{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Лабораторная 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Информация об экономике стран"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['stock index', 'country', 'year', 'index price', 'log_indexprice',\n",
      "       'inflationrate', 'oil prices', 'exchange_rate', 'gdppercent',\n",
      "       'percapitaincome', 'unemploymentrate', 'manufacturingoutput',\n",
      "       'tradebalance', 'USTreasury'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv(\".//static//scv//Economic Data - 9 Countries (1980-2020).csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Столбцы на русском:\n",
    "'stock index' - индекс акций\n",
    "'country' - страна\n",
    "'year'- год\n",
    "'index price' - индекс стоимости\n",
    "'log_indexprice' - индексная цена журнала\n",
    "'inflationrate' - ставка инфляции\n",
    "'oil prices' - цена на нефть\n",
    "'exchange_rate' - ставка обмена\n",
    "'gdppercent' - процент ВВП\n",
    "'percapitaincome' - доход на душу населения\n",
    "'unemploymentrate' - уровень безработицы\n",
    "'manufacturingoutput' - объем производства\n",
    "'tradebalance' - торговый баланс\n",
    "'USTreasury' - UST казначейство"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 369 entries, 0 to 368\n",
      "Data columns (total 14 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   stock index          369 non-null    object \n",
      " 1   country              369 non-null    object \n",
      " 2   year                 369 non-null    float64\n",
      " 3   index price          317 non-null    float64\n",
      " 4   log_indexprice       369 non-null    float64\n",
      " 5   inflationrate        326 non-null    float64\n",
      " 6   oil prices           369 non-null    float64\n",
      " 7   exchange_rate        367 non-null    float64\n",
      " 8   gdppercent           350 non-null    float64\n",
      " 9   percapitaincome      368 non-null    float64\n",
      " 10  unemploymentrate     348 non-null    float64\n",
      " 11  manufacturingoutput  278 non-null    float64\n",
      " 12  tradebalance         365 non-null    float64\n",
      " 13  USTreasury           369 non-null    float64\n",
      "dtypes: float64(12), object(2)\n",
      "memory usage: 40.5+ KB\n"
     ]
    },
    {
     "data": {
      "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>stock index</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>index price</th>\n",
       "      <th>log_indexprice</th>\n",
       "      <th>inflationrate</th>\n",
       "      <th>oil prices</th>\n",
       "      <th>exchange_rate</th>\n",
       "      <th>gdppercent</th>\n",
       "      <th>percapitaincome</th>\n",
       "      <th>unemploymentrate</th>\n",
       "      <th>manufacturingoutput</th>\n",
       "      <th>tradebalance</th>\n",
       "      <th>USTreasury</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NASDAQ</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>168.61</td>\n",
       "      <td>2.23</td>\n",
       "      <td>0.14</td>\n",
       "      <td>21.59</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>12575.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.06</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NASDAQ</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>203.15</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.10</td>\n",
       "      <td>31.77</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>13976.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.52</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NASDAQ</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>188.98</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.06</td>\n",
       "      <td>28.52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>14434.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.97</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NASDAQ</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>285.43</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.03</td>\n",
       "      <td>26.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>15544.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-51.64</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NASDAQ</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>248.89</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.04</td>\n",
       "      <td>25.88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>17121.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-102.73</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stock index                   country    year  index price  log_indexprice  \\\n",
       "0      NASDAQ  United States of America  1980.0       168.61            2.23   \n",
       "1      NASDAQ  United States of America  1981.0       203.15            2.31   \n",
       "2      NASDAQ  United States of America  1982.0       188.98            2.28   \n",
       "3      NASDAQ  United States of America  1983.0       285.43            2.46   \n",
       "4      NASDAQ  United States of America  1984.0       248.89            2.40   \n",
       "\n",
       "   inflationrate  oil prices  exchange_rate  gdppercent  percapitaincome  \\\n",
       "0           0.14       21.59            1.0        0.09          12575.0   \n",
       "1           0.10       31.77            1.0        0.12          13976.0   \n",
       "2           0.06       28.52            1.0        0.04          14434.0   \n",
       "3           0.03       26.19            1.0        0.09          15544.0   \n",
       "4           0.04       25.88            1.0        0.11          17121.0   \n",
       "\n",
       "   unemploymentrate  manufacturingoutput  tradebalance  USTreasury  \n",
       "0              0.07                  NaN        -13.06        0.11  \n",
       "1              0.08                  NaN        -12.52        0.14  \n",
       "2              0.10                  NaN        -19.97        0.13  \n",
       "3              0.10                  NaN        -51.64        0.11  \n",
       "4              0.08                  NaN       -102.73        0.12  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объект наблюдения - экономика\n",
    "Атрибуты - содержит набор информации об обучении, такие как:\n",
    "Фондовый рынок, ВВП, страна, год, стоимость топлива, уровень инфлции,уровень безработицы и так далее"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.scatter(df['inflationrate'], df['percapitaincome'], c=df['percapitaincome'], alpha=0.6)\n",
    "\n",
    "plt.title(\"Номер 1\")\n",
    "plt.ylabel(\"Доход на душу населения\")\n",
    "plt.xlabel(\"Уровень инфляции\")\n",
    "plt.grid(visible='true')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_condition = df.groupby('gdppercent')['unemploymentrate'].mean().reset_index()\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.plot(year_condition['gdppercent'], year_condition['unemploymentrate'], marker='.')\n",
    "\n",
    "plt.title(\"Диаграмма 2\")\n",
    "plt.xlabel(\"GDP percent\")\n",
    "plt.ylabel(\"Unemployent Rate\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Присутствует связь между атрибутами, уровень инфляции влияет и зависит от многих атрибутов.\n",
    "Для примера на графике приведена связь между инфляцией и доходом на душу населения. На втором графике показана связь уровня ВВП и безработицы\n",
    "Примеры бизнес целей\n",
    "\n",
    "    1.Прогнозирование уровня инфляции на основе уровня ВВП.\n",
    "    2.Наблюдение за изменениями уровня безработицы с уровнем ВВП.\n",
    "    \n",
    "Эффект для бизнеса: влияние на инвестиции индекса акций и цен на нефть, исследование влияния фондового индекса на инвестиции, исследования инфляции и покупательской способности.\n",
    "Цели технического проекта\n",
    "\n",
    "Для первой цели:\n",
    "\n",
    "Вход: Доход на душу населения\n",
    "Целевой признак: Уровень инфляции.\n",
    "\n",
    "Для второй цели:\n",
    "\n",
    "Вход: Уровень безработицы\n",
    "Целевой признак: Уровень ВВП"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка на выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пропущенные значения по столбцам:\n",
      "stock index             0\n",
      "country                 0\n",
      "year                    0\n",
      "index price            52\n",
      "log_indexprice          0\n",
      "inflationrate          43\n",
      "oil prices              0\n",
      "exchange_rate           2\n",
      "gdppercent             19\n",
      "percapitaincome         1\n",
      "unemploymentrate       21\n",
      "manufacturingoutput    91\n",
      "tradebalance            4\n",
      "USTreasury              0\n",
      "dtype: int64\n",
      "\n",
      "Статистический обзор данных:\n",
      "              year   index price  log_indexprice  inflationrate  oil prices  \\\n",
      "count   369.000000    317.000000      369.000000     326.000000  369.000000   \n",
      "mean   2000.000000   7898.648297        3.610542       0.041748   39.743171   \n",
      "std      11.848225   7811.336862        0.482481       0.039579   25.452654   \n",
      "min    1980.000000    168.610000        2.230000      -0.040000   11.350000   \n",
      "25%    1990.000000   2407.100000        3.320000       0.020000   19.410000   \n",
      "50%    2000.000000   5160.100000        3.600000       0.030000   28.520000   \n",
      "75%    2010.000000  10279.500000        3.980000       0.057500   57.880000   \n",
      "max    2020.000000  47751.330000        4.680000       0.240000   98.560000   \n",
      "\n",
      "       exchange_rate  gdppercent  percapitaincome  unemploymentrate  \\\n",
      "count     367.000000  350.000000       368.000000        348.000000   \n",
      "mean       27.897548    0.037114     20719.964674          0.068908   \n",
      "std        49.620521    0.037850     17435.037783          0.043207   \n",
      "min         0.900000   -0.110000        27.000000          0.020000   \n",
      "25%         1.330000    0.020000      2090.250000          0.040000   \n",
      "50%         5.440000    0.030000     19969.500000          0.060000   \n",
      "75%        15.055000    0.060000     36384.000000          0.090000   \n",
      "max       249.050000    0.150000     65280.000000          0.260000   \n",
      "\n",
      "       manufacturingoutput  tradebalance  USTreasury  \n",
      "count           278.000000    365.000000  369.000000  \n",
      "mean            328.084820    -15.996384    0.059024  \n",
      "std             622.395923    154.557170    0.033086  \n",
      "min               0.590000   -770.930000    0.010000  \n",
      "25%              80.380000    -25.370000    0.030000  \n",
      "50%             188.160000     -0.140000    0.050000  \n",
      "75%             271.977500     19.080000    0.080000  \n",
      "max            3868.460000    366.140000    0.140000  \n"
     ]
    }
   ],
   "source": [
    "null_values = df.isnull().sum()\n",
    "print(\"Пропущенные значения по столбцам:\")\n",
    "print(null_values)\n",
    "\n",
    "stat_summary = df.describe()\n",
    "print(\"\\nСтатистический обзор данных:\")\n",
    "print(stat_summary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На основе данных выше можно выделить большое количество столбцов с пропущенными значениями\n",
    "Также проверим данные на выбросы и дубликаты:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Коэффициент асимметрии для столбца 'year': 0.0\n",
      "\n",
      "Коэффициент асимметрии для столбца 'index price': 1.7605604508668822\n",
      "\n",
      "Коэффициент асимметрии для столбца 'log_indexprice': -0.23716751168770417\n",
      "\n",
      "Коэффициент асимметрии для столбца 'inflationrate': 1.5616085380027898\n",
      "\n",
      "Коэффициент асимметрии для столбца 'oil prices': 0.9915046764713877\n",
      "\n",
      "Коэффициент асимметрии для столбца 'exchange_rate': 2.1575952097650455\n",
      "\n",
      "Коэффициент асимметрии для столбца 'gdppercent': -0.038272329611460466\n",
      "\n",
      "Коэффициент асимметрии для столбца 'percapitaincome': 0.3051430219264069\n",
      "\n",
      "Коэффициент асимметрии для столбца 'unemploymentrate': 1.8092896369785585\n",
      "\n",
      "Коэффициент асимметрии для столбца 'manufacturingoutput': 4.195480293406057\n",
      "\n",
      "Коэффициент асимметрии для столбца 'tradebalance': -2.266183907194849\n",
      "\n",
      "Коэффициент асимметрии для столбца 'USTreasury': 0.6687596580836408\n",
      "\n",
      "Количество дубликатов: 0\n"
     ]
    }
   ],
   "source": [
    "for column in df.select_dtypes(include=[np.number]).columns:\n",
    "    skewness = df[column].skew()\n",
    "    print(f\"\\nКоэффициент асимметрии для столбца '{column}': {skewness}\")\n",
    "\n",
    "duplicates = df.duplicated().sum()\n",
    "print(f\"\\nКоличество дубликатов: {duplicates}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На основе данных выше можно сказать, что для столбца объем производства присутствует выброс.\n",
    "Удаляем все найденные пустые значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "В наборе данных 'Economic' было удалено 150 строк с пустыми значениями.\n"
     ]
    }
   ],
   "source": [
    "def drop_missing_values(dataframe, name):\n",
    "    before_shape = dataframe.shape \n",
    "    cleaned_dataframe = dataframe.dropna()  \n",
    "    after_shape = cleaned_dataframe.shape \n",
    "    print(f\"В наборе данных '{name}' было удалено {before_shape[0] - after_shape[0]} строк с пустыми значениями.\")\n",
    "    return cleaned_dataframe\n",
    "\n",
    "cleaned_df = drop_missing_values(df, \"Economic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Очистка данных от шумов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы в датасете:\n",
      "    stock index  country    year  index price  log_indexprice  inflationrate  \\\n",
      "229      SZCOMP    China  2004.0      1467.57            3.17           0.04   \n",
      "230      SZCOMP    China  2005.0      1144.54            3.06           0.02   \n",
      "231      SZCOMP    China  2006.0      1687.14            3.23           0.02   \n",
      "232      SZCOMP    China  2007.0      4329.44            3.64           0.05   \n",
      "233      SZCOMP    China  2008.0      2912.90            3.46           0.06   \n",
      "234      SZCOMP    China  2009.0      2737.01            3.44          -0.01   \n",
      "235      SZCOMP    China  2010.0      2795.88            3.45           0.03   \n",
      "236      SZCOMP    China  2011.0      2639.19            3.42           0.06   \n",
      "237      SZCOMP    China  2012.0      2211.11            3.34           0.03   \n",
      "238      SZCOMP    China  2013.0      2182.52            3.34           0.03   \n",
      "239      SZCOMP    China  2014.0      2279.75            3.36           0.02   \n",
      "240      SZCOMP    China  2015.0      3657.40            3.56           0.01   \n",
      "241      SZCOMP    China  2016.0      2978.14            3.47           0.02   \n",
      "242      SZCOMP    China  2017.0      3257.35            3.51           0.02   \n",
      "243      SZCOMP    China  2018.0      2920.18            3.47           0.02   \n",
      "244      SZCOMP    China  2019.0      2928.94            3.47           0.03   \n",
      "245      SZCOMP    China  2020.0      3109.78            3.49           0.02   \n",
      "271      DAX 30  Germany  2005.0      5408.25            3.73           0.02   \n",
      "272      DAX 30  Germany  2006.0      6596.91            3.82           0.02   \n",
      "273      DAX 30  Germany  2007.0      8067.31            3.91           0.02   \n",
      "274      DAX 30  Germany  2008.0      4810.20            3.68           0.03   \n",
      "276      DAX 30  Germany  2010.0      6914.19            3.84           0.01   \n",
      "277      DAX 30  Germany  2011.0      5898.35            3.77           0.02   \n",
      "280      DAX 30  Germany  2014.0      9805.55            3.99           0.01   \n",
      "281      DAX 30  Germany  2015.0     10743.01            4.03           0.01   \n",
      "283      DAX 30  Germany  2017.0     12917.64            4.11           0.02   \n",
      "284      DAX 30  Germany  2018.0     10558.96            4.02           0.02   \n",
      "285      DAX 30  Germany  2019.0     13249.01            4.12           0.01   \n",
      "286      DAX 30  Germany  2020.0     13718.78            4.14           0.01   \n",
      "\n",
      "     oil prices  exchange_rate  gdppercent  percapitaincome  unemploymentrate  \\\n",
      "229       43.15           8.28        0.10           1509.0              0.04   \n",
      "230       59.41           8.19        0.11           1753.0              0.04   \n",
      "231       61.96           7.97        0.13           2099.0              0.04   \n",
      "232       91.69           7.61        0.14           2694.0              0.04   \n",
      "233       41.12           6.95        0.10           3468.0              0.04   \n",
      "234       74.47           6.83        0.09           3832.0              0.04   \n",
      "235       89.15           6.77        0.11           4550.0              0.04   \n",
      "236       98.56           6.46        0.10           5618.0              0.04   \n",
      "237       87.86           6.31        0.08           6317.0              0.04   \n",
      "238       97.63           6.15        0.08           7051.0              0.05   \n",
      "239       59.29           6.16        0.07           7679.0              0.05   \n",
      "240       37.19           6.28        0.07           8067.0              0.05   \n",
      "241       51.97           6.64        0.07           8148.0              0.05   \n",
      "242       57.88           6.76        0.07           8879.0              0.04   \n",
      "243       49.52           6.61        0.07           9977.0              0.04   \n",
      "244       59.88           6.91        0.06          10217.0              0.05   \n",
      "245       47.02           6.90        0.02          10500.0              0.05   \n",
      "271       59.41           1.24        0.01          34520.0              0.12   \n",
      "272       61.96           1.26        0.04          36354.0              0.11   \n",
      "273       91.69           1.37        0.03          41640.0              0.09   \n",
      "274       41.12           1.47        0.01          45613.0              0.08   \n",
      "276       89.15           1.33        0.04          41572.0              0.08   \n",
      "277       98.56           1.39        0.04          46706.0              0.07   \n",
      "280       59.29           1.33        0.02          48024.0              0.07   \n",
      "281       37.19           1.11        0.01          41103.0              0.06   \n",
      "283       57.88           1.13        0.03          44553.0              0.06   \n",
      "284       49.52           1.18        0.01          47811.0              0.05   \n",
      "285       59.88           1.12        0.01          46468.0              0.05   \n",
      "286       47.02           1.14       -0.05          45724.0              0.06   \n",
      "\n",
      "     manufacturingoutput  tradebalance  USTreasury  \n",
      "229               625.22         51.17        0.04  \n",
      "230               733.66        124.63        0.04  \n",
      "231               893.13        208.92        0.05  \n",
      "232              1149.72        308.04        0.05  \n",
      "233              1475.66        348.83        0.04  \n",
      "234              1611.95        220.13        0.03  \n",
      "235              1924.32        222.40        0.03  \n",
      "236              2421.37        180.89        0.03  \n",
      "237              2690.09        231.87        0.02  \n",
      "238              2935.34        234.87        0.02  \n",
      "239              3184.24        221.55        0.03  \n",
      "240              3202.50        358.84        0.02  \n",
      "241              3153.12        255.48        0.02  \n",
      "242              3460.33        215.70        0.02  \n",
      "243              3868.46        106.71        0.03  \n",
      "244              3823.41        164.99        0.02  \n",
      "245              3853.81        366.14        0.01  \n",
      "271               571.36        148.05        0.04  \n",
      "272               618.70        162.20        0.05  \n",
      "273               714.38        231.95        0.05  \n",
      "274               750.91        227.47        0.04  \n",
      "276               669.57        178.90        0.03  \n",
      "277               758.60        184.02        0.03  \n",
      "280               786.55        257.40        0.03  \n",
      "281               683.20        255.02        0.02  \n",
      "283               752.02        257.66        0.02  \n",
      "284               795.96        243.72        0.03  \n",
      "285               737.94        223.82        0.02  \n",
      "286               678.29        221.53        0.01  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(cleaned_df['manufacturingoutput'], cleaned_df['gdppercent'])\n",
    "plt.xlabel('Объем производства')\n",
    "plt.ylabel('ВВП')\n",
    "plt.title('Диаграмма рассеивания перед чисткой')\n",
    "plt.show()\n",
    "\n",
    "Q1 = cleaned_df[\"manufacturingoutput\"].quantile(0.25)\n",
    "Q3 = cleaned_df[\"manufacturingoutput\"].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "threshold = 1.5 * IQR\n",
    "lower_bound = Q1 - threshold\n",
    "upper_bound = Q3 + threshold\n",
    "\n",
    "outliers = (cleaned_df[\"manufacturingoutput\"] < lower_bound) | (cleaned_df[\"manufacturingoutput\"] > upper_bound)\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы в датасете:\")\n",
    "print(cleaned_df[outliers])\n",
    "\n",
    "# Заменяем выбросы на медианные значения\n",
    "median_score = cleaned_df[\"manufacturingoutput\"].median()\n",
    "cleaned_df.loc[outliers, \"manufacturingoutput\"] = median_score\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(cleaned_df['manufacturingoutput'], cleaned_df['gdppercent'])\n",
    "plt.xlabel('Объем производства')\n",
    "plt.ylabel('ВВП')\n",
    "plt.title('Диаграмма рассеивания после чистки')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 131\n",
      "Размер контрольной выборки: 44\n",
      "Размер тестовой выборки: 44\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2, random_state=42)\n",
    "\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим недостаток баланса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение ВВП в обучающей выборке:\n",
      "gdppercent\n",
      " 0.02    30\n",
      " 0.04    25\n",
      " 0.03    21\n",
      " 0.01    13\n",
      " 0.07     8\n",
      " 0.08     8\n",
      " 0.05     7\n",
      "-0.01     5\n",
      " 0.11     2\n",
      " 0.09     2\n",
      "-0.02     2\n",
      " 0.10     2\n",
      "-0.03     1\n",
      " 0.14     1\n",
      "-0.10     1\n",
      " 0.06     1\n",
      "-0.05     1\n",
      "-0.04     1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение ВВП в контрольной выборке:\n",
      "gdppercent\n",
      " 0.02    9\n",
      " 0.03    7\n",
      " 0.01    6\n",
      " 0.07    4\n",
      " 0.04    4\n",
      " 0.05    4\n",
      " 0.08    3\n",
      " 0.06    3\n",
      "-0.01    2\n",
      " 0.10    1\n",
      "-0.08    1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение ВВП в тестовой выборке:\n",
      "gdppercent\n",
      " 0.02    12\n",
      " 0.03     8\n",
      " 0.01     7\n",
      " 0.05     5\n",
      " 0.04     3\n",
      " 0.08     3\n",
      "-0.01     2\n",
      "-0.05     1\n",
      " 0.06     1\n",
      " 0.13     1\n",
      " 0.07     1\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def check_balance(df, name):\n",
    "    counts = df['gdppercent'].value_counts()\n",
    "    print(f\"Распределение ВВП в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "также используем oversampling и undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оверсэмплинг:\n",
      "Распределение ВВП в обучающей выборке:\n",
      "gdppercent\n",
      " 0.08    37\n",
      " 0.07    33\n",
      "-0.04    31\n",
      " 0.02    30\n",
      "-0.10    27\n",
      " 0.04    25\n",
      "-0.05    25\n",
      "-0.03    21\n",
      " 0.03    21\n",
      " 0.01    13\n",
      " 0.11    11\n",
      " 0.09    11\n",
      " 0.10     7\n",
      " 0.05     7\n",
      "-0.01     5\n",
      " 0.14     5\n",
      "-0.02     2\n",
      " 0.06     1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение ВВП в контрольной выборке:\n",
      "gdppercent\n",
      "-0.08    24\n",
      " 0.02     9\n",
      " 0.07     7\n",
      " 0.03     7\n",
      " 0.01     6\n",
      " 0.05     5\n",
      " 0.04     4\n",
      " 0.06     4\n",
      " 0.08     3\n",
      "-0.01     2\n",
      " 0.10     1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение ВВП в тестовой выборке:\n",
      "gdppercent\n",
      " 0.08    26\n",
      "-0.01    22\n",
      "-0.05    14\n",
      " 0.02    12\n",
      " 0.03     8\n",
      " 0.01     7\n",
      " 0.05     5\n",
      " 0.07     5\n",
      " 0.13     5\n",
      " 0.04     3\n",
      " 0.06     1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Андерсэмплинг:\n",
      "Распределение ВВП в обучающей выборке:\n",
      "gdppercent\n",
      " 0.01    2\n",
      " 0.08    2\n",
      "-0.10    1\n",
      "-0.03    1\n",
      "-0.05    1\n",
      "-0.04    1\n",
      " 0.03    1\n",
      " 0.02    1\n",
      " 0.14    1\n",
      " 0.07    1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение ВВП в контрольной выборке:\n",
      "gdppercent\n",
      "-0.08    1\n",
      " 0.02    1\n",
      " 0.08    1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Распределение ВВП в тестовой выборке:\n",
      "gdppercent\n",
      "-0.01    2\n",
      " 0.08    2\n",
      "-0.05    1\n",
      " 0.02    1\n",
      " 0.04    1\n",
      " 0.05    1\n",
      " 0.07    1\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "def binning(target, bins):\n",
    "    return pd.cut(target, bins=bins, labels=False)\n",
    "\n",
    "train_df['gdppercent_binned'] = binning(train_df['gdppercent'], bins=3)\n",
    "val_df['gdppercent_binned'] = binning(val_df['gdppercent'], bins=3)\n",
    "test_df['gdppercent_binned'] = binning(test_df['gdppercent'], bins=3)\n",
    "\n",
    "def oversample(df, target_column):\n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    x_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    resampled_df = pd.concat([x_resampled, y_resampled], axis=1) \n",
    "    return resampled_df\n",
    "\n",
    "def undersample(df, target_column):\n",
    "    X = df.drop(target_column, axis=1)\n",
    "    y = df[target_column]\n",
    "    \n",
    "    undersampler = RandomUnderSampler(random_state=42)\n",
    "    x_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    resampled_df = pd.concat([x_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_oversampled = oversample(train_df, 'gdppercent_binned')\n",
    "val_df_oversampled = oversample(val_df, 'gdppercent_binned')\n",
    "test_df_oversampled = oversample(test_df, 'gdppercent_binned')\n",
    "\n",
    "train_df_undersampled = undersample(train_df, 'gdppercent_binned')\n",
    "val_df_undersampled = undersample(val_df, 'gdppercent_binned')\n",
    "test_df_undersampled = undersample(test_df, 'gdppercent_binned')\n",
    "\n",
    "print(\"Оверсэмплинг:\")\n",
    "check_balance(train_df_oversampled, \"обучающей выборке\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке\")\n",
    "\n",
    "print(\"Андерсэмплинг:\")\n",
    "check_balance(train_df_undersampled, \"обучающей выборке\")\n",
    "check_balance(val_df_undersampled, \"контрольной выборке\")\n",
    "check_balance(test_df_undersampled, \"тестовой выборке\")"
   ]
  }
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