diff --git a/lab_3/Lab3.ipynb b/lab_3/Lab3.ipynb
index e37d516..6ad94cc 100644
--- a/lab_3/Lab3.ipynb
+++ b/lab_3/Lab3.ipynb
@@ -4,77 +4,159 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Приступаем к работе...\n",
+    "*Вариант задания:* Заработная плата рабочих мест в области Data Science (вариант - 8)  \n",
     "\n",
-    "*Вариант задания:* Продажи домов в округе Кинг (вариант - 6)  \n",
-    "Определим бизнес-цели и цели технического проекта  \n",
+    "Бизнес-цели для датасета о заработной плате в Data Science:\n",
+    "Оптимизация стратегии найма и оплаты труда в Data Science\n",
     "\n",
-    "### Бизнес-цели:  \n",
-    "1. Оптимизация процесса оценки стоимости дома  \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",
-    "**Формулировка:** Разработать модель, которая позволяет автоматически и точно оценивать стоимость дома на основании его характеристик (таких как площадь, количество комнат, состояние, местоположение).  \n",
-    "**Цель:** Увеличить точность оценки стоимости недвижимости для агенств и потенциальных покупателей, а также сократить время и затраты на оценку недвижимости, обеспечивая более точное предсказание цены.  \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",
-    "*Точность модели прогнозирования* (RMSE): Минимизация среднеквадратичной ошибки до уровня ниже 10% от реальной цены, чтобы учитывать большие отклонения оценке.\n",
-    "*Средная абсолютная ошибка* (MAE): Модель должна предсказать цену с минимальной ошибкой и снизить MAE до 5% или меньше учитывая большие отклонения в оценке.  \n",
-    "*Скорость оценки:* Уменьшение времени на оценку стоимости дома, чтобы быстрее получать результат.\n",
-    "*Доступность:* Внедрение модели в реальную систему для использования агентами недвижимости.\n",
+    "Ключевые показатели успеха (KPI):\n",
     "\n",
-    "2. Оптимизация затрат на ремонт перед продажей  \n",
+    "Точность модели прогнозирования зарплаты (RMSE): Минимизация среднеквадратичной ошибки до уровня ниже 10% от реальной зарплаты, чтобы учитывать большие отклонения в оценке.\n",
     "\n",
-    "**Формулировка:** Разработать модель, которая поможет продавцам домов и агентствам недвижимости определить, какие улучшения или реновации дадут наибольший прирост стоимости дома при минимальных затратах. Это поможет избежать ненужных расходов и максимизировать прибыль от продажи.  \n",
-    "**Цель:** Снизить затраты на ремонт перед продажей, рекомендовать только те улучшения, которые максимально увеличат стоимость недвижимости, и сократить время на принятие решений по реновациям.  \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",
-    "**Ключевые показатели успеха (KPI):**  \n",
-    "*Возврат инвестиций* (ROI): Продавцы должны получать не менее 20% прироста стоимости дома на каждый вложенный доллар в реновацию. Например, если на ремонт было потрачено $10,000, цена дома должна увеличиться как минимум на $12,000.  \n",
-    "*Средняя стоимость ремонта на 1 сделку* (CPA): Задача снизить расходы на ремонт, минимизировав ненужные траты. Например, оптимизация затрат до $5,000 на дом с учетом максимального прироста в цене.  \n",
-    "*Сокращение времени на принятие решений:* Модель должна сокращать время, необходимое на оценку вариантов реноваций, до нескольких минут, что ускорит подготовку дома к продажи.\n",
+    "Скорость оценки зарплаты: Уменьшение времени на оценку зарплаты для новых сотрудников, чтобы быстрее принимать решения о найме.\n",
     "\n",
-    "### Технические цели проекта для каждой выделенной бизнес-цели\n",
+    "Доступность: Внедрение модели в систему управления персоналом для использования HR-специалистами.\n",
     "\n",
-    "1. **Создание модели для точной оценки стоимости дома.**  \n",
-    "*Сбор и подготовка данных:* Очистка данных от пропусков, выбросов, дубликатов (аномальных значений в столбцах price, sqft_living, bedrooms). Преобразование категориальных переменных (view, condition, waterfront) в числовую форму с применением One-Hot-Encoding. Нормализация и стандартизация с применением методов масштабирования данных (нормировка, стандартизация для числовых признаков, чтобы привести их к 1ому масштабу). Разбиение набора данных на обучающую, контрольную и тестовую выборки для предотвращения утечек данных и переобучения.  \n",
-    "*Разработка и обучение модели:* Исследование моделей машинного обучения, проводя эксперименты с различными алгоритмами (линейная регрессия, случайный лес, градиентный бустинг, деревья решений) для предсказания стоимости недвижимости. Обучение модели на обучающей выборке с использованием метрик оценки качества, таких как RMSE (Root Mean Square Error) и MAE (Mean Absolute Error). Оценка качества моделей на тестовой выборке, минимизируя MAE и RMSE для получения точных прогнозов стоимости.  \n",
-    "*Развёртывание модели:* Интеграция модели в существующую систему или разработка API для доступа к модели с недвижимостью и частными продавцами. Создание веб-приложения или мобильного интерфейса для удобного использования модели и получения прогнозов в режиме реального времени.\n",
+    "Оптимизация распределения ресурсов в компании\n",
     "\n",
-    "2. **Разработка модели для рекомендаций по реновациям.**  \n",
-    "*Сбор и подготовка данных:* Сбор данных о типах и стоимости реноваций, а также их влияние на конечную стоимость дома. Очистка и устранение неточных или неполных данных о ремонтах. Преобразование категориальных признаков (реновации, например, обновление крыши, замена окон) в числовой формат для представления этих данных с применением One-Hot-Encoding. Разбиение данных на обучающую и тестовую выборки для обучения модели.  \n",
-    "*Разработка и обучение модели:* Использование модели регрессий (линейная регрессия, случайный лес) для предсказания и моделирования влияния конкретных реноваций на увеличение стоимости недвижимости. Оценка метрики (CPA - Cost Per Acquisition) оценка затрат на реновацию одной продажи и (ROI - Return on Investment) расчёт возврата на инвестиции от реновации дома, прирост стоимости после реновации. Обучение модели с целью прогнозирования изменений, которые могут принести наибольшую пользу для стоимости домов и реноваций.  \n",
-    "*Развёртывание модели:* Создание интерфейса, где пользователи смогут вводить информацию о текущем состоянии дома и получать рекомендации по реновациям с расчётом ROI. Создать рекомендационную систему для продавцов недвижимости, которая будет предлагать набор реноваций.\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": 23,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
-      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
-      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
-      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
+      "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",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.ticker as ticker\n",
-    "import seaborn as sns\n",
-    "\n",
-    "# Подключим датафрейм и выгрузим данные\n",
-    "df = pd.read_csv(\".//static//csv//kc_house_data.csv\")\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
     "print(df.columns)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -98,188 +180,118 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>date</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>...</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>zipcode</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>long</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</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>7129300520</td>\n",
-       "      <td>20141013T000000</td>\n",
-       "      <td>221900.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>5650</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1180</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1955</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98178</td>\n",
-       "      <td>47.5112</td>\n",
-       "      <td>-122.257</td>\n",
-       "      <td>1340</td>\n",
-       "      <td>5650</td>\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>6414100192</td>\n",
-       "      <td>20141209T000000</td>\n",
-       "      <td>538000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.25</td>\n",
-       "      <td>2570</td>\n",
-       "      <td>7242</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2170</td>\n",
-       "      <td>400</td>\n",
-       "      <td>1951</td>\n",
-       "      <td>1991</td>\n",
-       "      <td>98125</td>\n",
-       "      <td>47.7210</td>\n",
-       "      <td>-122.319</td>\n",
-       "      <td>1690</td>\n",
-       "      <td>7639</td>\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>5631500400</td>\n",
-       "      <td>20150225T000000</td>\n",
-       "      <td>180000.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>770</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6</td>\n",
-       "      <td>770</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1933</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98028</td>\n",
-       "      <td>47.7379</td>\n",
-       "      <td>-122.233</td>\n",
-       "      <td>2720</td>\n",
-       "      <td>8062</td>\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>2487200875</td>\n",
-       "      <td>20141209T000000</td>\n",
-       "      <td>604000.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1960</td>\n",
-       "      <td>5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1050</td>\n",
-       "      <td>910</td>\n",
-       "      <td>1965</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98136</td>\n",
-       "      <td>47.5208</td>\n",
-       "      <td>-122.393</td>\n",
-       "      <td>1360</td>\n",
-       "      <td>5000</td>\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>1954400510</td>\n",
-       "      <td>20150218T000000</td>\n",
-       "      <td>510000.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>8080</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>1680</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1987</td>\n",
-       "      <td>0</td>\n",
-       "      <td>98074</td>\n",
-       "      <td>47.6168</td>\n",
-       "      <td>-122.045</td>\n",
-       "      <td>1800</td>\n",
-       "      <td>7503</td>\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",
-       "<p>5 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
-       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
-       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
-       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
-       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
-       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
+       "   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",
-       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
-       "0      5650     1.0           0     0  ...      7        1180              0   \n",
-       "1      7242     2.0           0     0  ...      7        2170            400   \n",
-       "2     10000     1.0           0     0  ...      6         770              0   \n",
-       "3      5000     1.0           0     0  ...      7        1050            910   \n",
-       "4      8080     1.0           0     0  ...      8        1680              0   \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",
-       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
-       "0      1955             0    98178  47.5112 -122.257           1340   \n",
-       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
-       "2      1933             0    98028  47.7379 -122.233           2720   \n",
-       "3      1965             0    98136  47.5208 -122.393           1360   \n",
-       "4      1987             0    98074  47.6168 -122.045           1800   \n",
-       "\n",
-       "   sqft_lot15  \n",
-       "0        5650  \n",
-       "1        7639  \n",
-       "2        8062  \n",
-       "3        5000  \n",
-       "4        7503  \n",
-       "\n",
-       "[5 rows x 21 columns]"
+       "  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": 24,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -315,260 +327,86 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>price</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>sqft_living</th>\n",
-       "      <th>sqft_lot</th>\n",
-       "      <th>floors</th>\n",
-       "      <th>waterfront</th>\n",
-       "      <th>view</th>\n",
-       "      <th>condition</th>\n",
-       "      <th>grade</th>\n",
-       "      <th>sqft_above</th>\n",
-       "      <th>sqft_basement</th>\n",
-       "      <th>yr_built</th>\n",
-       "      <th>yr_renovated</th>\n",
-       "      <th>zipcode</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>long</th>\n",
-       "      <th>sqft_living15</th>\n",
-       "      <th>sqft_lot15</th>\n",
+       "      <th>work_year</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <th>remote_ratio</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>2.161300e+04</td>\n",
-       "      <td>2.161300e+04</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>2.161300e+04</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
-       "      <td>21613.000000</td>\n",
+       "      <td>3755.000000</td>\n",
+       "      <td>3.755000e+03</td>\n",
+       "      <td>3755.000000</td>\n",
+       "      <td>3755.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>4.580302e+09</td>\n",
-       "      <td>5.400881e+05</td>\n",
-       "      <td>3.370842</td>\n",
-       "      <td>2.114757</td>\n",
-       "      <td>2079.899736</td>\n",
-       "      <td>1.510697e+04</td>\n",
-       "      <td>1.494309</td>\n",
-       "      <td>0.007542</td>\n",
-       "      <td>0.234303</td>\n",
-       "      <td>3.409430</td>\n",
-       "      <td>7.656873</td>\n",
-       "      <td>1788.390691</td>\n",
-       "      <td>291.509045</td>\n",
-       "      <td>1971.005136</td>\n",
-       "      <td>84.402258</td>\n",
-       "      <td>98077.939805</td>\n",
-       "      <td>47.560053</td>\n",
-       "      <td>-122.213896</td>\n",
-       "      <td>1986.552492</td>\n",
-       "      <td>12768.455652</td>\n",
+       "      <td>2022.373635</td>\n",
+       "      <td>1.906956e+05</td>\n",
+       "      <td>137570.389880</td>\n",
+       "      <td>46.271638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>2.876566e+09</td>\n",
-       "      <td>3.671272e+05</td>\n",
-       "      <td>0.930062</td>\n",
-       "      <td>0.770163</td>\n",
-       "      <td>918.440897</td>\n",
-       "      <td>4.142051e+04</td>\n",
-       "      <td>0.539989</td>\n",
-       "      <td>0.086517</td>\n",
-       "      <td>0.766318</td>\n",
-       "      <td>0.650743</td>\n",
-       "      <td>1.175459</td>\n",
-       "      <td>828.090978</td>\n",
-       "      <td>442.575043</td>\n",
-       "      <td>29.373411</td>\n",
-       "      <td>401.679240</td>\n",
-       "      <td>53.505026</td>\n",
-       "      <td>0.138564</td>\n",
-       "      <td>0.140828</td>\n",
-       "      <td>685.391304</td>\n",
-       "      <td>27304.179631</td>\n",
+       "      <td>0.691448</td>\n",
+       "      <td>6.716765e+05</td>\n",
+       "      <td>63055.625278</td>\n",
+       "      <td>48.589050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>1.000102e+06</td>\n",
-       "      <td>7.500000e+04</td>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>6.000000e+03</td>\n",
+       "      <td>5132.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>290.000000</td>\n",
-       "      <td>5.200000e+02</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>290.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1900.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98001.000000</td>\n",
-       "      <td>47.155900</td>\n",
-       "      <td>-122.519000</td>\n",
-       "      <td>399.000000</td>\n",
-       "      <td>651.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>2.123049e+09</td>\n",
-       "      <td>3.219500e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.750000</td>\n",
-       "      <td>1427.000000</td>\n",
-       "      <td>5.040000e+03</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>95000.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>1190.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1951.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98033.000000</td>\n",
-       "      <td>47.471000</td>\n",
-       "      <td>-122.328000</td>\n",
-       "      <td>1490.000000</td>\n",
-       "      <td>5100.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>3.904930e+09</td>\n",
-       "      <td>4.500000e+05</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>2.250000</td>\n",
-       "      <td>1910.000000</td>\n",
-       "      <td>7.618000e+03</td>\n",
-       "      <td>1.500000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.380000e+05</td>\n",
+       "      <td>135000.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>7.000000</td>\n",
-       "      <td>1560.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1975.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98065.000000</td>\n",
-       "      <td>47.571800</td>\n",
-       "      <td>-122.230000</td>\n",
-       "      <td>1840.000000</td>\n",
-       "      <td>7620.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>7.308900e+09</td>\n",
-       "      <td>6.450000e+05</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>2550.000000</td>\n",
-       "      <td>1.068800e+04</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>2210.000000</td>\n",
-       "      <td>560.000000</td>\n",
-       "      <td>1997.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>98118.000000</td>\n",
-       "      <td>47.678000</td>\n",
-       "      <td>-122.125000</td>\n",
-       "      <td>2360.000000</td>\n",
-       "      <td>10083.000000</td>\n",
+       "      <td>2023.000000</td>\n",
+       "      <td>1.800000e+05</td>\n",
+       "      <td>175000.000000</td>\n",
+       "      <td>100.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>9.900000e+09</td>\n",
-       "      <td>7.700000e+06</td>\n",
-       "      <td>33.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>13540.000000</td>\n",
-       "      <td>1.651359e+06</td>\n",
-       "      <td>3.500000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>13.000000</td>\n",
-       "      <td>9410.000000</td>\n",
-       "      <td>4820.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>2015.000000</td>\n",
-       "      <td>98199.000000</td>\n",
-       "      <td>47.777600</td>\n",
-       "      <td>-121.315000</td>\n",
-       "      <td>6210.000000</td>\n",
-       "      <td>871200.000000</td>\n",
+       "      <td>2023.000000</td>\n",
+       "      <td>3.040000e+07</td>\n",
+       "      <td>450000.000000</td>\n",
+       "      <td>100.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
-       "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
-       "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
-       "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
-       "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
-       "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
-       "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
-       "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
-       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
-       "\n",
-       "           sqft_lot        floors    waterfront          view     condition  \\\n",
-       "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
-       "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
-       "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
-       "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
-       "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
-       "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
-       "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
-       "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
-       "\n",
-       "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
-       "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
-       "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
-       "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
-       "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
-       "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
-       "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
-       "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
-       "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
-       "\n",
-       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
-       "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
-       "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
-       "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
-       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
-       "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
-       "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
-       "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
-       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
+       "         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": 25,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -580,65 +418,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "id               0\n",
-      "date             0\n",
-      "price            0\n",
-      "bedrooms         0\n",
-      "bathrooms        0\n",
-      "sqft_living      0\n",
-      "sqft_lot         0\n",
-      "floors           0\n",
-      "waterfront       0\n",
-      "view             0\n",
-      "condition        0\n",
-      "grade            0\n",
-      "sqft_above       0\n",
-      "sqft_basement    0\n",
-      "yr_built         0\n",
-      "yr_renovated     0\n",
-      "zipcode          0\n",
-      "lat              0\n",
-      "long             0\n",
-      "sqft_living15    0\n",
-      "sqft_lot15       0\n",
+      "work_year             0\n",
+      "experience_level      0\n",
+      "employment_type       0\n",
+      "job_title             0\n",
+      "salary                0\n",
+      "salary_currency       0\n",
+      "salary_in_usd         0\n",
+      "employee_residence    0\n",
+      "remote_ratio          0\n",
+      "company_location      0\n",
+      "company_size          0\n",
       "dtype: int64\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "id               False\n",
-       "date             False\n",
-       "price            False\n",
-       "bedrooms         False\n",
-       "bathrooms        False\n",
-       "sqft_living      False\n",
-       "sqft_lot         False\n",
-       "floors           False\n",
-       "waterfront       False\n",
-       "view             False\n",
-       "condition        False\n",
-       "grade            False\n",
-       "sqft_above       False\n",
-       "sqft_basement    False\n",
-       "yr_built         False\n",
-       "yr_renovated     False\n",
-       "zipcode          False\n",
-       "lat              False\n",
-       "long             False\n",
-       "sqft_living15    False\n",
-       "sqft_lot15       False\n",
+       "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": 26,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -656,13 +474,6 @@
     "df.isnull().any()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ооо, пропущенных колонок нету :)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -672,16 +483,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Размер обучающей выборки:  17290\n",
-      "Размер контрольной выборки:  4323\n",
-      "Размер тестовой выборки:  4323\n"
+      "Размер обучающей выборки:  3004\n",
+      "Размер контрольной выборки:  751\n",
+      "Размер тестовой выборки:  751\n"
      ]
     }
    ],
@@ -701,12 +512,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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3A233zjvvYOLEiXjiiSfw1ltvISwsDCqVCmPHjq30X0CuZK/hvffeQ8eOHcvdp3QAMZlMuHz5Mvr27Xvd40qShF9++QVqtfqaxwSA5ORkAEB0dPQ1jxkZGYlly5aVe/vVwaBr1654++23HbZ99NFH+O6778q9/5EjR7BkyRJ89dVX5f5nZrVa0a5dO8yZM6fc+9evX7/C2qvKarWib9++eOWVV8q9vXnz5g4/jxo1CkOHDnXYVtEU40mTJqFHjx4oLi7G7t27MW3aNGRnZ5f71++19OvXDy+//DIA4MKFC5g5cyZuv/127Nq1y+Ev9uqo6v0fffRRjBkzBhcuXIDRaMS2bdvw0UcfOezz+uuv46+//rrmLEmLxYK+ffsiMzMTr776Klq2bAl/f39cvHgRjz32WLmf2fXr1yMpKQmTJk2qUs3VUdXPFWD7zE6cOBFPPvlkmffN9bRv3x7vv/8+AMjjanr37o09e/Y4fFbvuecejB49GkIInD59GtOmTcOgQYPKXRH5Rt8bVVFRq/iOHTvw9NNPw9/fH2+//TaGDh3qEKqq8zzXRgw7JFu9ejVuv/12+a8eu+zsbIem4CZNmmD79u0oLi526l8NV/9nI4TAiRMn0L59e/m8ABAUFISEhITrHm///v0oLi6+ZsCzH1cIgcaNG1fqP9jDhw9DkqRy/4orfcwNGzage/fulfoPMyIiosxjutYg4gkTJqBjx4548MEHKzz//v370adPnxvqWqyMJk2aID8/v1KvCWD7i/bqfUv/xVtau3bt5H0HDBiAc+fOYenSpVW+BENMTIzDOVu0aIFu3bph7dq1FQaKhg0bArDNtrrpppvk7SaTCadPn5aP17hxY3m/q/3777/w9/d3+PwMHz4c48ePx9dff42ioiL4+PiUeR0jIiKQlJSEw4cPy+F6//79eOmll+R9Dhw4gGPHjmHp0qV49NFH5e3r16+v8HlISEhAcHAwJk2aVGG9zloor6qfK8A28y01NbXc2ZLXExoa6vAa9+7dG7GxsVi8eLHDpIh69eo57BcQEIARI0aU2+1X+rW1dwHblX6uSr9Xrvbvv/8iIiKizHv8+PHj8vEB4MSJE7BarWWe/759+2LhwoUwGAxYu3YtRo0aJc/MA6r3PNdGHLNDMrVaDSGEw7ZVq1bh4sWLDtuGDBmC9PT0Mn+NAihz/6r48ssvkZeXJ/+8evVqXL58GQMGDAAAxMXFoUmTJpg9ezby8/PL3D8tLa1M7Wq1utxp3aXdf//9UKvVmDp1apn6hRDyjAzANvXzm2++QZcuXa75F9OwYcNgsVjw1ltvlbnNbDbf0BiUpKQkfPfdd3j33XcrDDLDhg3DxYsX8dlnn5W5raioCAUFBdU+f3nnSkpKwq+//lrmtuzsbKdeG8pqtUKlUt1wgLNPzb7WNPyEhARotVrMmzfP4X3x+eefIycnR17Zt06dOujcuTOWLl3q0JV08uRJfP/99xgwYIDDX9wREREYMGAAvvrqKyxbtgx33nlnubOfVCoV2rZti4SEBCQkJJRZidl+zNK1CSHw4YcfXvOxd+zYEVFRUfjss89QWFgob//jjz+wa9eu635eKqsqnyvANj5r+vTpGDdu3DVbTSurMq8xcKXFuLxWkU6dOiE6OhqLFi1yOM7Vz1VMTAw6duyIpUuXOny2Dx48iN9++63MjFTANqygtPnz5wOA/P+dXbdu3aBWq+Hv749FixZh69atDp/rqj7PtRVbdkg2aNAgTJs2DY8//ji6deuGAwcOYNmyZQ5/1QK2Zvgvv/wS48ePx44dO9CjRw8UFBRgw4YNeO6558oMoKussLAw3HbbbXj88ceRkpKCuXPnomnTpnIXh0qlwn//+18MGDAAbdq0weOPP466devi4sWL2LRpE4KCgvDDDz+goKAACxYswLx589C8eXOH9UDsIemff/5BUlIS4uPj0aRJE7z99tuYMGECzpw5g3vvvReBgYE4ffo01qxZg1GjRuGll17Chg0bMHHiRPzzzz/44YcfrvlYevXqhWeeeQYzZszAvn370K9fP/j4+OD48eNYtWoVPvzwQzzwwAPVep5+++039O3b95otKY888ghWrlyJ//f//h82bdqE7t27w2Kx4N9//8XKlSvx66+/XrfFKz8/H+vWrXPYZv/LdcuWLfDx8UHdunXx8ssv4/vvv8egQYPw2GOPIS4uDgUFBThw4ABWr16NM2fOVHsq8759+xAQEACz2Yzdu3fjyy+/xD333FPuL6ZrOXXqFL766isAtoHdH330EYKCgq45SLlOnTqYMGECpk6dijvvvBN33303jh49io8//hi33HKLw4DiWbNmoV+/foiPj8dTTz0lTz339fXF9OnTyxz70UcflV//8gJxZbRs2RJNmjTBSy+9hIsXLyIoKAjffPNNmbE7V/Px8cHMmTPx2GOPoXv37hg5ciQyMzPx4Ycfom7dumWmfFssFof3wb59+wDYuldKD6K3WCy4ePEiduzYgS5dulT6c2W3Z88eREREVNgdej0pKSnya5yeno5PPvkEGo2mTHg7d+4c1q1bJ3djTZ8+HQ0bNkSnTp3KtC5rNBrMmjULjz76KHr06IERI0bIXWT16tVzeK7ee+89DBgwAPHx8XjyySflqefBwcHltlSdPn0ad999N+68804kJSXhq6++wn/+8x906NChwsfYv39/PPzww3jllVcwePBgxMTEVPl5rrXcOfWLlGGfPrlz585r7mcwGMSLL74oYmJihF6vF927dxdJSUnlTussLCwUb7zxhmjcuLHw8fER0dHR4oEHHhAnT54UQlRvmvHXX38tJkyYICIjI4VerxcDBw50mD5rt3fvXnH//feL8PBwodPpRMOGDcWwYcPExo0bHc59va+rpyN/88034rbbbhP+/v7C399ftGzZUiQmJoqjR48KIYR4/vnnRc+ePcW6devK1HT11HO7Tz/9VMTFxQm9Xi8CAwNFu3btxCuvvCIuXbok71PVqeeSJIndu3c7bC/vNTKZTGLmzJmiTZs2QqfTidDQUBEXFyemTp0qcnJyypzv6uNd7/lbvHixvH9eXp6YMGGCaNq0qdBqtSIiIkJ069ZNzJ49W5hMJiFE9d4T9i+NRiMaNmwoXnjhBZGVlSWEqNrU89LHioiIEP369RNJSUnXva8QtqnmLVu2FD4+PiIqKko8++yzcg2lbdy4UXTv3l3o9XoRFBQkBg4cKA4cOFDuMY1GowgNDRXBwcGiqKioUnWUN/X88OHDIiEhQQQEBIiIiAjx9NNPi/3795d5fcp7f65YsUJ07NhRfm88+OCD4syZMw77jBw5slKfpdJfV78Pr/e5EuLK++2DDz5wuG9Fn6urXf1+DQkJEd27dxc///yzw36l95EkSURHR4v7779fHDlyRAhRduq53cqVK0WnTp2ETqcTYWFh4qGHHir3/6YNGzY4vAcGDx4sDh8+XO5jOnz4sHjggQdEYGCgCA0NFaNHjy7zXsBVS00IIUR6erqoU6eOuO+++xy2V+Z5rs0kIW6g34HICTZv3ozbb78dq1atqnZrR2lnzpxB48aNcfr06QrHH0yZMgVnzpzBkiVLbvh8tVGjRo0wZcqUclfMpeszm82IjY3F4MGDy4yRq8mWLFmCJUuWlFldma6wL1SZlpbmtMUb6fo4ZoeIyM3Wrl2LtLQ0h4HFROQ6HLNDXsc+u+JaA4jbt28vX/6Cqq5Xr17yYpRUedu3b8c///yDt956C506dUKvXr2ULsmp6tatW+4lW4iUxrBDXiciIkIeqFiR+++/303VeKelS5cqXUKNtHDhQnz11Vfo2LGjV3ah9u3b97rrWhEpgWN2iIiIyKtxzA4RERF5NYYdIiIi8mocswPbCpqXLl1CYGCgy5fWJyIiIucQQiAvLw+xsbEOF7a+GsMOgEuXLjn1wohERETkPufPn0e9evUqvJ1hB0BgYCAA25MVFBSkcDVERERUGbm5uahfv778e7wiDDuA3HUVFBTEsENERFTDXG8ICgcoExERkVdj2CEiIiKvxrBDREREXo1hh4iIiLwaww4RERF5NYYdIiIi8moMO0REROTVGHaIiIjIqzHsEBERkVdj2CEiIiKvxrBDREREXo1hh4iIiLwaww4RERF5NYYdIiIi8moapQsgR0IIZGZmAgBCQ0ORlZUFAAgLC7vuJeyJiIioLIYdD5OZmYn3f9gNAHi8e2Ms/us0AODFwXEIDw9XsjQiIqIaiWHHA/kFBpf7PREREVUdx+wQERGRV2PYISIiIq/GsENERERejWGHiIiIvBrDDhEREXk1zsbyAKXX1hFCKFwNERGRd2HY8QBXr61DREREzsOw4yGut56OEAIZGRkAuJoyERFRVTDseCghRMmlIgQACVlZWVxNmYiIqBo4QNlDFeXnYuHPu2AoMsjb/AKDuaIyERFRFTHseDBf/0ClSyAiIqrxGHaIiIjIqzHsEBERkVdj2CEiIiKvxrBDREREXo1hh4iIiLwaww4RERF5NYYdIiIi8moMO0REROTVGHY8WJFFhTyjVekyiIiIajSGHQ9lEQJJOUH46WQRCkwWpcshIiKqsXghUA+VVihgFCpAAKcyTUqXQ0REVGOxZcdDXcoX8vcnMgzX2JOIiIiuhWHHQ10quDJWJ6PQgrNZRgCAEAKZmZkQQlR0VyIiIiqFYccD5RosyDUBEgQi/Wwv0caTeQCAovxczFm7DZmZmUqWSEREVGMw7Hig8zm2MTqhGjOah/sAADadzJVbc/QBQYrVRkREVNMw7Hgge9ipoy1GvUA1fFQSkvPNSMk3K1wZERFRzaNo2LFYLJg4cSIaN24MvV6PJk2a4K233nIYjyKEwKRJkxATEwO9Xo+EhAQcP37c4TiZmZkYMWIEgoKCEBISgieffBL5+fnufjhOUVRsxeXcYgBAHR8TNCoJdYNtrTupBcVKlkZERFQjKRp2Zs6ciYULF+Kjjz7CkSNHMHPmTMyaNQvz58+X95k1axbmzZuHRYsWYfv27fD390f//v1hMFyZoTRixAgcOnQI69evx48//oitW7di1KhRSjykG3Y83QCLAPw0QIDaNkg5xFcNAMgxcL0dIiKiqlJ0nZ2///4b99xzDwYOHAgAaNSoEb7++mvs2LEDgK1VZ+7cuXjzzTdxzz33AAC+/PJLREVFYe3atRg+fDiOHDmCdevWYefOnejcuTMAYP78+bjrrrswe/ZsxMbGKvPgqim7JND4+0iQJNu2YIewIylUGRERUc2kaMtOt27dsHHjRhw7dgwAsH//fvz5558YMGAAAOD06dNITk5GQkKCfJ/g4GB07doVSUlJAICkpCSEhITIQQcAEhISoFKpsH37djc+GufIM9rCjlZ9ZRtbdoiIiKpP0Zad1157Dbm5uWjZsiXUajUsFgumT5+OESNGAACSk5MBAFFRUQ73i4qKkm9LTk5GZGSkw+0ajQZhYWHyPlczGo0wGo3yz7m5uU57TDfKfi0sXamwE6y3/VBULFBs4fo6REREVaFoy87KlSuxbNkyLF++HHv27MHSpUsxe/ZsLF261KXnnTFjBoKDg+Wv+vXru/R8VSG37KiudFdp1SqElQSeXBPDDhERUVUoGnZefvllvPbaaxg+fDjatWuHRx55BOPGjcOMGTMAANHR0QCAlJQUh/ulpKTIt0VHRyM1NdXhdrPZjMzMTHmfq02YMAE5OTny1/nz55390KottyTslG7ZAYC6wVrb7bxMFhERUZUoGnYKCwuhUjmWoFarYbXaunIaN26M6OhobNy4Ub49NzcX27dvR3x8PAAgPj4e2dnZ2L17t7zP77//DqvViq5du5Z7Xp1Oh6CgIIcvT1HemB0AqC+HHbbsEBERVYWiY3YGDx6M6dOno0GDBmjTpg327t2LOXPm4IknngAASJKEsWPH4u2330azZs3QuHFjTJw4EbGxsbj33nsBAK1atcKdd96Jp59+GosWLUJxcTFGjx6N4cOH17iZWACQWzJmR6uWgFJrCNYrWWsnj2GHiIioShQNO/Pnz8fEiRPx3HPPITU1FbGxsXjmmWcwadIkeZ9XXnkFBQUFGDVqFLKzs3Hbbbdh3bp18PX1lfdZtmwZRo8ejT59+kClUmHIkCGYN2+eEg/phuUZyu/GYssOERFR9UiCl89Gbm4ugoODkZOTo0iXVkZGBhZuPgEA+PZQFjILLbizkQZSXirCY+rDV69H94YBeHz1Gagl4O76Zrz54G0IDw93e61ERESeorK/v3ltLA9itVorbNmJCfSBSgIsAijicjtERESVxrDjQfLzclFsG7JTZoCyWiUhsCQB5RVzFWUiIqLKYtjxICUTsaCSAE05eca+knI+rwdKRERUaQw7HsRUEna0agmSVDbt2K+RlWdmyw4REVFlMex4EFPJpSCuHq9jF6izvVxF5vJvJyIiorIYdjyIsVTLTnn8fGwvl8HClh0iIqLKYtjxIKaSwcm664Ydd1VERERU8zHseBCj3I1VQdjRXgk7Vi6PREREVCkMOx7kygDl8m/Xa2wvl4CE7EIO3CEiIqoMhh0Pcr2WHZVKQsmELKQX8PLnRERElcGw40Hklp3yFtkp4VtyNbP0Ai62Q0REVBkMOx7EPhuropYdANCXBKH0fLbsEBERVQbDjgexr7NT0dRzANCXtOykcRllIiKiSmHY8SBXpp5XvI/cssMxO0RERJXCsOMhhBCV7May/ZvOlh0iIqJKYdjxEGYrYC1ZOufa3Vhs2SEiIqoKhh0PYTTb+rAkCGiu8ar4ymN2GHaIiIgqg2HHQ9jX2NFKoswVz4UQyMrKAiDgJ7fsFENwFWUiIqLrYtjxEPaWHR+VtcxtRfm5WPjzLhiKDPKigsUWgZwijtshIiK6HoYdD2E021ppfKTyW2t8/QMBAGqVBK3Ktk9qntE9xREREdVgDDse4nphpzR7605KrsGVJREREXkFhh0PYZC7sSoTdkpadnLZskNERHQ9DDseothS9ZYddmMRERFdH8OOhzCXLLKjrlLYYTcWERHR9TDseAh72FGB3VhERETOxLDjIUqG7LBlh4iIyMkYdjzElW6s6+8rt+xwzA4REdF1Mex4CEs1xuykM+wQERFdF8OOh6jKmB1dSdgpMFlgKLa4siwiIqIaj2HHQ1wZs3P9fTUS4FOyYwavfk5ERHRNDDseoipTzyUJCPPzAQBk5LMri4iI6FoYdjyEHHYq0Y0FAKFy2GHLDhER0bUw7HiIqszGAoAwPw0AIJ0tO0RERNfEsOMhLCVjdlSV6MYCgFB9ScsOx+wQERFdE8OOBxBCVGnMDnClGyuTYYeIiOiaGHY8gNkq5JE66krexz5Amd1YRERE18aw4wEMxVb5+0q37PjbxuxwgDIREdG1Mex4AEPJIjtSyVdlXBmzw5YdIiKia2HY8QD2lh21yraGzvUIIaAxFwFgyw4REdH1MOx4AHvLjqaSzTqGgjz8uOcUANuigkJUruuLiIioNmLY8QBG85WWncoKCQoCAJgsAvlGsyvKIiIi8goMOx7AfjHPyi4oCAAalQQfVcn1sdiVRUREVCGGHQ8gd2OpqpB2APj62C8GykHKREREFWHY8QDyAOWqZR34amwvXzpbdoiIiCrEsKMwIQQysvMAVG3MDgDofWx34CrKREREFWPYUVhmZiZ+3HEMQOVnY9n5auxjdtiNRUREVBGGHQ+g0vkCqH7LDruxiIiIKsaw4wEsJcvkVL1lx/by8crnREREFWPY8QAWYUs56irOxtKXzMbK5GwsIiKiCjHseABLyXVAq92yw24sIiKiCjHseAB7NxbH7BARETkfw44HkLuxqtiyo9fYp54bYbXy+lhERETlYdjxAPIA5Sq+Gvap51YBZBcVO7kqIiIi78Cw4wGutOxUrWlHpZIQ5KsGwLV2iIiIKsKw4wGqO2YHAML8fAAAaQw7RERE5WLY8QDVXWcHuBJ2OEiZiIiofAw7HsDejVXVMTsAEOZvCzvsxiIiIiofw44HkLuxqtGyEy637DDsEBERlYdhxwNcGbNT9bRjb9lJz2M3FhERUXkYdjyA3I11Q2N22LJDRERUHoYdD3Ajs7HC/Rl2iIiIroVhR2EWq4AV1VtBWQgBjbkIAGdjERERVYRhR2FGs1X+vqqzsYryc/HNHwcA2Fp2hOAlI4iIiK7GsKMwQ/GVsFOd2VjBgQEAbKEp32h2VllEREReg2FHYQazBYAt6EhVvFwEYGsN8uPVz4mIiCrEsKMwe8tOdQYn24VxkDIREVGFGHYUZh+zo6lGq46dffr56UvpHLdDRER0FYYdhRnMN96yE2jLOli5/SQyMzOdUBUREZH3YNhRWFFJN5amGqsn24Xq1QAAi1rnlJqIiIi8ieJh5+LFi3j44YcRHh4OvV6Pdu3aYdeuXfLtQghMmjQJMTEx0Ov1SEhIwPHjxx2OkZmZiREjRiAoKAghISF48sknkZ+f7+6HUi32bqzqzMSyCykJO0XFVmRmZrIri4iIqBRFw05WVha6d+8OHx8f/PLLLzh8+DDef/99hIaGyvvMmjUL8+bNw6JFi7B9+3b4+/ujf//+MBgM8j4jRozAoUOHsH79evz444/YunUrRo0apcRDqjKD3LJT/WOE+moAAPlFJsxZu41dWURERKVolDz5zJkzUb9+fSxevFje1rhxY/l7IQTmzp2LN998E/fccw8A4Msvv0RUVBTWrl2L4cOH48iRI1i3bh127tyJzp07AwDmz5+Pu+66C7Nnz0ZsbKx7H1QVXRmzc+PdWAYLoA8IckpdRERE3kLRlp3vv/8enTt3xtChQxEZGYlOnTrhs88+k28/ffo0kpOTkZCQIG8LDg5G165dkZSUBABISkpCSEiIHHQAICEhASqVCtu3by/3vEajEbm5uQ5fSjEU29bZqc5FQO1C9bbMajCz+4qIiOhqioadU6dOYeHChWjWrBl+/fVXPPvss3jhhRewdOlSAEBycjIAICoqyuF+UVFR8m3JycmIjIx0uF2j0SAsLEze52ozZsxAcHCw/FW/fn1nP7RKM95gy44QAqpi2/gkg8VpZREREXkNRcOO1WrFzTffjHfeeQedOnXCqFGj8PTTT2PRokUuPe+ECROQk5Mjf50/f96l57uWGx2zYyjIw+ot+wEAZqvti4iIiK5QNOzExMSgdevWDttatWqFc+fOAQCio6MBACkpKQ77pKSkyLdFR0cjNTXV4Xaz2YzMzEx5n6vpdDoEBQU5fCnF4ITZWP7+AbA3DBkZdoiIiBwoGna6d++Oo0ePOmw7duwYGjZsCMA2WDk6OhobN26Ub8/NzcX27dsRHx8PAIiPj0d2djZ2794t7/P777/DarWia9eubngUN8bghHV2JAnQlwz6MbIri4iIyIGis7HGjRuHbt264Z133sGwYcOwY8cOfPrpp/j0008B2C6MOXbsWLz99tto1qwZGjdujIkTJyI2Nhb33nsvAFtL0J133il3fxUXF2P06NEYPny4x8/EAkqP2bmx4/hqJBQUCxgsN9BERERE5IUUDTu33HIL1qxZgwkTJmDatGlo3Lgx5s6dixEjRsj7vPLKKygoKMCoUaOQnZ2N2267DevWrYOvr6+8z7JlyzB69Gj06dMHKpUKQ4YMwbx585R4SFVmcLg2VvVnU/myZYeIiKhcioYdABg0aBAGDRpU4e2SJGHatGmYNm1ahfuEhYVh+fLlrijP5Zxx1XOAYYeIiKgiil8uora70anndvYxOwYru7GIiIhKY9hRmDMWFQTYskNERFQRhh2FGZw1QFltDzts2SEiIiqNYUdhxRbboGS1dGMhhS07RERE5WPYUZgzp54DgIGLChIRETlg2FGYvWXnBscnywOUi60Sii1MPERERHYMOwozWuyXi7ixtKNVA/YjZBYW32BVRERE3oNhR2EmJ1wbC7CtR+RbsmpSZgHDDhERkR3DjsJM9gHKTnglfNW2fzPYskNERCRj2FGQ2WKFxWofs3PjU8btg5TZjUVERHQFw46CTKUGEt9oNxZwpWWH3VhERERXMOwoyD5eB3BSN5a9ZYdhh4iISMawoyCjHHYEnLHuMcfsEBERlcWwo6DSM7EkZ47ZYcsOERGRjGFHQUaz7doOznoR7FPP2bJDRER0BcOOggzF9pYd4ZTj2S8GmlVodsrxiIiIvAHDjoLss7Fu9FIRdvaWneyiYnlKOxERUW3HsKMgY7Fzw45ODQACVgFkFpicc1AiIqIajmFHIUIIpGdlA3BeN5ZKkqAreUXT841OOSYREVFNx7CjkMzMTKxKOg4AkITzupx0JdPPGXaIiIhsGHYUpNb5AQBUcGbYsR2LYYeIiMiGYUdBVmG/Lpbzwo6vvRsrj2N2iIiIAEBT3TsWFBRgy5YtOHfuHEwmx1+sL7zwwg0XVhvYL43lzMQpd2MVsGWHiIgIqGbY2bt3L+666y4UFhaioKAAYWFhSE9Ph5+fHyIjIxl2KsnsgpYduRuLLTtEREQAqtmoMG7cOAwePBhZWVnQ6/XYtm0bzp49i7i4OMyePdvZNXota8laOM644rkdBygTERE5qlbY2bdvH1588UWoVCqo1WoYjUbUr18fs2bNwuuvv+7sGr2WpaRBx6kDlEteUa6zQ0REZFOtsOPj4wOVynbXyMhInDt3DgAQHByM8+fPO686L2e22ruxnHdMezcWww4REZFNtcbsdOrUCTt37kSzZs3Qq1cvTJo0Cenp6fi///s/tG3b1tk1ei2rPEDZ+S076flGCCGccjV1IiKimqxaLTvvvPMOYmJiAADTp09HaGgonn32WaSlpeHTTz91aoHezCJc0bJj+9dotqLQZHHegYmIiGqoarXsdO7cWf4+MjIS69atc1pBtYn9Yp3ObNlRS4BOo4LRbEVmgQn+umqvLkBEROQVqtWyc8cddyA7O9vJpdQ+9gHKzpyNJUlAqJ8t4HBGFhERUTXDzubNm8ssJEhV54qWHQAI8/MBwEHKREREwA0s3suBrzdOnnruxKdSCAF/je3AGWzZISIiqv7lIu677z5otdpyb/v999+rXVBtIrfsOHEFZUNBHlIMagAqnE/LBtDAaccmIiKqiaodduLj4xEQEODMWmodV3Vj+et1QEExsgrNTj0uERFRTVStsCNJEl5++WVERkY6u55axRXdWADgWzL9PKuo2LkHJiIiqoGqNWZHCOe2RNRWrmrZ8dXY0lNWIcMOERFRtcLO5MmT2YXlBK6Yeg4AOjnssBuLiIioWt1YkydPBgCkpaXh6NGjAIAWLVqgTp06zqusFnBZy05JemI3FhERUTVbdgoLC/HEE08gNjYWPXv2RM+ePREbG4snn3wShYWFzq7Ra1lccCFQwLEbi12ORERU21Ur7IwbNw5btmzB999/j+zsbGRnZ+O7777Dli1b8OKLLzq7Rq91ZYCya8bsGM2C18ciIqJar1rdWN988w1Wr16N3r17y9vuuusu6PV6DBs2DAsXLnRWfV7N3rKjdvJxNSoJahVgsQIZ+bw+FhER1W7V7saKiooqsz0yMpLdWJUkhHBZyw4A6DW2lzajgKsoExFR7VatsBMfH4/JkyfDYDDI24qKijB16lTEx8c7rThvZrZeCTjVvmbHNfiWhB1eH4uIiGq7avVvzJ07F3feeSfq1auHDh06AAD2798PX19f/Prrr04t0FsZzVb5e1e07Pj62MbtZOQz7BARUe1WrbDTrl07HD9+HMuWLcO///4LAHjooYcwYsQI6PV6pxborYotrm3ZudKNxbBDRES1W7XCztatW9GtWzc8/fTTzq6n1rC37KgkwBUXkPf1sXdjccwOERHVbtVqVLj99tuRmZnp7FpqFZPlSthxBfv0c3ZjERFRbcdrYynEZC6Zdu6isMNuLCIiIptqL8CSlJSE0NDQcm/r2bNntQuqLewtO64KO1e6sRh2iIiodqt22LnvvvvK3S5JEiwWrtp7PaXH7LjClW4sjtkhIqLardoTgZKTk2G1Wst8MehUjn02ltoVU7EA6EtadtILTOx2JCKiWq1av2olV0wfqmXsLTtqFz2X9jE7JrMV+UazS85BRERUE3CAskLklh0X5UaNWoKfvXWHM7KIiKgWq1bYsVqtiIyMdHYttYqrx+wAQJi/DwAgneN2iIioFqtW2JkxYwa++OKLMtu/+OILzJw584aLqg1cPRtLCIEgre3g6XkMO0REVHtVK+x88sknaNmyZZntbdq0waJFi264qNrAZHZt2CnKz0VGdi4AtuwQEVHtVq2wk5ycjJiYmDLb69Spg8uXL99wUbWBqWTMjspFs7EAwE9rW1kgjWN2iIioFqvWr9r69evjr7/+KrP9r7/+Qmxs7A0XVRsYXdyyAwA6te1ftuwQEVFtVq1FBZ9++mmMHTsWxcXFuOOOOwAAGzduxCuvvIIXX3zRqQV6q2KLa6eeA4Cv2tZ6xDE7RERUm1Ur7Lz88svIyMjAc889B5PJ1kXi6+uLV199FRMmTHBqgd7KWHJtLFfOxmLLDhERUTXDjiRJmDlzJiZOnIgjR45Ar9ejWbNm0Ol0zq7PaxW7eDYWAGglW6BKySmCEIKLQRIRUa1U7WtjAUBAQABuueUWZ9VSq8hjdlQAXLRGo2TKBxCClFwjMjMzER4e7poTERERebBqh51du3Zh5cqVOHfunNyVZfftt9/ecGHezlR6BWUXhR2tZAtUZgEUmXjNMiIiqp2qNRtrxYoV6NatG44cOYI1a9aguLgYhw4dwu+//47g4GBn1+iV7IsKunLMjka60k2WWVjsuhMRERF5sGqFnXfeeQcffPABfvjhB2i1Wnz44Yf4999/MWzYMDRo0MDZNXolVy8qCACSBPhqbCdg2CEiotqqWmHn5MmTGDhwIABAq9WioKAAkiRh3Lhx+PTTT51aoLcyme3dWK4dNGwPOxkFDDtERFQ7VSvshIaGIi8vDwBQt25dHDx4EACQnZ2NwsJC51XnxeRuLBeuoAywZYeIiKhaA5R79uyJ9evXo127dhg6dCjGjBmD33//HevXr0efPn2cXaNXckc3FgDo2bJDRES1XLXaFT766CMMHz4cAPDGG29g/PjxSElJwZAhQ/D5559Xq5B3330XkiRh7Nix8jaDwYDExESEh4cjICAAQ4YMQUpKisP9zp07h4EDB8LPzw+RkZF4+eWXYTabq1WDO8nXxnJx2GHLDhER1XZVatnJzbVdRVuj0SAgIED++bnnnsNzzz1X7SJ27tyJTz75BO3bt3fYPm7cOPz0009YtWoVgoODMXr0aNx///3ydbksFgsGDhyI6Oho/P3337h8+TIeffRR+Pj44J133ql2Pe5gcsOiggDH7BAREVUp7ISEhFRqFV6LpfJruuTn52PEiBH47LPP8Pbbb8vbc3Jy8Pnnn2P58uXy9bcWL16MVq1aYdu2bbj11lvx22+/4fDhw9iwYQOioqLQsWNHvPXWW3j11VcxZcoUaLXaqjw8t3LHhUCBK91YbNkhIqLaqkphZ9OmTQ4/CyFw11134b///S/q1q1brQISExMxcOBAJCQkOISd3bt3o7i4GAkJCfK2li1bokGDBkhKSsKtt96KpKQktGvXDlFRUfI+/fv3x7PPPotDhw6hU6dO5Z7TaDTCaLxyvSh7C5U7FdsXFXRxP5bcjcWWHSIiqqWqFHZ69epVZptarcatt96Km266qconX7FiBfbs2YOdO3eWuS05ORlarRYhISEO26OiopCcnCzvUzro2G+331aRGTNmYOrUqVWu15nsLTsqyWULKAMAfEuajjLYskNERLWUiyc+V+z8+fMYM2YMli1bBl9fX7eee8KECcjJyZG/zp8/79bzA+6bjWVv2ck3WmAo5iUjiIio9rmhsHP+/HkUFhZW6wKTu3fvRmpqKm6++WZoNBpoNBps2bIF8+bNg0ajQVRUFEwmE7Kzsx3ul5KSgujoaABAdHR0mdlZ9p/t+5RHp9MhKCjI4cvdHK6N5UJa9ZUZX+n5xmvvTERE5IWq1I01b948+fv09HR8/fXXuOOOO6p1Paw+ffrgwIEDDtsef/xxtGzZEq+++irq168PHx8fbNy4EUOGDAEAHD16FOfOnUN8fDwAID4+HtOnT0dqaioiIyMBAOvXr0dQUBBat25d5ZrcxWoVMFvdM/VckiTo1ECRGTh1MQ31Qhu69oREREQepkph54MPPgBg+wUaERGBwYMH480336zWiQMDA9G2bVuHbf7+/ggPD5e3P/nkkxg/fjzCwsIQFBSE559/HvHx8bj11lsBAP369UPr1q3xyCOPYNasWUhOTsabb76JxMRE6HS6atXlDvZp54DrW3YAwE8jocgskJZvuv7OREREXqZKYef06dOuqqNcH3zwAVQqFYYMGQKj0Yj+/fvj448/lm9Xq9X48ccf8eyzzyI+Ph7+/v4YOXIkpk2b5tY6q6r02Bm1G0ZN6Ute5dQ8hh0iIqp9qnW5CFfZvHmzw8++vr5YsGABFixYUOF9GjZsiJ9//tnFlTmXfSaWBEDl4guBAoCfjwRAIIUtO0REVAspNhurNjMWl8zEctOz78eWHSIiqsUYdhRgMNu6sTSuHp1cwq9k+nkKww4REdVCDDsKkFt23NCFBQB+PrZ/OUCZiIhqI4YdBRhLWnbc1411pWVHCFeu10xEROR5GHYUYJDH7LinZcc+G8totiKniJeNICKi2oVhRwFGN4/ZUaskaFW2Fp3LOQa3nJOIiMhTMOwowODm2VgAoFfb/k1m2CEiolqGYUcB8pgdNw1QBgC9hi07RERUOzHsKMC+qKC7xuwApVp2chl2iIiodmHYUYD9chEad3ZjlbTsJOcUue+kREREHoBhRwFyy447u7FKWnbYjUVERLUNw44C7C07inRjMewQEVEtw7CjAHvLjjLdWAw7RERUuzDsKMDdl4sAAN+Slp08oxn5RrPbzktERKQ0hh0FGMzu78byUQEBOlviYesOERHVJgw7CjAqsKggAEQFagEw7BARUe3CsKMAg5svF2EXGWALO5c5/ZyIiGoRhh0FGN18IVC7yEB72GHLDhER1R4MOwq4crkI9563brAvAOBcZqF7T0xERKQghh0F2Ft23N2NVS9EBwA4m1Hg1vMSEREpiWFHAUYFZmMBQL0QW8vO2Qy27BARUe3BsKMAg0KzsexhJzXPiCKTxb0nJyIiUgjDjgKujNlxb8tOsF6DIF8NAI7bISKi2oNhRwFXLhfh5hHKABqG+wPguB0iIqo9GHYUcOVCoO4/d4NwPwBs2SEiotqDYUcB9pYdd3ZjCSGQmZmJSL3tnGfYskNERLWERukCaiM57LgxahoK8rBw42FcKLItLMgZWUREVFuwZcfNii1WWKwCgPvH7PgFBCNQZzvn6bQ8t56biIhIKQw7bmZv1QHcv84OAAT62M55OdcEs8V6nb2JiIhqPoYdN7MPTgbcf7kIANBrABUELFaBS9m8RhYREXk/hh03s7fsaNUSJDevswMAkiTB38f2/dlMDlImIiLvx7DjZsaSlh2tRrmn3l9jGzN0hoOUiYioFmDYcTP7pSJ0ioYd27/nOP2ciIhqAYYdN7NfKkLZsGNr2Tl+ORtCCMXqICIicgeGHTfzhJYdrdnWfbX3XBYyMzMVq4OIiMgdGHbczN6yo1ViKlaJALWthtxi27o/RERE3oxhx83ss7GUbNnxVVmhUQFCABeyjYrVQURE5A4MO25mX2dHybAjSUCQ1nb+0xlFitVBRETkDgw7biavs6Ng2AGAYPtlIxh2iIjIyzHsuJl9nR3Jagag3EyoYF+27BARUe3AsONm9padkxfTYChS7nINwTrbS38iNY/Tz4mIyKsx7LiZ3I3l46NoHUElYedkhgHpGRmK1kJERORKDDtuZh+grJKUbU0J1EpQAbBCQnIuZ2QREZH3YthxM3vLjoLL7AAAVJKEQK3te47bISIib8aw42ZGuWVH4UJwZUbWKYYdIiLyYgw7bma/XIRa4W4sAAjScvo5ERF5P4YdN7NfLkKtcB0AEFTSsnOGYYeIiLwYw46bXWnZUbgQAMHaK91YnH5ORETeimHHzewtO0rPxgKAIC0ACOQZLUjL54wsIiLyTgw7buYps7EAQK2S4K+xfX8iJV/ZYoiIiFyEYcfNDB40GwsAAn1sLUwn0hh2iIjIOzHsuNmVlh3lu7EAIKhkIecTqQw7RETknRh23MzesuMJ3VjAlZad4+zGIiIiL8Ww42b2lh12YxEREbkHw46beVo3VoDGVkdanhHZhSaFqyEiInI+hh03k7uxFK7DzlKUB73aFnj2nLikcDVERETOx7DjZp7WjQUAIb626MXLRhARkTdi2HEjq1XA5GHdWAAQ7Gt7GzDsEBGRN2LYcSOTxSp/70ktO0E6hh0iIvJeDDtuZCy+EnY8Zeo5AATbw04mww4REXkfhh03MpivrLHjSS079rBzOceIAmOxwtUQERE5F8OOG9lbdrQaz3rafTUSdGpAANh78rLS5RARETmVZ/3W9XL2lh1PCzsAEKy1NTWdSi9UuBIiIiLn8rzful7M3rLj64lhR2cLOyfTOW6HiIi8i+f91vViRnvLjtrznvYQne3fk2zZISIiL+N5v3W9mKGkZUfn0S07DDtERORdPO+3rhezt+zoNB40FauEPexczjUhz8AZWURE5D0YdtyoqNgedjzvadepJfiWXCPreCqvgE5ERN5D0d+6M2bMwC233ILAwEBERkbi3nvvxdGjRx32MRgMSExMRHh4OAICAjBkyBCkpKQ47HPu3DkMHDgQfn5+iIyMxMsvvwyz2ezOh1IpBUZbTX5aT7kMqKMgH1vYOZacp3AlREREzqNo2NmyZQsSExOxbds2rF+/HsXFxejXrx8KCgrkfcaNG4cffvgBq1atwpYtW3Dp0iXcf//98u0WiwUDBw6EyWTC33//jaVLl2LJkiWYNGmSEg/pmgqMtpYdf48NO7Z/j6Yw7BARkffQKHnydevWOfy8ZMkSREZGYvfu3ejZsydycnLw+eefY/ny5bjjjjsAAIsXL0arVq2wbds23Hrrrfjtt99w+PBhbNiwAVFRUejYsSPeeustvPrqq5gyZQq0Wq0SD61c9pYdvaeGHW1Jyw7DDhEReRGPGjySk5MDAAgLCwMA7N69G8XFxUhISJD3admyJRo0aICkpCQAQFJSEtq1a4eoqCh5n/79+yM3NxeHDh0q9zxGoxG5ubkOX+5QYCpp2fHxqKddZu/GOprMMTtEROQ9POa3rtVqxdixY9G9e3e0bdsWAJCcnAytVouQkBCHfaOiopCcnCzvUzro2G+331aeGTNmIDg4WP6qX7++kx9N+Ty+ZaekGys934iMfKOyxRARETmJx4SdxMREHDx4ECtWrHD5uSZMmICcnBz56/z58y4/JwAUmGxhx1PH7GhUQN1g2+qCx1LYukNERN7BI8LO6NGj8eOPP2LTpk2oV6+evD06OhomkwnZ2dkO+6ekpCA6Olre5+rZWfaf7ftcTafTISgoyOHLHeSWHQ/txgKApnX8AHDcDhEReQ9Ff+sKITB69GisWbMGv//+Oxo3buxwe1xcHHx8fLBx40Z529GjR3Hu3DnEx8cDAOLj43HgwAGkpqbK+6xfvx5BQUFo3bq1ex5IJRWaPHs2FgA0jbCFnX+T3TOOiYiIyNUUnY2VmJiI5cuX47vvvkNgYKA8xiY4OBh6vR7BwcF48sknMX78eISFhSEoKAjPP/884uPjceuttwIA+vXrh9atW+ORRx7BrFmzkJycjDfffBOJiYnQ6XRKPrwy7C07FmMRAKFsMRVoHmkLO4cuMewQEZF3UDTsLFy4EADQu3dvh+2LFy/GY489BgD44IMPoFKpMGTIEBiNRvTv3x8ff/yxvK9arcaPP/6IZ599FvHx8fD398fIkSMxbdo0dz2MSrOvs/PjtkOo4ysQoHA95WkR6Q8A+Dc5D8UWK3w88KKlREREVaFo2BHi+q0bvr6+WLBgARYsWFDhPg0bNsTPP//szNJcwj5A2c9PD1g974KbQgj4WvLhr1WjwGTBidQ8tIoJVrosIiKiG8I/293I3o2lkTyzC8tQkIeZX2+EXrLVuePYZYUrIiIiunEMO25kX1TQAy96LvP1D0REgG3BnaOpBdfZm4iIyPMx7LhJscUKk9kKANCoPLNlxy7U1/a2YNghIiJvwLDjJoUlg5MBz27ZAYAwOewUVmpcFRERkSdj2HGT/JLByT5qCSoPDzvBviqoJCDfaMH5zCKlyyEiIrohDDtuUlgyONnPgxcUtFNJEkL1tjoPXcpRuBoiIqIbw7DjJvn2sOPBl4ooLdzPtioBFxckIqKarmb85vUC9ktF1ISWHQCIkMMOW3aIiKhmY9hxkystOzUk7Pjbws6BizkcpExERDUaw46bFJpqzpgdwNaN5aOWkJ5v4iBlIiKq0Rh23MR+XSw/bc14ytUqSb5O1t7zWQpXQ0REVH014zevFyioYd1YQgg0C7N1Ze05y7BDREQ1F8OOmxTUsAHKRfm5OH32AgBg7/lsZYshIiK6AQw7biK37NSQbiwAiArSAbBNPy8qGXNERERU09Sc37w1XE0boAwAkiEPvmoBi1XgryPnlS6HiIioWhh23CTfPkC5hozZAQBJAurYp6Bfyle4GiIiouph2HGTmnS5iNIi9La3yO6zmVxvh4iIaiSGHTfJr4FjdgCgjp8tnO08m4uMjAyFqyEiIqq6mvWbtwaraZeLsAvTqyABMAoVkvNMSpdDRERUZQw7blLT1tmx06gkhPpKADhuh4iIaiaGHTcpqIGzsezC5bCTp3AlREREVcew4yY17XIRpUXo2bJDREQ1V837zVsDCSGutOzUsG4s4ErY+Te1AEazReFqiIiIqoZhxw0MxVbYZ23XxG6sAB9AqxIotggcupSrdDlERERVwrDjBvZp55IE+PrUvKdckiSE6Wxpbe+5bGWLISIiqqKa95u3Bios1YWlkiSFq6kee9jZc45XQCciopqFYccN5AUFdRqFK6m+MK3t331s2SEiohqGYccN7AsKBtTgsBOitUIlARezi5CcU6R0OURERJXGsOMG+TX0ulilWYryEKixdWVtPcQroBMRUc3BsOMGhSVr7PjX4JYdAIgK9AEA7LvIxQWJiKjmYNhxA/ulIvxrcMsOcOWioHsvMOwQEVHNwbDjBvYFBWt6y06kn+3tcjS1QO6aIyIi8nQMO25wpWWnZocdf60KAVoVrALYyynoRERUQzDsuEGByTvG7ABAdMm4nZ2nMxWuhIiIqHIYdtwg32DvxqrZY3YAIDrAFnb+Op6KjIwMCPt1MIiIiDwUw44bZBQYAQARATqFK7lxkQG2wLbvQi5mfb8LmZls4SEiIs/GsOMGaXm2sFMnsOaHHZ25AD6wwCKAIpW/0uUQERFdF8OOG3hT2JEkCaE+tjFIyXnFCldDRER0fQw7biCHHS/oxgKAMB/bGKRLuQw7RETk+Rh2XKzAaJZnY3lDyw4ARPiYAACX84pRVGxRuBoiIqJrY9hxMXurjp9W7RVTzwEgQG2Fv48EiwB2n89VuhwiIqJrYthxsbR87xmvYydJQGygbVbWX6eylS2GiIjoOhh2XMzbxuvYxQZcCTtca4eIiDwZw46LedNMrNKi/dVQScDFHCNOpRcoXQ4REVGFGHZczB52IgJ0yMjIKFmEr+a3hPioJfnSEZuPpilcDRERUcW8Y8SsB7OHnUCNFe//sBuF+bkwGowKV+Uc9YO1uJRbjN8OXMS9rYIQFhYGSZKULouIiMgBW3ZczD5AOdzfB36BwfALCFK4IudpEKIFAOw4m4Np3/LSEURE5JkYdlzM3rIT7u+jcCXOF+yrRscYPQSAM4Xe9/iIiMg7MOy4WGqeAQAQ4YVhBwDubh0CADiaZoDJbFW2GCIionIw7LiQ1SqQnm9bbTjcX6twNa7RrUEA/H1UMJgFNhzNULocIiKiMhh2XCir0ASL1TbzKtTPO8eCq1USWkb6AgBW7k1RuBoiIqKyGHZcyD44OcxfCx+19z7VLev4QiUBBy7nY93By2VuF0IgIyODiw8SEZEivPc3sAfw1tWTr6b3UaFdlB4A8Nq3B5CSa3C4PTMzE28t+52ztYiISBEMOy7krasnl6dTrB5Nw3XILizGiyv3w2p1bMXRB3rPlHsiIqpZGHZcyNvDjhACWVlZAARMhXkIyzsFFQT+PJGOUf+3C8fOXS7TdcUuLSIicjeGHReyh51ILw07Rfm5WPjzLhiKbN1W4YF63BxuhUYlYcORVAxatBtvf7cff5/OhsliCzelu7QYfIiIyB28c4qQh7APUI4I0HrNNbGu5usf6PBzgwCBlwa1xcSfT+J4WiE+33ZRvu2PhXvQMsofRZYApOWbANiCz8QRdyA8PFzeTwiBzMxMXn6CiIicgi07LmRv2dFLZsxe/YfcAuLtmkX648uH26JrtAqD2kSgXoitZSs134StJ7OwM8WKAQv3YPyao8hRBZRp2eGAZiIiciaGHRdKLXWpCH1A4HX29i5ajQo3Basw9a6m+O7pTnigqRpf/KcNEnvUR7ivrY1ry4ksbLpgxYgl+/Dr3lMOoYcDmomIyFnYjeVCcQ1CEeavRUyQd47ZKY+9C8r2vVX+3kcNdKgbiA51A5GZehlDurfByr3J+GZfMo6mG/HM/47glr/OYur9NyOq9jxdRETkBgw7LjTzgfYAgIyM2nMZBUNBHhZuPAyruRjGoiIs3GiC1VwMlfrKtcGEEAiWivD0zcHIvXwKp0zBOJ5pxs4LhRg470/c1SYCmuIbG99UOnRx7A8RUe3GsENO5xcQDKvZBGNRkfx9UWGhHD4MBblyIJKKjegSq0PzEGBvsgkXC1X46VA61BIQ8/cFjL0zBL4+6iqHl8zMTLz/w24AwIuD4xwGQBMRUe3CsENu4dDiYzAiPDRSDkQAEKSV0LWOFff0aI85v5/B/kv5WPTXBfxwKAOvDWiJ+LpazPlxD4Ar4eV6s7b8AoPd+hiJiMgzcYCyC9nXkfHWaedV5RcQDL+AigceCyEQqzPhvTtj0C1GQlSgFhezi/D813vx0NIDOFOohUp/ZaB3ZmYmpn21ESdOnOB6PUREVCG27LiQvSulMD8XRoMRAUoX5OFKt/5Ea30w4f4meP2nUzicYcXxtEIcB/D3uQJsOrUXrWJDoJMsOJyrxQvfHEGgrxajbm+BW1vWR4COb2siIrqCvxVczN6VYu+uoWsrPcanKC8HXRuHokVoDvafTkWaOhzZRoGLOUZczEkpuYd94LMFO74+DAmH0SDUFy2j/JFdYEC9YB+2+BAR1XIMO+SRDAV5WPjzLtRt2hpatYSmwUDXGD/AR4d20X5YvecSCg1GGExmSL6BMFglGM0CaQVmnM0y4GyWbQHH7eeB7Rf2oX/bWPRpFYnODUORl5NdZpwPV20mIvJeDDvksa6+FAUA+GpUuDnWD8fTw1CYl4OstBSEx0RA5+uL/g1U+O6UGemFFsQG+mD9iVxcyi3GhWwjPv/zND7/8zQCdWoEq4x4qncL3NwkGo0i/BGo08irNl996QoiIqr5GHbIK9guSnoCdZu2Rr0gW/BRq4Jgsgg0CtVh8d/nkCt8kWOwIA8aTPnlJICTAABJAvQaFYTVD3v+uw91gvSoH6pH44gAdG4UipsbhEKvVSv7AImIqNq8JuwsWLAA7733HpKTk9GhQwfMnz8fXbp0UboscjIhBLKyslDe7DZ7S1Dp4KPz9UXbYDPiwkyQNBLSCy1INmqg9wvAxVwzMgqLIQRQWGwFAJzLMuBclgG7z2bJx9WoJHRuEISBHeqhV/NI1A/Tl9vVJYRAZoEJybkGnLiYBp3eH8F6H0imQjSIiUCIXovCvGyEh4fL9y+9flBoaGjJY+NCiEREzuQVYed///sfxo8fj0WLFqFr166YO3cu+vfvj6NHjyIyMlLp8siJSgeZa7k6+Pj6ByI8JhJ6vQkBaSnQ+wrcpClGpiUNAZGxKC42o7DICLXeH0UmCzJyC1GsC0a6ASiyqLDtTA62nckBcAh1AnzQMiYYAToNJAm4mFmAtHwT0vNNMFmuNxhawM9HDT+dD/y0amgkgcz8IkgAIgJ1yMw3QaMCWtcNQXiQrYtNK5nROCoUMSF6RAf5IjrIFwG+GqhVtjBktlhhMluRkp6BwKBgBOm10GquvapEodGMUxdTodH7Iz0zG1YB1AkLgbEwH74+KkTXCYefVnPd4xAR1QReEXbmzJmDp59+Go8//jgAYNGiRfjpp5/wxRdf4LXXXlO4OnK28sbyVHX/0qs8hwXbvs8qLkR4hO37CKkA4TGBsBQbcSElHZkIBHT+OJpmQFp+MdKOp1d4Pq1KQCdZ4KNRw2QRMFklmIUEqwAACYXFVhQWG8vcL8t4ZVvK8SwAWaVuvVBm/5KsU3JcR/5aFUL9dQj184GPJKBSa2AyW5GWV4SsQjMMZmuF9Zfmo5bgq1FB76OCXquG3keFID9f+GnV0PuoYTEXQwgBH61Wvo9VACajCVqtbaac0WSCtuR2k8kEAND7qBESoIdeq4GfVg1fHzX8tGr4qFWwWgUKCgrg6+dnO55VoKCwEEIAvnp9yTls23x9bT9brAKFRbbQ6O/vB7VKglqSoFZJkCQJaglQqySoVBJUku02lUqCWgWopJJtKgnXaksTEMjLy0dAQECFrW7XmvjneP8K9hGO98jPt+2Pa1ZWdQKQZykKIZCXX4AAf/9yT3PlWRHIzy8AAAQGBAASHJ4H+3elH5v9vvZtlXkUFT2F13tuq36fa9xWjRmcTq9P2O5XUPKc+wf4Q4J03brzC/IBAAH+joudVHg/Fz+vQtg+z/7+/hjUIRZBvj7l3s/VanzYMZlM2L17NyZMmCBvU6lUSEhIQFJSUrn3MRqNMJb6xZKTkwMAyM3NdWpteXl5yEq9hML8PORmpkGl0cBqLkZuZpqtzlI/38j3nnQsT6rFWcey5qQhBMnw9QtEuKYYGQYBq9YfZosVRYWFiIysAx+YUZh6HvUaNgIstvuHhMTKx9LpA1FsNsNgMqGwyAD/kAiYzVbk5ecgLLo+1Bof3FLfH9vPFaDQYEBWRgZ8/INRbBUwmK2wqHQoskgwWlUwwzZ+6FpxJc8I5OXl49w19pEg4K9VwWIVEMIKs9kKoVLDKq78h2os+cqpxvufiKi0NnW6o3GEc1ecs//evl5ArfFhJz09HRaLBVFRUQ7bo6Ki8O+//5Z7nxkzZmDq1KllttevX98lNRJVxtdKF0BE5EId57ru2Hl5eQgOrvgSQTU+7FTHhAkTMH78ePlnq9WKzMxMh4Gj1ZGbm4v69evj/PnzCAqq+LII3oCP1TvxsXqn2vRYgdr1eGv7YxVCIC8vD7Gxsde8b40POxEREVCr1UhJSXHYnpKSgujo6HLvo9PpoNPpHLaFhIQ4raagoCCvf9PZ8bF6Jz5W71SbHitQux5vbX6s12rRsavxUy20Wi3i4uKwceNGeZvVasXGjRsRHx+vYGVERETkCWp8yw4AjB8/HiNHjkTnzp3RpUsXzJ07FwUFBfLsLCIiIqq9vCLsPPjgg0hLS8OkSZOQnJyMjh07Yt26dWUGLbuaTqfD5MmTy3SReSM+Vu/Ex+qdatNjBWrX4+VjrRxJ8JLQRERE5MVq/JgdIiIiomth2CEiIiKvxrBDREREXo1hh4iIiLwaw46TLFiwAI0aNYKvry+6du2KHTt2KF2SS2zduhWDBw9GbGwsJEnC2rVrlS7JZWbMmIFbbrkFgYGBiIyMxL333oujR48qXZZLLFy4EO3bt5cX64qPj8cvv/yidFlu8e6770KSJIwdO1bpUpxuypQpkCTJ4atly5ZKl+UyFy9exMMPP4zw8HDo9Xq0a9cOu3btUrosp2vUqFGZ11WSJCQmJipdmtNZLBZMnDgRjRs3hl6vR5MmTfDWW29V+WKtDDtO8L///Q/jx4/H5MmTsWfPHnTo0AH9+/dHamqq0qU5XUFBATp06IAFCxYoXYrLbdmyBYmJidi2bRvWr1+P4uJi9OvXDwUFBUqX5nT16tXDu+++i927d2PXrl244447cM899+DQoUNKl+ZSO3fuxCeffIL27dsrXYrLtGnTBpcvX5a//vzzT6VLcomsrCx0794dPj4++OWXX3D48GG8//77CA0NVbo0p9u5c6fDa7p+/XoAwNChQxWuzPlmzpyJhQsX4qOPPsKRI0cwc+ZMzJo1C/Pnz6/agQTdsC5duojExET5Z4vFImJjY8WMGTMUrMr1AIg1a9YoXYbbpKamCgBiy5YtSpfiFqGhoeK///2v0mW4TF5enmjWrJlYv3696NWrlxgzZozSJTnd5MmTRYcOHZQuwy1effVVcdtttyldhiLGjBkjmjRpIqxWq9KlON3AgQPFE0884bDt/vvvFyNGjKjScdiyc4NMJhN2796NhIQEeZtKpUJCQgKSkpIUrIycLScnBwAQFhamcCWuZbFYsGLFChQUFHj1JVcSExMxcOBAh8+uNzp+/DhiY2Nx0003YcSIETh37pzSJbnE999/j86dO2Po0KGIjIxEp06d8NlnnyldlsuZTCZ89dVXeOKJJ27oQtaeqlu3bti4cSOOHTsGANi/fz/+/PNPDBgwoErH8YoVlJWUnp4Oi8VSZrXmqKgo/PvvvwpVRc5mtVoxduxYdO/eHW3btlW6HJc4cOAA4uPjYTAYEBAQgDVr1qB169ZKl+USK1aswJ49e7Bz506lS3Gprl27YsmSJWjRogUuX76MqVOnokePHjh48CACAwOVLs+pTp06hYULF2L8+PF4/fXXsXPnTrzwwgvQarUYOXKk0uW5zNq1a5GdnY3HHntM6VJc4rXXXkNubi5atmwJtVoNi8WC6dOnY8SIEVU6DsMOUSUkJibi4MGDXjveAQBatGiBffv2IScnB6tXr8bIkSOxZcsWrws858+fx5gxY7B+/Xr4+voqXY5Llf7rt3379ujatSsaNmyIlStX4sknn1SwMuezWq3o3Lkz3nnnHQBAp06dcPDgQSxatMirw87nn3+OAQMGIDY2VulSXGLlypVYtmwZli9fjjZt2mDfvn0YO3YsYmNjq/S6MuzcoIiICKjVaqSkpDhsT0lJQXR0tEJVkTONHj0aP/74I7Zu3Yp69eopXY7LaLVaNG3aFAAQFxeHnTt34sMPP8Qnn3yicGXOtXv3bqSmpuLmm2+Wt1ksFmzduhUfffQRjEYj1Gq1ghW6TkhICJo3b44TJ04oXYrTxcTElAnmrVq1wjfffKNQRa539uxZbNiwAd9++63SpbjMyy+/jNdeew3Dhw8HALRr1w5nz57FjBkzqhR2OGbnBmm1WsTFxWHjxo3yNqvVio0bN3r1eIfaQAiB0aNHY82aNfj999/RuHFjpUtyK6vVCqPRqHQZTtenTx8cOHAA+/btk786d+6MESNGYN++fV4bdAAgPz8fJ0+eRExMjNKlOF337t3LLA1x7NgxNGzYUKGKXG/x4sWIjIzEwIEDlS7FZQoLC6FSOUYVtVoNq9VapeOwZccJxo8fj5EjR6Jz587o0qUL5s6di4KCAjz++ONKl+Z0+fn5Dn8Vnj59Gvv27UNYWBgaNGigYGXOl5iYiOXLl+O7775DYGAgkpOTAQDBwcHQ6/UKV+dcEyZMwIABA9CgQQPk5eVh+fLl2Lx5M3799VelS3O6wMDAMuOu/P39ER4e7nXjsV566SUMHjwYDRs2xKVLlzB58mSo1Wo89NBDSpfmdOPGjUO3bt3wzjvvYNiwYdixYwc+/fRTfPrpp0qX5hJWqxWLFy/GyJEjodF476/ywYMHY/r06WjQoAHatGmDvXv3Ys6cOXjiiSeqdiAnzhCr1ebPny8aNGggtFqt6NKli9i2bZvSJbnEpk2bBIAyXyNHjlS6NKcr73ECEIsXL1a6NKd74oknRMOGDYVWqxV16tQRffr0Eb/99pvSZbmNt049f/DBB0VMTIzQarWibt264sEHHxQnTpxQuiyX+eGHH0Tbtm2FTqcTLVu2FJ9++qnSJbnMr7/+KgCIo0ePKl2KS+Xm5ooxY8aIBg0aCF9fX3HTTTeJN954QxiNxiodRxKiissQEhEREdUgHLNDREREXo1hh4iIiLwaww4RERF5NYYdIiIi8moMO0REROTVGHaIiIjIqzHsEBERkVdj2CEiIiKX2Lp1KwYPHozY2FhIkoS1a9dW+RhCCMyePRvNmzeHTqdD3bp1MX369Codw3vXmCYiIiJFFRQUoEOHDnjiiSdw//33V+sYY8aMwW+//YbZs2ejXbt2yMzMRGZmZpWOwRWUicijPfbYY8jOzi7zF+HmzZtx++23IysrCyEhIYrURkSVJ0kS1qxZg3vvvVfeZjQa8cYbb+Drr79GdnY22rZti5kzZ6J3794AgCNHjqB9+/Y4ePAgWrRoUe1zsxuLiIiIFDF69GgkJSVhxYoV+OeffzB06FDceeedOH78OADghx9+wE033YQff/wRjRs3RqNGjfDUU09VuWWHYYeIvEJ2djaeeuop1KlTB0FBQbjjjjuwf/9++fYpU6agY8eODvfZvHkzJElCdnY2ACAjIwMPPfQQ6tatCz8/P7Rr1w5ff/21Gx8FUe1x7tw5LF68GKtWrUKPHj3QpEkTvPTSS7jtttuwePFiAMCpU6dw9uxZrFq1Cl9++SWWLFmC3bt344EHHqjSuThmh4i8wtChQ6HX6/HLL78gODgYn3zyCfr06YNjx44hLCysUscwGAyIi4vDq6++iqCgIPz000945JFH0KRJE3Tp0sXFj4Codjlw4AAsFguaN2/usN1oNCI8PBwAYLVaYTQa8eWXX8r7ff7554iLi8PRo0cr3bXFsENENd6ff/6JHTt2IDU1FTqdDgAwe/ZsrF27FqtXr8aoUaMqdZy6devipZdekn9+/vnn8euvv2LlypUMO0ROlp+fD7Vajd27d0OtVjvcFhAQAACIiYmBRqNxCEStWrUCYGsZYtgholpj//79yM/Pl/8atCsqKsLJkyflnw8cOCD/JwoAFovFYX+LxYJ33nkHK1euxMWLF2EymWA0GuHn5+faB0BUC3Xq1AkWiwWpqano0aNHuft0794dZrMZJ0+eRJMmTQAAx44dAwA0bNiw0udi2CGiGi8/Px8xMTHYvHlzmdtKz9Rq0aIFvv/+e/nn7du34+GHH5Z/fu+99/Dhhx9i7ty5aNeuHfz9/TF27FiYTCZXlk/ktfLz83HixAn559OnT2Pfvn0ICwtD8+bNMWLECDz66KN4//330alTJ6SlpWHjxo1o3749Bg4ciISEBNx888144oknMHfuXFitViQmJqJv375lur+uhWGHiGq8m2++GcnJydBoNGjUqFGF+2m1WjRt2lT++cKFCw63//XXX7jnnnvkAGS1WnHs2DG0bt3aJXUTebtdu3bh9ttvl38eP348AGDkyJFYsmQJFi9ejLfffhsvvvgiLl68iIiICNx6660YNGgQAEClUuGHH37A888/j549e8Lf3x8DBgzA+++/X6U6GHaIyOPl5ORg3759Dtvsfy0eOHAAt956K+Lj43Hvvfdi1qxZaN68OS5duoSffvoJ9913Hzp37lyp8zRr1gyrV6/G33//jdDQUMyZMwcpKSkMO0TV1Lt3b1xrOT8fHx9MnToVU6dOrXCf2NhYfPPNNzdUB8MOEXm8zZs3o1OnTuXe1rNnT2zatAk///wz3njjDTz++ONIS0tDdHQ0evbsiaioqEqf580338SpU6fQv39/+Pn5YdSoUbj33nuRk5PjrIdCRArgCspEVKM1atQIS5YskVdcJSK6GhcVJKIarXXr1g4zrIiIrsaWHSIiIvJqbNkhIiIir8awQ0RERF6NYYeIiIi8GsMOEREReTWGHSIiIvJqDDtERETk1Rh2iIiIyKsx7BAREZFXY9ghIiIir/b/AbKNrL3+MNjZAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -716,7 +527,7 @@
     },
     {
      "data": {
-      "image/png": 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8/fTTdjFozVrD4sWL0adPnzq3qfnL2WAwICcnp97+7Jr7FQQBv/76K5RK5RX3CUD6K6rmL5e69hkcHIxvvvmmzvWX/5Lp378/Xn31VZtlH374IX788cc6H3/s2DGsXLkSX3/9dZ3jQcxmM3r16oV33323zsdHRETUW3tjmc1mjBw5Es8991yd662/kK2mTZuGe+65x2bZ1KlT63zsyy+/jEGDBqGqqgqJiYlYuHAhiouL62yJuJJRo0Zhzpw5AIBz587hzTffxM0334yDBw9K48Hqc+jQIYwZMwbDhw/HnDlz8K9//avWoNrG6NChQ62xJWvXrsWnn37a5H02t6KiIhgMhiu+x61qvnezs7Px5ptv4s4778TRo0dtxgE15ufeGFu2bMG2bduQkJDQ6MfOmTMHo0aNgslkwtGjR7Fw4UKIoogVK1bYbGf9Q1Gv12Pnzp3SYOuPPvqo1j7/6f10uatpDa/PX3/9hR9//BEzZszAtGnTsGPHDpv1jf3MOjOGHRe0bt063Hzzzfjss89slhcXFyMoKEi637lzZ+zfvx9VVVXNMsjWytpyYyWKItLT09G7d2/puICl+bbmX1r1OXToEKqqqq4Y8Kz7FUURHTt2bNCHPCUlBYIg1PlXcs19btu2DQMHDmzQL7+goKBaz+lKg4jj4+PRp08f3HffffUe/9ChQxg+fHiL/DK9/Fg6na5BPxPA0gpx+bZ1/YUMAL169ZK2HTNmDDIzM/HFF19csbukLmFhYTbH7Nq1K2688UZs2LDhH0+70KtXL6xduxYeHh5Yu3Ytpk2bhsOHD0vdo5GRkQCAEydOoFOnTtLjDAYDMjIy6nyuly9LSkpq1PO53OWfHQBITU2VwkbNGi93/PhxBAUF2fwMUlJSAFxqUbmSy9+7UVFRGDhwIHbv3m0Tdhryc4+MjMThw4dhNpttWneOHz9u8zysRFHE888/jzvvvBMDBgz4x1ovFxMTI9U0evRo6PV6vPDCC3jttddsLutQ8w/FcePG4dChQ9i0aVOd++zYsSO2bNmCsrIym2751NRUmM1mm5+J2WxGWlqazeucl5eH4uLiWs81PT0doijafJ5TU1MBoNbg8v/+97+47bbboFQqceutt+Kzzz7DlClTpPWN/cw6M47ZcUFKpRKiKNosW7t2ba3Tr0+YMAEFBQX48MMPa+3j8sc3hnVGidW6deuQk5ODMWPGAAD69euHzp074+2335ZmqtR0/vz5WrVbP+xXctddd0GpVGLBggW16hdFEYWFhdJ9o9GI77//HjfccMMVm/jvvfdemEymOmd1GI3GqxqDkpCQgB9//BFvvPFGvUHm3nvvRVZWVp2zUy5evIjy8vImH7+uYyUkJGDz5s211hUXFzc6mFyJ9UvwagPcxYsXAaBB0/CvvfZaeHl5QaFQ4L///S9Onz6NhQsXSutHjBgBtVqN999/3+b989lnn6GkpATjxo27qlobYsOGDTaf0z///BP79++XPjthYWHo06cPvvjiC5v33pEjR7Bly5ZaMyZXr14NtVqNm266qdG1WFtg62ol/Sdjx45Fbm4uvvvuO2mZ0WjEBx98AG9vbwwZMqRWnYcPH65zRmJTWN8XNaef18VsNtf7/MaOHQuTyVTr96O1ldX6frC+5pfPfLp8O6vs7GybGVqlpaX48ssv0adPn1otcNZZouPGjcP999+POXPm2JwtvzU/s/aOLTsu6NZbb8XChQvxyCOP4MYbb0RycjK++eYbm79WActA4i+//BKzZ8/Gn3/+iUGDBqG8vBzbtm3Dk08+idtvv71Jxw8ICMBNN92ERx55BHl5eViyZAmioqKkpm7rl82YMWPQo0cPPPLII2jbti2ysrLw22+/QavV4qeffkJ5eTmWLVuG999/H9HR0TbnBbGGpMOHDyMhIQGxsbHo3LkzXn31VWlq6B133AEfHx9kZGRg/fr1mDZtGp599lls27YNL730Eg4fPoyffvrpis9lyJAhePzxx7Fo0SIkJSVh1KhRcHNzQ1paGtauXYulS5fi7rvvbtLrtGXLFowcOfKKf5U9/PDDWLNmDZ544gn89ttvGDhwIEwmE44fP441a9Zg8+bN/9jipdPpav31am0Z2LVrF9zc3NC2bVvMmTMH//vf/3Drrbdi8uTJ6NevH8rLy5GcnIx169bh9OnTNi2DjZGUlARvb28YjUYkJibiyy+/xO23397oL9JTp07h66+/BmAZ2P3hhx9Cq9U2apAyYDkvzNy5c/HGG2/g/vvvR+/evdGmTRvEx8djwYIFuOWWW3DbbbfhxIkT+Oijj3D99dfjX//6V6OO0RRRUVG46aabMH36dOj1eixZsgSBgYE23RSLFy/GmDFjEBsbiylTpkhTz319faVzN6WlpWHevHn49ttv8fzzz9cad1IX6yBYwDIQ+s0334Svry9uvvnmRj+PadOm4ZNPPsHkyZORmJiIDh06YN26ddizZw+WLFli01ICWD4LU6dOvWIr65UkJCRApVJJ3VgffPAB+vbtW6ulJCEhAQUFBVI31vbt2/Hss8/Wuc+xY8dixIgR+M9//oOMjAz06dMHO3bswPfff48nnnhCOs/SNddcg0mTJuHTTz9FcXExhgwZgj///BNffPEF7rjjjlqvX3R0NKZMmYIDBw4gJCQEn3/+OfLy8mp1uV1u6dKl6N69O2bOnCmN4WrJz6zDkWkWGLWAhkyfFEXL1PNnnnlGDAsLEz08PMSBAweKCQkJtaZWiqJluvd//vMfsWPHjqKbm5sYGhoq3n333eLJkydFUWzaNONvv/1WjI+PF4ODg0UPDw9x3Lhx4pkzZ2o9/u+//xbvuusuMTAwUNRoNGJkZKR47733itu3b7c59j/dLp+e+v3334s33XST6OXlJXp5eYndunUT4+LixBMnToiiKIozZ84UBw8eLG7atKlWTZdPPbf69NNPxX79+okeHh6ij4+P2KtXL/G5554Ts7OzpW0aO/VcEAQxMTHRZnldPyODwSC++eabYo8ePUSNRiP6+/uL/fr1ExcsWCCWlJTUOt7l+/un16/mtN2ysjIxPj5ejIqKEtVqtRgUFCTeeOON4ttvvy0aDAZRFJv2nrDeVCqVGBkZKf773/8WL1y4IIpi46ae19xXUFCQOGrUKDEhIaFBj738fVJZWSl269ZNvP7660Wj0Sgt//DDD8Vu3bqJbm5uYkhIiDh9+nSpVquWmnq+ePFi8Z133hEjIiJEjUYjDho0SDx06FCt7bdt2yYOHDhQ9PDwELVarTh+/HgxJSVFWv/tt9+KPXv2FJcuXWpzeof66qjvtd23b1+dNV7u8p+7KIpiXl6e+Mgjj4hBQUGiWq0We/XqVWuKeM0p91lZWTbr6vqZ1fe6WW8KhUJs166dOGnSJJsp/NbfndabWq0Wo6KixJdfflnU6/WiKNb92dPpdOKsWbPE8PBw0c3NTYyKihLfeOONWqf2qKqqEhcsWCD9Ho2IiBDj4+PFysrKWs9p3Lhx4ubNm8XevXuLGo1G7NatW61TM9Scel7TF198IQIQ//e//0nLGvKZdQWCKF5FfwRRI+zcuRM333wz1q5d2+TWjppOnz6Njh07IiMjo94z7M6fPx+nT5/GypUrr/p4rqhDhw6YP3/+P56pllqW9b2+ePHielsayPF16NABPXv2xMaNG+UuxelwzA4RERE5NY7ZIYfl7e2Nhx566IoDiHv37m0z24IaZ8iQITYnsSMickQMO+SwgoKCpMGo9bnrrrtaqRrndPmFGYmIHBHH7BAREZFT45gdIiIicmoMO0REROTU7GbMzhtvvIH4+Hg89dRT0pkmKysr8cwzz2D16tXQ6/UYPXo0PvroI5sLymVmZmL69On47bff4O3tjUmTJmHRokWNunil2WxGdnY2fHx8WvyU+0RERNQ8RFFEWVkZwsPDa11Ytia7CDsHDhzAJ598Il0byWrWrFn4+eefsXbtWvj6+mLGjBm46667sGfPHgCAyWTCuHHjEBoair179yInJwcTJ06Em5sbXn/99QYfPzs7u1kvmEhERESt5+zZs2jXrl2962UfoKzT6XDttdfio48+wquvvoo+ffpgyZIlKCkpQZs2bbBq1SrpBHTHjx9H9+7dkZCQgAEDBuDXX3/FrbfeiuzsbKm15+OPP8bcuXNx/vx5qNXqBtVQUlICPz8/nD17tkGnTSciIiL5lZaWIiIiAsXFxfD19a13O9lbduLi4jBu3DiMGDECr776qrQ8MTERVVVVNtcF6tatG9q3by+FnYSEBPTq1cumW2v06NGYPn06jh49ir59+9Z5TL1eb3NhQOtFKbVaLcMOERGRg/mnISiyhp3Vq1fjr7/+woEDB2qty83NhVqthp+fn83ykJAQ5ObmStvUDDrW9dZ19Vm0aBEWLFhwldUTERGRI5BtNtbZs2fx1FNP4ZtvvoG7u3urHjs+Ph4lJSXS7ezZs616fCIiImo9soWdxMRE5Ofn49prr4VKpYJKpcKuXbvw/vvvQ6VSISQkBAaDAcXFxTaPy8vLQ2hoKAAgNDQUeXl5tdZb19VHo9FIXVbsuiIiInJusoWd4cOHIzk5GUlJSdLtuuuuw0MPPST9383NDdu3b5cec+LECWRmZiI2NhYAEBsbi+TkZOTn50vbbN26FVqtFjExMa3+nIiIiMj+yDZmx8fHBz179rRZ5uXlhcDAQGn5lClTMHv2bAQEBECr1WLmzJmIjY3FgAEDAACjRo1CTEwMHn74Ybz11lvIzc3Fiy++iLi4OGg0mlZ/TkRERGR/ZJ+NdSXvvfceFAoFJkyYYHNSQSulUomNGzdi+vTpiI2NhZeXFyZNmoSFCxfKWDURERHZE9nPs2MPSktL4evri5KSEo7fISIichAN/f7mtbGIiIjIqTHsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJwaww4RERE5NYYdIiIicmp2fVJBsmU0GpGamirdj46OhkrFHyEREdGV8JvSgaSmpuKddbsQFB6JguwzeOZu8BpgRERE/4Bhx8EEhUciNDJK7jKIiIgcBsfsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJwaww4RERE5NYYdIiIicmoMO0REROTUGHaIiIjIqTHsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJwaww4RERE5NYYdIiIicmoMO0REROTUGHaIiIjIqTHsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJwaww4RERE5NYYdIiIicmoMO0REROTUGHaIiIjIqTHsEBERkVOTNewsX74cvXv3hlarhVarRWxsLH799Vdp/dChQyEIgs3tiSeesNlHZmYmxo0bB09PTwQHB2POnDkwGo2t/VSIiIjITqnkPHi7du3wxhtvoEuXLhBFEV988QVuv/12/P333+jRowcAYOrUqVi4cKH0GE9PT+n/JpMJ48aNQ2hoKPbu3YucnBxMnDgRbm5ueP3111v9+TQHo9GI1NRUm2XR0dFQqWT9URERETksWb9Bx48fb3P/tddew/Lly7Fv3z4p7Hh6eiI0NLTOx2/ZsgUpKSnYtm0bQkJC0KdPH7zyyiuYO3cu5s+fD7Va3eLPobmlpqbinXW7EBQeCQAoyD6DZ+4GYmJiZK6MiIjIMdnNmB2TyYTVq1ejvLwcsbGx0vJvvvkGQUFB6NmzJ+Lj41FRUSGtS0hIQK9evRASEiItGz16NEpLS3H06NF6j6XX61FaWmpzsydB4ZEIjYxCaGSUFHqIiIioaWTvG0lOTkZsbCwqKyvh7e2N9evXS60YDz74ICIjIxEeHo7Dhw9j7ty5OHHiBH744QcAQG5urk3QASDdz83NrfeYixYtwoIFC1roGREREZE9kT3sdO3aFUlJSSgpKcG6deswadIk7Nq1CzExMZg2bZq0Xa9evRAWFobhw4fj5MmT6Ny5c5OPGR8fj9mzZ0v3S0tLERERcVXPg4iIiOyT7N1YarUaUVFR6NevHxYtWoRrrrkGS5curXPb/v37AwDS09MBAKGhocjLy7PZxnq/vnE+AKDRaKQZYNYbEREROSfZw87lzGYz9Hp9neuSkpIAAGFhYQCA2NhYJCcnIz8/X9pm69at0Gq1HNBLREREAGTuxoqPj8eYMWPQvn17lJWVYdWqVdi5cyc2b96MkydPYtWqVRg7diwCAwNx+PBhzJo1C4MHD0bv3r0BAKNGjUJMTAwefvhhvPXWW8jNzcWLL76IuLg4aDQaOZ8aERER2QlZw05+fj4mTpyInJwc+Pr6onfv3ti8eTNGjhyJs2fPYtu2bViyZAnKy8sRERGBCRMm4MUXX5Qer1QqsXHjRkyfPh2xsbHw8vLCpEmTbM7L4+jMJpPUbZeeng6zWVFruRXPx0NERFSbrN+Mn332Wb3rIiIisGvXrn/cR2RkJH755ZfmLMuuFOVl4avUEnTKVyEt6SCC2kcj/LLlAM/HQ0REVB82AzgA/5C2CI2MQkF2Zp3LiYiIqH52N0CZiIiIqDkx7BAREZFTY9ghIiIip8awQ0RERE6NYYeIiIicGsMOEREROTWGHSIiInJqDDtERETk1Bh2iIiIyKkx7BAREZFTY9ghIiIip8awQ0RERE6NYYeIiIicGsMOEREROTWGHQdUYTAiz+iBchN/fERERP9EJXcB1HAXzUqklHpjy+8ZAIKgqTLjWrMIhUKQuzQiIiK7xaYBB5Jl9Eax0Q0AIECEXlTgXPFFmasiIiKybww7DqTUrAYADO3aBuGqcgBAal6ZnCURERHZPYYdB2EWRSnshPt6IERZAQBIz9fBZBblLI2IiMiuMew4iAvlBpiggBIiAr3V8FPooRHM0BvNOFNULnd5REREdothx0HkllYCALQqIxSCAEEAQtQGAEBqnk7O0oiIiOwaw46DsIYdX5VJWhaqsYSdU+d1MLIri4iIqE4MOw4ir0QPAPBVGaVlvkoTvDUqVJlEFFXKVRkREZF9Y9hxACYRKCivHXYEAQjRagAARXpZSiMiIrJ7DDsOoNSogigCGsEId4Vtd1WI1h0Aww4REVF9GHYcQIlRCQDQKgy11lnDzgWGHSIiojox7DiAEpPlqh51hZ1gH0s3lq4KKNObaq0nIiJydQw7DuBi9QU/vYSqWuvc3ZTw9bBcQiKtsHYYIiIicnUMOw5Ab7b8mDSKultuQqpbd9IK2ZdFRER0OYYdO2cWAb1ouaq5Rqgn7FSP22HYISIiqo1hx84ZRCUAyxmT1TDXuU1w9fTztAKGHSIiossx7Ng5S9gBvNQqCELd27Sp7sY6X2HC+TIGHiIiopoYduyc3hp2NMp6t9GolPCxjFHGkayS1iiLiIjIYTDs2Dlr2PHWqK64XYClcQdHsxl2iIiIapI17Cxfvhy9e/eGVquFVqtFbGwsfv31V2l9ZWUl4uLiEBgYCG9vb0yYMAF5eXk2+8jMzMS4cePg6emJ4OBgzJkzB0aj8fJDOaxLLTtXDju+1WGHV0AnIiKyJWvYadeuHd544w0kJibi4MGDGDZsGG6//XYcPXoUADBr1iz89NNPWLt2LXbt2oXs7Gzcdddd0uNNJhPGjRsHg8GAvXv34osvvsDKlSvx8ssvy/WUml1Dw45Wbfk3Na+spUsiIiJyKFf+Bm1h48ePt7n/2muvYfny5di3bx/atWuHzz77DKtWrcKwYcMAACtWrED37t2xb98+DBgwAFu2bEFKSgq2bduGkJAQ9OnTB6+88grmzp2L+fPnQ61Wy/G0mlXNbqy652JZ+FY/1VMF5TCazFAp2UNJREQE2NGYHZPJhNWrV6O8vByxsbFITExEVVUVRowYIW3TrVs3tG/fHgkJCQCAhIQE9OrVCyEhIdI2o0ePRmlpqdQ6VBe9Xo/S0lKbm71q6JgdLxWgUQowGM3ILKpojdKIiIgcguxhJzk5Gd7e3tBoNHjiiSewfv16xMTEIDc3F2q1Gn5+fjbbh4SEIDc3FwCQm5trE3Ss663r6rNo0SL4+vpKt4iIiOZ9Us1I6sZS1z8bCwAEQUCEr2VKFsftEBERXSJ72OnatSuSkpKwf/9+TJ8+HZMmTUJKSkqLHjM+Ph4lJSXS7ezZsy16vKaqMplhrP4Rebv/c49jez9LX1Yax+0QERFJZB2zAwBqtRpRUVEAgH79+uHAgQNYunQp7rvvPhgMBhQXF9u07uTl5SE0NBQAEBoaij///NNmf9bZWtZt6qLRaKDRaJr5mTS/cr1lVpkCItQNGIMT6VfdspPPlh0iIiIr2Vt2Lmc2m6HX69GvXz+4ublh+/bt0roTJ04gMzMTsbGxAIDY2FgkJycjPz9f2mbr1q3QarWIiYlp9dqbm6467LgrzBDqO31yDWzZISIiqk3Wlp34+HiMGTMG7du3R1lZGVatWoWdO3di8+bN8PX1xZQpUzB79mwEBARAq9Vi5syZiI2NxYABAwAAo0aNQkxMDB5++GG89dZbyM3NxYsvvoi4uDiHaLn5J+V6y4U/NYorzcO6JLJ6zM6p85yRRUREZCVr2MnPz8fEiRORk5MDX19f9O7dG5s3b8bIkSMBAO+99x4UCgUmTJgAvV6P0aNH46OPPpIer1QqsXHjRkyfPh2xsbHw8vLCpEmTsHDhQrmeUrOytuw0NOwEe6vg4abExSoTzhRVoHMb75Ysj4iIyCHIGnY+++yzK653d3fHsmXLsGzZsnq3iYyMxC+//NLcpdmFcinsiA3aXiEIiAr2RnJWCdLyyhh2iIiIYIdjduiSmmN2GqpLiCXgcPo5ERGRBcOOHZO6sYSGh53oEB8AvGwEERGRFcOOHStv5JgdAIiq7ro6db68RWoiIiJyNAw7dkoURZQbrLOxGjZmBwA6tfECAGQUlMNsbvjjiIiInBXDjp0ymkWYqsOKuhEtOxEBnlApBFysMuH3xGSkpKTAaDS2VJlERER2j2HHTlVWWVp1BIi48lWxbLkpFQjxtkyyW5FwFu+s24XU1NQWqJCIiMgxMOzYqcoqS2uOG8xowMmTbbTTWk4uCJ82CAqPbObKiIiIHAvDjp2ytuyoGjETy6qt1tKyU1xe1aw1EREROSKGHTtVabSEHbcmhB1ry86Fi4ZmrYmIiMgRMezYKX2NbqzGalt9jawL5Qw7REREsl4ugup3Nd1Y7bSWq5+XVhphavzDiYiInArDjp2qNFa37DQw7JhNJqSnpwMACs5lQCUoYBQF6Dhsh4iIXBzDjp3SVzVuzE5RXha+Si1Bp3wV0pIOwtO3H0pNKpQx7BARkYvjmB07ZZ16rmrEmB3/kLYIjYyCf3A4vJSWsFTKYTtEROTiGHbs1NXMxgIAT6XlcWzZISIiV8ewY6cqG9mNdTkvheXxDDtEROTqGHbsVOVVTD0HAC9ryw67sYiIyMUx7NgpvdE69dzUpMd7Vo/ZMZiB0sqm7YOIiMgZMOzYIZMoospkueJ5U7uxlALgrbFMtjtXyr4sIiJyXQw7dqiqRkOMCmKT9+PvZTmTMsMOERG5MoYdO2SobszRqBSNvuJ5Tf6eljMpZ5Uw7BARketi2LFD+uqWHXc35VXtRwo7nJJFREQujGHHDllbdtzdru7H4+9Z3Y3Flh0iInJhDDt2yGBt2VE1T8tOdlkVTOamj/0hIiJyZAw7dkgas3OVLTs+7iooBMBoBrIuXGyGyoiIiBwPw44dMjTTmB1BEOBj6cnCyQLdVVZFRETkmBh27JA0Zucqu7EASGHn1Pnyq94XERGRI2LYsUOXWnau/sfjYxm2gwy27BARkYti2LFDl8bssGWHiIjoajHs2KFLs7Gar2WHYYeIiFwVw44dunSenatv2dFWt+zkllaiXG+86v0RERE5GoYdO9Rcs7EAQK0U4Otu+TFnFLB1h4iIXA/Djp0xmUWba2M1h7bVA3cYdoiIyBUx7NiZiiqz9P/maNkBgPDqvqzTDDtEROSCGHbsjK66WcdNKUCpuIpLntfQVsuWHSIicl2yhp1Fixbh+uuvh4+PD4KDg3HHHXfgxIkTNtsMHToUgiDY3J544gmbbTIzMzFu3Dh4enoiODgYc+bMgdHomINxy/SWsNNcrTpAjbBTyLBDRESuRyXnwXft2oW4uDhcf/31MBqNeOGFFzBq1CikpKTAy8tL2m7q1KlYuHChdN/T01P6v8lkwrhx4xAaGoq9e/ciJycHEydOhJubG15//fVWfT7NQVc9Orm5xusAQLiP5cfMbiwiInJFsoadTZs22dxfuXIlgoODkZiYiMGDB0vLPT09ERoaWuc+tmzZgpSUFGzbtg0hISHo06cPXnnlFcydOxfz58+HWq1u0efQ3CqqLFcnVzdr2LG07FyoqEJxhQF+no71mhAREV0NuxqzU1JSAgAICAiwWf7NN98gKCgIPXv2RHx8PCoqKqR1CQkJ6NWrF0JCQqRlo0ePRmlpKY4ePVrncfR6PUpLS21u9qK8esyOWtl8Pxp3NwVCte4AOG6HiIhcj6wtOzWZzWY8/fTTGDhwIHr27Cktf/DBBxEZGYnw8HAcPnwYc+fOxYkTJ/DDDz8AAHJzc22CDgDpfm5ubp3HWrRoERYsWNBCz+TqWGdjaZrhIqA1dQjyRG5pJTIKytG3vX+z7puIiMie2U3YiYuLw5EjR/DHH3/YLJ82bZr0/169eiEsLAzDhw/HyZMn0blz5yYdKz4+HrNnz5bul5aWIiIiommFNzOpZacZu7EAoGOQN/adKuK4HSIicjl20Y01Y8YMbNy4Eb/99hvatWt3xW379+8PAEhPTwcAhIaGIi8vz2Yb6/36xvloNBpotVqbm72wtuw0f9ixDOo+xbBDREQuRtawI4oiZsyYgfXr12PHjh3o2LHjPz4mKSkJABAWFgYAiI2NRXJyMvLz86Vttm7dCq1Wi5iYmBapuyVd6sZq/pYdADjN6edERORiZO3GiouLw6pVq/Djjz/Cx8dHGmPj6+sLDw8PnDx5EqtWrcLYsWMRGBiIw4cPY9asWRg8eDB69+4NABg1ahRiYmLw8MMP46233kJubi5efPFFxMXFQaPRyPn0mqS5ByibTSakp6dDHWQAAJzML0NVVRXc3NyaZf9ERET2TtaWneXLl6OkpARDhw5FWFiYdPvuu+8AAGq1Gtu2bcOoUaPQrVs3PPPMM5gwYQJ++uknaR9KpRIbN26EUqlEbGws/vWvf2HixIk25+VxJM3dslOUl4Wvdh3DtqNZEABcrBKx//CxZtk3ERGRI5C1ZUcUxSuuj4iIwK5du/5xP5GRkfjll1+aqyxZlbfAeXb8Q9qibYcu8MnKQGmlEdmljnl2aSIioqawiwHKdMnFFhqgDEA6mWBWaVWz75uIiMheMezYmZaaeg4A/p6WcTrZDDtERORCGHbsTEudVBCo0bJTxrBDRESug2HHjhhNZlQaq8fsNOPlIqz8qlt22I1FRESuxG7OoExAud4k/b9Fxux4WLuxDDhy9CgUgoDo6GioVHwbEBGR82LLjh0prbS0uCgFQKkQmn3/Wnc3CBBRZRbw8e7TeGfdLqSmpjb7cYiIiOwJ/6S3Izq9ZUq4WwtFUIVCgIdgRIXoBreAtvBya/5ARUREZG/YsmNHyiotYacFerAknoLlGMUVhpY7CBERkR1h2LEjOr2lG6ulWnYAwFNhDTscpExERK6BYceOWFt2WjLseAiWkHOBLTtEROQiGHbsSGuEHall5yJbdoiIyDVwgLIdaZWwUz1mp/RiFcxXvjQZERGRU2hy2CkvL8euXbuQmZkJg8G2S+Tf//73VRfmilpjzI5GMEEBEWZRQDkbd4iIyAU0Kez8/fffGDt2LCoqKlBeXo6AgAAUFBTA09MTwcHBDDtN1BotO4IAeCpN0JlU0DHsEBGRC2jS1+qsWbMwfvx4XLhwAR4eHti3bx/OnDmDfv364e23327uGl2GrhXCDgB4KizX3+IlsoiIyBU06Ws1KSkJzzzzDBQKBZRKJfR6PSIiIvDWW2/hhRdeaO4aXUZpa4UdJcMOERG5jiZ9rbq5uUGhsDw0ODgYmZmZAABfX1+cPXu2+apzEUajESkpKcgvKgEAqISWHTnspbRcg6uMs8+JiMgFNGnMTt++fXHgwAF06dIFQ4YMwcsvv4yCggJ89dVX6NmzZ3PX6PRSU1PxzrpdyDS1BwAYKnQtejxrNxbH7BARkStoUsvO66+/jrCwMADAa6+9Bn9/f0yfPh3nz5/Hp59+2qwFuoqg8EiICstVyVu6ZcezumWn3AhUmTj/nIiInFuTWnauu+466f/BwcHYtGlTsxXkygxGS4tLS4cdtSBCrVTAYDIjp6wK17To0YiIiOTVpJadYcOGobi4uJlLcW2iKEJvtLS4tHTYEQTAz9PSipRVyr4sIiJybk0KOzt37qx1IkG6OmYR0hmNVYqW71pi2CEiIlfR5EnOgiA0Zx0ur8ps/Z8IZSscz89DDQDILjO2wtGIiIjk0+TLRdx5551Qq9V1rtuxY0eTC3JV1rCjhIjWyJH+bNkhIiIX0eSwExsbC29v7+asxaVZw45KMF95w2bi51ndssOwQ0RETq5JYUcQBMyZMwfBwcHNXY/LksIOWmcquHXMTuFFE8r1Rnhpmpx7iYiI7FqTxuyIIs/N0txau2XH3U0JdfVP/3Rheasck4iISA5NCjvz5s1jF1Yzu9Sy0zphBwB8LI07OF1Q0WrHJCIiam1N6ruYN28eAOD8+fM4ceIEAKBr165o06ZN81XmYqQByi18jp2afNRAoR7IKGjZy1MQERHJqUktOxUVFXj00UcRHh6OwYMHY/DgwQgPD8eUKVNQUcFWgqaQo2XHu7plJ4MtO0RE5MSaFHZmzZqFXbt24X//+x+Ki4tRXFyMH3/8Ebt27cIzzzzT3DW6BGMrj9kBLnVjsWWHiIicWZO6sb7//nusW7cOQ4cOlZaNHTsWHh4euPfee7F8+fLmqs9lyNWNBQCnC9myQ0REzqvJ3VghISG1lgcHB7Mbq4nk7MYqKjegpILn2yEiIufUpLATGxuLefPmobKyUlp28eJFLFiwALGxsc1WnCu51I3Vei07bgoBgZ6Wi1OcZFcWERE5qSaFnSVLlmDPnj1o164dhg8fjuHDhyMiIgJ79+7F0qVLG7yfRYsW4frrr4ePjw+Cg4Nxxx13SLO7rCorKxEXF4fAwEB4e3tjwoQJyMvLs9kmMzMT48aNg6enJ4KDgzFnzhwYjY51zadLl4tovZYdAGintTTvnMxn2CEiIufUpLDTq1cvpKWlYdGiRejTpw/69OmDN954A2lpaejRo0eD97Nr1y7ExcVh37592Lp1K6qqqjBq1CiUl186yd2sWbPw008/Ye3atdi1axeys7Nx1113SetNJhPGjRsHg8GAvXv34osvvsDKlSvx8ssvN+WpyabKesXzVmzZMZtM8BEt3Y5/Hj/jcAGRiIioIZo0QHn37t248cYbMXXq1Ks6+KZNm2zur1y5EsHBwUhMTMTgwYNRUlKCzz77DKtWrcKwYcMAACtWrED37t2xb98+DBgwAFu2bEFKSgq2bduGkJAQ9OnTB6+88grmzp2L+fPn13uxUntjlKFlpygvC5llSkAZjp3HcpCamoqYmJhWOz4REVFraFLLzs0334yioqLmrgUlJSUAgICAAABAYmIiqqqqMGLECGmbbt26oX379khISAAAJCQkoFevXjYDpkePHo3S0lIcPXq0zuPo9XqUlpba3ORWJcOYHQAI8vUBAOiVnq16XCIiotZiN9fGMpvNePrppzFw4ED07NkTAJCbmwu1Wg0/Pz+bbUNCQpCbmyttc/nMMOt96zaXW7RoEXx9faVbREREMz+bxpOjZQcAvJSW4+kMgMnMa54REZHzafKlrhMSEuDv71/nusGDBzd6f3FxcThy5Aj++OOPppbUYPHx8Zg9e7Z0v7S0VNbAYzSLMMkwZgcA3BVmKBUCTGYReTojerXq0YmIiFpek8POnXfeWedyQRBgMpkata8ZM2Zg48aN2L17N9q1ayctDw0NhcFgQHFxsU3rTl5eHkJDQ6Vt/vzzT5v9WWdrWbe5nEajgUajaVSNLeli1aXWnNZu2REEwN/TDQU6A86V8lw7RETkfJrUjQVYuojMZnOtW2OCjiiKmDFjBtavX48dO3agY8eONuv79esHNzc3bN++XVp24sQJZGZmSufziY2NRXJyMvLz86Vttm7dCq1W6zCDbSuqw45KIUAhtP7x/T0tg7gZdoiIyBk1qWVHEJrnGzkuLg6rVq3Cjz/+CB8fH2mMja+vLzw8PODr64spU6Zg9uzZCAgIgFarxcyZMxEbG4sBAwYAAEaNGoWYmBg8/PDDeOutt5Cbm4sXX3wRcXFxdtV6cyUV1fPO3ZRNzp5XRQo7JQw7RETkfJoUdpprgLL1Glo1r7EFWKaXT548GQDw3nvvQaFQYMKECdDr9Rg9ejQ++ugjaVulUomNGzdi+vTpiI2NhZeXFyZNmoSFCxc2S42twdqyo1bJFHa8LCcWZMsOERE5oyaFHbO5ecaVNCQ0ubu7Y9myZVi2bFm920RGRuKXX35plprkcLFm2GnccKdmYW3ZyWLLDhEROaEmNSUsWrQIn3/+ea3ln3/+Od58882rLsrVSC07MndjXag0oeQiAw8RETmXJn27fvLJJ+jWrVut5T169MDHH3981UW5movVY3bk6sZSqxTwsFwPFKfO8xpZRETkXJr07Zqbm4uwsLBay9u0aYOcnJyrLsrVyN2yAwA+1VfVOHm+/MobEhEROZgmfbtGRERgz549tZbv2bMH4eHhV12Uq5F7gDIAaKvDTlp+mWw1EBERtYQmDVCeOnUqnn76aVRVVUkX6Ny+fTuee+45PPPMM81aoCu4aActO9awk57HbiwiInIuTQo7c+bMQWFhIZ588kkYDAYAlllTc+fORXx8fLMW6AoqZB6zAwC+ltnnSOeYHSIicjJNPqngm2++iZdeegnHjh2Dh4cHunTp4jAn8bM31pYdN6UMp0+uZm3ZySyqQGWVCe5uStlqISIiak5NvjYWAHh7e+P6669vrlpcVs0xO3Jdd1yjBHzUCpQZzDh5Xoce4b4yVUJERNS8mhx2Dh48iDVr1iAzM1PqyrL64YcfrrowV1Iz7OhlqkEQBLT3c8PRfD3S8xl2iIjIeTRpkMjq1atx44034tixY1i/fj2qqqpw9OhR7NixA76+/JJsLOk8OzIOUAaA9r6Wvqz0fI7bISIi59Gkb9fXX38d7733Hn766Seo1WosXboUx48fx7333ov27ds3d41Ozx6mngNARPUo5TTOyCIiIifSpG/XkydPYty4cQAAtVqN8vJyCIKAWbNm4dNPP23WAl2BPZxUEADa+1WHHZ5rh4iInEiTvl39/f1RVmb5Qmzbti2OHDkCACguLkZFRUXzVeci7KVlx9qNdaawAgZj81zslYiISG5N+nYdPHgwtm7dCgC455578NRTT2Hq1Kl44IEHMHz48GYt0NnpjSZYc4XcLTuBnkp4a1QwmkWcKeRlI4iIyDk0aTbWhx9+iMrKSgDAf/7zH7i5uWHv3r2YMGECXnzxxWYt0NmV603S/91kbtkRBAGdg71x6Gwx0vJ16BLiI2s9REREzaFRYae0tNTyIJUK3t7e0v0nn3wSTz75ZPNX5wJ0lUYAgEoAFIJ8JxW06mINO3k6oJfc1RAREV29RoUdPz8/CA34QjaZTP+4DVno9NVhR95GHUmXYG8AwIncUqSkpEjLo6OjoVJd1TkoiYiIZNGob6/ffvvN5r4oihg7diz++9//om3bts1amKuwhh03Owk70aGWrqsjZwvxzqmTCAqPREH2GTxzNxATEyNzdURERI3XqLAzZMiQWsuUSiUGDBiATp06NVtRrqTczlp2ulWHnXOlVbihU3uERkbJXBEREdHVsZOvWNdVZmctO6Fad/i4q2AWgTLDP29PRERk767qK/bs2bOoqKhAYGBgc9XjcsrtLOwIgiC17hQz7BARkRNoVDfW+++/L/2/oKAA3377LYYNG8brYV0FaTaWnYQdAIgO8cGB0xdQKtdVSYmIiJpRo8LOe++9B8Dy139QUBDGjx/P8+pcJXvrxgLAlh0iInIqjQo7GRkZLVWHy5IGKMt/ih1JdPXJBEsYdoiIyAnYUXuCa7J2Y9lTy07X6padCqPlchZERESOzI6+Yl2TzmB/Y3b8PNUI9FACAIrK2bxDRESOzY6+Yl2TPbbsAEAHf8sV0At0DDtEROTY7Owr1vXY2xmUrSL93AAAhTpOySIiIsdmZ1+xrsfeW3YK2bJDREQOzs6+Yl2PvV0I1KqDn7UbSw9RFGWuhoiIqOns7CvW9ZRVVgGwv5ad9n5uEABUGs24aJS7GiIioqazs69Y1yKKot2O2VErFdBaGndwgT1ZRETkwOzsK9a1VBhMMFf3ENlb2AEAP43l32KOUSYiIgdmh1+xrsPaqqMQAKUdnUHZqnqMMsMOERE5NIYdGZVVz8TydFNAEOwv7UgtO+zGIiIiByZr2Nm9ezfGjx+P8PBwCIKADRs22KyfPHkyBEGwud1yyy022xQVFeGhhx6CVquFn58fpkyZAp1O14rPoumsg5O91PaZOa1hR1cFVBjM8hZDRETURLJ+y5aXl+Oaa67BsmXL6t3mlltuQU5OjnT79ttvbdY/9NBDOHr0KLZu3YqNGzdi9+7dmDZtWkuX3iys3Vie9jhgB4BGKcBbY7lWbAZHKRMRkYNq1FXPm9uYMWMwZsyYK26j0WgQGhpa57pjx45h06ZNOHDgAK677joAwAcffICxY8fi7bffRnh4eLPX3Jx0UjeW/XVhWbXx0UCnN+IUww4RETko+2xSqGHnzp0IDg5G165dMX36dBQWFkrrEhIS4OfnJwUdABgxYgQUCgX2799f7z71ej1KS0ttbnKoOWZHbmaTCenp6UhJSUFKSgrS09NhNpvRxtvSl3WyiKOUiYjIMcnasvNPbrnlFtx1113o2LEjTp48iRdeeAFjxoxBQkIClEolcnNzERwcbPMYlUqFgIAA5Obm1rvfRYsWYcGCBS1d/j8qs6NurKK8LHyVWoJO+Za3RFrSQQS1j0ZQ2zAAYMsOERE5LLsOO/fff7/0/169eqF3797o3Lkzdu7cieHDhzd5v/Hx8Zg9e7Z0v7S0FBEREVdVa1NI3VhqBWAH43/9Q9oiNDIKAFCQnQkAUsvOmQtVMJrMUCnlD2ZERESN4VDfXJ06dUJQUBDS09MBAKGhocjPz7fZxmg0oqioqN5xPoBlHJBWq7W5yUGajWUHLTv18fVwg0oAqswiThWUy10OERFRo9nvt2wdzp07h8LCQoSFWbpWYmNjUVxcjMTERGmbHTt2wGw2o3///nKV2WD2PhsLAARBkKagp2TLM7aJiIjoasj6LavT6ZCUlISkpCQAQEZGBpKSkpCZmQmdToc5c+Zg3759OH36NLZv347bb78dUVFRGD16NACge/fuuOWWWzB16lT8+eef2LNnD2bMmIH777/f7mdiATXH7NjvbCzg0vl2UnIYdoiIyPHIGnYOHjyIvn37om/fvgCA2bNno2/fvnj55ZehVCpx+PBh3HbbbYiOjsaUKVPQr18//P7779BoNNI+vvnmG3Tr1g3Dhw/H2LFjcdNNN+HTTz+V6yk1is2YHTvmV33ZCLbsEBGRI5J1gPLQoUMhimK96zdv3vyP+wgICMCqVauas6xWYx2zY8/dWADgX6NlRxRFu7y0BRERUX3s+1vWyTnCmB0A0KotFystKjcgv4zn2yEiIsdi39+yTk5nRycVvBKVQkA7rRsAdmUREZHjse9vWSdnPYOyvV4ItKZOAZaBOxykTEREjsb+v2WdlNksQmdwjJYdAOjkXx122LJDREQOxv6/ZZ1URZUJ1rHZ9j71HAA6BVhGKbNlh4iIHA3DjkysM7HclALUSgcIO9UtO6cLy6WB1URERI6AYUcm1sHJ3hqVQ0zl9vNQIkSrgSgCJ3LZukNERI6DYUcm1rMne7vb9bVYbcSEWa4hdpTjdoiIyIEw7MjkUsuOm8yVNFzPtr4AgORzJTJXQkRE1HAMOzKxTjv3caCWHSnsZDHsEBGR42DYkYlObxmg7KOx/7BjNpmQnp4O94p8AEBavg6VVSaZqyIiImoY+/+mdVLWlh1HGLNTlJeFr1JL0LGbEm4wocqsxPHcMvSJ8JO7NCIion/Elh2ZOFo3ln9IW4R16IJATyUAdmUREZHjYNiRifVcNY40QBm4dAX0IxykTEREDoJhRyY6B2vZsao+kTJbdoiIyGEw7MikzDpA2cHCjrVlJzWvjIOUiYjIITDsyKSsxhmUHYmnCvBRK2A0i0jNK5O7HCIion/EsCOTS2N2HCvsCIKAqEDLdbLYlUVERI6AYUcmjjT1/HJRgZa+rCMMO0RE5AAYdmRiHaCsdXes2VhmkwnehiIAwIH0PKSkpMBo5FXQiYjIfjHsyMRRu7GK8rKQeDQdAHCqyIDFa3chNTVV5qqIiIjqx7AjA7NZvBR2HLAbKyQ4GBqVAmYAqsD2cpdDRER0RQw7MtAZLnX7OFrLDgAIAhDsYxm3c0EvczFERET/gGFHBiUVlnPsaFQKuLspZa6maYK17gAYdoiIyP4x7Mig5KIl7Ph6ONbg5JqsLTtFDDtERGTnGHZkUOpEYadED1SZRJmrISIiqh/DjgycoWXH18MN6upBypnFBrnLISIiqhfDjgxKKy1hR+vAYUcQBKl1J72IYYeIiOwXw44MnKFlB7jUlZVeyIE7RERkvxh2ZOA8YccyIyuNLTtERGTHGHZkYA07jtyNBQDBWkvLTkaRAVUms8zVEBER1Y1hRwalF63XxXK8EwrW5OfhBjcFUGUWkZank7scIiKiOjHsyMBZurEEQYCf2vL/I9m8AjoREdknhh0ZOEvYAQB/y7AdHMli2CEiIvvEsCMDZzipoFWAZdgOkhl2iIjITskadnbv3o3x48cjPDwcgiBgw4YNNutFUcTLL7+MsLAweHh4YMSIEUhLS7PZpqioCA899BC0Wi38/PwwZcoU6HT2PX7EGc6zY+VfHXaO5ZTCyEHKRERkh2QNO+Xl5bjmmmuwbNmyOte/9dZbeP/99/Hxxx9j//798PLywujRo1FZWSlt89BDD+Ho0aPYunUrNm7ciN27d2PatGmt9RQaTRRFqRsr/9xppKSkID09HWazYwYFHzfAw01AZZUZ6eftO2QSEZFrknU60JgxYzBmzJg614miiCVLluDFF1/E7bffDgD48ssvERISgg0bNuD+++/HsWPHsGnTJhw4cADXXXcdAOCDDz7A2LFj8fbbbyM8PLzVnktDXawySdeS+vyXvQht2x5pSQcR1D4a9lftPxMEAZ0DNDiSV4nkcyXoFqqVuyQiIiIbdjtmJyMjA7m5uRgxYoS0zNfXF/3790dCQgIAICEhAX5+flLQAYARI0ZAoVBg//799e5br9ejtLTU5tZarK06SgEICY9AaGQU/IMdMeZcEhVgmZJ1NLv1XkciIqKGstuwk5ubCwAICQmxWR4SEiKty83NRXBwsM16lUqFgIAAaZu6LFq0CL6+vtItIiKimauvn/UcO15qBQRBaLXjtqSoQMvAHQ5SJiIie2S3YaclxcfHo6SkRLqdPXu21Y5tbdnxVjvHS282maDWWYLlkaxi6A1VMldERERky26/cUNDQwEAeXl5Nsvz8vKkdaGhocjPz7dZbzQaUVRUJG1TF41GA61Wa3NrLVLY0Shb7ZgtqSgvC1sOHINKAPRGEdsPHJW7JCIiIht2G3Y6duyI0NBQbN++XVpWWlqK/fv3IzY2FgAQGxuL4uJiJCYmStvs2LEDZrMZ/fv3b/WaG0IKO252+9I3WkBIW4T4egAAUgt4BXQiIrIvss7G0ul0SE9Pl+5nZGQgKSkJAQEBaN++PZ5++mm8+uqr6NKlCzp27IiXXnoJ4eHhuOOOOwAA3bt3xy233IKpU6fi448/RlVVFWbMmIH777/fLmdiAZdOKOitcZ6wAwChWndkFV/EiUKGHSIisi+yhp2DBw/i5ptvlu7Pnj0bADBp0iSsXLkSzz33HMrLyzFt2jQUFxfjpptuwqZNm+Du7i495ptvvsGMGTMwfPhwKBQKTJgwAe+//36rP5eGshmz45in1qlTSPUV0NmyQ0RE9kbWsDN06FCIoljvekEQsHDhQixcuLDebQICArBq1aqWKK9F2ISdyn/Y2IGE+FoCaMYFAy4aTPBQO8eYJCIicnzO1ZfiAKzdWF5OMhvLykejgrsSMIvAUV4BnYiI7IhzfeM6AOt1sXycLOwIgoCA6t7FpLPFstZCRERUk3N94zoAZ5t6XlP1uQUZdoiIyK4w7LQyZzupYE3Wlp1D54plrYOIiKgm5/vGtXMlTjpmBwACqlt2zhZdRKGOs7KIiMg+ON83rp2zXhvLGVt21EoB7bRuANi6Q0RE9sP5vnHtmMFoxsUqEwDnDDsA0DXI0ryTdJYzsoiIyD445zeunbJ2YQmCc3ZjmU0m+IulAIA9x87BaDTKXBERERHDTqu6NBNLBYUgyFxN8yvKy8LRtAwAwKFsHU6cOCFzRURERAw7rcoadnw93GSupOW0bRMApSDACCVyytiyQ0RE8mPYaUXWEwo6c9hRCEAbH8u4nRO8ThYREdkBhp1WdKHcAADw83TesANcuigoww4REdkDhp1WVFQddoK8NTJX0rJCqy8KyiugExGRPWDYaUUFOkvYCfBSy1xJywrRWsLOySIDDEazzNUQEZGrY9hpRdazCjt7y46fhxvUCqDKLOJ4bqnc5RARkYtj2GlF1m6sQCdv2al5BfRDvCgoERHJjGGnFRWUu0Y3FnDpOll/M+wQEZHMGHZakbUbK9DJu7EAIKi6ZSfxzAV5CyEiIpfHsNOKXKUbCwAC3QEBwJnCCuSXVspdDhERuTCGnVZy0WBChcFyEdBAb+cPO2qlgI7+luf55+kimashIiJXxrDTSgrLLV1YapUC3hqVzNW0PLPJhHYaS4vOlr9OIiUlhRcGJSIiWTDstJJC3aUuLMEJLwJ6uaK8LGSdOwcA2H2qFO+s24XU1FSZqyIiIlfEsNNKrC07rtCFZdU2SAsAKDYA2pD2MldDRESuimGnlVxq2XH+mVhW7gpRuuhpIccoExGRTBh2WkmhC83EqinczzIH/TzDDhERyYRhp5VcOseOq4UdDwBAwUWZCyEiIpfFsNNKCqWzJ7tONxYAtK0OO4WVQCUvCkpERDJg2Gkl0pgdF2vZ8fNwg7dGBTOAo3nsyyIiotbHsNNKrLOxglws7AiCgPYBngCAv3PYl0VERK2PYaeVFOlcsxsLACIDLWHnr2yGHSIian0MO61AFEXpiueuNhsLACL8LWHndHEVr5NFREStjmGnFZQbTDBUD851tTE7AOChVsK/ukHrj/QCeYshIiKXw7DTCqzTzj3clPBUO/91seoSamncwe9pDDtERNS6GHZaQYGLzsSqKcQyAx2/pxVAFEV5iyEiIpfCsNMKilx4vI5VkAegUQko0OmRklMqdzlERORC7DrszJ8/H4Ig2Ny6desmra+srERcXBwCAwPh7e2NCRMmIC8vT8aK63bp7MmuNxPLSikIuDbM0ryz+UiuzNUQEZErseuwAwA9evRATk6OdPvjjz+kdbNmzcJPP/2EtWvXYteuXcjOzsZdd90lY7V1c9XrYl3upkgvAMAvDDtERNSK7H60rEqlQmhoaK3lJSUl+Oyzz7Bq1SoMGzYMALBixQp0794d+/btw4ABA1q71HoVVLfsBLjwmB0AuKGdJ9yUAtLzdUjLK0OXEB+5SyIiIhdg9y07aWlpCA8PR6dOnfDQQw8hMzMTAJCYmIiqqiqMGDFC2rZbt25o3749EhISrrhPvV6P0tJSm1tLyi2xnFsmVOveosexd15qBQZ1aQMA+CWZrTtERNQ67Drs9O/fHytXrsSmTZuwfPlyZGRkYNCgQSgrK0Nubi7UajX8/PxsHhMSEoLc3Ct/kS5atAi+vr7SLSIiogWfBZBdbDlzsPUK4K5sTE9LK92vR3JkroSIiFyFXXdjjRkzRvp/79690b9/f0RGRmLNmjXw8Gh6cIiPj8fs2bOl+6WlpS0aeLKqw05bhh2MjAmBSiHgeG4ZTp3XoVMbb7lLIiIiJ2fXLTuX8/PzQ3R0NNLT0xEaGgqDwYDi4mKbbfLy8uoc41OTRqOBVqu1ubWUyiqTdJ4dhh3Az1ONG6OCAAD/O5QtczVEROQKHCrs6HQ6nDx5EmFhYejXrx/c3Nywfft2af2JEyeQmZmJ2NhYGau0Ze3C8lQr4efpJnM19uGuvm0BAN8dOAujySxzNURE5Ozsuhvr2Wefxfjx4xEZGYns7GzMmzcPSqUSDzzwAHx9fTFlyhTMnj0bAQEB0Gq1mDlzJmJjY+1qJlZ2sWVwcrifBwRBkLka+ZhNJqSnpwMAOqpE+Hu6IaekEjuO52NUjyu3xBEREV0Nuw47586dwwMPPIDCwkK0adMGN910E/bt24c2bSwzet577z0oFApMmDABer0eo0ePxkcffSRz1bayiisAWLqwjEYjUlNTAQDp6ekwmx2qYe2qFOVl4avUEnTKV6Eg+wyGdeyF74+W4Ot9Z9BOKJK2i46Ohkpl129LIiJyMHb9rbJ69eorrnd3d8eyZcuwbNmyVqqo8bJqtOykpqbinXW7EBQeibSkgwhqH41wmetrTf4hbREaGQUAGBXtg++PluD3tAIYzx5CRLt2KMg+g2fuBmJiYmSulIiInInrNC3IJOuCZcxOO3/L4OSg8EiERkbBP9iVYo4ts8mE8rwzuDbMAyKAQk04QiOjEBQeKXdpRETkhBh2Wtilc+y49gkFayrKy8JXu47BE5bXJr0EuFhlkrkqIiJyVgw7LezSOXY8Za7EvviHtMW13aPgLRhggoDEMxfkLomIiJwUw04LMptF5JSwZac+giCgs7oEAHDobDEuGkWZKyIiImfEsNOCCnR6VJlEKAQgxMWvi1WfQEUlfJVGGM0ijrFxh4iIWgDDTgs6V92FFap1h5uSL3VdBAGI8rS8TidLgMxig8wVERGRs+E3cAviBUAbJkBlRMcgL5gBLE0ogNnM7iwiImo+DDstyDrtvK0/w86VCAJwc9c2UAnAsfN6fLXvjNwlERGRE2HYaUFs2Wk4H3c39LZcHxRvbTqOM4Xl8hZEREROg2GnBV2ads6w0xBRWqBHsAblBhMe/yoRFQaj3CUREZETYNhpQdZLRTDsNIwgCJg7OBhB3hoczy3D3O+TIYocv0NERFeHYacF+bir4OOu4pidRgjyVOGjh66FSiHgp0PZeG9bmtwlERGRg7PrC4E6ujWPxwIAWyca6YaOAZh/Ww+8uOEI3t+eBoUAPD0iWu6yiIjIQbFlpxUIgiB3CQ7nXwMiET+mGwBgybY0vLnpOEyckk5ERE3AsEN2a8rASDx6rT8AYPnOk3hkxZ8oruBJB4mIqHEYdshupaam4szxQ+gfDChgxu60AoxZ+jt+Sc5h1yARETUYx+yQXQsKj0TPyCj4pqYhuViFnJJKPPnNX7gm1B0P9vZDzxB3CIKA6OhoqFR8OxMRUW38diCH4K8RsGx8W+zMU2P5b+k4lFuJQ7m58NcAIWIhXrnLjOv79ITRaERqaqr0OIYgIiLitwDZDbPJhPT0dOl+eno6zOZLPa3uKgVmj4xGH+1FvLb9HE7rBFzQi7iAQDy45jQG7i9Ad49SHD95DsERHVCQfQbP3A3ExMTI8XSIiMhOMOyQ3SjKy8JXqSXolG95W6YlHURQ+2iEwzYI6fLO4NogBYZd0xEp2aX4+1QuKsxu2JlRjp1Qwl0RgX4IQHAIx/UQERHDDtkZ/5C2CI2MAgAUZGdKy2sGISkEdVThug4B0GQnodzNFxd9IpCSVYRKsxJ70guhFACN9wX8p1MVfNzd2MVFROSi+JueHIY1CNUMQYDlqum+KhMGdAtGQNERnFcEIE/wR4HOgNXJxdh6aifm3tINPTxL8e73uxEUHskuLiIiF8KwQ05FKQBtNQYM79MeiSnpyChXIbvMgOe+P4y+Ye5o26a91HJERESugefZIackCALaeQv4+PZ2iB/TDRqVAn/nVGJTJnAsp5Tn6SEiciFs2WlBl48RuXx2EbU8lULA40M6Y2RMCOK+3Idj5/XYkpKHCG9ggt4kd3lERNQKGHZaUGpqKt5ZtwtB4ZEAbGcXUcu7fCr7tKgKfGZUIKVYwFkd8ORPWfjAJww3RgXJWCUREbU0hp0WFhQeWefsImp5dU1lD28fjR7X9cbPSWdRWGHCg//djykDO+C2DoCb8tIFWzlTi4jIefC3OTm1uqayh2rdMToCqFD54NfUMny25zS+22vAje3U8NcInKlFRORkOICEXJJKIWDmgCD838TroNUooBPV2HIWOFLhA6/g9nKXR0REzYhhh1zayJgQfHRbW0R4W+4fzS7Fz2eAD/cV4NR5nbzFERFRs2A3FrmkmoOXi7JOY0CwAgO6RuD3tALkllbil9Qy/PLOLnQL0uCe/h0xrHsoOgR6QhCEf9gzERHZG4Ydckl1XX6ie8do3HtdO/yxZy9OXnRHiUKL4wV6vPLzcbzy83G09fNA3/Z+iAnXokuwD8J83RHm644ALzVMJhMvRUFEZKf425hcVl2XnxAEAf5KPfoHuaF9TEccTDmJMqOAMzoBWcUXkVV8ERsP59jsR61SIMBdgYqKcni6a2Cu1KFr8Em0Cw6At1oBH40CXTtGINDbHYGeKpTmnZVmfjUkFPGaXkREV4e/MYnq4aVRIbAyC24lJejTtScOHT2GSrU/BN9Q6KqAcr0RBqhgMJqRqzMD0KC0AgC8kZ8LILf40s72FNTYswh3pQC1qEfPdnnoGhGMUK07PNVKaFRKaNwU0KgUUCkUUCiAc2fP4oc/kuEfGIyKohw8fZsJA/r2bPEuNYYsInIW/M1F9A/8Q9qiXccuKM49C6W7Bt37dgEAHEnYgeKSUoRF9UDqsaPwDI5Em4jOOJ1+HCalO7wDQ1FpNKGktAwaJWCAGwrKq2AUBVSagEposDezAnszTzegimAgGwDC8MCaTHisP4v2vmpE+qoQHahGTIgnInzd0L1b12YLJDVPitnQ6fhXCkgMT0QkF6f5TbNs2TIsXrwYubm5uOaaa/DBBx/ghhtukLsscnKBIeHoHNUFF8+fhVJdhe7hWpjP6KB0B7p3CwYAHEk4htILJejUvRdSk/6Etl1XhHXphZRDf6P4ogGegeG4UKpDqJ8X1B6eqDKJKCm/iEKdAUq1BpUV5VCqNXDTeEBXUYkqKHGxSsSJAj1OFOix5WQ5gAtQwoweobkY2K0t+kT4oW+EH4K17g16HpVVJuSX6pFXVonckkrkl+mRmVWMXE17lFT5oURlxptbTyI4sQhqlQC1UkC7sBB4atzg7qaARqWEu5sCuVnn8P3uJAQGh6L0fDYeH1uF6OhoAJbw9H+/7Id/SFtcyMvClDFV6BXTFd4aFTzclBz83cIYNsmVOcU7/bvvvsPs2bPx8ccfo3///liyZAlGjx6NEydOIDg4WO7yiGzGBykVIkK07jivuojQNt7o3rcLjiTsQGneaXTq3gsAkJb+J7q0j0b3vj1xJGEHlO7e6N63O44k7ICg8UZo9DUoKjfg6PFUlMIDOlGNKpMCh3MrcTj3pHTccF93RIf6IMBTDR93y8e9yiyiuMKAgjIDzusqkV9SifIqc/3FX7gAQEBaqQDklNRcUc8DQoGzABCOfT+cA3Cuxrq2wBnLv3vXnwPWW9YpBMBLrYLWww1+nm5wRxW07gr4apTo3C4EbXwsA8EDvdUI8NIgwEsNrbuqUQHJbBZRelGPlONpMJhEGMwi2rZrDxMEKBUCNCoF3JSWm1pluWlUCqiVinqPI4oijGYRBqMZBqMZVSYzDCYzLuqrkHYyA2ZRhFIhQCkAUZ07QaN2g0oQkX3mFNRKAYIgtEroMJtFJB09jiUb9qBNaDsU52Zizj08cSa5DqcIO++++y6mTp2KRx55BADw8ccf4+eff8bnn3+O559/XubqiBqmrrM910UhAIHeGgR6a6A/VQKluwld+1yPP/f+gTydCYqAtsgvM6ACamSXVCK7pLJBx1cKgMpUCTeY4K/1QsWFfHh4axEUHIoLOZlQqNzg1yYMRrOIstIStA/0gtrDGxcNRhSX6WAwidBV6FFaBUCpQlWVCRAEQBAgwBIMTGYRgiBANJsgioBZsJzqyywCZXojyvRGZBVftC3sWGmd9bopBfh7qhHgpYabUoBer4cClkOqNe6orDKj3GDERYMJ5QYjKusMdOfqWFabNfhoVAqYRaDSUAWjGagyiRAbtAcAyLK5JwBQwAytRyZ8PTXwUKvgpVbCQ62El1oFT7USnholPK3/VyvhoVZBFC3hymgWYTSZUVllhk5vtNwqjZf+rzeirLIKZRcNuFhlrbMdkAEA7bH7qwx4qs/CU2M5npdGBS+b/6tsLqEiADCLIkqKi6Vl/v5+UCgU1esFKARAoRAsP/bq+xBFXLhQZHm+AtAmKAgqlRIKQYBSASgVCigFWEKhQnFpmbTOsj9RBESI1f9a3k9WNdddum/ZRoQl7JlE0fKvWYRJvLSsymhC/vnzMIuW92FAQABUykv1CYIlDCsEQCEI1TdLvUL1fWk7QYBCUXM7y7biZXXWrBH1PBfr/+vaVtq63v3VvRw1HiuKl45ree5i9Q2oMppw/nyBdN/6uigUClhzv/TOqP58V/8X1nsmswkFBYUwV9fi5x+Ax4dEIcBLDTk4fNgxGAxITExEfHy8tEyhUGDEiBFISEio8zF6vR56vV66X1Ji+Wu1tLTuX6pNpdPpkH3qOPQXKwAA+ecyoFR7wtPDvUH/b+hjnGU7e6jBkZ97RXYq/NSe6OATgEJdJob3CIdZG4ZcnQlnss8jMT0bGk8vlBadh0oQERTgh+KsUwgIbIPOUVFwUwCnjhyEUu2JDhE9cDLzOJR6T3QIEmHKT4JS7YnIQMt7u7A8E2M7h6NTp044deo0vko4gDZBIdCnp6BLeEd06NoDhTmZGNfLsg0AnDp1Cj8fzkZgWHucTD5g2V90DNKO/IXSMh3atOsIo6hATnY23APD4RPcFvm5ObhoFOHm5YvKKjM8vLxRYVKg0ihCDyC3ohy5Ncd+N5BSAGA2AmYTlEoFRAgwmUwQBSWgUEKEbUtOpR5oUGQURSgEEaLZBKUgQKlSWUKd2QyFQoAIBarMl77YTAAK9ZUoLC5r/JO4SmYAJZVAyT9ueSW5TXhM9lUdseXZe31yudrXJQdju/lBFeTdLNVYWb+3awbGujh82CkoKIDJZEJISIjN8pCQEBw/frzOxyxatAgLFiyotTwiIqJFaiSSw9d2sP+WroGIHEefJS2377KyMvj6+ta73uHDTlPEx8dj9uzZ0n2z2YyioiIEBgY2epBkaWkpIiIicPbsWWi12uYu1aXwtWwefB2bB1/H5sHXsXnwdaybKIooKytDeHj4Fbdz+LATFBQEpVKJvLw8m+V5eXkIDQ2t8zEajQYajcZmmZ+f31XVodVq+QZsJnwtmwdfx+bB17F58HVsHnwda7tSi46Vw18IVK1Wo1+/fti+fbu0zGw2Y/v27YiNjZWxMiIiIrIHDt+yAwCzZ8/GpEmTcN111+GGG27AkiVLUF5eLs3OIiIiItflFGHnvvvuw/nz5/Hyyy8jNzcXffr0waZNm2oNWm4JGo0G8+bNq9UtRo3H17J58HVsHnwdmwdfx+bB1/HqCOI/zdciIiIicmAOP2aHiIiI6EoYdoiIiMipMewQERGRU2PYISIiIqfGsHOVli1bhg4dOsDd3R39+/fHn3/+KXdJDmf37t0YP348wsPDIQgCNmzYIHdJDmfRokW4/vrr4ePjg+DgYNxxxx04ceKE3GU5pOXLl6N3797SydtiY2Px66+/yl2WQ3vjjTcgCAKefvppuUtxOPPnz4cgCDa3bt26yV2Ww2HYuQrfffcdZs+ejXnz5uGvv/7CNddcg9GjRyM/P1/u0hxKeXk5rrnmGixbtkzuUhzWrl27EBcXh3379mHr1q2oqqrCqFGjUF5eLndpDqddu3Z44403kJiYiIMHD2LYsGG4/fbbcfToUblLc0gHDhzAJ598gt69e8tdisPq0aMHcnJypNsff/whd0kOh1PPr0L//v1x/fXX48MPPwRgOXNzREQEZs6cieeff17m6hyTIAhYv3497rjjDrlLcWjnz59HcHAwdu3ahcGDB8tdjsMLCAjA4sWLMWXKFLlLcSg6nQ7XXnstPvroI7z66qvo06cPlixZIndZDmX+/PnYsGEDkpKS5C7FobFlp4kMBgMSExMxYsQIaZlCocCIESOQkJAgY2VEQElJCQDLlzQ1nclkwurVq1FeXs7LzzRBXFwcxo0bZ/N7khovLS0N4eHh6NSpEx566CFkZmbKXZLDcYozKMuhoKAAJpOp1lmaQ0JCcPz4cZmqIrK0MD799NMYOHAgevbsKXc5Dik5ORmxsbGorKyEt7c31q9fj5iYGLnLciirV6/GX3/9hQMHDshdikPr378/Vq5cia5duyInJwcLFizAoEGDcOTIEfj4+MhdnsNg2CFyMnFxcThy5Aj79a9C165dkZSUhJKSEqxbtw6TJk3Crl27GHga6OzZs3jqqaewdetWuLu7y12OQxszZoz0/969e6N///6IjIzEmjVr2K3aCAw7TRQUFASlUom8vDyb5Xl5eQgNDZWpKnJ1M2bMwMaNG7F79260a9dO7nIcllqtRlRUFACgX79+OHDgAJYuXYpPPvlE5socQ2JiIvLz83HttddKy0wmE3bv3o0PP/wQer0eSqVSxgodl5+fH6Kjo5Geni53KQ6FY3aaSK1Wo1+/fti+fbu0zGw2Y/v27ezbp1YniiJmzJiB9evXY8eOHejYsaPcJTkVs9kMvV4vdxkOY/jw4UhOTkZSUpJ0u+666/DQQw8hKSmJQecq6HQ6nDx5EmFhYXKX4lDYsnMVZs+ejUmTJuG6667DDTfcgCVLlqC8vByPPPKI3KU5FJ1OZ/NXSkZGBpKSkhAQEID27dvLWJnjiIuLw6pVq/Djjz/Cx8cHubm5AABfX194eHjIXJ1jiY+Px5gxY9C+fXuUlZVh1apV2LlzJzZv3ix3aQ7Dx8en1ngxLy8vBAYGchxZIz377LMYP348IiMjkZ2djXnz5kGpVOKBBx6QuzSHwrBzFe677z6cP38eL7/8MnJzc9GnTx9s2rSp1qBlurKDBw/i5ptvlu7Pnj0bADBp0iSsXLlSpqocy/LlywEAQ4cOtVm+YsUKTJ48ufULcmD5+fmYOHEicnJy4Ovri969e2Pz5s0YOXKk3KWRCzp37hweeOABFBYWok2bNrjpppuwb98+tGnTRu7SHArPs0NEREROjWN2iIiIyKkx7BAREZFTY9ghIiIip8awQ0RERE6NYYeIiIicGsMOEREROTWGHSIiInJqDDtERETUInbv3o3x48cjPDwcgiBgw4YNjd6HKIp4++23ER0dDY1Gg7Zt2+K1115r1D54BmUiIiJqEeXl5bjmmmvw6KOP4q677mrSPp566ils2bIFb7/9Nnr16oWioiIUFRU1ah88gzIR2bXJkyejuLi41l+EO3fuxM0334wLFy7Az89PltqIqOEEQcD69etxxx13SMv0ej3+85//4Ntvv0VxcTF69uyJN998U7r0zbFjx9C7d28cOXIEXbt2bfKx2Y1FREREspgxYwYSEhKwevVqHD58GPfccw9uueUWpKWlAQB++ukndOrUCRs3bkTHjh3RoUMHPPbYY41u2WHYISKnUFxcjMceewxt2rSBVqvFsGHDcOjQIWn9/Pnz0adPH5vH7Ny5E4IgoLi4GABQWFiIBx54AG3btoWnpyd69eqFb7/9thWfBZHryMzMxIoVK7B27VoMGjQInTt3xrPPPoubbroJK1asAACcOnUKZ86cwdq1a/Hll19i5cqVSExMxN13392oY3HMDhE5hXvuuQceHh749ddf4evri08++QTDhw9HamoqAgICGrSPyspK9OvXD3PnzoVWq8XPP/+Mhx9+GJ07d8YNN9zQws+AyLUkJyfDZDIhOjraZrler0dgYCAAwGw2Q6/X48svv5S2++yzz9CvXz+cOHGiwV1bDDtE5PD++OMP/Pnnn8jPz4dGowEAvP3229iwYQPWrVuHadOmNWg/bdu2xbPPPivdnzlzJjZv3ow1a9Yw7BA1M51OB6VSicTERCiVSpt13t7eAICwsDCoVCqbQNS9e3cAlpYhhh0ichmHDh2CTqeT/hq0unjxIk6ePCndT05Oln6JAoDJZLLZ3mQy4fXXX8eaNWuQlZUFg8EAvV4PT0/Pln0CRC6ob9++MJlMyM/Px6BBg+rcZuDAgTAajTh58iQ6d+4MAEhNTQUAREZGNvhYDDtE5PB0Oh3CwsKwc+fOWutqztTq2rUr/ve//0n39+/fj3/961/S/cWLF2Pp0qVYsmQJevXqBS8vLzz99NMwGAwtWT6R09LpdEhPT5fuZ2RkICkpCQEBAYiOjsZDDz2EiRMn4p133kHfvn1x/vx5bN++Hb1798a4ceMwYsQIXHvttXj00UexZMkSmM1mxMXFYeTIkbW6v66EYYeIHN61116L3NxcqFQqdOjQod7t1Go1oqKipPvnzp2zWb9nzx7cfvvtUgAym81ITU1FTExMi9RN5OwOHjyIm2++Wbo/e/ZsAMCkSZOwcuVKrFixAq+++iqeeeYZZGVlISgoCAMGDMCtt94KAFAoFPjpp58wc+ZMDB48GF5eXhgzZgzeeeedRtXBsENEdq+kpARJSUk2y6x/LSYnJ2PAgAGIjY3FHXfcgbfeegvR0dHIzs7Gzz//jDvvvBPXXXddg47TpUsXrFu3Dnv37oW/vz/effdd5OXlMewQNdHQoUNxpdP5ubm5YcGCBViwYEG924SHh+P777+/qjoYdojI7u3cuRN9+/atc93gwYPx22+/4ZdffsF//vMfPPLIIzh//jxCQ0MxePBghISENPg4L774Ik6dOoXRo0fD09MT06ZNwx133IGSkpLmeipEJAOeQZmIHFqHDh2wcuVK6YyrRESX40kFicihxcTE2MywIiK6HFt2iIiIyKmxZYeIiIicGsMOEREROTWGHSIiInJqDDtERETk1Bh2iIiIyKkx7BAREZFTY9ghIiIip8awQ0RERE6NYYeIiIic2v8DVUgMJUvTdw0AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -726,7 +537,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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gUK6rpKrKXpdbHyUlJSbb/Pnnn1AoFHjhhRdMls+YMQNCCGzZsqXCsu90Ti2lbPDwndx6bktKSpCeno7g4GA4OTlVeC6qqqrv7bvJzs5GWloa4uLi8OGHH8JgMBjf2zUhhMD69esxbNgwCCFMzvGAAQOQnZ1trL8lPwf//PNPdO7cGT169DAus7Ozw6RJk3D16lWcPXvWZPs71V+v1+Pvv//GiBEj0LRpU+M+3t7e+M9//oO9e/ciJyenwjiWLVuG9PR0zJ07t1rxZ2ZmIi0tDVeuXMGiRYugUCjQu3fvcttV9/x36tTJpByNRoNnn30WSUlJxvPw559/wsvLy2Rcr5WVFV544QXk5eVhz549JmWOGDHC+J0HAJ07d0aXLl2M3zO3unLlCoYMGYK2bdviu+++M/kcycjIwM6dOzFmzBjj519aWhrS09MxYMAAXLx4EQkJCVWqp1TYnVdNy5YtQ/PmzaFUKuHp6YnQ0FCTN4XBYMCSJUvw+eefIzY21mRsS1mXH1DaDRgaGgql0rKnICQkxOS5TCZDcHCwcRzGxYsXAQDjx4+vtIzs7Gw4Ozsbn6elpZUr93YXL16EEKLS7W7vdisbT3OnL7mLFy8iOzsbHh4eFa5PSUkxef73339XezDn3Llz4ePjg8mTJ+OXX34pd/yYmJhKy7z9+BVZvXq1sevTy8sL27ZtQ2BgoHH9mTNn8Oabb2Lnzp3lPpSzs7NNnvv4+MDW1tZkWdmg8KtXr6Jr164ASi+1/vjjj3Hu3DmTxCQoKMj4f5lMBrlcXu58OTo6wtvb2/h+uXbtGgAgNDTUZDuVSoWmTZsa11fX119/ja+//rrc8ltfm2vXrsHHx8fkRwEA44Dxyo59p3NqKVlZWXdN0AoLCzF//nysWrUKCQkJJuNtbj+3VZWfn2/yfvT398eMGTPKdbdUxYgRI4z/l8vlePPNNzF69Giz4rpVamoqsrKysGLFCqxYsaLCbcr+diz5OXjt2rUKu9hufb+0atXKuPxO9U9NTUVBQUG5931ZeQaDAfHx8QgPDzdZl52djffffx8vvfRSpd1alWnfvr3x/2q1Gp999lm5oRXmnP8WLVpUWAeg9HOjS5cuuHbtGkJCQky+y27d7va/tYo+55s3b461a9eWi3fAgAFITk6Gq6trucT40qVLEEJg9uzZmD17doXxp6SkmCRsdQ2TqGrq3LmzSUvC7d5//33Mnj0bTz31FN555x24uLhALpdj2rRp5Vp4pFAWw8KFC43jY25365dDcXExEhMTcf/999+1XJlMhi1btkChUNyxTABISkoCUJpY3KlMDw8PrFmzpsL1tyc3Xbp0wbvvvmuy7LPPPsPmzZsr3D8mJgarV6/G999/X+HYKoPBgIiICHzyyScV7u/v719p7GWGDRuG4OBgpKSk4IsvvsAjjzyCvXv3okmTJsjKykLv3r3h4OCAefPmoVmzZrC2tsbx48fx6quvmvV++f777zFhwgSMGDECM2fOhIeHBxQKBebPn28cvwf8r5VEqvmghg8fXm5w+Ztvvml8X5jrbufUUpKSku743gWA559/HqtWrcK0adMQGRkJR0dHyGQyjB071uzPAmtra/z2228ASlsuV65ciWnTpsHb2xtjxoypVlkfffQR2rRpg5KSEhw5cgTvvvsulEpltVtQbldWt8cee6zSH2tlY6ekVBv1X7BgAeRyOWbOnIn09PRq7fv999/D09MTWq0WO3fuxNSpU2FtbW0ycL665//W1lAppKWlwdbWFr/99htGjBiB+fPnm7y+Ze+Vl19+GQMGDKiwjODg4HsSq7mYRFnYL7/8gr59+5b7lZ2VlQU3Nzfj82bNmuHQoUMoKSmx6Id9WUtTGSEELl26ZPzQKhuE6eDgUKUBiydPnkRJSckdE8eycoUQCAoKqtIl82fPnoVMJqvwl96tZW7fvh3du3ev0oeBm5tbuTrdafD3rFmz0LZtWzzyyCOVHv/kyZPo16+f2cmGr6+v8VfUqFGj4ObmhuXLl2PBggXYvXs30tPTsWHDBvTq1cu4T9mVnre7ceMG8vPzTVqjLly4AADGq2B++eUXNG3aFBs2bDCJ+fYvhqCgIBgMBly8eNFkKoCcnBwkJiYar3Qsaxk6f/68SbdGcXExYmNjzR706ufnV27fxYsXmyRRgYGB2L59O3Jzc01ao8q6OW9ttSpzt3NqKWfPnjVpOajIL7/8gvHjx+Pjjz82LtNqtSZXNVaXQqEwed2GDBkCFxcXbN26tdpJVIcOHYxXLg4aNAgJCQlYsGABZs+eXa5Fojrc3d1hb28PvV5/1/eHJT8HAwMDcf78+XLLK3u/3Kn+7u7usLGxqbQ8uVxe7kfUjRs3sGTJEsyfPx/29vbVTqK6d+9u/DseOnQozpw5g/nz55skUdU9/0FBQXd8TcqOFxgYiFOnTsFgMJic+8peu9u/Z4DSz6Lbr8azsbHB1q1b0aJFC0yfPh3vv/8+xowZY/zMKftMsbKyqtEAeilxTJSFKRSKcpfJrlu3rly/7ujRo5GWlobPPvusXBm3718dZVdNlPnll1+QmJiIQYMGASj94GjWrBk++ugj5OXllds/NTW1XOwKhaLC6QNuNWrUKCgUCrz99tvl4hdCmHyg6HQ6rF+/Hp07d75jl8iYMWOg1+vxzjvvlFun0+lq9GV04MABbN68GR988EGlCdKYMWOQkJCA//73v+XWFRYWIj8/v1rHzM7ORnFxsXF6hLIWu1tfr+LiYnz++ecV7q/T6fDll1+abPvll1/C3d0dHTp0qLTMQ4cO4cCBAyZllV3tuXjxYpPlS5YsMV4xBZSOAVSpVPj0009Nyvz666+RnZ2NIUOGVP0FqKbBgwdDr9eX+xtZtGgRZDKZ8T1dpirn1BKOHj2Ky5cv33X8UEWfBUuXLi03fUVNlJVfUetvdRUWFkKn00Gn09WoHIVCgdGjR2P9+vXlLnsHTD9jLPk5OHjwYBw+fNjkvZ6fn48VK1agSZMmaNmy5R33v7X+CoUCDzzwADZv3mwyJUVycjJ++OEH9OjRAw4ODib7v/322/D09MQzzzxTrbjvFM/dplK52/kve032799vXKbVarF8+XJ4eXkZPzcGDx6MpKQk/Pzzz8btdDodli5dCjs7u3JjszZt2mTynXb48GEcOnSo3N+ku7u7sTtx3rx58PPzw8SJE41xe3h4oE+fPvjyyy+RmJhYLv7bv4/qIrZEWdjQoUMxb948PPnkk+jWrRtOnz6NNWvWmPyKB0oHgH/77bd46aWXcPjwYfTs2RP5+fnG6fiHDx9u1vFdXFzQo0cPPPnkk0hOTsbixYsRHByMiRMnAijt+//qq68waNAghIeH48knn4Svry8SEhKwa9cuODg44LfffkN+fj6WLVuGTz/9FM2bN8fu3buNxyhLvk6dOoUDBw4gMjISzZo1w7vvvotZs2bh6tWrGDFiBOzt7REbG4uNGzdi0qRJePnll7F9+3bMnj0bp06dMjZLV6Z3796YPHky5s+fj6ioKDzwwAOwsrLCxYsXsW7dOixZssTsGYH//vtv3H///Xf89fP4449j7dq1eOaZZ7Br1y50794der0e586dw9q1a/HXX39V2kJ3+vRpzJgxA/fddx88PDxw48YNrFy5EgaDwTh4s1u3bnB2dsb48ePxwgsvQCaT4bvvvqv0y8PHxwcLFizA1atX0bx5c/z888+IiorCihUrjL/ihw4dig0bNmDkyJEYMmQIYmNj8cUXX6Bly5YmSXN4eDiefvpprFixApmZmejTpw+OHz+OlStXYtCgQcYky93dHbNmzcLbb7+NgQMH4sEHH8T58+fx+eefo1OnTnjsscfMev2rYtiwYejbty/eeOMNXL16FW3atMHff/+NzZs3Y9q0aSbTgABVO6c1NW/ePCxZsgRNmzYtNx3C7YYOHYrvvvsOjo6OaNmyJQ4cOIDt27ebjI2sLr1ej61btwIo7c5ZtWoV8vPzTcb3VNW2bdtw/fp1Y3fWmjVr8OCDDxovTKiJDz74ALt27UKXLl0wceJEtGzZEhkZGTh+/Di2b9+OjIwMAJb9HHzttdfw448/YtCgQXjhhRfg4uKCb775BrGxsVi/fn251rW71f/dd9/Ftm3b0KNHDzz77LNQKpX48ssvUVRUVOF95/7++2+sWbPG7Ndv06ZNcHNzM3bn/fvvv8apIMpU9/y/8sorWLNmjfE1cXNzw/fff4+zZ89izZo1xrFokyZNwpdffokJEybg2LFjaNKkCX755Rfs27cPixcvLjcuMTg4GD169MCUKVNQVFSExYsXw9XVFa+88kql9dNoNFixYgX69++P5cuXG287s2zZMvTo0QMRERGYOHEimjZtiuTkZBw4cADXr1+vcI7FOuWeXgtYj5VdhnrkyJE7bqfVasWMGTOEt7e30Gg0onv37uLAgQMVXrpcUFAg3njjDREUFCSsrKyEl5eXeOihh4yX1JozxcGPP/4oZs2aJTw8PIRGoxFDhgwR165dK7f/iRMnxKhRo4Srq6tQq9UiMDBQjBkzRuzYscPk2Hd73HpJtxBCrF+/XvTo0UPY2toKW1tb0aJFCzF16lRx/vx5IYQQzz//vOjVq5fYunVruZgqu3x5xYoVokOHDkKj0Qh7e3sREREhXnnlFXHjxg3jNtWd4kAmk4ljx46ZLK/oHBUXF4sFCxaI8PBwoVarhbOzs+jQoYN4++23RXZ2drnjlblx44Z48MEHhaenp7CyshLe3t5i6NChYu/evSbb7du3T3Tt2lVoNBrh4+MjXnnlFfHXX3+VuwS7d+/eIjw8XBw9elRERkYKa2trERgYKD777DOT8gwGg3j//fdFYGCgUKvVol27duL333+v8HL0kpISMW/ePOP7z9/fX7zyyisVXpr/2WefiRYtWggrKyvh6ekppkyZIjIzMyusu6WmOBCi9BL16dOnCx8fH2FlZSVCQkLEwoULy13+Xp1zWpOY/Pz8xFNPPWXy3itz+xQHmZmZ4sknnxRubm7Czs5ODBgwQJw7d67cdlWNt2xairKHnZ2daN++vfjuu+9M6lHVKQ7KHkqlUgQGBooXXnih0nNaWTyVTXEghBDJycli6tSpwt/f3/j51q9fP7FixQqT7e72OXirO01xIIQQly9fFg899JBwcnIS1tbWonPnzuL33383u/7Hjx8XAwYMEHZ2dsLGxkb07dtX7N+/32Sbsu+Gtm3bmrwv7/Z3cPv+ZQ+VSiWCg4PFnDlzTC77N/f8l70mjo6OwtraWnTq1Els2rSpXBzJycnG96tKpRIRERHlYr/1O+njjz8W/v7+Qq1Wi549e4qTJ0+abFvZ++PJJ58UDg4OJlMkXL58WTzxxBPCy8tLWFlZCV9fXzF06FCT6WDqKt72pYHYvXs3+vbti3Xr1lnkfk1Xr15FUFAQYmNjK5yxGSidhPHq1at3nZGXaq5Pnz5IS0ursHuEiOheKPteWLhwIV5++WWpw6kTOCaKiIiIyAwcE0UVsrOzw7hx4+448Lt169bG29gQERE1NkyiqEJlAxDvZNSoUfcoGiIiorqHY6KIiIiIzMAxUURERERmYBJFREREZAaOiULp/Xtu3LgBe3t7ye4lRkRERNUjhEBubi58fHxqdLsiczGJQuk9j6pyM1kiIiKqe+Lj4+Hn53fPj8skCjBOaR8fH1/ufkhERERUN+Xk5MDf37/crWnuFSZRgLELz8HBgUkUERFRPSPVUBwOLCciIiIyA5MoIiIiIjMwiSIiIiIyA5MoIiIiIjMwiSIiIiIyA5MoIiIiIjMwiSIiIiIyA5MoIiIiIjMwiSIiIiIyA5MoIiIiIjMwiSIiIiIyg6RJ1D///INhw4bBx8cHMpkMmzZtMlkvhMCcOXPg7e0NjUaD/v374+LFiybbZGRkYNy4cXBwcICTkxOefvpp5OXl3cNaEBERUWMkaRKVn5+PNm3aYNmyZRWu//DDD/Hpp5/iiy++wKFDh2Bra4sBAwZAq9Uatxk3bhzOnDmDbdu24ffff8c///yDSZMm3asqEBERUSMlE0IIqYMASu/AvHHjRowYMQJAaSuUj48PZsyYgZdffhkAkJ2dDU9PT6xevRpjx45FTEwMWrZsiSNHjqBjx44AgK1bt2Lw4MG4fv06fHx8qnTsnJwcODo6Ijs7Gw4ODrVSPyIiIrIsqb+/lff8iFUUGxuLpKQk9O/f37jM0dERXbp0wYEDBzB27FgcOHAATk5OxgQKAPr37w+5XI5Dhw5h5MiRFZZdVFSEoqIi4/OcnJzaqwjRXcTFxSEtLc1i5bm5uSEgIMBi5RERUcXqbBKVlJQEAPD09DRZ7unpaVyXlJQEDw8Pk/VKpRIuLi7GbSoyf/58vP322xaOmKj64uLi0CIsDIUFBRYrU2Njg3MxMUykiIhqWZ1NomrTrFmz8NJLLxmf5+TkwN/fX8KIqLFKS0tDYUEBxr26EJ4BzWpcXnLcZaxZMBNpaWlMooiIalmdTaK8vLwAAMnJyfD29jYuT05ORtu2bY3bpKSkmOyn0+mQkZFh3L8iarUaarXa8kETmckzoBn8QsKlDoOIiKqhzs4TFRQUBC8vL+zYscO4LCcnB4cOHUJkZCQAIDIyEllZWTh27Jhxm507d8JgMKBLly73PGYiIiJqPCRticrLy8OlS5eMz2NjYxEVFQUXFxcEBARg2rRpePfddxESEoKgoCDMnj0bPj4+xiv4wsLCMHDgQEycOBFffPEFSkpK8Nxzz2Hs2LFVvjKPiIiIyBySJlFHjx5F3759jc/LximNHz8eq1evxiuvvIL8/HxMmjQJWVlZ6NGjB7Zu3Qpra2vjPmvWrMFzzz2Hfv36QS6XY/To0fj000/veV2IiIiocZE0ierTpw/uNE2VTCbDvHnzMG/evEq3cXFxwQ8//FAb4RERERFVqs6OiSIiIiKqy5hEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZRSB0BUH8XFxSEtLa3G5cTExFggGiIikgKTKKJqiouLQ4uwMBQWFFiszLy8PIuVRURE9waTKKJqSktLQ2FBAca9uhCeAc1qVFbM4T3Y8s0SaLVaC0VHRET3CpMoIjN5BjSDX0h4jcpIjrtsoWiIiOhe48ByojpOCCF1CEREVAG2RBHVMXlaHaLis3AjuxCZBcUo0Qu42angaW+NCD9HuNmppQ6RiIjAJIqozijS6XHgcjqib+RAbzBtfUrOKUJyThFOJ2Qj3McBkc1cYaPiny8RkZT4KUxUBxQaFFh79Doy8osBAD5O1ojwdYSrrRpKhQypuUW4kJyLy6n5iL6Rg2sZBRjZ1hfOtiqJIyciaryYRBFJTO3bEse17tChGLZqBR5o6QV/Zw1kMplxG2cbFZp72iMhsxDbzyUjq6AE645dx/C2PvB0sJYweiKixosDy4kklA81PB6eCx3k8HKwxqOdAhDgYmOSQN3K11mDhzv4wcNejcISPTYcT0BmQfE9jpqIiAAmUUSSySkswRn4Q662haO8CKPb+8JWfffGYRuVEqPb+8Hb0RrFegP+OJWIYp3hHkRMRES3YhJFJAGd3oBfT95AMaxQnHoN4eoMKBVV/3NUKeUYEuENG5UC6fnF2BGTzKkQiIjuMSZRRBL491Ia0vOLYQUdUtbNgZWs+gmQrVqJwRHekMuACyl5iEnMrYVIiYioMkyiiO6xK6l5OHU9GwDQHDegz003uyxfJw26NnUFAPx7KRVFeouESEREVcAkiugeKijWYXtMCgCgXYATnJFf4zLbBzjD1U4FbYkBp7MUNS6PiIiqhkkU0T2091IaCkv0cLNToVszV4uUqZDLcF+oBwDgWr4Car+a3c+PiIiqhkkU0T2SkFloHLfUr4UnlHLL/fn5OGnQyscBAODc92kOMiciugeYRBHdA3qDwK7zpd14rXwc4OVo+QkyuzZ1hUImoPZpjiM3iixePhERmWISRXQPnLqehfT8YmisFOge7FYrx7BVKxFsXzpf1I/RuTAY2BpFRFSbmEQR1bIinR6Hr2YAACKbucLaqvYGfzd30MOgzcO1bB1+P51Ya8chIiImUUS17vi1LGhLDHC2sUK4t0OtHkslB3KObAIALNl+ga1RRES1iDcgJqpF+UU6HI/LBAB0a+YGubzie+JZUs7RzfDqPQ6XU/Px1Z8H0NGnZuOv3NzcEBAQYKHoiIgaDiZRRLXocGwGdAYBLwdrNHO3rfXj5WSkQhQXIvnABjh2GY05P/yD5B9n1ahMjY0NzsXEMJEiIroNkyiiWpKn1eHMjRwAQLdmrpDJar8VqjCv9HidWzTBBQhYB0TgyU82wlltXrdectxlrFkwE2lpaUyiiIhuwySKqJYci8uEXgj4OFnD38Xmnh7by9sbMgcHnEvKxXU4IyLE+54en4ioMeDAcqJakF+kQ3RC6f3xOjdxkSSG9gHOAICLKXnI1ZZIEgMRUUPGJIqoFpyIz4LOIODpoEbAPW6FKuNur4avkwZCwNitSERElsMkisjCikr0OHU9CwDQOcjlnoyFqkyEryMAIPpGNqc7ICKyMCZRRBZ2+kY2SvQCrrYqBLnW/hV5d9LMwxYaKwXyi/SITc+XNBYiooaGSRSRBekNAifjS8dCtQ9wlrQVCgCUcjla3rwx8embY7SIiMgymEQRWdCF5FzkFelgo1KguZed1OEAKL3hMQBcSy9AdiEHmBMRWQqTKCILEUIYZydv4+8Epbxu/Hk52aiMg9vPcoA5EZHF1I1P+Uro9XrMnj0bQUFB0Gg0aNasGd555x0I8b8BskIIzJkzB97e3tBoNOjfvz8uXrwoYdTUWF3PLERaXjGUchla3xzQXVe0vHnPvpikHJO/HyIiMl+dTqIWLFiA5cuX47PPPkNMTAwWLFiADz/8EEuXLjVu8+GHH+LTTz/FF198gUOHDsHW1hYDBgyAVquVMHJqjE7evCIvzNsB1lYKaYO5TTN3W6gUcuRqdUjIKpQ6HCKiBqFOJ1H79+/H8OHDMWTIEDRp0gQPPfQQHnjgARw+fBhAaSvU4sWL8eabb2L48OFo3bo1vv32W9y4cQObNm2SNnhqVHK0JbiSWnr1Wxu/utUKBQBKhRzNPUvHaJ1NZJceEZEl1Okkqlu3btixYwcuXLgAADh58iT27t2LQYMGAQBiY2ORlJSE/v37G/dxdHREly5dcODAgUrLLSoqQk5OjsmDqCZOX8+GAODnrIGrnVrqcCoUdrNL71JKHop1BomjISKq/+r0vfNee+015OTkoEWLFlAoFNDr9Xjvvfcwbtw4AEBSUhIAwNPT02Q/T09P47qKzJ8/H2+//XbtBU6Nik5vMM4I3sbPSdpg7sDb0RqOGitkF5bgcmqeMakiIiLz1OmWqLVr12LNmjX44YcfcPz4cXzzzTf46KOP8M0339So3FmzZiE7O9v4iI+Pt1DE1BhdSMlDYYke9tZKNHWTdnLNO5HJZAjztgdQOsCciIhqpk63RM2cOROvvfYaxo4dCwCIiIjAtWvXMH/+fIwfPx5eXl4AgOTkZHh7/+8u9cnJyWjbtm2l5arVaqjVdbPLheqfshsNt/J1hFwu7eSadxPqaY+DVzJwPaMQBcU62Kjq9EcAEVGdVqdbogoKCiC/ba4dhUIBg6F0PEdQUBC8vLywY8cO4/qcnBwcOnQIkZGR9zRWapzS84qQmK2FTAaE14PuMScbFTzs1RAoHRtFRETmq9M/Q4cNG4b33nsPAQEBCA8Px4kTJ/DJJ5/gqaeeAlDaPTFt2jS8++67CAkJQVBQEGbPng0fHx+MGDFC2uCpUYi+ORaqqZstbNV1+s/JqLmnPVJyi3AhOQ+t6/AYLiKiuq5Of+ovXboUs2fPxrPPPouUlBT4+Phg8uTJmDNnjnGbV155Bfn5+Zg0aRKysrLQo0cPbN26FdbW1hJGTo2BXgDnbk4XEO5T96Y1qEyIpx32XkpDQlYh8op0sKsnyR8RUV1Tpz897e3tsXjxYixevLjSbWQyGebNm4d58+bdu8CIANwokEOrM8BOrUSgq43U4VSZg7UVvB2tkZitxcXkXLQLcJY6JCKieqlOj4kiqsti80r/fMJ9HCCX1e0B5bdr7ll6ld6FZI6LIiIyF5MoIjMonX2QWiSHDEBLn7o/oPx2IR6ls5cn5WiRU1gicTRERPUTkygiM9i1fgAAEOhqAwdrK4mjqT5btRJ+zhoAwIWUXImjISKqn5hEEVVTiV7ALqIfgNK5oeqr5h6lXXoX2aVHRGQWJlFE1XQ0UQuFrTOs5QJNXOvuDOV3E+xhB5kMSMktQmZBsdThEBHVO0yiiKpp25VCAECgnQGKOj5D+Z1oVAoEOJdeVcjWKCKi6mMSRVQNidmFOJlUBABoYquXOJqa+99VehwXRURUXUyiiKph04kbEAC0cadhV//Gk5fTzN0WCpkM6fnFSM8rkjocIqJ6hUkUURUJIbDh+HUAQP6ZnRJHYxlqKwUCbk4UynvpERFVD5MooiqKTsjBxZQ8qBRA/rl9UodjMcE354y6lMokioioOphEEVXR+putUJ19rCGKCySOxnKautlCLgPS8op5lR4RUTUwiSKqghK9Ab+dvAEA6NNEI3E0lmVtpYCfM7v0iIiqi0kUURX8cyEV6fnFcLNTo42nWupwLK7sNjBMooiIqo5JFFEVbDieAAAY3tanXs8NVZmm7raQoXTiTd5Lj4ioaphEEd1FdmEJtsUkAwBGtfeVOJraYaNSwvfmvfTYGkVEVDVMooju4s/TiSjWGRDqaY+W3g5Sh1Nrgt15lR4RUXUwiSK6i7K5oUa194VM1vC68so0uzkuKjFbi1wtu/SIiO6GSRTRHVxLz8eRq5mQy4AR7RpmV14ZO7US3o7WAIDLqfkSR0NEVPcxiSK6g40nSgeUdw92g6eDtcTR1D5epUdEVHVMoogqIYQwJlENdUD57cq69BKyCpFfpJM4GiKiuo1JFFEljsdl4lp6AWxUCgwI95I6nHvCwdoKng6l82Bd5gBzIqI7YhJFVIn1N+eGGtTKGzYqpcTR3Du8lx4RUdUwiSKqQJFOj99v3uZldCPpyitTNtXB9cxCFOklDoaIqA5jEkVUgZ0xKcjR6uDtaI2uTV2lDueecrJRwd1ODSGAxEJ+RBARVYafkEQVKOvKG9HOF/IGeJuXuynr0rtewI8IIqLK8BOS6DbpeUXYfT4FADCqgc8NVZmyJCpFK4NMbStxNEREdROTKKLb/H4qETqDQISvI0I87aUORxIutiq42qogIINNcGepwyEiqpOYRBHd5tbbvDRmZXNG2TTvJnEkRER1E5MooltcSsnFyevZUMplGNbGR+pwJFU2e7mmaQcUlhgkjoaIqO5hEkV0iw03B5T3CXWHm51a4mik5Wqrgp1SQKZU4VhikdThEBHVOUyiiG4yGAQ23bzNy8h2fhJHIz2ZTAZfm9IWqAPXtRJHQ0RU9zCJIrrpYGw6bmRrYW+tRL8wD6nDqRPKkqjjiUUoLObMm0REt2ISRXRTWVfe0NY+sLZSSBxN3eBkJaDLSkKRXmDPhRSpwyEiqlOYRBEBKCzWY8vpRACN7zYvdyKTAfkX9gMAtkQnSRwNEVHdwiSKCMDfZ5OQX6xHgIsNOgQ6Sx1OnVJwfh8AYEdMCop07NIjIirDJIoI/7vNy8h2vpDJGt9tXu6k+MYFuGjkyCvSYe/FNKnDISKqM5hEUaOXkqPF3oupADjBZsUEIv2sAQB/nmaXHhFRGSZR1OhtjroBgwA6Bjoj0JX3iatI15tJ1LazSSjWceJNIiKASRQR1t+8zctItkJVqoWrCm52auRodThwJV3qcIiI6gQmUdSonb2Rg3NJuVAp5Bga0bhv83InCrkMA8I9AQBboxMljoaIqG5gEkWNWtnNhvu39ICjjZXE0dRtgyO8AQB/nUmGTs8uPSIipdQBEN0LcXFxSEszvbJMbxD45WjpBJKtHYpw/PjxKpUVExNj8fjqgy5BLnC2sUJGfjEOX81At2ZuUodERCQpJlHU4MXFxaFFWBgKCwpMllsHtYfnmHnQF2RjyogRgEFXrXLz8vIsGGXdp1TIcX9LT6w9eh1bTicxiSKiRo9JFDV4aWlpKCwowLhXF8IzoJlx+aE0Ba4XAM097DBm6doqlxdzeA+2fLMEWm3juynvoAhvrD16HVvPJOHtB8Mhl3NOLSJqvJhEUaPhGdAMfiHhAICiEj0Sr8cCEOgU1gSeDtZVLic57nItRVj3dW/mBntrJVJzi3AsLhOdmrhIHRIRkWQ4sJwapQspedAbBFxtVfCwV0sdTr2hUspxf1jpVXpbOPEmETVyTKKoUYpJzAEAtPR24G1eqmlgKy8ApVMdCCEkjoaISDpMoqjRycwvRmK2FjIZEOplL3U49U6v5u6wVSlwI1uLk9ezpQ6HiEgyTKKo0Tl7sxWqiastbNUcFlhd1lYK9G3hAQDYcpoTbxJR48UkihoVgxA4l5QLAAjzZiuUucom3twSncQuPSJqtJhEUaMSn1GAvCIdrJVyBLnxZsPm6hPqDmsrOeIyCnDmRo7U4RARSYJJFDUqZV15oV72UMr59jeXjUqJPs1Lu/S2RvMqPSJqnPgtQo1GsQG4nJoPAAjzdpA4mvpvUETpVXp/8io9ImqkmERRo3G9QM65oSzovhYeUCnkuJKaj4spjesWOEREAJMoakSu5ZW+3Tk3lGXYW1uhZ0jp/fP+5FV6RNQIMYmiRkHp4oeMYjnnhrKwQTev0uO4KCJqjJhEUaNg1+YBAEAQ54ayqPvDPGGlkOFcUi4uJOdKHQ4R0T3FJIoavBK9gF2rfgCAcF8OKLckRxsr9AktvUpvw/EEiaMhIrq3mERRg3coQQuFjSM0CoEmLpwbytJGtfMFAGyOSoDBwKv0iKjxqPNJVEJCAh577DG4urpCo9EgIiICR48eNa4XQmDOnDnw9vaGRqNB//79cfHiRQkjprpm+5UCAEATWwPkcg4ot7T7wjzgYK1EYrYWB6+kSx0OEdE9U6eTqMzMTHTv3h1WVlbYsmULzp49i48//hjOzs7GbT788EN8+umn+OKLL3Do0CHY2tpiwIAB0Gq1EkZOdcW19HycSimGEAYE2umlDqdBUisVGNLaBwCw4QS79Iio8ajTSdSCBQvg7++PVatWoXPnzggKCsIDDzyAZs2aAShthVq8eDHefPNNDB8+HK1bt8a3336LGzduYNOmTdIGT3XCT0fiAQDa2OOw5XjyWjOqfWmX3pbTiSgsZrJKRI1DnU6ifv31V3Ts2BEPP/wwPDw80K5dO/z3v/81ro+NjUVSUhL69+9vXObo6IguXbrgwIEDlZZbVFSEnJwckwc1PCV6A9YdvQ4AyI36S+JoGraOgc7wd9Egv1iPv89yugMiahzqdBJ15coVLF++HCEhIfjrr78wZcoUvPDCC/jmm28AAElJpR/Wnp6eJvt5enoa11Vk/vz5cHR0ND78/f1rrxIkmR0xyUjLK4KTtRyFlw9LHU6DJpPJMLKdHwBepUdEjUedTqIMBgPat2+P999/H+3atcOkSZMwceJEfPHFFzUqd9asWcjOzjY+4uPjLRQx1SU/Hi49r/c10QAGdjHVtpE3r9L792IqUnI5JpGIGj6zR4nk5+djz549iIuLQ3Fxscm6F154ocaBAYC3tzdatmxpsiwsLAzr168HAHh5ld4ANTk5Gd7e3sZtkpOT0bZt20rLVavVUKt577SGLD6jAP9cTAUA9G9qg0USx9MYBLnZol2AE07EZeHXqBv4v55NpQ6JiKhWmZVEnThxAoMHD0ZBQQHy8/Ph4uKCtLQ02NjYwMPDw2JJVPfu3XH+/HmTZRcuXEBgYCAAICgoCF5eXtixY4cxacrJycGhQ4cwZcoUi8RA9dO6o/EQAuge7AovO44ov1dGtffDibgsbDiewCSKiBo8s7rzpk+fjmHDhiEzMxMajQYHDx7EtWvX0KFDB3z00UcWC2769Ok4ePAg3n//fVy6dAk//PADVqxYgalTpwIoHYcxbdo0vPvuu/j1119x+vRpPPHEE/Dx8cGIESMsFgfVL8U6A3642ZX3aOcAiaNpXIZGeMNKIcPZxBycT+JtYIioYTMriYqKisKMGTMgl8uhUChQVFQEf39/fPjhh3j99dctFlynTp2wceNG/Pjjj2jVqhXeeecdLF68GOPGjTNu88orr+D555/HpEmT0KlTJ+Tl5WHr1q2wtra2WBxUv/x5OhFpeUXwdFBjQLiX1OE0Ks62KvS9eRuY9cevSxwNEVHtMiuJsrKyglxeuquHhwfi4uIAlE4vYOlB2kOHDsXp06eh1WoRExODiRMnmqyXyWSYN28ekpKSoNVqsX37djRv3tyiMVD9snr/VQDAY10CYaWo09dONEgPdyy92nX9seso1hkkjoaIqPaYNVikXbt2OHLkCEJCQtC7d2/MmTMHaWlp+O6779CqVStLx0hUZVHxWYiKz4JKIcejXdiVJ4W+oe7wdFAjOacI284mY0hr77vvRERUD5n1M/399983Xg333nvvwdnZGVOmTEFqaipWrFhh0QCJquObm61QQ9t4w82OV2BKQamQ45GbrVE/Ho6TOBoiotpjVktUx44djf/38PDA1q1bLRYQkblScrX4/dQNAMCEbk2kDaaRG9PJH0t3XcLeS2m4lp6PQFdbqUMiIrI4s5Ko++67Dxs2bICTk5OFwyEy34+H4lGiF2gf4ITWfk5Sh9OgxMTEVHufdp5qHE8qwpLfjuCx1g4AADc3NwQEsJuViBoGs5Ko3bt3l5tgk0hKxToDvj90DQAwnq1QFpOTUTph6WOPPVbtfTUhXeEx6k2sO3Ydi55+EjDooLGxwbmYGCZSRNQgmD0LoUwms2QcRDWyJToRqblF8LBXY1ArDmS2lMK80ptzD5n8BkJbd6jWvgYBbLkhoLV1xiML1kGZdglrFsxEWloakygiahDMTqJGjhwJlUpV4bqdO3eaHRCROcqmNRjXJRAqJac1sDRXn0D4hYRXe78IRRqOXM3EDYMjOgU0q4XIiIikY3YSFRkZCTs7O0vGQmSWE3GZOBGXBSuFDI928Zc6HLpFuI8jjlzNRFxGAcI4/y0RNTBmJVEymQwzZ86Eh4eHpeMhqrYv9lwGADzYxhce9vymrkscNVYIdLHBtYwCXM1TSB0OEZFFmdXvIYSwdBxEZrmUkou/ziQDAKb04Q1v66JWvo4AgKt5ckDORIqIGg6zkqi5c+eyK4/qhC/3XAEAPNDSE8Ee9hJHQxUJcrOFjUqBIoMMNqE9pA6HiMhizOrOmzt3LgAgNTUV58+fBwCEhobC3d3dcpER3cWNrEJsikoAADzTh4OW6yqFXIbWfo44eCUDDp2GsyWbiBoMs1qiCgoK8NRTT8HHxwe9evVCr1694OPjg6effhoFBQWWjpGoQl/vjUWJXqBLkAvaBzhLHQ7dQYSvI+QQUHs3x7n0EqnDISKyCLOSqOnTp2PPnj349ddfkZWVhaysLGzevBl79uzBjBkzLB0jUTmZ+cXG+7JNYStUnWejUiLA1gAA+O1CvsTREBFZhlndeevXr8cvv/yCPn36GJcNHjwYGo0GY8aMwfLlyy0VH1GFvj1wDQXFeoR5O6B3c3Yj1wch9gZczVfgcIIW8RkF8HexkTokIqIaMbs7z9PTs9xyDw8PdudRrSso1mH1/lgApa1QnD2/fnBQCRTGHodB/G9yVCKi+sysJCoyMhJz586FVqs1LissLMTbb7+NyMhIiwVHVJGfj8Qjs6AEAS42GNzKS+pwqBpyjm4GUHoOc7UcG0VE9ZtZ3XmLFy/GwIED4efnhzZt2gAATp48CWtra/z1118WDZDoVtoSvXFyzYm9mkKp4C1e6hPtlePwtVcgIVeHdUev46keQVKHRERkNrO+gSIiInDx4kXMnz8fbdu2Rdu2bfHBBx/g4sWLCA+v/v21iKpqzaE4JOcUwcfRGmM6+kkdDlWbwNAQWwDAqv2x0Bs43QER1V9mtUT9888/6NatGyZOnGjpeIgqlV+kw/LdlwAAL/QLgVrJ2a/roz5NbPDzuULEZxRi29lkDGSXLBHVU2a1RPXt2xcZGRmWjoXojr45cBVpecUIdLXB6A5shaqv1EoZ/tM5AEDpfQ85+SYR1Ve8dx7VCznaEuMtXqb1D4EVx0LVaxO6N4FaKUdUfBb2XkqTOhwiIrOY1Z0HAAcOHICzc8WzRPfq1cvsgIgq8vW/scguLEGwhx0ebOMrdThUQx721ni0cwBW77+KpTsuoWcI5/oiovrH7CRq5MiRFS6XyWTQ6/VmB0R0u8z8Yny9t3ReqJfubw6FnPNCNQTP9G6GHw7F4fDVDBy8ko6uTV2lDomIqFrM7hNJSkqCwWAo92ACRZb25T9XkFekQ0tvBwwM5yDkhsLL0RoP37zCcunOixJHQ0RUfWYlUZwhmu6VlFwtvrk5u/WMB5pDzlaoBmVKn2ZQymXYdykdx65lSh0OEVG1cGA51WmLtl1EYYkebf2dcF8LD6nDIQvzc7bBqPalY9zYGkVE9Y1ZSZTBYICHB7/QqHadT8rFz0fiAABvDAljC2gD9WyfYMhlwO7zqTh1PUvqcIiIqsysJGr+/PlYuXJlueUrV67EggULahwUEQC892cMDAIY1MoLnZq4SB0O1ZImbrYY3rasNeqSxNEQEVWdWUnUl19+iRYtWpRbHh4eji+++KLGQRHtPp+Cfy6kwkohw2uDyr/XqGGZ2jcYMhmw7WwyohOypQ6HiKhKzEqikpKS4O3tXW65u7s7EhMTaxwUNW46vQHv/xkDABgf2QSBrrYSR0S1rXT+Lx8AwIKt5ySOhoioasxKovz9/bFv375yy/ft2wcfH58aB0WN289H43EhOQ9ONlZ4/r4QqcOhe2TG/aGwUsjw78U07L3IWcyJqO4zK4maOHEipk2bhlWrVuHatWu4du0aVq5cienTp/OmxFQjudoSfPL3BQDAi/1C4GhjJXFEdK8EuNpgXJdAAKWtUQYDrwImorrNrBnLZ86cifT0dDz77LMoLi4GAFhbW+PVV1/FrFmzLBogNS7Ld19Gen4xmrrZ4rGugVKHQ/fY8/cF45dj13E6IRt/nE7EsDZs2SaiusvsyTYXLFiA1NRUHDx4ECdPnkRGRgbmzJlj6fioEYlNy8dX/5be3uW1QS14k+FGyNVOjUm9mgIAPvr7PIp1BokjIiKqnNn3zgMAOzs7dOrUyVKxUCMmhMDbv51Bsd6AniFuuL+lJ+Li4pCWVvOxMTExMRaIkO6Vp3sE4dsD13AtvQA/HYnDE5FNpA6JiKhCZidRR48exdq1axEXF2fs0iuzYcOGGgdGjcu2s8nYfb50SoO3HwxHfHw8WoSFobCgwGLHyMvLs1hZVHts1Uq82D8EszdF49MdFzGqvR/s1DX6vUdEVCvM+mT66aef8MQTT2DAgAH4+++/8cADD+DChQtITk7GyJEjLR0jNXCFxXq8/dtZAMD/9WyKpu52OH78AgoLCjDu1YXwDGhWo/JjDu/Blm+WQKvVWiJcugfGdvLH1/9ewdX0Aqz45wpeur+51CEREZVjVhL1/vvvY9GiRZg6dSrs7e2xZMkSBAUFYfLkyRXOH0V0J8t3X0JCViF8HK3x/H3BJus8A5rBLyS8RuUnx12u0f5071kp5HhlYAs8u+Y4vtxzGWM6+sHP2UbqsIiITJg1cvfy5csYMmQIAEClUiE/Px8ymQzTp0/HihUrLBogNWzX0vPxxT9XAABvDm0JGxW7bajUoFZe6BLkgiKdAe/9wXFtRFT3mJVEOTs7Izc3FwDg6+uL6OhoAEBWVhYKLDiGhRq+t387i2KdAT2C3TColZfU4VAdIpPJ8NaD4ZDLgC3RSdh3iRNwElHdYlYS1atXL2zbtg0A8PDDD+PFF1/ExIkT8eijj6Jfv34WDZAaru1nk7HzXAqsFKVfljKZTOqQqI4J83bA4zfnC3vr1zMo0XPKAyKqO8zqO/nss8+Mg3TfeOMNWFlZYf/+/Rg9ejTefPNNiwZIDZO2RI+3fz8DAHiqRxCCPewkjojqqpfuD8VvpxJxMSUP3x64hqd7BEkdEhERgGomUTk5OaU7KZWws7MzPn/22Wfx7LPPWj46arC+2HMZ8RmF8HKwxgu8Px7dgaONFWYOCMWsDaexeNsFPNjGB+72aqnDIiKqXhLl5ORUpS4XvV5vdkDU8MVnFGD57tIr5t4YEgZbzgFEdzGmoz9+OBSH0wnZ+HDrOSx8uI3UIRERVS+J2rVrl8lzIQQGDx6Mr776Cr6+vhYNjOqfqs4wPn9vBop0BkR4qOCjS8Tx40nltuEs4w2Xuef2Py2UmJUArDt2He0cC9HCTQU3NzcEBARYOEIioqqpVhLVu3fvcssUCgW6du2Kpk2bWiwoqn/i4uKqNMO4pmlHeDz8FoReh7/en4LfZ8TfcXvOMt5w5GSkAgAee+wxs8twHfQi7Frfj5d/Po7E1S9CY63CuZgYJlJEJAn2o5BFpKWl3XWGcb0AtiVaIV8HNHeS4aF5Syotj7OMNzyFeaVjKIdMfgOhrTuYVUaRHtiWKAD3QPSauQL/LHgKaWlpTKKISBI1SqLi4+NRUFAAV1dXS8VD9dydZhg/FJuOfF0GbNUK9G/fDCpl5TNscJbxhsvVJ7BGs9CXOOXgrzPJiIc7lM4+FoyMiKh6qpVEffrpp8b/p6Wl4ccff8R9990HR0dHiwdGDUtOYQmOXM0EAPQMdr9jAkV0J6Ge9jiXmItrGQVwHfg8hBBSh0REjVS1kqhFixYBKJ1J2M3NDcOGDeO8UFQley6kQm8Q8HPWoLkn54Qi88lkMvRt4YHvDsTCOiACO2ML0cG83kEiohqpVhIVGxtbW3FQA3Y1LR9X0vIhlwF9mrtzZnKqMUeNFVo66nE6S4lvTuVgwoAizh1FRPccB5Y3YlWdkqAqKrtsXWcwYPeF0quy2vo7wdWOX3RkGcH2Bhw9dwnwCsZbv53Bsv+0lzokImpkmEQ1UlWdkqC6bp+S4Pi1LGQXlsBWpUCXIF6AQJYjlwEZW5fC98kl+ONUIga3SsSQ1t5Sh0VEjQiTqEaqKlMSVEdFUxKUDibPAAD0CHHjYHKyuOLkyxjVwg6/xORh9uZodGnqAje2dhLRPcIkqpG705QE1VHRlAT/XEyFziDg66RBqKd9jY9BVJGHW9ohOlOGc0m5mL0pGp+Pa89xd0R0T7BpgGrFtfR8XE7Nh0wG9AnlYHKqPVYKGT56uA2Uchm2RCfht1OJUodERI0EkyiyOINB4N+LpQPW2/g5sXuFal0rX0c8d18wAGDO5mik5HKmeyKqfUyiyOKib2QjPb8Y1ko5ugS5SB0ONRJT+wajpbcDsgpK8MbGaE7CSUS1jkkUWZROyHDwSulg8i5NXWFtpZA4ImosrBRyfDymDawUMmw7m4xNUQlSh0REDVy9SqI++OADyGQyTJs2zbhMq9Vi6tSpcHV1hZ2dHUaPHo3k5GTpgmzk4krsUFiih7ONFSJ8eTsgurfCvB3wYr8QAMDczWeQnMNuPSKqPfUmiTpy5Ai+/PJLtG7d2mT59OnT8dtvv2HdunXYs2cPbty4gVGjRkkUZeOmdPTEdV3pLV16hLhBIedgcrr3nundDBG+jsjR6jBrw2l26xFRrakXSVReXh7GjRuH//73v3B2djYuz87Oxtdff41PPvkE9913Hzp06IBVq1Zh//79OHjwoIQRN05OfSZAQAZ/Fw2CXG2lDocaKeXNbj2VQo6d51Kw7th1qUMiogaqXiRRU6dOxZAhQ9C/f3+T5ceOHUNJSYnJ8hYtWiAgIAAHDhyotLyioiLk5OSYPKhmsqGBbYueAAR6BnNKA5JWc097TL+/OQDgnd/OIiGrUOKIiKghqvNJ1E8//YTjx49j/vz55dYlJSVBpVLBycnJZLmnpyeSkpIqLXP+/PlwdHQ0Pvz9/S0ddqMihEAsPAEAXooC3giW6oRJvZqifYATcot0mLnuJAwGdusRkWXV6SQqPj4eL774ItasWQNra2uLlTtr1ixkZ2cbH/Hx8RYruzG6nJqPPGhgKC5EkCpX6nCIAAAKuQwfj2kLays59l9Ox3cHr0kdEhE1MHU6iTp27BhSUlLQvn17KJVKKJVK7NmzB59++imUSiU8PT1RXFyMrKwsk/2Sk5Ph5eVVablqtRoODg4mDzKPQQgcuJwOAMg5uhkqmUHiiIj+J8jNFrMGhQEA5m+JwZXUvLvsQURUdXU6ierXrx9Onz6NqKgo46Njx44YN26c8f9WVlbYsWOHcZ/z588jLi4OkZGREkbeeJxLzEVGQTGU0CHn0AapwyEq5/Gugege7AptiQEz1p2Ent16RGQhdfoGxPb29mjVqpXJMltbW7i6uhqXP/3003jppZfg4uICBwcHPP/884iMjETXrl2lCLlR0RkMOBhb2grlh3RcLi6QOCKi8uRyGT58qA0GLvoHJ+Ky8OU/l/Fsn2CpwyKiBqBOt0RVxaJFizB06FCMHj0avXr1gpeXFzZsYIvIvRCdkINcrQ62KgW8kSl1OESV8nXSYM6wlgCARdsu4FwSr8glopqr0y1RFdm9e7fJc2trayxbtgzLli2TJqBGqlhnwOHY0tu7dA5yQcl5dpFQ3fZQBz/8dSYZ22OSMf3nk9g8tTtUynr/O5KIJMRPEDJL1PUsFJbo4aixQrgPb+9CdZ9MJsP7o1rB2cYKMYk5WLrzotQhEVE9xySKqk1bosexa6Xdd12buvD2LlRveNhb472REQCAz3dfRlR8lrQBEVG9xiSKqu14XCaKdQa42akQ6mkvdThE1TI4whvD2/pAbxB4aW0UtCV6qUMionqKSRRVi7ZEj5Px2QCArk1deXsXqpfefjAcHvZqXEnNx4dbz0sdDhHVU0yiqFqi4rNQrDfA1U6Fpm68yTDVT042Kix4qDUAYOW+WOOEsURE1cEkiqqsSKc3jiHp0sSFrVBUr/UN9cCjnUvvm/nyupPIK9JJHBER1TdMoqjKTl7PRpHOABcbFYI97KQOh6jG3hjSEv4uGiRkFeLd389KHQ4R1TNMoqhKinUGnIgrvSKvU5AzW6GoQbBTK7HwoTaQyYCfjsRj17kUqUMionqESRRVyemEbGhLDHDSWKG5B6/Io4aja1NXPNU9CADw6vpTyC4okTgiIqovmETRXZXoDcZ5oTo1cYGc80JRAzNzQCiautsiJbcIb/9+RupwiKieYBJFdxWdkI3CEj0crJUI9WIrFDU81lYKfPRwG8hlwIbjCdh+NlnqkIioHmASRXekM5i2QnF2cmqo2gc44/96NgUAvL7xNLv1iOiumETRHZ1LykV+sR52aiXCvB2kDoeoVr10f/P/dev9xm49IrozpdQBUN0lhMDxm61Q7fyd2ApFdVJMTIxFynFzc0NAQAA+ergNHlq+HxtOJGBwhDf6t/S0SPlE1PAwiaJKXUnLR2ZBCVRKOcJ92QpFdUtORioA4LHHHrNIeRobG5yLiUH7gAD8X8+mWPHPFby+8TQ6NXGBo42VRY5BRA0LkyiqVNlYqNa+jlArFRJHQ2SqMC8HADBk8hsIbd2hRmUlx13GmgUzkZaWhoCAALx0f3Nsj0nGldR8vP3bGXzySFsLRExEDQ2TKKrQjaxCJGZroZDJ0NbfSepwiCrl6hMIv5Bwi5ZZdrUeu/WI6E44sJwqVNYK1cLbHrZq5trU+Nx6td6sjaeRVVAscUREVNcwiaJyMvKLcSUtHwDQIcBZ4miIpFN2tV5qbhHm/cZ76xGRKSZRVM7xm/fIa+ZuC2dblcTREEnHZBLOE5yEk4hMMYkiE3lFOpxLzAUAdAhkKxRR+wBnTGS3HhFVgEkUmTgZnwW9EPBxtIa3o0bqcIjqhOns1iOiCjCJIqMSvQGnE7IBAO3ZCkVkxG49IqoIkygyiknMQZHOAEeNFYLcbKUOh6hOYbceEd2OSRQBKL3Fy4n4LABAW38nyGW8xQvR7W7t1nub3XpEjR6TKAIAXE0vQFZBCVQKOVryRsNEFbq1W2/jiQRsY7ceUaPGJIoAACfiS6c1aOXrAJWSbwuiytzarfc6u/WIGjVORU1IyytCfEYhZADa+DlJHQ6RZGJiYqq0XR83gd/tFUjILcKL3+zFi12cym3j5uaGgIAAC0dIRHUJkyjCibgsAEAzDzs4aHi3emp8cjJSAQCPPfZYlfdR+YTCa9yH2HOtEOs+mYXCS4dN1mtsbHAuJoaJFFEDxiSqkdPqgfPJpZNrtuONhqmRKszLAQAMmfwGQlt3qPJ+pzOBC7lAwMOzcb93CVSK0uXJcZexZsFMpKWlMYkiasCYRDVyV/Lk0BsEPB3U8Ha0ljocIkm5+gTCLyS8ytt76Q1IPRyHzIISXNS7YkALr1qMjojqGo4gbswUSlzJLf3p3M7fGTJOa0BULUqFHPe39IQMwLmkXFxJzZM6JCK6h5hENWK2Yb1RZJDBTq1EsIed1OEQ1Uvejhq0Dyid4X/HuRRoS/QSR0RE9wqTqEZKCAGHTiMAAG38HKGQsxWKyFxdm7rA2cYKBcV67LmQKnU4RHSPMIlqpKJTiqHyCIJCJtDK11HqcIjqtdu79W4U8EcJUWPAJKqR+u1CPgAg0NYAayuFxNEQ1X+3dusdz1BCbsMfJ0QNHZOoRig2LR9HE4sAAMH2HL9BZCldm7rAxVaFIoMMboOnwyCE1CERUS1iEtUIrdoXCwAouHQY9pxbk8hilAo5BrXyglwmoGnWEb/fbPElooaJSVQjk1VQjHVHrwMAco9uljgaoobHzU6N1k6lLbzfn87F6evZEkdERLWFSVQj8+PheBSW6BHoqIT22kmpwyFqkJraGVBwfj90BuD5H48jr0gndUhEVAuYRDUiJXoDvtl/FQAwrLmttMEQNWAyGZC+9VO42chxNb0AczZFSx0SEdUCJlGNyJ+nE5GUo4WbnRo9AzRSh0PUoBm0eZjWxRlyGbDhRALWHY2XOiQisjAmUY2EEAIr95YOKH+8ayCsFJzHhqi2tXRXYVr/5gCANzdFc3wUUQPDJKqROHYtEyevZ0OllGNcV95Vnuheea5vMPq18ECRzoDJ3x1FWl6R1CERkYUwiWokvvq3tBVqVDtfuNmpJY6GqPGQy2VYNLYtmrrZ4ka2FlPXHEeJ3iB1WERkAUqpA6DaF59RgL/PJgEAnuoRJHE0RI1HTEyM8f/TOmrw6vYCHIrNwLTVe/B0u+rNaO7m5oaAALYiE9UlTKIagVX7rsIggJ4hbmjuaS91OEQNXk5G6U2IH3vsMZPlmuAu8Bg9G39cLMA3i95F/pmdVS5TY2ODczExTKSI6hAmUQ1crrYEa29eFfQ0W6GI7onCvBwAwJDJbyC0dQeTdWez9IjJUcBj6HT0eOp5uFvf/dYwyXGXsWbBTKSlpTGJIqpDmEQ1cD8fiUdekQ4hHnbo3dxd6nCIGhVXn0D4hYSbLPMVAsWnE3E5NR8HM9R4uIMfxykS1VMcWN6A6fQGrL45ueZTPYIgk3FaAyKpyWQyDAz3go+TNYp1BmyKSkBOYYnUYRGRGZhENWB/n03G9cxCuNiqMLKdr9ThENFNSoUcw1r7wNVWhfwiPTZFJaCwRC91WERUTUyiGrCvb06uOa5LAKytFBJHQ0S3srZSYHhbH9iplcgsKMGvUTc49QFRPcMxUQ3U8bhMHLuWCSuFDI93DZQ6HCKqgL21FUa288Xao/FIytHit5M3MKyND6wUFf++vXXKhJrgdAlElsEkqoFavvsyAGBkO194OFhLHA0RVcbFVoXhbX2w8UQC4jMLsTnqBh5s4wOV8n+JVGVTJpiL0yUQWQaTqAboUkoutp1NhkwGTOrVTOpwiOguvB01GNnOF5tO3EBCViE2RSVgeFsfqJWl3fB3mjKhujhdApHlMIlqgL7ccwUAcH+YJ4I97CSOhoiqwttRg5HtfbHpRAISs7XYdOIGRrT1gfqW8YwVTZlARNLhwPIGJjG79FcsADzTh61QRPWJl4M1RrX3hbVSjqQcLTacSEB+kU7qsIioEkyiGpiv/41FiV6gS5AL2gc4Sx0OEVWTh701RrX3g8ZKgZTcIqw9Go8CqKQOi4gqwCSqAckqKMYPh+MAsBWKqD5zt1fj4Y5+cNRYIUerwyk0gdqP3XhEdU2dTqLmz5+PTp06wd7eHh4eHhgxYgTOnz9vso1Wq8XUqVPh6uoKOzs7jB49GsnJyRJFLK3vDlxDQbEeLbzs0Ye3eCGq15xtVBjT0Q9eDtbQQQHPR95Fik4jdVhEdIs6nUTt2bMHU6dOxcGDB7Ft2zaUlJTggQceQH5+vnGb6dOn47fffsO6deuwZ88e3LhxA6NGjZIwamkUFuuNt3iZ0qcZb/FC1ADYqJQY3d4XrsiBTGmFmGJnHI7NgBB3v2kxEdW+On113tatW02er169Gh4eHjh27Bh69eqF7OxsfP311/jhhx9w3333AQBWrVqFsLAwHDx4EF27dpUibEmsOxaP9Pxi+DlrMCTCW+pwiMhClAo5WiABWw7vgEPnkThwJR3peUXo39Kz0kk5iejeqFd/gdnZ2QAAFxcXAMCxY8dQUlKC/v37G7dp0aIFAgICcODAgUrLKSoqQk5OjsmjPivS6Y2Ta07s2RRKfrASNSgyAJm7vkaIKgtyGXAhJQ+/HLuOXC1vXEwkpXrzbWswGDBt2jR0794drVq1AgAkJSVBpVLBycnJZFtPT08kJSVVWtb8+fPh6OhofPj7+9dm6LVu7dHrSMzWwtNBjUc61e+6EFHlfJQFGNXuf1fu/XQkHonZhVKHRdRo1ZskaurUqYiOjsZPP/1U47JmzZqF7Oxs4yM+Pt4CEUqjSKfH57suAQCe7RPMGw0TNXC+zhqM7eQPNzsVCor1WH8sAWcT63drOlF9VS+SqOeeew6///47du3aBT8/P+NyLy8vFBcXIysry2T75ORkeHl5VVqeWq2Gg4ODyaO+WnskHonZWng5WLMViqiRcNBY4eEO/mjmbgu9ENh2Nhn/XEyFgQPOie6pOp1ECSHw3HPPYePGjdi5cyeCgoJM1nfo0AFWVlbYsWOHcdn58+cRFxeHyMjIex3uPact0WPZrtKxUM/2bcZWKKJGRKWUY0iENzoHlY4RPRGXhV9P3kBRiV7iyIgajzp9dd7UqVPxww8/YPPmzbC3tzeOc3J0dIRGo4GjoyOefvppvPTSS3BxcYGDgwOef/55REZGNoor89YejUdSTmkr1JiObIUiamxkMhkim7rCzVaFv88m41p6AX4+Go9hbXzgbMNZzolqW51uiVq+fDmys7PRp08feHt7Gx8///yzcZtFixZh6NChGD16NHr16gUvLy9s2LBBwqjvjdJWqNKxUFPZCkXUqIV42uPhjn6wUyuRWVCCn47E41p6/t13JKIaqdMtUVWZUM7a2hrLli3DsmXL7kFEdcfPR+KRnFMEb0drjOFYKKJGz8PeGmM7+eOP04lIzNZic9QN9AhxQzt/J06+S1RL6nRLFFVMW6LH57tvXpHXNxhqJVuhiAiwVSsxqr0vWno7QAD492IatsekQGcwSB0aUYPEJKoeWr3/KpJziuDjaI0xHf3uvgMRNRpKuRz9wzzQK8QNMgBnE3Ow4XgC8ot0UodG1OAwiapnMvOLjWOhXnoglK1QRFSOTCZDuwBnDG/rA7VSjsRsLX46Eo+UHK3UoRE1KEyi6plPd15ErlaHMG8HjGznK3U4RFSHBbra4pFO/nC2sUJekQ7rjl1HQgHHRxFZCpOoeuRaej6+P3gNAPD64BZQyPlhSER35myjwiMd/RHoagOdQeBgmhK2re6TOiyiBoFJVD3y4dbzKNEL9Grujp4h7lKHQ0T1hNpKgQdb+yDM2x6ADG5DXsLvFzgFAlFNMYmqJ47HZeKP04mQyYBZg1pIHQ4R1TNyuQz3h3ki2L50RvOVUTlYtO1ClaaSIaKKMYmqB4QQeP+PGADAQ+39EOZdf+/1R0TSkclkaO2kR+Y/3wIAluy4iLd/O8tEishMTKLqgb/OJOPotUxYW8kx44FQqcMhonpMJgNyDqzFxHalP8ZW77+K2ZujmUgRmYFJVB2nLdHj/T9LW6H+r0dTeDlaSxwRETUEg0Js8dHDbSCTAd8fjMPszdEwGJhIEVVHnb7tS0MQFxeHtLQ0s/f/MToXcRkFcNHIEemUiwMHDkCtVtc4rpiYmBqXQUT120MdSifrnfnLSXx/MA5CAO8MbwU5r/wlqhImUbUoLi4OLcLCUFhQYNb+Smcf+Dy1DDKlFc7/8C56vLUfgAyA5X4t5uXlWawsIqp/HurgBxmAl385iTWH4gAwkSKqKiZRtSgtLQ2FBQUY9+pCeAY0q9a+QgB7U5VI0crhaW3AqBdexrkje7DlmyUYMvkNhLbuUKPYYg6XlqXVcgZjosZu9M0WqbJESiYrTaR442KiO2MSdQ94BjSDX0h4tfY5n5SLlPgkKOQyDGrfFI4aK6TEXwYAuPoEVru82yXHXa7R/kTUsIzu4AeZDJixrrRrz0ohx5yhLZlIEd0BB5bXQUU6Pf65mAoA6NTEGY4aK4kjIqLGYFR7PywY1RoAsGrfVXyw5Ryv2iO6AyZRddDByxkoKNbDSWOFDoHOUodDRI3ImE7+eG9kKwDAl/9cwSfbLkgcEVHdxSSqjknK0eLk9SwAQN8WHlDKeYqI6N4a1yUQbw1rCQBYuvMSlu64KHFERHUTv6HrEJ3egG1nkiEAhHraI8DFRuqQiKiRmtA9CK8PLr3F1MfbLuDLPRxHSXQ7JlF1yIEr6cgoKIaNSoHeobzBMBFJa1KvZnj5geYAgPlbzmHl3liJIyKqW5hE1REJmYU4HpcFAOgX5gGNlULagIiIADx3XwheuC8YADDv97P47uA1iSMiqjuYRNUBxToDtsUkAwBaejugqZudxBEREf3P9Pub45nepXPdzd4UjZ+PxEkcEVHdwCSqDth3KQ3ZhSWwt1aiV3M3qcMhIjIhk8nw6sBQPNU9CADw2obT2HD8usRREUmPSZTErqXn41RCNgCgf5gn1Ep24xFR3SOTyTB7aBge7xoIIYCX153EbydvSB0WkaSYREmooFhn7MZr7efIq/GIqE6TyWR4+8FwjO3kD4MApv0cha3RiVKHRSQZJlESEULgrzPJyC/Sw8VGhR7B7MYjorpPLpfh/ZERGNXOF3qDwPM/nsCOmz8GiRobJlESOXotE3EZBVDKZRgU4QUrBU8FEdUPcrkMCx9ug2FtfFCiF5jy/XHsuZAqdVhE9xy/uSWQkFmIA5fTAQB9Qt3hZqeWOCIioupRyGX4ZEwbDAz3QrHegEnfHmWLFDU6SqkDaGwKinXYeiYJAkALL3u09HaQOiQiaoRiYmIsUs6TLYC8Agfsjc3BpO+O4eOH22BEO1+LlE1U1zGJuoeEEPj7bDLyinRwtrFC31APyGQyqcMiokYkJ6O02+2xxx6zWJkaO3s8tmQL/r6QhWk/RyGroBgTbk6HQNSQMYm6hw7FZuBaegEUchkGR3hDpWRvKhHdW4V5OQCAIZPfQGjrDjUuLznuMtYsmIlJbazh49YEq/dfxVu/nUVmQQmm9Q/hD0Vq0JhE3SMXU3JxKDYDAHBfqAfHQRGRpFx9AuEXEm6x8uQyGeYOawlnGxUWbb+AJTsuIjWvCG8/GM4LZ6jB4jv7HsgqluHvM6UDLtv5O6GlD8dBEVHDI5PJ8GL/ELz9YDhkMuCHQ3EYv/IwsgqKpQ6NqFYwiaplco0DDqQqoTMIBLjYcD4oImrwxndrghWPd4StSoH9l9MxfNk+XErJlTosIotjElWLSvQC7iNmoUAvg6PGCoNaeUEu5/gAImr47m/pifXPdoOfswbX0gswctl+7D6fInVYRBbFMVG1aGVUDqwDIqCUCTzYxgfWVrwvHhE1TJVNmfBOT3t8uF+HmLQSPLX6CEaH2WFMSzsoKvlB6ebmhoCAgNoMlchimETVksz8YhxPLIIQBnR218PFViV1SEREFlelKRPkSrjc/wzs2w7EurN5+O7vw0j77SPoc8q3TGlsbHAuJoaJFNULTKJqibOtCgv6u2LY/70M7+emSR0OEVGtqM6UCfH5OhzPUMDaryWCnv0a7V308LM1GNeXTZeQlpbGJIrqBSZRtcjJWoGCmH8ATJM6FCKiWlWVKRP8ALQsLMHW6CQk5WhxKF2JbCt79Apxh0bF4Q5U/3BgORER3TOOGis81MEPnZu4AADOJeXi24NXcfZGDoSQODiiamJLFBER3VMKuQyRzVzRxM0GO8+lIC2vGNtikuGmVkLpwvvuUf3BligiIpKEt6MGYzsFoEewG5RyGdKK5PB56jOsispBZj4n6KS6j0kUERFJRiGXoUOgMx7rGghPawNkCiv8diEfvRbuwvLdl6Et0UsdIlGl2J1HRESSc9RYobu7Dss/fhdtnpiLJC2wYOs5fLXnAh4Jt0fvQA2sFNWfrJjzTlFtYhJFRER1Qm5mKrSxx3Fo3kjYhveBU8/HkA4PfH40G5/uuIjsg+uQd3o7oC+pcpmcd4pqE5MoIiKqE4xzTk2ahdDWHaA3AJfzdLiQowAcPeA6YCp8Bz+L5vZ6NLEzwOouA1I47xTVNiZRRERUp9w651QggF56A87cyMHRa5nIK9LhVJYS53LlCPd1QFs/JzhorKQNmBotJlFERFSnKRVytPF3QitfR8Qk5uB4XCYyC0pwIi4LUXFZaOZuh7YBTvBxtIZMxpu8073DJIqIiOoFhVyGVr6OCPdxwLX0ApyIz0JcRgEupebhUmoePOzVaOvvhBAPOygVvPicah+TKCIiqldkMhmauNmiiZst0vOKEBWfhZikXKTkFuHvs8nYcyEVYd4OcNdJHSk1dEyiiIio3nK1U6NfmCe6NXPD6RvZiE7IRq5Wh6j4LAAqeD46H/9cK0R4az3USt6fjyyLSRQREdV7GpUCnZu4oGOgM66lFyA6IRtX0vJgHRCBxYey8M3pHRjV3g+j2/uhpY+D1OFSA8FOYyIiajDkMhmC3GwxrI0PBvmUIGvvGrhq5MgsKMHXe2Mx+NN/MXDxP/jvP1eQkqOVOlyq59gSRUREDZKNEsje9yO+WDwDObZ+WH/8OrafTcG5pFy892cM5m+JQc8Qd4xq74sHWnpBo2J3363i4uKQlpZmkbIa6szxTKKIiKhBU8hl6BfmiX5hnsguKMHvp29gw/EEHLuWiT0XUrHnQio0Vgr0CXXHoAhv3NfCA3bqxv31GBcXhxZhYSgsKLBIeQ115vjG/S4hIqJGxdHGCuO6BGJcl0DEpuVj44kEbDxxHfEZhdgSnYQt0UlQKeXoGeyGPi080Ke5O/xdbKQO+55LS0tDYUEBxr26EJ4BzWpUVkOeOZ5JFBERNUpBbrZ46f7mmN4/BGdu5GBLdCK2nE7ClbR87DiXgh3nUgAATd1s0au5OyKbuaJDoDPc7NQSR37veAY0M84eT+UxiSIiokZNJiudxLOVryNefiAUF5LzsD2mdL6p49cycSUtH1fS8rF6/1UAQBNXG7QPdEa7AGe09LZHc0972Fvz1jONEZMoIiKim2QyGUK97BHqZY+pfYORqy3B/svp+OdCKo5czcCF5DxcTS/A1fQCbDieYNzP10mD5p52CHKzg6+zBr5O1vB1soG3kzWcNFb3fAZ1vUGgWGdAkU6PIp0BxToDivUG6PQCJXoDdAYBnd6AEr2AzlC6XGcQkMtKr3C8nKiFdZN2SNHKIDIKIJfJYKWQwUohh/Lmv1YKORTyxn2bHSZRRERElbC3tsKAcC8MCPcCAGQXlOB4fCaOX8tEVHwWLibnISlHi4SsQiRkFWLX+dRKylHC2UYFJxsr2KqUUCnl/3sobj6UpYmWQYjShwHQG/8vUKw3oKjEgKJbkqPS5zf/fzNZKtLpUaIXNa675yPv4N8UACkJlW4jlwEqpRwaKwU0KkXpv7f830alRIFWBqWTF3SGmsdU1zCJIiKiBi0mJsZiZRUVFcFRrUZfN6CvmxUAZ+QWGRCfo0NcdgmS8/VILdAjNV+PtAI9MrUGAECuVodcrQ5xGRYLpcrkMsBKLoNSXnqlolIOKGT/e66QAUq5DGWNSgYBFBYW4uq1OLj5NYGVyhp6Q2lLVYnegBK9AWX5kEEA2hIDtCUGZBaUVBKBFXwnf4W4bB0635Ma3zsNJolatmwZFi5ciKSkJLRp0wZLly5F584N7XQREVFV5WSUtgo99thjFixVBqAaLSoyOeTWdpBrHKDQ2EFu7QCZyhoyhRIyhar0X6UVoLCCTGFVWrbBAAgBIQzAzYcQAkJXDKErKf1XXwzoSiD0N5/riiH0Oghdkck2QldSWoaZhi34Fi3adSm3XH9LQlWkM0BbokdhsR4FJXpoi/UovPk8v1iP7LwC5BQUwUXT8Ob3bhBJ1M8//4yXXnoJX3zxBbp06YLFixdjwIABOH/+PDw8PKQOj4iIJFCYlwMAGDL5DYS27lDj8mIO78GWb5ZYpDxLllWb5Wm1Fc/qrpDLoJArYG2lgP1dyrp+8Qw+mfoQHMcdrXFcdU2DSKI++eQTTJw4EU8++SQA4IsvvsAff/yBlStX4rXXXpM4OiIikpKrT6BFLtNPjrtssfIsWVZtlmdJMlnDG4Re75Oo4uJiHDt2DLNmzTIuk8vl6N+/Pw4cOFDhPkVFRSgqKjI+z87OBgDk5ORYNLa8vDwApVl4UWHNZ30te1MnXb2Ay7Y1m/zNkmXV9fIYW90oj7HVjfIYW90ory7HZunyUq/HAij9TrT092xZeUJINGhd1HMJCQkCgNi/f7/J8pkzZ4rOnTtXuM/cuXMFSju1+eCDDz744IOPev6Ij4+/FylHOfW+Jcocs2bNwksvvWR8bjAYkJGRAVdXV7OaG3NycuDv74/4+Hg4ODhYMtQ6pzHVFWhc9WVdG67GVN/GVFegcdW3oroKIZCbmwsfHx9JYqr3SZSbmxsUCgWSk5NNlicnJ8PLy6vCfdRqNdRq02n7nZycahyLg4NDg38Tl2lMdQUaV31Z14arMdW3MdUVaFz1vb2ujo6OksVS7683VKlU6NChA3bs2GFcZjAYsGPHDkRGRkoYGRERETVk9b4lCgBeeukljB8/Hh07dkTnzp2xePFi5OfnG6/WIyIiIrK0BpFEPfLII0hNTcWcOXOQlJSEtm3bYuvWrfD09Lwnx1er1Zg7d265LsKGqDHVFWhc9WVdG67GVN/GVFegcdW3LtZVJoRU1wUSERER1V/1fkwUERERkRSYRBERERGZgUkUERERkRmYRBERERGZgUlUDS1btgxNmjSBtbU1unTpgsOHD0sdkom33noLMpnM5NGiRQvjeq1Wi6lTp8LV1RV2dnYYPXp0uYlL4+LiMGTIENjY2MDDwwMzZ86ETqcz2Wb37t1o37491Go1goODsXr16nKx1MZr9c8//2DYsGHw8fGBTCbDpk2bTNYLITBnzhx4e3tDo9Ggf//+uHjxosk2GRkZGDduHBwcHODk5ISnn37aeN/DMqdOnULPnj1hbW0Nf39/fPjhh+ViWbduHVq0aAFra2tERETgzz//rHYsNanrhAkTyp3rgQMH1su6zp8/H506dYK9vT08PDwwYsQInD9/3mSbuvTerUosNalrnz59yp3bZ555pt7VFQCWL1+O1q1bGydMjIyMxJYtW6pVfkOpa0M6r7f74IMPIJPJMG3atGodo97VV5KbzTQQP/30k1CpVGLlypXizJkzYuLEicLJyUkkJydLHZrR3LlzRXh4uEhMTDQ+UlNTjeufeeYZ4e/vL3bs2CGOHj0qunbtKrp162Zcr9PpRKtWrUT//v3FiRMnxJ9//inc3NzErFmzjNtcuXJF2NjYiJdeekmcPXtWLF26VCgUCrF161bjNrX1Wv3555/ijTfeEBs2bBAAxMaNG03Wf/DBB8LR0VFs2rRJnDx5Ujz44IMiKChIFBYWGrcZOHCgaNOmjTh48KD4999/RXBwsHj00UeN67Ozs4Wnp6cYN26ciI6OFj/++KPQaDTiyy+/NG6zb98+oVAoxIcffijOnj0r3nzzTWFlZSVOnz5drVhqUtfx48eLgQMHmpzrjIwMk23qS10HDBggVq1aJaKjo0VUVJQYPHiwCAgIEHl5ecZt6tJ7926x1LSuvXv3FhMnTjQ5t9nZ2fWurkII8euvv4o//vhDXLhwQZw/f168/vrrwsrKSkRHR1ep/IZU14Z0Xm91+PBh0aRJE9G6dWvx4osvVvkY9bG+TKJqoHPnzmLq1KnG53q9Xvj4+Ij58+dLGJWpuXPnijZt2lS4LisrS1hZWYl169YZl8XExAgA4sCBA0KI0i9uuVwukpKSjNssX75cODg4iKKiIiGEEK+88ooIDw83KfuRRx4RAwYMMD6/F6/V7YmFwWAQXl5eYuHChcZlWVlZQq1Wix9//FEIIcTZs2cFAHHkyBHjNlu2bBEymUwkJCQIIYT4/PPPhbOzs7G+Qgjx6quvitDQUOPzMWPGiCFDhpjE06VLFzF58uQqx1KTugpRmkQNHz680n3qa12FECIlJUUAEHv27DGWV1feu1WJpSZ1FaL0y/bWL6Pb1de6lnF2dhZfffVVgz6vt9dViIZ5XnNzc0VISIjYtm2bSf0a6rlld56ZiouLcezYMfTv39+4TC6Xo3///jhw4ICEkZV38eJF+Pj4oGnTphg3bhzi4uIAAMeOHUNJSYlJHVq0aIGAgABjHQ4cOICIiAiTiUsHDBiAnJwcnDlzxrjNrWWUbVNWhlSvVWxsLJKSkkyO6+joiC5dupjUz8nJCR07djRu079/f8jlchw6dMi4Ta9evaBSqUzqd/78eWRmZhq3udNrUJVYLGH37t3w8PBAaGgopkyZgvT0dOO6+lzX7OxsAICLiwuAuvXerUosNalrmTVr1sDNzQ2tWrXCrFmzUFBQYFxXX+uq1+vx008/IT8/H5GRkQ36vN5e1zIN7bxOnToVQ4YMKRdTQz23DWLGcimkpaVBr9eXmxXd09MT586dkyiq8rp06YLVq1cjNDQUiYmJePvtt9GzZ09ER0cjKSkJKpWq3M2XPT09kZSUBABISkqqsI5l6+60TU5ODgoLC5GZmSnJa1UWX0XHvTV2Dw8Pk/VKpRIuLi4m2wQFBZUro2yds7Nzpa/BrWXcLZaaGjhwIEaNGoWgoCBcvnwZr7/+OgYNGoQDBw5AoVDU27oaDAZMmzYN3bt3R6tWrYzHqCvv3arEUpO6AsB//vMfBAYGwsfHB6dOncKrr76K8+fPY8OGDfWyrqdPn0ZkZCS0Wi3s7OywceNGtGzZElFRUQ3uvFZWV6DhndeffvoJx48fx5EjR8qta6h/s0yiGrhBgwYZ/9+6dWt06dIFgYGBWLt2LTQajYSRkaWNHTvW+P+IiAi0bt0azZo1w+7du9GvXz8JI6uZqVOnIjo6Gnv37pU6lFpXWV0nTZpk/H9ERAS8vb3Rr18/XL58Gc2aNbvXYdZYaGgooqKikJ2djV9++QXjx4/Hnj17pA6rVlRW15YtWzao8xofH48XX3wR27Ztg7W1tdTh3DPszjOTm5sbFApFudH8ycnJ8PLykiiqu3NyckLz5s1x6dIleHl5obi4GFlZWSbb3FoHLy+vCutYtu5O2zg4OECj0Uj2WpWVfafjenl5ISUlxWS9TqdDRkaGRV6DW9ffLRZLa9q0Kdzc3HDp0iVjDPWtrs899xx+//137Nq1C35+fsbldem9W5VYalLXinTp0gUATM5tfaqrSqVCcHAwOnTogPnz56NNmzZYsmRJgzyvldW1IvX5vB47dgwpKSlo3749lEollEol9uzZg08//RRKpRKenp4N7twCTKLMplKp0KFDB+zYscO4zGAwYMeOHSb93XVNXl4eLl++DG9vb3To0AFWVlYmdTh//jzi4uKMdYiMjMTp06dNvny3bdsGBwcHY5N0ZGSkSRll25SVIdVrFRQUBC8vL5Pj5uTk4NChQyb1y8rKwrFjx4zb7Ny5EwaDwfiBFhkZiX/++QclJSUm9QsNDYWzs7Nxmzu9BlWJxdKuX7+O9PR0eHt717u6CiHw3HPPYePGjdi5c2e5Lsa69N6tSiw1qWtFoqKiAMDk3NaHulbGYDCgqKioQZ3Xu9W1IvX5vPbr1w+nT59GVFSU8dGxY0eMGzfO+P8GeW6rNQydTPz0009CrVaL1atXi7Nnz4pJkyYJJycnkysLpDZjxgyxe/duERsbK/bt2yf69+8v3NzcREpKihCi9DLPgIAAsXPnTnH06FERGRkpIiMjjfuXXXL6wAMPiKioKLF161bh7u5e4SWnM2fOFDExMWLZsmUVXnJaG69Vbm6uOHHihDhx4oQAID755BNx4sQJce3aNSFE6aX2Tk5OYvPmzeLUqVNi+PDhFU5x0K5dO3Ho0CGxd+9eERISYnLZf1ZWlvD09BSPP/64iI6OFj/99JOwsbEpd9m/UqkUH330kYiJiRFz586t8LL/u8Vibl1zc3PFyy+/LA4cOCBiY2PF9u3bRfv27UVISIjQarX1rq5TpkwRjo6OYvfu3SaXfxcUFBi3qUvv3bvFUpO6Xrp0ScybN08cPXpUxMbGis2bN4umTZuKXr161bu6CiHEa6+9Jvbs2SNiY2PFqVOnxGuvvSZkMpn4+++/q1R+Q6lrQzuvFbn96sOGdG7LMImqoaVLl4qAgAChUqlE586dxcGDB6UOycQjjzwivL29hUqlEr6+vuKRRx4Rly5dMq4vLCwUzz77rHB2dhY2NjZi5MiRIjEx0aSMq1evikGDBgmNRiPc3NzEjBkzRElJick2u3btEm3bthUqlUo0bdpUrFq1qlwstfFa7dq1SwAo9xg/frwQovRy+9mzZwtPT0+hVqtFv379xPnz503KSE9PF48++qiws7MTDg4O4sknnxS5ubkm25w8eVL06NFDqNVq4evrKz744INysaxdu1Y0b95cqFQqER4eLv744w+T9VWJxdy6FhQUiAceeEC4u7sLKysrERgYKCZOnFguSa0vda2ongBM3ld16b1blVjMrWtcXJzo1auXcHFxEWq1WgQHB4uZM2eazCdUX+oqhBBPPfWUCAwMFCqVSri7u4t+/foZE6iqlt8Q6trQzmtFbk+iGtK5LSMTQojqtV0REREREcdEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQRERGRGZhEEREREZmBSRQR3dGtt4AhIqL/YRJFRCauXLmCKVOmoGXLlnB1dYVGo8G5c+ekDotuevzxx/H+++9LGsPZs2fh5+eH/Px8SeMgkhqTKCKJ/d///R9CQkJgY2MDZ2dnREZG4vvvv5cklpiYGHTo0AE6nQ4rV67EoUOHcPnyZbRo0UKSeMjUyZMn8eeff+KFF14wLmvSpAkWL15cbtu33noLbdu2NT5PTU3FlClTEBAQALVaDS8vLwwYMAD79u0zKUsmk0Emk0Gj0aBJkyYYM2YMdu7caVJ2y5Yt0bVrV3zyyScWryNRfcIkikhirq6u+Oqrr3Dx4kUcPnwYU6ZMwTPPPIMvvvjinsfy3HPPYerUqfjvf/+Lrl27Ijg4GIGBgfc8DqrY0qVL8fDDD8POzq7a+44ePRonTpzAN998gwsXLuDXX39Fnz59kJ6ebrLdvHnzkJiYiPPnz+Pbb7+Fk5MT+vfvj/fee89kuyeffBLLly+HTqerUZ2I6rVq322PiGrdyJEjxaOPPmp8/u2334oOHToIOzs74enpKR599FGRnJxsXF92c+Lff/9dRERECLVaLbp06SJOnz5t3CYtLU2MHTtW+Pj4CI1GI1q1aiV++OEH4/q8vDwhk8nEzJkzRXBwsFCr1aJVq1Zi06ZNJrGdOnVK9O3bV1hbWwsXFxcxceJE402M586dW+kNdXv37i2EEGL8+PFi+PDhJmWuWrVKODo6Gp9funRJPPjgg8LDw0PY2tqKjh07im3btpnsc+PGDTFy5Ejh4uJicpzMzMwKX9PY2NhKY1u0aJHJtuPHjy+3za03Uv34449Fq1athI2NjfDz8xNTpkwxvgaV3Si67FGVc1ERnU4nHB0dxe+//26yPDAwsFz8QpSeizZt2gghhMjMzBQAxO7du+94jMrKmjNnjpDL5eLcuXPGZUVFRUKtVovt27ffsUyihowtUUR1iBACx44dw/79+zFw4EDj8pKSErzzzjs4efIkNm3ahKtXr2LChAnl9p85cyY+/vhjHDlyBO7u7hg2bJhxYLhWq0WHDh3wxx9/IDo6GpMmTcLjjz+Ow4cPAwDS09MhhMCXX36JefPm4dSpUxg9ejRGjRqFqKgoAEB+fj4GDBgAZ2dnHDlyBOvWrcP27dvx3HPPAQBefvllJCYmIjExETNmzEBkZKTx+YYNG6r8OuTl5WHw4MHYsWMHTpw4gYEDB2LYsGGIi4szbjNjxgxcuHABW7duRWJiItavX1+lsrdv326MKTExEX5+fuW2EUJg4MCBxm0iIyNN1svlcnz66ac4c+YMvvnmG+zcuROvvPIKAKBbt27G/cpiuvV4VTkXFTl16hSys7PRsWPHKtXzVnZ2drCzs8OmTZtQVFRU7f1ffPFFCCGwefNm4zKVSoW2bdvi33//rXZ5RA2GtDkcEQkhxMaNG4Wtra1QKpVCJpOJOXPm3HH7I0eOCADlWj9++ukn4zbp6elCo9GIn3/+udJyhgwZImbMmCGE+F9LzXvvvWeyTb9+/cS4ceOEEEKsWLFCODs7i7y8POP6P/74Q8jlcpGUlGSy39y5c42tT7eqSktURcLDw8XSpUuNz8PCwkxiLXsN7tYSdeLECZPlFbW+PProo+Khhx4yPu/du7dJS9Tt1q1bJ1xdXcstL4upKm49FxXZuHGjUCgUwmAw3DV+IUxbooQQ4pdffhHOzs7C2tpadOvWTcyaNUucPHmySmUJIYSnp6eYMmWKybKRI0eKCRMm3LliRA0YW6KI6oD7778fUVFROHLkCJYvX44lS5aYjIk6duwYhg0bhoCAANjb26N3794AYNIyA8CkxcTFxQWhoaGIiYkBAOj1erzzzjuIiIiAi4sL7Ozs8Ndff5Uro3v37ibPe/TogbNnzwIoHXjepk0b2NrammxvMBhw/vz5Ktf3999/N7aO2NnZ4ZlnnjFZn5eXh5dffhlhYWFwcnKCnZ0dYmJiTGINCgrCn3/+iYyMjCoft6pycnJM6ni77du3o1+/fvD19YW9vT0ef/xxpKeno6CgoErlV/Vc3KqwsBBqtRoymaza9QFKx0TduHEDv/76KwYOHIjdu3ejffv2WL16dZX2F0KUO7ZGo6lynYkaIiZRRHWAra0tgoOD0bZtW0yePBkvv/wyPvroIwD/60JzcHDAmjVrcOTIEWzcuBEAUFxcXOVjLFy4EEuWLMGrr76KXbt2ISoqCgMGDDCW4ezsXOm+5n5xV6Zv376IiooyPubNm2ey/uWXX8bGjRvx/vvv499//0VUVBQiIiJM6rto0SIUFRXBzc0NdnZ2GDRokMXiu3HjBnx8fCpcd/XqVQwdOhStW7fG+vXrcezYMSxbtgxA1c/H3c5FRdzc3FBQUFBuGwcHB2RnZ5fbPisrC46OjibLrK2tcf/992P27NnYv38/JkyYgLlz59413vT0dKSmpiIoKMhkeUZGBtzd3e+6P1FDxSSKqA4SQsBgMAAAzp07h/T0dHzwwQfo2bMnWrRogZSUlAr3O3jwoPH/mZmZuHDhAsLCwgAA+/btw/Dhw/HYY4+hTZs2aNq0KS5cuGDc3tHREV5eXiaXvAPA3r170bJlSwBAWFgYTp48aTI/0L59+yCXyxEaGlrl+pUljWUPDw8Pk/X79u3DhAkTMHLkSERERMDLywtXr1412aZ58+aYMGECmjRpgkOHDuGrr76q8vHvJD8/HzExMWjXrl2F648dOwaDwYCPP/4YXbt2RfPmzXHjxo1qHeNu56IiZdMVlLUKlgkNDcWxY8fKbX/8+HE0b978jmW2bNmySnM9LVmyBHK5HCNGjDBZHh0dXenrRNQYMIkiklBOTg7GjBmD7du3Iz4+HhcuXMDXX3+NhQsXGru4AgICoFKpsHTpUly5cgW//vor3nnnnQrLmzdvHnbs2IHo6GhMmDABbm5uxi++kJAQbNu2Dfv370dMTAwmT56M5ORkk/2nT5+OBQsW4KeffsKFCxfw1ltvYdeuXXj55ZcBAOPGjYO1tTXGjx+P6Oho7Nq1C88//zwef/xxeHp6Wux1CQkJwYYNGxAVFYWTJ0/iP//5jzGpLHPw4EG8/vrr+OWXXxAeHg5fX98aH/fcuXN49NFH4eTkVGnLVnBwMEpKSozn47vvvqv2dBRVORe3c3d3R/v27bF3716T5dOnT8cff/yB9957DzExMYiOjsYbb7yBAwcO4MUXXwRQ2pJ033334fvvv8epU6cQGxuLdevW4cMPP8Tw4cNNysvNzUVSUhLi4+Pxzz//YNKkSXj33Xfx3nvvITg42Ljd1atXkZCQgP79+1er7kQNitSDsogas6KiIvHMM8+I1q1bCwcHB+Hs7Cx69eol1q5da7LdDz/8IJo0aSLUarWIjIwUv/76q8kg6bIBzL/99psIDw8XKpVKdO7c2WTgcHp6uhg+fLiws7MTHh4e4s033xRPPPGEySBvnU4n3nzzTeHj4yOsrKxEREREtaY4uFVNBpbHxsaKvn37Co1GI/z9/cVnn31mMrg7JSVF+Pn5ia+++sq4jyUGlj/yyCNi0KBBIjo62mSb2weWf/LJJ8Lb21toNBoxYMAA8e2331Z47MoGllflXFTk888/F127di23/K+//hLdu3cXzs7OwtXVVfTp00fs2bPHuF6r1YrXXntNtG/fXjg6OgobGxsRGhoq3nzzTVFQUGDyWuDmVAwqlUoEBASIMWPGiJ07d5Y75vvvvy8GDBhwx3iJGjqZEEJIl8IRkSXs3r0bffv2RWZmJpycnKQOh2pJYWEhQkND8fPPP5ebduFeKi4uRkhICH744YdyFyIQNSbsziMiqic0Gg2+/fZbpKWlSRpHXFwcXn/9dSZQ1OgppQ6AiIiqrk+fPlKHYLwggKixY3ceERERkRnYnUdERERkBiZRRERERGZgEkVERERkBiZRRERERGZgEkVERERkBiZRRERERGZgEkVERERkBiZRRERERGb4fwii+c91cfqzAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -738,40 +549,46 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Средняя цена в обучающей выборке:  537768.04794679\n",
-      "Средняя цена в контрольной выборке:  549367.443673375\n",
-      "Средняя цена в тестовой выборке:  549367.443673375\n"
+      "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": [
-    "# Оценка сбалансированности целевой переменной (цена)\n",
-    "# Визуализация распределения цены в выборках (гистограмма)\n",
-    "def plot_price_distribution(data, title):\n",
-    "    sns.histplot(data['price'], kde=True)\n",
+    "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('Цена')\n",
+    "    plt.xlabel('Заработная плата (USD)')\n",
     "    plt.ylabel('Частота')\n",
     "    plt.show()\n",
     "\n",
-    "plot_price_distribution(train_data, 'Распределение цены в обучающей выборке')\n",
-    "plot_price_distribution(val_data, 'Распределение цены в контрольной выборке')\n",
-    "plot_price_distribution(test_data, 'Распределение цены в тестовой выборке')\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 (price)\n",
-    "print(\"Средняя цена в обучающей выборке: \", train_data['price'].mean())\n",
-    "print(\"Средняя цена в контрольной выборке: \", val_data['price'].mean())\n",
-    "print(\"Средняя цена в тестовой выборке: \", test_data['price'].mean())"
+    "# 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": 29,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -781,7 +598,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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x5MkTJCQkICEhAampqWjRogVOnDiB7OxslWm+evXqrdekt23bhuzsbHh5eUnTTEhIgLW1NapWraqWuNPT0wG8Xl8F2bx5M0xMTNCyZUuVabq4uMDIyEhtmhkZGSr1EhIS3nqa9+HDh1i0aBHGjx8PIyMjtfnXqFEDDg4OKtPMuRxd2P8izp8/Dy8vL3h6eqr9VwVAOlMJvP6PJSEhAQ0aNIAQAleuXCnUPHKEhIQgMTER3bp1U2mzpqYm6tWrV6T/fA4dOoT09HQMHTpU2qcBoG/fvvl2mtfS0kL//v2l9zo6Oujfvz/i4+Nx6dKlfOexbds2XLhwAdOnT39reyZPngwTExP8/PPPhV6GrKwsJCQk4J9//sG8efOQnJwsnYEFVNf9q1evkJCQgPr16wOAyuc1R84ZrRw5n9/cZ/RyTzMpKQkJCQlwdXXF33//jaSkJLXp5a7ftGlTuLi4SOs2JiYGYWFh6NmzJ8zNzaV6tWrVQsuWLQt9JjGvnPWS+/XixQuVOkXd/jmEEBgzZgw8PT1Rr169YrXvbQpz/NDU1JT6qGVnZ+Pp06fIzMxE3bp18922hZWzTVNSUoo9jcLau3cvNDU11fb5ESNGQAiBffv2AXjdNy87OxsTJkxQ2VaA+vdJYY+9xT32Xbx4EfHx8Rg4cKDK94aHhwccHBxU9pvvvvsOly5dkq4sAcBff/0FXV1ddOzYEUDxvrN++uknlff6+vrQ0dHBsWPH1Lq15K2XIyUlBQkJCWjcuDFevHiB27dvq9Q1MjJC165dpffVq1eHqakpatSoobLf5/z9999/q82vMMeTwjIyMsIPP/wgvdfR0cHXX3+tMt/9+/ejbNmy6NChg1Smp6dX7JtqZLs0GRAQgGrVqkFLSwtWVlaoXr26yo6fnZ2NBQsWYMmSJYiMjFS5tpxz+RJ4fUmzevXq0NIq2UWpWrWqyvucPj8518FzHhbp4+NT4DSSkpKkU8IAkJCQoDbdvO7evQshRIH18l7CyOljkjf85J1mUlISLC0t8x0eHx+v8v7gwYPS5bnCmjhxImxtbdG/f3/p+n7u+d+6davAaeadf34ePnwIDw8PpKam4smTJ/mG7KioKEyYMAG7du1SO0jk/eJ+m5ztm3PAzMvY2LjQ08rpu1G9enWVch0dHVSqVEmtb4etra1ap9hq1aoBeN0XJyfg5MjKysKvv/4Kb29v1KpV641tiYyMxLJlyxAYGFikjqp3796VLr/o6OhgyZIl8PLykoY/ffoUkyZNwsaNG9W2Z37rPu/+XblyZWhoaKj0Mzl9+jQmTpyI0NBQtXCTlJQEExMTaT9wcHBQm0eNGjWkfbGgbZBT78CBA8XqjHz79u23flaKuv1zrF+/Hjdv3sSmTZtU+kKWpMIcPwBg9erVmDNnDm7fvo2MjAypvGLFisWed+4uAqampujWrRtmzZr1XjqE//PPP7C1tVW7pJWzT+dsg/v370NDQwOOjo5vnWZhj73FPfa9aZ91cHDAqVOnpPddunTB8OHDpb7NQghs3rwZbdq0kY5VxfnOyrt9dXV1MWPGDIwYMQJWVlaoX78+2rVrhx49esDa2lqqd/PmTYwbNw5HjhxBcnKy2jxyK1eunNrx3MTEBHZ2dmplAPINgIU5nhRWfu0xMzPDtWvXpPf//PMPKleurFYvb7/gwpItiH399dfSXZP5mTp1KsaPH4/evXtj8uTJMDc3h4aGBoYOHaqW2uWQ04ZZs2bB2dk53zq5P6Dp6emIiYlBy5Yt3zpdhUKBffv2QVNT843TBF53nASg8iHIb5qWlpZYv359vsPzHiTq1auH3377TaVs8eLF2LlzZ77j37p1C6tWrcK6devy7euSnZ0NJycnzJ07N9/x837g8nPv3j18+eWXmDdvHrp3747Vq1erHFCysrLQsmVLPH36FKNHj4aDgwMMDQ3x8OFD9OzZs8j7TE79tWvX5rtuSzr4v4sVK1bgwYMHOHDgwFvrjh07FlWrVoWPjw9OnjxZ6HmUL18eISEhSElJQXBwMIYNGwY7Ozvpxg4vLy+cOXMGI0eOhLOzM4yMjJCdnY3WrVsXat3nPaDdv38fLVq0gIODA+bOnQs7Ozvo6Ohg7969mDdvnjTN3P95y6FChQr4/fffVco2b96M5cuXv9N009PTMX78ePTp00cK4e9DYY4f69atQ8+ePfHNN99g5MiRsLS0hKamJqZNm6ZyBqaocv4ZT0tLw7FjxzB79mwAr29q+C8o7LH3XY99hWFra4vGjRtj06ZN+PXXX3H27FlERUVhxowZKm0BCv+dBeT/+Ro6dCjat2+PHTt24MCBAxg/fjymTZuGI0eOoE6dOkhMTISrqyuMjY3h7++PypUrQ09PD5cvX8bo0aPVjgf5fc+9qVzkc3NFXu9yNexd5ltcH8+3SR5btmxBs2bN1O7OSkxMlDrfAq+T77lz55CRkVEiHc5z5P15FCEE7t27J51xyOl4amxsrPKfXUGuXr2KjIyMN4bPnOkKIVCxYsVCHYDDw8OhUCjy/a8p9zQPHTqEhg0bFuqLq3Tp0mrL9KZOpWPGjIGzszO+++67Aud/9epVtGjRotgfkJzLwlZWVti5cydGjBiBtm3bSiHy+vXruHPnDlavXq3ScTckJKRY88vZvpaWloXavm9ib28PAIiIiEClSpWk8vT0dERGRqpN/9GjR2pnZ+7cuQMAandkvXjxApMmTcLAgQOl+RTkypUr2LhxI3bs2FHgwaYgBgYGUjs7deqEBw8eYPLkyWjXrh2ePXuGw4cPY9KkSZgwYYI0zpt+Yuju3bsq/23fu3cP2dnZ0vLt3r0baWlp2LVrF8qXLy/Vy3spJ2caERERamcvb9++LU0v9zbI6/bt2yhdunSxzsQYGhqqbb+wsDCV90Xd/sDrMBIfH5/vHdUlKTw8HGXKlFG5ypDXli1bUKlSJWzbtk3l8ztx4sR3mnfuf8Y9PDxw9epV7N+//52mWRB7e3scOnQIKSkpKmfFci6T5WyjypUrIzs7G+Hh4QWGlRyFWXfvcuzLvd/k3bcjIiLUPu/fffcdBg4ciIiICPz1118wMDBA+/btVdoCFP47600qV66MESNGYMSIEbh79y6cnZ0xZ84crFu3DseOHcOTJ0+wbds2NGnSRBon54kI78Pbjiclzd7eHuHh4RBCqGzXe/fuFWt6H83jK/LS1NRUS6CbN29WuW0XeH0HR0JCAhYvXqw2jXdJsGvWrFHpu7BlyxbExMRId024uLigcuXKmD17Np4/f642/uPHj9Xarqmpme+jIXLr3Lmz9PDNvO0XQqg8DiAzMxNbt27F119//cbT415eXsjKysLkyZPVhmVmZqrdQl8UoaGh2LlzJ6ZPn17ggcbLywsPHz5UO3MAAC9fvkRqaupb51OtWjVYWVkBABYtWoTs7GwMGTJEGp4TLHKvMyEEFixYUKTlyeHu7g5jY2NMnTpV5VJMjrzb903c3Nygo6ODhQsXqrRvxYoVSEpKgoeHh0r9zMxMLFu2THqfnp6OZcuWoUyZMnBxcVGpu2DBAqSmpmLs2LFvbcf//d//oWHDhir9GoojKysLz549k26/z2/dA1D5SaW88v680qJFiwBA+nzlN82kpCQEBQWpjFenTh1YW1tj6dKlKo8DOHnyJC5evCh93mxsbODs7IzVq1er7O83btzAwYMH1e6mLklF3f4pKSmYMmUKhg0b9sazLe8qJSUFe/fuLfDye478tsW5c+cQGhpaou3Jzs4u8j8IhdW2bVtkZWWpfU/MmzcPCoVC2u+++eYbaGhowN/fX+3MTe7lL+y6e5djX926dWFpaam2b+/btw+3bt1S2288PT2hqamJP//8E5s3b0a7du1U/rko6ndWfl68eKHWX7hy5cooVarUG48H6enp7/VM59uOJyXN3d0dDx8+xK5du6SyV69e5budC+OjPSPWrl07+Pv7o1evXmjQoAGuX7+O9evXq/xHCbzuVL9mzRoMHz4c58+fR+PGjZGamopDhw5h4MCBUkfFojI3N0ejRo3Qq1cvxMXFYf78+ahSpYrUGU9DQwN//PEH2rRpg5o1a6JXr14oW7YsHj58iKNHj8LY2Bi7d+9GamoqAgICsHDhQlSrVk3l+SI5H4Zr164hNDQUSqUSlStXxm+//YYxY8bgwYMH+Oabb1CqVClERkZi+/bt6NevH3755RccOnQI48ePx7Vr1976UzWurq7o378/pk2bhrCwMLRq1Qra2tq4e/cuNm/ejAULFuDbb78t1no6ePAgWrZs+cb/sLp3745Nmzbhp59+wtGjR9GwYUNkZWXh9u3b2LRpEw4cOPDWM4W5WVtbY9asWfjxxx/xww8/oG3btnBwcEDlypXxyy+/4OHDhzA2NsbWrVvf2KH0TYyNjREYGIju3bvjyy+/RNeuXVGmTBlERUVhz549aNiwYb7hPz9lypTBmDFjMGnSJLRu3RodOnRAREQElixZgq+++kqlYyjw+jLDjBkz8ODBA1SrVg1//fUXwsLCsHz5crWzvgcPHsSUKVPe+F957roFPePuTZo0aYKmTZuifPnyeP78ObZs2YIrV65Il5KMjY3RpEkTzJw5ExkZGShbtiwOHjz4xv+AIyMj0aFDB7Ru3RqhoaFYt24dvv/+e9SuXRsA0KpVK+jo6KB9+/bo378/nj9/jt9//x2WlpaIiYmRpqOlpYWZM2eiR48eaNy4Mby9vaXHV5QrV07l0Q+zZs1CmzZtoFQq0adPH+nxFSYmJu/1zFNRt//ly5dRunRpjBo16r21adOmTZg0aRKePXuG//u//3tj3Xbt2mHbtm3o1KkTPDw8EBkZiaVLl8LR0THfL/TCCg0NRUJCgnRp8vDhw/jll1+KPb03ad++PZo1a4axY8fiwYMHqF27Ng4ePIidO3di6NCh0tmiKlWqYOzYsZg8eTIaN26Mzp07Q1dXFxcuXICtrS2mTZtWpHX3Lsc+bW1tzJgxA7169YKrqyu6desmPb6iQoUKGDZsmEp9S0tLNGvWDHPnzkVKSoraFYrCfme9yZ07d9CiRQt4eXnB0dERWlpa2L59O+Li4qQO9w0aNICZmRl8fHzw888/Q6FQYO3ate/10t7bjiclrX///li8eDG6deuGIUOGwMbGBuvXr5f63Rb5yk+R7rEsAYW5dVmI14+vGDFihLCxsRH6+vqiYcOGIjQ0NN/bml+8eCHGjh0rKlasKLS1tYW1tbX49ttvxf3794UQxXt8xZ9//inGjBkjLC0thb6+vvDw8Mj3EQhXrlwRnTt3FhYWFkJXV1fY29sLLy8vcfjwYZV5v+2V9xb5rVu3ikaNGglDQ0NhaGgoHBwchK+vr4iIiBBCCDF48GDRpEkTsX//frU2FXTr+PLly4WLi4vQ19cXpUqVEk5OTmLUqFHi0aNHUp2iPr5CoVCIS5cuqZTnt43S09PFjBkzRM2aNYWurq4wMzMTLi4uYtKkSSIpKUltfm+bnhBCNG/eXJQvX16kpKQIIYQIDw8Xbm5uwsjISJQuXVr07dtXXL16Ve2W57zLkN/jK3IcPXpUuLu7CxMTE6GnpycqV64sevbsKS5evCjVedvjK3IsXrxYODg4CG1tbWFlZSUGDBggnj17prasNWvWFBcvXhRKpVLo6ekJe3t7sXjxYrV2ARA2NjYiNTX1jcuUsz907Ngx32m87VbrAQMGiIoVKwpdXV1hbm4u6tevr/K4FCGE+Pfff0WnTp2EqampMDExEV26dBGPHj0qsC3h4eHi22+/FaVKlRJmZmZi0KBBKrfoCyHErl27RK1atYSenp6oUKGCmDFjhvS4mryPYdi0aZOoU6eO1MZu3brl+3k9dOiQaNiwodDX1xfGxsaiffv2Ijw8vMBlL4nHV+Qo7PYHIObNm6dS/qbHQRSnTZ06dRJt2rQR586dU6ub9xEM2dnZYurUqcLe3l7o6uqKOnXqiODgYLV6hW1vzndAzktHR0dUqVJFTJgwQXpMRkk/vkKI14+kGTZsmLC1tRXa2tqiatWqYtasWWqPpRDi9WORcvYnMzMz4erqKkJCQoQQRVt3QrzbsU8IIf766y+Vfdvb21t6zFNev//+uwAgSpUqpfZ5yvG27ywh/rf9Hj9+rDJuQkKC8PX1FQ4ODsLQ0FCYmJiIevXqiU2bNqnUO336tKhfv77Q19cXtra2YtSoUdLjJ/I+LiK//bWg76G827sox5PCPr4iv/bkt13//vtv4eHhIfT19UWZMmXEiBEjxNatWwUAlcduFIbi/y8c/X/Hjh1Ds2bNsHnz5mKfJcrtwYMHqFixIiIjIwu8Xu3n54cHDx5g1apV7zw/+m9r2rQpEhIScOPGDbmb8l7kPNj08ePHKn09iYiK6mM7nsyfPx/Dhg3Dv//+Kz2KqzA+2j5iRERERB+j3D+dB7zuI7Zs2TJUrVq1SCEM+Ij7iH0qjIyM4O3t/cbO9LVq1ZJ+somIiIg+bp07d0b58uXh7OyMpKQkrFu3Drdv3y7wMVFvwiD2npUuXRrr1q17Y53cv3NIREREHzd3d3f88ccfWL9+PbKysuDo6IiNGzcW+BinN2EfMSIiIiKZsI8YERERkUwYxIiIiIhkwj5ieP1E50ePHqFUqVLv9BtVRERE9OEIIZCSkgJbW1toaPw3zy0xiOH1b/uV1I+vEhER0YcVHR2NcuXKyd2MYmEQA6QfgY2OjoaxsbHMrSEiIqLCSE5Ohp2dncqPuf/XMIjhf78LZWxszCBGRET0H/Nf7lb037ygSkRERPQJYBAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZaMndgP8ql5Fr5G4C/X+XZvV47/Pg9v54cHt/Xri9Py8fYnt/bHhGjIiIiEgmDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZMIgRERERyUTWIFahQgUoFAq1l6+vLwDg1atX8PX1hYWFBYyMjODp6Ym4uDiVaURFRcHDwwMGBgawtLTEyJEjkZmZKcfiEBERERWJrEHswoULiImJkV4hISEAgC5dugAAhg0bht27d2Pz5s04fvw4Hj16hM6dO0vjZ2VlwcPDA+np6Thz5gxWr16NVatWYcKECbIsDxEREVFRyBrEypQpA2tra+kVHByMypUrw9XVFUlJSVixYgXmzp2L5s2bw8XFBUFBQThz5gzOnj0LADh48CDCw8Oxbt06ODs7o02bNpg8eTICAgKQnp4u56IRERERvdVH00csPT0d69atQ+/evaFQKHDp0iVkZGTAzc1NquPg4IDy5csjNDQUABAaGgonJydYWVlJddzd3ZGcnIybN28WOK+0tDQkJyervIiIiIg+tI8miO3YsQOJiYno2bMnACA2NhY6OjowNTVVqWdlZYXY2FipTu4QljM8Z1hBpk2bBhMTE+llZ2dXcgtCREREVEgfTRBbsWIF2rRpA1tb2/c+rzFjxiApKUl6RUdHv/d5EhEREeWlJXcDAOCff/7BoUOHsG3bNqnM2toa6enpSExMVDkrFhcXB2tra6nO+fPnVaaVc1dlTp386OrqQldXtwSXgIiIiKjoPoozYkFBQbC0tISHh4dU5uLiAm1tbRw+fFgqi4iIQFRUFJRKJQBAqVTi+vXriI+Pl+qEhITA2NgYjo6OH24BiIiIiIpB9jNi2dnZCAoKgo+PD7S0/tccExMT9OnTB8OHD4e5uTmMjY0xePBgKJVK1K9fHwDQqlUrODo6onv37pg5cyZiY2Mxbtw4+Pr68owXERERffRkD2KHDh1CVFQUevfurTZs3rx50NDQgKenJ9LS0uDu7o4lS5ZIwzU1NREcHIwBAwZAqVTC0NAQPj4+8Pf3/5CLQERERFQssgexVq1aQQiR7zA9PT0EBAQgICCgwPHt7e2xd+/e99U8IiIiovfmo+gjRkRERPQ5YhAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZMIgRERERyYRBjIiIiEgmDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUxkD2IPHz7EDz/8AAsLC+jr68PJyQkXL16UhgshMGHCBNjY2EBfXx9ubm64e/euyjSePn0Kb29vGBsbw9TUFH369MHz588/9KIQERERFYmsQezZs2do2LAhtLW1sW/fPoSHh2POnDkwMzOT6sycORMLFy7E0qVLce7cORgaGsLd3R2vXr2S6nh7e+PmzZsICQlBcHAwTpw4gX79+smxSERERESFpiXnzGfMmAE7OzsEBQVJZRUrVpT+FkJg/vz5GDduHDp27AgAWLNmDaysrLBjxw507doVt27dwv79+3HhwgXUrVsXALBo0SK0bdsWs2fPhq2t7YddKCIiIqJCkvWM2K5du1C3bl106dIFlpaWqFOnDn7//XdpeGRkJGJjY+Hm5iaVmZiYoF69eggNDQUAhIaGwtTUVAphAODm5gYNDQ2cO3cu3/mmpaUhOTlZ5UVERET0ockaxP7++28EBgaiatWqOHDgAAYMGICff/4Zq1evBgDExsYCAKysrFTGs7KykobFxsbC0tJSZbiWlhbMzc2lOnlNmzYNJiYm0svOzq6kF42IiIjorWQNYtnZ2fjyyy8xdepU1KlTB/369UPfvn2xdOnS9zrfMWPGICkpSXpFR0e/1/kRERER5UfWIGZjYwNHR0eVsho1aiAqKgoAYG1tDQCIi4tTqRMXFycNs7a2Rnx8vMrwzMxMPH36VKqTl66uLoyNjVVeRERERB+arEGsYcOGiIiIUCm7c+cO7O3tAbzuuG9tbY3Dhw9Lw5OTk3Hu3DkolUoAgFKpRGJiIi5duiTVOXLkCLKzs1GvXr0PsBRERERExSPrXZPDhg1DgwYNMHXqVHh5eeH8+fNYvnw5li9fDgBQKBQYOnQofvvtN1StWhUVK1bE+PHjYWtri2+++QbA6zNorVu3li5pZmRkYNCgQejatSvvmCQiIqKPmqxB7KuvvsL27dsxZswY+Pv7o2LFipg/fz68vb2lOqNGjUJqair69euHxMRENGrUCPv374eenp5UZ/369Rg0aBBatGgBDQ0NeHp6YuHChXIsEhEREVGhyRrEAKBdu3Zo165dgcMVCgX8/f3h7+9fYB1zc3Ns2LDhfTSPiIiI6L2R/SeOiIiIiD5XDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZMIgRERERyYRBjIiIiEgmDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikomsQczPzw8KhULl5eDgIA1/9eoVfH19YWFhASMjI3h6eiIuLk5lGlFRUfDw8ICBgQEsLS0xcuRIZGZmfuhFISIiIioyLbkbULNmTRw6dEh6r6X1vyYNGzYMe/bswebNm2FiYoJBgwahc+fOOH36NAAgKysLHh4esLa2xpkzZxATE4MePXpAW1sbU6dO/eDLQkRERFQUsgcxLS0tWFtbq5UnJSVhxYoV2LBhA5o3bw4ACAoKQo0aNXD27FnUr18fBw8eRHh4OA4dOgQrKys4Oztj8uTJGD16NPz8/KCjo/OhF4eIiIio0GTvI3b37l3Y2tqiUqVK8Pb2RlRUFADg0qVLyMjIgJubm1TXwcEB5cuXR2hoKAAgNDQUTk5OsLKykuq4u7sjOTkZN2/eLHCeaWlpSE5OVnkRERERfWiyBrF69eph1apV2L9/PwIDAxEZGYnGjRsjJSUFsbGx0NHRgampqco4VlZWiI2NBQDExsaqhLCc4TnDCjJt2jSYmJhILzs7u5JdMCIiIqJCkPXSZJs2baS/a9WqhXr16sHe3h6bNm2Cvr7+e5vvmDFjMHz4cOl9cnIywxgRERF9cLJfmszN1NQU1apVw71792BtbY309HQkJiaq1ImLi5P6lFlbW6vdRZnzPr9+Zzl0dXVhbGys8iIiIiL60D6qIPb8+XPcv38fNjY2cHFxgba2Ng4fPiwNj4iIQFRUFJRKJQBAqVTi+vXriI+Pl+qEhITA2NgYjo6OH7z9REREREUh66XJX375Be3bt4e9vT0ePXqEiRMnQlNTE926dYOJiQn69OmD4cOHw9zcHMbGxhg8eDCUSiXq168PAGjVqhUcHR3RvXt3zJw5E7GxsRg3bhx8fX2hq6sr56IRERERvZWsQezff/9Ft27d8OTJE5QpUwaNGjXC2bNnUaZMGQDAvHnzoKGhAU9PT6SlpcHd3R1LliyRxtfU1ERwcDAGDBgApVIJQ0ND+Pj4wN/fX65FIiIiIio0WYPYxo0b3zhcT08PAQEBCAgIKLCOvb099u7dW9JNIyIiInrvPqo+YkRERESfEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUy0ijtiamoqjh8/jqioKKSnp6sM+/nnn9+5YURERESfumIFsStXrqBt27Z48eIFUlNTYW5ujoSEBBgYGMDS0pJBjIiIiKgQinVpctiwYWjfvj2ePXsGfX19nD17Fv/88w9cXFwwe/bskm4jERER0SepWEEsLCwMI0aMgIaGBjQ1NZGWlgY7OzvMnDkTv/76a0m3kYiIiOiTVKwgpq2tDQ2N16NaWloiKioKAGBiYoLo6OiSax0RERHRJ6xYfcTq1KmDCxcuoGrVqnB1dcWECROQkJCAtWvX4osvvijpNhIRERF9kop1Rmzq1KmwsbEBAEyZMgVmZmYYMGAAHj9+jOXLl5doA4mIiIg+VcU6I1a3bl3pb0tLS+zfv7/EGkRERET0uSjWGbHmzZsjMTGxhJtCRERE9HkpVhA7duyY2kNciYiIiKhoiv0TRwqFoiTbQURERPTZKfZPHHXq1Ak6Ojr5Djty5EixG0RERET0uSh2EFMqlTAyMirJthARERF9VooVxBQKBUaOHAlLS8uSbg8RERHRZ6NYfcSEECXdDiIiIqLPTrGC2MSJE3lZkoiIiOgdFevS5MSJEwEAjx8/RkREBACgevXqKFOmTMm1jIiIiOgTV6wzYi9evEDv3r1ha2uLJk2aoEmTJrC1tUWfPn3w4sWLkm4jERER0SepWEFs2LBhOH78OHbt2oXExEQkJiZi586dOH78OEaMGFHSbSQiIiL6JBXr0uTWrVuxZcsWNG3aVCpr27Yt9PX14eXlhcDAwJJqHxEREdEnq9iXJq2srNTKLS0teWmSiIiIqJCKFcSUSiUmTpyIV69eSWUvX77EpEmToFQqi9WQ6dOnQ6FQYOjQoVLZq1ev4OvrCwsLCxgZGcHT0xNxcXEq40VFRcHDwwMGBgawtLTEyJEjkZmZWaw2EBEREX1Ixbo0OX/+fLRu3RrlypVD7dq1AQBXr16Fnp4eDhw4UOTpXbhwAcuWLUOtWrVUyocNG4Y9e/Zg8+bNMDExwaBBg9C5c2ecPn0aAJCVlQUPDw9YW1vjzJkziImJQY8ePaCtrY2pU6cWZ9GIiIiIPphinRFzcnLC3bt3MW3aNDg7O8PZ2RnTp0/H3bt3UbNmzSJN6/nz5/D29sbvv/8OMzMzqTwpKQkrVqzA3Llz0bx5c7i4uCAoKAhnzpzB2bNnAQAHDx5EeHg41q1bB2dnZ7Rp0waTJ09GQEAA0tPTC5xnWloakpOTVV5EREREH1qxgtiJEyego6ODvn37Ys6cOZgzZw5+/PFH6OvrF3lavr6+8PDwgJubm0r5pUuXkJGRoVLu4OCA8uXLIzQ0FAAQGhoKJycnlf5q7u7uSE5Oxs2bNwuc57Rp02BiYiK97OzsitxuIiIiondVrCDWrFkzPH369J1nvnHjRly+fBnTpk1TGxYbGwsdHR2YmpqqlFtZWSE2Nlaqk/emgZz3OXXyM2bMGCQlJUmv6Ojod1wSIiIioqIrVh+xkvityejoaAwZMgQhISHQ09N75+kVha6uLnR1dT/oPImIiIjyKlYQA15fFszdpyu3Jk2avHX8S5cuIT4+Hl9++aVUlpWVhRMnTmDx4sU4cOAA0tPTkZiYqHJWLC4uDtbW1gAAa2trnD9/XmW6OXdV5tQhIiIi+lgVO4h16tQp33KFQoGsrKy3jt+iRQtcv35dpaxXr15wcHDA6NGjYWdnB21tbRw+fBienp4AgIiICERFRUmPyFAqlZgyZQri4+NhaWkJAAgJCYGxsTEcHR2Lu2hEREREH0Sxg1hsbKwUfoqjVKlS+OKLL1TKDA0NYWFhIZX36dMHw4cPh7m5OYyNjTF48GAolUrUr18fANCqVSs4Ojqie/fumDlzJmJjYzFu3Dj4+vry0iMRERF99IoVxBQKRUm3I1/z5s2DhoYGPD09kZaWBnd3dyxZskQarqmpieDgYAwYMABKpRKGhobw8fGBv7//B2kfERER0buQrbN+fo4dO6byXk9PDwEBAQgICChwHHt7e+zdu/e9tIeIiIjofSpWEMvOzi7pdhARERF9dor1HLFp06Zh5cqVauUrV67EjBkz3rlRRERERJ+DYgWxZcuWwcHBQa28Zs2aWLp06Ts3ioiIiOhzUKwgFhsbCxsbG7XyMmXKICYm5p0bRURERPQ5KFYQs7Ozw+nTp9XKT58+DVtb23duFBEREdHnoFid9fv27YuhQ4ciIyMDzZs3BwAcPnwYo0aNwogRI0q0gURERESfqmIFsZEjR+LJkycYOHAg0tPTAbx+1MTo0aMxZsyYEm0gERER0aeq2A90nTFjBsaPH49bt25BX18fVatW5dPsiYiIiIqg2D9xBABGRkb46quvSqotRERERJ+VYgexixcvYtOmTYiKipIuT+bYtm3bOzeMiIiI6FNXrLsmN27ciAYNGuDWrVvYvn07MjIycPPmTRw5cgQmJiYl3UYiIiKiT1KxgtjUqVMxb9487N69Gzo6OliwYAFu374NLy8vlC9fvqTbSERERPRJKlYQu3//Pjw8PAAAOjo6SE1NhUKhwLBhw7B8+fISbSARERHRp6pYQczMzAwpKSkAgLJly+LGjRsAgMTERLx48aLkWkdERET0CStWZ/0mTZogJCQETk5O6NKlC4YMGYIjR44gJCQELVq0KOk2EhEREX2SihXEFi9ejFevXgEAxo4dC21tbZw5cwaenp4YN25ciTaQiIiI6FNVpCCWnJz8eiQtLRgZGUnvBw4ciIEDB5Z864iIiIg+YUUKYqamplAoFG+tl5WVVewGEREREX0uihTEjh49qvJeCIG2bdvijz/+QNmyZUu0YURERESfuiIFMVdXV7UyTU1N1K9fH5UqVSqxRhERERF9Dor1+AoiIiIienfvFMSio6Px4sULWFhYlFR7iIiIiD4bRbo0uXDhQunvhIQE/Pnnn2jevDl/X5KIiIioGIoUxObNmwcAUCgUKF26NNq3b8/nhhEREREVU5GCWGRk5PtqBxEREdFnh531iYiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTWYNYYGAgatWqBWNjYxgbG0OpVGLfvn3S8FevXsHX1xcWFhYwMjKCp6cn4uLiVKYRFRUFDw8PGBgYwNLSEiNHjkRmZuaHXhQiIiKiIpM1iJUrVw7Tp0/HpUuXcPHiRTRv3hwdO3bEzZs3AQDDhg3D7t27sXnzZhw/fhyPHj1C586dpfGzsrLg4eGB9PR0nDlzBqtXr8aqVaswYcIEuRaJiIiIqNCK9KPfJa19+/Yq76dMmYLAwECcPXsW5cqVw4oVK7BhwwY0b94cABAUFIQaNWrg7NmzqF+/Pg4ePIjw8HAcOnQIVlZWcHZ2xuTJkzF69Gj4+flBR0dHjsUiIiIiKpSPpo9YVlYWNm7ciNTUVCiVSly6dAkZGRlwc3OT6jg4OKB8+fIIDQ0FAISGhsLJyQlWVlZSHXd3dyQnJ0tn1fKTlpaG5ORklRcRERHRhyZ7ELt+/TqMjIygq6uLn376Cdu3b4ejoyNiY2Oho6MDU1NTlfpWVlaIjY0FAMTGxqqEsJzhOcMKMm3aNJiYmEgvOzu7kl0oIiIiokKQPYhVr14dYWFhOHfuHAYMGAAfHx+Eh4e/13mOGTMGSUlJ0is6Ovq9zo+IiIgoP7L2EQMAHR0dVKlSBQDg4uKCCxcuYMGCBfjuu++Qnp6OxMRElbNicXFxsLa2BgBYW1vj/PnzKtPLuasyp05+dHV1oaurW8JLQkRERFQ0sp8Ryys7OxtpaWlwcXGBtrY2Dh8+LA2LiIhAVFQUlEolAECpVOL69euIj4+X6oSEhMDY2BiOjo4fvO1ERERERSHrGbExY8agTZs2KF++PFJSUrBhwwYcO3YMBw4cgImJCfr06YPhw4fD3NwcxsbGGDx4MJRKJerXrw8AaNWqFRwdHdG9e3fMnDkTsbGxGDduHHx9fXnGi4iIiD56sgax+Ph49OjRAzExMTAxMUGtWrVw4MABtGzZEgAwb948aGhowNPTE2lpaXB3d8eSJUuk8TU1NREcHIwBAwZAqVTC0NAQPj4+8Pf3l2uRiIiIiApN1iC2YsWKNw7X09NDQEAAAgICCqxjb2+PvXv3lnTTiIiIiN67j66PGBEREdHngkGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZMIgRERERyYRBjIiIiEgmDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDKRNYhNmzYNX331FUqVKgVLS0t88803iIiIUKnz6tUr+Pr6wsLCAkZGRvD09ERcXJxKnaioKHh4eMDAwACWlpYYOXIkMjMzP+SiEBERERWZrEHs+PHj8PX1xdmzZxESEoKMjAy0atUKqampUp1hw4Zh9+7d2Lx5M44fP45Hjx6hc+fO0vCsrCx4eHggPT0dZ86cwerVq7Fq1SpMmDBBjkUiIiIiKjQtOWe+f/9+lferVq2CpaUlLl26hCZNmiApKQkrVqzAhg0b0Lx5cwBAUFAQatSogbNnz6J+/fo4ePAgwsPDcejQIVhZWcHZ2RmTJ0/G6NGj4efnBx0dHTkWjYiIiOitPqo+YklJSQAAc3NzAMClS5eQkZEBNzc3qY6DgwPKly+P0NBQAEBoaCicnJxgZWUl1XF3d0dycjJu3ryZ73zS0tKQnJys8iIiIiL60D6aIJadnY2hQ4eiYcOG+OKLLwAAsbGx0NHRgampqUpdKysrxMbGSnVyh7Cc4TnD8jNt2jSYmJhILzs7uxJeGiIiIqK3+2iCmK+vL27cuIGNGze+93mNGTMGSUlJ0is6Ovq9z5OIiIgoL1n7iOUYNGgQgoODceLECZQrV04qt7a2Rnp6OhITE1XOisXFxcHa2lqqc/78eZXp5dxVmVMnL11dXejq6pbwUhAREREVjaxnxIQQGDRoELZv344jR46gYsWKKsNdXFygra2Nw4cPS2URERGIioqCUqkEACiVSly/fh3x8fFSnZCQEBgbG8PR0fHDLAgRERFRMch6RszX1xcbNmzAzp07UapUKalPl4mJCfT19WFiYoI+ffpg+PDhMDc3h7GxMQYPHgylUon69esDAFq1agVHR0d0794dM2fORGxsLMaNGwdfX1+e9SIiIqKPmqxBLDAwEADQtGlTlfKgoCD07NkTADBv3jxoaGjA09MTaWlpcHd3x5IlS6S6mpqaCA4OxoABA6BUKmFoaAgfHx/4+/t/qMUgIiIiKhZZg5gQ4q119PT0EBAQgICAgALr2NvbY+/evSXZNCIiIqL37qO5a5KIiIjoc8MgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZMIgRERERyYRBjIiIiEgmDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZyBrETpw4gfbt28PW1hYKhQI7duxQGS6EwIQJE2BjYwN9fX24ubnh7t27KnWePn0Kb29vGBsbw9TUFH369MHz588/4FIQERERFY+sQSw1NRW1a9dGQEBAvsNnzpyJhQsXYunSpTh37hwMDQ3h7u6OV69eSXW8vb1x8+ZNhISEIDg4GCdOnEC/fv0+1CIQERERFZuWnDNv06YN2rRpk+8wIQTmz5+PcePGoWPHjgCANWvWwMrKCjt27EDXrl1x69Yt7N+/HxcuXEDdunUBAIsWLULbtm0xe/Zs2NrafrBlISIiIiqqj7aPWGRkJGJjY+Hm5iaVmZiYoF69eggNDQUAhIaGwtTUVAphAODm5gYNDQ2cO3euwGmnpaUhOTlZ5UVERET0oX20QSw2NhYAYGVlpVJuZWUlDYuNjYWlpaXKcC0tLZibm0t18jNt2jSYmJhILzs7uxJuPREREdHbfbRB7H0aM2YMkpKSpFd0dLTcTSIiIqLP0EcbxKytrQEAcXFxKuVxcXHSMGtra8THx6sMz8zMxNOnT6U6+dHV1YWxsbHKi4iIiOhD+2iDWMWKFWFtbY3Dhw9LZcnJyTh37hyUSiUAQKlUIjExEZcuXZLqHDlyBNnZ2ahXr94HbzMRERFRUch61+Tz589x79496X1kZCTCwsJgbm6O8uXLY+jQofjtt99QtWpVVKxYEePHj4etrS2++eYbAECNGjXQunVr9O3bF0uXLkVGRgYGDRqErl278o5JIiIi+ujJGsQuXryIZs2aSe+HDx8OAPDx8cGqVaswatQopKamol+/fkhMTESjRo2wf/9+6OnpSeOsX78egwYNQosWLaChoQFPT08sXLjwgy8LERERUVHJGsSaNm0KIUSBwxUKBfz9/eHv719gHXNzc2zYsOF9NI+IiIjovfpo+4gRERERfeoYxIiIiIhkwiBGREREJBMGMSIiIiKZMIgRERERyYRBjIiIiEgmDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCYMYkREREQyYRAjIiIikgmDGBEREZFMGMSIiIiIZMIgRkRERCQTBjEiIiIimTCIEREREcmEQYyIiIhIJgxiRERERDJhECMiIiKSCYMYERERkUwYxIiIiIhkwiBGREREJBMGMSIiIiKZMIgRERERyYRBjIiIiEgmDGJEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpl8MkEsICAAFSpUgJ6eHurVq4fz58/L3SQiIiKiN/okgthff/2F4cOHY+LEibh8+TJq164Nd3d3xMfHy900IiIiogJpyd2AkjB37lz07dsXvXr1AgAsXboUe/bswcqVK/F///d/avXT0tKQlpYmvU9KSgIAJCcnF3qeWWkv37HVVFKKst2Ki9v748Ht/Xnh9v68FHV759QXQryP5nwQCvFfbj2A9PR0GBgYYMuWLfjmm2+kch8fHyQmJmLnzp1q4/j5+WHSpEkfsJVERET0vkRHR6NcuXJyN6NY/vNnxBISEpCVlQUrKyuVcisrK9y+fTvfccaMGYPhw4dL77Ozs/H06VNYWFhAoVC81/Z+TJKTk2FnZ4fo6GgYGxvL3Rx6z7i9Py/c3p+Xz3V7CyGQkpICW1tbuZtSbP/5IFYcurq60NXVVSkzNTWVpzEfAWNj48/qg/u54/b+vHB7f14+x+1tYmIidxPeyX++s37p0qWhqamJuLg4lfK4uDhYW1vL1CoiIiKit/vPBzEdHR24uLjg8OHDUll2djYOHz4MpVIpY8uIiIiI3uyTuDQ5fPhw+Pj4oG7duvj6668xf/58pKamSndRUv50dXUxceJEtcu09Gni9v68cHt/Xri9/7v+83dN5li8eDFmzZqF2NhYODs7Y+HChahXr57czSIiIiIq0CcTxIiIiIj+a/7zfcSIiIiI/qsYxIiIiIhkwiBGREREJBMGsU9Q06ZNMXToULmbQR+RvPtEhQoVMH/+fNnaQ0Xzts+0QqHAjh07Cj29Y8eOQaFQIDEx8Z3bRh+vt+0XxdkP/Pz84Ozs/M5to//5JB5fQURFc+HCBRgaGsrdDCohMTExMDMzk7sZ9B/ToEEDxMTE/OefTP9fxyBG9BkqU6aM3E2gEsRfEfn0ZGRkQFtb+73OQ0dHh/vOR4CXJj9xz549Q48ePWBmZgYDAwO0adMGd+/eBfD6x1LLlCmDLVu2SPWdnZ1hY2MjvT916hR0dXXx4sWLD972z0HTpk0xePBgDB06FGZmZrCyssLvv/8uPZC4VKlSqFKlCvbt2yeNc+PGDbRp0wZGRkawsrJC9+7dkZCQIA1PTU1Fjx49YGRkBBsbG8yZM0dtvrkvTT548AAKhQJhYWHS8MTERCgUChw7dgzA/y5hHDhwAHXq1IG+vj6aN2+O+Ph47Nu3DzVq1ICxsTG+//577ivvSXZ2NkaNGgVzc3NYW1vDz89PGpb3EtSZM2fg7OwMPT091K1bFzt27FDbxgBw6dIl1K1bFwYGBmjQoAEiIiI+zMJ8YpYvXw5bW1tkZ2erlHfs2BG9e/cGAOzcuRNffvkl9PT0UKlSJUyaNAmZmZlSXYVCgcDAQHTo0AGGhob47bffUKVKFcyePVtlmmFhYVAoFLh3716h2paQkIBOnTrBwMAAVatWxa5du6Rh+V2a/P3332FnZwcDAwN06tQJc+fOzfe3mNeuXYsKFSrAxMQEXbt2RUpKSqHaQ+oYxD5xPXv2xMWLF7Fr1y6EhoZCCIG2bdsiIyMDCoUCTZo0kb5snz17hlu3buHly5e4ffs2AOD48eP46quvYGBgIONSfNpWr16N0qVL4/z58xg8eDAGDBiALl26oEGDBrh8+TJatWqF7t2748WLF0hMTETz5s1Rp04dXLx4Efv370dcXBy8vLyk6Y0cORLHjx/Hzp07cfDgQRw7dgyXL18ukbb6+flh8eLFOHPmDKKjo+Hl5YX58+djw4YN2LNnDw4ePIhFixaVyLxI1erVq2FoaIhz585h5syZ8Pf3R0hIiFq95ORktG/fHk5OTrh8+TImT56M0aNH5zvNsWPHYs6cObh48SK0tLSk0EBF06VLFzx58gRHjx6Vyp4+fYr9+/fD29sbJ0+eRI8ePTBkyBCEh4dj2bJlWLVqFaZMmaIyHT8/P3Tq1AnXr19Hnz590Lt3bwQFBanUCQoKQpMmTVClSpVCtW3SpEnw8vLCtWvX0LZtW3h7e+Pp06f51j19+jR++uknDBkyBGFhYWjZsqVaGwHg/v372LFjB4KDgxEcHIzjx49j+vTphWoP5UPQJ8fV1VUMGTJE3LlzRwAQp0+floYlJCQIfX19sWnTJiGEEAsXLhQ1a9YUQgixY8cOUa9ePdGxY0cRGBgohBDCzc1N/Prrrx9+IT4Trq6uolGjRtL7zMxMYWhoKLp37y6VxcTECAAiNDRUTJ48WbRq1UplGtHR0QKAiIiIECkpKUJHR0favkII8eTJE6Gvry+GDBkildnb24t58+YJIYSIjIwUAMSVK1ek4c+ePRMAxNGjR4UQQhw9elQAEIcOHZLqTJs2TQAQ9+/fl8r69+8v3N3d32WVUD7y7idCCPHVV1+J0aNHCyGEACC2b98uhBAiMDBQWFhYiJcvX0p1f//9d5VtnN/23LNnjwCgMh4VXseOHUXv3r2l98uWLRO2trYiKytLtGjRQkydOlWl/tq1a4WNjY30HoAYOnSoSp2HDx8KTU1Nce7cOSGEEOnp6aJ06dJi1apVhWoTADFu3Djp/fPnzwUAsW/fPiHE//aDZ8+eCSGE+O6774SHh4fKNLy9vYWJiYn0fuLEicLAwEAkJydLZSNHjhT16tUrVJtIHc+IfcJu3boFLS0tlZ96srCwQPXq1XHr1i0AgKurK8LDw/H48WMcP34cTZs2RdOmTXHs2DFkZGTgzJkzaNq0qUxL8HmoVauW9LempiYsLCzg5OQklVlZWQEA4uPjcfXqVRw9ehRGRkbSy8HBAcDr/1Lv37+P9PR0lW1ubm6O6tWrl3hbraysYGBggEqVKqmUxcfHl8i8SFXudQ8ANjY2+a7riIgI1KpVC3p6elLZ119//dZp5nRJ4PYrHm9vb2zduhVpaWkAgPXr16Nr167Q0NDA1atX4e/vr/K57du3L2JiYlQu5detW1dlmra2tvDw8MDKlSsBALt370ZaWhq6dOlS6Hbl3saGhoYwNjYucBtHRESo7Sv57TsVKlRAqVKlpPcF7YtUOOys/5lzcnKCubk5jh8/juPHj2PKlCmwtrbGjBkzcOHCBWRkZKBBgwZyN/OTlrdDrkKhUClTKBQAXvcRev78Odq3b48ZM2aoTcfGxqbQ/UZy09B4/f+YyPVrZxkZGW9ta9525pTl7SdDJeN9rOuC9jMquvbt20MIgT179uCrr77CyZMnMW/ePADA8+fPMWnSJHTu3FltvNyBOb87mX/88Ud0794d8+bNQ1BQEL777rsidRV53/tNSU3zc8Yg9gmrUaMGMjMzce7cOSlMPXnyBBEREXB0dATw+gPUuHFj7Ny5Ezdv3kSjRo1gYGCAtLQ0LFu2DHXr1uVjDj4iX375JbZu3YoKFSpAS0v941u5cmVoa2vj3LlzKF++PIDXff/u3LkDV1fXfKeZcwdlTEwM6tSpAwBqnbrpv6N69epYt24d0tLSoKurC+D140ro/dLT00Pnzp2xfv163Lt3D9WrV8eXX34J4PXnNiIiotD9unJr27YtDA0NERgYiP379+PEiRMl3XRJ9erV1fYV7jvvHy9NfsKqVq2Kjh07om/fvjh16hSuXr2KH374AWXLlkXHjh2lek2bNsWff/4JZ2dnGBkZQUNDA02aNMH69esL/PImefj6+uLp06fo1q0bLly4gPv37+PAgQPo1asXsrKyYGRkhD59+mDkyJE4cuQIbty4gZ49e0pnvfKjr6+P+vXrY/r06bh16xaOHz+OcePGfcClopL0/fffIzs7G/369cOtW7dw4MAB6c67nLNe9H54e3tjz549WLlyJby9vaXyCRMmYM2aNZg0aRJu3ryJW7duYePGjYX6nGlqaqJnz54YM2YMqlatCqVS+d7aP3jwYOzduxdz587F3bt3sWzZMuzbt4/7zXvGIPaJCwoKgouLC9q1awelUgkhBPbu3atyatnV1RVZWVkqfcGaNm2qVkbys7W1xenTp5GVlYVWrVrByckJQ4cOhampqRS2Zs2ahcaNG6N9+/Zwc3NDo0aN4OLi8sbprly5EpmZmXBxccHQoUPx22+/fYjFoffA2NgYu3fvRlhYGJydnTF27FhMmDABgOplMCp5zZs3h7m5OSIiIvD9999L5e7u7ggODsbBgwfx1VdfoX79+pg3bx7s7e0LNd0+ffogPT0dvXr1el9NBwA0bNgQS5cuxdy5c1G7dm3s378fw4YN437znilE7o4hRET0yVm/fj169eqFpKQk6Ovry90cKqKTJ0+iRYsWiI6Olm7e+VD69u2L27dv4+TJkx90vp8T9hEjIvrErFmzBpUqVULZsmVx9epVjB49Gl5eXgxh/zFpaWl4/Pgx/Pz80KVLlw8SwmbPno2WLVvC0NAQ+/btw+rVq7FkyZL3Pt/PGS9NEhF9YmJjY/HDDz+gRo0aGDZsGLp06YLly5fL3Swqoj///BP29vZITEzEzJkzVYatX79e5XEYuV81a9Ys9jzPnz+Pli1bwsnJCUuXLsXChQvx448/vuui0Bvw0iQREdF/TEpKCuLi4vIdpq2tXej+ZyQ/BjEiIiIimfDSJBEREZFMGMSIiIiIZMIgRkRERCQTBjEiojwK+q1NIqKSxiBGRJ+9v//+GwMGDICjoyMsLCygr6+P27dvy92sfHl6euLkyZPIyspCt27dEBwcLHeTiOgdMIgRFUPPnj3xzTffqJQ9fvwYX3zxBerVq4ekpCR5GkZFduvWLbi4uCAzMxMrV67EuXPncP/+fTg4OMjdtHwNGjQIHh4e0NPTQ2RkJNzc3ORuEhG9Az5Zn6gEPH78GM2bN4e+vj4OHjwIExMTuZtEhTRo0CD4+vr+Z35fs1mzZnj8+DGePn0Ka2tr/iAz0X8cz4gRvaOEhAS0aNECurq6CAkJUQlhc+fOhZOTEwwNDWFnZ4eBAwfi+fPnAIBjx45BoVAU+Mpx6tQpNG7cGPr6+rCzs8PPP/+M1NRUaXiFChXUxv3ll1+k4YGBgahcuTJ0dHRQvXp1rF27VqX9CoUCgYGBaNOmDfT19VGpUiVs2bJFGv7gwQMoFAqEhYVJZePHj4dCocD8+fOlstu3b6Nly5YwMTGR2mFqalrgejtx4gRq1KgBAwMDmJiYwN3dHXfv3pWGr127FnXr1kWpUqVgbW2N77//HvHx8dLwnPW3Z88e1KpVC3p6eqhfvz5u3Lgh1Xny5Am6deuGsmXLwsDAAE5OTvjzzz+l4ampqTh69CjS09NRtWpV6OnpwcnJCTt37lRp6/Xr16WgbWFhgX79+knb0c/Pr8Bt2LRpUwD5n0FdtWqV2vopzLbasWMHdHV1YWNjg5UrV0KhUGDo0KEFruc3tS8xMVFt+nlfOdv9beuyZ8+eBc6nZ8+eAICmTZsW2NahQ4dK64voc8IgRvQOnjx5Ajc3N2hpaSEkJETti1VDQwMLFy7EzZs3sXr1ahw5cgSjRo0CADRo0AAxMTGIiYnB1q1bAUB6HxMTAwC4f/8+WrduDU9PT1y7dg1//fUXTp06hUGDBqnMx9/fX2XciRMnAgC2b9+OIUOGYMSIEbhx4wb69++PXr164ejRoyrjjx8/Hp6enrh69Sq8vb3RtWtX3Lp1K99l/vfffzF//ny13y3s3bs3MjIycPr0acTExKiEtPyULVsWixcvxs2bN3Hq1CloaGigf//+0vCMjAxMnjwZV69exY4dO/DgwQPpCz23kSNHYs6cObhw4QLKlCmD9u3bS53tX716BRcXF+zZswc3btxAv3790L17d5w/fx7A6+0nhMCyZcvg7++Pa9euwdPTE507d5YCSGpqKtzd3WFmZoYLFy5g8+bNOHTokLQNfvnlF2m9jxgxAkqlUnq/bdu2N66D3Aq7rXKkpqZi/PjxMDIyeuu0a9asqbJ/5OxvueU82zsoKAgxMTHSOsrxtnW5YMECafpeXl7w8vKS3i9YsKDQ64HosyOIqMh8fHxEkyZNhLOzs9DW1hb169cXmZmZbx1v8+bNwsLCQq386NGjIr+PY58+fUS/fv1Uyk6ePCk0NDTEy5cvhRBC2Nvbi3nz5uU7vwYNGoi+ffuqlHXp0kW0bdtWeg9A/PTTTyp16tWrJwYMGCCEECIyMlIAEFeuXBFCCNGjRw/Rp08ftfnq6+uL9evXS++DgoKEiYlJvu3K6+XLl6JXr16iSZMmBda5cOGCACBSUlKEEP9bZxs3bpTqPHnyROjr64u//vqrwOl4eHiIESNGqCzblClTVOq0aNFCeHt7CyGEWL58uTAzMxPPnz+Xhu/Zs0doaGiI2NhYlfEmTpwoXF1d1ebp4+MjOnbsqFKWd/0Udltt375dCCHEhAkTRIsWLYSrq6sYMmRIgcs7ceJEUbt2bZWynHX37NkzqSwtLU0AEMHBwUII9e2en9zrMu/y+vj4qJW/qa1DhgzJd90Rfep4RoyomE6cOIHs7GyEhYXh3r17aj/KCwCHDh1CixYtULZsWZQqVQrdu3fHkydP8OLFi0LN4+rVq1i1apXKD/q6u7sjOzsbkZGRbx3/1q1baNiwoUpZw4YN1c52KZVKtff5nRG7fPkytm/fjsmTJ6sNq1ixIrZv317oZQOAqKgoGBkZwdDQEOfPn8eqVaukYZcuXUL79u1Rvnx5lCpVCq6urtI4BbXd3Nwc1atXl9qelZWFyZMnw8nJCebm5jAyMsKBAwfUppF3HTVq1Ajh4eEAXq/D2rVrw9DQUKV+dnY2IiIiCr2swcHBKtvxp59+Uhle2G0FAI8ePcLcuXMxZ86cQs//bZKTkwFAZTlzK+y6fJslS5bAyMgIFhYWqFevHnbv3v3ObSf6L2MQIyqmSpUq4fDhw3B0dMSSJUvg5+eHa9euScMfPHiAdu3aoVatWti6dSsuXbqEgIAAAEB6enqh5vH8+XP0798fYWFh0uvq1au4e/cuKleu/F6W601GjBiBX375BTY2NmrDVqxYgWvXrqFUqVL5Bo382NraIiwsDMePH4eZmRmmT58O4H+XA42NjbF+/XpcuHAB27dvB1D4dQcAs2bNwoIFCzB69GgcPXoUYWFhcHd3l6ZhZmZW4Lgl3Qm+WbNmKtvR39+/2NMaO3YsunTpgtq1a5dY+x49egTg9TbJz9vWZWF5e3sjLCwMJ06cQOPGjfHtt9/i4cOH79x+ov8qBjGiYnJyckLp0qUBAF26dEHnzp3Ro0cP6Yvp0qVLyM7Oxpw5c1C/fn1Uq1ZN+rIrrC+//BLh4eGoUqWK2ktHR+et49eoUQOnT59WKTt9+jQcHR1Vys6ePav2vkaNGiplu3btwp07d1RuBMitfv366NChA+rWrYsrV64UKmhoaWmhSpUqaNSoEX755ResX78ewOuO/0+ePMH06dPRuHFjODg4qHTUL6jtz549w507d6S2nz59Gh07dsQPP/yA2rVro1KlSrhz545U38TEBNbW1mrr6NSpU9I6qlGjBq5evapyg8Tp06ehoaGB6tWrv3UZcxgaGqpsP0tLS5Xhhd1WYWFh2LJlS4nf5XnhwgWUKlWqwID/tnVZWCYmJqhSpQpq1qyJSZMmIT09vcD+iESfAz6+gqiEBAQE4IsvvsCkSZMwZcoUVKlSBRkZGVi0aBHat2+P06dPY+nSpUWa5ujRo1G/fn0MGjQIP/74IwwNDREeHo6QkBAsXrz4reOPHDkSXl5eqFOnDtzc3LB7925s27YNhw4dUqm3efNm1K1bF40aNcL69etx/vx5rFixQqXOzJkzsWjRIhgYGOQ7r61bt2LVqlW4dOkSypcvrxY08goODoaZmRns7Ozw77//YubMmahTpw4AoHz58tDR0cGiRYvw008/4caNG/leDgVe36hgYWEBKysrjB07FqVLl5buUKxatSq2bNmCM2fOwMzMDHPnzkVcXJxKuBk2bBimTJmCSpUq4csvv8SGDRtw9OhRXL58GcDrMzgTJ06Ej48P/Pz88PjxYwwePBjdu3eHlZXVG5exKAq7rWbPno0RI0YUeOaqqLKzsxEcHIxff/0VPXr0gKamZr71CrMuCyMrKwuvXr1CWloaVqxYAW1tbVSvXp0PpqXPl9yd1Ij+i/LrfC2EEMHBwUJTU1OcPXtWCCHE3LlzhY2NjdDX1xfu7u5izZo1ap2khSi4s74QQpw/f160bNlSGBkZCUNDQ1GrVi2VzuVv6qwvhBBLliwRlSpVEtra2qJatWpizZo1KsMBiICAANGyZUuhq6srKlSooNLZPafTdu3atUVWVla+842IiBCmpqbi4MGD0vC3ddYPDAwUlStXFjo6OsLS0lJ06dJF/PPPP9LwDRs2iAoVKghdXV2hVCrFrl27VDqP56yz3bt3i5o1awodHR3x9ddfi6tXr0rTePLkiejYsaMwMjISlpaWYty4caJHjx4q2y4zM1OMGzdO2NraCm1tbeHk5CR27Nih0tZr166JZs2aCT09PWFubi769u0r3TSQ27t01heicNvK2tpa5caBd+2sn5CQIMqWLStGjhwpXr16JdXJ21m/MOsy9/IW1FkfgAAgdHR0RM2aNcWmTZuEEOysT58vhRD//55lIvosKRQKbN++Xe05Vx+7Y8eOoVmzZnj27Nkbn1dGRPQxYx8xIiIiIpkwiBERERHJhJcmiYiIiGTCM2JEREREMmEQIyIiIpIJgxgRERGRTBjEiIiIiGTCIEZEREQkEwYxIiIiIpkwiBERERHJhEGMiIiISCb/D7uJEaSRYTGjAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -791,7 +608,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -803,36 +620,42 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Размер обучающей выборки после oversampling и undersampling:  17620\n"
+      "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",
-    "# Преобразование целевой переменной (цены) в категориальные диапазоны с использованием квантилей\n",
-    "train_data['price_category'] = pd.qcut(train_data['price'], q=4, labels=['low', 'medium', 'high', 'very_high'])\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 цен после преобразования в категории\n",
-    "sns.countplot(x=train_data['price_category'])\n",
-    "plt.title('Распределение категорий цены в обучающей выборке')\n",
-    "plt.xlabel('Категория цены')\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=['price', 'price_category'])\n",
-    "y_train = train_data['price_category']\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 цен после oversampling\n",
+    "# Визуализация распределения заработной платы после oversampling\n",
     "sns.countplot(x=y_resampled)\n",
-    "plt.title('Распределение категорий цены после oversampling')\n",
-    "plt.xlabel('Категория цены')\n",
+    "plt.title('Распределение категорий заработной платы после oversampling')\n",
+    "plt.xlabel('Категория заработной платы')\n",
     "plt.ylabel('Частота')\n",
     "plt.show()\n",
     "\n",
@@ -840,10 +663,10 @@
     "rus = RandomUnderSampler(random_state=42)\n",
     "X_resampled, y_resampled = rus.fit_resample(X_resampled, y_resampled)\n",
     "\n",
-    "# Визуализация распределения цен после undersampling\n",
+    "# Визуализация распределения заработной платы после undersampling\n",
     "sns.countplot(x=y_resampled)\n",
-    "plt.title('Распределение категорий цены после undersampling')\n",
-    "plt.xlabel('Категория цен')\n",
+    "plt.title('Распределение категорий заработной платы после undersampling')\n",
+    "plt.xlabel('Категория заработной платы')\n",
     "plt.ylabel('Частота')\n",
     "plt.show()\n",
     "\n",
@@ -855,33 +678,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Конструирование признаков  \n",
-    "\n",
+    "### Конструирование признаков\n",
     "Теперь приступим к конструированию признаков для решения каждой задачи.\n",
     "\n",
-    "**Процесс конструирования признаков**  \n",
-    "Задача 1: Прогнозирование цен недвижимости. Цель технического проекта: Разработка модели машинного обучения для точного прогнозирования рыночной стоимости недвижимости.  \n",
-    "Задача 2: Оптимизация затрат на ремонт перед продажей. Цель технического проекта: Разработка модели машинного обучения для точного прогнозирования по рекомендациям по реновациям.\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**\n",
     "Унитарное кодирование категориальных признаков (one-hot encoding). Преобразование категориальных признаков в бинарные векторы.\n",
     "\n",
-    "**Дискретизация числовых признаков**  \n",
+    "**Дискретизация числовых признаков**\n",
     "Процесс преобразования непрерывных числовых значений в дискретные категории или интервалы (бины)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Столбцы train_data_encoded: ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'price_category', 'date_20140502T000000', 'date_20140503T000000', 'date_20140504T000000', 'date_20140505T000000', 'date_20140506T000000', 'date_20140507T000000', 'date_20140508T000000', 'date_20140509T000000', 'date_20140510T000000', 'date_20140511T000000', 'date_20140512T000000', 'date_20140513T000000', 'date_20140514T000000', 'date_20140515T000000', 'date_20140516T000000', 'date_20140517T000000', 'date_20140518T000000', 'date_20140519T000000', 'date_20140520T000000', 'date_20140521T000000', 'date_20140522T000000', 'date_20140523T000000', 'date_20140524T000000', 'date_20140525T000000', 'date_20140526T000000', 'date_20140527T000000', 'date_20140528T000000', 'date_20140529T000000', 'date_20140530T000000', 'date_20140531T000000', 'date_20140601T000000', 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'date_20141022T000000', 'date_20141023T000000', 'date_20141024T000000', 'date_20141027T000000', 'date_20141028T000000', 'date_20141029T000000', 'date_20141030T000000', 'date_20141031T000000', 'date_20141101T000000', 'date_20141103T000000', 'date_20141104T000000', 'date_20141105T000000', 'date_20141106T000000', 'date_20141107T000000', 'date_20141108T000000', 'date_20141109T000000', 'date_20141110T000000', 'date_20141111T000000', 'date_20141112T000000', 'date_20141113T000000', 'date_20141114T000000', 'date_20141115T000000', 'date_20141116T000000', 'date_20141117T000000', 'date_20141118T000000', 'date_20141119T000000', 'date_20141120T000000', 'date_20141121T000000', 'date_20141122T000000', 'date_20141123T000000', 'date_20141124T000000', 'date_20141125T000000', 'date_20141126T000000', 'date_20141128T000000', 'date_20141201T000000', 'date_20141202T000000', 'date_20141203T000000', 'date_20141204T000000', 'date_20141205T000000', 'date_20141206T000000', 'date_20141208T000000', 'date_20141209T000000', 'date_20141210T000000', 'date_20141211T000000', 'date_20141212T000000', 'date_20141214T000000', 'date_20141215T000000', 'date_20141216T000000', 'date_20141217T000000', 'date_20141218T000000', 'date_20141219T000000', 'date_20141220T000000', 'date_20141222T000000', 'date_20141223T000000', 'date_20141224T000000', 'date_20141226T000000', 'date_20141227T000000', 'date_20141229T000000', 'date_20141230T000000', 'date_20141231T000000', 'date_20150102T000000', 'date_20150105T000000', 'date_20150106T000000', 'date_20150107T000000', 'date_20150108T000000', 'date_20150109T000000', 'date_20150112T000000', 'date_20150113T000000', 'date_20150114T000000', 'date_20150115T000000', 'date_20150116T000000', 'date_20150119T000000', 'date_20150120T000000', 'date_20150121T000000', 'date_20150122T000000', 'date_20150123T000000', 'date_20150124T000000', 'date_20150126T000000', 'date_20150127T000000', 'date_20150128T000000', 'date_20150129T000000', 'date_20150130T000000', 'date_20150131T000000', 'date_20150202T000000', 'date_20150203T000000', 'date_20150204T000000', 'date_20150205T000000', 'date_20150206T000000', 'date_20150207T000000', 'date_20150209T000000', 'date_20150210T000000', 'date_20150211T000000', 'date_20150212T000000', 'date_20150213T000000', 'date_20150214T000000', 'date_20150216T000000', 'date_20150217T000000', 'date_20150218T000000', 'date_20150219T000000', 'date_20150220T000000', 'date_20150221T000000', 'date_20150222T000000', 'date_20150223T000000', 'date_20150224T000000', 'date_20150225T000000', 'date_20150226T000000', 'date_20150227T000000', 'date_20150228T000000', 'date_20150301T000000', 'date_20150302T000000', 'date_20150303T000000', 'date_20150304T000000', 'date_20150305T000000', 'date_20150306T000000', 'date_20150307T000000', 'date_20150309T000000', 'date_20150310T000000', 'date_20150311T000000', 'date_20150312T000000', 'date_20150313T000000', 'date_20150315T000000', 'date_20150316T000000', 'date_20150317T000000', 'date_20150318T000000', 'date_20150319T000000', 'date_20150320T000000', 'date_20150321T000000', 'date_20150323T000000', 'date_20150324T000000', 'date_20150325T000000', 'date_20150326T000000', 'date_20150327T000000', 'date_20150328T000000', 'date_20150329T000000', 'date_20150330T000000', 'date_20150331T000000', 'date_20150401T000000', 'date_20150402T000000', 'date_20150403T000000', 'date_20150404T000000', 'date_20150406T000000', 'date_20150407T000000', 'date_20150408T000000', 'date_20150409T000000', 'date_20150410T000000', 'date_20150411T000000', 'date_20150412T000000', 'date_20150413T000000', 'date_20150414T000000', 'date_20150415T000000', 'date_20150416T000000', 'date_20150417T000000', 'date_20150419T000000', 'date_20150420T000000', 'date_20150421T000000', 'date_20150422T000000', 'date_20150423T000000', 'date_20150424T000000', 'date_20150425T000000', 'date_20150426T000000', 'date_20150427T000000', 'date_20150428T000000', 'date_20150429T000000', 'date_20150430T000000', 'date_20150501T000000', 'date_20150502T000000', 'date_20150503T000000', 'date_20150504T000000', 'date_20150505T000000', 'date_20150506T000000', 'date_20150507T000000', 'date_20150508T000000', 'date_20150509T000000', 'date_20150511T000000', 'date_20150512T000000', 'date_20150513T000000', 'date_20150514T000000', 'date_20150527T000000', 'waterfront_0', 'waterfront_1', 'view_0', 'view_1', 'view_2', 'view_3', 'view_4', 'condition_1', 'condition_2', 'condition_3', 'condition_4', 'condition_5']\n"
+      "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"
      ]
     }
    ],
@@ -890,7 +715,7 @@
     "# Унитарное кодирование категориальных признаков (применение one-hot encoding)\n",
     "\n",
     "# Пример категориальных признаков\n",
-    "categorical_features = ['date', 'waterfront', 'view', 'condition']\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",
@@ -903,14 +728,15 @@
     "print(\"Столбцы test_data_encoded:\", test_data_encoded.columns.tolist())\n",
     "\n",
     "\n",
-    "# Дискретизация числовых признаков (цены). Например, можно разделить площадь жилья на категории\n",
-    "# Пример дискретизации признака 'Общая площадь'\n",
-    "train_data_encoded['sqtf'] = pd.cut(train_data_encoded['sqft_living'], bins=5, labels=False)\n",
-    "val_data_encoded['sqtf'] = pd.cut(val_data_encoded['sqft_living'], bins=5, labels=False)\n",
-    "test_data_encoded['sqtf'] = pd.cut(test_data_encoded['sqft_living'], bins=5, labels=False)\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",
-    "# Пример дискретизации признака 'sqft_living' на 5 категорий\n",
-    "df_encoded['sqtf'] = pd.cut(df_encoded['sqft_living'], bins=5, labels=False)"
+    "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"
    ]
   },
   {
@@ -918,22 +744,48 @@
    "metadata": {},
    "source": [
     "### Ручной синтез\n",
-    "Создание новых признаков на основе экспертных знаний и логики предметной области. К примеру, для данных о продаже домов можно создать признак цена за квадратный фут."
+    "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": 31,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "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": [
-    "# Ручной синтез признаков\n",
-    "train_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
-    "val_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
-    "test_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
+    "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",
-    "# Пример создания нового признака - цена за квадратный фут\n",
-    "df_encoded['price_per_sqft'] = df_encoded['price'] / df_encoded['sqft_living']"
+    "# Вывод первых нескольких строк датафрейма для проверки\n",
+    "print(df.head())"
    ]
   },
   {
@@ -945,19 +797,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
     "\n",
     "# Пример масштабирования числовых признаков\n",
-    "numerical_features = ['bedrooms', 'sqft_living']\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])"
+    "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])\n"
    ]
   },
   {
@@ -969,166 +821,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "               id     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
-      "9876   1219000473  164950.0 -0.395263       1.75    -0.555396     15330   \n",
-      "14982  6308000010  585000.0 -0.395263       2.50     0.238192      5089   \n",
-      "1464   3630120700  757000.0 -0.395263       3.25     1.230177      5283   \n",
-      "19209  1901600090  359000.0  1.752138       1.75    -0.147580      6654   \n",
-      "2039   3395040550  320000.0 -0.395263       2.50    -0.599484      2890   \n",
-      "...           ...       ...       ...        ...          ...       ...   \n",
-      "13184  1523049207  220000.0  0.678437       2.00    -0.412109      8043   \n",
-      "5759   1954420170  580000.0 -0.395263       2.50     0.083883      7484   \n",
-      "8433   1721801010  225000.0 -0.395263       1.00    -0.312911      6120   \n",
-      "10253  2422049104   85000.0 -1.468964       1.00    -1.371028      9000   \n",
-      "11363  7701960990  870000.0  0.678437       2.50     1.230177     14565   \n",
-      "\n",
-      "       floors  grade  sqft_above  sqft_basement  ...  view_2  view_3  view_4  \\\n",
-      "9876      1.0      7        1080            490  ...   False   False   False   \n",
-      "14982     2.0      9        2290              0  ...   False   False   False   \n",
-      "1464      2.0      9        3190              0  ...   False   False   False   \n",
-      "19209     1.5      7        1940              0  ...   False   False   False   \n",
-      "2039      2.0      7        1530              0  ...   False   False   False   \n",
-      "...       ...    ...         ...            ...  ...     ...     ...     ...   \n",
-      "13184     1.0      7         850            850  ...   False   False   False   \n",
-      "5759      2.0      8        2150              0  ...   False   False   False   \n",
-      "8433      1.0      6        1790              0  ...   False   False   False   \n",
-      "10253     1.0      6         830              0  ...   False   False   False   \n",
-      "11363     2.0     11        3190              0  ...   False   False   False   \n",
-      "\n",
-      "       condition_1  condition_2  condition_3  condition_4 condition_5  sqtf  \\\n",
-      "9876         False        False         True        False       False     0   \n",
-      "14982        False        False         True        False       False     0   \n",
-      "1464         False        False         True        False       False     1   \n",
-      "19209        False        False        False         True       False     0   \n",
-      "2039         False        False         True        False       False     0   \n",
-      "...            ...          ...          ...          ...         ...   ...   \n",
-      "13184        False        False         True        False       False     0   \n",
-      "5759         False        False         True        False       False     0   \n",
-      "8433         False        False         True        False       False     0   \n",
-      "10253        False        False         True        False       False     0   \n",
-      "11363        False        False         True        False       False     1   \n",
-      "\n",
-      "       price_per_sqft  \n",
-      "9876       105.063694  \n",
-      "14982      255.458515  \n",
-      "1464       237.304075  \n",
-      "19209      185.051546  \n",
-      "2039       209.150327  \n",
-      "...               ...  \n",
-      "13184      129.411765  \n",
-      "5759       269.767442  \n",
-      "8433       125.698324  \n",
-      "10253      102.409639  \n",
-      "11363      272.727273  \n",
-      "\n",
-      "[224 rows x 400 columns]\n"
-     ]
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
-      "id                                                                         \n",
-      "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
-      "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
-      "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
-      "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
-      "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
-      "\n",
-      "            grade  sqft_above  sqft_basement  yr_built  ...  view_2  view_3  \\\n",
-      "id                                                      ...                   \n",
-      "7129300520      7        1180              0      1955  ...   False   False   \n",
-      "6414100192      7        2170            400      1951  ...   False   False   \n",
-      "5631500400      6         770              0      1933  ...   False   False   \n",
-      "2487200875      7        1050            910      1965  ...   False   False   \n",
-      "1954400510      8        1680              0      1987  ...   False   False   \n",
-      "\n",
-      "            view_4  condition_1  condition_2  condition_3  condition_4  \\\n",
-      "id                                                                       \n",
-      "7129300520   False        False        False         True        False   \n",
-      "6414100192   False        False        False         True        False   \n",
-      "5631500400   False        False        False         True        False   \n",
-      "2487200875   False        False        False        False        False   \n",
-      "1954400510   False        False        False         True        False   \n",
-      "\n",
-      "            condition_5  sqtf  price_per_sqft  \n",
-      "id                                             \n",
-      "7129300520        False     0      188.050847  \n",
-      "6414100192        False     0      209.338521  \n",
-      "5631500400        False     0      233.766234  \n",
-      "2487200875         True     0      308.163265  \n",
-      "1954400510        False     0      303.571429  \n",
-      "\n",
-      "[5 rows x 402 columns]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "e:\\MII\\laboratory\\mai\\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",
+      "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",
-      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
-      "  warnings.warn(\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 идентификатору\n",
-    "df = df.drop_duplicates(subset='id')\n",
-    "duplicates = train_data_encoded[train_data_encoded['id'].duplicated(keep=False)]\n",
-    "\n",
-    "# Удаление дубликатов из столбца \"id\", сохранив первое вхождение\n",
-    "df_encoded = df_encoded.drop_duplicates(subset='id', keep='first')\n",
-    "\n",
-    "print(duplicates)\n",
-    "\n",
+    "# Удаление дубликатов по всем столбцам\n",
+    "df = df.drop_duplicates()\n",
     "\n",
     "# Создание EntitySet\n",
-    "es = ft.EntitySet(id='house_data')\n",
+    "es = ft.EntitySet(id='data_science_jobs')\n",
     "\n",
-    "# Добавление датафрейма с домами\n",
-    "es = es.add_dataframe(dataframe_name='houses', dataframe=df_encoded, index='id')\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='houses', max_depth=2)\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",
-    "train_data_encoded = train_data_encoded.drop_duplicates(subset='id')\n",
-    "train_data_encoded = train_data_encoded.drop_duplicates(subset='id', keep='first')  # or keep='last'\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",
-    "# Определение сущностей (Создание EntitySet)\n",
-    "es = ft.EntitySet(id='house_data')\n",
-    "\n",
-    "es = es.add_dataframe(dataframe_name='houses', dataframe=train_data_encoded, index='id')\n",
-    "\n",
-    "# Генерация признаков\n",
-    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='houses', max_depth=2)\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_encoded.index)\n",
-    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)"
+    "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())"
    ]
   },
   {
@@ -1152,34 +1001,176 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Время обучения модели: 5.18 секунд\n",
-      "Среднеквадратичная ошибка: 125198557176601739264.00\n"
+      "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",
-    "from sklearn.model_selection import train_test_split\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\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
     "\n",
-    "# Разделение данных на обучающую и валидационную выборки. Удаляем целевую переменную\n",
-    "X = feature_matrix.drop('price', axis=1)\n",
-    "y = feature_matrix['price']\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
     "\n",
-    "# One-hot encoding для категориальных переменных (преобразование категориальных объектов в числовые)\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",
@@ -1192,24 +1183,72 @@
     "# Время обучения модели\n",
     "train_time = time.time() - start_time\n",
     "\n",
-    "# Предсказания и оценка модели и вычисляем среднеквадратичную ошибку\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: {mse:.2f}')\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": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "e:\\MII\\laboratory\\mai\\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",
+      "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"
      ]
     },
@@ -1217,16 +1256,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "RMSE: 8277.602700993119\n",
+      "R²: 0.9826437806135544\n",
+      "MAE: 1270.2934354194408 \n",
       "\n",
-      "RMSE: 17870.38470608543\n",
-      "R²: 0.9973762630189477\n",
-      "MAE: 5924.569330616996 \n",
+      "Кросс-валидация RMSE: 13606.980806552549 \n",
       "\n",
-      "Кросс-валидация RMSE: 34577.766841359786 \n",
-      "\n",
-      "Train RMSE: 12930.759734777745\n",
-      "Train R²: 0.9987426148033223\n",
-      "Train MAE: 2495.3698282637165\n",
+      "Train RMSE: 4839.006207438376\n",
+      "Train R²: 0.9941174224388726\n",
+      "Train MAE: 664.4994041278297\n",
       "\n"
      ]
     },
@@ -1234,13 +1272,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "e:\\MII\\laboratory\\mai\\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",
+      "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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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1250,31 +1288,47 @@
     }
    ],
    "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\n",
-    "from sklearn.model_selection import cross_val_score\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",
-    "val_feature_matrix = val_feature_matrix.dropna()\n",
-    "test_feature_matrix = test_feature_matrix.dropna()\n",
     "\n",
     "# Разделение данных на обучающую и тестовую выборки\n",
-    "X_train = feature_matrix.drop('price', axis=1)\n",
-    "y_train = feature_matrix['price']\n",
-    "X_val = val_feature_matrix.drop('price', axis=1)\n",
-    "y_val = val_feature_matrix['price']\n",
-    "X_test = test_feature_matrix.drop('price', axis=1)\n",
-    "y_test = test_feature_matrix['price']\n",
-    "\n",
-    "X_test = X_test.reindex(columns=X_train.columns, fill_value=0)  \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 = pd.get_dummies(X, drop_first=True)\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, y, test_size=0.2, random_state=42)\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",
@@ -1289,7 +1343,6 @@
     "r2 = r2_score(y_test, y_pred)\n",
     "mae = mean_absolute_error(y_test, y_pred)\n",
     "\n",
-    "print()\n",
     "print(f\"RMSE: {rmse}\")\n",
     "print(f\"R²: {r2}\")\n",
     "print(f\"MAE: {mae} \\n\")\n",
@@ -1319,9 +1372,9 @@
     "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 цена')\n",
-    "plt.ylabel('Прогнозируемая цена')\n",
-    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
+    "plt.xlabel('Фактическая зарплата (USD)')\n",
+    "plt.ylabel('Прогнозируемая зарплата (USD)')\n",
+    "plt.title('Фактическая зарплата по сравнению с прогнозируемой')\n",
     "plt.show()"
    ]
   },
@@ -1329,25 +1382,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Выводы и итог  \n",
+    "# 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",
-    "**Модель случайного леса (RandomForestRegressor)** показала удовлетворительные результаты при прогнозировании цен на недвижимость. Метрики качества и кросс-валидация позволяют предположить, что модель не сильно переобучена и может быть использована для практических целей.  \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",
-    "*Точность предсказаний:* Модель демонстрирует довольно высокий R² (0.9987), что указывает на большую часть вариации целевого признака (цены недвижимости). Однако, значения RMSE и MAE остаются высоки (12930 и 2495), что свидетельствует о том, что модель не всегда точно предсказывает значения, особенно для объектов с высокими или низкими ценами.  \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",
-    "*Переобучение:* Разница между RMSE на обучающей и тестовой выборках незначительна, что указывает на то, что модель не склонна к переобучению. Однако в будущем стоит следить за этой метрикой при добавлении новых признаков или усложнении модели, чтобы избежать излишней подгонки под тренировочные данные. Также стоит быть осторожным и продолжать мониторинг этого показателя.  \n",
+    "Кросс-валидация: Значение RMSE после кросс-валидации немного выше, чем на тестовой выборке, что может указывать на некоторую нестабильность модели.\n",
     "\n",
-    "*Кросс-валидация:* При кросс-валидации наблюдается небольшое увеличение ошибки RMSE по сравнению с тестовой выборкой (рост на 2-3%). Это может указывать на небольшую нестабильность модели при использовании разных подвыборок данных. Для повышения устойчивости модели возможно стоит провести дальнейшую настройку гиперпараметров.  \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.\n",
-    "\n",
-    "Кажется на этом закончили :)"
+    "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": "mai",
+   "display_name": "aimenv",
    "language": "python",
    "name": "python3"
   },
@@ -1361,7 +1411,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
diff --git a/lab_4/Lab4.ipynb b/lab_4/Lab4.ipynb
new file mode 100644
index 0000000..c43967a
--- /dev/null
+++ b/lab_4/Lab4.ipynb
@@ -0,0 +1,1948 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Начало лабораторной работы**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": 10,
+   "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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>salary</th>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <th>remote_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>3755.000000</td>\n",
+       "      <td>3.755000e+03</td>\n",
+       "      <td>3755.000000</td>\n",
+       "      <td>3755.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2022.373635</td>\n",
+       "      <td>1.906956e+05</td>\n",
+       "      <td>137570.389880</td>\n",
+       "      <td>46.271638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.691448</td>\n",
+       "      <td>6.716765e+05</td>\n",
+       "      <td>63055.625278</td>\n",
+       "      <td>48.589050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>6.000000e+03</td>\n",
+       "      <td>5132.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>95000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.380000e+05</td>\n",
+       "      <td>135000.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2023.000000</td>\n",
+       "      <td>1.800000e+05</td>\n",
+       "      <td>175000.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2023.000000</td>\n",
+       "      <td>3.040000e+07</td>\n",
+       "      <td>450000.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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",
+      "work_year             False\n",
+      "experience_level      False\n",
+      "employment_type       False\n",
+      "job_title             False\n",
+      "salary                False\n",
+      "salary_currency       False\n",
+      "salary_in_usd         False\n",
+      "employee_residence    False\n",
+      "remote_ratio          False\n",
+      "company_location      False\n",
+      "company_size          False\n",
+      "dtype: bool\n"
+     ]
+    }
+   ],
+   "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",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print(df.isnull().any())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Классификация"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'X_train'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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",
+       "      <th>above_median_salary</th>\n",
+       "      <th>salary_category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1809</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>182000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>182000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1082</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Machine Learning Engineer</td>\n",
+       "      <td>126000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>126000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1686</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>BI Developer</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1600</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1376</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>226700</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>226700</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2706</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>160000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>160000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>928</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>200000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>200000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>297300</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>297300</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>133832</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>133832</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3004 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      work_year experience_level employment_type                  job_title  \\\n",
+       "1809       2023               SE              FT              Data Engineer   \n",
+       "1082       2023               SE              FT  Machine Learning Engineer   \n",
+       "1686       2023               SE              FT               BI Developer   \n",
+       "1600       2023               SE              FT             Data Scientist   \n",
+       "1376       2023               SE              FT              Data Engineer   \n",
+       "...         ...              ...             ...                        ...   \n",
+       "2706       2022               SE              FT              Data Engineer   \n",
+       "928        2023               MI              FT              Data Engineer   \n",
+       "564        2023               MI              FT              Data Engineer   \n",
+       "716        2023               SE              FT             Data Scientist   \n",
+       "1299       2023               SE              FT              Data Engineer   \n",
+       "\n",
+       "      salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
+       "1809  182000             USD         182000                 US           100   \n",
+       "1082  126000             USD         126000                 US             0   \n",
+       "1686  140000             USD         140000                 US           100   \n",
+       "1600  140000             USD         140000                 US             0   \n",
+       "1376  226700             USD         226700                 US             0   \n",
+       "...      ...             ...            ...                ...           ...   \n",
+       "2706  160000             USD         160000                 US           100   \n",
+       "928   200000             USD         200000                 US             0   \n",
+       "564   140000             USD         140000                 US             0   \n",
+       "716   297300             USD         297300                 US           100   \n",
+       "1299  133832             USD         133832                 US             0   \n",
+       "\n",
+       "     company_location company_size  above_median_salary salary_category  \n",
+       "1809               US            M                    1               1  \n",
+       "1082               US            M                    0               1  \n",
+       "1686               US            M                    1               1  \n",
+       "1600               US            M                    1               1  \n",
+       "1376               US            M                    1               2  \n",
+       "...               ...          ...                  ...             ...  \n",
+       "2706               US            M                    1               1  \n",
+       "928                US            M                    1               1  \n",
+       "564                US            M                    1               1  \n",
+       "716                US            M                    1               2  \n",
+       "1299               US            M                    0               1  \n",
+       "\n",
+       "[3004 rows x 13 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'y_train'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>above_median_salary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1809</th>\n",
+       "      <td>1</td>\n",
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+       "      <th>1082</th>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>1686</th>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>1600</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1376</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2706</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>928</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>564</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>716</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3004 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      above_median_salary\n",
+       "1809                    1\n",
+       "1082                    0\n",
+       "1686                    1\n",
+       "1600                    1\n",
+       "1376                    1\n",
+       "...                   ...\n",
+       "2706                    1\n",
+       "928                     1\n",
+       "564                     1\n",
+       "716                     1\n",
+       "1299                    0\n",
+       "\n",
+       "[3004 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'X_test'"
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+       "</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",
+       "      <th>above_median_salary</th>\n",
+       "      <th>salary_category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3459</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Research Scientist</td>\n",
+       "      <td>59000</td>\n",
+       "      <td>EUR</td>\n",
+       "      <td>61989</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3724</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Business Data Analyst</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>EUR</td>\n",
+       "      <td>59102</td>\n",
+       "      <td>LU</td>\n",
+       "      <td>100</td>\n",
+       "      <td>LU</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1795</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>180000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3535</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>50000</td>\n",
+       "      <td>NG</td>\n",
+       "      <td>100</td>\n",
+       "      <td>NG</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3255</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Analyst</td>\n",
+       "      <td>106260</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>106260</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1943</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Engineer</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>120000</td>\n",
+       "      <td>US</td>\n",
+       "      <td>100</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>573</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Autonomous Vehicle Technician</td>\n",
+       "      <td>7000</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>7000</td>\n",
+       "      <td>GH</td>\n",
+       "      <td>0</td>\n",
+       "      <td>GH</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3013</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Machine Learning Engineer</td>\n",
+       "      <td>129300</td>\n",
+       "      <td>USD</td>\n",
+       "      <td>129300</td>\n",
+       "      <td>US</td>\n",
+       "      <td>0</td>\n",
+       "      <td>US</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>EN</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Scientist</td>\n",
+       "      <td>70000</td>\n",
+       "      <td>CAD</td>\n",
+       "      <td>51753</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>100</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>L</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1565</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>SE</td>\n",
+       "      <td>FT</td>\n",
+       "      <td>Data Analyst</td>\n",
+       "      <td>48000</td>\n",
+       "      <td>EUR</td>\n",
+       "      <td>51508</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>0</td>\n",
+       "      <td>ES</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>751 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      work_year experience_level employment_type  \\\n",
+       "3459       2022               MI              FT   \n",
+       "3724       2021               EN              FT   \n",
+       "1795       2023               SE              FT   \n",
+       "3535       2021               MI              FT   \n",
+       "3255       2022               MI              FT   \n",
+       "...         ...              ...             ...   \n",
+       "1943       2022               MI              FT   \n",
+       "573        2023               EN              FT   \n",
+       "3013       2022               SE              FT   \n",
+       "327        2023               EN              FT   \n",
+       "1565       2023               SE              FT   \n",
+       "\n",
+       "                          job_title  salary salary_currency  salary_in_usd  \\\n",
+       "3459             Research Scientist   59000             EUR          61989   \n",
+       "3724          Business Data Analyst   50000             EUR          59102   \n",
+       "1795                  Data Engineer  180000             USD         180000   \n",
+       "3535                 Data Scientist   50000             USD          50000   \n",
+       "3255                   Data Analyst  106260             USD         106260   \n",
+       "...                             ...     ...             ...            ...   \n",
+       "1943                  Data Engineer  120000             USD         120000   \n",
+       "573   Autonomous Vehicle Technician    7000             USD           7000   \n",
+       "3013      Machine Learning Engineer  129300             USD         129300   \n",
+       "327                  Data Scientist   70000             CAD          51753   \n",
+       "1565                   Data Analyst   48000             EUR          51508   \n",
+       "\n",
+       "     employee_residence  remote_ratio company_location company_size  \\\n",
+       "3459                 AT             0               AT            L   \n",
+       "3724                 LU           100               LU            L   \n",
+       "1795                 US             0               US            M   \n",
+       "3535                 NG           100               NG            L   \n",
+       "3255                 US             0               US            M   \n",
+       "...                 ...           ...              ...          ...   \n",
+       "1943                 US           100               US            M   \n",
+       "573                  GH             0               GH            S   \n",
+       "3013                 US             0               US            M   \n",
+       "327                  CA           100               CA            L   \n",
+       "1565                 ES             0               ES            M   \n",
+       "\n",
+       "      above_median_salary salary_category  \n",
+       "3459                    0               0  \n",
+       "3724                    0               0  \n",
+       "1795                    1               1  \n",
+       "3535                    0               0  \n",
+       "3255                    0               1  \n",
+       "...                   ...             ...  \n",
+       "1943                    0               1  \n",
+       "573                     0               0  \n",
+       "3013                    0               1  \n",
+       "327                     0               0  \n",
+       "1565                    0               0  \n",
+       "\n",
+       "[751 rows x 13 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'y_test'"
+      ]
+     },
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+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1565</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>751 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      above_median_salary\n",
+       "3459                    0\n",
+       "3724                    0\n",
+       "1795                    1\n",
+       "3535                    0\n",
+       "3255                    0\n",
+       "...                   ...\n",
+       "1943                    0\n",
+       "573                     0\n",
+       "3013                    0\n",
+       "327                     0\n",
+       "1565                    0\n",
+       "\n",
+       "[751 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "work_year                 int64\n",
+      "experience_level         object\n",
+      "employment_type          object\n",
+      "job_title                object\n",
+      "salary                    int64\n",
+      "salary_currency          object\n",
+      "salary_in_usd             int64\n",
+      "employee_residence       object\n",
+      "remote_ratio              int64\n",
+      "company_location         object\n",
+      "company_size             object\n",
+      "above_median_salary       int64\n",
+      "salary_category        category\n",
+      "dtype: object\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from typing import Tuple\n",
+    "import pandas as pd\n",
+    "from pandas import DataFrame\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 целевого признака\n",
+    "median_salary = df['salary_in_usd'].median()\n",
+    "df['above_median_salary'] = np.where(df['salary_in_usd'] > median_salary, 1, 0)\n",
+    "\n",
+    "# Разделение на признаки и целевую переменную\n",
+    "X = df.drop(columns=['salary_in_usd', 'above_median_salary'])\n",
+    "y = df['above_median_salary']\n",
+    "\n",
+    "# Примерная категоризация\n",
+    "df['salary_category'] = pd.cut(df['salary_in_usd'], bins=[0, 100000, 200000, np.inf], labels=[0, 1, 2])\n",
+    "\n",
+    "# Выбор признаков и целевых переменных\n",
+    "X = df.drop(columns=['salary_in_usd', 'salary_category'])\n",
+    "\n",
+    "def split_stratified_into_train_val_test(\n",
+    "    df_input,\n",
+    "    stratify_colname=\"y\",\n",
+    "    frac_train=0.6,\n",
+    "    frac_val=0.15,\n",
+    "    frac_test=0.25,\n",
+    "    random_state=None,\n",
+    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
+    "   \n",
+    "    if frac_train + frac_val + frac_test != 1.0:\n",
+    "        raise ValueError(\n",
+    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
+    "            % (frac_train, frac_val, frac_test)\n",
+    "        )\n",
+    "    \n",
+    "    if stratify_colname not in df_input.columns:\n",
+    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
+    "    X = df_input  # Contains all columns.\n",
+    "    y = df_input[\n",
+    "        [stratify_colname]\n",
+    "    ]  # Dataframe of just the column on which to stratify.\n",
+    "    \n",
+    "    # Split original dataframe into train and temp dataframes.\n",
+    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
+    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
+    "    )\n",
+    "\n",
+    "    if frac_val <= 0:\n",
+    "        assert len(df_input) == len(df_train) + len(df_temp)\n",
+    "        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
+    "    # Split the temp dataframe into val and test dataframes.\n",
+    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
+    "\n",
+    "    df_val, df_test, y_val, y_test = train_test_split(\n",
+    "        df_temp,\n",
+    "        y_temp,\n",
+    "        stratify=y_temp,\n",
+    "        test_size=relative_frac_test,\n",
+    "        random_state=random_state,\n",
+    "    )\n",
+    "\n",
+    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
+    "    return df_train, df_val, df_test, y_train, y_val, y_test\n",
+    "\n",
+    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
+    "    df, stratify_colname=\"above_median_salary\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=42\n",
+    ")\n",
+    "\n",
+    "display(\"X_train\", X_train)\n",
+    "display(\"y_train\", y_train)\n",
+    "\n",
+    "display(\"X_test\", X_test)\n",
+    "display(\"y_test\", y_test)\n",
+    "\n",
+    "# Проверка преобразования\n",
+    "print(df.dtypes)\n",
+    "\n",
+    "# Визуализация распределения зарплат\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df['salary_in_usd'], bins=50, kde=True)\n",
+    "plt.title('Распределение зарплат')\n",
+    "plt.xlabel('Зарплата (USD)')\n",
+    "plt.ylabel('Частота')\n",
+    "plt.show()\n",
+    "\n",
+    "# Визуализация зависимости между зарплатой и уровнем опыта\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x='experience_level', y='salary_in_usd', data=df)\n",
+    "plt.title('Зависимость зарплаты от уровня опыта')\n",
+    "plt.xlabel('Уровень опыта')\n",
+    "plt.ylabel('Зарплата (USD)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Теперь перейдем к делению на выборки и созданию ориентира"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: (3004, 10)\n",
+      "Размер тестовой выборки: (751, 10)\n",
+      "Baseline Accuracy: 0.5126498002663116\n",
+      "Baseline F1 Score: 0.3474826991241725\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "\n",
+    "# Создание целевого признака\n",
+    "median_salary = df['salary_in_usd'].median()\n",
+    "df['above_median_salary'] = np.where(df['salary_in_usd'] > median_salary, 1, 0)\n",
+    "\n",
+    "# Разделение на признаки и целевую переменную\n",
+    "features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'salary', 'salary_currency', 'remote_ratio', 'employee_residence', 'company_location', 'company_size']\n",
+    "target = 'above_median_salary'\n",
+    "\n",
+    "# Разделение данных на тренировочный и тестовый наборы\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df[features], df[target], test_size=0.2, random_state=42, stratify=df[target])\n",
+    "\n",
+    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
+    "print(\"Размер тестовой выборки:\", X_test.shape)\n",
+    "\n",
+    "# Создание ориентира (baseline)\n",
+    "baseline_threshold = y_train.mean()\n",
+    "baseline_predictions = [1 if pred > baseline_threshold else 0 for pred in [baseline_threshold] * len(y_test)]\n",
+    "\n",
+    "# Вычисление метрик для ориентира\n",
+    "baseline_accuracy = accuracy_score(y_test, baseline_predictions)\n",
+    "baseline_f1 = f1_score(y_test, baseline_predictions, average='weighted')\n",
+    "\n",
+    "print('Baseline Accuracy:', baseline_accuracy)\n",
+    "print('Baseline F1 Score:', baseline_f1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Создание конвейера и обучение моделей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: Logistic Regression\n",
+      "Accuracy: 0.7523\n",
+      "F1 Score: 0.7609\n",
+      "----------------------------------------\n",
+      "Model: Decision Tree\n",
+      "Accuracy: 0.9960\n",
+      "F1 Score: 0.9959\n",
+      "----------------------------------------\n",
+      "Model: Gradient Boosting\n",
+      "Accuracy: 0.9947\n",
+      "F1 Score: 0.9945\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "\n",
+    "# Создание целевого признака\n",
+    "median_salary = df['salary_in_usd'].median()\n",
+    "df['above_median_salary'] = np.where(df['salary_in_usd'] > median_salary, 1, 0)\n",
+    "\n",
+    "# Разделение на признаки и целевую переменную\n",
+    "X = df.drop(columns=['salary_in_usd', 'above_median_salary'])\n",
+    "y = df['above_median_salary']\n",
+    "\n",
+    "# Разделение данных на тренировочный и тестовый наборы\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "\n",
+    "# Определение столбцов\n",
+    "numeric_columns = [\"work_year\", \"salary\", \"remote_ratio\"]\n",
+    "cat_columns = [\"experience_level\", \"employment_type\", \"job_title\", \"salary_currency\", \"employee_residence\", \"company_location\", \"company_size\"]\n",
+    "\n",
+    "# Предобработка данных\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', StandardScaler(), numeric_columns),\n",
+    "        ('cat', OneHotEncoder(handle_unknown='ignore'), cat_columns)])\n",
+    "\n",
+    "# Создание конвейеров для моделей\n",
+    "pipeline_logistic_regression = Pipeline(steps=[\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('classifier', LogisticRegression(random_state=42))])\n",
+    "\n",
+    "pipeline_decision_tree = Pipeline(steps=[\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('classifier', DecisionTreeClassifier(random_state=42))])\n",
+    "\n",
+    "pipeline_gradient_boosting = Pipeline(steps=[\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('classifier', GradientBoostingClassifier(random_state=42))])\n",
+    "\n",
+    "# Список конвейеров \n",
+    "pipelines = [\n",
+    "    ('Logistic Regression', pipeline_logistic_regression),\n",
+    "    ('Decision Tree', pipeline_decision_tree),\n",
+    "    ('Gradient Boosting', pipeline_gradient_boosting)\n",
+    "]\n",
+    "\n",
+    "# Обучение моделей и вывод результатов\n",
+    "for name, pipeline in pipelines:\n",
+    "    pipeline.fit(X_train, y_train)\n",
+    "    y_pred = pipeline.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    f1 = f1_score(y_test, y_pred)\n",
+    "    print(f\"Model: {name}\")\n",
+    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
+    "    print(f\"F1 Score: {f1:.4f}\")\n",
+    "    print(\"-\" * 40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оценка качества моделей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: Logistic Regression\n",
+      "Accuracy: 0.7523302263648469\n",
+      "F1 Score: 0.7517841210039291\n",
+      "\n",
+      "Model: Decision Tree\n",
+      "Accuracy: 0.996005326231691\n",
+      "F1 Score: 0.9960048583691977\n",
+      "\n",
+      "Model: Gradient Boosting\n",
+      "Accuracy: 0.9946737683089214\n",
+      "F1 Score: 0.9946728986768623\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "for name, pipeline in pipelines:\n",
+    "    y_pred = pipeline.predict(X_test)\n",
+    "    print(f\"Model: {name}\")\n",
+    "    print('Accuracy:', accuracy_score(y_test, y_pred))\n",
+    "    print('F1 Score:', f1_score(y_test, y_pred, average='weighted'))\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Регрессия\n",
+    "Цель: Разработать модель регрессии, которая будет предсказывать зарплату (salary_in_usd) на основе демографических данных, типа работы и других факторов."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер данных до удаления выбросов: (3755, 11)\n",
+      "Размер данных после удаления выбросов: (3708, 11)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from scipy import stats\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "\n",
+    "# Определение числовых признаков\n",
+    "numeric_features = ['work_year', 'salary', 'salary_in_usd', 'remote_ratio']\n",
+    "\n",
+    "# Вычисление z-оценок для числовых признаков\n",
+    "z_scores = stats.zscore(df[numeric_features])\n",
+    "\n",
+    "# Определение порога для удаления выбросов\n",
+    "threshold = 3\n",
+    "\n",
+    "# Удаление выбросов\n",
+    "df_cleaned = df[(z_scores < threshold).all(axis=1)]\n",
+    "\n",
+    "print(\"Размер данных до удаления выбросов:\", df.shape)\n",
+    "print(\"Размер данных после удаления выбросов:\", df_cleaned.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: (2966, 9)\n",
+      "Размер тестовой выборки: (742, 9)\n",
+      "Baseline MAE: 48988.97819674187\n",
+      "Baseline MSE: 3791583837.2779293\n",
+      "Baseline R²: -0.005051587587466155\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "# Определение признаков и целевой переменной\n",
+    "features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'salary_currency', 'remote_ratio', 'employee_residence', 'company_location', 'company_size']\n",
+    "target = 'salary_in_usd'\n",
+    "\n",
+    "# Разделение данных на тренировочный и тестовый наборы\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df_cleaned[features], df_cleaned[target], test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
+    "print(\"Размер тестовой выборки:\", X_test.shape)\n",
+    "\n",
+    "# Создание ориентира (baseline)\n",
+    "baseline_predictions = [y_train.mean()] * len(y_test)\n",
+    "\n",
+    "# Вычисление метрик для ориентира\n",
+    "print('Baseline MAE:', mean_absolute_error(y_test, baseline_predictions))\n",
+    "print('Baseline MSE:', mean_squared_error(y_test, baseline_predictions))\n",
+    "print('Baseline R²:', r2_score(y_test, baseline_predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер данных до удаления выбросов: (3755, 11)\n",
+      "Размер данных после удаления выбросов: (3733, 11)\n",
+      "Размер обучающей выборки: (2986, 9)\n",
+      "Размер тестовой выборки: (747, 9)\n",
+      "Baseline MAE: 47593.92288600708\n",
+      "Baseline MSE: 3680965527.9964128\n",
+      "Baseline R²: -0.0016576422593919116\n",
+      "Model: Linear Regression trained.\n",
+      "Model: Decision Tree trained.\n",
+      "Model: Gradient Boosting trained.\n",
+      "Model: Linear Regression\n",
+      "MAE: 36617.65439873256\n",
+      "MSE: 2194684192.4416404\n",
+      "R²: 0.4027865306031213\n",
+      "\n",
+      "Model: Decision Tree\n",
+      "MAE: 36516.71804922624\n",
+      "MSE: 2246643776.062331\n",
+      "R²: 0.38864738324451775\n",
+      "\n",
+      "Model: Gradient Boosting\n",
+      "MAE: 35842.80843437428\n",
+      "MSE: 2125285552.2470944\n",
+      "R²: 0.42167116230764956\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from scipy import stats\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import GradientBoostingRegressor\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "\n",
+    "# Определение числовых признаков\n",
+    "numeric_features = ['work_year', 'salary_in_usd', 'remote_ratio']\n",
+    "\n",
+    "# Вычисление z-оценок для числовых признаков\n",
+    "z_scores = stats.zscore(df[numeric_features])\n",
+    "\n",
+    "# Определение порога для удаления выбросов\n",
+    "threshold = 3\n",
+    "\n",
+    "# Удаление выбросов\n",
+    "df_cleaned = df[(z_scores < threshold).all(axis=1)]\n",
+    "\n",
+    "print(\"Размер данных до удаления выбросов:\", df.shape)\n",
+    "print(\"Размер данных после удаления выбросов:\", df_cleaned.shape)\n",
+    "\n",
+    "# Разделение на выборки и создание ориентира\n",
+    "features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'salary_currency', 'remote_ratio', 'employee_residence', 'company_location', 'company_size']\n",
+    "target = 'salary_in_usd'\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df_cleaned[features], df_cleaned[target], test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
+    "print(\"Размер тестовой выборки:\", X_test.shape)\n",
+    "\n",
+    "# Создание ориентира (baseline)\n",
+    "baseline_predictions = [y_train.mean()] * len(y_test)\n",
+    "\n",
+    "print('Baseline MAE:', mean_absolute_error(y_test, baseline_predictions))\n",
+    "print('Baseline MSE:', mean_squared_error(y_test, baseline_predictions))\n",
+    "print('Baseline R²:', r2_score(y_test, baseline_predictions))\n",
+    "\n",
+    "# Создание конвейера и обучение моделей\n",
+    "categorical_features = ['experience_level', 'employment_type', 'job_title', 'salary_currency', 'employee_residence', 'company_location', 'company_size']\n",
+    "numeric_features = ['work_year', 'remote_ratio']\n",
+    "\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', StandardScaler(), numeric_features),\n",
+    "        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)])\n",
+    "\n",
+    "pipeline_linear_regression = Pipeline(steps=[\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('regressor', LinearRegression())])\n",
+    "\n",
+    "pipeline_decision_tree = Pipeline(steps=[\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('regressor', DecisionTreeRegressor(random_state=42))])\n",
+    "\n",
+    "pipeline_gradient_boosting = Pipeline(steps=[\n",
+    "    ('preprocessor', preprocessor),\n",
+    "    ('regressor', GradientBoostingRegressor(random_state=42))])\n",
+    "\n",
+    "pipelines = [\n",
+    "    ('Linear Regression', pipeline_linear_regression),\n",
+    "    ('Decision Tree', pipeline_decision_tree),\n",
+    "    ('Gradient Boosting', pipeline_gradient_boosting)\n",
+    "]\n",
+    "\n",
+    "for name, pipeline in pipelines:\n",
+    "    pipeline.fit(X_train, y_train)\n",
+    "    print(f\"Model: {name} trained.\")\n",
+    "\n",
+    "# Оценка качества моделей\n",
+    "for name, pipeline in pipelines:\n",
+    "    y_pred = pipeline.predict(X_test)\n",
+    "    print(f\"Model: {name}\")\n",
+    "    print('MAE:', mean_absolute_error(y_test, y_pred))\n",
+    "    print('MSE:', mean_squared_error(y_test, y_pred))\n",
+    "    print('R²:', r2_score(y_test, y_pred))\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: Linear Regression\n",
+      "MAE: 36617.65439873256\n",
+      "MSE: 2194684192.4416404\n",
+      "R²: 0.4027865306031213\n",
+      "\n",
+      "Model: Decision Tree\n",
+      "MAE: 36516.71804922624\n",
+      "MSE: 2246643776.062331\n",
+      "R²: 0.38864738324451775\n",
+      "\n",
+      "Model: Gradient Boosting\n",
+      "MAE: 35842.80843437428\n",
+      "MSE: 2125285552.2470944\n",
+      "R²: 0.42167116230764956\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+    "\n",
+    "for name, pipeline in pipelines:\n",
+    "    y_pred = pipeline.predict(X_test)\n",
+    "    print(f\"Model: {name}\")\n",
+    "    print('MAE:', mean_absolute_error(y_test, y_pred))\n",
+    "    print('MSE:', mean_squared_error(y_test, y_pred))\n",
+    "    print('R²:', r2_score(y_test, y_pred))\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пропущенные значения:\n",
+      " work_year             0\n",
+      "experience_level      0\n",
+      "employment_type       0\n",
+      "job_title             0\n",
+      "salary                0\n",
+      "salary_currency       0\n",
+      "salary_in_usd         0\n",
+      "employee_residence    0\n",
+      "remote_ratio          0\n",
+      "company_location      0\n",
+      "company_size          0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "from scipy.stats import uniform, randint\n",
+    "from sklearn.model_selection import RandomizedSearchCV\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "\n",
+    "# Проверка на пропущенные значения\n",
+    "print(\"Пропущенные значения:\\n\", df.isnull().sum())\n",
+    "\n",
+    "# Удаление строк с пропущенными значениями\n",
+    "df = df.dropna()\n",
+    "\n",
+    "# Выбор признаков и целевой переменной\n",
+    "features = ['work_year', 'experience_level', 'employment_type', 'job_title', 'employee_residence', 'remote_ratio', 'company_location', 'company_size']\n",
+    "target = 'salary_in_usd'\n",
+    "\n",
+    "# Определение категориальных и числовых признаков\n",
+    "categorical_features = ['experience_level', 'employment_type', 'job_title', 'employee_residence', 'company_location', 'company_size']\n",
+    "numeric_features = ['work_year', 'remote_ratio']\n",
+    "\n",
+    "# Создание пайплайна для обработки данных\n",
+    "categorical_transformer = Pipeline(steps=[\n",
+    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
+    "])\n",
+    "\n",
+    "numeric_transformer = Pipeline(steps=[\n",
+    "    ('scaler', StandardScaler())\n",
+    "])\n",
+    "\n",
+    "preprocessor = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        ('num', numeric_transformer, numeric_features),\n",
+    "        ('cat', categorical_transformer, categorical_features)\n",
+    "    ])\n",
+    "\n",
+    "# Преобразование данных\n",
+    "X = preprocessor.fit_transform(df[features])\n",
+    "y = df[target]\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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:320: UserWarning: The total space of parameters 4 is smaller than n_iter=10. Running 4 iterations. For exhaustive searches, use GridSearchCV.\n",
+      "  warnings.warn(\n",
+      "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:540: FitFailedWarning: \n",
+      "6 fits failed out of a total of 12.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "6 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 888, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\pipeline.py\", line 473, in fit\n",
+      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\base.py\", line 1473, in wrapper\n",
+      "    return fit_method(estimator, *args, **kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_base.py\", line 609, in fit\n",
+      "    X, y = self._validate_data(\n",
+      "           ^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\base.py\", line 650, in _validate_data\n",
+      "    X, y = check_X_y(X, y, **check_params)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 1301, in check_X_y\n",
+      "    X = check_array(\n",
+      "        ^^^^^^^^^^^^\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 971, in check_array\n",
+      "    array = _ensure_sparse_format(\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\utils\\validation.py\", line 595, in _ensure_sparse_format\n",
+      "    raise TypeError(\n",
+      "TypeError: Sparse data was passed for X, but dense data is required. Use '.toarray()' to convert to a dense numpy array.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "d:\\MII\\AIM-PIbd-32-Kaznacheeva-E-K\\aimenv\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1103: UserWarning: One or more of the test scores are non-finite: [       nan 0.37308723        nan 0.37316524]\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14908\\2948510432.py:70: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n",
+      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14908\\2948510432.py:70: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n",
+      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_14908\\2948510432.py:70: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
+    "from scipy.stats import uniform, randint\n",
+    "\n",
+    "# Загрузка данных\n",
+    "df = pd.read_csv(\"..//static//csv//ds_salaries.csv\")\n",
+    "\n",
+    "# ... (ваш код предобработки данных, как в предыдущем примере) ...\n",
+    "\n",
+    "# Определение распределений для гиперпараметров\n",
+    "param_distributions = {\n",
+    "  'Linear Regression': {\n",
+    "    'regressor__fit_intercept': [True, False],\n",
+    "    'regressor__positive': [True, False]\n",
+    "  },\n",
+    "  'Random Forest': {\n",
+    "    'regressor__n_estimators': randint(50, 200),\n",
+    "    'regressor__max_depth': [None, 10, 20],\n",
+    "    'regressor__min_samples_split': randint(2, 11),\n",
+    "    'regressor__min_samples_leaf': randint(1, 5),\n",
+    "    'regressor__bootstrap': [True, False]\n",
+    "  },\n",
+    "  'Gradient Boosting': {\n",
+    "    'regressor__n_estimators': randint(50, 200),\n",
+    "    'regressor__learning_rate': uniform(0.01, 0.49), # uniform distribution for learning rate\n",
+    "    'regressor__max_depth': [3, 5, 7],\n",
+    "    'regressor__min_samples_split': randint(2, 11),\n",
+    "    'regressor__min_samples_leaf': randint(1, 5),\n",
+    "    'regressor__subsample': uniform(0.5, 0.5) # uniform distribution for subsample\n",
+    "\n",
+    "  }\n",
+    "}\n",
+    "\n",
+    "# Словарь для хранения лучших моделей и их гиперпараметров\n",
+    "best_models = {}\n",
+    "\n",
+    "# Цикл для обучения и настройки гиперпараметров каждой модели\n",
+    "for model_name, model_params in param_distributions.items():\n",
+    "  if model_name == 'Linear Regression':\n",
+    "    model = LinearRegression()\n",
+    "  elif model_name == 'Random Forest':\n",
+    "    model = RandomForestRegressor(random_state=42)\n",
+    "  elif model_name == 'Gradient Boosting':\n",
+    "    model = GradientBoostingRegressor(random_state=42)\n",
+    "  else:\n",
+    "    continue #Обработка неизвестных моделей\n",
+    "\n",
+    "  pipeline = Pipeline([('regressor', model)])\n",
+    "  random_search = RandomizedSearchCV(pipeline, param_distributions=model_params, n_iter=10, cv=3, n_jobs=-1, random_state=42)\n",
+    "  random_search.fit(X_train, y_train)\n",
+    "  best_models[model_name] = random_search.best_params_\n",
+    "\n",
+    "\n",
+    "# Визуализация лучших гиперпараметров\n",
+    "\n",
+    "fig, axes = plt.subplots(len(best_models), 1, figsize=(10, 5 * len(best_models)))\n",
+    "if len(best_models) == 1:\n",
+    "  axes = [axes] # обработка случая с одной моделью\n",
+    "\n",
+    "for i, (model_name, params) in enumerate(best_models.items()):\n",
+    "  axes[i].bar(params.keys(), params.values())\n",
+    "  axes[i].set_title(f\"Лучшие гиперпараметры для {model_name}\")\n",
+    "  axes[i].set_xticklabels(params.keys(), rotation=45, ha=\"right\") #Поворачиваем подписи на оси х\n",
+    "  axes[i].tick_params(axis='x', which='major', labelsize=8) # Размер шрифта подписей оси х\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aimenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
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+}