From 0fb7059c716bd65a58d6bae8d56c49996117b175 Mon Sep 17 00:00:00 2001
From: Factorino73 <masenkin73@xmail.ru>
Date: Fri, 18 Oct 2024 01:38:52 +0400
Subject: [PATCH] lab_2: 2nd dataset is done

---
 lab_2/lab2.ipynb           |  990 ++++++++--
 static/csv/ds_salaries.csv | 3756 ++++++++++++++++++++++++++++++++++++
 2 files changed, 4628 insertions(+), 118 deletions(-)
 create mode 100644 static/csv/ds_salaries.csv

diff --git a/lab_2/lab2.ipynb b/lab_2/lab2.ipynb
index cd3c03b..aac19b2 100644
--- a/lab_2/lab2.ipynb
+++ b/lab_2/lab2.ipynb
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -166,114 +166,106 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>est_diameter_min</th>\n",
-       "      <th>est_diameter_max</th>\n",
-       "      <th>relative_velocity</th>\n",
-       "      <th>miss_distance</th>\n",
-       "      <th>absolute_magnitude</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>count</th>\n",
-       "      <td>9.083600e+04</td>\n",
-       "      <td>90836.000000</td>\n",
-       "      <td>90836.000000</td>\n",
-       "      <td>90836.000000</td>\n",
-       "      <td>9.083600e+04</td>\n",
-       "      <td>90836.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
+       "      <th>id</th>\n",
+       "      <td>90836.0</td>\n",
        "      <td>1.438288e+07</td>\n",
-       "      <td>0.127432</td>\n",
-       "      <td>0.284947</td>\n",
-       "      <td>48066.918918</td>\n",
-       "      <td>3.706655e+07</td>\n",
-       "      <td>23.527103</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
        "      <td>2.087202e+07</td>\n",
-       "      <td>0.298511</td>\n",
-       "      <td>0.667491</td>\n",
-       "      <td>25293.296961</td>\n",
-       "      <td>2.235204e+07</td>\n",
-       "      <td>2.894086</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
        "      <td>2.000433e+06</td>\n",
-       "      <td>0.000609</td>\n",
-       "      <td>0.001362</td>\n",
-       "      <td>203.346433</td>\n",
-       "      <td>6.745533e+03</td>\n",
-       "      <td>9.230000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
        "      <td>3.448110e+06</td>\n",
-       "      <td>0.019256</td>\n",
-       "      <td>0.043057</td>\n",
-       "      <td>28619.020645</td>\n",
-       "      <td>1.721082e+07</td>\n",
-       "      <td>21.340000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
        "      <td>3.748362e+06</td>\n",
-       "      <td>0.048368</td>\n",
-       "      <td>0.108153</td>\n",
-       "      <td>44190.117890</td>\n",
-       "      <td>3.784658e+07</td>\n",
-       "      <td>23.700000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
        "      <td>3.884023e+06</td>\n",
-       "      <td>0.143402</td>\n",
-       "      <td>0.320656</td>\n",
-       "      <td>62923.604633</td>\n",
-       "      <td>5.654900e+07</td>\n",
-       "      <td>25.700000</td>\n",
+       "      <td>5.427591e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>5.427591e+07</td>\n",
-       "      <td>37.892650</td>\n",
-       "      <td>84.730541</td>\n",
-       "      <td>236990.128088</td>\n",
+       "      <th>est_diameter_min</th>\n",
+       "      <td>90836.0</td>\n",
+       "      <td>1.274321e-01</td>\n",
+       "      <td>2.985112e-01</td>\n",
+       "      <td>6.089126e-04</td>\n",
+       "      <td>1.925551e-02</td>\n",
+       "      <td>4.836765e-02</td>\n",
+       "      <td>1.434019e-01</td>\n",
+       "      <td>3.789265e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>est_diameter_max</th>\n",
+       "      <td>90836.0</td>\n",
+       "      <td>2.849469e-01</td>\n",
+       "      <td>6.674914e-01</td>\n",
+       "      <td>1.361570e-03</td>\n",
+       "      <td>4.305662e-02</td>\n",
+       "      <td>1.081534e-01</td>\n",
+       "      <td>3.206564e-01</td>\n",
+       "      <td>8.473054e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>relative_velocity</th>\n",
+       "      <td>90836.0</td>\n",
+       "      <td>4.806692e+04</td>\n",
+       "      <td>2.529330e+04</td>\n",
+       "      <td>2.033464e+02</td>\n",
+       "      <td>2.861902e+04</td>\n",
+       "      <td>4.419012e+04</td>\n",
+       "      <td>6.292360e+04</td>\n",
+       "      <td>2.369901e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>miss_distance</th>\n",
+       "      <td>90836.0</td>\n",
+       "      <td>3.706655e+07</td>\n",
+       "      <td>2.235204e+07</td>\n",
+       "      <td>6.745533e+03</td>\n",
+       "      <td>1.721082e+07</td>\n",
+       "      <td>3.784658e+07</td>\n",
+       "      <td>5.654900e+07</td>\n",
        "      <td>7.479865e+07</td>\n",
-       "      <td>33.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>absolute_magnitude</th>\n",
+       "      <td>90836.0</td>\n",
+       "      <td>2.352710e+01</td>\n",
+       "      <td>2.894086e+00</td>\n",
+       "      <td>9.230000e+00</td>\n",
+       "      <td>2.134000e+01</td>\n",
+       "      <td>2.370000e+01</td>\n",
+       "      <td>2.570000e+01</td>\n",
+       "      <td>3.320000e+01</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 id  est_diameter_min  est_diameter_max  relative_velocity  \\\n",
-       "count  9.083600e+04      90836.000000      90836.000000       90836.000000   \n",
-       "mean   1.438288e+07          0.127432          0.284947       48066.918918   \n",
-       "std    2.087202e+07          0.298511          0.667491       25293.296961   \n",
-       "min    2.000433e+06          0.000609          0.001362         203.346433   \n",
-       "25%    3.448110e+06          0.019256          0.043057       28619.020645   \n",
-       "50%    3.748362e+06          0.048368          0.108153       44190.117890   \n",
-       "75%    3.884023e+06          0.143402          0.320656       62923.604633   \n",
-       "max    5.427591e+07         37.892650         84.730541      236990.128088   \n",
+       "                      count          mean           std           min  \\\n",
+       "id                  90836.0  1.438288e+07  2.087202e+07  2.000433e+06   \n",
+       "est_diameter_min    90836.0  1.274321e-01  2.985112e-01  6.089126e-04   \n",
+       "est_diameter_max    90836.0  2.849469e-01  6.674914e-01  1.361570e-03   \n",
+       "relative_velocity   90836.0  4.806692e+04  2.529330e+04  2.033464e+02   \n",
+       "miss_distance       90836.0  3.706655e+07  2.235204e+07  6.745533e+03   \n",
+       "absolute_magnitude  90836.0  2.352710e+01  2.894086e+00  9.230000e+00   \n",
        "\n",
-       "       miss_distance  absolute_magnitude  \n",
-       "count   9.083600e+04        90836.000000  \n",
-       "mean    3.706655e+07           23.527103  \n",
-       "std     2.235204e+07            2.894086  \n",
-       "min     6.745533e+03            9.230000  \n",
-       "25%     1.721082e+07           21.340000  \n",
-       "50%     3.784658e+07           23.700000  \n",
-       "75%     5.654900e+07           25.700000  \n",
-       "max     7.479865e+07           33.200000  "
+       "                             25%           50%           75%           max  \n",
+       "id                  3.448110e+06  3.748362e+06  3.884023e+06  5.427591e+07  \n",
+       "est_diameter_min    1.925551e-02  4.836765e-02  1.434019e-01  3.789265e+01  \n",
+       "est_diameter_max    4.305662e-02  1.081534e-01  3.206564e-01  8.473054e+01  \n",
+       "relative_velocity   2.861902e+04  4.419012e+04  6.292360e+04  2.369901e+05  \n",
+       "miss_distance       1.721082e+07  3.784658e+07  5.654900e+07  7.479865e+07  \n",
+       "absolute_magnitude  2.134000e+01  2.370000e+01  2.570000e+01  3.320000e+01  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,7 +275,7 @@
     "df.info()\n",
     "\n",
     "# Статистическое описание числовых столбцов\n",
-    "df.describe()"
+    "df.describe().transpose()"
    ]
   },
   {
@@ -299,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -367,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -486,7 +478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +688,7 @@
       "True      8840\n",
       "Name: count, dtype: int64 \n",
       "\n",
-      "Обучающая выборка:  (54501, 6)\n",
+      "Обучающая выборка:  (54501, 10)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
       "False    49197\n",
@@ -705,7 +697,7 @@
       "Процент объектов класса \"False\": 90.27%\n",
       "Процент объектов класса \"True\": 9.73%\n",
       "\n",
-      "Контрольная выборка:  (18167, 6)\n",
+      "Контрольная выборка:  (18167, 10)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
       "False    16399\n",
@@ -714,7 +706,7 @@
       "Процент объектов класса \"False\": 90.27%\n",
       "Процент объектов класса \"True\": 9.73%\n",
       "\n",
-      "Тестовая выборка:  (18168, 6)\n",
+      "Тестовая выборка:  (18168, 10)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
       "False    16400\n",
@@ -753,7 +745,7 @@
     "]].copy()\n",
     "\n",
     "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
-    "   data, \n",
+    "   df, \n",
     "   stratify_colname=\"hazardous\", \n",
     "   frac_train=0.60, \n",
     "   frac_val=0.20, \n",
@@ -835,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -843,32 +835,32 @@
      "output_type": "stream",
      "text": [
       "После применения метода oversampling:\n",
-      "Обучающая выборка:  (100699, 6)\n",
+      "Обучающая выборка:  (98782, 21784)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
-      "True     51502\n",
+      "True     49585\n",
       "False    49197\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"True\": 51.14%\n",
-      "Процент объектов класса \"False\": 48.86%\n",
+      "Процент объектов класса \"True\": 50.20%\n",
+      "Процент объектов класса \"False\": 49.80%\n",
       "\n",
-      "Контрольная выборка:  (32759, 6)\n",
+      "Контрольная выборка:  (33168, 11762)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
+      "True     16769\n",
       "False    16399\n",
-      "True     16360\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"False\": 50.06%\n",
-      "Процент объектов класса \"True\": 49.94%\n",
+      "Процент объектов класса \"True\": 50.56%\n",
+      "Процент объектов класса \"False\": 49.44%\n",
       "\n",
-      "Тестовая выборка:  (33556, 6)\n",
+      "Тестовая выборка:  (32695, 11820)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
-      "True     17156\n",
       "False    16400\n",
+      "True     16295\n",
       "Name: count, dtype: int64\n",
-      "Процент объектов класса \"True\": 51.13%\n",
-      "Процент объектов класса \"False\": 48.87%\n",
+      "Процент объектов класса \"False\": 50.16%\n",
+      "Процент объектов класса \"True\": 49.84%\n",
       "\n",
       "Для обучающей выборки аугментация данных не требуется\n",
       "Для контрольной выборки аугментация данных не требуется\n",
@@ -877,7 +869,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1500x500 with 3 Axes>"
       ]
@@ -889,7 +881,7 @@
    "source": [
     "# Метод приращения с избытком (oversampling)\n",
     "def oversample(df: DataFrame, column: str) -> DataFrame:\n",
-    "    X: DataFrame = df.drop(column, axis=1)\n",
+    "    X: DataFrame = pd.get_dummies(df.drop(column, axis=1))\n",
     "    y: DataFrame = df[column] # type: ignore\n",
     "    \n",
     "    adasyn = ADASYN()\n",
@@ -921,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +921,7 @@
      "output_type": "stream",
      "text": [
       "После применения метода undersampling:\n",
-      "Обучающая выборка:  (10608, 6)\n",
+      "Обучающая выборка:  (10608, 21784)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
       "False    5304\n",
@@ -938,7 +930,7 @@
       "Процент объектов класса \"False\": 50.00%\n",
       "Процент объектов класса \"True\": 50.00%\n",
       "\n",
-      "Контрольная выборка:  (3536, 6)\n",
+      "Контрольная выборка:  (3536, 11762)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
       "False    1768\n",
@@ -947,7 +939,7 @@
       "Процент объектов класса \"False\": 50.00%\n",
       "Процент объектов класса \"True\": 50.00%\n",
       "\n",
-      "Тестовая выборка:  (3536, 6)\n",
+      "Тестовая выборка:  (3536, 11820)\n",
       "Распределение выборки данных по классам \"hazardous\":\n",
       " hazardous\n",
       "False    1768\n",
@@ -975,7 +967,7 @@
    "source": [
     "# Метод приращения с недостатком (undersampling)\n",
     "def undersample(df: DataFrame, column: str) -> DataFrame:\n",
-    "    X: DataFrame = df.drop(column, axis=1)\n",
+    "    X: DataFrame = pd.get_dummies(df.drop(column, axis=1))\n",
     "    y: DataFrame = df[column] # type: ignore\n",
     "    \n",
     "    undersampler = RandomUnderSampler()\n",
@@ -1004,6 +996,768 @@
     "# Визуализация сбалансированности классов\n",
     "visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'hazardous')"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Датасет №2: [Зарплаты в области Data Science](https://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary).\n",
+    "\n",
+    "### Описание датасета:\n",
+    "Данный набор данных предназначен для исследования факторов, влияющих на заработную плату специалистов по данным (Data Scientists) в 2023 году. Набор данных содержит информацию о различных характеристиках работников, таких как уровень опыта, тип занятости, местоположение сотрудника и компании, удалённость работы и размер компании. Этот анализ помогает понять, какие факторы наиболее значимо влияют на уровень зарплат в области Data Science, и как изменяются заработные платы в зависимости от этих факторов.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Анализ сведений:\n",
+    "**Проблемная область:**\n",
+    "Основная задача – изучить, как различные факторы, такие как опыт, тип занятости, местоположение и удалённость работы, влияют на уровень зарплаты специалистов по данным. Это важно для понимания рыночных тенденций и формирования конкурентоспособной системы оплаты труда.\n",
+    "\n",
+    "**Актуальность:**\n",
+    "Данный набор данных актуален для компаний, стремящихся выстроить конкурентоспособные стратегии оплаты труда, а также для специалистов по данным, желающих оценить свои зарплатные ожидания в зависимости от их опыта, географии и типа занятости.\n",
+    "\n",
+    "**Объекты наблюдения:**\n",
+    "Объектами наблюдения являются специалисты по данным, работающие в различных компаниях и странах, с разным уровнем опыта и типом занятости.\n",
+    "\n",
+    "**Атрибуты объектов:**\n",
+    "- work_year: Год, в который была выплачена зарплата.\n",
+    "- experience_level: Уровень опыта сотрудника.\n",
+    "    - EN: Начальный.\n",
+    "    - MI: Средний.\n",
+    "    - SE: Старший.\n",
+    "    - EX: Исполнительный.\n",
+    "- employment_type: Тип занятости.\n",
+    "    - PT: Полная.\n",
+    "    - FT: Частичная.\n",
+    "    - CT: Контрактная.\n",
+    "    - FL: Фриланс.\n",
+    "- job_title: Должность, которую занимал сотрудник.\n",
+    "- salary: Общая сумма выплаченной заработной платы.\n",
+    "- salary_currency: Валюта, в которой выплачена зарплата.\n",
+    "- salary_in_usd: Заработная плата, конвертированная в доллары США (USD).\n",
+    "- employee_residence: Страна проживания сотрудника в год выплаты зарплаты.\n",
+    "- remote_ratio: Доля удалённой работы (например, 100% удалённо или частично удалённо).\n",
+    "- company_location: Страна, в которой расположена основная офисная компания работодателя.\n",
+    "- company_size: Среднее количество сотрудников, работающих в компании.\n",
+    "\n",
+    "**Связь между объектами:**\n",
+    "Набор данных позволяет исследовать взаимосвязи между факторами, такими как уровень опыта, тип занятости и местоположение сотрудника, с уровнем его заработной платы. Взаимосвязи между этими факторами могут дать полезную информацию о влиянии определённых условий работы на доход.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Качество набора данных:\n",
+    "**Информативность:**\n",
+    "Датасет предоставляет важную информацию для анализа различных факторов, влияющих на зарплату специалистов по данным. Он включает множество атрибутов, которые можно использовать для построения моделей и анализа.\n",
+    "\n",
+    "**Степень покрытия:**\n",
+    "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",
+    "\n",
+    "**Согласованность меток:**\n",
+    "Все категории, такие как уровни опыта или типы занятости, имеют четко определённые метки, что упрощает анализ и моделирование.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### Бизес-цели:\n",
+    "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",
+    "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",
+    "\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",
+    "\n",
+    "---\n",
+    "\n",
+    "### Технические цели:\n",
+    "1. **Построение модели прогнозирования зарплат:**\n",
+    "Создание модели, которая будет предсказывать уровень зарплаты специалиста по данным на основе таких факторов, как опыт, регион и удалённость работы.\n",
+    "2. **Анализ влияния опыта и удалённости на зарплату:**\n",
+    "Исследование того, как уровень опыта и удалённость работы влияют на заработную плату, что может помочь компаниям лучше планировать условия найма.\n",
+    "3. **Оптимизация найма специалистов:**\n",
+    "С помощью анализа компания может определить наиболее значимые факторы для назначения зарплат, чтобы предлагать более конкурентные условия и привлекать лучших специалистов.\n",
+    "\n",
+    "**Входные данные:**\n",
+    "Год, уровень опыта, тип занятости, должность, зарплата, страна проживания, удалённость работы.\n",
+    "\n",
+    "**Целевой признак:**\n",
+    "Признак \"salary_in_usd\" – заработная плата в долларах США.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Выгрузка данных из файла в DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df: DataFrame = pd.read_csv('..//static//csv//ds_salaries.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Краткая информация о DataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 3755 entries, 0 to 3754\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column              Non-Null Count  Dtype \n",
+      "---  ------              --------------  ----- \n",
+      " 0   work_year           3755 non-null   int64 \n",
+      " 1   experience_level    3755 non-null   object\n",
+      " 2   employment_type     3755 non-null   object\n",
+      " 3   job_title           3755 non-null   object\n",
+      " 4   salary              3755 non-null   int64 \n",
+      " 5   salary_currency     3755 non-null   object\n",
+      " 6   salary_in_usd       3755 non-null   int64 \n",
+      " 7   employee_residence  3755 non-null   object\n",
+      " 8   remote_ratio        3755 non-null   int64 \n",
+      " 9   company_location    3755 non-null   object\n",
+      " 10  company_size        3755 non-null   object\n",
+      "dtypes: int64(4), object(7)\n",
+      "memory usage: 322.8+ KB\n"
+     ]
+    },
+    {
+     "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>work_year</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>2022.373635</td>\n",
+       "      <td>0.691448</td>\n",
+       "      <td>2020.0</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>2022.0</td>\n",
+       "      <td>2023.0</td>\n",
+       "      <td>2023.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>salary</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>190695.571771</td>\n",
+       "      <td>671676.500508</td>\n",
+       "      <td>6000.0</td>\n",
+       "      <td>100000.0</td>\n",
+       "      <td>138000.0</td>\n",
+       "      <td>180000.0</td>\n",
+       "      <td>30400000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>137570.389880</td>\n",
+       "      <td>63055.625278</td>\n",
+       "      <td>5132.0</td>\n",
+       "      <td>95000.0</td>\n",
+       "      <td>135000.0</td>\n",
+       "      <td>175000.0</td>\n",
+       "      <td>450000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>remote_ratio</th>\n",
+       "      <td>3755.0</td>\n",
+       "      <td>46.271638</td>\n",
+       "      <td>48.589050</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                count           mean            std     min       25%  \\\n",
+       "work_year      3755.0    2022.373635       0.691448  2020.0    2022.0   \n",
+       "salary         3755.0  190695.571771  671676.500508  6000.0  100000.0   \n",
+       "salary_in_usd  3755.0  137570.389880   63055.625278  5132.0   95000.0   \n",
+       "remote_ratio   3755.0      46.271638      48.589050     0.0       0.0   \n",
+       "\n",
+       "                    50%       75%         max  \n",
+       "work_year        2022.0    2023.0      2023.0  \n",
+       "salary         138000.0  180000.0  30400000.0  \n",
+       "salary_in_usd  135000.0  175000.0    450000.0  \n",
+       "remote_ratio        0.0     100.0       100.0  "
+      ]
+     },
+     "execution_count": 19,
+     "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 DataFrame не имеет пустых значений признаков. Нет необходимости использовать методы заполнения пропущенных данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "work_year             False\n",
+      "experience_level      False\n",
+      "employment_type       False\n",
+      "job_title             False\n",
+      "salary                False\n",
+      "salary_currency       False\n",
+      "salary_in_usd         False\n",
+      "employee_residence    False\n",
+      "remote_ratio          False\n",
+      "company_location      False\n",
+      "company_size          False\n",
+      "dtype: bool \n",
+      "\n",
+      "work_year             0\n",
+      "experience_level      0\n",
+      "employment_type       0\n",
+      "job_title             0\n",
+      "salary                0\n",
+      "salary_currency       0\n",
+      "salary_in_usd         0\n",
+      "employee_residence    0\n",
+      "remote_ratio          0\n",
+      "company_location      0\n",
+      "company_size          0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Проверка пропущенных данных\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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка work_year:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 76\n",
+      "\tМинимальное значение: 2020\n",
+      "\tМаксимальное значение: 2023\n",
+      "\t1-й квартиль (Q1): 2022.0\n",
+      "\t3-й квартиль (Q3): 2023.0\n",
+      "\n",
+      "Колонка salary:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 113\n",
+      "\tМинимальное значение: 6000\n",
+      "\tМаксимальное значение: 30400000\n",
+      "\t1-й квартиль (Q1): 100000.0\n",
+      "\t3-й квартиль (Q3): 180000.0\n",
+      "\n",
+      "Колонка salary_in_usd:\n",
+      "\tЕсть выбросы: Да\n",
+      "\tКоличество выбросов: 63\n",
+      "\tМинимальное значение: 5132\n",
+      "\tМаксимальное значение: 450000\n",
+      "\t1-й квартиль (Q1): 95000.0\n",
+      "\t3-й квартиль (Q3): 175000.0\n",
+      "\n",
+      "Колонка remote_ratio:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0\n",
+      "\tМаксимальное значение: 100\n",
+      "\t1-й квартиль (Q1): 0.0\n",
+      "\t3-й квартиль (Q3): 100.0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Числовые столбцы DataFrame\n",
+    "numeric_columns: list[str] = [\n",
+    "    'work_year',\n",
+    "    'salary',\n",
+    "    'salary_in_usd',\n",
+    "    'remote_ratio'\n",
+    "]\n",
+    "\n",
+    "# Проверка выбросов\n",
+    "check_outliers(df, numeric_columns)\n",
+    "visualize_outliers(df, numeric_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка work_year:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 2020.5\n",
+      "\tМаксимальное значение: 2023.0\n",
+      "\t1-й квартиль (Q1): 2022.0\n",
+      "\t3-й квартиль (Q3): 2023.0\n",
+      "\n",
+      "Колонка salary:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 6000.0\n",
+      "\tМаксимальное значение: 300000.0\n",
+      "\t1-й квартиль (Q1): 100000.0\n",
+      "\t3-й квартиль (Q3): 180000.0\n",
+      "\n",
+      "Колонка salary_in_usd:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 5132.0\n",
+      "\tМаксимальное значение: 295000.0\n",
+      "\t1-й квартиль (Q1): 95000.0\n",
+      "\t3-й квартиль (Q3): 175000.0\n",
+      "\n",
+      "Колонка remote_ratio:\n",
+      "\tЕсть выбросы: Нет\n",
+      "\tКоличество выбросов: 0\n",
+      "\tМинимальное значение: 0\n",
+      "\tМаксимальное значение: 100\n",
+      "\t1-й квартиль (Q1): 0.0\n",
+      "\t3-й квартиль (Q3): 100.0\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/nnzyyey5556pqKjIXnvtlQMPPDC33377UtUNAMuLNdZYI3fccccil62//vp57LHHarV9+k+tP/2n2qeeempOPfXUWm0jRowo//v4448vDNZfe+217L333qmsrFyC6gHg68UN6oWdccYZGTJkSL7zne+kqqoqZ599dh5//PHy8pYtW+bUU0/NCy+8kIYNG2bjjTfObbfdlgYNPp5J+sYbb8ywYcOyySabpEePHjnnnHPygx/8oK52B76witKn/w8a4Esye/bsrLjiihkzZkx22WWXui4HAFgG3nnnnYwfPz677bZbnnnmmfLNewDg89tmm22y5ppr5pxzzqnrUoD/xxPpwJeupqYmM2bMyBlnnJE2bdpkxx13rOuSAIBlZP31188777yTU045RYgOAF/QghvU48ePX6opWIEvnyAd+MKuvvrq7L///otc1r1799x6663p2bNnVlpppYwdOzaNGvlPDwAsLyZPnlzXJQDAcqPoBvWaa6652C8tvfjii7P33nsv8Xb+8Y9/ZODAgYtdPnv27CUeC74pTO0CfGGzZs3K9OnTF7mscePG6d69+1dcEQAAACxfXnnllXz44YeLXNapU6e0bNlyicd6//3389prry12+be//e2lrg+Wd4J0AAAAAAAo0KCuCwAAAAAAgK8zQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToA9c748eNTUVGR8ePH13UpAAAAwDeAIL0euP7661NRUbHI11prrVXX5cE3Rr9+/bLvvvsmSfbdd9/069evTuv5Ik4++eTcdNNNdV3GZ7rgggsyduzYZTrm8ccfnx49eiRJxo4dm4qKimU6PgAAALD8aVTXBbDkfvOb32SNNdYov//d735Xh9UA9dnJJ5+c3XbbLYMGDarrUgpdcMEFWWGFFco3MBbYaqut8v7776dJkyZ1UxgAAADwjSJIr0e+973v1XoC9rLLLsuMGTPqriCg0EcffZSamhph7/9TKpUyd+7cNGvW7AuP1aBBgzRt2nQZVAUAAADw2UztUg988MEHST4Ojj7LgmkKJk+eXG6rqanJOuusk4qKilpTJPz73//Ovvvum5VXXjlNmzZN586dM3To0Pzvf/+rNebxxx+/yGllGjX6/+/D9OvXL2uttVYef/zx9OnTJ82aNUvPnj1z0UUXLbQvxx57bDbccMO0bt06zZs3z3e+852MGzeuVr/JkyeXt/Pp6Sfmzp2btm3bpqKiIqeffvpCdXbs2DEffvhhrXX+/Oc/l8f75M2Hv/3tb9luu+1SVVWVysrKrLLKKjnxxBMzf/78zzzWC7b33HPPZY899kirVq3Svn37/PznP8/cuXNr9R0zZky23nrrdOzYMZWVlendu3cuvPDChcbcaaed0qNHjzRt2jQdO3bMjjvumP/85z+1+izYj9GjRy+0/uqrr56KioocfPDB5ba33347RxxxRNZee+20aNEirVq1ysCBA/PUU0/VWnfw4MFp2rRpnn322VrtAwYMSNu2bfP666+X215++eXsvvvuadeuXb71rW9ls802y6233lprvQVzWC94VVZWZtVVV82oUaNSKpWKD+7/s7hzb1FTqnzynPn065PefPPNDBs2LN26dUvDhg3LfVq0aLFENS3Ogu2ffvrpGT16dFZZZZVUVlbmmWeeSZI899xz2W233dKuXbs0bdo0G220UW6++eZaYyz4/P7zn//MoYcemg4dOqRNmzbZf//988EHH+Tdd9/NT37yk7Rt2zZt27bNkUceudCxnDNnTn7xi1+ka9euqayszGqrrZbTTz+9Vr+KiorMmTMnl19+eXn/P/nE92uvvZahQ4emU6dOqayszJprrpk//vGPS31MevToke233z533nlnNtpoozRr1iwXX3xxkiX7TPTo0SNPP/107r///oV+9oubI/26667LhhtumGbNmmWFFVbIPvvsk9dee22pawcAAAD4JE+k1wMLgvTKysrPtf6VV165UBibJHfffXdefvnlDBkyJJ07d87TTz+dSy65JE8//XQefvjhhQLICy+8sFbY+Olg/5133sm2226bPfbYI3vttVeuvfbaHHDAAWnSpEmGDh2aJKmurs5ll12WvfbaK/vtt19mzZqVP/zhDxkwYEAeffTRrLfeerXGbNq0acaMGVNr+om//vWvCwXVnzRr1qzccsst2XnnncttY8aMSdOmTRdab+zYsWnRokUOP/zwtGjRIvfdd1+OPfbYVFdX57TTTlvsNj5pjz32SI8ePTJq1Kg8/PDDOeecc/LOO+/kiiuuqHXs1lxzzey4445p1KhR/v73v+fAAw9MTU1NDjrooFrjDR8+PJ07d87rr7+e8847L/3798+kSZPyrW99a6HjMmLEiHLbQw89lFdeeWWh+l5++eXcdNNN2X333dOzZ89Mnz49F198cfr27ZtnnnkmVVVVSZKzzz479913XwYPHpwJEyakYcOGufjii3PXXXflyiuvLPebPn16+vTpk/feey+HHnpo2rdvn8svvzw77rhjrr/++lrHPfn/pyR6//33c8011+Q3v/lNOnbsmGHDhi3R8V1w/Bace0cddVRh3+HDh+c73/lOko/PlRtvvLHW8sGDB+eee+7JIYccknXXXTcNGzbMJZdckieeeGKJ6ykyZsyYzJ07N8OHD09lZWXatWuXp59+OltssUVWXHHF/PrXv07z5s1z7bXXZtCgQbnhhhsWOmaHHHJIOnfunBNOOCEPP/xwLrnkkrRp0yYPPfRQunXrlpNPPjm33XZbTjvttKy11lr5yU9+kuTjJ7533HHHjBs3LsOGDct6662XO++8M7/85S/z2muv5ayzzkry8X8TfvrTn2aTTTbJ8OHDkySrrLJKko9/vptttln5hkyHDh1y++23Z9iwYamurq51zi2J559/PnvttVf233//7LfffllttdWSLNlnYvTo0TnkkEPSokWL/Pa3v02SdOrUabHbGjt2bIYMGZKNN944o0aNyvTp03P22WfnwQcfzJNPPpk2bdosVe0AAAAAZSW+9kaPHl1KUnrqqadqtfft27e05ppr1mobM2ZMKUlp0qRJpVKpVJo7d26pW7dupYEDB5aSlMaMGVPu+9577y20rT//+c+lJKUHHnig3HbccceVkpTeeuutxdbYt2/fUpLSGWecUW6bN29eab311it17Nix9MEHH5RKpVLpo48+Ks2bN6/Wuu+8806pU6dOpaFDh5bbJk2aVEpS2muvvUqNGjUqTZs2rbxsm222Kf3oRz8qJSmddtppC9W51157lbbffvty+yuvvFJq0KBBaa+99lpoPxZ1DPbff//St771rdLcuXMXu7+f3N6OO+5Yq/3AAw9c6Oe1qO0MGDCgtPLKKxdu49prry0lKf3rX/8qtyUp7bbbbqVGjRrVah82bFj5uBx00EHl9rlz55bmz59fa9xJkyaVKisrSyNHjqzVfuedd5aSlE466aTSyy+/XGrRokVp0KBBtfqMGDGilKT0j3/8o9w2a9asUs+ePUs9evQob2vcuHGlJKVx48bVqqVBgwalAw88sHC/F/jNb35TSlKaMWNGuW3NNdcs9e3bd6G+L7zwQilJ6fLLLy+3LfgZLfD++++XGjRoUNp///1rrTt48OBS8+bNl6imxVlwzrZq1ar05ptv1lq2zTbblNZee+1a51RNTU2pT58+pV69epXbFnx+BwwYUKqpqSm3b7755qWKiorSz372s3LbRx99VFpppZVqHYubbrqp/PP7pN12261UUVFRevHFF8ttzZs3Lw0ePHih/Rg2bFipS5cutY55qVQq/fCHPyy1bt16kefy4nTv3r2UpHTHHXcstGxJPxOL+3l/+vz64IMPSh07diyttdZapffff7/c75ZbbiklKR177LFLXDcAAADAp5napR5YMNVKhw4dlnrd888/P//73/9y3HHHLbTsk/MUz507NzNmzMhmm22WJJ/r6dxGjRpl//33L79v0qRJ9t9//7z55pt5/PHHkyQNGzYszxddU1OTt99+Ox999FE22mijRW5zgw02yJprrpkrr7wySfLKK69k3LhxC33x4CcNHTo0d9xxR6ZNm5Ykufzyy7P55ptn1VVXXajvJ4/BrFmzMmPGjHznO9/Je++9l+eee26J9vvTT5QfcsghSZLbbrttkduZOXNmZsyYkb59++bll1/OzJkza63/3nvvZcaMGZk4cWIuvfTSdOrUaaHaO3XqlO222y5jxowpr3PttddmyJAhC9VXWVlZ/uuB+fPn53//+19atGiR1VZbbaFj/v3vfz/7779/Ro4cmV122SVNmzYtT8WxwG233ZZNNtkkW265ZbmtRYsWGT58eCZPnlyeyuTT+ztlypSceuqpqampydZbb72II7mwBX9BsCRzYS/JX27MmTMnNTU1ad++/RJt//PYdddda31W33777dx3333ZY489yufYjBkz8r///S8DBgzICy+8sNDUI8OGDav1FyGbbrppSqVSraf4GzZsmI022igvv/xyue22225Lw4YNc+ihh9Ya7xe/+EVKpVJuv/32wtpLpVJuuOGG7LDDDimVSuVaZ8yYkQEDBmTmzJlL/d+Gnj17ZsCAAQu1L81nYkn861//yptvvpkDDzyw1vmy3XbbZfXVV19o6iEAAACApSFIrwdeeeWVNGrUaKmD9JkzZ+bkk0/O4YcfvsjpEN5+++38/Oc/T6dOndKsWbN06NAhPXv2LK+7tKqqqtK8efNabQsC4E/O2X755ZdnnXXWSdOmTdO+fft06NAht95662K3OWTIkHJgPHbs2PTp0ye9evVabB3rrbde1lprrVxxxRUplUrl6R4W5emnn87OO++c1q1bp1WrVunQoUP22WefJEt+DD5dyyqrrJIGDRrU2ucHH3ww/fv3T/PmzdOmTZt06NAhv/nNbxa5nZEjR6ZDhw5Zf/31M3ny5IwfPz4tW7ZcaLtDhgzJn/70p8ybNy/XXXdd2rZtu8iAuqamJmeddVZ69eqVysrKrLDCCunQoUP+/e9/L3IfTz/99LRr1y4TJ07MOeeck44dO9Za/sorr5Sn5/ikNdZYo7z8kwYNGpQOHTqke/fuOf7443P00Udn1113XWj9RZkxY0YaN25ca1qbxXn33XeTpHCu8/bt26dXr1657LLLctddd+XNN9/MjBkzMm/evCWqZ0ks+Awt8OKLL6ZUKuWYY45Jhw4dar0W3OB68803a63TrVu3Wu9bt26dJOnatetC7e+88075/SuvvJKqqqqFzpfF/Ww+7a233sq7776bSy65ZKFaF3yGPl3rZ/n08VhgaT4TS2LBvi3q3Fx99dU/c98BAAAAipgjvR54/vnns/LKK9f6cs8lccopp6RBgwb55S9/udAXiCYfz+390EMP5Ze//GXWW2+9tGjRIjU1NfnBD36QmpqaZVV+LVdddVX23XffDBo0KL/85S/TsWPHNGzYMKNGjcpLL720yHX22WefHHnkkXn44Ydz+eWX5+ijj/7M7QwdOjQXXHBBNtlkk0ybNi177LFHzjjjjFp93n333fTt2zetWrXKyJEjs8oqq6Rp06Z54okn8qtf/epzH4NPzy3/0ksvZZtttsnqq6+eM888M127dk2TJk1y22235ayzzlpoOz/96U+zzTbb5NVXX81ZZ52VXXfdNQ899FA5TF1gu+22S5MmTXLTTTdlzJgxGTx48CK/kPbkk0/OMccck6FDh+bEE09Mu3bt0qBBg4wYMWKR+/jkk0+Ww9L//Oc/2WuvvT7XcVjg9NNPz7rrrpsPP/wwjz32WE466aQ0atRokX8l8WmTJ09Ot27dFjqmi7LgLxA6d+5c2O+aa67J3nvvvdBT0p++CfR5ffJJ6yTlY3zEEUcs8snsJPn2t79d633Dhg0X2W9R7aUl/OLWJbGg1n322SeDBw9eZJ911llnqcb89PFIlv4zAQAAAFDXBOlfc/PmzcvEiRNrfdnmknj99ddz9tlnZ9SoUWnZsuVCQfo777yTe++9NyeccEKOPfbYcvsLL7zwuWt9/fXXM2fOnFqB5P/93/8lSXr06JEkuf7667Pyyivnr3/9a61wtChUbd++fXbcccfyNDF77LFHZsyYUVjL3nvvnV/+8pf5+c9/nt12222RT3SPHz8+//vf//LXv/41W221Vbl90qRJS7S/C7zwwgu1nrp98cUXU1NTU97nv//975k3b15uvvnmWk8ajxs3bpHjffvb3y4Hq/3790+3bt3ypz/9KQcccECtfo0aNcqPf/zj/O53v8vTTz+dP/7xj4sc7/rrr893v/vd/OEPf6jV/u6772aFFVao1TZnzpwMGTIkvXv3Tp8+fXLqqadm5513zsYbb1zu07179zz//PMLbWfBVDjdu3ev1b7hhhumX79+SZKBAwfmtddeyymnnJJjjjlmkcH/Ah999FGeeuqp/OAHP1hsn0965plnUlFRscgnkj9p/fXXz6WXXprvfOc7GTlyZDbbbLOcdtppefDBB5doO0tr5ZVXTpI0btw4/fv3/1K2sUD37t1zzz33ZNasWbXO+UX9bBZ1c6JDhw5p2bJl5s+f/6XWujSfiSW5iZL8//v2/PPPL/SXGc8///xC5yUAAADA0jC1y9fcgqk7ttlmm6Va74QTTkinTp3ys5/9bJHLFzzZ+umnWUePHv256kw+Dj4/OZ/2Bx98kIsvvjgdOnTIhhtuuNjtPvLII5kwYULh2EOHDs2///3v7L777oVTdyzQrl277LTTTvn3v/+doUOHLrLPomr54IMPcsEFF3zm+J90/vnn13p/7rnnJvk4NF7cdmbOnFmerqbIghsGi5t6ZOjQofnPf/6TrbbaqhzYflrDhg0X+jlfd911C83LnSS/+tWvMmXKlFx++eU588wz06NHjwwePLjW9rfddts8+uijtX5mc+bMySWXXJIePXqkd+/ehfv0/vvv56OPPspHH31U2O+uu+7KzJkzs9NOOxX2Sz4+92644YZssskmn3l+VFdX58c//nF23HHHHH300enfv3+6dOnymdv4vDp27Jh+/frl4osvzhtvvLHQ8rfeemuZbWvbbbfN/Pnzc95559VqP+uss1JRUVE+J5OPn8BfMB3OAg0bNsyuu+6aG264If/973+/tFqX5jOxqDoXZaONNkrHjh1z0UUX1Tpfb7/99jz77LPZbrvtvnjhAAAAwDeWJ9K/pubMmZNzzz03I0eOLAehV111Va0+06dPz+zZs3PVVVfle9/7Xq150O+6665cffXV5S/2/LRWrVplq622yqmnnpoPP/wwK664Yu66666lfhr7k6qqqnLKKadk8uTJWXXVVXPNNddk4sSJueSSS9K4ceMkyfbbb5+//vWv2XnnnbPddttl0qRJueiii9K7d+/Mnj17sWP/4Ac/yFtvvbVEIfoCY8eOzfnnn7/QU9cL9OnTJ23bts3gwYNz6KGHpqKiIldeeeVST5UxadKk7LjjjvnBD36QCRMm5KqrrsqPfvSjrLvuukk+/gLPJk2aZIcddsj++++f2bNn59JLL03Hjh1rBau33XZbLrvssvTp0yft2rXLyy+/nEsvvTTNmzfPzjvvvMhtr7HGGpkxY8Yip89YYPvtt8/IkSMzZMiQ9OnTJ//5z39y9dVXLxS833fffbngggty3HHHZYMNNkiSjBkzJv369csxxxyTU089NUny61//On/+858zcODAHHrooWnXrl0uv/zyTJo0KTfccMNCT5nffffdefXVV8tTu1x99dXZcccdF3tuJh9Pv3LEEUeksrIy77//fq1zf+bMmZk/f35uuummDBo0KPfcc0+OOeaY/Pvf/87f//73xY65wEEHHZT3338/l1122Wf2XVbOP//8bLnllll77bWz3377ZeWVV8706dMzYcKEvPrqq3nqqaeWyXZ22GGHfPe7381vf/vbTJ48Oeuuu27uuuuu/O1vf8uIESOyyiqrlPtuuOGGueeee3LmmWemqqoqPXv2zKabbprf//73GTduXDbddNPst99+6d27d95+++088cQTueeee/L2229/4TqX9DOxoM4LL7wwJ510Ur797W+nY8eOi/wugMaNG+eUU07JkCFD0rdv3+y1116ZPn16zj777PTo0SOHHXbYF64bAAAA+AYr8bU0adKkUpIlfo0bN65UKpVKY8aMKSUprbfeeqWampqFxhszZky57dVXXy3tvPPOpTZt2pRat25d2n333Uuvv/56KUnpuOOOK/c77rjjSklKb7311mLr7du3b2nNNdcs/etf/yptvvnmpaZNm5a6d+9eOu+882r1q6mpKZ188sml7t27lyorK0vrr79+6ZZbbikNHjy41L1794XqPe200wqPzyeXf1adi1r+4IMPljbbbLNSs2bNSlVVVaUjjzyydOedd9Y6pouzYLxnnnmmtNtuu5VatmxZatu2benggw8uvf/++7X63nzzzaV11lmn1LRp01KPHj1Kp5xySumPf/xjKUlp0qRJpVKpVPrvf/9b+v73v19q3759qUmTJqWuXbuWfvjDH5b+/e9/1xorSemggw5abF2fXj537tzSL37xi1KXLl1KzZo1K22xxRalCRMmlPr27Vvq27dvqVQqlaqrq0vdu3cvbbDBBqUPP/yw1niHHXZYqUGDBqUJEyaU21566aXSbrvtVmrTpk2padOmpU022aR0yy231Fpv3Lhxtc7RRo0albp371469NBDS++8807hse3evftnnvMLzpdDDjmktNVWW5XuuOOOhcZZ8DNa4M9//nOpoqJiob6DBw8uNW/evLCmz/JZ5+xLL71U+slPflLq3LlzqXHjxqUVV1yxtP3225euv/76cp8Fn9/HHntskfvx6XN7UXXPmjWrdNhhh5WqqqpKjRs3LvXq1at02mmn1frvQalUKj333HOlrbbaqtSsWbNSktLgwYPLy6ZPn1466KCDSl27di01bty41Llz59I222xTuuSSS5bqmHTv3r203XbbLXLZknwmSqVSadq0aaXtttuu1LJly1KS8jm74Pz69Of0mmuuKa2//vqlysrKUrt27Up777136dVXX12qugEAAAA+raJUWobfVMcyM3ny5PTs2TPjxo0rzy/9Rfp92fr165cZM2YscjqI5dXxxx+fE044IW+99dZin3rn8+nRo0eOP/747LvvvotcPn78+Oy7776ZPHnyV1oXAAAAAN9M5kgHAAAAAIAC5kj/mmrRokX23nvvWvOef5F+UJ/svPPOtebz/rROnTotdt54vnxvvfVW5s+fv9jlTZo0Sbt27b7CigAAAAC+XKZ2YZkwtYupXfjm6NGjR1555ZXFLu/bt2/Gjx//1RUEAAAA8CUTpAOwVB588MG8//77i13etm3bbLjhhl9hRQAAAABfLkE6AAAAAAAU8GWjAAAAAABQwJeNfkJNTU1ef/31tGzZMhUVFXVdDgDfQKVSKbNmzUpVVVUaNHC/GwAAAL4OBOmf8Prrr6dr1651XQYAZOrUqVlppZXqugwAAAAggvRaWrZsmeTj8KJVq1Z1XA0A30TV1dXp2rVr+ZoEAAAA1D1B+icsmM6lVatWgnQA6pQpxgAAAODrw+SrAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUOBrEaQ/8MAD2WGHHVJVVZWKiorcdNNNtZaXSqUce+yx6dKlS5o1a5b+/fvnhRdeqNXn7bffzt57751WrVqlTZs2GTZsWGbPnv0V7gUAAAAAAMujr0WQPmfOnKy77ro5//zzF7n81FNPzTnnnJOLLroojzzySJo3b54BAwZk7ty55T577713nn766dx999255ZZb8sADD2T48OFf1S4AAAAAALCcqiiVSqW6LuKTKioqcuONN2bQoEFJPn4avaqqKr/4xS9yxBFHJElmzpyZTp06ZezYsfnhD3+YZ599Nr17985jjz2WjTbaKElyxx13ZNttt82rr76aqqqqJdp2dXV1WrdunZkzZ6ZVq1Zfyv4BQBHXIgAAAPj6+Vo8kV5k0qRJmTZtWvr3719ua926dTbddNNMmDAhSTJhwoS0adOmHKInSf/+/dOgQYM88sgjX3nNAAAAAAAsPxrVdQGfZdq0aUmSTp061Wrv1KlTedm0adPSsWPHWssbNWqUdu3alfssyrx58zJv3rzy++rq6mVVNtSpKVOmZMaMGZ9r3ffffz+TJ09etgUtpR49eqRZs2afa90VVlgh3bp1W8YVAQAAAPBN9rUP0r9Mo0aNygknnFDXZcAyNWXKlKy2+hqZ+/57dV1KnWja7Ft5/rlnhekAAAAALDNf+yC9c+fOSZLp06enS5cu5fbp06dnvfXWK/d58803a6330Ucf5e233y6vvyhHHXVUDj/88PL76urqdO3adRlWD1+9GTNmZO7776X99r9I4/ZLfz6XPvogH82c/iVUtuQate6UikZNlnq9D/83Nf+75YzMmDFDkA4AAADAMvO1D9J79uyZzp0759577y0H59XV1XnkkUdywAEHJEk233zzvPvuu3n88cez4YYbJknuu+++1NTUZNNNN13s2JWVlamsrPzS9wHqQuP2XVPZ+dufb+WVei/bYgAAAACgHvtaBOmzZ8/Oiy++WH4/adKkTJw4Me3atUu3bt0yYsSInHTSSenVq1d69uyZY445JlVVVRk0aFCSZI011sgPfvCD7Lfffrnooovy4Ycf5uCDD84Pf/jDVFVV1dFeAQAAAACwPPhaBOn/+te/8t3vfrf8fsF0K4MHD87YsWNz5JFHZs6cORk+fHjefffdbLnllrnjjjvStGnT8jpXX311Dj744GyzzTZp0KBBdt1115xzzjlf+b4AAAAAALB8+VoE6f369UupVFrs8oqKiowcOTIjR45cbJ927drlT3/605dRHgAAAAAA32AN6roAAAAAAAD4OhOkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAEABQToAAAAAABQQpAMAAAAAQAFBOgAAAAAAFBCkAwAAAABAAUE6AAAAAAAUEKQDAAAAAECBehOkz58/P8ccc0x69uyZZs2aZZVVVsmJJ56YUqlU7lMqlXLsscemS5cuadasWfr3758XXnihDqsGAAAAAKC+qzdB+imnnJILL7ww5513Xp599tmccsopOfXUU3PuueeW+5x66qk555xzctFFF+WRRx5J8+bNM2DAgMydO7cOKwcAAAAAoD5rVNcFLKmHHnooO+20U7bbbrskSY8ePfLnP/85jz76aJKPn0YfPXp0jj766Oy0005JkiuuuCKdOnXKTTfdlB/+8Id1VjsAAAAAAPVXvXkivU+fPrn33nvzf//3f0mSp556Kv/85z8zcODAJMmkSZMybdq09O/fv7xO69ats+mmm2bChAmLHHPevHmprq6u9QIAAAAAgE+qN0+k//rXv051dXVWX331NGzYMPPnz8/vfve77L333kmSadOmJUk6depUa71OnTqVl33aqFGjcsIJJ3y5hQMAAAAAUK/VmyfSr7322lx99dX505/+lCeeeCKXX355Tj/99Fx++eWfe8yjjjoqM2fOLL+mTp26DCsGAAAAAGB5UG+eSP/lL3+ZX//61+W5ztdee+288sorGTVqVAYPHpzOnTsnSaZPn54uXbqU15s+fXrWW2+9RY5ZWVmZysrKL712AAAAAADqr3rzRPp7772XBg1ql9uwYcPU1NQkSXr27JnOnTvn3nvvLS+vrq7OI488ks033/wrrRUAAAAAgOVHvXkifYcddsjvfve7dOvWLWuuuWaefPLJnHnmmRk6dGiSpKKiIiNGjMhJJ52UXr16pWfPnjnmmGNSVVWVQYMG1W3xAAAAAADUW/UmSD/33HNzzDHH5MADD8ybb76Zqqqq7L///jn22GPLfY488sjMmTMnw4cPz7vvvpstt9wyd9xxR5o2bVqHlQMAAAAAUJ/VmyC9ZcuWGT16dEaPHr3YPhUVFRk5cmRGjhz51RUGAAAAAMByrd7MkQ4AAAAAAHVBkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABepVkP7aa69ln332Sfv27dOsWbOsvfba+de//lVeXiqVcuyxx6ZLly5p1qxZ+vfvnxdeeKEOKwYAAAAAoL6rN0H6O++8ky222CKNGzfO7bffnmeeeSZnnHFG2rZtW+5z6qmn5pxzzslFF12URx55JM2bN8+AAQMyd+7cOqwcAAAAAID6rFFdF7CkTjnllHTt2jVjxowpt/Xs2bP871KplNGjR+foo4/OTjvtlCS54oor0qlTp9x000354Q9/+JXXDAAAAABA/Vdvnki/+eabs9FGG2X33XdPx44ds/766+fSSy8tL580aVKmTZuW/v37l9tat26dTTfdNBMmTFjkmPPmzUt1dXWtFwAAAAAAfFK9CdJffvnlXHjhhenVq1fuvPPOHHDAATn00ENz+eWXJ0mmTZuWJOnUqVOt9Tp16lRe9mmjRo1K69aty6+uXbt+uTsBAAAAAEC9U2+C9JqammywwQY5+eSTs/7662f48OHZb7/9ctFFF33uMY866qjMnDmz/Jo6deoyrBgAAAAAgOVBvQnSu3Tpkt69e9dqW2ONNTJlypQkSefOnZMk06dPr9Vn+vTp5WWfVllZmVatWtV6AQAAAADAJ9WbIH2LLbbI888/X6vt//7v/9K9e/ckH3/xaOfOnXPvvfeWl1dXV+eRRx7J5ptv/pXWCgAAAADA8qNRXRewpA477LD06dMnJ598cvbYY488+uijueSSS3LJJZckSSoqKjJixIicdNJJ6dWrV3r27JljjjkmVVVVGTRoUN0WDwAAAABAvVVvgvSNN944N954Y4466qiMHDkyPXv2zOjRo7P33nuX+xx55JGZM2dOhg8fnnfffTdbbrll7rjjjjRt2rQOKwcAAAAAoD6rN0F6kmy//fbZfvvtF7u8oqIiI0eOzMiRI7/CqgAAAAAAWJ7VmznSAQAAAACgLgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKBAvQzSf//736eioiIjRowot82dOzcHHXRQ2rdvnxYtWmTXXXfN9OnT665IAAAAAACWC/UuSH/sscdy8cUXZ5111qnVfthhh+Xvf/97rrvuutx///15/fXXs8suu9RRlQAAAAAALC/qVZA+e/bs7L333rn00kvTtm3bcvvMmTPzhz/8IWeeeWa23nrrbLjhhhkzZkweeuihPPzww3VYMQAAAAAA9V29CtIPOuigbLfddunfv3+t9scffzwffvhhrfbVV1893bp1y4QJExY73rx581JdXV3rBQAAAAAAn9SorgtYUn/5y1/yxBNP5LHHHlto2bRp09KkSZO0adOmVnunTp0ybdq0xY45atSonHDCCcu6VAAAAAAAliP14on0qVOn5uc//3muvvrqNG3adJmNe9RRR2XmzJnl19SpU5fZ2AAAAAAALB/qRZD++OOP580338wGG2yQRo0apVGjRrn//vtzzjnnpFGjRunUqVM++OCDvPvuu7XWmz59ejp37rzYcSsrK9OqVataLwAAAAAA+KR6MbXLNttsk//85z+12oYMGZLVV189v/rVr9K1a9c0btw49957b3bdddckyfPPP58pU6Zk8803r4uSAQAAAABYTtSLIL1ly5ZZa621arU1b9487du3L7cPGzYshx9+eNq1a5dWrVrlkEMOyeabb57NNtusLkoGAAAAAGA5US+C9CVx1llnpUGDBtl1110zb968DBgwIBdccEFdlwUAAAAAQD1Xb4P08ePH13rftGnTnH/++Tn//PPrpiAAAAAAAJZL9eLLRgEAAAAAoK4I0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACgQL0J0keNGpWNN944LVu2TMeOHTNo0KA8//zztfrMnTs3Bx10UNq3b58WLVpk1113zfTp0+uoYgAAAAAAlgf1Jki///77c9BBB+Xhhx/O3XffnQ8//DDf//73M2fOnHKfww47LH//+99z3XXX5f7778/rr7+eXXbZpQ6rBgAAAACgvmtU1wUsqTvuuKPW+7Fjx6Zjx455/PHHs9VWW2XmzJn5wx/+kD/96U/ZeuutkyRjxozJGmuskYcffjibbbZZXZQNAAAAAEA9V2+eSP+0mTNnJknatWuXJHn88cfz4Ycfpn///uU+q6++erp165YJEyYscox58+alurq61gsAAAAAAD6pXgbpNTU1GTFiRLbYYoustdZaSZJp06alSZMmadOmTa2+nTp1yrRp0xY5zqhRo9K6devyq2vXrl926QAAAAAA1DP1Mkg/6KCD8t///jd/+ctfvtA4Rx11VGbOnFl+TZ06dRlVCAAAAADA8qLezJG+wMEHH5xbbrklDzzwQFZaaaVye+fOnfPBBx/k3XffrfVU+vTp09O5c+dFjlVZWZnKysovu2QAAAAAAOqxevNEeqlUysEHH5wbb7wx9913X3r27Flr+YYbbpjGjRvn3nvvLbc9//zzmTJlSjbffPOvulwAAAAAAJYT9eaJ9IMOOih/+tOf8re//S0tW7Ysz3veunXrNGvWLK1bt86wYcNy+OGHp127dmnVqlUOOeSQbL755tlss83quHoAAAAAAOqrehOkX3jhhUmSfv361WofM2ZM9t133yTJWWedlQYNGmTXXXfNvHnzMmDAgFxwwQVfcaUAAAAAACxP6k2QXiqVPrNP06ZNc/755+f888//CioCAAAAAOCboN7MkQ4AAAAAAHVBkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUGC5C9LPP//89OjRI02bNs2mm26aRx99tK5LAgAAAACgHluugvRrrrkmhx9+eI477rg88cQTWXfddTNgwIC8+eabdV0aAAAAAAD11HIVpJ955pnZb7/9MmTIkPTu3TsXXXRRvvWtb+WPf/xjXZcGAAAAAEA9tdwE6R988EEef/zx9O/fv9zWoEGD9O/fPxMmTKjDygAAAAAAqM8a1XUBy8qMGTMyf/78dOrUqVZ7p06d8txzzy1ynXnz5mXevHnl99XV1V9qjfBV6dyiImu892Qavf36Uq9bmv9h5s96+0uoask1bNkuFQ0bL/V6H703PWlR8SVUBAAAAMA32XITpH8eo0aNygknnFDXZcAytcIKK+SgzZrn6N431nUpX72q5KR3mmeFFVao60oAAAAAWI4sN0H6CiuskIYNG2b69Om12qdPn57OnTsvcp2jjjoqhx9+ePl9dXV1unbt+qXWCV+2bt26Zd9zx+fZ11/4XOvPmzcvr7++9E+yL0tVVVWprKz8XOvu+8NeWalbt2VcEQAAAADfZMtNkN6kSZNsuOGGuffeezNo0KAkSU1NTe69994cfPDBi1ynsrLyc4d18HW20uobJqtv+LnXX2/ZlQIAAAAA9d5yE6QnyeGHH57Bgwdno402yiabbJLRo0dnzpw5GTJkSF2XBgAAAABAPbVcBel77rln3nrrrRx77LGZNm1a1ltvvdxxxx0LfQEpAAAAAAAsqYpSqVSq6yK+Lqqrq9O6devMnDkzrVq1qutyAPgGci0CAACAr58GdV0AAAAAAAB8nQnSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKCNIBAAAAAKCAIB0AAAAAAAoI0gEAAAAAoECjui7g66RUKiVJqqur67gSAL6pFlyDFlyTAAAAgLonSP+EWbNmJUm6du1ax5UA8E03a9astG7duq7LAAAAAJJUlDzyVlZTU5PXX389LVu2TEVFRV2XA/VSdXV1unbtmqlTp6ZVq1Z1XQ7UO6VSKbNmzUpVVVUaNDADGwAAAHwdCNKBZaq6ujqtW7fOzJkzBekAAAAALBc86gYAAAAAAAUE6QAAAAAAUECQDixTlZWVOe6441JZWVnXpQAAAADAMmGOdAAAAAAAKOCJdAAAAAAAKCBIBwAAAACAAoJ0AAAAAAAoIEgHlokHHnggO+ywQ6qqqlJRUZGbbrqprksCAAAAgGVCkA4sE3PmzMm6666b888/v65LAQAAAIBlqlFdFwAsHwYOHJiBAwfWdRkAAAAAsMx5Ih0AAAAAAAoI0gEAAAAAoIAgHQAAAAAACgjSAQAAAACggCAdAAAAAAAKNKrrAoDlw+zZs/Piiy+W30+aNCkTJ05Mu3bt0q1btzqsDAAAAAC+mIpSqVSq6yKA+m/8+PH57ne/u1D74MGDM3bs2K++IAAAAABYRgTpAAAAAABQwBzpAAAAAABQQJAOAAAAAAAFBOkAAAAAAFBAkA4AAAAAAAUE6QAAAAAAUECQDgAAAAAABQTpAAAAAABQQJAOAAAAAAAFBOnAcqdHjx4ZPXp0XZcBAAAAwHJCkA4slX79+mXEiBF1XUaSZOzYsWnTps1C7Y899liGDx/+1RcEAAAAwHJJkA710AcffFDXJXypvuj+dejQId/61reWUTUAAAAAfNMJ0qEe6NevXw4++OCMGDEiK6ywQgYMGJD//ve/GThwYFq0aJFOnTrlxz/+cWbMmFFrnUMOOSQjRoxI27Zt06lTp1x66aWZM2dOhgwZkpYtW+bb3/52br/99lrbuv/++7PJJpuksrIyXbp0ya9//et89NFHSZJ99903999/f84+++xUVFSkoqIikydPTpLPrGdp9y9JzjzzzKy99tpp3rx5unbtmgMPPDCzZ89OkowfPz5DhgzJzJkzy7Ucf/zxSRae2mXKlCnZaaed0qJFi7Rq1Sp77LFHpk+f/nl+FAAAAAB8AwnSoZ64/PLL06RJkzz44IP5/e9/n6233jrrr79+/vWvf+WOO+7I9OnTs8ceeyy0zgorrJBHH300hxxySA444IDsvvvu6dOnT5544ol8//vfz49//OO89957SZLXXnst2267bTbeeOM89dRTufDCC/OHP/whJ510UpLk7LPPzuabb5799tsvb7zxRt5444107do177777hLVs6T7d9FFFyVJGjRokHPOOSdPP/10Lr/88tx333058sgjkyR9+vTJ6NGj06pVq3ItRxxxxELj1tTUZKeddsrbb7+d+++/P3fffXdefvnl7Lnnnp/r5wAAAADAN09FqVQq1XURQLF+/fqluro6TzzxRJLkpJNOyj/+8Y/ceeed5T6vvvpqunbtmueffz6rrrpq+vXrl/nz5+cf//hHkmT+/Plp3bp1dtlll1xxxRVJkmnTpqVLly6ZMGFCNttss/z2t7/NDTfckGeffTYVFRVJkgsuuCC/+tWvMnPmzDRo0CD9+vXLeuutV+uJ7yWpZ2n2b3Guv/76/OxnPys/6T527NiMGDEi7777bq1+PXr0yIgRIzJixIjcfffdGThwYCZNmpSuXbsmSZ555pmsueaaefTRR7PxxhsXbhMAAAAAPJEO9cSGG25Y/vdTTz2VcePGpUWLFuXX6quvniR56aWXyv3WWWed8r8bNmyY9u3bZ+211y63derUKUny5ptvJkmeffbZbL755uUQPUm22GKLzJ49O6+++upia1vSepZ0/xa45557ss0222TFFVdMy5Yt8+Mf/zj/+9//yk/QL4lnn302Xbt2LYfoSdK7d++0adMmzz777BKPAwAAAMA3V6O6LgBYMs2bNy//e/bs2dlhhx1yyimnLNSvS5cu5X83bty41rKKiopabQsC85qami9U25LWU+ST+5ckkydPzvbbb58DDjggv/vd79KuXbv885//zLBhw/LBBx/4MlEAAAAAvjKCdKiHNthgg9xwww3p0aNHGjVadh/jNdZYIzfccENKpVI5ZH/wwQfTsmXLrLTSSkmSJk2aZP78+V96PY8//nhqampyxhlnpEGDj/945tprr63VZ1G1LGqfpk6dmqlTp9aa2uXdd99N7969l0mtAAAAACzfTO0C9dBBBx2Ut99+O3vttVcee+yxvPTSS7nzzjszZMiQzwyWixx44IGZOnVqDjnkkDz33HP529/+luOOOy6HH354Oczu0aNHHnnkkUyePDkzZsxITU3Nl1LPt7/97Xz44Yc599xz8/LLL+fKK68sfwnpAj169Mjs2bNz7733ZsaMGYuc8qV///5Ze+21s/fee+eJJ57Io48+mp/85Cfp27dvNtpoo89VGwAAAADfLIJ0qIeqqqry4IMPZv78+fn+97+ftddeOyNGjEibNm3KgffnseKKK+a2227Lo48+mnXXXTc/+9nPMmzYsBx99NHlPkcccUQaNmyY3r17p0OHDpkyZcqXUs+6666bM888M6ecckrWWmutXH311Rk1alStPn369MnPfvaz7LnnnunQoUNOPfXUhcapqKjI3/72t7Rt2zZbbbVV+vfvn5VXXjnXXHPN56oLAAAAgG+eilKpVKrrIgAAAAAA4OvKE+kAAAAAAFBAkA58qaZMmZIWLVos9jVlypS6LhEAAAAACpnaBfhSffTRR5k8efJil/fo0SONGjX66goCAAAAgKUkSAcAAAAAgAKmdgEAAAAAgAKCdAAAAAAAKCBIBwAAAACAAoJ0AAAAAAAoIEgHAAAAAIACgnQAAAAAACggSAcAAAAAgAKCdAAAAAAAKPD/AbYjeXlGhk1oAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Устраняем выборсы\n",
+    "df: DataFrame = remove_outliers(df, numeric_columns)\n",
+    "\n",
+    "# Проверка выбросов\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 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",
+    "- Средняя зарплата: зарплаты между первым квартилем (25%) и третьим квартилем (75%) — это зарплаты от 95000 до 175000.\n",
+    "- Высокая зарплата: зарплаты выше третьего квартиля (75%) и до максимального значения — это зарплаты выше 175000.\n",
+    "\n",
+    "Весь набор данных состоит из 3755 объектов, из которых 1867 (около 49.7%) имеют средний уровень зарплаты (medium), 956 (около 25.4%) – низкий уровень зарплаты (low), и 932 (около 24.8%) – высокий уровень зарплаты (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 зарплаты (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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "salary_in_usd\n",
+      "100000.0    99\n",
+      "150000.0    98\n",
+      "120000.0    91\n",
+      "160000.0    84\n",
+      "130000.0    82\n",
+      "            ..\n",
+      "39916.0      1\n",
+      "26005.0      1\n",
+      "22611.0      1\n",
+      "5679.0       1\n",
+      "40038.0      1\n",
+      "Name: count, Length: 1002, dtype: int64 \n",
+      "\n",
+      "count      3755.000000\n",
+      "mean     136959.779760\n",
+      "std       61098.121137\n",
+      "min        5132.000000\n",
+      "25%       95000.000000\n",
+      "50%      135000.000000\n",
+      "75%      175000.000000\n",
+      "max      295000.000000\n",
+      "Name: salary_in_usd, dtype: float64 \n",
+      "\n",
+      "salary_category\n",
+      "medium    1867\n",
+      "low        956\n",
+      "high       932\n",
+      "Name: count, dtype: int64 \n",
+      "\n",
+      "Обучающая выборка:  (2253, 12)\n",
+      "Распределение выборки данных по классам \"salary_category\":\n",
+      " salary_category\n",
+      "medium    1120\n",
+      "low        574\n",
+      "high       559\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 49.71%\n",
+      "Процент объектов класса \"low\": 25.48%\n",
+      "Процент объектов класса \"high\": 24.81%\n",
+      "\n",
+      "Контрольная выборка:  (751, 12)\n",
+      "Распределение выборки данных по классам \"salary_category\":\n",
+      " salary_category\n",
+      "medium    373\n",
+      "low       191\n",
+      "high      187\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 49.67%\n",
+      "Процент объектов класса \"low\": 25.43%\n",
+      "Процент объектов класса \"high\": 24.90%\n",
+      "\n",
+      "Тестовая выборка:  (751, 12)\n",
+      "Распределение выборки данных по классам \"salary_category\":\n",
+      " salary_category\n",
+      "medium    374\n",
+      "low       191\n",
+      "high      186\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"medium\": 49.80%\n",
+      "Процент объектов класса \"low\": 25.43%\n",
+      "Процент объектов класса \"high\": 24.77%\n",
+      "\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(df.salary_in_usd.value_counts(), '\\n')\n",
+    "\n",
+    "# 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",
+    "labels: list[str] = ['low', 'medium', 'high']\n",
+    "\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(df['salary_category'].value_counts(), '\\n')\n",
+    "\n",
+    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
+    "   df,\n",
+    "   stratify_colname=\"salary_category\", \n",
+    "   frac_train=0.60, \n",
+    "   frac_val=0.20, \n",
+    "   frac_test=0.20\n",
+    ")\n",
+    "\n",
+    "# Проверка сбалансированности\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",
+    "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",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df_train, df_val, df_test, 'salary_category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Приращение данных:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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",
+      " salary_category\n",
+      "low       1121\n",
+      "medium    1120\n",
+      "high      1119\n",
+      "Name: count, dtype: int64\n",
+      "Процент объектов класса \"low\": 33.36%\n",
+      "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",
+      " salary_category\n",
+      "low       373\n",
+      "medium    373\n",
+      "high      373\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:  (1121, 162)\n",
+      "Распределение выборки данных по классам \"salary_category\":\n",
+      " salary_category\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",
+      "\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, 'salary_category')\n",
+    "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",
+    "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",
+    "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",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'salary_category')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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",
+      " salary_category\n",
+      "low       559\n",
+      "medium    559\n",
+      "high      559\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:  (1119, 157)\n",
+      "Распределение выборки данных по классам \"salary_category\":\n",
+      " salary_category\n",
+      "low       373\n",
+      "medium    373\n",
+      "high      373\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:  (558, 162)\n",
+      "Распределение выборки данных по классам \"salary_category\":\n",
+      " salary_category\n",
+      "low       186\n",
+      "medium    186\n",
+      "high      186\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",
+      "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, 'salary_category')\n",
+    "df_val_undersampled: DataFrame = oversample(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",
+    "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",
+    "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",
+    "   \n",
+    "# Визуализация сбалансированности классов\n",
+    "visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'salary_category')"
+   ]
   }
  ],
  "metadata": {
diff --git a/static/csv/ds_salaries.csv b/static/csv/ds_salaries.csv
new file mode 100644
index 0000000..2339741
--- /dev/null
+++ b/static/csv/ds_salaries.csv
@@ -0,0 +1,3756 @@
+work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size
+2023,SE,FT,Principal Data Scientist,80000,EUR,85847,ES,100,ES,L
+2023,MI,CT,ML Engineer,30000,USD,30000,US,100,US,S
+2023,MI,CT,ML Engineer,25500,USD,25500,US,100,US,S
+2023,SE,FT,Data Scientist,175000,USD,175000,CA,100,CA,M
+2023,SE,FT,Data Scientist,120000,USD,120000,CA,100,CA,M
+2023,SE,FT,Applied Scientist,222200,USD,222200,US,0,US,L
+2023,SE,FT,Applied Scientist,136000,USD,136000,US,0,US,L
+2023,SE,FT,Data Scientist,219000,USD,219000,CA,0,CA,M
+2023,SE,FT,Data Scientist,141000,USD,141000,CA,0,CA,M
+2023,SE,FT,Data Scientist,147100,USD,147100,US,0,US,M
+2023,SE,FT,Data Scientist,90700,USD,90700,US,0,US,M
+2023,SE,FT,Data Analyst,130000,USD,130000,US,100,US,M
+2023,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
+2023,EN,FT,Applied Scientist,213660,USD,213660,US,0,US,L
+2023,EN,FT,Applied Scientist,130760,USD,130760,US,0,US,L
+2023,SE,FT,Data Modeler,147100,USD,147100,US,0,US,M
+2023,SE,FT,Data Modeler,90700,USD,90700,US,0,US,M
+2023,SE,FT,Data Scientist,170000,USD,170000,US,0,US,M
+2023,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M
+2023,MI,FT,Data Analyst,150000,USD,150000,US,100,US,M
+2023,MI,FT,Data Analyst,110000,USD,110000,US,100,US,M
+2023,SE,FT,Research Engineer,275000,USD,275000,DE,0,DE,M
+2023,SE,FT,Research Engineer,174000,USD,174000,DE,0,DE,M
+2023,SE,FT,Analytics Engineer,230000,USD,230000,GB,100,GB,M
+2023,SE,FT,Analytics Engineer,143200,USD,143200,GB,100,GB,M
+2023,SE,FT,Business Intelligence Engineer,225000,USD,225000,US,0,US,M
+2023,SE,FT,Business Intelligence Engineer,156400,USD,156400,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,130000,USD,130000,US,0,US,M
+2023,SE,FT,Data Strategist,90000,USD,90000,CA,0,CA,M
+2023,SE,FT,Data Strategist,72000,USD,72000,CA,0,CA,M
+2023,SE,FT,Data Engineer,253200,USD,253200,US,0,US,M
+2023,SE,FT,Data Engineer,90700,USD,90700,US,0,US,M
+2023,SE,FT,Computer Vision Engineer,342810,USD,342810,US,0,US,M
+2023,SE,FT,Computer Vision Engineer,184590,USD,184590,US,0,US,M
+2023,MI,FT,Data Engineer,162500,USD,162500,US,0,US,M
+2023,MI,FT,Data Engineer,130000,USD,130000,US,0,US,M
+2023,MI,FT,Data Analyst,105380,USD,105380,US,0,US,M
+2023,MI,FT,Data Analyst,64500,USD,64500,US,0,US,M
+2023,EN,FT,Data Quality Analyst,100000,USD,100000,NG,100,NG,L
+2023,EN,FT,Compliance Data Analyst,30000,USD,30000,NG,100,NG,L
+2022,MI,FT,Machine Learning Engineer,1650000,INR,20984,IN,50,IN,L
+2023,EN,FT,Applied Scientist,204620,USD,204620,US,0,US,L
+2023,EN,FT,Applied Scientist,110680,USD,110680,US,0,US,L
+2023,SE,FT,Data Engineer,270703,USD,270703,US,0,US,M
+2023,SE,FT,Data Engineer,221484,USD,221484,US,0,US,M
+2023,SE,FT,Data Scientist,212750,USD,212750,US,100,US,M
+2023,SE,FT,Data Scientist,185000,USD,185000,US,100,US,M
+2023,SE,FT,Data Scientist,262000,USD,262000,US,100,US,M
+2023,SE,FT,Data Scientist,245000,USD,245000,US,100,US,M
+2023,SE,FT,Data Scientist,275300,USD,275300,US,100,US,M
+2023,SE,FT,Data Scientist,183500,USD,183500,US,100,US,M
+2023,SE,FT,Data Scientist,218500,USD,218500,US,100,US,M
+2023,SE,FT,Data Scientist,199098,USD,199098,US,100,US,M
+2023,SE,FT,Data Engineer,203300,USD,203300,US,100,US,M
+2023,SE,FT,Data Engineer,123600,USD,123600,US,100,US,M
+2023,SE,FT,Research Engineer,189110,USD,189110,US,0,US,M
+2023,SE,FT,Research Engineer,139000,USD,139000,US,0,US,M
+2023,EX,FT,Data Scientist,258750,USD,258750,US,0,US,M
+2023,EX,FT,Data Scientist,185000,USD,185000,US,0,US,M
+2023,SE,FT,Data Engineer,231500,USD,231500,US,100,US,M
+2023,SE,FT,Data Engineer,166000,USD,166000,US,100,US,M
+2023,SE,FT,Data Scientist,172500,USD,172500,US,100,US,M
+2023,SE,FT,Data Scientist,110500,USD,110500,US,100,US,M
+2023,SE,FT,Data Engineer,238000,USD,238000,US,0,US,M
+2023,SE,FT,Data Engineer,176000,USD,176000,US,0,US,M
+2023,SE,FT,Data Engineer,237000,USD,237000,US,100,US,M
+2023,SE,FT,Data Engineer,201450,USD,201450,US,100,US,M
+2023,SE,FT,Applied Scientist,309400,USD,309400,US,0,US,L
+2023,SE,FT,Applied Scientist,159100,USD,159100,US,0,US,L
+2023,SE,FT,Data Engineer,115000,USD,115000,US,0,US,M
+2023,SE,FT,Data Engineer,81500,USD,81500,US,0,US,M
+2023,SE,FT,Data Scientist,237000,USD,237000,US,100,US,M
+2023,SE,FT,Data Scientist,201450,USD,201450,US,100,US,M
+2023,SE,FT,Computer Vision Engineer,280000,USD,280000,US,0,US,M
+2023,SE,FT,Computer Vision Engineer,210000,USD,210000,US,0,US,M
+2023,SE,FT,Data Architect,280100,USD,280100,US,100,US,M
+2023,SE,FT,Data Architect,168100,USD,168100,US,100,US,M
+2023,SE,FT,Data Engineer,193500,USD,193500,US,100,US,M
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+2023,MI,FT,Data Engineer,154000,USD,154000,US,0,US,M
+2023,MI,FT,Data Engineer,116000,USD,116000,US,0,US,M
+2023,SE,FT,Data Scientist,190000,USD,190000,US,0,US,M
+2023,SE,FT,Data Scientist,136000,USD,136000,US,0,US,M
+2023,MI,FT,Data Analyst,65000,GBP,78990,GB,100,GB,M
+2023,MI,FT,Data Analyst,36050,GBP,43809,GB,100,GB,M
+2023,SE,FT,Data Analyst,180000,USD,180000,US,0,US,M
+2023,SE,FT,Data Analyst,110000,USD,110000,US,0,US,M
+2023,SE,FT,Data Scientist,275300,USD,275300,US,100,US,M
+2023,SE,FT,Data Scientist,183000,USD,183000,US,100,US,M
+2023,SE,FT,Data Engineer,170000,USD,170000,US,0,US,M
+2023,SE,FT,Data Engineer,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Engineer,154000,USD,154000,US,0,US,M
+2023,SE,FT,Data Engineer,116000,USD,116000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
+2023,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+2023,MI,FT,Data Engineer,200000,USD,200000,US,0,US,M
+2023,MI,FT,Data Engineer,120000,USD,120000,US,0,US,M
+2023,SE,FT,Data Scientist,175000,USD,175000,US,100,US,M
+2023,SE,FT,Data Scientist,160000,USD,160000,US,100,US,M
+2023,MI,FT,Data Engineer,105000,GBP,127599,GB,0,GB,M
+2023,MI,FT,Data Engineer,85000,GBP,103294,GB,0,GB,M
+2023,SE,FT,Data Engineer,153600,USD,153600,US,0,US,M
+2023,SE,FT,Data Engineer,106800,USD,106800,US,0,US,M
+2023,EN,FT,Data Analyst,85000,USD,85000,US,100,US,M
+2023,EN,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,SE,FT,Data Scientist,225000,USD,225000,US,0,US,M
+2023,SE,FT,Data Scientist,156400,USD,156400,US,0,US,M
+2023,SE,FT,Analytics Engineer,150000,USD,150000,US,0,US,M
+2023,SE,FT,Analytics Engineer,120000,USD,120000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Data Analyst,145000,USD,145000,US,100,US,M
+2023,SE,FT,Data Analyst,90000,USD,90000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
+2023,SE,FT,Data Analyst,85500,USD,85500,US,0,US,M
+2023,SE,FT,Data Engineer,167500,USD,167500,US,0,US,M
+2023,SE,FT,Data Engineer,106500,USD,106500,US,0,US,M
+2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M
+2023,MI,FT,Data Analytics Manager,155000,USD,155000,US,0,US,M
+2023,MI,FT,Data Analytics Manager,140000,USD,140000,US,0,US,M
+2023,SE,FT,Research Scientist,250000,USD,250000,US,0,US,M
+2023,SE,FT,Research Scientist,200000,USD,200000,US,0,US,M
+2023,SE,FT,Data Scientist,260000,USD,260000,US,0,US,M
+2023,SE,FT,Data Scientist,186000,USD,186000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
+2023,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+2023,SE,FT,Research Scientist,200000,USD,200000,US,0,US,M
+2023,SE,FT,Research Scientist,150000,USD,150000,US,0,US,M
+2023,SE,FT,Data Scientist,45000,EUR,48289,ES,0,ES,M
+2023,SE,FT,Data Scientist,36000,EUR,38631,ES,0,ES,M
+2023,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M
+2023,SE,FT,Data Scientist,120000,USD,120000,US,0,US,M
+2023,EN,FT,Data Analyst,30000,USD,30000,IN,50,IN,M
+2023,MI,FT,Research Scientist,185000,USD,185000,US,0,US,M
+2023,MI,FT,Research Scientist,125000,USD,125000,US,0,US,M
+2022,EN,PT,Data Analyst,34320,USD,34320,US,100,US,S
+2022,MI,FT,Business Data Analyst,48000,BRL,9289,BR,100,BR,M
+2023,SE,FT,Head of Data,70000,EUR,75116,PT,100,PT,L
+2022,EX,FT,Data Science Manager,106000,USD,106000,UZ,0,RU,L
+2023,SE,FT,Data Analyst,175000,USD,175000,US,100,US,M
+2023,SE,FT,Data Analyst,130000,USD,130000,US,100,US,M
+2023,SE,FT,Data Analyst,122000,USD,122000,US,100,US,M
+2023,SE,FT,Data Analyst,93800,USD,93800,US,100,US,M
+2023,SE,FT,Data Science Manager,150000,USD,150000,MX,100,MX,M
+2023,SE,FT,Data Science Manager,90000,USD,90000,MX,100,MX,M
+2023,SE,FT,Data Analyst,165000,USD,165000,US,100,US,M
+2023,SE,FT,Data Analyst,112000,USD,112000,US,100,US,M
+2023,EN,FT,Computer Vision Engineer,220000,USD,220000,US,0,US,M
+2023,SE,FT,BI Data Analyst,67000,EUR,71897,DE,100,DE,M
+2023,EN,FT,AI Developer,60000,EUR,64385,DE,0,DE,M
+2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
+2023,SE,FT,Data Analyst,1300000,INR,15806,IN,100,IN,S
+2023,MI,FT,Machine Learning Engineer,200000,USD,200000,US,0,US,M
+2023,MI,FT,Machine Learning Engineer,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Engineer,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Engineer,129300,USD,129300,US,0,US,M
+2023,SE,FT,Data Analyst,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Analyst,121700,USD,121700,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
+2023,MI,FT,Data Scientist,40000,USD,40000,FR,50,FR,L
+2023,SE,FT,Data Science Consultant,1000000,THB,29453,TH,50,TH,M
+2023,SE,FT,Data Analyst,180180,USD,180180,US,0,US,M
+2023,SE,FT,Data Analyst,106020,USD,106020,US,0,US,M
+2023,SE,FT,Data Scientist,136000,USD,136000,US,100,US,M
+2023,SE,FT,Data Scientist,104000,USD,104000,US,100,US,M
+2023,SE,FT,Data Analyst,125000,USD,125000,US,0,US,M
+2023,SE,FT,Data Analyst,110000,USD,110000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,153400,USD,153400,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,122700,USD,122700,US,0,US,M
+2023,EX,FT,Data Engineer,310000,USD,310000,US,100,US,M
+2023,EX,FT,Data Engineer,239000,USD,239000,US,100,US,M
+2023,SE,FT,Data Analyst,152380,USD,152380,US,0,US,M
+2023,SE,FT,Data Analyst,121904,USD,121904,US,0,US,M
+2023,SE,FT,Data Scientist,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,247500,USD,247500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,172200,USD,172200,US,0,US,M
+2023,SE,FT,Data Architect,168400,USD,168400,US,0,US,M
+2023,SE,FT,Data Architect,105200,USD,105200,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,128280,USD,128280,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,106900,USD,106900,US,0,US,M
+2022,SE,FT,Lead Data Scientist,192000,USD,192000,US,100,US,L
+2023,MI,FT,Data Engineer,140000,USD,140000,US,0,US,M
+2023,MI,FT,Data Engineer,100000,USD,100000,US,0,US,M
+2023,SE,FT,Research Engineer,100000,EUR,107309,DE,100,DE,S
+2023,SE,FT,Research Engineer,80000,EUR,85847,DE,100,DE,S
+2023,SE,FT,Machine Learning Engineer,275000,USD,275000,DE,0,DE,M
+2023,SE,FT,Machine Learning Engineer,174000,USD,174000,DE,0,DE,M
+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Data Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Data Analyst,170500,USD,170500,US,100,US,M
+2023,SE,FT,Data Analyst,85000,USD,85000,US,100,US,M
+2023,SE,FT,Data Manager,60027,GBP,72946,GB,0,GB,M
+2023,SE,FT,Data Manager,44737,GBP,54365,GB,0,GB,M
+2023,EX,FT,Head of Data Science,131899,GBP,160288,GB,0,GB,M
+2023,EX,FT,Head of Data Science,104891,GBP,127467,GB,0,GB,M
+2023,SE,FT,Data Engineer,80000,USD,80000,US,0,US,M
+2023,SE,FT,Data Engineer,65000,USD,65000,US,0,US,M
+2023,SE,FT,Data Engineer,124740,USD,124740,US,0,US,M
+2023,SE,FT,Data Engineer,65488,USD,65488,US,0,US,M
+2023,SE,FT,Data Quality Analyst,72200,USD,72200,US,0,US,M
+2023,SE,FT,Data Quality Analyst,64980,USD,64980,US,0,US,M
+2023,SE,FT,Data Engineer,153600,USD,153600,US,0,US,M
+2023,SE,FT,Data Engineer,106800,USD,106800,US,0,US,M
+2023,SE,FT,Data Analyst,179975,USD,179975,US,100,US,M
+2023,SE,FT,Data Analyst,86466,USD,86466,US,100,US,M
+2023,SE,FT,Data Engineer,120000,USD,120000,US,0,US,M
+2023,SE,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,MI,FT,Insight Analyst,42000,GBP,51039,GB,0,GB,M
+2023,MI,FT,Insight Analyst,35000,GBP,42533,GB,0,GB,M
+2023,SE,FT,Data Scientist,149076,USD,149076,US,0,US,M
+2023,SE,FT,Data Scientist,82365,USD,82365,US,0,US,M
+2023,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M
+2023,SE,FT,Data Analyst,110600,USD,110600,US,0,US,M
+2023,SE,FT,Data Science Manager,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Science Manager,120000,USD,120000,US,0,US,M
+2023,SE,FT,Data Analyst,230000,USD,230000,US,0,US,M
+2023,SE,FT,Data Analyst,180000,USD,180000,US,0,US,M
+2023,SE,FT,Data Analyst,153600,USD,153600,US,0,US,M
+2023,SE,FT,Data Analyst,106800,USD,106800,US,0,US,M
+2023,SE,FT,Data Manager,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Manager,120000,USD,120000,US,0,US,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,205920,USD,205920,US,0,US,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,171600,USD,171600,US,0,US,M
+2023,SE,FT,Data Analyst,165000,USD,165000,US,100,US,M
+2023,SE,FT,Data Analyst,125000,USD,125000,US,100,US,M
+2023,SE,FT,Data Engineer,265000,USD,265000,US,0,US,M
+2023,SE,FT,Data Engineer,185000,USD,185000,US,0,US,M
+2023,MI,FT,Applied Machine Learning Engineer,130000,USD,130000,US,0,US,M
+2022,EN,FT,Data Scientist,168000,USD,168000,US,100,US,M
+2023,MI,FT,AI Scientist,36000,EUR,38631,ES,50,ES,L
+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
+2023,SE,FT,Data Analyst,85500,USD,85500,US,0,US,M
+2023,SE,FT,Data Scientist,147100,USD,147100,US,0,US,M
+2023,SE,FT,Data Scientist,90700,USD,90700,US,0,US,M
+2023,SE,FT,Data Engineer,167580,USD,167580,US,0,US,M
+2023,SE,FT,Data Engineer,87980,USD,87980,US,0,US,M
+2023,SE,FT,Data Engineer,202000,USD,202000,US,100,US,M
+2023,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
+2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Data Engineer,104000,USD,104000,US,100,US,M
+2023,SE,FT,Data Engineer,65000,USD,65000,US,100,US,M
+2023,SE,FT,Research Engineer,230000,USD,230000,US,0,US,M
+2023,SE,FT,Research Engineer,148000,USD,148000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,269000,USD,269000,CA,100,CA,M
+2023,SE,FT,Machine Learning Engineer,158000,USD,158000,CA,100,CA,M
+2023,SE,FT,Analytics Engineer,197000,USD,197000,US,0,US,M
+2023,SE,FT,Analytics Engineer,106000,USD,106000,US,0,US,M
+2023,MI,FT,Deep Learning Engineer,150000,USD,150000,US,100,US,M
+2023,MI,FT,Deep Learning Engineer,100000,USD,100000,US,100,US,M
+2023,SE,FT,Data Engineer,290000,USD,290000,US,100,US,M
+2023,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
+2023,SE,FT,Data Engineer,192000,USD,192000,US,0,US,M
+2023,SE,FT,Data Engineer,172800,USD,172800,US,0,US,M
+2023,SE,FT,Data Scientist,300240,USD,300240,US,0,US,M
+2023,SE,FT,Data Scientist,200160,USD,200160,US,0,US,M
+2023,SE,FT,Data Scientist,300240,USD,300240,US,0,US,M
+2023,SE,FT,Data Scientist,200160,USD,200160,US,0,US,M
+2023,SE,FT,Analytics Engineer,175000,USD,175000,US,0,US,M
+2023,SE,FT,Analytics Engineer,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Manager,169000,USD,169000,US,0,US,M
+2023,SE,FT,Data Manager,100000,USD,100000,US,0,US,M
+2023,SE,FT,Data Scientist,370000,USD,370000,US,0,US,M
+2023,SE,FT,Data Scientist,245000,USD,245000,US,0,US,M
+2023,MI,FT,Data Engineer,95000,GBP,115447,GB,100,GB,L
+2023,SE,FT,Data Analyst,110000,USD,110000,US,100,US,S
+2023,SE,FT,Data Analyst,80000,USD,80000,US,100,US,S
+2023,EN,FT,Data Analyst,55000,USD,55000,US,0,US,M
+2023,EN,FT,Data Analyst,48000,USD,48000,US,0,US,M
+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
+2023,SE,FT,Data Analyst,85000,USD,85000,US,0,US,M
+2023,SE,FT,Data Engineer,137500,USD,137500,US,100,US,M
+2023,SE,FT,Data Engineer,81500,USD,81500,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,323300,USD,323300,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,184700,USD,184700,US,0,US,M
+2021,MI,FT,AI Scientist,30000,USD,30000,GH,0,GH,S
+2023,SE,FT,Machine Learning Engineer,186000,USD,186000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,153088,USD,153088,US,100,US,M
+2023,MI,FT,Data Infrastructure Engineer,190000,USD,190000,US,100,US,M
+2023,MI,FT,Data Infrastructure Engineer,160000,USD,160000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,150000,USD,150000,US,100,US,M
+2023,MI,FT,Data Infrastructure Engineer,190000,USD,190000,US,0,US,M
+2023,MI,FT,Data Infrastructure Engineer,183310,USD,183310,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,240000,USD,240000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,180000,USD,180000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,150000,USD,150000,US,100,US,M
+2023,SE,FT,Data Science Manager,299500,USD,299500,US,0,US,M
+2023,SE,FT,Data Science Manager,245100,USD,245100,US,0,US,M
+2023,SE,FT,Data Engineer,144000,USD,144000,US,100,US,M
+2023,SE,FT,Data Engineer,66000,USD,66000,US,100,US,M
+2023,MI,FT,Machine Learning Engineer,148500,USD,148500,US,0,US,M
+2023,MI,FT,Machine Learning Engineer,126277,USD,126277,US,0,US,M
+2023,SE,FT,Data Architect,228000,USD,228000,US,0,US,M
+2023,SE,FT,Data Architect,120000,USD,120000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,180000,USD,180000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,90000,USD,90000,US,0,US,M
+2023,SE,FT,Data Scientist,126500,USD,126500,US,0,US,M
+2023,SE,FT,Data Scientist,78000,USD,78000,US,0,US,M
+2023,SE,FT,Data Engineer,180000,USD,180000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,272000,USD,272000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,170000,USD,170000,US,0,US,M
+2023,MI,FT,Data Analyst,80000,USD,80000,US,0,US,M
+2023,MI,FT,Data Analyst,60000,USD,60000,US,0,US,M
+2023,SE,FT,Data Engineer,259000,USD,259000,US,100,US,M
+2023,SE,FT,Data Engineer,146000,USD,146000,US,100,US,M
+2023,SE,FT,Data Engineer,200000,USD,200000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,0,US,M
+2023,EX,FT,Data Engineer,310000,USD,310000,US,100,US,M
+2023,EX,FT,Data Engineer,239000,USD,239000,US,100,US,M
+2023,SE,FT,Data Analyst,142000,USD,142000,US,100,US,M
+2023,SE,FT,Data Analyst,95000,USD,95000,US,100,US,M
+2023,SE,FT,Data Scientist,155000,USD,155000,US,0,US,M
+2023,SE,FT,Data Scientist,139500,USD,139500,US,0,US,M
+2023,MI,FT,Data Engineer,140000,USD,140000,US,100,US,M
+2023,MI,FT,Data Engineer,120000,USD,120000,US,100,US,M
+2023,SE,FT,Data Engineer,259000,USD,259000,US,100,US,M
+2023,SE,FT,Data Engineer,146000,USD,146000,US,100,US,M
+2023,MI,FT,Data Analyst,90000,GBP,109371,HR,0,HR,M
+2023,MI,FT,Data Analyst,60000,GBP,72914,HR,0,HR,M
+2023,EN,PT,Data Analyst,78000,PLN,17779,PL,100,IN,L
+2023,EN,FT,Data Scientist,101400,BRL,19522,BR,100,BR,L
+2023,SE,FT,Data Science Lead,247500,USD,247500,US,0,US,M
+2023,SE,FT,Data Science Lead,172200,USD,172200,US,0,US,M
+2023,SE,FT,Data Scientist,225000,USD,225000,US,0,US,M
+2023,SE,FT,Data Scientist,156400,USD,156400,US,0,US,M
+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Data Engineer,109400,USD,109400,US,0,US,M
+2023,MI,FT,Data Engineer,125000,USD,125000,US,0,US,M
+2023,MI,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,100000,EUR,107309,FR,100,FR,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,70000,EUR,75116,FR,100,FR,M
+2023,SE,FT,Data Scientist,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Data Analyst,120000,USD,120000,US,100,US,M
+2023,SE,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M
+2023,SE,FT,Data Analyst,110600,USD,110600,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,288000,USD,288000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,140000,USD,140000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,288000,USD,288000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,140000,USD,140000,US,100,US,M
+2023,MI,FT,Data Engineer,120000,USD,120000,US,0,US,M
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+2022,MI,FT,Machine Learning Developer,100000,CAD,76814,CA,100,CA,M
+2020,SE,FT,Machine Learning Manager,157000,CAD,117104,CA,50,CA,L
+2022,EX,FT,Director of Data Science,250000,CAD,192037,CA,50,CA,L
+2022,MI,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,S
+2022,MI,FT,Business Data Analyst,1400000,INR,17805,IN,100,IN,M
+2022,MI,FT,Data Scientist,2400000,INR,30523,IN,100,IN,L
+2022,MI,FT,Machine Learning Infrastructure Engineer,53000,EUR,55685,PT,50,PT,L
+2022,MI,PT,Data Engineer,50000,EUR,52533,DE,50,DE,L
+2022,EN,FT,Data Scientist,1400000,INR,17805,IN,100,IN,M
+2022,MI,FT,Applied Machine Learning Scientist,75000,USD,75000,BO,100,US,L
+2022,MI,FT,Applied Data Scientist,157000,USD,157000,US,100,US,L
+2022,MI,FT,Business Data Analyst,90000,CAD,69133,CA,50,CA,L
+2022,EN,FT,Data Engineer,65000,USD,65000,US,100,US,S
+2022,SE,FT,Machine Learning Engineer,65000,EUR,68293,IE,100,IE,S
+2021,MI,FT,Data Scientist,109000,USD,109000,US,50,US,L
+2022,MI,FT,Data Scientist,88000,CAD,67597,CA,100,CA,M
+2022,EN,FT,Computer Vision Engineer,10000,USD,10000,PT,100,LU,M
+2022,MI,FT,Data Analyst,20000,USD,20000,GR,100,GR,S
+2021,SE,FT,Head of Data,87000,EUR,102839,SI,100,SI,L
+2022,SE,FT,Head of Data,200000,USD,200000,MY,100,US,M
+2022,EN,FT,Data Scientist,66500,CAD,51081,CA,100,CA,L
+2022,MI,FT,Data Scientist,78000,USD,78000,US,100,US,M
+2022,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
+2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
+2022,MI,FT,Machine Learning Engineer,121000,AUD,83864,AU,100,AU,L
+2022,EN,FT,Data Scientist,40000,USD,40000,JP,100,MY,L
+2022,MI,FT,Head of Data,30000,EUR,31520,EE,100,EE,S
+2022,SE,FT,Machine Learning Engineer,57000,EUR,59888,NL,100,NL,L
+2020,EN,FT,Data Engineer,48000,EUR,54742,PK,100,DE,L
+2022,SE,FT,Research Scientist,85000,EUR,89306,FR,50,FR,L
+2022,EN,FT,Data Scientist,120000,AUD,83171,AU,50,AU,M
+2022,SE,FT,Data Scientist,165000,USD,165000,US,100,US,M
+2022,MI,FT,Machine Learning Scientist,153000,USD,153000,US,50,US,M
+2022,SE,FT,Data Scientist,100000,USD,100000,BR,100,US,M
+2022,SE,FT,Machine Learning Developer,100000,CAD,76814,CA,100,CA,M
+2022,MI,FT,Data Scientist,150000,PLN,33609,PL,100,PL,L
+2022,MI,FT,Principal Data Analyst,75000,USD,75000,CA,100,CA,S
+2020,MI,FT,Product Data Analyst,20000,USD,20000,HN,0,HN,S
+2022,EN,CT,Applied Machine Learning Scientist,29000,EUR,30469,TN,100,CZ,M
+2021,MI,FT,Research Scientist,69999,USD,69999,CZ,50,CZ,L
+2022,EN,FT,Data Engineer,52800,EUR,55475,PK,100,DE,M
+2022,MI,FT,Research Scientist,59000,EUR,61989,AT,0,AT,L
+2022,SE,FT,Data Science Manager,152500,USD,152500,US,100,US,M
+2022,EN,FT,Research Scientist,120000,USD,120000,US,100,US,L
+2022,MI,FT,Data Scientist,135000,USD,135000,US,100,US,L
+2022,SE,FT,Data Analytics Lead,405000,USD,405000,US,100,US,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,0,US,L
+2021,SE,FT,Data Science Manager,240000,USD,240000,US,0,US,L
+2021,MI,FT,Data Analyst,135000,USD,135000,US,100,US,L
+2021,EN,FT,Data Scientist,80000,USD,80000,US,100,US,M
+2022,SE,FT,Applied Data Scientist,380000,USD,380000,US,100,US,L
+2022,MI,FT,Data Scientist,115000,CHF,120402,CH,0,CH,L
+2022,SE,FT,Applied Data Scientist,177000,USD,177000,US,100,US,L
+2022,MI,FT,Data Engineer,62000,EUR,65141,FR,100,FR,M
+2022,MI,FT,Data Scientist,48000,USD,48000,RU,100,US,S
+2022,EN,FT,Data Analytics Engineer,20000,USD,20000,PK,0,PK,M
+2021,SE,FT,Principal Data Scientist,220000,USD,220000,US,0,US,L
+2021,MI,FT,ML Engineer,8500000,JPY,77364,JP,50,JP,S
+2021,MI,FT,ML Engineer,7000000,JPY,63711,JP,50,JP,S
+2022,EN,FT,Computer Vision Software Engineer,150000,USD,150000,AU,100,AU,S
+2021,MI,FT,Data Analyst,90000,USD,90000,US,100,US,M
+2022,MI,FL,Data Scientist,100000,USD,100000,CA,100,US,M
+2021,EN,FT,Data Scientist,100000,USD,100000,US,0,US,S
+2022,EN,PT,Data Scientist,100000,USD,100000,DZ,50,DZ,M
+2022,SE,FT,Research Scientist,144000,USD,144000,US,50,US,L
+2022,SE,FT,Principal Data Scientist,148000,EUR,155499,DE,100,DE,M
+2021,SE,FT,Computer Vision Engineer,24000,USD,24000,BR,100,BR,M
+2021,MI,FT,Applied Machine Learning Scientist,38400,USD,38400,VN,100,US,M
+2022,EN,FT,Financial Data Analyst,100000,USD,100000,US,50,US,L
+2021,MI,FT,Data Scientist,82500,USD,82500,US,100,US,S
+2021,EN,FT,Data Scientist,42000,EUR,49646,FR,50,FR,M
+2021,SE,FT,Lead Data Scientist,3000000,INR,40570,IN,50,IN,L
+2022,EN,FT,Data Engineer,120000,USD,120000,US,100,US,M
+2022,SE,FT,Lead Machine Learning Engineer,80000,EUR,84053,DE,0,DE,M
+2021,EN,FT,Machine Learning Engineer,20000,USD,20000,IN,100,IN,S
+2022,EN,FT,Computer Vision Engineer,125000,USD,125000,US,0,US,M
+2021,MI,FT,Data Scientist,700000,INR,9466,IN,0,IN,S
+2021,SE,FT,Machine Learning Scientist,120000,USD,120000,US,50,US,S
+2021,EN,PT,Data Analyst,8760,EUR,10354,ES,50,ES,M
+2021,EN,FT,Applied Data Scientist,80000,GBP,110037,GB,0,GB,L
+2022,EN,FT,ML Engineer,20000,EUR,21013,PT,100,PT,L
+2021,EN,FT,Data Analyst,50000,USD,50000,US,100,US,M
+2021,SE,FT,Principal Data Engineer,200000,USD,200000,US,100,US,M
+2021,MI,FT,Big Data Engineer,60000,USD,60000,ES,50,RO,M
+2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,EN,FT,Machine Learning Developer,100000,USD,100000,IQ,50,IQ,S
+2021,MI,FT,Data Engineer,100000,USD,100000,US,100,US,L
+2021,SE,FT,Machine Learning Engineer,70000,EUR,82744,BE,50,BE,M
+2020,MI,FT,Data Engineer,51999,EUR,59303,DE,100,DE,S
+2021,MI,FT,Research Scientist,53000,EUR,62649,FR,50,FR,M
+2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L
+2021,MI,FT,Data Architect,170000,USD,170000,US,100,US,L
+2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L
+2021,EN,FT,BI Data Analyst,55000,USD,55000,US,50,US,S
+2021,EX,FT,Director of Data Science,250000,USD,250000,US,0,US,L
+2021,EN,FT,Data Engineer,80000,USD,80000,US,100,US,L
+2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L
+2021,EX,FT,Director of Data Science,110000,EUR,130026,DE,50,DE,M
+2021,EN,FT,Data Science Consultant,54000,EUR,63831,DE,50,DE,L
+2020,SE,FT,Data Scientist,60000,EUR,68428,GR,100,US,L
+2021,EX,FT,Head of Data Science,85000,USD,85000,RU,0,RU,M
+2021,EX,FT,Head of Data,230000,USD,230000,RU,50,RU,L
+2021,EN,FT,Machine Learning Engineer,125000,USD,125000,US,100,US,S
+2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,100,US,M
+2020,MI,FT,Research Scientist,450000,USD,450000,US,0,US,M
+2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L
+2020,MI,FT,Data Engineer,65000,EUR,74130,AT,50,AT,L
+2021,SE,FT,Data Science Engineer,159500,CAD,127221,CA,50,CA,L
+2021,SE,FT,Data Science Manager,144000,USD,144000,US,100,US,L
+2021,EN,FT,Data Scientist,13400,USD,13400,UA,100,UA,L
+2021,MI,FT,Data Scientist,95000,CAD,75774,CA,100,CA,L
+2021,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
+2020,MI,FT,Data Science Consultant,103000,USD,103000,US,100,US,L
+2021,SE,FT,Data Engineer,153000,USD,153000,US,100,US,L
+2021,MI,FT,Data Engineer,90000,USD,90000,US,100,US,L
+2021,EN,FT,Data Analyst,90000,USD,90000,US,100,US,S
+2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S
+2021,MI,FT,Data Scientist,50000,USD,50000,NG,100,NG,L
+2021,EN,PT,AI Scientist,12000,USD,12000,PK,100,US,M
+2021,MI,PT,3D Computer Vision Researcher,400000,INR,5409,IN,50,IN,M
+2021,MI,CT,ML Engineer,270000,USD,270000,US,100,US,L
+2021,MI,FT,Applied Data Scientist,68000,CAD,54238,GB,50,CA,L
+2021,MI,FT,Machine Learning Engineer,40000,EUR,47282,ES,100,ES,S
+2021,EX,FT,Director of Data Science,130000,EUR,153667,IT,100,PL,L
+2021,MI,FT,Data Engineer,110000,PLN,28476,PL,100,PL,L
+2021,MI,FT,Data Analytics Engineer,110000,USD,110000,US,100,US,L
+2021,EN,FT,Research Scientist,60000,GBP,82528,GB,50,GB,L
+2020,EN,FT,Machine Learning Engineer,250000,USD,250000,US,50,US,L
+2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M
+2021,SE,FT,Data Analyst,80000,USD,80000,BG,100,US,S
+2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S
+2020,EN,FT,Machine Learning Engineer,138000,USD,138000,US,100,US,S
+2021,MI,FT,Data Engineer,140000,USD,140000,US,100,US,L
+2021,SE,FT,Data Analytics Engineer,67000,EUR,79197,DE,100,DE,L
+2021,SE,FT,Lead Data Analyst,170000,USD,170000,US,100,US,L
+2021,EN,FT,Data Analyst,80000,USD,80000,US,100,US,M
+2020,MI,FT,Data Scientist,45760,USD,45760,PH,100,US,S
+2021,MI,FT,BI Data Analyst,100000,USD,100000,US,100,US,M
+2021,SE,FT,Data Scientist,45000,EUR,53192,FR,50,FR,L
+2021,EX,FT,Head of Data,235000,USD,235000,US,100,US,L
+2021,EX,FT,BI Data Analyst,150000,USD,150000,IN,100,US,L
+2020,EX,FT,Data Engineer,70000,EUR,79833,ES,50,ES,L
+2021,EN,FT,Machine Learning Scientist,225000,USD,225000,US,100,US,L
+2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,100,DE,S
+2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180,PT,0,PT,M
+2021,SE,FT,Marketing Data Analyst,75000,EUR,88654,GR,100,DK,L
+2021,SE,FT,Lead Data Engineer,75000,GBP,103160,GB,100,GB,S
+2021,SE,FT,Data Engineer,82500,GBP,113476,GB,100,GB,M
+2021,SE,FT,Machine Learning Engineer,80000,EUR,94564,DE,50,DE,L
+2021,EN,FT,Data Engineer,2250000,INR,30428,IN,100,IN,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M
+2021,SE,FT,Data Engineer,115000,USD,115000,US,100,US,S
+2021,MI,FT,Research Scientist,235000,CAD,187442,CA,100,CA,L
+2021,MI,FT,Data Analyst,37456,GBP,51519,GB,50,GB,L
+2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L
+2020,MI,FT,Data Engineer,88000,GBP,112872,GB,50,GB,L
+2021,MI,FT,BI Data Analyst,11000000,HUF,36259,HU,50,US,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,L
+2020,EN,PT,ML Engineer,14000,EUR,15966,DE,100,DE,S
+2021,MI,FT,Computer Vision Software Engineer,81000,EUR,95746,DE,100,US,S
+2021,EN,FT,Computer Vision Software Engineer,70000,USD,70000,US,100,US,M
+2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S
+2021,MI,FT,Cloud Data Engineer,120000,SGD,89294,SG,50,SG,L
+2021,EN,FT,Data Scientist,2200000,INR,29751,IN,50,IN,L
+2021,SE,FT,Lead Data Engineer,276000,USD,276000,US,0,US,L
+2020,SE,FT,Data Engineer,188000,USD,188000,US,100,US,L
+2021,SE,FT,Cloud Data Engineer,160000,USD,160000,BR,100,US,S
+2020,MI,FT,Data Scientist,105000,USD,105000,US,100,US,L
+2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,SE,FT,Data Engineer,174000,USD,174000,US,100,US,L
+2021,MI,FT,Data Analyst,93000,USD,93000,US,100,US,L
+2021,EN,FT,Data Scientist,2100000,INR,28399,IN,100,IN,M
+2021,SE,FT,Research Scientist,51400,EUR,60757,PT,50,PT,L
+2021,EN,FT,Data Scientist,90000,USD,90000,US,100,US,S
+2020,MI,FT,Data Engineer,61500,EUR,70139,FR,50,FR,L
+2020,EN,FT,Data Analyst,450000,INR,6072,IN,0,IN,S
+2020,SE,FT,Data Engineer,720000,MXN,33511,MX,0,MX,S
+2021,SE,FT,Principal Data Analyst,170000,USD,170000,US,100,US,M
+2021,SE,FT,Data Engineer,70000,GBP,96282,GB,50,GB,L
+2021,MI,FT,Data Engineer,108000,TRY,12103,TR,0,TR,M
+2021,EN,FT,Data Scientist,31000,EUR,36643,FR,50,FR,L
+2021,MI,FT,Data Engineer,52500,GBP,72212,GB,50,GB,L
+2020,EN,FT,Data Analyst,91000,USD,91000,US,100,US,L
+2021,SE,FT,Big Data Architect,125000,CAD,99703,CA,50,CA,M
+2021,SE,FT,Data Scientist,165000,USD,165000,US,100,US,L
+2021,MI,FT,Data Analyst,80000,USD,80000,US,100,US,L
+2021,SE,FT,Data Scientist,130000,CAD,103691,CA,100,CA,L
+2021,EN,FT,Data Engineer,1600000,INR,21637,IN,50,IN,M
+2020,EN,FT,Research Scientist,42000,USD,42000,NL,50,NL,L
+2020,MI,FT,Lead Data Scientist,115000,USD,115000,AE,0,AE,L
+2021,MI,FT,Research Scientist,80000,CAD,63810,CA,100,CA,M
+2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S
+2021,MI,FT,Head of Data Science,110000,USD,110000,US,0,US,S
+2021,MI,FT,Data Architect,180000,USD,180000,US,100,US,L
+2021,SE,FT,Data Analyst,200000,USD,200000,US,100,US,L
+2020,SE,FT,Big Data Engineer,85000,GBP,109024,GB,50,GB,M
+2021,SE,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,SE,FT,ML Engineer,256000,USD,256000,US,100,US,S
+2021,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
+2020,MI,FT,Data Scientist,70000,EUR,79833,DE,0,DE,L
+2021,EN,FT,Data Engineer,72500,USD,72500,US,100,US,L
+2021,SE,FT,Machine Learning Engineer,185000,USD,185000,US,50,US,L
+2021,MI,PT,Data Engineer,59000,EUR,69741,NL,100,NL,L
+2021,EN,FT,Research Scientist,100000,USD,100000,JE,0,CN,L
+2021,MI,FT,Data Engineer,112000,USD,112000,US,100,US,L
+2020,SE,FT,Machine Learning Engineer,150000,USD,150000,US,50,US,L
+2021,SE,FT,Data Scientist,180000,TRY,20171,TR,50,TR,L
+2021,SE,FT,AI Scientist,55000,USD,55000,ES,100,ES,L
+2021,EN,FT,Data Scientist,58000,USD,58000,US,50,US,L
+2021,EN,FT,Data Scientist,100000,USD,100000,US,100,US,M
+2021,SE,FT,Data Scientist,65720,EUR,77684,FR,50,FR,M
+2021,EN,FT,Machine Learning Engineer,85000,USD,85000,NL,100,DE,S
+2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,0,DE,L
+2021,SE,CT,Staff Data Scientist,105000,USD,105000,US,100,US,M
+2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L
+2021,EN,FT,Data Engineer,55000,EUR,65013,DE,50,DE,M
+2021,MI,FT,Data Engineer,250000,TRY,28016,TR,100,TR,M
+2021,MI,FT,Data Engineer,111775,USD,111775,US,0,US,M
+2021,MI,FT,Data Engineer,93150,USD,93150,US,0,US,M
+2021,SE,FT,Lead Data Engineer,160000,USD,160000,PR,50,US,S
+2021,MI,FT,Data Scientist,21600,EUR,25532,RS,100,DE,S
+2021,SE,FT,Machine Learning Engineer,4900000,INR,66265,IN,0,IN,L
+2021,MI,FT,Data Scientist,1250000,INR,16904,IN,100,IN,S
+2021,SE,FT,Data Analyst,54000,EUR,63831,DE,50,DE,L
+2020,SE,FT,Lead Data Scientist,190000,USD,190000,US,100,US,S
+2021,EX,FT,Director of Data Science,120000,EUR,141846,DE,0,DE,L
+2021,EN,FT,Big Data Engineer,1200000,INR,16228,IN,100,IN,L
+2021,SE,FT,Data Analyst,90000,CAD,71786,CA,100,CA,M
+2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
+2021,SE,FT,Data Scientist,135000,USD,135000,US,0,US,L
+2021,EN,FT,Machine Learning Engineer,21000,EUR,24823,DE,50,DE,M
+2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L
+2021,SE,FT,Machine Learning Engineer,1799997,INR,24342,IN,100,IN,L
+2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S
+2021,MI,FT,Data Scientist,147000,USD,147000,US,50,US,L
+2021,SE,FT,Research Scientist,120500,CAD,96113,CA,50,CA,L
+2021,SE,FT,Data Science Manager,174000,USD,174000,US,100,US,L
+2020,MI,FT,Business Data Analyst,135000,USD,135000,US,100,US,L
+2021,EN,FT,Machine Learning Engineer,21844,USD,21844,CO,50,CO,M
+2020,SE,FT,Lead Data Engineer,125000,USD,125000,NZ,50,NZ,S
+2020,EN,FT,Data Scientist,45000,EUR,51321,FR,0,FR,S
+2020,MI,FT,Data Scientist,3000000,INR,40481,IN,0,IN,L
+2021,EX,FT,Data Science Consultant,59000,EUR,69741,FR,100,ES,S
+2021,SE,FT,Data Analytics Engineer,50000,USD,50000,VN,100,GB,M
+2020,EN,FT,Data Scientist,35000,EUR,39916,FR,0,FR,M
+2020,MI,FT,Lead Data Analyst,87000,USD,87000,US,100,US,L
+2021,MI,FT,Data Engineer,22000,EUR,26005,RO,0,US,L
+2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
+2021,MI,FT,Big Data Engineer,1672000,INR,22611,IN,0,IN,L
+2021,MI,FT,Data Scientist,420000,INR,5679,IN,100,US,S
+2021,EN,FT,Machine Learning Engineer,81000,USD,81000,US,50,US,S
+2021,MI,FT,Data Scientist,30400000,CLP,40038,CL,100,CL,L
+2021,EN,FT,Data Science Consultant,90000,USD,90000,US,100,US,S
+2021,MI,FT,Data Scientist,52000,EUR,61467,DE,50,AT,M
+2021,SE,FT,Machine Learning Infrastructure Engineer,195000,USD,195000,US,100,US,M
+2021,MI,FT,Data Scientist,32000,EUR,37825,ES,100,ES,L
+2020,MI,FT,Data Analyst,85000,USD,85000,US,100,US,L
+2021,EX,CT,Principal Data Scientist,416000,USD,416000,US,100,US,S
+2021,SE,FT,Machine Learning Scientist,225000,USD,225000,US,100,CA,L
+2021,MI,FT,Data Scientist,40900,GBP,56256,GB,50,GB,L
+2021,MI,FT,Data Scientist,2500000,INR,33808,IN,0,IN,M
+2021,MI,FT,Data Scientist,85000,GBP,116914,GB,50,GB,L
+2021,MI,FT,Machine Learning Engineer,180000,PLN,46597,PL,100,PL,L
+2020,MI,FT,Data Analyst,8000,USD,8000,PK,50,PK,L
+2020,EN,FT,Data Engineer,4450000,JPY,41689,JP,100,JP,S
+2020,SE,FT,Big Data Engineer,100000,EUR,114047,PL,100,GB,S
+2021,MI,FT,Machine Learning Engineer,75000,EUR,88654,BE,100,BE,M
+2020,EN,FT,Data Science Consultant,423000,INR,5707,IN,50,IN,M
+2020,MI,FT,Lead Data Engineer,56000,USD,56000,PT,100,US,M
+2021,EN,PT,Computer Vision Engineer,180000,DKK,28609,DK,50,DK,S
+2021,MI,FT,Data Scientist,75000,EUR,88654,DE,50,DE,L
+2020,MI,FT,Product Data Analyst,450000,INR,6072,IN,100,IN,L
+2020,SE,FT,Data Engineer,42000,EUR,47899,GR,50,GR,L
+2020,MI,FT,BI Data Analyst,98000,USD,98000,US,0,US,M
+2021,MI,FT,Data Engineer,48000,GBP,66022,HK,50,GB,S
+2021,MI,FT,Research Scientist,48000,EUR,56738,FR,50,FR,S
+2021,MI,FT,Machine Learning Engineer,21000,EUR,24823,SI,50,SI,L
+2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,0,US,L
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