AIM-PIbd-31-Zhirnova-A-E/lab_3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало лабораторной\n",
    "\n",
    "Цены на кофе - https://www.kaggle.com/datasets/mayankanand2701/starbucks-stock-price-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Бизнес-цели**\n",
    "1. Предсказание цены закрытия акций (Close) \n",
    "Максимизация прибыли путем предсказания изменения цены акций, что может помочь в принятии инвестиционных решений.\n",
    "2. Предсказание объема торгов (Volume)\n",
    "Оптимизация торговых операций и улучшение ликвидности на рынке, что помогает в планировании запасов и управлении финансовыми потоками.\n",
    "\n",
    "\n",
    "**Технические цели проекта**:\n",
    "\n",
    "**Сбор и подготовка данных**\n",
    "Разработка подхода для сбора исторических данных о ценах акций и объемах торгов.\n",
    "Обеспечение качества данных: устранение дубликатов, обработка пропусков и аномалий.\n",
    "Факторизация данных: создание дополнительных признаков, таких как скользящие средние, индекс относительной силы (RSI) и другие технические индикаторы.\n",
    "\n",
    "**Выбор и разработка моделей**\n",
    "Исследование различных методов машинного обучения и глубокого обучения для предсказания цен закрытия акций и объемов торгов.\n",
    "Проведение сравнительного анализа моделей (например, линейная регрессия, деревья решений, нейронные сети) с использованием метрик, таких как RMSE (корень среднеквадратичной ошибки) и MAE (средняя абсолютная ошибка).\n",
    "Оптимизация гиперпараметров моделей для повышения точности предсказаний.\n",
    "\n",
    "**Валидация и тестирование моделей**\n",
    "Разработка стратегий кросс-валидации для оценки производительности моделей на различных временных интервалах и рыночных условиях.\n",
    "Оценка устойчивости моделей к различным сценариям (например, резкие падения или росты рынков).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "print(df.columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date         False\n",
       "Open         False\n",
       "High         False\n",
       "Low          False\n",
       "Close        False\n",
       "Adj Close    False\n",
       "Volume       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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",
    "# Проверка на пропущенные данные\n",
    "print(df.isnull().sum())\n",
    "\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных значений нет."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и\n",
    "тестовую выборки для устранения проблемы просачивания данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 5142\n",
      "Размер контрольной выборки: 1286\n",
      "Размер тестовой выборки: 1608\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тест)\n",
    "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную (80% - обучение, 20% - контроль)\n",
    "train_data, val_data = train_test_split(train_data, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_data))\n",
    "print(\"Размер контрольной выборки:\", len(val_data))\n",
    "print(\"Размер тестовой выборки:\", len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Загрузим данные\n",
    "df = pd.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "\n",
    "# Определяем целевую переменную и признаки\n",
    "target_column = 'Close'\n",
    "X = df.drop(columns=[target_column, 'Date'])\n",
    "y = df[target_column]\n",
    "\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\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",
    "# Создаём DataFrame для каждой выборки\n",
    "train_data = pd.DataFrame(X_train, columns=X.columns)\n",
    "train_data['Close'] = y_train.reset_index(drop=True)\n",
    "\n",
    "val_data = pd.DataFrame(X_val, columns=X.columns)\n",
    "val_data['Close'] = y_val.reset_index(drop=True)\n",
    "\n",
    "test_data = pd.DataFrame(X_test, columns=X.columns)\n",
    "test_data['Close'] = y_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+/v4YOnSo9LGla/Xbb79BoVBg/PjxJq+dPHkyhBDYvHmzyfbQ0FCEhIRIH9evXx/PPvsstm7dCr1eb7Kv8fuoWrVq+Pnnn02+x8tznZ0Bu7GcRExMDJo2bQqlUgl/f380a9bMZBqmwWDAp59+isWLFyMhIcHkm8TY1QXkdX81a9YMSqV1vzQenHYpk8nQuHFjqd/+4sWLAIDIyMgij5GWloYaNWpIH6ekpDx0OufFixchhChyvwe7m4xrdzwYMB48ZlpamvSL5kEPLiC2bdu2Uk/TnjlzJurUqYPXX38da9euNTv/uXPnijxmcQuYXb16FUDeuI8HNW/eHL///juAgmDbvHlzs/1atGgBIG+chqWlDQp391SvXh2DBw/GggUL4OnpiUuXLkEIgRkzZmDGjBlF1m8M6QDg6+uLiIgIfPfdd+jQoQO+++47REZGWpxmfO3aNbz33nv4+eefzcZPpaWlWTzfg/71r39J/1epVPjiiy8sdqEWdvXqVXTq1Mlsu/FaXb16Fa1atYJMJkOdOnWgVqtN9jN+vxYexzJs2DCsXr0a+/fvx+OPP44dO3YgKSnJ5Jdp+/bt4ebmhtmzZ+PLL7+Uvj90Op3Z1/by5cvx8ccf4/z589DpdNL2oKAgs7oXLFiABQsWSOd48FiFvwbK6/bt20hNTcXSpUuL7Aa19DWt1+vxn//8B0OGDCn1lP3+/ftL/5fL5Zg+fbrFdY4Kf5/4+flh9OjRmD17NhQKhdm+MpkMTZs2Nfu6fPBaXb16FXXq1DH546/wfsbvUSNLP7uaNm2KrKws3L59GwEBAdL2fv364cKFC/Dz8zMb/1PW6+wsGHacRMeOHaXZWJZ88MEHmDFjBkaOHIn3338fPj4+kMvlmDBhglmLiSMYa1iwYEGR4wcKBxCtVotbt26hd+/eDz2uTCbD5s2bLf6AejDUJCYmAoDJDxBLx/Tz88MPP/xg8fkHQ0inTp0wd+5ck21ffPEFNm7caPH1586dw7Jly7BixQqLY38MBgNat26NTz75xOLr69WrV2TtJVW4haS0jME7JycHe/bskQatLl68WPo8T5kyRRpk+6DGjRubbRs5ciSGDRuGcePGYd++ffjmm29MBkoDeb/8evfujbt37+Kdd95B8+bN4enpiRs3bmD48OEl/jpfsWIF/P39kZ2djV27diEqKgpubm4mg1HLqjTXNTw8HP7+/lixYgUef/xxrFixAgEBASZh0t/fH59//jmioqLQtGlTk9cXbo1asWIFhg8fjv79+2Pq1Knw8/ODQqFAdHS0NL6vsKFDh2LYsGEWx8BZm/Hz8sorrxT5x46lMPPtt9/iypUr2Lp1a6nP+dFHH6Ft27bQ6XQ4evQo5s6dC6VSiZkzZ5rst27dOqjVamRlZWH9+vX473//C7VajbffftvsmOX5nrGW8+fPY/PmzRg0aBAmT56M2NhY6bmyXmdnwbBTRaxduxZPPPEEvv32W5PtqampqFWrlvRxo0aNcPjwYYt/GZaHseXGSAiBS5cuSd9cxoHParW62IGgRn/++Sd0Ol2xAc94XCEEgoKCzH4ZWHL27FnIZDKLLR+Fj7ljxw507dq1RD/gatWqZfaeihtEPG3aNLRr1w4vvvhikef/888/0atXr1J3LQYGBgIALly4YNZsf+HCBen5WrVqwcvLCxcuXDA7xvnz5wHkzUaxpHDwjoiIwMmTJ7FlyxYAQMOGDQHktaiV5PNs1LdvX7i5ueGll15Ct27d0KhRI7Owc+rUKfz1119Yvnw5hg0bJm3fvn17ic8DAF27dpXeW79+/XDmzBlER0cXG3YCAwOLvVbG6xoUFIRt27YhIyPD5C/7v/76CwaDweSaKhQKvPzyy1i2bBk+/PBDbNiwAaNHjzYL7a+++ioGDBiA06dPS92wxm4xo7Vr16Jhw4b46aefTL5mHvzlbtSwYUOEhYWhadOmOHDggNnPg4d9DZSGr68vqlWrBr1eX+KviaysLMyePRtvvvmmdG1LIyQkRJol1rdvX9y4cQMffvghZsyYYdIy8/jjj0s/H5955hkcOHAAW7ZssRh2goKCcOLECRgMBpNjPHitAgMDsWPHDrOvgQe/Vowe/NkJ5H29eHh4mP1h9fPPP+Oxxx5DdHQ0xo4di1deeUXq7i3LdXYmHLNTRSgUCrNmzTVr1piNjxg4cCBSUlLwxRdfmB3jwdeXxvfff28yhmPt2rW4desW+vbtCyDvh0+jRo3w0UcfITMz0+z1D049XrNmDRQKhcVp3YUNGDAACoUCs2fPNqtfCCHNOgKA3NxcrFu3Dh07diy2G2vQoEHQ6/V4//33zZ7Lzc01Wca+tOLi4rBx40bMmzevyCAzaNAg3Lhxw+KU1vv37xc7vqBDhw7w8/PDkiVLTKapb968GefOnUNERASAvKb9J598Ehs3bpSWLwDyxjAsX74cHTp0KPHq3NnZ2dIvaD8/P/To0QNfffUVbt26Zbbvg59nI6VSiWHDhuHkyZMm40cKM56j8OdZCIFPP/20RHUW5f79+w+d0v/UU0/hyJEjiIuLk7ZpNBosXboUDRo0QHBwsLSfXq83+/4yttIZr7/R0KFDce/ePbz++uvIzMzEK6+8YvH8Pj4+ePzxxxEWFoawsDCT7l7A8rU5fPiwSb2WPPnkk0hLS8OyZcukbcYucZVKZZVfmgqFAgMHDsS6detw+vRps+ctfU18+umn0Gg0ePfdd8t9fiDvc5ybm4vc3Nwi9xFCQAhhsYUYyPvcJiYmYvXq1dI2S9eqqK+BhQsXQiaTST8TjeLi4kzG0ly/fh0bN25Enz59zGoxziB988030aVLF7z++uvSsgtluc7OhC07VUS/fv0wZ84cjBgxAl26dMGpU6fwww8/SH9pGw0bNgzff/89Jk2ahCNHjuCxxx6DRqPBjh078Oabb+LZZ58t0/l9fHzQrVs3jBgxAklJSVi0aBEaN26M0aNHA8j75frNN9+gb9++aNmyJUaMGIG6devixo0b2L17N9RqNX755RdoNBrExMTgs88+Q9OmTbFnzx7pHMaQdPLkScTFxSE0NBSNGjXC3Llzpam9/fv3R7Vq1ZCQkID169fjtddew5QpU7Bjxw7MmDEDJ0+exC+//FLse+nevTtef/11REdHIz4+Hn369IGLiwsuXryINWvW4NNPP8Xzzz9fpuu0bds29O7du9hfIkOHDsWPP/6IN954A7t370bXrl2h1+tx/vx5/Pjjj9i6dWuRLV4uLi748MMPMWLECHTv3h2DBw+Wpp43aNDAZODsnDlzsGXLFnTr1g1vvvkmVCoVvv76a6SlpeHjjz8usr7Nmzfj/PnzUjfWvn37MGXKFOn5mJgYdOvWDa1bt8bo0aPRsGFDJCUlIS4uDv/884/FtZ8A4P3338fUqVPNfpEbNW/eHI0aNcKUKVNw48YNqNVqrFu3zmzszsNs2LABtWrVkrqx9u/f/9Apuf/+97/xf//3f+jbty/Gjx8PHx8fLF++HAkJCVi3bp30l/5TTz2FsLAwvPvuu0hISEC7du2wa9curFu3Dm+88YbZVOL27dujVatW0qD0wuOJSqNfv3746aef8NxzzyEiIgIJCQlYsmQJgoODLf5xYfTqq69iyZIlePPNN3Hq1Ck0a9YMGzduxPbt2xEdHW0y3g/Iax00tuIBed+TcrncZJuxBWzv3r1Qq9Xw8fHBvHnzsHv3bnTq1AmjR49GcHAw7t69ixMnTmDHjh24e/euyXm2bduG//73v2bnL6nt27fjn3/+kbqxfvjhBzzzzDNmCz3u2rXLpBvr0qVLRX4tjBo1Cl9++SWGDx+OY8eOISgoCBs2bMDOnTsxb948qdann34aTzzxBN59911cuXIFbdu2xbZt27Bx40ZMmDDBZHkPIG+tovDwcJOp5wAwe/bsIt+fTCbDN998g3bt2mHmzJmYP38+AJT6OjsVu879Iqsr6XTf7OxsMXnyZFG7dm3h7u4uunbtKuLi4symqAqRN3333XffFUFBQcLFxUUEBASI559/Xly+fFkIUbap5//3f/8npk2bJvz8/IS7u7uIiIiwOBX4jz/+EAMGDBA1a9YUKpVKBAYGikGDBomdO3eanPthj8JTQoUQYt26daJbt27C09NTeHp6iubNm4uoqChx4cIFIYQQ48aNE48//rjYsmWLWU0PTj03Wrp0qQgJCRHu7u6iWrVqonXr1uLtt98WN2/elPYp7dRzmUwmjh8/brLd0udIq9WKDz/8ULRs2VKoVCpRo0YNERISImbPni3S0tLMzveg1atXi/bt2wuVSiV8fHzEkCFDpGUKCjtx4oQIDw8Xnp6ewsPDQ/To0cNsyruR8WvR+HB1dRWNGzcW7733ntm058uXL4thw4aJgIAA4eLiIurWrSv69esn1q5da3a8or62LT1/9uxZERYWJry8vEStWrXE6NGjxZ9//ikAiNjY2GKvSXH1F54mX5TLly+L559/XlSvXl24ubmJjh07ik2bNpntl5mZKSZOnCjq1KkjXFxcROPGjcW8efPMloYwmj9/vgAgPvjgg4fWYPTg14zBYBAffPCBCAwMFCqVSrRv315s2rRJREZGisDAQJPX4oEp3ElJSWLEiBGiVq1awtXVVbRs2VIsXbrU5DUl/b588FF4CnhSUpKIiooS9erVk37u9OrVy+Rcxp8ntWvXFhqNpti6LTG+3vhQKpUiMDBQjB8/Xty7d0/az/g9b3y4u7uL4OBgsXDhQmmfB6eeCyFEcnKyGDlypHStWrVqJb7++muzOjIyMky+Bpo0aSIWLFhgsuyG8T1FRUWJFStWiCZNmkifu8LXrXC9D5o9e7ZQKpXixIkT0raSXGdnxHtjkU3t2bMHTzzxBNasWVPm1o7Crly5gqCgICQkJBQ5XmDWrFm4cuWKSdM7UWX16aefYuLEibhy5Qrq16/v6HKspkGDBli2bJnZCstUQCaTISoqyuKwAiodjtkhIqqghBD49ttv0b17d6cKOkT2xjE7VKl4eXlhyJAhxQ4gbtOmjXT7C6LKSKPR4Oeff8bu3btx6tSpIpcpqMy6d+8OHx8fR5dBVQTDDlUqtWrVwooVK4rdZ8CAAXaqhsg2bt++jZdffhnVq1fHf/7zHzzzzDOOLsnqli9f7ugSqArhmB0iIiJyahyzQ0RERE6NYYeIiIicGsfsIG+Vy5s3b6JatWrlurM3ERER2Y8QAhkZGahTp47FmwMbMewAuHnzplVunkhERET2d/36dTzyyCNFPs+wA0g3Y7t+/TrUarWDqyEiIqKSSE9PR7169UxuqmqJQ8POrFmzzO7v0axZM+nur9nZ2Zg8eTJWrVqFnJwchIeHY/HixSY3ILx27RrGjBmD3bt3w8vLC5GRkYiOjoZSWfK3Zuy6UqvVDDtERESVzMOGoDi8Zadly5bYsWOH9HHhkDJx4kT8+uuvWLNmDby9vTF27FgMGDAABw4cAADo9XpEREQgICAABw8exK1btzBs2DC4uLjggw8+sPt7ISIioorH4WFHqVQiICDAbHtaWhq+/fZbrFy5Ej179gQAxMbGokWLFjh06BA6d+6Mbdu24ezZs9ixYwf8/f3Rrl07vP/++3jnnXcwa9YsszvYEhERUdXj8KnnFy9eRJ06ddCwYUMMGTIE165dAwAcP34cOp0OYWFh0r7NmzdH/fr1ERcXBwCIi4tD69atTbq1wsPDkZ6ejjNnzhR5zpycHKSnp5s8iIiIyDk5NOx06tQJy5Ytw5YtW/Dll18iISEBjz32GDIyMpCYmAhXV1dUr17d5DX+/v5ITEwEACQmJpoEHePzxueKEh0dDW9vb+nBmVhERETOy6HdWH379pX+36ZNG3Tq1AmBgYH48ccf4e7ubrPzTps2DZMmTZI+No7mJiIiIufj8G6swqpXr46mTZvi0qVLCAgIgFarRWpqqsk+SUlJ0hifgIAAJCUlmT1vfK4oKpVKmnnFGVhERETOrUKFnczMTFy+fBm1a9dGSEgIXFxcsHPnTun5Cxcu4Nq1awgNDQUAhIaG4tSpU0hOTpb22b59O9RqNYKDg+1ePxEREVU8Du3GmjJlCp5++mkEBgbi5s2bmDlzJhQKBQYPHgxvb2+MGjUKkyZNgo+PD9RqNcaNG4fQ0FB07twZANCnTx8EBwdj6NChmD9/PhITEzF9+nRERUVBpVI58q0RERFRBeHQsPPPP/9g8ODBuHPnDnx9fdGtWzccOnQIvr6+AICFCxdCLpdj4MCBJosKGikUCmzatAljxoxBaGgoPD09ERkZiTlz5jjqLREREVEFIxNCCEcX4Wjp6enw9vZGWloax+8QERFVEiX9/V2hxuwQERERWRvDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMezYmUajgUajcXQZREREVQbDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQERGRU2PYISIiIqfGsENEREROjWGHiIiInBrDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQERGRU2PYISIiIqfGsENEREROjWGHiIiInBrDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQERGRU2PYISIiIqfGsENEREROjWGHiIiInBrDDhERETk1hh07EkJAo9FACOHoUoiIiKoMhh07ysrKwkcbjyArK8vRpRAREVUZDDt25qrycHQJREREVQrDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQERGRU2PYISIiIqfGsENEREROjWGHiIiInBrDDhERETk1hh0iIiJyagw7RERE5NQYdoiIiMipMewQERGRU2PYsTMhBDQaDYQQji6FiIioSmDYsTOdNhtfbDuDrKwsR5dCRERUJTDsOICLyt3RJRAREVUZDDtERETk1Bh2iIiIyKkx7BAREZFTY9ghIiIip8awQ0RERE6NYYeIiIicGsMOEREROTWGHSIiInJqDDtERETk1Bh2iIiIyKkx7BAREZFTY9ghIiIip1Zhws68efMgk8kwYcIEaVt2djaioqJQs2ZNeHl5YeDAgUhKSjJ53bVr1xAREQEPDw/4+flh6tSpyM3NtXP1REREVFFViLBz9OhRfPXVV2jTpo3J9okTJ+KXX37BmjVrsHfvXty8eRMDBgyQntfr9YiIiIBWq8XBgwexfPlyLFu2DO+995693wIRERFVUA4PO5mZmRgyZAi+/vpr1KhRQ9qelpaGb7/9Fp988gl69uyJkJAQxMbG4uDBgzh06BAAYNu2bTh79ixWrFiBdu3aoW/fvnj//fcRExMDrVbrqLdEREREFYjDw05UVBQiIiIQFhZmsv348ePQ6XQm25s3b4769esjLi4OABAXF4fWrVvD399f2ic8PBzp6ek4c+ZMkefMyclBenq6yYOIiIick9KRJ1+1ahVOnDiBo0ePmj2XmJgIV1dXVK9e3WS7v78/EhMTpX0KBx3j88bnihIdHY3Zs2eXs/rSEUJAo9EAEHY9LxERUVXnsJad69ev46233sIPP/wANzc3u5572rRpSEtLkx7Xr1+3+TmzsrLw0cYjHDxNRERkZw4LO8ePH0dycjL+9a9/QalUQqlUYu/evfjss8+gVCrh7+8PrVaL1NRUk9clJSUhICAAABAQEGA2O8v4sXEfS1QqFdRqtcnDHlxVHnY5DxERERVwWNjp1asXTp06hfj4eOnRoUMHDBkyRPq/i4sLdu7cKb3mwoULuHbtGkJDQwEAoaGhOHXqFJKTk6V9tm/fDrVajeDgYLu/JyIiIqp4HDZmp1q1amjVqpXJNk9PT9SsWVPaPmrUKEyaNAk+Pj5Qq9UYN24cQkND0blzZwBAnz59EBwcjKFDh2L+/PlITEzE9OnTERUVBZVKZff3RERERBWPQwcoP8zChQshl8sxcOBA5OTkIDw8HIsXL5aeVygU2LRpE8aMGYPQ0FB4enoiMjISc+bMcWDVREREVJHIhBBVfnpQeno6vL29kZaWZrPxOxqNBtHrDiNLkw5X92qY8UJneHp62uRcREREVUFJf387fJ0dIiIiIlti2CEiIiKnxrBDRERETo1hh4iIiJwaww4RERE5NYYdOxJC4E62DDn6Kj8BjoiIyG4q9Do7ziblvgF7k5SonalzdClERERVBlt27ChLl9eik6QxQJPDG4ISERHZA8OOHRl7rwwAjl5NdWQpREREVQbDjh0ZCg3ViUu457hCiIiIqhCGHTsqHHYOXmbYISIisgeGHTsqHHb+vpOFm6n3HVcMERFRFcGwY0eGB+65+vvFFAdVQkREVHUw7NiR4YHldfZfvO2YQoiIiKoQhh07Ms7GUrvk/Wf/xdsQggsMEhER2RLDjh0ZW3Z83PL+Tb2fi0yut0NERGRTDDt2ZAw7LnJAlr/tvlbvsHqIiIiqAoYdOzIgL+3IASjzr3wWww4REZFNMezYkbFlRy4DlPK8th2GHSIiItti2LEj07CT9//7Oo7ZISIisiWGHTvSFwo7ivyWnftagwMrIiIicn4MO3ZkyG/aKdyyk6Vlyw4REZEtMezYkdSNhYIxO/d1HLNDRERkSww7dmRpzA4HKBMREdkWw44dcTYWERGR/THs2FHhAcrK/FUFs9mNRUREZFMMO3ZkvOu5acsOBygTERHZEsOOHRknmXMFZSIiIvth2LEjS2N2eG8sIiIi21I6uoCqxGBhzA5bdoiIiGyLYceOjGFHJgOUMs7GIiIisgd2Y9mRPn+AskKW9wB4bywiIiJbY9ixo8IrKMsMOgCAJlvnuIKIiIiqAHZj2ZHJmJ38bezGIiIisi227NiRyZgd49TznFyI/O4tIiIisj6GHTsRQpi07Cjyr3xKZg6ysrIcVxgREZGTYzeWnegMBa03cuS17gAFt5AgIiIi22DYsROd3iD9Xy4DZPktO7kGsBuLiIjIhhh27ERXqAlHLst7AIB44DkiIiKyLo7ZsZOClh0BGQoGKANAFu98TkREZDMMO3ZibL0xjteRy2RS6w7vj0VERGQ7DDt2YmzZMQYcoOD+WPfZskNERGQzDDt2IrXsFAo7xunn2TqDhVcQERGRNTDs2AlbdoiIiByDYcdOCo/ZMZJWUeaYHSIiIpth2LETSy07xm6s++zGIiIishmGHTuxNGZHydlYRERENsewYycWx+wYByjnMuwQERHZCsOOnRSM2SlYLVmRf4Os+1p2YxEREdkKw46dFNeywxWUiYiIbIdhx04sjtkxDlDmmB0iIiKbYdixE2PLjszCAOWMrBze+ZyIiMhGGHbspLh1do4l3EZWVpb9iyIiIqoCGHbsxOI6O/n/N8gUDqiIiIioamDYsZPixuzkGtiFRUREZCsMO3YitewU2lYQduxfDxERUVXBsGMnBS075uvssGWHiIjIdhh27KS4dXbYskNERGQ7DDt2ojMUfW8sPVt2iIiIbIZhx04sjdlRsGWHiIjI5hh27ISzsYiIiBzDoWHnyy+/RJs2baBWq6FWqxEaGorNmzdLz2dnZyMqKgo1a9aEl5cXBg4ciKSkJJNjXLt2DREREfDw8ICfnx+mTp2K3Nxce7+Vh7I4Zif//7kCMHAFZSIiIptwaNh55JFHMG/ePBw/fhzHjh1Dz5498eyzz+LMmTMAgIkTJ+KXX37BmjVrsHfvXty8eRMDBgyQXq/X6xEREQGtVouDBw9i+fLlWLZsGd577z1HvaUiaXOLHqAMANk69mURERHZgkxUsJsy+fj4YMGCBXj++efh6+uLlStX4vnnnwcAnD9/Hi1atEBcXBw6d+6MzZs3o1+/frh58yb8/f0BAEuWLME777yD27dvw9XVtUTnTE9Ph7e3N9LS0qBWq23yvl5ffgRbz91G2xp6NKouh6uLEkIIrL6YF3L2TeqC+n41bHJuIiIiZ1TS398VZsyOXq/HqlWroNFoEBoaiuPHj0On0yEsLEzap3nz5qhfvz7i4uIAAHFxcWjdurUUdAAgPDwc6enpUutQRVHQjVWQLWUymXTLiGwd73xORERkC0pHF3Dq1CmEhoYiOzsbXl5eWL9+PYKDgxEfHw9XV1dUr17dZH9/f38kJiYCABITE02CjvF543NFycnJQU5OjvRxenq6ld5N0SxNPQfyurL0euC+lt1YREREtuDwlp1mzZohPj4ehw8fxpgxYxAZGYmzZ8/a9JzR0dHw9vaWHvXq1bPp+QDLA5SBgpuBZrFlh4iIyCYcHnZcXV3RuHFjhISEIDo6Gm3btsWnn36KgIAAaLVapKammuyflJSEgIAAAEBAQIDZ7Czjx8Z9LJk2bRrS0tKkx/Xr1637piyQpp4/sN04SPk+ww4REZFNODzsPMhgMCAnJwchISFwcXHBzp07pecuXLiAa9euITQ0FAAQGhqKU6dOITk5Wdpn+/btUKvVCA4OLvIcKpVKmu5ufNiasWVH9mA3Vv7H97UMO0RERLbg0DE706ZNQ9++fVG/fn1kZGRg5cqV2LNnD7Zu3Qpvb2+MGjUKkyZNgo+PD9RqNcaNG4fQ0FB07twZANCnTx8EBwdj6NChmD9/PhITEzF9+nRERUVBpVI58q2ZsbSoIFCwinIOl1EmIiKyCYeGneTkZAwbNgy3bt2Ct7c32rRpg61bt6J3794AgIULF0Iul2PgwIHIyclBeHg4Fi9eLL1eoVBg06ZNGDNmDEJDQ+Hp6YnIyEjMmTPHUW+pSMaWHUVRLTtcZ4eIiMgmHBp2vv3222Kfd3NzQ0xMDGJiYorcJzAwEL/99pu1S7O6osbsGFt2OPWciIjINircmB1nVdSYHUX+hmx2YxEREdkEw46dFIzZMV2wWsmWHSIiIpti2LGTh62zw3tjERER2QbDjp1wnR0iIiLHYNixE+1DWnY49ZyIiMg2GHbsQAjx0HV22I1FRERkGww7dpBrKBiUbNaNJa2zw24sIiIiW2DYsQPj4GSALTtERET2xrBjB7rcQi07Rayzk5PLlh0iIiJbKFPYadiwIe7cuWO2PTU1FQ0bNix3Uc5GW6hl54Gsw9tFEBER2ViZws6VK1eg15u3ROTk5ODGjRvlLsrZFF5jx2wFZS4qSEREZFOlujfWzz//LP3feGdyI71ej507d6JBgwZWK85ZFHUTUKCgZYdjdoiIiGyjVGGnf//+AACZTIbIyEiT51xcXNCgQQN8/PHHVivOWRS1ejJQqGWH6+wQERHZRKnCjsGQ9ws5KCgIR48eRa1atWxSlLPR5lpeYwcofLsIdmMRERHZQqnCjlFCQoK163BqBS075mlHyannRERENlWmsAMAO3fuxM6dO5GcnCy1+Bh999135S7MmRTbjZW/LdcgoNMb4KLgagBERETWVKawM3v2bMyZMwcdOnRA7dq1IbPQYkEFirovFmA6aDlbp2fYISIisrIyhZ0lS5Zg2bJlGDp0qLXrcUpF3RfLuE0GQCDvlhHV3FzsWhsREZGzK1MzglarRZcuXaxdi9PS5hY99VwmkxXMyNJy3A4REZG1lSnsvPrqq1i5cqW1a3FaxQ1QBgoGKWdpc+1VEhERUZVRpm6s7OxsLF26FDt27ECbNm3g4mLa9fLJJ59YpThnUdwAZaAgcd7L0AC11fYpioiIqIooU9g5efIk2rVrBwA4ffq0yXMcrGzO2I1VVNgxtuzkcGFBIiIiqytT2Nm9e7e163BqxQ1QBriwIBERkS1xnrMdFNwbq/gxO7zzORERkfWVqWXniSeeKLa7ateuXWUuyBk9bMwOW3aIiIhsp0xhxzhex0in0yE+Ph6nT582u0EoPXzMjhR2OGaHiIjI6soUdhYuXGhx+6xZs5CZmVmugpyRJjsHACATlsOMUp63rCBbdoiIiKzPqmN2XnnlFd4Xy4KSd2OxZYeIiMjarBp24uLi4ObmZs1DOoWHzsaSBiizZYeIiMjaytSNNWDAAJOPhRC4desWjh07hhkzZlilMGfysJYdZf52rrNDRERkfWUKO97e3iYfy+VyNGvWDHPmzEGfPn2sUpgzMbbsFDV/Tbo3FruxiIiIrK5MYSc2NtbadTi1XEPx3VjGlh12YxEREVlfmcKO0fHjx3Hu3DkAQMuWLdG+fXurFOVsch82QJktO0RERDZTprCTnJyMl156CXv27EH16tUBAKmpqXjiiSewatUq+Pr6WrPGSk+f37JT1DqMCmnMDlt2iIiIrK1Ms7HGjRuHjIwMnDlzBnfv3sXdu3dx+vRppKenY/z48dausdKTurGKeJ63iyAiIrKdMrXsbNmyBTt27ECLFi2kbcHBwYiJieEAZQtyH3ojUC4qSEREZCtlatkxGAxwcXEx2+7i4gKDga0TD8otYTcWx+wQERFZX5nCTs+ePfHWW2/h5s2b0rYbN25g4sSJ6NWrl9WKcxYl7cbivbGIiIisr0xh54svvkB6ejoaNGiARo0aoVGjRggKCkJ6ejo+//xza9dY6eXmt3Y9vGWH3VhERETWVqYxO/Xq1cOJEyewY8cOnD9/HgDQokULhIWFWbU4Z1EwZsdy2uHUcyIiItspVcvOrl27EBwcjPT0dMhkMvTu3Rvjxo3DuHHj8Oijj6Jly5bYv3+/rWqttB7WjaXgooJEREQ2U6qws2jRIowePRpqtdrsOW9vb7z++uv45JNPrFacs3jYAGXjmB2dXkhr8hAREZF1lCrs/Pnnn3jyySeLfL5Pnz44fvx4uYtyNg+fel7wf47bISIisq5ShZ2kpCSLU86NlEolbt++Xe6inE1JV1AG2JVFRERkbaUKO3Xr1sXp06eLfP7kyZOoXbt2uYtyNsbZWEVdbJlMVjBuR8uwQ0REZE2lCjtPPfUUZsyYgezsbLPn7t+/j5kzZ6Jfv35WK85ZPOyu50DBuB3eH4uIiMi6SjX1fPr06fjpp5/QtGlTjB07Fs2aNQMAnD9/HjExMdDr9Xj33XdtUmhlpnvImB0AUMhlgF7gvpbTz4mIiKypVGHH398fBw8exJgxYzBt2jQIYRyLIkN4eDhiYmLg7+9vk0IrM2nMTjH7FNwMlC07RERE1lTqRQUDAwPx22+/4d69e7h06RKEEGjSpAlq1Khhi/qcQkm6sYw3A2XYISIisq4yraAMADVq1MCjjz5qzVqcVq6+0O0iilhGR7o/FsMOERGRVZXp3lhUOg9bQRkAlPnNPgw7RERE1sWwYwcPW0EZKLg/FqeeExERWRfDjh08bAVlAFDmJyGO2SEiIrIuhh0bMxiENEyn+Knnef/yzudERETWxbBjYzpDQXgpfuo5W3aIiIhsgWHHxoxdWEDJVlDmAGUiIiLrYtixsZKGHeO9sbK0uTauiIiIqGph2LGx3BJ2Y7nkp52sHLbsEBERWRPDjo3lFrpVhKyYuecyvQ4AoGHLDhERkVUx7NiYLn/15OK6sICCMTtZXGeHiIjIqhh2bExfggUFgYLZWJoctuwQERFZE8OOjen0D79VBMCWHSIiIltxaNiJjo7Go48+imrVqsHPzw/9+/fHhQsXTPbJzs5GVFQUatasCS8vLwwcOBBJSUkm+1y7dg0RERHw8PCAn58fpk6ditzcitFCYhyg/LBuLJf85zlmh4iIyLocGnb27t2LqKgoHDp0CNu3b4dOp0OfPn2g0WikfSZOnIhffvkFa9aswd69e3Hz5k0MGDBAel6v1yMiIgJarRYHDx7E8uXLsWzZMrz33nuOeEtmSnKrCKBQyw5nYxEREVmVTAghHr6bfdy+fRt+fn7Yu3cvHn/8caSlpcHX1xcrV67E888/DwA4f/48WrRogbi4OHTu3BmbN29Gv379cPPmTfj7+wMAlixZgnfeeQe3b9+Gq6vrQ8+bnp4Ob29vpKWlQa1WW/U9xV9PRf+YA/B0keHpIDm0OdmAXAlXF6XJfhqdwC8JBri5yHH+/b5WrYGIiMgZlfT3d4Uas5OWlgYA8PHxAQAcP34cOp0OYWFh0j7NmzdH/fr1ERcXBwCIi4tD69atpaADAOHh4UhPT8eZM2csnicnJwfp6ekmD1vJLeVsrGydQRrUTEREROVXYcKOwWDAhAkT0LVrV7Rq1QoAkJiYCFdXV1SvXt1kX39/fyQmJkr7FA46xueNz1kSHR0Nb29v6VGvXj0rv5sCJR6gXCgMcRVlIiIi66kwYScqKgqnT5/GqlWrbH6uadOmIS0tTXpcv37dZucq6dRzuQxQcPo5ERGR1VWIsDN27Fhs2rQJu3fvxiOPPCJtDwgIgFarRWpqqsn+SUlJCAgIkPZ5cHaW8WPjPg9SqVRQq9UmD1vRlXA2lkwmg3t+886dtEyb1UNERFTVODTsCCEwduxYrF+/Hrt27UJQUJDJ8yEhIXBxccHOnTulbRcuXMC1a9cQGhoKAAgNDcWpU6eQnJws7bN9+3ao1WoEBwfb540Uo6SzsQDA3SXv06HhWjtERERWo3z4LrYTFRWFlStXYuPGjahWrZo0xsbb2xvu7u7w9vbGqFGjMGnSJPj4+ECtVmPcuHEIDQ1F586dAQB9+vRBcHAwhg4divnz5yMxMRHTp09HVFQUVCqVI98eAECf37IjK/Y2oHmMYYcLCxIREVmPQ8POl19+CQDo0aOHyfbY2FgMHz4cALBw4ULI5XIMHDgQOTk5CA8Px+LFi6V9FQoFNm3ahDFjxiA0NBSenp6IjIzEnDlz7PU2iqUrQ8sOww4REZH1ODTslGSJHzc3N8TExCAmJqbIfQIDA/Hbb79ZszSrKekKygDgwbBDRERkdRVigLIzM47ZedhsLIAtO0RERLbAsGNjuYaSrbMDAG4MO0RERFbHsGNjJV1BGQDclQw7RERE1sawY2M6qRuLs7GIiIgcgWHHxowrKJdqNpaOYYeIiMhaGHZsrKQrKANs2SEiIrIFhh0bk2ZjlWBfhh0iIiLrY9ixsdwSdmMJIaAw5N0AlGGHiIjIehh2bKyks7F02hzsO3cDAKDR8q7nRERE1sKwY2PGlp2SzMZSuboCYMsOERGRNTHs2Jh01/MS7Ju/zA7DDhERkRUx7NhYae6NxbBDRERkfQw7NqYrxb2xlPn7MOwQERFZD8OOjZXmdhHGlp37OgMMhoffEZ6IiIgejmHHxvSluBGoslAgus9VlImIiKyCYcfGdNI6Ow9v2pHLChYf5PRzIiIi62DYsTFjN1ZJxuzIZIUGKeewZYeIiMgaGHZsrKQrKBsp83dkyw4REZF1MOzYWGkGKAOAC6efExERWRXDjo1JKyiXcH+lIm/PzBy27BAREVkDw46NSSsol7Zlh2N2iIiIrIJhx8YKVlAuWdrhmB0iIiLrYtixsdKsoAwUhJ0sdmMRERFZBcOOjUktOyXc39iNpeEAZSIiIqtg2LGx0o7ZkVp22I1FRERkFQw7NibNxirpAGVF3r8aDlAmIiKyCoYdGyvtOjts2SEiIrIuhh0bk1ZQLuFKOxyzQ0REZF0MOzaWy9lYREREDsWwY2MF6+yUbH8XaZ0dtuwQERFZA8OOjZX6RqD5A5Q5ZoeIiMg6GHZsrPS3i8jbMf0+ww4REZE1MOzYmC5/NlZJbwTqmn8j0LT7OhtVREREVLUw7NhYQTdWyeKOKr8bKz1bB0P+a4mIiKjsGHZsSAgBfSnH7BhbdoQAMrLZlUVERFReDDs2lFuoZaakU88Vchnc8xfbSb2vtUVZREREVQrDjg0ZBycDJW/ZEUJA7aYEwHE7RERE1qB0dAHOzLjGDpCXKksyAkeXk42c/DV2UrMYdoiIiMqLLTs2VLhlp6TdWACgyl9sJ5UtO0REROXGsGNDOkPBtPOSzsYCCmZkpWVxzA4REVF5MezYkHEmllJRimYdAK5KrrVDRERkLQw7NmTsxlKWdHRyPlV+OOKYHSIiovJj2LEh4+rJpQ07xrV2OGaHiIio/Bh2bChX6sYq3WVW8ZYRREREVsOwY0PGbixFGVt20tiNRUREVG4MOzZkXGen9GN28v7lCspERETlx7BjQ7r8lh0XzsYiIiJyGIYdG5KmnnM2FhERkcMw7NhQbv5srLKO2cnJNSBbp7d6XURERFUJw44N6aSWndJdZhc5YOz5YlcWERFR+TDs2JCxZae0KyjLZDJ4u7sAYFcWERFReTHs2FBuGcfsAIDaLe+G9Km8PxYREVG5MOzYUFlvFyGEgJcq71PDVZSJiIjKR+noApyZtM5OKbuxdDnZuJOeNzCZY3aIiIjKhy07NlTWFZQBwM0lL4dyFWUiIqLyYdixoYIVlEt/maW1driKMhERUbkw7NiQtIJyGVp2XPNvGcFuLCIiovJh2LEhaQXlUo7ZAQCVkqsoExERWQPDjg3pyriCMlDozuds2SEiIioXhh0bKs86OyqGHSIiIqtg2LGhghWUS3+ZXfNfwm4sIiKi8mHYsaHytOzIDXmzsLiCMhERUfk4NOzs27cPTz/9NOrUqQOZTIYNGzaYPC+EwHvvvYfatWvD3d0dYWFhuHjxosk+d+/exZAhQ6BWq1G9enWMGjUKmZmZdnwXRSvrCspAQctORnauNNCZiIiISs+hYUej0aBt27aIiYmx+Pz8+fPx2WefYcmSJTh8+DA8PT0RHh6O7OxsaZ8hQ4bgzJkz2L59OzZt2oR9+/bhtddes9dbKJaujCsoAwVTzwWAdI7bISIiKjOH3i6ib9++6Nu3r8XnhBBYtGgRpk+fjmeffRYA8P3338Pf3x8bNmzASy+9hHPnzmHLli04evQoOnToAAD4/PPP8dRTT+Gjjz5CnTp17PZeLNEbV1CWyaAv5WvlMhlcFYBWD6Rk5qCGp6v1CyQiIqoCKuyYnYSEBCQmJiIsLEza5u3tjU6dOiEuLg4AEBcXh+rVq0tBBwDCwsIgl8tx+PDhIo+dk5OD9PR0k4ct5JZjnR0A8HDJ+/TcSst+yJ5ERERUlAobdhITEwEA/v7+Jtv9/f2l5xITE+Hn52fyvFKphI+Pj7SPJdHR0fD29pYe9erVs3L1eYzr7LiU4XYRAODpkheSEhl2iIiIyqzChh1bmjZtGtLS0qTH9evXbXKe8qygDADu+WGHLTtERERlV2HDTkBAAAAgKSnJZHtSUpL0XEBAAJKTk02ez83Nxd27d6V9LFGpVFCr1SYPW9CV467nAOCZ3411/U4GhOCMLCIiorKosGEnKCgIAQEB2Llzp7QtPT0dhw8fRmhoKAAgNDQUqampOH78uLTPrl27YDAY0KlTJ7vX/KCCu56XsWUnf/j4wYtJyMrKslZZREREVYpDZ2NlZmbi0qVL0scJCQmIj4+Hj48P6tevjwkTJmDu3Llo0qQJgoKCMGPGDNSpUwf9+/cHALRo0QJPPvkkRo8ejSVLlkCn02Hs2LF46aWXHD4TCyi0zk4Zu7FUyAUA3M+1WklERERVjkPDzrFjx/DEE09IH0+aNAkAEBkZiWXLluHtt9+GRqPBa6+9htTUVHTr1g1btmyBm5ub9JoffvgBY8eORa9evSCXyzFw4EB89tlndn8vlpS3Zccj/7Oj0RmsVRIREVGV49Cw06NHj2LHoshkMsyZMwdz5swpch8fHx+sXLnSFuWVW8EKymXrLTR2Y2n1wH2dHp7WKoyIiKgKqbBjdpyBLn82lj5Xi7y1kEvHRQ4o8xuFktNzrFgZERFR1cGwY0P6/G6sLSf+Rm5u6W/5IJPJpNadxAyGHSIiorJg2LEh49RzV1dVmY/h4ZL3bxJbdoiIiMqEYceGcvNXUC7PRXbP78dKYssOERFRmTDs2JBxBWVZ2SZjASiYkcWWHSIiorJh2LEhYzdWGWeeAwBc89faYdghIiIqG4YdGzKus1OesOOuyAtM7MYiIiIqG4YdGzKus1O+MTt5x0hkyw4REVGZOHRRQWfXs7kfmt3TQJmdVuZjuCvy/r2j0UGba4CrkvmUiIioNPib04am9wvGRwOC4a0q+2V2lRd0gyWlZ1upMiIioqqDYaeCk8kKTT9n2CEiIio1hp1KwDj9/GYaww4REVFpMexUAp4ueS07V1I0Dq6EiIio8mHYqQTU+WN+Ehh2iIiISo1hpxKo5prXsnMxKR1ClP7u6URERFUZw04loM4POxcSM6DRsHWHiIioNBh2KgEvFxlkAHQGIEWjdXQ5RERElQrDTiWgkMtQ21sFALh6576DqyEiIqpcGHYqicAabgCAhDtZDq6EiIiocmHYqSSMYecKww4REVGpMOxUEvV9jC077MYiIiIqDYadSqJ+fsvOVbbsEBERlQrDTiVh7Mb6JzUbOr3BwdUQERFVHgw7lYSflwuUciDXIHDtLlt3iIiISophp5KQyWTwVikAAH/f5sKCREREJcWwU4kY75F1+XamgyshIiKqPBh2KhEvZd59sf66lerYQoiIiCoRhp1KpIZb3qfrXCJbdoiIiEqKYacSqemef/fzZA2ydXoHV0NERFQ5MOxUIh5KGdwUMuQaBM4nZji6HCIiokqBYacSkclkqOmR9yk79U+qY4shIiKqJBh2KgEhBDQaDQABH/e8T9nJf9IcWxQREVElwbBTCei02fh8y5/IzdWhpnveWjt/Xr8HIYSDKyMiIqr4GHYqCVdV3u0iCg9SvpPGcTtEREQPw7BTySj1OVApBASAC0mcgk5ERPQwDDuVjEwmQ023vNadMzfZskNERPQwDDuVkE9+2Dl9i2GHiIjoYRh2KiEfVV7YOXUj3cGVEBERVXwMO5VQTXdABiDhzn1cSeEd0ImIiIrDsGNDhdfHsSaVQoYAr7wp6BtPXOMUdCIiomIw7NhQVlYWPtp4BLm5Oqsfu763EgCw/GACsrKyrH58IiIiZ8GwY2OuKg+bHLe+WgEZgLvZAjdTs21yDiIiImfAsFNJubvI0a5uNQDA9vO3odFo2J1FRERkAcNOJaXNvo+ugXmtRlvOJCF63SF2ZxEREVnAsFOJPR6kBgCcvJkJrVzl4GqIiIgqJoadSszXywUBXnkDlS+k5Di4GiIiooqJYaeSa+Gbd4PQCyk50OYaHFwNERFRxcOwU0kJIZCVlYX63grU8lQiO1dg27nbji6LiIiowmHYqaR02hx8tfMccrIy4afM68JaefSGg6siIiKqeBh2KjGlKq8Lq2lNF8hlwJ830vHn9VTHFkVERFTBMOw4ATelDA3yV1ReuOMvB1dDRERUsTDsOIk2/q5QymXYc+E2Dl5KcXQ5REREFQbDjpOo5irDc218AQDRm8/DYOBqykRERADDjtPQ5WRDl54CD1cFTt1Iw8Z4DlYmIiICGHacitrLA0M7BAAAZv58Bmeu3eb9soiIqMpj2HEiQggMCK6GWu4ypGfnYvA3R5F0N93RZRERETkUw44T0eVk46vtp9CxphZucgPStQJT15+FnuN3iIioCmPYcTKuKje4KYDQABkUMmD/pbv4cPM5aDQadmkREVGVxLDjpGqogE4BMgDA0v0JGP3tAWRlZQHI6+5i+CEioqqCYceJ1a8mR2s/FwDAoVs6/BR/CwCQlZWF6HWHpPBDRETkzJSOLoBsq62fC2p6uWPP3+mY8csFHL+egUfruiEpR4mbqfdRB4CHhwdkMpnF1xtvOFrcPkRERBUZW3acXK42BzVy76B9gCvkMmBD/E28++vf2H45E70/P4xHo/dg6qpj+Oee5VYetgIREVFl5zRhJyYmBg0aNICbmxs6deqEI0eOOLqkCkPl5oZWvi74uF8gnmhcA/4eclR3k0MhA7JygbV/JuOJj/bg3fWncOjvO9DpDSavd3XzcFDlRERE5ecU3VirV6/GpEmTsGTJEnTq1AmLFi1CeHg4Lly4AD8/P0eXVyHocrKx78+LmNK9Kf7v+H3IFAoMfrQeFmy/jDMpOiTfF/jh8DX8cPga3F3kcHdRQKc3INcgoM01YMP53xFcxxtNfN3R3N8LbQJrwUulRPb9+wAAL09PeLopUU2lZHcXERFVKDLhBFNyOnXqhEcffRRffPEFAMBgMKBevXoYN24c/v3vfz/09enp6fD29kZaWhrUarXV6tJoNIhedxi5uVppmzYnG5Ar4epimjOttf1hr8nV5cLDy0v62FXlBm1ONpKzFfDwroG9l1KRYyh7WPFwVaBudXfU9naDr6cSHm4qAIBenwsXpRJ6g0BaVg6ycwUy7muRpdXDVSlHNTclani6wV0J+FRzh7e7C6q55dWvNwB6gwF6g4BBADqdFi4urngwUz1YtQCg02rh6uoKmUwGIQS0Oi1cXVylQCaT5Y1L0mp1cHXNG8yt1eqgcjU/vhCAVqeFytUVZk9aOL/Jc0U8KSvmVUW/xrrnKeopba4BOTo9srVauKtUcFHIIJfLoJTLoJDLoZAj71+ZDPL8YxT+YVL4J4so9Ixxu+m+ln8MFexr/nrzc5gfSwggR5/3PrRaLdzdVGb1K+UF76ssX/ll+QFalp+6okxnKuu5ynIe+/0qsdf1K+tbqujXvEyvKuM11+kNyMzKgZAr8HKnQHi7u5Tl7EUq6e/vSt+yo9Vqcfz4cUybNk3aJpfLERYWhri4OIuvycnJQU5OjvRxWloagLyLZk0ajQYZqSnIzc2VtuVqsyGTK5GjNL301tpektfoC4WvnPtZyNVmw0uuhEi+jV413ZCpB3K1WsggoM/Vw8PDA3qFCi0DffHryUSk62TINsiRaxDI1RsgRN73gYAMmTnAhYwMXLhe1qtGRETOqEt9DwTV8rLqMY2/tx8W/Cp92ElJSYFer4e/v7/Jdn9/f5w/f97ia6KjozF79myz7fXq1bNJjURERFVdu0W2O3ZGRga8vb2LfL7Sh52ymDZtGiZNmiR9bDAYcPfuXdSsWdNq403S09NRr149XL9+3apdY86M16z0eM3Khtet9HjNSo/XrPRKe82EEMjIyECdOnWK3a/Sh51atWpBoVAgKSnJZHtSUhICAgIsvkalUkGlUplsq169uk3qU6vV/CIvJV6z0uM1Kxtet9LjNSs9XrPSK801K65Fx6jSTz13dXVFSEgIdu7cKW0zGAzYuXMnQkNDHVgZERERVQSVvmUHACZNmoTIyEh06NABHTt2xKJFi6DRaDBixAhHl0ZEREQO5hRh58UXX8Tt27fx3nvvITExEe3atcOWLVvMBi3bk0qlwsyZM826y6hovGalx2tWNrxupcdrVnq8ZqVnq2vmFOvsEBERERWl0o/ZISIiIioOww4RERE5NYYdIiIicmoMO0REROTUGHZsICYmBg0aNICbmxs6deqEI0eOOLqkCm3fvn14+umnUadOHchkMmzYsMHRJVV40dHRePTRR1GtWjX4+fmhf//+uHDhgqPLqtC+/PJLtGnTRlqsLDQ0FJs3b3Z0WZXKvHnzIJPJMGHCBEeXUqHNmjULMpnM5NG8eXNHl1Xh3bhxA6+88gpq1qwJd3d3tG7dGseOHbPKsRl2rGz16tWYNGkSZs6ciRMnTqBt27YIDw9HcnKyo0ursDQaDdq2bYuYmBhHl1Jp7N27F1FRUTh06BC2b98OnU6HPn36QKPROLq0CuuRRx7BvHnzcPz4cRw7dgw9e/bEs88+izNnzji6tErh6NGj+Oqrr9CmTRtHl1IptGzZErdu3ZIev//+u6NLqtDu3buHrl27wsXFBZs3b8bZs2fx8ccfo0aNGtY5gSCr6tixo4iKipI+1uv1ok6dOiI6OtqBVVUeAMT69esdXUalk5ycLACIvXv3OrqUSqVGjRrim2++cXQZFV5GRoZo0qSJ2L59u+jevbt46623HF1ShTZz5kzRtm1bR5dRqbzzzjuiW7duNjs+W3asSKvV4vjx4wgLC5O2yeVyhIWFIS4uzoGVkbNLS0sDAPj4+Di4kspBr9dj1apV0Gg0vK1MCURFRSEiIsLkZxsV7+LFi6hTpw4aNmyIIUOG4Nq1a44uqUL7+eef0aFDB7zwwgvw8/ND+/bt8fXXX1vt+Aw7VpSSkgK9Xm+2crO/vz8SExMdVBU5O4PBgAkTJqBr165o1aqVo8up0E6dOgUvLy+oVCq88cYbWL9+PYKDgx1dVoW2atUqnDhxAtHR0Y4updLo1KkTli1bhi1btuDLL79EQkICHnvsMWRkZDi6tArr77//xpdffokmTZpg69atGDNmDMaPH4/ly5db5fhOcbsIoqosKioKp0+f5piAEmjWrBni4+ORlpaGtWvXIjIyEnv37mXgKcL169fx1ltvYfv27XBzc3N0OZVG3759pf+3adMGnTp1QmBgIH788UeMGjXKgZVVXAaDAR06dMAHH3wAAGjfvj1Onz6NJUuWIDIystzHZ8uOFdWqVQsKhQJJSUkm25OSkhAQEOCgqsiZjR07Fps2bcLu3bvxyCOPOLqcCs/V1RWNGzdGSEgIoqOj0bZtW3z66aeOLqvCOn78OJKTk/Gvf/0LSqUSSqUSe/fuxWeffQalUgm9Xu/oEiuF6tWro2nTprh06ZKjS6mwateubfZHR4sWLazW/cewY0Wurq4ICQnBzp07pW0GgwE7d+7kuACyKiEExo4di/Xr12PXrl0ICgpydEmVksFgQE5OjqPLqLB69eqFU6dOIT4+Xnp06NABQ4YMQXx8PBQKhaNLrBQyMzNx+fJl1K5d29GlVFhdu3Y1Wz7jr7/+QmBgoFWOz24sK5s0aRIiIyPRoUMHdOzYEYsWLYJGo8GIESMcXVqFlZmZafIXT0JCAuLj4+Hj44P69es7sLKKKyoqCitXrsTGjRtRrVo1aUyYt7c33N3dHVxdxTRt2jT07dsX9evXR0ZGBlauXIk9e/Zg69atji6twqpWrZrZODBPT0/UrFmT48OKMWXKFDz99NMIDAzEzZs3MXPmTCgUCgwePNjRpVVYEydORJcuXfDBBx9g0KBBOHLkCJYuXYqlS5da5wQ2m+dVhX3++eeifv36wtXVVXTs2FEcOnTI0SVVaLt37xYAzB6RkZGOLq3CsnS9AIjY2FhHl1ZhjRw5UgQGBgpXV1fh6+srevXqJbZt2+bosiodTj1/uBdffFHUrl1buLq6irp164oXX3xRXLp0ydFlVXi//PKLaNWqlVCpVKJ58+Zi6dKlVju2TAghrBObiIiIiCoejtkhIiIip8awQ0RERE6NYYeIiIicGsMOEREROTWGHSIiInJqDDtERETk1Bh2iIiIyKkx7BBRpdagQQMsWrTI0WUQkQX79u3D008/jTp16kAmk2HDhg2lPsbWrVvRuXNnVKtWDb6+vhg4cCCuXLlSqmMw7BCRwzz99NN48sknLT63f/9+yGQynDx50s5VEZG1aDQatG3bFjExMWV6fUJCAp599ln07NkT8fHx2Lp1K1JSUjBgwIBSHYdhh4gcZtSoUdi+fTv++ecfs+diY2PRoUMHtGnTxgGVEZE19O3bF3PnzsVzzz1n8fmcnBxMmTIFdevWhaenJzp16oQ9e/ZIzx8/fhx6vR5z585Fo0aN8K9//QtTpkxBfHw8dDpdietg2CEih+nXrx98fX2xbNkyk+2ZmZlYs2YNRo0ahXXr1qFly5ZQqVRo0KABPv744yKPd+XKFchkMsTHx0vbUlNTIZPJpB+ge/bsgUwmw9atW9G+fXu4u7ujZ8+eSE5OxubNm9GiRQuo1Wq8/PLLyMrKko5jMBgQHR2NoKAguLu7o23btli7dq01LwdRlTN27FjExcVh1apVOHnyJF544QU8+eSTuHjxIgAgJCQEcrkcsbGx0Ov1SEtLw//+9z+EhYXBxcWl5Cey2l22iIjKYOrUqaJRo0bCYDBI27777jvh7u4u9uzZI+RyuZgzZ464cOGCiI2NFe7u7iY3PA0MDBQLFy4UQgiRkJAgAIg//vhDev7evXsCgNi9e7cQouDGs507dxa///67OHHihGjcuLHo3r276NOnjzhx4oTYt2+fqFmzppg3b550nLlz54rmzZuLLVu2iMuXL4vY2FihUqnEnj17bHl5iJwGALF+/Xrp46tXrwqFQiFu3Lhhsl+vXr3EtGnTpI/37Nkj/Pz8hEKhEABEaGiouHfvXunOXZ7CiYjK69y5cyZhRAghHnvsMfHKK6+Il19+WfTu3dtk/6lTp4rg4GDp47KGnR07dkj7REdHCwDi8uXL0rbXX39dhIeHCyGEyM7OFh4eHuLgwYMmtYwaNUoMHjy4PG+fqMp4MOxs2rRJABCenp4mD6VSKQYNGiSEEOLWrVuiSZMmYurUqeLEiRNi7969onv37qJXr14mfyA9jNKKrVFERKXWvHlzdOnSBd999x169OiBS5cuYf/+/ZgzZw4mTZqEZ5991mT/rl27YtGiRdDr9VAoFGU+b+GxQP7+/vDw8EDDhg1Nth05cgQAcOnSJWRlZaF3794mx9BqtWjfvn2ZayCqyjIzM6FQKHD8+HGz72UvLy8AQExMDLy9vTF//nzpuRUrVqBevXo4fPgwOnfuXKJzMewQkcONGjUK48aNQ0xMDGJjY9GoUSN079691MeRy/OGIeb9EZmnqEGMhfv7ZTKZWf+/TCaDwWAAkPdDGQB+/fVX1K1b12Q/lUpV6jqJCGjfvj30ej2Sk5Px2GOPWdwnKytL+r42MgYj4/dnSXCAMhE53KBBgyCXy7Fy5Up8//33GDlyJGQyGVq0aIEDBw6Y7HvgwAE0bdrUYquOr68vAODWrVvStsKDlcsqODgYKpUK165dQ+PGjU0e9erVK/fxiZxVZmYm4uPjpe/DhIQExMfH49q1a2jatCmGDBmCYcOG4aeffkJCQgKOHDmC6Oho/PrrrwCAiIgIHD16FHPmzMHFixdx4sQJjBgxAoGBgaVqVWXLDhE5nJeXF1588UVMmzYN6enpGD58OABg8uTJePTRR/H+++/jxRdfRFxcHL744gssXrzY4nHc3d3RuXNnzJs3D0FBQUhOTsb06dPLXV+1atUwZcoUTJw4EQaDAd26dUNaWhoOHDgAtVqNyMjIcp+DyBkdO3YMTzzxhPTxpEmTAACRkZFYtmwZYmNjMXfuXEyePBk3btxArVq10LlzZ/Tr1w8A0LNnT6xcuRLz58/H/Pnz4eHhgdDQUGzZsgXu7u4lL8TK44+IiMrk4MGDAoB46qmnTLavXbtWBAcHCxcXF1G/fn2xYMECk+cLD1AWQoizZ8+K0NBQ4e7uLtq1aye2bdtmcYBy4dkcsbGxwtvb2+S4M2fOFG3btpU+NhgMYtGiRaJZs2bCxcVF+Pr6ivDwcLF3715rvH0isiGZEIU6t4mIiIicDMfsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJwaww4RERE5NYYdIiIicmoMO0REROTUGHaIiIjIqTHsEBERkVNj2CEiIiKnxrBDRERETo1hh4iIiJza/wPXyY//bbWyFAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Гистограмма распределения объема в обучающей выборке\n",
    "sns.histplot(train_data['Volume'], kde=True)\n",
    "plt.title('Распределение объема в обучающей выборке')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения объема в контрольной выборке\n",
    "sns.histplot(val_data['Volume'], kde=True)\n",
    "plt.title('Распределение объема в контрольной выборке')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения объема в тестовой выборке\n",
    "sns.histplot(test_data['Volume'], kde=True)\n",
    "plt.title('Распределение объема в тестовой выборке')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Statistics for 'Close':\n",
      "count    3935.000000\n",
      "mean       30.118718\n",
      "std        33.588282\n",
      "min         0.347656\n",
      "25%         4.505000\n",
      "50%        13.627500\n",
      "75%        55.299999\n",
      "max       126.059998\n",
      "Name: Close, dtype: float64\n",
      "Training Data Statistics for 'Volume':\n",
      "count    3.935000e+03\n",
      "mean     1.483718e+07\n",
      "std      1.475393e+07\n",
      "min      1.847800e+06\n",
      "25%      7.912000e+06\n",
      "50%      1.182380e+07\n",
      "75%      1.789760e+07\n",
      "max      5.855088e+08\n",
      "Name: Volume, dtype: float64\n",
      "\n",
      "\n",
      "Validation Data Statistics for 'Close':\n",
      "count    199.000000\n",
      "mean      22.080320\n",
      "std       29.184280\n",
      "min        0.359375\n",
      "25%        2.698243\n",
      "50%        7.840000\n",
      "75%       32.589999\n",
      "max      119.800003\n",
      "Name: Close, dtype: float64\n",
      "Validation Data Statistics for 'Volume':\n",
      "count    1.990000e+02\n",
      "mean     1.516312e+07\n",
      "std      9.809615e+06\n",
      "min      2.659200e+06\n",
      "25%      8.404200e+06\n",
      "50%      1.236850e+07\n",
      "75%      1.983870e+07\n",
      "max      6.235520e+07\n",
      "Name: Volume, dtype: float64\n",
      "\n",
      "\n",
      "Test Data Statistics for 'Close':\n",
      "count    189.000000\n",
      "mean      39.538342\n",
      "std       37.190211\n",
      "min        0.425781\n",
      "25%        5.460000\n",
      "50%       27.674999\n",
      "75%       70.669998\n",
      "max      122.629997\n",
      "Name: Close, dtype: float64\n",
      "Test Data Statistics for 'Volume':\n",
      "count    1.890000e+02\n",
      "mean     1.363519e+07\n",
      "std      9.243478e+06\n",
      "min      1.504000e+06\n",
      "25%      7.248100e+06\n",
      "50%      1.056700e+07\n",
      "75%      1.726080e+07\n",
      "max      5.744800e+07\n",
      "Name: Volume, dtype: float64\n",
      "\n",
      "\n",
      "Аугментация данных успешно применена для обучающей выборки.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 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.utils import resample\n",
    "\n",
    "# Загрузим данные\n",
    "df = pd.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "\n",
    "# Определяем целевую переменную и признаки\n",
    "target_column_close = 'Close'\n",
    "target_column_volume = 'Volume'  # Здесь используем колонку Volume\n",
    "X = df.drop(columns=[target_column_close, target_column_volume, 'Date'])\n",
    "y_close = df[target_column_close]\n",
    "y_volume = df[target_column_volume]\n",
    "\n",
    "# Сначала разделяем на учебную и временную выборки\n",
    "X_train, X_temp, y_train_close, y_temp_close = train_test_split(X, y_close, test_size=0.3, random_state=42)\n",
    "y_train_volume, y_temp_volume = train_test_split(y_volume, test_size=0.3, random_state=42)\n",
    "\n",
    "# Затем разделяем временную выборку на валидационную и тестовую\n",
    "X_val, X_test, y_val_close, y_test_close = train_test_split(X_temp, y_temp_close, test_size=0.5, random_state=42)\n",
    "y_val_volume, y_test_volume = train_test_split(y_temp_volume, test_size=0.5, random_state=42)\n",
    "\n",
    "# Создаем DataFrame для каждой выборки\n",
    "train_data = pd.DataFrame(X_train, columns=X.columns)\n",
    "train_data['Close'] = y_train_close.reset_index(drop=True)\n",
    "train_data['Volume'] = y_train_volume.reset_index(drop=True)\n",
    "\n",
    "val_data = pd.DataFrame(X_val, columns=X.columns)\n",
    "val_data['Close'] = y_val_close.reset_index(drop=True)\n",
    "val_data['Volume'] = y_val_volume.reset_index(drop=True)\n",
    "\n",
    "test_data = pd.DataFrame(X_test, columns=X.columns)\n",
    "test_data['Close'] = y_test_close.reset_index(drop=True)\n",
    "test_data['Volume'] = y_test_volume.reset_index(drop=True)\n",
    "\n",
    "# Шаг 1: Оценка сбалансированности выборок с использованием Volume\n",
    "def plot_distribution(data, column, title):\n",
    "    plt.figure(figsize=(10, 4))\n",
    "    plt.title(f'Distribution of \"{column}\" for {title}')\n",
    "    data[column].hist(bins=30, color='blue', alpha=0.7)\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "# График для каждой выборки\n",
    "plot_distribution(train_data, 'Volume', 'Training Data')\n",
    "plot_distribution(val_data, 'Volume', 'Validation Data')\n",
    "plot_distribution(test_data, 'Volume', 'Test Data')\n",
    "\n",
    "# Проверка на сбалансированность\n",
    "def print_statistics(data, name):\n",
    "    print(f\"{name} Statistics for 'Close':\")\n",
    "    print(data['Close'].describe())\n",
    "    print(f\"{name} Statistics for 'Volume':\")\n",
    "    print(data['Volume'].describe())\n",
    "    print(\"\\n\")\n",
    "\n",
    "print_statistics(train_data, 'Training Data')\n",
    "print_statistics(val_data, 'Validation Data')\n",
    "print_statistics(test_data, 'Test Data')\n",
    "\n",
    "# Шаг 2: Применение аугментации данных для 'Volume'\n",
    "def augment_data(data, target_column, n_samples):\n",
    "    # Увеличим выборку путем бутстреппинга\n",
    "    augmented_data = resample(data, replace=True, n_samples=n_samples, random_state=42)\n",
    "    return augmented_data\n",
    "\n",
    "# Оценим нужно ли нам увеличение выборки по обучающим данным\n",
    "if train_data['Volume'].value_counts(normalize=True).max() < 0.75:  # 75% как порог\n",
    "    # Допустим, мы хотим увеличить обучающую выборку до 2000 примеров\n",
    "    augmented_train_data = augment_data(train_data, 'Volume', n_samples=2000)\n",
    "\n",
    "    # Объединяем оригинальные и увеличенные данные\n",
    "    train_data = pd.concat([train_data, augmented_train_data]).drop_duplicates().reset_index(drop=True)\n",
    "    print(\"Аугментация данных успешно применена для обучающей выборки.\")\n",
    "\n",
    "# Проверяем новую распределение после аугментации\n",
    "plot_distribution(train_data, 'Volume', 'Augmented Training Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучающая выборка содержит 3935 примеров, валидационная — 199, тестовая — 199. Основные статистики по целевой переменной Close показывают, что значения варьируются от 0.35 до 126.06 с учетом значительного разброса (стандартное отклонение 33.59).\n",
    "Распределение объема торгов в обучающей выборке имеет большой разброс с минимумом в 1.85 миллиона и максимумом в 585.51 миллиона. Среднее значение объема составляет примерно 14.84 миллиона. Это говорит о разнообразии торговой активности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Столбцы train_data_encoded:\n",
      "['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume', 'Year_1992', 'Year_1993', 'Year_1994', 'Year_1995', 'Year_1996', 'Year_1997', 'Year_1998', 'Year_1999', 'Year_2000', 'Year_2001', 'Year_2002', 'Year_2003', 'Year_2004', 'Year_2005', 'Year_2006', 'Year_2007', 'Year_2008', 'Year_2009', 'Year_2010', 'Year_2011', 'Year_2012', 'Year_2013', 'Year_2014', 'Year_2015', 'Year_2016', 'Year_2017', 'Year_2018', 'Year_2019', 'Year_2020', 'Year_2021', 'Year_2022', 'Year_2023', 'Year_2024', 'Month_1', 'Month_2', 'Month_3', 'Month_4', 'Month_5', 'Month_6', 'Month_7', 'Month_8', 'Month_9', 'Month_10', 'Month_11', 'Month_12', 'Close_Category']\n",
      "Первые 5 строк закодированного датафрейма:\n",
      "        Date      Open      High       Low     Close  Adj Close     Volume  \\\n",
      "0 1992-06-26  0.328125  0.347656  0.320313  0.335938   0.260703  224358400   \n",
      "1 1992-06-29  0.339844  0.367188  0.332031  0.359375   0.278891   58732800   \n",
      "2 1992-06-30  0.367188  0.371094  0.343750  0.347656   0.269797   34777600   \n",
      "3 1992-07-01  0.351563  0.359375  0.339844  0.355469   0.275860   18316800   \n",
      "4 1992-07-02  0.359375  0.359375  0.347656  0.355469   0.275860   13996800   \n",
      "\n",
      "   Year_1992  Year_1993  Year_1994  ...  Month_4  Month_5  Month_6  Month_7  \\\n",
      "0       True      False      False  ...    False    False     True    False   \n",
      "1       True      False      False  ...    False    False     True    False   \n",
      "2       True      False      False  ...    False    False     True    False   \n",
      "3       True      False      False  ...    False    False    False     True   \n",
      "4       True      False      False  ...    False    False    False     True   \n",
      "\n",
      "   Month_8  Month_9  Month_10  Month_11  Month_12  Close_Category  \n",
      "0    False    False     False     False     False               0  \n",
      "1    False    False     False     False     False               0  \n",
      "2    False    False     False     False     False               0  \n",
      "3    False    False     False     False     False               0  \n",
      "4    False    False     False     False     False               0  \n",
      "\n",
      "[5 rows x 53 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Загрузим данные\n",
    "df = pd.read_csv(\"./static/csv/Starbucks Dataset.csv\")\n",
    "\n",
    "# Убедимся, что дата в нужном формате\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "# Конструирование признаков\n",
    "\n",
    "df['Year'] = df['Date'].dt.year\n",
    "df['Month'] = df['Date'].dt.month\n",
    "\n",
    "# Условия для создания категорий\n",
    "categorical_features = ['Year', 'Month']\n",
    "\n",
    "# Применение one-hot encoding к новым категориальным признакам\n",
    "train_data_encoded = pd.get_dummies(df, columns=categorical_features)\n",
    "\n",
    "# Отделяем метки 'Close' и 'Volume'\n",
    "y_close = df['Close']\n",
    "y_volume = df['Volume']\n",
    "\n",
    "\n",
    "X = train_data_encoded.drop(columns=['Date', 'Close', 'Volume', 'Adj Close'])\n",
    "\n",
    "# Дискретизация числовых признаков (например, 'Close').\n",
    "# Создадим категории для 'Close' на 5 категорий\n",
    "train_data_encoded['Close_Category'] = pd.cut(train_data_encoded['Close'], bins=5, labels=False)\n",
    "\n",
    "# Выводим информацию о столбцах\n",
    "print(\"Столбцы train_data_encoded:\")\n",
    "print(train_data_encoded.columns.tolist())\n",
    "\n",
    "# Пример вывода первых нескольких строк закодированного датафрейма\n",
    "print(\"Первые 5 строк закодированного датафрейма:\")\n",
    "print(train_data_encoded.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Названия столбцов в датасете:\n",
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
      "Статистические параметры:\n",
      "              Open         High          Low        Close    Adj Close  \\\n",
      "count  8036.000000  8036.000000  8036.000000  8036.000000  8036.000000   \n",
      "mean     30.054280    30.351487    29.751322    30.058857    26.674025   \n",
      "std      33.615577    33.906613    33.314569    33.615911    31.728090   \n",
      "min       0.328125     0.347656     0.320313     0.335938     0.260703   \n",
      "25%       4.392031     4.531250     4.304922     4.399610     3.414300   \n",
      "50%      13.325000    13.493750    13.150000    13.330000    10.352452   \n",
      "75%      55.250000    55.722501    54.852499    55.267499    47.464829   \n",
      "max     126.080002   126.320000   124.809998   126.059998   118.010414   \n",
      "\n",
      "             Volume  \n",
      "count  8.036000e+03  \n",
      "mean   1.470459e+07  \n",
      "std    1.340021e+07  \n",
      "min    1.504000e+06  \n",
      "25%    7.817750e+06  \n",
      "50%    1.169815e+07  \n",
      "75%    1.778795e+07  \n",
      "max    5.855088e+08  \n",
      "После дискретизации 'Close':\n",
      "      Close Close_Disc\n",
      "0  0.335938       0-50\n",
      "1  0.359375       0-50\n",
      "2  0.347656       0-50\n",
      "3  0.355469       0-50\n",
      "4  0.355469       0-50\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Загружаем данные\n",
    "df = pd.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "\n",
    "# Проверка на наличие числовых признаков\n",
    "print(\"Названия столбцов в датасете:\")\n",
    "print(df.columns)\n",
    "\n",
    "# Выводим основные статистические параметры для количественных признаков\n",
    "print(\"Статистические параметры:\")\n",
    "print(df.describe())\n",
    "\n",
    "# Дискретизация столбца 'Close' на группы\n",
    "bins = [0, 50, 100, 150, 200, 250, 300]  # Определяем границы корзин\n",
    "labels = ['0-50', '51-100', '101-150', '151-200', '201-250', '251-300']  # Названия категорий\n",
    "\n",
    "# Создание нового столбца 'Close_Disc' на основе дискретизации\n",
    "df['Close_Disc'] = pd.cut(df['Close'], bins=bins, labels=labels, include_lowest=True)\n",
    "\n",
    "# Проверка результата\n",
    "print(\"После дискретизации 'Close':\")\n",
    "print(df[['Close', 'Close_Disc']].head())\n",
    "\n",
    "# Если нужно, можно повторить дискретизацию для других числовых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первые строки данных:\n",
      "         Date      Open      High       Low     Close  Adj Close     Volume\n",
      "0  1992-06-26  0.328125  0.347656  0.320313  0.335938   0.260703  224358400\n",
      "1  1992-06-29  0.339844  0.367188  0.332031  0.359375   0.278891   58732800\n",
      "2  1992-06-30  0.367188  0.371094  0.343750  0.347656   0.269797   34777600\n",
      "3  1992-07-01  0.351563  0.359375  0.339844  0.355469   0.275860   18316800\n",
      "4  1992-07-02  0.359375  0.359375  0.347656  0.355469   0.275860   13996800\n",
      "Новые признаки:\n",
      "   Price_Change  Percentage_Change  Average_Price  Price_Range  Volume_Impact\n",
      "0      0.007813           2.381105       0.333008     0.027343   6.134632e+06\n",
      "1      0.019531           5.747049       0.349610     0.035157   2.064869e+06\n",
      "2     -0.019532          -5.319346       0.357422     0.027344   9.509587e+05\n",
      "3      0.003906           1.111038       0.351563     0.019531   3.577454e+05\n",
      "4     -0.003906          -1.086887       0.355469     0.011719   1.640285e+05\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Загружаем данные\n",
    "df = pd.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "\n",
    "# Просмотр первых строк, чтобы понять структуру данных\n",
    "print(\"Первые строки данных:\")\n",
    "print(df.head())\n",
    "\n",
    "# 1. Изменение цены\n",
    "df['Price_Change'] = df['Close'] - df['Open']\n",
    "\n",
    "# 2. Процентное изменение\n",
    "df['Percentage_Change'] = (df['Price_Change'] / df['Open']) * 100\n",
    "\n",
    "# 3. Средняя цена\n",
    "df['Average_Price'] = (df['High'] + df['Low'] + df['Open'] + df['Close']) / 4\n",
    "\n",
    "# 4. Диапазон цены\n",
    "df['Price_Range'] = df['High'] - df['Low']\n",
    "\n",
    "# 5. Объем изменений\n",
    "df['Volume_Impact'] = df['Price_Range'] * df['Volume']\n",
    "\n",
    "# Проверка создания новых признаков\n",
    "print(\"Новые признаки:\")\n",
    "print(df[['Price_Change', 'Percentage_Change', 'Average_Price', 'Price_Range', 'Volume_Impact']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Результаты после масштабирования:\n",
      "   Price_Change  Percentage_Change  Average_Price  Price_Range  Volume_Impact\n",
      "0      0.005718           1.121421      -0.884290    -0.751681      -0.099935\n",
      "1      0.026425           2.750040      -0.883796    -0.741427      -0.403159\n",
      "2     -0.042603          -2.604458      -0.883564    -0.751680      -0.486153\n",
      "3     -0.001186           0.506897      -0.883738    -0.761932      -0.530351\n",
      "4     -0.014990          -0.556574      -0.883622    -0.772183      -0.544784\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "# Шаг 1: Загрузка обработанных данных\n",
    "df = pd.read_csv(\".//static//csv//Starbucks_Dataset_Processed.csv\")\n",
    "\n",
    "# Шаг 2: Признаки для масштабирования\n",
    "features_to_scale = ['Price_Change', 'Percentage_Change', 'Average_Price', 'Price_Range', 'Volume_Impact']\n",
    "\n",
    "# Шаг 3: Нормализация (Min-Max Scaling)\n",
    "min_max_scaler = MinMaxScaler()\n",
    "df[features_to_scale] = min_max_scaler.fit_transform(df[features_to_scale])\n",
    "\n",
    "# Шаг 4: Стандартизация (Z-score Scaling)\n",
    "standard_scaler = StandardScaler()\n",
    "df[features_to_scale] = standard_scaler.fit_transform(df[features_to_scale])\n",
    "\n",
    "# Вывод результатов после масштабирования\n",
    "print(\"Результаты после масштабирования:\")\n",
    "print(df[features_to_scale].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В целом, результаты масштабирования показывают высокую волатильность рынка, однако стабильность в среднем значении цены может указывать на определенные тренды, которые стоит учитывать в дальнейших аналитических прогнозах и торговых стратегиях"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\a3012\\AIM-PIbd-31-Zhirnova-A-E\\aimenv\\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",
      "c:\\Users\\a3012\\AIM-PIbd-31-Zhirnova-A-E\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\dfs.py:321: UnusedPrimitiveWarning: Some specified primitives were not used during DFS:\n",
      "  agg_primitives: ['count', 'mean', 'sum']\n",
      "This may be caused by a using a value of max_depth that is too small, not setting interesting values, or it may indicate no compatible columns for the primitive were found in the data. If the DFS call contained multiple instances of a primitive in the list above, none of them were used.\n",
      "  warnings.warn(warning_msg, UnusedPrimitiveWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
      "Built 9 features\n",
      "Elapsed: 00:00 | Progress: 100%|██████████\n",
      "        Open      High       Low     Close  Adj Close     Volume MONTH(Date)  \\\n",
      "id                                                                             \n",
      "0   0.328125  0.347656  0.320313  0.335938   0.260703  224358400           6   \n",
      "1   0.339844  0.367188  0.332031  0.359375   0.278891   58732800           6   \n",
      "2   0.367188  0.371094  0.343750  0.347656   0.269797   34777600           6   \n",
      "3   0.351563  0.359375  0.339844  0.355469   0.275860   18316800           7   \n",
      "4   0.359375  0.359375  0.347656  0.355469   0.275860   13996800           7   \n",
      "\n",
      "   WEEKDAY(Date) YEAR(Date)  \n",
      "id                           \n",
      "0              4       1992  \n",
      "1              0       1992  \n",
      "2              1       1992  \n",
      "3              2       1992  \n",
      "4              3       1992  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import featuretools as ft\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "print(df.columns)\n",
    "\n",
    "# Преобразование колонки 'Date' в datetime формат\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df['id'] = df.index  # Добавляем уникальный идентификатор\n",
    "\n",
    "# Создание сущности\n",
    "es = ft.EntitySet(id='starbucks_data')\n",
    "es = es.add_dataframe(dataframe_name='prices', dataframe=df, index='id', make_index=False)\n",
    "\n",
    "# Конструирование признаков\n",
    "features, feature_defs = ft.dfs(entityset=es,\n",
    "                                 target_dataframe_name='prices',\n",
    "                                 agg_primitives=['count', 'mean', 'sum'],\n",
    "                                 trans_primitives=['month', 'year', 'weekday'],\n",
    "                                 verbose=True)\n",
    "\n",
    "# Печать созданных признаков\n",
    "print(features.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Система смогла успешно сгенерировать 9 признаков, среди которых температуры открытия, закрытия акций, а также даты с разбивкой по месяцам и дням недели. Это может помочь в анализе сезонных и временных закономерностей, но также требует более глубокого исследования для выявления значимых корреляций"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 9.94 секунд\n",
      "Среднеквадратичная ошибка: 66373961335213.34\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Разделение данных на обучающую и валидационную выборки. Удаляем целевую переменную\n",
    "X = feature_matrix.drop('Volume', axis=1)\n",
    "y = feature_matrix['Volume']\n",
    "\n",
    "# One-hot encoding для категориальных переменных (преобразование категориальных объектов в числовые)\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Проверяем, есть ли пропущенные значения, и заполняем их медианой или другим подходящим значением\n",
    "X.fillna(X.median(), inplace=True)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model = LinearRegression()\n",
    "\n",
    "# Начинаем отсчет времени\n",
    "start_time = time.time()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Предсказания и оценка модели и вычисляем среднеквадратичную ошибку\n",
    "predictions = model.predict(X_val)\n",
    "mse = mean_squared_error(y_val, predictions)\n",
    "\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Время обучения модели составило 9.94 секунд. Это говорит о том, что процесс обучения с использованием линейной регрессии не является очень затратным по времени\n",
    "\n",
    "Среднеквадратичная ошибка (MSE) равна 66373961335213.34. Это значение является крайне высоким, что сигнализирует о том, что модель требует дополнительной настройки или улучшения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Линейная регрессия:\n",
      "MSE: 0.05698523693575633\n",
      "R^2: 0.999949312678354\n",
      "\n",
      "Дерево решений:\n",
      "MSE: 0.1502495406307128\n",
      "R^2: 0.999866355793138\n"
     ]
    },
    {
     "data": {
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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",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import  r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Загрузка данных\n",
    "data = pd.read_csv('.//static//csv//Starbucks Dataset.csv')\n",
    "\n",
    "# Предварительная обработка\n",
    "data['Date'] = pd.to_datetime(data['Date'])  # Преобразуем в datetime\n",
    "data.set_index('Date', inplace=True)\n",
    "\n",
    "# Отбираем признаки и целевую переменную\n",
    "features = ['Open', 'High', 'Low', 'Volume']  # Исключаем 'Close' из признаков\n",
    "target = 'Close'\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X = data[features]\n",
    "y = data[target]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Модель линейной регрессии\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)\n",
    "y_pred_lin = lin_reg.predict(X_test)\n",
    "\n",
    "# Оценка производительности\n",
    "r2_lin = r2_score(y_test, y_pred_lin)\n",
    "\n",
    "print(\"Линейная регрессия:\")\n",
    "print(\"MSE:\", mse_lin)\n",
    "print(\"R^2:\", r2_lin)\n",
    "\n",
    "# Модель дерева решений\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(X_train, y_train)\n",
    "y_pred_tree = tree_reg.predict(X_test)\n",
    "\n",
    "# Оценка производительности\n",
    "r2_tree = r2_score(y_test, y_pred_tree)\n",
    "\n",
    "print(\"\\nДерево решений:\")\n",
    "print(\"MSE:\", mse_tree)\n",
    "print(\"R^2:\", r2_tree)\n",
    "\n",
    "# Визуализация важности признаков\n",
    "feature_importance = np.abs(lin_reg.coef_)\n",
    "features_names = X.columns\n",
    "importance_df = pd.DataFrame({\n",
    "    'Feature': features_names,\n",
    "    'Importance': feature_importance\n",
    "}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
    "plt.title('Важность признаков (Линейная регрессия)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Корреляция признаков с 'Close':\n",
      "Close        1.000000\n",
      "High         0.999931\n",
      "Low          0.999930\n",
      "Open         0.999858\n",
      "Adj Close    0.997965\n",
      "Date         0.889680\n",
      "Volume      -0.319534\n",
      "Name: Close, dtype: float64\n",
      "Mean Squared Error: 0.05698523693575633\n",
      "R^2 Score: 0.999949312678354\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",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import  r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 1. Загрузка данных\n",
    "data = pd.read_csv('.//static//csv//Starbucks Dataset.csv')\n",
    "\n",
    "# 2. Предварительная обработка данных\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data.sort_values('Date', inplace=True)\n",
    "\n",
    "# 3. Оценка корреляции между признаками и целевой переменной\n",
    "correlation_matrix = data.corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True)\n",
    "plt.title('Корреляционная матрица')\n",
    "plt.show()\n",
    "\n",
    "# Рассмотрим корреляцию с целевой переменной 'Close'\n",
    "correlation_with_close = correlation_matrix['Close'].sort_values(ascending=False)\n",
    "print(\"Корреляция признаков с 'Close':\")\n",
    "print(correlation_with_close)\n",
    "\n",
    "# 4. Обучение модели и оценка предсказательной способности\n",
    "# Выберем признаки для модели: Open, High, Low и Volume\n",
    "features = data[['Open', 'High', 'Low', 'Volume']]\n",
    "target = data['Close']\n",
    "\n",
    "# Разделим данные на обучающую и тестовую выборки\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "\n",
    "# Обучим линейную регрессию\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказание\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Оценка качества\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R^2 Score:\", r2)\n",
    "\n",
    "\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('Фактическая цена закрытия')\n",
    "plt.ylabel('Прогнозируемая цена закрытия')\n",
    "plt.title('Фактическая цена закрытия по сравнению с прогнозируемой')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На основании представленных данных о корреляции признаков с целевой переменной 'Close', а также значений Mean Squared Error (MSE), можно сделать несколько важных выводов:\n",
    "\n",
    "**Высокая корреляция признаков**\n",
    "\n",
    "Показатели High, Low, Open и Adj Close имеют крайне высокую положительную корреляцию с целевой переменной Close:\n",
    "High: 0.999931\n",
    "Low: 0.999930\n",
    "Open: 0.999858\n",
    "Adj Close: 0.997965\n",
    "Это говорит о том, что данные переменные практически линейно зависимы от значения Close. Таким образом, знание значений этих признаков позволяет с высокой степенью уверенности предсказывать значение Close.\n",
    "\n",
    "Date имеет также значимую корреляцию (0.889680), что может указывать на временную зависимость или тренды в данных по мере их изменений.\n",
    "Volume имеет отрицательную корреляцию (-0.319534) с Close, что может говорить о том, что увеличение объема торгов не всегда приводит к росту цен, хотя эта зависимость слабая.\n",
    "\n",
    "Эффективность модели\n",
    "Значение Mean Squared Error (MSE) составляет 0.05698523693575633, что указывает на очень низкий уровень ошибок предсказания, подтверждая хорошую точность модели."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из последнего графика видно, что результаты прогнозируемые и фактические практически совпали. Это говорит о том, что модель будет давать неплохой результат на практике. \n",
    "\n",
    "На основании проведенного анализа с использованием моделей линейной регрессии и дерева решений на наборе данных Starbucks, можно сделать следующие выводы:\n",
    "\n",
    "**1. Модели и их производительность**\n",
    "\n",
    "Линейная регрессия показала отличные результаты с значениям:\n",
    "MSE (среднеквадратичная ошибка): 0.05698523693575633, что указывает на низкий уровень ошибок предсказания по сравнению с реальными значениями.\n",
    "R² (коэффициент детерминации): 0.999949312678354, что свидетельствует о том, что модель объясняет почти 100% дисперсии целевой переменной, показывая великолепную подгонку данных.\n",
    "Дерево решений также продемонстрировало высокие показатели:\n",
    "MSE: 0.1502495406307128, однако это значение значительно выше, чем у линейной регрессии.\n",
    "R²: 0.999866355793138, что, несмотря на немного менее удачную подгонку по сравнению с линейной регрессией, все еще указывает на очень хорошую соответствие модели данным.\n",
    "\n",
    "**2. Сравнение моделей**\n",
    "\n",
    "Хотя обе модели показали исключительные результаты, линейная регрессия в этом случае оказалась более точной, что можно объяснить ее способностью находить прямые зависимости в данных и меньшей склонностью к переобучению в данном контексте. Деревья решений могут быть полезны для выявления более сложных закономерностей, однако в данной задаче они не продемонстрировали такой же уровень точности.\n",
    "\n",
    "**3. Важность признаков**\n",
    "\n",
    "Результаты визуализации важности признаков, полученные из линейной регрессии, помогают понять, какие из входных переменных (Open, High, Low, Volume) наибольшим образом влияют на целевую переменную (Close). Это может быть полезным для дальнейшего анализа и при принятии бизнес-решений, связанных с управлением и стратегией Starbucks.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
 ],
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