1302 lines
48 KiB
Plaintext
1302 lines
48 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Лабораторная работа 2. Анализ нескольких датасетов."
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"### 1.Выбрать три набора данных, которые не соответствуют Вашему варианту задания\n",
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"Выбранны варианты: Данные по инсультам(Вариант 4), Продажи домов(Вариант 6), Цены на мобильные устройства (Вариант 18)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. Провести анализ сведений о каждом наборе данных со страницы загрузки в Kaggle. Какова проблемная область?\n",
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"\n",
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"#### Данные по инсультам:\n",
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"- **Проблемная область:** Анализ данных о пациентах с инсультом\n",
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"- **Цели:** Анализ данных о пациентах с инсультом, определение факторов, влияющих на исход лечения\n",
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"- **Набор данных:** 5111 записей, 12 переменных:\n",
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" - id\n",
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" - gender\n",
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" - age\n",
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" - hypertension\n",
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" - heart_disease\n",
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" - ever_married\n",
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" - work_type\n",
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" - residence_typr\n",
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" - avg_glucose_level\n",
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" - bmi\n",
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" - smoking_status\n",
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" - stroke\n",
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"- **Описание данных:** Сведения о пациентах с инсультом, их лечении и исходе лечения\n",
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"\n",
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"#### Продажи домов:\n",
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"- **Проблемная область:** Анализ продаж домов и их цен в зависисмости от различных факторов \n",
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"- **Цели:** Анализ продаж домов, определение факторов, влияющих на цены\n",
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"- **Набор данных:** 21614 записей, 21 переменная:\n",
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" - id\n",
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" - date\n",
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" - price\n",
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" - bedrooms\n",
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" - bathrooms\n",
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" - sqft_living\n",
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" - sqft_loft\n",
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" - floors\n",
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" - waterfront\n",
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" - view\n",
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" - condition\n",
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" - grade\n",
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" - sqft_above\n",
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" - sqft_basment\n",
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" - yr_build\n",
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" - yr_renovated\n",
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" - zipcode\n",
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" - lat\n",
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" - longsqft_living15\n",
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" - sqft_lot15\n",
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"- **Описание данных:** Сведения о проданных домах в King County, США\n",
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"\n",
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"#### Цены на мобильные устройства:\n",
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"- **Проблемная область:** Анализ цен на мобильные устройства\n",
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"- **Цели:** Анализ цен на мобильные устройства, определение факторов, влияющих на цены\n",
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"- **Набор данных:** 1371 записей, 18 переменных:\n",
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" - id\n",
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" - name\n",
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" - rating\n",
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" - spec_score\n",
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" - no_of_sim\n",
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" - ram\n",
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" - battery\n",
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" - camera\n",
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" - external_memory\n",
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" - android_version\n",
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" - price\n",
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" - company\n",
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" - inbuild_memory\n",
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" - fast_charging\n",
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" - screen_resolution\n",
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" - processor\n",
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" - processor_name\n",
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"- **Описание данных:** Сведения о ценах на мобильные устройства в зависимости от различных факторов"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Данные по инсультам:\n",
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"Каждая строка в датасете содержит соответствующую информацию о пациенте, что позволяет проводить анализ и строить модели для предсказания риска инсульта."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>gender</th>\n",
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" <th>age</th>\n",
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" <th>hypertension</th>\n",
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" <th>heart_disease</th>\n",
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||
" <th>ever_married</th>\n",
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" <th>work_type</th>\n",
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" <th>Residence_type</th>\n",
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" <th>avg_glucose_level</th>\n",
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" <th>bmi</th>\n",
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" <th>smoking_status</th>\n",
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" <th>stroke</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>9046</td>\n",
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" <td>Male</td>\n",
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" <td>67.0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>Yes</td>\n",
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" <td>Private</td>\n",
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" <td>Urban</td>\n",
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" <td>228.69</td>\n",
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" <td>36.6</td>\n",
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" <td>formerly smoked</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>51676</td>\n",
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" <td>Female</td>\n",
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||
" <td>61.0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>Yes</td>\n",
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||
" <td>Self-employed</td>\n",
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||
" <td>Rural</td>\n",
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||
" <td>202.21</td>\n",
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||
" <td>NaN</td>\n",
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||
" <td>never smoked</td>\n",
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||
" <td>1</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>2</th>\n",
|
||
" <td>31112</td>\n",
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||
" <td>Male</td>\n",
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||
" <td>80.0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>1</td>\n",
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||
" <td>Yes</td>\n",
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||
" <td>Private</td>\n",
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||
" <td>Rural</td>\n",
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||
" <td>105.92</td>\n",
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||
" <td>32.5</td>\n",
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||
" <td>never smoked</td>\n",
|
||
" <td>1</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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||
" <th>3</th>\n",
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||
" <td>60182</td>\n",
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||
" <td>Female</td>\n",
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||
" <td>49.0</td>\n",
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||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
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||
" <td>Yes</td>\n",
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||
" <td>Private</td>\n",
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||
" <td>Urban</td>\n",
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||
" <td>171.23</td>\n",
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||
" <td>34.4</td>\n",
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||
" <td>smokes</td>\n",
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||
" <td>1</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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||
" <td>1665</td>\n",
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||
" <td>Female</td>\n",
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||
" <td>79.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Self-employed</td>\n",
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||
" <td>Rural</td>\n",
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||
" <td>174.12</td>\n",
|
||
" <td>24.0</td>\n",
|
||
" <td>never smoked</td>\n",
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||
" <td>1</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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||
" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>5105</th>\n",
|
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" <td>18234</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>80.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Yes</td>\n",
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||
" <td>Private</td>\n",
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||
" <td>Urban</td>\n",
|
||
" <td>83.75</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>never smoked</td>\n",
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||
" <td>0</td>\n",
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||
" </tr>\n",
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" <tr>\n",
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" <th>5106</th>\n",
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" <td>44873</td>\n",
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" <td>Female</td>\n",
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||
" <td>81.0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>Yes</td>\n",
|
||
" <td>Self-employed</td>\n",
|
||
" <td>Urban</td>\n",
|
||
" <td>125.20</td>\n",
|
||
" <td>40.0</td>\n",
|
||
" <td>never smoked</td>\n",
|
||
" <td>0</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
|
||
" <th>5107</th>\n",
|
||
" <td>19723</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>35.0</td>\n",
|
||
" <td>0</td>\n",
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||
" <td>0</td>\n",
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||
" <td>Yes</td>\n",
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||
" <td>Self-employed</td>\n",
|
||
" <td>Rural</td>\n",
|
||
" <td>82.99</td>\n",
|
||
" <td>30.6</td>\n",
|
||
" <td>never smoked</td>\n",
|
||
" <td>0</td>\n",
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||
" </tr>\n",
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" <tr>\n",
|
||
" <th>5108</th>\n",
|
||
" <td>37544</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>51.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Private</td>\n",
|
||
" <td>Rural</td>\n",
|
||
" <td>166.29</td>\n",
|
||
" <td>25.6</td>\n",
|
||
" <td>formerly smoked</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5109</th>\n",
|
||
" <td>44679</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Yes</td>\n",
|
||
" <td>Govt_job</td>\n",
|
||
" <td>Urban</td>\n",
|
||
" <td>85.28</td>\n",
|
||
" <td>26.2</td>\n",
|
||
" <td>Unknown</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
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" </tbody>\n",
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"</table>\n",
|
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"<p>5110 rows × 12 columns</p>\n",
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"</div>"
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],
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"text/plain": [
|
||
" id gender age hypertension heart_disease ever_married \\\n",
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||
"0 9046 Male 67.0 0 1 Yes \n",
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||
"1 51676 Female 61.0 0 0 Yes \n",
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||
"2 31112 Male 80.0 0 1 Yes \n",
|
||
"3 60182 Female 49.0 0 0 Yes \n",
|
||
"4 1665 Female 79.0 1 0 Yes \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"5105 18234 Female 80.0 1 0 Yes \n",
|
||
"5106 44873 Female 81.0 0 0 Yes \n",
|
||
"5107 19723 Female 35.0 0 0 Yes \n",
|
||
"5108 37544 Male 51.0 0 0 Yes \n",
|
||
"5109 44679 Female 44.0 0 0 Yes \n",
|
||
"\n",
|
||
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
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||
"0 Private Urban 228.69 36.6 formerly smoked \n",
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||
"1 Self-employed Rural 202.21 NaN never smoked \n",
|
||
"2 Private Rural 105.92 32.5 never smoked \n",
|
||
"3 Private Urban 171.23 34.4 smokes \n",
|
||
"4 Self-employed Rural 174.12 24.0 never smoked \n",
|
||
"... ... ... ... ... ... \n",
|
||
"5105 Private Urban 83.75 NaN never smoked \n",
|
||
"5106 Self-employed Urban 125.20 40.0 never smoked \n",
|
||
"5107 Self-employed Rural 82.99 30.6 never smoked \n",
|
||
"5108 Private Rural 166.29 25.6 formerly smoked \n",
|
||
"5109 Govt_job Urban 85.28 26.2 Unknown \n",
|
||
"\n",
|
||
" stroke \n",
|
||
"0 1 \n",
|
||
"1 1 \n",
|
||
"2 1 \n",
|
||
"3 1 \n",
|
||
"4 1 \n",
|
||
"... ... \n",
|
||
"5105 0 \n",
|
||
"5106 0 \n",
|
||
"5107 0 \n",
|
||
"5108 0 \n",
|
||
"5109 0 \n",
|
||
"\n",
|
||
"[5110 rows x 12 columns]"
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]
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"var4 = pd.read_csv(\"./datasets/var4/healthcare-dataset-stroke-data.csv\")\n",
|
||
"\n",
|
||
"var4"
|
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]
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},
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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{
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"data": {
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"text/plain": [
|
||
"id int64\n",
|
||
"gender object\n",
|
||
"age float64\n",
|
||
"hypertension int64\n",
|
||
"heart_disease int64\n",
|
||
"ever_married object\n",
|
||
"work_type object\n",
|
||
"Residence_type object\n",
|
||
"avg_glucose_level float64\n",
|
||
"bmi float64\n",
|
||
"smoking_status object\n",
|
||
"stroke int64\n",
|
||
"dtype: object"
|
||
]
|
||
},
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"execution_count": 2,
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||
"metadata": {},
|
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"output_type": "execute_result"
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}
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],
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"source": [
|
||
"var4.dtypes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Продажи домов\n",
|
||
"Каждая строка в датасете содержит соответствующую информацию о доме, что позволяет проводить анализ и строить модели для предсказания его цены."
|
||
]
|
||
},
|
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{
|
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"cell_type": "code",
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"metadata": {},
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|
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" <th>id</th>\n",
|
||
" <th>date</th>\n",
|
||
" <th>price</th>\n",
|
||
" <th>bedrooms</th>\n",
|
||
" <th>bathrooms</th>\n",
|
||
" <th>sqft_living</th>\n",
|
||
" <th>sqft_lot</th>\n",
|
||
" <th>floors</th>\n",
|
||
" <th>waterfront</th>\n",
|
||
" <th>view</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>grade</th>\n",
|
||
" <th>sqft_above</th>\n",
|
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" <th>sqft_basement</th>\n",
|
||
" <th>yr_built</th>\n",
|
||
" <th>yr_renovated</th>\n",
|
||
" <th>zipcode</th>\n",
|
||
" <th>lat</th>\n",
|
||
" <th>long</th>\n",
|
||
" <th>sqft_living15</th>\n",
|
||
" <th>sqft_lot15</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>7129300520</td>\n",
|
||
" <td>20141013T000000</td>\n",
|
||
" <td>221900.0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1.00</td>\n",
|
||
" <td>1180</td>\n",
|
||
" <td>5650</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>1180</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1955</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98178</td>\n",
|
||
" <td>47.5112</td>\n",
|
||
" <td>-122.257</td>\n",
|
||
" <td>1340</td>\n",
|
||
" <td>5650</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>6414100192</td>\n",
|
||
" <td>20141209T000000</td>\n",
|
||
" <td>538000.0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2.25</td>\n",
|
||
" <td>2570</td>\n",
|
||
" <td>7242</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>2170</td>\n",
|
||
" <td>400</td>\n",
|
||
" <td>1951</td>\n",
|
||
" <td>1991</td>\n",
|
||
" <td>98125</td>\n",
|
||
" <td>47.7210</td>\n",
|
||
" <td>-122.319</td>\n",
|
||
" <td>1690</td>\n",
|
||
" <td>7639</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>5631500400</td>\n",
|
||
" <td>20150225T000000</td>\n",
|
||
" <td>180000.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1.00</td>\n",
|
||
" <td>770</td>\n",
|
||
" <td>10000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>770</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1933</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98028</td>\n",
|
||
" <td>47.7379</td>\n",
|
||
" <td>-122.233</td>\n",
|
||
" <td>2720</td>\n",
|
||
" <td>8062</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2487200875</td>\n",
|
||
" <td>20141209T000000</td>\n",
|
||
" <td>604000.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>1960</td>\n",
|
||
" <td>5000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>1050</td>\n",
|
||
" <td>910</td>\n",
|
||
" <td>1965</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98136</td>\n",
|
||
" <td>47.5208</td>\n",
|
||
" <td>-122.393</td>\n",
|
||
" <td>1360</td>\n",
|
||
" <td>5000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1954400510</td>\n",
|
||
" <td>20150218T000000</td>\n",
|
||
" <td>510000.0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2.00</td>\n",
|
||
" <td>1680</td>\n",
|
||
" <td>8080</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>1680</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1987</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98074</td>\n",
|
||
" <td>47.6168</td>\n",
|
||
" <td>-122.045</td>\n",
|
||
" <td>1800</td>\n",
|
||
" <td>7503</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21608</th>\n",
|
||
" <td>263000018</td>\n",
|
||
" <td>20140521T000000</td>\n",
|
||
" <td>360000.0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2.50</td>\n",
|
||
" <td>1530</td>\n",
|
||
" <td>1131</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>1530</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98103</td>\n",
|
||
" <td>47.6993</td>\n",
|
||
" <td>-122.346</td>\n",
|
||
" <td>1530</td>\n",
|
||
" <td>1509</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21609</th>\n",
|
||
" <td>6600060120</td>\n",
|
||
" <td>20150223T000000</td>\n",
|
||
" <td>400000.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>2.50</td>\n",
|
||
" <td>2310</td>\n",
|
||
" <td>5813</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>2310</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2014</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98146</td>\n",
|
||
" <td>47.5107</td>\n",
|
||
" <td>-122.362</td>\n",
|
||
" <td>1830</td>\n",
|
||
" <td>7200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21610</th>\n",
|
||
" <td>1523300141</td>\n",
|
||
" <td>20140623T000000</td>\n",
|
||
" <td>402101.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.75</td>\n",
|
||
" <td>1020</td>\n",
|
||
" <td>1350</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>1020</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2009</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98144</td>\n",
|
||
" <td>47.5944</td>\n",
|
||
" <td>-122.299</td>\n",
|
||
" <td>1020</td>\n",
|
||
" <td>2007</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21611</th>\n",
|
||
" <td>291310100</td>\n",
|
||
" <td>20150116T000000</td>\n",
|
||
" <td>400000.0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2.50</td>\n",
|
||
" <td>1600</td>\n",
|
||
" <td>2388</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>1600</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2004</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98027</td>\n",
|
||
" <td>47.5345</td>\n",
|
||
" <td>-122.069</td>\n",
|
||
" <td>1410</td>\n",
|
||
" <td>1287</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21612</th>\n",
|
||
" <td>1523300157</td>\n",
|
||
" <td>20141015T000000</td>\n",
|
||
" <td>325000.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.75</td>\n",
|
||
" <td>1020</td>\n",
|
||
" <td>1076</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>1020</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2008</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>98144</td>\n",
|
||
" <td>47.5941</td>\n",
|
||
" <td>-122.299</td>\n",
|
||
" <td>1020</td>\n",
|
||
" <td>1357</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>21613 rows × 21 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" id date price bedrooms bathrooms \\\n",
|
||
"0 7129300520 20141013T000000 221900.0 3 1.00 \n",
|
||
"1 6414100192 20141209T000000 538000.0 3 2.25 \n",
|
||
"2 5631500400 20150225T000000 180000.0 2 1.00 \n",
|
||
"3 2487200875 20141209T000000 604000.0 4 3.00 \n",
|
||
"4 1954400510 20150218T000000 510000.0 3 2.00 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"21608 263000018 20140521T000000 360000.0 3 2.50 \n",
|
||
"21609 6600060120 20150223T000000 400000.0 4 2.50 \n",
|
||
"21610 1523300141 20140623T000000 402101.0 2 0.75 \n",
|
||
"21611 291310100 20150116T000000 400000.0 3 2.50 \n",
|
||
"21612 1523300157 20141015T000000 325000.0 2 0.75 \n",
|
||
"\n",
|
||
" sqft_living sqft_lot floors waterfront view ... grade \\\n",
|
||
"0 1180 5650 1.0 0 0 ... 7 \n",
|
||
"1 2570 7242 2.0 0 0 ... 7 \n",
|
||
"2 770 10000 1.0 0 0 ... 6 \n",
|
||
"3 1960 5000 1.0 0 0 ... 7 \n",
|
||
"4 1680 8080 1.0 0 0 ... 8 \n",
|
||
"... ... ... ... ... ... ... ... \n",
|
||
"21608 1530 1131 3.0 0 0 ... 8 \n",
|
||
"21609 2310 5813 2.0 0 0 ... 8 \n",
|
||
"21610 1020 1350 2.0 0 0 ... 7 \n",
|
||
"21611 1600 2388 2.0 0 0 ... 8 \n",
|
||
"21612 1020 1076 2.0 0 0 ... 7 \n",
|
||
"\n",
|
||
" sqft_above sqft_basement yr_built yr_renovated zipcode lat \\\n",
|
||
"0 1180 0 1955 0 98178 47.5112 \n",
|
||
"1 2170 400 1951 1991 98125 47.7210 \n",
|
||
"2 770 0 1933 0 98028 47.7379 \n",
|
||
"3 1050 910 1965 0 98136 47.5208 \n",
|
||
"4 1680 0 1987 0 98074 47.6168 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"21608 1530 0 2009 0 98103 47.6993 \n",
|
||
"21609 2310 0 2014 0 98146 47.5107 \n",
|
||
"21610 1020 0 2009 0 98144 47.5944 \n",
|
||
"21611 1600 0 2004 0 98027 47.5345 \n",
|
||
"21612 1020 0 2008 0 98144 47.5941 \n",
|
||
"\n",
|
||
" long sqft_living15 sqft_lot15 \n",
|
||
"0 -122.257 1340 5650 \n",
|
||
"1 -122.319 1690 7639 \n",
|
||
"2 -122.233 2720 8062 \n",
|
||
"3 -122.393 1360 5000 \n",
|
||
"4 -122.045 1800 7503 \n",
|
||
"... ... ... ... \n",
|
||
"21608 -122.346 1530 1509 \n",
|
||
"21609 -122.362 1830 7200 \n",
|
||
"21610 -122.299 1020 2007 \n",
|
||
"21611 -122.069 1410 1287 \n",
|
||
"21612 -122.299 1020 1357 \n",
|
||
"\n",
|
||
"[21613 rows x 21 columns]"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"var6 = pd.read_csv(\"./datasets/var6/kc_house_data.csv\")\n",
|
||
"var6"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"id int64\n",
|
||
"date object\n",
|
||
"price float64\n",
|
||
"bedrooms int64\n",
|
||
"bathrooms float64\n",
|
||
"sqft_living int64\n",
|
||
"sqft_lot int64\n",
|
||
"floors float64\n",
|
||
"waterfront int64\n",
|
||
"view int64\n",
|
||
"condition int64\n",
|
||
"grade int64\n",
|
||
"sqft_above int64\n",
|
||
"sqft_basement int64\n",
|
||
"yr_built int64\n",
|
||
"yr_renovated int64\n",
|
||
"zipcode int64\n",
|
||
"lat float64\n",
|
||
"long float64\n",
|
||
"sqft_living15 int64\n",
|
||
"sqft_lot15 int64\n",
|
||
"dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"var6.dtypes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Цены на мобильные устройства\n",
|
||
"Каждая строка в датасете содержит соответствующую информацию о мобильном устройстве, что позволяет проводить анализ и строить модели для предсказания его цены."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Unnamed: 0</th>\n",
|
||
" <th>Name</th>\n",
|
||
" <th>Rating</th>\n",
|
||
" <th>Spec_score</th>\n",
|
||
" <th>No_of_sim</th>\n",
|
||
" <th>Ram</th>\n",
|
||
" <th>Battery</th>\n",
|
||
" <th>Display</th>\n",
|
||
" <th>Camera</th>\n",
|
||
" <th>External_Memory</th>\n",
|
||
" <th>Android_version</th>\n",
|
||
" <th>Price</th>\n",
|
||
" <th>company</th>\n",
|
||
" <th>Inbuilt_memory</th>\n",
|
||
" <th>fast_charging</th>\n",
|
||
" <th>Screen_resolution</th>\n",
|
||
" <th>Processor</th>\n",
|
||
" <th>Processor_name</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Samsung Galaxy F14 5G</td>\n",
|
||
" <td>4.65</td>\n",
|
||
" <td>68</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, 5G, VoLTE,</td>\n",
|
||
" <td>4 GB RAM</td>\n",
|
||
" <td>6000 mAh Battery</td>\n",
|
||
" <td>6.6 inches</td>\n",
|
||
" <td>50 MP + 2 MP Dual Rear &amp; 13 MP Front Camera</td>\n",
|
||
" <td>Memory Card Supported, upto 1 TB</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>9,999</td>\n",
|
||
" <td>Samsung</td>\n",
|
||
" <td>128 GB inbuilt</td>\n",
|
||
" <td>25W Fast Charging</td>\n",
|
||
" <td>2408 x 1080 px Display with Water Drop Notch</td>\n",
|
||
" <td>Octa Core Processor</td>\n",
|
||
" <td>Exynos 1330</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Samsung Galaxy A11</td>\n",
|
||
" <td>4.20</td>\n",
|
||
" <td>63</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
|
||
" <td>2 GB RAM</td>\n",
|
||
" <td>4000 mAh Battery</td>\n",
|
||
" <td>6.4 inches</td>\n",
|
||
" <td>13 MP + 5 MP + 2 MP Triple Rear &amp; 8 MP Fro...</td>\n",
|
||
" <td>Memory Card Supported, upto 512 GB</td>\n",
|
||
" <td>10</td>\n",
|
||
" <td>9,990</td>\n",
|
||
" <td>Samsung</td>\n",
|
||
" <td>32 GB inbuilt</td>\n",
|
||
" <td>15W Fast Charging</td>\n",
|
||
" <td>720 x 1560 px Display with Punch Hole</td>\n",
|
||
" <td>1.8 GHz Processor</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Samsung Galaxy A13</td>\n",
|
||
" <td>4.30</td>\n",
|
||
" <td>75</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
|
||
" <td>4 GB RAM</td>\n",
|
||
" <td>5000 mAh Battery</td>\n",
|
||
" <td>6.6 inches</td>\n",
|
||
" <td>50 MP Quad Rear &amp; 8 MP Front Camera</td>\n",
|
||
" <td>Memory Card Supported, upto 1 TB</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>11,999</td>\n",
|
||
" <td>Samsung</td>\n",
|
||
" <td>64 GB inbuilt</td>\n",
|
||
" <td>25W Fast Charging</td>\n",
|
||
" <td>1080 x 2408 px Display with Water Drop Notch</td>\n",
|
||
" <td>2 GHz Processor</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Samsung Galaxy F23</td>\n",
|
||
" <td>4.10</td>\n",
|
||
" <td>73</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
|
||
" <td>4 GB RAM</td>\n",
|
||
" <td>6000 mAh Battery</td>\n",
|
||
" <td>6.4 inches</td>\n",
|
||
" <td>48 MP Quad Rear &amp; 13 MP Front Camera</td>\n",
|
||
" <td>Memory Card Supported, upto 1 TB</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>11,999</td>\n",
|
||
" <td>Samsung</td>\n",
|
||
" <td>64 GB inbuilt</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>720 x 1600 px</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" <td>Helio G88</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>Samsung Galaxy A03s (4GB RAM + 64GB)</td>\n",
|
||
" <td>4.10</td>\n",
|
||
" <td>69</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
|
||
" <td>4 GB RAM</td>\n",
|
||
" <td>5000 mAh Battery</td>\n",
|
||
" <td>6.5 inches</td>\n",
|
||
" <td>13 MP + 2 MP + 2 MP Triple Rear &amp; 5 MP Fro...</td>\n",
|
||
" <td>Memory Card Supported, upto 1 TB</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>11,999</td>\n",
|
||
" <td>Samsung</td>\n",
|
||
" <td>64 GB inbuilt</td>\n",
|
||
" <td>15W Fast Charging</td>\n",
|
||
" <td>720 x 1600 px Display with Water Drop Notch</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" <td>Helio P35</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1365</th>\n",
|
||
" <td>1365</td>\n",
|
||
" <td>TCL 40R</td>\n",
|
||
" <td>4.05</td>\n",
|
||
" <td>75</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, 5G, VoLTE,</td>\n",
|
||
" <td>4 GB RAM</td>\n",
|
||
" <td>5000 mAh Battery</td>\n",
|
||
" <td>6.6 inches</td>\n",
|
||
" <td>50 MP + 2 MP + 2 MP Triple Rear &amp; 8 MP Fro...</td>\n",
|
||
" <td>Memory Card (Hybrid)</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>18,999</td>\n",
|
||
" <td>TCL</td>\n",
|
||
" <td>64 GB inbuilt</td>\n",
|
||
" <td>15W Fast Charging</td>\n",
|
||
" <td>720 x 1612 px</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" <td>Dimensity 700 5G</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1366</th>\n",
|
||
" <td>1366</td>\n",
|
||
" <td>TCL 50 XL NxtPaper 5G</td>\n",
|
||
" <td>4.10</td>\n",
|
||
" <td>80</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, VoLTE,</td>\n",
|
||
" <td>8 GB RAM</td>\n",
|
||
" <td>5000 mAh Battery</td>\n",
|
||
" <td>6.8 inches</td>\n",
|
||
" <td>50 MP + 2 MP Dual Rear &amp; 16 MP Front Camera</td>\n",
|
||
" <td>Memory Card (Hybrid)</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>24,990</td>\n",
|
||
" <td>TCL</td>\n",
|
||
" <td>128 GB inbuilt</td>\n",
|
||
" <td>33W Fast Charging</td>\n",
|
||
" <td>1200 x 2400 px</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" <td>Dimensity 7050</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1367</th>\n",
|
||
" <td>1367</td>\n",
|
||
" <td>TCL 50 XE NxtPaper 5G</td>\n",
|
||
" <td>4.00</td>\n",
|
||
" <td>80</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, 5G, VoLTE,</td>\n",
|
||
" <td>6 GB RAM</td>\n",
|
||
" <td>5000 mAh Battery</td>\n",
|
||
" <td>6.6 inches</td>\n",
|
||
" <td>50 MP + 2 MP Dual Rear &amp; 16 MP Front Camera</td>\n",
|
||
" <td>Memory Card Supported, upto 1 TB</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>23,990</td>\n",
|
||
" <td>TCL</td>\n",
|
||
" <td>256 GB inbuilt</td>\n",
|
||
" <td>18W Fast Charging</td>\n",
|
||
" <td>720 x 1612 px</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" <td>Dimensity 6080</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1368</th>\n",
|
||
" <td>1368</td>\n",
|
||
" <td>TCL 40 NxtPaper 5G</td>\n",
|
||
" <td>4.50</td>\n",
|
||
" <td>79</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, 5G, VoLTE,</td>\n",
|
||
" <td>6 GB RAM</td>\n",
|
||
" <td>5000 mAh Battery</td>\n",
|
||
" <td>6.6 inches</td>\n",
|
||
" <td>50 MP + 2 MP + 2 MP Triple Rear &amp; 8 MP Fro...</td>\n",
|
||
" <td>Memory Card Supported, upto 1 TB</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>22,499</td>\n",
|
||
" <td>TCL</td>\n",
|
||
" <td>256 GB inbuilt</td>\n",
|
||
" <td>15W Fast Charging</td>\n",
|
||
" <td>720 x 1612 px</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" <td>Dimensity 6020</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1369</th>\n",
|
||
" <td>1369</td>\n",
|
||
" <td>TCL Trifold</td>\n",
|
||
" <td>4.65</td>\n",
|
||
" <td>93</td>\n",
|
||
" <td>Dual Sim, 3G, 4G, 5G, VoLTE, Vo5G,</td>\n",
|
||
" <td>12 GB RAM</td>\n",
|
||
" <td>4600 mAh Battery</td>\n",
|
||
" <td>10 inches</td>\n",
|
||
" <td>Foldable Display, Dual Display</td>\n",
|
||
" <td>50 MP + 48 MP + 8 MP Triple Rear &amp; 32 MP F...</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>1,19,990</td>\n",
|
||
" <td>TCL</td>\n",
|
||
" <td>256 GB inbuilt</td>\n",
|
||
" <td>67W Fast Charging</td>\n",
|
||
" <td>1916 x 2160 px</td>\n",
|
||
" <td>Octa Core</td>\n",
|
||
" <td>Snapdragon 8 Gen2</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>1370 rows × 18 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Unnamed: 0 Name Rating Spec_score \\\n",
|
||
"0 0 Samsung Galaxy F14 5G 4.65 68 \n",
|
||
"1 1 Samsung Galaxy A11 4.20 63 \n",
|
||
"2 2 Samsung Galaxy A13 4.30 75 \n",
|
||
"3 3 Samsung Galaxy F23 4.10 73 \n",
|
||
"4 4 Samsung Galaxy A03s (4GB RAM + 64GB) 4.10 69 \n",
|
||
"... ... ... ... ... \n",
|
||
"1365 1365 TCL 40R 4.05 75 \n",
|
||
"1366 1366 TCL 50 XL NxtPaper 5G 4.10 80 \n",
|
||
"1367 1367 TCL 50 XE NxtPaper 5G 4.00 80 \n",
|
||
"1368 1368 TCL 40 NxtPaper 5G 4.50 79 \n",
|
||
"1369 1369 TCL Trifold 4.65 93 \n",
|
||
"\n",
|
||
" No_of_sim Ram Battery \\\n",
|
||
"0 Dual Sim, 3G, 4G, 5G, VoLTE, 4 GB RAM 6000 mAh Battery \n",
|
||
"1 Dual Sim, 3G, 4G, VoLTE, 2 GB RAM 4000 mAh Battery \n",
|
||
"2 Dual Sim, 3G, 4G, VoLTE, 4 GB RAM 5000 mAh Battery \n",
|
||
"3 Dual Sim, 3G, 4G, VoLTE, 4 GB RAM 6000 mAh Battery \n",
|
||
"4 Dual Sim, 3G, 4G, VoLTE, 4 GB RAM 5000 mAh Battery \n",
|
||
"... ... ... ... \n",
|
||
"1365 Dual Sim, 3G, 4G, 5G, VoLTE, 4 GB RAM 5000 mAh Battery \n",
|
||
"1366 Dual Sim, 3G, 4G, VoLTE, 8 GB RAM 5000 mAh Battery \n",
|
||
"1367 Dual Sim, 3G, 4G, 5G, VoLTE, 6 GB RAM 5000 mAh Battery \n",
|
||
"1368 Dual Sim, 3G, 4G, 5G, VoLTE, 6 GB RAM 5000 mAh Battery \n",
|
||
"1369 Dual Sim, 3G, 4G, 5G, VoLTE, Vo5G, 12 GB RAM 4600 mAh Battery \n",
|
||
"\n",
|
||
" Display Camera \\\n",
|
||
"0 6.6 inches 50 MP + 2 MP Dual Rear & 13 MP Front Camera \n",
|
||
"1 6.4 inches 13 MP + 5 MP + 2 MP Triple Rear & 8 MP Fro... \n",
|
||
"2 6.6 inches 50 MP Quad Rear & 8 MP Front Camera \n",
|
||
"3 6.4 inches 48 MP Quad Rear & 13 MP Front Camera \n",
|
||
"4 6.5 inches 13 MP + 2 MP + 2 MP Triple Rear & 5 MP Fro... \n",
|
||
"... ... ... \n",
|
||
"1365 6.6 inches 50 MP + 2 MP + 2 MP Triple Rear & 8 MP Fro... \n",
|
||
"1366 6.8 inches 50 MP + 2 MP Dual Rear & 16 MP Front Camera \n",
|
||
"1367 6.6 inches 50 MP + 2 MP Dual Rear & 16 MP Front Camera \n",
|
||
"1368 6.6 inches 50 MP + 2 MP + 2 MP Triple Rear & 8 MP Fro... \n",
|
||
"1369 10 inches Foldable Display, Dual Display \n",
|
||
"\n",
|
||
" External_Memory Android_version \\\n",
|
||
"0 Memory Card Supported, upto 1 TB 13 \n",
|
||
"1 Memory Card Supported, upto 512 GB 10 \n",
|
||
"2 Memory Card Supported, upto 1 TB 12 \n",
|
||
"3 Memory Card Supported, upto 1 TB 12 \n",
|
||
"4 Memory Card Supported, upto 1 TB 11 \n",
|
||
"... ... ... \n",
|
||
"1365 Memory Card (Hybrid) 12 \n",
|
||
"1366 Memory Card (Hybrid) 14 \n",
|
||
"1367 Memory Card Supported, upto 1 TB 13 \n",
|
||
"1368 Memory Card Supported, upto 1 TB 13 \n",
|
||
"1369 50 MP + 48 MP + 8 MP Triple Rear & 32 MP F... 13 \n",
|
||
"\n",
|
||
" Price company Inbuilt_memory fast_charging \\\n",
|
||
"0 9,999 Samsung 128 GB inbuilt 25W Fast Charging \n",
|
||
"1 9,990 Samsung 32 GB inbuilt 15W Fast Charging \n",
|
||
"2 11,999 Samsung 64 GB inbuilt 25W Fast Charging \n",
|
||
"3 11,999 Samsung 64 GB inbuilt NaN \n",
|
||
"4 11,999 Samsung 64 GB inbuilt 15W Fast Charging \n",
|
||
"... ... ... ... ... \n",
|
||
"1365 18,999 TCL 64 GB inbuilt 15W Fast Charging \n",
|
||
"1366 24,990 TCL 128 GB inbuilt 33W Fast Charging \n",
|
||
"1367 23,990 TCL 256 GB inbuilt 18W Fast Charging \n",
|
||
"1368 22,499 TCL 256 GB inbuilt 15W Fast Charging \n",
|
||
"1369 1,19,990 TCL 256 GB inbuilt 67W Fast Charging \n",
|
||
"\n",
|
||
" Screen_resolution Processor \\\n",
|
||
"0 2408 x 1080 px Display with Water Drop Notch Octa Core Processor \n",
|
||
"1 720 x 1560 px Display with Punch Hole 1.8 GHz Processor \n",
|
||
"2 1080 x 2408 px Display with Water Drop Notch 2 GHz Processor \n",
|
||
"3 720 x 1600 px Octa Core \n",
|
||
"4 720 x 1600 px Display with Water Drop Notch Octa Core \n",
|
||
"... ... ... \n",
|
||
"1365 720 x 1612 px Octa Core \n",
|
||
"1366 1200 x 2400 px Octa Core \n",
|
||
"1367 720 x 1612 px Octa Core \n",
|
||
"1368 720 x 1612 px Octa Core \n",
|
||
"1369 1916 x 2160 px Octa Core \n",
|
||
"\n",
|
||
" Processor_name \n",
|
||
"0 Exynos 1330 \n",
|
||
"1 Octa Core \n",
|
||
"2 Octa Core \n",
|
||
"3 Helio G88 \n",
|
||
"4 Helio P35 \n",
|
||
"... ... \n",
|
||
"1365 Dimensity 700 5G \n",
|
||
"1366 Dimensity 7050 \n",
|
||
"1367 Dimensity 6080 \n",
|
||
"1368 Dimensity 6020 \n",
|
||
"1369 Snapdragon 8 Gen2 \n",
|
||
"\n",
|
||
"[1370 rows x 18 columns]"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"var18 = pd.read_csv(\"./datasets/var18/mobile_phone_price_prediction.csv\")\n",
|
||
"var18"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Unnamed: 0 int64\n",
|
||
"Name object\n",
|
||
"Rating float64\n",
|
||
"Spec_score int64\n",
|
||
"No_of_sim object\n",
|
||
"Ram object\n",
|
||
"Battery object\n",
|
||
"Display object\n",
|
||
"Camera object\n",
|
||
"External_Memory object\n",
|
||
"Android_version object\n",
|
||
"Price object\n",
|
||
"company object\n",
|
||
"Inbuilt_memory object\n",
|
||
"fast_charging object\n",
|
||
"Screen_resolution object\n",
|
||
"Processor object\n",
|
||
"Processor_name object\n",
|
||
"dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"var18.dtypes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 3. Провести анализ содержимого каждого набора данных. Что является объектом/объектами наблюдения? Каковы атрибуты объектов? Есть ли связи между объектами?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": []
|
||
}
|
||
],
|
||
"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
|
||
}
|