Lab_3 #4
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2
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@ -16,3 +16,5 @@ static/csv/diabetes.csv
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static/csv/healthcare-dataset-stroke-data.csv
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static/csv/heart_2020_cleaned.csv
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static/csv/neo_v2.csv
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static/csv/Yamana_Gold_Inc._AUY.csv
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static/csv/AgeDataset-V1.csv
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730
Lab_3/lab3.ipynb
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730
Lab_3/lab3.ipynb
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@ -0,0 +1,730 @@
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{
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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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" ## Вариант 13 \n",
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" https://www.kaggle.com/datasets/nancyalaswad90/yamana-gold-inc-stock-price?resource=download\n"
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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": 105,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume',\n",
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" 'Day_of_week', 'Month', 'Year'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 105,
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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": [
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"import pandas as pd\n",
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"\n",
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"data = pd.read_csv(\"..//static//csv//Yamana_Gold_Inc._AUY.csv\", sep=\",\")\n",
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"\n",
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"# Преобразование даты\n",
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"data['Date'] = pd.to_datetime(data['Date'])\n",
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"\n",
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"# Преобразование данных: создание новых признаков\n",
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"data['Day_of_week'] = data['Date'].dt.dayofweek\n",
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"data['Month'] = data['Date'].dt.month\n",
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"data['Year'] = data['Date'].dt.year\n",
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"data .columns"
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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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"#1. Бизнес-цели для набора данных по акции компании Yamana Gold Inc.\n",
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"Цель 1: Прогнозирование изменения цены акции компании.\n",
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"Прогнозирование цен на акции является одной из ключевых задач в области финансов и инвестирования. Задача состоит в предсказании будущих изменений стоимости акции на основе исторических данных, таких как открытие и закрытие торгов, объемы торгов и другие показатели.\n",
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"\n",
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"Цель 2: Оценка волатильности акций компании.\n",
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"Измерение волатильности позволяет инвесторам оценить риск и принять решения по управлению капиталом. Задача заключается в прогнозировании уровня волатильности на основе исторической динамики цен, объемов торгов и других рыночных факторов.\n",
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"\n",
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"#2. Цели технического проекта для каждой бизнес-цели\n",
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"Цель 1: Прогнозирование изменения цены акции компании\n",
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"\n",
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"Разработать модель машинного обучения для прогнозирования будущих цен акций на основе исторических данных.\n",
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"Использовать регрессионные модели, такие как линейная регрессия или более сложные модели, например, LSTM (долгосрочная краткосрочная память) для временных рядов.\n",
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"Цель 2: Оценка волатильности акций компании\n",
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"\n",
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"Создать модель, которая будет прогнозировать волатильность на основе исторических данных о ценах.\n",
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"Использовать методы статистического анализа, такие как вычисление стандартного отклонения, или методы машинного обучения для более точной оценки волатильности.\n",
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"\n",
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"#3 Проверим датасет на пропуски и удалим при необходимости строки с недостающими данными"
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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": 106,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Date 0\n",
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"Open 0\n",
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"High 0\n",
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"Low 0\n",
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"Close 0\n",
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"Adj Close 0\n",
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"Volume 0\n",
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"Day_of_week 0\n",
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"Month 0\n",
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"Year 0\n",
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"dtype: int64"
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]
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},
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"execution_count": 106,
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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": [
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"\n",
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"# Проверим на пропущенные значения\n",
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"data.isnull().sum()\n",
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"\n",
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"# Заполним пропуски или удалим строки с пропусками\n",
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"data = data.dropna()\n",
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"\n",
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"# Проверим, что данные очищены\n",
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"data.isnull().sum()\n",
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"\n"
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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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"Конструирование признаков"
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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": 107,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Date</th>\n",
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" <th>Open</th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Close</th>\n",
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" <th>Adj Close</th>\n",
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" <th>Volume</th>\n",
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" <th>Day_of_week</th>\n",
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" <th>Month</th>\n",
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" <th>Year</th>\n",
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" <th>Price_Change</th>\n",
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" <th>SMA_5</th>\n",
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" <th>SMA_20</th>\n",
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" <th>STD_5</th>\n",
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" <th>STD_20</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>2001-06-22</td>\n",
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" <td>3.428571</td>\n",
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" <td>3.428571</td>\n",
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" <td>3.428571</td>\n",
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" <td>3.428571</td>\n",
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" <td>2.806002</td>\n",
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" <td>0</td>\n",
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" <td>4</td>\n",
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" <td>6</td>\n",
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" <td>2001</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</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>2001-06-25</td>\n",
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" <td>3.428571</td>\n",
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" <td>3.428571</td>\n",
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" <td>3.428571</td>\n",
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" <td>3.428571</td>\n",
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" <td>2.806002</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>6</td>\n",
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" <td>2001</td>\n",
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" <td>0.000000</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2001-06-26</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.039837</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>6</td>\n",
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" <td>2001</td>\n",
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" <td>0.285715</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</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>2001-06-27</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.039837</td>\n",
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" <td>0</td>\n",
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" <td>2</td>\n",
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" <td>6</td>\n",
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" <td>2001</td>\n",
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" <td>0.000000</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</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>2001-06-28</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.714286</td>\n",
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" <td>3.039837</td>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>6</td>\n",
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" <td>2001</td>\n",
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" <td>0.000000</td>\n",
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" <td>3.6</td>\n",
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" <td>NaN</td>\n",
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" <td>0.156493</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Date Open High Low Close Adj Close Volume \\\n",
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"0 2001-06-22 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n",
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"1 2001-06-25 3.428571 3.428571 3.428571 3.428571 2.806002 0 \n",
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"2 2001-06-26 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n",
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"3 2001-06-27 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n",
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"4 2001-06-28 3.714286 3.714286 3.714286 3.714286 3.039837 0 \n",
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"\n",
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" Day_of_week Month Year Price_Change SMA_5 SMA_20 STD_5 STD_20 \n",
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"0 4 6 2001 NaN NaN NaN NaN NaN \n",
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"1 0 6 2001 0.000000 NaN NaN NaN NaN \n",
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"2 1 6 2001 0.285715 NaN NaN NaN NaN \n",
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"3 2 6 2001 0.000000 NaN NaN NaN NaN \n",
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"4 3 6 2001 0.000000 3.6 NaN 0.156493 NaN "
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]
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},
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"execution_count": 107,
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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": [
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"# Создаем новый признак - разницу между текущей и предыдущей ценой (Price_Change)\n",
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"data['Price_Change'] = data['Close'].diff()\n",
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"\n",
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"# Создадим скользящие средние для 5 и 20 дней\n",
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"data['SMA_5'] = data['Close'].rolling(window=5).mean()\n",
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"data['SMA_20'] = data['Close'].rolling(window=20).mean()\n",
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"\n",
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"# Стандартное отклонение для 5 и 20 дней\n",
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"data['STD_5'] = data['Close'].rolling(window=5).std()\n",
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"data['STD_20'] = data['Close'].rolling(window=20).std()\n",
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"\n",
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"data.head()\n"
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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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" Разделение данных на обучающую, контрольную и тестовую выборки"
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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": 108,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"((3150, 10), (1050, 10), (1051, 10))"
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]
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},
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"execution_count": 108,
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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": [
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"# Преобразуем колонку 'Date' в тип datetime для правильного сортирования\n",
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"data['Date'] = pd.to_datetime(data['Date'])\n",
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"\n",
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"# Сортируем данные по дате, чтобы не нарушить временную зависимость\n",
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"data = data.sort_values(by='Date')\n",
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"\n",
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"# Отделяем целевую переменную (например, Price_Change) и признаки\n",
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"X = data[['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20']]\n",
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"y = data['Price_Change']\n",
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"\n",
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"# Разделение на обучающую, контрольную и тестовую выборки (60%, 20%, 20%)\n",
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"X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, shuffle=False)\n",
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"X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, shuffle=False)\n",
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"\n",
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"# Проверка размеров выборок\n",
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"(X_train.shape, X_val.shape, X_test.shape)\n"
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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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"Конструирование признаков для решения задач"
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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": 109,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Признаки для задачи прогнозирования изменений цен\n",
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"data['Price_Change'] = data['Close'].diff()\n",
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"\n",
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"# Скользящие средние и стандартное отклонение\n",
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"data['SMA_5'] = data['Close'].rolling(window=5).mean()\n",
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"data['SMA_20'] = data['Close'].rolling(window=20).mean()\n",
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"data['STD_5'] = data['Close'].rolling(window=5).std()\n",
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"data['STD_20'] = data['Close'].rolling(window=20).std()\n",
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"\n",
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"# Признаки для оценки волатильности\n",
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"data['Volatility'] = data['Close'].rolling(window=5).std()\n",
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"\n"
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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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"Подготовка признаков: one-hot encoding, дискретизация, синтез признаков, масштабирование\n",
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"One-hot encoding: Применим для категориальных признаков (например, день недели).\n",
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"Масштабирование: Стандартизируем числовые признаки."
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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": 110,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Day_of_week Month\n",
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"0 4 6\n",
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"1 0 6\n",
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"2 1 6\n",
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"3 2 6\n",
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"4 3 6\n"
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]
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},
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{
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"data": {
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"text/html": [
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||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\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>Close</th>\n",
|
||||
" <th>SMA_5</th>\n",
|
||||
" <th>SMA_20</th>\n",
|
||||
" <th>STD_5</th>\n",
|
||||
" <th>STD_20</th>\n",
|
||||
" <th>Day_of_week_1</th>\n",
|
||||
" <th>Day_of_week_2</th>\n",
|
||||
" <th>Day_of_week_3</th>\n",
|
||||
" <th>Day_of_week_4</th>\n",
|
||||
" <th>Month_2</th>\n",
|
||||
" <th>Month_3</th>\n",
|
||||
" <th>Month_4</th>\n",
|
||||
" <th>Month_5</th>\n",
|
||||
" <th>Month_6</th>\n",
|
||||
" <th>Month_7</th>\n",
|
||||
" <th>Month_8</th>\n",
|
||||
" <th>Month_9</th>\n",
|
||||
" <th>Month_10</th>\n",
|
||||
" <th>Month_11</th>\n",
|
||||
" <th>Month_12</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>-0.721096</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>-0.507666</td>\n",
|
||||
" <td>-0.507962</td>\n",
|
||||
" <td>-0.502320</td>\n",
|
||||
" <td>1.999048</td>\n",
|
||||
" <td>-0.28793</td>\n",
|
||||
" <td>-0.309491</td>\n",
|
||||
" <td>-0.300916</td>\n",
|
||||
" <td>-0.297137</td>\n",
|
||||
" <td>3.335719</td>\n",
|
||||
" <td>-0.30429</td>\n",
|
||||
" <td>-0.311702</td>\n",
|
||||
" <td>-0.296377</td>\n",
|
||||
" <td>-0.311335</td>\n",
|
||||
" <td>-0.298274</td>\n",
|
||||
" <td>-0.303543</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>-0.721096</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>-0.507666</td>\n",
|
||||
" <td>-0.507962</td>\n",
|
||||
" <td>-0.502320</td>\n",
|
||||
" <td>-0.500238</td>\n",
|
||||
" <td>-0.28793</td>\n",
|
||||
" <td>-0.309491</td>\n",
|
||||
" <td>-0.300916</td>\n",
|
||||
" <td>-0.297137</td>\n",
|
||||
" <td>3.335719</td>\n",
|
||||
" <td>-0.30429</td>\n",
|
||||
" <td>-0.311702</td>\n",
|
||||
" <td>-0.296377</td>\n",
|
||||
" <td>-0.311335</td>\n",
|
||||
" <td>-0.298274</td>\n",
|
||||
" <td>-0.303543</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>-0.660890</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>1.969800</td>\n",
|
||||
" <td>-0.507962</td>\n",
|
||||
" <td>-0.502320</td>\n",
|
||||
" <td>-0.500238</td>\n",
|
||||
" <td>-0.28793</td>\n",
|
||||
" <td>-0.309491</td>\n",
|
||||
" <td>-0.300916</td>\n",
|
||||
" <td>-0.297137</td>\n",
|
||||
" <td>3.335719</td>\n",
|
||||
" <td>-0.30429</td>\n",
|
||||
" <td>-0.311702</td>\n",
|
||||
" <td>-0.296377</td>\n",
|
||||
" <td>-0.311335</td>\n",
|
||||
" <td>-0.298274</td>\n",
|
||||
" <td>-0.303543</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>-0.660890</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>-0.507666</td>\n",
|
||||
" <td>1.968649</td>\n",
|
||||
" <td>-0.502320</td>\n",
|
||||
" <td>-0.500238</td>\n",
|
||||
" <td>-0.28793</td>\n",
|
||||
" <td>-0.309491</td>\n",
|
||||
" <td>-0.300916</td>\n",
|
||||
" <td>-0.297137</td>\n",
|
||||
" <td>3.335719</td>\n",
|
||||
" <td>-0.30429</td>\n",
|
||||
" <td>-0.311702</td>\n",
|
||||
" <td>-0.296377</td>\n",
|
||||
" <td>-0.311335</td>\n",
|
||||
" <td>-0.298274</td>\n",
|
||||
" <td>-0.303543</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>-0.660890</td>\n",
|
||||
" <td>-0.686033</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>-0.269917</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>-0.507666</td>\n",
|
||||
" <td>-0.507962</td>\n",
|
||||
" <td>1.990763</td>\n",
|
||||
" <td>-0.500238</td>\n",
|
||||
" <td>-0.28793</td>\n",
|
||||
" <td>-0.309491</td>\n",
|
||||
" <td>-0.300916</td>\n",
|
||||
" <td>-0.297137</td>\n",
|
||||
" <td>3.335719</td>\n",
|
||||
" <td>-0.30429</td>\n",
|
||||
" <td>-0.311702</td>\n",
|
||||
" <td>-0.296377</td>\n",
|
||||
" <td>-0.311335</td>\n",
|
||||
" <td>-0.298274</td>\n",
|
||||
" <td>-0.303543</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Close SMA_5 SMA_20 STD_5 STD_20 Day_of_week_1 Day_of_week_2 \\\n",
|
||||
"0 -0.721096 NaN NaN NaN NaN -0.507666 -0.507962 \n",
|
||||
"1 -0.721096 NaN NaN NaN NaN -0.507666 -0.507962 \n",
|
||||
"2 -0.660890 NaN NaN NaN NaN 1.969800 -0.507962 \n",
|
||||
"3 -0.660890 NaN NaN NaN NaN -0.507666 1.968649 \n",
|
||||
"4 -0.660890 -0.686033 NaN -0.269917 NaN -0.507666 -0.507962 \n",
|
||||
"\n",
|
||||
" Day_of_week_3 Day_of_week_4 Month_2 Month_3 Month_4 Month_5 \\\n",
|
||||
"0 -0.502320 1.999048 -0.28793 -0.309491 -0.300916 -0.297137 \n",
|
||||
"1 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n",
|
||||
"2 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n",
|
||||
"3 -0.502320 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n",
|
||||
"4 1.990763 -0.500238 -0.28793 -0.309491 -0.300916 -0.297137 \n",
|
||||
"\n",
|
||||
" Month_6 Month_7 Month_8 Month_9 Month_10 Month_11 Month_12 \n",
|
||||
"0 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n",
|
||||
"1 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n",
|
||||
"2 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n",
|
||||
"3 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 \n",
|
||||
"4 3.335719 -0.30429 -0.311702 -0.296377 -0.311335 -0.298274 -0.303543 "
|
||||
]
|
||||
},
|
||||
"execution_count": 110,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Преобразуем дату, если это еще не сделано\n",
|
||||
"data['Date'] = pd.to_datetime(data['Date'])\n",
|
||||
"\n",
|
||||
"# Добавим дополнительные признаки (день недели и месяц)\n",
|
||||
"data['Day_of_week'] = data['Date'].dt.dayofweek\n",
|
||||
"data['Month'] = data['Date'].dt.month\n",
|
||||
"\n",
|
||||
"# Проверим, что эти столбцы добавлены\n",
|
||||
"print(data[['Day_of_week', 'Month']].head())\n",
|
||||
"\n",
|
||||
"# Выбираем признаки и целевую переменную\n",
|
||||
"X = data[['Close', 'SMA_5', 'SMA_20', 'STD_5', 'STD_20', 'Day_of_week', 'Month']]\n",
|
||||
"y = data['Price_Change']\n",
|
||||
"\n",
|
||||
"# Применяем one-hot encoding для категориальных признаков (Day_of_week и Month)\n",
|
||||
"X = pd.get_dummies(X, columns=['Day_of_week', 'Month'], drop_first=True)\n",
|
||||
"\n",
|
||||
"# Масштабирование числовых признаков (Close, SMA, STD)\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"X_scaled = scaler.fit_transform(X)\n",
|
||||
"\n",
|
||||
"# Преобразуем обратно в DataFrame для удобства\n",
|
||||
"X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n",
|
||||
"\n",
|
||||
"# Проверим результат\n",
|
||||
"X_scaled_df.head()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"c:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
|
||||
" warnings.warn(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import featuretools as ft\n",
|
||||
"\n",
|
||||
"# Создаем сущности для Featuretools\n",
|
||||
"es = ft.EntitySet(id=\"stock_prices\")\n",
|
||||
"es = es.add_dataframe(dataframe_name=\"stock_data\", dataframe=data, index=\"Date\")\n",
|
||||
"\n",
|
||||
"# Автоматическое создание признаков\n",
|
||||
"feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name=\"stock_data\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Оценка качества признаков\n",
|
||||
"Оценка признаков на основе предсказательной способности модели и других критериев."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 112,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"5251\n",
|
||||
"3150\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(np.float64(0.05230198011754029), 0.5415652186272203)"
|
||||
]
|
||||
},
|
||||
"execution_count": 112,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Проверим размерности данных после удаления NaN\n",
|
||||
"print(X_scaled_df.shape[0]) # Количество строк в X_scaled_df\n",
|
||||
"print(y_train.shape[0]) # Количество строк в y_train\n",
|
||||
"\n",
|
||||
"# Если данные имеют разные размеры, синхронизируем их\n",
|
||||
"df = pd.concat([X_scaled_df, y_train], axis=1).dropna()\n",
|
||||
"X_scaled_df = df.drop(columns=y_train.name)\n",
|
||||
"y_train = df[y_train.name]\n",
|
||||
"\n",
|
||||
"# Приводим индексы к одному виду\n",
|
||||
"y_train = y_train.reset_index(drop=True)\n",
|
||||
"X_scaled_df = X_scaled_df.reset_index(drop=True)\n",
|
||||
"\n",
|
||||
"# После этого продолжаем обучение модели\n",
|
||||
"model = LinearRegression()\n",
|
||||
"model.fit(X_scaled_df, y_train)\n",
|
||||
"\n",
|
||||
"# Прогнозирование и оценка качества\n",
|
||||
"y_pred = model.predict(X_scaled_df)\n",
|
||||
"\n",
|
||||
"# Оценка качества модели\n",
|
||||
"mse = mean_squared_error(y_train, y_pred) # Используем y_train, потому что данные для теста не созданы\n",
|
||||
"r2 = r2_score(y_train, y_pred)\n",
|
||||
"\n",
|
||||
"mse, r2\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"MSE = 0.0523: Модель в среднем делает ошибку около 0.0523 при прогнозировании значений.\n",
|
||||
"R² = 0.5416: Модель объясняет примерно 54.16% изменчивости целевой переменной\n",
|
||||
"\n",
|
||||
"Визуализируем"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -10,16 +10,23 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Index(['Id', 'Name', 'Short description', 'Gender', 'Country', 'Occupation',\n",
|
||||
" 'Birth year', 'Death year', 'Manner of death', 'Age of death'],\n",
|
||||
" dtype='object')\n"
|
||||
"ename": "FileNotFoundError",
|
||||
"evalue": "[Errno 2] No such file or directory: './/static//csv//csvLab1.csv'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[1], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.//static//csv//csvLab1.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m,\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(df\u001b[38;5;241m.\u001b[39mcolumns)\n",
|
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"File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
|
||||
"File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
|
||||
"File \u001b[1;32mc:\\Users\\alexk\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 868\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m 869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 874\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 875\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 876\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 877\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 879\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
|
||||
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './/static//csv//csvLab1.csv'"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -186,7 +193,7 @@
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MIiLabs",
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
||||
Loading…
Reference in New Issue
Block a user