2023_ML_LR4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0688b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import metrics \n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50072746",
   "metadata": {},
   "source": [
    "# <font color = 'red'> ЛР 4. Предварительная обработка признаков. Простейший линейный классификатор - логистическая регрессия. </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b1bdbfb",
   "metadata": {},
   "source": [
    "Сложность: <font color = 'green'> Легко  </font>.\n",
    "\n",
    "Дата составления: 10.10.2023\n",
    "\n",
    "Срок выполнения: 2 недели (с момента первой практики после выдачи).\n",
    "\n",
    "Автор: ст. преподаватель Кушнеров А.В."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61b8275",
   "metadata": {},
   "source": [
    "## <font color = 'green'> 1. Предварительная обработка признаков.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de55f55",
   "metadata": {},
   "source": [
    "<font color = 'red' size = 5>Задание 1 </font>\n",
    "\n",
    "1. Изучите примеры точечной работы с признаками в исходных данных из файла *LR4Pr_1(Features)*. \n",
    "2. Поработав в документацией и примерами проведите отбор и подготовку признаков из файла telecom.csv.\n",
    "        2.1 Визуализируйте признаки.\n",
    "        2.2 Удалите аномальные данные, где необходимо.\n",
    "        2.3 Определите важные признаки с помощью описательной статитсики и корреляции.\n",
    "        2.4 Заполните пропуски и поработайте с данными по своему усмотрению.\n",
    "        2.5 Подготовленный датасет сохраните в новый csv файл.\n",
    "        \n",
    "\n",
    "\n",
    "[https://scikit-learn.org/stable/modules/preprocessing.html](https://scikit-learn.org/stable/modules/preprocessing.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "874689b6-0e91-449d-9109-07397e9f0b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b35c4509-67a0-4a58-b1e9-7bf7b163eda1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-06T17:57:55.920053Z",
     "start_time": "2023-10-06T17:57:54.076738800Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('telecom_churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0d804c7-c7c8-4a89-9928-d1fffacfd05c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-06T17:57:55.921023900Z",
     "start_time": "2023-10-06T17:57:54.139391800Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>account length</th>\n",
       "      <th>area code</th>\n",
       "      <th>phone number</th>\n",
       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
       "      <th>total day calls</th>\n",
       "      <th>total day charge</th>\n",
       "      <th>...</th>\n",
       "      <th>total eve calls</th>\n",
       "      <th>total eve charge</th>\n",
       "      <th>total night minutes</th>\n",
       "      <th>total night calls</th>\n",
       "      <th>total night charge</th>\n",
       "      <th>total intl minutes</th>\n",
       "      <th>total intl calls</th>\n",
       "      <th>total intl charge</th>\n",
       "      <th>customer service calls</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
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       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>88</td>\n",
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       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
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       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "  state  account length  area code phone number international plan  \\\n",
       "0    KS             128        415     382-4657                 no   \n",
       "1    OH             107        415     371-7191                 no   \n",
       "2    NJ             137        415     358-1921                 no   \n",
       "3    OH              84        408     375-9999                yes   \n",
       "4    OK              75        415     330-6626                yes   \n",
       "\n",
       "  voice mail plan  number vmail messages  total day minutes  total day calls  \\\n",
       "0             yes                     25              265.1              110   \n",
       "1             yes                     26              161.6              123   \n",
       "2              no                      0              243.4              114   \n",
       "3              no                      0              299.4               71   \n",
       "4              no                      0              166.7              113   \n",
       "\n",
       "   total day charge  ...  total eve calls  total eve charge  \\\n",
       "0             45.07  ...               99             16.78   \n",
       "1             27.47  ...              103             16.62   \n",
       "2             41.38  ...              110             10.30   \n",
       "3             50.90  ...               88              5.26   \n",
       "4             28.34  ...              122             12.61   \n",
       "\n",
       "   total night minutes  total night calls  total night charge  \\\n",
       "0                244.7                 91               11.01   \n",
       "1                254.4                103               11.45   \n",
       "2                162.6                104                7.32   \n",
       "3                196.9                 89                8.86   \n",
       "4                186.9                121                8.41   \n",
       "\n",
       "   total intl minutes  total intl calls  total intl charge  \\\n",
       "0                10.0                 3               2.70   \n",
       "1                13.7                 3               3.70   \n",
       "2                12.2                 5               3.29   \n",
       "3                 6.6                 7               1.78   \n",
       "4                10.1                 3               2.73   \n",
       "\n",
       "   customer service calls  churn  \n",
       "0                       1  False  \n",
       "1                       1  False  \n",
       "2                       0  False  \n",
       "3                       2  False  \n",
       "4                       3  False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "762b35d3-183f-4faf-97c3-c9007c469f29",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-06T17:57:55.921023900Z",
     "start_time": "2023-10-06T17:57:54.170504800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['state', 'account length', 'area code', 'phone number',\n",
      "       'international plan', 'voice mail plan', 'number vmail messages',\n",
      "       'total day minutes', 'total day calls', 'total day charge',\n",
      "       'total eve minutes', 'total eve calls', 'total eve charge',\n",
      "       'total night minutes', 'total night calls', 'total night charge',\n",
      "       'total intl minutes', 'total intl calls', 'total intl charge',\n",
      "       'customer service calls', 'churn'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "baf4396c-9dc7-4d57-a826-5fc99c60e124",
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     "start_time": "2023-10-06T17:57:54.192493100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 21 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   state                   3333 non-null   object \n",
      " 1   account length          3333 non-null   int64  \n",
      " 2   area code               3333 non-null   int64  \n",
      " 3   phone number            3333 non-null   object \n",
      " 4   international plan      3333 non-null   object \n",
      " 5   voice mail plan         3333 non-null   object \n",
      " 6   number vmail messages   3333 non-null   int64  \n",
      " 7   total day minutes       3333 non-null   float64\n",
      " 8   total day calls         3333 non-null   int64  \n",
      " 9   total day charge        3333 non-null   float64\n",
      " 10  total eve minutes       3333 non-null   float64\n",
      " 11  total eve calls         3333 non-null   int64  \n",
      " 12  total eve charge        3333 non-null   float64\n",
      " 13  total night minutes     3333 non-null   float64\n",
      " 14  total night calls       3333 non-null   int64  \n",
      " 15  total night charge      3333 non-null   float64\n",
      " 16  total intl minutes      3333 non-null   float64\n",
      " 17  total intl calls        3333 non-null   int64  \n",
      " 18  total intl charge       3333 non-null   float64\n",
      " 19  customer service calls  3333 non-null   int64  \n",
      " 20  churn                   3333 non-null   bool   \n",
      "dtypes: bool(1), float64(8), int64(8), object(4)\n",
      "memory usage: 524.2+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "727d7c03-a069-40c8-ab60-97bdde213e3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop([\"phone number\", \"state\", 'area code'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be885442-1fa7-446f-9f75-1187fb6a6ec2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-06T17:57:55.922021300Z",
     "start_time": "2023-10-06T17:57:54.201422100Z"
    }
   },
   "outputs": [],
   "source": [
    "data['churn']=data['churn'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a5338fc9-560c-421f-92a4-c5bfdb8855bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-10-06T17:57:55.922021300Z",
     "start_time": "2023-10-06T17:57:54.230345400Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "      <th>number vmail messages</th>\n",
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       "      <td>128</td>\n",
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       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>no</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account length international plan voice mail plan  number vmail messages  \\\n",
       "0             128                 no             yes                     25   \n",
       "1             107                 no             yes                     26   \n",
       "2             137                 no              no                      0   \n",
       "3              84                yes              no                      0   \n",
       "4              75                yes              no                      0   \n",
       "\n",
       "   total day minutes  total day calls  total day charge  total eve minutes  \\\n",
       "0              265.1              110             45.07              197.4   \n",
       "1              161.6              123             27.47              195.5   \n",
       "2              243.4              114             41.38              121.2   \n",
       "3              299.4               71             50.90               61.9   \n",
       "4              166.7              113             28.34              148.3   \n",
       "\n",
       "   total eve calls  total eve charge  total night minutes  total night calls  \\\n",
       "0               99             16.78                244.7                 91   \n",
       "1              103             16.62                254.4                103   \n",
       "2              110             10.30                162.6                104   \n",
       "3               88              5.26                196.9                 89   \n",
       "4              122             12.61                186.9                121   \n",
       "\n",
       "   total night charge  total intl minutes  total intl calls  \\\n",
       "0               11.01                10.0                 3   \n",
       "1               11.45                13.7                 3   \n",
       "2                7.32                12.2                 5   \n",
       "3                8.86                 6.6                 7   \n",
       "4                8.41                10.1                 3   \n",
       "\n",
       "   total intl charge  customer service calls  churn  \n",
       "0               2.70                       1      0  \n",
       "1               3.70                       1      0  \n",
       "2               3.29                       0      0  \n",
       "3               1.78                       2      0  \n",
       "4               2.73                       3      0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a1a8e69-46d2-41b4-988f-6ed7cc23bcc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\"no\": 0, \"yes\": 1}\n",
    "data[\"international plan\"] = data[\"international plan\"].map(d)\n",
    "data[\"voice mail plan\"] = data[\"voice mail plan\"].map(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9e10848-3a09-4cd7-8f73-d99d91fe190f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account length</th>\n",
       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
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       "  <tbody>\n",
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       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
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       "      <td>122</td>\n",
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       "      <td>186.9</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account length  international plan  voice mail plan  number vmail messages  \\\n",
       "0             128                   0                1                     25   \n",
       "1             107                   0                1                     26   \n",
       "2             137                   0                0                      0   \n",
       "3              84                   1                0                      0   \n",
       "4              75                   1                0                      0   \n",
       "\n",
       "   total day minutes  total day calls  total day charge  total eve minutes  \\\n",
       "0              265.1              110             45.07              197.4   \n",
       "1              161.6              123             27.47              195.5   \n",
       "2              243.4              114             41.38              121.2   \n",
       "3              299.4               71             50.90               61.9   \n",
       "4              166.7              113             28.34              148.3   \n",
       "\n",
       "   total eve calls  total eve charge  total night minutes  total night calls  \\\n",
       "0               99             16.78                244.7                 91   \n",
       "1              103             16.62                254.4                103   \n",
       "2              110             10.30                162.6                104   \n",
       "3               88              5.26                196.9                 89   \n",
       "4              122             12.61                186.9                121   \n",
       "\n",
       "   total night charge  total intl minutes  total intl calls  \\\n",
       "0               11.01                10.0                 3   \n",
       "1               11.45                13.7                 3   \n",
       "2                7.32                12.2                 5   \n",
       "3                8.86                 6.6                 7   \n",
       "4                8.41                10.1                 3   \n",
       "\n",
       "   total intl charge  customer service calls  churn  \n",
       "0               2.70                       1      0  \n",
       "1               3.70                       1      0  \n",
       "2               3.29                       0      0  \n",
       "3               1.78                       2      0  \n",
       "4               2.73                       3      0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14f1fbed-9bb8-4b5d-bb20-5781ee41f185",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = data.drop(\"voice mail plan\", axis = 1).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6afe7b3a-1baf-4275-8f4a-a5b6e556a069",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>account length</th>\n",
       "      <th>international plan</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>account length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024735</td>\n",
       "      <td>-0.004628</td>\n",
       "      <td>0.006216</td>\n",
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       "      <td>-0.013176</td>\n",
       "      <td>-0.008960</td>\n",
       "      <td>0.009514</td>\n",
       "      <td>0.020661</td>\n",
       "      <td>0.009546</td>\n",
       "      <td>-0.003796</td>\n",
       "      <td>0.016541</td>\n",
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       "    <tr>\n",
       "      <th>international plan</th>\n",
       "      <td>0.024735</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.049396</td>\n",
       "      <td>0.003755</td>\n",
       "      <td>0.049398</td>\n",
       "      <td>0.019100</td>\n",
       "      <td>0.006114</td>\n",
       "      <td>0.019106</td>\n",
       "      <td>-0.028905</td>\n",
       "      <td>0.012451</td>\n",
       "      <td>-0.028913</td>\n",
       "      <td>0.045871</td>\n",
       "      <td>0.017366</td>\n",
       "      <td>0.045780</td>\n",
       "      <td>-0.024522</td>\n",
       "      <td>0.259852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number vmail messages</th>\n",
       "      <td>-0.004628</td>\n",
       "      <td>0.008745</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>-0.009548</td>\n",
       "      <td>0.000776</td>\n",
       "      <td>0.017562</td>\n",
       "      <td>-0.005864</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.007681</td>\n",
       "      <td>0.007123</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>0.013957</td>\n",
       "      <td>0.002884</td>\n",
       "      <td>-0.013263</td>\n",
       "      <td>-0.089728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total day minutes</th>\n",
       "      <td>0.006216</td>\n",
       "      <td>0.049396</td>\n",
       "      <td>0.000778</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007043</td>\n",
       "      <td>0.015769</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.022972</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>-0.010155</td>\n",
       "      <td>0.008033</td>\n",
       "      <td>-0.010092</td>\n",
       "      <td>-0.013423</td>\n",
       "      <td>0.205151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total day calls</th>\n",
       "      <td>0.038470</td>\n",
       "      <td>0.003755</td>\n",
       "      <td>-0.009548</td>\n",
       "      <td>0.006750</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006753</td>\n",
       "      <td>-0.021451</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>-0.021449</td>\n",
       "      <td>0.022938</td>\n",
       "      <td>-0.019557</td>\n",
       "      <td>0.022927</td>\n",
       "      <td>0.021565</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>0.021666</td>\n",
       "      <td>-0.018942</td>\n",
       "      <td>0.018459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total day charge</th>\n",
       "      <td>0.006214</td>\n",
       "      <td>0.049398</td>\n",
       "      <td>0.000776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007050</td>\n",
       "      <td>0.015769</td>\n",
       "      <td>0.007036</td>\n",
       "      <td>0.004324</td>\n",
       "      <td>0.022972</td>\n",
       "      <td>0.004301</td>\n",
       "      <td>-0.010157</td>\n",
       "      <td>0.008032</td>\n",
       "      <td>-0.010094</td>\n",
       "      <td>-0.013427</td>\n",
       "      <td>0.205151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total eve minutes</th>\n",
       "      <td>-0.006757</td>\n",
       "      <td>0.019100</td>\n",
       "      <td>0.017562</td>\n",
       "      <td>0.007043</td>\n",
       "      <td>-0.021451</td>\n",
       "      <td>0.007050</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012584</td>\n",
       "      <td>0.007586</td>\n",
       "      <td>-0.012593</td>\n",
       "      <td>-0.011035</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>-0.011067</td>\n",
       "      <td>-0.012985</td>\n",
       "      <td>0.092796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total eve calls</th>\n",
       "      <td>0.019260</td>\n",
       "      <td>0.006114</td>\n",
       "      <td>-0.005864</td>\n",
       "      <td>0.015769</td>\n",
       "      <td>0.006462</td>\n",
       "      <td>0.015769</td>\n",
       "      <td>-0.011430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011423</td>\n",
       "      <td>-0.002093</td>\n",
       "      <td>0.007710</td>\n",
       "      <td>-0.002056</td>\n",
       "      <td>0.008703</td>\n",
       "      <td>0.017434</td>\n",
       "      <td>0.008674</td>\n",
       "      <td>0.002423</td>\n",
       "      <td>0.009233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total eve charge</th>\n",
       "      <td>-0.006745</td>\n",
       "      <td>0.019106</td>\n",
       "      <td>0.017578</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>-0.021449</td>\n",
       "      <td>0.007036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011423</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012592</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>-0.012601</td>\n",
       "      <td>-0.011043</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>-0.011074</td>\n",
       "      <td>-0.012987</td>\n",
       "      <td>0.092786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total night minutes</th>\n",
       "      <td>-0.008955</td>\n",
       "      <td>-0.028905</td>\n",
       "      <td>0.007681</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.022938</td>\n",
       "      <td>0.004324</td>\n",
       "      <td>-0.012584</td>\n",
       "      <td>-0.002093</td>\n",
       "      <td>-0.012592</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011204</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>-0.015207</td>\n",
       "      <td>-0.012353</td>\n",
       "      <td>-0.015180</td>\n",
       "      <td>-0.009288</td>\n",
       "      <td>0.035493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total night calls</th>\n",
       "      <td>-0.013176</td>\n",
       "      <td>0.012451</td>\n",
       "      <td>0.007123</td>\n",
       "      <td>0.022972</td>\n",
       "      <td>-0.019557</td>\n",
       "      <td>0.022972</td>\n",
       "      <td>0.007586</td>\n",
       "      <td>0.007710</td>\n",
       "      <td>0.007596</td>\n",
       "      <td>0.011204</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011188</td>\n",
       "      <td>-0.013605</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>-0.013630</td>\n",
       "      <td>-0.012802</td>\n",
       "      <td>0.006141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total night charge</th>\n",
       "      <td>-0.008960</td>\n",
       "      <td>-0.028913</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.004300</td>\n",
       "      <td>0.022927</td>\n",
       "      <td>0.004301</td>\n",
       "      <td>-0.012593</td>\n",
       "      <td>-0.002056</td>\n",
       "      <td>-0.012601</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.011188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015214</td>\n",
       "      <td>-0.012329</td>\n",
       "      <td>-0.015186</td>\n",
       "      <td>-0.009277</td>\n",
       "      <td>0.035496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total intl minutes</th>\n",
       "      <td>0.009514</td>\n",
       "      <td>0.045871</td>\n",
       "      <td>0.002856</td>\n",
       "      <td>-0.010155</td>\n",
       "      <td>0.021565</td>\n",
       "      <td>-0.010157</td>\n",
       "      <td>-0.011035</td>\n",
       "      <td>0.008703</td>\n",
       "      <td>-0.011043</td>\n",
       "      <td>-0.015207</td>\n",
       "      <td>-0.013605</td>\n",
       "      <td>-0.015214</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032304</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>-0.009640</td>\n",
       "      <td>0.068239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total intl calls</th>\n",
       "      <td>0.020661</td>\n",
       "      <td>0.017366</td>\n",
       "      <td>0.013957</td>\n",
       "      <td>0.008033</td>\n",
       "      <td>0.004574</td>\n",
       "      <td>0.008032</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>0.017434</td>\n",
       "      <td>0.002541</td>\n",
       "      <td>-0.012353</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>-0.012329</td>\n",
       "      <td>0.032304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032372</td>\n",
       "      <td>-0.017561</td>\n",
       "      <td>-0.052844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total intl charge</th>\n",
       "      <td>0.009546</td>\n",
       "      <td>0.045780</td>\n",
       "      <td>0.002884</td>\n",
       "      <td>-0.010092</td>\n",
       "      <td>0.021666</td>\n",
       "      <td>-0.010094</td>\n",
       "      <td>-0.011067</td>\n",
       "      <td>0.008674</td>\n",
       "      <td>-0.011074</td>\n",
       "      <td>-0.015180</td>\n",
       "      <td>-0.013630</td>\n",
       "      <td>-0.015186</td>\n",
       "      <td>0.999993</td>\n",
       "      <td>0.032372</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009675</td>\n",
       "      <td>0.068259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer service calls</th>\n",
       "      <td>-0.003796</td>\n",
       "      <td>-0.024522</td>\n",
       "      <td>-0.013263</td>\n",
       "      <td>-0.013423</td>\n",
       "      <td>-0.018942</td>\n",
       "      <td>-0.013427</td>\n",
       "      <td>-0.012985</td>\n",
       "      <td>0.002423</td>\n",
       "      <td>-0.012987</td>\n",
       "      <td>-0.009288</td>\n",
       "      <td>-0.012802</td>\n",
       "      <td>-0.009277</td>\n",
       "      <td>-0.009640</td>\n",
       "      <td>-0.017561</td>\n",
       "      <td>-0.009675</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.208750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>churn</th>\n",
       "      <td>0.016541</td>\n",
       "      <td>0.259852</td>\n",
       "      <td>-0.089728</td>\n",
       "      <td>0.205151</td>\n",
       "      <td>0.018459</td>\n",
       "      <td>0.205151</td>\n",
       "      <td>0.092796</td>\n",
       "      <td>0.009233</td>\n",
       "      <td>0.092786</td>\n",
       "      <td>0.035493</td>\n",
       "      <td>0.006141</td>\n",
       "      <td>0.035496</td>\n",
       "      <td>0.068239</td>\n",
       "      <td>-0.052844</td>\n",
       "      <td>0.068259</td>\n",
       "      <td>0.208750</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        account length  international plan  \\\n",
       "account length                1.000000            0.024735   \n",
       "international plan            0.024735            1.000000   \n",
       "number vmail messages        -0.004628            0.008745   \n",
       "total day minutes             0.006216            0.049396   \n",
       "total day calls               0.038470            0.003755   \n",
       "total day charge              0.006214            0.049398   \n",
       "total eve minutes            -0.006757            0.019100   \n",
       "total eve calls               0.019260            0.006114   \n",
       "total eve charge             -0.006745            0.019106   \n",
       "total night minutes          -0.008955           -0.028905   \n",
       "total night calls            -0.013176            0.012451   \n",
       "total night charge           -0.008960           -0.028913   \n",
       "total intl minutes            0.009514            0.045871   \n",
       "total intl calls              0.020661            0.017366   \n",
       "total intl charge             0.009546            0.045780   \n",
       "customer service calls       -0.003796           -0.024522   \n",
       "churn                         0.016541            0.259852   \n",
       "\n",
       "                        number vmail messages  total day minutes  \\\n",
       "account length                      -0.004628           0.006216   \n",
       "international plan                   0.008745           0.049396   \n",
       "number vmail messages                1.000000           0.000778   \n",
       "total day minutes                    0.000778           1.000000   \n",
       "total day calls                     -0.009548           0.006750   \n",
       "total day charge                     0.000776           1.000000   \n",
       "total eve minutes                    0.017562           0.007043   \n",
       "total eve calls                     -0.005864           0.015769   \n",
       "total eve charge                     0.017578           0.007029   \n",
       "total night minutes                  0.007681           0.004323   \n",
       "total night calls                    0.007123           0.022972   \n",
       "total night charge                   0.007663           0.004300   \n",
       "total intl minutes                   0.002856          -0.010155   \n",
       "total intl calls                     0.013957           0.008033   \n",
       "total intl charge                    0.002884          -0.010092   \n",
       "customer service calls              -0.013263          -0.013423   \n",
       "churn                               -0.089728           0.205151   \n",
       "\n",
       "                        total day calls  total day charge  total eve minutes  \\\n",
       "account length                 0.038470          0.006214          -0.006757   \n",
       "international plan             0.003755          0.049398           0.019100   \n",
       "number vmail messages         -0.009548          0.000776           0.017562   \n",
       "total day minutes              0.006750          1.000000           0.007043   \n",
       "total day calls                1.000000          0.006753          -0.021451   \n",
       "total day charge               0.006753          1.000000           0.007050   \n",
       "total eve minutes             -0.021451          0.007050           1.000000   \n",
       "total eve calls                0.006462          0.015769          -0.011430   \n",
       "total eve charge              -0.021449          0.007036           1.000000   \n",
       "total night minutes            0.022938          0.004324          -0.012584   \n",
       "total night calls             -0.019557          0.022972           0.007586   \n",
       "total night charge             0.022927          0.004301          -0.012593   \n",
       "total intl minutes             0.021565         -0.010157          -0.011035   \n",
       "total intl calls               0.004574          0.008032           0.002541   \n",
       "total intl charge              0.021666         -0.010094          -0.011067   \n",
       "customer service calls        -0.018942         -0.013427          -0.012985   \n",
       "churn                          0.018459          0.205151           0.092796   \n",
       "\n",
       "                        total eve calls  total eve charge  \\\n",
       "account length                 0.019260         -0.006745   \n",
       "international plan             0.006114          0.019106   \n",
       "number vmail messages         -0.005864          0.017578   \n",
       "total day minutes              0.015769          0.007029   \n",
       "total day calls                0.006462         -0.021449   \n",
       "total day charge               0.015769          0.007036   \n",
       "total eve minutes             -0.011430          1.000000   \n",
       "total eve calls                1.000000         -0.011423   \n",
       "total eve charge              -0.011423          1.000000   \n",
       "total night minutes           -0.002093         -0.012592   \n",
       "total night calls              0.007710          0.007596   \n",
       "total night charge            -0.002056         -0.012601   \n",
       "total intl minutes             0.008703         -0.011043   \n",
       "total intl calls               0.017434          0.002541   \n",
       "total intl charge              0.008674         -0.011074   \n",
       "customer service calls         0.002423         -0.012987   \n",
       "churn                          0.009233          0.092786   \n",
       "\n",
       "                        total night minutes  total night calls  \\\n",
       "account length                    -0.008955          -0.013176   \n",
       "international plan                -0.028905           0.012451   \n",
       "number vmail messages              0.007681           0.007123   \n",
       "total day minutes                  0.004323           0.022972   \n",
       "total day calls                    0.022938          -0.019557   \n",
       "total day charge                   0.004324           0.022972   \n",
       "total eve minutes                 -0.012584           0.007586   \n",
       "total eve calls                   -0.002093           0.007710   \n",
       "total eve charge                  -0.012592           0.007596   \n",
       "total night minutes                1.000000           0.011204   \n",
       "total night calls                  0.011204           1.000000   \n",
       "total night charge                 0.999999           0.011188   \n",
       "total intl minutes                -0.015207          -0.013605   \n",
       "total intl calls                  -0.012353           0.000305   \n",
       "total intl charge                 -0.015180          -0.013630   \n",
       "customer service calls            -0.009288          -0.012802   \n",
       "churn                              0.035493           0.006141   \n",
       "\n",
       "                        total night charge  total intl minutes  \\\n",
       "account length                   -0.008960            0.009514   \n",
       "international plan               -0.028913            0.045871   \n",
       "number vmail messages             0.007663            0.002856   \n",
       "total day minutes                 0.004300           -0.010155   \n",
       "total day calls                   0.022927            0.021565   \n",
       "total day charge                  0.004301           -0.010157   \n",
       "total eve minutes                -0.012593           -0.011035   \n",
       "total eve calls                  -0.002056            0.008703   \n",
       "total eve charge                 -0.012601           -0.011043   \n",
       "total night minutes               0.999999           -0.015207   \n",
       "total night calls                 0.011188           -0.013605   \n",
       "total night charge                1.000000           -0.015214   \n",
       "total intl minutes               -0.015214            1.000000   \n",
       "total intl calls                 -0.012329            0.032304   \n",
       "total intl charge                -0.015186            0.999993   \n",
       "customer service calls           -0.009277           -0.009640   \n",
       "churn                             0.035496            0.068239   \n",
       "\n",
       "                        total intl calls  total intl charge  \\\n",
       "account length                  0.020661           0.009546   \n",
       "international plan              0.017366           0.045780   \n",
       "number vmail messages           0.013957           0.002884   \n",
       "total day minutes               0.008033          -0.010092   \n",
       "total day calls                 0.004574           0.021666   \n",
       "total day charge                0.008032          -0.010094   \n",
       "total eve minutes               0.002541          -0.011067   \n",
       "total eve calls                 0.017434           0.008674   \n",
       "total eve charge                0.002541          -0.011074   \n",
       "total night minutes            -0.012353          -0.015180   \n",
       "total night calls               0.000305          -0.013630   \n",
       "total night charge             -0.012329          -0.015186   \n",
       "total intl minutes              0.032304           0.999993   \n",
       "total intl calls                1.000000           0.032372   \n",
       "total intl charge               0.032372           1.000000   \n",
       "customer service calls         -0.017561          -0.009675   \n",
       "churn                          -0.052844           0.068259   \n",
       "\n",
       "                        customer service calls     churn  \n",
       "account length                       -0.003796  0.016541  \n",
       "international plan                   -0.024522  0.259852  \n",
       "number vmail messages                -0.013263 -0.089728  \n",
       "total day minutes                    -0.013423  0.205151  \n",
       "total day calls                      -0.018942  0.018459  \n",
       "total day charge                     -0.013427  0.205151  \n",
       "total eve minutes                    -0.012985  0.092796  \n",
       "total eve calls                       0.002423  0.009233  \n",
       "total eve charge                     -0.012987  0.092786  \n",
       "total night minutes                  -0.009288  0.035493  \n",
       "total night calls                    -0.012802  0.006141  \n",
       "total night charge                   -0.009277  0.035496  \n",
       "total intl minutes                   -0.009640  0.068239  \n",
       "total intl calls                     -0.017561 -0.052844  \n",
       "total intl charge                    -0.009675  0.068259  \n",
       "customer service calls                1.000000  0.208750  \n",
       "churn                                 0.208750  1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "681bb187-039b-40a8-9fa2-29eb7e19da1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_matrix,cmap=\"crest\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "995a6827-85ad-4e39-b333-89a8e78576f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['total day charge',\n",
    "                      'total eve charge', 'total night charge', 'total intl charge'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64966468-c5e1-4024-8aff-6ebd42d7bf1a",
   "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>account length</th>\n",
       "      <th>international plan</th>\n",
       "      <th>voice mail plan</th>\n",
       "      <th>number vmail messages</th>\n",
       "      <th>total day minutes</th>\n",
       "      <th>total day calls</th>\n",
       "      <th>total eve minutes</th>\n",
       "      <th>total eve calls</th>\n",
       "      <th>total night minutes</th>\n",
       "      <th>total night calls</th>\n",
       "      <th>total intl minutes</th>\n",
       "      <th>total intl calls</th>\n",
       "      <th>customer service calls</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>156.2</td>\n",
       "      <td>77</td>\n",
       "      <td>215.5</td>\n",
       "      <td>126</td>\n",
       "      <td>279.1</td>\n",
       "      <td>83</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>231.1</td>\n",
       "      <td>57</td>\n",
       "      <td>153.4</td>\n",
       "      <td>55</td>\n",
       "      <td>191.3</td>\n",
       "      <td>123</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>180.8</td>\n",
       "      <td>109</td>\n",
       "      <td>288.8</td>\n",
       "      <td>58</td>\n",
       "      <td>191.9</td>\n",
       "      <td>91</td>\n",
       "      <td>14.1</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>213.8</td>\n",
       "      <td>105</td>\n",
       "      <td>159.6</td>\n",
       "      <td>84</td>\n",
       "      <td>139.2</td>\n",
       "      <td>137</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>234.4</td>\n",
       "      <td>113</td>\n",
       "      <td>265.9</td>\n",
       "      <td>82</td>\n",
       "      <td>241.4</td>\n",
       "      <td>77</td>\n",
       "      <td>13.7</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      account length  international plan  voice mail plan  \\\n",
       "0                128                   0                1   \n",
       "1                107                   0                1   \n",
       "2                137                   0                0   \n",
       "3                 84                   1                0   \n",
       "4                 75                   1                0   \n",
       "...              ...                 ...              ...   \n",
       "3328             192                   0                1   \n",
       "3329              68                   0                0   \n",
       "3330              28                   0                0   \n",
       "3331             184                   1                0   \n",
       "3332              74                   0                1   \n",
       "\n",
       "      number vmail messages  total day minutes  total day calls  \\\n",
       "0                        25              265.1              110   \n",
       "1                        26              161.6              123   \n",
       "2                         0              243.4              114   \n",
       "3                         0              299.4               71   \n",
       "4                         0              166.7              113   \n",
       "...                     ...                ...              ...   \n",
       "3328                     36              156.2               77   \n",
       "3329                      0              231.1               57   \n",
       "3330                      0              180.8              109   \n",
       "3331                      0              213.8              105   \n",
       "3332                     25              234.4              113   \n",
       "\n",
       "      total eve minutes  total eve calls  total night minutes  \\\n",
       "0                 197.4               99                244.7   \n",
       "1                 195.5              103                254.4   \n",
       "2                 121.2              110                162.6   \n",
       "3                  61.9               88                196.9   \n",
       "4                 148.3              122                186.9   \n",
       "...                 ...              ...                  ...   \n",
       "3328              215.5              126                279.1   \n",
       "3329              153.4               55                191.3   \n",
       "3330              288.8               58                191.9   \n",
       "3331              159.6               84                139.2   \n",
       "3332              265.9               82                241.4   \n",
       "\n",
       "      total night calls  total intl minutes  total intl calls  \\\n",
       "0                    91                10.0                 3   \n",
       "1                   103                13.7                 3   \n",
       "2                   104                12.2                 5   \n",
       "3                    89                 6.6                 7   \n",
       "4                   121                10.1                 3   \n",
       "...                 ...                 ...               ...   \n",
       "3328                 83                 9.9                 6   \n",
       "3329                123                 9.6                 4   \n",
       "3330                 91                14.1                 6   \n",
       "3331                137                 5.0                10   \n",
       "3332                 77                13.7                 4   \n",
       "\n",
       "      customer service calls  churn  \n",
       "0                          1      0  \n",
       "1                          1      0  \n",
       "2                          0      0  \n",
       "3                          2      0  \n",
       "4                          3      0  \n",
       "...                      ...    ...  \n",
       "3328                       2      0  \n",
       "3329                       3      0  \n",
       "3330                       2      0  \n",
       "3331                       2      0  \n",
       "3332                       0      0  \n",
       "\n",
       "[3333 rows x 14 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4a667e32-350c-4f21-9884-5f04612c8082",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = list(set(data.columns) - set(['international plan', 'voice mail plan',  'churn']))\n",
    "data[features].hist(figsize=(20,12));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56b7a512-72e8-4c4e-880c-f4406b3175fc",
   "metadata": {},
   "source": [
    "sns.pairplot(data[features + ['churn']], hue='churn');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93ef8f73-13b8-4b27-a6c0-035a0faf2f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop('number vmail messages', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e27e6ac-9d55-41d1-8170-c3c60bfb188d",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file = 'new_telecom.csv'\n",
    "data.to_csv(output_file, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56618ad",
   "metadata": {},
   "source": [
    "## <font color = 'green'> 2. Логистическая регрессия.  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5b8a97e",
   "metadata": {},
   "source": [
    "**Логистическая регрессия** - модель машинного обучения для линейной классификации данных. Основная идея состоит в вычислении вероятности попадания объекта в один из двух классов(в случае бинарной классификации) и последующей классификации этого объекта с помощью порога вероятности.\n",
    "\n",
    "Общий алгоритм можно описать так. \n",
    "\n",
    "1. Вычисляем зачение функции-регрессора для заданной функции $reg = \\overline{w}^{T}\\overline{x}$.\n",
    "2. Вычисляем вероятность попадания в класс \"1\"  $p_{1} = \\frac{1}{1+e^{-reg}}$.\n",
    "3. Классифицируем объект в класс \"1\" в случае если $p_{1}>0.5$ (хотя порог можно взять другим) и в класс \"0\" в противоположном случае.\n",
    "\n",
    "Подробнее изучить можно в конспекте лекции и по [ссылке](https://habr.com/ru/articles/485872/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ffa53e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 12.61563938 -12.63844576]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.53737763])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_data = np.random.randint(0,40,(300,2))\n",
    "rows = np.where(X_data[:,0]!=X_data[:,1])\n",
    "X_data = X_data[rows]\n",
    "y_data = np.array([int(np.sign(elem[0]-elem[1])==1) for elem in X_data])\n",
    "\n",
    "plt.scatter(X_data[:,0],X_data[:,1],c = y_data);\n",
    "\n",
    "inner_model = LogisticRegression(penalty='none')\n",
    "inner_model.fit(X_data, y_data)\n",
    "\n",
    "inner_preds = inner_model.predict(X_data)\n",
    "inner_preds_pr = inner_model.predict_proba(X_data)\n",
    " # accuracy\n",
    "(inner_preds == y_data).mean()\n",
    "\n",
    "inner_preds_pr\n",
    "\n",
    "print(inner_model.coef_)\n",
    "\n",
    "inner_model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cff8e3",
   "metadata": {},
   "source": [
    "<font color = 'red' size = 5>Задание 2 </font>\n",
    "\n",
    "1. Реализуйте собственные методы для классификации данных методом логистической регрессии. Для минимизации функции потерь используйте градиентный спуск. \n",
    "2. Протестируйте ваш метод на искуственных данных. Сравните с работой встроенной функции. \n",
    "3. Графически изобразите результаты классификации. Изобразите разделяющую гиперплоскость или кривую.\n",
    "4. Попробуйте подобрать данные, которые станут проблемой (покажут низкое качество на валидации) для описанной модели."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56db9d04-0663-4517-94fc-c107edc2a970",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "edd5278f-c133-43b8-9b4c-dab27e20425c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class myLogisticRegression:\n",
    "    def __init__(self, learning_rate=0.01, num_iterations=1000, threshold = 0.5):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.num_iterations = num_iterations\n",
    "        self.threshold = threshold\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        num_samples, num_features = X.shape\n",
    "        self.weights = np.zeros(num_features)\n",
    "        self.bias = 0\n",
    "\n",
    "        for _ in range(self.num_iterations):\n",
    "            linear_model = np.dot(X, self.weights) + self.bias\n",
    "            y_predicted = self.sigmoid(linear_model)\n",
    "            dw = (1 / num_samples) * np.dot(X.T, (y_predicted - y))\n",
    "            db = (1 / num_samples) * np.sum(y_predicted - y)\n",
    "            \n",
    "            self.weights -= self.learning_rate * dw\n",
    "            self.bias -= self.learning_rate * db\n",
    "    \n",
    "    def predict(self, X):\n",
    "        linear_model = np.dot(X, self.weights) + self.bias\n",
    "        y_predicted = self.sigmoid(linear_model)\n",
    "        y_predicted_class = [1 if i > self.threshold else 0 for i in y_predicted]\n",
    "        return y_predicted_class\n",
    "    \n",
    "    def sigmoid(self, x):\n",
    "        return 1 / (1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "e2f063f5-ba4f-4334-86cf-f5c6828bfbbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class myLogisticRegressionMinus:\n",
    "    def __init__(self, learning_rate=0.01, num_iterations=1000, threshold = 0):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.num_iterations = num_iterations\n",
    "        self.threshold = threshold\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        num_samples, num_features = X.shape\n",
    "        self.weights = np.zeros(num_features)\n",
    "        self.bias = 0\n",
    "\n",
    "        for _ in range(self.num_iterations):\n",
    "            linear_model = np.dot(X, self.weights) + self.bias\n",
    "            y_predicted = self.sigmoid(np.dot(linear_model.T, y))\n",
    "            dw = (1 / num_samples) * np.dot(np.dot(X.T, y), y_predicted)\n",
    "            db = (1 / num_samples) * np.sum(np.dot(y, y_predicted))\n",
    "            \n",
    "            self.weights += self.learning_rate * dw\n",
    "            self.bias += self.learning_rate * db\n",
    "    \n",
    "    def predict(self, X):\n",
    "        linear_model = np.dot(X, self.weights) + self.bias\n",
    "        y_predicted = np.tanh(linear_model)\n",
    "        y_predicted_class = [1 if i > self.threshold else -1 for i in y_predicted]\n",
    "        return y_predicted_class\n",
    "    \n",
    "    def sigmoid(self, x):\n",
    "        return 1 / (1 + np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "5b15b9fd-15d6-4e97-8204-4926ee4e69cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Моя реализация с классами -1, 1:  0.8620689655172413 \n",
      "Веса:  [0.69685257 9.94191507]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_data, y_data = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=28)\n",
    "y_data[y_data == 0] = -1\n",
    "\n",
    "plt.scatter(X_data[:,0],X_data[:,1],c = y_data);\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.28, random_state=32)\n",
    "\n",
    "model = myLogisticRegressionMinus()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Моя реализация с классами -1, 1: \", accuracy, \"\\nВеса: \", model.weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "5e2029e3-fcbd-4e75-afaa-fe0c1fdf38c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          -1       0.85      0.92      0.88      1511\n",
      "           1       0.91      0.83      0.87      1489\n",
      "\n",
      "    accuracy                           0.88      3000\n",
      "   macro avg       0.88      0.88      0.88      3000\n",
      "weighted avg       0.88      0.88      0.88      3000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "X, y = make_classification(n_samples=10000, n_features=20, n_informative=2, n_redundant=10, n_classes=2, random_state=1)\n",
    "y[y == 0] = -1\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "poly = PolynomialFeatures(degree=3)\n",
    "X_train_poly = poly.fit_transform(X_train)\n",
    "X_test_poly = poly.transform(X_test)\n",
    "\n",
    "model = myLogisticRegressionMinus()\n",
    "model.fit(X_train_poly, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test_poly)\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b72c49-cc9a-486d-bf73-d1a5acf8d2ea",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3538acb6-c2e0-4bb6-8085-8e4eae0661a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Моя реализация с классами 0, 1:  0.896551724137931 \n",
      "Веса:  [-0.05638959  1.61663324]\n",
      "scikit-learn реализация:  0.896551724137931 \n",
      "Веса:  [[-0.10100607  2.01257843]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_data, y_data = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=28)\n",
    "\n",
    "plt.scatter(X_data[:,0],X_data[:,1],c = y_data);\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.28, random_state=32)\n",
    "\n",
    "model = myLogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Моя реализация с классами 0, 1: \", accuracy, \"\\nВеса: \", model.weights)\n",
    "\n",
    "sklearn_model = LogisticRegression()\n",
    "sklearn_model.fit(X_train, y_train)\n",
    "sklearn_y_pred = sklearn_model.predict(X_test)\n",
    "sklearn_accuracy = accuracy_score(y_test, sklearn_y_pred)\n",
    "print(\"scikit-learn реализация: \", sklearn_accuracy, \"\\nВеса: \", sklearn_model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce06f2e-da54-4743-a3cb-b44f96ad0587",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "579419bb-df0f-4f40-92a1-aa21fe6d3eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=28)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X, y)\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))\n",
    "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.contourf(xx, yy, Z, alpha=0.8)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.title('Logistic Regression Classification')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b17e0dd-001f-40b8-85a5-428ad0d6fe2e",
   "metadata": {},
   "source": [
    "## 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7a21d90d-eb58-4bf3-a012-2c863c166592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Точность = 0.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_circles\n",
    "\n",
    "X, y = make_circles(n_samples=100, noise=0.1, factor=0.5, random_state=42)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Точность =\", accuracy)\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))\n",
    "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k')\n",
    "#plt.contourf(xx, yy, Z, alpha=0.8)\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.title('Logistic Regression Classification')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a649cc",
   "metadata": {},
   "source": [
    "<font color = 'red' size = 5>Задание 3 </font>\n",
    "\n",
    "Для каждого из подзаданий:\n",
    "\n",
    "1. Проведите предварительную обработку данных.\n",
    "2. Постройте модель классификации на основе метода логистической регрессии из встроенной библиотеки. \n",
    "3. Подберите оптимальные гиперпараметры модели используя различные оценки, кросс-валидацию и валидационные кривые.\n",
    "4. Сделайте выводы о точности моделей.\n",
    "5. Используйте регуляризацию, где необходимо. Определите оптимальный коэффициент регуляризации. \n",
    "6. Оцените качество модели с помощью ROC-кривых.\n",
    "\n",
    "[ROC](https://loginom.ru/blog/logistic-regression-roc-auc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53471a68",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### 3.1 Скоринг отказа от услуг оператора связи.\n",
    "\n",
    "Используйте данные из файла [telecom.csv](https://www.kaggle.com/datasets/mnassrib/telecom-churn-datasets/code) для предсказания отказа от услуг связи. Столбец \"churn\". \n",
    "Предврительно изучите и подготовьте данные.\n",
    "\n",
    "#####  3.2 Предсказание диабета у пациентов.\n",
    "\n",
    "Используйте данные из файла [diabetes.csv](https://www.kaggle.com/datasets/saurabh00007/diabetescsv) для предсказания исхода для пациентов. Столбец \"outcome\". Предварительно изучите и подготовьте данные. \n",
    "\n",
    "#####  3.3 Данные теста микрочипов\n",
    "\n",
    "Используйте данные из файла microchip_tests.txt для предсказания брака микрочипов. Предварительно изучите и подготовьте данные. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8e2517-f866-458b-94af-408462f7439e",
   "metadata": {},
   "source": [
    "## 3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f43034d1-5145-4f95-93f1-03158d21e846",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11508/2326033200.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'new_telecom.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'pd' is not defined"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('new_telecom.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75d7a9c7-138b-4772-a26d-a51e449ee72e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_11508/2364786433.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'churn'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'churn'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mscaler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mscaled_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "y = data['churn']\n",
    "data = data.drop(['churn'], axis=1)\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee423e47-5622-43ef-a4c0-d2f097e51333",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=3) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.28,\n",
    "random_state=28)\n",
    "\n",
    "model_2 = LogisticRegression(solver='sag')\n",
    "model_2.fit(X_train,y_train)\n",
    "\n",
    "pred = model_2.predict(X_test)\n",
    "\n",
    "accurancy = metrics.accuracy_score(y_test, pred)\n",
    "accurancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a78cb655-24f7-457d-a355-af727f23907f",
   "metadata": {},
   "outputs": [],
   "source": [
    "degrees = list(range(2,6))\n",
    "scores_test = []\n",
    "\n",
    "for i in degrees:\n",
    "    poly = PolynomialFeatures(degree=i) \n",
    "    scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "    model = LogisticRegression(solver='sag')\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores_test.append(auc)\n",
    "    \n",
    "\n",
    "plt.plot(degrees, scores_test, 'b-o', label='Test')\n",
    "plt.legend()\n",
    "plt.xlabel('Степень PolynomialFeatures')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние степени PolynomialFeatures на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88692d31-36ff-46f6-a539-c71926fcc881",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.logspace(-5, 5, 20)\n",
    "scores_test = []\n",
    "\n",
    "poly = PolynomialFeatures(degree=3) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "    \n",
    "for alpha in alphas:\n",
    "    reg = LogisticRegression(C=alpha)\n",
    "    reg.fit(X_train, y_train)\n",
    "\n",
    "    pred_proba = reg.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores_test.append(auc)\n",
    "    \n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(np.log10(alphas), scores_test, 'b-o', label='Test')\n",
    "plt.legend()\n",
    "plt.xlabel('log10(alpha)')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние регуляризации на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "339637d8-cb5a-47f7-82f1-fd9bc6a0cc37",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratios = np.linspace(0,1,20)\n",
    "scores = []\n",
    "\n",
    "\n",
    "for ratio in ratios:\n",
    "    model = LogisticRegression(C=0.01, l1_ratio=ratio)\n",
    "    model.fit(X_train, y_train)\n",
    "    pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores.append(auc)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(ratios, scores, 'b-o')\n",
    "plt.xlabel('l1_ratio')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние параметра l1_ratio на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b25bf22c-ccd2-419b-baa4-59545eca92e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=3) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.28,random_state=28)\n",
    "model = LogisticRegression(C=0.01)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "r2 = model.score(X_test, y_test)\n",
    "print(f\"R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74604e62-ab70-462a-9cdf-971d1b5c130b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_proba = model.predict_proba(X_test)[::,1]\n",
    "auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20da9c6e-eb73-4553-9e61-782a19d6feae",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "sns.set_color_codes(\"muted\")\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "pred_proba = model.predict_proba(X_test)[::,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')  \n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef652302-a122-4244-a711-af076c3a77bb",
   "metadata": {},
   "source": [
    "## 3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6283dcdb-caa2-4fac-b859-d7987463bf2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14aefe7e-70de-4d11-bfcb-74585583b0c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375e0dda-dcff-491a-b82b-b1957c84b048",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_idx = data.columns\n",
    "print(col_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7cc79d2-6fa4-4a04-a3cc-de11fc01147d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faf5c292-c599-4cee-9983-47f2b9897cf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = data.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2015d2a4-5938-456e-ae71-a71d75339238",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(corr_matrix,cmap=\"crest\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "253a6e1c-19b7-4080-92bc-19eaaed8ba9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"SkinThickness\"] = data[\"SkinThickness\"].replace(0, np.NaN)\n",
    "data[\"Insulin\"] = data[\"Insulin\"].replace(0, np.NaN)\n",
    "data[\"BMI\"] = data[\"BMI\"].replace(0, np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55655ca4-48ff-42be-830c-bc3eb135b5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "medians = data.median() \n",
    "data = data.fillna(medians)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "447fd54e-5887-4b9b-bff0-cbc6abeade3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Outcome']\n",
    "data = data.drop(['Outcome'], axis=1)\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fa6401c-c2d8-49e3-8d58-a3a92dfc79a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=3) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,\n",
    "random_state=28)\n",
    "\n",
    "model_2 = LogisticRegression(solver='sag')\n",
    "model_2.fit(X_train,y_train)\n",
    "\n",
    "pred = model_2.predict(X_test)\n",
    "\n",
    "accurancy = metrics.accuracy_score(y_test, pred)\n",
    "accurancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1bdef87-9ba0-433c-a34d-b8646f807f07",
   "metadata": {},
   "outputs": [],
   "source": [
    "degrees = list(range(1,6))\n",
    "scores_test = []\n",
    "\n",
    "for i in degrees:\n",
    "    poly = PolynomialFeatures(degree=i) \n",
    "    scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "    model = LogisticRegression()\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores_test.append(auc)\n",
    "    \n",
    "\n",
    "plt.plot(degrees, scores_test, 'b-o', label='Test')\n",
    "plt.legend()\n",
    "plt.xlabel('Степень PolynomialFeatures')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние степени PolynomialFeatures на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d76c6f6-22eb-48b9-bd14-0c121200779e",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.logspace(-5, 5, 20)\n",
    "scores_test = []\n",
    "\n",
    "poly = PolynomialFeatures(degree=2) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "    \n",
    "for alpha in alphas:\n",
    "    reg = LogisticRegression(C=alpha)\n",
    "    reg.fit(X_train, y_train)\n",
    "    \n",
    "    pred_proba = reg.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores_test.append(auc)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(np.log10(alphas), scores_test, 'b-o', label='Test')\n",
    "plt.legend()\n",
    "plt.xlabel('log10(alpha)')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние регуляризации на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9235d8bc-9e82-4c97-93cf-ac5e63ec76f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ratios = np.linspace(0,1,20)\n",
    "scores = []\n",
    "\n",
    "\n",
    "for ratio in ratios:\n",
    "    model = LogisticRegression(C=1000, l1_ratio=ratio)\n",
    "    model.fit(X_train, y_train)\n",
    "    pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores.append(auc)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(ratios, scores, 'b-o')\n",
    "plt.xlabel('l1_ratio')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние параметра l1_ratio на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "493757a7-35a6-44da-98bc-cf070de6db55",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=2) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.28,random_state=28)\n",
    "model = LogisticRegression(C=1000, solver = \"sag\")\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "r2 = model.score(X_test, y_test)\n",
    "print(f\"R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac7c732a-a1c5-4ee4-8590-74a0d839329c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_proba = model.predict_proba(X_test)[::,1]\n",
    "auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2583041a-eb25-4649-8aa2-54554c868bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "sns.set_color_codes(\"muted\")\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "pred_proba = model.predict_proba(X_test)[::,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')  \n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b203dc2-054e-4f0d-a8ed-eae55ef6440f",
   "metadata": {},
   "source": [
    "## 3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "7a0c0f33-db40-4013-a908-479268203e80",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "    \"microchip_tests.txt\",\n",
    "    header=None,\n",
    "    names=(\"test1\", \"test2\", \"outcome\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "8e0a704b-a978-4d05-b085-132e3312565c",
   "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>test1</th>\n",
       "      <th>test2</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.051267</td>\n",
       "      <td>0.699560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092742</td>\n",
       "      <td>0.684940</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.213710</td>\n",
       "      <td>0.692250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.502190</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.465640</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>-0.720620</td>\n",
       "      <td>0.538740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>-0.593890</td>\n",
       "      <td>0.494880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>-0.484450</td>\n",
       "      <td>0.999270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>-0.006336</td>\n",
       "      <td>0.999270</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.632650</td>\n",
       "      <td>-0.030612</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        test1     test2  outcome\n",
       "0    0.051267  0.699560        1\n",
       "1   -0.092742  0.684940        1\n",
       "2   -0.213710  0.692250        1\n",
       "3   -0.375000  0.502190        1\n",
       "4   -0.513250  0.465640        1\n",
       "..        ...       ...      ...\n",
       "113 -0.720620  0.538740        0\n",
       "114 -0.593890  0.494880        0\n",
       "115 -0.484450  0.999270        0\n",
       "116 -0.006336  0.999270        0\n",
       "117  0.632650 -0.030612        0\n",
       "\n",
       "[118 rows x 3 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "8893c967-01bb-435e-afc2-70cdb9d0df5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test1      float64\n",
       "test2      float64\n",
       "outcome      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "180b393f-24cc-4374-9552-541f1da011a0",
   "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>test1</th>\n",
       "      <th>test2</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.138709</td>\n",
       "      <td>-0.074115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test2</th>\n",
       "      <td>-0.138709</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outcome</th>\n",
       "      <td>-0.074115</td>\n",
       "      <td>0.005697</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            test1     test2   outcome\n",
       "test1    1.000000 -0.138709 -0.074115\n",
       "test2   -0.138709  1.000000  0.005697\n",
       "outcome -0.074115  0.005697  1.000000"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = data.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "21587a59-2395-40e2-9e63-13eb7dbfb994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cREStDIdKiEhnaMlTXRncRKQ7GNxERK0Ng5uIqHXR05LkZnATke7QjtxmcBOR7tCS3GZwE5Hu4MVJIqJWRluCW9INOCUlJQ0uq66uxq1bt1RWEBGR2qjxLe/N6YnBnZycDGdnZwwePBiurq71vg/t/PnzcHNzU1d9REQqoyW53XBwb9q0CXFxcfDx8UFkZCR69uyJxYsX44MPPkBtbW1z1khEpBLqeB63JjQ4xr1x40bMmjUL8+bNAwAEBAQgNTUVy5Ytg4GBAZYvX95sRRIRqUQLD2SpGgzu69evY8iQIQptgYGBMDAwwOLFi/Hiiy/iww8/VHuBRESqoiW53XBwd+7cGVeuXFF6aeW0adOQn5+PlJQUdOvWDfb29movkohIFVr6EIhUDQa3p6cn4uPj0alTJwwePFjhpZkLFixAfn4+li5dKn9DMRFRS6ctwd3gxcng4GD069cPoaGhSq/S0dPTQ0xMDMaOHYuffvpJ7UUSEamElkwrabDHbWRkhLVr1+Jf//oX6ntJTps2bbBixQqMHTsWOTk5ai2SiEgVtOUhU43egNOnTx9cvHgRd+/erXe5nZ0drK2tVV4YEZGqact0QEl3TkZGRiIvL6/eZRcuXJC/yZiIqCXTluBucKgkKCgIf/zxBwBACIHg4GC0a9dOab2ioiL07NlTbQUSEalKC89jyRoM7tmzZyMzMxMAkJmZif79+8PMzExhHX19fZiYmMDf31+9VRIRqYKWJHeDwe3o6AhHR0cAQE1NDd5//31YWFg0V11ERCrX0odApJL0WNelS5cCAO7fv48XXngBALBr1y4UFBTA3d2dgU5ErYKW5La0i5O5ubkYPXo0kpOTAQBxcXEICQnBkiVLMG7cOJw6dUqtRRIRqYSWzOOWFNxffPEFDAwMMHLkSFRWVmLjxo3w8fHBiRMnMGzYMM4qIaJWQV9P+qeptm3bhrFjx8Le3h7e3t7YsmWL5G0LCgrg5OSEL7/8Utp5SFnp+PHjmD9/Pvr3749jx46hrKwM/v7+MDIywuTJk/Hbb79JLpCISGPU1OPOyspCWFgYXFxcsHr1ajg7OyM8PBzZ2dmNbiuEQFRUFMrLyyUfT9IYd1VVFUxNTQEA+/fvx/PPPw8nJycADy9ctmnDN6ARUcunrhGQmJgYeHt7IyoqCgDg6uqKkpISrFq1CmPGjHniths3bkRubm6Tjiepx21jY4OcnBwUFhYiOzsbw4YNQ5s2bVBVVYX09HTY2Ng06aBERJqgjhtw8vLyIJPJMHr0aIV2Ly8v5ObmNnjzYt22K1euxGeffdak85DUVQ4NDUVwcDDS09PRrl07zJo1S15YUVERkpKSmnRQIiLNkJ7IpaWlKC0tVWo3MTFReFpqXW/ZyspKYT1LS0sAwJUrV+qdeVdbW4uIiAh4e3tj+PDhkusCJAa3i4sLtm7dinPnzsHBwQEvvfQSAGDGjBkYPHgwn1VCRK1CU3rSqampSExMVGqfM2cOQkJC5N/LysoAPHww36MMDQ0BoMGx69TUVOTl5T1Vx1fy4LSFhQUsLCxQXV2NwsJCdOzYEdOmTWvyAYmINKUpwR0YGAhfX1+l9kd72wDkT0/Ve2znde36+soj0rm5uYiLi0N8fDyMjY2lF/V/JAf3b7/9htjYWBw7dgw1NTXIyMhAWloaLCwsEBwc3OQDExE1t6YE9+NDIg2pC97He9YVFRUKy+vU1NQgIiICY8aMgYuLC6qrq+XLamtrUV1d3eiED0kXJ0+dOoWpU6fi3r17ePfdd+V/k3Tr1g2JiYnYuHGjlN0QEWmdurFtmUym0H7t2jWF5XUKCgpw9uxZbNmyBXZ2dvIPACQkJMj//CSSetwrV67E0KFDkZSUhOrqaqxevRoAMHfuXPz111/YtGkTpk6dKmVXREQao45nlVhaWsLc3BzZ2dkYNWqUvD0nJwc9e/ZE9+7dFdbv0qWL/AF+j5o4cSKmTJmCN954o9FjSgru8+fPIz4+HoDyOI67uzu+++47KbshItIodc3jDg4ORmRkJExNTeHm5oY9e/YgKytLfld5cXExZDIZrK2tYWRkhP79+9e7ny5dujS47FGShkoMDQ1RVFRU77Jbt27Jr54SEbVkevrSP03h5+eHRYsW4ZdffkFwcDCOHTuG6Oho+Pj4AAD27t0Lf39/nD9/XiXnIanH7eHhgbi4OPTp0we2trYAHva8CwsLsWbNGowYMUIlxRARqZM6nx01efJkTJ48ud5lfn5+8PPze+L2Fy9elHwsScEdFhaGc+fOYeLEiejatSsAYMGCBcjPz0fXrl0RFhYm+YBERBrTwp/6J5Wk4L506RLS09OxY8cOHDlyBFZWVvIHTHl6euLo0aPw9vZWd61ERM9ES3JbWnAHBARg8+bNmDRpEiZNmqSw7MiRIwgPD2dwE1GLp/VvwAkPD0dBQQGAh3cALVy4UOmWTgC4evUqOnfurL4KiYhURUuSu8Frp97e3jAwMICBgQEAyP/86Kdt27ZwcnJCXFxcc9VLRPTU1PF0QE1osMft5uYGNzc3AMDbb7+NhQsXolevXs1VFxGRyrXwPJZM0hh3WlqauusgIlK7lt6TloqvriEi3cHgJiJqXbQktxncRKQ7OFRCRNTKMLiJiFoZbQnuJj4Di4iINI09biLSGdrS42ZwE5HO0JLcZnATke5o6gsSWioGNxHpDPa4iYhaGy0Z5GZwE5HO0I7YZnATkQ7Rkg43g5uIdAeDm4iolWFwExG1MlqS25oP7nt/actP2XK9ucJS0yVovYwPr2m6BJ3w/0494w60JG40HtxERM1FS3KbD5kiIt2hzpcFb9u2DWPHjoW9vT28vb2xZcuWJ65fWFiIjz/+GO7u7hgwYAD8/PyQlZUl6VjscRORzlDXxcmsrCyEhYUhICAArq6u2L17N8LDw9G+fXuMGTNGaf3Kykq88847KCsrQ2hoKLp06YKdO3di7ty5qKmpweuvv/7E4zG4iUhnqCu4Y2Ji4O3tjaioKACAq6srSkpKsGrVqnqDe//+/fjXv/6FjIwM2NvbAwBcXFxw48YNrF27ttHg5lAJEekMvSZ8pMrLy4NMJsPo0aMV2r28vJCbm4u8vDylbQwNDeHv74/+/fsrtL/88suQyWSNHpM9biLSGerocefm5gIArKysFNotLR/O5rpy5QosLCwUlg0ZMgRDhgxRaKuqqsK+ffvQu3fvRo/Z5OC+efMmjhw5gtu3b8PX1xeFhYWwtrZGu3btmrorIqJm1ZTgLi0tRWlpqVK7iYkJTExM5N/LysoAAEZGRgrrGRoaAgDKy8slHW/lypW4evUqVq9e3ei6TQru6OhopKWlobq6Gnp6enBxcUFMTAxu3bqF1NRUdOrUqSm7IyJqsVJTU5GYmKjUPmfOHISEhMi/CyEAAHqP/a1Q166v/+QRaSEEVqxYgXXr1mHmzJnw9PRstDbJwZ2cnIy0tDQsWLAA7u7uGDVqlPwkQkNDERsbi8WLF0vdHRFRs9NvQo87MDAQvr6+Su2P9rYBwNjYGIByz7qiokJheX0qKysRERGB7du3Y+bMmViwYIGk2iQH9+bNmxESEoKAgADU1NTI2wcMGIC5c+di1apVUndFRKQRTRkqeXxIpCF1Y9symQy2trby9mvXriksf1x5eTmCgoJw6tQpREVFITAwUHJtkmeV3L59W+kKaJ2XXnoJ9+7dk3xQIiJNUMesEktLS5ibmyM7O1uhPScnBz179kT37t2VtqmpqcHs2bNx9uxZxMTENCm0gSb0uHv06IEDBw5g6NChSstOnDihdNWUiKjFUdM87uDgYERGRsLU1BRubm7Ys2cPsrKyEBsbCwAoLi6GTCaDtbU1jIyM8N133+HYsWPw9/fH3/72N5w5c+Y/JerpwcHB4YnHkxzcgYGB+O///m9UV1fDw8MDenp6yMvLw8mTJ/HNN98gLCzs6c6YiKiZqOsGHD8/P1RWViIlJQUZGRmwsLBAdHQ0fHx8AAB79+5FZGQk1q9fj0GDBmHnzp0AHg5Bb968WWFfBgYG+P333598HqLu0qcEa9asQVJSEv766y/5FdO2bdtixowZmDdvXpNOtM6Xh+KfajuS7s6fmq5A+/HpgM3j3Kkvnmn7gM3Sr8Wt9//HMx1LnZo0HTAoKAhvvfUWTp06hZKSEhgbG8PBwQEdO3ZUV31ERCrTlFklLVmTb8AxMjLC8OHD1VELEZFa6dwbcG7cuIHPPvsMZ86ckd8p9LjffvtNZYUREamaluS29OD+6KOPcObMGbzxxhvo0KGDGksiIlIPnetxnzlzBp988gn8/PzUWQ8RkfroWnB37tyZPW0iatW05eKk5Dsng4KCkJCQgPz8fHXWQ0SkNuq4c1ITJPe43dzc8M0338DT0xNmZmZo3769wnI9PT3s3r1b5QUSEamKzo1xh4eHIz8/HyNGjEDnzp3VWRMRkVpoSW5LD+6TJ09i0aJF9T7mkIioNdC5HreZmRnMzMzUWQsRkVppS3BLvjg5c+ZMJCQk4MaNG+qsh4hIbfT1pH9aMsk97gMHDuDKlSsYOXIkOnXqJH+f2qPqnnhFRNQStfA8lkxycHfs2FHp9fNERK2JtgyVSA7upUuXqrMOIiK105LcbvrTAfft24djx46hrKwMHTt2xGuvvQZXV1d11EZEpFI61+N+8OABZs+ejUOHDqFt27YwMzNDUVERkpOT4ezsjOTkZDz33HPqrJWI6JloSW5Ln1USFxeHM2fOIDY2Fr/++iv27duHc+fO4YsvvsD58+eRmJiozjqJiJ6ZtswqkRzcO3bsQGhoKLy9vaH3f//e0NPTg4+PD+bMmYPt27errUgiIlXQ05P+ackkD5WUlpbCxsam3mU2Nja4c+eOyooiIlKHlh7IUknucVtZWeHAgQP1Ltu3bx/Mzc1VVhQRkTro3NMBAwICEBkZiaqqKowdOxadO3fGnTt3sH37dmzcuBEfffSROuskInpmelrS5ZYc3BMmTIBMJsPXX3+N9PR0AIAQAu3atZO//Z2IqCVr6RcdpWrSPO7Q0FAEBgbi7NmzKCkpgYmJCRwdHWFqaqqu+oiIVEZLclv6GDfwcCw7KSkJw4cPx7hx49CxY0fMnTsXR44cUVd9REQqo5PTAd977z1cvnxZ3vb888+jtrYWM2fOxP79+9VSIBGRqqjz4uS2bdswduxY2Nvbw9vbG1u2bHni+hUVFVi0aBFcXFwwYMAAzJo1C1evXpV0LMnBnZSUhLfeegvJycnytt69eyM1NRWTJ09GfHy81F0REWmEuuZxZ2VlISwsDC4uLli9ejWcnZ0RHh6O7OzsBreZN28esrOzERYWhujoaNy6dQsBAQEoKytr9HiSx7hlMhmioqLqXebp6Ykff/xR6q6IiDRCXSMgMTEx8Pb2lmekq6srSkpKsGrVKowZM0Zp/RMnTmDfvn1Yu3Ythg8fDgB47bXXMHLkSGzatAnvvvvuE48nucfdqVMnnD9/vt5lFy9e5AVKImrx1DHGnZeXB5lMpvTYay8vL+Tm5iIvL09pm4MHD8LQ0BAuLi7yNjMzMwwcOFDSsLPk4B43bhwSExPx3Xff4c6dOxBCoKioCBkZGUhISMC4ceOk7oqISCPUMVSSm5sL4OFNio+ytLQEAFy5cqXebSwtLWFgYKDQ3qNHj3rXf5zkoZLg4GDk5uZi4cKFWLRokbxdCIHRo0cjNDRU6q6IiDSiKUMlpaWlKC0tVWo3MTGBiYmJ/HvdmLSRkZHCenVvCSsvL1faR3l5udL6ddvUt/7jJAd327ZtER8fj0uXLuHkyZO4d+8ejI2N4eTkhD59+kjdDRGRxjRlCCQ1NbXep57OmTMHISEh8u9CCADKd2XWtevrKw9s1C2rt8Z61n+c5OBOTEzEm2++id69e6N3794Ky/Lz8/Htt9/i448/lro7IqJm15Qed2BgIHx9fZXaH+1tA4CxsTEA5Z51RUWFwvJHGRkZ4fr160rtFRUV9fbEHyd5jHv16tW4detWvcvOnDmDzZs3S90VEZFGNGWM28TEBObm5kqfx4O7bmxbJpMptF+7dk1h+ePb5OXlKfW8r127Vu/6j3tij3vKlCk4c+YMgIdde39//wbXdXR0bPRgRESapI47Ii0tLWFubo7s7GyMGjVK3p6Tk4OePXuie/fuStsMGzYMSUlJOHTokHxmSXFxMU6cOIGgoKBGj/nE4F68eDFycnIghEB8fDwmTZqEbt26KaxjYGAAY2PjeucqEhG1JOqaxx0cHIzIyEiYmprCzc0Ne/bsQVZWFmJjYwE8DGWZTAZra2sYGRlh4MCBcHZ2xvz58xEWFoYOHTogISEBxsbGmDJlSqPHe2Jw9+rVC7NnzwYA1NbW4s0330TXrl1VcJpERM1PXU919fPzQ2VlJVJSUpCRkQELCwtER0fDx8cHALB3715ERkZi/fr1GDRoEICH1w2XLVuG5cuXo7a2Fk5OToiLi5N0T4yeeNLlzUc0NL79qKcJ9S8P8VZ5dbvzp6Yr0H4ZH17TdAk64dypL55p+5X7pedN2PCWO8VZ8qySESNGNPoQ8gsXLjxzQURE6tKkx6G2YJKDe8mSJUrBff/+fZw4cQJHjx7FkiVLVF4cEZEqtfTHtUolObj9/PzqbX/rrbewdOlSbN26FW5ubqqqi4hI5bTkzWWq+ZeDh4cH9u7dq4pdERGpjc69LPhJzp49izZtVLIrIiK10ZYet+S0/eSTT5TaampqcPPmTRw5cgQTJ05UaWFERKqmJbktPbgPHjyo1KanpwcjIyPMmjUL7733nkoLIyJSNZ3rce/ZswcAcOnSJRw7dgwVFRXo2LEjXn31VfTq1UttBRIRqYrOzSoRQuCTTz7BDz/8oPBgFD09PUyYMKHe6YJERC2Jzs3jXrNmDbZs2YIPPvgA48aNQ+fOnVFYWIitW7ciPj4eL7/8MmbNmqXOWomInom29C0lB3dmZibee+89vPPOO/K2bt26YdasWXjw4AEyMzMZ3ETUomlJbkv/l0NhYSGcnJzqXfbqq6+ioKBAZUUREamDOt45qQmSg9vCwgKnT5+ud9np06fx4osvqqwoIiJ10LkbcCZOnIiYmBi88MIL8PHxQefOnXHnzh1s374da9askfTwbyIiTZLwOsdWQXJwv/3227hw4QKWLVuG6OhoebsQAuPHj5c/t5uIqKVq6T1pqSQHt4GBAaKjozFr1iwcP34cJSUlMDExgbOzM6ytrdVZIxGRSrT0sWupmvyAEWtrawY1EbVKWjJSopqHTBERtQY62+MmImqt9PUkvamxxZP8zkkiImoZtGXIh4hIZzC4iYhaGQY3EVErw+AmImplGNxERK0Mg5uIqJVhcBMRtTIMbiKiVkYng1sd9xzxPiZF/I2bH38f3aFzwf3zzz8jPDxcpfs8ffr0E59HvmHDBowaNUqlx2zJmus3Li8vR3R0NDw9PeHo6Ihx48Zh48aNOhlgly9fxpQpUzRdBjUTnXtWSWpqKmpqalS6z8zMTPzxxx/1Ltu1axeWLVuGv/3tbyo9ZkvWXL/xvHnz8OuvvyI0NBQvv/wyDh06hM8++wxlZWU692KPnTt3NviGKtI+OhfczaWkpASJiYlIS0uDiYmJpsvROhcuXMD+/fsRFxcHb29vAMCQIUNQWlqKtWvX6lxwk27RqaGSt99+G4cPH8axY8dga2uLo0eP4u7du/j4448xZMgQ2NvbY8qUKTh58qTCdgcPHsSkSZMwYMAADBw4EO+//z4uX74MAIiIiEBmZiby8/Nha2uLH3/8EQCwfv165OTkIDY2Fh4eHs1+rprSXL+xEAL+/v4YMmSIwn5efvlllJWV4e7du812zs+quroa69atw9ixY2Fvb4+RI0fiq6++kv+rxcPDAx999JHCNj/++CNsbW1x8+ZNJCQkYNWqVQAAW1tbJCQkAAAqKysRFxcHDw8PODg4YNy4cdixY4fCfrZs2QJfX184Ojpi+PDhiI6Oxl9//SVfHhERgaCgIKSnp8v3M3PmTBQWFiIzMxOenp4YMGAApk+fjuvXryvs+/vvv4ePjw/69esHDw8PJCcn6+QwlloIHXLp0iXxxhtviAkTJojTp0+LsrIyMX78eOHi4iK+//578fPPP4ugoCDRr18/cfbsWSGEEDKZTNjb24tFixaJw4cPi+zsbOHl5SVGjhwpamtrxbVr18R7770nXFxcxOnTp0VRUZEQQojc3Fzx4MEDIYQQ4eHhwtPTU2Pn3Zya8zeuz7Rp04SLi4uora1trlN+ZgsWLBB2dnYiISFB/PLLLyI+Pl7Y2dmJyMhIIYQQ7u7uIioqSmGbH374QdjY2IiCggJRUFAgPvnkE2FjYyNOnz4tCgoKhBBChISECAcHB7FmzRpx6NAhsWjRImFrayv27NkjhBBi1apVwtbWVnz++efiwIEDIiUlRTg6OorAwED57xceHi4GDBggfH19xZ49e0RmZqbo16+f8PLyEuPHjxe7du0SGRkZwtHRUQQFBcnrS0pKEra2tmLJkiXiwIED4ssvvxR2dnZi6dKlzfGTaj2dCm4hhAgMDBTTpk0TQgixefNmYWtrK3799Vf58pqaGuHr6yumT58uhBBi27ZtwsbGRty8eVO+ztmzZ0VMTIwoKysTQggRFRUl3N3dGzymLgW3EJr5jYUQYt26dcLGxkasW7dO1aekNv/+97+FjY2N+PrrrxXak5OThY2Njbh48WKjwS2EEKtXrxY2Njby5RcvXhQ2NjZiw4YNCtvNnDlTLF26VNy9e1f069dPLFq0SGH59u3bhY2Njfj555+FEA//37WxsREymUy+TlBQkFLbp59+KpydnYUQQpSWlgoHBwfx6aefKux73bp1om/fviI/P78pPxHVQ6eGSh53+PBhdO3aFX379kV1dTWqq6tRW1sLd3d3HD9+HJWVlXBwcMBzzz2HiRMn4vPPP8eBAwfQp08fzJs3D0ZGRpo+hRavuX7jDRs2YOnSpfD29kZAQICaz0p1jh8/DgB4/fXXFdrHjx+vsLyp6oaiHp/N9PXXXyMiIgJnz55FZWUlxo4dq7B8zJgxaNu2LY4ePSpv69SpEywsLBS+m5mZKbR16NABZWVlAB7OAPrzzz/h4eEh/29eXV0NDw8P1NTU4MiRI091TvQfOn1x8t69e7h58ybs7OzqXX737l2Ym5tjw4YNSE5ORmZmJtavXw8TExNMnToVc+fOhZ62vAtJTdT9G9fW1mLFihVISUnB66+/jujo6Fb136SkpATAwzB8VN33ujBsqnv37tW738eP++KLLyq06+vrw8zMDOXl5fI2Q0NDpe2ff/75Ro89Y8aMepffvn27wW1JGp0ObmNjY/Tq1QvR0dH1Lu/YsSMAwN7eHomJiaisrMTJkyexefNmJCUl4ZVXXoGXl1dzltzqqPM3rqqqwgcffICdO3dixowZWLBgQasKbQDyGUdFRUXo2rWrvL2wsBDAf36f2tpahe3u37//xP0aGxsDAIqLixXC+d///jf+/PNPmJqayo/To0cP+fLa2loUFxfLj/s06o4dGxur0Cuv06VLl6feNz2kc0MlBgYG8j8PHDgQN27cQJcuXdC/f3/556effkJaWhratm2LtLQ0eHh4oLKyEu3atcOQIUPw2WefAQAKCgqU9knN9xtHRUUhJycHkZGRCA8Pb3WhDQDOzs4AgG3btim01313cnKCkZGR/Heo8/isnMd/HycnJwAPb4Z61Oeff46YmBg4ODigXbt22L59u8Ly7OxsVFVVybd/Gg4ODmjbti1u376t8N+8uroasbGx8r+U6OnpXI/b2NgYJ06cwOHDh+Hp6YkNGzbg73//O4KCgtC1a1fs3bsX3377LebMmQM9PT0MHjwYy5cvR3BwMKZNmwYDAwN89913eO655+Du7i7f5507d7Bv3z707dtX53sUzfEb//777/jnP/8JDw8PODo64syZMwo1vPLKK2jXrp0Gzr5pevfujfHjxyM2NhZ//vknBgwYgNOnTyMpKQn/9V//BWtra7i7u2PNmjVITk6Gvb099uzZozROXNfL3bZtGxwdHdG3b1+MHj0aS5cuxf3792Fra4vdu3fj2LFj+Oabb9ChQwfMnDkTSUlJaNOmDUaMGIFLly4hISEBzs7OcHV1fepzMjMzw4wZMxAbG4vy8nI4OTnhxo0biI2NhbGxMXr37v1MvxlBt6YDCiHEmTNnhJubm7CzsxPbt28XhYWFIiIiQgwePFj0799f+Pj4iLS0NIVtDh48KCZPnixeffVV4eDgIN566y1x/Phx+fJr164JHx8fYWdnJ9auXat0TF2bVdIcv3FERISwsbFp8FM326I1qKqqEgkJCcLd3V3Y2dmJUaNGiaSkJFFdXS2EEKKiokJ8/PHHYuDAgcLR0VHMnTtX/PTTTwrnWVRUJPz9/YWdnZ18psiDBw/E8uXLhaurq7C3txe+vr7y2SJ10tLShJeXl7CzsxMjRowQ0dHR4v79+/Ll9f2/W98Mn/j4eNG3b1/599raWrF+/XoxZswYYWdnJ1xcXERERIS4ffu2yn43Xca3vBMRtTI6N8ZNRNTaMbiJiFoZBjcRUSvD4CYiamUY3ERErQyDm4iolWFwExG1MgxuIqJWhsFNRNTK/H95AHcAwqpnvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(corr_matrix,cmap=\"crest\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "7ede41a9-33f6-46f0-9a5a-c7bc100e1c26",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['outcome']\n",
    "data = data.drop(['outcome'], axis=1)\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "064ae4e1-124b-44f1-8b31-81aad24a7997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly = PolynomialFeatures(degree=3) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,\n",
    "random_state=28)\n",
    "\n",
    "model_2 = LogisticRegression(solver='sag')\n",
    "model_2.fit(X_train,y_train)\n",
    "\n",
    "pred = model_2.predict(X_test)\n",
    "\n",
    "accurancy = metrics.accuracy_score(y_test, pred)\n",
    "accurancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "4c75bec3-d7ab-4a90-bbb8-1400ddb8d9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "degrees = list(range(1,6))\n",
    "scores_test = []\n",
    "\n",
    "for i in degrees:\n",
    "    poly = PolynomialFeatures(degree=i) \n",
    "    scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "    model = LogisticRegression(solver='sag')\n",
    "    model.fit(X_train, y_train)\n",
    "    pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores_test.append(auc)\n",
    "\n",
    "plt.plot(degrees, scores_test, 'b-o', label='Test')\n",
    "plt.legend()\n",
    "plt.xlabel('Степень PolynomialFeatures')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние степени PolynomialFeatures на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "13ba4c8a-e51f-4f68-8eed-9bda9b742ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oNBCJRAgNDUWfPn303ovk5GRkZGTkW3ba+LXHq1QqPHv2LM9z5UXJycmQyWQ4evQojh49mu8xsbGxxb4XrevXr2Pv3r1YvXo1bty4UeLzGAsme5QvBwcHtGrVCkeOHNE1t5XEqVOncPLkSWzbtk2v5gYATExMsHr1asyePRtTp04t9MEXEBCQp3amoCaw+/fvY/v27Vi8eHGeB5X2S3rq1Klo2rRpvq/P7wv5RS8/QIHcX75A7n2VxjWA3Fqd5cuXIzQ0FKNHj8aJEycwfPjwPO9lYYr7Xhd0f2Jx3saAV5Xxi7777jvEx8dj3bp1mDNnDvz9/eHh4YElS5Zg7dq1qF+/Ppo2bYr+/fvDz88Pc+bMea0veyC31trDwwPDhg3L0+n7RWvWrEFOTg4+/PBD3Lp1q8jn79+/PwYMGKD7d0JCAj799FPdv9PS0jBs2DBERUWhXbt26NKlC+rWrQs3NzcMGTKkSNcorCy0n7XVq1fDwsKi0PMFBARg7dq1+Oeff7BixQrMnz8f33zzDQCgX79++OKLL3Dt2jVkZWUhPj6+2LXILxo3bhwCAgKQk5ODqKgorF69GqNGjcLBgwchlUoLfF10dDTeeustZGRkICAgAL1790a9evWg0WgwadKkEsfzosLe16LGoG1mfLkLAwBd/7TCatS1f4/5xVQQ7QAMjUaD1NRU7NmzBx9//DEAFDvh69q1K5YuXYobN27AzMwMERER6N27N9atW5cnxvxoP4PaGsvHjx8jJyfnlUm99vsyMDCwwB8qhfVnLYhGo8G8efPQrl07dO7cmckemOzRK2j/gPN7wBeFdnBE37594efnl+8xvr6+GDNmDB48eAB/f3+8+eabkMvleg9MLRcXF71BCa+yYMEC3YjDl2lri6ysrPKc7/r160hNTS30oamtTXnRkydPYGJiAnd3d11zz+tcAwA8PT3RuHFjHD9+HL6+vsjMzES/fv0KfV1+ivNeF3R/L9d6FVbGycnJuH//Pvz9/XW1kdWrV9eNRu3evTvWrl2L/v3756kBTUhIKNF9al2+fBl//vkngoOD830Qa0VFRWHTpk348ssvX5mA5MfDw0OvfF8e7bd582Y8fPgQGzdu1NXCaGMrqsLKQvt5rlatGurVq6d3nHYQgVqtxt27dyGVSuHu7g5XV1d07NgRV69exf79+3XJXmBgIL799lscP34cmZmZsLe3R7t27fJcv6g/NmrWrKn3/lhbW2PmzJk4f/68btBBflasWIHExESEhobqfeb27dtXpOsWxcvvq1KpRHR0tK6cihqDtkYvIiIiT+3enTt3YG9vD1tbW105RURE6DWParcBKLSm/0V2dnZ6723Xrl3RunVr7Nu3r9jJXs2aNeHl5aVrcq5Tp06eAS6Ojo6wsrLSxfqiR48e6cV/7tw52NnZ5Vsr/eL5LC0toVKp8nxHxsTE4NatW3l+qBf1c7dv3z7cuHHjlT/wKhv22aN8JSQk4Pz586hXr57eA1ClUhX51+fu3bsRExOTbzKhdePGDcycORM+Pj5YtmwZunbtimbNmr1W7H///Tf+/vtvfP755/nub9iwIVxcXPDLL7/o9ZlKT0/HtGnTEBQUVOiqIf/88w+uXr2q+3dCQgL27t2L1q1bw87OrlSuodWvXz9dk4Svry/q16+vt7+oZVKc93rPnj16/axOnjyJBw8eoEePHnrHFVbGGzduxJgxY3D37l3dNm3TsaWlpW46j5cfCidPnsTjx491fQ1LYsmSJejSpUu+ycqLli9fjpo1a+Y7zc3rSklJAaB/fxqNBlu2bAGAIt1fYWWh7U/3008/6dW+3L59Gx988AE2bdqEzMxMDB06NE9CnZ6ervdAtbW1RceOHXHy5EmcPHkSgYGBep8tbbyvSp5fRfsjqLBm4ZSUFFhaWqJ69ep6r922bRuA/2qFXsf27dt1TbJA7qhwmUyme1+LGkO7du3g4uKC4OBgvT59ly9fxqVLl3Q/ONu2bQtzc3Ns2LBB77hnz55h3759aNy4sd5ghOJSqVTIycl5ZXeFV+nSpQuOHz+O0NDQfJNFExMTtG/fHmfPnsXNmzd12zUaDdatWweRSIROnToByB1g0759+1dWFJiamqJDhw44efKkbuoZrYULF2LSpElITk4G8N97XdTP3ZIlSzB8+PBCR2RXJqzZIxw9elQ3hYNGo8GzZ8+wY8cOZGZm6poFrly5gmvXriEhIaHIo92ePXuGKVOmFPhrVaFQICgoCGKxGIsWLSrxAyS/67755pto2LBhvvvNzMzw5ZdfYtq0aRg0aBAGDx4Mc3NzhISEICYmBt9//32hsUgkErz33nt4++23YWFhgd9++w1qtVo3ArY0rqHVu3dvLFy4EIcOHdIbPHDnzh1cvXoVd+/ezVNT8LLivtcymQxDhw7F0KFDkZSUhE2bNqFGjRp4++239Y4rrIzffPNNbNmyBe+++y7efvttmJqaIjg4WJdUODo6onr16ropGapWrYrr16/jjz/+gLm5eb4DGPbs2aP3b+2cc0ePHtUbNJOYmFjoPGPae1iyZEmJa7BfpUOHDvjll18wceJEDB48GEqlEqGhobhx4wbEYnGRBmgUVha1a9fG6NGj8csvvyAlJQXdunVDSkoKtmzZAmtra0ydOhXW1tYYMmQIfv31V0yZMgXt2rXDhQsXcOXKlTyrPvTt2xdTp04FAMydO1e3/ezZs7qRj0VNSq5evQoTExNkZ2cjIiICO3bsgLu7O5o3b17o+3b8+HFMnDgRPXv2hEwmw+7du3W1cUV53wrz+PFjjBo1Cv369cOjR4+wdetW+Pv765KzosZgYWGBL7/8ElOnTsWwYcPwxhtvICsrCxs2bECVKlV0o8kdHBwwffp0LFiwAMOHD0e/fv0gl8uxdetWqNVqfPHFF8WKPyEhAXv27IFarUZ8fDz279+P7OxsvPHGGyV6P7p27YoNGzYAKLgZeMaMGbhw4QJGjx6tm3bqyJEjOH/+PN555x1Ur15d18exUaNGef5WExISkJGRgbNnz6Jdu3a6840cORIjR45E9erVceLECfz1119466234Obmht27d+tqU52dnYt0L1lZWcUaaFUZMNkjvak3TExMYGdnh0aNGmHevHm6Jo0dO3Zg9+7d8Pb2LnRUo5abm1ueeZJetGrVKty7dw+ffvppkfqvFZW1tXWeUaEvCwwMRHBwMFavXo1Vq1ZBLBajVq1aWL16dZFGHjZt2hR9+vTBqlWrIJPJ0KJFC3zyySd6SdfrXkPL2dkZbdq0wZkzZ/T6Tx09ehQrVqxAtWrVCp2epLjv9TvvvIP09HQsX74cEokEb7zxBqZPn56nRqawMvb09MSGDRuwdOlS3a//+vXr45NPPtEliGvXrsXChQuxefNmaDQaeHp6Yvbs2VCpVJg3bx5u3Lihl7i/PA+c1vz58/WSvbfffhteXl6F3qt2CpKy0KFDB8ydOxfBwcFYuHAh7Ozs0KBBA2zfvh1ffvllgRP/vqgoZfH555/D19cX27Ztw6JFiyCVStGiRQtMnTpVV7sxa9Ys2NnZYd++fThx4oQuEZk4caLe9Tp37gwbGxvY2NjovS8//fQTLl68iIYNGxbYx+pl27dvx/bt2wFAF9Nnn31WaM3esGHDkJaWhpCQEMydOxfOzs5o2rQpVqxYoZvAfOzYsUWKoSAzZszA1atX8f3330MqlWLs2LGYMmWKLukvTgyBgYFYtWoVVq9ejR9//BESiQStWrXCzJkz4ejoqLvm2LFj4erqiuDgYCxduhSWlpbw9/fHRx99VOw59CIiIvR+XPr4+GD+/Pm66VOKq1mzZrC3t4e7uzs8PT3zPcbT0xM7duzA//73P2zbtg1ZWVmoUaMG5s2bh8GDB+Pp06dYsmQJgNwuDAVZs2YN2rVrpzvfsmXLsGPHDmRkZMDDwwNBQUEYPXo0nj17hlmzZsHS0hJvv/12gXG9bOrUqSXuZ260NERULJ07d9aMGjWqXK85btw4zeDBg8v8OlFRUZratWtrli1bVubXKk2///67pnPnzkKHUaqEKovs7GxN8+bNNd999125Xre8nD9/XlO7dm3N77//LnQoRkf7mY2KiirwmGXLlpX79ydpNOyzR2Tgnjx5gr///ptzRVG5OHDgAGQyGT9vREaEzbhEBurUqVP4448/EB4eDgcHhzKdRb+i8/T01GvCpeILDg7G5cuXcerUKXTu3Jmd26nYrKys0K9fv1cOEqlTp06p9c+mouM7TmSgLC0tcfr0aTg5OWHBggUlHmVXGbRo0aLM+t1VFjk5OThz5gyaNGmiNzCDqKgcHR3x/fffv/KYl0f0U/kQaTRFmMKdiIiIiCok9tkjIiIiMmJM9oiIiIiMGPvsFSI5WQ61mi3dReHkZIPExPTCD6RywzIxTCwXw8MyMTwsk+IRi0VwcLDOdx+TvUKo1Rome8XA98rwsEwME8vF8LBMDA/LpHSwGZeIiIjIiDHZIyIiIjJiTPaIiIiIjBiTPSIiIiIjxmSPiIiIyIhxNC4RERG9tsxMOdLTU5GToyyV8z1/LoZarS6Vc1VkJiZmsLGxg6Vl/tOqFAWTPSIiInotSqUCMlky7O2dYWZmDpFI9NrnNDUVQ6Wq3MmeRqOBUpmNlJQEmJqawcxMUqLzsBmXiIiIXotMlgIbGztIJBalkuhRLpFIBInEAtbWdkhPTynxeZjsERER0WtRqRQwN7cUOgyjZWFhCaVSUeLXsxmXiMiAnbstQ8iZZCTKVHCSmmJIgAPa1pMKHRaRHrU6B2KxidBhGC2x2ARqdU6JX89kj4jIQJ27LUPwkQQoVLlLRiXKVAg+kgAATPjI4LD5tuy87nvLZlwiIgMVciZJl+hpKVQahJxJFigiIioLGk3ZrgHMmj0iIgOTrVTj7zvpSJTl32yTKFMhR62BiZg1KURlZd68rxEauv+VxzRt2gwrVqx9rev88ssGmJiYYMSIMa91nldhskdEZCAS0pQ4djUNJ27IIM9Sw0QM5BQw88QnP0eiaxM7dGokhdSSfaWIStvYsePRv/+bun8vXboQJiYmmDr1U902a+uSz32ntX79GowZ8+5rn+dVmOwREQlIo9HgdlQWjlxJxeWIDIgANK9pje5+tkhMU2LD0US9plyJqQidG0sRlaBEyJkk7P47Ga3rWqO7nx28Xc2FuxEiI+Pm5g43N3fdv62srGFiYoqGDRsJGFXJMNkjIhJAtlKNs7fTcfRKKp4mKmFjIUbflvbo0sQWTlLtV7MlRCJRgaNxnyYocPRqKs7cSsfpm+mo7WaBHn62aFbDGqYmbOKliu3MrTRsP5lk0CPRr169jHXrVuPOnVuwsLBAhw6d8eGHUyGV5sapVquxfv0aHDlyEAkJ8XB2dkG3boEYN24iTE1NERDQAgCwYcM6bNiwDmfOXCyTOJnsERGVo/hUJXaHx+BgWCIystXwcpVgfA8XtK5jDYlZ3jFzbetJC3zAuTtLMLabC4YEOOLUTRmOXU3Div3P4WBjgq5NbNGpkS1srdjESxVPRRiJfvXqZUyb9iFatmyFOXMWITk5CWvXrsKDB/exevXPMDU1xa+/bsIff+zE5Mkfo1q16rh16wbWrl0FMzMzvPvuBKxZswGTJo1Hr1590bfvgDKLlckeEVEZ02g0uBmZiSNX03D1YQZEYqBFzdym19rVX39pKWsLE/Rqbo9APztce5yBI1fSsPNsMnafT0brOjbo7mcHnyps4qXydeaWDKduyEr02gexWVC9ND5JodJg/eF4nPineOfs0FCKgPqlnyD+9NMKeHv7YtGiHyAW5/5Qq127Dt59dxSOHz+CHj164cqVy6hbtx569+4HAPDzaw4LCwvY2OTGo20SdnFxLdPmYSZ7RERlJEuR21R75EoqYpKUkFqK0a+VPQZ3rgYoskv9emKxCH6+1vDztUZ0ogJHr6bhzC0ZztxKR81q5ujhZ4cWtdjES4bv5USvsO3lLSsrCzdv3sCoUWOhVquhVueOpPLxqYGqVashPPwCevTohWbNmmPNmhX48MPxCAjogDZtAvDmm2+Ve7xM9oiISllcshJHr6Xi9M10ZGSr4V1Fggk9XeBf2xoSUzFc7CSIjy/9ZO9Fbk4SvN3VGUMCHHH6pgxHrqRi1Z/PYW9tgi6NbdG5sRR21nwEUNkJqF/yGrWP10UiUabKs91JaorZQ6u/bmivTSZLg1qtxubNwdi8OTjPfnd3DwDAiBFjYGlphQMH9mL16uVYtWoZfHx88fHHM9GsWYtyi1fwv/T9+/dj9erViIqKgpubGyZOnIgBAwYUePydO3ewePFiXLp0Cebm5ujYsSM++eQTVKlSRXdMeno6Vq5ciSNHjiAhIQEeHh4YPnw4hg8fzhm+iei15beEWeu6NrjxJBNHrqTh+qMMiMVAy1q5TbU1q71+U21JWZmLEdjMDt39bPHPo0wcvpKKXX8nY29YMvxr26B7U1vUqGZR4H0ZSv8oqlyGBDjo9dkDckeiDwlwEDCq/1hbW0MkEmH48FHo0qV7nv1WVlYAALFYjDffHIo33xyK5OQk/P33WWzeHIzPP5+JffsOw9S0fNIwQZO90NBQzJgxA2PGjEH79u1x9OhRfPbZZ7CwsEDPnj3zHB8ZGYmRI0fC2dkZ3377Lezs7LBx40YMHz4cu3fvhq2tLQDg448/xvXr1zFlyhT4+vri3LlzmDNnDmQyGSZOnFjet0lERiS/juPrDsVj68lEpGaoYWtlgv6t7dG5sS0cbAT/Pa0jFonQxNcKTXytEJuU28R7+pYM526no0ZVc3i7SnD6VrpBd4inyqNtPSnEJiKDHY1rZWWNWrXqICoqEnXr1tdtl8vT8eWXs9C9e094enrjww/Ho3btupg2bQYcHBzRu3c/pKenY9myJcjMzIRUKtX19ytLgn4TLV26FL169cLs2bMBAO3bt0dqaip+/PHHfJO9zZs3Q6VSYcOGDahePbcat3Xr1ujZsyfWr1+P6dOn4/bt2zh16hT+97//oVevXgCANm3aIC0tDevWrWOyR0SvJeRMcp4lzHLUgDxLjYk9XeBf2wZmpobdglDNUYLRXZwxuJ0jTt+S4ejVNBy7nrfTu3ZpNkN5wFLlElDfFq1r2wgdRoHee+8DfPbZx5g372t07doDSqUCW7ZsQkTEA3z00TQAuQMytmzZCEdHRzRs2BgJCfHYtm0Lmjf3103PYmMjxY0b13H16mU0aeJXJq0Agq2NGxUVhcjISPTo0UNve2BgICIiIhAVFZXnNY8ePULt2rV1iR4AmJubo1GjRjh58iSA3FFvb731Ftq0aaP3Wl9fX8hkMiQnc01JIiq5/PoRAYBKDbSrLzX4RO9FluZi9PCzw8Kx7gUeU9D9ElV2bdq0w5Ily/D0aRQ+//xTzJ//LaytbbB8+U/w9a0JAHj33Ql4++1xOHBgL2bMmILly39Aq1ZtMWfOAt15xo2bgNu3b2HGjCmIj39eJrEKVrMXEREBAPDx8dHb7uXlBSA3sfPw8NDbV61aNdy/fx8qlUqvnfvp06e65LB+/fr49ttv81zv6NGjcHFxgb29fWneBhFVMlJLMWSZedcw+28i5IpHLBLBSWpaYId4IkK+a+C2bNkaLVu2LvA1JiYmePfdCXj33QkFHjNgwGAMGDC4VGIsiGA1ezJZbpOBjY1+Fa12nbn09PQ8rxkwYACeP3+Ozz//HDExMUhMTMQPP/yA+/fvIzMzs8Brbdq0CWFhYXjvvfc4QIOISkSj0eDg5VTIMtV4+VvEkDqOl9SQAAdI8qmVbFHLSoBoiKg0CfaTTaPJ7fPycvKl3Z5fh8UWLVpg/vz5WLBgAXbv3g2RSITu3btj+PDhCAkJyfc6W7ZswYIFC9CrVy+MGTOm2HE6ORlufwFD5OLCvj2GhmXy+nJyNFizPxr7zyeiXQM7tKwjxW/H4xCfooSLvRne7lEVXfwci3VOQyuX/i5SSKUW2HT4GeJTlHCyNYWpqQiHr6TBo6o1BgW4GP2PZUMrk4rk+XMxTE1Lv/6oLM5ZUYnF4hJ/RgVL9rQdE1+uwZPL5Xr7XzZo0CD0798fkZGRkEqlcHZ2RlBQUJ7mWbVajcWLFyM4OBh9+/bFokWLSvRFlZiYDrVaU/iBBBcXKeLjSzZbOpUNlsnry8xWY+WBOFx/nIneLewwtL0jxCIRmr2r382kOO+zoZZLI3czfP/CfSmUavx0MB7r/4zFw6fpGN3Z2WgnZDbUMqko1Go1VKq83Rteh6mpuNTPWZGp1epXfkbFYlGBFVSCpczavnqRkZF62588eaK3/0UPHz7Enj17YGJiAh8fHzg7OwMAbt26hfr1/xv6rFQqMW3aNAQHB+Pdd9/F999/X25z2RCR8UhIU2Hu9hjceJKJd7o5Y1gHJ4iNvHbrRRIzMSb1dUXflvb467oMS3c/Q0Y2H75EFY1gyZ6Xlxfc3d1x8OBBve2HDx+Gt7e33ohbrbt372LmzJl6I3UvXLiAO3fuoFu3brpts2fPxuHDhxEUFITPPvvM6JseiKj0RTzLwje/RSNRpsKMQVXRubGt0CEJQiwSYWh7R4zr4YzbUZmYsy0a8alKocMiomIQtLpr0qRJCAoKgp2dHTp16oTjx48jNDQUP/zwAwAgKSkJkZGRqFmzJmxsbNCpUye4u7tj+vTpmDx5MpKSkrBgwQI0adIE/frlLjJ84sQJ7N27F126dEHTpk1x9epVvWvWr18fEomkvG+ViCqQ8Pty/BT6HLZWJpg1pBrcnPid0bGhLVxszbBsXxy+2RqDj/tX0a28QQTk9rln5UrZ0I5nKCmR5nXP8Jq2bduG4OBgxMbGwsPDAxMmTNAtl7Zr1y4EBQVh8+bNaNWqFYDcKVnmzZuHK1euwNLSEt27d8fHH3+sWz0jKCgIu3btKvB6J0+eRNWqVYscH/vsFR37vBgelknxaDQa/HkxFTtOJ8G3mjmmvVGlTNaPrcjlEpOkwNI/niE5PQcTe+VOIm0MKnKZGILnz5/CyakqTExK7++Fffb+k5OjQmLiM7i6Fjwn5qv67Ame7Bk6JntFxy9Lw8MyKTpVjgabjyfgxD8y+Ne2xoRAF0jMyqanS0Uvl7SMHPy49xnux2RjSIAj+ra0q/A1OhW9TISWlPQcFhaWsLIqvRHNTPb+I5fLoFBkwsHBtcBjDHKABhGRoZBn5WDJH89w4h8Z+vnb48M+rmWW6BkDWysTfDa4GlrXsUbImST8fDgBqhz+KK7MpFJ7pKenQqHIeu0mR/qPRqOBQpEFuTwVNjb2JT4Ph6gSUaUWn6rE0t3P8CxZifcCXdC+AedaKwqJqRgf9HZFVYdk7D6fgvg0Jab0qwJrCxOhQyMBmJlJIJU6IC0tCSpV6QzgEYvFUKtZs2dqagap1AFmZiXvO8xkj4gqrfsxWfjfnmfIUQMz36yGeh6WQodUoYhEIgxq64gq9mZYfzge326NwfSBVVHF3kzo0EgAlpbWsLS0LrXzsWm99LCdgogqpQt307EwJBaWEjG+Gl6did5raFdfis8GV0NaZg6+3RqNe9FZQodERC9gskdElYpGo8HeC8lYeeA5vKtI8NUIN1Rz5NQqr6uuuyW+Gu4GK3MTLNwZg7/v5F3fnIiEwWSPiCoNVY4G6w/HY+fZZLSpa4NZg6tDask+ZqWlqoMZ/m94ddSoaoHVfz7H7vPJ7KxPZACY7BFRpZCemYPFv8fi9M10DGhtj/d7ucDMtGJPF2KIpJYmmPlmNbSrZ4Nd55Kx9mA8lComfERC4gANIjJ6cclKLNn9DAlpSrzfywVt63HEbVkyMxVhQk8XVHUww+/nkpGQpsKUN6qwFpVIIKzZIyKjdjc6C99sjUZ6Zg4+e7MaE71yIhKJ0L+1Az7o7YqIZ9n4dms0YpMVQodFVCkx2SMio3XutgyLdsbAxtIEXw13Qx13jrgtb23q2mDWkGrIyFbj260xuBOVKXRIRJUOm3GJqEyduy1DyJlkJMpUcJKaYkiAQ5nVrr14LUuJGJkKNeq5W2ByvyqwYROiYGpVt8D/DXfD0t3PsOj3WHRoKMX1R5nl8pkgItbsEVEZOndbhuAjCUiUqQAAiTIVgo8k4Nzt0p8o9eVrZSrUEIuAgAY2TPQMQBV7M/zfsOqoYm+Kv67LyuUzQUS5WLNHRGUm5EwyFC+NxFSoNNh4NAEPY7NL9Vqnb8ryXEutAXadS0H7Bralei0qGWsLE2Qr847MVag0CDmTzNo9ojLCZI+Iyoy29uZlWUoNzt0u3Ul3s/JJIl4VAwkjUZZTwHaWE1FZYbJHRGXGSWqa70PcSWqKH97zLNVrfbwussBrkeF41WeCiMoG++wRUZkZEuAA0UvzFktMRRgS4FAm15K8NElyWV2LSi6/chKLwHIiKkP8KUVEZaaBpyU0GsBSIkKmQlOmIy+15yyvkb9UMi+Xk7mZCAqlBt5VzAWOjMh4MdkjojJz8UEGAODLYW5wd5aU+fXa1pMyuasAXiyntIwczNwQhS1/JeLTQVUherkqmIheG5txiajMhN1NR3VHM7g5mQkdChkoWysTDGjjgBtPMnElIkPocIiMEpM9IioTKXIV7kRnwb+2NWtr6JW6NbFFdUcz/HYiEQqVWuhwiIwOkz0iKhMX78uh0QD+tW2EDoUMnKmJCKM6O+F5qgqHLqcKHQ6R0WGyR0RlIvyeHNUdzcqlrx5VfA29rNC8phX2nE9BEufcIypVTPaIqNSlyFW48zS3CZeoqEZ0dIJGA2w/nSR0KERGhckeEZW6i/fl0IBNuFQ8LnZm6N3CDn/fScfd6CyhwyEyGkz2iKjUhd2Tw82JTbhUfH397eFoY4JfjidArc5/CTwiKh4me0RUqlLSVbj7NIu1elQi5mZiDOvohMh4BU7ckAkdDpFRYLJHRKUqXNeEy/56VDKtalujjpsFdp5JgjwrR+hwiCo8JntEVKq0TbhuTmzCpZIRiUQY3cUJ8mw1dp1LFjocogqPyR4RlZqUdBXuRWehFZtw6TV5upijS2NbHLuWhqh4hdDhEFVoTPaIqNRom3BbsgmXSsGgtg6wNBdjy4kEaDQcrEFUUkz2iKjUhN2Tw51NuFRKpJYmeLOtA25HZSH8vlzocIgqLCZ7RFQqkv9twvWvwyZcKj2dG9vCw1mCrSeTkK3kurlEJcFkj4hKBUfhUlkwEYswurMTEmUq/HmR6+YSlQSTPSIqFWH35PBwlqC6I5twqXTV9bBEqzrW2B+WgoQ0pdDhEFU4TPaI6LUlyf5twmWtHpWRYR2cIBIBW09y3Vyi4mKyR0Sv7eK/neeZ7FFZcZKaop+/PcLvy3ErMlPocIgqFCZ7RPTaLtxLh4ezBNXYhEtlqFdzOzjbmuKXvxKQw3VziYqMyR4RvZYkmQr3Y7JZq0dlTmImxoiOTohOVOLYtTShwyGqMJjsEdFrCWcTLpWj5jWt0MDTErvOJSMtg+vmEhUFkz0iei1h99Lh6cImXCofIpEIozo7IUuhxu/nOFiDqCiY7BFRibEJl4Tg5iRBdz9bnLguw+Pn2UKHQ2TwmOwRUYmF/duEy7VwqbwNaO0AG0sxfjnOdXOJCsNkj4hKLOzuv024DmzCpfJlbWGCIQGOuB+Tjb/vcN1coldhskdEJZIoU+FBLJtwSTgdGkjhXUWC7acSkaXgurlEBWGyR0QlEn4vHQDgX9tG4EioshKLRRjd2RnJ8hzsC0sROhwig8Vkj4hKJOyeHJ4uElR1MBM6FKrEalW3QLt6Ngi9lIK4FK6bS5QfJntEVGwJablNuK3qsAmXhDe0vSNMxSL8djJR6FCIDBKTPSIqtvD7/zbh1mITLgnPwcYU/Vs74MrDDFx/lCF0OEQGh8keERVb2D05vFwlqMImXDIQPfzsUMXeFL+eSIQqh1OxEL2IyR4RFUtCmhIPY7M5MIMMipmpCCM7OSM2WYkjV1KFDofIoDDZI6JiCb/HtXDJMDX1tUITH0v8cT4ZKXKV0OEQGQwme0RULGH35PB2laCKPZtwyfCM6OQEpUqDkDNcN5dIi8keERVZQpoSD5+xCZcMVzUHCXo2s8Ppm+l4GJsldDhEBoHJHhEVGZtwqSJ4o7UD7KxN8MtfiVBz3VwiJntEVHQX7snhXUUCVzbhkgGzlIgxrL0jIp5l4+ytdKHDIRIckz0iKpL4VCUinmWjFZtwqQJoU88GNaqZY8fpJGRkc91cqtxMhQ6AiCqG8PtswqWKQywSYXRnJ3z9WwymrXuCLIUGTlJTDAlwQNt6UqHDIypXrNkjoiIJuyuHTxVzuNixCZcqhmfJSohFQJYit99eokyF4CMJOHdbJnBkROWLyR4RFSo+VYmIuGzW6lGFEnImGeqXxmcoVBqEnEkWJiAigQie7O3fvx99+vRB48aN0atXL+zevfuVx9+5cwfjxo1D06ZN0apVK8ycORNxcXF6x6hUKvzvf/9Dx44d0aRJE4wYMQLXr18vw7sgMm5hHIVLFVCiLP+JlQvaTmSsBE32QkNDMWPGDLRr1w4rV66Ev78/PvvsMxw8eDDf4yMjIzFy5Eg8ffoU3377Lb777jvEx8dj+PDhSEtL0x03b948bNy4Ee+99x5++OEHmJiYYOzYsYiKiiqvWyMyKmH35PBlEy5VME7S/LulF7SdyFgJmuwtXboUvXr1wuzZs9G+fXt888036NWrF3788cd8j9+8eTNUKhU2bNiAN954Ax07dsSaNWug0Wiwfv16AMDTp0+xfft2fPbZZxg1ahS6dOmCn3/+GXZ2drpjiKjo4lOVeBSXjZas1aMKZkiAAySmIr1tZiYiDAlwECgiImEIluxFRUUhMjISPXr00NseGBiIiIiIfGvhHj16hNq1a6N69eq6bebm5mjUqBFOnjwJADh//jxycnIQGBioO0YikaBTp044depUGd0NkfFiEy5VVG3rSfFud2e9mrxa1c05GpcqHcHqsiMiIgAAPj4+etu9vLwA5CZ2Hh4eevuqVauG+/fvQ6VSwdT0v9CfPn2qSw4jIiJgZ2cHR0fHPOeNiYlBVlYWLCwsSv1+iIzVhXvpbMKlCqttPakuufv5cDzO3U5HqlwFO2s25VLlIdinXSbLHfpuY6M/Qau1dW7tQXp63lnPBwwYgJ07d+Lzzz/H1KlTYW5ujs2bN+sSQO3rXj7ni+eVy+XFSvacnDiBbHG4uPAXs6F5nTKJTcrG4zgFxvWqxrItZXw/y9+oHhKcunkHZ+5mYWxgtTz7WSaGh2VSOgRL9jT/rlcoEony3S4W521hbtGiBebPn48FCxZg9+7dEIlE6N69O4YPH46QkBC91xf1eoVJTEyH+uWx+5QvFxcp4uM5f5Uhed0yORiWAgBo4GbKsi1F/FsRhjmAFjWtse/vBHRpYAVL8/+eMywTw8MyKR6xWFRgBZVgffak0txs/eUaPLlcrrf/ZYMGDcL58+dx8OBBnDlzBsuXL4dMJoO9vT2A3JpC7TnyO29+tX5ElL+w++nwrWoOZ1s24ZJx6NPSDhnZavz1T1rhBxMZCcGSPW1fvcjISL3tT5480dv/oocPH2LPnj0wMTGBj48PnJ2dAQC3bt1C/fr1AQC+vr5ISUlBampqnvO6u7tDIpGU+r0QGaO4FCUexynQigMzyIj4VrVAfU9LHLyUCqWKrTZUOQiW7Hl5ecHd3T3PnHqHDx+Gt7e33ohbrbt372LmzJl6I3UvXLiAO3fuoFu3bgCAtm3bAgAOHTqkO0ahUODkyZO6fURUOO0oXE65Qsamb0s7pMhzcJbLplElIehwpEmTJiEoKAh2dnbo1KkTjh8/jtDQUPzwww8AgKSkJERGRqJmzZqwsbFBp06d4O7ujunTp2Py5MlISkrCggUL0KRJE/Tr1w8A4ObmhoEDB2Lu3LnIyMiAl5cXNmzYgNTUVIwfP17I2yWqUMLvpaMGm3DJCDXwtIR3FQkOhKeiQwMpxOLi9eUmqmgETfYGDRoEhUKB4OBghISEwMPDA4sWLULv3r0BACdOnEBQUBA2b96MVq1awcrKCuvXr8e8efPw8ccfw9LSEr1798bHH3+sNxXLt99+C1tbW6xduxYZGRlo0KABNmzYoJvWhYheLS5ZicfPFRje0bHwg4kqGJFIhL4t7bFi/3NcfCCHf2325SbjJtIUNHyVAHA0bnFw5JThKWmZ7AtLRsiZZCwd7wlnW85HVtr4tyI8tVqDzzY+haW5CN+McIOrqy3LxMDw76R4DHI0LhEZrrB7ctSoZs5Ej4yWWCxC7xZ2eBynwM3ITKHDISpTTPaISE9cshJPnnMULhm/gPpS2Fub4EB4auEHE1VgTPaISE/Y/dy5L1uyHxMZOTNTEQKb2eFmZCbuPc0QOhyiMsNkj4j0XLgrR81q5nqLxxMZqy6NbWFlLkbIyedCh0JUZpjsEZHOs2QlIuMV8K/DWj2qHCzNxejaxBZnb6YiNlkhdDhEZYLJHhHphN3LbcL1r8X+elR59PCzhZmJCH+y7x4ZKSZ7RKQTdk+OWtXN4cgmXKpE7KxN0aOFI87ckiFJphI6HKJSx2SPiAAAsckKRMYrODCDKqVB7V2g1gCHLrN2j4wPkz0iAgCE/7sWLptwqTKq5miOVrWt8df1NMizcoQOh6hUMdkjIgC5o3DZhEuVWV9/e2QpNTh2LU3oUIhKFZM9IkJskgJRCQquEUqVmqeLORp7W+LQ5VQolGqhwyEqNUz2iAhh/zbhtmQTLlVyff3tIctU49RNrslKxoPJHhFxFC7Rv+q4WaBmNXP8eTEVOWqN0OEQlQome0SVXMy/Tbit2IRLBJFIhL7+9khIU+HCXbnQ4RCVCiZ7RJVc2D05RABasAmXCADQ1NcKbk5mOBCeAo2GtXtU8THZI6rkwu6mo5abBZtwif4lFonQu4U9ohIUuP4oU+hwiF4bkz2iSiw6UYGniUr412atHtGL2tS1gaPUBPvDU4QOhei1MdkjqsTC/23C5ShcIn2mJiL0am6Pu9FZuB+TJXQ4RK+FyR5RJXTutgwfr4vErr+TYWIC3I5iUxXRyzo1ksLaQszaParwmOwRVTLnbssQfCQBif8u+K7KAYKPJODcbc4rRvQiczMxevjZ4crDDDxNUAgdDlGJMdkjqmRCziRDodIfYahQaRByJlmgiIgMV7emtpCYinDgYorQoRCVGJM9okpGW6NX1O1ElZnU0gSdG0tx/k46EtKUQodDVCJM9ogqGacCplgpaDtRZdezuT0A4OClVGEDISohJntElcyQAAeYvPSXLzEVYUiAgzABERk4J6kp2tS1wYl/ZJBl5ggdDlGxMdkjqmTa1pNCaimGqUnuv52kpni3uzPa1pMKGxiRAevT0h4KlQZHrrB2jyoettsQVTJPExRIkasxposTujW1EzocogrBzUmCZjWscORqGnq3sIeFhHUlVHHw00pUyYTdS+dauEQl0LelPeRZapy4wWmKqGJhskdUiWg0GoTdk6OuuwXsrVmxT1QcNatboK67BQ5eTIEqR1P4C4gMBJM9okokOlGJmCQlWnItXKIS6dvSHknpOfj7TrrQoRAVGZM9okok7F46RCI24RKVVCNvS3i6SLA/PAVqDWv3qGJgskdUSWg0GlxgEy7RaxGJROjT0h6xSUpceZghdDhERcJkj6iSiE5UIjZJCf/aNkKHQlSh+de2houdKfaHpUDD2j2qAJjsEVUSF+7+24Rb00roUIgqNBOxCL1b2OPhs2zcfZoldDhEhWKyR1QJvDgK145NuESvrX19G9hamWB/eIrQoRAViskeUSXwNEGJ2GQlWrEJl6hUSMzECGxmi+uPM/HkebbQ4RC9EpM9okrgAkfhEpW6Lo1tYSER4QBr98jAMdkjMnIajQbh9+So524BWysTocMhMhrWFibo2tgWF+7JEZeiFDocogIx2SMyclEJCsQmK+Ffh024RKUtsJkdTMRA6MUUoUMhKhCTPSIjF3ZP/u8oXDbhEpU2extTBNSX4vTNdKTIVUKHQ5QvJntERkw7CreehyWbcInKSO8W9lDlaHD4cprQoRDli8kekRF79CwLz5KVaMW1cInKTFUHM7SsbY1j11KRka0WOhyiPDjhFpERO/1PCsQioDmbcInKVJ+W9gi7J8f09ZHIyFbDSWqKIQEOaFtPKnRoRKzZIzJWGo0Gp6+nsAmXqBzEJikgEkFXs5coUyH4SALO3ZYJHBlRCZK9mJgYfP/990hNTdVtW7t2LRYuXIjExMRSDY6ISi4yXoHoRAX82YRLVOZCziTj5WVyFSoNQs4kCxMQ0QuKlezdu3cPAwcOxIYNGxAbG6vbnpaWht9++w0DBgxAVFRUqQdJRMUXdk8OsZhNuETlIVGW/0jcgrYTladiJXtLliyBtbU1Dhw4gLp16+q2z5gxAwcOHICZmRm+//77Ug+SiIondxRuOpr42rAJl6gcOEnz7wJf0Hai8lSsZO/q1at4++234e3tnWefh4cHRo0ahfDw8NKKjYhK6Em8AnEpKrRvZC90KESVwpAAB0hMRXrbJKYiDAlwECgiov8UK9nTaDTIzi54wWeNRoOsrKzXDoqIXk/4PTnEIqBtAzuhQyGqFNrWk+Ld7s56NXndm0o5GpcMQrGSvSZNmmD79u1IS8s7caRcLkdISAiaNGlSasERUfFpNBpcuJuO+p6WsLNmExJReWlbT4of3vPEz1N84GBtgkdxCqFDIgJQzHn2PvroI4waNQp9+/ZFv3794OXlBZFIhMjISBw4cADx8fFYsGBBWcVKREXwJF6B56kq9PW3FzoUokrJzFSEns3tsPVUEh7GZqFGNQuhQ6JKrljJXpMmTbBhwwYsWrQIwcHB0Lwwzrxu3bpYsGAB/Pz8Sj1IIiq6sLtyTqRMJLBOjW2x50IKDoSnYMobVYUOhyq5YrfxtGjRAiEhIUhKSkJ0dDTUajWqVasGV1fXsoiPiIpBOwq3vqclpJYchUskFEuJGN2a2mLfhRTEJClQ3VEidEhUiZV4BQ1HR0c0atQITZo0YaJHZCCePM9twuVauETC6+FnB1MTEf4MTy38YKIyVKyavTFjxhTpuM2bN5coGCJ6PRfupcNEDDRjEy6R4GytTNCxoRR//ZOGQW0d4Mg590ggxfrkPX36NM82tVqN5ORkZGdnw83NDbVq1Sq14Iio6DQaDcLvyVHfg024RIaiVws7HL+ehoOXUzGio5PQ4VAlVaxk7/jx4/luz8nJwbFjx/DFF19g3LhxpRIYERXP43+bcPu1shc6FCL6l4udGVrXscGJ62l4w98eNvwhRgIocZ+9F5mYmKBHjx4YMmQIl0sjEkjY3dwm3OY12IRLZEj6tLRHllKDY9fyzlFLVB5KJdnT8vb2xp07d0rzlERUBBqNBmH35WjgacmaAyID4+EiQRMfKxy+kopspVrocKgSKrVkT6FQYO/evXByKl6fhP3796NPnz5o3LgxevXqhd27d7/y+KSkJAQFBSEgIAD+/v6YOHEiHj9+rHeMXC7H/Pnz0blzZzRr1gyjR4/G9evXi3lHRBXHozgF4lNV8K9tI3QoRJSPvv72kGWqceqGTOhQqBIqldG4CoUCjx49QlpaGiZPnlzk84WGhmLGjBkYM2YM2rdvj6NHj+Kzzz6DhYUFevbsmed4jUaDSZMmITIyEp9++ins7e2xbNkyjBkzBvv27YOdXe46oP/3f/+H48ePY8aMGfDy8sKGDRvw9ttvY+/evfDw8CjOLRNVCGHaUbg1rIQOhYjyUcfNArWqmyP0Uio6N7aFqYlI6JCoEnnt0bhAbp89X19f9O3bFyNGjCjy+ZYuXYpevXph9uzZAID27dsjNTUVP/74Y77J3uPHj3H58mUsWrQIAwYMAADUqFED3bp1w/HjxzFw4EBkZWUhNDQUH374IUaOHAkA8PPzQ9u2bbFnzx589NFHxbllIoOXO5Eym3CJDF3flvb4YU8cLtxNR7v6UqHDoUqkVEbjvigpKQmOjo6FHhcVFYXIyEhMnz5db3tgYCBCQ0MRFRWVpxYuOzsbAGBt/V8HdG1tXkpKCgBAqVRCrVbDxua/5iwrKyuYm5vrjiEyJo/ispGQpsKA1g5Ch0JEr9DE1wpuTmbYH56CNvVsIBaxdo/KR7FneNy9ezcOHz6MjIwMqNX/dTTNycmBXC7HgwcPcOPGjULPExERAQDw8fHR2+7l5QUAePToUZ5kr27dumjVqhVWrlwJX19fODg4YOHChbCyskK3bt0AAFKpFAMHDsSmTZvQrFkzeHl5Yd26dZDL5ejdu3dxb5fI4IXdk+eOwq3JJlwiQyYWidC3pT1+OhiPa48y4OfLkfNUPoqV7K1btw5LliyBRCKBjY0NkpOTUbVqVaSkpCAzMxMWFhYYPXp0kc4lk+V2Un2xBg74r9YuPT0939d9/fXXGD9+vC5xk0gkWLlypV5i+PHHH2PChAkYMmQIAEAkEmHu3Llo1qxZcW6XyOBpm3AbelnC2oJNuESGrlUdG+w8m4wD4alM9qjcFCvZ27VrF+rVq4dffvkFycnJ6N69OzZv3ozq1atj+/btmDNnDpo0aVKkc2k0GgC5iVh+28XivAOFHz58iGHDhsHT0xOzZ8+GhYUFduzYgSlTpmD9+vVo0aIFEhMTMXToUEgkEixZsgROTk44dOgQvvrqK1hZWRW7ds/JiaMbi8PFhf1QytPdqAwkpKkwpke1At97lolhYrkYnvIqkyGdXLFmXwzi5CI09OYz5lX4d1I6ipXsRUdHY/r06bCxsYGNjQ3s7Oxw8eJFDBw4ECNGjMClS5ewadOmfAdXvEwqzS3Al2vw5HK53v4Xbdy4EQAQHBys66vXrl07jBgxAvPnz8euXbsQEhKCZ8+e4ciRI7ravjZt2kAmk2HOnDno2bNnvolkQRIT06FWa4p8fGXm4iJFfDynFShPhy4kwkQM1HIV5/ves0wME8vF8JRnmTT3lkBqKcavR2IxfUDVcrlmRcS/k+IRi0UFVlAVa549U1NTvcERXl5euHv3ru7frVq1yjPnXUG0ffUiIyP1tj958kRv/4tiYmJQo0YNXaIH5NYMNm/eHA8ePNAd4+Likqe/X4sWLZCUlISkpKQixUdk6DQaDcLvswmXqKIxNxOje1M7XI3IQFS8QuhwqBIoVrJXo0YNXLlyRfdvHx8fvcEYaWlpUCiK9sH18vKCu7s7Dh48qLf98OHD8Pb2RvXq1fO8xsfHB/fv30dqaqre9mvXrsHNzU13TEJCQp6k8+rVq7raSCJjEPEsdxQuJ1Imqni6NrWFuZkIBy6mCB0KVQLFSvYGDRqEXbt2YcaMGcjIyECXLl1w8eJFrFixAn/++Sc2btyIunXrFvl8kyZNwv79+/Htt9/i1KlT+PrrrxEaGoqpU6cCyJ3G5erVq7qm3rFjx8LExATjxo3D4cOHcerUKXzyyScICwvTTeY8ePBgVKtWDRMmTMC+fftw7tw5zJ07F7t378b7778PMzOz4twykcHSjsLlRMpEFY/U0gSdGtni/J10xKcqhQ6HjFyx+uwNHz4cz549w6+//gpTU1P06NEDffr0wYoVKwDkjqydMWNGkc83aNAgKBQKBAcHIyQkBB4eHli0aJFuEMWJEycQFBSEzZs3o1WrVnB3d8fWrVuxePFizJo1C2KxGLVr18aGDRvQtm1bALl9/bTHzJ07FwqFAr6+vli6dCn69OlTnNslMlj/jcK1YhMuUQXVq7kdjl5NReilVIzp4ix0OGTERBrt8NdiUKlUMDX9L0+8ePEiUlJS4OfnV+y1cQ0dB2gUHTvTlp+HsVn4ZmsMJvR0QcArZuJnmRgmlovhEapM1h16jgt35Vg63hO2Vvzh9iL+nRRPqQ3Q0Hox0QNyBz9069bN6BI9IkN14d8mXD9fNuESVWS9W9hDqdLgyJXUwg8mKqESJXtEJBy1RoOwe+lo5M0mXKKKzs1JgmY1rXD0ahoyFerCX0BUAkz2iCqYiNhsJMly0Ko2Z98nMgZ9W9pDnq3GiX/ShA6FjBSTPaIK5sI9OUxNAL8aTPaIjEGNahao526BgxdToVSxjziVPiZ7RBWIWqNB+P10NPKygpU5/3yJjEUff3sky3Nw7g4HJFDp49OCqAJ5+G8Trj+bcImMSiMvS3i6SPBneCpngKBSx2SPqAIJu5fOJlwiIyQSidDX3x6xyUpcepghdDhkZJjsEVUQao0G4ffkaOTNJlwiY9SyljVc7UxxIDwFJZgCl6hAfGIQVRAPY7ORlJ4D/1qs1SMyRiZiEXq3tEfEs2zcjsoSOhwyIkz2iCqIsHvpMDMRoRmbcImMVkB9G9hZmWB/eIrQoZARYbJHVAGo/10Lt5G3JSzZhEtktCSmYvRoZocbTzLxOC5b6HDISPCpQVQBPIjJRnI6R+ESVQZdm9jCUiJi7R6VGiZ7RBWAtgnXz5fJHpGxszIXo0sTW4Tfl+NZslLocMgIMNkjMnD/jcJlEy5RZRHoZwdTsQihF1OEDoWMAJ8cRAbuQUw2kuU58K9tI3QoRFRO7G1MEdDABqdvyZCSrhI6HKrgmOwRGThdE24NK6FDIaJy1Lu5PXLUwKErqUKHQhUckz0iA6Ztwm3sbQlLCf9ciSqTKg5m8K9tjePX0pCRrRY6HKrA+PQgMmD3tU24ddiES1QZ9Wlpj0yFBseupQkdClVgTPaIDFjY3dwm3Ka+bMIlqoy8Xc3R0MsShy+nQqFk7R6VDJM9IgOl1mgQfl+Oxj5swiWqzPq2tEdqRg7O3EoXOhSqoPgEITJQ96OzkCLPQSuOwiWq1Op5WMC3qjn+vJiCHLVG6HCoAmKyR2SgLtyTswmXiCASidC3pT2ep6oQfk8udDhUATHZIzJAarUGF+/L0cTHEhZswiWq9JrVtEI1BzPsD0+BRsPaPSoePkWIDNC9mNwmXE6kTEQAIBaJ0KelPSLjFfjnSabQ4VAFw2SPyACF3WUTLhHpa1vPBg42JjgQliJ0KFTBMNkjMjBqtQbhD+Ro6mvFJlwi0jE1EaFnczvcfpqFBzFZQodDFQifJEQG5l50FlLlOWhZ21roUIjIwHRqZAtrczEOhKcIHQpVIKZCB0BEuc7dliHkTDISZbmLnmcrOIEqEemzlIhRx90clx5mYMzSCDhJTTEkwAFt60mFDo0MGJM9IgNw7rYMwUcSoFD9N8rul78SYWYq4pc4Eemcuy3DP0/+a8JNlKkQfCQBAPhdQQViMy6RAQg5k6yX6AGAQqVByJlkgSIiIkMUciYZSn5XUDEx2SMyANqm26JuJ6LK6VXfFXefZnIOPsoXkz0iA+Akzb9HRUHbiahyKug7QQRg3o5YfLklGif/SYNCyT6/9B8me0QGYEiAA8Qi/W0SUxGGBDgIExARGaQhAQ6QmOp/WUhMRXi3hzPe6eYMtVqDn48kYOq6SGw/nYiENLYOEAdoEBmEmtUsoNYAFhIRshQajrAjonxpvxO0I/df/q7o1EiKO0+zcPhKKv68mPtf8xpW6O5nh7ruFhCJRK86PRkpJntEBiD0UipMTYBFYz3gYMM/SyIqWNt60gJ/CIpEItTzsEQ9D0skpClx7GoaTtyQ4eKDDHg4S9CtqS3a1rOBuRkb9ioTPlWIBJYqV+HUDRna1ZMy0SOiUuNsa4a3OjhhQBsHnL+TjsNX0rDhaAJ2nE5Cx0ZSdG1iCxc7M6HDpHLAJwuRwA5fSYMqR4PeLe2EDoWIjJC5mRgdG9miQ0Mp7kZn4ciVNBy8lIrQS6nw87VCDz871PNgE68xY7JHJKDMbDWOXUtDi1rWqOYgETocIjJiIpEIdd0tUdfdEokyFY5fS8Nf19Nw+WEG3JzM0N3PDu3YxGuUmOwRCeivf9KQka1Gn5b2QodCRJVI7sAOR/RvbY8Ld+U4fCUVG/9t4u3QUIpuTWzham+mt4wjB45VXEz2iASiVGlw8FIq6ntawrequdDhEFElJDEVo30DKQLq2+B+TDYOX0nF4cupOHQpFZ4uZohOUkKVk3ssl2aruJjsEQnk7G0ZUuQ5mNCTffWISFgikQi13SxQ280CSTIVjl9Pw76wFLy8IId2aTYmexULG+aJBKBWa3AgPBXeVSRo4GkpdDhERDqOUlMMbueYJ9HT4jKOFQ+TPSIBXHwgR1yKEn1b2nMEHBEZJC7jaDyY7BGVM41Gg/3hKahib4YWNa2FDoeIKF/5Lc0mAjCwrb0g8VDJMdkjKmc3IzPxOE6BPi3tIH55QVwiIgPRtp4U73Z31tXkSS3F0AC4H50tbGBUbKyLJSpnB8JTYW9tgnbs4ExEBu7lpdl2nk3C3gspqFHNHJ0a2QoYGRUHa/aIylHEs2zcjMxEYDM7mJmyVo+IKpZBbRzQ0MsSvxxPRMQz1vBVFEz2iMrRgfAUWJmL0aUxfxETUcUjFovwQW9X2FqZYPm+OMgyc4QOiYqAyR5ROYlNVuDifTm6NrGFpTn/9IioYpJammByvypIzVBh9Z/PoVYXMEcLGQw+cYjKyZ/hqTA1EaGHH2v1iKhi861qjjFdnHHjSSb++DtZ6HCoEBygQVQOkmQqnLklQ6dGtrCz5p8dEVV8nRrZ4kFMNvZcSIFvNXP4+VacqaQq25q/rNkjKgeHLqdCowF6t+DSaERkPMZ0cYK3qwQ/hcYjLlkpdDhFcu62DMFHEnQrgWjX/D13WyZwZGWHyR5RGZNn5eCv62nwr20NFzszocMhIio1EjMxJverApEIWLYvDtlKtdAhFSrkTBIUKv1+hto1f40Vkz2iMnbsWhqylBr09bcXOhQiolLnYmeGD3q54mmCAhuPJkBT0KK6BkCVo0GiLP8RxMa85i+TPaIypFCqcehyKhp7W8LTxVzocIiIykRjHysMbOOAs7fTcfy6YTaHpmfm4LvfYwvcb8xr/jLZIypDp27KIMtUs1aPiIzeG63t0cTHElv+SsCDmCyhw9ETl6zEt9ti8CA2C10bS/Ou+SsC3mxnL0xw5YDJHlEZyVFr8OfFVNSsZo46bhZCh0NEVKbEIhHe7+UKJ6kplu+PQ1qGYUy4fPdpJr7eGo30zBx8Nrg63u7morfmr5W5GBoNkJxuGPGWBeOtsyQS2IW7ciSkqTCqsxNEIi6NRkTGz9oid8Llb7fGYOWBOMx8sxpMxMJ9/529JcPPR+LhbGuGTwZURRWH3EFyL675q9FosOrP59h5Nhk+VczR0MtKsHjLCmv2iMqARqPBgfAUuDmZoamv8X1xEBEVxMvVHO90d8btqCzsPJskSAwajQa7ziXhp4PxqFXNAv83rLou0XuZSCTCuO4uqO5ohtV/PkdCmvEN1BA82du/fz/69OmDxo0bo1evXti9e/crj09KSkJQUBACAgLg7++PiRMn4vHjx3mO27ZtG3r16oVGjRohMDAQmzdvLpsbIMrH9ceZiEpQoHcLe4hZq0dElUxAfSm6NJbiQHgqwu/Ly/XaCpUaa0Ljsft8Cto3sMGnb1aDjaXJK19jIRFjSr8qUOZosGJfHJQqwx1RXBKCJnuhoaGYMWMG2rVrh5UrV8Lf3x+fffYZDh48mO/xGo0GkyZNwqlTpzBjxgx89913iI+Px5gxY5Camqo7bsOGDfjmm28QGBiItWvXIjAwEPPmzcPWrVvL69aoktsflgJHqQna1LUROhQiIkGM7OQM36rmWHfoOWKTFOVyTVlmDhbtjMXfd9IxJMAB43u4wNSkaD+4qzlKMCHQFRFx2dhyIqGMIy1fgvbZW7p0KXr16oXZs2cDANq3b4/U1FT8+OOP6NmzZ57jHz9+jMuXL2PRokUYMGAAAKBGjRro1q0bjh8/joEDB0Iul2PZsmWYOHEipk2bBgBo06YNoqOjcfbsWQwfPry8bo8qqfsxWbgbnYWRnZyK/CVDRGRszExFmNyvCv5vy1Ms2xeHr4a7wUJSdnVMsUkKLPnjGZLTczCpjyta1Sn+j+0WtazRp6UdDoSnomY1C7RvYBxLqAlWsxcVFYXIyEj06NFDb3tgYCAiIiIQFRWV5zXZ2dkAAGvr/9bfs7PLXX4qJSUFAHDmzBlkZGRgxIgReq9dsmQJVqxYUZq3QJSv/eEpsLYQo1Mj4/iSICIqKSepKT7sUwUxSUr8fCS+zCZcvhWZiW+2xiBToUbQkGolSvS0BrdzRD0PC2w8moDHz7NLMUrhCJbsRUREAAB8fHz0tnt5eQEAHj16lOc1devWRatWrbBy5Uo8fPgQSUlJmDt3LqysrNCtWzcAwN27d2Fvb4/Y2FgMGzYMDRs2RMeOHdlnj8rF0wQFrjzMQA8/O5ibCd4llohIcA08LTG4nSMu3JXj8JW0Uj//6ZsyLN4VC3trE3w9wg01q7/eVFcmYhEm9akCG0sxlu+LQ3pmxZ+SRbCnkUyWO8O2jY1+9q2ttUtPT8/3dV9//TXS0tLQu3dvtGnTBqGhofjxxx/h4eEBIHcAh1KpxAcffIAePXpg3bp16Nq1K+bNm4ddu3aV4R0RAQcupkBiKkK3prZCh0JEZDD6trRD8xpW2HYqEXefZpbKOdUaDULOJGHdoXjUdbfEl8Oql9r647ZWJpjctwqSZCqsCX0OtQEvAVcUgvXZ01blvjz/mHa7WJw3D3348CGGDRsGT09PzJ49GxYWFtixYwemTJmC9evXo0WLFlAqlZDL5Zg+fTpGjRoFILfPXkxMDJYvX45BgwYVK04nJ3awLw4Xl8rbdBmXrMD5O+no18YZvp72QoejU5nLxJCxXAwPy6RsBY2yxtQV97AqNB4rPqoNR9vCE7OCyiRbqcbSkEic+icVPVs6YlJ/91LvI+3iIsX7mSKs3BONo9czMLJb1VI9f3kSLNmTSnML8OUaPLlcrrf/RRs3bgQABAcH6/rqtWvXDiNGjMD8+fOxa9cuXc1gx44d9V7bvn17/PXXX5DJZPmeuyCJielQqyt2Rl9eXFykiI83zDURy8Nvf+WO3upY38pg3ofKXiaGiuVieFgm5WNSH1d8/Vs0vt0cgc8GV3tlglZQmaRl5OCHPc8QEZuNYR0c0au5HZKT8m8NfF3+vhJcrW+DX4/FoYqtCE18DHfeVLFYVGAFlWDNuNq+epGRkXrbnzx5orf/RTExMahRo4Yu0QNyawabN2+OBw8eAPivz59CoT/MW6lU6o4nKm2yzByc+EeGNnVtjHoxbSKi1+HuLMG4Hi64G52F7aeLP+FydKICX/8Wjah4BT7qVwW9W9iX6XNdJBJhbFdnuDtLsObP54hPVZbZtcqSYMmel5cX3N3d88ypd/jwYXh7e6N69ep5XuPj44P79+/rzakHANeuXYObmxuA3Bo8ADhw4IDeMX/99Rfq1KmTp48gUWk4ciUVCpUGfVraCx0KEZFBa1PXBj38bHHocirO3y16jdyNJxn4dms0lDkazB5aDS1rWRf+olJgbibGlDeqQANg2b44KJTqcrluaRK0CmLSpEkICgqCnZ0dOnXqhOPHjyM0NBQ//PADgNzBFpGRkahZsyZsbGwwduxY7N27F+PGjcOECRNgYWGBPXv2ICwsTPcaT09PDB8+HD/99BNMTU3RtGlTHDhwAOfPn8eqVauEvF0yUlkKNY5cTUOzGlZwc5IIHQ4RkcEb1sEJj+Ky8fPheLg7SeDu/Orvzr+up2HTsQS4OUnw8YCqcLYt3/Slir0ZJvZywQ+747DpeALG93CpUC2FIk1ZTXpTRNu2bUNwcDBiY2Ph4eGBCRMm6CZM3rVrF4KCgrB582a0atUKQO4gjcWLFyMsLAxisRi1a9fGRx99hLZt2+rOqVarsW7dOuzYsQPPnz+Hj48PPvroozxz+hUF++wVXWXt83Lwcip+O5GI/xtW/bWH/Je2ylomho7lYnhYJuUvOV2F/9sSDUtzMb4Z4QZLc/3GRhcXKeKep2HH6ST8eTEVjb0tMalPlTzHlaffzyZhz4UUvNPNGZ0bG9asC6/qsyd4smfomOwVXWX8slTlaDDj50i42pth9tC8XQ+EVhnLpCJguRgelokw7jzNxMKQWDSrYYXJ/aro1ZZJ7awx95eHuPQgA12b2GJUZyeYiIWtTVOrNVjyxzPcfpqJL96qDt+qhvMD/1XJHnuSE72Gv++kIyk9B+90dxE6FCKiCqeuuyXe6uCIrSeTsPrP57gfk41EmQoONiaQmIrxPEWJkZ2c0MPP1iCaTcViEd7v7Yqvfo3Gsn1xmDPKHVJLE6HDKhSn+CcqIbVGg/3hKfBwlqCxt6XQ4RARVUg9m9nBt6oE5+/KkShTAQCS03MQl6JEYDNbBDazM4hET0tqaYKP+laBLEONVQeeV4jWPyZ7RCV05WEGYpOU6OtftkP/iYiMmUgkQoo8/yXJwu9nlHM0ReNb1RxjujjhZmQmfj+XLHQ4hWKyR1QCGo0G+8NS4GJnCv/a5TP8n4jIWCXJ8k/2tDV9hqhjI1t0bCjFvrAUXH4oFzqcV2KyR1QCd59m4eGzbPRuYS94h2EiooquoMnoDX2S+tFdnOBdRYKfQp/jWbLhTrjMZI+oBPaHp8DWygTt63OSbiKi1zUkwAESU/0fzuZmIgwJcBAooqKRmIoxpV8ViMUiLN8Xh2wDnXCZyR5RMT15no3rjzMR2MwWEjP+CRERva629aR4t7uzribPSWqKKQPd0bZe0deyF4qzrRk+6O2KpwkKbDiSAEOc0c6w60eJDNCB8BRYSEToYmATahIRVWRt60n1kruKNPdhY28rDGrrgN/PJaNmdXN0a2ondEh6WC1BVAxxKUpcuCdHl8a2sLYw/LmViIiofPRrZY+mvlb49UQi7sdkCR2OHiZ7RMUQeikVJmIgsJlh/WojIiJhiUUiTOzpAiepKVbsi0Oq3HBGEjPZIyqiVLkKp2/IEFBfCgcb9oAgIiJ91hYmmPJGFcizcydczjGQCZeZ7BEV0eEraVDlaNC7hb3QoRARkYHydDHH2G7OuP00CyFnkoQOBwAHaBAV6txtGXacTkJSeg4kpiJEPMtCVQczocMiIiIDFVBfioex2fjzYipO35RBlqmGk9QUQwIcBBlhzJo9olc4d1uG4CMJSErPnd1dodIg+EgCzt2uGCPEiIhIGL5VJRABkGXmzr2XKFMJ9vxgskf0CiFnkqFQ6fe5UKg0CDlj+GshEhGRcHadS8HLPfaEen4w2SN6hYLWZTTk9RqJiEh4hvT8YLJH9AqWkvzXvTX09RqJiEhYhrTeL5M9ogJcjchApkID8Uv5nsTU8NdrJCIiYeW33q9Qzw8me0T5eJ6ixJrQ5/B0keRZr/Hd7s4VYr1GIiISTn7r/Qr1/GBbFNFLFEo1lu2LAwBM6VcFrvZm6NCQ6+ASEVHxvLzer1BYs0f0Ao1Gg43HEhAZr8AHvVzhas/59IiIqGJjskf0gr/+keHMrXQMaG2PJr5WQodDRET02pjsEf3rYWwWfjmegMbelhjQhgMwiIjIODDZIwKQlpGD5fvi4GBjivd7uUIsyn/KFSIiooqGyR5Vemq1Bqv+fA5ZphpT+lWBjaWJ0CERERGVGiZ7VOn9fi4ZtyIz8XZXZ3hXMRc6HCIiolLFZI8qtUsP5NgXloLOjaTo0FD44fFERESljckeVVqxyQqsPfgcvlXMMaqzs9DhEBERlQkme1QpZSvVWL43DiZiET7qVwVmphyQQURExonJHlU6Go0GwUcSEJ2oxId9XOFsy4VkiIjIeDHZo0rnyNU0/H0nHW+2c0BDL06cTERExo3JHlUq96KzsPVkIvxqWKGvv73Q4RAREZU5JntUaaTIVVixPw7OtqaYEOjCiZOJiKhSYLJHlYIqR4OV+58jI1uNKf2qwtqCEycTEVHlwGSPKoWQM0m4G52Fd7s7w8NFInQ4RERE5YbJHhm9sHvpCL2Uim5NbdG2HidOJiKiyoXJHhm16EQF1h2KR81q5hjR0UnocIiIiModkz0yWpkKNZbti4O5mRgf9a0CUxMOyCAiosqHyR4ZJY1Gg/WH4hGXrMSkPq5wlHLiZCIiqpyY7JFROngpFeH35Rja3hH1PCyFDoeIiEgwTPbI6NyOysT200loWcsavZrbCR0OERGRoJjskVFJkqmw8sBzVHEww/hAF4g4cTIREVVyTPbIaKhyNFixPw4KpRpT+lWBpYQfbyIiIj4NyWhsPZmIB7HZGB/oAjcnTpxMREQEMNkjI3HutgxHrqahZ3M7+Ne2ETocIiIig8Fkjyq8yPhsBB9JQB03CwwNcBQ6HCIiIoPCyceoQjp3W4aQM8lIlKkgFgHmZiJM6uvKiZOJiIhewpo9qnDO3ZYh+EgCEmUqAIBaA6hygFuRmQJHRkREZHiY7FGFE3ImGQqVRm+bMkeDkDPJAkVERERkuNiMSxWGRqPBvegsXY3eywraTkREVJkx2SODp1Cq8feddBy5mobIeAVEADT5HOfE9W+JiIjy4NORDFZCmgrHrqXixD8yyLPU8HCW4J1uzhCLgV+OJ+o15UpMRRgS4CBgtERERIaJyR4ZFI1GgztPs3DkSiouPcwAADSvYYXufnao626hW/7MzESkG43rJDXFkAAHtK0nFTJ0IiIig8RkjwxCtlKNc7fTcfRqGqISFLC2EKN3Czt0bWILZ1uzPMe3rSdlckdERFQETPZIUPGpShy9moZTN2SQZ6vh6SLBuB7OaFPHBhIzDhYnIiJ6XUz2qNxpNBrcisptqr0SkQERgBa1rNG9qS1qu/3XVEtERESvj8kelZtspRpnb6XjyNVURCcqIbUUo29Le3RpYsuRtERERGWET1iBvLjcV1kPMCivax2/koTg0Ng813meosTRa7lNtRnZani7SvBeoAta1bGGxJRNtURERGWJyZ4AtMt9aacOSZSpEHwkAQBKPQkrr2uduy3DhqMJyFb+d531h+Px58UURMUrIRbnNtX28LNDzWrmbKolIiIqJ4Ine/v378fq1asRFRUFNzc3TJw4EQMGDCjw+KSkJCxevBinT5+GQqGAn58fgoKC4O3tne/x6enp6NevH9q2bYt58+aVzU0UU37LfSlUGvx2IhE2lialeq3fTiSWy7V+O5GoS/S0VDlAVIISb7SyR+fGtnBkUy0REVG5E/TpGxoaihkzZmDMmDFo3749jh49is8++wwWFhbo2bNnnuM1Gg0mTZqEyMhIfPrpp7C3t8eyZcswZswY7Nu3D3Z2dnles2DBAsTExJTH7RRZQct6pWWq8f2uZ+USQ3ldS6MB3mznWObXISIiovwJmuwtXboUvXr1wuzZswEA7du3R2pqKn788cd8k73Hjx/j8uXLWLRoka72r0aNGujWrRuOHz+OgQMH6h1/8uRJhIaGQio1rPnYnKSm+SZ8dlYmmPJGlVK91rK9cUjNyCnzaxV0HQ68ICIiEpZgT+KoqChERkZi+vTpetsDAwMRGhqKqKgoeHh46O3Lzs4GAFhbW+u2aWvzUlJS9I5NTU3FF198gU8//RTr1q0rgzsouSEBDnr96IDc5b6Gd3REreoWpXqt4R0dy+Vawzs66vXZ016HS5gREREJS7ChkBEREQAAHx8fve1eXl4AgEePHuV5Td26ddGqVSusXLkSDx8+RFJSEubOnQsrKyt069ZN79g5c+agRo0aGDZsWBndQcm1rSfFu92ddbVeTlJTvNvduUxGyJbXtdrWk2LKQPdyuSciIiIqOsFq9mQyGQDAxsZGb7u21i49PT3f13399dcYP348evfuDQCQSCRYuXKlXi3gkSNHcOzYMezbt89gR32W53Jf5XWtLn6OaOSed2kzIiIiEo5gyZ5Gk9vc93Iypt0uFuetdHz48CGGDRsGT09PzJ49GxYWFtixYwemTJmC9evXo0WLFkhKSsJXX32FmTNnwt3d/bXjdHKyKfwg0nFxYU2eoWGZGCaWi+FhmRgelknpECzZ0w6aeLkGTy6X6+1/0caNGwEAwcHBur567dq1w4gRIzB//nzs2rULX3/9NWrUqIHBgwdDpfpvEIRGo4FKpYKpafFuOTExHWq1pvADCS4uUsTHy4QOg17AMjFMLBfDwzIxPCyT4hGLRQVWUAnWZ0/bVy8yMlJv+5MnT/T2vygmJgY1atTQm2JFJBKhefPmePDgAQDg0KFDCAsLQ8OGDdGgQQM0aNAA0dHR+P3339GgQQM8ffq0rG6JiIiIyOAIVrPn5eUFd3d3HDx4EN27d9dtP3z4MLy9vVG9evU8r/Hx8cEff/yB1NRUvYTv2rVrcHNzAwDs3Lkzz+s++OADNG7cGB988AFcXV3L4G6IiIiIDJOgk6BNmjQJQUFBsLOzQ6dOnXD8+HGEhobihx9+AJC7WkZkZCRq1qwJGxsbjB07Fnv37sW4ceMwYcIEWFhYYM+ePQgLC9O9plGjRnmuI5FI4ODgkO8+IiIiImMmaLI3aNAgKBQKBAcHIyQkBB4eHli0aJFupO2JEycQFBSEzZs3o1WrVnB3d8fWrVuxePFizJo1C2KxGLVr18aGDRvQtm1bIW+FiIiIyCCJNNrhr5QvDtAoOnamNTwsE8PEcjE8LBPDwzIpHoMcoEFEREREZY/JHhEREZER4yr1hRCLDXMFDkPF98vwsEwME8vF8LBMDA/LpOhe9V6xzx4RERGREWMzLhEREZERY7JHREREZMSY7BEREREZMSZ7REREREaMyR4RERGREWOyR0RERGTEmOwRERERGTEme0RERERGjMkeERERkRFjskdlJjY2Fs2bN8eqVauEDqXSi4+PxxdffIHOnTvDz88PgwYNQmhoqNBhVTr79+9Hnz590LhxY/Tq1Qu7d+8WOqRKTa1WY+vWrejXrx/8/PzQrVs3LFiwAOnp6UKHRv/66KOP0L17d6HDqPC4Ni6VCY1Gg9mzZ/NL0wAoFAqMHz8eMpkMU6ZMgaurKw4dOoRp06YhJycHffv2FTrESiE0NBQzZszAmDFj0L59exw9ehSfffYZLCws0LNnT6HDq5TWr1+P//3vfxg3bhzatGmDR48eYdmyZXjw4AF+/vlnocOr9Pbs2YMjR47A09NT6FAqPCZ7VCZ+++03RERECB0GATh16hTu3LmDkJAQNG7cGADQrl07xMTEYN26dUz2ysnSpUvRq1cvzJ49GwDQvn17pKam4scff2SyJwCNRoP169fjrbfewieffAIAaNu2LRwcHPDxxx/j9u3bqFevnsBRVl5xcXGYN28eqlatKnQoRoHNuFTqoqKi8P3332POnDlCh0IArK2t8dZbb6FRo0Z62319fREZGSlQVJVLVFQUIiMj0aNHD73tgYGBiIiIQFRUlECRVV5yuRxvvPFGnh87vr6+AMC/DYF98cUXaNeuHdq0aSN0KEaBNXtUqtRqNWbNmoVevXqhQ4cOQodDANq0aZPnC1OpVOLkyZOoVauWQFFVLtpabh8fH73tXl5eAIBHjx7Bw8Oj3OOqzGxsbPDFF1/k2X706FEAQM2aNcs7JPpXSEgIbt68if379+O7774TOhyjwGSPikSlUuHAgQMF7nd2dka7du2wadMmREVFYc2aNeUYXeVV1HJ52ffff4/Hjx9j5cqVZRke/UsmkwHITTBeZG1tDQDs22ogrl27hrVr16Jbt26oUaOG0OFUStHR0ViwYAEWLFgAR0dHocMxGkz2qEiys7Mxc+bMAvf7+/ujWrVq+N///odly5ZBKpWWY3SVV1HK5cVkT6PRYPHixdi4cSPGjRuHbt26lUeYlZ5GowEAiESifLeLxexRI7RLly7h/fffh7u7O+bOnSt0OJWSdmBfx44dERgYKHQ4RoXJHhWJtbU17t69W+D+nJwcDB8+HD179kS7du2gUql0+9RqNVQqFUxN+XErbYWVy4sUCgVmzZqFAwcOYNy4ca9MEql0aX/8vFyDJ5fL9faTMP7880/MmjUL3t7eWL9+PRwcHIQOqVL69ddfcffuXezbt0/3DNH+IFKpVDAxMcnzg4mKhk9fKhWxsbG4du0arl27lmfusOXLl2P58uVFTkqo9KWnp2PixIm4fPkyZs+ejbffflvokCoVbV+9yMhI1KlTR7f9yZMnevup/G3YsAGLFi2Cv78/Vq5cycRbQIcOHUJycjICAgLy7GvQoAEWLFiAQYMGCRBZxcdkj0qFq6srdu7cmWf74MGDMXz4cLz55psCREVAbq3rBx98gGvXrumm/6Dy5eXlBXd3dxw8eFBvgtjDhw/D29sb1atXFzC6yiskJAQLFy5E7969sWjRIkgkEqFDqtS++eYbXW231sqVK3H79m2sWLEC7u7uAkVW8THZo1IhkUjyTO2h5erqWuA+Knvbtm1DWFgY3nrrLVSrVg1Xr17V7ROJRGjSpIlwwVUikyZNQlBQEOzs7NCpUyccP34coaGh+OGHH4QOrVJKTEzEvHnz4ObmhpEjR+LWrVt6+z09PTlAoJxpp715kb29/SufL1Q0TPaIjNyhQ4cAANu3b8f27dv19pmYmOR5yFHZGDRoEBQKBYKDgxESEgIPDw8sWrQIvXv3Fjq0Sun06dPIzMxEdHQ0Ro4cmWf/d999h/79+wsQGVHpE2m0vR+JiIiIyOhwvD8RERGREWOyR0RERGTEmOwRERERGTEme0RERERGjMkeERERkRFjskdERERkxJjsEVGF0qVLF4wePbpcrrV69Wp06dIl331JSUkICgpC27Zt0axZM3zwwQeIiorK99iMjAx06tQJly9fLtb1R48eXeD1y+J1BVm1ahUmT55caucjovLFZI+IKB+nT5/GypUr892nUCgwfvx4HDp0CMOHD8fkyZNx48YNjBw5EsnJyXmOX758OerUqYNmzZqVddhl4u2330Z4eDhOnjwpdChEVAJM9oiIXrJ9+3Z88MEHUCqV+e7fvXs3bt68ieXLl2Py5Ml45513sHHjRiQmJmLjxo16x0ZFRWHz5s14//33yyHysmFtbY0xY8Zg3rx5UKvVQodDRMXEZI+I6AXvvvsu/u///g+tWrVCgwYN8j3mwIED8PT0RLt27XTbatSogdatW+PAgQN6x/7yyy+oWrUq/Pz8yjTustavXz88efIEf/31l9ChEFExMdkjogrt4sWLGDt2LPz8/ODn54cxY8YgPDw8z3EnT57EkCFD0LRpU3Tt2hVbtmzB559/nqdvW3R0NL766iusX78e1tbW+V7z5s2b+SaCDRo0QFRUFFJTUwEAWVlZ2LVrF7p165bn2IMHD2LUqFFo3rw5GjZsiC5duuC7776DQqEo8F5Hjx6NsWPH4vjx4+jduzcaN26MAQMG6NY/ftmZM2cwaNAgNGrUCJ06dcKqVavy1MwVNQ4PDw/Url0bv/76a4HxEZFhYrJHRBXWsWPHMHr0aMTGxuKDDz7ABx98gNjYWIwdOxbHjh3THffXX3/pmmU//vhjBAYGYtGiRTh69Giec+7btw8jRoyASCTK95pyuRwymQxVqlTJs8/FxQUAEBsbCwC4dOkSZDIZOnXqpHdcSEgIpk6dCqlUihkzZmDmzJlwc3PDzz//jLVr177ynh8+fIgpU6agZcuWmDFjBsRiMaZMmYJ9+/bpHRcfH4/JkyejdevWmD17NqpXr44ff/wRmzdvLnEcXbp0QVhYGLKysl4ZIxEZFlOhAyAiKgmVSoVvv/0WVapUwe+//w4bGxsAwLBhw9C3b19888036NChA8zMzDB//nx4eHhg27ZtsLCwAAA0a9YMkyZNylN7J5FIXnlduVwOALrzvEi7LSMjA0BusgcAderU0TsuODgYfn5+WLVqlS6pHDFiBLp27YpDhw7ho48+KvD6z58/R1BQEMaOHQsAGDp0KN544w1899136NOnD8Ti3N/wCoUCS5cuRffu3QHkNsN27NgRR44c0b22uHHUqVMHSqUSV69eRevWrV/5PhGR4WDNHhFVSLdu3cKzZ88wcuRIXaIHALa2thg1ahTi4uJw48YN3LlzB5GRkRg2bJhegtatWzfUqFGj2NfVaDQAUGDNHwBdwhUVFQVLS0s4Ojrq7d+7dy/Wrl2rd47ExETY2trqEsWCSKVSjBgxQvdvCwsLDB8+HM+fP8eNGzd02y0tLfWaqG1sbODr64uEhIQSx+Hu7g4AePr06StjJCLDwpo9IqqQtAmHj49Pnn2+vr4AgJiYGJia5n7NeXl55TnOx8cHt2/fLtZ1tTWB2dnZefZpmze1x6SkpOglolpmZmYIDw/H/v37ERERgcjISCQmJgIA3NzcXnl9T0/PPLWP2nuLjo5G48aNAQD29vYwMTHRO87CwkJ3nZLEob2X/KaXISLDxZo9IqqQtDVsr9pnZmYGlUoFIP/mWXNz82Jf18bGBra2tnj+/Hmefdptrq6uAHJr+PKLc8mSJXjnnXdw+/Zt1KtXD5MnT8aePXvQokWLQq9vZmaWZ5t20MWLyZ22dvFVihuH9l5eTiKJyLCxZo+IKiRtzVNERESefY8ePQIAVK1aVbft8ePHCAgI0Dvu8ePHJbp2vXr1cOvWrTzbb926BS8vL9jZ2QEAnJycdCNztaKjo7F27Vr0798f3333nd6+F5tYC/L06VNoNBq9plftfeRXe1mQksShrdFzcnIq8nWISHis2SOiCqlBgwZwcXHB1q1bkZ6ertuenp6O3377DS4uLmjYsCEaNmyIatWqYefOnXrTiVy9ejXfhK0oAgMDERERgXPnzum2PXz4EOfPn0fv3r1126pXrw6lUon4+HjdNm3yV7NmTb1znjx5Eo8fP9bVRBYkISEBoaGhun9nZmZi69at8Pb2zjMQ5FVKEkdcXJzuvoio4mDNHhFVSGZmZvjyyy8xbdo0vPnmmxg8eDAAYOfOnXj+/DmWLVuma8qcNWsWpk2bhmHDhqF///5ISkrC5s2bCx15W5AhQ4Zgy5YtmDJlCsaNGwdLS0v8/PPPqFKlCt5++23dca1bt8by5ctx7do13Vx7NWvWRPXq1bFmzRpkZ2ejatWquH79Ov744w+Ym5vrRvu+6r6DgoJw8+ZNuLq64vfff0dcXBzWrFlTrHsoSRxXr16FlZUVmjRpUqxrEZGwWLNHRBVWYGAggoOD4erqipUrV+Knn36Cu7s7Nm/erDeRcc+ePfHDDz9ApVJh8eLF2L9/P4KCgtCwYcMSJXwSiQQbN25Ehw4dsH79eqxatQpNmjTBpk2b4ODgoDvOz88Ptra2uilYtK9du3Yt/Pz8sHnzZixatAg3b97E7NmzMWPGDKSnp+uNqn2Zq6srlixZgsOHD+OHH36AVCrFhg0b8jRRF+UeihvHpUuX0KpVqxInyUQkDJHmVb2ciYgquJycHKSmpuaZ/gTInXvO1ta2TFeFmD9/Pg4fPoy//vrrldO1FMXo0aMRHR2N48ePl1J0Rff48WMEBgZi1apV6Nq1a7lfn4hKjjV7RGTUcnJy0KFDB/zf//2f3vZ79+7h/v37uqlKysrYsWMRHx+P8+fPl+l1ytru3bvh4+OTZ3k5IjJ87LNHREZNIpGgZ8+e2LlzJ0QiERo2bIjnz59j69atcHBwwDvvvFOm169evTqGDx+OtWvXok2bNmV6rbKSnp6OrVu3Ys6cOa9dO0lE5Y/JHhEZvblz58LHxwd79+7FH3/8AalUijZt2mDatGm6OfHK0rRp09CnTx+Eh4ejZcuWZX690rZx40Y0a9YMPXr0EDoUIioB9tkjIiIiMmLss0dERERkxJjsERERERkxJntERERERozJHhEREZERY7JHREREZMSY7BEREREZsf8H9ii7IadsZmoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphas = np.logspace(-5, 5, 20)\n",
    "scores_test = []\n",
    "\n",
    "poly = PolynomialFeatures(degree=4) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.3,random_state=28)\n",
    "    \n",
    "for alpha in alphas:\n",
    "    reg = LogisticRegression(C=alpha)\n",
    "    reg.fit(X_train, y_train)\n",
    "    pred_proba = reg.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores_test.append(auc)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(np.log10(alphas), scores_test, 'b-o', label='Test')\n",
    "plt.legend()\n",
    "plt.xlabel('log10(alpha)')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние регуляризации на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "e8c2aa4c-63e0-4a0c-8ce9-b23b2cfe7752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratios = np.linspace(0,1,20)\n",
    "scores = []\n",
    "\n",
    "\n",
    "for ratio in ratios:\n",
    "    model = LogisticRegression(C=1, l1_ratio=ratio)\n",
    "    model.fit(X_train, y_train)\n",
    "    pred_proba = model.predict_proba(X_test)[::,1]\n",
    "    auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "    scores.append(auc)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(ratios, scores, 'b-o')\n",
    "plt.xlabel('l1_ratio')\n",
    "plt.ylabel('auc')\n",
    "plt.title('Влияние параметра l1_ratio на результат работы модели')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "c4cf973f-9365-421b-a664-80603a1723a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.7647058823529411\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(degree=4) \n",
    "scaled_data_1 = poly.fit_transform(scaled_data)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_data_1, y, test_size=0.28,random_state=28)\n",
    "model = LogisticRegression(C=1)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "r2 = model.score(X_test, y_test)\n",
    "print(f\"R2: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "206f0a48-3ed6-4da2-823d-a64f88c9c85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9228070175438597"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_proba = model.predict_proba(X_test)[::,1]\n",
    "auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "7c6760b4-6785-4a11-ab0a-360bcc821b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5)\n",
    "sns.set_color_codes(\"muted\")\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "pred_proba = model.predict_proba(X_test)[::,1]\n",
    "fpr, tpr, thresholds = roc_curve(y_test, pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, pred_proba)\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')  \n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584d5943-f30e-46f5-9e88-cb0e785eca03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}