{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Лабораторная 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Информация об экономике стран"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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": 133,
   "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": 133,
     "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": 134,
   "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": 135,
   "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": 136,
   "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": 137,
   "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": 138,
   "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": 139,
   "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": {
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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": 140,
   "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": 143,
   "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": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оверсэмплинг:\n",
      "Распределение Дохода на душу населения 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 Дохода на душу населения 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 Дохода на душу населения 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 Дохода на душу населения 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 Дохода на душу населения 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 Дохода на душу населения 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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