{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "*Вариант задания:* Заработная плата рабочих мест в области Data Science (вариант - 8) \n", "\n", "Бизнес-цели для датасета о заработной плате в Data Science:\n", "Оптимизация стратегии найма и оплаты труда в Data Science\n", "\n", "Формулировка: Разработать модель, которая позволяет точно прогнозировать и оптимизировать заработную плату для специалистов в области Data Science на основе их опыта, типа занятости, местоположения и других факторов.\n", "\n", "Цель: Увеличить привлекательность компании для талантливых специалистов в Data Science, обеспечивая конкурентоспособные зарплаты, а также оптимизировать расходы на персонал, избегая переплат и недоплат.\n", "\n", "Ключевые показатели успеха (KPI):\n", "\n", "Точность модели прогнозирования зарплаты (RMSE): Минимизация среднеквадратичной ошибки до уровня ниже 10% от реальной зарплаты, чтобы учитывать большие отклонения в оценке.\n", "\n", "Средняя абсолютная ошибка (MAE): Модель должна предсказать зарплату с минимальной ошибкой и снизить MAE до 5% или меньше, учитывая большие отклонения в оценке.\n", "\n", "Скорость оценки зарплаты: Уменьшение времени на оценку зарплаты для новых сотрудников, чтобы быстрее принимать решения о найме.\n", "\n", "Доступность: Внедрение модели в систему управления персоналом для использования HR-специалистами.\n", "\n", "Оптимизация распределения ресурсов в компании\n", "\n", "Формулировка: Разработать модель, которая поможет компаниям определить оптимальное распределение ресурсов (бюджета) на Data Science проекты и команды, учитывая уровень зарплат, опыт и другие факторы.\n", "\n", "Цель: Снизить затраты на Data Science проекты, оптимизировать распределение бюджета, обеспечивая максимальную эффективность и результативность проектов.\n", "\n", "Ключевые показатели успеха (KPI):\n", "\n", "Возврат инвестиций (ROI): Проекты должны показывать не менее 20% прироста в результатах (например, увеличение прибыли, улучшение показателей) на каждый вложенный доллар в Data Science.\n", "\n", "Средняя стоимость проекта на 1 сотрудника (CPA): Задача снизить расходы на проекты, минимизировав ненужные траты. Например, оптимизация затрат до $50,000 на проект с учетом максимального прироста в результатах.\n", "\n", "Сокращение времени на принятие решений: Модель должна сокращать время, необходимое на оценку вариантов распределения ресурсов, до нескольких минут, что ускорит принятие решений.\n", "\n", "Оптимизация стратегии развития карьеры в Data Science\n", "\n", "Формулировка: Разработать модель, которая поможет специалистам в Data Science определить оптимальные пути развития карьеры, учитывая текущий уровень зарплаты, опыт и перспективы роста.\n", "\n", "Цель: Повысить удовлетворенность и мотивацию специалистов в Data Science, обеспечивая им четкие пути развития карьеры и возможность получения конкурентоспособных зарплат.\n", "\n", "Ключевые показатели успеха (KPI):\n", "\n", "Уровень удовлетворенности сотрудников: Увеличение уровня удовлетворенности сотрудников на 15% за счет предоставления четких путей развития карьеры и возможностей для роста.\n", "\n", "Средний срок пребывания в компании: Увеличение среднего срока пребывания сотрудников в компании на 20% за счет предоставления привлекательных перспектив развития.\n", "\n", "Доступность: Внедрение модели в систему управления карьерой для использования сотрудниками и HR-специалистами.\n", "\n", "**Технические цели проекта для каждой выделенной бизнес-цели**\n", "\n", "Оптимизация стратегии найма и оплаты труда в Data Science\n", "\n", "Сбор и подготовка данных:\n", "\n", "Сбор данных: Получение данных о заработных платах специалистов в Data Science из различных источников (например, Glassdoor, LinkedIn, Kaggle).\n", "\n", "Очистка данных: Удаление пропусков, выбросов и дубликатов. Преобразование категориальных переменных (например, experience_level, employment_type, employee_residence, company_location) в числовую форму с использованием One-Hot Encoding.\n", "\n", "Нормализация и стандартизация: Применение методов масштабирования данных (нормировка, стандартизация) для числовых признаков (например, salary_in_usd, remote_ratio).\n", "\n", "Разбиение данных: Разделение набора данных на обучающую, контрольную и тестовую выборки для предотвращения утечек данных и переобучения.\n", "\n", "Разработка и обучение модели:\n", "\n", "Исследование моделей: Эксперименты с различными алгоритмами (линейная регрессия, случайный лес, градиентный бустинг, деревья решений) для предсказания заработной платы.\n", "\n", "Обучение модели: Обучение модели на обучающей выборке с использованием метрик оценки качества, таких как RMSE (Root Mean Square Error) и MAE (Mean Absolute Error).\n", "\n", "Оценка качества: Оценка качества моделей на тестовой выборке, минимизируя MAE и RMSE для получения точных прогнозов заработной платы.\n", "\n", "Развёртывание модели:\n", "\n", "Интеграция модели: Интеграция модели в существующую систему управления персоналом или разработка API для доступа к модели.\n", "\n", "Создание интерфейса: Создание веб-приложения или мобильного интерфейса для удобного использования модели и получения прогнозов в режиме реального времени.\n", "\n", "Оптимизация распределения ресурсов в компании\n", "\n", "Сбор и подготовка данных:\n", "\n", "Сбор данных: Получение данных о затратах на Data Science проекты, результатах проектов, уровнях зарплат сотрудников и других релевантных факторов.\n", "\n", "Очистка данных: Удаление пропусков, выбросов и дубликатов. Преобразование категориальных переменных в числовую форму с использованием One-Hot Encoding.\n", "\n", "Нормализация и стандартизация: Применение методов масштабирования данных для числовых признаков.\n", "\n", "Разбиение данных: Разделение набора данных на обучающую, контрольную и тестовую выборки.\n", "\n", "Разработка и обучение модели:\n", "\n", "Исследование моделей: Эксперименты с различными алгоритмами (линейная регрессия, случайный лес, градиентный бустинг) для предсказания оптимального распределения ресурсов.\n", "\n", "Обучение модели: Обучение модели на обучающей выборке с использованием метрик оценки качества, таких как ROI (Return on Investment) и CPA (Cost Per Acquisition).\n", "\n", "Оценка качества: Оценка качества моделей на тестовой выборке, минимизируя CPA и максимизируя ROI.\n", "\n", "Развёртывание модели:\n", "\n", "Интеграция модели: Интеграция модели в систему управления проектами или разработка API для доступа к модели.\n", "\n", "Создание интерфейса: Создание веб-приложения или мобильного интерфейса для удобного использования модели и получения рекомендаций по распределению ресурсов.\n", "\n", "Оптимизация стратегии развития карьеры в Data Science\n", "\n", "Сбор и подготовка данных:\n", "\n", "Сбор данных: Получение данных о карьерных траекториях специалистов в Data Science, уровнях зарплат, опыте и других релевантных факторах.\n", "\n", "Очистка данных: Удаление пропусков, выбросов и дубликатов. Преобразование категориальных переменных в числовую форму с использованием One-Hot Encoding.\n", "\n", "Нормализация и стандартизация: Применение методов масштабирования данных для числовых признаков.\n", "\n", "Разбиение данных: Разделение набора данных на обучающую, контрольную и тестовую выборки.\n", "\n", "Разработка и обучение модели:\n", "\n", "Исследование моделей: Эксперименты с различными алгоритмами (линейная регрессия, случайный лес, градиентный бустинг) для предсказания оптимальных путей развития карьеры.\n", "\n", "Обучение модели: Обучение модели на обучающей выборке с использованием метрик оценки качества, таких как MAE (Mean Absolute Error) и RMSE (Root Mean Square Error).\n", "\n", "Оценка качества: Оценка качества моделей на тестовой выборке, минимизируя MAE и RMSE.\n", "\n", "Развёртывание модели:\n", "\n", "Интеграция модели: Интеграция модели в систему управления карьерой или разработка API для доступа к модели.\n", "\n", "Создание интерфейса: Создание веб-приложения или мобильного интерфейса для удобного использования модели и получения рекомендаций по развитию карьеры." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n", " 'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n", " 'remote_ratio', 'company_location', 'company_size'],\n", " dtype='object')\n" ] } ], "source": [ "import pandas as pd\n", "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n", "print(df.columns)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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work_yearexperience_levelemployment_typejob_titlesalarysalary_currencysalary_in_usdemployee_residenceremote_ratiocompany_locationcompany_size
02023SEFTPrincipal Data Scientist80000EUR85847ES100ESL
12023MICTML Engineer30000USD30000US100USS
22023MICTML Engineer25500USD25500US100USS
32023SEFTData Scientist175000USD175000CA100CAM
42023SEFTData Scientist120000USD120000CA100CAM
\n", "
" ], "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Для наглядности\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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work_yearsalarysalary_in_usdremote_ratio
count3755.0000003.755000e+033755.0000003755.000000
mean2022.3736351.906956e+05137570.38988046.271638
std0.6914486.716765e+0563055.62527848.589050
min2020.0000006.000000e+035132.0000000.000000
25%2022.0000001.000000e+0595000.0000000.000000
50%2022.0000001.380000e+05135000.0000000.000000
75%2023.0000001.800000e+05175000.000000100.000000
max2023.0000003.040000e+07450000.000000100.000000
\n", "
" ], "text/plain": [ " work_year salary salary_in_usd remote_ratio\n", "count 3755.000000 3.755000e+03 3755.000000 3755.000000\n", "mean 2022.373635 1.906956e+05 137570.389880 46.271638\n", "std 0.691448 6.716765e+05 63055.625278 48.589050\n", "min 2020.000000 6.000000e+03 5132.000000 0.000000\n", "25% 2022.000000 1.000000e+05 95000.000000 0.000000\n", "50% 2022.000000 1.380000e+05 135000.000000 0.000000\n", "75% 2023.000000 1.800000e+05 175000.000000 100.000000\n", "max 2023.000000 3.040000e+07 450000.000000 100.000000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Описание данных (основные статистические показатели)\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "work_year 0\n", "experience_level 0\n", "employment_type 0\n", "job_title 0\n", "salary 0\n", "salary_currency 0\n", "salary_in_usd 0\n", "employee_residence 0\n", "remote_ratio 0\n", "company_location 0\n", "company_size 0\n", "dtype: int64\n" ] }, { "data": { "text/plain": [ "work_year False\n", "experience_level False\n", "employment_type False\n", "job_title False\n", "salary False\n", "salary_currency False\n", "salary_in_usd False\n", "employee_residence False\n", "remote_ratio False\n", "company_location False\n", "company_size False\n", "dtype: bool" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Процент пропущенных значений признаков\n", "for i in df.columns:\n", " null_rate = df[i].isnull().sum() / len(df) * 100\n", " if null_rate > 0:\n", " print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n", "\n", "# Проверка на пропущенные данные\n", "print(df.isnull().sum())\n", "\n", "df.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Разбиваем на выборки (обучающую, тестовую, контрольную)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Размер обучающей выборки: 3004\n", "Размер контрольной выборки: 751\n", "Размер тестовой выборки: 751\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n", "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n", "\n", "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n", "train_data, val_data = train_test_split(df, test_size=0.2, random_state=42)\n", "\n", "print(\"Размер обучающей выборки: \", len(train_data))\n", "print(\"Размер контрольной выборки: \", len(val_data))\n", "print(\"Размер тестовой выборки: \", len(test_data))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Средняя заработная плата в обучающей выборке: 138055.9893475366\n", "Средняя заработная плата в контрольной выборке: 135627.99201065247\n", "Средняя заработная плата в тестовой выборке: 135627.99201065247\n" ] } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "# Предположим, что у вас уже есть данные, разделенные на обучающую, контрольную и тестовую выборки\n", "# train_data, val_data, test_data\n", "\n", "# Визуализация распределения заработной платы в выборках (гистограмма)\n", "def plot_salary_distribution(data, title):\n", " sns.histplot(data['salary_in_usd'], kde=True)\n", " plt.title(title)\n", " plt.xlabel('Заработная плата (USD)')\n", " plt.ylabel('Частота')\n", " plt.show()\n", "\n", "plot_salary_distribution(train_data, 'Распределение заработной платы в обучающей выборке')\n", "plot_salary_distribution(val_data, 'Распределение заработной платы в контрольной выборке')\n", "plot_salary_distribution(test_data, 'Распределение заработной платы в тестовой выборке')\n", "\n", "# Оценка сбалансированности данных по целевой переменной (salary_in_usd)\n", "print(\"Средняя заработная плата в обучающей выборке: \", train_data['salary_in_usd'].mean())\n", "print(\"Средняя заработная плата в контрольной выборке: \", val_data['salary_in_usd'].mean())\n", "print(\"Средняя заработная плата в тестовой выборке: \", test_data['salary_in_usd'].mean())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Размер обучающей выборки после oversampling и undersampling: 3044\n" ] } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from sklearn.model_selection import train_test_split\n", "from imblearn.over_sampling import RandomOverSampler\n", "from imblearn.under_sampling import RandomUnderSampler\n", "\n", "# Предположим, что у вас уже есть данные, разделенные на обучающую, контрольную и тестовую выборки\n", "# train_data, val_data, test_data\n", "\n", "# Преобразование целевой переменной (заработная плата) в категориальные диапазоны с использованием квантилей\n", "train_data['salary_category'] = pd.qcut(train_data['salary_in_usd'], q=4, labels=['low', 'medium', 'high', 'very_high'])\n", "\n", "# Визуализация распределения заработной платы после преобразования в категории\n", "sns.countplot(x=train_data['salary_category'])\n", "plt.title('Распределение категорий заработной платы в обучающей выборке')\n", "plt.xlabel('Категория заработной платы')\n", "plt.ylabel('Частота')\n", "plt.show()\n", "\n", "# Балансировка категорий с помощью RandomOverSampler (увеличение меньшинств)\n", "ros = RandomOverSampler(random_state=42)\n", "X_train = train_data.drop(columns=['salary_in_usd', 'salary_category'])\n", "y_train = train_data['salary_category']\n", "\n", "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n", "\n", "# Визуализация распределения заработной платы после oversampling\n", "sns.countplot(x=y_resampled)\n", "plt.title('Распределение категорий заработной платы после oversampling')\n", "plt.xlabel('Категория заработной платы')\n", "plt.ylabel('Частота')\n", "plt.show()\n", "\n", "# Применение RandomUnderSampler для уменьшения большего класса\n", "rus = RandomUnderSampler(random_state=42)\n", "X_resampled, y_resampled = rus.fit_resample(X_resampled, y_resampled)\n", "\n", "# Визуализация распределения заработной платы после undersampling\n", "sns.countplot(x=y_resampled)\n", "plt.title('Распределение категорий заработной платы после undersampling')\n", "plt.xlabel('Категория заработной платы')\n", "plt.ylabel('Частота')\n", "plt.show()\n", "\n", "# Печать размеров выборки после балансировки\n", "print(\"Размер обучающей выборки после oversampling и undersampling: \", len(X_resampled))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Конструирование признаков\n", "Теперь приступим к конструированию признаков для решения каждой задачи.\n", "\n", "**Процесс конструирования признаков**\n", "Задача 1: Прогнозирование заработной платы в Data Science. Цель технического проекта: Разработка модели машинного обучения для точного прогнозирования заработной платы специалистов в области Data Science.\n", "Задача 2: Оптимизация распределения ресурсов в компании. Цель технического проекта: Разработка модели машинного обучения для оптимизации распределения ресурсов на Data Science проекты.\n", "\n", "**Унитарное кодирование**\n", "Унитарное кодирование категориальных признаков (one-hot encoding). Преобразование категориальных признаков в бинарные векторы.\n", "\n", "**Дискретизация числовых признаков**\n", "Процесс преобразования непрерывных числовых значений в дискретные категории или интервалы (бины)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Столбцы train_data_encoded: ['work_year', 'job_title', 'salary', 'salary_currency', 'salary_in_usd', 'remote_ratio', 'salary_category', 'experience_level_EN', 'experience_level_EX', 'experience_level_MI', 'experience_level_SE', 'employment_type_CT', 'employment_type_FL', 'employment_type_FT', 'employment_type_PT', 'employee_residence_AE', 'employee_residence_AM', 'employee_residence_AR', 'employee_residence_AT', 'employee_residence_AU', 'employee_residence_BA', 'employee_residence_BE', 'employee_residence_BG', 'employee_residence_BO', 'employee_residence_BR', 'employee_residence_CA', 'employee_residence_CF', 'employee_residence_CH', 'employee_residence_CL', 'employee_residence_CN', 'employee_residence_CO', 'employee_residence_CR', 'employee_residence_CY', 'employee_residence_CZ', 'employee_residence_DE', 'employee_residence_DK', 'employee_residence_DZ', 'employee_residence_EE', 'employee_residence_EG', 'employee_residence_ES', 'employee_residence_FI', 'employee_residence_FR', 'employee_residence_GB', 'employee_residence_GH', 'employee_residence_GR', 'employee_residence_HK', 'employee_residence_HN', 'employee_residence_HR', 'employee_residence_HU', 'employee_residence_ID', 'employee_residence_IE', 'employee_residence_IL', 'employee_residence_IN', 'employee_residence_IQ', 'employee_residence_IR', 'employee_residence_IT', 'employee_residence_JP', 'employee_residence_KE', 'employee_residence_KW', 'employee_residence_LT', 'employee_residence_LU', 'employee_residence_LV', 'employee_residence_MA', 'employee_residence_MD', 'employee_residence_MK', 'employee_residence_MX', 'employee_residence_MY', 'employee_residence_NG', 'employee_residence_NL', 'employee_residence_NZ', 'employee_residence_PH', 'employee_residence_PK', 'employee_residence_PL', 'employee_residence_PR', 'employee_residence_PT', 'employee_residence_RO', 'employee_residence_RS', 'employee_residence_RU', 'employee_residence_SE', 'employee_residence_SG', 'employee_residence_SI', 'employee_residence_SK', 'employee_residence_TH', 'employee_residence_TN', 'employee_residence_TR', 'employee_residence_UA', 'employee_residence_US', 'employee_residence_UZ', 'employee_residence_VN', 'company_location_AE', 'company_location_AL', 'company_location_AM', 'company_location_AR', 'company_location_AS', 'company_location_AT', 'company_location_AU', 'company_location_BA', 'company_location_BE', 'company_location_BO', 'company_location_BR', 'company_location_CA', 'company_location_CF', 'company_location_CH', 'company_location_CL', 'company_location_CO', 'company_location_CR', 'company_location_CZ', 'company_location_DE', 'company_location_DK', 'company_location_DZ', 'company_location_EE', 'company_location_EG', 'company_location_ES', 'company_location_FI', 'company_location_FR', 'company_location_GB', 'company_location_GH', 'company_location_GR', 'company_location_HN', 'company_location_HR', 'company_location_HU', 'company_location_ID', 'company_location_IE', 'company_location_IL', 'company_location_IN', 'company_location_IQ', 'company_location_IR', 'company_location_IT', 'company_location_JP', 'company_location_KE', 'company_location_LT', 'company_location_LU', 'company_location_LV', 'company_location_MA', 'company_location_MD', 'company_location_MK', 'company_location_MX', 'company_location_MY', 'company_location_NG', 'company_location_NL', 'company_location_NZ', 'company_location_PH', 'company_location_PK', 'company_location_PL', 'company_location_PR', 'company_location_PT', 'company_location_RO', 'company_location_RU', 'company_location_SE', 'company_location_SG', 'company_location_SI', 'company_location_SK', 'company_location_TH', 'company_location_TR', 'company_location_UA', 'company_location_US', 'company_location_VN', 'company_size_L', 'company_size_M', 'company_size_S']\n", "Столбцы val_data_encoded: ['work_year', 'job_title', 'salary', 'salary_currency', 'salary_in_usd', 'remote_ratio', 'experience_level_EN', 'experience_level_EX', 'experience_level_MI', 'experience_level_SE', 'employment_type_FL', 'employment_type_FT', 'employment_type_PT', 'employee_residence_AE', 'employee_residence_AR', 'employee_residence_AS', 'employee_residence_AT', 'employee_residence_AU', 'employee_residence_BE', 'employee_residence_BR', 'employee_residence_CA', 'employee_residence_CF', 'employee_residence_CH', 'employee_residence_CO', 'employee_residence_DE', 'employee_residence_DO', 'employee_residence_ES', 'employee_residence_FR', 'employee_residence_GB', 'employee_residence_GH', 'employee_residence_GR', 'employee_residence_HK', 'employee_residence_HR', 'employee_residence_IE', 'employee_residence_IN', 'employee_residence_IT', 'employee_residence_JE', 'employee_residence_JP', 'employee_residence_LV', 'employee_residence_MT', 'employee_residence_MX', 'employee_residence_NG', 'employee_residence_NL', 'employee_residence_PK', 'employee_residence_PR', 'employee_residence_PT', 'employee_residence_RU', 'employee_residence_TH', 'employee_residence_TR', 'employee_residence_UA', 'employee_residence_US', 'employee_residence_UZ', 'company_location_AE', 'company_location_AS', 'company_location_AT', 'company_location_AU', 'company_location_BE', 'company_location_BS', 'company_location_CA', 'company_location_CF', 'company_location_CH', 'company_location_CN', 'company_location_CO', 'company_location_DE', 'company_location_ES', 'company_location_FI', 'company_location_FR', 'company_location_GB', 'company_location_GH', 'company_location_GR', 'company_location_HK', 'company_location_HR', 'company_location_IE', 'company_location_IN', 'company_location_JP', 'company_location_LU', 'company_location_LV', 'company_location_MT', 'company_location_MX', 'company_location_NG', 'company_location_NL', 'company_location_PK', 'company_location_PR', 'company_location_PT', 'company_location_RU', 'company_location_SG', 'company_location_TH', 'company_location_TR', 'company_location_UA', 'company_location_US', 'company_size_L', 'company_size_M', 'company_size_S']\n", "Столбцы test_data_encoded: ['work_year', 'job_title', 'salary', 'salary_currency', 'salary_in_usd', 'remote_ratio', 'experience_level_EN', 'experience_level_EX', 'experience_level_MI', 'experience_level_SE', 'employment_type_FL', 'employment_type_FT', 'employment_type_PT', 'employee_residence_AE', 'employee_residence_AR', 'employee_residence_AS', 'employee_residence_AT', 'employee_residence_AU', 'employee_residence_BE', 'employee_residence_BR', 'employee_residence_CA', 'employee_residence_CF', 'employee_residence_CH', 'employee_residence_CO', 'employee_residence_DE', 'employee_residence_DO', 'employee_residence_ES', 'employee_residence_FR', 'employee_residence_GB', 'employee_residence_GH', 'employee_residence_GR', 'employee_residence_HK', 'employee_residence_HR', 'employee_residence_IE', 'employee_residence_IN', 'employee_residence_IT', 'employee_residence_JE', 'employee_residence_JP', 'employee_residence_LV', 'employee_residence_MT', 'employee_residence_MX', 'employee_residence_NG', 'employee_residence_NL', 'employee_residence_PK', 'employee_residence_PR', 'employee_residence_PT', 'employee_residence_RU', 'employee_residence_TH', 'employee_residence_TR', 'employee_residence_UA', 'employee_residence_US', 'employee_residence_UZ', 'company_location_AE', 'company_location_AS', 'company_location_AT', 'company_location_AU', 'company_location_BE', 'company_location_BS', 'company_location_CA', 'company_location_CF', 'company_location_CH', 'company_location_CN', 'company_location_CO', 'company_location_DE', 'company_location_ES', 'company_location_FI', 'company_location_FR', 'company_location_GB', 'company_location_GH', 'company_location_GR', 'company_location_HK', 'company_location_HR', 'company_location_IE', 'company_location_IN', 'company_location_JP', 'company_location_LU', 'company_location_LV', 'company_location_MT', 'company_location_MX', 'company_location_NG', 'company_location_NL', 'company_location_PK', 'company_location_PR', 'company_location_PT', 'company_location_RU', 'company_location_SG', 'company_location_TH', 'company_location_TR', 'company_location_UA', 'company_location_US', 'company_size_L', 'company_size_M', 'company_size_S']\n", "Столбцы train_data_encoded после дискретизации: ['work_year', 'job_title', 'salary', 'salary_currency', 'salary_in_usd', 'remote_ratio', 'salary_category', 'experience_level_EN', 'experience_level_EX', 'experience_level_MI', 'experience_level_SE', 'employment_type_CT', 'employment_type_FL', 'employment_type_FT', 'employment_type_PT', 'employee_residence_AE', 'employee_residence_AM', 'employee_residence_AR', 'employee_residence_AT', 'employee_residence_AU', 'employee_residence_BA', 'employee_residence_BE', 'employee_residence_BG', 'employee_residence_BO', 'employee_residence_BR', 'employee_residence_CA', 'employee_residence_CF', 'employee_residence_CH', 'employee_residence_CL', 'employee_residence_CN', 'employee_residence_CO', 'employee_residence_CR', 'employee_residence_CY', 'employee_residence_CZ', 'employee_residence_DE', 'employee_residence_DK', 'employee_residence_DZ', 'employee_residence_EE', 'employee_residence_EG', 'employee_residence_ES', 'employee_residence_FI', 'employee_residence_FR', 'employee_residence_GB', 'employee_residence_GH', 'employee_residence_GR', 'employee_residence_HK', 'employee_residence_HN', 'employee_residence_HR', 'employee_residence_HU', 'employee_residence_ID', 'employee_residence_IE', 'employee_residence_IL', 'employee_residence_IN', 'employee_residence_IQ', 'employee_residence_IR', 'employee_residence_IT', 'employee_residence_JP', 'employee_residence_KE', 'employee_residence_KW', 'employee_residence_LT', 'employee_residence_LU', 'employee_residence_LV', 'employee_residence_MA', 'employee_residence_MD', 'employee_residence_MK', 'employee_residence_MX', 'employee_residence_MY', 'employee_residence_NG', 'employee_residence_NL', 'employee_residence_NZ', 'employee_residence_PH', 'employee_residence_PK', 'employee_residence_PL', 'employee_residence_PR', 'employee_residence_PT', 'employee_residence_RO', 'employee_residence_RS', 'employee_residence_RU', 'employee_residence_SE', 'employee_residence_SG', 'employee_residence_SI', 'employee_residence_SK', 'employee_residence_TH', 'employee_residence_TN', 'employee_residence_TR', 'employee_residence_UA', 'employee_residence_US', 'employee_residence_UZ', 'employee_residence_VN', 'company_location_AE', 'company_location_AL', 'company_location_AM', 'company_location_AR', 'company_location_AS', 'company_location_AT', 'company_location_AU', 'company_location_BA', 'company_location_BE', 'company_location_BO', 'company_location_BR', 'company_location_CA', 'company_location_CF', 'company_location_CH', 'company_location_CL', 'company_location_CO', 'company_location_CR', 'company_location_CZ', 'company_location_DE', 'company_location_DK', 'company_location_DZ', 'company_location_EE', 'company_location_EG', 'company_location_ES', 'company_location_FI', 'company_location_FR', 'company_location_GB', 'company_location_GH', 'company_location_GR', 'company_location_HN', 'company_location_HR', 'company_location_HU', 'company_location_ID', 'company_location_IE', 'company_location_IL', 'company_location_IN', 'company_location_IQ', 'company_location_IR', 'company_location_IT', 'company_location_JP', 'company_location_KE', 'company_location_LT', 'company_location_LU', 'company_location_LV', 'company_location_MA', 'company_location_MD', 'company_location_MK', 'company_location_MX', 'company_location_MY', 'company_location_NG', 'company_location_NL', 'company_location_NZ', 'company_location_PH', 'company_location_PK', 'company_location_PL', 'company_location_PR', 'company_location_PT', 'company_location_RO', 'company_location_RU', 'company_location_SE', 'company_location_SG', 'company_location_SI', 'company_location_SK', 'company_location_TH', 'company_location_TR', 'company_location_UA', 'company_location_US', 'company_location_VN', 'company_size_L', 'company_size_M', 'company_size_S']\n", "Столбцы val_data_encoded после дискретизации: ['work_year', 'job_title', 'salary', 'salary_currency', 'salary_in_usd', 'remote_ratio', 'experience_level_EN', 'experience_level_EX', 'experience_level_MI', 'experience_level_SE', 'employment_type_FL', 'employment_type_FT', 'employment_type_PT', 'employee_residence_AE', 'employee_residence_AR', 'employee_residence_AS', 'employee_residence_AT', 'employee_residence_AU', 'employee_residence_BE', 'employee_residence_BR', 'employee_residence_CA', 'employee_residence_CF', 'employee_residence_CH', 'employee_residence_CO', 'employee_residence_DE', 'employee_residence_DO', 'employee_residence_ES', 'employee_residence_FR', 'employee_residence_GB', 'employee_residence_GH', 'employee_residence_GR', 'employee_residence_HK', 'employee_residence_HR', 'employee_residence_IE', 'employee_residence_IN', 'employee_residence_IT', 'employee_residence_JE', 'employee_residence_JP', 'employee_residence_LV', 'employee_residence_MT', 'employee_residence_MX', 'employee_residence_NG', 'employee_residence_NL', 'employee_residence_PK', 'employee_residence_PR', 'employee_residence_PT', 'employee_residence_RU', 'employee_residence_TH', 'employee_residence_TR', 'employee_residence_UA', 'employee_residence_US', 'employee_residence_UZ', 'company_location_AE', 'company_location_AS', 'company_location_AT', 'company_location_AU', 'company_location_BE', 'company_location_BS', 'company_location_CA', 'company_location_CF', 'company_location_CH', 'company_location_CN', 'company_location_CO', 'company_location_DE', 'company_location_ES', 'company_location_FI', 'company_location_FR', 'company_location_GB', 'company_location_GH', 'company_location_GR', 'company_location_HK', 'company_location_HR', 'company_location_IE', 'company_location_IN', 'company_location_JP', 'company_location_LU', 'company_location_LV', 'company_location_MT', 'company_location_MX', 'company_location_NG', 'company_location_NL', 'company_location_PK', 'company_location_PR', 'company_location_PT', 'company_location_RU', 'company_location_SG', 'company_location_TH', 'company_location_TR', 'company_location_UA', 'company_location_US', 'company_size_L', 'company_size_M', 'company_size_S', 'salary_category']\n", "Столбцы test_data_encoded после дискретизации: ['work_year', 'job_title', 'salary', 'salary_currency', 'salary_in_usd', 'remote_ratio', 'experience_level_EN', 'experience_level_EX', 'experience_level_MI', 'experience_level_SE', 'employment_type_FL', 'employment_type_FT', 'employment_type_PT', 'employee_residence_AE', 'employee_residence_AR', 'employee_residence_AS', 'employee_residence_AT', 'employee_residence_AU', 'employee_residence_BE', 'employee_residence_BR', 'employee_residence_CA', 'employee_residence_CF', 'employee_residence_CH', 'employee_residence_CO', 'employee_residence_DE', 'employee_residence_DO', 'employee_residence_ES', 'employee_residence_FR', 'employee_residence_GB', 'employee_residence_GH', 'employee_residence_GR', 'employee_residence_HK', 'employee_residence_HR', 'employee_residence_IE', 'employee_residence_IN', 'employee_residence_IT', 'employee_residence_JE', 'employee_residence_JP', 'employee_residence_LV', 'employee_residence_MT', 'employee_residence_MX', 'employee_residence_NG', 'employee_residence_NL', 'employee_residence_PK', 'employee_residence_PR', 'employee_residence_PT', 'employee_residence_RU', 'employee_residence_TH', 'employee_residence_TR', 'employee_residence_UA', 'employee_residence_US', 'employee_residence_UZ', 'company_location_AE', 'company_location_AS', 'company_location_AT', 'company_location_AU', 'company_location_BE', 'company_location_BS', 'company_location_CA', 'company_location_CF', 'company_location_CH', 'company_location_CN', 'company_location_CO', 'company_location_DE', 'company_location_ES', 'company_location_FI', 'company_location_FR', 'company_location_GB', 'company_location_GH', 'company_location_GR', 'company_location_HK', 'company_location_HR', 'company_location_IE', 'company_location_IN', 'company_location_JP', 'company_location_LU', 'company_location_LV', 'company_location_MT', 'company_location_MX', 'company_location_NG', 'company_location_NL', 'company_location_PK', 'company_location_PR', 'company_location_PT', 'company_location_RU', 'company_location_SG', 'company_location_TH', 'company_location_TR', 'company_location_UA', 'company_location_US', 'company_size_L', 'company_size_M', 'company_size_S', 'salary_category']\n" ] } ], "source": [ "# Конструирование признаков\n", "# Унитарное кодирование категориальных признаков (применение one-hot encoding)\n", "\n", "# Пример категориальных признаков\n", "categorical_features = ['experience_level', 'employment_type', 'employee_residence', 'company_location', 'company_size']\n", "\n", "# Применение one-hot encoding\n", "train_data_encoded = pd.get_dummies(train_data, columns=categorical_features)\n", "val_data_encoded = pd.get_dummies(val_data, columns=categorical_features)\n", "test_data_encoded = pd.get_dummies(test_data, columns=categorical_features)\n", "df_encoded = pd.get_dummies(df, columns=categorical_features)\n", "\n", "print(\"Столбцы train_data_encoded:\", train_data_encoded.columns.tolist())\n", "print(\"Столбцы val_data_encoded:\", val_data_encoded.columns.tolist())\n", "print(\"Столбцы test_data_encoded:\", test_data_encoded.columns.tolist())\n", "\n", "\n", "# Пример дискретизации признака 'salary_in_usd' на 5 категорий\n", "train_data_encoded['salary_category'] = pd.cut(train_data_encoded['salary_in_usd'], bins=5, labels=False)\n", "val_data_encoded['salary_category'] = pd.cut(val_data_encoded['salary_in_usd'], bins=5, labels=False)\n", "test_data_encoded['salary_category'] = pd.cut(test_data_encoded['salary_in_usd'], bins=5, labels=False)\n", "df_encoded['salary_category'] = pd.cut(df_encoded['salary_in_usd'], bins=5, labels=False)\n", "\n", "print(\"Столбцы train_data_encoded после дискретизации:\", train_data_encoded.columns.tolist())\n", "print(\"Столбцы val_data_encoded после дискретизации:\", val_data_encoded.columns.tolist())\n", "print(\"Столбцы test_data_encoded после дискретизации:\", test_data_encoded.columns.tolist())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ручной синтез\n", "Создание новых признаков на основе экспертных знаний и логики предметной области. К примеру, для данных о заработной плате в Data Science можно создать признак \"зарплата в месяц\"." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 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 Salary in month \n", "0 ES L 6666 \n", "1 US S 2500 \n", "2 US S 2125 \n", "3 CA M 14583 \n", "4 CA M 10000 \n" ] } ], "source": [ "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n", "# Создание нового признака 'Salary in month'\n", "df['Salary in month'] = df['salary'] // 12\n", "\n", "# Вывод первых нескольких строк датафрейма для проверки\n", "print(df.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "\n", "# Пример масштабирования числовых признаков\n", "numerical_features = ['work_year', 'salary']\n", "\n", "scaler = StandardScaler()\n", "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n", "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n", "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Конструирование признаков с применением фреймворка Featuretools" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " work_year experience_level employment_type job_title \\\n", "id \n", "1 2023 SE FT Principal Data Scientist \n", "2 2023 MI CT ML Engineer \n", "3 2023 MI CT ML Engineer \n", "4 2023 SE FT Data Scientist \n", "5 2023 SE FT Data Scientist \n", "\n", " salary salary_currency salary_in_usd employee_residence remote_ratio \\\n", "id \n", "1 80000 EUR 85847 ES 100 \n", "2 30000 USD 30000 US 100 \n", "3 25500 USD 25500 US 100 \n", "4 175000 USD 175000 CA 100 \n", "5 120000 USD 120000 CA 100 \n", "\n", " company_location company_size \n", "id \n", "1 ES L \n", "2 US S \n", "3 US S \n", "4 CA M \n", "5 CA M \n", " work_year experience_level employment_type job_title salary \\\n", "id \n", "2385 2022 SE FT Data Engineer 175000 \n", "941 2023 SE FT Analytics Engineer 150000 \n", "1617 2023 MI FT Data Analyst 65000 \n", "1443 2023 MI FT Data Analyst 61200 \n", "416 2023 SE FT Data Scientist 175000 \n", "\n", " salary_currency salary_in_usd employee_residence remote_ratio \\\n", "id \n", "2385 USD 175000 US 100 \n", "941 USD 150000 US 0 \n", "1617 GBP 78990 GB 0 \n", "1443 USD 61200 US 0 \n", "416 USD 175000 US 100 \n", "\n", " company_location company_size \n", "id \n", "2385 US M \n", "941 US M \n", "1617 GB M \n", "1443 US M \n", "416 US M \n", " work_year experience_level employment_type job_title salary \\\n", "id \n", "2321 2022 SE FT Analytics Engineer 116250 \n", "473 2023 EX FT Data Engineer 286000 \n", "2269 2022 EN FT Data Engineer 135000 \n", "430 2023 SE FT Data Analyst 208450 \n", "3574 2020 MI FT Data Engineer 88000 \n", "\n", " salary_currency salary_in_usd employee_residence remote_ratio \\\n", "id \n", "2321 USD 116250 US 100 \n", "473 USD 286000 US 100 \n", "2269 USD 135000 US 0 \n", "430 USD 208450 US 100 \n", "3574 GBP 112872 GB 50 \n", "\n", " company_location company_size \n", "id \n", "2321 US M \n", "473 US M \n", "2269 US M \n", "430 US M \n", "3574 GB L \n" ] } ], "source": [ "import pandas as pd\n", "import featuretools as ft\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Загрузка данных\n", "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n", "\n", "# Создание уникального идентификатора для каждой строки\n", "df['id'] = range(1, len(df) + 1)\n", "\n", "# Предобработка данных (например, кодирование категориальных признаков, удаление дубликатов)\n", "# Удаление дубликатов по всем столбцам\n", "df = df.drop_duplicates()\n", "\n", "# Создание EntitySet\n", "es = ft.EntitySet(id='data_science_jobs')\n", "\n", "# Добавление датафрейма с данными о рабочих местах\n", "es = es.add_dataframe(\n", " dataframe_name='jobs',\n", " dataframe=df,\n", " index='id'\n", ")\n", "\n", "# Генерация признаков с помощью глубокой синтезы признаков\n", "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='jobs', max_depth=1)\n", "\n", "# Выводим первые 5 строк сгенерированного набора признаков\n", "print(feature_matrix.head())\n", "\n", "# Разделение данных на обучающую и тестовую выборки\n", "train_data, test_data = train_test_split(df, test_size=0.3, random_state=42)\n", "\n", "# Разделение оставшейся части на валидационную и тестовую выборки\n", "val_data, test_data = train_test_split(test_data, test_size=0.5, random_state=42)\n", "\n", "# Преобразование признаков для контрольной и тестовой выборок\n", "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_data['id'])\n", "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data['id'])\n", "\n", "# Вывод первых 5 строк сгенерированных признаков для валидационной и тестовой выборок\n", "print(val_feature_matrix.head())\n", "print(test_feature_matrix.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Оценка качества каждого набора признаков \n", "\n", "*Предсказательная способность Метрики:* RMSE, MAE, R² \n", "\n", "*Методы:* Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках. \n", "\n", "*Скорость вычисления Методы:* Измерение времени выполнения генерации признаков и обучения модели. \n", "\n", "*Надежность Методы:* Кросс-валидация, анализ чувствительности модели к изменениям в данных. \n", "\n", "*Корреляция Методы:* Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков. \n", "\n", "*Цельность Методы:* Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Время обучения модели: 1.81 секунд\n", "Среднеквадратичная ошибка (RMSE): 49834.60\n", "Средняя абсолютная ошибка (MAE): 37776.22\n", "Коэффициент детерминации (R²): 0.37\n", "Кросс-валидация RMSE: 51653687796568.14 (± 37705548691705.71)\n", "Корреляционная матрица признаков:\n", " work_year remote_ratio experience_level_EX \\\n", "work_year 1.000000 -0.236430 0.003156 \n", "remote_ratio -0.236430 1.000000 0.007190 \n", "experience_level_EX 0.003156 0.007190 1.000000 \n", "experience_level_MI -0.128381 -0.000650 -0.092433 \n", "experience_level_SE 0.194923 -0.035201 -0.252152 \n", "... ... ... ... \n", "company_location_UA 0.005969 -0.005896 -0.005778 \n", "company_location_US 0.267002 -0.077706 0.022562 \n", "company_location_VN 0.014787 -0.015545 -0.002888 \n", "company_size_M 0.421975 -0.154550 -0.003061 \n", "company_size_S -0.257948 0.108512 0.012020 \n", "\n", " experience_level_MI experience_level_SE \\\n", "work_year -0.128381 0.194923 \n", "remote_ratio -0.000650 -0.035201 \n", "experience_level_EX -0.092433 -0.252152 \n", "experience_level_MI 1.000000 -0.744400 \n", "experience_level_SE -0.744400 1.000000 \n", "... ... ... \n", "company_location_UA -0.017059 0.005553 \n", "company_location_US -0.255712 0.324686 \n", "company_location_VN -0.008526 -0.023258 \n", "company_size_M -0.097174 0.236746 \n", "company_size_S 0.060936 -0.163489 \n", "\n", " employment_type_FL employment_type_FT \\\n", "work_year -0.050350 0.116310 \n", "remote_ratio 0.025238 -0.068702 \n", "experience_level_EX -0.009144 0.001938 \n", "experience_level_MI 0.035964 -0.033295 \n", "experience_level_SE -0.040667 0.113486 \n", "... ... ... \n", "company_location_UA 0.156722 -0.079394 \n", "company_location_US -0.053906 0.082093 \n", "company_location_VN -0.000843 0.001628 \n", "company_size_M -0.047840 0.125424 \n", "company_size_S 0.095761 -0.173783 \n", "\n", " employment_type_PT job_title_AI Developer \\\n", "work_year -0.093825 0.027726 \n", "remote_ratio 0.041919 -0.016126 \n", "experience_level_EX -0.011933 -0.009591 \n", "experience_level_MI -0.006230 -0.004301 \n", "experience_level_SE -0.096100 -0.045802 \n", "... ... ... \n", "company_location_UA -0.002202 0.300345 \n", "company_location_US -0.078434 -0.099216 \n", "company_location_VN -0.001101 -0.000885 \n", "company_size_M -0.100277 -0.043467 \n", "company_size_S 0.108664 0.064994 \n", "\n", " job_title_AI Programmer ... company_location_SG \\\n", "work_year 0.004219 ... -0.021620 \n", "remote_ratio 0.001772 ... 0.016794 \n", "experience_level_EX -0.004085 ... -0.007079 \n", "experience_level_MI -0.012059 ... 0.044089 \n", "experience_level_SE -0.032896 ... -0.042828 \n", "... ... ... ... \n", "company_location_UA -0.000754 ... -0.001306 \n", "company_location_US -0.047600 ... -0.082490 \n", "company_location_VN -0.000377 ... -0.000653 \n", "company_size_M -0.021372 ... -0.055210 \n", "company_size_S -0.004676 ... -0.008104 \n", "\n", " company_location_SI company_location_SK \\\n", "work_year -0.017648 -0.008821 \n", "remote_ratio 0.027712 0.018050 \n", "experience_level_EX -0.005778 -0.002888 \n", "experience_level_MI 0.042620 -0.008526 \n", "experience_level_SE -0.029172 0.011453 \n", "... ... ... \n", "company_location_UA -0.001066 -0.000533 \n", "company_location_US -0.067335 -0.033654 \n", "company_location_VN -0.000533 -0.000266 \n", "company_size_M -0.030233 -0.037352 \n", "company_size_S -0.006615 0.080574 \n", "\n", " company_location_TH company_location_TR \\\n", "work_year -0.001648 -0.051424 \n", "remote_ratio 0.011871 -0.004714 \n", "experience_level_EX -0.005003 -0.006461 \n", "experience_level_MI 0.008196 0.052106 \n", "experience_level_SE -0.020249 -0.036503 \n", "... ... ... \n", "company_location_UA -0.000923 -0.001192 \n", "company_location_US -0.058306 -0.075293 \n", "company_location_VN -0.000462 -0.000596 \n", "company_size_M -0.039024 -0.003949 \n", "company_size_S -0.005728 -0.007397 \n", "\n", " company_location_UA company_location_US \\\n", "work_year 0.005969 0.267002 \n", "remote_ratio -0.005896 -0.077706 \n", "experience_level_EX -0.005778 0.022562 \n", "experience_level_MI -0.017059 -0.255712 \n", "experience_level_SE 0.005553 0.324686 \n", "... ... ... \n", "company_location_UA 1.000000 -0.067335 \n", "company_location_US -0.067335 1.000000 \n", "company_location_VN -0.000533 -0.033654 \n", "company_size_M -0.030233 0.314961 \n", "company_size_S 0.035342 -0.229439 \n", "\n", " company_location_VN company_size_M company_size_S \n", "work_year 0.014787 0.421975 -0.257948 \n", "remote_ratio -0.015545 -0.154550 0.108512 \n", "experience_level_EX -0.002888 -0.003061 0.012020 \n", "experience_level_MI -0.008526 -0.097174 0.060936 \n", "experience_level_SE -0.023258 0.236746 -0.163489 \n", "... ... ... ... \n", "company_location_UA -0.000533 -0.030233 0.035342 \n", "company_location_US -0.033654 0.314961 -0.229439 \n", "company_location_VN 1.000000 -0.037352 -0.003306 \n", "company_size_M -0.037352 1.000000 -0.463577 \n", "company_size_S -0.003306 -0.463577 1.000000 \n", "\n", "[250 rows x 250 columns]\n", "Коэффициенты модели:\n", " Feature Coefficient\n", "0 work_year 3996.696898\n", "1 remote_ratio 5.199270\n", "2 experience_level_EX 88740.552288\n", "3 experience_level_MI 20170.854874\n", "4 experience_level_SE 44093.474726\n", ".. ... ...\n", "245 company_location_UA -64984.628104\n", "246 company_location_US 40574.678578\n", "247 company_location_VN 24478.024917\n", "248 company_size_M -2895.244061\n", "249 company_size_S -23506.811439\n", "\n", "[250 rows x 2 columns]\n" ] } ], "source": [ "import time\n", "import pandas as pd\n", "from sklearn.model_selection import train_test_split, cross_val_score\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", "\n", "# Загрузка данных\n", "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n", "\n", "# Разделение данных на признаки и целевую переменную\n", "X = df.drop(['salary_in_usd', 'salary', 'salary_currency'], axis=1) # Удаляем целевую переменную и ненужные столбцы\n", "y = df['salary_in_usd']\n", "\n", "# One-hot encoding для категориальных переменных\n", "X = pd.get_dummies(X, drop_first=True)\n", "\n", "# Проверяем, есть ли пропущенные значения, и заполняем их медианой или другим подходящим значением\n", "X.fillna(X.median(), inplace=True)\n", "\n", "# Разделение данных на обучающую и валидационную выборки\n", "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "# Обучение модели\n", "model = LinearRegression()\n", "\n", "# Начинаем отсчет времени\n", "start_time = time.time()\n", "model.fit(X_train, y_train)\n", "\n", "# Время обучения модели\n", "train_time = time.time() - start_time\n", "\n", "# Предсказания и оценка модели\n", "predictions = model.predict(X_val)\n", "mse = mean_squared_error(y_val, predictions)\n", "mae = mean_absolute_error(y_val, predictions)\n", "r2 = r2_score(y_val, predictions)\n", "\n", "print(f'Время обучения модели: {train_time:.2f} секунд')\n", "print(f'Среднеквадратичная ошибка (RMSE): {mse**0.5:.2f}')\n", "print(f'Средняя абсолютная ошибка (MAE): {mae:.2f}')\n", "print(f'Коэффициент детерминации (R²): {r2:.2f}')\n", "\n", "# Кросс-валидация\n", "cv_scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')\n", "cv_rmse_scores = (-cv_scores)**0.5\n", "print(f'Кросс-валидация RMSE: {cv_rmse_scores.mean():.2f} (± {cv_rmse_scores.std():.2f})')\n", "\n", "# Анализ корреляции\n", "correlation_matrix = X.corr()\n", "print(\"Корреляционная матрица признаков:\")\n", "print(correlation_matrix)\n", "\n", "# Цельность: Проверка логической связи между признаками и целевой переменной\n", "# В данном случае, мы можем проанализировать коэффициенты модели\n", "coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_})\n", "print(\"Коэффициенты модели:\")\n", "print(coefficients)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " pd.to_datetime(\n", "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "RMSE: 8277.602700993119\n", "R²: 0.9826437806135544\n", "MAE: 1270.2934354194408 \n", "\n", "Кросс-валидация RMSE: 13606.980806552549 \n", "\n", "Train RMSE: 4839.006207438376\n", "Train R²: 0.9941174224388726\n", "Train MAE: 664.4994041278297\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", " warnings.warn(\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", "from sklearn.model_selection import train_test_split, cross_val_score\n", "\n", "# Загрузка данных\n", "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n", "\n", "# Создание уникального идентификатора для каждой строки\n", "df['id'] = range(1, len(df) + 1)\n", "\n", "# Предобработка данных (например, кодирование категориальных признаков, удаление дубликатов)\n", "# Удаление дубликатов по всем столбцам\n", "df = df.drop_duplicates()\n", "\n", "# Создание EntitySet\n", "es = ft.EntitySet(id='data_science_jobs')\n", "\n", "# Добавление датафрейма с данными о рабочих местах\n", "es = es.add_dataframe(\n", " dataframe_name='jobs',\n", " dataframe=df,\n", " index='id'\n", ")\n", "\n", "# Генерация признаков с помощью глубокой синтезы признаков\n", "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='jobs', max_depth=1)\n", "\n", "# Удаление строк с NaN\n", "feature_matrix = feature_matrix.dropna()\n", "\n", "# Разделение данных на обучающую и тестовую выборки\n", "X_train = feature_matrix.drop('salary_in_usd', axis=1)\n", "y_train = feature_matrix['salary_in_usd']\n", "\n", "# Кодирования категориальных переменных с использованием одноразового кодирования\n", "X_train = pd.get_dummies(X_train, drop_first=True)\n", "\n", "# Разобьём тренировочный тест и примерку модели\n", "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n", "\n", "# Выбор модели\n", "model = RandomForestRegressor(random_state=42)\n", "\n", "# Обучение модели\n", "model.fit(X_train, y_train)\n", "\n", "# Предсказание и оценка\n", "y_pred = model.predict(X_test)\n", "\n", "rmse = mean_squared_error(y_test, y_pred, squared=False)\n", "r2 = r2_score(y_test, y_pred)\n", "mae = mean_absolute_error(y_test, y_pred)\n", "\n", "print(f\"RMSE: {rmse}\")\n", "print(f\"R²: {r2}\")\n", "print(f\"MAE: {mae} \\n\")\n", "\n", "# Кросс-валидация\n", "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n", "rmse_cv = (-scores.mean())**0.5\n", "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n", "\n", "# Анализ важности признаков\n", "feature_importances = model.feature_importances_\n", "feature_names = X_train.columns\n", "\n", "# Проверка на переобучение\n", "y_train_pred = model.predict(X_train)\n", "\n", "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n", "r2_train = r2_score(y_train, y_train_pred)\n", "mae_train = mean_absolute_error(y_train, y_train_pred)\n", "\n", "print(f\"Train RMSE: {rmse_train}\")\n", "print(f\"Train R²: {r2_train}\")\n", "print(f\"Train MAE: {mae_train}\")\n", "print()\n", "\n", "# Визуализация результатов\n", "plt.figure(figsize=(10, 6))\n", "plt.scatter(y_test, y_pred, alpha=0.5)\n", "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n", "plt.xlabel('Фактическая зарплата (USD)')\n", "plt.ylabel('Прогнозируемая зарплата (USD)')\n", "plt.title('Фактическая зарплата по сравнению с прогнозируемой')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Выводы и итог\n", "Модель случайного леса (RandomForestRegressor) показала удовлетворительные результаты при прогнозировании зарплат в области Data Science. Метрики качества и кросс-валидация позволяют предположить, что модель не сильно переобучена и может быть использована для практических целей.\n", "\n", "Точность предсказаний: Модель показывает довольно высокий R² (0.8029), что указывает на хорошее объяснение вариации зарплат. Однако, значения RMSE и MAE довольно высоки, что говорит о том, что модель не очень точно предсказывает зарплаты, особенно для высоких значений.\n", "\n", "Переобучение: Разница между RMSE на обучающей и тестовой выборках не очень большая, что указывает на то, что переобучение не является критическим. Однако, стоит быть осторожным и продолжать мониторинг этого показателя.\n", "\n", "Кросс-валидация: Значение RMSE после кросс-валидации немного выше, чем на тестовой выборке, что может указывать на некоторую нестабильность модели.\n", "\n", "Рекомендации: Следует уделить внимание дополнительной обработке категориальных признаков, улучшению метода feature engineering, а также возможной оптимизации модели (например, через подбор гиперпараметров) для повышения точности предсказаний на экстремальных значениях. Также стоит рассмотреть возможность использования других моделей, таких как градиентный бустинг или нейронные сети, для сравнения результатов и выбора наиболее эффективной модели." ] } ], "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 }