From e5e3511ef422bd29a98aa629a59c81e4e678e9df Mon Sep 17 00:00:00 2001
From: salih <salih1504200473@mail.ru>
Date: Thu, 12 Dec 2024 16:14:13 +0400
Subject: [PATCH] =?UTF-8?q?=D0=93=D0=BE=D1=82=D0=BE=D0=B2=D0=B0=D1=8F=204?=
=?UTF-8?q?=20laba?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
---
README.md | 4 +-
lab_4/laba4.ipynb | 1158 +++++++++++
static/csv/ds_salaries.csv | 3756 ++++++++++++++++++++++++++++++++++++
3 files changed, 4917 insertions(+), 1 deletion(-)
create mode 100644 lab_4/laba4.ipynb
create mode 100644 static/csv/ds_salaries.csv
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..fe2cd53
--- /dev/null
+++ b/lab_4/laba4.ipynb
@@ -0,0 +1,1158 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "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": 26,
+ "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": 27,
+ "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": 28,
+ "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",
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+ " <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",
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+ " <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",
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+ " <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": 28,
+ "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": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training LinearRegression...\n",
+ "Training RandomForestRegressor..."
+ ]
+ },
+ {
+ "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": [
+ "\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": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_60a89_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
+ " <th id=\"T_60a89_level0_col1\" class=\"col_heading level0 col1\" >RMSE</th>\n",
+ " <th id=\"T_60a89_level0_col2\" class=\"col_heading level0 col2\" >R2</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_60a89_level0_row0\" class=\"row_heading level0 row0\" >GradientBoostingRegressor</th>\n",
+ " <td id=\"T_60a89_row0_col0\" class=\"data row0 col0\" >35404.550426</td>\n",
+ " <td id=\"T_60a89_row0_col1\" class=\"data row0 col1\" >45669.354450</td>\n",
+ " <td id=\"T_60a89_row0_col2\" class=\"data row0 col2\" >0.418786</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_60a89_level0_row1\" class=\"row_heading level0 row1\" >RandomForestRegressor</th>\n",
+ " <td id=\"T_60a89_row1_col0\" class=\"data row1 col0\" >35382.494479</td>\n",
+ " <td id=\"T_60a89_row1_col1\" class=\"data row1 col1\" >45711.498654</td>\n",
+ " <td id=\"T_60a89_row1_col2\" class=\"data row1 col2\" >0.417713</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_60a89_level0_row2\" class=\"row_heading level0 row2\" >LinearRegression</th>\n",
+ " <td id=\"T_60a89_row2_col0\" class=\"data row2 col0\" >35903.747612</td>\n",
+ " <td id=\"T_60a89_row2_col1\" class=\"data row2 col1\" >45746.923741</td>\n",
+ " <td id=\"T_60a89_row2_col2\" class=\"data row2 col2\" >0.416810</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x27c059b8500>"
+ ]
+ },
+ "execution_count": 30,
+ "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": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Transformed feature shape (train): (1886, 41)\n",
+ "Transformed feature shape (test): (472, 41)\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: 37037.92339197355\n",
+ "RMSE: 45540.3787622507\n",
+ "R2: 0.4345993379829327\n",
+ "\n",
+ "Model: RandomForestRegressor\n",
+ "Best Params: {'model__n_estimators': 300, 'model__min_samples_split': 10, 'model__max_depth': 10}\n",
+ "MAE: 36990.476148447306\n",
+ "RMSE: 45909.387319133624\n",
+ "R2: 0.4253994599029146\n",
+ "\n",
+ "Model: GradientBoostingRegressor\n",
+ "Best Params: {'model__n_estimators': 100, 'model__max_depth': 3, 'model__learning_rate': 0.1}\n",
+ "MAE: 37119.53085174526\n",
+ "RMSE: 45945.52202948378\n",
+ "R2: 0.42449458199205714\n"
+ ]
+ },
+ {
+ "data": {
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+ " <th class=\"blank level0\" > </th>\n",
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+ " <tr>\n",
+ " <th id=\"T_d0b41_level0_row0\" class=\"row_heading level0 row0\" >LinearRegression</th>\n",
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+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x27c00e56ae0>"
+ ]
+ },
+ "execution_count": 38,
+ "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": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'Leather interior'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+ "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+ "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'Leather interior'",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[39], line 14\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m accuracy_score, confusion_matrix, f1_score\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# Преобразование целевой переменной для классификации\u001b[39;00m\n\u001b[1;32m---> 14\u001b[0m y_class \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mLeather interior\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mmap({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYes\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m1\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m0\u001b[39m}) \u001b[38;5;66;03m# Преобразуем в 0/1\u001b[39;00m\n\u001b[0;32m 15\u001b[0m X \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLeather interior\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mID\u001b[39m\u001b[38;5;124m'\u001b[39m]) \u001b[38;5;66;03m# Признаки\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \u001b[38;5;66;03m# Определение числовых и категориальных признаков\u001b[39;00m\n",
+ "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 4101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 4104\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+ "File \u001b[1;32mc:\\Users\\salih\\OneDrive\\Рабочий стол\\3 курас\\МИИ\\laba1\\AIM-PIbd-31-Yaruskin-S-A\\aimenv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3810\u001b[0m ):\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'Leather interior'"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.compose import ColumnTransformer\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+ "from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
+ "\n",
+ "# Преобразование целевой переменной для классификации\n",
+ "y_class = df['Leather interior'].map({'Yes': 1, 'No': 0}) # Преобразуем в 0/1\n",
+ "X = df.drop(columns=['Leather interior', 'ID']) # Признаки\n",
+ "\n",
+ "# Определение числовых и категориальных признаков\n",
+ "numeric_features = ['work_year'] # Пример числового признака\n",
+ "categorical_features = ['experience_level', 'employment_type', 'employee_residence', \n",
+ " 'company_location', 'company_size', 'remote_ratio']\n",
+ "\n",
+ "# Обработка числовых данных\n",
+ "numeric_transformer = Pipeline(steps=[\n",
+ " ('imputer', SimpleImputer(strategy='median')),\n",
+ " ('scaler', StandardScaler())\n",
+ "])\n",
+ "\n",
+ "# Обработка категориальных данных\n",
+ "categorical_transformer = Pipeline(steps=[\n",
+ " ('imputer', SimpleImputer(strategy='most_frequent')),\n",
+ " ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
+ "])\n",
+ "\n",
+ "# Комбинированный трансформер\n",
+ "preprocessor = ColumnTransformer(\n",
+ " transformers=[\n",
+ " ('num', numeric_transformer, numeric_features),\n",
+ " ('cat', categorical_transformer, categorical_features)\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "# Разделение данных на обучающую и тестовую выборки\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', 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)\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}\")\n"
+ ]
+ }
+ ],
+ "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
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+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
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+2023,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
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+2023,EN,FT,Applied Scientist,130760,USD,130760,US,0,US,L
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+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
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+2023,MI,FT,Data Scientist,510000,HKD,65062,HK,0,HK,L
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+2022,EN,FT,AI Developer,300000,USD,300000,IN,50,IN,L
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+2023,EX,FT,Data Engineer,115500,USD,115500,US,0,US,M
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+2023,SE,FT,Data Analyst,55000,USD,55000,US,0,US,M
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+2023,MI,FT,Data Analyst,65000,USD,65000,US,100,US,M
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+2023,MI,FT,Data Engineer,45000,EUR,48289,SI,100,SI,M
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+2023,SE,FT,Machine Learning Engineer,83300,GBP,101228,GB,0,GB,M
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+2023,SE,FT,Machine Learning Engineer,247500,USD,247500,US,0,US,M
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+2023,MI,FT,Data Engineer,72000,USD,72000,MX,100,MX,M
+2023,MI,FT,Data Engineer,60000,USD,60000,MX,100,MX,M
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+2023,MI,FT,Machine Learning Scientist,125000,USD,125000,US,0,US,S
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+2023,SE,FT,Data Engineer,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Engineer,154000,USD,154000,US,0,US,M
+2023,SE,FT,Data Engineer,116000,USD,116000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
+2023,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+2023,MI,FT,Data Engineer,200000,USD,200000,US,0,US,M
+2023,MI,FT,Data Engineer,120000,USD,120000,US,0,US,M
+2023,SE,FT,Data Scientist,175000,USD,175000,US,100,US,M
+2023,SE,FT,Data Scientist,160000,USD,160000,US,100,US,M
+2023,MI,FT,Data Engineer,105000,GBP,127599,GB,0,GB,M
+2023,MI,FT,Data Engineer,85000,GBP,103294,GB,0,GB,M
+2023,SE,FT,Data Engineer,153600,USD,153600,US,0,US,M
+2023,SE,FT,Data Engineer,106800,USD,106800,US,0,US,M
+2023,EN,FT,Data Analyst,85000,USD,85000,US,100,US,M
+2023,EN,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,SE,FT,Data Scientist,225000,USD,225000,US,0,US,M
+2023,SE,FT,Data Scientist,156400,USD,156400,US,0,US,M
+2023,SE,FT,Analytics Engineer,150000,USD,150000,US,0,US,M
+2023,SE,FT,Analytics Engineer,120000,USD,120000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Data Analyst,145000,USD,145000,US,100,US,M
+2023,SE,FT,Data Analyst,90000,USD,90000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
+2023,SE,FT,Data Analyst,85500,USD,85500,US,0,US,M
+2023,SE,FT,Data Engineer,167500,USD,167500,US,0,US,M
+2023,SE,FT,Data Engineer,106500,USD,106500,US,0,US,M
+2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M
+2023,MI,FT,Data Analytics Manager,155000,USD,155000,US,0,US,M
+2023,MI,FT,Data Analytics Manager,140000,USD,140000,US,0,US,M
+2023,SE,FT,Research Scientist,250000,USD,250000,US,0,US,M
+2023,SE,FT,Research Scientist,200000,USD,200000,US,0,US,M
+2023,SE,FT,Data Scientist,260000,USD,260000,US,0,US,M
+2023,SE,FT,Data Scientist,186000,USD,186000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
+2023,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+2023,SE,FT,Research Scientist,200000,USD,200000,US,0,US,M
+2023,SE,FT,Research Scientist,150000,USD,150000,US,0,US,M
+2023,SE,FT,Data Scientist,45000,EUR,48289,ES,0,ES,M
+2023,SE,FT,Data Scientist,36000,EUR,38631,ES,0,ES,M
+2023,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M
+2023,SE,FT,Data Scientist,120000,USD,120000,US,0,US,M
+2023,EN,FT,Data Analyst,30000,USD,30000,IN,50,IN,M
+2023,MI,FT,Research Scientist,185000,USD,185000,US,0,US,M
+2023,MI,FT,Research Scientist,125000,USD,125000,US,0,US,M
+2022,EN,PT,Data Analyst,34320,USD,34320,US,100,US,S
+2022,MI,FT,Business Data Analyst,48000,BRL,9289,BR,100,BR,M
+2023,SE,FT,Head of Data,70000,EUR,75116,PT,100,PT,L
+2022,EX,FT,Data Science Manager,106000,USD,106000,UZ,0,RU,L
+2023,SE,FT,Data Analyst,175000,USD,175000,US,100,US,M
+2023,SE,FT,Data Analyst,130000,USD,130000,US,100,US,M
+2023,SE,FT,Data Analyst,122000,USD,122000,US,100,US,M
+2023,SE,FT,Data Analyst,93800,USD,93800,US,100,US,M
+2023,SE,FT,Data Science Manager,150000,USD,150000,MX,100,MX,M
+2023,SE,FT,Data Science Manager,90000,USD,90000,MX,100,MX,M
+2023,SE,FT,Data Analyst,165000,USD,165000,US,100,US,M
+2023,SE,FT,Data Analyst,112000,USD,112000,US,100,US,M
+2023,EN,FT,Computer Vision Engineer,220000,USD,220000,US,0,US,M
+2023,SE,FT,BI Data Analyst,67000,EUR,71897,DE,100,DE,M
+2023,EN,FT,AI Developer,60000,EUR,64385,DE,0,DE,M
+2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
+2023,SE,FT,Data Analyst,1300000,INR,15806,IN,100,IN,S
+2023,MI,FT,Machine Learning Engineer,200000,USD,200000,US,0,US,M
+2023,MI,FT,Machine Learning Engineer,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Engineer,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Engineer,129300,USD,129300,US,0,US,M
+2023,SE,FT,Data Analyst,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Analyst,121700,USD,121700,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,204500,USD,204500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
+2023,MI,FT,Data Scientist,40000,USD,40000,FR,50,FR,L
+2023,SE,FT,Data Science Consultant,1000000,THB,29453,TH,50,TH,M
+2023,SE,FT,Data Analyst,180180,USD,180180,US,0,US,M
+2023,SE,FT,Data Analyst,106020,USD,106020,US,0,US,M
+2023,SE,FT,Data Scientist,136000,USD,136000,US,100,US,M
+2023,SE,FT,Data Scientist,104000,USD,104000,US,100,US,M
+2023,SE,FT,Data Analyst,125000,USD,125000,US,0,US,M
+2023,SE,FT,Data Analyst,110000,USD,110000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,153400,USD,153400,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,122700,USD,122700,US,0,US,M
+2023,EX,FT,Data Engineer,310000,USD,310000,US,100,US,M
+2023,EX,FT,Data Engineer,239000,USD,239000,US,100,US,M
+2023,SE,FT,Data Analyst,152380,USD,152380,US,0,US,M
+2023,SE,FT,Data Analyst,121904,USD,121904,US,0,US,M
+2023,SE,FT,Data Scientist,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,247500,USD,247500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,172200,USD,172200,US,0,US,M
+2023,SE,FT,Data Architect,168400,USD,168400,US,0,US,M
+2023,SE,FT,Data Architect,105200,USD,105200,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,128280,USD,128280,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,106900,USD,106900,US,0,US,M
+2022,SE,FT,Lead Data Scientist,192000,USD,192000,US,100,US,L
+2023,MI,FT,Data Engineer,140000,USD,140000,US,0,US,M
+2023,MI,FT,Data Engineer,100000,USD,100000,US,0,US,M
+2023,SE,FT,Research Engineer,100000,EUR,107309,DE,100,DE,S
+2023,SE,FT,Research Engineer,80000,EUR,85847,DE,100,DE,S
+2023,SE,FT,Machine Learning Engineer,275000,USD,275000,DE,0,DE,M
+2023,SE,FT,Machine Learning Engineer,174000,USD,174000,DE,0,DE,M
+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Data Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Data Analyst,170500,USD,170500,US,100,US,M
+2023,SE,FT,Data Analyst,85000,USD,85000,US,100,US,M
+2023,SE,FT,Data Manager,60027,GBP,72946,GB,0,GB,M
+2023,SE,FT,Data Manager,44737,GBP,54365,GB,0,GB,M
+2023,EX,FT,Head of Data Science,131899,GBP,160288,GB,0,GB,M
+2023,EX,FT,Head of Data Science,104891,GBP,127467,GB,0,GB,M
+2023,SE,FT,Data Engineer,80000,USD,80000,US,0,US,M
+2023,SE,FT,Data Engineer,65000,USD,65000,US,0,US,M
+2023,SE,FT,Data Engineer,124740,USD,124740,US,0,US,M
+2023,SE,FT,Data Engineer,65488,USD,65488,US,0,US,M
+2023,SE,FT,Data Quality Analyst,72200,USD,72200,US,0,US,M
+2023,SE,FT,Data Quality Analyst,64980,USD,64980,US,0,US,M
+2023,SE,FT,Data Engineer,153600,USD,153600,US,0,US,M
+2023,SE,FT,Data Engineer,106800,USD,106800,US,0,US,M
+2023,SE,FT,Data Analyst,179975,USD,179975,US,100,US,M
+2023,SE,FT,Data Analyst,86466,USD,86466,US,100,US,M
+2023,SE,FT,Data Engineer,120000,USD,120000,US,0,US,M
+2023,SE,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,MI,FT,Insight Analyst,42000,GBP,51039,GB,0,GB,M
+2023,MI,FT,Insight Analyst,35000,GBP,42533,GB,0,GB,M
+2023,SE,FT,Data Scientist,149076,USD,149076,US,0,US,M
+2023,SE,FT,Data Scientist,82365,USD,82365,US,0,US,M
+2023,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M
+2023,SE,FT,Data Analyst,110600,USD,110600,US,0,US,M
+2023,SE,FT,Data Science Manager,175000,USD,175000,US,0,US,M
+2023,SE,FT,Data Science Manager,120000,USD,120000,US,0,US,M
+2023,SE,FT,Data Analyst,230000,USD,230000,US,0,US,M
+2023,SE,FT,Data Analyst,180000,USD,180000,US,0,US,M
+2023,SE,FT,Data Analyst,153600,USD,153600,US,0,US,M
+2023,SE,FT,Data Analyst,106800,USD,106800,US,0,US,M
+2023,SE,FT,Data Manager,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Manager,120000,USD,120000,US,0,US,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,205920,USD,205920,US,0,US,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,171600,USD,171600,US,0,US,M
+2023,SE,FT,Data Analyst,165000,USD,165000,US,100,US,M
+2023,SE,FT,Data Analyst,125000,USD,125000,US,100,US,M
+2023,SE,FT,Data Engineer,265000,USD,265000,US,0,US,M
+2023,SE,FT,Data Engineer,185000,USD,185000,US,0,US,M
+2023,MI,FT,Applied Machine Learning Engineer,130000,USD,130000,US,0,US,M
+2022,EN,FT,Data Scientist,168000,USD,168000,US,100,US,M
+2023,MI,FT,AI Scientist,36000,EUR,38631,ES,50,ES,L
+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
+2023,SE,FT,Data Analyst,85500,USD,85500,US,0,US,M
+2023,SE,FT,Data Scientist,147100,USD,147100,US,0,US,M
+2023,SE,FT,Data Scientist,90700,USD,90700,US,0,US,M
+2023,SE,FT,Data Engineer,167580,USD,167580,US,0,US,M
+2023,SE,FT,Data Engineer,87980,USD,87980,US,0,US,M
+2023,SE,FT,Data Engineer,202000,USD,202000,US,100,US,M
+2023,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
+2023,SE,FT,Data Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Data Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,163800,USD,163800,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,126000,USD,126000,US,0,US,M
+2023,SE,FT,Data Engineer,104000,USD,104000,US,100,US,M
+2023,SE,FT,Data Engineer,65000,USD,65000,US,100,US,M
+2023,SE,FT,Research Engineer,230000,USD,230000,US,0,US,M
+2023,SE,FT,Research Engineer,148000,USD,148000,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,269000,USD,269000,CA,100,CA,M
+2023,SE,FT,Machine Learning Engineer,158000,USD,158000,CA,100,CA,M
+2023,SE,FT,Analytics Engineer,197000,USD,197000,US,0,US,M
+2023,SE,FT,Analytics Engineer,106000,USD,106000,US,0,US,M
+2023,MI,FT,Deep Learning Engineer,150000,USD,150000,US,100,US,M
+2023,MI,FT,Deep Learning Engineer,100000,USD,100000,US,100,US,M
+2023,SE,FT,Data Engineer,290000,USD,290000,US,100,US,M
+2023,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
+2023,SE,FT,Data Engineer,192000,USD,192000,US,0,US,M
+2023,SE,FT,Data Engineer,172800,USD,172800,US,0,US,M
+2023,SE,FT,Data Scientist,300240,USD,300240,US,0,US,M
+2023,SE,FT,Data Scientist,200160,USD,200160,US,0,US,M
+2023,SE,FT,Data Scientist,300240,USD,300240,US,0,US,M
+2023,SE,FT,Data Scientist,200160,USD,200160,US,0,US,M
+2023,SE,FT,Analytics Engineer,175000,USD,175000,US,0,US,M
+2023,SE,FT,Analytics Engineer,140000,USD,140000,US,0,US,M
+2023,SE,FT,Data Manager,169000,USD,169000,US,0,US,M
+2023,SE,FT,Data Manager,100000,USD,100000,US,0,US,M
+2023,SE,FT,Data Scientist,370000,USD,370000,US,0,US,M
+2023,SE,FT,Data Scientist,245000,USD,245000,US,0,US,M
+2023,MI,FT,Data Engineer,95000,GBP,115447,GB,100,GB,L
+2023,SE,FT,Data Analyst,110000,USD,110000,US,100,US,S
+2023,SE,FT,Data Analyst,80000,USD,80000,US,100,US,S
+2023,EN,FT,Data Analyst,55000,USD,55000,US,0,US,M
+2023,EN,FT,Data Analyst,48000,USD,48000,US,0,US,M
+2023,SE,FT,Data Analyst,95000,USD,95000,US,0,US,M
+2023,SE,FT,Data Analyst,85000,USD,85000,US,0,US,M
+2023,SE,FT,Data Engineer,137500,USD,137500,US,100,US,M
+2023,SE,FT,Data Engineer,81500,USD,81500,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,323300,USD,323300,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,184700,USD,184700,US,0,US,M
+2021,MI,FT,AI Scientist,30000,USD,30000,GH,0,GH,S
+2023,SE,FT,Machine Learning Engineer,186000,USD,186000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,153088,USD,153088,US,100,US,M
+2023,MI,FT,Data Infrastructure Engineer,190000,USD,190000,US,100,US,M
+2023,MI,FT,Data Infrastructure Engineer,160000,USD,160000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,150000,USD,150000,US,100,US,M
+2023,MI,FT,Data Infrastructure Engineer,190000,USD,190000,US,0,US,M
+2023,MI,FT,Data Infrastructure Engineer,183310,USD,183310,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,240000,USD,240000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,180000,USD,180000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,150000,USD,150000,US,100,US,M
+2023,SE,FT,Data Science Manager,299500,USD,299500,US,0,US,M
+2023,SE,FT,Data Science Manager,245100,USD,245100,US,0,US,M
+2023,SE,FT,Data Engineer,144000,USD,144000,US,100,US,M
+2023,SE,FT,Data Engineer,66000,USD,66000,US,100,US,M
+2023,MI,FT,Machine Learning Engineer,148500,USD,148500,US,0,US,M
+2023,MI,FT,Machine Learning Engineer,126277,USD,126277,US,0,US,M
+2023,SE,FT,Data Architect,228000,USD,228000,US,0,US,M
+2023,SE,FT,Data Architect,120000,USD,120000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,180000,USD,180000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,90000,USD,90000,US,0,US,M
+2023,SE,FT,Data Scientist,126500,USD,126500,US,0,US,M
+2023,SE,FT,Data Scientist,78000,USD,78000,US,0,US,M
+2023,SE,FT,Data Engineer,180000,USD,180000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,272000,USD,272000,US,0,US,M
+2023,SE,FT,Machine Learning Software Engineer,170000,USD,170000,US,0,US,M
+2023,MI,FT,Data Analyst,80000,USD,80000,US,0,US,M
+2023,MI,FT,Data Analyst,60000,USD,60000,US,0,US,M
+2023,SE,FT,Data Engineer,259000,USD,259000,US,100,US,M
+2023,SE,FT,Data Engineer,146000,USD,146000,US,100,US,M
+2023,SE,FT,Data Engineer,200000,USD,200000,US,0,US,M
+2023,SE,FT,Data Engineer,160000,USD,160000,US,0,US,M
+2023,EX,FT,Data Engineer,310000,USD,310000,US,100,US,M
+2023,EX,FT,Data Engineer,239000,USD,239000,US,100,US,M
+2023,SE,FT,Data Analyst,142000,USD,142000,US,100,US,M
+2023,SE,FT,Data Analyst,95000,USD,95000,US,100,US,M
+2023,SE,FT,Data Scientist,155000,USD,155000,US,0,US,M
+2023,SE,FT,Data Scientist,139500,USD,139500,US,0,US,M
+2023,MI,FT,Data Engineer,140000,USD,140000,US,100,US,M
+2023,MI,FT,Data Engineer,120000,USD,120000,US,100,US,M
+2023,SE,FT,Data Engineer,259000,USD,259000,US,100,US,M
+2023,SE,FT,Data Engineer,146000,USD,146000,US,100,US,M
+2023,MI,FT,Data Analyst,90000,GBP,109371,HR,0,HR,M
+2023,MI,FT,Data Analyst,60000,GBP,72914,HR,0,HR,M
+2023,EN,PT,Data Analyst,78000,PLN,17779,PL,100,IN,L
+2023,EN,FT,Data Scientist,101400,BRL,19522,BR,100,BR,L
+2023,SE,FT,Data Science Lead,247500,USD,247500,US,0,US,M
+2023,SE,FT,Data Science Lead,172200,USD,172200,US,0,US,M
+2023,SE,FT,Data Scientist,225000,USD,225000,US,0,US,M
+2023,SE,FT,Data Scientist,156400,USD,156400,US,0,US,M
+2023,SE,FT,Data Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Data Engineer,109400,USD,109400,US,0,US,M
+2023,MI,FT,Data Engineer,125000,USD,125000,US,0,US,M
+2023,MI,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,100000,EUR,107309,FR,100,FR,M
+2023,SE,FT,Machine Learning Infrastructure Engineer,70000,EUR,75116,FR,100,FR,M
+2023,SE,FT,Data Scientist,185900,USD,185900,US,0,US,M
+2023,SE,FT,Data Scientist,129300,USD,129300,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,139500,USD,139500,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,109400,USD,109400,US,0,US,M
+2023,SE,FT,Data Analyst,120000,USD,120000,US,100,US,M
+2023,SE,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,SE,FT,Data Analyst,169000,USD,169000,US,0,US,M
+2023,SE,FT,Data Analyst,110600,USD,110600,US,0,US,M
+2023,SE,FT,Machine Learning Engineer,288000,USD,288000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,140000,USD,140000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,288000,USD,288000,US,100,US,M
+2023,SE,FT,Machine Learning Engineer,140000,USD,140000,US,100,US,M
+2023,MI,FT,Data Engineer,120000,USD,120000,US,0,US,M
+2023,MI,FT,Data Engineer,90000,USD,90000,US,0,US,M
+2023,MI,FT,Data Science Lead,60000,GBP,72914,GB,0,GB,M
+2023,MI,FT,Data Science Lead,50000,GBP,60761,GB,0,GB,M
+2023,SE,FT,Data Scientist,215050,USD,215050,US,100,US,M
+2023,SE,FT,Data Scientist,156400,USD,156400,US,100,US,M
+2023,SE,FT,Data Architect,198000,USD,198000,US,100,US,M
+2023,SE,FT,Data Architect,114000,USD,114000,US,100,US,M
+2023,EN,FT,Data Analyst,75000,USD,75000,US,100,US,M
+2023,EN,FT,Data Analyst,60000,USD,60000,US,100,US,M
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+2023,SE,FT,Machine Learning Engineer,142200,USD,142200,US,0,US,M
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+2022,MI,FT,Business Data Analyst,90000,CAD,69133,CA,50,CA,L
+2022,EN,FT,Data Engineer,65000,USD,65000,US,100,US,S
+2022,SE,FT,Machine Learning Engineer,65000,EUR,68293,IE,100,IE,S
+2021,MI,FT,Data Scientist,109000,USD,109000,US,50,US,L
+2022,MI,FT,Data Scientist,88000,CAD,67597,CA,100,CA,M
+2022,EN,FT,Computer Vision Engineer,10000,USD,10000,PT,100,LU,M
+2022,MI,FT,Data Analyst,20000,USD,20000,GR,100,GR,S
+2021,SE,FT,Head of Data,87000,EUR,102839,SI,100,SI,L
+2022,SE,FT,Head of Data,200000,USD,200000,MY,100,US,M
+2022,EN,FT,Data Scientist,66500,CAD,51081,CA,100,CA,L
+2022,MI,FT,Data Scientist,78000,USD,78000,US,100,US,M
+2022,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
+2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
+2022,MI,FT,Machine Learning Engineer,121000,AUD,83864,AU,100,AU,L
+2022,EN,FT,Data Scientist,40000,USD,40000,JP,100,MY,L
+2022,MI,FT,Head of Data,30000,EUR,31520,EE,100,EE,S
+2022,SE,FT,Machine Learning Engineer,57000,EUR,59888,NL,100,NL,L
+2020,EN,FT,Data Engineer,48000,EUR,54742,PK,100,DE,L
+2022,SE,FT,Research Scientist,85000,EUR,89306,FR,50,FR,L
+2022,EN,FT,Data Scientist,120000,AUD,83171,AU,50,AU,M
+2022,SE,FT,Data Scientist,165000,USD,165000,US,100,US,M
+2022,MI,FT,Machine Learning Scientist,153000,USD,153000,US,50,US,M
+2022,SE,FT,Data Scientist,100000,USD,100000,BR,100,US,M
+2022,SE,FT,Machine Learning Developer,100000,CAD,76814,CA,100,CA,M
+2022,MI,FT,Data Scientist,150000,PLN,33609,PL,100,PL,L
+2022,MI,FT,Principal Data Analyst,75000,USD,75000,CA,100,CA,S
+2020,MI,FT,Product Data Analyst,20000,USD,20000,HN,0,HN,S
+2022,EN,CT,Applied Machine Learning Scientist,29000,EUR,30469,TN,100,CZ,M
+2021,MI,FT,Research Scientist,69999,USD,69999,CZ,50,CZ,L
+2022,EN,FT,Data Engineer,52800,EUR,55475,PK,100,DE,M
+2022,MI,FT,Research Scientist,59000,EUR,61989,AT,0,AT,L
+2022,SE,FT,Data Science Manager,152500,USD,152500,US,100,US,M
+2022,EN,FT,Research Scientist,120000,USD,120000,US,100,US,L
+2022,MI,FT,Data Scientist,135000,USD,135000,US,100,US,L
+2022,SE,FT,Data Analytics Lead,405000,USD,405000,US,100,US,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,0,US,L
+2021,SE,FT,Data Science Manager,240000,USD,240000,US,0,US,L
+2021,MI,FT,Data Analyst,135000,USD,135000,US,100,US,L
+2021,EN,FT,Data Scientist,80000,USD,80000,US,100,US,M
+2022,SE,FT,Applied Data Scientist,380000,USD,380000,US,100,US,L
+2022,MI,FT,Data Scientist,115000,CHF,120402,CH,0,CH,L
+2022,SE,FT,Applied Data Scientist,177000,USD,177000,US,100,US,L
+2022,MI,FT,Data Engineer,62000,EUR,65141,FR,100,FR,M
+2022,MI,FT,Data Scientist,48000,USD,48000,RU,100,US,S
+2022,EN,FT,Data Analytics Engineer,20000,USD,20000,PK,0,PK,M
+2021,SE,FT,Principal Data Scientist,220000,USD,220000,US,0,US,L
+2021,MI,FT,ML Engineer,8500000,JPY,77364,JP,50,JP,S
+2021,MI,FT,ML Engineer,7000000,JPY,63711,JP,50,JP,S
+2022,EN,FT,Computer Vision Software Engineer,150000,USD,150000,AU,100,AU,S
+2021,MI,FT,Data Analyst,90000,USD,90000,US,100,US,M
+2022,MI,FL,Data Scientist,100000,USD,100000,CA,100,US,M
+2021,EN,FT,Data Scientist,100000,USD,100000,US,0,US,S
+2022,EN,PT,Data Scientist,100000,USD,100000,DZ,50,DZ,M
+2022,SE,FT,Research Scientist,144000,USD,144000,US,50,US,L
+2022,SE,FT,Principal Data Scientist,148000,EUR,155499,DE,100,DE,M
+2021,SE,FT,Computer Vision Engineer,24000,USD,24000,BR,100,BR,M
+2021,MI,FT,Applied Machine Learning Scientist,38400,USD,38400,VN,100,US,M
+2022,EN,FT,Financial Data Analyst,100000,USD,100000,US,50,US,L
+2021,MI,FT,Data Scientist,82500,USD,82500,US,100,US,S
+2021,EN,FT,Data Scientist,42000,EUR,49646,FR,50,FR,M
+2021,SE,FT,Lead Data Scientist,3000000,INR,40570,IN,50,IN,L
+2022,EN,FT,Data Engineer,120000,USD,120000,US,100,US,M
+2022,SE,FT,Lead Machine Learning Engineer,80000,EUR,84053,DE,0,DE,M
+2021,EN,FT,Machine Learning Engineer,20000,USD,20000,IN,100,IN,S
+2022,EN,FT,Computer Vision Engineer,125000,USD,125000,US,0,US,M
+2021,MI,FT,Data Scientist,700000,INR,9466,IN,0,IN,S
+2021,SE,FT,Machine Learning Scientist,120000,USD,120000,US,50,US,S
+2021,EN,PT,Data Analyst,8760,EUR,10354,ES,50,ES,M
+2021,EN,FT,Applied Data Scientist,80000,GBP,110037,GB,0,GB,L
+2022,EN,FT,ML Engineer,20000,EUR,21013,PT,100,PT,L
+2021,EN,FT,Data Analyst,50000,USD,50000,US,100,US,M
+2021,SE,FT,Principal Data Engineer,200000,USD,200000,US,100,US,M
+2021,MI,FT,Big Data Engineer,60000,USD,60000,ES,50,RO,M
+2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,EN,FT,Machine Learning Developer,100000,USD,100000,IQ,50,IQ,S
+2021,MI,FT,Data Engineer,100000,USD,100000,US,100,US,L
+2021,SE,FT,Machine Learning Engineer,70000,EUR,82744,BE,50,BE,M
+2020,MI,FT,Data Engineer,51999,EUR,59303,DE,100,DE,S
+2021,MI,FT,Research Scientist,53000,EUR,62649,FR,50,FR,M
+2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L
+2021,MI,FT,Data Architect,170000,USD,170000,US,100,US,L
+2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L
+2021,EN,FT,BI Data Analyst,55000,USD,55000,US,50,US,S
+2021,EX,FT,Director of Data Science,250000,USD,250000,US,0,US,L
+2021,EN,FT,Data Engineer,80000,USD,80000,US,100,US,L
+2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L
+2021,EX,FT,Director of Data Science,110000,EUR,130026,DE,50,DE,M
+2021,EN,FT,Data Science Consultant,54000,EUR,63831,DE,50,DE,L
+2020,SE,FT,Data Scientist,60000,EUR,68428,GR,100,US,L
+2021,EX,FT,Head of Data Science,85000,USD,85000,RU,0,RU,M
+2021,EX,FT,Head of Data,230000,USD,230000,RU,50,RU,L
+2021,EN,FT,Machine Learning Engineer,125000,USD,125000,US,100,US,S
+2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,100,US,M
+2020,MI,FT,Research Scientist,450000,USD,450000,US,0,US,M
+2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L
+2020,MI,FT,Data Engineer,65000,EUR,74130,AT,50,AT,L
+2021,SE,FT,Data Science Engineer,159500,CAD,127221,CA,50,CA,L
+2021,SE,FT,Data Science Manager,144000,USD,144000,US,100,US,L
+2021,EN,FT,Data Scientist,13400,USD,13400,UA,100,UA,L
+2021,MI,FT,Data Scientist,95000,CAD,75774,CA,100,CA,L
+2021,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
+2020,MI,FT,Data Science Consultant,103000,USD,103000,US,100,US,L
+2021,SE,FT,Data Engineer,153000,USD,153000,US,100,US,L
+2021,MI,FT,Data Engineer,90000,USD,90000,US,100,US,L
+2021,EN,FT,Data Analyst,90000,USD,90000,US,100,US,S
+2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S
+2021,MI,FT,Data Scientist,50000,USD,50000,NG,100,NG,L
+2021,EN,PT,AI Scientist,12000,USD,12000,PK,100,US,M
+2021,MI,PT,3D Computer Vision Researcher,400000,INR,5409,IN,50,IN,M
+2021,MI,CT,ML Engineer,270000,USD,270000,US,100,US,L
+2021,MI,FT,Applied Data Scientist,68000,CAD,54238,GB,50,CA,L
+2021,MI,FT,Machine Learning Engineer,40000,EUR,47282,ES,100,ES,S
+2021,EX,FT,Director of Data Science,130000,EUR,153667,IT,100,PL,L
+2021,MI,FT,Data Engineer,110000,PLN,28476,PL,100,PL,L
+2021,MI,FT,Data Analytics Engineer,110000,USD,110000,US,100,US,L
+2021,EN,FT,Research Scientist,60000,GBP,82528,GB,50,GB,L
+2020,EN,FT,Machine Learning Engineer,250000,USD,250000,US,50,US,L
+2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M
+2021,SE,FT,Data Analyst,80000,USD,80000,BG,100,US,S
+2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S
+2020,EN,FT,Machine Learning Engineer,138000,USD,138000,US,100,US,S
+2021,MI,FT,Data Engineer,140000,USD,140000,US,100,US,L
+2021,SE,FT,Data Analytics Engineer,67000,EUR,79197,DE,100,DE,L
+2021,SE,FT,Lead Data Analyst,170000,USD,170000,US,100,US,L
+2021,EN,FT,Data Analyst,80000,USD,80000,US,100,US,M
+2020,MI,FT,Data Scientist,45760,USD,45760,PH,100,US,S
+2021,MI,FT,BI Data Analyst,100000,USD,100000,US,100,US,M
+2021,SE,FT,Data Scientist,45000,EUR,53192,FR,50,FR,L
+2021,EX,FT,Head of Data,235000,USD,235000,US,100,US,L
+2021,EX,FT,BI Data Analyst,150000,USD,150000,IN,100,US,L
+2020,EX,FT,Data Engineer,70000,EUR,79833,ES,50,ES,L
+2021,EN,FT,Machine Learning Scientist,225000,USD,225000,US,100,US,L
+2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,100,DE,S
+2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180,PT,0,PT,M
+2021,SE,FT,Marketing Data Analyst,75000,EUR,88654,GR,100,DK,L
+2021,SE,FT,Lead Data Engineer,75000,GBP,103160,GB,100,GB,S
+2021,SE,FT,Data Engineer,82500,GBP,113476,GB,100,GB,M
+2021,SE,FT,Machine Learning Engineer,80000,EUR,94564,DE,50,DE,L
+2021,EN,FT,Data Engineer,2250000,INR,30428,IN,100,IN,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M
+2021,SE,FT,Data Engineer,115000,USD,115000,US,100,US,S
+2021,MI,FT,Research Scientist,235000,CAD,187442,CA,100,CA,L
+2021,MI,FT,Data Analyst,37456,GBP,51519,GB,50,GB,L
+2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L
+2020,MI,FT,Data Engineer,88000,GBP,112872,GB,50,GB,L
+2021,MI,FT,BI Data Analyst,11000000,HUF,36259,HU,50,US,L
+2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,L
+2020,EN,PT,ML Engineer,14000,EUR,15966,DE,100,DE,S
+2021,MI,FT,Computer Vision Software Engineer,81000,EUR,95746,DE,100,US,S
+2021,EN,FT,Computer Vision Software Engineer,70000,USD,70000,US,100,US,M
+2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S
+2021,MI,FT,Cloud Data Engineer,120000,SGD,89294,SG,50,SG,L
+2021,EN,FT,Data Scientist,2200000,INR,29751,IN,50,IN,L
+2021,SE,FT,Lead Data Engineer,276000,USD,276000,US,0,US,L
+2020,SE,FT,Data Engineer,188000,USD,188000,US,100,US,L
+2021,SE,FT,Cloud Data Engineer,160000,USD,160000,BR,100,US,S
+2020,MI,FT,Data Scientist,105000,USD,105000,US,100,US,L
+2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,SE,FT,Data Engineer,174000,USD,174000,US,100,US,L
+2021,MI,FT,Data Analyst,93000,USD,93000,US,100,US,L
+2021,EN,FT,Data Scientist,2100000,INR,28399,IN,100,IN,M
+2021,SE,FT,Research Scientist,51400,EUR,60757,PT,50,PT,L
+2021,EN,FT,Data Scientist,90000,USD,90000,US,100,US,S
+2020,MI,FT,Data Engineer,61500,EUR,70139,FR,50,FR,L
+2020,EN,FT,Data Analyst,450000,INR,6072,IN,0,IN,S
+2020,SE,FT,Data Engineer,720000,MXN,33511,MX,0,MX,S
+2021,SE,FT,Principal Data Analyst,170000,USD,170000,US,100,US,M
+2021,SE,FT,Data Engineer,70000,GBP,96282,GB,50,GB,L
+2021,MI,FT,Data Engineer,108000,TRY,12103,TR,0,TR,M
+2021,EN,FT,Data Scientist,31000,EUR,36643,FR,50,FR,L
+2021,MI,FT,Data Engineer,52500,GBP,72212,GB,50,GB,L
+2020,EN,FT,Data Analyst,91000,USD,91000,US,100,US,L
+2021,SE,FT,Big Data Architect,125000,CAD,99703,CA,50,CA,M
+2021,SE,FT,Data Scientist,165000,USD,165000,US,100,US,L
+2021,MI,FT,Data Analyst,80000,USD,80000,US,100,US,L
+2021,SE,FT,Data Scientist,130000,CAD,103691,CA,100,CA,L
+2021,EN,FT,Data Engineer,1600000,INR,21637,IN,50,IN,M
+2020,EN,FT,Research Scientist,42000,USD,42000,NL,50,NL,L
+2020,MI,FT,Lead Data Scientist,115000,USD,115000,AE,0,AE,L
+2021,MI,FT,Research Scientist,80000,CAD,63810,CA,100,CA,M
+2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S
+2021,MI,FT,Head of Data Science,110000,USD,110000,US,0,US,S
+2021,MI,FT,Data Architect,180000,USD,180000,US,100,US,L
+2021,SE,FT,Data Analyst,200000,USD,200000,US,100,US,L
+2020,SE,FT,Big Data Engineer,85000,GBP,109024,GB,50,GB,M
+2021,SE,FT,Data Engineer,200000,USD,200000,US,100,US,L
+2021,SE,FT,ML Engineer,256000,USD,256000,US,100,US,S
+2021,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
+2020,MI,FT,Data Scientist,70000,EUR,79833,DE,0,DE,L
+2021,EN,FT,Data Engineer,72500,USD,72500,US,100,US,L
+2021,SE,FT,Machine Learning Engineer,185000,USD,185000,US,50,US,L
+2021,MI,PT,Data Engineer,59000,EUR,69741,NL,100,NL,L
+2021,EN,FT,Research Scientist,100000,USD,100000,JE,0,CN,L
+2021,MI,FT,Data Engineer,112000,USD,112000,US,100,US,L
+2020,SE,FT,Machine Learning Engineer,150000,USD,150000,US,50,US,L
+2021,SE,FT,Data Scientist,180000,TRY,20171,TR,50,TR,L
+2021,SE,FT,AI Scientist,55000,USD,55000,ES,100,ES,L
+2021,EN,FT,Data Scientist,58000,USD,58000,US,50,US,L
+2021,EN,FT,Data Scientist,100000,USD,100000,US,100,US,M
+2021,SE,FT,Data Scientist,65720,EUR,77684,FR,50,FR,M
+2021,EN,FT,Machine Learning Engineer,85000,USD,85000,NL,100,DE,S
+2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,0,DE,L
+2021,SE,CT,Staff Data Scientist,105000,USD,105000,US,100,US,M
+2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L
+2021,EN,FT,Data Engineer,55000,EUR,65013,DE,50,DE,M
+2021,MI,FT,Data Engineer,250000,TRY,28016,TR,100,TR,M
+2021,MI,FT,Data Engineer,111775,USD,111775,US,0,US,M
+2021,MI,FT,Data Engineer,93150,USD,93150,US,0,US,M
+2021,SE,FT,Lead Data Engineer,160000,USD,160000,PR,50,US,S
+2021,MI,FT,Data Scientist,21600,EUR,25532,RS,100,DE,S
+2021,SE,FT,Machine Learning Engineer,4900000,INR,66265,IN,0,IN,L
+2021,MI,FT,Data Scientist,1250000,INR,16904,IN,100,IN,S
+2021,SE,FT,Data Analyst,54000,EUR,63831,DE,50,DE,L
+2020,SE,FT,Lead Data Scientist,190000,USD,190000,US,100,US,S
+2021,EX,FT,Director of Data Science,120000,EUR,141846,DE,0,DE,L
+2021,EN,FT,Big Data Engineer,1200000,INR,16228,IN,100,IN,L
+2021,SE,FT,Data Analyst,90000,CAD,71786,CA,100,CA,M
+2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
+2021,SE,FT,Data Scientist,135000,USD,135000,US,0,US,L
+2021,EN,FT,Machine Learning Engineer,21000,EUR,24823,DE,50,DE,M
+2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L
+2021,SE,FT,Machine Learning Engineer,1799997,INR,24342,IN,100,IN,L
+2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S
+2021,MI,FT,Data Scientist,147000,USD,147000,US,50,US,L
+2021,SE,FT,Research Scientist,120500,CAD,96113,CA,50,CA,L
+2021,SE,FT,Data Science Manager,174000,USD,174000,US,100,US,L
+2020,MI,FT,Business Data Analyst,135000,USD,135000,US,100,US,L
+2021,EN,FT,Machine Learning Engineer,21844,USD,21844,CO,50,CO,M
+2020,SE,FT,Lead Data Engineer,125000,USD,125000,NZ,50,NZ,S
+2020,EN,FT,Data Scientist,45000,EUR,51321,FR,0,FR,S
+2020,MI,FT,Data Scientist,3000000,INR,40481,IN,0,IN,L
+2021,EX,FT,Data Science Consultant,59000,EUR,69741,FR,100,ES,S
+2021,SE,FT,Data Analytics Engineer,50000,USD,50000,VN,100,GB,M
+2020,EN,FT,Data Scientist,35000,EUR,39916,FR,0,FR,M
+2020,MI,FT,Lead Data Analyst,87000,USD,87000,US,100,US,L
+2021,MI,FT,Data Engineer,22000,EUR,26005,RO,0,US,L
+2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
+2021,MI,FT,Big Data Engineer,1672000,INR,22611,IN,0,IN,L
+2021,MI,FT,Data Scientist,420000,INR,5679,IN,100,US,S
+2021,EN,FT,Machine Learning Engineer,81000,USD,81000,US,50,US,S
+2021,MI,FT,Data Scientist,30400000,CLP,40038,CL,100,CL,L
+2021,EN,FT,Data Science Consultant,90000,USD,90000,US,100,US,S
+2021,MI,FT,Data Scientist,52000,EUR,61467,DE,50,AT,M
+2021,SE,FT,Machine Learning Infrastructure Engineer,195000,USD,195000,US,100,US,M
+2021,MI,FT,Data Scientist,32000,EUR,37825,ES,100,ES,L
+2020,MI,FT,Data Analyst,85000,USD,85000,US,100,US,L
+2021,EX,CT,Principal Data Scientist,416000,USD,416000,US,100,US,S
+2021,SE,FT,Machine Learning Scientist,225000,USD,225000,US,100,CA,L
+2021,MI,FT,Data Scientist,40900,GBP,56256,GB,50,GB,L
+2021,MI,FT,Data Scientist,2500000,INR,33808,IN,0,IN,M
+2021,MI,FT,Data Scientist,85000,GBP,116914,GB,50,GB,L
+2021,MI,FT,Machine Learning Engineer,180000,PLN,46597,PL,100,PL,L
+2020,MI,FT,Data Analyst,8000,USD,8000,PK,50,PK,L
+2020,EN,FT,Data Engineer,4450000,JPY,41689,JP,100,JP,S
+2020,SE,FT,Big Data Engineer,100000,EUR,114047,PL,100,GB,S
+2021,MI,FT,Machine Learning Engineer,75000,EUR,88654,BE,100,BE,M
+2020,EN,FT,Data Science Consultant,423000,INR,5707,IN,50,IN,M
+2020,MI,FT,Lead Data Engineer,56000,USD,56000,PT,100,US,M
+2021,EN,PT,Computer Vision Engineer,180000,DKK,28609,DK,50,DK,S
+2021,MI,FT,Data Scientist,75000,EUR,88654,DE,50,DE,L
+2020,MI,FT,Product Data Analyst,450000,INR,6072,IN,100,IN,L
+2020,SE,FT,Data Engineer,42000,EUR,47899,GR,50,GR,L
+2020,MI,FT,BI Data Analyst,98000,USD,98000,US,0,US,M
+2021,MI,FT,Data Engineer,48000,GBP,66022,HK,50,GB,S
+2021,MI,FT,Research Scientist,48000,EUR,56738,FR,50,FR,S
+2021,MI,FT,Machine Learning Engineer,21000,EUR,24823,SI,50,SI,L
+2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,0,US,L
+2021,MI,FL,Data Engineer,20000,USD,20000,IT,0,US,L
+2020,EX,FT,Director of Data Science,325000,USD,325000,US,100,US,L
+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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