Online_Market_Place_EN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H6cQIZr3ElW1"
   },
   "source": [
    "# Analysis of online store "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business objective and work plan "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Objective:**\n",
    "The main objective of the project is to develop a solution that will personalize offers to loyal customers to increase their purchasing activity.\n",
    "\n",
    "**Criteria**:\n",
    "\n",
    "Target attribute - purchase activity. \n",
    "\n",
    "**Plan of Work**:\n",
    "1) Data Loading:\n",
    "- Import datasets for analysis.\n",
    "- Import libraries\n",
    "  \n",
    "2) Data preprocessing:\n",
    "- Performing data cleaning to handle omissions and remove duplicates.\n",
    "- Converting data types as needed.\n",
    "- Merging datasets\n",
    "\n",
    "3) Exploratory Data Analysis:\n",
    "- Correlation analysis\n",
    "\n",
    "4) Model building and selection of the best model\n",
    "5) Analyzing the importance of attributes\n",
    "6) Customer segmentation and development of recommendations\n",
    "7) Final conclusions\n",
    "\n",
    "\n",
    "**Datasets:**\n",
    "\n",
    "**market_file.csv**\n",
    "\n",
    "contains data on customer behavior on the website, customer communications and product behavior, and target attribute \n",
    "\n",
    "**market_money.csv**\n",
    "\n",
    "contains data about the revenue the store receives from the customer, i.e. how much the customer spent during the period of interaction with the site.\n",
    "\n",
    "**market_money.csv**\n",
    "\n",
    "contains data on the time (in minutes) that the customer spent on the site during the period.\n",
    "\n",
    "**market_money.csv** contains data on the average monthly profit of the customer for the last 3 months: what profit the store gets from sales to each customer.\n",
    "\n",
    "Translated with DeepL.com (free version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Upload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: shap in /opt/anaconda3/lib/python3.11/site-packages (0.46.0)\n",
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     ]
    }
   ],
   "source": [
    "!pip install shap\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install --upgrade ipywidgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy==1.23\n",
      "  Using cached numpy-1.23.0.tar.gz (10.7 MB)\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25hBuilding wheels for collected packages: numpy\n",
      "  Building wheel for numpy (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for numpy: filename=numpy-1.23.0-cp311-cp311-macosx_14_0_x86_64.whl size=5714429 sha256=067811668713bede51883349f56e14ac8b84ad58fa5d1f650e00ab0967c13b3e\n",
      "  Stored in directory: /Users/D063858/Library/Caches/pip/wheels/6d/36/1a/3ec6b85008bea3151efb003f5d41baa7bf4966cb43c1c2470b\n",
      "Successfully built numpy\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.26.4\n",
      "    Uninstalling numpy-1.26.4:\n",
      "      Successfully uninstalled numpy-1.26.4\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "gensim 4.3.0 requires FuzzyTM>=0.4.0, which is not installed.\n",
      "pandas 2.1.4 requires numpy<2,>=1.23.2; python_version == \"3.11\", but you have numpy 1.23.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed numpy-1.23.0\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy==1.23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: python-dateutil>=2.7 in /opt/anaconda3/lib/python3.11/site-packages (from matplotlib>=2.2.3->phik) (2.8.2)\n",
      "Collecting numpy>=1.18.0 (from phik)\n",
      "  Using cached numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl.metadata (61 kB)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/lib/python3.11/site-packages (from pandas>=0.25.1->phik) (2023.3.post1)\n",
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      "Using cached numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl (20.6 MB)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.23.0\n",
      "    Uninstalling numpy-1.23.0:\n",
      "      Successfully uninstalled numpy-1.23.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "gensim 4.3.0 requires FuzzyTM>=0.4.0, which is not installed.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed numpy-1.26.4\n"
     ]
    }
   ],
   "source": [
    "!pip install phik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n",
    "import phik\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "\n",
    "from sklearn.preprocessing import (OneHotEncoder,  \n",
    "                                    OrdinalEncoder,\n",
    "                                   StandardScaler,\n",
    "                                   MinMaxScaler, \n",
    "                                    LabelEncoder,\n",
    "                                    PolynomialFeatures\n",
    "                                    )\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "from sklearn.metrics import (\n",
    "                mean_squared_error,\n",
    "                mean_absolute_error,\n",
    "                r2_score,\n",
    "                accuracy_score, \n",
    "                confusion_matrix,\n",
    "                 recall_score,\n",
    "                precision_score,\n",
    "                 roc_auc_score\n",
    "             ) \n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "#upload datasets\n",
    "pth1_market_file = 'market_file.csv'\n",
    "pth2_market_file = '/datasets/market_file.csv'\n",
    "\n",
    "pth1_market_money = 'market_money.csv'\n",
    "pth2_market_money = '/datasets/market_money.csv'\n",
    "\n",
    "pth1_market_time = 'market_time.csv'\n",
    "pth2_market_time = '/datasets/market_time.csv'\n",
    "\n",
    "pth1_money = 'money.csv'\n",
    "pth2_money = '/datasets/money.csv'\n",
    "\n",
    "# reading files \n",
    "if os.path.exists(pth1_market_file):\n",
    "    df_market_file = pd.read_csv(pth1_market_file)\n",
    "elif os.path.exists(pth2_market_file):\n",
    "    df_market_file = pd.read_csv(pth2_market_file)\n",
    "else:\n",
    "    print('Something is wrong with market_file.csv')\n",
    "\n",
    "if os.path.exists(pth1_market_money):\n",
    "    df_market_money = pd.read_csv(pth1_market_money)\n",
    "elif os.path.exists(pth2_market_money):\n",
    "    df_market_money = pd.read_csv(pth2_market_money)\n",
    "else:\n",
    "    print('Something is wrong with market_money.csv')\n",
    "\n",
    "if os.path.exists(pth1_market_time):\n",
    "    df_market_time = pd.read_csv(pth1_market_time)\n",
    "elif os.path.exists(pth2_market_time):\n",
    "    df_market_time = pd.read_csv(pth2_market_time)\n",
    "else:\n",
    "    print('Something is wrong with market_time.csv')\n",
    "\n",
    "if os.path.exists(pth1_money):\n",
    "    df_money = pd.read_csv(pth1_money, sep=';', decimal=',')\n",
    "elif os.path.exists(pth2_money):\n",
    "    df_money = pd.read_csv(pth2_money, sep=';', decimal=',')\n",
    "else:\n",
    "    print('Something is wrong with money.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>id</th>\n",
       "      <th>Покупательская активность</th>\n",
       "      <th>Тип сервиса</th>\n",
       "      <th>Разрешить сообщать</th>\n",
       "      <th>Маркет_актив_6_мес</th>\n",
       "      <th>Маркет_актив_тек_мес</th>\n",
       "      <th>Длительность</th>\n",
       "      <th>Акционные_покупки</th>\n",
       "      <th>Популярная_категория</th>\n",
       "      <th>Средний_просмотр_категорий_за_визит</th>\n",
       "      <th>Неоплаченные_продукты_штук_квартал</th>\n",
       "      <th>Ошибка_сервиса</th>\n",
       "      <th>Страниц_за_визит</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215348</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215349</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4</td>\n",
       "      <td>819</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215350</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>стандартт</td>\n",
       "      <td>нет</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3</td>\n",
       "      <td>539</td>\n",
       "      <td>0.14</td>\n",
       "      <td>Домашний текстиль</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id Покупательская активность Тип сервиса Разрешить сообщать  \\\n",
       "0  215348                 Снизилась     премиум                 да   \n",
       "1  215349                 Снизилась     премиум                 да   \n",
       "2  215350                 Снизилась   стандартт                нет   \n",
       "\n",
       "   Маркет_актив_6_мес  Маркет_актив_тек_мес  Длительность  Акционные_покупки  \\\n",
       "0                 3.4                     5           121               0.00   \n",
       "1                 4.4                     4           819               0.75   \n",
       "2                 4.9                     3           539               0.14   \n",
       "\n",
       "  Популярная_категория  Средний_просмотр_категорий_за_визит  \\\n",
       "0     Товары для детей                                    6   \n",
       "1     Товары для детей                                    4   \n",
       "2    Домашний текстиль                                    5   \n",
       "\n",
       "   Неоплаченные_продукты_штук_квартал  Ошибка_сервиса  Страниц_за_визит  \n",
       "0                                   2               1                 5  \n",
       "1                                   4               2                 5  \n",
       "2                                   2               1                 5  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Format to lower case and rename the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_market_file.columns = df_market_file.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>покупательская_активность</th>\n",
       "      <th>тип_сервиса</th>\n",
       "      <th>разрешить_сообщать</th>\n",
       "      <th>маркет_актив_6_мес</th>\n",
       "      <th>маркет_актив_тек_мес</th>\n",
       "      <th>длительность</th>\n",
       "      <th>акционные_покупки</th>\n",
       "      <th>популярная_категория</th>\n",
       "      <th>средний_просмотр_категорий_за_визит</th>\n",
       "      <th>неоплаченные_продукты_штук_квартал</th>\n",
       "      <th>ошибка_сервиса</th>\n",
       "      <th>страниц_за_визит</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215348</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215349</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4</td>\n",
       "      <td>819</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215350</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>стандартт</td>\n",
       "      <td>нет</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3</td>\n",
       "      <td>539</td>\n",
       "      <td>0.14</td>\n",
       "      <td>Домашний текстиль</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id покупательская_активность тип_сервиса разрешить_сообщать  \\\n",
       "0  215348                 Снизилась     премиум                 да   \n",
       "1  215349                 Снизилась     премиум                 да   \n",
       "2  215350                 Снизилась   стандартт                нет   \n",
       "\n",
       "   маркет_актив_6_мес  маркет_актив_тек_мес  длительность  акционные_покупки  \\\n",
       "0                 3.4                     5           121               0.00   \n",
       "1                 4.4                     4           819               0.75   \n",
       "2                 4.9                     3           539               0.14   \n",
       "\n",
       "  популярная_категория  средний_просмотр_категорий_за_визит  \\\n",
       "0     Товары для детей                                    6   \n",
       "1     Товары для детей                                    4   \n",
       "2    Домашний текстиль                                    5   \n",
       "\n",
       "   неоплаченные_продукты_штук_квартал  ошибка_сервиса  страниц_за_визит  \n",
       "0                                   2               1                 5  \n",
       "1                                   4               2                 5  \n",
       "2                                   2               1                 5  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file = df_market_file.rename(columns=\n",
    "                                     {'покупательская активность': 'покупательская_активность',\n",
    "                                      'тип сервиса': 'тип_сервиса',\n",
    "                                      'разрешить сообщать': 'разрешить_сообщать', \n",
    "                                      }) \n",
    "df_market_file.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <td>препредыдущий_месяц</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215348</td>\n",
       "      <td>текущий_месяц</td>\n",
       "      <td>3293.1</td>\n",
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       "      <th>2</th>\n",
       "      <td>215348</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "       id               Период  Выручка\n",
       "0  215348  препредыдущий_месяц      0.0\n",
       "1  215348        текущий_месяц   3293.1\n",
       "2  215348     предыдущий_месяц      0.0"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_money.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "       id               период  выручка\n",
       "0  215348  препредыдущий_месяц      0.0\n",
       "1  215348        текущий_месяц   3293.1\n",
       "2  215348     предыдущий_месяц      0.0"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_money.columns = df_market_money.columns.str.lower()\n",
    "df_market_money.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10</td>\n",
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      "text/plain": [
       "       id            Период  минут\n",
       "0  215348     текущий_месяц     14\n",
       "1  215348  предыдцщий_месяц     13\n",
       "2  215349     текущий_месяц     10"
      ]
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     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_market_time.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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      ],
      "text/plain": [
       "       id            период  минут\n",
       "0  215348     текущий_месяц     14\n",
       "1  215348  предыдцщий_месяц     13\n",
       "2  215349     текущий_месяц     10"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_time.columns = df_market_time.columns.str.lower()\n",
    "df_market_time.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Прибыль</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215348</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215349</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215350</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  Прибыль\n",
       "0  215348     0.98\n",
       "1  215349     4.16\n",
       "2  215350     3.13"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>прибыль</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215348</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215349</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215350</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  прибыль\n",
       "0  215348     0.98\n",
       "1  215349     4.16\n",
       "2  215350     3.13"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money.columns = df_money.columns.str.lower()\n",
    "df_money.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing of df_market_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1300 entries, 0 to 1299\n",
      "Data columns (total 13 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   id                                   1300 non-null   int64  \n",
      " 1   покупательская_активность            1300 non-null   object \n",
      " 2   тип_сервиса                          1300 non-null   object \n",
      " 3   разрешить_сообщать                   1300 non-null   object \n",
      " 4   маркет_актив_6_мес                   1300 non-null   float64\n",
      " 5   маркет_актив_тек_мес                 1300 non-null   int64  \n",
      " 6   длительность                         1300 non-null   int64  \n",
      " 7   акционные_покупки                    1300 non-null   float64\n",
      " 8   популярная_категория                 1300 non-null   object \n",
      " 9   средний_просмотр_категорий_за_визит  1300 non-null   int64  \n",
      " 10  неоплаченные_продукты_штук_квартал   1300 non-null   int64  \n",
      " 11  ошибка_сервиса                       1300 non-null   int64  \n",
      " 12  страниц_за_визит                     1300 non-null   int64  \n",
      "dtypes: float64(2), int64(7), object(4)\n",
      "memory usage: 132.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_market_file.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df_market_file.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "покупательская_активность\n",
       "Прежний уровень    802\n",
       "Снизилась          498\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file['покупательская_активность'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "тип_сервиса\n",
       "стандарт     914\n",
       "премиум      376\n",
       "стандартт     10\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file['тип_сервиса'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "тип_сервиса\n",
       "стандарт    924\n",
       "премиум     376\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file['тип_сервиса'] = df_market_file['тип_сервиса'].replace({\n",
    "    'стандартт': 'стандарт'\n",
    "    })\n",
    "df_market_file['тип_сервиса'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "разрешить_сообщать\n",
       "да     962\n",
       "нет    338\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file['разрешить_сообщать'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "популярная_категория\n",
       "Товары для детей                        330\n",
       "Домашний текстиль                       251\n",
       "Косметика и аксесуары                   223\n",
       "Техника для красоты и здоровья          184\n",
       "Мелкая бытовая техника и электроника    174\n",
       "Кухонная посуда                         138\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file['популярная_категория'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__ \n",
    "- there are 1300 observations and 12 features + IDs in the dataset.\n",
    "- Missing values were not found.\n",
    "- duplicates were checked. One implicit duplicate of type_service was detected and corrected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing df_market_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3900 entries, 0 to 3899\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   id       3900 non-null   int64  \n",
      " 1   период   3900 non-null   object \n",
      " 2   выручка  3900 non-null   float64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_market_money.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(df_market_money.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "период\n",
       "препредыдущий_месяц    1300\n",
       "текущий_месяц          1300\n",
       "предыдущий_месяц       1300\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_money['период'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__ \n",
    "- there are 3900 observations and 2 traits + ID in the dataset.\n",
    "- Missing values were not found.\n",
    "- check for duplicates was performed. One implicit duplicate of a period was detected and corrected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing df_market_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2600 entries, 0 to 2599\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      2600 non-null   int64 \n",
      " 1   период  2600 non-null   object\n",
      " 2   минут   2600 non-null   int64 \n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 61.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_market_time.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "период\n",
       "текущий_месяц       1300\n",
       "предыдцщий_месяц    1300\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_time['период'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "период\n",
       "текущий_месяц       1300\n",
       "предыдущий_месяц    1300\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_time['период'] = df_market_time['период'].replace({\n",
    "    'предыдцщий_месяц': 'предыдущий_месяц'\n",
    "    })\n",
    "df_market_time['период'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__ \n",
    "- there are 2600 observations and 2 traits + ID in the dataset.\n",
    "- Missing values are not detected.\n",
    "- check for duplicates was performed, no duplicates were found"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing of df_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1300 entries, 0 to 1299\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   id       1300 non-null   int64  \n",
      " 1   прибыль  1300 non-null   float64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 20.4 KB\n"
     ]
    }
   ],
   "source": [
    "df_money.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Findings:__ \n",
    "- there are 1300 observations and 1 feature + ID in the dataset\n",
    "- No missing values detected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA df_market_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by analyzing the quantitative features. Let's look at the general statistical indicators df_market_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "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>id</th>\n",
       "      <th>маркет_актив_6_мес</th>\n",
       "      <th>маркет_актив_тек_мес</th>\n",
       "      <th>длительность</th>\n",
       "      <th>акционные_покупки</th>\n",
       "      <th>средний_просмотр_категорий_за_визит</th>\n",
       "      <th>неоплаченные_продукты_штук_квартал</th>\n",
       "      <th>ошибка_сервиса</th>\n",
       "      <th>страниц_за_визит</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.00000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>4.253769</td>\n",
       "      <td>4.011538</td>\n",
       "      <td>601.898462</td>\n",
       "      <td>0.319808</td>\n",
       "      <td>3.27000</td>\n",
       "      <td>2.840000</td>\n",
       "      <td>4.185385</td>\n",
       "      <td>8.176923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>375.421985</td>\n",
       "      <td>1.014814</td>\n",
       "      <td>0.696868</td>\n",
       "      <td>249.856289</td>\n",
       "      <td>0.249843</td>\n",
       "      <td>1.35535</td>\n",
       "      <td>1.971451</td>\n",
       "      <td>1.955298</td>\n",
       "      <td>3.978126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>215348.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>215672.750000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>405.500000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>606.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>216322.250000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>806.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>216647.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1079.000000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  маркет_актив_6_мес  маркет_актив_тек_мес  длительность  \\\n",
       "count    1300.000000         1300.000000           1300.000000   1300.000000   \n",
       "mean   215997.500000            4.253769              4.011538    601.898462   \n",
       "std       375.421985            1.014814              0.696868    249.856289   \n",
       "min    215348.000000            0.900000              3.000000    110.000000   \n",
       "25%    215672.750000            3.700000              4.000000    405.500000   \n",
       "50%    215997.500000            4.200000              4.000000    606.000000   \n",
       "75%    216322.250000            4.900000              4.000000    806.000000   \n",
       "max    216647.000000            6.600000              5.000000   1079.000000   \n",
       "\n",
       "       акционные_покупки  средний_просмотр_категорий_за_визит  \\\n",
       "count        1300.000000                           1300.00000   \n",
       "mean            0.319808                              3.27000   \n",
       "std             0.249843                              1.35535   \n",
       "min             0.000000                              1.00000   \n",
       "25%             0.170000                              2.00000   \n",
       "50%             0.240000                              3.00000   \n",
       "75%             0.300000                              4.00000   \n",
       "max             0.990000                              6.00000   \n",
       "\n",
       "       неоплаченные_продукты_штук_квартал  ошибка_сервиса  страниц_за_визит  \n",
       "count                         1300.000000     1300.000000       1300.000000  \n",
       "mean                             2.840000        4.185385          8.176923  \n",
       "std                              1.971451        1.955298          3.978126  \n",
       "min                              0.000000        0.000000          1.000000  \n",
       "25%                              1.000000        3.000000          5.000000  \n",
       "50%                              3.000000        4.000000          8.000000  \n",
       "75%                              4.000000        6.000000         11.000000  \n",
       "max                             10.000000        9.000000         20.000000  "
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_file.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's look at categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = [\n",
    "    ('покупательская_активность', 'purchasing_activity', 45),\n",
    "    ('тип_сервиса', 'service_type', 90),\n",
    "    ('разрешить_сообщать', 'contact_allowed', 90),\n",
    "    ('популярная_категория', 'popular category', 45)\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "\n",
    "for i, (column, title, angle) in enumerate(columns, 1):\n",
    "    plt.subplot(3, 2, i)\n",
    "    data = df_market_file[column].value_counts()\n",
    "    data.plot(kind='pie', autopct='%1.1f%%', startangle=angle, wedgeprops={'linewidth': 3, 'edgecolor': 'white'})\n",
    "    plt.title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion:__ \n",
    "- No anomalies were found in the quantitative features\n",
    "- Categorical data also looks good"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA df_market_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "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>id</th>\n",
       "      <th>выручка</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3900.000000</td>\n",
       "      <td>3900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>5025.696051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>375.325686</td>\n",
       "      <td>1777.704104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>215348.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>215672.750000</td>\n",
       "      <td>4590.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>4957.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>216322.250000</td>\n",
       "      <td>5363.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>216647.000000</td>\n",
       "      <td>106862.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id        выручка\n",
       "count    3900.000000    3900.000000\n",
       "mean   215997.500000    5025.696051\n",
       "std       375.325686    1777.704104\n",
       "min    215348.000000       0.000000\n",
       "25%    215672.750000    4590.150000\n",
       "50%    215997.500000    4957.500000\n",
       "75%    216322.250000    5363.000000\n",
       "max    216647.000000  106862.200000"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_money.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.boxplot(df_market_money['выручка'], vert=False)  \n",
    "plt.xlabel('Revenue')\n",
    "plt.title('Revenue Statistics')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "df_market_money['выручка'].hist(bins=30)\n",
    "plt.title('Revenue Distribution')\n",
    "plt.xlabel('Revenue')\n",
    "plt.ylabel('Quantity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uu++WJGVmZmrevHlatGiRdu3aJZfLpeTkZJ08edIaIzU1VatXr9bKlSu1bds2nTp1SoMGDVJ5eblPagIAAA2HT4NQy5Yt5XK5rK9//vOfuuqqq5SUlCRjjBYsWKCZM2dq6NChSkhI0PLly1VcXKysrCxJUmFhoZYsWaJnnnlG/fr1U+fOnbVixQrt2bNHGzdu9GVpAACgAag3H7paWlqqFStWKC0tTQ6HQ19++aXy8vKUkpJi9XE6nUpKStL27ds1YcIE7d69W2VlZR59YmJilJCQoO3bt6t///7V7svtdsvtdlvLRUVFks5+0FtZWZnXaqocy5tj1kfU6V+o07/YoU471ChR54X6Xop6E4TWrFmjEydOaMyYMZKkvLw8SVJUVJRHv6ioKB0+fNjqExwcrKZNm1bpU7l9debMmaPZs2dXac/OzlZoaOillFGtykt+/o46/Qt1+hc71GmHGiXq/LHi4uJL3k+9CUJLlizRgAEDFBMT49HucDg8lo0xVdrO9VN9ZsyYobS0NGu5qKhIsbGxSklJUURERC1mX72ysjLl5OQoOTlZQUFBXhu3vqFO/0Kd/sUOddqhRok6q1N5RedS1IsgdPjwYW3cuFGvv/661eZyuSSdPesTHR1ttefn51tniVwul0pLS1VQUOBxVig/P189e/Y87/6cTqecTmeV9qCgoDo5uOpq3PqGOv0LdfpW+0feqvW2X80dWKWtvtbpTXaoUaLOc/tcqnrxPkJLly5Vq1atNHDg//3yxsXFyeVyeZwaKy0t1ZYtW6yQ06VLFwUFBXn0yc3N1d69ey8YhAAAAKR6cEaooqJCS5cu1ejRoxUY+H/TcTgcSk1NVUZGhuLj4xUfH6+MjAyFhoZq+PDhkqTIyEiNHz9eU6dOVfPmzdWsWTNNmzZNiYmJ6tevn69KAgAADYTPg9DGjRt15MgRjRs3rsq66dOnq6SkRBMnTlRBQYG6deum7OxshYeHW33mz5+vwMBADRs2TCUlJerbt6+WLVumgICAy1kGAABogHwehFJSUmSMqXadw+FQenq60tPTz7t9SEiIFi5cqIULF9bRDAEAgL+qF/cIAQAA+AJBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2BZBCAAA2JbPH58HAHjXjz+ewxlglHmzlJC+Qe7yC39Oo1T9x3MA/owzQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLYIQgAAwLZ8HoSOHTum++67T82bN1doaKhuvPFG7d6921pvjFF6erpiYmLUuHFj9e7dW/v27fMYw+12a/LkyWrRooXCwsI0ePBgHT169HKXAgAAGhifBqGCggL16tVLQUFBWrdunT799FM988wzuuKKK6w+mZmZmjdvnhYtWqRdu3bJ5XIpOTlZJ0+etPqkpqZq9erVWrlypbZt26ZTp05p0KBBKi8v90FVAACgoQj05c6ffvppxcbGaunSpVZb+/btre+NMVqwYIFmzpypoUOHSpKWL1+uqKgoZWVlacKECSosLNSSJUv0yiuvqF+/fpKkFStWKDY2Vhs3blT//v0va00AAKDh8OkZoTfeeENdu3bV3XffrVatWqlz58568cUXrfWHDh1SXl6eUlJSrDan06mkpCRt375dkrR7926VlZV59ImJiVFCQoLVBwAAoDo+PSP05ZdfavHixUpLS9Ojjz6q9957Tw8++KCcTqdGjRqlvLw8SVJUVJTHdlFRUTp8+LAkKS8vT8HBwWratGmVPpXbn8vtdsvtdlvLRUVFkqSysjKVlZV5rb7Ksbw5Zn1Enf6FOusHZ4DxzjiNjMefP6W+vh4XUt9/lt5Cnefveyl8GoQqKirUtWtXZWRkSJI6d+6sffv2afHixRo1apTVz+FweGxnjKnSdq4L9ZkzZ45mz55dpT07O1uhoaE1LeMn5eTkeH3M+og6/Qt1+lbmzd4d74muFRfVb+3atd7d8WVUX3+W3kad/6e4uPiS9+PTIBQdHa3rr7/eo+26667TqlWrJEkul0vS2bM+0dHRVp/8/HzrLJHL5VJpaakKCgo8zgrl5+erZ8+e1e53xowZSktLs5aLiooUGxurlJQURUREeKc4nU2qOTk5Sk5OVlBQkNfGrW+o079Qp/ckpG+ok3FrwtnI6ImuFXrs/UZyV1z4P5CStDe94d1XyTHrX2pSZ+UVnUvh0yDUq1cvHThwwKPt888/V7t27SRJcXFxcrlcysnJUefOnSVJpaWl2rJli55++mlJUpcuXRQUFKScnBwNGzZMkpSbm6u9e/cqMzOz2v06nU45nc4q7UFBQXVycNXVuPUNdfoX6rx07vKfDh6Xi7vCcVHzacg/c45Z/3IxdXrjdfBpEHrooYfUs2dPZWRkaNiwYXrvvff0wgsv6IUXXpB09pJYamqqMjIyFB8fr/j4eGVkZCg0NFTDhw+XJEVGRmr8+PGaOnWqmjdvrmbNmmnatGlKTEy0niIDAACojk+D0E033aTVq1drxowZevzxxxUXF6cFCxZoxIgRVp/p06erpKREEydOVEFBgbp166bs7GyFh4dbfebPn6/AwEANGzZMJSUl6tu3r5YtW6aAgABflAUAABoInwYhSRo0aJAGDRp03vUOh0Pp6elKT08/b5+QkBAtXLhQCxcurIMZAgAAf+Xzj9gAAADwFYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwrUBfTwAAUH+0f+StWm/71dyBXpwJcHlwRggAANgWQQgAANgWQQgAANiWT4NQenq6HA6Hx5fL5bLWG2OUnp6umJgYNW7cWL1799a+ffs8xnC73Zo8ebJatGihsLAwDR48WEePHr3cpQAAgAbI52eEOnbsqNzcXOtrz5491rrMzEzNmzdPixYt0q5du+RyuZScnKyTJ09afVJTU7V69WqtXLlS27Zt06lTpzRo0CCVl5f7ohwAANCA+PypscDAQI+zQJWMMVqwYIFmzpypoUOHSpKWL1+uqKgoZWVlacKECSosLNSSJUv0yiuvqF+/fpKkFStWKDY2Vhs3blT//v0vay0AAKBh8XkQOnjwoGJiYuR0OtWtWzdlZGToyiuv1KFDh5SXl6eUlBSrr9PpVFJSkrZv364JEyZo9+7dKisr8+gTExOjhIQEbd++/bxByO12y+12W8tFRUWSpLKyMpWVlXmttsqxvDlmfUSd/oU6vccZYOps7IueQyPj8Wdd8tUxwzHrX2pSpzdeC4cxxme/qevWrVNxcbGuueYaffPNN3ryySf12Wefad++fTpw4IB69eqlY8eOKSYmxtrmP//zP3X48GFt2LBBWVlZGjt2rEeokaSUlBTFxcXpz3/+c7X7TU9P1+zZs6u0Z2VlKTQ01LtFAgCAOlFcXKzhw4ersLBQERERtRrDp2eEBgwYYH2fmJioHj166KqrrtLy5cvVvXt3SZLD4fDYxhhTpe1cP9VnxowZSktLs5aLiooUGxurlJSUWr+Q1SkrK1NOTo6Sk5MVFBTktXHrG+r0L9TpPQnpG+pk3JpwNjJ6omuFHnu/kdwVF/6781LtTffN7Qgcs/6lJnVWXtG5FD6/NPZjYWFhSkxM1MGDBzVkyBBJUl5enqKjo60++fn5ioqKkiS5XC6VlpaqoKBATZs29ejTs2fP8+7H6XTK6XRWaQ8KCqqTg6uuxq1vqNO/UOelc5fXbfCoCXeFo87n4+vjhWPWv1xMnd54HXz+1NiPud1u7d+/X9HR0YqLi5PL5VJOTo61vrS0VFu2bLFCTpcuXRQUFOTRJzc3V3v37r1gEAIAAJB8fEZo2rRpuvPOO9W2bVvl5+frySefVFFRkUaPHi2Hw6HU1FRlZGQoPj5e8fHxysjIUGhoqIYPHy5JioyM1Pjx4zV16lQ1b95czZo107Rp05SYmGg9RQYAAHA+Pg1CR48e1b333qvvvvtOLVu2VPfu3bVz5061a9dOkjR9+nSVlJRo4sSJKigoULdu3ZSdna3w8HBrjPnz5yswMFDDhg1TSUmJ+vbtq2XLlikgIMBXZQEAgAbCp0Fo5cqVF1zvcDiUnp6u9PT08/YJCQnRwoULtXDhQi/PDgAA+Lt6dY8QAADA5UQQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtlWrIJSenq7Dhw97ey4AAACXVa2C0JtvvqmrrrpKffv2VVZWls6cOePteQEAANS5WgWh3bt364MPPlCnTp300EMPKTo6Wvfff7927drl7fkBAADUmVrfI9SpUyfNnz9fx44d01/+8hcdO3ZMvXr1UmJiop599lkVFhZ6c54AAABed8k3S1dUVKi0tFRut1vGGDVr1kyLFy9WbGys/va3v3ljjgAAAHWi1kFo9+7deuCBBxQdHa2HHnpInTt31v79+7VlyxZ99tlnmjVrlh588EFvzhUAAMCrahWEOnXqpO7du+vQoUNasmSJvv76a82dO1dXX3211WfUqFH69ttvL3rMOXPmyOFwKDU11Wozxig9PV0xMTFq3LixevfurX379nls53a7NXnyZLVo0UJhYWEaPHiwjh49WpuyAACAzdQqCN1999366quv9NZbb2nIkCEKCAio0qdly5aqqKi4qPF27dqlF154QZ06dfJoz8zM1Lx587Ro0SLt2rVLLpdLycnJOnnypNUnNTVVq1ev1sqVK7Vt2zadOnVKgwYNUnl5eW1KAwAANlKrIGSMUdOmTau0l5SU6PHHH6/RWKdOndKIESP04osveoxpjNGCBQs0c+ZMDR06VAkJCVq+fLmKi4uVlZUlSSosLNSSJUv0zDPPqF+/furcubNWrFihPXv2aOPGjbUpDQAA2EitgtDs2bN16tSpKu3FxcWaPXt2jcaaNGmSBg4cqH79+nm0Hzp0SHl5eUpJSbHanE6nkpKStH37dkln71MqKyvz6BMTE6OEhASrDwAAwPkE1mYjY4wcDkeV9o8//ljNmjW76HFWrlypDz74oNr3H8rLy5MkRUVFebRHRUVZ72qdl5en4ODgKmenoqKirO2r43a75Xa7reWioiJJUllZmcrKyi56/j+lcixvjlkfUad/oU7vcQaYOhv7oufQyHj8WZd8dcxwzPqXmtTpjdeiRkGoadOmcjgccjgcuuaaazzCUHl5uU6dOqXf/va3FzXW119/rSlTpig7O1shISHn7Xdu4DpfCKtJnzlz5lR75io7O1uhoaE/MfOay8nJ8fqY9RF1+hfqvHSZN9fZ0DX2RNeLu2fzUqxdu7bO93EhHLP+5WLqLC4uvuT91CgILViwQMYYjRs3TrNnz1ZkZKS1Ljg4WO3bt1ePHj0uaqzdu3crPz9fXbp0sdrKy8u1detWLVq0SAcOHJB09qxPdHS01Sc/P986S+RyuVRaWqqCggKPs0L5+fnq2bPnefc9Y8YMpaWlWctFRUWKjY1VSkqKIiIiLmr+F6OsrEw5OTlKTk5WUFCQ18atb6jTv1Cn9ySkb6iTcWvC2cjoia4Veuz9RnJXXPg/kZdqb3r/Oh3/fDhm/UtN6qy8onMpahSERo8eLUmKi4tTz549L+kH0bdvX+3Zs8ejbezYserQoYMefvhhXXnllXK5XMrJyVHnzp0lSaWlpdqyZYuefvppSVKXLl0UFBSknJwcDRs2TJKUm5urvXv3KjMz87z7djqdcjqdVdqDgoLq5OCqq3HrG+r0L9R56dzldRs8asJd4ajz+fj6eOGY9S8XU6c3XoeLDkJFRUXW2ZLOnTurpKREJSUl1fa9mLMq4eHhSkhI8GgLCwtT8+bNrfbU1FRlZGQoPj5e8fHxysjIUGhoqIYPHy5JioyM1Pjx4zV16lQ1b95czZo107Rp05SYmFjl5msAAIBzXXQQatq0qXJzc9WqVStdccUV1d6DU3lvjrfew2f69OkqKSnRxIkTVVBQoG7duik7O1vh4eFWn/nz5yswMFDDhg1TSUmJ+vbtq2XLllX73kYAAAA/dtFB6J133rGeCNu0aVOdTGbz5s0eyw6HQ+np6UpPTz/vNiEhIVq4cKEWLlxYJ3MCAAD+66KDUFJSkvV9XFycYmNjq32i6+uvv/be7AAAAOpQrd5QMS4urtrPETt+/Lji4uIueVIAAACXQ60/YqO6e4ROnTp1wfcEAgAAqE9q9Ph85XvvOBwOPfbYYx5vPlheXq53331XN954o1cnCAAAUFdqFIQ+/PBDSWfPCO3Zs0fBwcHWuuDgYN1www2aNm2ad2cIAABQR2oUhCqfFhs7dqyeffZZr74LMwAAwOVWqw9dXbp0qbfnAQAAcNnVKgidPn1ac+fO1dtvv638/HxVVHh+mN+XX37plckBAADUpVoFoV//+tfasmWLRo4cqejo6J/8NHgAAID6qFZBaN26dXrrrbfUq1cvb88HAADgsqnV+wg1bdrU+rgNAACAhqpWQeiJJ57Q73//exUXF3t7PgAAAJdNrS6NPfPMM/rf//1fRUVFqX379goKCvJY/8EHH3hlcgAAAHWpVkFoyJAhXp4GAADA5VerIDRr1ixvzwMAAOCyq9U9QgAAAP6gVmeEysvLNX/+fP3973/XkSNHVFpa6rH++PHjXpkcAABAXarVGaHZs2dr3rx5GjZsmAoLC5WWlqahQ4eqUaNGSk9P9/IUAQAA6katgtBf//pXvfjii5o2bZoCAwN177336qWXXtLvf/977dy509tzBAAAqBO1CkJ5eXlKTEyUJDVp0kSFhYWSpEGDBumtt97y3uwAAADqUK2CUJs2bZSbmytJuvrqq5WdnS1J2rVrl5xOp/dmBwAAUIdqFYR+/vOf6+2335YkTZkyRY899pji4+M1atQojRs3zqsTBAAAqCu1emps7ty51ve//OUv1aZNG23fvl1XX321Bg8e7LXJAQAA1KVaBaFzde/eXd27d/fGUAAAAJdNrYLQyy+/fMH1o0aNqtVkAAAALqdaBaEpU6Z4LJeVlam4uFjBwcEKDQ0lCAEAgAahVjdLFxQUeHydOnVKBw4c0C233KJXX33V23MEAACoE177rLH4+HjNnTu3ytkiAACA+sqrH7oaEBCgf//7394cEgAAoM7U6h6hN954w2PZGKPc3FwtWrRIvXr18srEAAAA6lqtgtCQIUM8lh0Oh1q2bKnbbrtNzzzzjDfmBQAAUOdqFYQqKiokSd9++62Cg4MVGRnp1UkBAABcDjW+R+jEiROaNGmSWrRoIZfLpWbNmsnlcmnGjBkqLi6uizkCAADUiRqdETp+/Lh69OihY8eOacSIEbruuutkjNH+/fu1cOFC5eTkaNu2bfr444/17rvv6sEHH6yreQMAAFyyGgWhxx9/XMHBwfrf//1fRUVFVVmXkpKikSNHKjs7W88995xXJwoAAOBtNQpCa9as0Z///OcqIUiSXC6XMjMzdccdd2jWrFkaPXq01yYJAABQF2p0j1Bubq46dux43vUJCQlq1KiRZs2adckTAwAAqGs1OiPUokULffXVV2rTpk216w8dOqRWrVp5ZWIAgIal/SNv1Xrbr+YO9OJMgItXozNCt99+u2bOnKnS0tIq69xutx577DHdfvvtXpscAABAXarRGaHZs2era9euio+P16RJk9ShQwdJ0qeffqrnn39ebrdbL7/8cp1MFAAAwNtqdEaoTZs22rFjh66//nrNmDFDQ4YM0ZAhQzRz5kxdf/31+te//qW2bdte9HiLFy9Wp06dFBERoYiICPXo0UPr1q2z1htjlJ6erpiYGDVu3Fi9e/fWvn37PMZwu92aPHmyWrRoobCwMA0ePFhHjx6tSVkAAMCmavyGinFxcVq3bp2+++477dy5Uzt37tS3336r9evX6+qrr67RWG3atNHcuXP1/vvv6/3339dtt92mu+66ywo7mZmZmjdvnhYtWqRdu3bJ5XIpOTlZJ0+etMZITU3V6tWrtXLlSm3btk2nTp3SoEGDVF5eXtPSAACAzdTqIzYkqWnTprr55psvaed33nmnx/JTTz2lxYsXa+fOnbr++uu1YMECzZw5U0OHDpUkLV++XFFRUcrKytKECRNUWFioJUuW6JVXXlG/fv0kSStWrFBsbKw2btyo/v37X9L8AACAf6t1EPK28vJyvfbaazp9+rR69OihQ4cOKS8vTykpKVYfp9OppKQkbd++XRMmTNDu3btVVlbm0ScmJkYJCQnavn37eYOQ2+2W2+22louKiiRJZWVlKisr81pNlWN5c8z6iDr9C3V6jzPA1NnYFz2HRsbjz/rqUn4OHLP+pSZ1euO1cBhjfPrbsWfPHvXo0UNnzpxRkyZNlJWVpTvuuEPbt29Xr169dOzYMcXExFj9//M//1OHDx/Whg0blJWVpbFjx3qEGklKSUlRXFyc/vznP1e7z/T0dM2ePbtKe1ZWlkJDQ71bIAAAqBPFxcUaPny4CgsLFRERUasxfH5G6Nprr9VHH32kEydOaNWqVRo9erS2bNlirXc4HB79jTFV2s71U31mzJihtLQ0a7moqEixsbFKSUmp9QtZnbKyMuXk5Cg5OVlBQUFeG7e+oU7/Qp3ek5C+oU7GrQlnI6MnulbosfcbyV1x4b87fWlveu1vZeCY9S81qbPyis6l8HkQCg4Otm6y7tq1q3bt2qVnn31WDz/8sCQpLy9P0dHRVv/8/HzrIz5cLpdKS0tVUFCgpk2bevTp2bPneffpdDrldDqrtAcFBdXJwVVX49Y31OlfqPPSucvrT/BwVzjq1XzO5Y2fAcesf7mYOr3xOtT4qbG6ZoyR2+1WXFycXC6XcnJyrHWlpaXasmWLFXK6dOmioKAgjz65ubnau3fvBYMQAACA5OMzQo8++qgGDBig2NhYnTx5UitXrtTmzZu1fv16ORwOpaamKiMjQ/Hx8YqPj1dGRoZCQ0M1fPhwSVJkZKTGjx+vqVOnqnnz5mrWrJmmTZumxMRE6ykyAACA8/FpEPrmm280cuRI5ebmKjIyUp06ddL69euVnJwsSZo+fbpKSko0ceJEFRQUqFu3bsrOzlZ4eLg1xvz58xUYGKhhw4appKREffv21bJlyxQQEOCrsgAAQAPh0yC0ZMmSC653OBxKT09Xenr6efuEhIRo4cKFWrhwoZdnBwAA/F29u0cIAADgciEIAQAA2/L54/MAUJ+1f+QtX08BQB3ijBAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtghAAALAtnwahOXPm6KabblJ4eLhatWqlIUOG6MCBAx59jDFKT09XTEyMGjdurN69e2vfvn0efdxutyZPnqwWLVooLCxMgwcP1tGjRy9nKQAAoAHyaRDasmWLJk2apJ07dyonJ0c//PCDUlJSdPr0aatPZmam5s2bp0WLFmnXrl1yuVxKTk7WyZMnrT6pqalavXq1Vq5cqW3btunUqVMaNGiQysvLfVEWAABoIAJ9ufP169d7LC9dulStWrXS7t279bOf/UzGGC1YsEAzZ87U0KFDJUnLly9XVFSUsrKyNGHCBBUWFmrJkiV65ZVX1K9fP0nSihUrFBsbq40bN6p///6XvS4AANAw1Kt7hAoLCyVJzZo1kyQdOnRIeXl5SklJsfo4nU4lJSVp+/btkqTdu3errKzMo09MTIwSEhKsPgAAANXx6RmhHzPGKC0tTbfccosSEhIkSXl5eZKkqKgoj75RUVE6fPiw1Sc4OFhNmzat0qdy+3O53W653W5ruaioSJJUVlamsrIy7xT0/4/34z/9FXX6F+r05Awwl2M6dcbZyHj8WV9dyvHGMetfalKnN16LehOEHnjgAX3yySfatm1blXUOh8Nj2RhTpe1cF+ozZ84czZ49u0p7dna2QkNDazDri5OTk+P1Mesj6vQv1HlW5s2XaSJ17ImuFb6ewgWtXbv2ksfgmPUvF1NncXHxJe+nXgShyZMn64033tDWrVvVpk0bq93lckk6e9YnOjraas/Pz7fOErlcLpWWlqqgoMDjrFB+fr569uxZ7f5mzJihtLQ0a7moqEixsbFKSUlRRESE1+oqKytTTk6OkpOTFRQU5LVx6xvq9C/U6SkhfcNlnJX3ORsZPdG1Qo+930juigv/B9KX9qbX/n5Ojln/UpM6K6/oXAqfBiFjjCZPnqzVq1dr8+bNiouL81gfFxcnl8ulnJwcde7cWZJUWlqqLVu26Omnn5YkdenSRUFBQcrJydGwYcMkSbm5udq7d68yMzOr3a/T6ZTT6azSHhQUVCcHV12NW99Qp3+hzrPc5fU3PNSEu8JRr2vxxrHGMetfLqZOb7wOPg1CkyZNUlZWlv7xj38oPDzcuqcnMjJSjRs3lsPhUGpqqjIyMhQfH6/4+HhlZGQoNDRUw4cPt/qOHz9eU6dOVfPmzdWsWTNNmzZNiYmJ1lNkAAAA1fFpEFq8eLEkqXfv3h7tS5cu1ZgxYyRJ06dPV0lJiSZOnKiCggJ169ZN2dnZCg8Pt/rPnz9fgYGBGjZsmEpKStS3b18tW7ZMAQEBl6sUAADQAPn80thPcTgcSk9PV3p6+nn7hISEaOHChVq4cKEXZwcAAPxdvXofIQAAgMuJIAQAAGyrXjw+DwCwt/aPvFXrbQ8+kfLTnYDz4IwQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwLYIQAACwrUBfTwAAgEuRkL5BmTef/dNd7qjRtl/NHVhHs0JDwRkhAABgW5wRAuD32j/yVpU2Z4Cp9VkEAP6DM0IAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2CEIAAMC2fBqEtm7dqjvvvFMxMTFyOBxas2aNx3pjjNLT0xUTE6PGjRurd+/e2rdvn0cft9utyZMnq0WLFgoLC9PgwYN19OjRy1gFAABoqHwahE6fPq0bbrhBixYtqnZ9Zmam5s2bp0WLFmnXrl1yuVxKTk7WyZMnrT6pqalavXq1Vq5cqW3btunUqVMaNGiQysvLL1cZAACggfLpZ40NGDBAAwYMqHadMUYLFizQzJkzNXToUEnS8uXLFRUVpaysLE2YMEGFhYVasmSJXnnlFfXr10+StGLFCsXGxmrjxo3q37//ZasFAAA0PPX2Q1cPHTqkvLw8paSkWG1Op1NJSUnavn27JkyYoN27d6usrMyjT0xMjBISErR9+/bzBiG32y23220tFxUVSZLKyspUVlbmtRoqx/LmmPURdfoXf6zTGWCqtjUyHn/6KzvUeSk1NqTj3B9/N6tTkzq98VrU2yCUl5cnSYqKivJoj4qK0uHDh60+wcHBatq0aZU+ldtXZ86cOZo9e3aV9uzsbIWGhl7q1KvIycnx+pj1EXX6F3+qM/Pm8697omvF5ZuID9mhztrUuHbt2jqYSd3yp9/NC7mYOouLiy95P/U2CFVyOBwey8aYKm3n+qk+M2bMUFpamrVcVFSk2NhYpaSkKCIi4tIm/CNlZWXKyclRcnKygoKCvDZufUOd/sUf60xI31ClzdnI6ImuFXrs/UZyV1z475SGzA51XkqNe9Mbzi0U/vi7WZ2a1Fl5RedS1Nsg5HK5JJ096xMdHW215+fnW2eJXC6XSktLVVBQ4HFWKD8/Xz179jzv2E6nU06ns0p7UFBQnRxcdTVufUOd/sWf6nSXn/8fR3eF44Lr/YUd6qxNjQ3xGPen380LuZg6vfE61Nv3EYqLi5PL5fI4NVZaWqotW7ZYIadLly4KCgry6JObm6u9e/deMAgBAABIPj4jdOrUKX3xxRfW8qFDh/TRRx+pWbNmatu2rVJTU5WRkaH4+HjFx8crIyNDoaGhGj58uCQpMjJS48eP19SpU9W8eXM1a9ZM06ZNU2JiovUUGQAAwPn4NAi9//776tOnj7Vced/O6NGjtWzZMk2fPl0lJSWaOHGiCgoK1K1bN2VnZys8PNzaZv78+QoMDNSwYcNUUlKivn37atmyZQoICLjs9QAAgIbFp0God+/eMub8jzs6HA6lp6crPT39vH1CQkK0cOFCLVy4sA5mCAAA/Fm9vUcIAACgrhGEAACAbRGEAACAbdXb9xECgB9r/8hbvp4CAD9EEAIA2NalBOyv5g704kzgK1waAwAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAtkUQAgAAthXo6wkAsI/2j7zl6ykAXnMpx/NXcwd6cSa4FJwRAgAAtkUQAgAAtkUQAgAAtkUQAgAAtsXN0gBqhBueAfgTzggBAADbIggBAADbIggBAADbIggBAADb4mZpAAAus9o8dOAMMMq8uQ4mY3OcEQIAALZFEAIAALZFEAIAALZFEAIAALbFzdIAADQgCekb5C531Grbr+YO9PJsGj7OCAEAANsiCAEAANsiCAEAANviHiHAhi70Zm6Vb9p2KfchAEBD4TdB6Pnnn9cf/vAH5ebmqmPHjlqwYIFuvfVWX08LAIB6ozbvaF3JX2+09otLY3/729+UmpqqmTNn6sMPP9Stt96qAQMG6MiRI76eGgAAqMf84ozQvHnzNH78eP3617+WJC1YsEAbNmzQ4sWLNWfOHB/PrvaXGPw1fQMAUF80+CBUWlqq3bt365FHHvFoT0lJ0fbt2300KwAA/Iu/XlZr8EHou+++U3l5uaKiojzao6KilJeXV+02brdbbrfbWi4sLJQkHT9+XGVlZV6bW1lZmYqLixVY1kjlFTU/I/T99997bS51qbLO77//XkFBQb6eTp2pb3V2m/N2rbe90C9+YIVRcXFFrY/bhoI6/YcdapQadp01+fesJn/Xnjx5UpJkjKn13Bp8EKrkcHgeFMaYKm2V5syZo9mzZ1dpj4uLq5O51VaLZ3w9A9jVcF9P4DKhTv9hhxqlhltnXf97dvLkSUVGRtZq2wYfhFq0aKGAgIAqZ3/y8/OrnCWqNGPGDKWlpVnLFRUVOn78uJo3b37e8FQbRUVFio2N1ddff62IiAivjVvfUKd/oU7/Yoc67VCjRJ3VMcbo5MmTiomJqfX+GnwQCg4OVpcuXZSTk6Of//znVntOTo7uuuuuardxOp1yOp0ebVdccUWdzTEiIsKvD9pK1OlfqNO/2KFOO9QoUee5ansmqFKDD0KSlJaWppEjR6pr167q0aOHXnjhBR05ckS//e1vfT01AABQj/lFELrnnnv0/fff6/HHH1dubq4SEhK0du1atWvXztdTAwAA9ZhfBCFJmjhxoiZOnOjraXhwOp2aNWtWlctw/oY6/Qt1+hc71GmHGiXqrCsOcynPnAEAADRgfvERGwAAALVBEAIAALZFEAIAALZFEAIAALZFEKpDzz//vOLi4hQSEqIuXbro//2//+frKZ3X1q1bdeeddyomJkYOh0Nr1qzxWG+MUXp6umJiYtS4cWP17t1b+/bt8+jjdrs1efJktWjRQmFhYRo8eLCOHj3q0aegoEAjR45UZGSkIiMjNXLkSJ04caKOqztrzpw5uummmxQeHq5WrVppyJAhOnDggEcff6hz8eLF6tSpk/VmZD169NC6deus9f5QY3XmzJkjh8Oh1NRUq80fak1PT5fD4fD4crlc1np/qLHSsWPHdN9996l58+YKDQ3VjTfeqN27d1vr/aHW9u3bV/l5OhwOTZo0yW9q/OGHH/Rf//VfiouLU+PGjXXllVfq8ccfV0VFhdWnXtVpUCdWrlxpgoKCzIsvvmg+/fRTM2XKFBMWFmYOHz7s66lVa+3atWbmzJlm1apVRpJZvXq1x/q5c+ea8PBws2rVKrNnzx5zzz33mOjoaFNUVGT1+e1vf2tat25tcnJyzAcffGD69OljbrjhBvPDDz9YfW6//XaTkJBgtm/fbrZv324SEhLMoEGDLkuN/fv3N0uXLjV79+41H330kRk4cKBp27atOXXqlF/V+cYbb5i33nrLHDhwwBw4cMA8+uijJigoyOzdu9dvajzXe++9Z9q3b286depkpkyZYrX7Q62zZs0yHTt2NLm5udZXfn6+X9VojDHHjx837dq1M2PGjDHvvvuuOXTokNm4caP54osv/KrW/Px8j59lTk6OkWQ2bdrkNzU++eSTpnnz5uaf//ynOXTokHnttddMkyZNzIIFC6w+9alOglAdufnmm81vf/tbj7YOHTqYRx55xEczunjnBqGKigrjcrnM3LlzrbYzZ86YyMhI86c//ckYY8yJEydMUFCQWblypdXn2LFjplGjRmb9+vXGGGM+/fRTI8ns3LnT6rNjxw4jyXz22Wd1XFVV+fn5RpLZsmWLMcZ/6zTGmKZNm5qXXnrJL2s8efKkiY+PNzk5OSYpKckKQv5S66xZs8wNN9xQ7Tp/qdEYYx5++GFzyy23nHe9P9X6Y1OmTDFXXXWVqaio8JsaBw4caMaNG+fRNnToUHPfffcZY+rfz5JLY3WgtLRUu3fvVkpKikd7SkqKtm/f7qNZ1d6hQ4eUl5fnUY/T6VRSUpJVz+7du1VWVubRJyYmRgkJCVafHTt2KDIyUt26dbP6dO/eXZGRkT55XQoLCyVJzZo1k+SfdZaXl2vlypU6ffq0evTo4Zc1Tpo0SQMHDlS/fv082v2p1oMHDyomJkZxcXH61a9+pS+//FKSf9X4xhtvqGvXrrr77rvVqlUrde7cWS+++KK13p9qrVRaWqoVK1Zo3LhxcjgcflPjLbfcorfffluff/65JOnjjz/Wtm3bdMcdd0iqfz9Lv3ln6frku+++U3l5uaKiojzao6KilJeX56NZ1V7lnKur5/Dhw1af4OBgNW3atEqfyu3z8vLUqlWrKuO3atXqsr8uxhilpaXplltuUUJCgjU/yT/q3LNnj3r06KEzZ86oSZMmWr16ta6//nrrLwd/qFGSVq5cqQ8++EC7du2qss5ffp7dunXTyy+/rGuuuUbffPONnnzySfXs2VP79u3zmxol6csvv9TixYuVlpamRx99VO+9954efPBBOZ1OjRo1yq9qrbRmzRqdOHFCY8aMseYmNfwaH374YRUWFqpDhw4KCAhQeXm5nnrqKd17773W/Crn/GO+qpMgVIccDofHsjGmSltDUpt6zu1TXX9fvC4PPPCAPvnkE23btq3KOn+o89prr9VHH32kEydOaNWqVRo9erS2bNly3vk1xBq//vprTZkyRdnZ2QoJCTlvv4Ze64ABA6zvExMT1aNHD1111VVavny5unfvXu38GlqNklRRUaGuXbsqIyNDktS5c2ft27dPixcv1qhRo847z4ZYa6UlS5ZowIABiomJ8Whv6DX+7W9/04oVK5SVlaWOHTvqo48+UmpqqmJiYjR69OjzztFXdXJprA60aNFCAQEBVRJpfn5+lQTcEFQ+oXKhelwul0pLS1VQUHDBPt98802V8b/99tvL+rpMnjxZb7zxhjZt2qQ2bdpY7f5UZ3BwsK6++mp17dpVc+bM0Q033KBnn33Wr2rcvXu38vPz1aVLFwUGBiowMFBbtmzRc889p8DAQGse/lDrj4WFhSkxMVEHDx70q59ndHS0rr/+eo+26667TkeOHLHmKPlHrZJ0+PBhbdy4Ub/+9a+tNn+p8Xe/+50eeeQR/epXv1JiYqJGjhyphx56SHPmzLHmJ9WfOglCdSA4OFhdunRRTk6OR3tOTo569uzpo1nVXlxcnFwul0c9paWl2rJli1VPly5dFBQU5NEnNzdXe/futfr06NFDhYWFeu+996w+7777rgoLCy/L62KM0QMPPKDXX39d77zzjuLi4jzW+0ud1THGyO12+1WNffv21Z49e/TRRx9ZX127dtWIESP00Ucf6corr/SbWn/M7XZr//79io6O9qufZ69evaq8ncXnn3+udu3aSfK/38+lS5eqVatWGjhwoNXmLzUWFxerUSPPeBEQEGA9Pl/v6rzo26pRI5WPzy9ZssR8+umnJjU11YSFhZmvvvrK11Or1smTJ82HH35oPvzwQyPJzJs3z3z44YfW4/5z5841kZGR5vXXXzd79uwx9957b7WPOrZp08Zs3LjRfPDBB+a2226r9lHHTp06mR07dpgdO3aYxMTEy/ZI5/33328iIyPN5s2bPR5fLS4utvr4Q50zZswwW7duNYcOHTKffPKJefTRR02jRo1Mdna239R4Pj9+aswY/6h16tSpZvPmzebLL780O3fuNIMGDTLh4eHW3yX+UKMxZ98CITAw0Dz11FPm4MGD5q9//asJDQ01K1assPr4S63l5eWmbdu25uGHH66yzh9qHD16tGndurX1+Pzrr79uWrRoYaZPn14v6yQI1aE//vGPpl27diY4ONj8x3/8h/WYdn20adMmI6nK1+jRo40xZx93nDVrlnG5XMbpdJqf/exnZs+ePR5jlJSUmAceeMA0a9bMNG7c2AwaNMgcOXLEo8/3339vRowYYcLDw014eLgZMWKEKSgouCw1VlefJLN06VKrjz/UOW7cOOu4a9mypenbt68VgozxjxrP59wg5A+1Vr6/SlBQkImJiTFDhw41+/bts9b7Q42V3nzzTZOQkGCcTqfp0KGDeeGFFzzW+0utGzZsMJLMgQMHqqzzhxqLiorMlClTTNu2bU1ISIi58sorzcyZM43b7bb61Kc6HcYYc/HnjwAAAPwH9wgBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBAADbIggBuKzGjBkjh8Mhh8OhwMBAtW3bVvfff3+VD1cEgMuBIATgsrv99tuVm5urr776Si+99JLefPNNTZw40dfTAmBDBCEAl53T6ZTL5VKbNm2UkpKie+65R9nZ2db6pUuX6rrrrlNISIg6dOig559/3lrXo0cPPfLIIx7jffvttwoKCtKmTZsknf0k6+nTp6t169YKCwtTt27dtHnzZqv/smXLdMUVV2jDhg267rrr1KRJEyucVerdu7dSU1M99jNkyBCNGTPGWv6p/QCo/whCAHzqyy+/1Pr16xUUFCRJevHFFzVz5kw99dRT2r9/vzIyMvTYY49p+fLlkqQRI0bo1Vdf1Y8/JvFvf/uboqKilJSUJEkaO3as/vWvf2nlypX65JNPdPfdd+v222/XwYMHrW2Ki4v13//933rllVe0detWHTlyRNOmTavR3C9mPwDqudp9tiwA1M7o0aNNQECACQsLMyEhIUaSkWTmzZtnjDEmNjbWZGVleWzzxBNPmB49ehhjjMnPzzeBgYFm69at1voePXqY3/3ud8YYY7744gvjcDjMsWPHPMbo27evmTFjhjHGmKVLlxpJ5osvvrDW//GPfzRRUVHW8rmfZG+MMXfddZcZPXr0Re8HQP0X6OMcBsCG+vTpo8WLF6u4uFgvvfSSPv/8c02ePFnffvutvv76a40fP16/+c1vrP4//PCDIiMjJUktW7ZUcnKy/vrXv+rWW2/VoUOHtGPHDi1evFiS9MEHH8gYo2uuucZjn263W82bN7eWQ0NDddVVV1nL0dHRys/Pv+gaLnY/AOo3ghCAyy4sLExXX321JOm5555Tnz59NHv2bD3wwAOSzl4e69atm8c2AQEB1vcjRozQlClTtHDhQmVlZaljx4664YYbJEkVFRUKCAjQ7t27PbaRpCZNmljfV16Kq+RwODwutzVq1MhjWZLKysqs7y92PwDqN4IQAJ+bNWuWBgwYoPvvv1+tW7fWl19+qREjRpy3/5AhQzRhwgStX79eWVlZGjlypLWuc+fOKi8vV35+vm699dZaz6lly5YeN0+Xl5dr79696tOnj1f3A8C3CEIAfK53797q2LGjMjIylJ6ergcffFAREREaMGCA3G633n//fRUUFCgtLU3S2TNKd911lx577DHt379fw4cPt8a65pprNGLECI0aNUrPPPOMOnfurO+++07vvPOOEhMTdccdd1zUnG677TalpaXprbfe0lVXXaX58+frxIkTXt8PAN/iqTEA9UJaWppefPFF9e/fXy+99JKWLVumxMREJSUladmyZYqLi/PoP2LECH388ce69dZb1bZtW491S5cu1ahRozR16lRde+21Gjx4sN59913FxsZe9HzGjRun0aNHa9SoUUpKSlJcXJx1Nsib+wHgWw5z7kVwAAAAm+CMEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsC2CEAAAsK3/D6HRlVB2gMp5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate mean not considering for outliers  \n",
    "mean_value = df_market_money[(df_market_money['выручка'] <= 80000)]['выручка'].mean()\n",
    "# replace outlier with feature mean value\n",
    "df_market_money['выручка'] = df_market_money['выручка'].apply(lambda x: mean_value if x > 80000 else x)\n",
    "\n",
    "df_market_money['выручка'].hist(bins=30)\n",
    "plt.title('Revenue Distribution')\n",
    "plt.xlabel('Revenue')\n",
    "plt.ylabel('Quantity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion:__ \n",
    "- outliers were detected in the df_market_money dataset and replaced with the mean value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA df_market_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "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>id</th>\n",
       "      <th>минут</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2600.000000</td>\n",
       "      <td>2600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>13.336154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>375.349754</td>\n",
       "      <td>4.080198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>215348.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>215672.750000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>216322.250000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>216647.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id        минут\n",
       "count    2600.000000  2600.000000\n",
       "mean   215997.500000    13.336154\n",
       "std       375.349754     4.080198\n",
       "min    215348.000000     4.000000\n",
       "25%    215672.750000    10.000000\n",
       "50%    215997.500000    13.000000\n",
       "75%    216322.250000    16.000000\n",
       "max    216647.000000    23.000000"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_market_time.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df_market_time['минут'], vert=False)  \n",
    "plt.xlabel('Minutes')\n",
    "plt.title('Minutes spent on web site ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion:__ \n",
    "- no anomalies detected in the df_market_time dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA df_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "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>id</th>\n",
       "      <th>прибыль</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>3.996631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>375.421985</td>\n",
       "      <td>1.013722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>215348.000000</td>\n",
       "      <td>0.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>215672.750000</td>\n",
       "      <td>3.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>215997.500000</td>\n",
       "      <td>4.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>216322.250000</td>\n",
       "      <td>4.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>216647.000000</td>\n",
       "      <td>7.430000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id      прибыль\n",
       "count    1300.000000  1300.000000\n",
       "mean   215997.500000     3.996631\n",
       "std       375.421985     1.013722\n",
       "min    215348.000000     0.860000\n",
       "25%    215672.750000     3.300000\n",
       "50%    215997.500000     4.045000\n",
       "75%    216322.250000     4.670000\n",
       "max    216647.000000     7.430000"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_money.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df_money['прибыль'], vert=False)  \n",
    "plt.xlabel('Revenue')\n",
    "plt.title('Revenue statistics')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ Conclusion:__ \n",
    "- Revenue in the df_money and the df_market_money has different rounding "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customers with purchasing activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's select customers with purchasing activity for at least three months, meaning those who have made a purchase during this period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3900, 3)\n"
     ]
    }
   ],
   "source": [
    "print(df_market_money.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1300, 3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pivot = df_market_money.pivot_table(index='id', columns='период', values='выручка')\n",
    "display(df_pivot.shape)\n",
    "df_filtered = df_pivot[((df_pivot['текущий_месяц'] == 0)) |\n",
    "                      ((df_pivot['предыдущий_месяц'] == 0)) |\n",
    "                      ((df_pivot['препредыдущий_месяц'] == 0))]\n",
    "display(df_filtered.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three customers with low purchasing power have been identified; let's remove them from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3891, 3)\n"
     ]
    }
   ],
   "source": [
    "to_delete = df_filtered.index\n",
    "df_market_money = df_market_money[~df_market_money['id'].isin(to_delete)]\n",
    "print(df_market_money.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:**\n",
    "\n",
    "- Anomalies have been corrected.\n",
    "- Three inactive users have been removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging the tables:  market_file.csv, market_money.csv, market_time.csv. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "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>id</th>\n",
       "      <th>покупательская_активность</th>\n",
       "      <th>тип_сервиса</th>\n",
       "      <th>разрешить_сообщать</th>\n",
       "      <th>маркет_актив_6_мес</th>\n",
       "      <th>маркет_актив_тек_мес</th>\n",
       "      <th>длительность</th>\n",
       "      <th>акционные_покупки</th>\n",
       "      <th>популярная_категория</th>\n",
       "      <th>средний_просмотр_категорий_за_визит</th>\n",
       "      <th>неоплаченные_продукты_штук_квартал</th>\n",
       "      <th>ошибка_сервиса</th>\n",
       "      <th>страниц_за_визит</th>\n",
       "      <th>период</th>\n",
       "      <th>выручка</th>\n",
       "      <th>период_time</th>\n",
       "      <th>минут</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215349</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4</td>\n",
       "      <td>819</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>препредыдущий_месяц</td>\n",
       "      <td>4472.0</td>\n",
       "      <td>текущий_месяц</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215349</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4</td>\n",
       "      <td>819</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>препредыдущий_месяц</td>\n",
       "      <td>4472.0</td>\n",
       "      <td>предыдущий_месяц</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>215349</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4</td>\n",
       "      <td>819</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>текущий_месяц</td>\n",
       "      <td>4971.6</td>\n",
       "      <td>текущий_месяц</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id покупательская_активность тип_сервиса разрешить_сообщать  \\\n",
       "0  215349                 Снизилась     премиум                 да   \n",
       "1  215349                 Снизилась     премиум                 да   \n",
       "2  215349                 Снизилась     премиум                 да   \n",
       "\n",
       "   маркет_актив_6_мес  маркет_актив_тек_мес  длительность  акционные_покупки  \\\n",
       "0                 4.4                     4           819               0.75   \n",
       "1                 4.4                     4           819               0.75   \n",
       "2                 4.4                     4           819               0.75   \n",
       "\n",
       "  популярная_категория  средний_просмотр_категорий_за_визит  \\\n",
       "0     Товары для детей                                    4   \n",
       "1     Товары для детей                                    4   \n",
       "2     Товары для детей                                    4   \n",
       "\n",
       "   неоплаченные_продукты_штук_квартал  ошибка_сервиса  страниц_за_визит  \\\n",
       "0                                   4               2                 5   \n",
       "1                                   4               2                 5   \n",
       "2                                   4               2                 5   \n",
       "\n",
       "                период  выручка       период_time  минут  \n",
       "0  препредыдущий_месяц   4472.0     текущий_месяц     10  \n",
       "1  препредыдущий_месяц   4472.0  предыдущий_месяц     12  \n",
       "2        текущий_месяц   4971.6     текущий_месяц     10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_market = df_market_file.merge(df_market_money, on = 'id', suffixes=('','_money')).merge(df_market_time, on = 'id', suffixes=('','_time'))\n",
    "display(df_market.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "период_time  предыдущий_месяц  текущий_месяц\n",
      "id                                          \n",
      "215349                     12             10\n",
      "215350                      8             13\n",
      "215351                     11             13\n",
      "215352                      8             11\n",
      "215353                     10             10\n",
      "...                       ...            ...\n",
      "216643                     14              7\n",
      "216644                     12             11\n",
      "216645                     12             18\n",
      "216646                     18              7\n",
      "216647                     15             10\n",
      "\n",
      "[1297 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# group by id revenue and time per period\n",
    "df_piv_profit = df_market.pivot_table(index = 'id', columns = 'период', values = 'выручка',  aggfunc='first')\n",
    "df_piv_time = df_market.pivot_table(index = 'id', columns = 'период_time',values = 'минут', aggfunc = 'first') \n",
    "print(df_piv_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        выручка_предыдущий_месяц  выручка_предпредыдущий_месяц  \\\n",
      "id                                                               \n",
      "215349                    5216.0                        4472.0   \n",
      "215350                    5457.5                        4826.0   \n",
      "215351                    6158.0                        4793.0   \n",
      "215352                    5807.5                        4594.0   \n",
      "215353                    4738.5                        5124.0   \n",
      "...                          ...                           ...   \n",
      "216643                    3664.0                        4704.0   \n",
      "216644                    4729.0                        5299.0   \n",
      "216645                    5160.0                        5334.0   \n",
      "216646                    4923.0                        4725.0   \n",
      "216647                    4720.0                        5183.0   \n",
      "\n",
      "        выручка_текущий_месяц  \n",
      "id                             \n",
      "215349                 4971.6  \n",
      "215350                 5058.4  \n",
      "215351                 6610.4  \n",
      "215352                 5872.5  \n",
      "215353                 5388.5  \n",
      "...                       ...  \n",
      "216643                 4741.7  \n",
      "216644                 5353.0  \n",
      "216645                 5139.2  \n",
      "216646                 5224.6  \n",
      "216647                 4448.3  \n",
      "\n",
      "[1297 rows x 3 columns]\n",
      "        минут_предыдущий_месяц  минут_текущий_месяц\n",
      "id                                                 \n",
      "215349                      12                   10\n",
      "215350                       8                   13\n",
      "215351                      11                   13\n",
      "215352                       8                   11\n",
      "215353                      10                   10\n",
      "...                        ...                  ...\n",
      "216643                      14                    7\n",
      "216644                      12                   11\n",
      "216645                      12                   18\n",
      "216646                      18                    7\n",
      "216647                      15                   10\n",
      "\n",
      "[1297 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "#переименуем колонки\n",
    "df_piv_profit.columns = ['выручка_предыдущий_месяц', 'выручка_предпредыдущий_месяц','выручка_текущий_месяц']\n",
    "df_piv_time.columns = [ 'минут_предыдущий_месяц', 'минут_текущий_месяц']\n",
    "print(df_piv_profit)\n",
    "print(df_piv_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1297, 18)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = df_market_file.merge(df_piv_profit, on='id').merge(df_piv_time, on='id')\n",
    "df_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by examining the interaction between features.  \n",
    "We will create a correlation matrix between the features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df_final.set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/phik/data_quality.py:59: UserWarning: The number of unique values of variable выручка_предыдущий_месяц is large: 1121. Are you sure this is not an interval variable? Analysis for pairs of variables including выручка_предыдущий_месяц can be slow.\n",
      "  warnings.warn(\n",
      "/opt/anaconda3/lib/python3.11/site-packages/phik/data_quality.py:59: UserWarning: The number of unique values of variable выручка_текущий_месяц is large: 1239. Are you sure this is not an interval variable? Analysis for pairs of variables including выручка_текущий_месяц can be slow.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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52pw5cyy+H3/++ad27dp13/uz83lk5d46V65cMWZsZXZ/YmKi+vfvr1GjRj3Qz1lkZKR+++03Va1aVZ988olxvVmzZrp06ZLmzJmj3bt3a/fu3ZKU7bGU/v/P2c6dO5WQkKBevXqlq3O/n58RI0YoIiJCU6ZM0aRJk9Il7bIzRtk1ZswYeXh4WBwTN+y5/40AAAAAcD8OpsfzeEI8cEKqbNmycnV11caNGzOtc/cv/ElJSYqIiDBmQVWpUkWHDh1SyZIl5ePjY3HcPWPn119/Vc2aNe8bT7FixSzayGhmRmRkpJYtW6bPPvss03aio6M1ceLELGdQPazff/9dt2/fNs537dols9msokWLysfHR05OTtq+fbtRnpiYqH379ql8+fKSpBdeeEHbtm3LdH+iNB4eHmrevLl69eqVbnaUdOeNgpGRkbp165ZxbceOHXJwcEi3BPBBnyc7OnbsqPfee0/z5s3TmjVrtHz5cqNs27Ztql69unr37q3KlSvLx8cn2xtxS3eWsRUpUkR//fVXuu9Z2gyhrGTn80hz9/f9ypUrOnr0qPF9TzNy5EjVrFnTYlnivWbOnCmz2ayOHTtm9zHTxfzSSy8pNDRUP//8sy5evKhLly4pKipKn376qerXr6/y5cvrypUrVred9nMWEBCg9957T8uWLbMoz87PT8GCBfXcc8+pT58+KlasmNasWWNRnp0xyq5Bgwbp2rVrFkdww5cful0AAAAAwMN54PVoLi4uGjBggPr37y8nJyfVqFFDFy5c0KFDh4xlfF9++aXKli2r8uXLa/Lkybpy5Yq6dOkiSXr//fc1Z84cvf322woJCVH+/Pl1/PhxLVmyRHPmzFFycrJWr16tTZs2aenSpYqJiZEkXbt2Tampqbpw4YIKFChgVcwTJkxQcHBwprOn0mJu1aqVqlSp8oAjc38JCQnq2rWrPv30U/3vf//TsGHD1KdPHzk4OChXrlx67733FBISonz58ql48eIaN26cYmNj1bVrV0l39uaaNm2a2rVrp0GDBsnDw0O7du3Syy+/LD8/P4u+Bg4cKD8/P7Vt2zZdHB06dNCwYcPUqVMnhYaG6sKFC/rggw/UsWNHi83pH+Z5siNfvnyS7rylbfz48erdu7dq166t/Pnzy8fHRwsXLtT69etVqlQpLVq0SHv37s1WMilNaGioPvzwQ+XOnVuvv/664uPjtW/fPl25ckX9+vXL8t7sfB5pRowYIU9PTxUqVEiDBw9W/vz5Ld4wFxsbq9mzZ2v//v1Z9jlu3DitWrXK6tlBW7ZsUXJyssqUKaPLly9r+PDhKlasmPLnz6+UlBR5enpq9uzZ8vb21unTpzVw4ECr2pfuLKGNiYnRxYsXFR4eni7hltXPz9WrV7Vy5Uq98sorcnJy0o8//qiDBw+qcuXKRp3sjlF2OTs7y9nZ2eLajX9hGS4AAAAAwDoP9ZvZkCFD5OjoqKFDh+rcuXPy9va2WMLz+eefa+zYsTpw4IDKlCmjH374Qfnz55ckFS5cWDt27NCAAQPUuHFjxcfHq0SJEnrttdfk4OCgbdu26a233pIk43/v9tJLL1n9mnd3d3eLTagzkpKSolGjRlnVrrXq16+vsmXLqlatWoqPj1e7du0UGhpqlH/++edKSUlRx44ddePGDVWtWlXr1683NpD29PTUpk2bFBISotq1aytHjhzy9/e32OcojZ+fX6aJBzc3N61fv14fffSRXnrpJbm5ualVq1aaNGnSI30ea/Ts2VPLli1T7969tXTpUvXq1UuRkZFq27atTCaT3n77bfXu3Vs//fRTttvs1q2b3NzcNH78ePXv31+5cuXS888/r6CgoGzdf7/P4+56H330kY4dO6ZKlSpp1apVcnJyMsoTExPVs2fP+84+q1u3rurVq5ft50tz5coVDR48WCdPnpSbm5uqVatmzD5ycHDQkiVL9OGHH6pixYry8/PTF198oTp16ljVR4MGDSTdmX33yiuvaPHixRblWf38pKamKjw8XH379lV8fLxKly6tmTNnWuxDl90xAgAAAAA82UypaZvkPEKnTp1SqVKldODAgSz3asrKli1bFBoaqi1btqQru3r1qvz9/a1OSD0OAgMDjZkiT4On7XkexJYtW1S3bl1duXJFefLksXc4uI8bU63fKN6eUmu+Zu8QrHIzV/ZnVz4Oct06b+8Qss0x7rq9Q7BKvHtBe4fwVHOKtX7Ztb38+urH9g7BKvV/7G/vEKxyueRL9g7BKnnPHbR3CFZJ2Hf/PUcfF5daBtk7BKu4zsjem6QfF+5t3rZ3CFYxpaR/k/XjKldAC3uH8MBuzfnU3iFkKFf3zLcpepw88B5S/zYnJydjKde9HBwcrF6uBwAAAAAAgMfDY5uQql69ulasWJFhWe7cubV3716bxnP69GmZzeZMj9OnT9s0nicBY/ZojR49OtOxfP311x/btgEAAADgaWRycHgsjyfFv7K7b8mSJfUvrAS0q8KFCysyMjLL8uwIDw9/NAE9JrJ6nkc1Zo+7OnXq2OT73qtXL7Vp0ybDMldX18e2bQAAAAAA7sXrprLJ0dFRPj4+9g7jicKYPVr58uXLdBnr49w2AAAAAAD3enLmcgEAAAAAAOCpQEIKAAAAAAAANkVCCgAAAAAAADbFHlIAAAAAAADWMpnsHcETjRlSAAAAAAAAsCkSUgAAAAAAALApElIAAAAAAACwKRJSAAAAAAAAsCkSUgAAAAAAALAp3rIHAAAAAABgLQfm+DwMRg8AAAAAAAA2RUIKAAAAAAAANkVCCgAAAAAAADbFHlIAnik5ixa1dwhWueGc294hWMXt9mV7h2AVx7jr9g4h2+LNBewdglVSTU/Wv3m5XI+xdwhWMaWm2juEbKv/Y397h2CVjW+Ms3cIVin/f2vtHYJVTCnJ9g7BKn9+vdPeIWRbudvx9g7BKv+36Yi9Q7DK8+2d7B2CVVJyONs7BOC+nqz/WgQAAAAAAMATjxlSAAAAAAAA1jKZ7B3BE40ZUgAAAAAAALApElIAAAAAAACwKRJSAAAAAAAAsCkSUgAAAAAAALApElIAAAAAAACwKd6yBwAAAAAAYCWTA3N8HgajBwAAAAAAAJsiIQUAAAAAAACbIiEFAAAAAAAAmyIhBQAAAAAAAJsiIQUAAAAAAACb4i17AAAAAAAA1jIxx+dhMHoAAAAAAACwKRJSAAAAAAAAsCkSUgAAAAAAALApElIAAAAAAACwKRJSAAAAAAAAsCmrElJ16tRRUFCQxbXQ0FD5+/sb52FhYSpfvrxcXFxUrlw5zZgxw6L+wYMHVa9ePbm6usrT01M9evTQzZs3jfLAwECZTKZ0R548eSzaOXXqVIb1rl69Kkk6ceKE3nzzTRUqVEhms1kvvfSSfvnll3T3R0ZGZvos93uejNqQpJIlS2rKlCnGuclk0sqVK7Mcy3vvuVuLFi0UGBhonCckJKh///4qUqSIcuXKpWrVqmnLli0Z3nuv8PDwDMft7ufYsmWLTCaT1qxZo0qVKsnFxUXVqlXTwYMHLdpavny5KlSoIGdnZ5UsWVITJ07MVn9pY5yamiofHx9NmDDB4p4///xTDg4OOnHihCTL8UtNTVXnzp1VsWJFXbp0KcvnKVmypKSMP9c0K1eulMlkytbY3dvO1atXZTKZLMY+MDBQLVq0MP6cWWyBgYFauHChPD09FR8fb9FPq1at9O6772bYZ2RkpPLmzav//ve/2Yo5q7FLaz8tJkdHx3SfY3Jysrp27apSpUrJ1dVVfn5+mjp1qkUfac88fPhwFSxYULlz51bPnj2VkJCQrXGQpHXr1unVV19Vnjx55OnpqTfeeMP4/O9Wp06ddG1k9nMDAAAAAP8qB9PjeTwhHukMqTlz5mjw4MEaNWqUoqKiNHr0aA0ZMkQLFiyQJMXGxuq1115T3rx5tXfvXn333Xf65Zdf1KdPH4t2XnvtNUVHRxtHRr9wpqamSpJ++eUXRUdHa/ny5RblN2/eVJMmTfTLL7/owIEDaty4sZo1a6bTp08/suexl86dO2vHjh1asmSJ/vjjD7Vu3Vqvvfaajh07lq37c+fObTG+e/bsybBeSEiIJkyYoL1796pgwYJq3ry5EhMTJUkRERFq06aN2rVrp4MHDyo0NFRDhgxReHh4lv0FBwcb100mk7p06aKwsDCL+vPnz1fNmjVVpkyZdG0FBQVp69at2rBhgzw9PdW2bVuL70nRokWN871792ZrPP4tU6dONWJp06aN2rRpY5xPnTpVrVu3VnJyslatWmXcc/HiRf3444/q3LlzuvaOHDmiRo0aaeDAgerVq5fV8dw7dmkqVKig6OhonTp1Sh999JE+/vhjRUVFSZJSUlJUtGhRLV26VIcPH9bQoUP1ySefaOnSpRZtb9y4UVFRUdq8ebO++eYbff/99xo+fHi2xkGSbt26pX79+mnv3r3auHGjHBwc9J///EcpKSkW/aSmpqp79+7G/UWLFrV6HAAAAAAA9uf4KBsbOXKkJk6cqJYtW0qSSpUqpcOHD2vWrFnq1KmTFi9erNu3b2vhwoXKlSuXJGn69Olq1qyZxo4dq0KFCkmSnJ2d5eXlZbTr4eGRrq+0xIiXl5e8vLyUL18+i/JKlSqpUqVKxvlnn32m77//XqtWrVKfPn3k6uoqSbp9+/YDP489nDhxQt98843Onj2rwoULS5I+/vhjrVu3TmFhYRo9evR92zCZTBbjGxcXl2G9YcOGqWHDhpKkBQsWqGjRovr+++/Vpk0bTZo0SfXr19eQIUMkSb6+vjp8+LDGjx9vMZsrPj5eTk5ORn9ms9mij86dO2vo0KHas2ePXn75ZSUmJuqrr77S+PHj08UzZMgQLVu2TNu3b5e3t7ckydXV1fgsPTw8lCNHDotnsycPDw/ju5sW472xtW/fXmFhYWrdurUkafHixSpatKjq1KljUe9///ufGjRooG7dumnAgAFWx5LR2KVxdHQ04ipevLgcHR2NzylnzpxGYkm68zOwc+dOLV26VG3atDGuOzk5af78+XJzc1OFChU0YsQIhYSEaOTIkdkah1atWlmcz5s3TwULFtThw4dVsWJF43piYqI8PDyM+3PkyJHlc8fHx6ebgZaamCTnnI/0rz4AAAAAgJWsniE1Y8YMmc1m40hLgFy4cEFnzpxR165dLco/++wzY+lNVFSUKlWqZCSjJKlGjRpKSUnRkSNHrIrj+vXrkmTR1t1u3bql/v3767nnnlOePHlkNpv1f//3f8YMqYIFCyp//vz69ttv083CyO7zpKlevbpFnYxmYb399tsWdbZt25auzoABA2Q2m1WgQAHVrFlTmzZtSldn//79Sk1Nla+vr0V7v/76a4ZLnB5GQECA8ed8+fLJz8/PmDkTFRWlGjVqWNSvUaOGjh07puTkZOPapUuXlDt37kz78Pb2VtOmTTV//nxJ0o8//qi4uDgjQZPmyy+/1GeffSY/Pz9jKZ41Dh48KLPZLA8PD5UrV06hoaHGLDt76t69u37++Wf9/fffku4sEU1b4pbm6tWratCggc6ePavGjRtb3cf9xi5tbFxcXPT2229rypQpKlasmFH+3//+V1WrVlWBAgVkNps1Z86cdN/xSpUqyc3NzTgPCAjQzZs3debMmWzFeOLECbVv316lS5dW7ty5VapUKUlK18/169cz/ZnPyJgxY4yEWNoxfsUv978RAAAAAPCvsnqaQIcOHTR48GDj/IsvvtDWrVuNpM6cOXNUrVo1i3vSZjGkpqZmul9PdvfxSXPu3Dk5ODhkOhsmJCRE69ev14QJE+Tj4yNXV1e99dZbxr42JpNJM2fOVGBgoGbOnKmcOXMqISFBzz33nCRl63nSfPvttypfvrxxfu/sFkmaPHmyGjRoYJx36NAhw5gDAwMVGxuradOm6c0339TZs2ct6qSkpChHjhyKiIhIF8e9s4/+DWmfU0afZUYJnr/++uu+CaRu3bqpY8eOmjx5ssLCwtS2bVuL5IYk7d69W2vXrlVgYKBmzZpl9ZI1Pz8/rVq1SikpKYqIiFDXrl1VrFgxi6Vr9lC5cmVVqlRJCxcuVOPGjXXw4EGtXr3aos7//vc/tW/fXh06dFCXLl30xx9/WJWUud/Y3Ts2PXv2VLly5VS/fn0tXbpUffv21cSJExUQECB3d3eNHz9eu3fvzlbf2f25btasmYoVK6Y5c+aocOHCSklJUcWKFY2f1zTR0dHGzMDsGDRokPr162dxLXXtrGzfDwAAAAD4d1idkPLw8JCPj49xnrZUrlChQipSpIj++uuvDJMtkvTcc89pwYIFunXrlvEL9Y4dO+Tg4CBfX1+r4ti7d6/KlSsnFxeXDMu3bdumwMBA/ec//5F0Z0+pU6dOWdR566231Lx5c509e1YpKSlGci27z5OmWLFiFmPi6Jh+WL28vCzqpC1dulv+/PmNOsOGDdPcuXPT7QtVuXJlJScn6/z586pZs2aWcT2sXbt2qXjx4pKkK1eu6OjRoypXrpykO5/l9u3bLerv3LlTvr6+FomyrVu3qn379ln206RJE+XKlUszZ87UTz/9ZHwGd5syZYpef/11zZgxQ4GBgXrttdesminl5ORkjK2vr6/mzZunAwcOWCQJ7aVbt26aPHmy/v77bzVo0MBidpJ0Z5lc2r5lq1at0sCBAzVt2rRst3+/sctobFauXKn69etr27Ztql69unr37m3Uz2gm3u+//67bt28b3+tdu3bJbDZna4+nS5cuKSoqSrNmzTK+0/d+t9L6vXz5sipXrpztZ3d2dpazs7PFtTiW6wEAAACA3T3STc1DQ0M1ZswYTZ06VUePHtXBgwcVFhamSZMmSbozK8jFxUWdOnXSn3/+qc2bN+uDDz5Qx44djf2j7ichIUGLFi3SpEmT1KVLl0zr+fj4aMWKFYqMjNTvv/+u9u3bZ7g0z8nJSaVLl5aPj0+6faju9zyPWlJSkuLi4nTlyhXNnj1brq6u6Tb29vX1VYcOHfTuu+9qxYoVOnnypPbu3auxY8dq7dq1jzSeESNGaOPGjfrzzz8VGBio/PnzG2+QCw4O1saNGzVy5EgdPXpUCxYs0PTp0/Xxxx9LurM317Rp03TixAm99tpriomJUUxMjG7evKmkpCRdvnzZ6CdHjhwKDAzUoEGD5OPjY7FUME3aZ9OqVSs1bdpUXbt2tWrJXWpqquLi4hQbG6vt27dr3759ev75560ek7R24uLijL2JEhISjGt3L1fMrg4dOujvv//WnDlzMvxO586dW46OjnJ0dFR4eLjmzJmT7bcqSvcfu6SkJMXExOjcuXNau3at9uzZYyQefXx8tG/fPq1fv15Hjx7VkCFDMtwsPiEhQV27dtXhw4f1008/adiwYerTp48cHO7/V0zevHnl6emp2bNn6/jx49q0aVO6WU379u1Tx44d9fzzz6tq1arZfnYAAAAA+LeYTA6P5fGkeKSRduvWTXPnzlV4eLief/551a5dW+Hh4cZ+MG5ublq/fr0uX76sl156SW+99Zbq16+v6dOnZ7uPu9/o1rdv30zrTZ48WXnz5lX16tXVrFkzNW7cWFWqVHmkz/OohYSEyNXVVYULF9aaNWu0fPly5c2bN129sLAwvfvuuwoODpafn5+aN2+u3bt3p5tZ87A+//xzffTRR3rxxRcVHR2tVatWycnJSZJUpUoVLV26VEuWLFHFihU1dOhQjRgxwtjQ/Ntvv9WHH36olJQUBQQEyNvbW97e3po4caIOHTpkbBSfpmvXrkpISMgyyZhm+vTp+vPPPzVz5sxsP8sff/whV1dXubu7q127durZs6e6d++e/cG4px1XV1djuWjjxo2Na1999ZXVbebOnVutWrWS2Ww2En6ZeeGFFzR48GB16dJFt27dsrqvjMbu0KFD8vb2VrFixdSjRw/16tXLWNbXq1cvtWzZUm3btlW1atV06dIli9lSaerXr6+yZcuqVq1aatOmjZo1a6bQ0NBsxeTg4KAlS5YoIiJCFStWVN++fdNtat+3b18VLVpUa9eutXp5LwAAAADg8WNKfRx2dsZjZcuWLapbt66uXLmiPHnyPFAb4eHh2rJli8LDw9OVRUZGKigoyGKWz44dO1SnTh2dPXs227PlniYNGzZU+fLl9cUXX9g7FKsFBgbq6tWrWrlypb1DyZa45ZPtHYJVbpSrbu8QrJIzMfM3lz6OcsZevn+lx0S8uYC9Q7BKqkPWb8F83Lhcj7F3CFYxPUH/+eZw86q9Q7DKxjfG2TsEq5T/v0c7Q/7fVvjUDnuHYJWIj6faO4RsK/efF+0dglX+7/sIe4dglefn3v9t5o+TlBzO96/0mMjjX8feITywuG/G2juEDLm8bf2b2e2BzVTwr3B1dZWHh0eGZTlz5jSWkcXHx+vMmTMaMmSI2rRp88wloy5fvqyff/5ZmzZtsmqmIAAAAAAAT7InZ3EhsuX111+X2WzO8Bg92nZZ/bZt22rq1Iz/RalChQpasWKFJOmbb76Rn5+frl27pnHj7PsvkhUqVMh07BYvXvyv9FmlShX17NlTY8eOlZ+fn1X3Ll68ONN4K1So8K/ECwAAAADAo8AMqafM3Llzdft2xktm7t20PTN16tSxasPwhxEYGGjsO2Vva9euVWJiYoZl/9bMrXvf/GiN5s2bq1q1ahmW5cyZ84HbtVZGyzIBAAAAAMgKCamnTJEiRewdwhOrRIkS9g7BKu7u7nJ3d7d3GAAAAADwbHLghUsPgyV7AAAAAAAAsCkSUgAAAAAAALApElIAAAAAAACwKRJSAAAAAAAAsCkSUgAAAAAAALAp3rIHAAAAAABgLRNzfB4GowcAAAAAAACbIiEFAAAAAAAAmyIhBQAAAAAAAJsiIQUAAAAAAACbIiEFAAAAAAAAm+ItewAAAAAAANYymewdwRONGVIAAAAAAACwKWZIAXimJBX1sXcIVknK4WzvEKxyyzmvvUOwSp7UFHuHkG3JOZzsHYJVHJPi7B2CVf4pVMneITy1XJJu2TsEq5T/v7X2DsEqUeWa2DsEq+TbO8veIVilQLkC9g4h23I4P1n/P5FwM8neITzVkh2frO8Dnk3MkAIAAAAAAIBNkZACAAAAAACATZGQAgAAAAAAgE2xhxQAAAAAAIC1HJjj8zAYPQAAAAAAgGfYjBkzVKpUKbm4uOjFF1/Utm3bsnXfjh075OjoKH9/f6v7JCEFAAAAAADwjPr2228VFBSkwYMH68CBA6pZs6Zef/11nT59Osv7rl27pnfffVf169d/oH5JSAEAAAAAADyjJk2apK5du6pbt24qX768pkyZomLFimnmzJlZ3tezZ0+1b99eAQEBD9QvCSkAAAAAAICnRHx8vK5fv25xxMfHZ1g3ISFBERERatSokcX1Ro0aaefOnZn2ERYWphMnTmjYsGEPHCcJKQAAAAAAgKfEmDFj5OHhYXGMGTMmw7oXL15UcnKyChUqZHG9UKFCiomJyfCeY8eOaeDAgVq8eLEcHR/8XXm8ZQ8AAAAAAMBapsdzjs+gQYPUr18/i2vOzs5Z3mMymSzOU1NT012TpOTkZLVv317Dhw+Xr6/vQ8VJQgoAAAAAAOAp4ezsfN8EVJr8+fMrR44c6WZDnT9/Pt2sKUm6ceOG9u3bpwMHDqhPnz6SpJSUFKWmpsrR0VE///yz6tWrl62+H890HgAAAAAAAP5VTk5OevHFF7VhwwaL6xs2bFD16tXT1c+dO7cOHjyoyMhI4+jVq5f8/PwUGRmpatWqZbtvZkgBAAAAAAA8o/r166eOHTuqatWqCggI0OzZs3X69Gn16tVL0p0lgH///bcWLlwoBwcHVaxY0eL+ggULysXFJd31+yEhBQAAAAAA8Ixq27atLl26pBEjRig6OloVK1bU2rVrVaJECUlSdHS0Tp8+/cj7JSEF2ElSUtJDvZEAAAAAAIBHoXfv3urdu3eGZeHh4VneGxoaqtDQUKv7ZA8pwAaSkpI0adIk1ah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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval_cols = ['маркет_актив_6_мес', 'длительность', 'акционные_покупки', \n",
    "                 'выручка_за_предпредыдущий_месяц', 'выручка_за_предыдущий_месяц', 'выручка_за_текущий_месяц']\n",
    "\n",
    "# Определяем количество бинов для интервальных переменных\n",
    "bins = {\n",
    "    'маркет_актив_6_мес': 5,\n",
    "    'длительность': 5,\n",
    "    'акционные_покупки': 5,\n",
    "    'выручка_за_предпредыдущий_месяц': 5,\n",
    "    'выручка_за_предыдущий_месяц': 5,\n",
    "    'выручка_за_текущий_месяц': 5\n",
    "}\n",
    "\n",
    "# Вычисление phik корреляционной матрицы\n",
    "phik_matrix = df_final.phik_matrix(interval_cols=interval_cols, bins=bins)\n",
    "\n",
    "# визуализировать матрицу корреляций\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(phik_matrix, cmap='coolwarm', center=0, annot=True, fmt=\".2f\")\n",
    "plt.title(\"Phik Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "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>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>маркет_актив_6_мес</td>\n",
       "      <td>1.222024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>маркет_актив_тек_мес</td>\n",
       "      <td>1.044482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>выручка_предпредыдущий_месяц</td>\n",
       "      <td>1.612463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>длительность</td>\n",
       "      <td>1.099467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>акционные_покупки</td>\n",
       "      <td>1.186329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>средний_просмотр_категорий_за_визит</td>\n",
       "      <td>1.178637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>неоплаченные_продукты_штук_квартал</td>\n",
       "      <td>1.198188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ошибка_сервиса</td>\n",
       "      <td>1.079304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>страниц_за_визит</td>\n",
       "      <td>1.417234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>выручка_текущий_месяц</td>\n",
       "      <td>3.872051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>выручка_предыдущий_месяц</td>\n",
       "      <td>4.237893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>минут_текущий_месяц</td>\n",
       "      <td>1.321100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>минут_предыдущий_месяц</td>\n",
       "      <td>1.527000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                feature       VIF\n",
       "0                    маркет_актив_6_мес  1.222024\n",
       "1                  маркет_актив_тек_мес  1.044482\n",
       "2          выручка_предпредыдущий_месяц  1.612463\n",
       "3                          длительность  1.099467\n",
       "4                     акционные_покупки  1.186329\n",
       "5   средний_просмотр_категорий_за_визит  1.178637\n",
       "6    неоплаченные_продукты_штук_квартал  1.198188\n",
       "7                        ошибка_сервиса  1.079304\n",
       "8                      страниц_за_визит  1.417234\n",
       "9                 выручка_текущий_месяц  3.872051\n",
       "10             выручка_предыдущий_месяц  4.237893\n",
       "11                  минут_текущий_месяц  1.321100\n",
       "12               минут_предыдущий_месяц  1.527000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantitative_features = ['маркет_актив_6_мес', 'маркет_актив_тек_мес', 'выручка_предпредыдущий_месяц','длительность', 'акционные_покупки', 'средний_просмотр_категорий_за_визит', 'неоплаченные_продукты_штук_квартал', 'ошибка_сервиса', 'страниц_за_визит', 'выручка_текущий_месяц', 'выручка_предыдущий_месяц', 'минут_текущий_месяц', 'минут_предыдущий_месяц']\n",
    "vif_data = df_final[quantitative_features]\n",
    "vif_data = vif_data.assign(const=1)\n",
    "vif = pd.DataFrame({\n",
    "    'feature': quantitative_features,\n",
    "    'VIF': [variance_inflation_factor(vif_data.values, i) for i in range(len(quantitative_features))]})\n",
    "display(vif)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion:__  \n",
    "**Multicollinearity**  \n",
    "- High multicollinearity was detected between the feature \"revenue_current_month\" and other features according to the phik matrix (>0.8).\n",
    "- This is also observable in the VIF factor (3.8).\n",
    "- To avoid multicollinearity, the \"revenue_current_month\" feature can be excluded from the model selection process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pipeline creation \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage, we will take the following steps:\n",
    "1) Split the dataset into training and testing sets. The target variable is \"purchasing_activity.\"\n",
    "2) Process numerical and categorical features in a pipeline:\n",
    "   - For numerical features, we will use StandardScaler() and MinMaxScaler().\n",
    "   - For categorical features, we will use OneHotEncoder.\n",
    "   - For the target variable, we will use LabelEncoder.\n",
    "3) Prepare the final pipeline and a dictionary for four models, which will include hyperparameters for:\n",
    "   - KNeighborsClassifier()\n",
    "   - DecisionTreeClassifier()\n",
    "   - LogisticRegression()\n",
    "   - SVC()\n",
    "4) We will use ROC AUC as the metric because it allows us to determine the best ROC curve for binary classification of objects.\n",
    "   - The ROC curve displays the relationship between the True Positive Rate and the False Positive Rate, allowing us to assess how well the model distinguishes between classes 0 and 1, considering different thresholds.\n",
    "   - A significant advantage of this metric is that it evaluates model quality across different classification thresholds, making it more general and effective.\n",
    "   - It takes into account False Positive (Type I) and False Negative (Type II) errors.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 42\n",
    "TEST_SIZE = 0.25\n",
    "\n",
    "\n",
    "X = df_final.drop(['покупательская_активность', 'выручка_текущий_месяц'], axis=1)\n",
    "y = df_final['покупательская_активность']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size = TEST_SIZE, \n",
    "    random_state = RANDOM_STATE,\n",
    "    stratify = y\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode targer feature  \n",
    "label_encoder = LabelEncoder()\n",
    "y_train_encoded = label_encoder.fit_transform(y_train)\n",
    "y_test_encoded = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical features\n",
    "ohe_columns = [  \n",
    "    'популярная_категория'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ordinal features \n",
    "ord_columns = [\n",
    "    'тип_сервиса', 'разрешить_сообщать' \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "#numeric featurs \n",
    "num_columns = ['маркет_актив_6_мес', \n",
    "               'маркет_актив_тек_мес',\n",
    "               'длительность', \n",
    "               'акционные_покупки', \n",
    "               'средний_просмотр_категорий_за_визит',\n",
    "               'неоплаченные_продукты_штук_квартал',\n",
    "                'ошибка_сервиса',\n",
    "               'страниц_за_визит', \n",
    "               'выручка_предыдущий_месяц',\n",
    "                'выручка_предпредыдущий_месяц',\n",
    "               'минут_предыдущий_месяц',\n",
    "               'минут_текущий_месяц'\n",
    "               ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create pipeline to prepare features from ohe_columns\n",
    "ohe_pipe = Pipeline(\n",
    "    [\n",
    "        (\n",
    "            'simpleImputer_ohe', \n",
    "            SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
    "        ),\n",
    "        (\n",
    "            'ohe', \n",
    "            OneHotEncoder(drop='first', handle_unknown='ignore', sparse_output=False)\n",
    "        ), \n",
    "        \n",
    "    ]\n",
    ") \n",
    "# create pipeline to prepare features from  ord_columns\n",
    "ord_pipe = Pipeline(\n",
    "    [\n",
    "        (\n",
    "            'simpleImputer_before_ord', \n",
    "            SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
    "        ),\n",
    "        (  'ord',  \n",
    "            OrdinalEncoder(\n",
    "                categories=[\n",
    "                    ['стандарт', 'премиум'], \n",
    "                    ['нет', 'да'],\n",
    "                ], \n",
    "                handle_unknown='use_encoded_value', unknown_value=np.nan\n",
    "            )\n",
    "        ),\n",
    "        (\n",
    "            'simpleImputer_after_ord', \n",
    "            SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
    "        )\n",
    "    ]\n",
    ") \n",
    "# create pipeline to prepare features from  num_columns\n",
    "num_preprocessor_standard = Pipeline([\n",
    "    ('simpleImputer_num', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "num_preprocessor_minmax = Pipeline([\n",
    "    ('simpleImputer_num', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', MinMaxScaler())\n",
    "])\n",
    "\n",
    "# create data preprocessing pipeline \n",
    "data_preprocessor = ColumnTransformer(\n",
    "    [\n",
    "        ('ohe', ohe_pipe, ohe_columns),\n",
    "        ('ord', ord_pipe, ord_columns),\n",
    "        ('num', StandardScaler(), num_columns)\n",
    "    ], \n",
    "    remainder='passthrough'\n",
    ") \n",
    "\n",
    "\n",
    "# final pipeline: data preprocessing and model \n",
    "pipe_final = Pipeline(\n",
    "    [\n",
    "        ('preprocessor', data_preprocessor),\n",
    "        ('models', DecisionTreeClassifier(random_state=RANDOM_STATE))\n",
    "    ]\n",
    ")\n",
    "\n",
    "param_grid = [\n",
    "    # grid for DecisionTreeClassifier()\n",
    "    {\n",
    "        'models': [DecisionTreeClassifier(random_state=RANDOM_STATE)],\n",
    "        'models__max_depth': range(2, 7),\n",
    "        'models__max_features': range(2, 8),\n",
    "        'preprocessor__num': [StandardScaler(), MinMaxScaler(), 'passthrough']  \n",
    "    },\n",
    "    \n",
    "    # grid for KNeighborsClassifier() \n",
    "    {\n",
    "        'models': [KNeighborsClassifier()],\n",
    "        'models__n_neighbors': range(2, 7),\n",
    "        'preprocessor__num': [StandardScaler(), MinMaxScaler(), 'passthrough']   \n",
    "    },\n",
    "\n",
    "    # grid for LogisticRegression()\n",
    "    {\n",
    "        'models': [LogisticRegression(\n",
    "            random_state=RANDOM_STATE, \n",
    "            solver='liblinear'\n",
    "        )],\n",
    "        'models__C': range(1, 5),\n",
    "        'preprocessor__num': [StandardScaler(), MinMaxScaler(), 'passthrough']  \n",
    "    }, \n",
    "    # grid for SVC()\n",
    "    {\n",
    "        'models': [SVC(random_state=RANDOM_STATE, probability=True)],\n",
    "        'models__C': range(1, 5),\n",
    "        'preprocessor__num': [StandardScaler(), MinMaxScaler(), 'passthrough']  \n",
    "\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "randomized_search = RandomizedSearchCV(\n",
    "    pipe_final, \n",
    "    param_grid, \n",
    "    cv=5,\n",
    "    scoring='roc_auc',\n",
    "    random_state=RANDOM_STATE,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dummy Classifier Score: 0.6184615384615385\n"
     ]
    }
   ],
   "source": [
    "# create and train DummyClassifier\n",
    "clf_dummy = DummyClassifier(strategy=\"most_frequent\")\n",
    "clf_dummy.fit(X_train, y_train)\n",
    "\n",
    "# test model on test data\n",
    "score = clf_dummy.score(X_test, y_test)\n",
    "\n",
    "print(f'Dummy Classifier Score: {score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a `DummyClassifier` to establish a reference point with an ROC AUC score of 0.6. This means that our model should not perform worse than this baseline metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                   estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                                transformers=[(&#x27;ohe&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleImputer_ohe&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                               (&#x27;ohe&#x27;,\n",
       "                                                                                                OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                              handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                                              sparse_output=False))]),\n",
       "                                                                               [&#x27;популярная_категория&#x27;]),\n",
       "                                                                              (&#x27;ord&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleImpu...\n",
       "                                                               &#x27;passthrough&#x27;]},\n",
       "                                        {&#x27;models&#x27;: [LogisticRegression(C=4,\n",
       "                                                                       random_state=42,\n",
       "                                                                       solver=&#x27;liblinear&#x27;)],\n",
       "                                         &#x27;models__C&#x27;: range(1, 5),\n",
       "                                         &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                               MinMaxScaler(),\n",
       "                                                               &#x27;passthrough&#x27;]},\n",
       "                                        {&#x27;models&#x27;: [SVC(probability=True,\n",
       "                                                        random_state=42)],\n",
       "                                         &#x27;models__C&#x27;: range(1, 5),\n",
       "                                         &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                               MinMaxScaler(),\n",
       "                                                               &#x27;passthrough&#x27;]}],\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                                                transformers=[(&#x27;ohe&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleImputer_ohe&#x27;,\n",
       "                                                                                                SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                                               (&#x27;ohe&#x27;,\n",
       "                                                                                                OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                                              handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                                              sparse_output=False))]),\n",
       "                                                                               [&#x27;популярная_категория&#x27;]),\n",
       "                                                                              (&#x27;ord&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;simpleImpu...\n",
       "                                                               &#x27;passthrough&#x27;]},\n",
       "                                        {&#x27;models&#x27;: [LogisticRegression(C=4,\n",
       "                                                                       random_state=42,\n",
       "                                                                       solver=&#x27;liblinear&#x27;)],\n",
       "                                         &#x27;models__C&#x27;: range(1, 5),\n",
       "                                         &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                               MinMaxScaler(),\n",
       "                                                               &#x27;passthrough&#x27;]},\n",
       "                                        {&#x27;models&#x27;: [SVC(probability=True,\n",
       "                                                        random_state=42)],\n",
       "                                         &#x27;models__C&#x27;: range(1, 5),\n",
       "                                         &#x27;preprocessor__num&#x27;: [StandardScaler(),\n",
       "                                                               MinMaxScaler(),\n",
       "                                                               &#x27;passthrough&#x27;]}],\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;ohe&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleImputer_ohe&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                                  (&#x27;ohe&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                                 sparse_output=False))]),\n",
       "                                                  [&#x27;популярная_категория&#x27;]),\n",
       "                                                 (&#x27;ord&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;simpleImputer_before_ord&#x27;,\n",
       "                                                                   SimpleImputer(stra...\n",
       "                                                  [&#x27;маркет_актив_6_мес&#x27;,\n",
       "                                                   &#x27;маркет_актив_тек_мес&#x27;,\n",
       "                                                   &#x27;длительность&#x27;,\n",
       "                                                   &#x27;акционные_покупки&#x27;,\n",
       "                                                   &#x27;средний_просмотр_категорий_за_визит&#x27;,\n",
       "                                                   &#x27;неоплаченные_продукты_штук_квартал&#x27;,\n",
       "                                                   &#x27;ошибка_сервиса&#x27;,\n",
       "                                                   &#x27;страниц_за_визит&#x27;,\n",
       "                                                   &#x27;выручка_предыдущий_месяц&#x27;,\n",
       "                                                   &#x27;выручка_предпредыдущий_месяц&#x27;,\n",
       "                                                   &#x27;минут_предыдущий_месяц&#x27;,\n",
       "                                                   &#x27;минут_текущий_месяц&#x27;])])),\n",
       "                (&#x27;models&#x27;, DecisionTreeClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;ohe&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleImputer_ohe&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ohe&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;,\n",
       "                                                                sparse_output=False))]),\n",
       "                                 [&#x27;популярная_категория&#x27;]),\n",
       "                                (&#x27;ord&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;simpleImputer_before_ord&#x27;,\n",
       "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
       "                                                 (&#x27;ord&#x27;,\n",
       "                                                  Or...\n",
       "                                 [&#x27;тип_сервиса&#x27;, &#x27;разрешить_сообщать&#x27;]),\n",
       "                                (&#x27;num&#x27;, StandardScaler(),\n",
       "                                 [&#x27;маркет_актив_6_мес&#x27;, &#x27;маркет_актив_тек_мес&#x27;,\n",
       "                                  &#x27;длительность&#x27;, &#x27;акционные_покупки&#x27;,\n",
       "                                  &#x27;средний_просмотр_категорий_за_визит&#x27;,\n",
       "                                  &#x27;неоплаченные_продукты_штук_квартал&#x27;,\n",
       "                                  &#x27;ошибка_сервиса&#x27;, &#x27;страниц_за_визит&#x27;,\n",
       "                                  &#x27;выручка_предыдущий_месяц&#x27;,\n",
       "                                  &#x27;выручка_предпредыдущий_месяц&#x27;,\n",
       "                                  &#x27;минут_предыдущий_месяц&#x27;,\n",
       "                                  &#x27;минут_текущий_месяц&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ohe</label><div class=\"sk-toggleable__content\"><pre>[&#x27;популярная_категория&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ord</label><div class=\"sk-toggleable__content\"><pre>[&#x27;тип_сервиса&#x27;, &#x27;разрешить_сообщать&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;стандарт&#x27;, &#x27;премиум&#x27;], [&#x27;нет&#x27;, &#x27;да&#x27;]],\n",
       "               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=nan)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;маркет_актив_6_мес&#x27;, &#x27;маркет_актив_тек_мес&#x27;, &#x27;длительность&#x27;, &#x27;акционные_покупки&#x27;, &#x27;средний_просмотр_категорий_за_визит&#x27;, &#x27;неоплаченные_продукты_штук_квартал&#x27;, &#x27;ошибка_сервиса&#x27;, &#x27;страниц_за_визит&#x27;, &#x27;выручка_предыдущий_месяц&#x27;, &#x27;выручка_предпредыдущий_месяц&#x27;, &#x27;минут_предыдущий_месяц&#x27;, &#x27;минут_текущий_месяц&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('preprocessor',\n",
       "                                              ColumnTransformer(remainder='passthrough',\n",
       "                                                                transformers=[('ohe',\n",
       "                                                                               Pipeline(steps=[('simpleImputer_ohe',\n",
       "                                                                                                SimpleImputer(strategy='most_frequent')),\n",
       "                                                                                               ('ohe',\n",
       "                                                                                                OneHotEncoder(drop='first',\n",
       "                                                                                                              handle_unknown='ignore',\n",
       "                                                                                                              sparse_output=False))]),\n",
       "                                                                               ['популярная_категория']),\n",
       "                                                                              ('ord',\n",
       "                                                                               Pipeline(steps=[('simpleImpu...\n",
       "                                                               'passthrough']},\n",
       "                                        {'models': [LogisticRegression(C=4,\n",
       "                                                                       random_state=42,\n",
       "                                                                       solver='liblinear')],\n",
       "                                         'models__C': range(1, 5),\n",
       "                                         'preprocessor__num': [StandardScaler(),\n",
       "                                                               MinMaxScaler(),\n",
       "                                                               'passthrough']},\n",
       "                                        {'models': [SVC(probability=True,\n",
       "                                                        random_state=42)],\n",
       "                                         'models__C': range(1, 5),\n",
       "                                         'preprocessor__num': [StandardScaler(),\n",
       "                                                               MinMaxScaler(),\n",
       "                                                               'passthrough']}],\n",
       "                   random_state=42, scoring='roc_auc')"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomized_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model and its parameters:\n",
      "\n",
      " Pipeline(steps=[('preprocessor',\n",
      "                 ColumnTransformer(remainder='passthrough',\n",
      "                                   transformers=[('ohe',\n",
      "                                                  Pipeline(steps=[('simpleImputer_ohe',\n",
      "                                                                   SimpleImputer(strategy='most_frequent')),\n",
      "                                                                  ('ohe',\n",
      "                                                                   OneHotEncoder(drop='first',\n",
      "                                                                                 handle_unknown='ignore',\n",
      "                                                                                 sparse_output=False))]),\n",
      "                                                  ['популярная_категория']),\n",
      "                                                 ('ord',\n",
      "                                                  Pipeline(steps=[('simpleImputer_before_ord',\n",
      "                                                                   SimpleImputer(stra...\n",
      "                                                   'маркет_актив_тек_мес',\n",
      "                                                   'длительность',\n",
      "                                                   'акционные_покупки',\n",
      "                                                   'средний_просмотр_категорий_за_визит',\n",
      "                                                   'неоплаченные_продукты_штук_квартал',\n",
      "                                                   'ошибка_сервиса',\n",
      "                                                   'страниц_за_визит',\n",
      "                                                   'выручка_предыдущий_месяц',\n",
      "                                                   'выручка_предпредыдущий_месяц',\n",
      "                                                   'минут_предыдущий_месяц',\n",
      "                                                   'минут_текущий_месяц'])])),\n",
      "                ('models',\n",
      "                 LogisticRegression(C=4, random_state=42, solver='liblinear'))])\n"
     ]
    }
   ],
   "source": [
    "print('Best model and its parameters:\\n\\n', randomized_search.best_estimator_) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC metric for the best model:\n",
      " 0.8981188965825331\n"
     ]
    }
   ],
   "source": [
    "print('ROC-AUC metric for the best model:\\n', randomized_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   rank_test_score                                       param_models  \\\n",
      "4                1  LogisticRegression(C=4, random_state=42, solve...   \n",
      "7                2  LogisticRegression(C=4, random_state=42, solve...   \n",
      "3                3            DecisionTreeClassifier(random_state=42)   \n",
      "0                4            DecisionTreeClassifier(random_state=42)   \n",
      "5                5            DecisionTreeClassifier(random_state=42)   \n",
      "1                6            DecisionTreeClassifier(random_state=42)   \n",
      "9                7            DecisionTreeClassifier(random_state=42)   \n",
      "8                8            DecisionTreeClassifier(random_state=42)   \n",
      "6                9            DecisionTreeClassifier(random_state=42)   \n",
      "2               10            DecisionTreeClassifier(random_state=42)   \n",
      "\n",
      "   mean_test_score                                             params  \n",
      "4         0.898119  {'preprocessor__num': StandardScaler(), 'model...  \n",
      "7         0.897960  {'preprocessor__num': StandardScaler(), 'model...  \n",
      "3         0.833939  {'preprocessor__num': MinMaxScaler(), 'models_...  \n",
      "0         0.833524  {'preprocessor__num': MinMaxScaler(), 'models_...  \n",
      "5         0.832341  {'preprocessor__num': 'passthrough', 'models__...  \n",
      "1         0.829046  {'preprocessor__num': MinMaxScaler(), 'models_...  \n",
      "9         0.821985  {'preprocessor__num': 'passthrough', 'models__...  \n",
      "8         0.805955  {'preprocessor__num': StandardScaler(), 'model...  \n",
      "6         0.803018  {'preprocessor__num': StandardScaler(), 'model...  \n",
      "2         0.799274  {'preprocessor__num': MinMaxScaler(), 'models_...  \n"
     ]
    }
   ],
   "source": [
    "result = pd.DataFrame(randomized_search.cv_results_)\n",
    "print(result[\n",
    "    ['rank_test_score', 'param_models', 'mean_test_score','params']\n",
    "].sort_values('rank_test_score'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion:__ \n",
    "- As a result of the tests, the best model identified was Logistic Regression.\n",
    "- The metric indicates a strong performance with a score of 0.89."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.022156520440985854\n"
     ]
    }
   ],
   "source": [
    "best_index = randomized_search.best_index_\n",
    "std_test_score = randomized_search.cv_results_['std_test_score'][best_index]\n",
    "print(std_test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A small standard deviation of 0.2 indicates the model's stability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score: 0.92\n"
     ]
    }
   ],
   "source": [
    "# save the best model \n",
    "best_model = randomized_search.best_estimator_\n",
    "#predict on test data\n",
    "y_test_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "#metric\n",
    "roc_auc = roc_auc_score(y_test, y_test_proba)\n",
    "print(f'ROC AUC Score: {roc_auc:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This metric also exceeds that of the Dummy model, indicating that the model tuning was successful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the class distribution using the **confusion matrix**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "                 Прежний уровень  Снизилась\n",
      "Прежний уровень              189         12\n",
      "Снизилась                     21        103\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = best_model.predict(X_test)\n",
    "\n",
    "# create confusion matrix \n",
    "cm = confusion_matrix(y_test, y_test_pred)\n",
    "\n",
    "# convert  to DataFrame \n",
    "cm_df = pd.DataFrame(cm, index=np.unique(y_test), columns=np.unique(y_test))\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The confusion matrix allows us to see how the best model distributes objects across classes:\n",
    "- 189 times, the model correctly predicted that the level of purchasing activity would remain the same.\n",
    "- 103 times, it correctly predicted that the level of purchasing activity would decrease.\n",
    "- 12 times, the model predicted that the level would remain the same, but it actually decreased (Type I error).\n",
    "- 21 times, it predicted that the level would decrease, but it remained the same (Type II error).\n",
    "\n",
    "This analysis shows that the model performs well in classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's evaluate feature importance for the best model and create a feature importance plot using the SHAP method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'preprocessor': ColumnTransformer(remainder='passthrough',\n",
      "                  transformers=[('ohe',\n",
      "                                 Pipeline(steps=[('simpleImputer_ohe',\n",
      "                                                  SimpleImputer(strategy='most_frequent')),\n",
      "                                                 ('ohe',\n",
      "                                                  OneHotEncoder(drop='first',\n",
      "                                                                handle_unknown='ignore',\n",
      "                                                                sparse_output=False))]),\n",
      "                                 ['популярная_категория']),\n",
      "                                ('ord',\n",
      "                                 Pipeline(steps=[('simpleImputer_before_ord',\n",
      "                                                  SimpleImputer(strategy='most_frequent')),\n",
      "                                                 ('ord',\n",
      "                                                  Or...\n",
      "                                 ['тип_сервиса', 'разрешить_сообщать']),\n",
      "                                ('num', StandardScaler(),\n",
      "                                 ['маркет_актив_6_мес', 'маркет_актив_тек_мес',\n",
      "                                  'длительность', 'акционные_покупки',\n",
      "                                  'средний_просмотр_категорий_за_визит',\n",
      "                                  'неоплаченные_продукты_штук_квартал',\n",
      "                                  'ошибка_сервиса', 'страниц_за_визит',\n",
      "                                  'выручка_предыдущий_месяц',\n",
      "                                  'выручка_предпредыдущий_месяц',\n",
      "                                  'минут_предыдущий_месяц',\n",
      "                                  'минут_текущий_месяц'])]), 'models': LogisticRegression(C=4, random_state=42, solver='liblinear')}\n"
     ]
    }
   ],
   "source": [
    "print(randomized_search.best_estimator_.named_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x830 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# save the steps in to the variables \n",
    "model = randomized_search.best_estimator_.named_steps['models']\n",
    "preprocessor = randomized_search.best_estimator_.named_steps['preprocessor']\n",
    "\n",
    "# обработаем тренировочный и тестовый сет используя пайплайн \n",
    "X_train_preprocessed = preprocessor.transform(X_train)\n",
    "X_test_preprocessed = preprocessor.transform(X_test)\n",
    "\n",
    "# initiate SHAP for logistic regression \n",
    "explainer = shap.LinearExplainer(model, X_train_preprocessed)\n",
    "shap_values = explainer.shap_values(X_test_preprocessed)\n",
    "\n",
    "# create SHAP visalozation for feature importance \n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "shap.summary_plot(shap_values, X_test_preprocessed, \n",
    "                  feature_names=preprocessor.get_feature_names_out(), \n",
    "                  plot_type=\"bar\", max_display=17, show=False)\n",
    "ax.set_title(\"SHAP график\")\n",
    "ax.set_xlabel(\"Важность признаков\")\n",
    "ax.set_ylabel(\"Признаки\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x790 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "explanation_object = shap.Explanation(values=shap_values, \n",
    "                                      base_values=explainer.expected_value, \n",
    "                                      data=X_test_preprocessed, \n",
    "                                      feature_names=preprocessor.get_feature_names_out())\n",
    "\n",
    "# создаем SHAP beeswarm график\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "shap.plots.beeswarm(explanation_object, max_display=16, show=False)\n",
    "ax.set_title(\"SHAP Beeswarm visualization\")\n",
    "ax.set_xlabel(\"SHAP input on model results\")\n",
    "ax.set_ylabel(\"Features\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Conclusion:__\n",
    "\n",
    "*The most important features* are:\n",
    "- Average category views per visit (product behavior)\n",
    "- Number of pages visited per visit (website behavior)\n",
    "- Minutes spent on the site in the previous month (website behavior)\n",
    "- Number of unpaid products (product behavior)\n",
    "- Promotional purchases (product behavior)\n",
    "\n",
    "*Less significant features* include:\n",
    "- Popular category (product behavior)\n",
    "- Service type (customer communication)\n",
    "- Service error\n",
    "- Revenue from the previous month (financial behavior)\n",
    "- Duration (time since customer registration on the site) (customer communication)\n",
    "\n",
    "__SHAP Beeswarm Analysis:__\n",
    "- Purchase activity is the target feature.\n",
    "- Class 1 - 'Decreased,' Class 0 - 'Remained the same.'\n",
    "- Analyzing the SHAP beeswarm plot, we can observe that the most influential features (average category views per visit, number of pages visited per visit, minutes spent on the site in the previous month) tend to lead the model to classify objects as class 0, meaning that the purchase activity remained the same.\n",
    "\n",
    "__Model Improvement:__\n",
    "\n",
    "- Less significant features can be excluded from the model, for example, using the *filtering* method.\n",
    "- Logistic regression captures only linear dependencies, so it can be improved by adding *polynomial features* through polynomial expansion to the feature description.\n",
    "- Apply *regularization* to reduce the risk of model overfitting.\n",
    "\n",
    "__Business Decisions:__\n",
    "- Website behavior contributes the most to the model's prediction, indicating that these factors greatly influence purchase activity. A possible recommendation here would be to create promotional banners on the website, improve navigation, or enhance the user interface.\n",
    "- Product behavior is also important. It is evident that certain categories are frequently viewed by customers, so focusing on these through discounts could be beneficial. Additionally, attention should be given to unpaid products. It may be worth offering discounts on these items."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customers Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_res = pd.DataFrame({\n",
    "    'id': X_test.index,\n",
    "    'покупательская_активность': y_test_pred\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_res = model_res.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = model_res.merge(df_money, on='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "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>id</th>\n",
       "      <th>покупательская_активность</th>\n",
       "      <th>прибыль</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>216446</td>\n",
       "      <td>Прежний уровень</td>\n",
       "      <td>4.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215469</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id покупательская_активность  прибыль\n",
       "0  216446           Прежний уровень     4.47\n",
       "1  215469                 Снизилась     3.76"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customer segmentation is essential for understanding customer purchasing behavior and for developing personalized marketing strategies. We can segment customers using the following method:\n",
    "\n",
    "1) **Profitability** (how much the customer has spent): For this, we use data from the dataset `df_money`.\n",
    "2) **Model predictions**: We utilize the model's predictions, where 0 indicates that the customer's purchasing power has remained the same, and 1 indicates that it will decrease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "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>cегмент</th>\n",
       "      <th>кол-во_клиентов</th>\n",
       "      <th>средняя_прибыль</th>\n",
       "      <th>общая_прибыль</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>высокая_прибыль_прежняя_активность</td>\n",
       "      <td>104</td>\n",
       "      <td>4.775769</td>\n",
       "      <td>496.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>высокая_прибыль_снизилась_активность</td>\n",
       "      <td>59</td>\n",
       "      <td>4.723051</td>\n",
       "      <td>278.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>низкая_прибыль_прежняя_активность</td>\n",
       "      <td>106</td>\n",
       "      <td>3.130377</td>\n",
       "      <td>331.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>низкая_прибыль_снизилась_активность</td>\n",
       "      <td>56</td>\n",
       "      <td>3.186964</td>\n",
       "      <td>178.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                cегмент  кол-во_клиентов  средняя_прибыль  \\\n",
       "0    высокая_прибыль_прежняя_активность              104         4.775769   \n",
       "1  высокая_прибыль_снизилась_активность               59         4.723051   \n",
       "2     низкая_прибыль_прежняя_активность              106         3.130377   \n",
       "3   низкая_прибыль_снизилась_активность               56         3.186964   \n",
       "\n",
       "   общая_прибыль  \n",
       "0         496.68  \n",
       "1         278.66  \n",
       "2         331.82  \n",
       "3         178.47  "
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create new column Segment \n",
    "merged_df['cегмент'] = 'None'\n",
    "# calculate average revenue to group customers\n",
    "median_revenue = merged_df['прибыль'].median()\n",
    "\n",
    "merged_df.loc[(merged_df['покупательская_активность'] == 'Прежний уровень') & (merged_df['прибыль'] >= median_revenue), 'cегмент'] = 'высокая_прибыль_прежняя_активность'\n",
    "merged_df.loc[(merged_df['покупательская_активность'] == 'Прежний уровень') & (merged_df['прибыль'] < median_revenue), 'cегмент'] = 'низкая_прибыль_прежняя_активность'\n",
    "merged_df.loc[(merged_df['покупательская_активность'] == 'Снизилась') & (merged_df['прибыль'] >= median_revenue), 'cегмент'] = 'высокая_прибыль_снизилась_активность'\n",
    "merged_df.loc[(merged_df['покупательская_активность'] == 'Снизилась') & (merged_df['прибыль'] < median_revenue), 'cегмент'] = 'низкая_прибыль_снизилась_активность'\n",
    "\n",
    "# Анализ сегментов\n",
    "segment_analysis = merged_df.groupby('cегмент')['прибыль'].agg(['count', 'mean', 'sum']).reset_index()\n",
    "segment_analysis.columns = ['cегмент', 'кол-во_клиентов', 'средняя_прибыль', 'общая_прибыль']\n",
    "segment_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the analysis, the segment chosen was *high profitability with decreasing activity*. Since we are predicting a potential decrease in activity, this could lead to a reduction in profitability, which is critical for the business."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df = df_final.merge(merged_df, on='id', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The target segment is *high_profitability_with_decreased_activity*.\n",
    "goal_seg = result_df[result_df['cегмент'] == 'высокая_прибыль_снизилась_активность']\n",
    "# best segment for evaluation\n",
    "best_seg = result_df[result_df['cегмент'] == 'высокая_прибыль_прежняя_активность']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider the feature **Unpaid Products**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    59.000000\n",
      "mean      3.932203\n",
      "std       2.164481\n",
      "min       0.000000\n",
      "25%       2.000000\n",
      "50%       4.000000\n",
      "75%       5.000000\n",
      "max       9.000000\n",
      "Name: неоплаченные_продукты_штук_квартал, dtype: float64\n",
      "count    104.000000\n",
      "mean       2.288462\n",
      "std        1.505157\n",
      "min        0.000000\n",
      "25%        1.000000\n",
      "50%        2.000000\n",
      "75%        3.250000\n",
      "max        5.000000\n",
      "Name: неоплаченные_продукты_штук_квартал, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "statistics_goal = goal_seg['неоплаченные_продукты_штук_квартал'].describe()\n",
    "print(statistics_goal)\n",
    "statistics_best = best_seg['неоплаченные_продукты_штук_квартал'].describe()\n",
    "print(statistics_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider the feature **Marketing Activity in the Last 6 Months**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    59.000000\n",
      "mean      3.620339\n",
      "std       0.991130\n",
      "min       0.900000\n",
      "25%       3.150000\n",
      "50%       3.900000\n",
      "75%       4.100000\n",
      "max       5.600000\n",
      "Name: маркет_актив_6_мес, dtype: float64\n",
      "count    104.000000\n",
      "mean       4.600000\n",
      "std        0.690153\n",
      "min        3.000000\n",
      "25%        4.100000\n",
      "50%        4.400000\n",
      "75%        5.200000\n",
      "max        6.300000\n",
      "Name: маркет_актив_6_мес, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "statistics_goal = goal_seg['маркет_актив_6_мес'].describe()\n",
    "print(statistics_goal)\n",
    "statistics_best = best_seg['маркет_актив_6_мес'].describe()\n",
    "print(statistics_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "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>id</th>\n",
       "      <th>покупательская_активность_x</th>\n",
       "      <th>тип_сервиса</th>\n",
       "      <th>разрешить_сообщать</th>\n",
       "      <th>маркет_актив_6_мес</th>\n",
       "      <th>маркет_актив_тек_мес</th>\n",
       "      <th>длительность</th>\n",
       "      <th>акционные_покупки</th>\n",
       "      <th>популярная_категория</th>\n",
       "      <th>средний_просмотр_категорий_за_визит</th>\n",
       "      <th>...</th>\n",
       "      <th>ошибка_сервиса</th>\n",
       "      <th>страниц_за_визит</th>\n",
       "      <th>выручка_предыдущий_месяц</th>\n",
       "      <th>выручка_предпредыдущий_месяц</th>\n",
       "      <th>выручка_текущий_месяц</th>\n",
       "      <th>минут_предыдущий_месяц</th>\n",
       "      <th>минут_текущий_месяц</th>\n",
       "      <th>покупательская_активность_y</th>\n",
       "      <th>прибыль</th>\n",
       "      <th>cегмент</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215349</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>премиум</td>\n",
       "      <td>да</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4</td>\n",
       "      <td>819</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5216.0</td>\n",
       "      <td>4472.0</td>\n",
       "      <td>4971.6</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>4.16</td>\n",
       "      <td>высокая_прибыль_снизилась_активность</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>215351</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>стандарт</td>\n",
       "      <td>да</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5</td>\n",
       "      <td>896</td>\n",
       "      <td>0.99</td>\n",
       "      <td>Товары для детей</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6158.0</td>\n",
       "      <td>4793.0</td>\n",
       "      <td>6610.4</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>4.87</td>\n",
       "      <td>высокая_прибыль_снизилась_активность</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>215356</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>стандарт</td>\n",
       "      <td>да</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4</td>\n",
       "      <td>192</td>\n",
       "      <td>0.14</td>\n",
       "      <td>Косметика и аксесуары</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4146.5</td>\n",
       "      <td>4433.0</td>\n",
       "      <td>4566.4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Снизилась</td>\n",
       "      <td>4.93</td>\n",
       "      <td>высокая_прибыль_снизилась_активность</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id покупательская_активность_x тип_сервиса разрешить_сообщать  \\\n",
       "0  215349                   Снизилась     премиум                 да   \n",
       "1  215351                   Снизилась    стандарт                 да   \n",
       "5  215356                   Снизилась    стандарт                 да   \n",
       "\n",
       "   маркет_актив_6_мес  маркет_актив_тек_мес  длительность  акционные_покупки  \\\n",
       "0                 4.4                     4           819               0.75   \n",
       "1                 3.2                     5           896               0.99   \n",
       "5                 4.2                     4           192               0.14   \n",
       "\n",
       "    популярная_категория  средний_просмотр_категорий_за_визит  ...  \\\n",
       "0       Товары для детей                                    4  ...   \n",
       "1       Товары для детей                                    5  ...   \n",
       "5  Косметика и аксесуары                                    2  ...   \n",
       "\n",
       "   ошибка_сервиса  страниц_за_визит  выручка_предыдущий_месяц  \\\n",
       "0               2                 5                    5216.0   \n",
       "1               6                 4                    6158.0   \n",
       "5               1                 3                    4146.5   \n",
       "\n",
       "   выручка_предпредыдущий_месяц  выручка_текущий_месяц  \\\n",
       "0                        4472.0                 4971.6   \n",
       "1                        4793.0                 6610.4   \n",
       "5                        4433.0                 4566.4   \n",
       "\n",
       "   минут_предыдущий_месяц  минут_текущий_месяц  покупательская_активность_y  \\\n",
       "0                      12                   10                    Снизилась   \n",
       "1                      11                   13                    Снизилась   \n",
       "5                       6                    7                    Снизилась   \n",
       "\n",
       "  прибыль                               cегмент  \n",
       "0    4.16  высокая_прибыль_снизилась_активность  \n",
       "1    4.87  высокая_прибыль_снизилась_активность  \n",
       "5    4.93  высокая_прибыль_снизилась_активность  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goal_seg.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider the feature **Promotional Purchases**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    59.000000\n",
      "mean      0.514237\n",
      "std       0.321683\n",
      "min       0.120000\n",
      "25%       0.250000\n",
      "50%       0.330000\n",
      "75%       0.930000\n",
      "max       0.990000\n",
      "Name: акционные_покупки, dtype: float64\n",
      "count    104.000000\n",
      "mean       0.227788\n",
      "std        0.138893\n",
      "min        0.110000\n",
      "25%        0.150000\n",
      "50%        0.210000\n",
      "75%        0.252500\n",
      "max        0.950000\n",
      "Name: акционные_покупки, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "statistics_goal = goal_seg['акционные_покупки'].describe()\n",
    "print(statistics_goal)\n",
    "statistics_best = best_seg['акционные_покупки'].describe()\n",
    "print(statistics_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider the feature **Popular Category**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts = goal_seg['популярная_категория'].value_counts()\n",
    "\n",
    "# create pie chart \n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140, colors=sns.color_palette('viridis', len(category_counts)))\n",
    "plt.title('To analyze the distribution of popular categories within the \"high_profitability_with_decreased_activity\"')\n",
    "plt.axis('equal') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "category_counts = goal_seg['разрешить_сообщать'].value_counts()\n",
    "\n",
    "# Построение круговой диаграммы\n",
    "plt.figure(figsize=(6, 3))\n",
    "plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140, colors=sns.color_palette('viridis', len(category_counts)))\n",
    "plt.title('Distribution of \"allow to inform\"')\n",
    "plt.axis('equal')  # Убедиться, что круговая диаграмма круглая\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Personalized Recommendations**:\n",
    "\n",
    "- We can see that the popular categories for the target segment are children's products, cosmetics and accessories, and kitchenware. These are the categories that should be the focus of the following campaigns:\n",
    "  \n",
    "  - **Discounts on these types of products**: The target segment has a higher average number of unpaid items in their cart over the last 3 months (4 compared to 2, nearly 50% higher). Offering discounts on these products could encourage them to complete their purchases.\n",
    "  \n",
    "  - **Improve marketing activity**: Increase efforts in calls and advertising, as the average marketing activity for this segment (3.5) is lower than that of the best-performing segment (4.5). Enhancing marketing engagement could help retain these customers.\n",
    "  \n",
    "  - **Launch promotional campaigns focused on these categories**: Since this segment tends to take advantage of promotions more frequently, targeted promotions in these categories could resonate well with them.\n",
    "\n",
    "  - **Overall approach**: Customers in this segment are generally open to receiving information about offers. Therefore, focusing communication and marketing efforts on these specific categories could be particularly effective."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task\n",
    "\n",
    "The task is to develop a solution that will allow the personalization of offers for regular customers to increase their purchasing activity.\n",
    "\n",
    "### Initial Data and Preprocessing\n",
    "\n",
    "The initial data consisted of four datasets related to customer behavior on the website, revenue data that the store receives from customers, data on the time (in minutes) that customers spent on the website over a period, and data on the average monthly profit from customers over the last 3 months.\n",
    "\n",
    "The first three datasets were merged, data preprocessing was conducted, and a correlation analysis was performed.\n",
    "\n",
    "### Search for the Best Model\n",
    "\n",
    "To find the best model, a pipeline was created that included data preparation (encoding, scaling) and searching for the best model among kNN, Decision Tree Classifier, SVC, and Logistic Regression. Randomized search was used for hyperparameter tuning.\n",
    "\n",
    "### Best Model\n",
    "\n",
    "As a result of the tests, the best model was Logistic Regression, with a ROC_AUC metric of 0.9 on the test set, and 0.87 on validation.\n",
    "\n",
    "### Conclusions and Additional Recommendations for the Target Customer Segment\n",
    "\n",
    "The target segment chosen was high profitability with decreasing purchasing activity. An analytical and statistical analysis was conducted, leading to the following personalized recommendations:\n",
    "\n",
    "- **Focus of marketing activity** should be on the following categories:\n",
    "  - Children’s products, cosmetics and accessories, kitchenware.\n",
    "  \n",
    "- **Campaigns and promotions**:\n",
    "  - Offer discounts on these products to reduce the number of unpaid items in the cart.\n",
    "  \n",
    "- **Increase marketing activity**:\n",
    "  - Enhance calls and advertisements.\n",
    "  \n",
    "- **Promotions and offers**:\n",
    "  - Launch promotional campaigns for these categories, as the segment shows high promotional activity.\n",
    "  \n",
    "- **Communication**:\n",
    "  - Actively inform customers about offers, focusing on these categories.\n",
    "\n",
    "These recommendations will help increase customer purchasing activity in the selected segment."
   ]
  }
 ],
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