1573 lines
374 KiB
Plaintext
1573 lines
374 KiB
Plaintext
{
|
||
"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": 3,
|
||
"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": 83,
|
||
"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": 83,
|
||
"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": 84,
|
||
"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": 85,
|
||
"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": 86,
|
||
"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": 87,
|
||
"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": 88,
|
||
"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": 89,
|
||
"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": 90,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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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": 91,
|
||
"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": 92,
|
||
"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": 93,
|
||
"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": 94,
|
||
"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": 95,
|
||
"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": 96,
|
||
"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": 105,
|
||
"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": 109,
|
||
"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": 111,
|
||
"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": null,
|
||
"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": 116,
|
||
"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": 62,
|
||
"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": 62,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = pd.read_csv('csv/9.Stores.csv')\n",
|
||
"df.info()\n",
|
||
"df.describe().transpose()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Присутствуют ли пустые значения признаков в колонке:\n",
|
||
"Store ID False\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": [
|
||
"# 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": 64,
|
||
"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": {
|
||
"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>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": 64,
|
||
"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": 65,
|
||
"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)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|