AIM-PIbd-31-Alekseev-I-S/Lab_2/Lab2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1-й Датасет: Pima Indians Diabetes Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database\n",
    "\n",
    "Этот набор данных получен Национальным институтом диабета, болезней органов пищеварения и почек. Цель набора данных - диагностически предсказать, есть ли у пациента диабет, на основе определенных диагностических измерений, включенных в набор данных. При отборе этих случаев из более обширной базы данных было наложено несколько ограничений. В частности, все пациенты здесь - женщины не моложе 21 года индейского происхождения Пима.\n",
    "\n",
    "* Из описания датасета очевидно, что объектами иследования являются женьщины индейци пима.\n",
    "* Атрибуты объектов: Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome\n",
    "* Очевидная цель этого датасета - научиться предсказывать диабет.\n",
    "\n",
    "В качестве примера бизнес-целей можно привести:\n",
    "* Повышение качества жизни пациентов. Цель технического проекта: Разработать интерфейс для модели, который будет предоставлять пациентам персонализированные рекомендации по профилактике и лечению диабета на основе их индивидуальных рисков, определенных моделью.\n",
    "* Повышение эффективности скрининга диабета. Цель технического проекта: Разработать и обучить модель машинного обучения с точностью предсказания не менее 85% для автоматизированного скрининга диабета на основе данных датасета \"Диабет у индейцев Пима\".\n",
    "*  Снижение медицинских расходов. Цель технического проекта: Оптимизировать модель прогнозирования таким образом, чтобы минимизировать количество ложноотрицательных результатов (пациенты с диабетом, которые не были выявлены), что позволит снизить затраты на лечение осложнений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество колонок: 9\n",
      "колонки: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//diabetes.csv\", sep=\",\")\n",
    "print('количество колонок: ' + str(df.columns.size))  \n",
    "print('колонки: ' + ', '.join(df.columns))\n",
    "\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Получение сведений о пропущенных данных\n",
    "\n",
    "Типы пропущенных данных:\n",
    "\n",
    "* None - представление пустых данных в Python\n",
    "* NaN - представление пустых данных в Pandas\n",
    "* '' - пустая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n",
      "\n",
      "Pregnancies                 False\n",
      "Glucose                     False\n",
      "BloodPressure               False\n",
      "SkinThickness               False\n",
      "Insulin                     False\n",
      "BMI                         False\n",
      "DiabetesPedigreeFunction    False\n",
      "Age                         False\n",
      "Outcome                     False\n",
      "dtype: bool\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "# Процент пустых 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}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по статистике выше, пустые значения отсутсвуют. Проверим датасет на выбросы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'Pregnancies': 4\n",
      "Количество выбросов в столбце 'Glucose': 5\n",
      "Количество выбросов в столбце 'BloodPressure': 45\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\".//static//csv//diabetes.csv\")\n",
    "\n",
    "# Выбираем числовые столбцы\n",
    "numeric_columns = ['Pregnancies', 'Glucose', 'BloodPressure']\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['Pregnancies', 'Glucose', 'BloodPressure']\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(numeric_columns, 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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В принципе, количество выбросов для солбцов 'Pregnancies' и 'Glucose' не так критично, что нельзя сказать про столбец 'BloodPressure'. Сделаем очистку от выбросов для данного столбца:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество удаленных строк: 45\n",
      "Количество выбросов в столбце 'BloodPressure': 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "   \n",
    "# Выбираем столбцы для очистки\n",
    "columns_to_clean =  ['BloodPressure']\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",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['BloodPressure']\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",
    "\n",
    "# Создаем гистограммы для очищенных данных\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "# Гистограмма для relative_velocity\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(df_cleaned['BloodPressure'], kde=True)\n",
    "plt.title('Histogram of Blood Pressure (Cleaned)')\n",
    "plt.xlabel('Blood Pressure')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по данным на диаграмме выше, количество выбросов значительно сократилось и не превышает допустимые диапозоны. Теперь можно приступить к разбиению датасета на выборки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 433\n",
      "Размер контрольной выборки: 145\n",
      "Размер тестовой выборки: 145\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('Outcome', axis=1)  # Признаки\n",
    "y = df['Outcome']  # Целевая переменная\n",
    "\n",
    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\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",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", X_train.shape[0])\n",
    "print(\"Размер контрольной выборки:\", X_val.shape[0])\n",
    "print(\"Размер тестовой выборки:\", X_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сбалансированность обучающей выборки:\n",
      "Outcome\n",
      "0    0.658199\n",
      "1    0.341801\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность контрольной выборки:\n",
      "Outcome\n",
      "0    0.655172\n",
      "1    0.344828\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность тестовой выборки:\n",
      "Outcome\n",
      "0    0.662069\n",
      "1    0.337931\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('Outcome', axis=1)  # Признаки\n",
    "y = df['Outcome']  # Целевая переменная\n",
    "\n",
    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
    "\n",
    "# Функция для проверки сбалансированности выборок\n",
    "def check_balance(y_train, y_val, y_test):\n",
    "    print(\"Сбалансированность обучающей выборки:\")\n",
    "    print(y_train.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность контрольной выборки:\")\n",
    "    print(y_val.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность тестовой выборки:\")\n",
    "    print(y_test.value_counts(normalize=True))\n",
    "\n",
    "# Проверка сбалансированности\n",
    "check_balance(y_train, y_val, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По данным выше можно понять, что выборки сбалансиированы относительно. Воспользуемся приращением данных методом выборки с избытком (oversampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сбалансированность обучающей выборки после SMOTE:\n",
      "Outcome\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n",
      "Сбалансированность обучающей выборки:\n",
      "Outcome\n",
      "0    0.5\n",
      "1    0.5\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность контрольной выборки:\n",
      "Outcome\n",
      "0    0.655172\n",
      "1    0.344828\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Сбалансированность тестовой выборки:\n",
      "Outcome\n",
      "0    0.662069\n",
      "1    0.337931\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('Outcome', axis=1)  # Признаки\n",
    "y = df['Outcome']  # Целевая переменная\n",
    "\n",
    "# Разбиение на обучающую и оставшуюся часть (контрольная + тестовая)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
    "\n",
    "# Применение SMOTE для балансировки обучающей выборки\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Функция для проверки сбалансированности выборок\n",
    "def check_balance(y_train, y_val, y_test):\n",
    "    print(\"Сбалансированность обучающей выборки:\")\n",
    "    print(y_train.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность контрольной выборки:\")\n",
    "    print(y_val.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nСбалансированность тестовой выборки:\")\n",
    "    print(y_test.value_counts(normalize=True))\n",
    "\n",
    "# Проверка сбалансированности после SMOTE\n",
    "print(\"Сбалансированность обучающей выборки после SMOTE:\")\n",
    "print(y_train_resampled.value_counts(normalize=True))\n",
    "\n",
    "# Проверка сбалансированности контрольной и тестовой выборок\n",
    "check_balance(y_train_resampled, y_val, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборка сбалансирована"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-й Датасет: Starbucks Stock Price Dataset 📊🍵🧋🔥"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starbucks Corporation - всемирно известная сеть кофеен, основанная в 1971 году в Сиэтле, штат Вашингтон, Джерри Болдуином, Зевом Сиглом и Гордоном Боукером. Начав со скромного магазина, торгующего высококачественными кофейными зернами и оборудованием, Starbucks превратилась в одну из крупнейших в мире сетей кофеен с тысячами магазинов по всему миру. Известная своим кофе высшего сорта, инновационными напитками и уникальным обслуживанием клиентов, Starbucks стала культурной иконой кофейной индустрии.\n",
    "\n",
    "Этот набор данных предоставляет исчерпывающую информацию об изменениях цен на акции Starbucks за последние 25 лет. Он включает в себя важные столбцы, такие как дата, цена открытия, самая высокая цена дня, самая низкая цена дня, цена закрытия, скорректированная цена закрытия и объем торгов.\n",
    "\n",
    "Эти данные бесценны для проведения исторического анализа, прогнозирования динамики акций в будущем и понимания рыночных тенденций, связанных с акциями Starbucks.\n",
    "\n",
    "* Из описания датасета очевидно, что объектами иследования являются  записи о динамике цены акций.\n",
    "* Атрибуты объектов: Date,Open,High,Low,Close,Adj Close,Volume\n",
    "* Очевидная цель этого датасета - научиться предсказывать цены на акции.\n",
    "\n",
    "В качестве примера бизнес-целей можно привести:\n",
    "* Предсказание будущих цен акций: Использовать исторические данные для прогнозирования будущих цен акций Starbucks.\n",
    "* Анализ волатильности: Оценка волатильности акций на основе исторических данных, что поможет принять более информированные решения для инвестиций.\n",
    "* Оптимизация торговых стратегий: Разработка стратегий для покупки и продажи акций на основе определённых индикаторов или паттернов поведения цен."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество колонок: 7\n",
      "колонки: Date, Open, High, Low, Close, Adj Close, Volume\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8036 entries, 0 to 8035\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Date       8036 non-null   object \n",
      " 1   Open       8036 non-null   float64\n",
      " 2   High       8036 non-null   float64\n",
      " 3   Low        8036 non-null   float64\n",
      " 4   Close      8036 non-null   float64\n",
      " 5   Adj Close  8036 non-null   float64\n",
      " 6   Volume     8036 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 439.6+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1992-06-26</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.347656</td>\n",
       "      <td>0.320313</td>\n",
       "      <td>0.335938</td>\n",
       "      <td>0.260703</td>\n",
       "      <td>224358400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1992-06-29</td>\n",
       "      <td>0.339844</td>\n",
       "      <td>0.367188</td>\n",
       "      <td>0.332031</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.278891</td>\n",
       "      <td>58732800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992-06-30</td>\n",
       "      <td>0.367188</td>\n",
       "      <td>0.371094</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.347656</td>\n",
       "      <td>0.269797</td>\n",
       "      <td>34777600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1992-07-01</td>\n",
       "      <td>0.351563</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.339844</td>\n",
       "      <td>0.355469</td>\n",
       "      <td>0.275860</td>\n",
       "      <td>18316800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1992-07-02</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>0.347656</td>\n",
       "      <td>0.355469</td>\n",
       "      <td>0.275860</td>\n",
       "      <td>13996800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         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"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//sd.csv\", sep=\",\")\n",
    "print('количество колонок: ' + str(df.columns.size))  \n",
    "print('колонки: ' + ', '.join(df.columns))\n",
    "\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Получение сведений о пропущенных данных\n",
    "\n",
    "Типы пропущенных данных:\n",
    "\n",
    "* None - представление пустых данных в Python\n",
    "* NaN - представление пустых данных в Pandas\n",
    "* '' - пустая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
      "\n",
      "Date         False\n",
      "Open         False\n",
      "High         False\n",
      "Low          False\n",
      "Close        False\n",
      "Adj Close    False\n",
      "Volume       False\n",
      "dtype: bool\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "# Процент пустых 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}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по статистике выше, пустые значения отсутсвуют. Проверим датасет на выбросы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'Open': 0\n",
      "Количество выбросов в столбце 'High': 0\n",
      "Количество выбросов в столбце 'Low': 0\n",
      "Количество выбросов в столбце 'Close': 0\n",
      "Количество выбросов в столбце 'Adj Close': 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\".//static//csv//sd.csv\")\n",
    "\n",
    "# Выбираем числовые столбцы\n",
    "numeric_columns = ['Open','High','Low','Close','Adj Close']\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['Open','High','Low','Close','Adj Close']\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(numeric_columns, 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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по диаграммам, количетв выбросов либо полностью отсутсвует, либо имеется в пределах допустимых значений. Теперь можно приступить к разбиению датасета на выборки, но теперь используем прописанные реализации методов приращения данных: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "# Функция для применения oversampling\n",
    "def apply_oversampling(X, y):\n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)\n",
    "    return X_resampled, y_resampled\n",
    "\n",
    "# Функция для применения undersampling\n",
    "def apply_undersampling(X, y):\n",
    "    undersampler = RandomUnderSampler(random_state=42)\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y)\n",
    "    return X_resampled, y_resampled\n",
    "\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input,\n",
    "    stratify_colname=\"y\",\n",
    "    frac_train=0.6,\n",
    "    frac_val=0.15,\n",
    "    frac_test=0.25,\n",
    "    random_state=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
    "    following fractional ratios provided by the user, where each subset is\n",
    "    stratified by the values in a specific column (that is, each subset has\n",
    "    the same relative frequency of the values in the column). It performs this\n",
    "    splitting by running train_test_split() twice.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_input : Pandas dataframe\n",
    "        Input dataframe to be split.\n",
    "    stratify_colname : str\n",
    "        The name of the column that will be used for stratification. Usually\n",
    "        this column would be for the label.\n",
    "    frac_train : float\n",
    "    frac_val   : float\n",
    "    frac_test  : float\n",
    "        The ratios with which the dataframe will be split into train, val, and\n",
    "        test data. The values should be expressed as float fractions and should\n",
    "        sum to 1.0.\n",
    "    random_state : int, None, or RandomStateInstance\n",
    "        Value to be passed to train_test_split().\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    df_train, df_val, df_test :\n",
    "        Dataframes containing the three splits.\n",
    "    \"\"\"\n",
    "\n",
    "    if frac_train + frac_val + frac_test != 1.0:\n",
    "        raise ValueError(\n",
    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
    "            % (frac_train, frac_val, frac_test)\n",
    "        )\n",
    "\n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
    "\n",
    "    X = df_input  # Contains all columns.\n",
    "    y = df_input[\n",
    "        [stratify_colname]\n",
    "    ]  # Dataframe of just the column on which to stratify.\n",
    "\n",
    "    # Split original dataframe into train and temp dataframes.\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
    "    )\n",
    "\n",
    "    # Split the temp dataframe into val and test dataframes.\n",
    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
    "    df_val, df_test, y_val, y_test = train_test_split(\n",
    "        df_temp,\n",
    "        y_temp,\n",
    "        stratify=y_temp,\n",
    "        test_size=relative_frac_test,\n",
    "        random_state=random_state,\n",
    "    )\n",
    "\n",
    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
    "\n",
    "    return df_train, df_val, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка:  (4821, 4)\n",
      "Volume_Grouped\n",
      " 0    2802\n",
      " 1    1460\n",
      " 2     369\n",
      " 3     111\n",
      " 4      40\n",
      " 5      18\n",
      "-1      10\n",
      " 6       7\n",
      " 7       4\n",
      "Name: count, dtype: int64\n",
      "Обучающая выборка после oversampling:  (25218, 4)\n",
      "Volume_Grouped\n",
      " 0    2802\n",
      " 2    2802\n",
      " 1    2802\n",
      " 5    2802\n",
      " 3    2802\n",
      " 4    2802\n",
      " 7    2802\n",
      "-1    2802\n",
      " 6    2802\n",
      "Name: count, dtype: int64\n",
      "Контрольная выборка:  (1607, 4)\n",
      "Volume_Grouped\n",
      " 0    934\n",
      " 1    487\n",
      " 2    123\n",
      " 3     37\n",
      " 4     13\n",
      " 5      6\n",
      "-1      4\n",
      " 6      2\n",
      " 7      1\n",
      "Name: count, dtype: int64\n",
      "Тестовая выборка:  (1608, 4)\n",
      "Volume_Grouped\n",
      " 0    934\n",
      " 1    487\n",
      " 2    124\n",
      " 3     37\n",
      " 4     14\n",
      " 5      6\n",
      "-1      3\n",
      " 6      2\n",
      " 7      1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data = df[[\"Volume\", \"High\", \"Low\"]].copy()\n",
    "data[\"Volume_Grouped\"] = pd.cut(data[\"Volume\"], bins=50, labels=False)\n",
    "\n",
    "interval_counts = data[\"Volume_Grouped\"].value_counts().sort_index()\n",
    "\n",
    "min_samples_per_interval = 5\n",
    "for interval, count in interval_counts.items():\n",
    "    if count < min_samples_per_interval:\n",
    "        data.loc[data[\"Volume_Grouped\"] == interval, \"Volume_Grouped\"] = -1\n",
    "\n",
    "\n",
    "df_coffee_train, df_coffee_val, df_coffee_test = split_stratified_into_train_val_test(\n",
    "    data, stratify_colname=\"Volume_Grouped\", frac_train=0.60, frac_val=0.20, frac_test=0.20)\n",
    "\n",
    "print(\"Обучающая выборка: \", df_coffee_train.shape)\n",
    "print(df_coffee_train[\"Volume_Grouped\"].value_counts())\n",
    "\n",
    "X_resampled, y_resampled = apply_oversampling(df_coffee_train, df_coffee_train[\"Volume_Grouped\"])\n",
    "df_coffee_train_adasyn = pd.DataFrame(X_resampled)\n",
    "\n",
    "print(\"Обучающая выборка после oversampling: \", df_coffee_train_adasyn.shape)\n",
    "print(df_coffee_train_adasyn[\"Volume_Grouped\"].value_counts())\n",
    "\n",
    "print(\"Контрольная выборка: \", df_coffee_val.shape)\n",
    "print(df_coffee_val[\"Volume_Grouped\"].value_counts())\n",
    "\n",
    "print(\"Тестовая выборка: \", df_coffee_test.shape)\n",
    "print(df_coffee_test[\"Volume_Grouped\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборка сбалансирована"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3-й Датасет: Supermarket store branches sales analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Супермаркет - это магазин самообслуживания, предлагающий широкий ассортимент продуктов питания, напитков и товаров для дома, организованный по разделам. Этот магазин больше и имеет более широкий выбор, чем предыдущие продуктовые магазины, но меньше по размеру и более ограничен в ассортименте товаров, чем гипермаркет или рынок больших коробок. Однако в повседневном использовании в США термин \"продуктовый магазин\" является синонимом слова \"супермаркет\" и не используется для обозначения других типов магазинов, торгующих продуктами.\n",
    "\n",
    "* Из описания датасета очевидно, что объектами иследования являются магазины.\n",
    "* Атрибуты объектов: Store ID,Store_Area,Items_Available,Daily_Customer_Count,Store_Sales\n",
    "* Очевидная цель этого датасета - научиться предсказывать объем продаж на основе таких характеристик, как площадь магазина и другие факторы.\n",
    "\n",
    "В качестве примера бизнес-целей можно привести:\n",
    "* Оптимизация работы магазинов. Это может включать выявление тех характеристик магазинов (например, площадь, местоположение), которые наиболее сильно влияют на уровень продаж, и разработку стратегий для повышения этих продаж на основе этих факторов.\n",
    "* Другая возможная цель заключается в расширении или перемещении магазинов, где данные могут использоваться для принятия решений о выборе местоположений, оптимальном использовании пространства и планировке магазинов для максимизации продаж.\n",
    "* Также может быть целью управление запасами и ресурсами, поскольку понимание того, как площадь магазина влияет на объем продаж, поможет лучше управлять запасами и распределением ресурсов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "количество колонок: 5\n",
      "колонки: Store ID , Store_Area, Items_Available, Daily_Customer_Count, Store_Sales\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 896 entries, 0 to 895\n",
      "Data columns (total 5 columns):\n",
      " #   Column                Non-Null Count  Dtype\n",
      "---  ------                --------------  -----\n",
      " 0   Store ID              896 non-null    int64\n",
      " 1   Store_Area            896 non-null    int64\n",
      " 2   Items_Available       896 non-null    int64\n",
      " 3   Daily_Customer_Count  896 non-null    int64\n",
      " 4   Store_Sales           896 non-null    int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 35.1 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store ID</th>\n",
       "      <th>Store_Area</th>\n",
       "      <th>Items_Available</th>\n",
       "      <th>Daily_Customer_Count</th>\n",
       "      <th>Store_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1659</td>\n",
       "      <td>1961</td>\n",
       "      <td>530</td>\n",
       "      <td>66490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1461</td>\n",
       "      <td>1752</td>\n",
       "      <td>210</td>\n",
       "      <td>39820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1340</td>\n",
       "      <td>1609</td>\n",
       "      <td>720</td>\n",
       "      <td>54010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1451</td>\n",
       "      <td>1748</td>\n",
       "      <td>620</td>\n",
       "      <td>53730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1770</td>\n",
       "      <td>2111</td>\n",
       "      <td>450</td>\n",
       "      <td>46620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Store ID   Store_Area  Items_Available  Daily_Customer_Count  Store_Sales\n",
       "0          1        1659             1961                   530        66490\n",
       "1          2        1461             1752                   210        39820\n",
       "2          3        1340             1609                   720        54010\n",
       "3          4        1451             1748                   620        53730\n",
       "4          5        1770             2111                   450        46620"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//Stores.csv\", sep=\",\")\n",
    "print('количество колонок: ' + str(df.columns.size))  \n",
    "print('колонки: ' + ', '.join(df.columns))\n",
    "\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Получение сведений о пропущенных данных\n",
    "\n",
    "Типы пропущенных данных:\n",
    "\n",
    "* None - представление пустых данных в Python\n",
    "* NaN - представление пустых данных в Pandas\n",
    "* '' - пустая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Store ID                0\n",
      "Store_Area              0\n",
      "Items_Available         0\n",
      "Daily_Customer_Count    0\n",
      "Store_Sales             0\n",
      "dtype: int64\n",
      "\n",
      "Store ID                False\n",
      "Store_Area              False\n",
      "Items_Available         False\n",
      "Daily_Customer_Count    False\n",
      "Store_Sales             False\n",
      "dtype: bool\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "\n",
    "print()\n",
    "\n",
    "# Процент пустых 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}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по статистике выше, пустые значения отсутсвуют. Проверим датасет на выбросы:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'Store_Area': 5\n",
      "Количество выбросов в столбце 'Items_Available': 5\n",
      "Количество выбросов в столбце 'Daily_Customer_Count': 3\n",
      "Количество выбросов в столбце 'Store_Sales': 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv(\".//static//csv//Stores.csv\")\n",
    "\n",
    "# Выбираем числовые столбцы\n",
    "numeric_columns = ['Store_Area','Items_Available','Daily_Customer_Count','Store_Sales']\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['Store_Area','Items_Available','Daily_Customer_Count','Store_Sales']\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(numeric_columns, 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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по диаграммам, количетв выбросов либо полностью отсутсвует, либо имеется в пределах допустимых значений. Теперь можно приступить к разбиению датасета на выборки: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Обучающая выборка:  (537, 4)\n",
      "Sales_Grouped\n",
      " 2    184\n",
      " 3    148\n",
      " 1    135\n",
      " 4     45\n",
      " 0     20\n",
      "-1      5\n",
      "Name: count, dtype: int64\n",
      "Обучающая выборка после oversampling:  (1104, 4)\n",
      "Sales_Grouped\n",
      " 1    184\n",
      " 2    184\n",
      " 3    184\n",
      " 4    184\n",
      " 0    184\n",
      "-1    184\n",
      "Name: count, dtype: int64\n",
      "Контрольная выборка:  (179, 4)\n",
      "Sales_Grouped\n",
      " 2    61\n",
      " 3    49\n",
      " 1    45\n",
      " 4    15\n",
      " 0     7\n",
      "-1     2\n",
      "Name: count, dtype: int64\n",
      "Тестовая выборка:  (180, 4)\n",
      "Sales_Grouped\n",
      " 2    61\n",
      " 3    50\n",
      " 1    45\n",
      " 4    15\n",
      " 0     7\n",
      "-1     2\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data = df[[\"Store_Sales\", \"Store_Area\", \"Daily_Customer_Count\"]].copy()\n",
    "data[\"Sales_Grouped\"] = pd.cut(data[\"Store_Sales\"], bins=6, labels=False)\n",
    "\n",
    "interval_counts = data[\"Sales_Grouped\"].value_counts().sort_index()\n",
    "\n",
    "min_samples_per_interval = 10\n",
    "for interval, count in interval_counts.items():\n",
    "    if count < min_samples_per_interval:\n",
    "        data.loc[data[\"Sales_Grouped\"] == interval, \"Sales_Grouped\"] = -1\n",
    "\n",
    "df_shop_train, df_shop_val, df_shop_test = split_stratified_into_train_val_test(\n",
    "    data, stratify_colname=\"Sales_Grouped\", frac_train=0.60, frac_val=0.20, frac_test=0.20)\n",
    "\n",
    "\n",
    "print(\"Обучающая выборка: \", df_shop_train.shape)\n",
    "print(df_shop_train[\"Sales_Grouped\"].value_counts())\n",
    "\n",
    "X_resampled, y_resampled = apply_oversampling(df_shop_train, df_shop_train[\"Sales_Grouped\"])\n",
    "df_shop_train_adasyn = pd.DataFrame(X_resampled)\n",
    "\n",
    "print(\"Обучающая выборка после oversampling: \", df_shop_train_adasyn.shape)\n",
    "print(df_shop_train_adasyn[\"Sales_Grouped\"].value_counts())\n",
    "\n",
    "print(\"Контрольная выборка: \", df_shop_val.shape)\n",
    "print(df_shop_val[\"Sales_Grouped\"].value_counts())\n",
    "\n",
    "print(\"Тестовая выборка: \", df_shop_test.shape)\n",
    "print(df_shop_test[\"Sales_Grouped\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборка сбалансирована"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
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