diff --git a/README.md b/README.md
index 31a36b9..f9a3d0a 100644
--- a/README.md
+++ b/README.md
@@ -3,4 +3,6 @@
 https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data
 https://www.kaggle.com/datasets/muhammedtausif/world-population-by-countries
 https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data
-https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed
\ No newline at end of file
+https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed
+
+Датасет для 4 лабы: https://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary?resource=download
\ No newline at end of file
diff --git a/lab_4/laba4.ipynb b/lab_4/laba4.ipynb
new file mode 100644
index 0000000..35f9ef2
--- /dev/null
+++ b/lab_4/laba4.ipynb
@@ -0,0 +1,1314 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>work_year</th>\n",
+       "      <th>experience_level</th>\n",
+       "      <th>employment_type</th>\n",
+       "      <th>job_title</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>salary_currency</th>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <th>employee_residence</th>\n",
+       "      <th>remote_ratio</th>\n",
+       "      <th>company_location</th>\n",
+       "      <th>company_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Principal Data Scientist</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>EUR</td>\n",
+       "      <td>85847</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>100</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>ML Engineer</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>ML Engineer</td>\n",
+       "      <td>25500</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>25500</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>100</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>100</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   work_year experience_level employment_type                 job_title  \\\n",
+       "0       2023               SE              FT  Principal Data Scientist   \n",
+       "1       2023               MI              CT               ML Engineer   \n",
+       "2       2023               MI              CT               ML Engineer   \n",
+       "3       2023               SE              FT            Data Scientist   \n",
+       "4       2023               SE              FT            Data Scientist   \n",
+       "\n",
+       "   salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
+       "0   80000             EUR          85847                 ES           100   \n",
+       "1   30000             USD          30000                 US           100   \n",
+       "2   25500             USD          25500                 US           100   \n",
+       "3  175000             USD         175000                 CA           100   \n",
+       "4  120000             USD         120000                 CA           100   \n",
+       "\n",
+       "  company_location company_size  \n",
+       "0               ES            L  \n",
+       "1               US            S  \n",
+       "2               US            S  \n",
+       "3               CA            M  \n",
+       "4               CA            M  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Бизнес-цели</h2>\n",
+    "Задача регрессии: Построить модель для прогноза зарплаты в USD используя атрибуты.<br>\n",
+    "Задача классификации: Определение уровня опыта сотрудника (experience_level) на основе других характеристик, таких как job_title, salary_in_usd, и employment_type."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проведем обработку данных и сделаем выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transformed feature shape (train): (2029, 151)\n",
+      "Transformed feature shape (test): (508, 151)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "\n",
+    "# Удаление выбросов из столбца `salary_in_usd` с использованием IQR\n",
+    "Q1 = df['salary_in_usd'].quantile(0.25)\n",
+    "Q3 = df['salary_in_usd'].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "df = df[(df['salary_in_usd'] >= Q1 - 1.5 * IQR) & (df['salary_in_usd'] <= Q3 + 1.5 * IQR)]\n",
+    "\n",
+    "# Преобразование категориальных данных в числовые (если потребуется)\n",
+    "if 'remote_ratio' in df.columns:\n",
+    "    df['remote_ratio'] = df['remote_ratio'].astype(int)\n",
+    "\n",
+    "# Удаление дубликатов\n",
+    "df.drop_duplicates(inplace=True)\n",
+    "\n",
+    "# Определение целевой переменной и признаков\n",
+    "X = df.drop(columns=['salary_in_usd', 'salary_currency', 'job_title'])  # Признаки\n",
+    "y = df['salary_in_usd']  # Целевая переменная для регрессии\n",
+    "\n",
+    "# Определение числовых и категориальных признаков\n",
+    "numeric_features = ['work_year', 'remote_ratio']\n",
+    "categorical_features = ['experience_level', 'employment_type', \n",
+    "                        'employee_residence', 'company_location', 'company_size']\n",
+    "\n",
+    "# Обработка числовых данных\n",
+    "numeric_transformer = Pipeline(steps=[\n",
+    "    ('imputer', SimpleImputer(strategy='median')),  # Заполнение пропусков медианой\n",
+    "    ('scaler', StandardScaler())                   # Нормализация данных\n",
+    "])\n",
+    "\n",
+    "# Обработка категориальных данных\n",
+    "categorical_transformer = Pipeline(steps=[\n",
+    "    ('imputer', SimpleImputer(strategy='most_frequent')),  # Заполнение пропусков модой\n",
+    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))     # Преобразование в One-Hot Encoding\n",
+    "])\n",
+    "\n",
+    "# Комбинированный трансформер\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', numeric_transformer, numeric_features),   # Применяем числовую обработку\n",
+    "        ('cat', categorical_transformer, categorical_features)  # Применяем категориальную обработку\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Разделение данных на обучающую и тестовую выборки\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Применение пайплайна\n",
+    "X_train_transformed = preprocessor.fit_transform(X_train)\n",
+    "X_test_transformed = preprocessor.transform(X_test)\n",
+    "\n",
+    "# Проверка результата трансформации\n",
+    "print(f\"Transformed feature shape (train): {X_train_transformed.shape}\")\n",
+    "print(f\"Transformed feature shape (test): {X_test_transformed.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Выведим результаты"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>work_year</th>\n",
+       "      <th>remote_ratio</th>\n",
+       "      <th>experience_level_EN</th>\n",
+       "      <th>experience_level_EX</th>\n",
+       "      <th>experience_level_MI</th>\n",
+       "      <th>experience_level_SE</th>\n",
+       "      <th>employment_type_CT</th>\n",
+       "      <th>employment_type_FL</th>\n",
+       "      <th>employment_type_FT</th>\n",
+       "      <th>employment_type_PT</th>\n",
+       "      <th>...</th>\n",
+       "      <th>company_location_SI</th>\n",
+       "      <th>company_location_SK</th>\n",
+       "      <th>company_location_TH</th>\n",
+       "      <th>company_location_TR</th>\n",
+       "      <th>company_location_UA</th>\n",
+       "      <th>company_location_US</th>\n",
+       "      <th>company_location_VN</th>\n",
+       "      <th>company_size_L</th>\n",
+       "      <th>company_size_M</th>\n",
+       "      <th>company_size_S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-1.747172</td>\n",
+       "      <td>1.016983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-1.747172</td>\n",
+       "      <td>1.016983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.943539</td>\n",
+       "      <td>-1.057887</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.401816</td>\n",
+       "      <td>1.016983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.401816</td>\n",
+       "      <td>-0.020452</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 151 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   work_year  remote_ratio  experience_level_EN  experience_level_EX  \\\n",
+       "0  -1.747172      1.016983                  0.0                  0.0   \n",
+       "1  -1.747172      1.016983                  0.0                  0.0   \n",
+       "2   0.943539     -1.057887                  0.0                  0.0   \n",
+       "3  -0.401816      1.016983                  0.0                  0.0   \n",
+       "4  -0.401816     -0.020452                  0.0                  0.0   \n",
+       "\n",
+       "   experience_level_MI  experience_level_SE  employment_type_CT  \\\n",
+       "0                  0.0                  1.0                 0.0   \n",
+       "1                  0.0                  1.0                 0.0   \n",
+       "2                  0.0                  1.0                 0.0   \n",
+       "3                  1.0                  0.0                 0.0   \n",
+       "4                  1.0                  0.0                 0.0   \n",
+       "\n",
+       "   employment_type_FL  employment_type_FT  employment_type_PT  ...  \\\n",
+       "0                 0.0                 1.0                 0.0  ...   \n",
+       "1                 0.0                 1.0                 0.0  ...   \n",
+       "2                 0.0                 1.0                 0.0  ...   \n",
+       "3                 0.0                 1.0                 0.0  ...   \n",
+       "4                 0.0                 1.0                 0.0  ...   \n",
+       "\n",
+       "   company_location_SI  company_location_SK  company_location_TH  \\\n",
+       "0                  0.0                  0.0                  0.0   \n",
+       "1                  0.0                  0.0                  0.0   \n",
+       "2                  0.0                  0.0                  0.0   \n",
+       "3                  0.0                  0.0                  0.0   \n",
+       "4                  0.0                  0.0                  0.0   \n",
+       "\n",
+       "   company_location_TR  company_location_UA  company_location_US  \\\n",
+       "0                  0.0                  0.0                  0.0   \n",
+       "1                  0.0                  0.0                  1.0   \n",
+       "2                  0.0                  0.0                  1.0   \n",
+       "3                  0.0                  0.0                  1.0   \n",
+       "4                  0.0                  0.0                  0.0   \n",
+       "\n",
+       "   company_location_VN  company_size_L  company_size_M  company_size_S  \n",
+       "0                  0.0             1.0             0.0             0.0  \n",
+       "1                  0.0             1.0             0.0             0.0  \n",
+       "2                  0.0             0.0             1.0             0.0  \n",
+       "3                  0.0             0.0             1.0             0.0  \n",
+       "4                  0.0             1.0             0.0             0.0  \n",
+       "\n",
+       "[5 rows x 151 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Получим имена категориальных признаков после OneHotEncoder\n",
+    "categorical_feature_names = preprocessor.named_transformers_['cat']['onehot'].get_feature_names_out(categorical_features)\n",
+    "\n",
+    "# Объединим их с именами числовых признаков\n",
+    "feature_names = list(numeric_features) + list(categorical_feature_names)\n",
+    "\n",
+    "# Создадим DataFrame для преобразованных данных\n",
+    "X_train_transformed_df = pd.DataFrame(X_train_transformed.toarray() if hasattr(X_train_transformed, 'toarray') else X_train_transformed, columns=feature_names)\n",
+    "\n",
+    "# Выведем первые 5 строк обработанного набора данных\n",
+    "X_train_transformed_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Обучим три модели"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training LinearRegression...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 1 is smaller than n_iter=20. Running 1 iterations. For exhaustive searches, use GridSearchCV.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training RandomForestRegressor...\n",
+      "Training GradientBoostingRegressor...\n",
+      "\n",
+      "Model: LinearRegression\n",
+      "Best Params: {}\n",
+      "MAE: 35903.74761235383\n",
+      "RMSE: 45746.92374132039\n",
+      "R2: 0.41681042958060477\n",
+      "\n",
+      "Model: RandomForestRegressor\n",
+      "Best Params: {'model__n_estimators': 100, 'model__min_samples_split': 10, 'model__max_depth': 20}\n",
+      "MAE: 35382.49447920311\n",
+      "RMSE: 45711.49865435396\n",
+      "R2: 0.41771328994747514\n",
+      "\n",
+      "Model: GradientBoostingRegressor\n",
+      "Best Params: {'model__n_estimators': 50, 'model__max_depth': 5, 'model__learning_rate': 0.2}\n",
+      "MAE: 35404.55042553757\n",
+      "RMSE: 45669.354449671955\n",
+      "R2: 0.41878648590699374\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "# Задание случайного состояния\n",
+    "random_state = 42\n",
+    "\n",
+    "# Модели и параметры\n",
+    "models_regression = {\n",
+    "    \"LinearRegression\": LinearRegression(),\n",
+    "    \"RandomForestRegressor\": RandomForestRegressor(random_state=random_state),\n",
+    "    \"GradientBoostingRegressor\": GradientBoostingRegressor(random_state=random_state)\n",
+    "}\n",
+    "\n",
+    "param_grids_regression = {\n",
+    "    \"LinearRegression\": {},\n",
+    "    \"RandomForestRegressor\": {\n",
+    "        'model__n_estimators': [50, 100, 200],\n",
+    "        'model__max_depth': [None, 10, 20],\n",
+    "        'model__min_samples_split': [2, 5, 10]\n",
+    "    },\n",
+    "    \"GradientBoostingRegressor\": {\n",
+    "        'model__n_estimators': [50, 100, 200],\n",
+    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
+    "        'model__max_depth': [3, 5, 10]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Результаты\n",
+    "results_regression = {}\n",
+    "\n",
+    "# Перебор моделей\n",
+    "for name, model in models_regression.items():\n",
+    "    print(f\"Training {name}...\")\n",
+    "    \n",
+    "    # Создание пайплайна\n",
+    "    pipeline = Pipeline(steps=[\n",
+    "        ('preprocessor', preprocessor),  # Используем уже созданный preprocessor\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "    \n",
+    "    # Определение параметров для RandomizedSearchCV\n",
+    "    param_grid = param_grids_regression[name]\n",
+    "    search = RandomizedSearchCV(pipeline, param_distributions=param_grid, \n",
+    "                                cv=5, scoring='neg_mean_absolute_error', \n",
+    "                                n_jobs=-1, random_state=random_state, n_iter=20)\n",
+    "    search.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Лучшая модель\n",
+    "    best_model = search.best_estimator_\n",
+    "    y_pred = best_model.predict(X_test)\n",
+    "    \n",
+    "    # Метрики\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    \n",
+    "    # Сохранение результатов\n",
+    "    results_regression[name] = {\n",
+    "        \"Best Params\": search.best_params_,\n",
+    "        \"MAE\": mae,\n",
+    "        \"RMSE\": rmse,\n",
+    "        \"R2\": r2\n",
+    "    }\n",
+    "\n",
+    "# Печать результатов\n",
+    "for name, metrics in results_regression.items():\n",
+    "    print(f\"\\nModel: {name}\")\n",
+    "    for metric, value in metrics.items():\n",
+    "        print(f\"{metric}: {value}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_05579_row0_col0 {\n",
+       "  background-color: #25858e;\n",
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+       "<table id=\"T_05579\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_05579_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
+       "      <th id=\"T_05579_level0_col1\" class=\"col_heading level0 col1\" >RMSE</th>\n",
+       "      <th id=\"T_05579_level0_col2\" class=\"col_heading level0 col2\" >R2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_05579_level0_row0\" class=\"row_heading level0 row0\" >GradientBoostingRegressor</th>\n",
+       "      <td id=\"T_05579_row0_col0\" class=\"data row0 col0\" >35404.550426</td>\n",
+       "      <td id=\"T_05579_row0_col1\" class=\"data row0 col1\" >45669.354450</td>\n",
+       "      <td id=\"T_05579_row0_col2\" class=\"data row0 col2\" >0.418786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_05579_level0_row1\" class=\"row_heading level0 row1\" >RandomForestRegressor</th>\n",
+       "      <td id=\"T_05579_row1_col0\" class=\"data row1 col0\" >35382.494479</td>\n",
+       "      <td id=\"T_05579_row1_col1\" class=\"data row1 col1\" >45711.498654</td>\n",
+       "      <td id=\"T_05579_row1_col2\" class=\"data row1 col2\" >0.417713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_05579_level0_row2\" class=\"row_heading level0 row2\" >LinearRegression</th>\n",
+       "      <td id=\"T_05579_row2_col0\" class=\"data row2 col0\" >35903.747612</td>\n",
+       "      <td id=\"T_05579_row2_col1\" class=\"data row2 col1\" >45746.923741</td>\n",
+       "      <td id=\"T_05579_row2_col2\" class=\"data row2 col2\" >0.416810</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x27c0be5fc50>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Формирование таблицы метрик из результатов регрессионных моделей\n",
+    "reg_metrics = pd.DataFrame.from_dict(results_regression, orient=\"index\")[\n",
+    "    [\"MAE\", \"RMSE\", \"R2\"]\n",
+    "]\n",
+    "\n",
+    "# Визуализация результатов с помощью стилизации\n",
+    "styled_metrics = (\n",
+    "    reg_metrics.sort_values(by=\"RMSE\")\n",
+    "    .style.background_gradient(cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE\", \"MAE\"])\n",
+    "    .background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"R2\"])\n",
+    ")\n",
+    "\n",
+    "# Отобразим таблицу\n",
+    "styled_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Чтото слабоватые модели получились. Даже 50% нет, нужно попробовать улучшить данные."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transformed feature shape (train): (2029, 153)\n",
+      "Transformed feature shape (test): (508, 153)\n",
+      "Training LinearRegression...\n",
+      "Training RandomForestRegressor...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 1 is smaller than n_iter=20. Running 1 iterations. For exhaustive searches, use GridSearchCV.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training GradientBoostingRegressor...\n",
+      "\n",
+      "Model: LinearRegression\n",
+      "Best Params: {}\n",
+      "MAE: 35923.393167146765\n",
+      "RMSE: 45787.2465103007\n",
+      "R2: 0.41578189344376837\n",
+      "\n",
+      "Model: RandomForestRegressor\n",
+      "Best Params: {'model__n_estimators': 100, 'model__min_samples_split': 10, 'model__max_depth': 20}\n",
+      "MAE: 35428.00841155441\n",
+      "RMSE: 45772.13311276274\n",
+      "R2: 0.41616750576805106\n",
+      "\n",
+      "Model: GradientBoostingRegressor\n",
+      "Best Params: {'model__n_estimators': 100, 'model__max_depth': 3, 'model__learning_rate': 0.1}\n",
+      "MAE: 35575.964157916314\n",
+      "RMSE: 45645.593157690266\n",
+      "R2: 0.41939112731165484\n"
+     ]
+    },
+    {
+     "data": {
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+       "<table id=\"T_baae7\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_baae7_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_baae7_level0_row0\" class=\"row_heading level0 row0\" >GradientBoostingRegressor</th>\n",
+       "      <td id=\"T_baae7_row0_col0\" class=\"data row0 col0\" >35575.964158</td>\n",
+       "      <td id=\"T_baae7_row0_col1\" class=\"data row0 col1\" >45645.593158</td>\n",
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+       "    </tr>\n",
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+       "      <th id=\"T_baae7_level0_row1\" class=\"row_heading level0 row1\" >RandomForestRegressor</th>\n",
+       "      <td id=\"T_baae7_row1_col0\" class=\"data row1 col0\" >35428.008412</td>\n",
+       "      <td id=\"T_baae7_row1_col1\" class=\"data row1 col1\" >45772.133113</td>\n",
+       "      <td id=\"T_baae7_row1_col2\" class=\"data row1 col2\" >0.416168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_baae7_level0_row2\" class=\"row_heading level0 row2\" >LinearRegression</th>\n",
+       "      <td id=\"T_baae7_row2_col0\" class=\"data row2 col0\" >35923.393167</td>\n",
+       "      <td id=\"T_baae7_row2_col1\" class=\"data row2 col1\" >45787.246510</td>\n",
+       "      <td id=\"T_baae7_row2_col2\" class=\"data row2 col2\" >0.415782</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x27c0bec9850>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Функция для приведения выбросов к среднему значению\n",
+    "def handle_outliers(df, column):\n",
+    "    Q1 = df[column].quantile(0.25)\n",
+    "    Q3 = df[column].quantile(0.75)\n",
+    "    IQR = Q3 - Q1\n",
+    "    lower_bound = Q1 - 1.5 * IQR\n",
+    "    upper_bound = Q3 + 1.5 * IQR\n",
+    "    \n",
+    "    mean_value = df[column].mean()\n",
+    "    df[column] = np.where((df[column] < lower_bound) | (df[column] > upper_bound), mean_value, df[column])\n",
+    "    return df\n",
+    "\n",
+    "# Приведение выбросов в столбце `salary_in_usd` к среднему значению\n",
+    "df = handle_outliers(df, 'salary_in_usd')\n",
+    "\n",
+    "# Преобразование категориальных данных в строковые для корректной обработки\n",
+    "if 'remote_ratio' in df.columns:\n",
+    "    df['remote_ratio'] = df['remote_ratio'].astype(str)\n",
+    "\n",
+    "# Удаление дубликатов\n",
+    "df.drop_duplicates(inplace=True)\n",
+    "\n",
+    "# Определение целевой переменной и признаков\n",
+    "X = df.drop(columns=['salary_in_usd', 'salary_currency', 'job_title'])  # Признаки\n",
+    "y = df['salary_in_usd']  # Целевая переменная для регрессии\n",
+    "\n",
+    "# Определение числовых и категориальных признаков\n",
+    "numeric_features = ['work_year']  # Убрали 'remote_ratio', так как это категориальный признак\n",
+    "categorical_features = ['experience_level', 'employment_type', \n",
+    "                        'employee_residence', 'company_location', 'company_size', 'remote_ratio']\n",
+    "\n",
+    "# Обработка числовых данных\n",
+    "numeric_transformer = Pipeline(steps=[\n",
+    "    ('imputer', SimpleImputer(strategy='median')),  # Заполнение пропусков медианой\n",
+    "    ('scaler', StandardScaler())                   # Нормализация данных\n",
+    "])\n",
+    "\n",
+    "# Обработка категориальных данных\n",
+    "categorical_transformer = Pipeline(steps=[\n",
+    "    ('imputer', SimpleImputer(strategy='most_frequent')),  # Заполнение пропусков модой\n",
+    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))     # Преобразование в One-Hot Encoding\n",
+    "])\n",
+    "\n",
+    "# Комбинированный трансформер\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', numeric_transformer, numeric_features),   # Применяем числовую обработку\n",
+    "        ('cat', categorical_transformer, categorical_features)  # Применяем категориальную обработку\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Разделение данных на обучающую и тестовую выборки\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Применение пайплайна\n",
+    "X_train_transformed = preprocessor.fit_transform(X_train)\n",
+    "X_test_transformed = preprocessor.transform(X_test)\n",
+    "\n",
+    "# Проверка результата трансформации\n",
+    "print(f\"Transformed feature shape (train): {X_train_transformed.shape}\")\n",
+    "print(f\"Transformed feature shape (test): {X_test_transformed.shape}\")\n",
+    "\n",
+    "# Определение моделей и их параметров\n",
+    "models = {\n",
+    "    \"LinearRegression\": LinearRegression(),\n",
+    "    \"RandomForestRegressor\": RandomForestRegressor(random_state=42),\n",
+    "    \"GradientBoostingRegressor\": GradientBoostingRegressor(random_state=42)\n",
+    "}\n",
+    "\n",
+    "param_grids = {\n",
+    "    \"LinearRegression\": {},\n",
+    "    \"RandomForestRegressor\": {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__max_depth': [10, 20, None],\n",
+    "        'model__min_samples_split': [2, 5, 10]\n",
+    "    },\n",
+    "    \"GradientBoostingRegressor\": {\n",
+    "        'model__n_estimators': [100, 200, 300],\n",
+    "        'model__learning_rate': [0.01, 0.1, 0.2],\n",
+    "        'model__max_depth': [3, 5, 7]\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Результаты\n",
+    "results = {}\n",
+    "\n",
+    "# Обучение моделей с подбором гиперпараметров\n",
+    "for name, model in models.items():\n",
+    "    print(f\"Training {name}...\")\n",
+    "    pipeline = Pipeline(steps=[\n",
+    "        ('preprocessor', preprocessor),\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "    param_grid = param_grids[name]\n",
+    "    search = RandomizedSearchCV(pipeline, param_distributions=param_grid, cv=5, \n",
+    "                                scoring='neg_mean_absolute_error', n_jobs=-1, \n",
+    "                                random_state=42, n_iter=20)\n",
+    "    search.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Лучшая модель\n",
+    "    best_model = search.best_estimator_\n",
+    "    y_pred = best_model.predict(X_test)\n",
+    "    \n",
+    "    # Метрики\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    \n",
+    "    # Сохранение результатов\n",
+    "    results[name] = {\n",
+    "        \"Best Params\": search.best_params_,\n",
+    "        \"MAE\": mae,\n",
+    "        \"RMSE\": rmse,\n",
+    "        \"R2\": r2\n",
+    "    }\n",
+    "\n",
+    "# Печать результатов\n",
+    "for name, metrics in results.items():\n",
+    "    print(f\"\\nModel: {name}\")\n",
+    "    for metric, value in metrics.items():\n",
+    "        print(f\"{metric}: {value}\")\n",
+    "\n",
+    "# Формирование таблицы метрик из результатов регрессионных моделей\n",
+    "reg_metrics = pd.DataFrame.from_dict(results, orient=\"index\")[\n",
+    "    [\"MAE\", \"RMSE\", \"R2\"]\n",
+    "]\n",
+    "\n",
+    "# Визуализация результатов с помощью стилизации\n",
+    "styled_metrics = (\n",
+    "    reg_metrics.sort_values(by=\"RMSE\")\n",
+    "    .style.background_gradient(cmap=\"viridis\", low=1, high=0.3, subset=[\"RMSE\", \"MAE\"])\n",
+    "    .background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"R2\"])\n",
+    ")\n",
+    "\n",
+    "# Отобразим таблицу\n",
+    "styled_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Переписал не много код, стало чуть лучше, но не намного. Думаю для моей первой работы подойдет"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Приступим к задаче классификации</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training LogisticRegression...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training RandomForestClassifier...\n",
+      "Training KNN...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 8 is smaller than n_iter=10. Running 8 iterations. For exhaustive searches, use GridSearchCV.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Model: LogisticRegression\n",
+      "Best Params: {'model__C': 0.1}\n",
+      "Accuracy: 1.0\n",
+      "F1 Score: 1.0\n",
+      "Confusion_matrix: [[ 50   0   0   0]\n",
+      " [  0 129   0   0]\n",
+      " [  0   0 316   0]\n",
+      " [  0   0   0  13]]\n",
+      "\n",
+      "Model: RandomForestClassifier\n",
+      "Best Params: {'model__n_estimators': 300, 'model__max_features': None, 'model__max_depth': 15, 'model__criterion': 'entropy'}\n",
+      "Accuracy: 1.0\n",
+      "F1 Score: 1.0\n",
+      "Confusion_matrix: [[ 50   0   0   0]\n",
+      " [  0 129   0   0]\n",
+      " [  0   0 316   0]\n",
+      " [  0   0   0  13]]\n",
+      "\n",
+      "Model: KNN\n",
+      "Best Params: {'model__weights': 'distance', 'model__n_neighbors': 9}\n",
+      "Accuracy: 0.9940944881889764\n",
+      "F1 Score: 0.9641274132899506\n",
+      "Confusion_matrix: [[ 50   0   0   0]\n",
+      " [  0 129   0   0]\n",
+      " [  0   0 316   0]\n",
+      " [  1   0   2  10]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
+    "\n",
+    "# Удалить строки с пропусками в experience_level\n",
+    "df = df.dropna(subset=['experience_level'])\n",
+    "\n",
+    "# Пересоздать y_class\n",
+    "y_class = df['experience_level'].map({'EN': 0, 'MI': 1, 'SE': 2, 'EX': 3})\n",
+    "\n",
+    "# Повторное разделение данных\n",
+    "X_train_clf, X_test_clf, y_train_clf, y_test_clf = train_test_split(X, y_class, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Определение моделей и их гиперпараметров\n",
+    "models_classification = {\n",
+    "    \"LogisticRegression\": LogisticRegression(max_iter=1000),\n",
+    "    \"RandomForestClassifier\": RandomForestClassifier(random_state=42),\n",
+    "    \"KNN\": KNeighborsClassifier()\n",
+    "}\n",
+    "\n",
+    "param_grids_classification = {\n",
+    "    \"LogisticRegression\": {\n",
+    "        'model__C': [0.1, 1, 10]\n",
+    "    },\n",
+    "    \"RandomForestClassifier\": {\n",
+    "        \"model__n_estimators\": [100, 200, 300],\n",
+    "        \"model__max_features\": [\"sqrt\", \"log2\", None],\n",
+    "        \"model__max_depth\": [5, 10, 15, None],\n",
+    "        \"model__criterion\": [\"gini\", \"entropy\"]\n",
+    "    },\n",
+    "    \"KNN\": {\n",
+    "        'model__n_neighbors': [3, 5, 7, 9],\n",
+    "        'model__weights': ['uniform', 'distance']\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# Результаты\n",
+    "results_classification = {}\n",
+    "\n",
+    "# Перебор моделей\n",
+    "for name, model in models_classification.items():\n",
+    "    print(f\"Training {name}...\")\n",
+    "    pipeline = Pipeline(steps=[\n",
+    "        ('preprocessor', preprocessor),\n",
+    "        ('model', model)\n",
+    "    ])\n",
+    "    param_grid = param_grids_classification[name]\n",
+    "    grid_search = RandomizedSearchCV(pipeline, param_distributions=param_grid, cv=5, scoring='f1_macro', n_jobs=-1, random_state=42)\n",
+    "    grid_search.fit(X_train_clf, y_train_clf)\n",
+    "\n",
+    "    # Лучшая модель\n",
+    "    best_model = grid_search.best_estimator_\n",
+    "    y_pred = best_model.predict(X_test_clf)\n",
+    "\n",
+    "    # Метрики\n",
+    "    acc = accuracy_score(y_test_clf, y_pred)\n",
+    "    f1 = f1_score(y_test_clf, y_pred, average='macro')\n",
+    "\n",
+    "    # Вычисление матрицы ошибок\n",
+    "    c_matrix = confusion_matrix(y_test_clf, y_pred)\n",
+    "\n",
+    "    # Сохранение результатов\n",
+    "    results_classification[name] = {\n",
+    "        \"Best Params\": grid_search.best_params_,\n",
+    "        \"Accuracy\": acc,\n",
+    "        \"F1 Score\": f1,\n",
+    "        \"Confusion_matrix\": c_matrix\n",
+    "    }\n",
+    "\n",
+    "# Печать результатов\n",
+    "for name, metrics in results_classification.items():\n",
+    "    print(f\"\\nModel: {name}\")\n",
+    "    for metric, value in metrics.items():\n",
+    "        print(f\"{metric}: {value}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Вывод: Все три модели показывают высокую точность, что указывает на их способность хорошо справляться с задачей классификации на данном наборе данных."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Нарисуем матрицу ошибок"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 7 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import ConfusionMatrixDisplay\n",
+    "\n",
+    "# Визуализация матриц ошибок\n",
+    "num_models = len(results_classification)\n",
+    "num_rows = (num_models // 2) + (num_models % 2)  # Количество строк для подграфиков\n",
+    "fig, ax = plt.subplots(num_rows, 2, figsize=(12, 10), sharex=False, sharey=False)\n",
+    "\n",
+    "for index, (name, metrics) in enumerate(results_classification.items()):\n",
+    "    c_matrix = metrics[\"Confusion_matrix\"]\n",
+    "    disp = ConfusionMatrixDisplay(\n",
+    "        confusion_matrix=c_matrix, display_labels=['EN', 'MI', 'SE', 'EX']\n",
+    "    ).plot(ax=ax.flat[index])\n",
+    "    disp.ax_.set_title(name)\n",
+    "\n",
+    "# Корректировка расположения графиков\n",
+    "plt.subplots_adjust(top=0.9, bottom=0.1, hspace=0.4, wspace=0.3)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Формируем таблицу метрик классификации"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_3cbcd_row0_col0, #T_3cbcd_row1_col0 {\n",
+       "  background-color: #21918c;\n",
+       "  color: #f1f1f1;\n",
+       "  background-color: #da5a6a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_3cbcd_row0_col1, #T_3cbcd_row1_col1 {\n",
+       "  background-color: #21918c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_3cbcd_row2_col0 {\n",
+       "  background-color: #440154;\n",
+       "  color: #f1f1f1;\n",
+       "  background-color: #4e02a2;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_3cbcd_row2_col1 {\n",
+       "  background-color: #440154;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_3cbcd\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_3cbcd_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
+       "      <th id=\"T_3cbcd_level0_col1\" class=\"col_heading level0 col1\" >F1 Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3cbcd_level0_row0\" class=\"row_heading level0 row0\" >LogisticRegression</th>\n",
+       "      <td id=\"T_3cbcd_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
+       "      <td id=\"T_3cbcd_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3cbcd_level0_row1\" class=\"row_heading level0 row1\" >RandomForestClassifier</th>\n",
+       "      <td id=\"T_3cbcd_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
+       "      <td id=\"T_3cbcd_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3cbcd_level0_row2\" class=\"row_heading level0 row2\" >KNN</th>\n",
+       "      <td id=\"T_3cbcd_row2_col0\" class=\"data row2 col0\" >0.994094</td>\n",
+       "      <td id=\"T_3cbcd_row2_col1\" class=\"data row2 col1\" >0.964127</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x27c0bf32b40>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Формируем таблицу метрик классификации\n",
+    "clf_metrics = pd.DataFrame.from_dict(results_classification, orient=\"index\")[[\"Accuracy\", \"F1 Score\"]]\n",
+    "\n",
+    "# Визуализация результатов с помощью стилизации\n",
+    "styled_metrics_clf = (\n",
+    "    clf_metrics.sort_values(by=\"F1 Score\", ascending=False)  # Сортировка по F1 Score\n",
+    "    .style.background_gradient(cmap=\"viridis\", low=0, high=1, subset=[\"F1 Score\", \"Accuracy\"])  # Стилизация столбцов\n",
+    "    .background_gradient(cmap=\"plasma\", low=0.3, high=1, subset=[\"Accuracy\"])\n",
+    ")\n",
+    "\n",
+    "styled_metrics_clf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В итоге RandomForestClassifier и LogisticRegression выдали точность в 100% что я считаю это очень хорошо, но я не уверен что это правда. KNN очень близко."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lab_5/laba5.ipynb b/lab_5/laba5.ipynb
new file mode 100644
index 0000000..db6e7cf
--- /dev/null
+++ b/lab_5/laba5.ipynb
@@ -0,0 +1,648 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>work_year</th>\n",
+       "      <th>experience_level</th>\n",
+       "      <th>employment_type</th>\n",
+       "      <th>job_title</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>salary_currency</th>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <th>employee_residence</th>\n",
+       "      <th>remote_ratio</th>\n",
+       "      <th>company_location</th>\n",
+       "      <th>company_size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Principal Data Scientist</td>\n",
+       "      <td>80000</td>\n",
+       "      <td>EUR</td>\n",
+       "      <td>85847</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>100</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>ML Engineer</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>30000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>ML Engineer</td>\n",
+       "      <td>25500</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>25500</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>100</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>100</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3750</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>412000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>412000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3751</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Principal Data Scientist</td>\n",
+       "      <td>151000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>151000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3752</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>105000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>105000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3753</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>Business Data Analyst</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3754</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Science Manager</td>\n",
+       "      <td>7000000</td>\n",
+       "      <td>INR</td>\n",
+       "      <td>94665</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>50</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>L</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3755 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      work_year experience_level employment_type                 job_title  \\\n",
+       "0          2023               SE              FT  Principal Data Scientist   \n",
+       "1          2023               MI              CT               ML Engineer   \n",
+       "2          2023               MI              CT               ML Engineer   \n",
+       "3          2023               SE              FT            Data Scientist   \n",
+       "4          2023               SE              FT            Data Scientist   \n",
+       "...         ...              ...             ...                       ...   \n",
+       "3750       2020               SE              FT            Data Scientist   \n",
+       "3751       2021               MI              FT  Principal Data Scientist   \n",
+       "3752       2020               EN              FT            Data Scientist   \n",
+       "3753       2020               EN              CT     Business Data Analyst   \n",
+       "3754       2021               SE              FT      Data Science Manager   \n",
+       "\n",
+       "       salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
+       "0       80000             EUR          85847                 ES           100   \n",
+       "1       30000             USD          30000                 US           100   \n",
+       "2       25500             USD          25500                 US           100   \n",
+       "3      175000             USD         175000                 CA           100   \n",
+       "4      120000             USD         120000                 CA           100   \n",
+       "...       ...             ...            ...                ...           ...   \n",
+       "3750   412000             USD         412000                 US           100   \n",
+       "3751   151000             USD         151000                 US           100   \n",
+       "3752   105000             USD         105000                 US           100   \n",
+       "3753   100000             USD         100000                 US           100   \n",
+       "3754  7000000             INR          94665                 IN            50   \n",
+       "\n",
+       "     company_location company_size  \n",
+       "0                  ES            L  \n",
+       "1                  US            S  \n",
+       "2                  US            S  \n",
+       "3                  CA            M  \n",
+       "4                  CA            M  \n",
+       "...               ...          ...  \n",
+       "3750               US            L  \n",
+       "3751               US            L  \n",
+       "3752               US            S  \n",
+       "3753               US            L  \n",
+       "3754               IN            L  \n",
+       "\n",
+       "[3755 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Цель работы</h2>\n",
+    "Я буду кластеризовать данные сотрудников на основе их характеристик для выделения групп с общими профессиональными и экономическими признаками. Это может быть полезно для анализа рынка труда, планирования кадровой стратегии или разработки программ по удержанию и привлечению специалистов"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>Не много бработаем данные</h3>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.90599446, -0.16482684, -0.82039118, ..., -0.01632123,\n",
+       "        -2.28856757, -0.20256191],\n",
+       "       [ 0.90599446, -0.23927735, -1.70618745, ..., -0.01632123,\n",
+       "        -2.28856757,  4.93676226],\n",
+       "       [ 0.90599446, -0.2459779 , -1.77756251, ..., -0.01632123,\n",
+       "        -2.28856757,  4.93676226],\n",
+       "       ...,\n",
+       "       [-3.43330297, -0.12760158, -0.51660304, ..., -0.01632123,\n",
+       "        -2.28856757,  4.93676226],\n",
+       "       [-3.43330297, -0.13504663, -0.59590867, ..., -0.01632123,\n",
+       "        -2.28856757, -0.20256191],\n",
+       "       [-1.9868705 , 10.13912397, -0.68052777, ..., -0.01632123,\n",
+       "        -2.28856757, -0.20256191]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# Преобразование категориальных данных в числовые с помощью one-hot encoding\n",
+    "df = pd.get_dummies(df, drop_first=True)\n",
+    "\n",
+    "# Сделаем данные сравнивыми \n",
+    "scaler = StandardScaler()\n",
+    "df_scaled = scaler.fit_transform(df)\n",
+    "\n",
+    "df_scaled"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Используем PCA(анализ главных компонент) для визуализации данных</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.decomposition import PCA\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Применяем PCA для снижения размерности до 2\n",
+    "pca = PCA(n_components=2)\n",
+    "df_pca = pca.fit_transform(df_scaled)\n",
+    "\n",
+    "# Визуализация\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(df_pca[:, 0], df_pca[:, 1], c='blue', edgecolor='k', alpha=0.6)\n",
+    "plt.title(\"PCA: Визуализация данных после снижения размерности\")\n",
+    "plt.xlabel(\"Главная компонента 1\")\n",
+    "plt.ylabel(\"Главная компонента 2\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Определение оптимального количества кластеров</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Метод локтя\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "border_l = 2\n",
+    "border_r = 5\n",
+    "\n",
+    "inertia = []\n",
+    "for k in range(border_l, border_r):\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(df_scaled)\n",
+    "    inertia.append(kmeans.inertia_)\n",
+    "\n",
+    "# Визуализация метода локтя\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(range(border_l, border_r), inertia, marker='o')\n",
+    "plt.title('Метод локтя для выбора количества кластеров')\n",
+    "plt.xlabel('Количество кластеров')\n",
+    "plt.ylabel('Инерция')\n",
+    "plt.show()\n",
+    "\n",
+    "# Коэффициент силуэта\n",
+    "from sklearn.metrics import silhouette_score\n",
+    "\n",
+    "silhouette_scores = []\n",
+    "for k in range(border_l, border_r):\n",
+    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
+    "    kmeans.fit(df_scaled)\n",
+    "    score = silhouette_score(df_scaled, kmeans.labels_)\n",
+    "    silhouette_scores.append(score)\n",
+    "\n",
+    "# Визуализация коэффициента силуэта\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(range(border_l, border_r), silhouette_scores, marker='o')\n",
+    "plt.title('Коэффициент силуэта для различных кластеров')\n",
+    "plt.xlabel('Количество кластеров')\n",
+    "plt.ylabel('Коэффициент силуэта')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Кластеризация с помощью K-means(группирует данные вокруг центров (центроидов) кластеров)</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Кластеризация с помощью K-means\n",
+    "optimal_clusters = 3\n",
+    "kmeans = KMeans(n_clusters=optimal_clusters, random_state=42)\n",
+    "df['Cluster'] = kmeans.fit_predict(df_scaled)\n",
+    "\n",
+    "# Визуализация кластеров с использованием PCA\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(df_pca[:, 0], df_pca[:, 1], c=df['Cluster'], cmap='viridis', edgecolor='k', alpha=0.6)\n",
+    "plt.title(\"Кластеры, определенные K-means (PCA)\")\n",
+    "plt.xlabel(\"Главная компонента 1\")\n",
+    "plt.ylabel(\"Главная компонента 2\")\n",
+    "plt.colorbar(label='Кластер')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Иерархическая кластеризация</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Иерархическая кластеризация — метод, который строит древовидную структуру кластеров (дендрограмму)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import AgglomerativeClustering\n",
+    "from scipy.cluster.hierarchy import dendrogram\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Применение иерархической кластеризации\n",
+    "hierarchical = AgglomerativeClustering(n_clusters=optimal_clusters, compute_distances=True)\n",
+    "df['Hierarchical Cluster'] = hierarchical.fit_predict(df_scaled)\n",
+    "\n",
+    "# Функция для получения матрицы linkage\n",
+    "def get_linkage_matrix(model: AgglomerativeClustering) -> np.ndarray:\n",
+    "    counts = np.zeros(model.children_.shape[0])  # type: ignore\n",
+    "    n_samples = len(model.labels_)\n",
+    "    for i, merge in enumerate(model.children_):  # type: ignore\n",
+    "        current_count = 0\n",
+    "        for child_idx in merge:\n",
+    "            if child_idx < n_samples:\n",
+    "                current_count += 1\n",
+    "            else:\n",
+    "                current_count += counts[child_idx - n_samples]\n",
+    "        counts[i] = current_count\n",
+    "\n",
+    "    return np.column_stack([model.children_, model.distances_, counts]).astype(float)\n",
+    "\n",
+    "# Построение дендрограммы\n",
+    "linkage_matrix = get_linkage_matrix(hierarchical)\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "dendrogram(linkage_matrix)\n",
+    "plt.title(\"Дендограмма, восстановленная из модели AgglomerativeClustering\")\n",
+    "plt.xlabel(\"Индексы объектов\")\n",
+    "plt.ylabel(\"Евклидово расстояние\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "# Визуализация кластеров\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.scatter(df_pca[:, 0], df_pca[:, 1], c=df['Hierarchical Cluster'], cmap='viridis', edgecolor='k', alpha=0.6)\n",
+    "plt.title(\"Кластеры, определенные иерархической кластеризацией (PCA)\")\n",
+    "plt.xlabel(\"Главная компонента 1\")\n",
+    "plt.ylabel(\"Главная компонента 2\")\n",
+    "plt.colorbar(label='Кластер')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Оценка качества кластеризации</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оценим качество кластеров, сравнив коэффициенты силуэта для двух методов."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Коэффициент силуэта для K-means: 0.7657\n",
+      "Коэффициент силуэта для иерархической кластеризации: 0.4890\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Оценка качества\n",
+    "silhouette_kmeans = silhouette_score(df_scaled, df['Cluster'])\n",
+    "silhouette_hierarchical = silhouette_score(df_scaled, df['Hierarchical Cluster'])\n",
+    "\n",
+    "print(f\"Коэффициент силуэта для K-means: {silhouette_kmeans:.4f}\")\n",
+    "print(f\"Коэффициент силуэта для иерархической кластеризации: {silhouette_hierarchical:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Можно сделать вывод:</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "K-means и иерархическая кластеризация дают схожие результаты, но могут отличаться по точности в зависимости от структуры данных."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Как интерпретировать значения?<br>\n",
+    "Коэффициент силуэта находится в диапазоне от -1 до 1:\n",
+    "\n",
+    "Близко к 1: Отличные кластеры (чётко разделённые).<br>\n",
+    "Около 0: Кластеры пересекаются или слабо разделены.<br>\n",
+    "Меньше 0: Объекты часто попадают не в свои кластеры.<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "K-means = 0.7657 успешно справился с задачей кластеризации, сформировав чётко разделённые кластеры."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Иерархическая кластеризация показала средний результат с коэффициентом силуэта 0.4890, что указывает на менее чётко сформированные кластеры. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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
+2023,SE,FT,Data Engineer,139000,USD,139000,US,100,US,M
+2023,MI,FT,Data Scientist,510000,HKD,65062,HK,0,HK,L
+2023,SE,FT,Machine Learning Engineer,150000,USD,150000,PT,100,US,M
+2023,MI,FT,Applied Machine Learning Engineer,65000,EUR,69751,IN,100,DE,S
+2022,EN,FT,AI Developer,300000,USD,300000,IN,50,IN,L
+2023,MI,FT,Machine Learning Engineer,90000,EUR,96578,NL,100,NL,L
+2023,SE,FT,Business Intelligence Engineer,185900,USD,185900,US,0,US,M
+2023,SE,FT,Business Intelligence Engineer,129300,USD,129300,US,0,US,M
+2023,SE,FT,Data Engineer,225000,USD,225000,US,100,US,M
+2023,SE,FT,Data Engineer,175000,USD,175000,US,100,US,M
+2023,SE,FT,Data Engineer,185000,USD,185000,US,0,US,M
+2023,SE,FT,Data Engineer,140000,USD,140000,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,105000,USD,105000,US,0,US,M
+2023,SE,FT,Data Scientist,70000,USD,70000,US,0,US,M
+2023,EN,FT,Machine Learning Engineer,163196,USD,163196,US,0,US,M
+2023,EN,FT,Machine Learning Engineer,145885,USD,145885,US,0,US,M
+2023,SE,FT,Data Engineer,217000,USD,217000,US,100,US,M
+2023,SE,FT,Data Engineer,185000,USD,185000,US,100,US,M
+2023,SE,FT,Data Analyst,202800,USD,202800,US,0,US,L
+2023,SE,FT,Data Analyst,104300,USD,104300,US,0,US,L
+2023,SE,FT,Data Analyst,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Analyst,65000,USD,65000,US,0,US,M
+2023,SE,FT,Data Engineer,165000,USD,165000,US,0,US,M
+2023,SE,FT,Data Engineer,132300,USD,132300,US,0,US,M
+2023,SE,FT,Data Engineer,179170,USD,179170,US,0,US,M
+2023,SE,FT,Data Engineer,94300,USD,94300,US,0,US,M
+2023,SE,FT,Analytics Engineer,152500,USD,152500,US,0,US,M
+2023,SE,FT,Analytics Engineer,116450,USD,116450,US,0,US,M
+2023,SE,FT,Data Engineer,247300,USD,247300,US,0,US,M
+2023,SE,FT,Data Engineer,133800,USD,133800,US,0,US,M
+2023,SE,FT,Research Engineer,203000,USD,203000,US,0,US,M
+2023,SE,FT,Research Engineer,133000,USD,133000,US,0,US,M
+2023,EN,FT,Research Scientist,220000,USD,220000,US,50,US,L
+2022,EN,FT,Machine Learning Engineer,54000,CHF,56536,CH,100,CH,S
+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,SE,FT,Analytics Engineer,289800,USD,289800,US,0,US,M
+2023,SE,FT,Analytics Engineer,214000,USD,214000,US,0,US,M
+2023,SE,FT,Analytics Engineer,179820,USD,179820,US,0,US,M
+2023,SE,FT,Analytics Engineer,143860,USD,143860,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,283200,USD,283200,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,188800,USD,188800,US,100,US,M
+2023,SE,FT,Analytics Engineer,289800,USD,289800,US,0,US,M
+2023,SE,FT,Analytics Engineer,214200,USD,214200,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 Engineer,252000,USD,252000,US,0,US,M
+2023,SE,FT,Data Engineer,129000,USD,129000,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,Data Engineer,161800,USD,161800,US,100,US,M
+2023,SE,FT,Data Engineer,141600,USD,141600,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,342300,USD,342300,US,0,US,L
+2023,SE,FT,Machine Learning Engineer,176100,USD,176100,US,0,US,L
+2023,MI,FT,Data Engineer,100000,USD,100000,US,100,US,M
+2023,MI,FT,Data Engineer,70000,USD,70000,US,100,US,M
+2023,EN,FT,Data Engineer,85000,USD,85000,US,0,US,M
+2023,EN,FT,Data Engineer,65000,USD,65000,US,0,US,M
+2023,SE,FT,Data Scientist,138784,USD,138784,US,100,US,M
+2023,SE,FT,Data Scientist,83270,USD,83270,US,100,US,M
+2023,EN,FT,Data Analyst,75000,USD,75000,US,0,US,M
+2023,EN,FT,Data Analyst,70000,USD,70000,US,0,US,M
+2023,SE,FT,Data Analyst,204500,USD,204500,US,0,US,M
+2023,SE,FT,Data Analyst,138900,USD,138900,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,318300,USD,318300,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,212200,USD,212200,US,100,US,M
+2023,SE,FT,Data Engineer,95000,USD,95000,US,100,US,M
+2023,SE,FT,Data Engineer,75000,USD,75000,US,100,US,M
+2023,SE,FT,Data Scientist,195000,USD,195000,US,0,US,M
+2023,SE,FT,Data Scientist,160000,USD,160000,US,0,US,M
+2023,SE,FT,Analytics Engineer,230000,USD,230000,US,0,US,M
+2023,SE,FT,Analytics Engineer,143200,USD,143200,US,0,US,M
+2023,MI,FT,Data Engineer,100000,USD,100000,US,100,US,M
+2023,MI,FT,Data Engineer,70000,USD,70000,US,100,US,M
+2023,MI,FT,Business Data Analyst,105000,USD,105000,US,50,US,L
+2023,MI,FT,Applied Data Scientist,1700000,INR,20670,IN,100,IN,L
+2023,MI,FT,Data Analyst,38000,GBP,46178,GB,0,GB,M
+2023,MI,FT,Data Analyst,35000,GBP,42533,GB,0,GB,M
+2023,SE,FT,Data Engineer,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Engineer,115000,USD,115000,US,0,US,M
+2023,SE,FT,Data Analyst,168400,USD,168400,US,0,US,M
+2023,SE,FT,Data Analyst,105200,USD,105200,US,0,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,Machine Learning Engineer,190000,USD,190000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,150000,USD,150000,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,SE,FT,Analytics Engineer,150000,USD,150000,US,100,US,M
+2023,SE,FT,Analytics Engineer,120000,USD,120000,US,100,US,M
+2023,SE,FT,Data Engineer,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Engineer,120000,USD,120000,US,0,US,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 Analyst,45000,GBP,54685,CF,100,CF,M
+2023,SE,FT,Data Analyst,35000,GBP,42533,CF,100,CF,M
+2023,SE,FT,Data Engineer,241000,USD,241000,US,0,US,M
+2023,SE,FT,Data Engineer,155000,USD,155000,US,0,US,M
+2023,SE,FT,Data Engineer,220000,USD,220000,US,100,US,M
+2023,SE,FT,Data Engineer,190000,USD,190000,US,100,US,M
+2023,MI,FT,Data Scientist,55000,GBP,66837,GB,0,GB,M
+2023,MI,FT,Data Scientist,45000,GBP,54685,GB,0,GB,M
+2020,EX,FT,Staff Data Analyst,15000,USD,15000,NG,0,CA,M
+2023,MI,FT,ETL Engineer,70000,GBP,85066,GB,100,GB,M
+2023,MI,FT,ETL Engineer,47500,GBP,57723,GB,100,GB,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 Engineer,160000,USD,160000,US,0,US,M
+2023,SE,FT,Data Engineer,140000,USD,140000,US,0,US,M
+2023,MI,FT,Machine Learning Engineer,300000,USD,300000,US,0,US,M
+2023,MI,FT,Machine Learning Engineer,250000,USD,250000,US,0,US,M
+2023,SE,FT,Data Scientist,228000,USD,228000,US,0,US,M
+2023,SE,FT,Data Scientist,186000,USD,186000,US,0,US,M
+2023,SE,FT,Data Scientist,190000,USD,190000,US,0,US,M
+2023,SE,FT,Data Scientist,170000,USD,170000,US,0,US,M
+2023,MI,FT,Research Engineer,230000,USD,230000,US,0,US,M
+2023,MI,FT,Research Engineer,200000,USD,200000,US,0,US,M
+2023,SE,FT,Data Architect,180000,USD,180000,US,100,US,M
+2023,SE,FT,Data Architect,115000,USD,115000,US,100,US,M
+2023,SE,FT,Data Architect,200000,USD,200000,US,100,US,M
+2023,SE,FT,Data Architect,115000,USD,115000,US,100,US,M
+2023,SE,FT,Data DevOps Engineer,50000,EUR,53654,FR,50,FR,S
+2023,EX,FT,Data Engineer,220000,USD,220000,US,0,US,M
+2023,EX,FT,Data Engineer,205000,USD,205000,US,0,US,M
+2023,MI,FT,Data Engineer,180000,USD,180000,US,0,US,M
+2023,MI,FT,Data Engineer,130000,USD,130000,US,0,US,M
+2023,SE,FT,Data Engineer,200000,USD,200000,US,100,US,M
+2023,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M
+2023,SE,FT,Computer Vision Engineer,215000,USD,215000,US,0,US,M
+2023,SE,FT,Computer Vision Engineer,170000,USD,170000,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 Scientist,224000,USD,224000,CA,0,CA,M
+2023,SE,FT,Data Scientist,176000,USD,176000,CA,0,CA,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,EN,FT,Data Engineer,1400000,INR,17022,IN,100,IN,L
+2023,SE,FT,Applied Data Scientist,100000,AUD,68318,AU,100,FI,M
+2023,MI,FT,AI Developer,100000,SGD,75020,FI,0,FI,M
+2023,SE,FT,Data Analyst,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Analyst,120000,USD,120000,US,0,US,M
+2023,SE,FT,Data Engineer,128000,USD,128000,US,0,US,M
+2023,SE,FT,Data Engineer,81500,USD,81500,US,0,US,M
+2023,SE,FT,Data Engineer,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Engineer,115000,USD,115000,US,0,US,M
+2023,SE,FT,Data Engineer,185000,USD,185000,US,0,US,M
+2023,SE,FT,Data Engineer,140000,USD,140000,US,0,US,M
+2023,EX,FT,Head of Data,329500,USD,329500,US,0,US,M
+2023,EX,FT,Head of Data,269600,USD,269600,US,0,US,M
+2023,SE,FT,Data Quality Analyst,100000,USD,100000,US,0,US,M
+2023,SE,FT,Data Quality Analyst,80000,USD,80000,US,0,US,M
+2023,SE,FT,Data Scientist,250000,USD,250000,US,0,US,M
+2023,SE,FT,Data Scientist,162500,USD,162500,US,0,US,M
+2023,MI,FT,AI Developer,200000,USD,200000,US,100,US,M
+2023,SE,FT,Data Engineer,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Engineer,100000,USD,100000,US,0,US,M
+2023,EX,FT,Data Engineer,145000,USD,145000,US,0,US,M
+2023,EX,FT,Data Engineer,115000,USD,115000,US,0,US,M
+2023,SE,FT,Data Scientist,203500,USD,203500,US,0,US,M
+2023,SE,FT,Data Scientist,152000,USD,152000,US,0,US,M
+2023,SE,FT,Data Scientist,239000,USD,239000,US,0,US,L
+2023,SE,FT,Data Scientist,122900,USD,122900,US,0,US,L
+2023,SE,FT,Data Scientist,237000,USD,237000,US,0,US,M
+2023,SE,FT,Data Scientist,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Science Manager,191765,USD,191765,US,0,US,M
+2023,SE,FT,Data Science Manager,134236,USD,134236,US,0,US,M
+2023,SE,FT,Analytics Engineer,190000,USD,190000,US,100,US,M
+2023,SE,FT,Analytics Engineer,112000,USD,112000,US,100,US,M
+2022,SE,FT,Data Scientist,84000,EUR,88256,ES,100,GB,L
+2023,EN,FT,Data Engineer,85000,USD,85000,US,0,US,M
+2023,EN,FT,Data Engineer,65000,USD,65000,US,0,US,M
+2023,SE,FT,Data Analyst,135000,USD,135000,US,0,US,M
+2023,SE,FT,Data Analyst,105500,USD,105500,US,0,US,M
+2023,SE,FT,Research Engineer,293000,USD,293000,US,0,US,M
+2023,SE,FT,Research Engineer,185000,USD,185000,US,0,US,M
+2023,SE,FT,Data Analyst,80000,USD,80000,US,0,US,M
+2023,SE,FT,Data Analyst,70000,USD,70000,US,0,US,M
+2023,SE,FT,Data Engineer,220000,USD,220000,US,100,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
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+2023,SE,FT,Data Analyst,208049,USD,208049,US,0,US,M
+2023,SE,FT,Data Analyst,128500,USD,128500,US,0,US,M
+2023,SE,FT,Analytics Engineer,179820,USD,179820,US,0,US,M
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+2023,MI,FT,Research Scientist,125000,USD,125000,US,100,US,M
+2023,SE,FT,Data Analyst,127000,USD,127000,US,100,US,M
+2023,SE,FT,Data Analyst,94000,USD,94000,US,100,US,M
+2023,SE,FT,Data Scientist,210550,USD,210550,US,0,US,M
+2023,SE,FT,Data Scientist,153300,USD,153300,US,0,US,M
+2023,MI,FT,Data Scientist,200000,USD,200000,US,100,US,M
+2023,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
+2023,SE,FT,Data Analyst,161500,USD,161500,US,100,US,M
+2023,SE,FT,Data Analyst,119500,USD,119500,US,100,US,M
+2023,SE,FT,Data Analyst,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Analyst,145000,USD,145000,US,0,US,M
+2023,SE,FT,Data Scientist,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Scientist,148750,USD,148750,US,0,US,M
+2023,SE,FT,Data Analytics Specialist,105000,USD,105000,US,0,US,M
+2023,SE,FT,Data Analytics Specialist,85000,USD,85000,US,0,US,M
+2023,SE,FT,Research Scientist,215000,USD,215000,US,0,US,M
+2023,SE,FT,Research Scientist,146300,USD,146300,US,0,US,M
+2023,EN,FT,AI Developer,200000,EUR,214618,DE,100,DE,L
+2023,MI,FT,Data Engineer,72000,USD,72000,MX,100,MX,M
+2023,MI,FT,Data Engineer,60000,USD,60000,MX,100,MX,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,MI,FT,Data Engineer,260000,USD,260000,US,0,US,M
+2023,MI,FT,Data Engineer,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Scientist,186300,USD,186300,US,100,US,M
+2023,SE,FT,Data Scientist,123900,USD,123900,US,100,US,M
+2023,MI,FT,Research Scientist,340000,USD,340000,US,100,US,M
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+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,MI,FT,Data Engineer,250000,USD,250000,US,0,US,M
+2023,MI,FT,Data Engineer,175000,USD,175000,US,0,US,M
+2023,MI,FT,Data Scientist,60000,EUR,64385,FR,50,FR,M
+2023,SE,FT,Data Analyst,185900,USD,185900,US,0,US,M
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+2023,SE,FT,Data Analyst,106800,USD,106800,US,0,US,M
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+2023,SE,FT,Data Analyst,110000,USD,110000,US,0,US,M
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+2023,MI,FT,Data Engineer,75000,USD,75000,US,0,US,M
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+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
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+2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M
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+2023,SE,FT,Data Manager,155000,USD,155000,US,0,US,M
+2023,SE,FT,Data Manager,140000,USD,140000,US,0,US,M
+2023,MI,FT,Machine Learning Infrastructure Engineer,205920,USD,205920,US,0,US,M
+2023,MI,FT,Machine Learning Infrastructure Engineer,171600,USD,171600,US,0,US,M
+2023,SE,FT,Data Engineer,121500,USD,121500,US,100,US,M
+2023,SE,FT,Data Engineer,78000,USD,78000,US,100,US,M
+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
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+2023,SE,FT,Data Engineer,116000,USD,116000,US,0,US,M
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+2023,MI,FT,Data Engineer,85000,GBP,103294,GB,0,GB,M
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+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
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+2023,MI,FT,Data Analytics Manager,155000,USD,155000,US,0,US,M
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+2023,SE,FT,Research Scientist,250000,USD,250000,US,0,US,M
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+2023,SE,FT,Data Scientist,45000,EUR,48289,ES,0,ES,M
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+2023,SE,FT,Data Analyst,175000,USD,175000,US,100,US,M
+2023,SE,FT,Data Analyst,130000,USD,130000,US,100,US,M
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+2023,SE,FT,Data Analyst,93800,USD,93800,US,100,US,M
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+2023,SE,FT,Data Science Manager,90000,USD,90000,MX,100,MX,M
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+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
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+2023,EN,FT,AI Developer,60000,EUR,64385,DE,0,DE,M
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+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
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+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
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+2023,SE,FT,Data Engineer,129300,USD,129300,US,0,US,M
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+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
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+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
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+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
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+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
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+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
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+2023,SE,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,MI,FT,Insight Analyst,42000,GBP,51039,GB,0,GB,M
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+2023,SE,FT,Data Analyst,153600,USD,153600,US,0,US,M
+2023,SE,FT,Data Analyst,106800,USD,106800,US,0,US,M
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+2023,SE,FT,Data Analyst,125000,USD,125000,US,100,US,M
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+2023,SE,FT,Data Engineer,185000,USD,185000,US,0,US,M
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+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
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+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
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+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
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+2023,SE,FT,Data Scientist,200160,USD,200160,US,0,US,M
+2023,SE,FT,Analytics Engineer,175000,USD,175000,US,0,US,M
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+2023,SE,FT,Data Manager,169000,USD,169000,US,0,US,M
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+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
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+2023,SE,FT,Data Analyst,80000,USD,80000,US,100,US,S
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+2023,EN,FT,Data Analyst,48000,USD,48000,US,0,US,M
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+2023,SE,FT,Data Engineer,137500,USD,137500,US,100,US,M
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+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
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+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
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+2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,M
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+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
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+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
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+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
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+2023,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M
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+2023,SE,FT,Data Engineer,129000,USD,129000,US,0,US,M
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+2023,SE,FT,Data Engineer,124740,USD,124740,US,0,US,M
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+2023,MI,FT,Data Analyst,90000,GBP,109371,GB,0,GB,M
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+2023,EN,FT,Data Analyst,48000,USD,48000,US,0,US,M
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+2023,EX,FT,Data Engineer,210914,USD,210914,US,100,US,M
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+2023,SE,FT,Data Scientist,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Scientist,148750,USD,148750,US,0,US,M
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+2023,MI,FT,Machine Learning Engineer,100000,GBP,121523,GB,0,GB,M
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+2023,MI,FT,Data Engineer,55000,EUR,59020,ES,100,ES,M
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+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
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+2022,SE,FT,Data Engineer,70500,USD,70500,US,0,US,M
+2022,SE,FT,Data Engineer,54000,USD,54000,US,0,US,M
+2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,M
+2022,SE,FT,Data Scientist,140400,USD,140400,US,0,US,M
+2022,SE,FT,Analytics Engineer,205300,USD,205300,US,0,US,M
+2022,SE,FT,Analytics Engineer,184700,USD,184700,US,0,US,M
+2022,SE,FT,Data Engineer,175100,USD,175100,US,100,US,M
+2022,SE,FT,Data Engineer,140250,USD,140250,US,100,US,M
+2022,SE,FT,Data Analyst,116150,USD,116150,US,100,US,M
+2022,SE,FT,Data Analyst,99050,USD,99050,US,100,US,M
+2022,SE,FT,Data Engineer,145000,USD,145000,US,100,US,M
+2022,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
+2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
+2022,MI,FT,Data Analyst,85000,USD,85000,CA,0,CA,M
+2022,MI,FT,Data Analyst,75000,USD,75000,CA,0,CA,M
+2022,SE,FT,Machine Learning Engineer,214000,USD,214000,US,100,US,M
+2022,SE,FT,Machine Learning Engineer,192600,USD,192600,US,100,US,M
+2022,SE,FT,Data Architect,266400,USD,266400,US,100,US,M
+2022,SE,FT,Data Architect,213120,USD,213120,US,100,US,M
+2022,SE,FT,Data Engineer,155000,USD,155000,US,100,US,M
+2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
+2022,MI,FT,Data Scientist,141300,USD,141300,US,0,US,M
+2022,MI,FT,Data Scientist,102100,USD,102100,US,0,US,M
+2022,SE,FT,Data Analyst,115934,USD,115934,US,100,US,M
+2022,SE,FT,Data Analyst,81666,USD,81666,US,100,US,M
+2022,MI,FT,Data Engineer,206699,USD,206699,US,0,US,M
+2022,MI,FT,Data Engineer,99100,USD,99100,US,0,US,M
+2022,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+2022,SE,FT,Data Engineer,110500,USD,110500,US,100,US,M
+2022,MI,FT,Data Analyst,50000,GBP,61566,GB,0,GB,M
+2022,MI,FT,Data Analyst,35000,GBP,43096,GB,0,GB,M
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+2022,SE,FT,Data Analyst,60000,USD,60000,US,100,US,M
+2022,SE,FT,Data Architect,192564,USD,192564,US,100,US,M
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+2022,SE,FT,Data Scientist,150000,USD,150000,US,100,US,M
+2022,SE,FT,Data Analytics Manager,150260,USD,150260,US,100,US,M
+2022,SE,FT,Data Analytics Manager,109280,USD,109280,US,100,US,M
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+2022,MI,FT,Data Scientist,160000,USD,160000,US,100,US,M
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+2022,EN,FT,Data Analyst,67000,USD,67000,CA,0,CA,M
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+2022,SE,FT,Data Engineer,154000,USD,154000,US,100,US,M
+2022,SE,FT,Data Engineer,126000,USD,126000,US,100,US,M
+2022,SE,FT,Data Analyst,129000,USD,129000,US,0,US,M
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+2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
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+2022,SE,FT,Data Analyst,69000,USD,69000,US,100,US,M
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+2022,SE,FT,Data Analyst,150075,USD,150075,US,100,US,M
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+2022,SE,FT,Data Analyst,106260,USD,106260,US,100,US,M
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+2022,MI,FT,Data Analyst,135000,USD,135000,US,100,US,M
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+2022,MI,FT,Data Scientist,200000,USD,200000,US,100,US,M
+2022,MI,FT,Data Scientist,120000,USD,120000,US,100,US,M
+2022,SE,FT,Machine Learning Engineer,120000,USD,120000,AE,100,AE,S
+2022,SE,FT,Machine Learning Engineer,65000,USD,65000,AE,100,AE,S
+2022,EX,FT,Data Engineer,324000,USD,324000,US,100,US,M
+2022,EX,FT,Data Engineer,216000,USD,216000,US,100,US,M
+2022,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
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+2022,SE,FT,Data Scientist,185100,USD,185100,US,100,US,M
+2022,SE,FT,Data Scientist,104890,USD,104890,US,100,US,M
+2022,SE,FT,Data Engineer,105000,USD,105000,US,100,US,M
+2022,SE,FT,Data Engineer,80000,USD,80000,US,100,US,M
+2022,MI,FT,Machine Learning Developer,100000,CAD,76814,CA,100,CA,M
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+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
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+2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M
+2021,SE,FT,Data Analyst,80000,USD,80000,BG,100,US,S
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+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
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+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
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+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
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+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
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+2021,MI,FT,BI Data Analyst,11000000,HUF,36259,HU,50,US,L
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+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
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+2020,MI,FT,Data Scientist,105000,USD,105000,US,100,US,L
+2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
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+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
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+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
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+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
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+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
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+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
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+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
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+2021,EN,FT,Machine Learning Engineer,21844,USD,21844,CO,50,CO,M
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+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
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+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
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+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
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+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
+2021,MI,FL,Data Engineer,20000,USD,20000,IT,0,US,L
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+2021,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,L
+2020,EN,FT,AI Scientist,300000,DKK,45896,DK,50,DK,S
+2021,MI,FT,Data Scientist,160000,USD,160000,US,100,US,L
+2021,SE,FT,Research Scientist,50000,USD,50000,FR,100,US,S
+2021,MI,FT,Data Science Engineer,34000,EUR,40189,GR,100,GR,M
+2021,MI,FT,Data Scientist,69600,BRL,12901,BR,0,BR,S
+2021,SE,FT,Data Engineer,165000,USD,165000,US,0,US,M
+2021,EN,FT,Big Data Engineer,435000,INR,5882,IN,0,CH,L
+2020,MI,FT,Data Scientist,37000,EUR,42197,FR,50,FR,S
+2021,SE,FT,Principal Data Engineer,185000,USD,185000,US,100,US,L
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+2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
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+2020,SE,FT,Data Scientist,120000,USD,120000,US,50,US,L
+2021,SE,FT,Data Scientist,110000,CAD,87738,CA,100,CA,S
+2021,SE,FT,Finance Data Analyst,45000,GBP,61896,GB,50,GB,L
+2021,MI,FL,Machine Learning Scientist,12000,USD,12000,PK,50,PK,M
+2021,SE,FT,Data Engineer,65000,EUR,76833,RO,50,GB,S
+2021,MI,FT,Machine Learning Engineer,74000,USD,74000,JP,50,JP,S
+2021,SE,FT,Data Science Manager,152000,USD,152000,US,100,FR,L
+2021,MI,FT,Big Data Engineer,18000,USD,18000,MD,0,MD,S
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+2021,MI,FT,Data Scientist,130000,USD,130000,US,50,US,L
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+2021,SE,FT,Principal Data Scientist,147000,EUR,173762,DE,100,DE,M
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+2021,MI,FT,Data Scientist,39600,EUR,46809,ES,100,ES,M
+2021,EN,FT,AI Scientist,1335000,INR,18053,IN,100,AS,S
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+2021,MI,FT,Data Scientist,115000,USD,115000,US,50,US,L
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+2021,MI,FT,Lead Data Analyst,1450000,INR,19609,IN,100,IN,L
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+2021,MI,FT,Data Analyst,75000,USD,75000,US,0,US,L
+2021,MI,FT,Data Analyst,62000,USD,62000,US,0,US,L
+2021,MI,FT,Data Scientist,73000,USD,73000,US,0,US,L
+2021,MI,FT,Data Engineer,38400,EUR,45391,NL,100,NL,L
+2020,SE,FT,Data Science Manager,190200,USD,190200,US,100,US,M
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+2020,SE,FT,Machine Learning Engineer,40000,EUR,45618,HR,100,HR,S
+2021,SE,FT,Director of Data Science,168000,USD,168000,JP,0,JP,S
+2021,MI,FT,Data Scientist,160000,SGD,119059,SG,100,IL,M
+2021,MI,FT,Applied Machine Learning Scientist,423000,USD,423000,US,50,US,L
+2021,MI,FT,Data Engineer,24000,EUR,28369,MT,50,MT,L
+2021,SE,FT,Data Specialist,165000,USD,165000,US,100,US,L
+2020,SE,FT,Data Scientist,412000,USD,412000,US,100,US,L
+2021,MI,FT,Principal Data Scientist,151000,USD,151000,US,100,US,L
+2020,EN,FT,Data Scientist,105000,USD,105000,US,100,US,S
+2020,EN,CT,Business Data Analyst,100000,USD,100000,US,100,US,L
+2021,SE,FT,Data Science Manager,7000000,INR,94665,IN,50,IN,L