{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customer Personality Analysis\n",
    "https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis Набор представляет собой данные о покупателях\n",
    "Пример цели: Узнать, кто больше всего покупает продукцию (вино)\n",
    "Входные данные: год рождения, степень образования, статус отношений, сколько детей, сколько подростков, сколько было потрачено на вино"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n",
      "       'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n",
      "       'remote_ratio', 'company_location', 'company_size'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\".//static//csv//ds_salaries.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация данных - ящик с усами. Как видим - выборка относительно сбалансирована, есть среднее смещение в среднюю сторону, медиана уравновешена\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df[\"salary_in_usd\"])\n",
    "plt.title(\"Box Plot для salary_in_usd\")\n",
    "plt.xlabel(\"salary_in_usd\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализируем отношение размера компании и зарплаты\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"salary_in_usd\"], df[\"experience_level\"])\n",
    "plt.xlabel(\"salary_in_usd\")\n",
    "plt.ylabel(\"experience_level\")\n",
    "plt.title(\"salary in usd vs experience_level\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы:\n",
      "      work_year experience_level employment_type  \\\n",
      "33         2023               SE              FT   \n",
      "68         2023               SE              FT   \n",
      "83         2022               EN              FT   \n",
      "133        2023               SE              FT   \n",
      "145        2023               SE              FT   \n",
      "...         ...              ...             ...   \n",
      "3522       2020               MI              FT   \n",
      "3675       2021               EX              CT   \n",
      "3697       2020               EX              FT   \n",
      "3747       2021               MI              FT   \n",
      "3750       2020               SE              FT   \n",
      "\n",
      "                               job_title  salary salary_currency  \\\n",
      "33              Computer Vision Engineer  342810             USD   \n",
      "68                     Applied Scientist  309400             USD   \n",
      "83                          AI Developer  300000             USD   \n",
      "133            Machine Learning Engineer  342300             USD   \n",
      "145            Machine Learning Engineer  318300             USD   \n",
      "...                                  ...     ...             ...   \n",
      "3522                  Research Scientist  450000             USD   \n",
      "3675            Principal Data Scientist  416000             USD   \n",
      "3697            Director of Data Science  325000             USD   \n",
      "3747  Applied Machine Learning Scientist  423000             USD   \n",
      "3750                      Data Scientist  412000             USD   \n",
      "\n",
      "      salary_in_usd employee_residence  remote_ratio company_location  \\\n",
      "33           342810                 US             0               US   \n",
      "68           309400                 US             0               US   \n",
      "83           300000                 IN            50               IN   \n",
      "133          342300                 US             0               US   \n",
      "145          318300                 US           100               US   \n",
      "...             ...                ...           ...              ...   \n",
      "3522         450000                 US             0               US   \n",
      "3675         416000                 US           100               US   \n",
      "3697         325000                 US           100               US   \n",
      "3747         423000                 US            50               US   \n",
      "3750         412000                 US           100               US   \n",
      "\n",
      "     company_size  \n",
      "33              M  \n",
      "68              L  \n",
      "83              L  \n",
      "133             L  \n",
      "145             M  \n",
      "...           ...  \n",
      "3522            M  \n",
      "3675            S  \n",
      "3697            L  \n",
      "3747            L  \n",
      "3750            L  \n",
      "\n",
      "[63 rows x 11 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Есть шумы, убираем\n",
    "\n",
    "\n",
    "# Статистический анализ для определения выбросов\n",
    "Q1 = df[\"salary_in_usd\"].quantile(0.25)\n",
    "Q3 = df[\"salary_in_usd\"].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Определение порога для выбросов\n",
    "threshold = 1.5 * IQR\n",
    "outliers = (df[\"salary_in_usd\"] < (Q1 - threshold)) | (\n",
    "    df[\"salary_in_usd\"] > (Q3 + threshold)\n",
    ")\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы:\")\n",
    "print(df[outliers])\n",
    "\n",
    "# Обработка выбросов\n",
    "# В данном случае мы уберем выбросы\n",
    "median_salary = df[\"salary_in_usd\"].median()\n",
    "df.loc[outliers, \"salary_in_usd\"] = 0\n",
    "df = df[df.salary_in_usd != 0]\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"salary_in_usd\"], df[\"experience_level\"])\n",
    "plt.xlabel(\"salary_in_usd\")\n",
    "plt.ylabel(\"experience_level\")\n",
    "plt.title(\"salary in usd vs experience_level\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь создадим выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 2214\n",
      "Размер контрольной выборки: 739\n",
      "Размер тестовой выборки: 739\n",
      "Распределение salary_in_usd в обучающей выборке:\n",
      "salary_in_usd\n",
      "130000    60\n",
      "150000    59\n",
      "100000    56\n",
      "160000    56\n",
      "120000    52\n",
      "          ..\n",
      "127500     1\n",
      "9466       1\n",
      "57872      1\n",
      "134024     1\n",
      "122900     1\n",
      "Name: count, Length: 741, dtype: int64\n",
      "\n",
      "Распределение salary_in_usd в контрольной выборке:\n",
      "salary_in_usd\n",
      "100000    25\n",
      "150000    20\n",
      "140000    19\n",
      "120000    16\n",
      "135000    16\n",
      "          ..\n",
      "240500     1\n",
      "93919      1\n",
      "77364      1\n",
      "87738      1\n",
      "99050      1\n",
      "Name: count, Length: 354, dtype: int64\n",
      "\n",
      "Распределение salary_in_usd в тестовой выборке:\n",
      "salary_in_usd\n",
      "120000    23\n",
      "150000    19\n",
      "100000    18\n",
      "160000    16\n",
      "200000    13\n",
      "          ..\n",
      "109000     1\n",
      "133000     1\n",
      "245000     1\n",
      "51039      1\n",
      "146300     1\n",
      "Name: count, Length: 364, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Загрузка данных\n",
    "train_df = pd.read_csv(\".//static//csv//train_data.csv\")\n",
    "val_df = pd.read_csv(\".//static//csv//val_data.csv\")\n",
    "test_df = pd.read_csv(\".//static//csv//test_data.csv\")\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "\n",
    "def check_balance(df, name):\n",
    "    counts = df[\"salary_in_usd\"].value_counts()\n",
    "    print(f\"Распределение salary_in_usd в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение salary_in_usd в обучающей выборке после oversampling:\n",
      "salary_in_usd\n",
      "127221    60\n",
      "105000    60\n",
      "100000    60\n",
      "260000    60\n",
      "130000    60\n",
      "          ..\n",
      "110000    60\n",
      "113900    60\n",
      "54685     60\n",
      "193900    60\n",
      "50000     60\n",
      "Name: count, Length: 741, dtype: int64\n",
      "\n",
      "Распределение salary_in_usd в контрольной выборке после oversampling:\n",
      "salary_in_usd\n",
      "99050     25\n",
      "126277    25\n",
      "38400     25\n",
      "56738     25\n",
      "215050    25\n",
      "          ..\n",
      "75000     25\n",
      "140000    25\n",
      "100000    25\n",
      "175000    25\n",
      "90734     25\n",
      "Name: count, Length: 354, dtype: int64\n",
      "\n",
      "Распределение salary_in_usd в тестовой выборке после oversampling:\n",
      "salary_in_usd\n",
      "219000    23\n",
      "143860    23\n",
      "72500     23\n",
      "140000    23\n",
      "66837     23\n",
      "          ..\n",
      "126000    23\n",
      "109000    23\n",
      "220000    23\n",
      "250000    23\n",
      "80000     23\n",
      "Name: count, Length: 364, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop(\"salary_in_usd\", axis=1)\n",
    "    y = df[\"salary_in_usd\"]\n",
    "\n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y)  # type: ignore\n",
    "\n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Forbes Billionaires Database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed Список биллионеров форбс\n",
    "Использование: Узнать, когда же разбогатеешь\n",
    "Входные данные: Имя, Возраст, Страна, компания, Индустрия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Rank ', 'Name', 'Networth', 'Age', 'Country', 'Source', 'Industry'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\".//static//csv//Forbes Billionaires.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Анализируем датафрейм при помощи \"ящика с усами\". Естьсмещение в сторону меньших значений, это можно исправить при помощи oversampling и undersampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Box plot для столбца Age\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df['Age'])\n",
    "plt.title('Box Plot для Age')\n",
    "plt.xlabel('Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы:\n",
      "      Rank                   Name  Networth  Age  Country       Source  \\\n",
      "1311   1292  Kevin David Lehmann        2.4   19  Germany   drugstores   \n",
      "1961   1929    Henrique Dubugras        1.5   26   Brazil      fintech   \n",
      "1975   1929     Pedro Franceschi        1.5   25   Brazil      fintech   \n",
      "2062   1929          Wang Zelong        1.5   25    China    chemicals   \n",
      "2190   2190   Alexandra Andresen        1.3   25   Norway  investments   \n",
      "2191   2190   Katharina Andresen        1.3   26   Norway  investments   \n",
      "\n",
      "                    Industry  \n",
      "1311       Fashion & Retail   \n",
      "1961  Finance & Investments   \n",
      "1975  Finance & Investments   \n",
      "2062        Metals & Mining   \n",
      "2190          diversified     \n",
      "2191          diversified     \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Имеется смещение в меньшую сторону, в том числе и медианное\n",
    "df_cleaned = df.dropna()\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"Age\"], df[\"Networth\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Networth\")\n",
    "plt.title(\"Scatter Plot of Age vs Networth\")\n",
    "plt.show()\n",
    "\n",
    "# уберем шумы\n",
    "\n",
    "# Статистический анализ для определения выбросов\n",
    "Q1 = df[\"Age\"].quantile(0.25)\n",
    "Q3 = df[\"Age\"].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Определение порога для выбросов\n",
    "threshold = 1.5 * IQR\n",
    "outliers = (df[\"Age\"] < (Q1 - threshold)) | (\n",
    "    df[\"Age\"] > (Q3 + threshold)\n",
    ")\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы:\")\n",
    "print(df[outliers])\n",
    "\n",
    "# Обработка выбросов\n",
    "# В данном случае мы занулим выбросы\n",
    "median_charge = df[\"Age\"].median()\n",
    "df.loc[outliers, \"Age\"] = 0\n",
    "\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"Age\"], df[\"Networth\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Networth\")\n",
    "plt.title(\"Scatter Plot of Age vs Networth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 1560\n",
      "Размер контрольной выборки: 520\n",
      "Размер тестовой выборки: 520\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим недостаток баланса:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Age в обучающей выборке:\n",
      "Age\n",
      "64    95\n",
      "66    53\n",
      "58    51\n",
      "59    50\n",
      "56    47\n",
      "      ..\n",
      "98     2\n",
      "30     1\n",
      "29     1\n",
      "27     1\n",
      "25     1\n",
      "Name: count, Length: 73, dtype: int64\n",
      "\n",
      "Распределение Age в контрольной выборке:\n",
      "Age\n",
      "64    27\n",
      "54    23\n",
      "60    17\n",
      "57    15\n",
      "81    15\n",
      "      ..\n",
      "27     1\n",
      "32     1\n",
      "29     1\n",
      "19     1\n",
      "42     1\n",
      "Name: count, Length: 66, dtype: int64\n",
      "\n",
      "Распределение Age в тестовой выборке:\n",
      "Age\n",
      "64     30\n",
      "68     24\n",
      "72     22\n",
      "65     22\n",
      "58     18\n",
      "       ..\n",
      "100     1\n",
      "88      1\n",
      "93      1\n",
      "91      1\n",
      "33      1\n",
      "Name: count, Length: 62, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def check_balance(df, name):\n",
    "    counts = df['Age'].value_counts()\n",
    "    print(f\"Распределение Age в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Используем oversample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Age в обучающей выборке после oversampling:\n",
      "Age\n",
      "59    95\n",
      "70    95\n",
      "71    95\n",
      "81    95\n",
      "67    95\n",
      "      ..\n",
      "94    95\n",
      "29    95\n",
      "96    95\n",
      "27    95\n",
      "25    95\n",
      "Name: count, Length: 73, dtype: int64\n",
      "\n",
      "Распределение Age в контрольной выборке после oversampling:\n",
      "Age\n",
      "57    27\n",
      "69    27\n",
      "72    27\n",
      "64    27\n",
      "54    27\n",
      "      ..\n",
      "29    27\n",
      "38    27\n",
      "19    27\n",
      "89    27\n",
      "42    27\n",
      "Name: count, Length: 66, dtype: int64\n",
      "\n",
      "Распределение Age в тестовой выборке после oversampling:\n",
      "Age\n",
      "68    30\n",
      "70    30\n",
      "76    30\n",
      "74    30\n",
      "64    30\n",
      "      ..\n",
      "42    30\n",
      "88    30\n",
      "93    30\n",
      "91    30\n",
      "33    30\n",
      "Name: count, Length: 62, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop('Age', axis=1)\n",
    "    y = df['Age']\n",
    "    \n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 100 Highest-Valued Unicorns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/ankanhore545/100-highest-valued-unicorns Самые взлетевшие компании.\n",
    "Цель: создать свою супер-компанию\n",
    "Входные данные: Название компании, оценочная стоимость, страна, штат, город, индустрия, год основания, имя основателя, количество работников"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         id  gender  age  hypertension  heart_disease ever_married  \\\n",
      "0      9046    Male   67             0              1          Yes   \n",
      "1     51676  Female   61             0              0          Yes   \n",
      "2     31112    Male   80             0              1          Yes   \n",
      "3     60182  Female   49             0              0          Yes   \n",
      "4      1665  Female   79             1              0          Yes   \n",
      "...     ...     ...  ...           ...            ...          ...   \n",
      "5105  18234  Female   80             1              0          Yes   \n",
      "5106  44873  Female   81             0              0          Yes   \n",
      "5107  19723  Female   35             0              0          Yes   \n",
      "5108  37544    Male   51             0              0          Yes   \n",
      "5109  44679  Female   44             0              0          Yes   \n",
      "\n",
      "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
      "0           Private          Urban             228.69  36.6  formerly smoked   \n",
      "1     Self-employed          Rural             202.21   NaN     never smoked   \n",
      "2           Private          Rural             105.92  32.5     never smoked   \n",
      "3           Private          Urban             171.23  34.4           smokes   \n",
      "4     Self-employed          Rural             174.12  24.0     never smoked   \n",
      "...             ...            ...                ...   ...              ...   \n",
      "5105        Private          Urban              83.75   NaN     never smoked   \n",
      "5106  Self-employed          Urban             125.20  40.0     never smoked   \n",
      "5107  Self-employed          Rural              82.99  30.6     never smoked   \n",
      "5108        Private          Rural             166.29  25.6  formerly smoked   \n",
      "5109       Govt_job          Urban              85.28  26.2          Unknown   \n",
      "\n",
      "      stroke  \n",
      "0          1  \n",
      "1          1  \n",
      "2          1  \n",
      "3          1  \n",
      "4          1  \n",
      "...      ...  \n",
      "5105       0  \n",
      "5106       0  \n",
      "5107       0  \n",
      "5108       0  \n",
      "5109       0  \n",
      "\n",
      "[5110 rows x 12 columns]\n",
      "Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
      "       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
      "       'smoking_status', 'stroke'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "df = pd.read_csv(\".//static//csv//healthcare-dataset-stroke-data.csv\", sep=\",\")\n",
    "\n",
    "df[\"age\"] = df[\"age\"].astype(int)\n",
    "print(df)\n",
    "df[\"age\"].dtype\n",
    "\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x=df[\"bmi\"])\n",
    "plt.title('Box Plot для bmi')\n",
    "plt.xlabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"bmi\"], df[\"avg_glucose_level\"])\n",
    "plt.xlabel(\"bmi\")\n",
    "plt.ylabel(\"avg_glucose_level\")\n",
    "plt.title(\"Scatter Plot of BMI vs avg_glucose_level\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удаление строк с пустыми значениями"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы:\n",
      "         id  gender  age  hypertension  heart_disease ever_married  \\\n",
      "21    13861  Female   52             1              0          Yes   \n",
      "113   41069  Female   45             0              0          Yes   \n",
      "254   32257  Female   47             0              0          Yes   \n",
      "258   28674  Female   74             1              0          Yes   \n",
      "270   72911  Female   57             1              0          Yes   \n",
      "...     ...     ...  ...           ...            ...          ...   \n",
      "4858   1696  Female   43             0              0          Yes   \n",
      "4906  72696  Female   53             0              0          Yes   \n",
      "4952  16245    Male   51             1              0          Yes   \n",
      "5009  40732  Female   50             0              0          Yes   \n",
      "5057  38349  Female   49             0              0          Yes   \n",
      "\n",
      "          work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
      "21    Self-employed          Urban             233.29  48.9     never smoked   \n",
      "113         Private          Rural             224.10  56.6     never smoked   \n",
      "254         Private          Urban             210.95  50.1          Unknown   \n",
      "258   Self-employed          Urban             205.84  54.6     never smoked   \n",
      "270         Private          Rural             129.54  60.9           smokes   \n",
      "...             ...            ...                ...   ...              ...   \n",
      "4858        Private          Urban             100.88  47.6           smokes   \n",
      "4906        Private          Urban              70.51  54.1     never smoked   \n",
      "4952  Self-employed          Rural             211.83  56.6     never smoked   \n",
      "5009  Self-employed          Rural             126.85  49.5  formerly smoked   \n",
      "5057       Govt_job          Urban              69.92  47.6     never smoked   \n",
      "\n",
      "      stroke  \n",
      "21         1  \n",
      "113        1  \n",
      "254        0  \n",
      "258        0  \n",
      "270        0  \n",
      "...      ...  \n",
      "4858       0  \n",
      "4906       0  \n",
      "4952       0  \n",
      "5009       0  \n",
      "5057       0  \n",
      "\n",
      "[110 rows x 12 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cleaned = df.dropna()\n",
    "df_cleaned = df_cleaned.loc[df_cleaned[\"bmi\"] != \"N/A\"]\n",
    "# уберем шумы\n",
    "\n",
    "# Статистический анализ для определения выбросов\n",
    "Q1 = df[\"bmi\"].quantile(0.25)\n",
    "Q3 = df[\"bmi\"].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Определение порога для выбросов\n",
    "threshold = 1.5 * IQR\n",
    "outliers = (df[\"bmi\"] < (Q1 - threshold)) | (df[\"bmi\"] > (Q3 + threshold))\n",
    "\n",
    "# Вывод выбросов\n",
    "print(\"Выбросы:\")\n",
    "print(df[outliers])\n",
    "\n",
    "# Обработка выбросов\n",
    "# В данном случае мы занулим выбросы на медиану\n",
    "median = df[\"bmi\"].median()\n",
    "df.loc[outliers, \"bmi\"] = median\n",
    "\n",
    "\n",
    "# Визуализация данных после обработки\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df[\"bmi\"], df[\"avg_glucose_level\"])\n",
    "plt.xlabel(\"bmi\")\n",
    "plt.ylabel(\"avg_glucose_level\")\n",
    "plt.title(\"Scatter Plot of BMI vs avg_glucose_level\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиение набора данных на обучающую, контрольную и тестовую выборки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применение методов приращения данных (аугментации)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 2945\n",
      "Размер контрольной выборки: 982\n",
      "Размер тестовой выборки: 982\n",
      "Распределение в обучающей выборке:\n",
      "age\n",
      "37    57\n",
      "52    55\n",
      "56    54\n",
      "57    54\n",
      "53    52\n",
      "      ..\n",
      "72    20\n",
      "68    20\n",
      "7     17\n",
      "4     14\n",
      "6     13\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n",
      "Распределение в контрольной выборке:\n",
      "age\n",
      "78    22\n",
      "51    21\n",
      "41    21\n",
      "18    18\n",
      "63    18\n",
      "      ..\n",
      "9      6\n",
      "10     5\n",
      "12     5\n",
      "74     4\n",
      "67     2\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n",
      "Распределение в тестовой выборке:\n",
      "age\n",
      "78    25\n",
      "44    23\n",
      "54    23\n",
      "50    21\n",
      "57    21\n",
      "      ..\n",
      "11     6\n",
      "76     5\n",
      "7      5\n",
      "77     4\n",
      "6      4\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n",
      "Распределение в обучающей выборке после oversampling:\n",
      "age\n",
      "32    57\n",
      "81    57\n",
      "42    57\n",
      "31    57\n",
      "23    57\n",
      "      ..\n",
      "10    57\n",
      "74    57\n",
      "76    57\n",
      "4     57\n",
      "29    57\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n",
      "Распределение в контрольной выборке после oversampling:\n",
      "age\n",
      "28    22\n",
      "74    22\n",
      "30    22\n",
      "14    22\n",
      "71    22\n",
      "      ..\n",
      "80    22\n",
      "18    22\n",
      "82    22\n",
      "65    22\n",
      "67    22\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n",
      "Распределение в тестовой выборке после oversampling:\n",
      "age\n",
      "80    25\n",
      "42    25\n",
      "66    25\n",
      "29    25\n",
      "47    25\n",
      "      ..\n",
      "7     25\n",
      "72    25\n",
      "76    25\n",
      "34    25\n",
      "13    25\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на обучающую и тестовую выборки\n",
    "train_df, test_df = train_test_split(df_cleaned, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение обучающей выборки на обучающую и контрольную\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки:\", len(train_df))\n",
    "print(\"Размер контрольной выборки:\", len(val_df))\n",
    "print(\"Размер тестовой выборки:\", len(test_df))\n",
    "\n",
    "def check_balance(df, name):\n",
    "    counts = df[\"age\"].value_counts()\n",
    "    print(f\"Распределение в {name}:\")\n",
    "    print(counts)\n",
    "    print()\n",
    "\n",
    "\n",
    "check_balance(train_df, \"обучающей выборке\")\n",
    "check_balance(val_df, \"контрольной выборке\")\n",
    "check_balance(test_df, \"тестовой выборке\")\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop(\"age\", axis=1)\n",
    "    y = df[\"age\"]\n",
    "\n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "\n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение в обучающей выборке после oversampling:\n",
      "age\n",
      "32    57\n",
      "81    57\n",
      "42    57\n",
      "31    57\n",
      "23    57\n",
      "      ..\n",
      "10    57\n",
      "74    57\n",
      "76    57\n",
      "4     57\n",
      "29    57\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n",
      "Распределение в контрольной выборке после oversampling:\n",
      "age\n",
      "28    22\n",
      "74    22\n",
      "30    22\n",
      "14    22\n",
      "71    22\n",
      "      ..\n",
      "80    22\n",
      "18    22\n",
      "82    22\n",
      "65    22\n",
      "67    22\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n",
      "Распределение в тестовой выборке после oversampling:\n",
      "age\n",
      "80    25\n",
      "42    25\n",
      "66    25\n",
      "29    25\n",
      "47    25\n",
      "      ..\n",
      "7     25\n",
      "72    25\n",
      "76    25\n",
      "34    25\n",
      "13    25\n",
      "Name: count, Length: 83, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "\n",
    "def oversample(df):\n",
    "    X = df.drop(\"age\", axis=1)\n",
    "    y = df[\"age\"]\n",
    "\n",
    "    oversampler = RandomOverSampler(random_state=42)\n",
    "    X_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n",
    "\n",
    "    resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return resampled_df\n",
    "\n",
    "\n",
    "train_df_oversampled = oversample(train_df)\n",
    "val_df_oversampled = oversample(val_df)\n",
    "test_df_oversampled = oversample(test_df)\n",
    "\n",
    "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n",
    "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n",
    "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")"
   ]
  }
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