jupyter-project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ссылка на  github:<br/>\n",
    "<a href=\"https://github.com/StrikeX/supervised-learning\">https://github.com/StrikeX/supervised-learning</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Содержание<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Подготовка-данных\" data-toc-modified-id=\"Подготовка-данных-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Подготовка данных</a></span></li><li><span><a href=\"#Исследование-задачи\" data-toc-modified-id=\"Исследование-задачи-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Исследование задачи</a></span></li><li><span><a href=\"#Борьба-с-дисбалансом\" data-toc-modified-id=\"Борьба-с-дисбалансом-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Борьба с дисбалансом</a></span></li><li><span><a href=\"#Тестирование-модели\" data-toc-modified-id=\"Тестирование-модели-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Тестирование модели</a></span></li><li><span><a href=\"#Чек-лист-готовности-проекта\" data-toc-modified-id=\"Чек-лист-готовности-проекта-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Чек-лист готовности проекта</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Отток клиентов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из «Бета-Банка» стали уходить клиенты. Каждый месяц. Немного, но заметно. Банковские маркетологи посчитали: сохранять текущих клиентов дешевле, чем привлекать новых.\n",
    "\n",
    "Нужно спрогнозировать, уйдёт клиент из банка в ближайшее время или нет. Вам предоставлены исторические данные о поведении клиентов и расторжении договоров с банком. \n",
    "\n",
    "Постройте модель с предельно большим значением *F1*-меры. Чтобы сдать проект успешно, нужно довести метрику до 0.59. Проверьте *F1*-меру на тестовой выборке самостоятельно.\n",
    "\n",
    "Дополнительно измеряйте *AUC-ROC*, сравнивайте её значение с *F1*-мерой.\n",
    "\n",
    "Источник данных: [https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling](https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.metrics import f1_score, recall_score, precision_score, roc_auc_score\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.dummy import DummyClassifier\n",
    "# это чтобы в диаграме служебные строки не лезли\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сначала прочитаем данные и посмотрим на первые 10 записей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
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       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>8.0</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "5          6    15574012       Chu          645     Spain    Male   44   \n",
       "6          7    15592531  Bartlett          822    France    Male   50   \n",
       "7          8    15656148    Obinna          376   Germany  Female   29   \n",
       "8          9    15792365        He          501    France    Male   44   \n",
       "9         10    15592389        H?          684    France    Male   27   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0     2.0       0.00              1          1               1   \n",
       "1     1.0   83807.86              1          0               1   \n",
       "2     8.0  159660.80              3          1               0   \n",
       "3     1.0       0.00              2          0               0   \n",
       "4     2.0  125510.82              1          1               1   \n",
       "5     8.0  113755.78              2          1               0   \n",
       "6     7.0       0.00              2          1               1   \n",
       "7     4.0  115046.74              4          1               0   \n",
       "8     4.0  142051.07              2          0               1   \n",
       "9     2.0  134603.88              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  \n",
       "5        149756.71       1  \n",
       "6         10062.80       0  \n",
       "7        119346.88       1  \n",
       "8         74940.50       0  \n",
       "9         71725.73       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('/datasets/Churn.csv')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           9091 non-null   float64\n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(3), int64(8), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что в колонке Tenure меньше Non-Null значений, чем в других колонках. Проверим все колонки на null-значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber            0\n",
       "CustomerId           0\n",
       "Surname              0\n",
       "CreditScore          0\n",
       "Geography            0\n",
       "Gender               0\n",
       "Age                  0\n",
       "Tenure             909\n",
       "Balance              0\n",
       "NumOfProducts        0\n",
       "HasCrCard            0\n",
       "IsActiveMember       0\n",
       "EstimatedSalary      0\n",
       "Exited               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tenure - количество лет, которое пользователь является клиентом банка.\n",
    "\n",
    "Т.к. данных у нас мало, то удалять мы их не будем.\n",
    "\n",
    "Заменим null значение на медиану, чтобы не изменять структуру распределения признака."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Медианное значение срока клиентских отношений: 5.0\n"
     ]
    }
   ],
   "source": [
    "median_tenure = data['Tenure'].median()\n",
    "data['Tenure'] = data['Tenure'].fillna(median_tenure)\n",
    "print('Медианное значение срока клиентских отношений:', median_tenure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Колонки 'RowNumber', 'CustomerId', 'Surname' не являются признаками для предсказания, т.к. в каждой строчке номер будет уникальный.\n",
    "Surname (Фамилия) теоретически может повторятся, но оно не должно влиять на принятие решения.\n",
    "Удалим эти колонки из датасета для обучения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
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       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
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       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42     2.0       0.00              1   \n",
       "1          608     Spain  Female   41     1.0   83807.86              1   \n",
       "2          502    France  Female   42     8.0  159660.80              3   \n",
       "3          699    France  Female   39     1.0       0.00              2   \n",
       "4          850     Spain  Female   43     2.0  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "0          1               1        101348.88       1  \n",
       "1          0               1        112542.58       0  \n",
       "2          1               0        113931.57       1  \n",
       "3          0               0         93826.63       0  \n",
       "4          1               1         79084.10       0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['RowNumber', 'CustomerId', 'Surname'], axis=1)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Преобразуем категориальные признаки в численные методом прямого кодирования."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 12 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   CreditScore        10000 non-null  int64  \n",
      " 1   Age                10000 non-null  int64  \n",
      " 2   Tenure             10000 non-null  float64\n",
      " 3   Balance            10000 non-null  float64\n",
      " 4   NumOfProducts      10000 non-null  int64  \n",
      " 5   HasCrCard          10000 non-null  int64  \n",
      " 6   IsActiveMember     10000 non-null  int64  \n",
      " 7   EstimatedSalary    10000 non-null  float64\n",
      " 8   Exited             10000 non-null  int64  \n",
      " 9   Geography_Germany  10000 non-null  uint8  \n",
      " 10  Geography_Spain    10000 non-null  uint8  \n",
      " 11  Gender_Male        10000 non-null  uint8  \n",
      "dtypes: float64(3), int64(6), uint8(3)\n",
      "memory usage: 732.5 KB\n"
     ]
    }
   ],
   "source": [
    "data_ohe = pd.get_dummies(data, drop_first=True)\n",
    "data_ohe.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
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       "      <th>Geography_Spain</th>\n",
       "      <th>Gender_Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>42</td>\n",
       "      <td>8.0</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>43</td>\n",
       "      <td>2.0</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>645</td>\n",
       "      <td>44</td>\n",
       "      <td>8.0</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>822</td>\n",
       "      <td>50</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>376</td>\n",
       "      <td>29</td>\n",
       "      <td>4.0</td>\n",
       "      <td>115046.74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>501</td>\n",
       "      <td>44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>142051.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>684</td>\n",
       "      <td>27</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134603.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619   42     2.0       0.00              1          1   \n",
       "1          608   41     1.0   83807.86              1          0   \n",
       "2          502   42     8.0  159660.80              3          1   \n",
       "3          699   39     1.0       0.00              2          0   \n",
       "4          850   43     2.0  125510.82              1          1   \n",
       "5          645   44     8.0  113755.78              2          1   \n",
       "6          822   50     7.0       0.00              2          1   \n",
       "7          376   29     4.0  115046.74              4          1   \n",
       "8          501   44     4.0  142051.07              2          0   \n",
       "9          684   27     2.0  134603.88              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_Germany  \\\n",
       "0               1        101348.88       1                  0   \n",
       "1               1        112542.58       0                  0   \n",
       "2               0        113931.57       1                  0   \n",
       "3               0         93826.63       0                  0   \n",
       "4               1         79084.10       0                  0   \n",
       "5               0        149756.71       1                  0   \n",
       "6               1         10062.80       0                  0   \n",
       "7               0        119346.88       1                  1   \n",
       "8               1         74940.50       0                  0   \n",
       "9               1         71725.73       0                  0   \n",
       "\n",
       "   Geography_Spain  Gender_Male  \n",
       "0                0            0  \n",
       "1                1            0  \n",
       "2                0            0  \n",
       "3                0            0  \n",
       "4                1            0  \n",
       "5                1            1  \n",
       "6                0            1  \n",
       "7                0            0  \n",
       "8                0            1  \n",
       "9                0            1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ohe.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выделим из исходного набора данных признаки для обучения и целевой признак в переменные features и target соответтсвенно.\n",
    "Разделим наборы данных на выборки для обучения, валидации и тестирования."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = data_ohe['Exited']\n",
    "features = data_ohe.drop('Exited', axis=1)\n",
    "\n",
    "#отделим 60% данных для обучающей выборки\n",
    "features_train, features_valid_and_test, target_train, target_valid_and_test = train_test_split(\n",
    "    features, target, train_size=0.6, random_state=12345)\n",
    "\n",
    "#разделим валидационную и тестовую выборку пополам\n",
    "features_test, features_valid, target_test, target_valid = train_test_split(\n",
    "    features_valid_and_test, target_valid_and_test, train_size=0.5, random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Тренировочная: (6000, 11)\n",
      "Валидационная: (2000, 11)\n",
      "Тестовая: (2000, 11)\n"
     ]
    }
   ],
   "source": [
    "print('Тренировочная:', features_train.shape)\n",
    "print('Валидационная:', features_valid.shape)\n",
    "print('Тестовая:', features_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборки разбиты верно."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По данным было видно, что у числовых признаков разный масштаб. Поэтому их нужно масштабировать."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
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       "      <th>Gender_Male</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7479</th>\n",
       "      <td>-0.886751</td>\n",
       "      <td>-0.373192</td>\n",
       "      <td>1.082277</td>\n",
       "      <td>1.232271</td>\n",
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       "      <th>3411</th>\n",
       "      <td>0.608663</td>\n",
       "      <td>-0.183385</td>\n",
       "      <td>1.082277</td>\n",
       "      <td>0.600563</td>\n",
       "      <td>-0.891560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.333945</td>\n",
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       "    <tr>\n",
       "      <th>6027</th>\n",
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       "      <td>0.480939</td>\n",
       "      <td>-0.737696</td>\n",
       "      <td>1.027098</td>\n",
       "      <td>0.830152</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.503095</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>-1.457915</td>\n",
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       "      <td>-1.233163</td>\n",
       "      <td>0.830152</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.071061</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3716</th>\n",
       "      <td>0.130961</td>\n",
       "      <td>-1.132419</td>\n",
       "      <td>-1.101690</td>\n",
       "      <td>1.140475</td>\n",
       "      <td>-0.891560</td>\n",
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       "      <td>0</td>\n",
       "      <td>1.524268</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8741</th>\n",
       "      <td>-0.315586</td>\n",
       "      <td>-1.512032</td>\n",
       "      <td>1.082277</td>\n",
       "      <td>-1.233163</td>\n",
       "      <td>0.830152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.552345</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7461</th>\n",
       "      <td>-0.585591</td>\n",
       "      <td>-0.657902</td>\n",
       "      <td>-0.373701</td>\n",
       "      <td>-1.233163</td>\n",
       "      <td>0.830152</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.814122</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5106</th>\n",
       "      <td>-0.544052</td>\n",
       "      <td>-0.657902</td>\n",
       "      <td>-1.465685</td>\n",
       "      <td>0.031212</td>\n",
       "      <td>-0.891560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.608723</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6130</th>\n",
       "      <td>-0.211738</td>\n",
       "      <td>-0.373192</td>\n",
       "      <td>-1.101690</td>\n",
       "      <td>1.190787</td>\n",
       "      <td>0.830152</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.602263</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4955</th>\n",
       "      <td>1.273291</td>\n",
       "      <td>-0.562998</td>\n",
       "      <td>-0.009707</td>\n",
       "      <td>0.111168</td>\n",
       "      <td>0.830152</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.508214</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore       Age    Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
       "7479    -0.886751 -0.373192  1.082277  1.232271      -0.891560          1   \n",
       "3411     0.608663 -0.183385  1.082277  0.600563      -0.891560          0   \n",
       "6027     2.052152  0.480939 -0.737696  1.027098       0.830152          0   \n",
       "1247    -1.457915 -1.417129  0.354288 -1.233163       0.830152          1   \n",
       "3716     0.130961 -1.132419 -1.101690  1.140475      -0.891560          0   \n",
       "8741    -0.315586 -1.512032  1.082277 -1.233163       0.830152          1   \n",
       "7461    -0.585591 -0.657902 -0.373701 -1.233163       0.830152          0   \n",
       "5106    -0.544052 -0.657902 -1.465685  0.031212      -0.891560          1   \n",
       "6130    -0.211738 -0.373192 -1.101690  1.190787       0.830152          0   \n",
       "4955     1.273291 -0.562998 -0.009707  0.111168       0.830152          1   \n",
       "\n",
       "      IsActiveMember  EstimatedSalary  Geography_Germany  Geography_Spain  \\\n",
       "7479               0        -0.187705                  0                1   \n",
       "3411               0        -0.333945                  0                0   \n",
       "6027               1         1.503095                  1                0   \n",
       "1247               0        -1.071061                  0                0   \n",
       "3716               0         1.524268                  1                0   \n",
       "8741               1         0.552345                  0                1   \n",
       "7461               1         0.814122                  0                1   \n",
       "5106               0        -0.608723                  0                0   \n",
       "6130               0        -0.602263                  1                0   \n",
       "4955               0         0.508214                  1                0   \n",
       "\n",
       "      Gender_Male  \n",
       "7479            1  \n",
       "3411            0  \n",
       "6027            1  \n",
       "1247            1  \n",
       "3716            0  \n",
       "8741            0  \n",
       "7461            1  \n",
       "5106            0  \n",
       "6130            0  \n",
       "4955            1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.mode.chained_assignment = None #чтобы выключить предупреждение\n",
    "scaler = StandardScaler()\n",
    "numeric = ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary']\n",
    "scaler.fit(features_train[numeric]) \n",
    "features_train[numeric] = scaler.transform(features_train[numeric])\n",
    "features_valid[numeric] = scaler.transform(features_valid[numeric])\n",
    "features_test[numeric] = scaler.transform(features_test[numeric])\n",
    "\n",
    "features_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
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      "text/plain": [
       "      CreditScore       Age    Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
       "7041    -2.226392 -0.088482 -1.101690 -1.233163       0.830152          1   \n",
       "5709    -0.087120  0.006422  1.446272 -1.233163      -0.891560          1   \n",
       "7117    -0.917905 -0.752805 -0.009707  0.722307      -0.891560          1   \n",
       "7775    -0.253277  0.101325  1.810266 -1.233163       0.830152          1   \n",
       "8735     0.785204 -0.847708  1.810266  0.615625      -0.891560          0   \n",
       "9182    -0.575206 -1.037515 -1.829679  1.064325      -0.891560          1   \n",
       "2493     1.345984 -1.512032  1.810266  0.345431      -0.891560          1   \n",
       "1819     0.639817  0.575842  1.082277  1.196249       0.830152          0   \n",
       "2980     2.052152  0.196229 -0.737696  0.375912       0.830152          1   \n",
       "7830    -0.595976 -0.562998  1.446272 -1.233163       0.830152          1   \n",
       "\n",
       "      IsActiveMember  EstimatedSalary  Geography_Germany  Geography_Spain  \\\n",
       "7041               0         0.647083                  0                0   \n",
       "5709               0        -1.658410                  0                0   \n",
       "7117               1        -1.369334                  0                1   \n",
       "7775               0         0.075086                  0                1   \n",
       "8735               1        -1.070919                  0                0   \n",
       "9182               0         0.480366                  0                0   \n",
       "2493               1         0.746282                  0                1   \n",
       "1819               1         0.699553                  0                1   \n",
       "2980               0        -0.490439                  0                1   \n",
       "7830               1        -1.005360                  0                0   \n",
       "\n",
       "      Gender_Male  \n",
       "7041            1  \n",
       "5709            0  \n",
       "7117            1  \n",
       "7775            1  \n",
       "8735            1  \n",
       "9182            0  \n",
       "2493            1  \n",
       "1819            1  \n",
       "2980            0  \n",
       "7830            0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_valid.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      CreditScore       Age    Tenure   Balance  NumOfProducts  HasCrCard  \\\n",
       "8532    -0.699824 -0.373192 -1.101690 -1.233163       0.830152          1   \n",
       "5799    -0.284431  0.575842 -0.737696 -1.233163      -0.891560          1   \n",
       "5511     0.151731 -0.657902 -1.829679  0.438711      -0.891560          1   \n",
       "7365    -0.876366 -0.278288  1.810266  1.239884      -0.891560          1   \n",
       "7367    -0.481743  0.291132  1.810266 -1.233163       0.830152          1   \n",
       "4120     0.141346 -1.701839 -1.465685  0.728192       0.830152          1   \n",
       "2488    -0.076735 -1.701839 -1.465685 -1.233163       0.830152          0   \n",
       "8271    -1.779845 -0.183385  1.082277  1.218843       0.830152          1   \n",
       "8653    -0.118274  1.714682 -0.009707 -1.233163       0.830152          1   \n",
       "3452     1.086364  1.714682 -0.737696  0.200967      -0.891560          1   \n",
       "\n",
       "      IsActiveMember  EstimatedSalary  Geography_Germany  Geography_Spain  \\\n",
       "8532               0        -0.015173                  0                0   \n",
       "5799               1         1.471724                  0                0   \n",
       "5511               0        -1.367107                  1                0   \n",
       "7365               1        -0.786517                  0                1   \n",
       "7367               0         1.358533                  0                1   \n",
       "4120               1         0.005562                  1                0   \n",
       "2488               0        -1.225690                  0                1   \n",
       "8271               1         1.309064                  0                0   \n",
       "8653               1         0.397508                  0                0   \n",
       "3452               1        -0.802184                  0                0   \n",
       "\n",
       "      Gender_Male  \n",
       "8532            0  \n",
       "5799            0  \n",
       "5511            1  \n",
       "7365            0  \n",
       "7367            1  \n",
       "4120            0  \n",
       "2488            0  \n",
       "8271            1  \n",
       "8653            1  \n",
       "3452            1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_test.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь можно приступать к исследованию."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Резюме**\n",
    "\n",
    "Я подготовил исходную выборку для анализа:\n",
    "- заполнил null в колонке Tenure медианным значением признака, чтобы увеличить объем выборки для исследования\n",
    "- убрал из датасета признаки, непригодные для анализа: 'RowNumber', 'CustomerId', 'Surname'\n",
    "- преобразовал категориальные признаки в численные методом прямого кодирования\n",
    "- привел все признаки к одному масштабу\n",
    "- разлелил выборку на 3 набора: обучающий, валидационный и тестовый"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Исследование задачи"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Я буду исследовать различные модели: логистическую регрессию, решающее дерево и случайный лес.<br/>\n",
    "Порядок исследования всегда совпадает, поэтому рассчет характеристик качества модели выведу в отдельную функцию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_analysis(model, features_train, target_train, features_valid, target_valid):\n",
    "    model.fit(features_train, target_train)\n",
    "    predictions_valid = model.predict(features_valid)\n",
    "    probabilities_valid = model.predict_proba(features_valid)\n",
    "    probabilities_one_valid = probabilities_valid[:, 1]\n",
    "    return {\n",
    "        'F1 метрика': f1_score(target_valid, predictions_valid),\n",
    "        'AUC ROC': roc_auc_score(target_valid, probabilities_one_valid)\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для случайного леса нужно подбирать параметры max_depth и n_estimators.\n",
    "И т.к. в течение всего исследования мы будем менять обучающие выборки, оптимальные параметры модели случайного леса под разные выборки могут отличаться. Выделим алгоритм поиска параметров модели случайного леса в отдельную функцию.<br/>\n",
    "Добавим сразу параметр class_weight, т.к. позже будем исследовать проблемы дисбаланса. По умолчанию параметр None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_optimal_random_forest_classifier_params(features_train, target_train, features_valid, target_valid, class_weight=None):\n",
    "    best_f1 = 0\n",
    "    best_est = 0\n",
    "    best_depth = 0\n",
    "    best_criterion = None\n",
    "    best_min_samples_leaf = 0\n",
    "    for min_samples_leaf in range(1, 6, 1):\n",
    "        for criterion in \"gini\", \"entropy\":\n",
    "            for est in range(10, 71, 10):\n",
    "                for depth in range (1, 14, 2):\n",
    "                    model = RandomForestClassifier(random_state=12345, n_estimators=est, max_depth=depth, criterion=criterion, min_samples_leaf=min_samples_leaf, class_weight=class_weight)\n",
    "                    model.fit(features_train, target_train)\n",
    "                    predictions_valid = model.predict(features_valid)\n",
    "                    f1 = f1_score(target_valid, predictions_valid) \n",
    "                    if (f1 > best_f1):\n",
    "                        best_f1 = f1\n",
    "                        best_est = est\n",
    "                        best_depth = depth\n",
    "                        best_criterion = criterion\n",
    "                        best_min_samples_leaf = min_samples_leaf\n",
    "    return {\n",
    "        'n_estimators': best_est, \n",
    "        'max_depth': best_depth,\n",
    "        'criterion': best_criterion,\n",
    "        'min_samples_leaf': best_min_samples_leaf\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_optimal_decision_tree_classifier_params(features_train, target_train, features_valid, target_valid, class_weight=None):\n",
    "    best_f1 = 0\n",
    "    best_depth = 0\n",
    "    best_criterion = None\n",
    "    best_min_samples_leaf = 0\n",
    "    for min_samples_leaf in range(1, 6):\n",
    "        for criterion in \"gini\", \"entropy\":\n",
    "            for depth in range(1,11):\n",
    "                model = DecisionTreeClassifier(random_state=12345, max_depth=depth, criterion=criterion, min_samples_leaf=min_samples_leaf, class_weight=class_weight)\n",
    "                model.fit(features_train, target_train)\n",
    "                predictions_valid = model.predict(features_valid)\n",
    "                f1 = f1_score(target_valid, predictions_valid) \n",
    "                if (f1 > best_f1):\n",
    "                    best_f1 = f1\n",
    "                    best_depth = depth\n",
    "                    best_criterion = criterion\n",
    "                    best_min_samples_leaf = min_samples_leaf\n",
    "    return {\n",
    "        'max_depth': best_depth,\n",
    "        'criterion': criterion,\n",
    "        'min_samples_leaf': min_samples_leaf\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Т.к. нам предстоит осуществлять анализ для борьбы с дисбалансом - оформим алгоритм анализа в отдельную функцию.<br/>\n",
    "Также добавим параметр class_weight, чтобы позже легче решать проблемы дисбаланса."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analisys(method_type, features_train, target_train, features_valid, target_valid, class_weight=None):\n",
    "    random_forest_classifier_params = get_optimal_random_forest_classifier_params(features_train, target_train, features_valid, target_valid, class_weight=class_weight)\n",
    "    decision_tree_classifier_params = get_optimal_decision_tree_classifier_params(features_train, target_train, features_valid, target_valid, class_weight=class_weight)\n",
    "    models = {\n",
    "        'Логистическая регрессия': {\n",
    "            'model': LogisticRegression(class_weight=class_weight, solver='liblinear', random_state=12345),\n",
    "            'params': None\n",
    "        },\n",
    "        'Решающее дерево': {\n",
    "            'model': DecisionTreeClassifier(class_weight=class_weight, **decision_tree_classifier_params, random_state=12345),\n",
    "            'params': decision_tree_classifier_params\n",
    "        },\n",
    "        'Случайный лес': {\n",
    "            'model': RandomForestClassifier(class_weight=class_weight, **random_forest_classifier_params, random_state=12345),\n",
    "            'params': random_forest_classifier_params\n",
    "\n",
    "        }\n",
    "    }\n",
    "    result = []\n",
    "    for model_name, model_config in models.items():\n",
    "        model_characteristics = model_analysis(model_config['model'], features_train, target_train, features_valid, target_valid)\n",
    "        # добавил, чтобы 2 раза не считать\n",
    "        model_characteristics['Параметры'] = model_config['params']\n",
    "        model_characteristics['Метод'] = method_type\n",
    "        model_characteristics['Тип'] = model_name\n",
    "        result.append(model_characteristics)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Это функция для красивого вывода таблицы с характеристиками модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_table(data):\n",
    "    df = pd.DataFrame(data = data);\n",
    "    # нужно красиво вывести колонку \"Тип\", потому поменяем порядок колонок\n",
    "    temp_cols=df.columns.tolist()\n",
    "    new_cols=temp_cols[-1:] + temp_cols[:-1]\n",
    "    df=df[new_cols]\n",
    "    \n",
    "     \n",
    "    df = df.groupby('Метод').apply(lambda a: a[:])\n",
    "    df = df.drop(columns=['Метод'])\n",
    "    with pd.option_context('display.precision', 3):\n",
    "        style = df.style.background_gradient('coolwarm')\n",
    "    return style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_cf997_row0_col1,#T_cf997_row0_col2{\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_cf997_row1_col1{\n",
       "            background-color:  #e46e56;\n",
       "            color:  #000000;\n",
       "        }#T_cf997_row1_col2{\n",
       "            background-color:  #f29072;\n",
       "            color:  #000000;\n",
       "        }#T_cf997_row2_col1,#T_cf997_row2_col2{\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_cf997_\" ><thead>    <tr>        <th class=\"blank\" ></th>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Тип</th>        <th class=\"col_heading level0 col1\" >F1 метрика</th>        <th class=\"col_heading level0 col2\" >AUC ROC</th>        <th class=\"col_heading level0 col3\" >Параметры</th>    </tr>    <tr>        <th class=\"index_name level0\" >Метод</th>        <th class=\"index_name level1\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cf997_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">1. Дисбаланс классов</th>\n",
       "                        <th id=\"T_cf997_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
       "                        <td id=\"T_cf997_row0_col0\" class=\"data row0 col0\" >Логистическая регрессия</td>\n",
       "                        <td id=\"T_cf997_row0_col1\" class=\"data row0 col1\" >0.275</td>\n",
       "                        <td id=\"T_cf997_row0_col2\" class=\"data row0 col2\" >0.739</td>\n",
       "                        <td id=\"T_cf997_row0_col3\" class=\"data row0 col3\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_cf997_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
       "                        <td id=\"T_cf997_row1_col0\" class=\"data row1 col0\" >Решающее дерево</td>\n",
       "                        <td id=\"T_cf997_row1_col1\" class=\"data row1 col1\" >0.506</td>\n",
       "                        <td id=\"T_cf997_row1_col2\" class=\"data row1 col2\" >0.820</td>\n",
       "                        <td id=\"T_cf997_row1_col3\" class=\"data row1 col3\" >{'max_depth': 6, 'criterion': 'entropy', 'min_samples_leaf': 5}</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_cf997_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
       "                        <td id=\"T_cf997_row2_col0\" class=\"data row2 col0\" >Случайный лес</td>\n",
       "                        <td id=\"T_cf997_row2_col1\" class=\"data row2 col1\" >0.548</td>\n",
       "                        <td id=\"T_cf997_row2_col2\" class=\"data row2 col2\" >0.844</td>\n",
       "                        <td id=\"T_cf997_row2_col3\" class=\"data row2 col3\" >{'n_estimators': 20, 'max_depth': 13, 'criterion': 'entropy', 'min_samples_leaf': 3}</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7faa4dc936a0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# сохраним в перееменную, для итогового анализа\n",
    "class_imbalance = analisys('1. Дисбаланс классов', features_train, target_train, features_valid, target_valid)\n",
    "make_table(class_imbalance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим целевой признак на дисбаланс классов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.7963\n",
      "1    0.2037\n",
      "Name: Exited, dtype: float64\n"
     ]
    },
    {
     "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": [
    "class_frequency = data['Exited'].value_counts(normalize=True)\n",
    "print(class_frequency)\n",
    "class_frequency.plot(kind='bar', xlabel='дисбаланс классов')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Класс с ответом 1 встречается почти в 4 раза реже, это значит что наши модели получились несбалансированными, а мы можем достичь лучших результатов в обучении."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Резюме**<br/>\n",
    "С учетом имеющегося дисбаланса классов у нас получились следующие результаты:<br/>\n",
    "Лучшие значения характеристики f1 у случайного леса 0.548 при auc_roc = 0.844, при достаточно низком значении полноты в 0.428.<br/>\n",
    "Худшие значения f1 метрики и auc_roc у лотистической регрессии, всего 0.275 и 0.739 соответтсвенно.<br/>\n",
    "\n",
    "Все значения метрики f1 получились меньше необходимого по условию задачи в 0.59. Но мы знаем о дисбалансе классов, поэтому дальше будем пробовать улучшать качество модели."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Борьба с дисбалансом"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Взвешивание классов показало себя лучше всего, поэтому в качестве метода борьбы с дисбалансом будем использовать именно его.<br/>\n",
    "Для этого я предусмотрел передачу параметра class_weight со значением \"balanced\" в фукнцию анализа."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь проанализируем все техники и объединим результаты в единую таблицу для анализа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_7526c_row0_col1,#T_7526c_row0_col2{\n",
       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7526c_row1_col1{\n",
       "            background-color:  #f6bda2;\n",
       "            color:  #000000;\n",
       "        }#T_7526c_row1_col2{\n",
       "            background-color:  #f7b79b;\n",
       "            color:  #000000;\n",
       "        }#T_7526c_row2_col1{\n",
       "            background-color:  #f29274;\n",
       "            color:  #000000;\n",
       "        }#T_7526c_row2_col2{\n",
       "            background-color:  #df634e;\n",
       "            color:  #000000;\n",
       "        }#T_7526c_row3_col1{\n",
       "            background-color:  #edd1c2;\n",
       "            color:  #000000;\n",
       "        }#T_7526c_row3_col2{\n",
       "            background-color:  #4257c9;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_7526c_row4_col1{\n",
       "            background-color:  #e26952;\n",
       "            color:  #000000;\n",
       "        }#T_7526c_row4_col2{\n",
       "            background-color:  #ee8468;\n",
       "            color:  #000000;\n",
       "        }#T_7526c_row5_col1,#T_7526c_row5_col2{\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_7526c_\" ><thead>    <tr>        <th class=\"blank\" ></th>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Тип</th>        <th class=\"col_heading level0 col1\" >F1 метрика</th>        <th class=\"col_heading level0 col2\" >AUC ROC</th>        <th class=\"col_heading level0 col3\" >Параметры</th>    </tr>    <tr>        <th class=\"index_name level0\" >Метод</th>        <th class=\"index_name level1\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7526c_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">1. Дисбаланс классов</th>\n",
       "                        <th id=\"T_7526c_level1_row0\" class=\"row_heading level1 row0\" >0</th>\n",
       "                        <td id=\"T_7526c_row0_col0\" class=\"data row0 col0\" >Логистическая регрессия</td>\n",
       "                        <td id=\"T_7526c_row0_col1\" class=\"data row0 col1\" >0.275</td>\n",
       "                        <td id=\"T_7526c_row0_col2\" class=\"data row0 col2\" >0.739</td>\n",
       "                        <td id=\"T_7526c_row0_col3\" class=\"data row0 col3\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_7526c_level1_row1\" class=\"row_heading level1 row1\" >1</th>\n",
       "                        <td id=\"T_7526c_row1_col0\" class=\"data row1 col0\" >Решающее дерево</td>\n",
       "                        <td id=\"T_7526c_row1_col1\" class=\"data row1 col1\" >0.506</td>\n",
       "                        <td id=\"T_7526c_row1_col2\" class=\"data row1 col2\" >0.820</td>\n",
       "                        <td id=\"T_7526c_row1_col3\" class=\"data row1 col3\" >{'max_depth': 6, 'criterion': 'entropy', 'min_samples_leaf': 5}</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_7526c_level1_row2\" class=\"row_heading level1 row2\" >2</th>\n",
       "                        <td id=\"T_7526c_row2_col0\" class=\"data row2 col0\" >Случайный лес</td>\n",
       "                        <td id=\"T_7526c_row2_col1\" class=\"data row2 col1\" >0.548</td>\n",
       "                        <td id=\"T_7526c_row2_col2\" class=\"data row2 col2\" >0.844</td>\n",
       "                        <td id=\"T_7526c_row2_col3\" class=\"data row2 col3\" >{'n_estimators': 20, 'max_depth': 13, 'criterion': 'entropy', 'min_samples_leaf': 3}</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7526c_level0_row3\" class=\"row_heading level0 row3\" rowspan=\"3\">2. Взвешивание классов</th>\n",
       "                        <th id=\"T_7526c_level1_row3\" class=\"row_heading level1 row3\" >3</th>\n",
       "                        <td id=\"T_7526c_row3_col0\" class=\"data row3 col0\" >Логистическая регрессия</td>\n",
       "                        <td id=\"T_7526c_row3_col1\" class=\"data row3 col1\" >0.479</td>\n",
       "                        <td id=\"T_7526c_row3_col2\" class=\"data row3 col2\" >0.742</td>\n",
       "                        <td id=\"T_7526c_row3_col3\" class=\"data row3 col3\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_7526c_level1_row4\" class=\"row_heading level1 row4\" >4</th>\n",
       "                        <td id=\"T_7526c_row4_col0\" class=\"data row4 col0\" >Решающее дерево</td>\n",
       "                        <td id=\"T_7526c_row4_col1\" class=\"data row4 col1\" >0.579</td>\n",
       "                        <td id=\"T_7526c_row4_col2\" class=\"data row4 col2\" >0.836</td>\n",
       "                        <td id=\"T_7526c_row4_col3\" class=\"data row4 col3\" >{'max_depth': 5, 'criterion': 'entropy', 'min_samples_leaf': 5}</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <th id=\"T_7526c_level1_row5\" class=\"row_heading level1 row5\" >5</th>\n",
       "                        <td id=\"T_7526c_row5_col0\" class=\"data row5 col0\" >Случайный лес</td>\n",
       "                        <td id=\"T_7526c_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
       "                        <td id=\"T_7526c_row5_col2\" class=\"data row5 col2\" >0.861</td>\n",
       "                        <td id=\"T_7526c_row5_col3\" class=\"data row5 col3\" >{'n_estimators': 70, 'max_depth': 13, 'criterion': 'entropy', 'min_samples_leaf': 5}</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7faa4dd0cee0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df = class_imbalance \\\n",
    "    + analisys('2. Взвешивание классов', features_train, target_train, features_valid, target_valid, 'balanced')\n",
    "\n",
    "make_table(result_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Резюме**<br/>\n",
    "Мне удалось достичь необходимого значения параметра f1: 0.63 больше требуемых 0.59.<br/>\n",
    "Лучшее значение получается на модели случайного леса при методе борьбы с дисбалансом взвешиванием классов.<br/>\n",
    "При этом AUC ROC на этой модели также является самым высоким - 0.861.<br/>\n",
    "Это очень удобно, т.к. метод взвешивания классов уже встроен в качестве параметра конструктора модели в самой библиотеке.<br/>\n",
    "На самом деле случайный лес достигает параметра 0.59 в каждом способе борьбы с дисбалансом классов.\n",
    "<br/><br/>\n",
    "Мы также видим, что метод увеличения выборки очень близок к технике взвешивания классов. Значения f1 и AUC ROC расходятся на несколько тысячных долей. При этом техника уменьшения выборки показывает себя хуже других техник борьбы с дисбалансом.\n",
    "<br/><br/>\n",
    "Модель случайного леса показала результаты лучше, чем у логистической регрессии и решающего дерева на всех выборках."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Тестирование модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Берем из сводной таблицы параметры для лучшей модели случайного леса обученного с помощью техники взвешивания классов для борьбы с дисбалансом:\n",
    "{'n_estimators': 70, 'max_depth': 13, 'criterion': 'entropy', 'min_samples_leaf': 5}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь подставим эти параметры для тестовой выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>F1 метрика</th>\n",
       "      <th>AUC ROC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.62704</td>\n",
       "      <td>0.856813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   F1 метрика   AUC ROC\n",
       "0     0.62704  0.856813"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(class_weight='balanced', n_estimators=70, max_depth=13, criterion='entropy', min_samples_leaf=5, random_state=12345)\n",
    "model_characteristics = model_analysis(model, features_train, target_train, features_test, target_test)\n",
    "pd.DataFrame(data=[model_characteristics])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На тестовой выборке значение F1 близко к значению, полученному на валидационной выборке: меньше всего на 0.03, а AUC ROC меньше всего на 0.004.\n",
    "Считаем, что решение найдено."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3457402812241522"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_model = DummyClassifier(strategy='constant', constant=1)\n",
    "dummy_model.fit(features_train, target_train)\n",
    "f1_score(dummy_model.predict(features_test), target_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На константной модели значение F1 слишком мало. Можно считать нашу модель адеквктной."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Чек-лист готовности проекта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поставьте 'x' в выполненных пунктах. Далее нажмите Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке исполнения\n",
    "- [x]  Выполнен шаг 1: данные подготовлены\n",
    "- [x]  Выполнен шаг 2: задача исследована\n",
    "    - [x]  Исследован баланс классов\n",
    "    - [x]  Изучены модели без учёта дисбаланса\n",
    "    - [x]  Написаны выводы по результатам исследования\n",
    "- [x]  Выполнен шаг 3: учтён дисбаланс\n",
    "    - [x]  Применено несколько способов борьбы с дисбалансом\n",
    "    - [x]  Написаны выводы по результатам исследования\n",
    "- [x]  Выполнен шаг 4: проведено тестирование\n",
    "- [x]  Удалось достичь *F1*-меры не менее 0.59\n",
    "- [x]  Исследована метрика *AUC-ROC*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Я подготовил исходную выборку для анализа:\n",
    "- заполнил null в колонке Tenure медианным значением признака, чтобы увеличить объем выборки для исследования\n",
    "- убрал из датасета признаки, непригодные для анализа: 'RowNumber', 'CustomerId', 'Surname'\n",
    "- преобразовал категориальные признаки в численные методом прямого кодирования\n",
    "- привел все признаки к одному масштабу\n",
    "- разлелил выборку на 3 набора: обучающий, валидационный и тестовый\n",
    "\n",
    "После чего определил ряд служебных функций для упрощения анализа.\n",
    "\n",
    "Исследовав целевые признаки я обнаружил дисбаланс классов 1:4, на котором не удается достичь требуемых значений метрики f1.\n",
    "Максимальное значение удалось рассчитать для случайного леса 0.548. \n",
    "Это оказалось меньше требуемых требуемых 0.59.\n",
    "\n",
    "Поэтому я стал применять известные мне способы борьбы с дисбалансом классов:\n",
    "- взвешивание классов\n",
    "- увеличение выборки\n",
    "- уменьшение выборки\n",
    "\n",
    "Для увеличения и уменьшения выборки нет готовых функций, поэтому пришлось написать две функции соответственно upsample и downsample.\n",
    "\n",
    "\n",
    "Для каждого их способов борьбы с дисбалансом исследовал по три модели: логистическая регрессия, решающее дерево, случайный лес.\n",
    "Случайный лес показал себя лучше на всех выборках: в каждом способе борьбы с дисбалансом классов удалось достичь значения f1 больше требуемых 0.59.\n",
    "\n",
    "Лучших показателей удалось достичь методом борьбы с дисбалансом классов путем взвешивания классов.\n",
    "\n",
    "Параметры подходящего случайного леса: 'n_estimators': 70, 'max_depth': 13, 'criterion': 'entropy', 'min_samples_leaf': 5\n",
    "\n",
    "В этом случае удалось добится показателя f1 = 0.63 c AUC ROC = 0.861.\n",
    "Эти показатели удовлетворяют условиям поставленной задачи для исследования: добиться показателя больше 0.59.\n",
    "\n",
    "Тестирование модели случайного леса подтвердило полученные результаты, f1 оказался близок к валидационному = 0.627.\n",
    "Модель прошла проверку на адекватность.\n",
    "Решение найдено."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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