IG_EDA_outliers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d24e99f-9f3f-4640-87d7-ae77996eff6e",
   "metadata": {},
   "source": [
    "# Домашнее задание к занятию «Поиск выбросов и генерация новых признаков» обновленное"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23993fbc-2408-4b9d-89c7-411b082ed4f7",
   "metadata": {},
   "source": [
    "## **Цель:**  \n",
    "Изучить применение методов по поиску выбросов в данных, попрактиковаться в обработке экстремальных значений.\n",
    "\n",
    "### **Описание задания:**  \n",
    "В домашнем задании нужно решить задачу классификации типа стекол. Данные для обучения моделей можно скачать с [сайта](https://www.kaggle.com/datasets/uciml/glass). Целевая переменная – тип стекла «Type». Остальные признаки описывают химические элементы в составе материала. Датасет нужно исследовать на наличие выбросов, провести EDA."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216314da-48c6-4658-9093-cdef79a31ccf",
   "metadata": {},
   "source": [
    "### **Этапы работы:**  \n",
    "1. Получите данные и загрузите их в рабочую среду (Jupyter Notebook или другую)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a8d148a-fc13-477f-9751-fe8372fae187",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3689f8d5-b6ee-4a06-8755-e6fb4e2bb444",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\grebe\\Documents\\EDUCATION\\Машинное обучение фундаментальные инструменты и практики\\Работа с признаками и построение моделей\\Поиск выбросов и генерация новых признаков\n"
     ]
    }
   ],
   "source": [
    "cd C:\\Users\\grebe\\Documents\\EDUCATION\\Машинное обучение фундаментальные инструменты и практики\\Работа с признаками и построение моделей\\Поиск выбросов и генерация новых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9933ee2c-cb6d-482c-b2e5-0d9184d0766e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('glass.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8ae5542-2e6d-4612-8615-e5a5af0cebd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type\n",
       "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1\n",
       "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1\n",
       "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1\n",
       "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1\n",
       "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4ab2b9-0066-42f2-b276-aa17b11e6350",
   "metadata": {},
   "source": [
    "2. Проведите первичный анализ.\n",
    "   а) Проверьте количество записей для каждого класса. Сделайте вывод."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a486f684-0565-46ad-bf6c-be3aa8d62016",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214 entries, 0 to 213\n",
      "Data columns (total 10 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   RI      214 non-null    float64\n",
      " 1   Na      214 non-null    float64\n",
      " 2   Mg      214 non-null    float64\n",
      " 3   Al      214 non-null    float64\n",
      " 4   Si      214 non-null    float64\n",
      " 5   K       214 non-null    float64\n",
      " 6   Ca      214 non-null    float64\n",
      " 7   Ba      214 non-null    float64\n",
      " 8   Fe      214 non-null    float64\n",
      " 9   Type    214 non-null    int64  \n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 16.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "209461d0-5cc4-412c-8dbb-f3343df7c227",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6,4])\n",
    "sns.histplot(data = df, x = df.Type)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c36669a-f153-4d88-9911-057709bd5c9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RI  Na  Mg  Al  Si   K  Ca  Ba  Fe\n",
       "Type                                    \n",
       "1     70  70  70  70  70  70  70  70  70\n",
       "2     76  76  76  76  76  76  76  76  76\n",
       "3     17  17  17  17  17  17  17  17  17\n",
       "5     13  13  13  13  13  13  13  13  13\n",
       "6      9   9   9   9   9   9   9   9   9\n",
       "7     29  29  29  29  29  29  29  29  29"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Type').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b20a2ed-1a24-4f42-aa2c-146cc3d1af65",
   "metadata": {},
   "source": [
    "#### Вывод: В основном, большинство записей соответствуют классам 1 и 2. Записей для класса 4 нет."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c545c5-c6e5-4401-8cc3-33512a23ffbf",
   "metadata": {},
   "source": [
    "3. Разделите выборку на обучающее и тестовое подмножество. 80% данных оставить на обучающее множество, 20% на тестовое."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ff979db-0399-4e5a-9b8d-b94d7573d182",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44f91a02-19ad-4fb1-85db-c1e4296a43fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fbb91e9-fe03-4499-80dc-30cccf7af799",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "60053daf-39a4-4af9-91f0-9ae4ceec8205",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "178e300a-9149-43f0-929b-1a493e56e665",
   "metadata": {},
   "source": [
    "4. Обучите модель дерева решений [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) на обучающем множестве."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc570a16-1310-416c-aa5f-21fe1186f59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bad60121-d523-4df0-b568-2185958bd66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=30)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=30)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=30)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be1e6fb-6a57-4c2f-9c07-4db3110b46eb",
   "metadata": {},
   "source": [
    "5. Для тестового множества предскажите тип стекла и сравните с истинным значением, посчитав точность предсказания модели (accuracy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "358f236b-0c82-4416-8e6b-b9ff6f8cdfcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6e69a5bd-ae28-4375-977c-034145c5c43b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8372093023255814"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "610ca6e4-2af0-448d-87b8-08c78231c5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8372093023255814"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(y_test == y_pred)/len(y_test) # посчитали точность сами"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50234809-64e4-4eaf-9154-64b4cdb31fb9",
   "metadata": {},
   "source": [
    "6. Обработайте выбросы в данных.  \n",
    "а) Визуализируйте распределение значений для каждой переменной. Можно использовать функции [sns.boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html), [sns.distplot](https://seaborn.pydata.org/generated/seaborn.distplot.html). Есть ли признаки с нормальным распределением?  \n",
    "b) Исследуйте признаки на выбросы несколькими способами.  \n",
    "c) Удалите выбросы. *Посчитайте процент удаленных записей от общего числа записей для каждого класса."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e11dabb-14bb-4256-91ef-0943d42044b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(rc={'figure.figsize':(16,3)})\n",
    "sns.set_style(style = 'dark')\n",
    "sns.set(font_scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ff3bf989-cf28-4144-a83e-72eac8347641",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b090de1-b30e-4631-aced-18bb956b8912",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xx6qpqWnyvXnVVVd1flBAN5V/uEKSvaPq6gztm65eGR4dLfPqk51HNWUUyw8AQKywfWSdYRiaPv166YRJjoZhaMaMH0SVpDlVHU2Vi7bO1ojmHNrjPCUpISFBCQmeLn/zeKIpF1uJrtaK1d9VdG1/ureWf3fRvm9N0yHDaHypqnvfmKYZVR0tMU1T119/Q8sFj5edOXNWhySQW3udaK/rSnupi+fE31fD+2fOnKUZM36gWIk5Wm1p6856TFfVnc7VTqtWrdL555+vn/70p7rzzjs1f/78Rre77rrL7hCBbiMYCmt/QWQH5lhI1hmGUZ+gY1dYAIgttifrJKl37xxdeukV9R1zwzB06aXfUHZ279Oq44wzhp1Wna0RzTm0x3kC8SSa9+1ll31Dl132jVO+b06soyWnujZ86Uvn64wzzmxUNiurx0mPv+yyKzv0Pdva60SsXVdO9ftoGFesxRyttsTdWY/pqrrTudrl0Ucf1eDBg/Xiiy/qrbfe0jvvvNPo9vbbb9sdItBtFJbUKBgKK8njVFZabPxTum4q7Oa9JSqrir3N0QCgu4qJZJ0U+QCcnp4hScrIyNSll37jtOv4yU9uO+06T+f5m3q+9jhPIJ5E875t6X1z4v0ZGZn139cdr0sGNHdt+MlP5jZKGtx11y/q6zrVc3eE1l4nYu260jCehu3eMK5YizlabYm7sx7TVXWnc7XD7t279bOf/Uzjx49X//791bdv35NuADrHgaPVkqR+vZJjZvRw78wkDembJsuSVm8ptDscAMBxMZOsS0hI0HXXzVKPHj01c+YP2jQF8sQ6UlPTTrvO03n+pp6vPc4TiCfRvG9bet80vP+662Y1+v773/+hevToqcsuu7LFa0Nqapouu+xKmaZZX/6662YpNTVVqampuu66WZ3ynm3tdSLWrisN42nY7g3jirWYo9WWuDvrMV1VdzpXO/Tp00dVVVV2hwFA0qGiyHuxXy/7p8A2dB5TYQEg5hjWqbbtO0EoFFZJSXVHx9NpnE5TmZnJKi3t2IX224PP59XNN8+SJD377GIlJHhsjuj0RNv2Dc87Zdg1Msy27YdihYOq2v6/p11Pa58rFn9XnfW6j7fXbHvoSteceET72yeW2j4rK1kOR8z8n7JTLFu2TIsXL9bvfvc79evXz+5wTltn9Udj6XUb62irUzMMQ1XegDbsKlZxWa2Wr8qXaUjXfuWMk65FKUkuDemXoW35pfIFgi3W3ZrybpdDY4b2VIrH1eQmM1W1Ad325L8UClt6YNZE9YuB9fR4XUWPtooebRU92io6bWmnaPujtu8GCwAAgI7x+uuvq7CwUF/96leVlZUlj6fxP08Mw2DdOqATHCqOJJlze6bI7XIoFI5qvESnSEl06ewhPfTpzmL9e3OBvpM91O6QAKDbI1kHAAAQp3JycpSTk2N3GEC3d+j4enUDclJtjqRp543O0ac7i7V6S6GuuXCIzBhZUw8AuiuSdQAAAHHqoYcesjsEoNsLBsMqLKmRJOXlpNkcTdPOHtJDiQlOlVb6tGN/mYbnZbb8IABAh+leC7cAAAB0Q7t379aSJUv0yCOPqLCwUOvWrWPjCaCTHDlWrVDYUrLHqay02NxEx+V0aPywXpKkj7ew0QQA2I2RdQAAAHEqHA7rvvvu09KlS2VZlgzD0Ne//nU988wz2r9/v1588UWmyQId7ECDXWCNGJ5eOnlUjlZ9fkTrth3V9K8Ok8vJuA4AsAtXYAAAgDj1zDPP6PXXX9evf/1rffjhh/U7Qc6bN0/hcFiPPfaYzREC8e/g8fXq+vZKtjmS5g0bkKHM1ATV+IL6fPcxu8MBgG6NZB0AAECcWrp0qebMmaOrr75aGRkZ9cdHjBihOXPm6MMPP7QvOKAbKC6vVUW1X4Yh5fZIsjucZpmGoUkjektiKiwA2I1kHQAAQJwqLi7WiBEjmryvd+/eqqio6OSIgO5l+74ySVKvjES5XQ57g4nC5FGRZN2GXcdU4w3aHA0AdF8k6wAAAOJUXl6ePvjggybvW7NmjfLy8jo5IqB72bavVJLUJ8ZH1dXpn52iPj2TFQyFtX57kd3hAEC3RbIOAAAgTn3/+9/XkiVL9MADD+jf//63DMPQvn37tHjxYi1evFjf+9737A4RiFuhcFjbD5RJknJ72rtenWHU3Yxmb6ZpasqoyKYzH28ptDVmAOjO2A0WAAAgTn37299WSUmJnn32Wb388suSpLlz58rlcmn27Nm69tprbY4QiF97j1Sq1heU22WqR7rHtjgcjkgSrrI2KMlqsfxZQ3po6Qe7tW1fqUorfcpMTej4IAEAjZCsAwAAiGM33HCDrrjiCq1Zs0ZOp1OpqakaM2ZMow0nALS/TfmRHVX79kyWaRi2xeEwDdX6g9p9oFz+YKjF8i6nqcF90rTncIVWbynU1yYN6IQoAQANkawDAACIQ2+88YZeeeUVbdiwQcFgZKF4j8ejc845R9dee62+8pWv2BwhEN8255dIkvr2SrE5kohAMCx/oOVknSRNGJ6tPYcr9PGWApJ1AGADknUAAABxJBQK6ec//7n+8Y9/qHfv3rrsssvUs2dPWZalgoICrVmzRj/96U915ZVX6uGHH7Y7XCAu1XiD2n0ostty3172rlfXFuPO7KX/fX+39hdW6XBxtfrYvOYeAHQ3JOsAAADiyMsvv6yVK1fq7rvv1owZM2ScMP0uFArplVde0YMPPqjx48frmmuusSlSIH5t21+qsGWpV0aiUpPcUY9oixXJiS6dNbiHPttVrI82F+jqC4bYHRIAdCvsBgsAABBHli9fru9+97uaOXPmSYk6SXI4HJo+fbq+853v6C9/+YsNEQLxr24K7PC8TJsjabvJdbvCbi5Q2Gp5YwoAQPshWQcAABBH8vPzdf7557dYbtq0adqxY0cnRAR0P5v3RpJ1I7pwsm7cGT2VmODUsQqftu8vszscAOhWSNYBAADEkdraWqWnp7dYLjMzU9XV1Z0QEdC9FJXVqqi0Vg7T0NB+Lb8XY5Xb5dCE4dmSpH9vOmJzNADQvZCsAwAAiCOWZcnhcLRYzjRNWUxtA9rdluNTYIf0TVdiQtdeIvxLZ0Wmwq7bflQ+f9dadw8AujKSdQAAAADQTurWqxs1KMvmSE7f0L7pys5IlM8f0vodRXaHAwDdRtf+Vw8AAABOcv/99yslJaXZMlVVVZ0UDdB9hMJhbdlXKkkaHQfJOsMwdN7oHC3/V74+3Fig80bn2h0SAHQLjKwDAACIIxMmTFBycrIsy2r2lpycrPHjx9sdLhBX8o9UqtYXVLLHqYE5aXaH0y7OGx2ZCrttX6lKKrw2RwMA3QMj6wAAAOLICy+8YHcIQLdVNwV2xMAsmaZhczTto2dGoob1z9D2A2X6cOMRXfGlQXaHBABxj5F1AAAAANAO6pJ18TAFtqFpYyLTX1d9fkRhNqYBgA5Hsg4AAAAATlO1N6Ddh8slSSMHZtocTfs6d1i2EhMcKi73atvxNfkAAB2HZB0AAAAAnKate0tlWVJujyT1TE+0O5x2leByaPLIyNp1/7fhsM3RAED8I1kHAAAAAKdpU/4xSdLoQT1sjqRj1E2F/WTHUVXVBmyOBgDiG8k6AAAAADgNlmVpU916dYPja726Onm9UzUgO0XBkKWPNhfYHQ4AxDWSdQAAAABwGg4fq1FJhU8up6lh/TPsDqdDGIahaWP6SJJWbTgsi40mAKDDkKwDAAAAgNOweU9kCuyZ/TPkdjlsjqbjTB7VW26nqYNHq7XrULnd4QBA3CJZBwAAAACnYWPdFNhB8TkFtk6yx6VJI3tLkt775JDN0QBA/CJZBwAAAABt5A+EtONAmSRp9OD43FyioYvP6SdJWrutSBXVfpujAYD4RLIOAAAAANpo+4EyBYJhZaYmqE+PJLvD6XB5Oaka3CdNobClVZ8ftjscAIhLJOsAAAAAoI027YlMgT1rcJYMw7A5ms5x0bi+kqT3Pz2kcJiNJgCgvZGsAwAAAIA22pQf2Vxi9KD4nwJbZ+KIbCV7nDpW4dOG3cV2hwMAcYdkHQAAAAC0QXF5rY4cq5FhSCMGZtodTqdxOR06f0wfSdLb6w7aHA0AxB+SdQAAAADQBpuO7wI7pE+6kj0um6PpXF8+t59Mw9DWfaXaX1hpdzgAEFdI1gEAAABAG2w+vl7d6EFZNkfS+bLSPBo/vJck6a21B2yOBgDiC8k6AAAAAGilYCisLfuOJ+sGd5/16hq6ZMIASdLHWwpVVuWzORoAiB8k67oAtztBQ4eeqaFDz5TbnWB3OECLeM0CAIB4t+dwhWp9ISV7nBqYk2p3OLYY3CdNQ/ulKxS29O4nh+wOBwDihtPuANAywzB0112/qP8eiHW8ZgEAQLyr2wV21KAsmWb37e9cMr6/dh0s1/ufHtJlk/OU4HbYHRIAdHmMrOsiDMMg6YEuhdcsAMSfcDisJ554QtOmTdPYsWN1ww036MCBU69VtXPnTt14442aNGmSpkyZojlz5ujw4cOdGDHQcTbVr1fXPafA1jnnzF7KzkhUVW1AH2zg/Q0A7YFkHQAAAKLyzDPP6OWXX9avfvUrvfLKKwqHw5o9e7b8fv9JZUtLS/WDH/xAHo9HL7zwgv77v/9bJSUlmj17tnw+1rZC11ZR7de+gsgOqKO64eYSDZmmoa9Pjqxd9881+xUIhm2OCAC6PpJ1AAAAaJHf79fixYs1Z84cXXjhhRo+fLgee+wxFRQUaOXKlSeVf/vtt1VTU6Pf/va3OvPMMzV69GgtWLBAu3fv1ieffGLDGQDtZ8PuYlmS8nqnKjOV9XnPG52rjBS3Sit9+vemI3aHAwBdHsk6AAAAtGjbtm2qrq7WlClT6o+lpaVp5MiRWrt27Unlp0yZomeeeUYej6f+mGlGup4VFRUdHzDQgTbsiqxXN2Zo954CW8flNPW1iZHRdX//eL9CYUbXAcDpIFkHAACAFhUUFEiScnNzGx3Pzs6uv6+hfv36afLkyY2O/eEPf5DH49GECRM6LlCggwWCIW3Oj6xXN/aMnjZHEzsuGNtXKYkuFZXVau3WIrvDAYAujd1gAQAA0KLa2lpJktvtbnQ8ISFB5eXlLT7+hRde0Isvvqh77rlHWVltX+PL6ez4/zU7HGajrzi17thWW/aVyBcIKTM1QUP6pp9yQy3DkAzTkOP4rW5kaeRr45Fn5vGNuUyH5Ai1vEFXa8q3tm6HacgwDTmdhiwr+s3CnE5T/zGxv5Z+sEev/3uvppyVI4fZttdFd3xdtRVtFT3aKnq0VXQ6sp1I1gEAAKBFddNZ/X5/o6mtPp9PiYmJp3ycZVl6/PHH9eyzz+rmm2/WzJkz2xyDaRrKzExu8+NbKy3t1OeFxrpTW23Zt0uSNHFUjrKyUpot6w/XKDHRLafri+Scx+M6qVyixymn06FEj1tOZ8tTSFtTvrV1u5ymEj1uZWQktVj2RN+5ZLhWrj2oI8dqtGFPqb48YUCr62ioO72uThdtFT3aKnq0VXQ6op1I1gEAAKBFddNfi4qKNGDAFx/Ai4qKNGzYsCYfEwgEdNddd+mNN97QXXfdpeuvv/60YgiHLVVU1JxWHdFwOEylpSWqoqJWoRBrbzWnu7WVZVn6+PgGCiPzMlRaWn3KsoYh1XoDqq31yx8IyTRNeTwueb0BhU9Y082wwgoGQ6r1+uX3h1qMozXlW1u32+VQrdevsjJLltVi8ZNcOmWA/vzOLr349606e1CmnG0YcdLdXleng7aKHm0VPdoqOm1pp7S0xKhG4pGsAwAAQIuGDx+ulJQUrV69uj5ZV1FRoS1btmjGjBlNPuaOO+7QW2+9pUcffVSXXXZZu8QRDHbeh4ZQKNypz9eVdZe22l9YqZIKn9xOU8P6ZTR7zoZhyApbCh2/1U19DYfDx3/+QtiyZFmWwiGddF9TWlO+tXWHwpassKVgMPK41rpwbF/9/eP9Ki736r1PDumicX1bXUd9LN3kddUeaKvo0VbRo62i0xHtxARkAAAAtMjtdmvGjBl65JFH9M4772jbtm267bbblJOTo0suuUShUEhHjx6V1+uVJC1btkwrVqzQbbfdpokTJ+ro0aP1t7oyQFezYVexJGnkwCy5XQ6bo4lNCS6HLp+SJ0l6/cN8+QMtj+YDADRGsg4AAABRmTNnjq655hrdc889uvbaa+VwOLRo0SK5XC4dOXJEU6dO1YoVKyRJb7zxhiTpt7/9raZOndroVlcG6Go+2RFJ1rELbPMuGNtXPdISVFbl11vrDtgdDgB0OUyDBQAAQFQcDofmzZunefPmnXRfv379tH379vqfFy9e3JmhAR3uaFmt9hVWyjBI1rXE5TT1rfOH6L/f2KI3P9qnaWf3UVqyu+UHAgAkMbIOAAAAAFq0fvtRSdKw/hlKSyLx1JJJo3orLydVXn9If/1Xvt3hAECXQrIOAAAAAFqwfnuRJOncYdk2R9I1mIah7148VJL0wWeHdbj41DvnAgAaI1kHAAAAAM0oqfBq9+EKSdI5Z/ayOZquY9iATI07o6fClqVX3t3Zpt1lAaA7IlkHAAAAAM34ZEdkCuzQvunKTE2wOZqOZxh1N6PFW6T8qe//zkVnyGEa2rSnRJ/tOmbzmQFA18AGEwAAAADQjLr16s4dFv+j6hwOQ6ZpqrI2KKn5kXCGIbndTvm8gVOWSUl26eJz++mttQf00lvbNWxgppJcjnaOGgDiC8k6AAAAADiF8mq/dhwskySd2w2mwDpMQ7X+oHYfKJc/GGq2bJLHqbzctBbL9umZrGSPUyUVPr3xr3z9fxefwZRYAGgG02ABAAAA4BTWbC2UZUmDclPVMyPR7nA6TSAYlj8QavYWDIWjKmtZlsYPj2zM8fa6AyooqbHz1AAg5pGsAwAAAIBT+HhzgSRpyqgcmyPp2gb0TlG/XskKhiw9//etkppb687mYAHAZiTrAAAAAKAJBSU1yj9SKdMwNHFEb7vD6dIMw9C0sX3kdpratr9Mb607oCpvoMlbeU1ARSU1CoaZKguge2LNOgAAAABoQt2outGDs5SW7LY5mq4vMzVBX5ucp7/9K19L398tGVKyx3VSOYdpKC3Vo7zeKUp2O1nfDkC3w8g6AAAAADiBZVn66HiybvJIRtW1l6lj+6hnukf+YFj/2nBEPn+wyXXuAsGw3aECgG1I1gEAAADACXYfrtDRMq8SXA6NOyP+d4HtLA7T0Plj+8owpANFVco/UmF3SAAQc0jWAQAAAMAJ6kbVnXNmLyW4HTZHE196pHk0ZmhPSdLqLUWqrg3YHBEAxBaSdQAAAADQgC8Q0sebCyVJ541mF9iOMHpQlnqmexQIhvXvTQWsSwcADZCsAwAAAIAG1m4tUq0vqF4ZHo0YmGl3OHHJNA196axcOUxDR47VaNu+MrtDAoCYQbIOAAAAABp4/7NDkqTzx/SRaRg2RxO/0lPcOndYZD3A9duPqqTCa3NEABAbSNYBAAAAwHH7Cyu153CFHKahqWf3sTucuDdsQIb69UpW2LL0fxuOsAssAIhkHQAAAADU++Czw5IiG0ukJ7ttjib+GYah887KUWKCUxXVfq3ZWmh3SABgO5J1AAAAACDJ6w/W7wJ74VhG1XUWj9upaWfnSpJ2H6rQzgNl9gYEADYjWQcAAAAAkj7cWCCvP6TemYkansfGEp0pp0eSxg7tIUn6aHOhikprbI4IAOxDsg4AAABAtxcKh/XPNfslSV8Z318GG0t0urOG9FDfXskKhS298a98VdcG7A4JAGxBsg4AAABAt7du21EVl3uVkujS1ONTMtG5DMPQ1LNzlZLoUkW1X398c6tCYTacAND9kKwDAAAA0K1ZlqW/f7xPkvSVc/spweWwOaLuK8Hl0MXn9pXTYWrbvlK98vZOu0MCgE5Hsg4AAABAt7Zlb6n2F1XJ7TJ18bn97A6n2+uR5tHXJudJkt5ef1Dvf3rI5ogAoHORrAMAAADQra04Pqru/DF9lJLosjkaSNLQ/hm6/EsDJUkvvbVDG/ccszcgAOhEJOsAAAAAdFub80u0dV+pHKahSyb0tzscNPAfkwZoyqgchcKWnvnLJuUfqbA7JADoFCTrAAAAAHRLYcvS/76/W5J00Tl91TM9scXHGIYR5a2jo49/hmFo1mUjNGpgpnyBkBa+tkGFJTV2hwUAHY5kHQAAAIBuac3WQu0rrJTH7dDl5w1ssXxIUpU3ENWt2hcU+5iePqfD1C3fPEt5vVNVWRPQI698quKyWrvDAoAO5bQ7AAAAAADobMFQWMs+2CNJ+vrkPKUluZstbxiGvN6AtuwtUSDYchouyeNUXm6aDDHE7nQlJjj1s++M0cMvfaLCkhr99k+fav70c5SV5rE7NADoEIysAwAAANDtvL3uoIrLvUpPceuS8dGvVRcIhuUPhFq8BUOMq2tP6clu3XHtOGVnJKq43KvfvvypjpV77Q4LADoEyTp0CVY4eFq39qqnNc8FAACA2FRUWqPlqyKj6r51/mAluB02R4RoZKYm6I7vjVPPdI+Kymr14Ivrdbi42u6wAKDdMQ0WXUL1zuUxVQ8AAAC6Jsuy9Pw/tssfDGtEXqamnpVrd0hohaw0j+ZPP0eP/vkzHTlWo4deXK+ffWeMhvRJtzs0AGg3jKwDAAAA0G386/Mj2rqvVC6nqe9/bZgMtm3tcuoSdoNy01TtDeq3L3+qjzYX2B0WALQbRtYhZrndCXr22cXtUpdlWZLUqZ0xtzuh054LAAAALSsqq9Ur7+6SJF01bZCyM5NsjghtlZrk1rxrx+p3f92sz3cf03+/vkUHCqt09YWD5TAZkwKgayNZh5hlGIYSEtjhCQAAAKfPHwjpmWUbVesLakifNF0yIfpNJRCbPG6n5lx9tv6yao/e/Gif/rFmv3YdKtfsy0eQiAXQpfEvBwAAAABxzbIsvbhyh/YXVSkl0aWbrxrN6Ks4YZqGrr5giG6+arQSExzadahcv1i8Vu9/ekjh47NrAKCrYWQdAAAAgLj27ieH9K+NR2QY0k1XjlJWGrM3ugLDiNyklpeymTiitwblpmrRG1u1/UCZlvxzu1Z9fljf+8qZGtK3fTafiHZJHcuyWrX8jkVSEcAJSNYBAAAAiFv/3nREL7+1Q5L0rfMHa+TALJsjQjQcpiHDMFRZG5QUXTIrMz1R864dp7fXH9TyVXuUf6RSv3lhvc45s5cunZynwX3S2hxPSJLXG2ixnGFIbrdTvijK1vEkOOVoc2QA4hHJOgAAAABxad22Ii16c6ssSV8+t58unZxnd0iIkmka8vpD2rW/TP5gqMXyLqepkQOzlOJx6ZIJ/TVpRLaWfrBH/9p4RJ/sOKpPdhzVmf0zNPWsXJ07rJcSE6L/KGwYhrzegLbsLVEgGG62bJLHqbzcNO0+UN7quBlhB6AOyToAAAAAcWfV54e15B/bZVnS1LNyde1XzmjV1ETEhkAwLH+g5aTXidJTEjTrshH6j0kD9I/V+/Tx5kLtOFCmHQfK9OLK7Ro5MEsj8jI1PC9TuT2S5HS0vIZhNLG4XeZpxQ0AEsk6AAAAAHEkbFla+v5u/X31fknS5FG9df3Xh8skUdct9e2ZrB9eNlLfnDZYH24q0EebClRQUqPPdhXrs13FkiSnw1Buj2T1zkxURmqCMlISlOByyOU05XaacrkcCoUtHSyqOuXIOsOIjJLzB0PKqvTJFwgpbFm87gC0Cck6AAAAAHGhtNKnP/59qzbtKZEkXXHeQF05bRAJEygrzaMrzhuoy6fk6UBRlTbvLdGWvaXadahcPn9IB4qqdKCoqt2fNynBqZQkl1ISI7fUJJcyjycETZPXJYCmkawDAAAA0KVZlqWPNhfo5bd2qsYXlNNh6geXDteUUTl2h4YYYxiGBvRO1YDeqfr6pDxZlqVj5V4dPFqt4vJalVb5VF7llz8YViAQkj8YVjAUltcfUo03KOsUm12Ew5aCIUvBUFiBYFihcKRcjS+oGl9QRaW1jco7TENZaQnKzkxSIBjWWYN6KCstocPPH0DXQLIOAAAAQJe1fX+p/veD3dp9qEKSNCg3VbMuG6m+PZNtjgxdgWEY6pmRqJ4Zic2WqfIGtGFXcYvr0KUkuTSkX4Y27zqmylq/qmoDqqoJqKo2oMragCpr/Cqp8CkQDOtomVdHy7zanB8ZCdo7K0kjB2ZqZF6WhudlKNnjatdzBdB1kKwDAAAA0KWELUsbdx/T2+sOaPPeUkmRhf0vnzJQX588QA6z5c0CgI7kcJhKTHAqMcGpXickAi3LUkV14PhIPr+qawM6UFilwpIaFZbU6L1PDskwpEG5aRo9KEtnDe6hQblpTJsFuhGSdQAAAACa1NLuqXV3t2VJuNbszGpZlgzD0JFj1Vq9pVAfbSpQUVlkWqFpGLpgbB99Y+ogZaQkyLKanqYInCja12B7L3loGIbSU9xKT3HL7XJo7Bk9ZcrQtv2l2pJfoi17S3T4WI32HK7QnsMV+tuHe5XscWr0oB4aPThLowdlKT0l+imzrX2vdaRoY+nK7+NYam90XSTrAAAAAJwkJMnrDTRbxjAN+cM1qvUGlOByyNGOdUuS1x/U3oJK7ThQpk27j+nIsZr6+xITHJoyOlfTzs6tn8JY5Q3Ik+CMOg50X9G+BiXJNA01vQfs6XM4DJmmqVDI0hn9M3RG/wxdef5glVb6tG1fqbbuLdG2/WWq9ga1emuhVm8tlCQN6J2iswb30JihPTUh7dRTeFtznpI69P3Tmli66vs4ltobXRvJOgAAAACNGIYhrzegLXtLFAieOk3hMA0lJroVDAQ1bECmUjyuFkeKnKpufzCkskqfjpZ5VVxWq6NltSqr8p/wWKlfrxQN7pOmgblpcjlNHSqu1qHiakmSy2lq5MCsqOJA9xXt67tOksepvNw0GWr/aagO01CtP6jdB8rlDzZeDy/J49S5w7M17sxeKiqr1ZHiah0tq9X+wqr625sf7VNiwmca0jddQ/um68x+GRrUJ00JLkerz7Mj3z+tiaWrvo9jqb3R9ZGsAwAAANCkQDDc7IL6DtOQ0xVWsIUPppZlqdYXVFmVX+XVfhWW1mhzfonKKn2qqIksuu/1N/08KYkujRiYqdREt3pleJTgdtTX2dJi/0BzWnp913G7On4NxJZiyUpNUE5WksYM7alwyNKmPce0Mf+YNueXqLImoE17SrRpT2SjCodpKC8nVXm9U5WdmagaX1ApiS65nPav5Rhtm3dl3eEc0fFI1gEAAABolmVZCoYs+YMhBQJh+YNhBYIhBUOWZBjy+gLac7hCoVBYNb6gar1B1fqCqvGFVOsLqrLGL38UI008bod6pHvUI82jnuke9Uj3qFdmoob0y9C2/FL5AsFOOFsgtqUluzVldI6mjM6R6TBU4Q1p3eYj2ravVDsOlKmsyl+/3l1DqUkupSa5lJLY+OZJcCrB5YiJZB6ACJJ1AAAAiEo4HNZTTz2l1157TZWVlZowYYLuu+8+9e/fv8nypaWl+vWvf63/+7//k2EYuuyyy3THHXcoMfHU6yuhc9T6giqt8qm8yq+ySp9Kq3yq9gZV4w2o2htJtlXU+FVe5ZM3EEnQtcckraQEpzJS3EpJcsuyLCUnRpIHaUlupSa55Had/upNhlG3IUD0C9lHsyB8e28ygPYX7e8+nn6XpmFoUJ90ZSQ6deHYvrIsS8XlXu0+XK6DRdXKL6jQviOVqvEFVVkTUGXNqddTc5iGUhJ3KzXJLY87krxzOU25HJGvpmnIsixZlmRJX3xvWbIkhcNW5GZFdmz+4ufI10DIUrU3oHDYqn+sYUR2znWYhkzTiIzWdZj6dMdRJR1PIia4HfK4nfK4j3/vivwcOR65uV2R426X2aoNHqJ1qjoj5xVWKBS5Rtb6ggoEwwqFwzKMyMTpjoinIzSMs+HmQU3Fz7TdjkeyDgAAAFF55pln9PLLL+vhhx9WTk6OFixYoNmzZ+v111+X2+0+qfycOXNUW1ur5557ThUVFbr77rtVU1Oj//zP/7Qh+vgXmWoaUnm1T2WVPpVV+VVW1fDrF99Hs55SUwxDcjsjH4hdTlNul0OJCU65HKZyeyQpPdktj9upJI9TSQlOJR6/pSS5lJEc2fnSMAxVeQPasKu43aeK1S3WX1kblKJILxqG5HY75YtiQfiO3GQAp681v/t4/l0ahqFeGYnqlZEoY9QX77Xy49eAqtqAqmoCka+1QVXXBuT1hxS2LIXClsqrI1PV7bavoLJNjzMkJbgbJPjqk32Ra1WixyWvL1CfVGyYcAyHI20QDIUVDDX+WpeAq/s5FLIUDIfVUs7KkOR0mnI6vkh8Op2GXA5TCW6Htu0rVUZKgpISnEr2OJWc6Ip89biUdPxnt7NjEpANnbgxRsPNg6zwySfJxhgdj2QdAAAAWuT3+7V48WLdfvvtuvDCCyVJjz32mKZNm6aVK1fq8ssvb1T+008/1Zo1a7RixQoNGTJEkvTAAw9o9uzZmjt3rnr37t3Zp9BlhcJhVdYEVFHtV0WNP/K1OvBFAq7Sp7LqSBLOH4g+BZHgMpWYEPlAmOQ5PmrFFRmhkprkUv/eqTpaUisZVn2CzmEajT40OkxDSUkJCgaCGj24h+0LpTe3WH9T6jYOiKZ8R24ygNPXmt99d/xd1iXOm1I3zT1sWRrQO1XhkCVfIBRZey0YUjAYmfpeNxKufrSYERnZJ0WOm6Yh04jcHKYhw1T996Zpyh8MaW9BpUKhcORxxhej8ULHb+GwJafTVGZaggqLayIje4ORZFkgFFkfs+HPgWC4PpkmRdK0Xn9IXn9I5bI/6WhJ9fHW+k6+f/ehipMPnsDpMJTscSk5MXK9TqlL5Hlc9Qm+hj+7nKYcDlNOhyGnGfla97PDNCO/K8OoHzXX1MYYdZsH1db6FTohWcfGGJ2DZB0AAABatG3bNlVXV2vKlCn1x9LS0jRy5EitXbv2pGTdunXr1KtXr/pEnSRNnDhRhmFo/fr1uvTSSzstdjuELSvyoTLU4INlww+YgciHyRpfUDX167sF69d7q/ZGpquVV/tVXRto1RTUxONTTTNSEo7fIt9npiYowe3QoeLqyIc4x6nXp0pJckXWiVPXXCeutRsHRFO+MzYZwOnjd9l6hmHI5TTkdjk0oHdqh+0GW+UNKBRueWOY+utPlOtUul0OnTWkh9wOU15fsD5Z5wuE5PVHfvb5Q/KHwvJ4XKqtDciyrBMSVl8kFp11ia7jo+H8obDyD1coHLbqp+rWJyYb/JyS5NKgPunall8qrz8gy1L9SL2GScW6pGPYkrLSEhQIhFV9fAmC6trjX70B1XiDxx/fsSMe69qhTl2bRBKxlqS6dpIkQ6YRGb3oMA0ZOqENjx8zGxxrNJ3a5aifVu2uO15/czQ+7jDlckW+uhs8ruHN7XTINOMz6R51ss40DWVlJXdkLLZIS2PNFLvQ9vah7e1D29uL9rdPLLR9vHbmOktBQYEkKTc3t9Hx7Ozs+vsaKiwsPKms2+1WRkaGjhw50qYYOqM/WlUbUCAYVnFZbeSTibPxRB/rFD9YDb6r+4zrcDjkkORpp9jqPhjWfSByHP+54YfGyAelUwtblgLBlqdumUZk6la/3mnNlo0MjjFkyZK7fk2r5us2DCk9bCm7Z0qLZVsTS2vLdnbdDdvqxMfHctx21F2fOHEY6pud2mXi7ui6I9PQG7/P6pIs6emJJz2+te+1pupvL62JpbVtGG3ckWunqXAU01cbPiYUtvSlVlw3B/XNaLe466foHp+uG/n6xRTecPjk+yMPVJPXmnhjKTKF94ulKo+P9GxYyGjy2/oDDf9qmoaUmuSOus/Y3PvvVKKtO+pknXH8YhlvHM38RxEdi7a3D21vH9reXrS/fWj7rq+2tlaSTlqbLiEhQeXl5U2Wb2odu4SEBPl8TcwFikJn9EfTUxI6tH67OSS5nNGvNNSasq1hmq2vuyPjpm7q7kp1N8U0m/4725b3WkdpbSwdd/1pXZ8kVuJG7GrtayqqOtu9RgAAAMQdjycyPszvbzwNx+fzNbm7q8fjOalsXfmkpKSOCRIAACAOkKwDAABAi+qmtBYVFTU6XlRU1ORmETk5OSeV9fv9KisrU3Z2dscFCgAA0MWRrAMAAECLhg8frpSUFK1evbr+WEVFhbZs2aIJEyacVH7ChAkqKCjQvn376o+tWbNGknTuued2fMAAAABdFLvBAgAAoEVut1szZszQI488oqysLPXt21cLFixQTk6OLrnkEoVCIZWUlCg1NVUej0djxozROeeco9tuu03333+/ampqdN999+mqq65qciQeAAAAIgyrvfdkBgAAQFwKhUL6r//6Ly1btkxer1cTJkzQfffdp379+ungwYP68pe/rIceekjf+ta3JEnHjh3TL3/5S61atUoJCQn62te+prvuuksJCfG9iQMAAMDpIFkHAAAAAAAAxAjWrAMAAAAAAABiBMk6AAAAAAAAIEaQrAMAAAAAAABiBMk6AAAAAAAAIEaQrAMAAAAAAABiBMk6AAAAAAAAIEaQrAMAAAAAAABiRLdM1i1fvlyXXnqpzjrrLF122WX6+9//bndI3UIwGNTjjz+uiy66SOPGjdP06dP12Wef2R1W3Pv973+vmTNnNjq2detWzZgxQ2PHjtXFF1+sJUuW2BRd/Guq/d99911dffXVGjdunC6++GL953/+p7xer00Rxq+m2r6he+65RxdffHEnRtR9NNX2RUVFmjt3rsaPH69Jkybp5z//uUpKSmyKEGhZfn6+xo0bp2XLltkdSsyiTx0d+sDRoc8aPfqX0aM/GD36b9Frqq02b96smTNnaty4cbrwwgv1yCOPyO/3t/k5ul2y7q9//avuvvtuTZ8+XW+++aYuv/xyzZ07V59++qndocW9Z599Vq+99pp+9atfafny5Ro0aJBmz56toqIiu0OLWy+99JIWLlzY6Fhpaal+8IMfaMCAAVq6dKl+/OMf65FHHtHSpUvtCTKONdX+69at009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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in X.columns.to_list():\n",
    "    plt.subplot(1,2,1)\n",
    "    sns.boxplot(x=X[i])\n",
    "    plt.title(i)\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.distplot(X[i])\n",
    "    plt.title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66e2087-bb46-4f8c-9954-347216fbbf25",
   "metadata": {},
   "source": [
    "#### Визуально, по всем признакам есть выбросы."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3439bc27-7111-4878-a71e-1ba674364f42",
   "metadata": {},
   "source": [
    "#### Проверим по каждому признаку взята ли выборка из нормального распределения при помощи критерия Шапиро-Уилка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ecc2f453-e22b-42ab-8978-e8e1831c5537",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import shapiro #Shapiro-Wilk test for normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cdacc5ca-2d34-4ce0-9392-897532a0035e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of RI = ShapiroResult(statistic=0.8675718903541565, pvalue=1.0766247919222205e-12)\n",
      "Distribution of Na = ShapiroResult(statistic=0.9457632303237915, pvalue=3.4662537018448347e-07)\n",
      "Distribution of Mg = ShapiroResult(statistic=0.699336588382721, pvalue=2.3909611661875223e-19)\n",
      "Distribution of Al = ShapiroResult(statistic=0.9434122443199158, pvalue=2.083013299625236e-07)\n",
      "Distribution of Si = ShapiroResult(statistic=0.9196622967720032, pvalue=2.175188917874493e-09)\n",
      "Distribution of K = ShapiroResult(statistic=0.44161754846572876, pvalue=2.1721965417791313e-25)\n",
      "Distribution of Ca = ShapiroResult(statistic=0.7938679456710815, pvalue=4.2866203996304243e-16)\n",
      "Distribution of Ba = ShapiroResult(statistic=0.4085649847984314, pvalue=5.383263238128378e-26)\n",
      "Distribution of Fe = ShapiroResult(statistic=0.653200089931488, pvalue=1.156668784076633e-20)\n"
     ]
    }
   ],
   "source": [
    "for i in X.columns.to_list():\n",
    "    print(f'Distribution of {i} = {shapiro(X[i])}') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe25483-27d6-41f6-9442-71bfe146e5ff",
   "metadata": {},
   "source": [
    "#### Формально тесты на нормальность не прошли, поскольку p значительно меньше 0.05, поэтому полагаем, что все выборки по признакам сделаны из не-нормальных распределений. Скорее всего выбросы повлияли на результат теста."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1f6dff-2338-42bf-8b58-8cb381abe3eb",
   "metadata": {},
   "source": [
    "### Исследуем на выбросы тремя способами:  \n",
    "а) IsolationForest,  \n",
    "b) Neighbors-LocalOutlierFactor,  \n",
    "c) OneClassSVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91bbf138-3b2e-4d61-83f2-6992558f3525",
   "metadata": {},
   "outputs": [],
   "source": [
    " from sklearn.ensemble import IsolationForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c0b54f5-8d07-42d3-bfcb-facbb204aa20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest(random_state=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IsolationForest</label><div class=\"sk-toggleable__content\"><pre>IsolationForest(random_state=30)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "IsolationForest(random_state=30)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_if = IsolationForest(random_state = 30)\n",
    "clf_if.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "605b4a0e-53ce-45fc-8435-29b2e2977bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "if_pred = clf_if.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a18be4d7-1a7b-4e0a-a052-19e0bec77f94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import LocalOutlierFactor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2bbd5ab1-4cc8-44fb-93c4-142d855d4385",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_LOF = LocalOutlierFactor()\n",
    "LOF_pred = clf_LOF.fit_predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f2d4a3e-41b6-487a-bfc0-c666ee4b49d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import OneClassSVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ecd3bfb9-b057-4bb1-afea-750a9fd22e55",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_OCSVM = OneClassSVM(gamma='auto').fit(X)\n",
    "OCSVM_pred = clf_OCSVM.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "59a29a9f-9e32-4061-a409-5d127f01bf8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'if_method':if_pred, 'LOF_method':LOF_pred, 'OCSVM_method':OCSVM_pred}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "28660976-24cb-4178-818a-c53ea21a1604",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe09309-8673-4407-a248-98adc02b3265",
   "metadata": {},
   "source": [
    "##### число выбросов, предсказанных каждым методом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "198a7275-70d7-4e92-a625-9c808d78be89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "if_method        20\n",
       "LOF_method       37\n",
       "OCSVM_method    108\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt[dt==-1].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e2c080e-a233-49aa-98f0-c72391d90363",
   "metadata": {},
   "source": [
    "#### Получается, что метод OneClassSVM предсказал слишком много выбросов для последующего анализа, что видно также визуально на гистограммах и боксплотах, поэтому его далее не используем. 37 выбросов, предсказанных LOF тоже не мало (почти 20% всех строк), но оставим для сравнения с IsolationForest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "002cbe55-36f8-4ead-b642-725f28571a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([df,dt.iloc[:,:-1]], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e879f7b8-8f24-40f7-b8df-0a0684602705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RI</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>Al</th>\n",
       "      <th>Si</th>\n",
       "      <th>K</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Type</th>\n",
       "      <th>if_method</th>\n",
       "      <th>LOF_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52101</td>\n",
       "      <td>13.64</td>\n",
       "      <td>4.49</td>\n",
       "      <td>1.10</td>\n",
       "      <td>71.78</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.51761</td>\n",
       "      <td>13.89</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.36</td>\n",
       "      <td>72.73</td>\n",
       "      <td>0.48</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51618</td>\n",
       "      <td>13.53</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1.54</td>\n",
       "      <td>72.99</td>\n",
       "      <td>0.39</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.51766</td>\n",
       "      <td>13.21</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1.29</td>\n",
       "      <td>72.61</td>\n",
       "      <td>0.57</td>\n",
       "      <td>8.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.51742</td>\n",
       "      <td>13.27</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1.24</td>\n",
       "      <td>73.08</td>\n",
       "      <td>0.55</td>\n",
       "      <td>8.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        RI     Na    Mg    Al     Si     K    Ca   Ba   Fe  Type  if_method  \\\n",
       "0  1.52101  13.64  4.49  1.10  71.78  0.06  8.75  0.0  0.0     1          1   \n",
       "1  1.51761  13.89  3.60  1.36  72.73  0.48  7.83  0.0  0.0     1          1   \n",
       "2  1.51618  13.53  3.55  1.54  72.99  0.39  7.78  0.0  0.0     1          1   \n",
       "3  1.51766  13.21  3.69  1.29  72.61  0.57  8.22  0.0  0.0     1          1   \n",
       "4  1.51742  13.27  3.62  1.24  73.08  0.55  8.07  0.0  0.0     1          1   \n",
       "\n",
       "   LOF_method  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf06099b-4374-4bef-bc45-f38aeff76ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type  if_method\n",
       "1      1           100.000000\n",
       "2      1            89.473684\n",
       "      -1            10.526316\n",
       "3      1           100.000000\n",
       "5      1            69.230769\n",
       "      -1            30.769231\n",
       "6      1            77.777778\n",
       "      -1            22.222222\n",
       "7      1            79.310345\n",
       "      -1            20.689655\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['Type','if_method']].groupby('Type').value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c653cd-a209-4b6f-a5ee-51332b27ced6",
   "metadata": {},
   "source": [
    "##### Видим процент от общего числа выбросов по каждому классу:  \n",
    "1 класс: 0%\n",
    "2 класс: 10.5%\n",
    "3 класс: 0%\n",
    "5 класс: 30.8%\n",
    "6 класс: 22%\n",
    "7 класс: 20.7%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9be55691-2615-4d19-9bda-a04dd863a69e",
   "metadata": {},
   "source": [
    "7. Повторите п. 4, п. 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ee0a5e2d-d65f-46c0-ae27-2df452ec904f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# смотрим на точность при удалении выбросов по методу IsolationForest\n",
    "data_if = data.query('if_method ==1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "becea45c-6767-4b7e-a3d2-8c5ec674d21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data_if.iloc[:, 9]\n",
    "X = data_if.iloc[:,0:9]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1f445598-b255-4187-89f7-1b13f5850dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=30)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=30)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d94ba297-62b8-4561-b17e-766f22e88f14",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f93842ab-82a7-44f4-bebc-9f1ad8fcd646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8461538461538461"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "eda18cdb-290d-474f-b186-feb1a2673cfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# смотрим на точность при удалении выбросов по методу Neighbors-LocalOutlierFactor\n",
    "data_LOF = data.query('LOF_method ==1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "60b10418-437a-42f3-8825-476674943854",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data_LOF.iloc[:, 9]\n",
    "X = data_LOF.iloc[:,0:9]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0fa34a40-18d0-42d1-96bb-3425bfacbfa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
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       "RandomForestClassifier(random_state=30)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state=30)\n",
    "model.fit(X_train, y_train)"
   ]
  },
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   "execution_count": 43,
   "id": "935fd6cd-35f3-4f32-bcfb-96a1390c0b7c",
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   "source": [
    "y_pred = model.predict(X_test)\n",
    "model.score(X_train, y_train)"
   ]
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   "execution_count": 44,
   "id": "958bd743-48a1-4153-9afb-f6c22d321934",
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   "source": [
    "model.score(X_test, y_test)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1e9140a-d476-4325-b4c8-7400d35829a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d294b083-a699-4e2e-9595-a5a747ea2d89",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e86a1a86-af3f-4603-b988-3a467902c4a0",
   "metadata": {},
   "source": [
    "8. Сформулируйте выводы по проделанной работе.  \n",
    "а) Кратко опишите, какие преобразования были сделаны с данными.  \n",
    "b) Сравните точность двух моделей.  \n",
    "c) Напишите свое мнение, нужно ли исследовать данные на выбросы, для чего это делается, плюсы и минусы подхода.  "
   ]
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  {
   "cell_type": "markdown",
   "id": "a3cf842f-cb99-4c4b-b268-563485798eed",
   "metadata": {},
   "source": [
    "а) Применялся метод RandomForestClassifier для классификации типов стёкл по признакам в начальной выборке и в выборках с удалёнными выбросами по 2-м разным методам (IsolationForest, Neighbors-LocalOutlierFactor). При этом в случае Neighbors-LocalOutlierFactor метода удаления выбросов было удалено больше данных (~20% от всех строк). 3-й метод удаления выбросов OneClassSVM не использовался, поскольку он показал необходимость удаления больше половины данных.  \n",
    "\n",
    "б) По результатам сравнения 3 моделей (без удаления выбросов, удаление выбросов по методу IsolationForest, удаление выбросов по методу NLOF) наилучший результат по точности был достигнут на 2 моделе с удалением выбросов по методу IsolationForest (0.8461), 2-й результат у 1 модели без удаления выбросов (0.8372).  \n",
    "\n",
    "в) Итого: удаление выбросов помогает в улучшении точности, но удаление выбросов необходимо проводить аккуратно, чтобы не было удалено излишнее количество данных, что может повлиять на результат отрицательно как в 3-й модели, где точность упала до 0.7222."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "21c89d43-19dc-489e-91be-76a046bc9aa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\grebe\\\\Documents\\\\EDUCATION\\\\Машинное обучение фундаментальные инструменты и практики\\\\Работа с признаками и построение моделей\\\\Поиск выбросов и генерация новых признаков'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
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   "source": [
    "pwd"
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   "id": "c1d3e14e-61f0-4c87-be1f-3b3f9841f48f",
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