{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Датасет 1. Зарплата специалистов по обработке данных в 2023 году\n",
    "https://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary\n",
    "## Анализ сведений\n",
    "### Краткое описание\n",
    "Этот датасет посвящен анализу факторов, влияющих на уровень заработных плат специалистов в области Data Science. Включенные данные позволяют исследовать взаимосвязь между различными характеристиками сотрудников и их доходами.\n",
    "### Проблемная область\n",
    "Датасет касается анализа факторов, влияющих на заработную плату специалистов в области Data Science, что является важным аспектом для понимания экономических и профессиональных тенденций на рынке труда в этой сфере. Проблемная область включает:\n",
    "- Анализ влияния опыта, типа занятости, географического положения и других факторов на размер заработной платы специалистов.\n",
    "- Определение ключевых факторов, влияющих на рост зарплаты в профессии Data Scientist.\n",
    "- Выявление тенденций, которые могут помочь работодателям и специалистам принимать решения о карьере, зарплате и условиях работы.\n",
    "\n",
    "### Актуальность\n",
    "- **Рост профессии**: Data Science — это одна из самых востребованных и динамично развивающихся областей на рынке труда. Понимание факторов, влияющих на зарплату, важно для профессионалов и компаний.\n",
    "- **Тенденции на рынке труда**: В условиях глобализации и удаленной работы важно понять, как тип занятости и местоположение компании влияют на оплату труда.\n",
    "- **Оптимизация карьерных решений**: Анализ данных поможет специалистам принимать обоснованные решения при выборе карьерных путей, а работодателям — разрабатывать конкурентоспособные предложения по зарплате и условиям работы.\n",
    "\n",
    "### Объекты наблюдений\n",
    "Объектами наблюдения являются **Data Scientists**, то есть специалисты, занимающиеся анализом данных. Каждый объект представляет собой запись, которая отражает характеристики работы конкретного специалиста в определенный год.\n",
    "\n",
    "### Атрибуты объектов\n",
    "Каждый объект имеет следующие атрибуты:\n",
    "- **work_year** — год, в котором была выплачена зарплата. Позволяет отслеживать изменения зарплат в разные годы.\n",
    "- **experience_level** — уровень опыта сотрудника (Entry-level, Mid-level, Senior-level, Executive-level). Это важный атрибут, который влияет на зарплату.\n",
    "- **employment_type** — тип занятости (Part-time, Full-time, Contract, Freelance). Определяет, является ли работа постоянной или временной.\n",
    "- **job_title** — должность, занимаемая сотрудником. Важно для анализа различий между зарплатами для разных специализаций.\n",
    "- **salary** — общая сумма заработной платы.\n",
    "- **salary_currency** — валюта, в которой выплачена зарплата.\n",
    "- **salaryinusd** — зарплата в долларах США. Этот атрибут используется для стандартизации данных.\n",
    "- **employee_residence** — страна проживания сотрудника. Влияет на размер зарплаты и может быть важным для анализа глобальных различий.\n",
    "- **remote_ratio** — доля работы, выполняемой удаленно. Важно для анализа влияния удаленной работы на уровень зарплаты.\n",
    "- **company_location** — страна, где находится основная офисная локация компании. Это атрибут, который позволяет анализировать региональные различия в зарплатах.\n",
    "- **company_size** — размер компании, выраженный через медиану числа сотрудников. Размер компании может влиять на оплату труда, так как крупные компании часто предлагают более высокие зарплаты.\n",
    "\n",
    "### Связь между объектами\n",
    "Связь между объектами заключается в том, что все атрибуты в совокупности описывают профессиональную деятельность и условия работы каждого специалиста. Например:\n",
    "- **experience_level** и **job_title** могут быть взаимосвязаны, так как более высокие должности (например, Senior или Executive) соответствуют большему опыту.\n",
    "- **salary** напрямую зависит от **experience_level**, **employment_type**, **employee_residence**, **company_location**, и **company_size**, а также от уровня удаленности работы (**remote_ratio**).\n",
    "- **salaryinusd** служит для нормализации и сопоставления зарплат между различными странами и валютами.\n",
    "- **employee_residence** и **company_location** могут быть связаны с различиями в заработной плате, так как зарплаты могут варьироваться в зависимости от страны проживания и местоположения компании.\n",
    "\n",
    "## Качество набора данных\n",
    "### Информативность\n",
    "Датасет содержит разнообразные атрибуты, которые предоставляют полезную информацию для анализа факторов, влияющих на зарплату специалистов в области Data Science. Включенные переменные, такие как **уровень опыта**, **тип занятости**, **зарплата**, **географическое расположение** и **удаленная работа**, позволяют провести многогранный анализ и выявить значимые закономерности. Однако, отсутствие информации о дополнительной квалификации или навыках специалистов (например, знание конкретных технологий или инструментов) может ограничить глубину анализа.\n",
    "\n",
    "### Степень покрытия\n",
    "Датасет охватывает достаточно широкий спектр факторов, влияющих на зарплату, включая географические данные (страна проживания, местоположение компании) и рабочие условия (удаленная работа, тип занятости). Однако степень покрытия может быть ограничена:\n",
    "- Данные охватывают только одну профессиональную категорию (Data Science), что не позволяет делать выводы о других областях.\n",
    "- Пропущенные данные по некоторым атрибутам могут снизить полноту информации (например, отсутствие данных по размеру компании или типу работы для некоторых записей).\n",
    "\n",
    "### Соответствие реальным данным\n",
    "Датасет в целом отражает реальные условия рынка труда для специалистов в области Data Science. Он содержит важные атрибуты, такие как уровень опыта и зарплата, которые широко используются в исследованиях зарплат. Однако стоит учитывать, что в реальной жизни могут существовать дополнительные переменные, которые не учтены в наборе данных, такие как текущее состояние отрасли или специфические тренды (например, спрос на специалистов в определенных областях).\n",
    "\n",
    "### Согласованность меток\n",
    "Метки в датасете, такие как **experience_level** (уровень опыта), **employment_type** (тип занятости), и **company_size** (размер компании), имеют четкие и логичные категории, что способствует легкости их интерпретации. Однако для некоторых меток могут возникнуть проблемы с точностью классификации, например:\n",
    "- В разных странах или компаниях могут существовать различные способы определения уровней опыта, и это может не всегда совпадать с метками в датасете.\n",
    "- Некоторые метки могут требовать дополнительного пояснения, например, категориальные значения для **remote_ratio** или **job_title** могут быть варьироваться в зависимости от контекста.\n",
    "\n",
    "## Бизнес-цели\n",
    "### 1. **Определение конкурентоспособных уровней зарплат для специалистов в области Data Science**\n",
    "\n",
    "**Эффект на бизнес:**\n",
    "Датасет поможет компаниям, работающим в сфере Data Science, определять конкурентоспособные уровни зарплат для специалистов в зависимости от уровня опыта, типа занятости и географического положения. Это способствует привлечению и удержанию талантливых специалистов, улучшая стратегию найма и оптимизируя расходы на оплату труда.\n",
    "\n",
    "**Примеры целей технического проекта:**\n",
    "- **Цель проекта:** Создание модели для предсказания конкурентоспособных зарплат для специалистов по Data Science в зависимости от их уровня опыта и местоположения.\n",
    "  - **Что поступает на вход:** Данные о годе работы, уровне опыта, типе занятости, местоположении компании и специалиста.\n",
    "  - **Целевой признак:** Прогнозируемая зарплата (в долларах США или эквивалент в локальной валюте).\n",
    "\n",
    "### 2. **Определение факторов, влияющих на рост зарплат в сфере Data Science**\n",
    "\n",
    "**Эффект на бизнес:**\n",
    "Анализ факторов, влияющих на рост зарплат, позволит компаниям лучше понимать, какие характеристики (например, удаленная работа, опыт работы в крупных компаниях) способствуют повышению заработной платы. Это может помочь в построении программ карьерного роста и мотивации для сотрудников.\n",
    "\n",
    "**Примеры целей технического проекта:**\n",
    "- **Цель проекта:** Разработка модели для анализа факторов, которые влияют на рост зарплат в сфере Data Science.\n",
    "  - **Что поступает на вход:** Данные о годе работы, уровне опыта, типе занятости, удаленной работе, размере компании и других характеристиках.\n",
    "  - **Целевой признак:** Изменение зарплаты за год (прибавка к зарплате или её снижение).\n",
    "\n",
    "### 3. **Улучшение стратегии удаленной работы и гибких условий занятости**\n",
    "\n",
    "**Эффект на бизнес:**\n",
    "Датасет поможет компаниям понять, как удаленная работа или гибкие условия занятости влияют на уровень зарплаты специалистов. Это даст возможность оптимизировать политику гибкости в работе и предложить лучшие условия для сотрудников, что повышает их удовлетворенность и снижает текучесть кадров.\n",
    "\n",
    "**Примеры целей технического проекта:**\n",
    "- **Цель проекта:** Создание модели для анализа влияния удаленной работы и типа занятости на уровень зарплаты в сфере Data Science.\n",
    "  - **Что поступает на вход:** Данные о проценте удаленной работы, типе занятости (фриланс, контракт, полная или частичная занятость).\n",
    "  - **Целевой признак:** Зарплата в зависимости от удаленности работы и типа занятости (фиксированная сумма или разница в зарплатах для разных типов занятости)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполним все необходимые импорты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Any\n",
    "from math import ceil\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import ADASYN\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Считаем данные для первого датасета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3755 entries, 0 to 3754\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   work_year           3755 non-null   int64 \n",
      " 1   experience_level    3755 non-null   object\n",
      " 2   employment_type     3755 non-null   object\n",
      " 3   job_title           3755 non-null   object\n",
      " 4   salary              3755 non-null   int64 \n",
      " 5   salary_currency     3755 non-null   object\n",
      " 6   salary_in_usd       3755 non-null   int64 \n",
      " 7   employee_residence  3755 non-null   object\n",
      " 8   remote_ratio        3755 non-null   int64 \n",
      " 9   company_location    3755 non-null   object\n",
      " 10  company_size        3755 non-null   object\n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 322.8+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>work_year</th>\n",
       "      <td>3755.0</td>\n",
       "      <td>2022.373635</td>\n",
       "      <td>0.691448</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2022.0</td>\n",
       "      <td>2023.0</td>\n",
       "      <td>2023.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>3755.0</td>\n",
       "      <td>190695.571771</td>\n",
       "      <td>671676.500508</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>138000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>30400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary_in_usd</th>\n",
       "      <td>3755.0</td>\n",
       "      <td>137570.389880</td>\n",
       "      <td>63055.625278</td>\n",
       "      <td>5132.0</td>\n",
       "      <td>95000.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>450000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remote_ratio</th>\n",
       "      <td>3755.0</td>\n",
       "      <td>46.271638</td>\n",
       "      <td>48.589050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count           mean            std     min       25%  \\\n",
       "work_year      3755.0    2022.373635       0.691448  2020.0    2022.0   \n",
       "salary         3755.0  190695.571771  671676.500508  6000.0  100000.0   \n",
       "salary_in_usd  3755.0  137570.389880   63055.625278  5132.0   95000.0   \n",
       "remote_ratio   3755.0      46.271638      48.589050     0.0       0.0   \n",
       "\n",
       "                    50%       75%         max  \n",
       "work_year        2022.0    2023.0      2023.0  \n",
       "salary         138000.0  180000.0  30400000.0  \n",
       "salary_in_usd  135000.0  175000.0    450000.0  \n",
       "remote_ratio        0.0     100.0       100.0  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('csv/8.ds_salaries.csv')\n",
    "df.info()\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод проверки пустых значений в датафрейме"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Проверка пропущенных данных\n",
    "def check_null_columns(dataframe: DataFrame) -> None:\n",
    "    print('Присутствуют ли пустые значения признаков в колонке:')\n",
    "    print(dataframe.isnull().any(), '\\n')\n",
    "\n",
    "    if any(dataframe.isnull().any()):\n",
    "        print('Количество пустых значений признаков в колонке:')\n",
    "        print(dataframe.isnull().sum(), '\\n')\n",
    "\n",
    "        print('Процент пустых значений признаков в колонке:')\n",
    "        for column in dataframe.columns:\n",
    "            null_rate: float = dataframe[column].isnull().sum() / len(dataframe) * 100\n",
    "            if null_rate > 0:\n",
    "                print(f\"{column} процент пустых значений: {null_rate:.2f}%\")            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим на пустые значения в колонках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Присутствуют ли пустые значения признаков в колонке:\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",
      "\n"
     ]
    }
   ],
   "source": [
    "check_null_columns(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка на наличие выборосов и зашумленности данных\n",
    "\n",
    "Зашумленность – это наличие случайных ошибок или вариаций в данных, которые могут затруднить выявление истинных закономерностей.\n",
    "\n",
    "Выбросы – это значения, которые значительно отличаются от остальных наблюдений в наборе данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция возвращает список числовых колонок датафрейма"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_numeric_columns(dataframe: DataFrame) -> list[str]:\n",
    "    w = []\n",
    "    for column in dataframe.columns:\n",
    "        if not pd.api.types.is_numeric_dtype(dataframe[column]):\n",
    "            continue\n",
    "        w.append(column)\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод для проверки датафрейма на наличие выбросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_outliers(dataframe: DataFrame) -> list[str]:\n",
    "    w = []\n",
    "    for column in get_numeric_columns(dataframe):\n",
    "        Q1: float = dataframe[column].quantile(0.25)\n",
    "        Q3: float = dataframe[column].quantile(0.75)\n",
    "        IQR: float = Q3 - Q1\n",
    "\n",
    "        lower_bound: float = Q1 - 1.5 * IQR\n",
    "        upper_bound: float = Q3 + 1.5 * IQR\n",
    "\n",
    "        outliers: DataFrame = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)]\n",
    "        outlier_count: int = outliers.shape[0]\n",
    "\n",
    "        if outlier_count > 0:\n",
    "            w.append(column)\n",
    "\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"\\tЕсть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"\\tКоличество выбросов: {outlier_count}\")\n",
    "        print(f\"\\tМинимальное значение: {dataframe[column].min()}\")\n",
    "        print(f\"\\tМаксимальное значение: {dataframe[column].max()}\")\n",
    "        print(f\"\\t1-й квартиль (Q1): {Q1}\")\n",
    "        print(f\"\\t3-й квартиль (Q3): {Q3}\\n\")\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод для визуализации выбросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_outliers(dataframe: DataFrame) -> None:\n",
    "    columns = get_numeric_columns(dataframe)\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    rows: int = ceil(len(columns) / 3)\n",
    "    for index, column in enumerate(columns, 1):\n",
    "        plt.subplot(rows, 3, index)\n",
    "        plt.boxplot(dataframe[column], vert=True, patch_artist=True)\n",
    "        plt.title(f\"Диаграмма размахов для \\\"{column}\\\"\")\n",
    "        plt.xlabel(column)\n",
    "            \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим на наличие выбросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка work_year:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 76\n",
      "\tМинимальное значение: 2020\n",
      "\tМаксимальное значение: 2023\n",
      "\t1-й квартиль (Q1): 2022.0\n",
      "\t3-й квартиль (Q3): 2023.0\n",
      "\n",
      "Колонка salary:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 113\n",
      "\tМинимальное значение: 6000\n",
      "\tМаксимальное значение: 30400000\n",
      "\t1-й квартиль (Q1): 100000.0\n",
      "\t3-й квартиль (Q3): 180000.0\n",
      "\n",
      "Колонка salary_in_usd:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 63\n",
      "\tМинимальное значение: 5132\n",
      "\tМаксимальное значение: 450000\n",
      "\t1-й квартиль (Q1): 95000.0\n",
      "\t3-й квартиль (Q3): 175000.0\n",
      "\n",
      "Колонка remote_ratio:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 0\n",
      "\tМаксимальное значение: 100\n",
      "\t1-й квартиль (Q1): 0.0\n",
      "\t3-й квартиль (Q3): 100.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "columns_with_outliers = check_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализируем выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jxgxFRkbKw8OjUP8ff/yhZ599Vh4eHnJ2dlbdunXl6+trLlta9erVK3QThoJB68p55pYvX642bdqY8zDVrVtXW7ZsueY2hw0bZg5yMTExCgwMVNOmTa+ZR7t27dSqVSt9+OGHMgxDMTExRQ6K0uVpNR5++GG5ubnJ1dVVdevWNX95lfQ1uDqXxo0by9bW1mKfv/zyS/Xq1UvVqlVTjRo1VLduXU2cOLHI7UyfPl1169ZV+/btdezYMSUkJKh69eqFtjts2DDFxsYqOztba9euVc2aNYscVPPz8zV37lw1bdpUjo6OqlOnjurWravvvvuuyH2cPXu2atWqpf379+tf//qX3N3dLfp//vln3XPPPYWWK5iq5OpL4kJDQ1W3bl01bNhQU6dO1aRJk0o80Jw+fVpVqlSxuBTvWs6cOSNJxc7PVrt2bTVt2lTvv/++tm3bppMnT+r06dPKzs4uUT7F8fDwUNOmTc2ieWJiooKCgtS1a1edOHFCR48e1Zdffqn8/HyzkH7q1Cn99ddf13w98/Pz9csvv1i0F3xGi/Lggw+qevXq2rp1q1xdXUu9DzVq1NCDDz6o2NhYs23lypW66667zPfWqVOndObMGS1ZskR169a1eBR8zk6ePGkuX5rPe3H7hspt9+7devDBB1WvXr0i5/u8nmvNEXmn3HyP4wCOAzgOKP/HAWPGjNHdd9+tPn36qH79+mYx6kql/VlcqbSf16LG1Pvvv19eXl7mF+b5+flatWqV+vfvX+R7DKgIGEMZQxlDGUPvhDG0fv36haaYqVmzpv7888+bXndRpk+frjNnzujuu+9W69atNWHCBIt7uxW8F4v6fVHUe/hORyG9gjl8+LAaNWpkcUOSknjzzTdla2urCRMmFNn/6KOP6r333tNTTz2luLg4bdu2zfxldavmJV6xYoWGDh2qxo0b64MPPtBnn32m+Ph49ejR45rbfPzxx/XTTz9p7969Wr58+TUH8isNHz5cy5Yt065du5Senq5HH320UMyZM2fUrVs3ffvtt5o+fbo2bdqk+Ph4c06uG30Nrv7ld+TIEfXs2VOnT5/WW2+9pS1btig+Pt6ch/rq7YwcOVLbtm3T0qVL5eTkpAEDBhT5y71v375ycHDQhg0btGzZMoWHhxd5E52CA8CuXbtqxYoV2rp1q+Lj49WyZcsi9/Gbb74xC6FFzQdYWrNnz1Z8fLw+/fRTTZkyRW+++aamTZtWomWPHTumBg0aFHpNi1Jw1oSnp2excWvWrFHt2rUVEhIiDw8P1a1bV6tXry5RPtfTpUsXJSYm6sKFC0pOTlZQUJBatWqlGjVqKDExUYmJiXJxcSk0r1ppXPlt+tUGDBigI0eOWJxRXlpDhgzR0aNHtWfPHp09e1affPKJBg0aZL63Ct4zjz/+uOLj44t8dO7cWVLpP+/F7Rsqt/Pnz6tt27ZasGDBDS3//PPPKy0tzeLRokULPfLII2Wc6a3BcQDHARwHlP/jAHd3d+3fv1+ffPKJHnroIe3cuVN9+vRReHi4GVPan8WVSvt5LWpMtbOz0+DBg/Xxxx/r4sWL2rlzp06cOGFxBhxQ0TCGMoYyhjKG3glj6LWuOjdKeHPZ67n6JsBdu3bVkSNHtHTpUrVq1Urvv/++OnTooPfff79Mtnen4WajFUh2drb2799vcYOQkjhx4oTmzZun6OhoVa9evdDdiP/8809t375d06ZN0+TJk832a13WUdJtnj9/3uKb9P/3//6fpMt3CJakdevWqVGjRoqLi7P4hV7U5XIFateurYceesi8tO3RRx+1uFt4UR577DFNmDBBzz77rAYOHFjkN4QJCQn6/fffFRcXp65du5rtqampJdrfAj/++KPFN5Y//fST8vPzzX3etGmTsrOz9cknn6hBgwZm3NV3Vy/QpEkTNWnSRJLUq1cvNWjQQLGxsXr66act4uzt7fXEE0/o9ddf14EDB7R06dIi17du3ToFBwfrgw8+sGg/c+ZMoZtQnj9/XsOGDVOLFi0UGBiomTNn6uGHH1bHjh3NmIYNG+rw4cOFtlNw+V7Dhg0t2v38/Mw7g/fp00e//fab3nzzTb3yyitFHqwUuHTpkr799ls98MAD14y50sGDB2VjY3Pdb0fbt2+v9957T0FBQZo+fbr8/f01a9YsffnllyXaTnGCgoK0bNkyrV69Wnl5eQoMDJStra1ZYD906JACAwPNQbJu3bqqWrXqNV9PW1tbeXt7l3j7s2bNkr29vcaMGaPq1atr8ODBpd6HBx54QHXr1tXKlSvVqVMn/fXXX3riiSfM/rp166p69erKy8tTr169il3XjXzegaL06dOn2JvsZGdn6+WXX9aqVat05swZtWrVSm+++ab5u8fFxcXiDJtvv/1WBw8e1OLFi2916jeN4wCOAzgOuHOOAxwcHPTggw/qwQcfVH5+vsaMGaN3331Xr7zyipo0aVKqn8WVyvLzOmTIEM2ZM0ebNm3Sf/7zH9WtW1chISGlXg9wJ2AMZQxlDGUMrWxjaM2aNc0rDArk5OQoLS2tUGytWrU0bNgwDRs2TOfOnVPXrl01depUjRw50nwvFvU6FfUevtNxRnoFUnC5Uc+ePUu13LRp0+Th4VHk3E7S/33bdfW3W2+//fYN5Sld/mV95RxgOTk5evfdd1W3bl35+fldc7v//e9/lZSUVOy6hw8fru+++06PPPJIsZcbFahVq5b69++v7777zrzL+dWKyiUnJ0cLFy687vqvdPUZku+8844kmUWforaTmZlpXmJXnIKDnGtdLjV8+HB9//336tq1q8Uc21eys7Mr9HNeu3atfvvtt0KxL774oo4fP67ly5frrbfeko+Pj8LDwy22/7e//U1fffWVxc/s/PnzWrJkiXx8fK47X9aFCxd06dIlXbp0qdi4bdu2KTMzU/379y82Trr83vv444913333Xff9kZWVpSeeeEIPPfSQJk2apF69esnLy+u62yiJgilb3nzzTbVp08acSywoKEjbt2/X119/bcZIl382vXv31saNGy0uX8zIyFBsbKy6dOlSqilabGxstGTJEg0cOFDh4eH65JNPSr0P9vb2GjRokD766CPFxMSodevWatOmjUXOAwYM0Mcff6yUlJRCy586dcoiVir95x0orbFjxyopKUmrV682x4oHHnjgmgfI77//vu6++26Lz2N5xXHAZRwHcBxQnPJwHHB1oc3W1tYcPwtev9L8LK5Ulp/XNm3aqE2bNnr//ff18ccf6x//+Eepz9QF7hSMoZcxhjKGFocxtOTuhDG0cePG5n3bCixZsqTQGelXv+YuLi5q0qSJ+Xp7eXmpXbt2Wr58ucVVHfHx8YXm8q8IytdPETfk/PnzeueddzR9+nTzF8aKFSssYjIyMnTu3DmtWLFC999/v8Xcbdu2bdPKlSvNm5FczdXVVV27dtXMmTOVm5uru+66S9u2bSv1N8hXqlevnt58800dO3ZMd999t9asWaP9+/dryZIlqlKliiSpX79+iouL08MPP6y+ffsqNTVVixcvVosWLXTu3LlrrvuBBx7QqVOnSjTwF4iJidGCBQuu+e1kYGCgatasqfDwcP3zn/+UjY2N/v3vf5f60pnU1FQ99NBDeuCBB5SUlKQVK1Zo8ODBatu2raTLNx0p+HZ19OjROnfunN577z25u7tbfCv46aef6v3331dgYKBq1aqlo0eP6r333lO1atX08MMPF7nt5s2b6/Tp08VOidGvXz9Nnz5dw4YNU2BgoL7//nutXLmy0MHCjh07tHDhQk2ZMkUdOnSQJC1btkzdu3fXK6+8opkzZ0qSXnrpJa1atUp9+vTRP//5T9WqVUvLly9XamqqPv7440LfjMfHx+vXX39Vbm6u/ve//2nlypV66KGHrvnelC5fMvb888/L0dFRFy5csHjvZ2ZmKi8vTxs2bFBoaKg+//xzvfLKK/ruu++0adOma66zQEREhC5cuHBLLllq0qSJPD09dfjwYfNGOdLly6ZefPFFSSpUuHvttdcUHx+vLl26aMyYMbK3t9e7776r7Oxs8zUvDVtbW61YsUKhoaF69NFH9emnn5b4hjQFhgwZon/961/auXOneXnmld544w3t3LlTnTp10pNPPqkWLVrojz/+0L59+/T555/rjz/+kHTjn3egNI4fP65ly5bp+PHjqlevnqTLU7l89tlnWrZsmWbMmGERf/HiRa1cuVIvvfSSNdItMY4DLHEcwHFAgfJ6HDBy5Ej98ccf6tGjh+rXr6+ff/5Z77zzjtq1a2fOfVvSn8XVyvrzOmTIED3//POSxLQuqJAYQy0xhjKGFmAMrfhj6MiRI/XUU09pwIABuv/++/Xtt99q69athT7LLVq0UPfu3eXn56datWrp66+/1rp16zR27FgzJjo6Wn379lWXLl00fPhw/fHHH3rnnXfUsmXLivf3vIE7XmpqqiGpxI+dO3cahmEYy5YtMyQZ7dq1M/Lz8wutb9myZWbbr7/+ajz88MNGjRo1DDc3N+ORRx4xTpw4YUgypkyZYsZNmTLFkGScOnXqmvl269bNaNmypfH1118bAQEBhpOTk9GwYUNj/vz5FnH5+fnGjBkzjIYNGxqOjo5G+/btjc2bNxvh4eFGw4YNC+U7a9asYl+fK/uvl2dR/V9++aXh7+9vODs7G/Xq1TNeeOEFY+vWrRav6bUUrO/gwYPGwIEDjerVqxs1a9Y0xo4da1y4cMEi9pNPPjHatGljODk5GT4+Psabb75pLF261JBkpKamGoZhGCkpKUbv3r2N2rVrGw4ODoa3t7fxj3/8w/juu+8s1iXJiIiIuGZeV/dfvHjReO655wwvLy/D2dnZ6Ny5s5GUlGR069bN6Natm2EYhpGVlWU0bNjQ6NChg5Gbm2uxvvHjxxu2trZGUlKS2XbkyBFj4MCBRo0aNQwnJyfjvvvuMzZv3myx3M6dOy3eo/b29kbDhg2Nf/7zn8aff/5Z7GvbsGHD677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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод устраняет выбросы в заданных колонках, задавая значениям выше максимального значение максимума, а ниже минимального - значение минимума."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outliers(dataframe: DataFrame, columns: list[str]) -> DataFrame:\n",
    "    print('Колонки с выбросами:', *columns, sep='\\n')\n",
    "    for column in columns:\n",
    "        Q1: float = dataframe[column].quantile(0.25)\n",
    "        Q3: float = dataframe[column].quantile(0.75)\n",
    "        IQR: float = Q3 - Q1\n",
    "\n",
    "        lower_bound: float = Q1 - 1.5 * IQR\n",
    "        upper_bound: float = Q3 + 1.5 * IQR\n",
    "\n",
    "        dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "    \n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Устраняем выбросы, если они имеются"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонки с выбросами:\n",
      "work_year\n",
      "salary\n",
      "salary_in_usd\n"
     ]
    }
   ],
   "source": [
    "df = remove_outliers(df, columns_with_outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим наличие выбросов и визуализируем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка work_year:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 2020.5\n",
      "\tМаксимальное значение: 2023.0\n",
      "\t1-й квартиль (Q1): 2022.0\n",
      "\t3-й квартиль (Q3): 2023.0\n",
      "\n",
      "Колонка salary:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 6000.0\n",
      "\tМаксимальное значение: 300000.0\n",
      "\t1-й квартиль (Q1): 100000.0\n",
      "\t3-й квартиль (Q3): 180000.0\n",
      "\n",
      "Колонка salary_in_usd:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 5132.0\n",
      "\tМаксимальное значение: 295000.0\n",
      "\t1-й квартиль (Q1): 95000.0\n",
      "\t3-й квартиль (Q3): 175000.0\n",
      "\n",
      "Колонка remote_ratio:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 0\n",
      "\tМаксимальное значение: 100\n",
      "\t1-й квартиль (Q1): 0.0\n",
      "\t3-й квартиль (Q3): 100.0\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_outliers(df)\n",
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Разбиение набора данных на выборки:¶\n",
    "Групповое разбиение данных – это метод разделения данных на несколько групп или подмножеств на основе определенного признака или характеристики. При этом наблюдения для одного объекта должны попасть только в одну выборку.\n",
    "\n",
    "Основные виды выборки данных:\n",
    "- Обучающая выборка (60-80%). Обучение модели (подбор коэффициентов некоторой математической функции для аппроксимации).\n",
    "- Контрольная выборка (10-20%). Выбор метода обучения, настройка гиперпараметров.\n",
    "- Тестовая выборка (10-20% или 20-30%). Оценка качества модели перед передачей заказчику.\n",
    "\n",
    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция для создания выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "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[Any, Any, Any]:\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",
    "\n",
    "    X: DataFrame = df_input\n",
    "    y: DataFrame = df_input[\n",
    "        [stratify_colname]\n",
    "    ]\n",
    "\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, \n",
    "        stratify=y, \n",
    "        test_size=(1.0 - frac_train), \n",
    "        random_state=random_state\n",
    "    )\n",
    "\n",
    "    relative_frac_test: float = frac_test / (frac_val + frac_test)\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",
    "\n",
    "    return df_train, df_val, df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция оценки сбалансированности по колонке"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_balance(dataframe: DataFrame, dataframe_name: str, column: str) -> None:\n",
    "    counts: Series[int] = dataframe[column].value_counts()\n",
    "    print(dataframe_name + \": \", dataframe.shape)\n",
    "    print(f\"Распределение выборки данных по классам в колонке \\\"{column}\\\":\\n\", counts)\n",
    "    total_count: int = len(dataframe)\n",
    "    for value in counts.index:\n",
    "        percentage: float = counts[value] / total_count * 100\n",
    "        print(f\"Процент объектов класса \\\"{value}\\\": {percentage:.2f}%\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция определения необходимости аугментации данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def need_augmentation(dataframe: DataFrame,\n",
    "                      column: str, \n",
    "                      first_value: Any, second_value: Any) -> bool:\n",
    "    counts: Series[int] = dataframe[column].value_counts()\n",
    "    ratio: float = counts[first_value] / counts[second_value]\n",
    "    return ratio > 1.5 or ratio < 0.67"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод визуализации сбалансированности классов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_balance(dataframe: DataFrame,\n",
    "                      column: str) -> None:\n",
    "   fig, axes = plt.subplots(1, 1, figsize=(15, 5))\n",
    "\n",
    "   counts_train: Series[int] = dataframe[column].value_counts()\n",
    "   axes.pie(counts_train, labels=counts_train.index, autopct='%1.1f%%', startangle=90)\n",
    "   axes.set_title(f\"Распределение классов \\\"{column}\\\"\\n\")\n",
    "\n",
    "   plt.tight_layout()\n",
    "   plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
    "\n",
    "Стратифицированное разбиение требует, чтобы в каждом классе, по которому происходит стратификация, было минимум по два элемента, иначе метод не сможет корректно разделить данные на тренировочные, валидационные и тестовые наборы.\n",
    "\n",
    "Чтобы решить эту проблему введём категории для значения зарплаты. Вместо того, чтобы использовать точные значения зарплаты для стратификации, мы создадим категории зарплат, основываясь на квартилях (25%, 50%, 75%) и минимальном и максимальном значении зарплаты. Это позволит создать более крупные классы, что устранит проблему с редкими значениями\n",
    "\n",
    "Категории для разбиения зарплат:\n",
    "- Низкая зарплата: зарплаты ниже первого квартиля (25%) — это значения меньше 95000.\n",
    "- Средняя зарплата: зарплаты между первым квартилем (25%) и третьим квартилем (75%) — это зарплаты от 95000 до 175000.\n",
    "- Высокая зарплата: зарплаты выше третьего квартиля (75%) и до максимального значения — это зарплаты выше 175000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение количества наблюдений по меткам (классам):\n",
      "salary_in_usd\n",
      "100000.0    99\n",
      "150000.0    98\n",
      "120000.0    91\n",
      "160000.0    84\n",
      "130000.0    82\n",
      "            ..\n",
      "39916.0      1\n",
      "26005.0      1\n",
      "22611.0      1\n",
      "5679.0       1\n",
      "40038.0      1\n",
      "Name: count, Length: 1002, dtype: int64 \n",
      "\n",
      "Статистическое описание целевого признака:\n",
      "count      3755.000000\n",
      "mean     136959.779760\n",
      "std       61098.121137\n",
      "min        5132.000000\n",
      "25%       95000.000000\n",
      "50%      135000.000000\n",
      "75%      175000.000000\n",
      "max      295000.000000\n",
      "Name: salary_in_usd, dtype: float64 \n",
      "\n",
      "Распределение количества наблюдений по меткам (классам):\n",
      "salary_category\n",
      "medium    1867\n",
      "low        956\n",
      "high       932\n",
      "Name: count, dtype: int64 \n",
      "\n",
      "Проверка сбалансированности:\n",
      "Весь датасет:  (3755, 12)\n",
      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
      " salary_category\n",
      "medium    1867\n",
      "low        956\n",
      "high       932\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"medium\": 49.72%\n",
      "Процент объектов класса \"low\": 25.46%\n",
      "Процент объектов класса \"high\": 24.82%\n",
      "\n",
      "Проверка необходимости аугментации:\n",
      "Для датасета аугментация данных ТРЕБУЕТСЯ\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Вывод распределения количества наблюдений по меткам (классам)\n",
    "print('Распределение количества наблюдений по меткам (классам):')\n",
    "print(df.salary_in_usd.value_counts(), '\\n')\n",
    "\n",
    "# Статистическое описание целевого признака\n",
    "print('Статистическое описание целевого признака:')\n",
    "print(df['salary_in_usd'].describe().transpose(), '\\n')\n",
    "\n",
    "# Определим границы для каждой категории зарплаты\n",
    "bins: list[float] = [df['salary_in_usd'].min() - 1, \n",
    "                     df['salary_in_usd'].quantile(0.25), \n",
    "                     df['salary_in_usd'].quantile(0.75), \n",
    "                     df['salary_in_usd'].max() + 1]\n",
    "labels: list[str] = ['low', 'medium', 'high']\n",
    "\n",
    "# Создаем новую колонку с категориями зарплат#\n",
    "df['salary_category'] = pd.cut(df['salary_in_usd'], bins=bins, labels=labels)\n",
    "\n",
    "# Вывод распределения количества наблюдений по меткам (классам)\n",
    "print('Распределение количества наблюдений по меткам (классам):')\n",
    "print(df['salary_category'].value_counts(), '\\n')\n",
    "\n",
    "# Проверка сбалансированности\n",
    "print('Проверка сбалансированности:')\n",
    "check_balance(df, 'Весь датасет', 'salary_category')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print('Проверка необходимости аугментации:')\n",
    "print(f\"Для датасета аугментация данных {'НЕ ' if not need_augmentation(df, 'salary_category', 'low', 'medium') else ''}ТРЕБУЕТСЯ\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df, 'salary_category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать низкие или высокие зарплаты (low или high), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
    "\n",
    "Для получения более сбалансированных данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод приращения с избытком (oversampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def oversample(df: DataFrame, column: str) -> DataFrame:\n",
    "    X: DataFrame = pd.get_dummies(df.drop(column, axis=1))\n",
    "    y: DataFrame = df[column] # type: ignore\n",
    "    \n",
    "    adasyn = ADASYN()\n",
    "    X_resampled, y_resampled = adasyn.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return df_resampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Проверка сбалансированности выборок после применения метода oversampling:\n",
      "Весь датасет:  (5601, 279)\n",
      "Распределение выборки данных по классам в колонке \"salary_category\":\n",
      " salary_category\n",
      "high      1868\n",
      "medium    1867\n",
      "low       1866\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"high\": 33.35%\n",
      "Процент объектов класса \"medium\": 33.33%\n",
      "Процент объектов класса \"low\": 33.32%\n",
      "\n",
      "Проверка необходимости аугментации выборок после применения метода oversampling:\n",
      "Для всего датасета аугментация данных НЕ ТРЕБУЕТСЯ\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Приращение данных (oversampling)\n",
    "df_oversampled: DataFrame = oversample(df, 'salary_category')\n",
    "\n",
    "# Проверка сбалансированности\n",
    "print('Проверка сбалансированности выборок после применения метода oversampling:')\n",
    "check_balance(df_oversampled, 'Весь датасет', 'salary_category')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print('Проверка необходимости аугментации выборок после применения метода oversampling:')\n",
    "print(f\"Для всего датасета аугментация данных {'НЕ ' if not need_augmentation(df_oversampled, 'salary_category', 'low', 'medium') else ''}ТРЕБУЕТСЯ\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df_oversampled, 'salary_category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделим датасет на выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тренировочная выборка\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Контрольная выборка\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тестовая выборка\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
    "   df_oversampled,\n",
    "   stratify_colname=\"salary_category\", \n",
    "   frac_train=0.60, \n",
    "   frac_val=0.20, \n",
    "   frac_test=0.20\n",
    ")\n",
    "\n",
    "print('Тренировочная выборка')\n",
    "visualize_balance(df_train, 'salary_category')\n",
    "print('Контрольная выборка')\n",
    "visualize_balance(df_val, 'salary_category')\n",
    "print('Тестовая выборка')\n",
    "visualize_balance(df_test, 'salary_category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Датасет 2. Анализ продаж филиалов супермаркетов\n",
    "https://www.kaggle.com/datasets/surajjha101/stores-area-and-sales-data\n",
    "## Анализ сведений о датасете\n",
    "\n",
    "### **Проблемная область**  \n",
    "Датасет описывает производственные и экономические характеристики магазинов супермаркетов с целью анализа их деятельности и выявления факторов, влияющих на прибыльность. Задачи включают:\n",
    "- Оценку производительности магазинов;\n",
    "- Поиск факторов, которые могут улучшить прибыль и эффективность;\n",
    "- Определение взаимосвязи между различными характеристиками магазинов.\n",
    "\n",
    "### **Актуальность**  \n",
    "Анализ эффективности супермаркетов актуален в сфере розничной торговли, поскольку помогает:\n",
    "- Повышать прибыльность магазинов;\n",
    "- Улучшать распределение ресурсов (например, товаров или пространства);\n",
    "- Оптимизировать маркетинговые и операционные стратегии;\n",
    "- Оценивать влияние внешних факторов (например, площади магазина или ассортимента товаров) на продажи.\n",
    "\n",
    "### **Объекты наблюдений**  \n",
    "Объектами наблюдения являются **магазины супермаркетов**, каждый из которых представлен в датасете через уникальный идентификатор (Store ID). Для каждого магазина представлены различные параметры, которые отражают его физическую структуру и экономическую деятельность.\n",
    "\n",
    "### **Атрибуты объектов**  \n",
    "Каждое наблюдение (магазин) имеет следующие атрибуты:\n",
    "- **Store ID** — уникальный идентификатор магазина (индекс);\n",
    "- **Store_Area** — физическая площадь магазина в квадратных ярдах (меряет размер магазина);\n",
    "- **Items_Available** — количество различных товаров, доступных в магазине (ассортимент);\n",
    "- **Daily_Customer_Count** — среднее количество клиентов, посещающих магазин ежедневно (популярность);\n",
    "- **Store_Sales** — объем продаж магазина в долларах США (экономическая эффективность).\n",
    "\n",
    "### **Связь между объектами**  \n",
    "Связь между атрибутами объектов (магазинов) может быть следующей:\n",
    "- **Store_Area ↔ Items_Available**: Большее количество товаров может требовать большей площади для их размещения.\n",
    "- **Store_Area ↔ Store_Sales**: Большая площадь магазина может свидетельствовать о большем объеме продаж, поскольку позволяет разместить больше товаров и обслуживать больше клиентов.\n",
    "- **Items_Available ↔ Daily_Customer_Count**: Магазины с большим ассортиментом товаров могут привлекать больше клиентов, особенно если товары соответствуют потребительским ожиданиям.\n",
    "- **Daily_Customer_Count ↔ Store_Sales**: Прямая зависимость — большее количество клиентов может привести к большему объему продаж.\n",
    "\n",
    "Для дальнейшего анализа можно использовать корреляционные методы, чтобы понять, как различные факторы (площадь, ассортимент, количество клиентов) влияют на продажи.\n",
    "\n",
    "### Качество набора данных\n",
    "\n",
    "1. **Информативность**:  \n",
    "   Датасет содержит несколько ключевых атрибутов, которые отражают как физические характеристики магазинов, так и их экономическую эффективность. Эти атрибуты (площадь, ассортимент товаров, количество клиентов и продажи) достаточно информативны для начального анализа производительности супермаркетов.\n",
    "\n",
    "2. **Степень покрытия**:  \n",
    "   Датасет охватывает информацию по нескольким магазинам компании, однако он может не быть репрезентативным для всей розничной сети, так как данные собраны только для определенных магазинов с их уникальными характеристиками. Это может ограничить выводы, если не все магазины покрыты в данных.\n",
    "\n",
    "3. **Соответствие реальным данным**:  \n",
    "   Данные, представленные в датасете, соответствуют реальной практической ситуации, поскольку информация о площади магазинов, количестве товаров и клиентском потоке довольно типична для анализа розничных торговых точек.\n",
    "\n",
    "4. **Согласованность меток**:  \n",
    "   Метки данных (например, Store ID, Store_Area, Items_Available и т.д.) хорошо согласованы и имеют понятные и логичные наименования. Однако для полной уверенности в корректности данных потребуется проверка на наличие пропусков или аномалий (например, если площадь магазина или количество товаров кажется необычно низким или высоким).\n",
    "\n",
    "### Бизнес цели, которые может решить датасет:\n",
    "\n",
    "1. **Оптимизация ассортимента товаров и пространства**  \n",
    "   **Цель**: Разработать стратегию по оптимальному размещению товаров и выбору ассортимента в зависимости от площади магазина и его клиентской базы.  \n",
    "   **Эффект на бизнес**: Поможет увеличить продажи путем улучшения доступности популярных товаров и оптимизации использования пространства в магазинах.  \n",
    "   \n",
    "   **Цели технического проекта**:\n",
    "   - **Входные данные**: Площадь магазина, количество товаров, ежедневное количество клиентов.\n",
    "   - **Целевой признак**: Объем продаж (Store_Sales).\n",
    "\n",
    "2. **Увеличение продаж через улучшение привлечения клиентов**  \n",
    "   **Цель**: Разработать стратегию по увеличению потока клиентов в магазины на основе текущего количества покупателей и их корреляции с объемом продаж.  \n",
    "   **Эффект на бизнес**: Увеличение количества клиентов может прямо повлиять на рост продаж и прибыльность, особенно если будет применена стратегия привлечения дополнительного потока потребителей.  \n",
    "   \n",
    "   **Цели технического проекта**:\n",
    "   - **Входные данные**: Количество товаров в магазине, площадь магазина, среднее количество клиентов.\n",
    "   - **Целевой признак**: Объем продаж (Store_Sales).\n",
    "\n",
    "3. **Предсказание и управление производительностью магазинов**  \n",
    "   **Цель**: Оценить, какие факторы (площадь, ассортимент, количество клиентов) влияют на эффективность магазина и как прогнозировать его продажи в будущем.  \n",
    "   **Эффект на бизнес**: Ожидаемый результат — повышение точности прогнозов продаж и улучшение стратегического планирования для различных магазинов сети.  \n",
    "   \n",
    "   **Цели технического проекта**:\n",
    "   - **Входные данные**: Площадь магазина, количество товаров, ежедневное количество клиентов.\n",
    "   - **Целевой признак**: Объем продаж (Store_Sales).\n",
    "\n",
    "### Примеры целей технического проекта для каждой бизнес-цели:\n",
    "\n",
    "1. **Оптимизация ассортимента товаров и пространства**\n",
    "   - **Задача**: Построить модель, которая на основе площади магазина и ассортимента товаров будет предсказывать оптимальный объем продаж.\n",
    "   - **Вход**: Площадь магазина (Store_Area), Количество товаров (Items_Available).\n",
    "   - **Цель**: Прогнозировать объем продаж (Store_Sales).\n",
    "\n",
    "2. **Увеличение продаж через улучшение привлечения клиентов**\n",
    "   - **Задача**: Разработать алгоритм, который будет анализировать связи между количеством клиентов и продажами для оценки эффективности маркетинговых усилий.\n",
    "   - **Вход**: Среднее количество клиентов (Daily_Customer_Count), Количество товаров (Items_Available), Площадь магазина (Store_Area).\n",
    "   - **Цель**: Прогнозировать объем продаж (Store_Sales).\n",
    "\n",
    "3. **Предсказание и управление производительностью магазинов**\n",
    "   - **Задача**: Построить модель для предсказания объемов продаж на основе характеристик магазинов, чтобы заранее прогнозировать производительность и принимать меры по улучшению результатов.\n",
    "   - **Вход**: Площадь магазина (Store_Area), Среднее количество клиентов (Daily_Customer_Count), Количество товаров (Items_Available).\n",
    "   - **Цель**: Прогнозировать объем продаж (Store_Sales)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 896 entries, 0 to 895\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count  Dtype\n",
      "---  ------                --------------  -----\n",
      " 0   Store ID              896 non-null    int64\n",
      " 1   Store_Area            896 non-null    int64\n",
      " 2   Items_Available       896 non-null    int64\n",
      " 3   Daily_Customer_Count  896 non-null    int64\n",
      " 4   Store_Sales           896 non-null    int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 35.1 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store ID</th>\n",
       "      <td>896.0</td>\n",
       "      <td>448.500000</td>\n",
       "      <td>258.797218</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.75</td>\n",
       "      <td>448.5</td>\n",
       "      <td>672.25</td>\n",
       "      <td>896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store_Area</th>\n",
       "      <td>896.0</td>\n",
       "      <td>1485.409598</td>\n",
       "      <td>250.237011</td>\n",
       "      <td>775.0</td>\n",
       "      <td>1316.75</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>1653.50</td>\n",
       "      <td>2229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Items_Available</th>\n",
       "      <td>896.0</td>\n",
       "      <td>1782.035714</td>\n",
       "      <td>299.872053</td>\n",
       "      <td>932.0</td>\n",
       "      <td>1575.50</td>\n",
       "      <td>1773.5</td>\n",
       "      <td>1982.75</td>\n",
       "      <td>2667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily_Customer_Count</th>\n",
       "      <td>896.0</td>\n",
       "      <td>786.350446</td>\n",
       "      <td>265.389281</td>\n",
       "      <td>10.0</td>\n",
       "      <td>600.00</td>\n",
       "      <td>780.0</td>\n",
       "      <td>970.00</td>\n",
       "      <td>1560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store_Sales</th>\n",
       "      <td>896.0</td>\n",
       "      <td>59351.305804</td>\n",
       "      <td>17190.741895</td>\n",
       "      <td>14920.0</td>\n",
       "      <td>46530.00</td>\n",
       "      <td>58605.0</td>\n",
       "      <td>71872.50</td>\n",
       "      <td>116320.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count          mean           std      min       25%  \\\n",
       "Store ID              896.0    448.500000    258.797218      1.0    224.75   \n",
       "Store_Area            896.0   1485.409598    250.237011    775.0   1316.75   \n",
       "Items_Available       896.0   1782.035714    299.872053    932.0   1575.50   \n",
       "Daily_Customer_Count  896.0    786.350446    265.389281     10.0    600.00   \n",
       "Store_Sales           896.0  59351.305804  17190.741895  14920.0  46530.00   \n",
       "\n",
       "                          50%       75%       max  \n",
       "Store ID                448.5    672.25     896.0  \n",
       "Store_Area             1477.0   1653.50    2229.0  \n",
       "Items_Available        1773.5   1982.75    2667.0  \n",
       "Daily_Customer_Count    780.0    970.00    1560.0  \n",
       "Store_Sales           58605.0  71872.50  116320.0  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('csv/9.Stores.csv')\n",
    "df.info()\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим колонку с ID магазинов, она нам вряд ли понадобится"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 896 entries, 0 to 895\n",
      "Data columns (total 4 columns):\n",
      " #   Column                Non-Null Count  Dtype\n",
      "---  ------                --------------  -----\n",
      " 0   Store_Area            896 non-null    int64\n",
      " 1   Items_Available       896 non-null    int64\n",
      " 2   Daily_Customer_Count  896 non-null    int64\n",
      " 3   Store_Sales           896 non-null    int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 28.1 KB\n"
     ]
    }
   ],
   "source": [
    "if \"Store ID \" in df.columns:\n",
    "    df = df.drop(columns=[\"Store ID \"])\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Присутствуют ли пустые значения признаков в колонке:\n",
      "Store_Area              False\n",
      "Items_Available         False\n",
      "Daily_Customer_Count    False\n",
      "Store_Sales             False\n",
      "dtype: bool \n",
      "\n"
     ]
    }
   ],
   "source": [
    "check_null_columns(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим на наличие выбросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка Store_Area:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 5\n",
      "\tМинимальное значение: 775\n",
      "\tМаксимальное значение: 2229\n",
      "\t1-й квартиль (Q1): 1316.75\n",
      "\t3-й квартиль (Q3): 1653.5\n",
      "\n",
      "Колонка Items_Available:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 5\n",
      "\tМинимальное значение: 932\n",
      "\tМаксимальное значение: 2667\n",
      "\t1-й квартиль (Q1): 1575.5\n",
      "\t3-й квартиль (Q3): 1982.75\n",
      "\n",
      "Колонка Daily_Customer_Count:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 3\n",
      "\tМинимальное значение: 10\n",
      "\tМаксимальное значение: 1560\n",
      "\t1-й квартиль (Q1): 600.0\n",
      "\t3-й квартиль (Q3): 970.0\n",
      "\n",
      "Колонка Store_Sales:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 1\n",
      "\tМинимальное значение: 14920\n",
      "\tМаксимальное значение: 116320\n",
      "\t1-й квартиль (Q1): 46530.0\n",
      "\t3-й квартиль (Q3): 71872.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "columns_with_outliers = check_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Визуализируем выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Устраняем выбросы, если они имеются"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонки с выбросами:\n",
      "Store_Area\n",
      "Items_Available\n",
      "Daily_Customer_Count\n",
      "Store_Sales\n"
     ]
    }
   ],
   "source": [
    "df = remove_outliers(df, columns_with_outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим наличие выбросов и визуализируем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка Store_Area:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 811.625\n",
      "\tМаксимальное значение: 2158.625\n",
      "\t1-й квартиль (Q1): 1316.75\n",
      "\t3-й квартиль (Q3): 1653.5\n",
      "\n",
      "Колонка Items_Available:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 964.625\n",
      "\tМаксимальное значение: 2593.625\n",
      "\t1-й квартиль (Q1): 1575.5\n",
      "\t3-й квартиль (Q3): 1982.75\n",
      "\n",
      "Колонка Daily_Customer_Count:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 45.0\n",
      "\tМаксимальное значение: 1525.0\n",
      "\t1-й квартиль (Q1): 600.0\n",
      "\t3-й квартиль (Q3): 970.0\n",
      "\n",
      "Колонка Store_Sales:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 14920.0\n",
      "\tМаксимальное значение: 109886.25\n",
      "\t1-й квартиль (Q1): 46530.0\n",
      "\t3-й квартиль (Q3): 71872.5\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_outliers(df)\n",
    "visualize_outliers(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Датасет 3. Прогнозирование стоимости медицинского страхования\n",
    "https://www.kaggle.com/datasets/harishkumardatalab/medical-insurance-price-prediction\n",
    "## Анализ сведений о датасете\n",
    "\n",
    "### **Проблемная область**:  \n",
    "Задача прогнозирования медицинских расходов на основе различных факторов, влияющих на стоимость страхования. Это важно для компаний медицинского страхования для оптимизации ценообразования и управления рисками.\n",
    "\n",
    "### **Актуальность**:  \n",
    "Прогнозирование медицинских расходов является ключевым элементом для страховых компаний, чтобы правильно оценить риски, установить справедливые страховые взносы и обеспечить финансовую устойчивость компании. Актуальность такого анализа возрастает с увеличением потребности в персонализированном страховании.\n",
    "\n",
    "### **Объекты наблюдений**:  \n",
    "Каждый объект наблюдения представляет собой запись о человеке, который является клиентом медицинской страховой компании.\n",
    "\n",
    "### **Атрибуты объектов**:\n",
    "- **Age (возраст)** — числовой атрибут, показывает возраст клиента.\n",
    "- **Sex (пол)** — категориальный атрибут (мужчина/женщина), который может повлиять на тип медицинских услуг и расходы.\n",
    "- **BMI (индекс массы тела)** — числовой атрибут, который может быть важным для оценки здоровья клиента и возможных заболеваний.\n",
    "- **Children (дети)** — числовой атрибут, который может показывать потребность в медицинских услугах для детей.\n",
    "- **Smoker (курящий)** — булев атрибут, показывающий, является ли человек курильщиком, что влияет на его здоровье и расходы.\n",
    "- **Region (регион)** — текстовый атрибут, который может учитывать различия в стоимости медицинских услуг в разных регионах.\n",
    "- **Charges (расходы)** — целевой числовой атрибут, показывающий медицинские расходы, которые следует предсказать.\n",
    "\n",
    "### **Связь между объектами**:\n",
    "  Атрибуты данных взаимосвязаны. Например, возраст, ИМТ и курение могут быть связанными с увеличением медицинских расходов, так как старение и ожирение повышают риски заболеваний. Регион может определять базовый уровень расходов, а наличие детей может указывать на дополнительные расходы на медицинские услуги для детей.\n",
    "\n",
    "## Качество набора данных\n",
    "\n",
    "### **Информативность**:  \n",
    "Набор данных содержит важные параметры для оценки медицинских расходов, такие как возраст, ИМТ, статус курящего и наличие детей. Однако дополнительные параметры, такие как хронические заболевания, история медицинских визитов или история страховки, могут улучшить модель.\n",
    "\n",
    "### **Степень покрытия**:  \n",
    "Набор данных охватывает несколько ключевых факторов (возраст, пол, ИМТ, количество детей, курение, регион), которые являются важными для прогнозирования расходов. Однако для более точных прогнозов могут быть полезны дополнительные данные, такие как образ жизни или медицинская история.\n",
    "\n",
    "### **Соответствие реальным данным**:  \n",
    "Данные вполне могут соответствовать реальной ситуации в медицинском страховании, так как параметры, такие как курение, возраст и ИМТ, действительно влияют на здоровье и, следовательно, на расходы на лечение. Однако важно, чтобы данные были сбалансированы и не содержали искажений.\n",
    "\n",
    "### **Согласованность меток**:  \n",
    "Метки, такие как пол, курящий/не курящий, и регион, должны быть корректно представлены. Необходимо убедиться в отсутствии противоречий в данных (например, отсутствие значений для категориальных переменных или неверных числовых значений).\n",
    "\n",
    "## Бизнес-цели, которые может решить этот датасет\n",
    "\n",
    "1. **Оптимизация ценообразования на медицинское страхование**\n",
    "   - **Эффект на бизнес**: Компании смогут более точно оценивать потенциальные расходы на медицинские услуги для клиентов, что позволит устанавливать адекватные страховые взносы, минимизируя риски и обеспечивая прибыльность.\n",
    "\n",
    "2. **Оценка рисков клиентов**\n",
    "   - **Эффект на бизнес**: Страховые компании смогут выявлять группы клиентов с высоким риском, что поможет предсказать, какие клиенты могут потребовать больше затрат на лечение, и соответственно, предлагать им более высокие премии или дополнительные услуги.\n",
    "\n",
    "3. **Разработка персонализированных предложений для клиентов**\n",
    "   - **Эффект на бизнес**: Возможность предложить клиентам индивидуальные страховые планы и дополнительные услуги, основанные на их рисках и потребностях, повысит их удовлетворенность и лояльность, а также улучшит финансовые результаты компании.\n",
    "\n",
    "## Примеры целей технического проекта для каждой бизнес-цели\n",
    "\n",
    "1. **Оптимизация ценообразования на медицинское страхование**\n",
    "   - **Цель технического проекта**: Построить модель регрессии для прогнозирования медицинских расходов на основе демографических данных (возраст, пол, ИМТ, курение и т.д.).\n",
    "   - **Что поступает на вход**: Возраст, пол, ИМТ, количество детей, курение, регион.\n",
    "   - **Целевой признак**: Расходы (charges).\n",
    "\n",
    "2. **Оценка рисков клиентов**\n",
    "   - **Цель технического проекта**: Разработать модель классификации для оценки уровня риска клиента (низкий, средний, высокий риск).\n",
    "   - **Что поступает на вход**: Возраст, пол, ИМТ, количество детей, курение, регион.\n",
    "   - **Целевой признак**: Риск (классификация на категории: низкий, средний, высокий).\n",
    "\n",
    "3. **Разработка персонализированных предложений для клиентов**\n",
    "   - **Цель технического проекта**: Создать систему рекомендаций, которая будет предлагать персонализированные страховые планы и услуги на основе характеристик клиента.\n",
    "   - **Что поступает на вход**: Все атрибуты клиента (возраст, пол, ИМТ, дети, курение, регион).\n",
    "   - **Целевой признак**: Рекомендуемый план страхования или дополнительная услуга.\n",
    "\n",
    "Каждый из этих проектов направлен на повышение прибыльности компании, улучшение персонализированного подхода к клиентам и снижение финансовых рисков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2772 entries, 0 to 2771\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       2772 non-null   int64  \n",
      " 1   sex       2772 non-null   object \n",
      " 2   bmi       2772 non-null   float64\n",
      " 3   children  2772 non-null   int64  \n",
      " 4   smoker    2772 non-null   object \n",
      " 5   region    2772 non-null   object \n",
      " 6   charges   2772 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 151.7+ KB\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>2772.0</td>\n",
       "      <td>39.109668</td>\n",
       "      <td>14.081459</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>26.000</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>51.0000</td>\n",
       "      <td>64.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>2772.0</td>\n",
       "      <td>30.701349</td>\n",
       "      <td>6.129449</td>\n",
       "      <td>15.9600</td>\n",
       "      <td>26.220</td>\n",
       "      <td>30.44750</td>\n",
       "      <td>34.7700</td>\n",
       "      <td>53.13000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>2772.0</td>\n",
       "      <td>1.101732</td>\n",
       "      <td>1.214806</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charges</th>\n",
       "      <td>2772.0</td>\n",
       "      <td>13261.369959</td>\n",
       "      <td>12151.768945</td>\n",
       "      <td>1121.8739</td>\n",
       "      <td>4687.797</td>\n",
       "      <td>9333.01435</td>\n",
       "      <td>16577.7795</td>\n",
       "      <td>63770.42801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count          mean           std        min       25%         50%  \\\n",
       "age       2772.0     39.109668     14.081459    18.0000    26.000    39.00000   \n",
       "bmi       2772.0     30.701349      6.129449    15.9600    26.220    30.44750   \n",
       "children  2772.0      1.101732      1.214806     0.0000     0.000     1.00000   \n",
       "charges   2772.0  13261.369959  12151.768945  1121.8739  4687.797  9333.01435   \n",
       "\n",
       "                 75%          max  \n",
       "age          51.0000     64.00000  \n",
       "bmi          34.7700     53.13000  \n",
       "children      2.0000      5.00000  \n",
       "charges   16577.7795  63770.42801  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('csv/5.medical_insurance.csv')\n",
    "df.info()\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Присутствуют ли пустые значения признаков в колонке:\n",
      "age         False\n",
      "sex         False\n",
      "bmi         False\n",
      "children    False\n",
      "smoker      False\n",
      "region      False\n",
      "charges     False\n",
      "dtype: bool \n",
      "\n"
     ]
    }
   ],
   "source": [
    "check_null_columns(df)"
   ]
  }
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