{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа №3\n",
    "\n",
    "### Набор данных \"Наблюдения НЛО в США\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Для набора данных \"Наблюдения НЛО в США\" можно выделить несколько бизнес-целей и соответствующие технические задачи. Давайте рассмотрим этот процесс поэтапно.\n",
    "#  \n",
    "#  1. Определение бизнес-целей\n",
    "#      Бизнес-цель 1: Прогнозирование местоположения и частоты наблюдений НЛО.\n",
    "#          Задача заключается в анализе географического распределения и времени наблюдений НЛО, чтобы определить, в каких местах и когда чаще всего происходят наблюдения.\n",
    "#      Бизнес-цель 2: Анализ факторов, влияющих на восприятие НЛО (например, форма, продолжительность, описание).\n",
    "#          Цель — понять, какие признаки, такие как форма НЛО, длительность наблюдения, могут быть связаны с более подробными или более эмоционально окрашенными отчетами.\n",
    "#  2. Цели технического проекта для каждой бизнес-цели\n",
    "#      Цель для бизнес-цели 1: Создать модель, которая предскажет вероятное местоположение и время наблюдений на основе данных о предыдущих наблюдениях.\n",
    "#          Технические задачи:\n",
    "#              Прогнозирование местоположения и времени (классификация или регрессия).\n",
    "#              Кластеризация по географическому положению.\n",
    "#              Анализ временных рядов для выявления сезонных колебаний.\n",
    "#      Цель для бизнес-цели 2: Анализировать текстовые описания наблюдений НЛО для выявления ключевых паттернов и факторов.\n",
    "#          Технические задачи:\n",
    "#              Анализ текста с использованием методов обработки естественного языка (NLP).\n",
    "#              Классификация описаний по типам объектов или возможным объяснениям (например, возможный самолет или атмосферное явление)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['summary', 'city', 'state', 'date_time', 'shape', 'duration', 'stats',\n",
      "       'report_link', 'text', 'posted', 'city_latitude', 'city_longitude'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"../../datasets/nuforc_reports.csv\")\n",
    "\n",
    "# Срез данных, первые 15000 строк\n",
    "df = df.iloc[:15000]\n",
    "\n",
    "# Вывод\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>summary</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>date_time</th>\n",
       "      <th>shape</th>\n",
       "      <th>duration</th>\n",
       "      <th>stats</th>\n",
       "      <th>report_link</th>\n",
       "      <th>text</th>\n",
       "      <th>posted</th>\n",
       "      <th>city_latitude</th>\n",
       "      <th>city_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Viewed some red lights in the sky appearing to...</td>\n",
       "      <td>Visalia</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-12-15T21:45:00</td>\n",
       "      <td>light</td>\n",
       "      <td>2 minutes</td>\n",
       "      <td>Occurred : 12/15/2021 21:45  (Entered as : 12/...</td>\n",
       "      <td>http://www.nuforc.org/webreports/165/S165881.html</td>\n",
       "      <td>Viewed some red lights in the sky appearing to...</td>\n",
       "      <td>2021-12-19T00:00:00</td>\n",
       "      <td>36.356650</td>\n",
       "      <td>-119.347937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Look like 1 or 3 crafts from North traveling s...</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>OH</td>\n",
       "      <td>2021-12-16T09:45:00</td>\n",
       "      <td>triangle</td>\n",
       "      <td>14 seconds</td>\n",
       "      <td>Occurred : 12/16/2021 09:45  (Entered as : 12/...</td>\n",
       "      <td>http://www.nuforc.org/webreports/165/S165888.html</td>\n",
       "      <td>Look like 1 or 3 crafts from North traveling s...</td>\n",
       "      <td>2021-12-19T00:00:00</td>\n",
       "      <td>39.174503</td>\n",
       "      <td>-84.481363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>seen dark rectangle moving slowly thru the sky...</td>\n",
       "      <td>Tecopa</td>\n",
       "      <td>CA</td>\n",
       "      <td>2021-12-10T00:00:00</td>\n",
       "      <td>rectangle</td>\n",
       "      <td>Several minutes</td>\n",
       "      <td>Occurred : 12/10/2021 00:00  (Entered as : 12/...</td>\n",
       "      <td>http://www.nuforc.org/webreports/165/S165810.html</td>\n",
       "      <td>seen dark rectangle moving slowly thru the sky...</td>\n",
       "      <td>2021-12-19T00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>One red light moving switly west to east, beco...</td>\n",
       "      <td>Knoxville</td>\n",
       "      <td>TN</td>\n",
       "      <td>2021-12-10T19:30:00</td>\n",
       "      <td>triangle</td>\n",
       "      <td>20-30 seconds</td>\n",
       "      <td>Occurred : 12/10/2021 19:30  (Entered as : 12/...</td>\n",
       "      <td>http://www.nuforc.org/webreports/165/S165825.html</td>\n",
       "      <td>One red light moving switly west to east, beco...</td>\n",
       "      <td>2021-12-19T00:00:00</td>\n",
       "      <td>35.961561</td>\n",
       "      <td>-83.980115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bright, circular Fresnel-lens shaped light sev...</td>\n",
       "      <td>Alexandria</td>\n",
       "      <td>VA</td>\n",
       "      <td>2021-12-07T08:00:00</td>\n",
       "      <td>circle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Occurred : 12/7/2021 08:00  (Entered as : 12/0...</td>\n",
       "      <td>http://www.nuforc.org/webreports/165/S165754.html</td>\n",
       "      <td>Bright, circular Fresnel-lens shaped light sev...</td>\n",
       "      <td>2021-12-19T00:00:00</td>\n",
       "      <td>38.798958</td>\n",
       "      <td>-77.095133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             summary        city state  \\\n",
       "0  Viewed some red lights in the sky appearing to...     Visalia    CA   \n",
       "1  Look like 1 or 3 crafts from North traveling s...  Cincinnati    OH   \n",
       "2  seen dark rectangle moving slowly thru the sky...      Tecopa    CA   \n",
       "3  One red light moving switly west to east, beco...   Knoxville    TN   \n",
       "4  Bright, circular Fresnel-lens shaped light sev...  Alexandria    VA   \n",
       "\n",
       "             date_time      shape         duration  \\\n",
       "0  2021-12-15T21:45:00      light        2 minutes   \n",
       "1  2021-12-16T09:45:00   triangle       14 seconds   \n",
       "2  2021-12-10T00:00:00  rectangle  Several minutes   \n",
       "3  2021-12-10T19:30:00   triangle    20-30 seconds   \n",
       "4  2021-12-07T08:00:00     circle              NaN   \n",
       "\n",
       "                                               stats  \\\n",
       "0  Occurred : 12/15/2021 21:45  (Entered as : 12/...   \n",
       "1  Occurred : 12/16/2021 09:45  (Entered as : 12/...   \n",
       "2  Occurred : 12/10/2021 00:00  (Entered as : 12/...   \n",
       "3  Occurred : 12/10/2021 19:30  (Entered as : 12/...   \n",
       "4  Occurred : 12/7/2021 08:00  (Entered as : 12/0...   \n",
       "\n",
       "                                         report_link  \\\n",
       "0  http://www.nuforc.org/webreports/165/S165881.html   \n",
       "1  http://www.nuforc.org/webreports/165/S165888.html   \n",
       "2  http://www.nuforc.org/webreports/165/S165810.html   \n",
       "3  http://www.nuforc.org/webreports/165/S165825.html   \n",
       "4  http://www.nuforc.org/webreports/165/S165754.html   \n",
       "\n",
       "                                                text               posted  \\\n",
       "0  Viewed some red lights in the sky appearing to...  2021-12-19T00:00:00   \n",
       "1  Look like 1 or 3 crafts from North traveling s...  2021-12-19T00:00:00   \n",
       "2  seen dark rectangle moving slowly thru the sky...  2021-12-19T00:00:00   \n",
       "3  One red light moving switly west to east, beco...  2021-12-19T00:00:00   \n",
       "4  Bright, circular Fresnel-lens shaped light sev...  2021-12-19T00:00:00   \n",
       "\n",
       "   city_latitude  city_longitude  \n",
       "0      36.356650     -119.347937  \n",
       "1      39.174503      -84.481363  \n",
       "2            NaN             NaN  \n",
       "3      35.961561      -83.980115  \n",
       "4      38.798958      -77.095133  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Для наглядности\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15000 entries, 0 to 14999\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   summary         14998 non-null  object \n",
      " 1   city            14961 non-null  object \n",
      " 2   state           14235 non-null  object \n",
      " 3   date_time       14560 non-null  object \n",
      " 4   shape           13082 non-null  object \n",
      " 5   duration        13598 non-null  object \n",
      " 6   stats           15000 non-null  object \n",
      " 7   report_link     15000 non-null  object \n",
      " 8   text            14999 non-null  object \n",
      " 9   posted          14560 non-null  object \n",
      " 10  city_latitude   12002 non-null  float64\n",
      " 11  city_longitude  12002 non-null  float64\n",
      "dtypes: float64(2), object(10)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Описание данных (основные статистические показатели)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пропущенных значений в каждом столбце:\n",
      "summary              74\n",
      "city                382\n",
      "state              9345\n",
      "date_time          2668\n",
      "shape              5922\n",
      "duration           6492\n",
      "stats                 0\n",
      "report_link           0\n",
      "text                 38\n",
      "posted             2668\n",
      "city_latitude     26804\n",
      "city_longitude    26804\n",
      "dtype: int64\n",
      "summary Процент пустых значений: %0.05\n",
      "city Процент пустых значений: %0.28\n",
      "state Процент пустых значений: %6.82\n",
      "date_time Процент пустых значений: %1.95\n",
      "shape Процент пустых значений: %4.32\n",
      "duration Процент пустых значений: %4.74\n",
      "text Процент пустых значений: %0.03\n",
      "posted Процент пустых значений: %1.95\n",
      "city_latitude Процент пустых значений: %19.57\n",
      "city_longitude Процент пустых значений: %19.57\n",
      "Количество выбросов в столбце 'city_latitude': 1025\n",
      "Количество выбросов в столбце 'city_longitude': 23\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество удаленных строк: 27832\n",
      "Количество выбросов в столбце 'city_latitude': 38\n",
      "Количество выбросов в столбце 'city_longitude': 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя цена в обучающей выборке: 38.655686510239775\n",
      "Средняя цена в контрольной выборке: 38.6586177634688\n",
      "Средняя цена в тестовой выборке: 38.61544768163524\n",
      "\n",
      "Стандартное отклонение цены в обучающей выборке: 5.380551235399826\n",
      "Стандартное отклонение цены в контрольной выборке: 5.34170765011401\n",
      "Стандартное отклонение цены в тестовой выборке: 5.3932492782181525\n",
      "\n",
      "Распределение по квартилам (обучающая):\n",
      "0.25    34.269424\n",
      "0.50    39.222500\n",
      "0.75    42.284678\n",
      "Name: city_latitude, dtype: float64\n",
      "\n",
      "Распределение по квартилам (контрольная):\n",
      "0.25    34.286571\n",
      "0.50    39.302247\n",
      "0.75    42.277381\n",
      "Name: city_latitude, dtype: float64\n",
      "\n",
      "Распределение по квартилам (тестовая):\n",
      "0.25    34.194501\n",
      "0.50    39.165900\n",
      "0.75    42.286500\n",
      "Name: city_latitude, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"../../datasets/nuforc_reports.csv\")\n",
    "\n",
    "\n",
    "#5. Устранение пропущенных данных\n",
    "    \n",
    "#Сведения о пропущенных данных\n",
    "print(\"Количество пропущенных значений в каждом столбце:\")\n",
    "print(df.isnull().sum())\n",
    "\n",
    "# Процент пропущенных значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "#6. Проблемы набора данных\n",
    "    #5.1Выбросы: Возможны аномалии в значениях скорости или расстояния.\n",
    "    #Смещение: Данные могут быть смещены в сторону объектов, которые легче обнаружить (крупные, близкие).\n",
    "\n",
    "#7. Решения для обнаруженных проблем\n",
    "    #Выбросы: Идентификация и обработка выбросов через методы (например, IQR или Z-оценка).\n",
    "    #Смещение: Использование методов балансировки данных, таких как oversampling.\n",
    "\n",
    "#7.1 Проверка набора данных на выбросы\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = df.select_dtypes(include=np.number).columns.tolist()#['city_latitude'  , 'sqft_living', 'bathrooms', 'yr_built']\n",
    "def Emissions(columns_to_check):\n",
    "\n",
    "    # Функция для подсчета выбросов\n",
    "    def count_outliers(df, columns):\n",
    "        outliers_count = {}\n",
    "        for col in columns:\n",
    "            Q1 = df[col].quantile(0.25)\n",
    "            Q3 = df[col].quantile(0.75)\n",
    "            IQR = Q3 - Q1\n",
    "            lower_bound = Q1 - 1.5 * IQR\n",
    "            upper_bound = Q3 + 1.5 * IQR\n",
    "            \n",
    "            # Считаем количество выбросов\n",
    "            outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "            outliers_count[col] = len(outliers)\n",
    "        \n",
    "        return outliers_count\n",
    "\n",
    "    # Подсчитываем выбросы\n",
    "    outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "    # Выводим количество выбросов для каждого столбца\n",
    "    for col, count in outliers_count.items():\n",
    "        print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "        \n",
    "    # Создаем гистограммы\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    for i, col in enumerate(columns_to_check, 1):\n",
    "        plt.subplot(2, 3, i)\n",
    "        sns.histplot(df[col], kde=True)\n",
    "        plt.title(f'Histogram of {col}')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "Emissions(columns_to_check)\n",
    "\n",
    "#Признак miss_distance не имеет выбросов, \n",
    "#признак absolute_magnitude имеет количество выбросов в приемлемом диапазоне\n",
    "#для признаков est_diameter_min, est_diameter_max и relative_velocity необходимо использовать метод решения проблемы выбросов. \n",
    "#Воспользуемся методом удаления наблюдений с такими выбросами:\n",
    "# Выбираем столбцы для очистки\n",
    "columns_to_clean = ['city_latitude']\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, columns):\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Удаляем строки, содержащие выбросы\n",
    "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
    "    \n",
    "    return df\n",
    "\n",
    "# Удаляем выбросы\n",
    "df_cleaned = remove_outliers(df, columns_to_clean)\n",
    "\n",
    "# Выводим количество удаленных строк\n",
    "print(f\"Количество удаленных строк: {len(df) - len(df_cleaned)}\")\n",
    "\n",
    "df = df_cleaned\n",
    "\n",
    "#Оценим выбросы в выборке после усреднения:\n",
    "Emissions(columns_to_clean)\n",
    "\n",
    "#Удалось избавиться от выбросов в соответствующих признаках как видно на диаграммах.\n",
    "\n",
    "\n",
    "\n",
    "#8. Разбиение данных на выборки\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_data, temp_data = train_test_split(df, test_size=0.3, random_state=42)\n",
    "val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)\n",
    "\n",
    "# Средние значения цены\n",
    "print(\"Средняя цена в обучающей выборке:\", train_data['city_latitude'  ].mean())\n",
    "print(\"Средняя цена в контрольной выборке:\", val_data['city_latitude'  ].mean())\n",
    "print(\"Средняя цена в тестовой выборке:\", test_data['city_latitude'  ].mean())\n",
    "print()\n",
    "\n",
    "# Стандартное отклонение цены\n",
    "print(\"Стандартное отклонение цены в обучающей выборке:\", train_data['city_latitude'  ].std())\n",
    "print(\"Стандартное отклонение цены в контрольной выборке:\", val_data['city_latitude'  ].std())\n",
    "print(\"Стандартное отклонение цены в тестовой выборке:\", test_data['city_latitude'  ].std())\n",
    "print()\n",
    "\n",
    "# Проверка распределений по количеству объектов в диапазонах\n",
    "print(\"Распределение по квартилам (обучающая):\")\n",
    "print(train_data['city_latitude'  ].quantile([0.25, 0.5, 0.75]))\n",
    "print()\n",
    "print(\"Распределение по квартилам (контрольная):\")\n",
    "print(val_data['city_latitude'  ].quantile([0.25, 0.5, 0.75]))\n",
    "print()\n",
    "print(\"Распределение по квартилам (тестовая):\")\n",
    "print(test_data['city_latitude'  ].quantile([0.25, 0.5, 0.75]))\n",
    "\n",
    "# Построение гистограмм для каждой выборки\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "sns.histplot(train_data['city_latitude'  ], color='blue', label='Train', kde=True)\n",
    "sns.histplot(val_data['city_latitude'  ], color='green', label='Validation', kde=True)\n",
    "sns.histplot(test_data['city_latitude'  ], color='red', label='Test', kde=True)\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('city_latitude'  )\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Распределение цены в обучающей, контрольной и тестовой выборках')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#9. Оценить  сбалансированность  выборок  для  каждого  набора  данных.  Оценить необходимость использования методов приращения (аугментации) данных. \n",
    "#Выводы по сбалансированности\n",
    "#Если распределение классов примерно равно (например, 50%/50%), выборка считается сбалансированной, и аугментация данных не требуется.\n",
    "#Если один из классов сильно доминирует (например, 90%/10%), выборка несбалансированная, и может потребоваться аугментация данных.\n",
    "\n",
    "#Выборки оказались недостаточно сбалансированными. Используем методы приращения данных с избытком и с недостатком:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нет пропущенных данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиваем на выборки (обучающую, тестовую, контрольную)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры выборок:\n",
      "Обучающая выборка: 65464 записей\n",
      "Валидационная выборка: 21822 записей\n",
      "Тестовая выборка: 21822 записей\n"
     ]
    },
    {
     "data": {
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YhmG88847hiRj1qxZ+fIu6DUeOHCg4e7ubty4caPQ6wWAP6OCak2eDz74wHB0dDR27txptX/evHmGJGPXrl2GYRjGsWPHDEdHR6Nr1675PkvdXIfy/L4+3Gz16tWGJOPf//631f6nn37acHBwMI4fP27uK+gz82OPPWbUqVPH3M7OzjYyMjKsYi5evGj4+/sb/fv3N/ddv37dcHR0NAYOHGgVezufcQ3Dusbk6dmzp+Hu7m5u387n71deecXq95G33a5dOyM7O9vcn1f/8upYZmam4efnZzz88MPG9evXzbi1a9cakoxx48ZZXZMkY+jQoea+3NxcIzo62nB2djZ++eUXq3wXLVpk5ObmGr169TLc3d2NvXv35sv7Tt5LAr/HsiqwO1euXJEklS9f/pZxeeMWi8Xc98gjj6hVq1bmtpubm/76178qLS3N/GtueHi4AgMDrdZSPXz4sA4ePGg1U87Pz09nz55VZmbmLfNwc3Mzf87KytL58+dVvXp1eXt7F/gX5MuXL+vs2bNKSEhQZGSk6tata45VrFhR/fr104EDB5Senn7L896um/O7du2azp07p8cee0yGYejbb781x65cuSJHR8ciz/aSflur9ty5c/rhhx80ffp0OTk5Wf0+8pw7d07nzp0rcMZdQW7n9/rFF18oICDAnEkuSWXLltWwYcN09epVbd++3eqYXbp0sbpZyaOPPqqmTZvqiy++yHf+H374QdHR0WrYsKE++OADOTpa/9N882t848YNnTt3Ts2aNZOk255FAAC2MGfOHCUkJCghIUFLly5VmzZt9MILL2jVqlVWcTf/O3fx4kVdvnxZLVq0KPDfuLy6mTdL6XbzOH/+vF555ZVCY27cuCFXV9c/PNad5FpUY8aMUePGjfXMM89Y7d+0aZMyMzM1fPhwq1rx4osvytPT0+obb9Jvs7LyamLe49dffy3wnFlZWfliC6qjN19/3jcDWrVqpR9++EGXL182x65cuaIKFSoU+dtNubm5Zh5JSUl6//33Vbly5Xw3aMvL+/z588rOzr6tYz///POqWLGiuV2jRg117txZ69evV05OjnJycrRx40Z16dLFvPeMJFWuXFk9e/bUV199ZfX+UPpteZSbZ+0PHjxYZcqUKbDuf/bZZ/rrX/+qkSNHFrj0zs2v8ZUrV8z74fz666/6/vvvb+saAeB+sHLlStWuXVu1atWyqk9t27aVJHNpktWrVys3N1fjxo3L91nqTuvQF198IScnJw0bNsxq/0svvSTDMPTll19a7f/111917tw5paWl6T//+Y/++9//ql27dua4k5OTnJ2dJf1W2y5cuKDs7Gw1adLE6r3DtWvXlJuba1Wf7lRGRobOnTtn9gO2bNlilcvvc7548eJtr/U+YsQIq2/G9+7dW/7+/uZ7j/379+vs2bP661//avV+Kjo6WrVq1cr3HkWSVQ3MW44uMzNTmzZtyhc7cuRILVu2TB9//LEeffTRfOP34v0Z7l80x2F38preeU3ywhTURK9Vq1a+uLwPaXlrdzo6OqpXr15avXq1+QF02bJlcnV1tfqQ+9hjj+nGjRsaO3asfvrpJ7PQ/97169c1btw4c72zSpUqydfXV5cuXbL6EJqnS5cu8vf3l8ViUc2aNf8w37uVmpqqfv36ycfHRx4eHvL19TUbzTfnFxYWptzcXP3tb3/TiRMnzGJ8Jxo3bixfX19Vq1ZNCxcu1OzZs/MVxmvXrsnX11e+vr5yc3NTlSpVNHPmzFse93Z+rz/++KNq1KiR781WXtyPP/5otb9GjRr5jvnQQw/le92vXbumyMhIpaen68KFCwW+ebtw4YL+9re/yd/fX25ubvL19VVISIgkFfjfAACUFo8++qjCw8MVHh6uXr16ad26dapTp4754SfP2rVr1axZM7m6usrHx0e+vr6aO3dugf/G5a3jeTvrb0q//Tv5+uuva8SIEYUua5WTk6NLly7Jy8vrD493J7kWxVdffaU1a9Zo0qRJ+WpCXq35fX13dnbWgw8+mK8Wff/992ZNzHsU9geCjRs35otdsGBBvrhdu3YpPDxc5cqVk7e3t3x9ffXPf/5TUv66n5iYqHfeeUc///yzzp07Z36d/XacOnXKzKNRo0Y6ceKE/vOf/+T7veflXalSJbm6uqpx48bm+rK/l/d6Flb38/7I/8svv+jXX38t9H1Ubm6uTp06ZbX/93Xfw8NDlStXzlf3k5KS1KNHD+Xk5BS4Vqv025IuXbt2lZeXlzw9PeXr62tOsKDuA7Anx44d05EjR/LVp7wlsM6ePStJOnHihBwdHVWnTp27PuePP/6owMDAfJPpCvvcN2XKFPn6+qpy5cp6+umn1aJFC02aNMkqZsmSJapfv75cXV1VsWJF+fr6at26dVb/plesWFE1atTQe++9p40bN+rs2bM6d+5cgWurF2bFihXy9fWVv7+/IiIiFBQUlO8+XdJvy3r5+vrKx8dH7u7uio6O1rFjxwo8ZmG108nJSTVq1LD6vCzlf4+S99zfv26Ojo5Wf4CWZP5ef18733nnHU2dOlWSCu0hlPT7M9zfWHMcdsfLy0uVK1fWwYMHbxl38OBB/eUvf5Gnp6ck679E/pE+ffpoypQpWr16tXr06KHly5erY8eOVh+6O3furP79+2vKlCm3XD9y6NChWrRokYYPH66wsDB5eXnJwcFB3bt3V25ubr74N998UzVq1NCTTz552/kWVU5Ojp544glduHBBo0ePVq1atVSuXDn9/PPP6tevn1V+3bt31zfffKNZs2bd9nqjv7d06VL5+/vrxo0b2rJli2JjY+Xq6mp1cytXV1etWbNG0m9/4Fi4cKGGDx+uypUr69lnn813zDv5vZaEc+fOqVy5clqzZo26dOmi+Pj4fI2LZ599Vrt379bIkSPVsGFDeXh4KDc3V+3bty/wvwEAKK0cHR3Vpk0bzZw5U8eOHVPdunW1c+dOde7cWS1bttTbb7+typUrq2zZslq0aJGWL1+e7xhpaWny8PBQuXLlbuuckyZNkqOjo0aOHFnojYxTU1OVm5tb4I2yb3anuRbF6NGjFRkZqbZt2xZ449A7UbVqVb377rtW+1auXFlgHW7atKn+/e9/W+2bPXu2PvvsM3P7xIkTateunWrVqqVp06YpKChIzs7O+uKLLzR9+nSrmhQfH6+ff/5ZgwYNKlLu/v7+Wrp0qaTfGsILFy5U+/bt9dVXX6levXoF5n369GlNmjRJXbt21ZEjR/Id09Y1X5L++9//KioqSu3atdPIkSP13HPPWa03funSJbVq1Uqenp6aOHGiqlWrJldXV33zzTcaPXo0dR+AXcnNzVW9evU0bdq0AseDgoLucUb59e7dW3369FFubq5++OEHvfrqq+rYsaM2bdokBwcHLV26VP369VOXLl00cuRI+fn5ycnJSfHx8Tpx4oTVsT766CP16tVLkZGRVvtv9z1PRESERo4cKem3+3VMmjRJbdq00f79+61qYExMjJ555hnl5OTou+++0/jx49WlS5dSWzv37Nmj1157Tfv27VNcXJzat29v3ndGujfvz3B/ozkOu9SxY0e9++67+uqrr/T444/nG9+5c6dOnjypgQMHmvtCQkKUnJycLzbv6603f6B++OGH1ahRIy1btkwPPPCAUlNTNWvWrHzPXbBggcaNG6cTJ06YH3aeeOIJq5hPPvlEffv2Nf9SKv321e+b74B9s9DQULVq1UoeHh63nW9RHTp0SP/73/+0ZMkS9enTx9yfkJCQL9bR0VFvvvmmDh06pJSUFL399ttKT0/Pd1O2W2nevLmZd8eOHXXkyBHFx8dbNcednJysbtAZHR0tHx8frV+/vsDm+O3+XoODg3Xw4EHl5uZazR7PiwsODrZ6fkF/ef/f//6X73V3d3fX+vXrVatWLcXFxen111/Xs88+a85MuHjxojZv3qwJEyZY3RiusL/sA0Bpl7f0Rd4s4v/85z9ydXXVhg0brJZKWbRoUYHPP3r0aL6lNQpz+vRpzZw5U/Hx8SpfvnyhzfH9+/dLkpo0aXLL491prndq9erVSkxMLPQrwHm1Jjk52Wq2VWZmplJSUvLdoLpcuXL59iUlJRV47EqVKuWLvflGZ9JvNzTPyMjQ559/ripVqpj7877WfrOKFSvqgw8+UN26dfX4449r4MCB2rhx423fUNLV1dUqn86dO8vHx0ezZ8/WO++8U2je1atXV/PmzbVjxw6rHCWZ37oqrO6XK1fO/LDt7u5eaJyjo2O+hsyxY8fUpk0bc/vq1as6c+aMOnToYBVXr149rVy5Um5ublq5cqViYmJ08OBB8yvo27Zt0/nz57Vq1Sqrm6enpKQU8koBwP2rWrVq5jIlt1oepVq1asrNzdXRo0etbnBZFMHBwdq0aZOuXLliNXu8sM99Dz74oFUd8vLyUs+ePbVnzx6FhYXpk08+0YMPPqhVq1ZZXUNB3+Rq1KiR3n33XbVo0UITJ05Us2bNNGXKFO3ateu2cq9cubJVLjVr1tRjjz1mTtrLU6NGDTMuMjJSv/76q/71r38pNTU13zFvrp03v/fIzc3VsWPH1KhRI6vXJTk52Vz2Jk9ycnK+1y3vjwl5s8Wl3z4vS/l7Ff3799c///lPnT59WnXq1FFcXJx5w1Op5N+f4f7HsiqwSyNHjpSbm5sGDhyY74PyhQsXNGjQILm7u5t/dZWkDh066Ouvv9bu3bvNfTdu3NDcuXMVEBCg0NBQq+P07t1bGzdu1IwZM1SxYkVFRUUVmEtwcLDatm1rfu3895ycnPKtAzZr1izl5OQUen0ODg6KiIjQhg0b9N1331ld25IlS9SkSZNCv1p+J/LWHLs5P8MwCl3GZNasWdqyZYuWLVum8PBwNW/e/K7Of/369T/8mllebjevj3az2/29dujQQWlpafroo4/MuOzsbM2aNUseHh751j5fvXq1fv75Z3P766+/1t69e/P9d+Dr62t+RW3ixIl64IE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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Разделение признаков (features) и целевой переменной (target)\n",
    "X = df.drop(columns=['city_latitude'])  # Признаки (все столбцы, кроме 'city_latitude')\n",
    "y = df['city_latitude'] \n",
    "# Целевая переменная (price)\n",
    "\n",
    "# Разбиение на обучающую (60%), валидационную (20%) и тестовую (20%) выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Проверка размеров выборок\n",
    "print(f\"Размеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape[0]} записей\")\n",
    "print(f\"Валидационная выборка: {X_val.shape[0]} записей\")\n",
    "print(f\"Тестовая выборка: {X_test.shape[0]} записей\")\n",
    "\n",
    "# Визуализация распределения цен в каждой выборке\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.hist(y_train, bins=30, color='blue', alpha=0.7)\n",
    "plt.title('Обучающая выборка')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Количество')\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.hist(y_val, bins=30, color='green', alpha=0.7)\n",
    "plt.title('Валидационная выборка')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Количество')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.hist(y_test, bins=30, color='red', alpha=0.7)\n",
    "plt.title('Тестовая выборка')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Количество')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Балансировка выборок**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры выборок:\n",
      "Обучающая выборка: 6000 записей\n",
      "Валидационная выборка: 2000 записей\n",
      "Тестовая выборка: 2000 записей\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Разделение признаков (features) и целевой переменной (target)\n",
    "X = df.drop(columns=['city_latitude']).head(10000)  # Признаки (все столбцы, кроме 'city_latitude')\n",
    "y = df['city_latitude'].head(10000)  # Целевая переменная (цена)\n",
    "\n",
    "# Применение one-hot encoding для категориальных признаков\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Разбиение на обучающую (60%), валидационную (20%) и тестовую (20%) выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Проверка размеров выборок\n",
    "print(f\"Размеры выборок:\")\n",
    "print(f\"Обучающая выборка: {X_train.shape[0]} записей\")\n",
    "print(f\"Валидационная выборка: {X_val.shape[0]} записей\")\n",
    "print(f\"Тестовая выборка: {X_test.shape[0]} записей\")\n",
    "\n",
    "# Удаление выбросов (цены выше 95-го процентиля)\n",
    "upper_limit = y_train.quantile(0.95)\n",
    "X_train = X_train[y_train <= upper_limit]\n",
    "y_train = y_train[y_train <= upper_limit]\n",
    "\n",
    "# Логарифмическое преобразование целевой переменной\n",
    "y_train_log = np.log1p(y_train)\n",
    "y_val_log = np.log1p(y_val)\n",
    "y_test_log = np.log1p(y_test)\n",
    "\n",
    "# Стандартизация признаков\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_val_scaled = scaler.transform(X_val)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Визуализация распределения цен в сбалансированной выборке\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(y_train_log, bins=30, color='orange', alpha=0.7)\n",
    "plt.title('Сбалансированная обучающая выборка (логарифмическое преобразование)')\n",
    "plt.xlabel('Логарифм цены')\n",
    "plt.ylabel('Количество')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Унитарное кодирование категориальных признаков**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до унитарного кодирования:\n",
      "                                                 summary           city state  \\\n",
      "0      Viewed some red lights in the sky appearing to...        Visalia    CA   \n",
      "1      Look like 1 or 3 crafts from North traveling s...     Cincinnati    OH   \n",
      "3      One red light moving switly west to east, beco...      Knoxville    TN   \n",
      "4      Bright, circular Fresnel-lens shaped light sev...     Alexandria    VA   \n",
      "5      I'm familiar with all the fakery and UFO sight...      Fullerton    CA   \n",
      "...                                                  ...            ...   ...   \n",
      "12494                    star like stop and go satellite  San Francisco    CA   \n",
      "12495           Two Balls of Light sighted in Tulsa, Ok.          Tulsa    OK   \n",
      "12496  Highley reflective silver oval/disk seen in sk...       St Louis    MO   \n",
      "12497  While walking on broadwalk on south west corne...          Tempe    AZ   \n",
      "12498  was sitting on my front porch with granddaught...        Elkhart    KS   \n",
      "\n",
      "                 date_time     shape         duration  \\\n",
      "0      2021-12-15T21:45:00     light        2 minutes   \n",
      "1      2021-12-16T09:45:00  triangle       14 seconds   \n",
      "3      2021-12-10T19:30:00  triangle    20-30 seconds   \n",
      "4      2021-12-07T08:00:00    circle              NaN   \n",
      "5      2020-07-07T23:00:00   unknown        2 minutes   \n",
      "...                    ...       ...              ...   \n",
      "12494  2000-06-05T23:30:00       NaN         1 minute   \n",
      "12495  2000-06-06T08:15:00    circle  about 5 minutes   \n",
      "12496  2000-06-06T17:22:00      oval       one minute   \n",
      "12497  2000-06-07T22:00:00   unknown        4 seconds   \n",
      "12498  2000-06-07T23:48:00    circle     1to2 minutes   \n",
      "\n",
      "                                                   stats  \\\n",
      "0      Occurred : 12/15/2021 21:45  (Entered as : 12/...   \n",
      "1      Occurred : 12/16/2021 09:45  (Entered as : 12/...   \n",
      "3      Occurred : 12/10/2021 19:30  (Entered as : 12/...   \n",
      "4      Occurred : 12/7/2021 08:00  (Entered as : 12/0...   \n",
      "5      Occurred : 7/7/2020 23:00  (Entered as : 07/07...   \n",
      "...                                                  ...   \n",
      "12494  Occurred : 6/5/2000 23:30  (Entered as : 06/05...   \n",
      "12495  Occurred : 6/6/2000 08:15  (Entered as : 06/06...   \n",
      "12496  Occurred : 6/6/2000 17:22  (Entered as : 06/06...   \n",
      "12497  Occurred : 6/7/2000 22:00  (Entered as : 060/7...   \n",
      "12498  Occurred : 6/7/2000 23:48  (Entered as : 6/7/2...   \n",
      "\n",
      "                                             report_link  \\\n",
      "0      http://www.nuforc.org/webreports/165/S165881.html   \n",
      "1      http://www.nuforc.org/webreports/165/S165888.html   \n",
      "3      http://www.nuforc.org/webreports/165/S165825.html   \n",
      "4      http://www.nuforc.org/webreports/165/S165754.html   \n",
      "5      http://www.nuforc.org/webreports/157/S157444.html   \n",
      "...                                                  ...   \n",
      "12494   http://www.nuforc.org/webreports/013/S13042.html   \n",
      "12495   http://www.nuforc.org/webreports/013/S13150.html   \n",
      "12496   http://www.nuforc.org/webreports/013/S13043.html   \n",
      "12497   http://www.nuforc.org/webreports/013/S13054.html   \n",
      "12498   http://www.nuforc.org/webreports/013/S13051.html   \n",
      "\n",
      "                                                    text               posted  \\\n",
      "0      Viewed some red lights in the sky appearing to...  2021-12-19T00:00:00   \n",
      "1      Look like 1 or 3 crafts from North traveling s...  2021-12-19T00:00:00   \n",
      "3      One red light moving switly west to east, beco...  2021-12-19T00:00:00   \n",
      "4      Bright, circular Fresnel-lens shaped light sev...  2021-12-19T00:00:00   \n",
      "5      I'm familiar with all the fakery and UFO sight...  2020-07-09T00:00:00   \n",
      "...                                                  ...                  ...   \n",
      "12494  star like stop and go satellite A white star-l...  2000-06-21T00:00:00   \n",
      "12495  Two Balls of Light sighted in Tulsa, Ok. At su...  2000-06-21T00:00:00   \n",
      "12496  Highley reflective silver oval/disk seen in sk...  2000-06-21T00:00:00   \n",
      "12497  On southwest corner of Tempe Lake heard and sa...  2000-06-21T00:00:00   \n",
      "12498  was sitting on my front porch with granddaught...  2000-06-21T00:00:00   \n",
      "\n",
      "       city_latitude  city_longitude  \n",
      "0          36.356650     -119.347937  \n",
      "1          39.174503      -84.481363  \n",
      "3          35.961561      -83.980115  \n",
      "4          38.798958      -77.095133  \n",
      "5          33.877422     -117.924978  \n",
      "...              ...             ...  \n",
      "12494      37.769992     -122.425394  \n",
      "12495      36.109456      -95.935245  \n",
      "12496      38.623825      -90.308528  \n",
      "12497      33.414036     -111.920920  \n",
      "12498      37.046000     -101.853100  \n",
      "\n",
      "[10000 rows x 12 columns]\n",
      "\n",
      "Данные после унитарного кодирования:\n",
      "       city_latitude  city_longitude  \\\n",
      "0          36.356650     -119.347937   \n",
      "1          39.174503      -84.481363   \n",
      "3          35.961561      -83.980115   \n",
      "4          38.798958      -77.095133   \n",
      "5          33.877422     -117.924978   \n",
      "...              ...             ...   \n",
      "12494      37.769992     -122.425394   \n",
      "12495      36.109456      -95.935245   \n",
      "12496      38.623825      -90.308528   \n",
      "12497      33.414036     -111.920920   \n",
      "12498      37.046000     -101.853100   \n",
      "\n",
      "       summary_ A couple stopped me and told me &quot;Am I crazy or those lights are moving?  ((Starlink satellites?))  \\\n",
      "0                                                  False                                                                 \n",
      "1                                                  False                                                                 \n",
      "3                                                  False                                                                 \n",
      "4                                                  False                                                                 \n",
      "5                                                  False                                                                 \n",
      "...                                                  ...                                                                 \n",
      "12494                                              False                                                                 \n",
      "12495                                              False                                                                 \n",
      "12496                                              False                                                                 \n",
      "12497                                              False                                                                 \n",
      "12498                                              False                                                                 \n",
      "\n",
      "       summary_ All total, I counted 15 aircraft flying in single file with no audible noise ((Starlink satellites?))  \\\n",
      "0                                                  False                                                                \n",
      "1                                                  False                                                                \n",
      "3                                                  False                                                                \n",
      "4                                                  False                                                                \n",
      "5                                                  False                                                                \n",
      "...                                                  ...                                                                \n",
      "12494                                              False                                                                \n",
      "12495                                              False                                                                \n",
      "12496                                              False                                                                \n",
      "12497                                              False                                                                \n",
      "12498                                              False                                                                \n",
      "\n",
      "       summary_ I and 3 others observed 4 lights in the sky.  \\\n",
      "0                                                  False       \n",
      "1                                                  False       \n",
      "3                                                  False       \n",
      "4                                                  False       \n",
      "5                                                  False       \n",
      "...                                                  ...       \n",
      "12494                                              False       \n",
      "12495                                              False       \n",
      "12496                                              False       \n",
      "12497                                              False       \n",
      "12498                                              False       \n",
      "\n",
      "       summary_ I kept watching and counted 10 objects that seemed to come from nowhere just appear out of the blackness.  ((Starlink satellites?))  \\\n",
      "0                                                  False                                                                                              \n",
      "1                                                  False                                                                                              \n",
      "3                                                  False                                                                                              \n",
      "4                                                  False                                                                                              \n",
      "5                                                  False                                                                                              \n",
      "...                                                  ...                                                                                              \n",
      "12494                                              False                                                                                              \n",
      "12495                                              False                                                                                              \n",
      "12496                                              False                                                                                              \n",
      "12497                                              False                                                                                              \n",
      "12498                                              False                                                                                              \n",
      "\n",
      "       summary_ I noticed the stars and another in a perfect line.    ((Starlink satellites))  \\\n",
      "0                                                  False                                        \n",
      "1                                                  False                                        \n",
      "3                                                  False                                        \n",
      "4                                                  False                                        \n",
      "5                                                  False                                        \n",
      "...                                                  ...                                        \n",
      "12494                                              False                                        \n",
      "12495                                              False                                        \n",
      "12496                                              False                                        \n",
      "12497                                              False                                        \n",
      "12498                                              False                                        \n",
      "\n",
      "       summary_ I saw what looked like a star moving then I seen more.  ((Starlink satellites?))  \\\n",
      "0                                                  False                                           \n",
      "1                                                  False                                           \n",
      "3                                                  False                                           \n",
      "4                                                  False                                           \n",
      "5                                                  False                                           \n",
      "...                                                  ...                                           \n",
      "12494                                              False                                           \n",
      "12495                                              False                                           \n",
      "12496                                              False                                           \n",
      "12497                                              False                                           \n",
      "12498                                              False                                           \n",
      "\n",
      "       summary_ I see 5 lights in a line, separated evenly, flying  at the same speed.  ((\"Starlink\" satellites??))((anonymous))  \\\n",
      "0                                                  False                                                                           \n",
      "1                                                  False                                                                           \n",
      "3                                                  False                                                                           \n",
      "4                                                  False                                                                           \n",
      "5                                                  False                                                                           \n",
      "...                                                  ...                                                                           \n",
      "12494                                              False                                                                           \n",
      "12495                                              False                                                                           \n",
      "12496                                              False                                                                           \n",
      "12497                                              False                                                                           \n",
      "12498                                              False                                                                           \n",
      "\n",
      "       summary_ We have witnessed multiple lights looking like satellites with varing degrees of brightness in orbit.   ((Starlink satellites?))  \\\n",
      "0                                                  False                                                                                           \n",
      "1                                                  False                                                                                           \n",
      "3                                                  False                                                                                           \n",
      "4                                                  False                                                                                           \n",
      "5                                                  False                                                                                           \n",
      "...                                                  ...                                                                                           \n",
      "12494                                              False                                                                                           \n",
      "12495                                              False                                                                                           \n",
      "12496                                              False                                                                                           \n",
      "12497                                              False                                                                                           \n",
      "12498                                              False                                                                                           \n",
      "\n",
      "       ...  posted_2020-07-03T00:00:00  posted_2020-07-09T00:00:00  \\\n",
      "0      ...                       False                       False   \n",
      "1      ...                       False                       False   \n",
      "3      ...                       False                       False   \n",
      "4      ...                       False                       False   \n",
      "5      ...                       False                        True   \n",
      "...    ...                         ...                         ...   \n",
      "12494  ...                       False                       False   \n",
      "12495  ...                       False                       False   \n",
      "12496  ...                       False                       False   \n",
      "12497  ...                       False                       False   \n",
      "12498  ...                       False                       False   \n",
      "\n",
      "       posted_2020-07-23T00:00:00  posted_2020-07-31T00:00:00  \\\n",
      "0                           False                       False   \n",
      "1                           False                       False   \n",
      "3                           False                       False   \n",
      "4                           False                       False   \n",
      "5                           False                       False   \n",
      "...                           ...                         ...   \n",
      "12494                       False                       False   \n",
      "12495                       False                       False   \n",
      "12496                       False                       False   \n",
      "12497                       False                       False   \n",
      "12498                       False                       False   \n",
      "\n",
      "       posted_2020-08-06T00:00:00  posted_2020-08-20T00:00:00  \\\n",
      "0                           False                       False   \n",
      "1                           False                       False   \n",
      "3                           False                       False   \n",
      "4                           False                       False   \n",
      "5                           False                       False   \n",
      "...                           ...                         ...   \n",
      "12494                       False                       False   \n",
      "12495                       False                       False   \n",
      "12496                       False                       False   \n",
      "12497                       False                       False   \n",
      "12498                       False                       False   \n",
      "\n",
      "       posted_2020-08-27T00:00:00  posted_2020-09-04T00:00:00  \\\n",
      "0                           False                       False   \n",
      "1                           False                       False   \n",
      "3                           False                       False   \n",
      "4                           False                       False   \n",
      "5                           False                       False   \n",
      "...                           ...                         ...   \n",
      "12494                       False                       False   \n",
      "12495                       False                       False   \n",
      "12496                       False                       False   \n",
      "12497                       False                       False   \n",
      "12498                       False                       False   \n",
      "\n",
      "       posted_2020-11-05T00:00:00  posted_2021-12-19T00:00:00  \n",
      "0                           False                        True  \n",
      "1                           False                        True  \n",
      "3                           False                        True  \n",
      "4                           False                        True  \n",
      "5                           False                       False  \n",
      "...                           ...                         ...  \n",
      "12494                       False                       False  \n",
      "12495                       False                       False  \n",
      "12496                       False                       False  \n",
      "12497                       False                       False  \n",
      "12498                       False                       False  \n",
      "\n",
      "[10000 rows x 53560 columns]\n"
     ]
    }
   ],
   "source": [
    "print(\"Данные до унитарного кодирования:\")\n",
    "print(df.head(10000))\n",
    "\n",
    "# Применение унитарного кодирования для категориальных признаков\n",
    "df_encoded = pd.get_dummies(df.head(10000), drop_first=True)\n",
    "\n",
    "print(\"\\nДанные после унитарного кодирования:\")\n",
    "print(df_encoded.head(10000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Дискретизация числовых признаков**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.1938\n",
      "54.4333\n",
      "32.23950000000001\n"
     ]
    }
   ],
   "source": [
    "print(df['city_latitude'].min())\n",
    "print(df['city_latitude'].max())\n",
    "print(df['city_latitude'].max() - df['city_latitude'].min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до дискретизации:\n",
      "[22, 36, 49, 63, 77, inf]\n",
      "\n",
      "Данные после дискретизации:\n",
      "    city_latitude price_bins\n",
      "0       36.356650      36-49\n",
      "1       39.174503      36-49\n",
      "3       35.961561      22-36\n",
      "4       38.798958      36-49\n",
      "5       33.877422      22-36\n",
      "6       36.141246      36-49\n",
      "8       40.294123      36-49\n",
      "10      40.698700      36-49\n",
      "12      44.072800      36-49\n",
      "13      42.312800      36-49\n"
     ]
    }
   ],
   "source": [
    "print(\"Данные до дискретизации:\")\n",
    "#print(df.head(10))\n",
    "\n",
    "\n",
    "# Определение интервалов и меток для дискретизации\n",
    "bins = [\n",
    "round(df['city_latitude'].min() + df['city_latitude'].max() * 0), \n",
    "round(df['city_latitude'].min() + df['city_latitude'].max() * 0.25), \n",
    "round(df['city_latitude'].min() + df['city_latitude'].max() * 0.50), \n",
    "round(df['city_latitude'].min() + df['city_latitude'].max() * 0.75),\n",
    "round(df['city_latitude'].min() + df['city_latitude'].max() * 1),\n",
    "float('inf')\n",
    "]\n",
    "print(bins)\n",
    "labels = [\n",
    "str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0)) + '-' + str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0.25)),\n",
    "str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0.25)) + '-' + str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0.5)),\n",
    "str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0.5)) + '-' + str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0.75)),\n",
    "str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0.75)) + '-' + str(round(df['city_latitude'].min() + df['city_latitude'].max() * 0.1)),\n",
    "str(round(df['city_latitude'].min() + df['city_latitude'].max() * 1)) + '+'\n",
    "]\n",
    "\n",
    "# Применение дискретизации\n",
    "df['price_bins'] = pd.cut(df['city_latitude'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "print(\"\\nДанные после дискретизации:\")\n",
    "print(df[['city_latitude', 'price_bins']].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**«Ручной» синтез признаков**\n",
    "\n",
    "Создание новых признаков на основе экспертных знаний и логики предметной области. К примеру, для данных о продаже домов можно создать признак цена за единицу товара."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до синтеза признака:\n",
      "                                              summary         city state  \\\n",
      "0   Viewed some red lights in the sky appearing to...      Visalia    CA   \n",
      "1   Look like 1 or 3 crafts from North traveling s...   Cincinnati    OH   \n",
      "3   One red light moving switly west to east, beco...    Knoxville    TN   \n",
      "4   Bright, circular Fresnel-lens shaped light sev...   Alexandria    VA   \n",
      "5   I'm familiar with all the fakery and UFO sight...    Fullerton    CA   \n",
      "6   I was driving up lakes mead towards the lake a...    Las Vegas    NV   \n",
      "8   Wing shaped craft seen at night, no lights, no...         Orem    UT   \n",
      "10  Yellow light floating across my grass as it de...  Springfield    NJ   \n",
      "12  A trail of star like lights moving from the W....   Janesville    MN   \n",
      "13  Large bright ball not registering on any app a...       Bangor    MI   \n",
      "\n",
      "              date_time     shape       duration  \\\n",
      "0   2021-12-15T21:45:00     light      2 minutes   \n",
      "1   2021-12-16T09:45:00  triangle     14 seconds   \n",
      "3   2021-12-10T19:30:00  triangle  20-30 seconds   \n",
      "4   2021-12-07T08:00:00    circle            NaN   \n",
      "5   2020-07-07T23:00:00   unknown      2 minutes   \n",
      "6   2020-04-23T03:00:00      oval     10 minutes   \n",
      "8   2020-04-18T23:00:00     other     10 seconds   \n",
      "10  2020-05-13T03:37:00     light      7 seconds   \n",
      "12  2020-04-18T21:05:00     light     15 minutes   \n",
      "13  2020-04-18T22:30:00  triangle     45 minutes   \n",
      "\n",
      "                                                stats  \\\n",
      "0   Occurred : 12/15/2021 21:45  (Entered as : 12/...   \n",
      "1   Occurred : 12/16/2021 09:45  (Entered as : 12/...   \n",
      "3   Occurred : 12/10/2021 19:30  (Entered as : 12/...   \n",
      "4   Occurred : 12/7/2021 08:00  (Entered as : 12/0...   \n",
      "5   Occurred : 7/7/2020 23:00  (Entered as : 07/07...   \n",
      "6   Occurred : 4/23/2020 03:00  (Entered as : 4/23...   \n",
      "8   Occurred : 4/18/2020 23:00  (Entered as : 04/1...   \n",
      "10  Occurred : 5/13/2020 03:37  (Entered as : 05/1...   \n",
      "12  Occurred : 4/18/2020 21:05  (Entered as : 04/1...   \n",
      "13  Occurred : 4/18/2020 22:30  (Entered as : 04/1...   \n",
      "\n",
      "                                          report_link  \\\n",
      "0   http://www.nuforc.org/webreports/165/S165881.html   \n",
      "1   http://www.nuforc.org/webreports/165/S165888.html   \n",
      "3   http://www.nuforc.org/webreports/165/S165825.html   \n",
      "4   http://www.nuforc.org/webreports/165/S165754.html   \n",
      "5   http://www.nuforc.org/webreports/157/S157444.html   \n",
      "6   http://www.nuforc.org/webreports/155/S155608.html   \n",
      "8   http://www.nuforc.org/webreports/155/S155512.html   \n",
      "10  http://www.nuforc.org/webreports/155/S155647.html   \n",
      "12  http://www.nuforc.org/webreports/155/S155497.html   \n",
      "13  http://www.nuforc.org/webreports/155/S155495.html   \n",
      "\n",
      "                                                 text               posted  \\\n",
      "0   Viewed some red lights in the sky appearing to...  2021-12-19T00:00:00   \n",
      "1   Look like 1 or 3 crafts from North traveling s...  2021-12-19T00:00:00   \n",
      "3   One red light moving switly west to east, beco...  2021-12-19T00:00:00   \n",
      "4   Bright, circular Fresnel-lens shaped light sev...  2021-12-19T00:00:00   \n",
      "5   I'm familiar with all the fakery and UFO sight...  2020-07-09T00:00:00   \n",
      "6   I was driving up lakes mead towards the lake a...  2020-05-01T00:00:00   \n",
      "8   Wing shaped craft seen at night, no lights, no...  2020-05-01T00:00:00   \n",
      "10  Yellow light floating across my grass as it de...  2020-05-15T00:00:00   \n",
      "12  A trail of star like lights moving from the W....  2020-05-15T00:00:00   \n",
      "13  Large bright ball not registering on any app a...  2020-05-15T00:00:00   \n",
      "\n",
      "    city_latitude  city_longitude price_bins  \n",
      "0       36.356650     -119.347937      36-49  \n",
      "1       39.174503      -84.481363      36-49  \n",
      "3       35.961561      -83.980115      22-36  \n",
      "4       38.798958      -77.095133      36-49  \n",
      "5       33.877422     -117.924978      22-36  \n",
      "6       36.141246     -115.186592      36-49  \n",
      "8       40.294123     -111.701685      36-49  \n",
      "10      40.698700      -74.329600      36-49  \n",
      "12      44.072800      -93.728600      36-49  \n",
      "13      42.312800      -86.081300      36-49  \n",
      "\n",
      "Данные после синтеза признака 'relative_price':\n",
      "    city_latitude state  relative_appearing\n",
      "0       36.356650    CA            1.018610\n",
      "1       39.174503    OH            0.969926\n",
      "3       35.961561    TN            1.001705\n",
      "4       38.798958    VA            1.026087\n",
      "5       33.877422    CA            0.949149\n",
      "6       36.141246    NV            0.968071\n",
      "8       40.294123    UT            1.003457\n",
      "10      40.698700    NJ            1.008448\n",
      "12      44.072800    MN            0.970854\n",
      "13      42.312800    MI            0.984641\n"
     ]
    }
   ],
   "source": [
    "# Проверка первых строк данных\n",
    "print(\"Данные до синтеза признака:\")\n",
    "print(df.head(10))\n",
    "\n",
    "# Вычисление средней цены по категориям\n",
    "mean_price_by_category = df.groupby('state')['city_latitude'].transform('mean')\n",
    "\n",
    "# Создание нового признака 'relative_price' (относительная цена)\n",
    "df['relative_appearing'] = df['city_latitude'] / mean_price_by_category\n",
    "\n",
    "# Проверка первых строк данных после синтеза признака\n",
    "print(\"\\nДанные после синтеза признака 'relative_price':\")\n",
    "print(df[['city_latitude', 'state', 'relative_appearing']].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Масштабирование признаков на основе нормировки и стандартизации**\n",
    "\n",
    "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Данные до масштабирования:\n",
      "   city_latitude  relative_city_latitude\n",
      "0      36.356650                1.018610\n",
      "1      39.174503                0.969926\n",
      "3      35.961561                1.001705\n",
      "4      38.798958                1.026087\n",
      "5      33.877422                0.949149\n",
      "\n",
      "Данные после нормировки:\n",
      "   city_latitude  relative_city_latitude\n",
      "0       0.439301                0.475114\n",
      "1       0.526705                0.349452\n",
      "3       0.427046                0.431477\n",
      "4       0.515056                0.494412\n",
      "5       0.362401                0.295823\n",
      "\n",
      "Данные после стандартизации:\n",
      "   city_latitude  relative_city_latitude\n",
      "0      -0.426560                0.533974\n",
      "1       0.097536               -0.862875\n",
      "3      -0.500043                0.048911\n",
      "4       0.027688                0.748491\n",
      "5      -0.887675               -1.459012\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "# Создание нового признака 'relative_city_latitude' (цена относительно средней цены в категории)\n",
    "mean_city_latitude_by_state = df.groupby('state')['city_latitude'].transform('mean')\n",
    "df['relative_city_latitude'] = df['city_latitude'] / mean_city_latitude_by_state\n",
    "\n",
    "# Проверка первых строк данных до масштабирования\n",
    "print(\"Данные до масштабирования:\")\n",
    "print(df[['city_latitude', 'relative_city_latitude']].head())\n",
    "\n",
    "# Масштабирование признаков на основе нормировки\n",
    "min_max_scaler = MinMaxScaler()\n",
    "df[['city_latitude', 'relative_city_latitude']] = min_max_scaler.fit_transform(df[['city_latitude', 'relative_city_latitude']])\n",
    "\n",
    "# Проверка первых строк данных после нормировки\n",
    "print(\"\\nДанные после нормировки:\")\n",
    "print(df[['city_latitude', 'relative_city_latitude']].head())\n",
    "\n",
    "# Стандартизация признаков\n",
    "standard_scaler = StandardScaler()\n",
    "df[['city_latitude', 'relative_city_latitude']] = standard_scaler.fit_transform(df[['city_latitude', 'relative_city_latitude']])\n",
    "\n",
    "# Проверка первых строк данных после стандартизации\n",
    "print(\"\\nДанные после стандартизации:\")\n",
    "print(df[['city_latitude', 'relative_city_latitude']].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Конструирование признаков с применением фреймворка Featuretools**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\tumvu\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Built 23 features\n",
      "Elapsed: 00:14 | Progress:  95%|█████████▌"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\woodwork\\logical_types.py:841: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  series = series.replace(ww.config.get_option(\"nan_values\"), np.nan)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed: 00:17 | Progress: 100%|██████████\n",
      "Новые признаки, созданные с помощью Featuretools:\n",
      "             city state     shape       duration  city_latitude  \\\n",
      "index                                                             \n",
      "0         Visalia    CA     light      2 minutes      -0.426560   \n",
      "1      Cincinnati    OH  triangle     14 seconds       0.097536   \n",
      "2       Knoxville    TN  triangle  20-30 seconds      -0.500043   \n",
      "3      Alexandria    VA    circle            NaN       0.027688   \n",
      "4       Fullerton    CA   unknown      2 minutes      -0.887675   \n",
      "\n",
      "       city_longitude price_bins  relative_appearing  relative_city_latitude  \\\n",
      "index                                                                          \n",
      "0         -119.347937      36-49            1.018610                0.775416   \n",
      "1          -84.481363      36-49            0.969926                0.301550   \n",
      "2          -83.980115      22-36            1.001705                0.977744   \n",
      "3          -77.095133      36-49            1.026087               -0.177743   \n",
      "4         -117.924978      22-36            0.949149                1.613646   \n",
      "\n",
      "      DAY(date_time)  ... NUM_CHARACTERS(stats) NUM_CHARACTERS(summary)  \\\n",
      "index                 ...                                                 \n",
      "0                 15  ...                   174                      91   \n",
      "1                 16  ...                   180                     114   \n",
      "2                 10  ...                   182                     108   \n",
      "3                  7  ...                   166                     127   \n",
      "4                  7  ...                   167                     134   \n",
      "\n",
      "      NUM_CHARACTERS(text)  NUM_WORDS(stats)  NUM_WORDS(summary)  \\\n",
      "index                                                              \n",
      "0                      811                22                  17   \n",
      "1                      302                22                  22   \n",
      "2                     1633                22                  20   \n",
      "3                     1813                21                  18   \n",
      "4                     1275                21                  28   \n",
      "\n",
      "       NUM_WORDS(text)  WEEKDAY(date_time)  WEEKDAY(posted)  YEAR(date_time)  \\\n",
      "index                                                                          \n",
      "0                  147                   2                6             2021   \n",
      "1                   59                   3                6             2021   \n",
      "2                  304                   4                6             2021   \n",
      "3                  322                   1                6             2021   \n",
      "4                  233                   1                3             2020   \n",
      "\n",
      "      YEAR(posted)  \n",
      "index               \n",
      "0             2021  \n",
      "1             2021  \n",
      "2             2021  \n",
      "3             2021  \n",
      "4             2020  \n",
      "\n",
      "[5 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Создание нового признака 'relative_city_latitude'\n",
    "mean_city_latitude_by_state = df.groupby('state')['city_latitude'].transform('mean')\n",
    "df['relative_city_latitude'] = df['city_latitude'] / mean_city_latitude_by_state\n",
    "\n",
    "# Создание EntitySet\n",
    "es = ft.EntitySet(id='jio_mart_items')\n",
    "\n",
    "# Добавление данных с явным указанием индексного столбца\n",
    "es = es.add_dataframe(dataframe_name='items_data', dataframe=df, index='index', make_index=True)\n",
    "\n",
    "# Конструирование признаков\n",
    "features, feature_defs = ft.dfs(entityset=es, target_dataframe_name='items_data', verbose=True)\n",
    "\n",
    "# Проверка первых строк новых признаков\n",
    "print(\"Новые признаки, созданные с помощью Featuretools:\")\n",
    "print(features.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Оценка качества**\n",
    "\n",
    "*Предсказательная способность Метрики:* RMSE, MAE, R²  \n",
    "\n",
    "*Методы:* Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.  \n",
    "\n",
    "*Скорость вычисления Методы:* Измерение времени выполнения генерации признаков и обучения модели.  \n",
    "\n",
    "*Надежность Методы:* Кросс-валидация, анализ чувствительности модели к изменениям в данных.  \n",
    "\n",
    "*Корреляция Методы:* Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.  \n",
    "\n",
    "*Цельность Методы:* Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.5489449749619157\n",
      "R²: 0.924423816721939\n",
      "MAE: 0.46826566221280963\n",
      "Training Time: 37.429771184921265 seconds\n",
      "Cross-validated RMSE: 1.535611497565107\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tumvu\\AppData\\Local\\Temp\\ipykernel_54788\\399707436.py:70: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x='Importance', y='Feature', data=importance_df_top, palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train RMSE: 0.4358844786848851\n",
      "Train R²: 0.994185027626814\n",
      "Train MAE: 0.12184558416960284\n",
      "Корреляция: 0.96\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\tumvu\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"../../datasets/nuforc_reports.csv\").head(2000)\n",
    "\n",
    "# Создание нового признака 'relative_city_latitude'\n",
    "mean_city_latitude_by_state = df.groupby('state')['city_latitude'].transform('mean')\n",
    "df['relative_city_latitude'] = df['city_latitude'] / mean_city_latitude_by_state\n",
    "\n",
    "# Предобработка данных\n",
    "# Преобразуем категориальные переменные в числовые\n",
    "df = pd.get_dummies(df, drop_first=True)\n",
    "\n",
    "# Разделение данных на признаки и целевую переменную\n",
    "X = df.drop('city_latitude', axis=1).dropna()\n",
    "y = df['city_latitude'].dropna()\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# Измерение времени обучения и предсказания\n",
    "start_time = time.time()\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказание и оценка\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "end_time = time.time()\n",
    "training_time = end_time - start_time\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "print(f\"RMSE: {rmse}\")\n",
    "print(f\"R²: {r2}\")\n",
    "print(f\"MAE: {mae}\")\n",
    "print(f\"Training Time: {training_time} seconds\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Cross-validated RMSE: {rmse_cv}\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "\n",
    "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
    "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Отобразим только топ-20 признаков\n",
    "top_n = 20\n",
    "importance_df_top = importance_df.head(top_n)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df_top, palette='viridis')\n",
    "plt.title(f'Top {top_n} Feature Importance')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train)\n",
    "\n",
    "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "mae_train = mean_absolute_error(y_train, y_train_pred)\n",
    "\n",
    "print(f\"Train RMSE: {rmse_train}\")\n",
    "print(f\"Train R²: {r2_train}\")\n",
    "print(f\"Train MAE: {mae_train}\")\n",
    "\n",
    "correlation = np.corrcoef(y_test, y_pred)[0, 1]\n",
    "print(f\"Корреляция: {correlation:.2f}\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Actual city_latitude')\n",
    "plt.ylabel('Predicted city_latitude')\n",
    "plt.title('Actual vs Predicted city_latitude')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выводы и итог  \n",
    "\n",
    "**Время обучения:**\n",
    "\n",
    "Время обучения модели составляет 37 секунды, что является средним. Это указывает на то, что модель обучается быстро и может эффективно обрабатывать данные.\n",
    "\n",
    "**Предсказательная способность:**\n",
    "\n",
    "MAE (Mean Absolute Error): 0.12184558416960284 — это средняя абсолютная ошибка предсказаний модели. Значение MAE  невелико, что означает, что предсказанные значения в среднем отклоняются от реальных на 0.12184558416960284. Это может быть приемлемым уровнем ошибки.\n",
    "\n",
    "RMSE (Mean Squared Error): 0.4358844786848851 — это среднее значение квадратов ошибок.\n",
    "\n",
    "R² (коэффициент детерминации): 0.994185027626814 — это средний уровень, указывающий на то, что модель объясняет 99,4% вариации целевой переменной. Это свидетельствует о средней предсказательной способности модели.\n",
    "\n",
    "**Корреляция:**\n",
    "\n",
    "Корреляция (0.96) между предсказанными и реальными значениями говорит о том, что предсказания модели имеют  сильную линейную зависимость с реальными значениями. Это подтверждает, что модель хорошо обучена и делает точные прогнозы.\n",
    "\n",
    "**Надежность (кросс-валидация):**\n",
    "\n",
    "Среднее RMSE (кросс-валидация): 1.535611497565107 — это значительно ниже, чем обычное RMSE, что указывает на отсутствие проблем с переобучением - что и подтверждается тестом переобучением. \n",
    "\n",
    "Результаты визуализации важности признаков, полученные из линейной регрессии, помогают понять, какие из входных переменных наибольшим образом влияют на целевую переменную (city_latitude)."
   ]
  }
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