помощи oversampling и undersampling.",
+   "id": "1d636f51fd70c157"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:50.204024Z",
+     "start_time": "2024-12-07T05:04:48.589403Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x=df['Birth year'])\n",
+    "plt.title('Box Plot для Birth year')\n",
+    "plt.xlabel('Birth year')\n",
+    "plt.show()"
+   ],
+   "id": "51358c8282ab6255",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Есть выбросы.",
+   "id": "3421b8f5f5d2178a"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:51.864318Z",
+     "start_time": "2024-12-07T05:04:50.235251Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Статистический анализ для определения выбросов\n",
+    "df = df[df['Birth year'] > 0]\n",
+    "\n",
+    "Q1 = df['Birth year'].quantile(0.25)\n",
+    "Q3 = df['Birth year'].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "# Определение порога для выбросов\n",
+    "threshold = 1.5 * IQR\n",
+    "outliers = (df['Birth year'] < (Q1 - threshold)) | (df['Birth year'] > (Q3 + threshold))\n",
+    "\n",
+    "# Вывод выбросов\n",
+    "print(\"Выбросы:\")\n",
+    "print(df[outliers])\n",
+    "\n",
+    "# Обработка выбросов\n",
+    "# В данном случае мы заменим выбросы на медианное значение\n",
+    "median_review_no = df['Birth year'].median()\n",
+    "df.loc[outliers, 'Birth year'] = median_review_no\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x=df['Birth year'])\n",
+    "plt.title('Box Plot для Birth year')\n",
+    "plt.xlabel('Birth year')\n",
+    "plt.show()"
+   ],
+   "id": "40e42ecfac902bc2",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Выбросы:\n",
+      "                Id                   Name  \\\n",
+      "8             Q297        Diego Velázquez   \n",
+      "9             Q301               El Greco   \n",
+      "11            Q307        Galileo Galilei   \n",
+      "13            Q346     Louis IX of France   \n",
+      "15            Q353     Blanche of Castile   \n",
+      "...            ...                    ...   \n",
+      "1222824  Q76625167    Francisco de Segura   \n",
+      "1222864  Q76737718             Hans Osara   \n",
+      "1222928  Q76879920       Luigi Anguissola   \n",
+      "1222934  Q76887267          Luigi Strozzi   \n",
+      "1223002  Q77233630  Matteo Gregorio Rossi   \n",
+      "\n",
+      "                                         Short description  Gender  \\\n",
+      "8                              Spanish painter (1599-1660)    Male   \n",
+      "9                    Greek painter, sculptor and architect    Male   \n",
+      "11       Italian mathematician, physicist, philosopher ...    Male   \n",
+      "13                                          king of France    Male   \n",
+      "15       Spanish princess and saint, queen consort of F...  Female   \n",
+      "...                                                    ...     ...   \n",
+      "1222824              Spanish poet of the ''siglo de oro''.    Male   \n",
+      "1222864               Finnish lieutenant in the Cudgel War    Male   \n",
+      "1222928                       16th century Italian painter    Male   \n",
+      "1222934       klerk uit Groothertogdom Toscane (1632-1700)    Male   \n",
+      "1223002                     Italian engraver and publisher     NaN   \n",
+      "\n",
+      "                                           Country  Occupation  Birth year  \\\n",
+      "8                                            Spain      Artist        1599   \n",
+      "9                        Spain; Republic of Venice      Artist        1541   \n",
+      "11       Grand Duchy of Tuscany; Duchy of Florence  Astronomer        1564   \n",
+      "13                                          France       Ruler        1214   \n",
+      "15                              Kingdom of Castile      Regent        1188   \n",
+      "...                                            ...         ...         ...   \n",
+      "1222824                                      Spain      Artist        1569   \n",
+      "1222864                            Finland; Sweden         NaN        1560   \n",
+      "1222928                                        NaN      Artist        1479   \n",
+      "1222934                     Grand Duchy of Tuscany  Politician        1632   \n",
+      "1223002                                        NaN   Publisher        1638   \n",
+      "\n",
+      "         Death year Manner of death  Age of death  \n",
+      "8            1660.0             NaN          61.0  \n",
+      "9            1614.0             NaN          73.0  \n",
+      "11           1642.0             NaN          78.0  \n",
+      "13           1270.0  natural causes          56.0  \n",
+      "15           1252.0             NaN          64.0  \n",
+      "...             ...             ...           ...  \n",
+      "1222824      1620.0             NaN          51.0  \n",
+      "1222864      1601.0             NaN          41.0  \n",
+      "1222928      1553.0             NaN          74.0  \n",
+      "1222934      1700.0             NaN          68.0  \n",
+      "1223002      1702.0             NaN          64.0  \n",
+      "\n",
+      "[103545 rows x 10 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:52.618966Z",
+     "start_time": "2024-12-07T05:04:51.884406Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ],
+   "id": "2b08e1385cc51e44",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Id                         0\n",
+      "Name                       0\n",
+      "Short description      67877\n",
+      "Gender                133600\n",
+      "Country               335192\n",
+      "Occupation            206844\n",
+      "Birth year                 0\n",
+      "Death year                 1\n",
+      "Manner of death      1168920\n",
+      "Age of death               1\n",
+      "dtype: int64\n",
+      "\n",
+      "Id                   False\n",
+      "Name                 False\n",
+      "Short description     True\n",
+      "Gender                True\n",
+      "Country               True\n",
+      "Occupation            True\n",
+      "Birth year           False\n",
+      "Death year            True\n",
+      "Manner of death       True\n",
+      "Age of death          True\n",
+      "dtype: bool\n",
+      "\n",
+      "Short description процент пустых значений: %5.55\n",
+      "Gender процент пустых значений: %10.93\n",
+      "Country процент пустых значений: %27.42\n",
+      "Occupation процент пустых значений: %16.92\n",
+      "Death year процент пустых значений: %0.00\n",
+      "Manner of death процент пустых значений: %95.62\n",
+      "Age of death процент пустых значений: %0.00\n"
+     ]
+    }
+   ],
+   "execution_count": 7
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:52.954779Z",
+     "start_time": "2024-12-07T05:04:52.657355Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "# Замена пустых данных на медиану\n",
+    "df[\"Birth year\"] = df[\"Birth year\"].fillna(df[\"Birth year\"].median())\n",
+    "df[\"Death year\"] = df[\"Death year\"].fillna(df[\"Death year\"].max())\n",
+    "df[\"Age of death\"] = df[\"Age of death\"].fillna(df[\"Death year\"] - df[\"Birth year\"] + 1)\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ],
+   "id": "f68b156fc306ada1",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Short description процент пустых значений: %5.55\n",
+      "Gender процент пустых значений: %10.93\n",
+      "Country процент пустых значений: %27.42\n",
+      "Occupation процент пустых значений: %16.92\n",
+      "Manner of death процент пустых значений: %95.62\n"
+     ]
+    }
+   ],
+   "execution_count": 8
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Manner of death имеет слишком много пустых значений </br> Age of death считается из других полей <br> в выборке их лучше исключить",
+   "id": "60f5a960c211b634"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:54.146614Z",
+     "start_time": "2024-12-07T05:04:52.968787Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Функция для создания выборок\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "dropna_df = df.dropna()\n",
+    "df_input = dropna_df[[\"Id\", \"Gender\", \"Country\", \"Birth year\", \"Death year\"]].query('Gender == \"Male\" or Gender == \"Female\"')\n",
+    "#[[\"Gender\"] == \"Male\" or [\"Gender\"] == \"Female\"]\n",
+    "\n",
+    "train_df, temp_df = train_test_split(df_input, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# Разделение остатка на контрольную и тестовую выборки\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Проверка размеров выборок\n",
+    "print(\"Размер обучающей выборки:\", len(train_df))\n",
+    "print(\"Размер контрольной выборки:\", len(val_df))\n",
+    "print(\"Размер тестовой выборки:\", len(test_df))\n",
+    "\n",
+    "# Сохранение выборок в файлы\n",
+    "train_df.to_csv(\".//static//csv//train_data.csv\", index=False)\n",
+    "val_df.to_csv(\".//static//csv//val_data.csv\", index=False)\n",
+    "test_df.to_csv(\".//static//csv//test_data.csv\", index=False)"
+   ],
+   "id": "ae1ee081769ff2aa",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 27438\n",
+      "Размер контрольной выборки: 9146\n",
+      "Размер тестовой выборки: 9146\n"
+     ]
+    }
+   ],
+   "execution_count": 9
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:54.249324Z",
+     "start_time": "2024-12-07T05:04:54.174250Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
+    "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
+    "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
+    "\n",
+    "# Оценка сбалансированности\n",
+    "def check_balance(df, name):\n",
+    "    counts = df['Gender'].value_counts()\n",
+    "    print(f\"Распределение Gender в {name}:\")\n",
+    "    print(counts)\n",
+    "    print(f\"Процент Male: {counts['Male'] / len(df) * 100:.2f}%\")\n",
+    "    print(f\"Процент Female: {counts['Female'] / len(df) * 100:.2f}%\")\n",
+    "    print()\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")"
+   ],
+   "id": "264b15ff4137ef94",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение Gender в обучающей выборке:\n",
+      "Gender\n",
+      "Male      22999\n",
+      "Female     4439\n",
+      "Name: count, dtype: int64\n",
+      "Процент Male: 83.82%\n",
+      "Процент Female: 16.18%\n",
+      "\n",
+      "Распределение Gender в контрольной выборке:\n",
+      "Gender\n",
+      "Male      7721\n",
+      "Female    1425\n",
+      "Name: count, dtype: int64\n",
+      "Процент Male: 84.42%\n",
+      "Процент Female: 15.58%\n",
+      "\n",
+      "Распределение Gender в тестовой выборке:\n",
+      "Gender\n",
+      "Male      7707\n",
+      "Female    1439\n",
+      "Name: count, dtype: int64\n",
+      "Процент Male: 84.27%\n",
+      "Процент Female: 15.73%\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 10
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Данные не сбалансированы: мужчин больше, чем женщин.",
+   "id": "bb251943566dd9d7"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:54.289751Z",
+     "start_time": "2024-12-07T05:04:54.267835Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from sklearn.utils import resample\n",
+    "\n",
+    "standard_data = df_input[df_input['Gender'] == 'Male']\n",
+    "free_data = df_input[df_input['Gender'] == 'Female']\n",
+    "\n",
+    "# Уменьшение количества male до количества female\n",
+    "standard_undersampled = resample(standard_data,\n",
+    "                                 replace=False,  # выборка без замены\n",
+    "                                 n_samples=len(free_data),  # количество образцов для выборки\n",
+    "                                 random_state=42)  # для воспроизводимости\n",
+    "\n",
+    "# Объединение данных обратно\n",
+    "df_input = pd.concat([standard_undersampled, free_data])\n",
+    "\n",
+    "train_df, temp_df = train_test_split(df_input, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# Разделение остатка на контрольную и тестовую выборки\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")"
+   ],
+   "id": "f09dba8137306afc",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение Gender в обучающей выборке:\n",
+      "Gender\n",
+      "Male      4419\n",
+      "Female    4344\n",
+      "Name: count, dtype: int64\n",
+      "Процент Male: 50.43%\n",
+      "Процент Female: 49.57%\n",
+      "\n",
+      "Распределение Gender в контрольной выборке:\n",
+      "Gender\n",
+      "Female    1487\n",
+      "Male      1434\n",
+      "Name: count, dtype: int64\n",
+      "Процент Male: 49.09%\n",
+      "Процент Female: 50.91%\n",
+      "\n",
+      "Распределение Gender в тестовой выборке:\n",
+      "Gender\n",
+      "Female    1472\n",
+      "Male      1450\n",
+      "Name: count, dtype: int64\n",
+      "Процент Male: 49.62%\n",
+      "Процент Female: 50.38%\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 11
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "### 2) Образование студентов",
+   "id": "34a46b89a2d7c8dc"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:54.414989Z",
+     "start_time": "2024-12-07T05:04:54.322842Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df = pd.read_csv(\"StudentsPerformance.csv\")\n",
+    "sampled_df = df.sample(frac=0.4)\n",
+    "print(df.columns)"
+   ],
+   "id": "b27758ba28415b32",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
+      "       'test preparation course', 'math score', 'reading score',\n",
+      "       'writing score'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "execution_count": 12
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:54.451091Z",
+     "start_time": "2024-12-07T05:04:54.438626Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df.info()\n",
+    "print(df.shape)\n",
+    "df.head()"
+   ],
+   "id": "22da6934e834877c",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   gender                       1000 non-null   object\n",
+      " 1   race/ethnicity               1000 non-null   object\n",
+      " 2   parental level of education  1000 non-null   object\n",
+      " 3   lunch                        1000 non-null   object\n",
+      " 4   test preparation course      1000 non-null   object\n",
+      " 5   math score                   1000 non-null   int64 \n",
+      " 6   reading score                1000 non-null   int64 \n",
+      " 7   writing score                1000 non-null   int64 \n",
+      "dtypes: int64(3), object(5)\n",
+      "memory usage: 62.6+ KB\n",
+      "(1000, 8)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "   gender race/ethnicity parental level of education         lunch  \\\n",
+       "0  female        group B           bachelor's degree      standard   \n",
+       "1  female        group C                some college      standard   \n",
+       "2  female        group B             master's degree      standard   \n",
+       "3    male        group A          associate's degree  free/reduced   \n",
+       "4    male        group C                some college      standard   \n",
+       "\n",
+       "  test preparation course  math score  reading score  writing score  \n",
+       "0                    none          72             72             74  \n",
+       "1               completed          69             90             88  \n",
+       "2                    none          90             95             93  \n",
+       "3                    none          47             57             44  \n",
+       "4                    none          76             78             75  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gender</th>\n",
+       "      <th>race/ethnicity</th>\n",
+       "      <th>parental level of education</th>\n",
+       "      <th>lunch</th>\n",
+       "      <th>test preparation course</th>\n",
+       "      <th>math score</th>\n",
+       "      <th>reading score</th>\n",
+       "      <th>writing score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>bachelor's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>72</td>\n",
+       "      <td>72</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>69</td>\n",
+       "      <td>90</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group B</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>90</td>\n",
+       "      <td>95</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group A</td>\n",
+       "      <td>associate's degree</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>47</td>\n",
+       "      <td>57</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>none</td>\n",
+       "      <td>76</td>\n",
+       "      <td>78</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 13
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:54.594512Z",
+     "start_time": "2024-12-07T05:04:54.586463Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ],
+   "id": "ca0b0adc2b6e172a",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gender                         0\n",
+      "race/ethnicity                 0\n",
+      "parental level of education    0\n",
+      "lunch                          0\n",
+      "test preparation course        0\n",
+      "math score                     0\n",
+      "reading score                  0\n",
+      "writing score                  0\n",
+      "dtype: int64\n",
+      "\n",
+      "gender                         False\n",
+      "race/ethnicity                 False\n",
+      "parental level of education    False\n",
+      "lunch                          False\n",
+      "test preparation course        False\n",
+      "math score                     False\n",
+      "reading score                  False\n",
+      "writing score                  False\n",
+      "dtype: bool\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 14
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:54.849186Z",
+     "start_time": "2024-12-07T05:04:54.744842Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x=df['math score'])\n",
+    "plt.title('Box Plot для math score')\n",
+    "plt.xlabel('math score')\n",
+    "plt.show()"
+   ],
+   "id": "9f8f17302f5e065c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 15
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Есть выбросы",
+   "id": "386fbfcb08f18b76"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:55.000194Z",
+     "start_time": "2024-12-07T05:04:54.880368Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Статистический анализ для определения выбросов\n",
+    "Q1 = df['math score'].quantile(0.25)\n",
+    "Q3 = df['math score'].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "# Определение порога для выбросов\n",
+    "threshold = 1.5 * IQR\n",
+    "outliers = (df['math score'] < (Q1 - threshold)) | (df['math score'] > (Q3 + threshold))\n",
+    "\n",
+    "# Вывод выбросов\n",
+    "print(\"Выбросы:\")\n",
+    "print(df[outliers])\n",
+    "\n",
+    "# Обработка выбросов\n",
+    "# В данном случае мы заменим выбросы на медианное значение\n",
+    "median_review_no = df['math score'].median()\n",
+    "df.loc[outliers, 'math score'] = median_review_no\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x=df['math score'])\n",
+    "plt.title('Box Plot для math score')\n",
+    "plt.xlabel('math score')\n",
+    "plt.show()"
+   ],
+   "id": "8856143e19bd7485",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Выбросы:\n",
+      "     gender race/ethnicity parental level of education         lunch  \\\n",
+      "17   female        group B            some high school  free/reduced   \n",
+      "59   female        group C            some high school  free/reduced   \n",
+      "145  female        group C                some college  free/reduced   \n",
+      "338  female        group B            some high school  free/reduced   \n",
+      "466  female        group D          associate's degree  free/reduced   \n",
+      "787  female        group B                some college      standard   \n",
+      "842  female        group B                 high school  free/reduced   \n",
+      "980  female        group B                 high school  free/reduced   \n",
+      "\n",
+      "    test preparation course  math score  reading score  writing score  \n",
+      "17                     none          18             32             28  \n",
+      "59                     none           0             17             10  \n",
+      "145                    none          22             39             33  \n",
+      "338                    none          24             38             27  \n",
+      "466                    none          26             31             38  \n",
+      "787                    none          19             38             32  \n",
+      "842               completed          23             44             36  \n",
+      "980                    none           8             24             23  \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 16
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:55.105781Z",
+     "start_time": "2024-12-07T05:04:55.087478Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Функция для создания выборок\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "dropna_df = df.dropna()\n",
+    "df_input = dropna_df[[\"math score\", \"reading score\", \"writing score\", \"lunch\"]]\n",
+    "\n",
+    "train_df, temp_df = train_test_split(df_input, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# Разделение остатка на контрольную и тестовую выборки\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Проверка размеров выборок\n",
+    "print(\"Размер обучающей выборки:\", len(train_df))\n",
+    "print(\"Размер контрольной выборки:\", len(val_df))\n",
+    "print(\"Размер тестовой выборки:\", len(test_df))\n",
+    "\n",
+    "# Сохранение выборок в файлы\n",
+    "train_df.to_csv(\".//static//csv//train_data.csv\", index=False)\n",
+    "val_df.to_csv(\".//static//csv//val_data.csv\", index=False)\n",
+    "test_df.to_csv(\".//static//csv//test_data.csv\", index=False)"
+   ],
+   "id": "19b2227d09187635",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 600\n",
+      "Размер контрольной выборки: 200\n",
+      "Размер тестовой выборки: 200\n"
+     ]
+    }
+   ],
+   "execution_count": 17
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:55.176635Z",
+     "start_time": "2024-12-07T05:04:55.144596Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
+    "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
+    "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
+    "\n",
+    "def check_balance(df, name):\n",
+    "    counts = df['lunch'].value_counts()\n",
+    "    print(f\"Распределение lunch в {name}:\")\n",
+    "    print(counts)\n",
+    "    print(f\"Процент standard: {counts['standard'] / len(df) * 100:.2f}%\")\n",
+    "    print(f\"Процент free/reduced: {counts['free/reduced'] / len(df) * 100:.2f}%\")\n",
+    "    print()\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")"
+   ],
+   "id": "3e5fe04e72cba1f1",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение lunch в обучающей выборке:\n",
+      "lunch\n",
+      "standard        394\n",
+      "free/reduced    206\n",
+      "Name: count, dtype: int64\n",
+      "Процент standard: 65.67%\n",
+      "Процент free/reduced: 34.33%\n",
+      "\n",
+      "Распределение lunch в контрольной выборке:\n",
+      "lunch\n",
+      "standard        134\n",
+      "free/reduced     66\n",
+      "Name: count, dtype: int64\n",
+      "Процент standard: 67.00%\n",
+      "Процент free/reduced: 33.00%\n",
+      "\n",
+      "Распределение lunch в тестовой выборке:\n",
+      "lunch\n",
+      "standard        117\n",
+      "free/reduced     83\n",
+      "Name: count, dtype: int64\n",
+      "Процент standard: 58.50%\n",
+      "Процент free/reduced: 41.50%\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 18
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:55.208465Z",
+     "start_time": "2024-12-07T05:04:55.195804Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "standard_data = df_input[df_input['lunch'] == 'standard']\n",
+    "free_data = df_input[df_input['lunch'] == 'free/reduced']\n",
+    "\n",
+    "# Уменьшение количества male до количества female\n",
+    "standard_undersampled = resample(standard_data,\n",
+    "                                 replace=False,  # выборка без замены\n",
+    "                                 n_samples=len(free_data),  # количество образцов для выборки\n",
+    "                                 random_state=42)  # для воспроизводимости\n",
+    "\n",
+    "# Объединение данных обратно\n",
+    "df_input = pd.concat([standard_undersampled, free_data])\n",
+    "\n",
+    "train_df, temp_df = train_test_split(df_input, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# Разделение остатка на контрольную и тестовую выборки\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")\n",
+    "\n",
+    "train_df.to_csv(\".//static//csv//train_data.csv\", index=False)\n",
+    "val_df.to_csv(\".//static//csv//val_data.csv\", index=False)\n",
+    "test_df.to_csv(\".//static//csv//test_data.csv\", index=False)"
+   ],
+   "id": "89d35c1d76854af5",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение lunch в обучающей выборке:\n",
+      "lunch\n",
+      "free/reduced    230\n",
+      "standard        196\n",
+      "Name: count, dtype: int64\n",
+      "Процент standard: 46.01%\n",
+      "Процент free/reduced: 53.99%\n",
+      "\n",
+      "Распределение lunch в контрольной выборке:\n",
+      "lunch\n",
+      "standard        75\n",
+      "free/reduced    67\n",
+      "Name: count, dtype: int64\n",
+      "Процент standard: 52.82%\n",
+      "Процент free/reduced: 47.18%\n",
+      "\n",
+      "Распределение lunch в тестовой выборке:\n",
+      "lunch\n",
+      "standard        84\n",
+      "free/reduced    58\n",
+      "Name: count, dtype: int64\n",
+      "Процент standard: 59.15%\n",
+      "Процент free/reduced: 40.85%\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 19
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "3) Зарплаты ",
+   "id": "90f5f62d0f207fcd"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:04:55.249310Z",
+     "start_time": "2024-12-07T05:04:55.226833Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df = pd.read_csv(\"ds_salaries.csv\")\n",
+    "sampled_df = df.sample(frac=0.4)\n",
+    "print(df.columns)\n",
+    "print(df)"
+   ],
+   "id": "aa48737cf6a10934",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n",
+      "       'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n",
+      "       'remote_ratio', 'company_location', 'company_size'],\n",
+      "      dtype='object')\n",
+      "      work_year experience_level employment_type                 job_title  \\\n",
+      "0          2023               SE              FT  Principal Data Scientist   \n",
+      "1          2023               MI              CT               ML Engineer   \n",
+      "2          2023               MI              CT               ML Engineer   \n",
+      "3          2023               SE              FT            Data Scientist   \n",
+      "4          2023               SE              FT            Data Scientist   \n",
+      "...         ...              ...             ...                       ...   \n",
+      "3750       2020               SE              FT            Data Scientist   \n",
+      "3751       2021               MI              FT  Principal Data Scientist   \n",
+      "3752       2020               EN              FT            Data Scientist   \n",
+      "3753       2020               EN              CT     Business Data Analyst   \n",
+      "3754       2021               SE              FT      Data Science Manager   \n",
+      "\n",
+      "       salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
+      "0       80000             EUR          85847                 ES           100   \n",
+      "1       30000             USD          30000                 US           100   \n",
+      "2       25500             USD          25500                 US           100   \n",
+      "3      175000             USD         175000                 CA           100   \n",
+      "4      120000             USD         120000                 CA           100   \n",
+      "...       ...             ...            ...                ...           ...   \n",
+      "3750   412000             USD         412000                 US           100   \n",
+      "3751   151000             USD         151000                 US           100   \n",
+      "3752   105000             USD         105000                 US           100   \n",
+      "3753   100000             USD         100000                 US           100   \n",
+      "3754  7000000             INR          94665                 IN            50   \n",
+      "\n",
+      "     company_location company_size  \n",
+      "0                  ES            L  \n",
+      "1                  US            S  \n",
+      "2                  US            S  \n",
+      "3                  CA            M  \n",
+      "4                  CA            M  \n",
+      "...               ...          ...  \n",
+      "3750               US            L  \n",
+      "3751               US            L  \n",
+      "3752               US            S  \n",
+      "3753               US            L  \n",
+      "3754               IN            L  \n",
+      "\n",
+      "[3755 rows x 11 columns]\n"
+     ]
+    }
+   ],
+   "execution_count": 20
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:08:29.826827Z",
+     "start_time": "2024-12-07T05:08:29.809667Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "df.info()\n",
+    "print(df.shape)\n",
+    "df.head()"
+   ],
+   "id": "efe4a65727cb2a42",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3755 entries, 0 to 3754\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column              Non-Null Count  Dtype \n",
+      "---  ------              --------------  ----- \n",
+      " 0   work_year           3755 non-null   int64 \n",
+      " 1   experience_level    3755 non-null   object\n",
+      " 2   employment_type     3755 non-null   object\n",
+      " 3   job_title           3755 non-null   object\n",
+      " 4   salary              3755 non-null   int64 \n",
+      " 5   salary_currency     3755 non-null   object\n",
+      " 6   salary_in_usd       3755 non-null   int64 \n",
+      " 7   employee_residence  3755 non-null   object\n",
+      " 8   remote_ratio        3755 non-null   int64 \n",
+      " 9   company_location    3755 non-null   object\n",
+      " 10  company_size        3755 non-null   object\n",
+      "dtypes: int64(4), object(7)\n",
+      "memory usage: 322.8+ KB\n",
+      "(3755, 11)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "   work_year experience_level employment_type                 job_title  \\\n",
+       "0       2023               SE              FT  Principal Data Scientist   \n",
+       "1       2023               MI              CT               ML Engineer   \n",
+       "2       2023               MI              CT               ML Engineer   \n",
+       "3       2023               SE              FT            Data Scientist   \n",
+       "4       2023               SE              FT            Data Scientist   \n",
+       "\n",
+       "   salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
+       "0   80000             EUR          85847                 ES           100   \n",
+       "1   30000             USD          30000                 US           100   \n",
+       "2   25500             USD          25500                 US           100   \n",
+       "3  175000             USD         175000                 CA           100   \n",
+       "4  120000             USD         120000                 CA           100   \n",
+       "\n",
+       "  company_location company_size  \n",
+       "0               ES            L  \n",
+       "1               US            S  \n",
+       "2               US            S  \n",
+       "3               CA            M  \n",
+       "4               CA            M  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>work_year</th>\n",
+       "      <th>experience_level</th>\n",
+       "      <th>employment_type</th>\n",
+       "      <th>job_title</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>salary_currency</th>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <th>employee_residence</th>\n",
+       "      <th>remote_ratio</th>\n",
+       "      <th>company_location</th>\n",
+       "      <th>company_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Principal Data Scientist</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>EUR</td>\n",
+       "      <td>85847</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>100</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>ML Engineer</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>ML Engineer</td>\n",
+       "      <td>25500</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>25500</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>100</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>100</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 21
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:09:03.492543Z",
+     "start_time": "2024-12-07T05:09:03.484743Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Количество пустых значений признаков\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Есть ли пустые значения признаков\n",
+    "print(df.isnull().any())\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Процент пустых значений признаков\n",
+    "for i in df.columns:\n",
+    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
+    "    if null_rate > 0:\n",
+    "        print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
+   ],
+   "id": "7fe305e3322450b0",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "work_year             0\n",
+      "experience_level      0\n",
+      "employment_type       0\n",
+      "job_title             0\n",
+      "salary                0\n",
+      "salary_currency       0\n",
+      "salary_in_usd         0\n",
+      "employee_residence    0\n",
+      "remote_ratio          0\n",
+      "company_location      0\n",
+      "company_size          0\n",
+      "dtype: int64\n",
+      "\n",
+      "work_year             False\n",
+      "experience_level      False\n",
+      "employment_type       False\n",
+      "job_title             False\n",
+      "salary                False\n",
+      "salary_currency       False\n",
+      "salary_in_usd         False\n",
+      "employee_residence    False\n",
+      "remote_ratio          False\n",
+      "company_location      False\n",
+      "company_size          False\n",
+      "dtype: bool\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 22
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:10:52.411701Z",
+     "start_time": "2024-12-07T05:10:52.310163Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x=df['salary_in_usd'])\n",
+    "plt.title('Box Plot для salary_in_usd')\n",
+    "plt.xlabel('math salary_in_usd')\n",
+    "plt.show()"
+   ],
+   "id": "ae7082a6ae02ce66",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 23
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Есть выбросы",
+   "id": "3f22eadffa90565b"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:12:21.725609Z",
+     "start_time": "2024-12-07T05:12:21.617687Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Статистический анализ для определения выбросов\n",
+    "Q1 = df['salary_in_usd'].quantile(0.25)\n",
+    "Q3 = df['salary_in_usd'].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "# Определение порога для выбросов\n",
+    "threshold = 1.5 * IQR\n",
+    "outliers = (df['salary_in_usd'] < (Q1 - threshold)) | (df['salary_in_usd'] > (Q3 + threshold))\n",
+    "\n",
+    "# Вывод выбросов\n",
+    "print(\"Выбросы:\")\n",
+    "print(df[outliers])\n",
+    "\n",
+    "# Обработка выбросов\n",
+    "# В данном случае мы заменим выбросы на медианное значение\n",
+    "median_review_no = df['salary_in_usd'].median()\n",
+    "df.loc[outliers, 'salary_in_usd'] = median_review_no\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x=df['salary_in_usd'])\n",
+    "plt.title('Box Plot для salary_in_usd')\n",
+    "plt.xlabel('salary_in_usd')\n",
+    "plt.show()"
+   ],
+   "id": "b5fa84c701764a4",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Выбросы:\n",
+      "      work_year experience_level employment_type  \\\n",
+      "33         2023               SE              FT   \n",
+      "68         2023               SE              FT   \n",
+      "83         2022               EN              FT   \n",
+      "133        2023               SE              FT   \n",
+      "145        2023               SE              FT   \n",
+      "...         ...              ...             ...   \n",
+      "3522       2020               MI              FT   \n",
+      "3675       2021               EX              CT   \n",
+      "3697       2020               EX              FT   \n",
+      "3747       2021               MI              FT   \n",
+      "3750       2020               SE              FT   \n",
+      "\n",
+      "                               job_title  salary salary_currency  \\\n",
+      "33              Computer Vision Engineer  342810             USD   \n",
+      "68                     Applied Scientist  309400             USD   \n",
+      "83                          AI Developer  300000             USD   \n",
+      "133            Machine Learning Engineer  342300             USD   \n",
+      "145            Machine Learning Engineer  318300             USD   \n",
+      "...                                  ...     ...             ...   \n",
+      "3522                  Research Scientist  450000             USD   \n",
+      "3675            Principal Data Scientist  416000             USD   \n",
+      "3697            Director of Data Science  325000             USD   \n",
+      "3747  Applied Machine Learning Scientist  423000             USD   \n",
+      "3750                      Data Scientist  412000             USD   \n",
+      "\n",
+      "      salary_in_usd employee_residence  remote_ratio company_location  \\\n",
+      "33           342810                 US             0               US   \n",
+      "68           309400                 US             0               US   \n",
+      "83           300000                 IN            50               IN   \n",
+      "133          342300                 US             0               US   \n",
+      "145          318300                 US           100               US   \n",
+      "...             ...                ...           ...              ...   \n",
+      "3522         450000                 US             0               US   \n",
+      "3675         416000                 US           100               US   \n",
+      "3697         325000                 US           100               US   \n",
+      "3747         423000                 US            50               US   \n",
+      "3750         412000                 US           100               US   \n",
+      "\n",
+      "     company_size  \n",
+      "33              M  \n",
+      "68              L  \n",
+      "83              L  \n",
+      "133             L  \n",
+      "145             M  \n",
+      "...           ...  \n",
+      "3522            M  \n",
+      "3675            S  \n",
+      "3697            L  \n",
+      "3747            L  \n",
+      "3750            L  \n",
+      "\n",
+      "[63 rows x 11 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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cd999cd5558V+++0XAwYMiAEDBuT26dixY/zud7+LBx98MC666KLIy8uLFi1axMMPP5x7Rur888+PSpUqxciRI2PkyJHRrFmz6NevX1x//fWlc8cAvqe8rKS3PQGgVD3zzDMxZMiQeOmll77T9u1dYWFhnHjiifHcc89t8jkyAJQez4AAsE0aP358jB8/Pp599tlo3759sfjIsqzYyd8bstNOO5V4wv63ffvtgdenTJkym3RuD8COSIAAbOV23XXXdT4DY3O2b68WLFgQI0aMiAMOOCBuuummYtsmTJgQvXv3LvEYt9xyy2afuL6+D8X8tmOOOeZ7fxo7wPbKS7AA2O4sWbIkPvnkkxL3q1u37ma/HfLbb79d4j7Vq1ff6KfaA+zIBAgAAJCMF6gCAADJCBAAACAZAQIAACQjQAAAgGQ26214//e/xfFDn7Kelxex2267bJFjs/UwzzsG87zjMNc7BvO84zDXO4Yfap6/Ps53tVkBkmWxxX4ot+Sx2XqY5x2Ded5xmOsdg3necZjrHUNpz7OXYAEAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIJmdS3sAwNZt7tw5sXjxl5u0b15exPz5lWPhwqWRZVt4YJSq7Xmud9mlatSsWau0hwGw3RIgwAbNnTsnLu17Qaxetaq0hwLJlC1XLu4adJ8IAdhCBAiwQYsXfxmrV62K5ft2jKIK1Up7ODusMssXRsVP/hnL9/lJFFXML+3hbNfKrFgU8fErsXjxlwIEYAsRIECJiipUi6LKNUp7GDu8oor55gGAbZ6T0AEAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACSzTQbIl18uKu0hAADA97KjPqbd5gJk9uwv4pxzesfs2V+U9lAAAOA72ZEf025zAbJ06ZLIsqJYunRJaQ8FAAC+kx35Me02FyAAAMC2S4AAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkIwAAQAAkhEgAABAMgIEAABIRoAAAADJCBAAACAZAQIAACQjQAAAgGQECAAAkMzOpT0AAADYUY0b90r87W9jY8aMGVGuXLnYffc6cfLJp8Y//vFSzJ79edSu/eM4/PAuMXXq1FiwYH5Ur75rNGhwYOy0006lPfTvTIAAAEBif/nL8xERMXr0s8Uuf/vtwnjhhb8Uu2zkyOHFvq5Zs1acfvrZ0br1IVt0jFuKl2ABAEBCjzwyIl555R+5r3faaado2rR5lC1brth+e+21T0RE7LzzV88ZHHnkUXHzzQNjzz33jttvvzXGj3893aB/QAIEAAASWb16VYwe/WxUrlwlIr6Kj4cffjKuuur/Yu3aNZGXlxd5eXlRpsxOMX36J9GsWYsYOfKJqFYtP1544f+LffbZN6666v+iefOD4+GHH4y1a9eW8j3afNvsS7Bmzfq0tIfAZsjLi5g/v3IsXLg0sqy0R8Omss7YUfnZ/4rf3TsOc53OuHGvRFFRUeTn58fSpUuiQ4dDo1y5cjF69HNRVFQUHTocGuPGvRx77bVnTJv2SZQrVz7KlSsXJ510Stx//z3xwgtjokePn8Uxx5wQv/71lfHee1PioIMKSvtubZZtNkAGD76jtIcAwHbK3zHAljZr1syIiDj44NYRETF79ucREXHUUUfHuHEvR/nyFSIiYunSJRER0aLFwcX223PPPSMiYsGC+amG/IPZZgPkkksuizp19ijtYbCJ8vIi8vP9y8q2ZtasTz0QY4fk75iv+N294zDX6Ywb90qMHv1s1KlTN2bNmhkTJ46PVq3aRu3aP46IiOeffzYiIlauXBERkXup1qRJEyMicvvNmDEjIiKqV9815fB/ENtsgNSps0fsu+/+pT0MNlFeXkSNGrvEvHmL/WIDtnr+jvmK3907DnOdzh577Bl/+cvzsXDhwoiIGDfu5Tj33AvjiCOOjEceeTBeffWVyMvLywXGqlUrY9WqVfHEE3+IMmV2iiOOODKKioriT396KmrVqh0NGhxYavflu9pmAwQAALY1ZcuWix49jo7nn38mIiLWrl0bvXufGI0aNY6ddto5Vq9eFRERWbY29tprn5g8eVKcfvpJsWbNmjjyyJ7x8ccfx5/+9FS8+ebEuPzy/tvk54EIEAAASOi0086MRYsW5N6Kd+3atVFYOHmd/aZP/yQiItasWRMREWPG/DnGjPlz1KpVOy6/vP82+zkgAgQAABLr1u2oeOWVf0SPHkfHypXLfRI6AACw5XXo0HG955z16PGzYl9va2+1uzE+iBAAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZLa5AKlcuUrk5ZWJypWrlPZQAADgO9mRH9PuXNoD2Fy1a/8ohg9/OKpWrVbaQwEAgO9kR35Mu809AxIRO+REAQCwfdlRH9NukwECAABsmwQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJLNzaQ8A2PqVWbGotIewQyuzfGGx/7Pl+FkH2PIECLBBu+xSNcqWKxfx8SulPRQiouIn/yztIewQypYrF7vsUrW0hwGw3RIgwAbVrFkr7hp0Xyxe/OUm7Z+XF5GfXzkWLlwaWbaFB0ep2p7nepddqkbNmrVKexgA2y0BAmxUzZq1NvnBWF5eRI0au8S8eYu3uwelFGeuAfiunIQOAAAkI0AAAIBkBAgAAJCMAAEAAJIRIAAAQDICBAAASEaAAAAAyQgQAAAgGQECAAAkI0AAAIBkBAgAAJCMAAEAAJIRIAAAQDICBAAASEaAAAAAyQgQAAAgGQECAAAkI0AAAIBkBAgAAJCMAAEAAJIRIAAAQDICBAAASEaAAAAAyQgQAAAgGQECAAAkI0AAAIBkBAgAAJCMAAEAAJIRIAAAQDICBAAASEaAAAAAyQgQAAAgGQECAAAkI0AAAIBkBAgAAJCMAAEAAJIRIAAAQDICBAAASEaAAAAAyQgQAAAgGQECAAAkI0AAAIBkBAgAAJDMzpuzc17eDz+Ar4+5JY7N1sM87xjM847DXO8YzPOOw1zvGH6oef7e18+yLPt+hwAAANg0XoIFAAAkI0AAAIBkBAgAAJCMAAEAAJIRIAAAQDICBAAASEaAAAAAyQgQAAAgGQECAAAkU2oBsnLlyrj66qujZcuW0b59+3jwwQdLayiU4MUXX4z69esX+++SSy6JiIgpU6bECSecEE2aNInjjjsu3nnnnWLXHT16dBx22GHRpEmT6NOnT8yfPz+3Lcuy+N3vfhdt2rSJVq1axW9/+9soKirKbV+wYEFcfPHF0axZs+jcuXM899xzae7wDmjVqlXRo0ePGD9+fO6yTz/9NM4444xo2rRpdOvWLV599dVi13n99dejR48e0aRJk+jdu3d8+umnxbY/9NBD0aFDh2jWrFlcffXVsXz58ty2ktZ/SbfNd7O+eb7pppvWWd+PPvpobvuWXMMl/f5g88yePTsuueSSaNWqVXTo0CFuueWWWLlyZURYz9ubjc21Nb39mD59epx99tnRrFmzOPTQQ2P48OG5bdv8ms5KyY033pj17Nkze+edd7K//vWvWbNmzbIxY8aU1nDYiHvvvTc7//zzszlz5uT+W7RoUbZ06dKsXbt22a233ppNnTo1GzBgQHbIIYdkS5cuzbIsywoLC7PGjRtnf/rTn7J33303O/XUU7Pzzjsvd9wHHngg69ixYzZx4sTsjTfeyNq3b58NHz48t/3888/PTj/99Oz999/PnnzyyaxRo0ZZYWFh8vu/vVuxYkXWp0+frF69etm//vWvLMuyrKioKOvZs2d2+eWXZ1OnTs2GDh2aNWnSJJs1a1aWZVk2a9asrGnTptkDDzyQffDBB9mll16a9ejRIysqKsqyLMvGjh2btWjRInvppZeywsLCrFu3btkNN9yQu82Nrf+SbpvvZn3znGVZdsYZZ2TDhg0rtr6XLVuWZdmWXcMl/f5g8xQVFWUnnnhids4552QffPBBNnHixOzwww/Pbr31Vut5O7Oxuc4ya3p7sXbt2qxLly7Z5Zdfnn3yySfZyy+/nDVv3jx7/vnnt4s1XSoBsnTp0qygoKDYX4L33HNPduqpp5bGcCjB5Zdfnt1+++3rXP7UU09lnTt3zv1AFxUVZYcffnj29NNPZ1mWZVdeeWXWr1+/3P6fffZZVr9+/WzGjBlZlmVZx44dc/tmWZY9++yzWadOnbIsy7Lp06dn9erVyz799NPc9quvvrrY8fj+Pvzww+yoo47KevbsWeyB6euvv541bdq02F8cp59+ejZ48OAsy7Js0KBBxdbrsmXLsmbNmuWu36tXr9y+WZZlEydOzBo3bpwtW7asxPVf0m2z+TY0z1mWZR06dMjGjRu33uttyTVc0u8PNs/UqVOzevXqZXPnzs1d9uc//zlr37699byd2dhcZ5k1vb2YPXt2dumll2aLFy/OXdanT5/suuuu2y7WdKm8BOu9996LNWvWRLNmzXKXtWjRIgoLC4s91cfW4aOPPoq99957ncsLCwujRYsWkZeXFxEReXl50bx583jrrbdy21u2bJnb/8c//nHsvvvuUVhYGLNnz47PP/88Dj744Nz2Fi1axKxZs2LOnDlRWFgYP/7xj6Nu3brFtk+ePHnL3Mkd1IQJE6J169bxxBNPFLu8sLAwDjzwwKhUqVLushYtWmxwbitWrBgHHXRQvPXWW7F27dp4++23i21v2rRprF69Ot57770S139Jt83m29A8L1myJGbPnr3e9R2xZddwSb8/2Dw1a9aM4cOHR40aNYpdvmTJEut5O7Oxubamtx+1atWKQYMGRZUqVSLLspg0aVJMnDgxWrVqtV2s6Z038/vxg5g7d25Ur149ypUrl7usRo0asXLlyli4cGHsuuuupTEs1iPLsvjkk0/i1VdfjWHDhsXatWuja9eucckll8TcuXNj//33L7b/brvtFh9++GFERMyZMydq1aq1zvYvvvgi5s6dGxFRbPvXv0y/3r6+686ePfsHv487sl69eq338g19/7/44osSt3/55ZexcuXKYtt33nnnyM/Pjy+++CLKlCmz0fVf0m2z+TY0zx999FHk5eXF0KFD45///Gfk5+fHmWeeGcccc0xEbNk1XNLvDzZP1apVo0OHDrmvi4qK4tFHH402bdpYz9uZjc21Nb196ty5c3z22WfRqVOnOOKII+I3v/nNNr+mSyVAli9fXuyORUTu61WrVpXGkNiAzz77LDdfgwYNipkzZ8ZNN90UK1as2OA8fj2HK1as2OD2FStW5L7+5raIr34GSjo2W1ZJ3/+NbV/f3H5ze5ZlG13/5j6djz/+OPLy8mLfffeNU089NSZOnBjXXHNNVKlSJQ4//PAtuobN85Y1cODAmDJlSvzxj3+Mhx56yHrejn1zrv/73/9a09uhwYMHx7x58+L666+PW265Zbv4O7pUAqR8+fLrDPTrrytUqFAaQ2ID6tSpE+PHj49q1apFXl5eNGzYMIqKiuLKK6+MVq1arXcev57DDc1zxYoVi/0wly9fPvfniK+eKtzQdf18pFG+fPlYuHBhscs2ZW6rVq26znx+c3vFihVj7dq1G13/Jd02P5yjjz46OnXqFPn5+RER0aBBg5g2bVo89thjcfjhh2/RNWyNbzkDBw6MkSNHxp133hn16tWznrdj357rAw44wJreDhUUFETEV+9OdcUVV8Rxxx1X7F2rIra9NV0q54DUrl07FixYEGvWrMldNnfu3KhQoUJUrVq1NIbERuTn5+de0xkRsd9++8XKlSujZs2aMW/evGL7zps3L/fUXO3atde7vWbNmlG7du2IiNxTvt/889fbN3RdtrwNff83ZW7z8/OjfPnyxbavWbMmFi5cmJvbja3/km6bH05eXl7ugcrX9t1339xLKrbkGjbPW8aAAQNixIgRMXDgwDjiiCMiwnreXq1vrq3p7ce8efPib3/7W7HL9t9//1i9evX3evy1tazpUgmQhg0bxs4771zshJVJkyZFQUFBlCnjsxG3JuPGjYvWrVsXK+1333038vPzcyefZVkWEV+dL/Lmm29GkyZNIiKiSZMmMWnSpNz1Pv/88/j888+jSZMmUbt27dh9992LbZ80aVLsvvvuUatWrWjatGnMmjWr2GsKJ02aFE2bNt3C95iIr+buv//9b+6p2oivvv8bmtvly5fHlClTokmTJlGmTJkoKCgotv2tt96KnXfeORo0aFDi+i/ptvnh3HXXXXHGGWcUu+y9996LfffdNyK27Bpu0qTJRn9/sPmGDBkSjz/+eNxxxx3RvXv33OXW8/ZnQ3NtTW8/Zs6cGRdddFGxc1/feeed2HXXXaNFixbb/prerPfM+gFdc801Wffu3bPCwsLsxRdfzJo3b5698MILpTUcNmDx4sVZhw4dsssuuyz76KOPspdffjlr3759dv/992eLFy/O2rRpkw0YMCD78MMPswEDBmTt2rXLvTXbm2++mR100EHZk08+mXu/8fPPPz937GHDhmXt27fP/vWvf2X/+te/svbt22cPPvhgbvtZZ52VnXrqqdm7776bPfnkk1lBQYHPAdmCvvn2rGvWrMm6deuW9e3bN/vggw+yYcOGZU2bNs29z/enn36aFRQUZMOGDcu9x3jPnj1zb784evTorHnz5tmLL76YFRYWZt27d88GDBiQu62Nrf+Sbpvv55vzXFhYmB144IHZ8OHDs+nTp2d/+MMfskaNGmVvvvlmlmVbdg2X9PuDzTN16tSsYcOG2Z133lns8x/mzJljPW9nNjbX1vT2Y82aNdmxxx6bnXXWWdmHH36Yvfzyy9khhxySPfTQQ9vFmi61AFm2bFl21VVXZU2bNs3at2+fjRgxorSGQgk++OCD7IwzzsiaNm2atWvXLrv77rtzP8SFhYXZ0UcfnRUUFGTHH3989t///rfYdZ9++umsY8eOWdOmTbM+ffpk8+fPz21bs2ZN9pvf/CZr2bJl1rp162zgwIG542ZZls2bNy87//zzs4KCgqxz587Zn//85zR3eAf17c+HmDZtWnbKKadkjRo1yrp375699tprxfZ/+eWXsy5dumSNGzfOTj/99Nz7yH9t2LBhWdu2bbMWLVpkv/rVr7IVK1bktpW0/ku6bb67b8/ziy++mPXs2TMrKCjIunbtus4/BG3JNVzS7w823bBhw7J69eqt978ss563JyXNtTW9/fjiiy+yPn36ZM2bN8/atWuX3Xfffbn52NbXdF6W/f/PlQEAAGxhTrgAAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIBkBAgAAJCNAAACAZAQIAACQjAAB2IbdfffdcdpppyW/3fHjx0f9+vWT325JnnnmmejcuXNpDwOAjRAgAGy2Zs2axauvvlrawwBgGyRAANhs5cqVi5o1a5b2MADYBgkQgK3Eww8/HJ06dYqCgoI49thj49///ndERPz973+Po48+OgoKCqJly5Zx2WWXxdKlS9d7jKeeeiq6du0ajRo1itatW8cNN9wQa9eujYiI/v37R//+/eOoo46Ktm3bxpAhQ6Jnz57Frv/ggw9Gr169ShzrN1+CNXPmzKhfv3789a9/jcMOOywKCgri/PPPj4ULF27S/f56XN9Uv379GD9+fEREvPHGG/Gzn/0sCgoK4qc//Wk8/vjjuf1mz54d55xzTjRt2jSOOeaYmDFjxibdJgClR4AAbAWmTJkSv/3tb+O6666LMWPGRMuWLaNv374xY8aMuPTSS6NXr14xZsyYGDRoULz++uvx5JNPrnOMCRMmxE033RSXXXZZjB07Nm644Yb44x//GH//+99z+zz33HPRt2/fGDZsWBx11FHxwQcfxCeffJLbPmbMmOjevft3ug9Dhw6NO+64Ix599NF4++23Y8SIEd/pON+0du3a6Nu3b3Tt2jXGjBkTl156adxwww0xderUiIi49NJLo6ioKJ566qk499xzY+TIkd/7NgHYsnYu7QEAEDFr1qzIy8uL3XffPerWrRt9+/aNTp06RVFRUfz617+OE088MSIi6tatG4ccckh8+OGH6xyjUqVKcfPNN0eXLl1y+44YMSI+/PDD3GUFBQXFTtJu3LhxjB07Ni644IKYNWtWTJkyJYYOHfqd7sMll1wSjRs3joiInj17xttvv/2djvNNixcvjoULF0aNGjWibt26Ubdu3ahVq1bUrFkzPvzww5g8eXL84x//iN133z0OOOCAeOedd2Ls2LHf+3YB2HI8AwKwFWjfvn3Uq1cvevbsGcccc0w8+OCDse+++8bee+8dP/nJT+K+++6Lyy67LHr27BljxoyJoqKidY7RqFGjaNCgQQwePDguueSSOOKII6KwsLDYvnXq1Cl2ne7du+cesI8ZMyZatWoVu+2223e6D3vttVfuz1WqVInVq1d/p+N8U35+fpx88snx61//Ojp16hQ33nhj7LLLLlGtWrWYOnVq5Ofnx+67757bv6Cg4HvfJgBblgAB2ApUrFgxnnrqqRg5cmS0atUqnnnmmTj22GNjwoQJ0b1795g6dWq0bNkybr755ujWrdt6jzFu3Lg49thjY968edGhQ4cYPHhwNG/evNg+5cuXL/Z1t27d4oMPPojp06fHCy+8sMFjb4qyZct+p+vl5eUV+3rNmjXFvr7++utj9OjRceKJJ0ZhYWGceOKJ8corr0RERJZlP8gYAEhHgABsBSZPnhzDhg2LNm3axK9+9asYO3ZsrFy5Mvr37x8HH3xw3H777dGrV69o3LhxTJ8+fZ0H3hFfnYB+3HHHxY033hgnnHBC7LfffjFjxoz17vu1WrVqRatWreLpp5+O9957L/dSrZTKli1b7KT6Tz/9NPfnuXPnxg033BB77bVXXHDBBfH0009HmzZt4qWXXop69erFokWLYvr06bn933333aRjB2DzOQcEYCtQoUKFuOeee6JGjRrRtm3bmDhxYixbtizOPffcGDVqVPznP/+JXXbZJZ544ol4++23Y4899ljnGPn5+TF58uR4//33o0yZMjFs2LCYO3durFq1aqO33aNHjxgwYEC0a9cuqlWrtqXu4gYVFBTELbfcEm+88Ubstttuccstt+SeyahWrVq8+OKLkWVZnHXWWTF79uxcKO23337Rtm3buPrqq+Oaa66JmTNnxqOPPhqVK1dOfh8A2HSeAQHYCjRs2DBuvvnmGD58eBx55JExdOjQGDhwYJx22mnRtGnTOOOMM6JXr17x2WefRZ8+fWLKlCnrHOOiiy6K3XbbLU466aQ488wzo3z58nHyySeX+KxAly5dYu3atd/r5Vffx89+9rM44ogj4sILL4xzzjknevToEbVq1YqIrz5v5N5774333nsvjjrqqOjbt28cf/zxccIJJ0RExJ133hnVq1ePn//853HHHXeUyqfCA7B58rKNPTcPwHZv2rRpcfTRR8drr73m2QMAtjgvwQLYQS1ZsiReffXVeOKJJ6J79+7iA4AkPAMCsINasmRJHHroobHnnnvG/fffHzVq1IiIiP/9739x2GGHbfS6kydP3qTbGDFiRAwePHiD23v27Bk33njjpg8agG2eAAGgmLVr18bMmTM3us83P/NjY7788stYsGDBBrdXqVLlO3/uCADbJgECAAAk412wAACAZAQIAACQjAABAACSESAAAEAyAgQAAEhGgAAAAMkIEAAAIJn/B6PQv1VhUzdKAAAAAElFTkSuQmCC"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 24
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:15:42.200731Z",
+     "start_time": "2024-12-07T05:15:42.186361Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Функция для создания выборок\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "dropna_df = df.dropna()\n",
+    "df_input = dropna_df[[\"salary_in_usd\", \"employee_residence\", \"company_size\", \"work_year\", \"company_location\"]]\n",
+    "\n",
+    "train_df, temp_df = train_test_split(df_input, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# Разделение остатка на контрольную и тестовую выборки\n",
+    "val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Проверка размеров выборок\n",
+    "print(\"Размер обучающей выборки:\", len(train_df))\n",
+    "print(\"Размер контрольной выборки:\", len(val_df))\n",
+    "print(\"Размер тестовой выборки:\", len(test_df))\n",
+    "\n",
+    "# Сохранение выборок в файлы\n",
+    "train_df.to_csv(\".//static//csv//train_data.csv\", index=False)\n",
+    "val_df.to_csv(\".//static//csv//val_data.csv\", index=False)\n",
+    "test_df.to_csv(\".//static//csv//test_data.csv\", index=False)"
+   ],
+   "id": "77ab4b875d5c36f2",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: 2253\n",
+      "Размер контрольной выборки: 751\n",
+      "Размер тестовой выборки: 751\n"
+     ]
+    }
+   ],
+   "execution_count": 25
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-12-07T05:18:01.515059Z",
+     "start_time": "2024-12-07T05:18:01.503530Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
+    "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
+    "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
+    "\n",
+    "def check_balance(df, name):\n",
+    "    counts = df['company_size'].value_counts()\n",
+    "    print(f\"Распределение company_size в {name}:\")\n",
+    "    print(counts)\n",
+    "    print(f\"Процент L: {counts['L'] / len(df) * 100:.2f}%\")\n",
+    "    print(f\"Процент M: {counts['M'] / len(df) * 100:.2f}%\")\n",
+    "    print(f\"Процент S: {counts['S'] / len(df) * 100:.2f}%\")\n",
+    "    print()\n",
+    "\n",
+    "check_balance(train_df, \"обучающей выборке\")\n",
+    "check_balance(val_df, \"контрольной выборке\")\n",
+    "check_balance(test_df, \"тестовой выборке\")"
+   ],
+   "id": "bdd7580094a49586",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение company_size в обучающей выборке:\n",
+      "company_size\n",
+      "M    1893\n",
+      "L     276\n",
+      "S      84\n",
+      "Name: count, dtype: int64\n",
+      "Процент L: 12.25%\n",
+      "Процент M: 84.02%\n",
+      "Процент S: 3.73%\n",
+      "\n",
+      "Распределение company_size в контрольной выборке:\n",
+      "company_size\n",
+      "M    622\n",
+      "L     97\n",
+      "S     32\n",
+      "Name: count, dtype: int64\n",
+      "Процент L: 12.92%\n",
+      "Процент M: 82.82%\n",
+      "Процент S: 4.26%\n",
+      "\n",
+      "Распределение company_size в тестовой выборке:\n",
+      "company_size\n",
+      "M    638\n",
+      "L     81\n",
+      "S     32\n",
+      "Name: count, dtype: int64\n",
+      "Процент L: 10.79%\n",
+      "Процент M: 84.95%\n",
+      "Процент S: 4.26%\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 27
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Данные не сбалансированы",
+   "id": "cb6aa8715719d743"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}