From 3ec9aaf5ffc59f37caa6be6746972c085c85f97b Mon Sep 17 00:00:00 2001
From: Factorino73 <masenkin73@xmail.ru>
Date: Sat, 19 Oct 2024 00:00:49 +0400
Subject: [PATCH] lab_2: 3rd dataset is done & refactoring

---
 lab_2/lab2.ipynb             | 1407 ++++++++++++++++++++++++++++++----
 static/csv/economic_data.csv |  370 +++++++++
 2 files changed, 1647 insertions(+), 130 deletions(-)
 create mode 100644 static/csv/economic_data.csv

diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb
index 916b187..e8f7ada 100644
--- a/lab_2/lab2.ipynb
+++ b/lab_2/lab2.ipynb
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -265,7 +265,7 @@
        "absolute_magnitude  2.134000e+01  2.370000e+01  2.570000e+01  3.320000e+01  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,13 +291,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Присутствуют ли пустые значения признаков в колонке:\n",
       "id                    False\n",
       "name                  False\n",
       "est_diameter_min      False\n",
@@ -310,6 +311,7 @@
       "hazardous             False\n",
       "dtype: bool \n",
       "\n",
+      "Количество пустых значений признаков в колонке:\n",
       "id                    0\n",
       "name                  0\n",
       "est_diameter_min      0\n",
@@ -320,7 +322,10 @@
       "sentry_object         0\n",
       "absolute_magnitude    0\n",
       "hazardous             0\n",
-      "dtype: int64\n"
+      "dtype: int64 \n",
+      "\n",
+      "Процент пустых значений признаков в колонке:\n",
+      "\n"
      ]
     }
    ],
@@ -328,16 +333,20 @@
     "# Проверка пропущенных данных\n",
     "def check_null_columns(dataframe: DataFrame) -> None:\n",
     "    # Присутствуют ли пустые значения признаков\n",
+    "    print('Присутствуют ли пустые значения признаков в колонке:')\n",
     "    print(dataframe.isnull().any(), '\\n')\n",
     "\n",
     "    # Количество пустых значений признаков\n",
-    "    print(dataframe.isnull().sum())\n",
+    "    print('Количество пустых значений признаков в колонке:')\n",
+    "    print(dataframe.isnull().sum(), '\\n')\n",
     "\n",
     "    # Процент пустых значений признаков\n",
-    "    for i in dataframe.columns:\n",
-    "        null_rate: float = dataframe[i].isnull().sum() / len(dataframe) * 100\n",
+    "    print('Процент пустых значений признаков в колонке:')\n",
+    "    for column in dataframe.columns:\n",
+    "        null_rate: float = dataframe[column].isnull().sum() / len(dataframe) * 100\n",
     "        if null_rate > 0:\n",
-    "            print(f\"{i} процент пустых значений: %{null_rate:.2f}\")\n",
+    "            print(f\"{column} процент пустых значений: {null_rate:.2f}%\")\n",
+    "    print()\n",
     "            \n",
     "\n",
     "# Проверка пропущенных данных\n",
@@ -359,13 +368,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Проверка наличия выбросов в колонках:\n",
       "Колонка est_diameter_min:\n",
       "\tЕсть выбросы: Да\n",
       "\tКоличество выбросов: 8306\n",
@@ -420,15 +430,6 @@
     }
    ],
    "source": [
-    "# Числовые столбцы DataFrame\n",
-    "numeric_columns: list[str] = [\n",
-    "    'est_diameter_min',\n",
-    "    'est_diameter_max', \n",
-    "    'relative_velocity', \n",
-    "    'miss_distance', \n",
-    "    'absolute_magnitude'\n",
-    "]\n",
-    "\n",
     "# Проверка выбросов в DataFrame\n",
     "def check_outliers(dataframe: DataFrame, columns: list[str]) -> None:\n",
     "    for column in columns:\n",
@@ -471,20 +472,31 @@
     "    plt.show()\n",
     "\n",
     "\n",
-    "# Проверка выбросов\n",
+    "# Числовые столбцы DataFrame\n",
+    "numeric_columns: list[str] = [\n",
+    "    'est_diameter_min',\n",
+    "    'est_diameter_max', \n",
+    "    'relative_velocity', \n",
+    "    'miss_distance', \n",
+    "    'absolute_magnitude'\n",
+    "]\n",
+    "\n",
+    "# Проверка наличия выбросов в колонках\n",
+    "print('Проверка наличия выбросов в колонках:')\n",
     "check_outliers(df, numeric_columns)\n",
     "visualize_outliers(df, numeric_columns)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Проверка наличия выбросов в колонках после их устранения:\n",
       "Колонка est_diameter_min:\n",
       "\tЕсть выбросы: Нет\n",
       "\tКоличество выбросов: 0\n",
@@ -564,7 +576,8 @@
     "# Устраняем выборсы\n",
     "df: DataFrame = remove_outliers(df, numeric_columns)\n",
     "\n",
-    "# Проверка выбросов\n",
+    "# Проверка наличия выбросов в колонках\n",
+    "print('Проверка наличия выбросов в колонках после их устранения:')\n",
     "check_outliers(df, numeric_columns)\n",
     "visualize_outliers(df, numeric_columns)"
    ]
@@ -595,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -676,20 +689,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Распределение количества наблюдений по меткам (классам):\n",
       "hazardous\n",
       "False    81996\n",
       "True      8840\n",
       "Name: count, dtype: int64 \n",
       "\n",
+      "Проверка сбалансированности выборок:\n",
       "Обучающая выборка:  (54501, 10)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
       "False    49197\n",
       "True      5304\n",
@@ -698,7 +713,7 @@
       "Процент объектов класса \"True\": 9.73%\n",
       "\n",
       "Контрольная выборка:  (18167, 10)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
       "False    16399\n",
       "True      1768\n",
@@ -707,7 +722,7 @@
       "Процент объектов класса \"True\": 9.73%\n",
       "\n",
       "Тестовая выборка:  (18168, 10)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
       "False    16400\n",
       "True      1768\n",
@@ -715,6 +730,7 @@
       "Процент объектов класса \"False\": 90.27%\n",
       "Процент объектов класса \"True\": 9.73%\n",
       "\n",
+      "Проверка необходимости аугментации выборок:\n",
       "Для обучающей выборки аугментация данных требуется\n",
       "Для контрольной выборки аугментация данных требуется\n",
       "Для тестовой выборки аугментация данных требуется\n"
@@ -732,31 +748,11 @@
     }
    ],
    "source": [
-    "# Вывод распределения количества наблюдений по меткам (классам)\n",
-    "print(df.hazardous.value_counts(), '\\n')\n",
-    "\n",
-    "data: DataFrame = df[[\n",
-    "   'est_diameter_min', \n",
-    "   'est_diameter_max', \n",
-    "   'relative_velocity', \n",
-    "   'miss_distance', \n",
-    "   'absolute_magnitude', \n",
-    "   'hazardous'\n",
-    "]].copy()\n",
-    "\n",
-    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
-    "   df, \n",
-    "   stratify_colname=\"hazardous\", \n",
-    "   frac_train=0.60, \n",
-    "   frac_val=0.20, \n",
-    "   frac_test=0.20\n",
-    ")\n",
-    "\n",
     "# Оценка сбалансированности\n",
     "def check_balance(dataframe: DataFrame, dataframe_name: str, column: str) -> None:\n",
     "    counts: Series[int] = dataframe[column].value_counts()\n",
     "    print(dataframe_name + \": \", dataframe.shape)\n",
-    "    print(f\"Распределение выборки данных по классам \\\"{column}\\\":\\n\", counts)\n",
+    "    print(f\"Распределение выборки данных по классам в колонке \\\"{column}\\\":\\n\", counts)\n",
     "    total_count: int = len(dataframe)\n",
     "    for value in counts.index:\n",
     "        percentage: float = counts[value] / total_count * 100\n",
@@ -798,12 +794,26 @@
     "   plt.show()\n",
     " \n",
     "\n",
-    "# Проверка сбалансированности\n",
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df.hazardous.value_counts(), '\\n')\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   df, \n",
+    "   stratify_colname=\"hazardous\", \n",
+    "   frac_train=0.60, \n",
+    "   frac_val=0.20, \n",
+    "   frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок:')\n",
     "check_balance(df_train, 'Обучающая выборка', 'hazardous')\n",
     "check_balance(df_val, 'Контрольная выборка', 'hazardous')\n",
     "check_balance(df_test, 'Тестовая выборка', 'hazardous')\n",
     "\n",
-    "# Проверка необходимости аугментации\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок:')\n",
     "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'hazardous', True, False) else ''}требуется\")\n",
     "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'hazardous', True, False) else ''}требуется\")\n",
     "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'hazardous', True, False) else ''}требуется\")\n",
@@ -827,41 +837,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "После применения метода oversampling:\n",
-      "Обучающая выборка:  (98782, 21784)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Проверка сбалансированности выборок после применения метода oversampling:\n",
+      "Обучающая выборка:  (99094, 21839)\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
-      "True     49585\n",
+      "True     49897\n",
       "False    49197\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"True\": 50.20%\n",
-      "Процент объектов класса \"False\": 49.80%\n",
+      "Процент объектов класса \"True\": 50.35%\n",
+      "Процент объектов класса \"False\": 49.65%\n",
       "\n",
-      "Контрольная выборка:  (33168, 11762)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Контрольная выборка:  (33065, 11737)\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
-      "True     16769\n",
+      "True     16666\n",
       "False    16399\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"True\": 50.56%\n",
-      "Процент объектов класса \"False\": 49.44%\n",
+      "Процент объектов класса \"True\": 50.40%\n",
+      "Процент объектов класса \"False\": 49.60%\n",
       "\n",
-      "Тестовая выборка:  (32695, 11820)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Тестовая выборка:  (33123, 11819)\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
+      "True     16723\n",
       "False    16400\n",
-      "True     16295\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"False\": 50.16%\n",
-      "Процент объектов класса \"True\": 49.84%\n",
+      "Процент объектов класса \"True\": 50.49%\n",
+      "Процент объектов класса \"False\": 49.51%\n",
       "\n",
+      "Проверка необходимости аугментации выборок после применения метода oversampling:\n",
       "Для обучающей выборки аугментация данных не требуется\n",
       "Для контрольной выборки аугментация данных не требуется\n",
       "Для тестовой выборки аугментация данных не требуется\n"
@@ -869,7 +880,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -896,13 +907,14 @@
     "df_val_oversampled: DataFrame = oversample(df_val, 'hazardous')\n",
     "df_test_oversampled: DataFrame = oversample(df_test, 'hazardous')\n",
     "\n",
-    "# Проверка сбалансированности\n",
-    "print('После применения метода oversampling:')\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
     "check_balance(df_train_oversampled, 'Обучающая выборка', 'hazardous')\n",
     "check_balance(df_val_oversampled, 'Контрольная выборка', 'hazardous')\n",
     "check_balance(df_test_oversampled, 'Тестовая выборка', 'hazardous')\n",
     "\n",
-    "# Проверка необходимости аугментации\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
     "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
     "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
     "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
@@ -913,16 +925,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "После применения метода undersampling:\n",
-      "Обучающая выборка:  (10608, 21784)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Проверка сбалансированности выборок после применения метода undersampling:\n",
+      "Обучающая выборка:  (10608, 21839)\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
       "False    5304\n",
       "True     5304\n",
@@ -930,8 +942,8 @@
       "Процент объектов класса \"False\": 50.00%\n",
       "Процент объектов класса \"True\": 50.00%\n",
       "\n",
-      "Контрольная выборка:  (3536, 11762)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Контрольная выборка:  (3536, 11737)\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
       "False    1768\n",
       "True     1768\n",
@@ -939,8 +951,8 @@
       "Процент объектов класса \"False\": 50.00%\n",
       "Процент объектов класса \"True\": 50.00%\n",
       "\n",
-      "Тестовая выборка:  (3536, 11820)\n",
-      "Распределение выборки данных по классам \"hazardous\":\n",
+      "Тестовая выборка:  (3536, 11819)\n",
+      "Распределение выборки данных по классам в колонке \"hazardous\":\n",
       " hazardous\n",
       "False    1768\n",
       "True     1768\n",
@@ -948,6 +960,7 @@
       "Процент объектов класса \"False\": 50.00%\n",
       "Процент объектов класса \"True\": 50.00%\n",
       "\n",
+      "Проверка необходимости аугментации выборок после применения метода undersampling:\n",
       "Для обучающей выборки аугментация данных не требуется\n",
       "Для контрольной выборки аугментация данных не требуется\n",
       "Для тестовой выборки аугментация данных не требуется\n"
@@ -982,13 +995,14 @@
     "df_val_undersampled: DataFrame = undersample(df_val, 'hazardous')\n",
     "df_test_undersampled: DataFrame = undersample(df_test, 'hazardous')\n",
     "\n",
-    "# Проверка сбалансированности\n",
-    "print('После применения метода undersampling:')\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок после применения метода undersampling:')\n",
     "check_balance(df_train_undersampled, 'Обучающая выборка', 'hazardous')\n",
     "check_balance(df_val_undersampled, 'Контрольная выборка', 'hazardous')\n",
     "check_balance(df_test_undersampled, 'Тестовая выборка', 'hazardous')\n",
     "\n",
-    "# Проверка необходимости аугментации\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок после применения метода undersampling:')\n",
     "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
     "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
     "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
@@ -1052,7 +1066,7 @@
     "Набор данных охватывает специалистов по данным с разным опытом, работающих в различных странах, что позволяет провести сравнительный анализ и выявить региональные и глобальные тренды.\n",
     "\n",
     "**Соответствие реальным данным:**\n",
-    "ДЗаработные платы специалистов по данным, приведенные в датасете, отражают реальную ситуацию на рынке труда в 2023 году, предоставляя точные данные для анализа текущих рыночных условий.\n",
+    "Заработные платы специалистов по данным, приведенные в датасете, отражают реальную ситуацию на рынке труда в 2023 году, предоставляя точные данные для анализа текущих рыночных условий.\n",
     "\n",
     "**Согласованность меток:**\n",
     "Все категории, такие как уровни опыта или типы занятости, имеют четко определённые метки, что упрощает анализ и моделирование.\n",
@@ -1063,9 +1077,9 @@
     "1. **Оптимизация структуры оплаты труда:**\n",
     "Компании могут использовать данный анализ для создания конкурентных предложений по оплате труда, основываясь на опыте, географии и других значимых факторах.\n",
     "2. **Планирование найма и удержание специалистов:**\n",
-    "Помогает работодателям понять, какие факторы могут привлечь или удержать специалистов по данным, и оптимизировать HR-процессы для сокращения текучести кадров.**\n",
+    "Помогает работодателям понять, какие факторы могут привлечь или удержать специалистов по данным, и оптимизировать HR-процессы для сокращения текучести кадров.\n",
     "3. **Анализ глобальных и региональных зарплатных трендов:**\n",
-    "Позволяет компаниям проводить сравнительный анализ зарплат по регионам, уровням опыта и типам занятости, помогая в принятии решений о расширении бизнеса в разные страны.**\n",
+    "Позволяет компаниям проводить сравнительный анализ зарплат по регионам, уровням опыта и типам занятости, помогая в принятии решений о расширении бизнеса в разные страны.\n",
     "\n",
     "**Эффект для бизнеса:**\n",
     "Компании, использующие данную информацию, могут предлагать конкурентоспособные зарплаты, улучшить процессы найма и удержания специалистов, а также сократить издержки, связанные с высокими зарплатными ожиданиями. Это также помогает улучшить планирование бюджета на персонал.\n",
@@ -1098,7 +1112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1114,7 +1128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1235,7 +1249,7 @@
        "remote_ratio        0.0     100.0       100.0  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1259,13 +1273,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Присутствуют ли пустые значения признаков в колонке:\n",
       "work_year             False\n",
       "experience_level      False\n",
       "employment_type       False\n",
@@ -1279,6 +1294,7 @@
       "company_size          False\n",
       "dtype: bool \n",
       "\n",
+      "Количество пустых значений признаков в колонке:\n",
       "work_year             0\n",
       "experience_level      0\n",
       "employment_type       0\n",
@@ -1290,7 +1306,10 @@
       "remote_ratio          0\n",
       "company_location      0\n",
       "company_size          0\n",
-      "dtype: int64\n"
+      "dtype: int64 \n",
+      "\n",
+      "Процент пустых значений признаков в колонке:\n",
+      "\n"
      ]
     }
    ],
@@ -1310,13 +1329,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Проверка наличия выбросов в колонках:\n",
       "Колонка work_year:\n",
       "\tЕсть выбросы: Да\n",
       "\tКоличество выбросов: 76\n",
@@ -1371,20 +1391,22 @@
     "    'remote_ratio'\n",
     "]\n",
     "\n",
-    "# Проверка выбросов\n",
+    "# Проверка наличия выбросов в колонках\n",
+    "print('Проверка наличия выбросов в колонках:')\n",
     "check_outliers(df, numeric_columns)\n",
     "visualize_outliers(df, numeric_columns)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Проверка наличия выбросов в колонках после их устранения:\n",
       "Колонка work_year:\n",
       "\tЕсть выбросы: Нет\n",
       "\tКоличество выбросов: 0\n",
@@ -1434,7 +1456,8 @@
     "# Устраняем выборсы\n",
     "df: DataFrame = remove_outliers(df, numeric_columns)\n",
     "\n",
-    "# Проверка выбросов\n",
+    "# Проверка наличия выбросов в колонках\n",
+    "print('Проверка наличия выбросов в колонках после их устранения:')\n",
     "check_outliers(df, numeric_columns)\n",
     "visualize_outliers(df, numeric_columns)"
    ]
@@ -1449,7 +1472,7 @@
     "\n",
     "Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
     "\n",
-    "Чтобы решить эту проблему введём категории для значения зарплаты. Вместо того, чтобы использовать точные значения зарплаты для стратификации, мы создадим категории зарплат, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении зарплаты. Это позволит создать более крупные классы, что устранит проблему с редкими значениями:\n",
+    "Чтобы решить эту проблему введём категории для значения зарплаты. Вместо того, чтобы использовать точные значения зарплаты для стратификации, мы создадим категории зарплат, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении зарплаты. Это позволит создать более крупные классы, что устранит проблему с редкими значениями\n",
     "\n",
     "Категории для разбиения зарплат:\n",
     "- Низкая зарплата: зарплаты ниже первого квартиля (25%) — это значения меньше 95000.\n",
@@ -1467,13 +1490,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Распределение количества наблюдений по меткам (классам):\n",
       "salary_in_usd\n",
       "100000.0    99\n",
       "150000.0    98\n",
@@ -1488,6 +1512,7 @@
       "40038.0      1\n",
       "Name: count, Length: 1002, dtype: int64 \n",
       "\n",
+      "Статистическое описание целевого признака:\n",
       "count      3755.000000\n",
       "mean     136959.779760\n",
       "std       61098.121137\n",
@@ -1498,14 +1523,16 @@
       "max      295000.000000\n",
       "Name: salary_in_usd, dtype: float64 \n",
       "\n",
+      "Распределение количества наблюдений по меткам (классам):\n",
       "salary_category\n",
       "medium    1867\n",
       "low        956\n",
       "high       932\n",
       "Name: count, dtype: int64 \n",
       "\n",
+      "Проверка сбалансированности выборок:\n",
       "Обучающая выборка:  (2253, 12)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "medium    1120\n",
       "low        574\n",
@@ -1516,7 +1543,7 @@
       "Процент объектов класса \"high\": 24.81%\n",
       "\n",
       "Контрольная выборка:  (751, 12)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "medium    373\n",
       "low       191\n",
@@ -1527,7 +1554,7 @@
       "Процент объектов класса \"high\": 24.90%\n",
       "\n",
       "Тестовая выборка:  (751, 12)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "medium    374\n",
       "low       191\n",
@@ -1537,6 +1564,7 @@
       "Процент объектов класса \"low\": 25.43%\n",
       "Процент объектов класса \"high\": 24.77%\n",
       "\n",
+      "Проверка необходимости аугментации выборок:\n",
       "Для обучающей выборки аугментация данных требуется\n",
       "Для контрольной выборки аугментация данных требуется\n",
       "Для тестовой выборки аугментация данных требуется\n"
@@ -1555,19 +1583,25 @@
    ],
    "source": [
     "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
     "print(df.salary_in_usd.value_counts(), '\\n')\n",
     "\n",
     "# Статистическое описание целевого признака\n",
+    "print('Статистическое описание целевого признака:')\n",
     "print(df['salary_in_usd'].describe().transpose(), '\\n')\n",
     "\n",
     "# Определим границы для каждой категории зарплаты\n",
-    "bins: list[int] = [0, 95000, 175000, 450000]\n",
+    "bins: list[float] = [df['salary_in_usd'].min() - 1, \n",
+    "                     df['salary_in_usd'].quantile(0.25), \n",
+    "                     df['salary_in_usd'].quantile(0.75), \n",
+    "                     df['salary_in_usd'].max() + 1]\n",
     "labels: list[str] = ['low', 'medium', 'high']\n",
     "\n",
-    "# Создаем новую колонку с категориями зарплат\n",
+    "# Создаем новую колонку с категориями зарплат#\n",
     "df['salary_category'] = pd.cut(df['salary_in_usd'], bins=bins, labels=labels)\n",
     "\n",
     "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
     "print(df['salary_category'].value_counts(), '\\n')\n",
     "\n",
     "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
@@ -1578,12 +1612,14 @@
     "   frac_test=0.20\n",
     ")\n",
     "\n",
-    "# Проверка сбалансированности\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок:')\n",
     "check_balance(df_train, 'Обучающая выборка', 'salary_category')\n",
     "check_balance(df_val, 'Контрольная выборка', 'salary_category')\n",
     "check_balance(df_test, 'Тестовая выборка', 'salary_category')\n",
     "\n",
-    "# Проверка необходимости аугментации\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок:')\n",
     "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
     "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
     "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
@@ -1601,16 +1637,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "После применения метода oversampling:\n",
-      "Обучающая выборка:  (3360, 241)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Проверка сбалансированности выборок после применения метода oversampling:\n",
+      "Обучающая выборка:  (3360, 240)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "low       1121\n",
       "medium    1120\n",
@@ -1620,8 +1656,8 @@
       "Процент объектов класса \"medium\": 33.33%\n",
       "Процент объектов класса \"high\": 33.30%\n",
       "\n",
-      "Контрольная выборка:  (1119, 157)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Контрольная выборка:  (1119, 154)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "low       373\n",
       "medium    373\n",
@@ -1631,17 +1667,18 @@
       "Процент объектов класса \"medium\": 33.33%\n",
       "Процент объектов класса \"high\": 33.33%\n",
       "\n",
-      "Тестовая выборка:  (1121, 162)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Тестовая выборка:  (1122, 159)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
+      "low       374\n",
       "medium    374\n",
       "high      374\n",
-      "low       373\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"medium\": 33.36%\n",
-      "Процент объектов класса \"high\": 33.36%\n",
-      "Процент объектов класса \"low\": 33.27%\n",
+      "Процент объектов класса \"low\": 33.33%\n",
+      "Процент объектов класса \"medium\": 33.33%\n",
+      "Процент объектов класса \"high\": 33.33%\n",
       "\n",
+      "Проверка необходимости аугментации выборок после применения метода oversampling:\n",
       "Для обучающей выборки аугментация данных не требуется\n",
       "Для контрольной выборки аугментация данных не требуется\n",
       "Для тестовой выборки аугментация данных не требуется\n"
@@ -1649,7 +1686,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -1664,13 +1701,14 @@
     "df_val_oversampled: DataFrame = oversample(df_val, 'salary_category')\n",
     "df_test_oversampled: DataFrame = oversample(df_test, 'salary_category')\n",
     "\n",
-    "# Проверка сбалансированности\n",
-    "print('После применения метода oversampling:')\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
     "check_balance(df_train_oversampled, 'Обучающая выборка', 'salary_category')\n",
     "check_balance(df_val_oversampled, 'Контрольная выборка', 'salary_category')\n",
     "check_balance(df_test_oversampled, 'Тестовая выборка', 'salary_category')\n",
     "\n",
-    "# Проверка необходимости аугментации\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
     "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
     "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
     "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
@@ -1681,16 +1719,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "После применения метода undersampling:\n",
-      "Обучающая выборка:  (1677, 241)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Проверка сбалансированности выборок после применения метода undersampling:\n",
+      "Обучающая выборка:  (1677, 240)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "low       559\n",
       "medium    559\n",
@@ -1700,8 +1738,8 @@
       "Процент объектов класса \"medium\": 33.33%\n",
       "Процент объектов класса \"high\": 33.33%\n",
       "\n",
-      "Контрольная выборка:  (561, 157)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Контрольная выборка:  (561, 154)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "low       187\n",
       "medium    187\n",
@@ -1711,8 +1749,8 @@
       "Процент объектов класса \"medium\": 33.33%\n",
       "Процент объектов класса \"high\": 33.33%\n",
       "\n",
-      "Тестовая выборка:  (558, 162)\n",
-      "Распределение выборки данных по классам \"salary_category\":\n",
+      "Тестовая выборка:  (558, 159)\n",
+      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
       " salary_category\n",
       "low       186\n",
       "medium    186\n",
@@ -1722,6 +1760,7 @@
       "Процент объектов класса \"medium\": 33.33%\n",
       "Процент объектов класса \"high\": 33.33%\n",
       "\n",
+      "Проверка необходимости аугментации выборок после применения метода undersampling:\n",
       "Для обучающей выборки аугментация данных не требуется\n",
       "Для контрольной выборки аугментация данных не требуется\n",
       "Для тестовой выборки аугментация данных не требуется\n"
@@ -1744,13 +1783,14 @@
     "df_val_undersampled: DataFrame = undersample(df_val, 'salary_category')\n",
     "df_test_undersampled: DataFrame = undersample(df_test, 'salary_category')\n",
     "\n",
-    "# Проверка сбалансированности\n",
-    "print('После применения метода undersampling:')\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок после применения метода undersampling:')\n",
     "check_balance(df_train_undersampled, 'Обучающая выборка', 'salary_category')\n",
     "check_balance(df_val_undersampled, 'Контрольная выборка', 'salary_category')\n",
     "check_balance(df_test_undersampled, 'Тестовая выборка', 'salary_category')\n",
     "\n",
-    "# Проверка необходимости аугментации\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок после применения метода undersampling:')\n",
     "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
     "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
     "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'salary_category', 'low', 'medium') else ''}требуется\")\n",
@@ -1758,6 +1798,1113 @@
     "# Визуализация сбалансированности классов\n",
     "visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'salary_category')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Датасет №3: [Экономика стран](https://www.kaggle.com/datasets/pratik453609/economic-data-9-countries-19802020).\n",
+    "\n",
+    "### Описание датасета:\n",
+    "Данный набор данных содержит информацию о ключевых макроэкономических показателях для восьми стран (Китай, Франция, Германия, Индия, Япония, Испания, Великобритания, США) и одного специального административного района (Гонконг) за период с 1980 по 2020 год. В наборе представлены данные о таких макроэкономических переменных, как инфляция, уровень безработицы, ВВП, обменные курсы (по отношению к доллару США), доход на душу населения и цены на основные фондовые индексы каждой страны. Этот датасет полезен для анализа взаимосвязей между экономическими показателями и динамикой фондовых индексов стран, что может быть использовано для экономического моделирования и прогноза.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Анализ сведений:\n",
+    "**Проблемная область:**\n",
+    "Основная задача – исследование взаимосвязей между макроэкономическими переменными и ценами на фондовые индексы, а также анализ влияния таких факторов, как инфляция, ВВП и обменные курсы на фондовые рынки и экономическую стабильность стран.\n",
+    "\n",
+    "**Актуальность:**\n",
+    "Этот датасет актуален для исследователей, аналитиков и экономистов, занимающихся изучением макроэкономических трендов, финансовых рынков и их взаимосвязей. Он предоставляет данные, необходимые для анализа экономических кризисов, изменений на фондовых рынках и долгосрочных макроэкономических прогнозов.\n",
+    "\n",
+    "**Объекты наблюдения:**\n",
+    "Страны (или регионы), данные по которым включены в датасет: Китай, Франция, Германия, Гонконг, Индия, Япония, Испания, Великобритания и США. Для каждого из них собирались данные за период с 1980 по 2020 год.\n",
+    "\n",
+    "**Атрибуты объектов:**\n",
+    "- stock index: Название основного фондового индекса страны.\n",
+    "- country: Название страны.\n",
+    "- year: Год, к которому относятся данные.\n",
+    "- index price: Средняя цена фондового индекса за год.\n",
+    "- log_indexprice: Логарифмическое значение цены индекса для учета валютных различий.\n",
+    "- inflationrate: Уровень инфляции в стране.\n",
+    "- oil prices: Цены на нефть в долларах США.\n",
+    "- exchange_rate: Обменный курс валюты страны по отношению к доллару США.\n",
+    "- gdppercent: Рост ВВП (в процентах).\n",
+    "- percapitaincome: Доход на душу населения.\n",
+    "- unemploymentrate: Уровень безработицы (в процентах).\n",
+    "- manufacturingoutput: Объем производства в промышленном секторе страны.\n",
+    "- tradebalance: Торговый баланс.\n",
+    "- USTreasury: Облигации.\n",
+    "\n",
+    "**Связь между объектами:**\n",
+    "Данные позволяют исследовать взаимосвязи между макроэкономическими факторами и ценами на фондовые индексы, а также между другими экономическими показателями. Например, можно анализировать, как инфляция и обменный курс влияют на фондовый рынок каждой страны или как колебания цен на нефть отражаются на экономике разных стран.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Качество набора данных:\n",
+    "**Информативность:**\n",
+    "Датасет включает широкий спектр макроэкономических показателей и цены фондовых индексов за 40-летний период, что делает его очень полезным для анализа долгосрочных экономических трендов и финансовых рынков.\n",
+    "\n",
+    "**Степень покрытия:**\n",
+    "Набор данных охватывает данные по ведущим экономикам мира, представляя достаточно полную картину макроэкономической динамики в разных странах и регионах за большой временной период (1980–2020 гг.).\n",
+    "\n",
+    "**Соответствие реальным данным:**\n",
+    "Все представленные макроэкономические показатели и цены фондовых индексов являются официальными экономическими данными, которые используются для анализа и прогнозирования в реальных условиях.\n",
+    "\n",
+    "**Согласованность меток:**\n",
+    "Названия признаков определены недостаточно чётко. Обычно названия признаков записываются в стиле \"snake_case\" – слова пишутся строчными буквами и разделяются знаком нижнего подчеркивания. В данном же случае некоторые названия переменных записаны в стиле \"snake_case\", у некоторых слова разделяются пробелом, у некоторых вовсе не разделяются и пишутся слитно. Также в описании датасета отсутствовала расшифровка нескольких столбцов датасета – их предназначение пришлось домысливать самому, основываясь лишь на собственной логике. Сами данные представляют собой легко интерпретируемые экономические показатели, что упрощает их анализ и использование в эконометрических моделях.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Бизес-цели:\n",
+    "1. **Оценка влияния макроэкономических факторов на фондовые рынки:**\n",
+    "Анализ взаимосвязей между инфляцией, обменными курсами, ВВП и динамикой фондовых индексов для прогнозирования изменений на фондовых рынках.\n",
+    "2. **Прогнозирование экономических кризисов:**\n",
+    "Использование данных для создания моделей, позволяющих прогнозировать экономические кризисы или спады на основе динамики ключевых макроэкономических переменных.\n",
+    "3. **Оптимизация инвестиционных решений:**\n",
+    "Помощь инвесторам и финансовым аналитикам в понимании влияния экономических факторов на фондовые рынки для принятия более обоснованных инвестиционных решений.\n",
+    "\n",
+    "**Эффект для бизнеса:**\n",
+    "Компании, использующие данные для анализа и прогнозирования, могут лучше управлять рисками, связанными с изменениями на фондовых рынках и макроэкономическими условиями. Это может привести к более точным инвестиционным стратегиям и повышению эффективности управления активами.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Технические цели:\n",
+    "1. **Построение модели прогнозирования цен на фондовые индексы:**\n",
+    "Создание модели машинного обучения для прогнозирования цен на фондовые индексы на основе макроэкономических показателей, таких как инфляция, ВВП и обменные курсы.\n",
+    "2. **Анализ корреляций между макроэкономическими переменными:**\n",
+    "Проведение анализа корреляций между такими показателями, как инфляция, доход на душу населения, цены на нефть и курс валют для выявления ключевых факторов, влияющих на фондовые рынки.\n",
+    "3. **Прогнозирование долгосрочных экономических трендов:**\n",
+    "Использование данных для построения прогнозов долгосрочных макроэкономических трендов и их влияния на экономику и финансовые рынки.\n",
+    "\n",
+    "**Входные данные:**\n",
+    "Инфляция, ВВП, обменные курсы, цены на нефть, цены фондовых индексов, доход на душу населения, уровень безработицы, объем производства в промышленном секторе страны, торговый баланс, облигации.\n",
+    "\n",
+    "**Целевой признак:**\n",
+    "Признак \"index_price\" – средняя цена фондового индекса страны.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Выгрузка данных из файла в DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df: DataFrame = pd.read_csv('..//static//csv//economic_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Краткая информация о DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>year</th>\n",
+       "      <td>369.0</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>11.848225</td>\n",
+       "      <td>1980.00</td>\n",
+       "      <td>1990.00</td>\n",
+       "      <td>2000.00</td>\n",
+       "      <td>2010.0000</td>\n",
+       "      <td>2020.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index price</th>\n",
+       "      <td>317.0</td>\n",
+       "      <td>7898.648297</td>\n",
+       "      <td>7811.336862</td>\n",
+       "      <td>168.61</td>\n",
+       "      <td>2407.10</td>\n",
+       "      <td>5160.10</td>\n",
+       "      <td>10279.5000</td>\n",
+       "      <td>47751.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>log_indexprice</th>\n",
+       "      <td>369.0</td>\n",
+       "      <td>3.610542</td>\n",
+       "      <td>0.482481</td>\n",
+       "      <td>2.23</td>\n",
+       "      <td>3.32</td>\n",
+       "      <td>3.60</td>\n",
+       "      <td>3.9800</td>\n",
+       "      <td>4.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inflationrate</th>\n",
+       "      <td>326.0</td>\n",
+       "      <td>0.041748</td>\n",
+       "      <td>0.039579</td>\n",
+       "      <td>-0.04</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.0575</td>\n",
+       "      <td>0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>oil prices</th>\n",
+       "      <td>369.0</td>\n",
+       "      <td>39.743171</td>\n",
+       "      <td>25.452654</td>\n",
+       "      <td>11.35</td>\n",
+       "      <td>19.41</td>\n",
+       "      <td>28.52</td>\n",
+       "      <td>57.8800</td>\n",
+       "      <td>98.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>exchange_rate</th>\n",
+       "      <td>367.0</td>\n",
+       "      <td>27.897548</td>\n",
+       "      <td>49.620521</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>1.33</td>\n",
+       "      <td>5.44</td>\n",
+       "      <td>15.0550</td>\n",
+       "      <td>249.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gdppercent</th>\n",
+       "      <td>350.0</td>\n",
+       "      <td>0.037114</td>\n",
+       "      <td>0.037850</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.0600</td>\n",
+       "      <td>0.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>percapitaincome</th>\n",
+       "      <td>368.0</td>\n",
+       "      <td>20719.964674</td>\n",
+       "      <td>17435.037783</td>\n",
+       "      <td>27.00</td>\n",
+       "      <td>2090.25</td>\n",
+       "      <td>19969.50</td>\n",
+       "      <td>36384.0000</td>\n",
+       "      <td>65280.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unemploymentrate</th>\n",
+       "      <td>348.0</td>\n",
+       "      <td>0.068908</td>\n",
+       "      <td>0.043207</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.0900</td>\n",
+       "      <td>0.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>manufacturingoutput</th>\n",
+       "      <td>278.0</td>\n",
+       "      <td>328.084820</td>\n",
+       "      <td>622.395923</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>80.38</td>\n",
+       "      <td>188.16</td>\n",
+       "      <td>271.9775</td>\n",
+       "      <td>3868.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tradebalance</th>\n",
+       "      <td>365.0</td>\n",
+       "      <td>-15.996384</td>\n",
+       "      <td>154.557170</td>\n",
+       "      <td>-770.93</td>\n",
+       "      <td>-25.37</td>\n",
+       "      <td>-0.14</td>\n",
+       "      <td>19.0800</td>\n",
+       "      <td>366.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>USTreasury</th>\n",
+       "      <td>369.0</td>\n",
+       "      <td>0.059024</td>\n",
+       "      <td>0.033086</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.0800</td>\n",
+       "      <td>0.14</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     count          mean           std      min      25%  \\\n",
+       "year                 369.0   2000.000000     11.848225  1980.00  1990.00   \n",
+       "index price          317.0   7898.648297   7811.336862   168.61  2407.10   \n",
+       "log_indexprice       369.0      3.610542      0.482481     2.23     3.32   \n",
+       "inflationrate        326.0      0.041748      0.039579    -0.04     0.02   \n",
+       "oil prices           369.0     39.743171     25.452654    11.35    19.41   \n",
+       "exchange_rate        367.0     27.897548     49.620521     0.90     1.33   \n",
+       "gdppercent           350.0      0.037114      0.037850    -0.11     0.02   \n",
+       "percapitaincome      368.0  20719.964674  17435.037783    27.00  2090.25   \n",
+       "unemploymentrate     348.0      0.068908      0.043207     0.02     0.04   \n",
+       "manufacturingoutput  278.0    328.084820    622.395923     0.59    80.38   \n",
+       "tradebalance         365.0    -15.996384    154.557170  -770.93   -25.37   \n",
+       "USTreasury           369.0      0.059024      0.033086     0.01     0.03   \n",
+       "\n",
+       "                          50%         75%       max  \n",
+       "year                  2000.00   2010.0000   2020.00  \n",
+       "index price           5160.10  10279.5000  47751.33  \n",
+       "log_indexprice           3.60      3.9800      4.68  \n",
+       "inflationrate            0.03      0.0575      0.24  \n",
+       "oil prices              28.52     57.8800     98.56  \n",
+       "exchange_rate            5.44     15.0550    249.05  \n",
+       "gdppercent               0.03      0.0600      0.15  \n",
+       "percapitaincome      19969.50  36384.0000  65280.00  \n",
+       "unemploymentrate         0.06      0.0900      0.26  \n",
+       "manufacturingoutput    188.16    271.9775   3868.46  \n",
+       "tradebalance            -0.14     19.0800    366.14  \n",
+       "USTreasury               0.05      0.0800      0.14  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Краткая информация о DataFrame\n",
+    "df.info()\n",
+    "\n",
+    "# Статистическое описание числовых столбцов\n",
+    "df.describe().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Проблема пропущенных данных:\n",
+    "\n",
+    "Проверка на отсутствие значений, представленная ниже, показала, что некоторые колонки DataFrame содержат пустые значения признаков.\n",
+    "\n",
+    "Решения проблемы отсутствия значений:\n",
+    "1. Удаление примеров с пустыми значениями (допустимо для набора данных с большим количеством наблюдений).\n",
+    "2. Использовать метод машинного обучения, который способен обработать пустые значения (например, деревья решений).\n",
+    "3. Использовать методы подстановки данных:\n",
+    "    - Заполнить средним значением признака (среднее по колонке).\n",
+    "    - Подставить магическое число (число за диапазоном доступных значений).\n",
+    "    - Обучить модель для предсказания пропущенного значения на основе других значений наблюдения.\n",
+    "\n",
+    "Воспользуемся методом подстановки среднего значения признака."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "До заполнения пустых значений:\n",
+      "Присутствуют ли пустые значения признаков в колонке:\n",
+      "stock index            False\n",
+      "country                False\n",
+      "year                   False\n",
+      "index price             True\n",
+      "log_indexprice         False\n",
+      "inflationrate           True\n",
+      "oil prices             False\n",
+      "exchange_rate           True\n",
+      "gdppercent              True\n",
+      "percapitaincome         True\n",
+      "unemploymentrate        True\n",
+      "manufacturingoutput     True\n",
+      "tradebalance            True\n",
+      "USTreasury             False\n",
+      "dtype: bool \n",
+      "\n",
+      "Количество пустых значений признаков в колонке:\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",
+      "Процент пустых значений признаков в колонке:\n",
+      "index price процент пустых значений: 14.09%\n",
+      "inflationrate процент пустых значений: 11.65%\n",
+      "exchange_rate процент пустых значений: 0.54%\n",
+      "gdppercent процент пустых значений: 5.15%\n",
+      "percapitaincome процент пустых значений: 0.27%\n",
+      "unemploymentrate процент пустых значений: 5.69%\n",
+      "manufacturingoutput процент пустых значений: 24.66%\n",
+      "tradebalance процент пустых значений: 1.08%\n",
+      "\n",
+      "После заполнения пустых значений:\n",
+      "Присутствуют ли пустые значения признаков в колонке:\n",
+      "stock index            False\n",
+      "country                False\n",
+      "year                   False\n",
+      "index price            False\n",
+      "log_indexprice         False\n",
+      "inflationrate          False\n",
+      "oil prices             False\n",
+      "exchange_rate          False\n",
+      "gdppercent             False\n",
+      "percapitaincome        False\n",
+      "unemploymentrate       False\n",
+      "manufacturingoutput    False\n",
+      "tradebalance           False\n",
+      "USTreasury             False\n",
+      "dtype: bool \n",
+      "\n",
+      "Количество пустых значений признаков в колонке:\n",
+      "stock index            0\n",
+      "country                0\n",
+      "year                   0\n",
+      "index price            0\n",
+      "log_indexprice         0\n",
+      "inflationrate          0\n",
+      "oil prices             0\n",
+      "exchange_rate          0\n",
+      "gdppercent             0\n",
+      "percapitaincome        0\n",
+      "unemploymentrate       0\n",
+      "manufacturingoutput    0\n",
+      "tradebalance           0\n",
+      "USTreasury             0\n",
+      "dtype: int64 \n",
+      "\n",
+      "Процент пустых значений признаков в колонке:\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Заполнить пропущенные данные средним значением\n",
+    "def fill_null_columns(dataframe: DataFrame) -> DataFrame:\n",
+    "    for column in dataframe.columns:\n",
+    "        null_rate: float = dataframe[column].isnull().sum() / len(dataframe) * 100\n",
+    "        if null_rate > 0:\n",
+    "            # Замена пустых данных на медиану\n",
+    "            df[column] = df[column].fillna(df[column].median())\n",
+    "    \n",
+    "    return dataframe\n",
+    "\n",
+    "\n",
+    "# Проверка пропущенных данных\n",
+    "print('До заполнения пустых значений:')\n",
+    "check_null_columns(df)\n",
+    "\n",
+    "# Заполнение пропущенных значений\n",
+    "df: DataFrame = fill_null_columns(df)\n",
+    "\n",
+    "# Проверка пропущенных данных\n",
+    "print('После заполнения пустых значений:')\n",
+    "check_null_columns(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Проблема зашумленности данных:\n",
+    "\n",
+    "Представленный ниже код помогает определить наличие выбросов в наборе данных и устранить их (при наличии), заменив значения ниже нижней границы (рассматриваемого минимума) на значения нижней границы, а значения выше верхней границы (рассматриваемого максимума) – на значения верхней границы."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Проверка наличия выбросов в колонках:\n",
+      "Колонка year:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 1980.0\n",
+      "\tМаксимальное значение: 2020.0\n",
+      "\t1-й квартиль (Q1): 1990.0\n",
+      "\t3-й квартиль (Q3): 2010.0\n",
+      "\n",
+      "Колонка index price:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 33\n",
+      "\tМинимальное значение: 168.61\n",
+      "\tМаксимальное значение: 47751.33\n",
+      "\t1-й квартиль (Q1): 2846.5\n",
+      "\t3-й квартиль (Q3): 9484.47\n",
+      "\n",
+      "Колонка log_indexprice:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 3\n",
+      "\tМинимальное значение: 2.23\n",
+      "\tМаксимальное значение: 4.68\n",
+      "\t1-й квартиль (Q1): 3.32\n",
+      "\t3-й квартиль (Q3): 3.98\n",
+      "\n",
+      "Колонка inflationrate:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 38\n",
+      "\tМинимальное значение: -0.04\n",
+      "\tМаксимальное значение: 0.24\n",
+      "\t1-й квартиль (Q1): 0.02\n",
+      "\t3-й квартиль (Q3): 0.05\n",
+      "\n",
+      "Колонка oil prices:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 11.35\n",
+      "\tМаксимальное значение: 98.56\n",
+      "\t1-й квартиль (Q1): 19.41\n",
+      "\t3-й квартиль (Q3): 57.88\n",
+      "\n",
+      "Колонка exchange_rate:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 85\n",
+      "\tМинимальное значение: 0.9\n",
+      "\tМаксимальное значение: 249.05\n",
+      "\t1-й квартиль (Q1): 1.33\n",
+      "\t3-й квартиль (Q3): 13.9\n",
+      "\n",
+      "Колонка gdppercent:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 41\n",
+      "\tМинимальное значение: -0.11\n",
+      "\tМаксимальное значение: 0.15\n",
+      "\t1-й квартиль (Q1): 0.02\n",
+      "\t3-й квартиль (Q3): 0.05\n",
+      "\n",
+      "Колонка percapitaincome:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 27.0\n",
+      "\tМаксимальное значение: 65280.0\n",
+      "\t1-й квартиль (Q1): 2099.0\n",
+      "\t3-й квартиль (Q3): 36354.0\n",
+      "\n",
+      "Колонка unemploymentrate:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 23\n",
+      "\tМинимальное значение: 0.02\n",
+      "\tМаксимальное значение: 0.26\n",
+      "\t1-й квартиль (Q1): 0.04\n",
+      "\t3-й квартиль (Q3): 0.08\n",
+      "\n",
+      "Колонка manufacturingoutput:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 35\n",
+      "\tМинимальное значение: 0.59\n",
+      "\tМаксимальное значение: 3868.46\n",
+      "\t1-й квартиль (Q1): 101.07\n",
+      "\t3-й квартиль (Q3): 245.75\n",
+      "\n",
+      "Колонка tradebalance:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 77\n",
+      "\tМинимальное значение: -770.93\n",
+      "\tМаксимальное значение: 366.14\n",
+      "\t1-й квартиль (Q1): -24.12\n",
+      "\t3-й квартиль (Q3): 18.15\n",
+      "\n",
+      "Колонка USTreasury:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0.01\n",
+      "\tМаксимальное значение: 0.14\n",
+      "\t1-й квартиль (Q1): 0.03\n",
+      "\t3-й квартиль (Q3): 0.08\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Числовые столбцы DataFrame\n",
+    "numeric_columns: list[str] = [\n",
+    "    'year',\n",
+    "    'index price',\n",
+    "    'log_indexprice',\n",
+    "    'inflationrate',\n",
+    "    'oil prices',\n",
+    "    'exchange_rate',\n",
+    "    'gdppercent',\n",
+    "    'percapitaincome',\n",
+    "    'unemploymentrate',\n",
+    "    'manufacturingoutput',\n",
+    "    'tradebalance',\n",
+    "    'USTreasury'\n",
+    "]\n",
+    "\n",
+    "# Проверка наличия выбросов в колонках\n",
+    "print('Проверка наличия выбросов в колонках:')\n",
+    "check_outliers(df, numeric_columns)\n",
+    "visualize_outliers(df, numeric_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Проверка наличия выбросов в колонках после их устранения:\n",
+      "Колонка year:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 1980.0\n",
+      "\tМаксимальное значение: 2020.0\n",
+      "\t1-й квартиль (Q1): 1990.0\n",
+      "\t3-й квартиль (Q3): 2010.0\n",
+      "\n",
+      "Колонка index price:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 168.61\n",
+      "\tМаксимальное значение: 19441.424999999996\n",
+      "\t1-й квартиль (Q1): 2846.5\n",
+      "\t3-й квартиль (Q3): 9484.47\n",
+      "\n",
+      "Колонка log_indexprice:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 2.3299999999999996\n",
+      "\tМаксимальное значение: 4.68\n",
+      "\t1-й квартиль (Q1): 3.32\n",
+      "\t3-й квартиль (Q3): 3.98\n",
+      "\n",
+      "Колонка inflationrate:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: -0.025000000000000005\n",
+      "\tМаксимальное значение: 0.095\n",
+      "\t1-й квартиль (Q1): 0.02\n",
+      "\t3-й квартиль (Q3): 0.05\n",
+      "\n",
+      "Колонка oil prices:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 11.35\n",
+      "\tМаксимальное значение: 98.56\n",
+      "\t1-й квартиль (Q1): 19.41\n",
+      "\t3-й квартиль (Q3): 57.88\n",
+      "\n",
+      "Колонка exchange_rate:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0.9\n",
+      "\tМаксимальное значение: 32.755\n",
+      "\t1-й квартиль (Q1): 1.33\n",
+      "\t3-й квартиль (Q3): 13.9\n",
+      "\n",
+      "Колонка gdppercent:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: -0.025000000000000005\n",
+      "\tМаксимальное значение: 0.095\n",
+      "\t1-й квартиль (Q1): 0.02\n",
+      "\t3-й квартиль (Q3): 0.05\n",
+      "\n",
+      "Колонка percapitaincome:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 27.0\n",
+      "\tМаксимальное значение: 65280.0\n",
+      "\t1-й квартиль (Q1): 2099.0\n",
+      "\t3-й квартиль (Q3): 36354.0\n",
+      "\n",
+      "Колонка unemploymentrate:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0.02\n",
+      "\tМаксимальное значение: 0.14\n",
+      "\t1-й квартиль (Q1): 0.04\n",
+      "\t3-й квартиль (Q3): 0.08\n",
+      "\n",
+      "Колонка manufacturingoutput:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0.59\n",
+      "\tМаксимальное значение: 462.77\n",
+      "\t1-й квартиль (Q1): 101.07\n",
+      "\t3-й квартиль (Q3): 245.75\n",
+      "\n",
+      "Колонка tradebalance:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: -87.52499999999999\n",
+      "\tМаксимальное значение: 81.55499999999999\n",
+      "\t1-й квартиль (Q1): -24.12\n",
+      "\t3-й квартиль (Q3): 18.15\n",
+      "\n",
+      "Колонка USTreasury:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0.01\n",
+      "\tМаксимальное значение: 0.14\n",
+      "\t1-й квартиль (Q1): 0.03\n",
+      "\t3-й квартиль (Q3): 0.08\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6+umn9cYbb1iXvZzX49GjR9WxY0d1795d999/v95//30988wzio6OVqdOnS4r5unTp+vNN9/Uhx9+aN3XjRo1uqyYVq1apbvvvlvVq1fXsGHDFBwcrNTUVC1fvlzDhg3T4MGDdeDAAa1atUpvvfXWVexh6ezZs4qLi9Ott96qyZMn27w3ivLOO+/o+PHjGjx4sCwWi1566SV1795du3fvtp5F/umnn6pXr16Kjo5WQkKCjh49qoEDB6pGjRo2fRljdO+992r16tUaOHCgmjRpoi+++EIjR47UH3/8oWnTpl3WtnTv3l2//vproUvcCgokkrR27VotXrxYTzzxhNzc3PTqq6+qY8eO2rhx42WPDfnWW2/p4YcfVrNmzfTII49IkmrXrn1ZfaD847iB4waOG8r3cUNAQIBmzZqlxx57TN26dVP37t0lyZp7peJz3ZIlS3Tq1Ck99thjqlatmjZu3KiXX35Zv//+u5YsWWJdfuXKlerRo4caNGighIQEHTlyRP3791fNmjULxTN48GDNnz9f/fv31xNPPKE9e/bolVde0aZNm7Ru3TqbK6527NihXr166dFHH1V8fLwSExPVs2dPrVixQnfeeaekkr+GinK5nzP333+/IiMjlZCQoB9//FHz5s1TYGCgXnzxRWub8ePHa9y4cWrZsqUmTJggV1dXfffdd/rqq6/UoUMHSec+9+Lj4xUXF6cXX3xRp06d0qxZs3Trrbdq06ZNDj3uKMo3cjo5nZxevnP6lQgPD5d0Lqf37Nnzkt9nL6W4Y4aS5veSvBdyc3MVFxennJwcPf744woODtYff/yh5cuX69ixY/L19b2i2Hv27Kk6dero+eeflzFG9913n4YMGaK3335bN910k03bt99+W23atCn0Hf26YK4z06dPN5LMTz/9ZDO/devW5sYbb7SZl5iYaCSZPXv2GGOMOX36tAkLCzOdOnUykkxiYqK17alTpwqt69133zWSzNdff22dN3bsWCPJHD58uNgYW7dubSSZKVOmWOfl5OSYJk2amMDAQJObm2uMMebs2bMmJyfHZtmjR4+aoKAgM2DAAOu8PXv2GEmmd+/exsXFxaSnp1ufa9eunfnb3/5mJJlJkyYVirN3797m7rvvts7/7bffjJOTk+ndu3eh7ShqHwwePNh4enqa06dPF7u956/v3nvvtZn/97//vdDfq6j1xMXFmVq1al10He+9956RZH744QfrPEnmvvvuMy4uLjbzBw4caN0vQ4YMsc4/ffq0ycvLs+l3z549xs3NzUyYMMFm/hdffGEkmX//+99m9+7dxsvLy3Tt2tWmzfDhw40k880331jnHT9+3ERGRpqIiAjrulavXm0kmdWrV9vE4uTkZP7+979fdLsLPPvss0aSyczMtM678cYbTevWrQu13bFjh5FkFixYYJ1X8Dcq8NdffxknJyczePBgm2Xj4+NN5cqVSxRTcQpeswEBAebYsWPW+aNGjTKSTOPGjc2ZM2es83v37m1cXV1tXmclfT0WvN/efPNN67ycnBwTHBxsevToYZ134edBgaL+NsW9z0sS09mzZ01kZKQJDw83R48etWmbn59v/f+QIUNMUR/hRcVjzP/26fmfW/Hx8UaS+ec//1mon/j4eBMeHl5o+WrVqpk///zTOv+jjz4ykswnn3xinRcdHW1q1qxpjh8/bp23Zs0aI8mmz2XLllnfI+e77777jMViMTt37iw29gKSzNixY63TkyZNKvLvVND2ws+A3377zbi7u5tu3boVu+0FLnwPGGNM5cqVTXx8fKG2cBwcN3DcwHFD+T9uOHz4cKF8cH7/xeW6ol4bCQkJxmKxmN9++806r0mTJqZ69eo2xyQrV64slNe++eYbI8m8/fbbNn2uWLGi0Pzw8HAjyXzwwQfWeVlZWaZ69ermpptuss4r6WuoqFx5uZ8z538OGGNMt27dTLVq1azTO3bsME5OTqZbt26FYio4Rjl+/LipUqWKGTRokM3z6enpxtfXt9B8oCyR08np5PTyn9MLXrPnvybPV9T3vb59+xpJpmrVqqZbt25m8uTJJjU19aLrKaqf4o4ZLie/l+S9sGnTJiPJLFmypNj4Luc78Pnv2Qv17t3bhISE2Lx2f/zxx2L7vh5cd0OjFFyedP4ZgiU1c+ZMHTlyxDrswfk8PDys/z99+rQyMzPVokULSbqiX8RcXFw0ePBg67Srq6sGDx6sQ4cOKSUlRZLk7OwsV1dXSecu0/jzzz919uxZ3XzzzUWus2nTprrxxhutZ5D+9ttvWr169UUv5R8wYIBWrFih9PR0SdKCBQsUGxurunXrFmp7/j44fvy4MjMzddttt+nUqVPavn17ibb7wl9xH3/8cUnnbmRQ1HqysrKUmZmp1q1ba/fu3dbLSAqcOnVKmZmZ2rx5s+bOnaugoKBCsQcFBalz585KTEy0LvPee++pf//+heJzc3Oz/mKfl5enI0eOyMvLS/Xq1Su0zzt06KDBgwdrwoQJ6t69u9zd3fXaa6/ZtPnss8/UrFkz3XrrrdZ5Xl5eeuSRR7R3715t27bNpn3B9u7bt08vvfSS8vPz1bZt2yL2ZGEFv9q7u7tfsm1JzpY4efKk8vPzVa1atRKt/0r07NnT5tfQ5s2bS5L69Oljcwlh8+bNlZuba3NZz+W8Hr28vNSnTx/rtKurq5o1a6bdu3eX6vaUJKZNmzZpz549Gj58eKExxi88m6S0PPbYYyVu26tXL1WtWtU6XXCWQMG+OnDggLZs2aK+ffvanOnSunVrRUdH2/T12WefydnZWU888YTN/KeeekrGGH3++eeXvS2XEhsbq5iYGOt0WFiYunTpoi+++KJEl27i+sNxA8cNHDdUnOOGiykq153/2jh58qQyMzPVsmVLGWOslxEfPHhQmzdvVnx8vM0xyZ133qkGDRrY9LdkyRL5+vrqzjvvVGZmpvURExMjLy+vQkMWhISE2Jzx5+Pjo759+2rTpk3W99DlvIYutn0l+Zx59NFHbaZvu+02HTlyRNnZ2ZLODQ+Tn5+vMWPGFDqDteAYZdWqVTp27Jh69+5tsw+cnZ3VvHnzQvsAKEvkdHI6Od0xcvqFEhMT9corrygyMlIffvihnn76aUVFRaldu3ZXNPTHhccMl5PfS/JeKDie+OKLL4ocguxKXZjHJalv3746cOCATYxvv/22PDw81KNHj1Jbd0Vy3RXCf/vtN7m4uFx28svKytLzzz+vESNGKCgoqNDzf/75p4YNG6agoCB5eHgoICBAkZGR1mUvV0hISKGB7Qs+tM8fp2zBggVq1KiR3N3dVa1aNQUEBOjTTz8tdp39+/e3fsjPnz9fLVu2VJ06dYqNo0mTJmrYsKHefPNNGWOsl4IUZevWrerWrZt8fX3l4+OjgIAAa3GxpPvgwlhq164tJycnm21et26d2rdvr8qVK6tKlSoKCAjQs88+W+R6JkyYoICAAN10003au3ev1qxZI29v70Lr7d+/v9555x3l5ORoyZIlqlq1apFJJT8/X9OmTVOdOnXk5uYmf39/BQQE6Oeffy5yGydPniw/Pz9t3rxZ//3vfwuNT/Xbb7+pXr16hZYruIv5hZdkde3aVQEBAQoPD9e4ceM0evToEn94ZWZmqlKlSiW6VOjYsWOSdNHxvapVq6Y6depo3rx5WrlypQ4dOqTMzEzl5OSUKJ6SCAsLs5kuSBihoaFFzj9/fK7LeT3WrFmzUJG5atWqxY7neaVKEtOuXbsk6bKH6bhSLi4uRV7aXZwL/yYFRfGCfVXwmr3hhhsKLXvhvN9++00hISGF3pPFvf5LQ1Gfd3Xr1tWpU6esN1IBzsdxA8cNHDdUnOOG4hSX6/bt26d+/frJz89PXl5eCggIUOvWrSX977VRsE+Let1f+LfYsWOHsrKyFBgYqICAAJvHiRMnrDfTLnDDDTcUOv648H17ua+h813u58ylcvyuXbvk5ORU6AeAC/eBJLVt27bQPij4uwP2Qk4np5PTK35OL3B+/nRyctKQIUOUkpKizMxMffTRR+rUqZO++uorPfDAA5fVb1HHDJeT30vyXoiMjNSIESM0b948+fv7Ky4uTjNnzryiz4vzFXzunO/OO+9U9erV9fbbb0s69zp+99131aVLlyLfD9eD626M8LS0NNWqVcvmbNKSePHFF+Xk5KSRI0cWuumFdG48q/Xr12vkyJFq0qSJvLy8lJ+fr44dOyo/P7+0wrexcOFC9evXT127dtXIkSMVGBgoZ2dnJSQkWItpF+rTp4/+8Y9/aMOGDVqwYIFGjx59yfUMGDBAr776qpo1a6b09HTdf//9mjJlik2bY8eOqXXr1vLx8dGECRNUu3Ztubu768cff9Qzzzxzxfvgwi8Hu3btUrt27VS/fn1NnTpVoaGhcnV11WeffaZp06YVWs/DDz+sdu3a6ffff9e0adPUo0cPrV+/vtCYS507d5arq6uWLVumxMRExcfHF3kTleeff17PPfecBgwYoIkTJ8rPz09OTk4aPnx4kdu4adMm64fili1b1Lt37yvaDwUmT56sxo0b68yZM/r+++/173//Wy4uLkWemXChvXv3KiwsrERnFRf86h8cHHzRdosXL9aDDz6ouLg4m/mldXdiZ2fny5pv/v9mIZf7erxUf1LxZ2OX9Czia/UeudDlxnn+mQ0lUZJ9Vdqudt+X9/WhfOO4geMGjhsqznFDcYrKdXl5ebrzzjv1559/6plnnlH9+vVVuXJl/fHHH+rXr98VvQbz8/MVGBho/bJ5oSs5C/VyX0Pnu9zPmdLI8QX9vvXWW0W+Hi73sxQoTeR0cjo5vfzn9IKz1ou6cbUk6xnUxZ3dXq1aNd17772699571aZNG61du1a//fabdSzxSynqmKGk+f1y3gtTpkxRv3799NFHH2nlypV64oknrOPjF3WiXoGLfSc9/2z0As7Ozvrb3/6muXPn6tVXX9W6det04MABmyvirzfX1ZFITk6ONm/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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Устраняем выборсы\n",
+    "df: DataFrame = remove_outliers(df, numeric_columns)\n",
+    "\n",
+    "# Проверка наличия выбросов в колонках\n",
+    "print('Проверка наличия выбросов в колонках после их устранения:')\n",
+    "check_outliers(df, numeric_columns)\n",
+    "visualize_outliers(df, numeric_columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Разбиение набора данных на выборки:\n",
+    "\n",
+    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
+    "\n",
+    "Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
+    "\n",
+    "Чтобы решить эту проблему введём категории для значения цены фондового рынка. Вместо того, чтобы использовать точные значения цен для стратификации, мы создадим категории, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении. Это позволит создать более крупные классы, что устранит проблему с редкими значениями.\n",
+    "\n",
+    "Категории для разбиения зарплат:\n",
+    "- Низкая цена индекса: значения ниже первого квартиля (25%) — это цены фондовых индексов ниже 2846.50.\n",
+    "- Средняя цена индекса: значения между первым квартилем (25%) и третьим квартилем (75%) — это цены от 2846.50 до 9484.47.\n",
+    "- Высокая цена индекса: значения выше третьего квартиля (75%) и до максимального значения — это цены выше 9484.47.\n",
+    "\n",
+    "Весь набор данных состоит из 369 объектов, из которых 184 (около 49.9%) имеют средний уровень цены фондового рынка (medium), 93 (около 25.2%) – низкий уровень цены (low), и 92 (около 24.9%) – высокий уровень цены (high).\n",
+    "\n",
+    "Все выборки показывают одинаковое распределение классов, что свидетельствует о том, что данные были отобраны случайным образом и не содержат явного смещения.\n",
+    "\n",
+    "Однако, несмотря на сбалансированность при разбиении данных, в целом данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать низкие или высокие цены фондовых рынков (low или high), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
+    "\n",
+    "Для получения более сбалансированных выборок данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "index price\n",
+      "5160.100     53\n",
+      "19441.425    33\n",
+      "1000.000      2\n",
+      "285.430       1\n",
+      "248.890       1\n",
+      "             ..\n",
+      "1647.170      1\n",
+      "1986.530      1\n",
+      "2099.320      1\n",
+      "2263.410      1\n",
+      "203.150       1\n",
+      "Name: count, Length: 284, dtype: int64 \n",
+      "\n",
+      "Статистическое описание целевого признака:\n",
+      "count      369.000000\n",
+      "mean      6948.930095\n",
+      "std       5682.147273\n",
+      "min        168.610000\n",
+      "25%       2846.500000\n",
+      "50%       5160.100000\n",
+      "75%       9484.470000\n",
+      "max      19441.425000\n",
+      "Name: index price, dtype: float64 \n",
+      "\n",
+      "Распределение количества наблюдений по меткам (классам):\n",
+      "index_price_category\n",
+      "medium    184\n",
+      "low        93\n",
+      "high       92\n",
+      "Name: count, dtype: int64 \n",
+      "\n",
+      "Проверка сбалансированности выборок:\n",
+      "Обучающая выборка:  (221, 15)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "medium    110\n",
+      "low        56\n",
+      "high       55\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 49.77%\n",
+      "Процент объектов класса \"low\": 25.34%\n",
+      "Процент объектов класса \"high\": 24.89%\n",
+      "\n",
+      "Контрольная выборка:  (74, 15)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "medium    37\n",
+      "high      19\n",
+      "low       18\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 50.00%\n",
+      "Процент объектов класса \"high\": 25.68%\n",
+      "Процент объектов класса \"low\": 24.32%\n",
+      "\n",
+      "Тестовая выборка:  (74, 15)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "medium    37\n",
+      "low       19\n",
+      "high      18\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 50.00%\n",
+      "Процент объектов класса \"low\": 25.68%\n",
+      "Процент объектов класса \"high\": 24.32%\n",
+      "\n",
+      "Проверка необходимости аугментации выборок:\n",
+      "Для обучающей выборки аугментация данных требуется\n",
+      "Для контрольной выборки аугментация данных требуется\n",
+      "Для тестовой выборки аугментация данных требуется\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df['index price'].value_counts(), '\\n')\n",
+    "\n",
+    "# Статистическое описание целевого признака\n",
+    "print('Статистическое описание целевого признака:')\n",
+    "print(df['index price'].describe().transpose(), '\\n')\n",
+    "\n",
+    "# Определим границы для каждой категории цен фондового рынка\n",
+    "bins: list[float] = [df['index price'].min() - 1, \n",
+    "                     df['index price'].quantile(0.25), \n",
+    "                     df['index price'].quantile(0.75), \n",
+    "                     df['index price'].max() + 1]\n",
+    "labels: list[str] = ['low', 'medium', 'high']\n",
+    "\n",
+    "# Создаем новую колонку с категориями зарплат\n",
+    "df['index_price_category'] = pd.cut(df['index price'], bins=bins, labels=labels)\n",
+    "\n",
+    "# Вывод распределения количества наблюдений по меткам (классам)\n",
+    "print('Распределение количества наблюдений по меткам (классам):')\n",
+    "print(df['index_price_category'].value_counts(), '\\n')\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   df,\n",
+    "   stratify_colname=\"index_price_category\", \n",
+    "   frac_train=0.60, \n",
+    "   frac_val=0.20, \n",
+    "   frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок:')\n",
+    "check_balance(df_train, 'Обучающая выборка', 'index_price_category')\n",
+    "check_balance(df_val, 'Контрольная выборка', 'index_price_category')\n",
+    "check_balance(df_test, 'Тестовая выборка', 'index_price_category')\n",
+    "\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок:')\n",
+    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df_train, df_val, df_test, 'index_price_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Приращение данных:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Проверка сбалансированности выборок после применения метода oversampling:\n",
+      "Обучающая выборка:  (335, 31)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "low       115\n",
+      "medium    110\n",
+      "high      110\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"low\": 34.33%\n",
+      "Процент объектов класса \"medium\": 32.84%\n",
+      "Процент объектов класса \"high\": 32.84%\n",
+      "\n",
+      "Контрольная выборка:  (110, 31)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "high      40\n",
+      "medium    37\n",
+      "low       33\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"high\": 36.36%\n",
+      "Процент объектов класса \"medium\": 33.64%\n",
+      "Процент объектов класса \"low\": 30.00%\n",
+      "\n",
+      "Тестовая выборка:  (115, 31)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "low       42\n",
+      "medium    37\n",
+      "high      36\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"low\": 36.52%\n",
+      "Процент объектов класса \"medium\": 32.17%\n",
+      "Процент объектов класса \"high\": 31.30%\n",
+      "\n",
+      "Проверка необходимости аугментации выборок после применения метода oversampling:\n",
+      "Для обучающей выборки аугментация данных не требуется\n",
+      "Для контрольной выборки аугментация данных не требуется\n",
+      "Для тестовой выборки аугментация данных не требуется\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Приращение данных (oversampling)\n",
+    "df_train_oversampled: DataFrame = oversample(df_train, 'index_price_category')\n",
+    "df_val_oversampled: DataFrame = oversample(df_val, 'index_price_category')\n",
+    "df_test_oversampled: DataFrame = oversample(df_test, 'index_price_category')\n",
+    "\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
+    "check_balance(df_train_oversampled, 'Обучающая выборка', 'index_price_category')\n",
+    "check_balance(df_val_oversampled, 'Контрольная выборка', 'index_price_category')\n",
+    "check_balance(df_test_oversampled, 'Тестовая выборка', 'index_price_category')\n",
+    "\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
+    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'index_price_category')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Проверка сбалансированности выборок после применения метода undersampling:\n",
+      "Обучающая выборка:  (165, 31)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "low       55\n",
+      "medium    55\n",
+      "high      55\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"low\": 33.33%\n",
+      "Процент объектов класса \"medium\": 33.33%\n",
+      "Процент объектов класса \"high\": 33.33%\n",
+      "\n",
+      "Контрольная выборка:  (54, 31)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "low       18\n",
+      "medium    18\n",
+      "high      18\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"low\": 33.33%\n",
+      "Процент объектов класса \"medium\": 33.33%\n",
+      "Процент объектов класса \"high\": 33.33%\n",
+      "\n",
+      "Тестовая выборка:  (54, 31)\n",
+      "Распределение выборки данных по классам в колонке \"index_price_category\":\n",
+      " index_price_category\n",
+      "low       18\n",
+      "medium    18\n",
+      "high      18\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"low\": 33.33%\n",
+      "Процент объектов класса \"medium\": 33.33%\n",
+      "Процент объектов класса \"high\": 33.33%\n",
+      "\n",
+      "Проверка необходимости аугментации выборок после применения метода undersampling:\n",
+      "Для обучающей выборки аугментация данных не требуется\n",
+      "Для контрольной выборки аугментация данных не требуется\n",
+      "Для тестовой выборки аугментация данных не требуется\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Приращение данных (undersampling)\n",
+    "df_train_undersampled: DataFrame = undersample(df_train, 'index_price_category')\n",
+    "df_val_undersampled: DataFrame = undersample(df_val, 'index_price_category')\n",
+    "df_test_undersampled: DataFrame = undersample(df_test, 'index_price_category')\n",
+    "\n",
+    "# Проверка сбалансированности выборок\n",
+    "print('Проверка сбалансированности выборок после применения метода undersampling:')\n",
+    "check_balance(df_train_undersampled, 'Обучающая выборка', 'index_price_category')\n",
+    "check_balance(df_val_undersampled, 'Контрольная выборка', 'index_price_category')\n",
+    "check_balance(df_test_undersampled, 'Тестовая выборка', 'index_price_category')\n",
+    "\n",
+    "# Проверка необходимости аугментации выборок\n",
+    "print('Проверка необходимости аугментации выборок после применения метода undersampling:')\n",
+    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'index_price_category', 'low', 'medium') else ''}требуется\")\n",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'index_price_category')"
+   ]
   }
  ],
  "metadata": {
diff --git a/static/csv/economic_data.csv b/static/csv/economic_data.csv
new file mode 100644
index 0000000..f3bed91
--- /dev/null
+++ b/static/csv/economic_data.csv
@@ -0,0 +1,370 @@
+stock index,country,year,index price,log_indexprice,inflationrate,oil prices,exchange_rate,gdppercent,percapitaincome,unemploymentrate,manufacturingoutput,tradebalance,USTreasury
+NASDAQ,United States of America,1980.00,168.61,2.23,0.14,21.59,1.00,0.09,12575.00,0.07,,-13.06,0.11
+NASDAQ,United States of America,1981.00,203.15,2.31,0.10,31.77,1.00,0.12,13976.00,0.08,,-12.52,0.14
+NASDAQ,United States of America,1982.00,188.98,2.28,0.06,28.52,1.00,0.04,14434.00,0.10,,-19.97,0.13
+NASDAQ,United States of America,1983.00,285.43,2.46,0.03,26.19,1.00,0.09,15544.00,0.10,,-51.64,0.11
+NASDAQ,United States of America,1984.00,248.89,2.40,0.04,25.88,1.00,0.11,17121.00,0.08,,-102.73,0.12
+NASDAQ,United States of America,1985.00,290.25,2.46,0.04,24.09,1.00,0.07,18237.00,0.07,,-114.02,0.11
+NASDAQ,United States of America,1986.00,366.97,2.56,0.02,12.51,1.00,0.06,19071.00,0.07,,-131.87,0.08
+NASDAQ,United States of America,1987.00,402.57,2.60,0.04,15.40,1.00,0.06,20039.00,0.06,,-144.77,0.08
+NASDAQ,United States of America,1988.00,374.43,2.57,0.04,12.58,1.00,0.08,21417.00,0.05,,-109.39,0.09
+NASDAQ,United States of America,1989.00,437.80,2.64,0.05,15.86,1.00,0.08,22857.00,0.05,,-86.74,0.08
+NASDAQ,United States of America,1990.00,409.18,2.61,0.05,27.28,1.00,0.02,23889.00,0.06,182.19,-77.85,0.09
+NASDAQ,United States of America,1991.00,491.69,2.69,0.04,19.50,1.00,,24342.00,0.07,185.83,-28.61,0.08
+NASDAQ,United States of America,1992.00,599.26,2.78,0.03,19.41,1.00,0.04,25419.00,0.07,189.27,-34.74,0.07
+NASDAQ,United States of America,1993.00,715.16,2.85,0.03,14.52,1.00,0.03,26387.00,0.07,166.43,-65.17,0.06
+NASDAQ,United States of America,1994.00,751.65,2.88,0.03,17.16,1.00,0.04,27695.00,0.06,187.05,-92.49,0.07
+NASDAQ,United States of America,1995.00,925.19,2.97,0.03,19.03,1.00,0.03,28691.00,0.06,208.44,-89.76,0.07
+NASDAQ,United States of America,1996.00,1164.95,3.07,0.03,25.23,1.00,0.04,29968.00,0.05,216.82,-96.38,0.06
+NASDAQ,United States of America,1997.00,1469.49,3.17,0.02,18.33,1.00,0.04,31459.00,0.05,235.67,-101.97,0.06
+NASDAQ,United States of America,1998.00,1794.91,3.25,0.02,11.35,1.00,0.05,32854.00,0.05,240.04,-162.71,0.05
+NASDAQ,United States of America,1999.00,2728.15,3.44,0.02,26.10,1.00,0.05,34514.00,0.04,234.62,-255.83,0.06
+NASDAQ,United States of America,2000.00,3783.67,3.58,0.03,28.44,1.00,0.04,36335.00,0.04,223.42,-375.05,0.06
+NASDAQ,United States of America,2001.00,2035.00,3.31,0.03,19.39,1.00,0.01,37133.00,0.05,205.70,-367.93,0.05
+NASDAQ,United States of America,2002.00,1539.73,3.19,0.02,29.46,1.00,0.02,38023.00,0.06,217.95,-425.40,0.05
+NASDAQ,United States of America,2003.00,1647.17,3.22,0.02,32.13,1.00,0.03,39496.00,0.06,240.22,-503.13,0.04
+NASDAQ,United States of America,2004.00,1986.53,3.30,0.03,43.15,1.00,0.04,41713.00,0.06,267.20,-619.08,0.04
+NASDAQ,United States of America,2005.00,2099.32,3.32,0.03,59.41,1.00,0.04,44115.00,0.05,268.36,-721.19,0.04
+NASDAQ,United States of America,2006.00,2263.41,3.35,0.03,61.96,1.00,0.03,46299.00,0.05,277.32,-770.93,0.05
+NASDAQ,United States of America,2007.00,2578.47,3.41,0.03,91.69,1.00,0.02,47976.00,0.05,299.29,-718.43,0.05
+NASDAQ,United States of America,2008.00,2161.65,3.33,0.04,41.12,1.00,,48383.00,0.06,278.83,-723.09,0.04
+NASDAQ,United States of America,2009.00,1845.38,3.27,,74.47,1.00,-0.03,47100.00,0.09,221.24,-396.45,0.03
+NASDAQ,United States of America,2010.00,2349.89,3.37,0.02,89.15,1.00,0.03,48467.00,0.10,236.41,-513.90,0.03
+NASDAQ,United States of America,2011.00,2677.44,3.43,0.03,98.56,1.00,0.02,49883.00,0.09,249.95,-579.46,0.03
+NASDAQ,United States of America,2012.00,2965.56,3.47,0.02,87.86,1.00,0.02,51603.00,0.08,252.36,-568.57,0.02
+NASDAQ,United States of America,2013.00,3541.29,3.55,0.01,97.63,1.00,0.02,53107.00,0.07,265.61,-490.78,0.02
+NASDAQ,United States of America,2014.00,4375.10,3.64,0.02,59.29,1.00,0.02,55050.00,0.06,287.86,-507.66,0.03
+NASDAQ,United States of America,2015.00,4945.55,3.69,,37.19,1.00,0.03,56863.00,0.05,274.30,-526.57,0.02
+NASDAQ,United States of America,2016.00,4987.79,3.70,0.01,51.97,1.00,0.02,58021.00,0.05,245.73,-512.51,0.02
+NASDAQ,United States of America,2017.00,6235.30,3.79,0.02,57.88,1.00,0.02,60110.00,0.04,242.59,-555.53,0.02
+NASDAQ,United States of America,2018.00,7425.96,3.87,0.02,49.52,1.00,0.03,63064.00,0.04,256.36,-609.46,0.03
+NASDAQ,United States of America,2019.00,7940.36,3.90,0.02,59.88,1.00,0.02,65280.00,0.04,245.75,-610.47,0.02
+NASDAQ,United States of America,2020.00,10201.51,4.01,0.01,47.02,1.00,-0.03,63544.00,0.08,227.14,,0.01
+FTSE 100,United Kingdom,1980.00,,3.33,0.18,21.59,2.32,-0.02,10032.00,0.05,,12.30,0.11
+FTSE 100,United Kingdom,1981.00,,3.33,0.12,31.77,2.02,-0.01,9599.00,0.04,,15.24,0.14
+FTSE 100,United Kingdom,1982.00,,3.33,0.09,28.52,1.75,0.02,9145.00,0.04,,9.77,0.13
+FTSE 100,United Kingdom,1983.00,,3.33,0.05,26.19,1.52,0.04,8691.00,0.04,,4.69,0.11
+FTSE 100,United Kingdom,1984.00,,3.33,0.05,25.88,1.34,0.02,8179.00,0.04,,-0.30,0.12
+FTSE 100,United Kingdom,1985.00,,3.33,0.06,24.09,1.30,0.04,8652.00,0.05,,5.39,0.11
+FTSE 100,United Kingdom,1986.00,,3.33,0.03,12.51,1.47,0.03,10611.00,0.04,,-3.63,0.08
+FTSE 100,United Kingdom,1987.00,,3.33,0.04,15.40,1.64,0.05,13118.00,0.05,,-7.00,0.08
+FTSE 100,United Kingdom,1988.00,,3.33,0.04,12.58,1.78,0.06,15988.00,0.05,,-29.79,0.09
+FTSE 100,United Kingdom,1989.00,,3.33,0.06,15.86,1.64,0.03,16240.00,0.05,,-33.87,0.08
+FTSE 100,United Kingdom,1990.00,2143.50,3.33,0.08,27.28,1.78,0.01,19096.00,0.07,182.19,-20.30,0.09
+FTSE 100,United Kingdom,1991.00,2493.10,3.40,0.07,19.50,1.77,-0.01,19900.00,0.09,185.83,-4.36,0.08
+FTSE 100,United Kingdom,1992.00,2846.50,3.45,0.05,19.41,1.77,,20488.00,0.10,189.27,-8.49,0.07
+FTSE 100,United Kingdom,1993.00,3418.40,3.53,0.03,14.52,1.50,0.02,18390.00,0.10,166.43,-3.90,0.06
+FTSE 100,United Kingdom,1994.00,3065.50,3.49,0.02,17.16,1.53,0.04,19708.00,0.10,187.05,-0.31,0.07
+FTSE 100,United Kingdom,1995.00,3689.30,3.57,0.03,19.03,1.58,0.03,23125.00,0.09,208.44,3.66,0.07
+FTSE 100,United Kingdom,1996.00,4118.50,3.61,0.03,25.23,1.56,0.02,24335.00,0.08,216.82,2.38,0.06
+FTSE 100,United Kingdom,1997.00,5135.50,3.71,0.02,18.33,1.64,0.05,26718.00,0.07,235.67,6.97,0.06
+FTSE 100,United Kingdom,1998.00,5882.58,3.77,0.02,11.35,1.66,0.04,28238.00,0.06,240.04,-11.17,0.05
+FTSE 100,United Kingdom,1999.00,6930.20,3.84,0.02,26.10,1.62,0.03,28668.00,0.06,234.62,-24.12,0.06
+FTSE 100,United Kingdom,2000.00,6222.46,3.79,0.01,28.44,1.52,0.04,28155.00,0.06,223.42,-29.59,0.06
+FTSE 100,United Kingdom,2001.00,5217.35,3.72,0.02,19.39,1.44,0.03,27719.00,0.05,205.70,-37.30,0.05
+FTSE 100,United Kingdom,2002.00,3940.36,3.60,0.02,29.46,1.50,0.02,29982.00,0.05,217.95,-47.84,0.05
+FTSE 100,United Kingdom,2003.00,4476.87,3.65,0.01,32.13,1.63,0.03,34385.00,0.05,240.22,-45.90,0.04
+FTSE 100,United Kingdom,2004.00,4814.30,3.68,0.01,43.15,1.83,0.02,40210.00,0.05,267.20,-61.87,0.04
+FTSE 100,United Kingdom,2005.00,5618.76,3.75,0.02,59.41,1.82,0.03,41933.00,0.05,268.36,-61.73,0.04
+FTSE 100,United Kingdom,2006.00,6220.81,3.79,0.02,61.96,1.84,0.03,44474.00,0.05,277.32,-57.48,0.05
+FTSE 100,United Kingdom,2007.00,6456.91,3.81,0.02,91.69,2.00,0.02,50448.00,0.05,299.29,-63.22,0.05
+FTSE 100,United Kingdom,2008.00,4434.17,3.65,0.04,41.12,1.85,,47270.00,0.06,278.83,-63.77,0.04
+FTSE 100,United Kingdom,2009.00,5412.88,3.73,0.02,74.47,1.57,-0.04,38736.00,0.08,221.24,-38.74,0.03
+FTSE 100,United Kingdom,2010.00,5899.94,3.77,0.02,89.15,1.55,0.02,39537.00,0.08,236.41,-48.09,0.03
+FTSE 100,United Kingdom,2011.00,5572.28,3.75,0.04,98.56,1.60,0.01,42048.00,0.08,249.95,-25.37,0.03
+FTSE 100,United Kingdom,2012.00,5897.81,3.77,0.03,87.86,1.59,0.01,42449.00,0.08,252.36,-33.47,0.02
+FTSE 100,United Kingdom,2013.00,6749.09,3.83,0.02,97.63,1.56,0.02,43401.00,0.08,265.61,-39.09,0.02
+FTSE 100,United Kingdom,2014.00,6566.09,3.82,0.01,59.29,1.65,0.03,47452.00,0.06,287.86,-47.61,0.03
+FTSE 100,United Kingdom,2015.00,6242.32,3.80,,37.19,1.53,0.02,45039.00,0.05,274.30,-43.19,0.02
+FTSE 100,United Kingdom,2016.00,7142.83,3.85,0.01,51.97,1.36,0.02,41048.00,0.05,245.73,-48.76,0.02
+FTSE 100,United Kingdom,2017.00,7687.77,3.89,0.03,57.88,1.29,0.02,40306.00,0.04,242.59,-38.47,0.02
+FTSE 100,United Kingdom,2018.00,6728.13,3.83,0.02,49.52,1.34,0.01,42996.00,0.04,256.36,-34.00,0.03
+FTSE 100,United Kingdom,2019.00,7542.44,3.88,0.02,59.88,1.28,0.01,42354.00,0.04,245.75,-35.06,0.02
+FTSE 100,United Kingdom,2020.00,6460.52,3.81,0.01,47.02,1.28,-0.10,40285.00,0.04,227.14,-9.69,0.01
+Nifty 50,India,1980.00,,3.02,0.11,21.59,7.89,0.07,267.00,0.03,31.21,-5.79,0.11
+Nifty 50,India,1981.00,,3.02,0.13,31.77,8.68,0.06,270.00,0.03,32.45,-5.10,0.14
+Nifty 50,India,1982.00,,3.02,0.08,28.52,9.48,0.03,274.00,0.03,32.86,-4.33,0.13
+Nifty 50,India,1983.00,,3.02,0.12,26.19,10.10,0.07,291.00,0.03,36.37,-4.40,0.11
+Nifty 50,India,1984.00,,3.02,0.08,25.88,11.35,0.04,277.00,0.03,35.45,-3.06,0.12
+Nifty 50,India,1985.00,,3.02,0.06,24.09,12.33,0.05,296.00,0.03,38.17,-5.56,0.11
+Nifty 50,India,1986.00,,3.02,0.09,12.51,12.60,0.05,310.00,0.03,40.39,-4.55,0.08
+Nifty 50,India,1987.00,,3.02,0.09,15.40,12.95,0.04,340.00,0.03,45.23,-3.84,0.08
+Nifty 50,India,1988.00,,3.02,0.09,12.58,13.90,0.10,354.00,0.03,47.76,-4.21,0.09
+Nifty 50,India,1989.00,,3.02,0.07,15.86,16.21,0.06,346.00,0.03,50.04,-3.36,0.08
+Nifty 50,India,1990.00,1048.29,3.02,0.09,27.28,17.49,0.06,368.00,0.06,53.27,-4.49,0.09
+Nifty 50,India,1991.00,1908.85,3.28,0.14,19.50,22.71,0.01,303.00,0.06,42.34,,0.08
+Nifty 50,India,1992.00,2615.37,3.42,0.12,19.41,28.16,0.05,317.00,0.06,45.54,-2.15,0.07
+Nifty 50,India,1993.00,3346.06,3.52,0.06,14.52,31.29,0.05,301.00,0.06,44.45,0.05,0.06
+Nifty 50,India,1994.00,3926.90,3.59,0.10,17.16,31.39,0.07,346.00,0.06,54.86,-0.99,0.07
+Nifty 50,India,1995.00,3110.49,3.49,0.10,19.03,32.42,0.08,374.00,0.06,64.37,-4.25,0.07
+Nifty 50,India,1996.00,3085.20,3.49,0.09,25.23,35.51,0.08,400.00,0.06,69.14,-4.55,0.06
+Nifty 50,India,1997.00,3658.98,3.56,0.07,18.33,36.36,0.04,415.00,0.06,68.70,-5.15,0.06
+Nifty 50,India,1998.00,3055.41,3.49,0.13,11.35,41.36,0.06,413.00,0.06,66.23,-7.01,0.05
+Nifty 50,India,1999.00,5005.82,3.70,0.05,26.10,43.13,0.09,442.00,0.06,69.65,-8.77,0.06
+Nifty 50,India,2000.00,3972.12,3.60,0.04,28.44,45.00,0.04,443.00,0.06,74.60,-4.25,0.06
+Nifty 50,India,2001.00,3262.33,3.51,0.04,19.39,47.22,0.05,452.00,0.06,74.31,-4.25,0.05
+Nifty 50,India,2002.00,3377.28,3.53,0.04,29.46,48.63,0.04,471.00,0.06,80.12,-5.05,0.05
+Nifty 50,India,2003.00,5838.96,3.77,0.04,32.13,46.59,0.08,547.00,0.06,94.72,-4.23,0.04
+Nifty 50,India,2004.00,6602.69,3.82,0.04,43.15,45.26,0.08,628.00,0.06,112.24,-12.66,0.04
+Nifty 50,India,2005.00,9397.93,3.97,0.04,59.41,44.00,0.08,715.00,0.06,131.04,-22.90,0.04
+Nifty 50,India,2006.00,13786.91,4.14,0.06,61.96,45.19,0.08,807.00,0.05,162.70,-29.98,0.05
+Nifty 50,India,2007.00,20286.99,4.31,0.06,91.69,41.18,0.08,1028.00,0.05,205.20,-49.73,0.05
+Nifty 50,India,2008.00,9647.31,3.98,0.08,41.12,43.39,0.03,999.00,0.06,205.00,-62.02,0.04
+Nifty 50,India,2009.00,17464.81,4.24,0.11,74.47,48.33,0.08,1102.00,0.06,230.05,-73.43,0.03
+Nifty 50,India,2010.00,20509.09,4.31,0.12,89.15,45.65,0.08,1358.00,0.06,285.36,-74.62,0.03
+Nifty 50,India,2011.00,15454.92,4.19,0.09,98.56,46.58,0.05,1458.00,0.06,294.23,-119.28,0.03
+Nifty 50,India,2012.00,19426.71,4.29,0.09,87.86,53.37,0.05,1444.00,0.06,289.08,-122.91,0.02
+Nifty 50,India,2013.00,21170.68,4.33,0.11,97.63,58.51,0.06,1450.00,0.06,283.21,-55.38,0.02
+Nifty 50,India,2014.00,27499.42,4.44,0.07,59.29,61.00,0.07,1574.00,0.06,307.21,-60.89,0.03
+Nifty 50,India,2015.00,26117.54,4.42,0.05,37.19,64.11,0.08,1606.00,0.06,327.82,-48.31,0.02
+Nifty 50,India,2016.00,26626.46,4.43,0.05,51.97,67.16,0.08,1733.00,0.05,347.94,-40.53,0.02
+Nifty 50,India,2017.00,34056.83,4.53,0.03,57.88,65.07,0.07,1981.00,0.05,398.20,-83.76,0.02
+Nifty 50,India,2018.00,36068.33,4.56,0.04,49.52,68.37,0.07,1997.00,0.05,401.20,-100.38,0.03
+Nifty 50,India,2019.00,41253.74,4.62,0.04,59.88,70.38,0.04,2101.00,0.07,382.56,-72.57,0.02
+Nifty 50,India,2020.00,47751.33,4.68,0.07,47.02,74.14,-0.08,1901.00,0.06,339.98,-8.31,0.01
+Nikkei 225,Japan,1980.00,6867.86,3.84,0.08,21.59,226.58,0.03,9463.00,0.02,71.01,-10.76,0.11
+Nikkei 225,Japan,1981.00,7518.55,3.88,0.05,31.77,220.45,0.04,10360.00,0.02,71.70,7.48,0.14
+Nikkei 225,Japan,1982.00,7397.27,3.87,0.03,28.52,249.05,0.03,9576.00,0.02,71.96,6.22,0.13
+Nikkei 225,Japan,1983.00,8816.24,3.95,0.02,26.19,237.45,0.04,10421.00,0.03,74.24,19.08,0.11
+Nikkei 225,Japan,1984.00,10567.53,4.02,0.02,25.88,237.59,0.05,10979.00,0.03,81.16,32.25,0.12
+Nikkei 225,Japan,1985.00,12557.45,4.10,0.02,24.09,238.47,0.05,11577.00,0.03,84.14,44.63,0.11
+Nikkei 225,Japan,1986.00,16392.74,4.21,0.01,12.51,168.50,0.03,17113.00,0.03,83.98,76.37,0.08
+Nikkei 225,Japan,1987.00,23232.14,4.37,,15.40,144.62,0.05,20749.00,0.03,86.87,70.12,0.08
+Nikkei 225,Japan,1988.00,27048.26,4.43,0.01,12.58,128.14,0.07,25059.00,0.03,94.91,60.32,0.09
+Nikkei 225,Japan,1989.00,34050.78,4.53,0.02,15.86,137.99,0.05,24823.00,0.02,100.40,41.28,0.08
+Nikkei 225,Japan,1990.00,29437.18,4.47,0.03,27.28,144.82,0.05,25371.00,0.02,104.65,24.33,0.09
+Nikkei 225,Japan,1991.00,24295.57,4.39,0.03,19.50,134.51,0.03,28915.00,0.02,106.64,53.70,0.08
+Nikkei 225,Japan,1992.00,18109.08,4.26,0.02,19.41,126.75,0.01,31415.00,0.02,100.55,80.30,0.07
+Nikkei 225,Japan,1993.00,19100.00,4.28,0.01,14.52,111.23,-0.01,35682.00,0.03,97.13,95.40,0.06
+Nikkei 225,Japan,1994.00,19935.83,4.30,0.01,17.16,102.20,0.01,39200.00,0.03,98.35,93.37,0.07
+Nikkei 225,Japan,1995.00,17329.70,4.24,,19.03,94.11,0.03,43429.00,0.03,101.34,68.88,0.07
+Nikkei 225,Japan,1996.00,21094.61,4.32,,25.23,108.80,0.03,38437.00,0.03,103.53,20.96,0.06
+Nikkei 225,Japan,1997.00,18397.52,4.26,0.02,18.33,121.09,0.01,35022.00,0.04,107.66,46.66,0.06
+Nikkei 225,Japan,1998.00,15355.99,4.19,0.01,11.35,130.82,-0.01,31903.00,0.04,100.48,73.27,0.05
+Nikkei 225,Japan,1999.00,16823.41,4.23,,26.10,113.71,,36027.00,0.05,101.07,70.92,0.06
+Nikkei 225,Japan,2000.00,17145.01,4.23,-0.01,28.44,107.82,0.03,38532.00,0.05,106.05,69.86,0.06
+Nikkei 225,Japan,2001.00,12093.56,4.08,-0.01,19.39,121.52,,33846.00,0.05,99.62,28.41,0.05
+Nikkei 225,Japan,2002.00,10123.14,4.01,-0.01,29.46,125.27,,32289.00,0.06,98.45,55.59,0.05
+Nikkei 225,Japan,2003.00,9311.42,3.97,,32.13,115.92,0.02,34808.00,0.05,102.29,75.37,0.04
+Nikkei 225,Japan,2004.00,11179.25,4.05,,43.15,108.16,0.02,37689.00,0.05,107.12,97.35,0.04
+Nikkei 225,Japan,2005.00,12422.58,4.09,,59.41,110.14,0.02,37218.00,0.05,108.86,71.78,0.04
+Nikkei 225,Japan,2006.00,16110.38,4.21,,61.96,116.35,0.01,35434.00,0.04,113.30,64.06,0.05
+Nikkei 225,Japan,2007.00,17002.24,4.23,,91.69,117.77,0.02,35275.00,0.04,116.60,85.46,0.05
+Nikkei 225,Japan,2008.00,12165.35,4.09,0.01,41.12,103.38,-0.01,39339.00,0.04,112.83,22.62,0.04
+Nikkei 225,Japan,2009.00,9346.11,3.97,-0.01,74.47,93.60,-0.05,40855.00,0.05,89.39,28.82,0.03
+Nikkei 225,Japan,2010.00,10006.49,4.00,-0.01,89.15,87.75,0.04,44508.00,0.05,102.78,83.25,0.03
+Nikkei 225,Japan,2011.00,9425.42,3.97,,98.56,79.71,,48168.00,0.05,99.93,-33.44,0.03
+Nikkei 225,Japan,2012.00,9102.56,3.96,,87.86,79.81,0.01,48603.00,0.05,100.06,-95.94,0.02
+Nikkei 225,Japan,2013.00,13577.87,4.13,,97.63,97.56,0.02,40454.00,0.04,99.46,-119.44,0.02
+Nikkei 225,Japan,2014.00,15460.43,4.19,0.03,59.29,105.86,,38109.00,0.04,101.24,-119.57,0.03
+Nikkei 225,Japan,2015.00,19203.77,4.28,0.01,37.19,121.02,0.01,34524.00,0.04,100.00,-18.39,0.02
+Nikkei 225,Japan,2016.00,16920.48,4.23,,51.97,108.76,0.01,38762.00,0.03,100.22,48.78,0.02
+Nikkei 225,Japan,2017.00,20209.03,4.31,,57.88,112.14,0.02,38387.00,0.03,102.84,45.27,0.02
+Nikkei 225,Japan,2018.00,22310.73,4.35,0.01,49.52,110.43,,39159.00,0.03,103.91,11.56,0.03
+Nikkei 225,Japan,2019.00,21697.23,4.34,,59.88,109.03,,40113.00,0.02,101.19,8.66,0.02
+Nikkei 225,Japan,2020.00,22705.02,4.36,,47.02,106.76,-0.05,,0.03,90.94,,0.01
+HSI,Hong Kong,1980.00,,3.46,,21.59,,0.10,5700.00,,,-0.15,0.11
+HSI,Hong Kong,1981.00,,3.46,,31.77,5.59,0.09,5991.00,,,-0.47,0.14
+HSI,Hong Kong,1982.00,,3.46,0.11,28.52,6.07,0.03,6134.00,,,0.18,0.13
+HSI,Hong Kong,1983.00,,3.46,0.10,26.19,7.26,0.06,5595.00,,,0.54,0.11
+HSI,Hong Kong,1984.00,,3.46,0.09,25.88,7.82,0.10,6208.00,,,2.68,0.12
+HSI,Hong Kong,1985.00,,3.46,0.04,24.09,7.79,0.01,6543.00,,,3.58,0.11
+HSI,Hong Kong,1986.00,,3.46,0.03,12.51,7.80,0.11,7435.00,,,3.74,0.08
+HSI,Hong Kong,1987.00,,3.46,0.06,15.40,7.80,0.13,9071.00,,,5.27,0.08
+HSI,Hong Kong,1988.00,2884.51,3.46,0.08,12.58,7.81,0.09,10610.00,,,5.33,0.09
+HSI,Hong Kong,1989.00,2556.72,3.41,0.10,15.86,7.80,0.02,12098.00,,,7.89,0.08
+HSI,Hong Kong,1990.00,2781.05,3.44,0.10,27.28,7.79,0.04,13486.00,,,6.57,0.09
+HSI,Hong Kong,1991.00,3027.13,3.48,0.11,19.50,7.77,0.06,15466.00,0.02,,6.05,0.08
+HSI,Hong Kong,1992.00,3829.37,3.58,0.10,19.41,7.74,0.06,17976.00,0.02,,5.42,0.07
+HSI,Hong Kong,1993.00,5545.98,3.74,0.09,14.52,7.74,0.06,20396.00,0.02,,8.12,0.06
+HSI,Hong Kong,1994.00,7695.99,3.89,0.09,17.16,7.73,0.06,22503.00,0.02,,1.57,0.07
+HSI,Hong Kong,1995.00,9453.52,3.98,0.09,19.03,7.74,0.02,23497.00,0.03,,-6.48,0.07
+HSI,Hong Kong,1996.00,9098.46,3.96,0.06,25.23,7.73,0.04,24818.00,0.03,,-2.37,0.06
+HSI,Hong Kong,1997.00,11646.54,4.07,0.06,18.33,7.74,0.05,27330.00,0.02,,-5.95,0.06
+HSI,Hong Kong,1998.00,13294.70,4.12,0.03,11.35,7.75,-0.06,25809.00,0.05,,0.91,0.05
+HSI,Hong Kong,1999.00,9484.47,3.98,-0.04,26.10,7.76,0.03,25092.00,0.06,,8.56,0.06
+HSI,Hong Kong,2000.00,12859.93,4.11,-0.04,28.44,7.79,0.08,25757.00,0.05,7.88,7.57,0.06
+HSI,Hong Kong,2001.00,15838.33,4.20,-0.02,19.39,7.80,0.01,25230.00,0.05,6.87,7.95,0.05
+HSI,Hong Kong,2002.00,12446.85,4.10,-0.03,29.46,7.80,0.02,24666.00,0.07,5.87,13.57,0.05
+HSI,Hong Kong,2003.00,10321.22,4.01,-0.03,32.13,7.79,0.03,23977.00,0.08,5.01,14.68,0.04
+HSI,Hong Kong,2004.00,10346.25,4.01,,43.15,7.79,0.09,24928.00,0.07,5.07,14.73,0.04
+HSI,Hong Kong,2005.00,12988.18,4.11,0.01,59.41,7.78,0.07,26650.00,0.06,5.10,22.18,0.04
+HSI,Hong Kong,2006.00,14402.00,4.16,0.02,61.96,7.77,0.07,28224.00,0.05,5.11,21.60,0.05
+HSI,Hong Kong,2007.00,17118.31,4.23,0.02,91.69,7.80,0.06,30594.00,0.04,4.15,22.59,0.05
+HSI,Hong Kong,2008.00,23700.45,4.37,0.04,41.12,7.79,0.02,31516.00,0.04,4.05,22.31,0.04
+HSI,Hong Kong,2009.00,20606.72,4.31,0.01,74.47,7.75,-0.02,30697.00,0.05,3.70,16.85,0.03
+HSI,Hong Kong,2010.00,18203.01,4.26,0.02,89.15,7.77,0.07,32550.00,0.04,3.91,13.43,0.03
+HSI,Hong Kong,2011.00,21336.42,4.33,0.05,98.56,7.78,0.05,35142.00,0.03,3.93,9.57,0.03
+HSI,Hong Kong,2012.00,21414.34,4.33,0.04,87.86,7.76,0.02,36731.00,0.03,3.95,2.97,0.02
+HSI,Hong Kong,2013.00,20686.69,4.32,0.04,97.63,7.76,0.03,38404.00,0.03,3.89,1.65,0.02
+HSI,Hong Kong,2014.00,22654.24,4.36,0.04,59.29,7.75,0.03,40315.00,0.03,3.60,0.60,0.03
+HSI,Hong Kong,2015.00,23287.69,4.37,0.03,37.19,7.75,0.02,42432.00,0.03,3.45,7.39,0.02
+HSI,Hong Kong,2016.00,24145.21,4.38,0.02,51.97,7.76,0.02,43731.00,0.03,3.46,7.33,0.02
+HSI,Hong Kong,2017.00,21511.54,4.33,0.01,57.88,7.79,0.04,46166.00,0.03,3.50,3.44,0.02
+HSI,Hong Kong,2018.00,26453.67,4.42,0.02,49.52,7.84,0.03,48543.00,0.03,3.52,-0.77,0.03
+HSI,Hong Kong,2019.00,28804.14,4.46,0.03,59.88,7.84,-0.02,48354.00,0.03,3.75,5.64,0.02
+HSI,Hong Kong,2020.00,27650.63,4.44,,47.02,7.76,-0.06,46324.00,0.06,,6.32,0.01
+SZCOMP,China,1980.00,,3.07,,21.59,,0.08,195.00,0.05,,-1.15,0.11
+SZCOMP,China,1981.00,,3.07,,31.77,1.71,0.05,197.00,0.05,,,0.14
+SZCOMP,China,1982.00,,3.07,,28.52,1.90,0.09,203.00,0.05,,4.81,0.13
+SZCOMP,China,1983.00,,3.07,,26.19,1.98,0.11,225.00,0.04,,2.57,0.11
+SZCOMP,China,1984.00,,3.07,,25.88,2.33,0.15,251.00,0.03,,0.05,0.12
+SZCOMP,China,1985.00,,3.07,,24.09,2.94,0.13,294.00,0.02,,-12.50,0.11
+SZCOMP,China,1986.00,,3.07,,12.51,3.46,0.09,282.00,0.02,,-7.39,0.08
+SZCOMP,China,1987.00,,3.07,0.07,15.40,3.73,0.12,252.00,0.02,,0.29,0.08
+SZCOMP,China,1988.00,,3.07,0.19,12.58,3.73,0.11,284.00,0.02,,-4.06,0.09
+SZCOMP,China,1989.00,,3.07,0.18,15.86,3.77,0.04,311.00,0.02,,-4.93,0.08
+SZCOMP,China,1990.00,,3.07,0.03,27.28,4.79,0.04,318.00,0.02,,10.67,0.09
+SZCOMP,China,1991.00,,3.07,0.04,19.50,5.33,0.09,333.00,0.03,,11.60,0.08
+SZCOMP,China,1992.00,,3.07,0.06,19.41,5.52,0.14,366.00,0.03,,5.00,0.07
+SZCOMP,China,1993.00,,3.07,0.15,14.52,5.78,0.14,377.00,0.02,,-11.79,0.06
+SZCOMP,China,1994.00,,3.07,0.24,17.16,8.64,0.13,473.00,0.02,,7.36,0.07
+SZCOMP,China,1995.00,,3.07,0.17,19.03,8.37,0.11,610.00,0.03,,11.96,0.07
+SZCOMP,China,1996.00,,3.07,0.08,25.23,8.34,0.10,709.00,0.03,,17.55,0.06
+SZCOMP,China,1997.00,1166.51,3.07,0.03,18.33,8.32,0.09,782.00,0.03,,42.82,0.06
+SZCOMP,China,1998.00,1257.32,3.10,-0.01,11.35,8.30,0.08,829.00,0.03,,43.84,0.05
+SZCOMP,China,1999.00,1381.49,3.14,-0.01,26.10,8.28,0.08,873.00,0.03,,30.64,0.06
+SZCOMP,China,2000.00,1897.42,3.28,,28.44,8.28,0.08,959.00,0.03,,28.79,0.06
+SZCOMP,China,2001.00,1940.96,3.29,0.01,19.39,8.28,0.08,1053.00,0.03,,28.09,0.05
+SZCOMP,China,2002.00,1561.31,3.19,-0.01,29.46,8.28,0.09,1149.00,0.03,,37.38,0.05
+SZCOMP,China,2003.00,1467.88,3.17,0.01,32.13,8.28,0.10,1289.00,0.04,,35.82,0.04
+SZCOMP,China,2004.00,1467.57,3.17,0.04,43.15,8.28,0.10,1509.00,0.04,625.22,51.17,0.04
+SZCOMP,China,2005.00,1144.54,3.06,0.02,59.41,8.19,0.11,1753.00,0.04,733.66,124.63,0.04
+SZCOMP,China,2006.00,1687.14,3.23,0.02,61.96,7.97,0.13,2099.00,0.04,893.13,208.92,0.05
+SZCOMP,China,2007.00,4329.44,3.64,0.05,91.69,7.61,0.14,2694.00,0.04,1149.72,308.04,0.05
+SZCOMP,China,2008.00,2912.90,3.46,0.06,41.12,6.95,0.10,3468.00,0.04,1475.66,348.83,0.04
+SZCOMP,China,2009.00,2737.01,3.44,-0.01,74.47,6.83,0.09,3832.00,0.04,1611.95,220.13,0.03
+SZCOMP,China,2010.00,2795.88,3.45,0.03,89.15,6.77,0.11,4550.00,0.04,1924.32,222.40,0.03
+SZCOMP,China,2011.00,2639.19,3.42,0.06,98.56,6.46,0.10,5618.00,0.04,2421.37,180.89,0.03
+SZCOMP,China,2012.00,2211.11,3.34,0.03,87.86,6.31,0.08,6317.00,0.04,2690.09,231.87,0.02
+SZCOMP,China,2013.00,2182.52,3.34,0.03,97.63,6.15,0.08,7051.00,0.05,2935.34,234.87,0.02
+SZCOMP,China,2014.00,2279.75,3.36,0.02,59.29,6.16,0.07,7679.00,0.05,3184.24,221.55,0.03
+SZCOMP,China,2015.00,3657.40,3.56,0.01,37.19,6.28,0.07,8067.00,0.05,3202.50,358.84,0.02
+SZCOMP,China,2016.00,2978.14,3.47,0.02,51.97,6.64,0.07,8148.00,0.05,3153.12,255.48,0.02
+SZCOMP,China,2017.00,3257.35,3.51,0.02,57.88,6.76,0.07,8879.00,0.04,3460.33,215.70,0.02
+SZCOMP,China,2018.00,2920.18,3.47,0.02,49.52,6.61,0.07,9977.00,0.04,3868.46,106.71,0.03
+SZCOMP,China,2019.00,2928.94,3.47,0.03,59.88,6.91,0.06,10217.00,0.05,3823.41,164.99,0.02
+SZCOMP,China,2020.00,3109.78,3.49,0.02,47.02,6.90,0.02,10500.00,0.05,3853.81,366.14,0.01
+DAX 30,Germany,1980.00,480.90,2.68,0.05,21.59,1.82,0.01,6206.00,0.03,,-22.41,0.11
+DAX 30,Germany,1981.00,490.40,2.69,0.06,31.77,2.26,0.01,5220.00,0.05,,-16.02,0.14
+DAX 30,Germany,1982.00,552.80,2.74,0.05,28.52,2.43,,5069.00,0.07,,-10.51,0.13
+DAX 30,Germany,1983.00,774.00,2.89,0.03,26.19,2.55,0.02,5044.00,0.08,,-11.79,0.11
+DAX 30,Germany,1984.00,820.90,2.91,0.02,25.88,2.85,0.03,4762.00,0.08,,-10.35,0.12
+DAX 30,Germany,1985.00,1366.20,3.14,0.02,24.09,2.94,0.02,4821.00,0.08,,-7.63,0.11
+DAX 30,Germany,1986.00,1432.30,3.16,,12.51,2.17,0.02,6883.00,0.08,,-1.98,0.08
+DAX 30,Germany,1987.00,1000.00,3.00,,15.40,1.80,0.01,8527.00,0.08,,-2.15,0.08
+DAX 30,Germany,1988.00,1327.87,3.12,0.01,12.58,1.76,0.04,9168.00,0.08,,-2.48,0.09
+DAX 30,Germany,1989.00,1790.37,3.25,0.03,15.86,1.88,0.04,9083.00,0.07,,-4.41,0.08
+DAX 30,Germany,1990.00,1398.20,3.15,0.03,27.28,1.62,0.05,11404.00,0.06,,-1.74,0.09
+DAX 30,Germany,1991.00,1578.00,3.20,0.04,19.50,1.66,0.05,11942.00,0.06,237.43,-4.66,0.08
+DAX 30,Germany,1992.00,1545.10,3.19,0.05,19.41,1.56,0.02,13518.00,0.08,254.79,-5.47,0.07
+DAX 30,Germany,1993.00,2266.70,3.36,0.04,14.52,1.65,-0.01,13049.00,0.09,225.80,0.53,0.06
+DAX 30,Germany,1994.00,2106.60,3.32,0.03,17.16,1.62,0.02,13844.00,0.10,234.55,1.84,0.07
+DAX 30,Germany,1995.00,2253.90,3.35,0.02,19.03,1.43,0.02,16186.00,0.09,271.52,5.38,0.07
+DAX 30,Germany,1996.00,2888.70,3.46,0.01,25.23,1.50,0.01,15588.00,0.10,256.13,9.12,0.06
+DAX 30,Germany,1997.00,4224.30,3.63,0.02,18.33,1.73,0.02,13786.00,0.11,228.78,12.19,0.06
+DAX 30,Germany,1998.00,5002.39,3.70,0.01,11.35,1.76,0.02,13953.00,0.11,233.65,14.07,0.05
+DAX 30,Germany,1999.00,6958.14,3.84,0.01,26.10,1.07,0.02,26735.00,0.11,438.87,12.13,0.06
+DAX 30,Germany,2000.00,6433.61,3.81,0.01,28.44,0.92,0.03,23695.00,0.10,400.23,3.30,0.06
+DAX 30,Germany,2001.00,5160.10,3.71,0.02,19.39,0.90,0.02,23628.00,0.09,395.80,31.65,0.05
+DAX 30,Germany,2002.00,2892.63,3.46,0.01,29.46,0.95,,25197.00,0.10,411.33,88.34,0.05
+DAX 30,Germany,2003.00,3965.16,3.60,0.01,32.13,1.13,-0.01,30310.00,0.11,496.31,94.41,0.04
+DAX 30,Germany,2004.00,4256.08,3.63,0.02,43.15,1.24,0.01,34107.00,0.11,563.82,146.36,0.04
+DAX 30,Germany,2005.00,5408.25,3.73,0.02,59.41,1.24,0.01,34520.00,0.12,571.36,148.05,0.04
+DAX 30,Germany,2006.00,6596.91,3.82,0.02,61.96,1.26,0.04,36354.00,0.11,618.70,162.20,0.05
+DAX 30,Germany,2007.00,8067.31,3.91,0.02,91.69,1.37,0.03,41640.00,0.09,714.38,231.95,0.05
+DAX 30,Germany,2008.00,4810.20,3.68,0.03,41.12,1.47,0.01,45613.00,0.08,750.91,227.47,0.04
+DAX 30,Germany,2009.00,5957.43,3.78,,74.47,1.39,-0.06,41650.00,0.08,603.23,170.94,0.03
+DAX 30,Germany,2010.00,6914.19,3.84,0.01,89.15,1.33,0.04,41572.00,0.08,669.57,178.90,0.03
+DAX 30,Germany,2011.00,5898.35,3.77,0.02,98.56,1.39,0.04,46706.00,0.07,758.60,184.02,0.03
+DAX 30,Germany,2012.00,7612.39,3.88,0.02,87.86,1.29,,43856.00,0.07,710.95,215.16,0.02
+DAX 30,Germany,2013.00,9552.16,3.98,0.02,97.63,1.33,,46299.00,0.07,743.97,215.01,0.02
+DAX 30,Germany,2014.00,9805.55,3.99,0.01,59.29,1.33,0.02,48024.00,0.07,786.55,257.40,0.03
+DAX 30,Germany,2015.00,10743.01,4.03,0.01,37.19,1.11,0.01,41103.00,0.06,683.20,255.02,0.02
+DAX 30,Germany,2016.00,11481.06,4.06,,51.97,1.11,0.02,42136.00,0.06,716.94,255.97,0.02
+DAX 30,Germany,2017.00,12917.64,4.11,0.02,57.88,1.13,0.03,44553.00,0.06,752.02,257.66,0.02
+DAX 30,Germany,2018.00,10558.96,4.02,0.02,49.52,1.18,0.01,47811.00,0.05,795.96,243.72,0.03
+DAX 30,Germany,2019.00,13249.01,4.12,0.01,59.88,1.12,0.01,46468.00,0.05,737.94,223.82,0.02
+DAX 30,Germany,2020.00,13718.78,4.14,0.01,47.02,1.14,-0.05,45724.00,0.06,678.29,221.53,0.01
+CAC 40,France,1980.00,394.44,2.60,0.14,21.59,4.23,0.02,1938.00,,19.77,-1.64,0.11
+CAC 40,France,1981.00,325.88,2.51,0.13,31.77,5.43,0.01,1693.00,,16.94,-1.38,0.14
+CAC 40,France,1982.00,353.24,2.55,0.12,28.52,6.58,0.03,1600.00,,15.58,-2.19,0.13
+CAC 40,France,1983.00,552.60,2.74,0.09,26.19,7.62,0.01,1524.00,0.09,14.93,-0.23,0.11
+CAC 40,France,1984.00,643.20,2.81,0.08,25.88,8.74,0.02,1436.00,0.10,13.97,0.10,0.12
+CAC 40,France,1985.00,930.21,2.97,0.06,24.09,8.99,0.02,1488.00,0.10,14.60,-0.31,0.11
+CAC 40,France,1986.00,1403.64,3.15,0.03,12.51,6.93,0.02,2064.00,0.11,19.92,-0.14,0.08
+CAC 40,France,1987.00,1000.00,3.00,0.03,15.40,6.01,0.03,2485.00,0.11,23.51,-1.56,0.08
+CAC 40,France,1988.00,1573.94,3.20,0.03,12.58,5.96,0.05,2695.00,0.10,25.43,-1.02,0.09
+CAC 40,France,1989.00,2001.08,3.30,0.03,15.86,6.38,0.04,2697.00,0.10,25.28,-1.07,0.08
+CAC 40,France,1990.00,1509.00,3.18,0.03,27.28,5.44,0.03,3322.00,0.09,31.36,-1.52,0.09
+CAC 40,France,1991.00,1765.70,3.25,0.03,19.50,5.64,0.01,3304.00,0.10,30.80,-0.61,0.08
+CAC 40,France,1992.00,1857.80,3.27,0.02,19.41,5.30,0.02,3630.00,0.11,33.31,1.43,0.07
+CAC 40,France,1993.00,2268.20,3.36,0.02,14.52,5.66,-0.01,3412.00,0.12,30.38,3.39,0.06
+CAC 40,France,1994.00,1881.20,3.27,0.02,17.16,5.55,0.02,3582.00,0.12,31.23,3.18,0.07
+CAC 40,France,1995.00,1872.00,3.27,0.02,19.03,4.99,0.02,4099.00,0.12,36.32,3.90,0.07
+CAC 40,France,1996.00,2315.70,3.36,0.02,25.23,5.12,0.01,4097.00,0.12,35.44,4.36,0.06
+CAC 40,France,1997.00,2998.90,3.48,0.01,18.33,5.84,0.02,3694.00,0.12,32.50,6.67,0.06
+CAC 40,France,1998.00,3942.66,3.60,0.01,11.35,5.90,0.04,3807.00,0.12,33.61,6.29,0.05
+CAC 40,France,1999.00,5958.32,3.78,0.01,26.10,1.07,0.03,24682.00,0.11,215.66,35.72,0.06
+CAC 40,France,2000.00,5926.42,3.77,0.02,28.44,0.92,0.04,22420.00,0.10,197.71,18.15,0.06
+CAC 40,France,2001.00,4624.58,3.67,0.02,19.39,0.90,0.02,22453.00,0.09,193.29,21.66,0.05
+CAC 40,France,2002.00,3063.91,3.49,0.02,29.46,0.95,0.01,24293.00,0.09,203.97,29.88,0.05
+CAC 40,France,2003.00,3557.90,3.55,0.02,32.13,1.13,0.01,29634.00,0.09,241.52,26.31,0.04
+CAC 40,France,2004.00,3821.16,3.58,0.02,43.15,1.24,0.03,33803.00,0.09,268.87,21.46,0.04
+CAC 40,France,2005.00,4715.23,3.67,0.02,59.41,1.24,0.02,34773.00,0.09,269.11,1.88,0.04
+CAC 40,France,2006.00,5541.75,3.74,0.02,61.96,1.26,0.02,36474.00,0.09,272.13,-5.41,0.05
+CAC 40,France,2007.00,5614.08,3.75,0.01,91.69,1.37,0.02,41561.00,0.08,309.42,-19.00,0.05
+CAC 40,France,2008.00,3217.97,3.51,0.03,41.12,1.47,,45519.00,0.07,325.40,-33.94,0.04
+CAC 40,France,2009.00,3936.33,3.60,,74.47,1.39,-0.03,41740.00,0.09,286.02,-21.37,0.03
+CAC 40,France,2010.00,3804.78,3.58,0.02,89.15,1.33,0.02,40678.00,0.09,273.17,-34.15,0.03
+CAC 40,France,2011.00,3159.81,3.50,0.02,98.56,1.39,0.02,43848.00,0.09,297.86,-55.81,0.03
+CAC 40,France,2012.00,3641.07,3.56,0.02,87.86,1.29,,40872.00,0.10,278.07,-34.78,0.02
+CAC 40,France,2013.00,4295.95,3.63,0.01,97.63,1.33,0.01,42605.00,0.10,291.15,-29.09,0.02
+CAC 40,France,2014.00,4272.75,3.63,0.01,59.29,1.33,0.01,43069.00,0.10,293.61,-32.71,0.03
+CAC 40,France,2015.00,4637.06,3.67,,37.19,1.11,0.01,36653.00,0.10,254.50,-13.82,0.02
+CAC 40,France,2016.00,4862.31,3.69,,51.97,1.11,0.01,37063.00,0.10,254.30,-14.96,0.02
+CAC 40,France,2017.00,5312.56,3.73,0.01,57.88,1.13,0.02,38781.00,0.09,263.05,-27.63,0.02
+CAC 40,France,2018.00,4730.69,3.67,0.02,49.52,1.18,0.02,41547.00,0.09,275.76,-29.02,0.03
+CAC 40,France,2019.00,5978.06,3.78,0.01,59.88,1.12,0.02,40380.00,0.08,266.63,-26.59,0.02
+CAC 40,France,2020.00,5551.41,3.74,,47.02,1.14,-0.08,38625.00,0.08,241.71,-57.65,0.01
+IEX 35,Spain,1980.00,,3.38,0.16,21.59,71.78,0.02,37.00,,,-0.04,0.11
+IEX 35,Spain,1981.00,,3.38,0.15,31.77,92.33,,32.00,,,-0.03,0.14
+IEX 35,Spain,1982.00,,3.38,0.14,28.52,109.92,0.01,31.00,,,-0.03,0.13
+IEX 35,Spain,1983.00,,3.38,0.12,26.19,143.54,0.02,27.00,,,-0.02,0.11
+IEX 35,Spain,1984.00,,3.38,0.11,25.88,160.91,0.02,27.00,,,0.01,0.12
+IEX 35,Spain,1985.00,,3.38,0.09,24.09,170.20,0.02,28.00,,,0.01,0.11
+IEX 35,Spain,1986.00,,3.38,0.09,12.51,140.14,0.03,39.00,,,0.02,0.08
+IEX 35,Spain,1987.00,2407.10,3.38,0.05,15.40,123.51,0.06,50.00,0.20,,-0.02,0.08
+IEX 35,Spain,1988.00,2727.50,3.44,0.05,12.58,116.49,0.05,58.00,0.19,,-0.05,0.09
+IEX 35,Spain,1989.00,3000.00,3.48,0.07,15.86,118.34,0.05,64.00,0.17,,-0.10,0.08
+IEX 35,Spain,1990.00,2248.80,3.35,0.07,27.28,101.95,0.04,83.00,0.15,,-0.13,0.09
+IEX 35,Spain,1991.00,2603.30,3.42,0.06,19.50,103.87,0.03,89.00,0.16,,-0.14,0.08
+IEX 35,Spain,1992.00,2344.57,3.37,0.06,19.41,102.47,0.01,97.00,0.17,,-0.14,0.07
+IEX 35,Spain,1993.00,3615.22,3.56,0.05,14.52,127.31,-0.01,80.00,0.21,,-0.05,0.06
+IEX 35,Spain,1994.00,3087.68,3.49,0.05,17.16,133.89,0.02,81.00,0.22,,-0.03,0.07
+IEX 35,Spain,1995.00,3630.76,3.56,0.05,19.03,124.68,0.03,93.00,0.21,0.60,-0.04,0.07
+IEX 35,Spain,1996.00,5154.77,3.71,0.04,25.23,126.68,0.03,97.00,0.20,0.63,-0.01,0.06
+IEX 35,Spain,1997.00,7255.40,3.86,0.02,18.33,146.45,0.04,89.00,0.18,0.59,0.01,0.06
+IEX 35,Spain,1998.00,9836.60,3.99,0.02,11.35,149.37,0.04,93.00,0.16,0.62,-0.02,0.05
+IEX 35,Spain,1999.00,11641.40,4.07,0.02,26.10,1.07,0.04,15721.00,0.14,103.49,-12.28,0.06
+IEX 35,Spain,2000.00,9109.80,3.96,0.03,28.44,0.92,0.05,14750.00,0.12,97.13,-17.69,0.06
+IEX 35,Spain,2001.00,8397.60,3.92,0.04,19.39,0.90,0.04,15369.00,0.11,99.40,-14.28,0.05
+IEX 35,Spain,2002.00,6036.90,3.78,0.03,29.46,0.95,0.03,17107.00,0.11,108.02,-14.11,0.05
+IEX 35,Spain,2003.00,7737.20,3.89,0.03,32.13,1.13,0.03,21511.00,0.11,133.45,-20.02,0.04
+IEX 35,Spain,2004.00,9080.80,3.96,0.03,43.15,1.24,0.03,24907.00,0.11,151.49,-39.56,0.04
+IEX 35,Spain,2005.00,10733.90,4.03,0.03,59.41,1.24,0.04,26429.00,0.09,158.16,-55.42,0.04
+IEX 35,Spain,2006.00,14146.50,4.15,0.04,61.96,1.26,0.04,28389.00,0.08,168.08,-71.77,0.05
+IEX 35,Spain,2007.00,15182.29,4.18,0.03,91.69,1.37,0.04,32591.00,0.08,190.13,-85.17,0.05
+IEX 35,Spain,2008.00,9195.80,3.96,0.04,41.12,1.47,0.01,35511.00,0.11,207.17,-77.28,0.04
+IEX 35,Spain,2009.00,11940.00,4.08,,74.47,1.39,-0.04,32170.00,0.18,172.86,-13.00,0.03
+IEX 35,Spain,2010.00,9859.10,3.99,0.02,89.15,1.33,,30532.00,0.20,162.09,-14.57,0.03
+IEX 35,Spain,2011.00,8566.30,3.93,0.03,98.56,1.39,-0.01,31678.00,0.21,170.26,4.10,0.03
+IEX 35,Spain,2012.00,8167.50,3.91,0.02,87.86,1.29,-0.03,28323.00,0.25,147.38,27.36,0.02
+IEX 35,Spain,2013.00,9916.70,4.00,0.01,97.63,1.33,-0.01,29068.00,0.26,151.68,53.24,0.02
+IEX 35,Spain,2014.00,10279.50,4.01,,59.29,1.33,0.01,29501.00,0.24,155.09,42.50,0.03
+IEX 35,Spain,2015.00,9544.20,3.98,-0.01,37.19,1.11,0.04,25742.00,0.22,135.11,36.35,0.02
+IEX 35,Spain,2016.00,9352.10,3.97,,51.97,1.11,0.03,26523.00,0.20,139.01,49.16,0.02
+IEX 35,Spain,2017.00,10043.90,4.00,0.02,57.88,1.13,0.03,28170.00,0.17,148.80,47.33,0.02
+IEX 35,Spain,2018.00,8539.90,3.93,0.02,49.52,1.18,0.02,30389.00,0.15,158.33,38.70,0.03
+IEX 35,Spain,2019.00,9549.20,3.98,0.01,59.88,1.12,0.02,29565.00,0.14,155.49,41.94,0.02
+IEX 35,Spain,2020.00,8073.70,3.91,,47.02,1.14,-0.11,27057.00,0.16,143.05,19.10,0.01