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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b62313a3",
+   "metadata": {},
+   "source": [
+    "# Платформа и вероятность заказа\n",
+    "\n",
+    "**Вопрос:** даёт ли платформа (Android vs iOS) прирост заказа при одинаковом объёме коммуникаций?\n",
+    "\n",
+    "**Гипотеза:** при контроле показов/кликов Android-клиенты конвертируются выше."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8c8f09b1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:12:03.874747Z",
+     "iopub.status.busy": "2025-12-12T19:12:03.874144Z",
+     "iopub.status.idle": "2025-12-12T19:12:10.515786Z",
+     "shell.execute_reply": "2025-12-12T19:12:10.513552Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "from pathlib import Path\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
+    "\n",
+    "project_root = Path.cwd().resolve()\n",
+    "while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
+    "    project_root = project_root.parent\n",
+    "    project_root = project_root.parent\n",
+    "sys.path.append(str(project_root / \"preanalysis\"))\n",
+    "import eda_utils as eda\n",
+    "\n",
+    "db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
+    "conn = sqlite3.connect(db_path)\n",
+    "df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
+    "conn.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "67ed5210",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:12:10.521535Z",
+     "iopub.status.busy": "2025-12-12T19:12:10.521072Z",
+     "iopub.status.idle": "2025-12-12T19:12:13.018480Z",
+     "shell.execute_reply": "2025-12-12T19:12:13.016893Z"
+    }
+   },
+   "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>active_imp_total</th>\n",
+       "      <th>passive_imp_total</th>\n",
+       "      <th>active_click_total</th>\n",
+       "      <th>passive_click_total</th>\n",
+       "      <th>orders_amt_total</th>\n",
+       "      <th>imp_total</th>\n",
+       "      <th>click_total</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender_cd</th>\n",
+       "      <th>device_platform_cd</th>\n",
+       "      <th>has_order</th>\n",
+       "      <th>ctr_all</th>\n",
+       "      <th>cr_click2order</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.198276</td>\n",
+       "      <td>0.130435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>236.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.126280</td>\n",
+       "      <td>0.054054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.372093</td>\n",
+       "      <td>0.062500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    active_imp_total  passive_imp_total  active_click_total  \\\n",
+       "id                                                            \n",
+       "1               33.0               35.0                14.0   \n",
+       "2               27.0               89.0                19.0   \n",
+       "3               57.0              236.0                37.0   \n",
+       "4               20.0               37.0                14.0   \n",
+       "5               23.0               20.0                13.0   \n",
+       "\n",
+       "    passive_click_total  orders_amt_total  imp_total  click_total   age  \\\n",
+       "id                                                                        \n",
+       "1                   3.0                 0       68.0         17.0  58.0   \n",
+       "2                   4.0                 3      116.0         23.0  54.0   \n",
+       "3                   0.0                 2      293.0         37.0  70.0   \n",
+       "4                   1.0                 0       57.0         15.0  43.0   \n",
+       "5                   3.0                 1       43.0         16.0  46.0   \n",
+       "\n",
+       "   gender_cd device_platform_cd  has_order   ctr_all  cr_click2order  \n",
+       "id                                                                    \n",
+       "1          M            Android          0  0.250000        0.000000  \n",
+       "2          M            Android          1  0.198276        0.130435  \n",
+       "3          F            Android          1  0.126280        0.054054  \n",
+       "4          F            Android          0  0.263158        0.000000  \n",
+       "5          M            Android          1  0.372093        0.062500  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for cols, name in [\n",
+    "    (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
+    "    (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
+    "    (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
+    "    (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
+    "    (eda.ORDER_COLS, \"orders_amt_total\"),\n",
+    "]:\n",
+    "    df[name] = df[cols].sum(axis=1)\n",
+    "\n",
+    "df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
+    "df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
+    "\n",
+    "client = df.groupby(\"id\").agg(\n",
+    "    {\n",
+    "        \"active_imp_total\": \"sum\",\n",
+    "        \"passive_imp_total\": \"sum\",\n",
+    "        \"active_click_total\": \"sum\",\n",
+    "        \"passive_click_total\": \"sum\",\n",
+    "        \"orders_amt_total\": \"sum\",\n",
+    "        \"imp_total\": \"sum\",\n",
+    "        \"click_total\": \"sum\",\n",
+    "        \"age\": \"median\",\n",
+    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
+    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n",
+    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
+    "client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
+    "client.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee977b3f",
+   "metadata": {},
+   "source": [
+    "## Заказы по платформам"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3cb9ed5d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:12:13.024492Z",
+     "iopub.status.busy": "2025-12-12T19:12:13.024166Z",
+     "iopub.status.idle": "2025-12-12T19:12:13.288887Z",
+     "shell.execute_reply": "2025-12-12T19:12:13.287256Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zVP9GrRPo1q2bHn30UcXGxmrjxo2aOXNmtvXyMg6v7PtWGztATkgggFtAenq69u3bp3bt2pmqX6lSJR0+fNiuPGuawpVn9zMyMnT06FG1bNky1z5dXV314osv6u+//9bx48f12muv6fTp03r++edNH8ebb76ptm3bys3NTREREfrkk0/Uo0cPmzr//fefUlJS7M5Q5mTp0qVKSEjQsGHDst2etQi3SpUq2SYYV/P09FTz5s1tyq6+s4tZlStX1o4dO3T+/Hmbs6BZn8PVSdtdd91ls+9SpUrp2Wef1W+//aYGDRpku4+qVavKYrHo0KFDqlWrVp5jLFOmjHWfHTt2VKdOnTRv3jy99957+vvvv/XPP//o7bff1iOPPGJts23btmz7Kl68uObMmaMaNWooPDxcs2fPVmhoqPXqSGJionbs2KFhw4bZJBb//PNPjv1lxda+fXvFxcVp5syZatOmjXUM5zTOy5Qpk+0Z+YceekiTJ0/WhAkT1LFjx2zPTjvqytiuvpp1+PDhPF1dMyNrSmJuV6F++ukn61n3xo0bW8uvvDNaXpQvX14ZGRk6e/ZsjlMRpcvTC0+dOmWdFpd17LGxsbrrrrus9dLT03X8+HG7797JkyftriRljZfsTnpk3cVsxIgRKlOmjJo1a6affvrJpk5ex2GWGzF2AGfBGgjgFvD5558rPT3d9NSa1q1ba/fu3dq1a5e1LDU1VStWrFDlypV1zz33WMu//vprXbhwQc2aNbtmv4sWLdLOnTs1ZcoUNW/e3GauvBlZazLuv/9+de7cWVOmTNHp06dt6nzxxReSZCqelJQUzZ49W+Hh4Tmefe3QoYPc3NwUExNjd2bRMIzr+gyNVq1aKTMzU0uWLLEpz7qi0KpVq1zbZ90qNbezve3atZOrq6tmzJhhdxY5pzOpObl48aJSU1Ot+8uao35lP4Zh5HjlyMfHx/oj9plnntEdd9yhV155xdo+uytAkrRw4cJrxpaZmamkpCRrbBUqVFCtWrW0Zs0am6l0f//9t7Zt26bWrVtn20/p0qXVtm1b7d+/X926dbvmfvOjbt26Klu2rJYvX27z2X3//fc6dOiQ7r///gLd3/r16+Xu7p7tnZKyZPdZpqena+nSpfnaZ9Z3+crb/u7evdvmv9LlKXIpKSnW+s2bN5e7u7sWLVpkE8vKlSuVnJxs97ldunTJ5upfenq6Pv74Y/n4+KhOnTrZxtatWzft379fXbt2zfbKXX7H4Y0YO4Cz4AoEcBNLTU3V4sWLNWPGDLm5uckwDK1du9amzunTp5Wamqq1a9cqODhY5cqV05AhQ/TFF19o8ODBCgsLk7e3t9asWaPjx48rOjparq6uSktLU1RUlJYtW6YGDRpcc8rQgQMHNGXKFD399NM2iyTz6+WXX9YDDzyg8ePHa/r06Tp9+rSioqK0cuVKde7c2dSc/r1796pMmTLWOfzZqVq1qkaMGKFp06YpLi5O7dq1U4kSJXT8+HFt3rxZPXv21KBBgxw+nuyEhISoadOmevfddxUXFyd/f39t27ZNX3/9tcLDw1W1alWb+vv379fatWtlGIaOHTumRYsW6Y477lDdunVz3Mfdd9+toUOHaubMmXrsscfUoUMHeXh4aM+ePapQoYKeffbZbNslJydr8ODBatmypSpUqKDExEStXbtWp06dUpcuXSRdnqdetWpVvf3224qPj1fJkiX15Zdfmlr7UqxYMY0fP179+/fX0qVL1bdvX5UsWVKNGzfW3LlzlZGRoYoVK2rbtm3ZngXv27evmjRpokqVKiklJUVffvml/vzzT+vtRqXLtxoePHiwevXqpe7du1tv41qqVCm7qVNXmjRpksaOHWtqPVF+uLu767nnntOYMWPUr18/de7c2Xob18qVK6t///4Fsp9//vlH0dHRWrdunYYMGZLrGfEGDRrI29tbo0ePVlhYmFxcXKxjLT+aNWumBg0aaPz48YqLi5Obm5v1OR1fffWVXF1ddccdd2jevHmqUqWKOnfuLOlykhkREaGYmBg98cQTCgkJ0eHDh7V06VIFBARYx16WChUqaM6cOYqLi1O1atW0fv167du3T+PHj7e53euVevbsqU6dOqlUqVLZbs/LOLza9R47gLMggQBuYgkJCZo2bZr19dixY3Os+8ILL+ijjz5SuXLlVK5cOS1fvlxTpkzR4sWLdfHiRfn7+2v27NnWs59JSUnasGGDevbsqeHDh+f6gLr09HQ9++yzqlu3rs09/R1RtmxZjRkzRi+++KK++eYblS5dWjt37tRTTz2Vp30MHTr0mlMJhgwZomrVqmnBggWaMWOGJOmOO+5QcHCw3cPPCpKrq6tmzZqlqKgorV+/XqtXr1blypX1wgsvaODAgXb1N23apE2bNsnFxUXlypVT06ZNNWLECOtzJXLyzDPPqEqVKlq8eLHeffddFS9eXP7+/nr44YdzbOPh4aE777xTK1as0JkzZ+Tl5aV77rlHs2fPtq4xcHd31+zZs/Xmm2/q/fffV9GiRdW+fXv17ds3176zBAUFqWvXrnrnnXfUrl07VaxYUdOmTdP48eO1dOlSGYah4OBgzZkzx24K3b333qvPPvtMJ0+elKenp6pVq2Y3lap58+aaO3euoqKiFBUVpSJFiqhx48Z6/vnnbabHXK1YsWI2d+G5Hrp27apixYppzpw5mjp1qjw9PdWuXTs9//zzDt3J7Ep79+7V33//rZdfftnuTl9XK1OmjGbPnq23335b7733nry8vNSlSxcFBQXlK4F2cXFRTEyMXn75Zc2fP19+fn566aWX9Mwzz2jo0KHavn27Vq5cqVq1amnixIk27/ewYcPk4+OjxYsXa+LEifL29lbPnj01atQou6TA29tbkyZN0ptvvqkVK1aoXLlyGjt2rPV5F9kxc7MJs+Pwajdi7ADOwMXI7+kFAIXu+PHjatu2rT766CM1bdrU4XoAcL3s2bNH3bt318SJE9W1a1eH+wsLC9PZs2ft7qwG4PpjDQQAAAAA00gggJuYp6enHnroIZUrV65A6gEAAFwLayCAm5iPj4+mTp1aYPUAAACuhTUQAAAAAExjChMAAAAA00ggAAAAAJjGGogc7Nq1S4Zh5PggGgAAAOBWkZGRIRcXFzVo0OCadUkgcmAYRr6fwAkAAADcTPLyu5cEIgdZVx4CAgIKORIAAADg+tqzZ4/puk63BuLQoUMaMGCAAgMDFRwcrMmTJys9Pd1U2/j4eL344otq1qyZ6tWrp9DQUH322WfXOWIAAADg9uFUVyASExMVHh6uatWqKTo6WvHx8Zo0aZIuXLigsWPH5tr25MmT6tWrl6pXr67x48erZMmSOnDggOnkAwAAAMC1OVUCsXz5cqWkpCgmJkalS5eWJGVmZmrcuHGKiIhQxYoVc2w7ZcoU3XHHHZo7d67c3NwkSUFBQTcibAAAAOC24VRTmLZs2aKgoCBr8iBJoaGhslgs2rZtW47tzp8/rw0bNuixxx6zJg8AAAAACp5TJRCxsbHy9fW1KfPy8lL58uUVGxubY7u9e/cqIyNDRYoUUb9+/VSnTh0FBwdrypQpysjIuN5hAwAAALcNp5rClJSUJC8vL7tyb29vJSYm5tju9OnTkqRXXnlFPXv21NNPP63du3crKipKrq6uevbZZ/MVj2EYSk1NzVdbAAAA4GZhGIZcXFxM1XWqBCK/LBaLJKl58+YaPXq0JKlZs2ZKSUnR/PnzFRkZqWLFiuW534yMDO3bt69AYwUAAACckYeHh6l6TpVAeHl5KTk52a48MTFR3t7eubaTLicNVwoKCtLs2bN15MgR+fv75zked3d33XPPPXluBwAAANxMDh48aLquUyUQvr6+dmsdkpOTderUKbu1EVe61o/8ixcv5iseFxcXeXp65qstAAAAcLMwO31JcrJF1K1atdL27duVlJRkLdu4caNcXV0VHBycY7vKlSvLz89P27dvtynfvn27ihUrxlUEAAAAoIA4VQLRu3dvlShRQpGRkdq6datWrVqlyZMnq3fv3jbPgAgPD1f79u1t2o4cOVLffPONJkyYoG3btmn27NmaP3+++vfvz1UEAAAAoIA41RQmb29vLVy4UOPHj1dkZKRKlCih7t27a+TIkTb1LBaLMjMzbcpCQkL0zjvvaObMmVq2bJkqVKigYcOGaciQITfyEAAAAIBbmothGEZhB+GM9uzZI0kKCAhwuC+LxZCrq/l5Zbi9MV4AAMCNlpffvk51BeJW5erqohnLtinuZM7PsgAkqXIFb0X2yXm9DwAAQGEjgbhB4k4m6p+4s4UdBgAAAOAQp1pEDQAAAMC5kUAAAAAAMI0EAgAAAIBpJBAAAAAATCOBAAAAAGAaCQQAAICTsVh4TBfMu9Hjhdu4AgAAOBmeIQWzCuMZUiQQAAAATohnSMFZMYUJAAAAgGkkEAAAAABMI4EAAAAAYBoJBAAAAADTSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADCNBAIAAACAaSQQAHJlsRiFHQJuIowXALj1FSnsAAA4N1dXF81Ytk1xJxMLOxQ4ucoVvBXZJ7iww7CyWAy5uroUdhi4iTBmAHNIIABcU9zJRP0Td7awwwDyhOQXeeFsCTDgzEggAAC3LJJfACh4rIEAAAAAYBoJBAAAAADTSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADCNBAIAAACAaSQQAAAAAEwjgQAAAABgGgkEAAAAANNIIAAAAACYVqSwA7jaoUOH9Oabb2rXrl0qUaKEHn74YY0YMUIeHh65tgsJCVFcXJxd+e7du1W0aNHrFS4AAABwW3GqBCIxMVHh4eGqVq2aoqOjFR8fr0mTJunChQsaO3bsNdt37NhRAwcOtCm7VuIBAAAAwDynSiCWL1+ulJQUxcTEqHTp0pKkzMxMjRs3ThEREapYsWKu7cuVK6fAwMDrHygAAABwm3KqNRBbtmxRUFCQNXmQpNDQUFksFm3btq3wAgMAAAAgyckSiNjYWPn6+tqUeXl5qXz58oqNjb1m+88//1x169ZVgwYNNHjwYO3fv/96hQoAAADclpxqClNSUpK8vLzsyr29vZWYmJhr25CQENWrV0+VKlXSsWPHNHv2bD322GNas2aN7rrrrnzFYxiGUlNT89U2i4uLi4oXL+5QH7j9pKWlyTCMwg6D8Yt8cYbxy9hFfjF+cbNydOwahiEXFxdTdZ0qgXDEK6+8Yv37fffdp+DgYIWGhmrevHl6/fXX89VnRkaG9u3b51BcxYsXV+3atR3qA7efw4cPKy0trbDDYPwiX5xh/DJ2kV+MX9ysCmLsmr35kFMlEF5eXkpOTrYrT0xMlLe3d576qlChgho1aqS9e/fmOx53d3fdc889+W4vyXQmB1ypevXqhX4GTGL8In+cYfwydpFfjF/crBwduwcPHjRd16kSCF9fX7u1DsnJyTp16pTd2ogbwcXFRZ6enjd8vwCXrnEzY/ziZsb4xc3K0bGbl8TVqRZRt2rVStu3b1dSUpK1bOPGjXJ1dVVwcHCe+oqPj9cvv/yigICAgg4TAAAAuG051RWI3r17a9GiRYqMjFRERITi4+M1efJk9e7d2+YZEOHh4Tpx4oQ2bdokSVq3bp2+/fZbtW7dWhUqVNCxY8f0wQcfyM3NTQMGDCiswwEAAABuOU6VQHh7e2vhwoUaP368IiMjVaJECXXv3l0jR460qWexWJSZmWl9XaVKFZ08eVJvvfWWkpOTVapUKTVr1kzDhw/P9x2YAAAAANhzqgRCkmrUqKEFCxbkWmfRokU2rwMDA+3KAAAAABQ8p1oDAQAAAMC5kUAAAAAAMI0EAgAAAIBpJBAAAAAATCOBAAAAAGAaCQQAAAAA00ggAAAAAJhGAgEAAADANBIIAAAAAKaRQAAAAAAwjQQCAAAAgGkkEAAAAABMI4EAAAAAYBoJBAAAAADTSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADCNBAIAAACAaSQQAAAAAEwjgQAAAABgGgkEAAAAANNIIAAAAACYRgIBAAAAwDQSCAAAAACmkUAAAAAAMI0EAgAAAIBpJBAAAAAATCOBAAAAAGAaCQQAAAAA00ggAAAAAJhGAgEAAADANBIIAAAAAKaRQAAAAAAwzekSiEOHDmnAgAEKDAxUcHCwJk+erPT09Dz1sWDBAvn7+ysiIuI6RQkAAADcnooUdgBXSkxMVHh4uKpVq6bo6GjFx8dr0qRJunDhgsaOHWuqj1OnTmnGjBkqW7bsdY4WAAAAuP04VQKxfPlypaSkKCYmRqVLl5YkZWZmaty4cYqIiFDFihWv2ceUKVMUEhKiEydOXOdoAQAAgNuPU01h2rJli4KCgqzJgySFhobKYrFo27Zt12z/v//9T5s3b9azzz57HaMEAAAAbl9OlUDExsbK19fXpszLy0vly5dXbGxsrm0zMzM1fvx4DR06VBUqVLieYQIAAAC3LaeawpSUlCQvLy+7cm9vbyUmJubadunSpUpLS1P//v0LLB7DMJSamupQHy4uLipevHgBRYTbRVpamgzDKOwwGL/IF2cYv4xd5BfjFzcrR8euYRhycXExVdepEoj8OnPmjKKiovT222/Lw8OjwPrNyMjQvn37HOqjePHiql27dgFFhNvF4cOHlZaWVthhMH6RL84wfhm7yC/GL25WBTF2zf6OdqoEwsvLS8nJyXbliYmJ8vb2zrHd9OnT5e/vr/vuu09JSUmSpEuXLunSpUtKSkqSp6enihTJ+6G6u7vrnnvuyXO7K5nN5IArVa9evdDPgEmMX+SPM4xfxi7yi/GLm5WjY/fgwYOm6zpVAuHr62u31iE5OVmnTp2yWxtxpcOHD+vnn39W48aN7bY1btxYc+bMUatWrfIcj4uLizw9PfPcDnAUl65xM2P84mbG+MXNytGxm5fE1akSiFatWmn27Nk2ayE2btwoV1dXBQcH59jupZdesl55yPLWW2+pWLFiGjVqlPz9/a9r3AAAAMDtIt8JhGEYSklJkbu7u4oWLVogwfTu3VuLFi1SZGSkIiIiFB8fr8mTJ6t37942z4AIDw/XiRMntGnTJklSrVq17Pry8vKSp6enmjZtWiCxAQAAAHDgNq4ZGRlq0qSJPvroowILxtvbWwsXLpSbm5siIyM1bdo0de/eXaNHj7apZ7FYlJmZWWD7BQAAAGBOvq9AeHh4qFy5cgV61yNJqlGjhhYsWJBrnUWLFl2zHzN1AAAAAOSNQw+Se/TRR7V27Vqlp6cXVDwAAAAAnJhDi6j9/f319ddf68EHH9Sjjz6qypUrq1ixYnb1OnTo4MhuAAAAADgJhxKIUaNGWf8+ffr0bOu4uLg4/DA2AAAAAM7BoQSiIBdQAwAAAHB+DiUQTZo0Kag4AAAAANwECuRBcunp6dq7d6/OnDmjhg0bysfHpyC6BQAAAOBkHLoLk3R5GlOLFi302GOPadiwYdq/f78kKSEhQU2bNtXKlSsdDhIAAACAc3AogVi1apXeeusttWzZUhMmTJBhGNZtPj4+atasmdavX+9wkAAAAACcg0MJxIcffqi2bdtq2rRpatOmjd32OnXq6MCBA47sAgAAAIATcSiBOHLkiFq1apXj9tKlS+vcuXOO7AIAAACAE3EogfDy8tLZs2dz3H7w4EGVL1/ekV0AAAAAcCIOJRCtWrXSihUrlJSUZLftwIED+uSTTxQSEuLILgAAAAA4EYdu4zpixAj17NlTDz74oNq0aSMXFxetWbNGq1at0ldffaXy5cvrqaeeKqhYAQAAABQyh65AVKxYUatXr1bLli21YcMGGYahtWvX6ttvv1Xnzp21YsUKngkBAAAA3EIcfpBc2bJlNWHCBE2YMEEJCQmyWCzy8fGRq6vDj5gAAAAA4GQK5EnUWbjaAAAAANza8pRAxMTE5HkHLi4uioyMzHM7AAAAAM7H4QTCxcVFkmyeQp1VbhgGCQQAAABwC8lTAvHXX3/ZvI6Pj9eQIUN07733Kjw8XNWrV5ckxcbGauHChTp06JDef//9gosWAAAAQKFyaKXzuHHjdPfdd2vq1KkKCAhQyZIlVbJkSdWrV0/Tpk1T1apV9cYbbxRUrAAAAAAKmUMJxM6dO9WsWbMctzdr1kw7duxwZBcAAAAAnIhDCUTRokX122+/5bh9165dKlq0qCO7AAAAAOBEHLqN60MPPaRFixbJy8tL/fr1U9WqVSVJR48e1aJFi7Ru3TqFhYUVSKAAAAAACp9DCcRzzz2ns2fPavHixVqyZIn14XEWi0WGYahz58567rnnCiRQAAAAAIXPoQTCw8NDU6ZM0aBBg7RlyxbFxcVJkipXrqxWrVqpZs2aBRIkAAAAAOeQ7wQiLS1Nzz//vDp06KAuXbqQLAAAAAC3gXwvoi5evLi2b9+uCxcuFGQ8AAAAAJyYQ3dhatSokXbt2lVQsQAAAABwcg4lEGPHjtUvv/yid999V//9919BxQQAAADASTm0iLpLly7KzMzUBx98oA8++EBubm7y8PCwqePi4qJffvnFoSABAAAAOAeHEoiOHTvKxcWloGIBAAAA4OQcSiAmTZpUUHEAAAAAuAk4tAYCAAAAwO3FoSsQknT+/HktWLBA3333nU6cOCFJqlSpku6//371799fJUuWdDhIAAAAAM7BoSsQ8fHxeuSRRxQTE6PU1FQ1bNhQDRs2VFpammJiYvToo4/q5MmTeerz0KFDGjBggAIDAxUcHKzJkycrPT39mu2ee+45dejQQYGBgWrcuLH69u2rrVu35vfQAAAAAGTDoSsQU6dO1enTp/X++++rdevWNtu+//57jRgxQtOmTdPbb79tqr/ExESFh4erWrVqio6OVnx8vCZNmqQLFy5o7NixubbNyMhQ//79Va1aNV28eFErV67UkCFD9NFHH+m+++7L9zECAAAA+H8OJRA//PCDwsPD7ZIHSWrdurXCwsK0YsUK0/0tX75cKSkpiomJUenSpSVJmZmZGjdunCIiIlSxYsUc206fPt3mdatWrdS2bVutXbuWBAIAAAAoIA5NYUpLS1PZsmVz3F6uXDmlpaWZ7m/Lli0KCgqyJg+SFBoaKovFom3btuUpNjc3N5UqVUoZGRl5agcAAAAgZw4lEDVq1NAXX3yR7RqFjIwMffHFF6pRo4bp/mJjY+Xr62tT5uXlpfLlyys2Nvaa7Q3D0KVLl3T27FnNmzdPR44cUa9evUzvHwAAAEDuHJrCNHjwYI0cOVI9evTQY489pmrVqkmSDh8+rOXLl2v//v169913TfeXlJQkLy8vu3Jvb28lJiZes/3KlSv1yiuvSJI8PT317rvvqkGDBqb3fzXDMJSamprv9tLlJ3EXL17coT5w+0lLS5NhGIUdBuMX+eIM45exi/xi/OJm5ejYNQzD9AOiHUogQkNDlZaWpmnTpum1116z7tQwDJUtW1ZvvfWWOnXq5Mgu8qRt27aqWbOmzp49q40bN2rEiBGKiYnJdo2GGRkZGdq3b59DMRUvXly1a9d2qA/cfg4fPpyn6X/XC+MX+eEM45exi/xi/OJmVRBj18PDw1Q9h58D0bVrV3Xp0kV//PGHzXMg6tatqyJF8ta9l5eXkpOT7coTExPl7e19zfY+Pj7y8fGRdHkRdWJioqZMmZLvBMLd3V333HNPvtpmMZvJAVeqXr16oZ8Bkxi/yB9nGL+MXeQX4xc3K0fH7sGDB03XdTiBkKQiRYooMDBQgYGBudZLSEhQjx49NHXq1GynFvn6+tqtdUhOTtapU6fs1kaYUadOHW3ZsiXP7bK4uLjI09Mz3+2B/OLSNW5mjF/czBi/uFk5Onbzkrg6tIg6rywWi+Li4nThwoVst7dq1Urbt29XUlKStWzjxo1ydXVVcHBwnvf3yy+/6K677sp3vAAAAABsFcgViILSu3dvLVq0SJGRkYqIiFB8fLwmT56s3r172zwDIjw8XCdOnNCmTZskSd99953WrFmj+++/X3feeacSExO1bt06bd26Ve+8805hHQ4AAABwy3GqBMLb21sLFy7U+PHjFRkZqRIlSqh79+4aOXKkTT2LxaLMzEzr67vuukvp6emaNm2azp49qzJlysjf31+LFi1SkyZNbvRhAAAAALcsp0ogpMvPlliwYEGudRYtWmTXZubMmdcxKgAAAADSDV4DAQAAAODmRgIBAAAAwDQSCAAAAACm3dAEwt3dXY0bNzb1UDgAAAAAzqfAF1GnpaXpiy++UHp6ulq3bq3KlStbt3l7e9stgAYAAABw83AogXjppZe0e/durVu3TpKUnp6unj176sCBA5KkUqVKaeHChapdu7bjkQIAAAAodA5NYfrxxx/Vvn176+t169bpwIEDmjp1qtatW6dy5copJibG4SABAAAAOAeHEojTp0/bTFHavHmz6tatqwcffFD33HOPevbsqd27dzscJAAAAADn4FACUbx4cSUnJ0uSLl26pJ9++kktWrSwbi9RooR1OwAAAICbn0NrIOrUqaMVK1aoadOm+uabb5SSkqKQkBDr9qNHj6ps2bIOBwkAAADAOTiUQIwYMUJPPPGEunXrJsMw1LFjR9WrV8+6fdOmTWrYsKHDQQIAAABwDg4lEAEBAdqwYYN+/fVXeXl5qUmTJtZtSUlJeuyxx2zKAAAAANzcHH4OhI+Pj9q1a2dX7uXlpfDwcEe7BwAAAOBECuxBcufPn9f58+dlsVjstlWqVKmgdgMAAACgEDmcQCxdulQLFizQsWPHcqyzb98+R3cDAAAAwAk4dBvXZcuW6Y033lDVqlU1YsQIGYah8PBwDRkyROXKlVPNmjU1YcKEgooVAAAAQCFzKIFYvHixWrRooblz56pnz56SpNatW2vkyJFav369UlJSdO7cuYKIEwAAAIATcCiBOHr0qNq0aSNJcnd3lyRlZGRIkkqVKqXu3btr6dKlDoYIAAAAwFk4lECUKlVKmZmZkqSSJUuqePHi+u+//6zbS5QoodOnTzsWIQAAAACn4VACce+99+qvv/6yvq5fv76WLVum+Ph4/fvvv/r4449VrVo1R2MEAAAA4CQcSiC6dOmiAwcOKD09XZI0bNgwHTp0SPfff79CQkJ0+PBhjRgxoiDiBAAAAOAEHLqNa7du3dStWzfr60aNGumLL77Q119/rSJFiig4OFjVq1d3OEgAAAAAzqHAHiSXkpKipKQkubm5qUOHDtbyEydO8CA5AAAA4BbhUAJx8eJFxcTEaOXKlbnerpUHyQEAAAC3BocSiNdff11r1qxRu3bt1KhRI3l7exdUXAAAAACckEMJxKZNm9SjRw+98cYbBRUPAAAAACfm0F2YXFxcVLt27YKKBQAAAICTcyiBaNu2rbZv315QsQAAAABwcnlKIM6dO2fz56mnntLx48f16quv6o8//lBCQoJdndwWVwMAAAC4ueRpDUSzZs3k4uJiU2YYhv7880+tXLkyx3bchQkAAAC4NeQpgYiMjLRLIAAAAADcPvKUQAwbNux6xQEAAADgJuDQImoAAAAAtxcSCAAAAACmOfQguevh0KFDevPNN7Vr1y6VKFFCDz/8sEaMGCEPD48c25w8eVILFizQtm3bdPToUZUqVUqNGzfWqFGjVLly5RsYPQAAAHBrc6oEIjExUeHh4apWrZqio6MVHx+vSZMm6cKFCxo7dmyO7fbu3atNmzapW7duql+/vs6ePatZs2apR48eWrdunXx8fG7gUQAAAAC3LqdKIJYvX66UlBTFxMSodOnSkqTMzEyNGzdOERERqlixYrbtGjVqpA0bNqhIkf8/nIYNG+r+++/XmjVrNHDgwBsRPgAAAHDLc6o1EFu2bFFQUJA1eZCk0NBQWSwWbdu2Lcd2Xl5eNsmDJN1xxx3y8fHRyZMnr1e4AAAAwG3HqRKI2NhY+fr62pR5eXmpfPnyio2NzVNfhw8f1pkzZ1SjRo2CDBEAAAC4rTnVFKakpCR5eXnZlXt7eysxMdF0P4Zh6M0331SFChXUuXPnfMdjGIZSU1Pz3V6SXFxcVLx4cYf6wO0nLS1NhmEUdhiMX+SLM4xfxi7yi/GLm5WjY9cwDNMPjHaqBKKgREdHa+fOnZo7d648PT3z3U9GRob27dvnUCzFixdX7dq1HeoDt5/Dhw8rLS2tsMNg/CJfnGH8MnaRX4xf3KwKYuzmdtfTKzlVAuHl5aXk5GS78sTERHl7e5vqY8WKFZoxY4YmTJigoKAgh+Jxd3fXPffc41AfZjM54ErVq1cv9DNgEuMX+eMM45exi/xi/OJm5ejYPXjwoOm6TpVA+Pr62q11SE5O1qlTp+zWRmRn06ZNev311zV8+HB1797d4XhcXFwcuoIB5BeXrnEzY/ziZsb4xc3K0bGbl8TVqRZRt2rVStu3b1dSUpK1bOPGjXJ1dVVwcHCubX/88UeNGjVKPXr0UGRk5PUOFQAAALgtOVUC0bt3b5UoUUKRkZHaunWrVq1apcmTJ6t37942z4AIDw9X+/btra8PHTqkyMhIVatWTQ8//LB+++0365+jR48WxqEAAAAAtySnmsLk7e2thQsXavz48YqMjFSJEiXUvXt3jRw50qaexWJRZmam9fXvv/+u5ORkJScnq0+fPjZ1H330UU2aNOmGxA8AAADc6pwqgZCkGjVqaMGCBbnWWbRokc3rrl27qmvXrtcxKgAAAACSk01hAgAAAODcSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADCNBAIAAACAaSQQAAAAAEwjgQAAAABgGgkEAAAAANNIIAAAAACYRgIBAAAAwDQSCAAAAACmkUAAAAAAMI0EAgAAAIBpJBAAAAAATCOBAAAAAGAaCQQAAAAA00ggAAAAAJhGAgEAAADANBIIAAAAAKaRQAAAAAAwjQQCAAAAgGkkEAAAAABMI4EAAAAAYBoJBAAAAADTSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADCNBAIAAACAaSQQAAAAAEwjgQAAAABgGgkEAAAAANNIIAAAAACY5nQJxKFDhzRgwAAFBgYqODhYkydPVnp6+jXbLVmyRBEREWrWrJn8/f21cePGGxAtAAAAcHtxqgQiMTFR4eHhysjIUHR0tEaOHKkVK1Zo0qRJ12y7du1anT17Vq1bt74BkQIAAAC3pyKFHcCVli9frpSUFMXExKh06dKSpMzMTI0bN04RERGqWLFirm1dXV11/PhxrVmz5sYEDAAAANxmnOoKxJYtWxQUFGRNHiQpNDRUFotF27Zty7Wtq6tTHQoAAABwS3KqX92xsbHy9fW1KfPy8lL58uUVGxtbSFEBAAAAyOJUU5iSkpLk5eVlV+7t7a3ExMQbHo9hGEpNTXWoDxcXFxUvXryAIsLtIi0tTYZhFHYYjF/kizOMX8Yu8ovxi5uVo2PXMAy5uLiYqutUCYSzycjI0L59+xzqo3jx4qpdu3YBRYTbxeHDh5WWllbYYTB+kS/OMH4Zu8gvxi9uVgUxdj08PEzVc6oEwsvLS8nJyXbliYmJ8vb2vuHxuLu765577nGoD7OZHHCl6tWrF/oZMInxi/xxhvHL2EV+MX5xs3J07B48eNB0XadKIHx9fe3WOiQnJ+vUqVN2ayNuBBcXF3l6et7w/QJcusbNjPGLmxnjFzcrR8duXhJXp1pE3apVK23fvl1JSUnWso0bN8rV1VXBwcGFGBkAAAAAycmuQPTu3VuLFi1SZGSkIiIiFB8fr8mTJ6t37942z4AIDw/XiRMntGnTJmvZnj17FBcXp4SEBEnS77//Lkny8fFRkyZNbuyBAAAAALcop0ogvL29tXDhQo0fP16RkZEqUaKEunfvrpEjR9rUs1gsyszMtClbsmSJPv30U+vr+fPnS5KaNGmiRYsWXf/gAQAAgNuAUyUQklSjRg0tWLAg1zrZJQSTJk3SpEmTrlNUAAAAACQnWwMBAAAAwLmRQAAAAAAwjQQCAAAAgGkkEAAAAABMI4EAAAAAYBoJBAAAAADTSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADCNBAIAAACAaSQQAAAAAEwjgQAAAABgGgkEAAAAANNIIAAAAACYRgIBAAAAwDQSCAAAAACmkUAAAAAAMI0EAgAAAIBpJBAAAAAATCOBAAAAAGAaCQQAAAAA00ggAAAAAJhGAgEAAADANBIIAAAAAKaRQAAAAAAwjQQCAAAAgGkkEAAAAABMI4EAAAAAYBoJBAAAAADTSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADDN6RKIQ4cOacCAAQoMDFRwcLAmT56s9PT0a7YzDEMffPCB7r//ftWrV0+9evXSb7/9dv0DBgAAAG4jTpVAJCYmKjw8XBkZGYqOjtbIkSO1YsUKTZo06Zpt58yZo6ioKPXv31/vv/++ypcvr4EDB+rYsWM3IHIAAADg9lCksAO40vLly5WSkqKYmBiVLl1akpSZmalx48YpIiJCFStWzLbdxYsX9f7772vgwIHq37+/JKlRo0bq1KmT5s2bp9dff/3GHAAAAABwi3OqKxBbtmxRUFCQNXmQpNDQUFksFm3bti3Hdr/++qvOnz+v0NBQa5mHh4fat2+vLVu2XM+QAQAAgNuKUyUQsbGx8vX1tSnz8vJS+fLlFRsbm2s7SXZta9SooRMnTujChQsFHywAAABwG3KqKUxJSUny8vKyK/f29lZiYmKu7Tw8PFS0aFGbci8vLxmGocTERBUrVixPsWRkZMgwDO3evTtP7bLj4uKizk3KK9NS1uG+cGtzc3XVnj17ZBhGYYdixfiFWc42fhm7yAvGL25WBTV2MzIy5OLiYqquUyUQziTrDTT7Rl6LV8m8JTC4vRXUuCsojF/khTONX8Yu8orxi5uVo2PXxcXl5kwgvLy8lJycbFeemJgob2/vXNulp6fr4sWLNlchkpKS5OLikmvbnDRo0CDPbQAAAIBbnVOtgfD19bVb65CcnKxTp07ZrW+4up0kHT582KY8NjZWlSpVyvP0JQAAAADZc6oEolWrVtq+fbuSkpKsZRs3bpSrq6uCg4NzbNewYUOVLFlSGzZssJZlZGToq6++UqtWra5rzAAAAMDtxKmmMPXu3VuLFi1SZGSkIiIiFB8fr8mTJ6t37942z4AIDw/XiRMntGnTJklS0aJFFRERoejoaPn4+MjPz0/Lli3TuXPnNGjQoMI6HAAAAOCW41QJhLe3txYuXKjx48crMjJSJUqUUPfu3TVy5EibehaLRZmZmTZlgwcPlmEYmj9/vhISElSrVi3NmzdPd9111408BAAAAOCW5mI4y/3KAAAAADg9p1oDAQAAAMC5kUAAAAAAMI0EAgAAAIBpJBAAAAAATCOBAAAAAGAaCQQAAAAA00ggkK0uXbrI399f//vf/wqsz6SkJPn7+2v16tUF0l90dLQaNGhwzXpPPfWUwsLCCmSfuHVlN57i4+M1duxYtW7dWnXr1lWrVq30yiuv6L///rNr/++//2rMmDEKCQlRQECAWrRoof79+2vt2rU36hBwGxs9erQefPBBm7Lff/9dTz75pJo2baqAgACFhoYqJiZGFy5csGv/22+/6YknnlBwcLDq1aunkJAQDR8+XL///vuNOgTcAq4chz/++KP8/f2tfxo0aKBHHnlEK1euVEE8QeDhhx/W6NGj7co/++wz9e7dWw0aNFCDBg3Uq1cvrVmzJts+PvvsM3Xv3l2NGjVSw4YNFRoaqpdffllnzpxxOL5bnVM9SA7O4cCBA9q/f78k6fPPP9d9991XyBFlr0ePHmrdunVhh4Fb1KFDhxQWFqZixYopMjJS1apV05EjRzR79mx98803WrRokWrUqCHpcnLcs2dPeXt7a9iwYapUqZL+++8/7dy5Uz/88IMefvjhQj4a3OqeeuoppaamWl+vX79ezz//vBo2bKhx48apTJky+vXXX/XBBx9oy5YtWrhwoYoXLy5J+uWXX/T444+rZcuWGjdunEqUKKEjR45o8+bN2r17t+rXr19Yh4WbzNXjUJImTpwoX19fJSUlaeXKlXr55Zd16dIl9e7du8D3P378eC1ZskTdunXTU089JRcXF3355ZcaPXq09uzZo1dffdVad86cOZo2bZr69++v4cOHyzAMHThwQJ9//rlOnjypsmXLFnh8txQDuMq0adOMmjVrGmFhYUaTJk2M9PT0Auk3MTHR8PPzM1atWpVjnYsXLxqZmZkFsr8sTz75pNGvX78C7RO3nqioKCMwMND6+tFHHzWCgoKMU6dO2dQ7deqUERQUZDz66KPWshUrVhh+fn5GXFycXb8FPZ6Ba/nvv/+MwMBA4/HHHzcuXbpks23Hjh2Gn5+fMXHiRGvZs88+a7Rv396urmEwfpF/O3fuNPz8/Izdu3dbyy5dumR06NDB6Ny5s8P9d+nSxXjxxRetrzdv3mz4+fkZUVFRdnWnT59u+Pn5GV9//bW1rGXLlsbo0aOz7Ztxf21MYYINwzC0bt06NWvWTAMGDNC5c+f0ww8/WLdnXZLctm2bnn32WTVo0EBt2rTRnDlz7PpasWKFQkJCVL9+fYWHh+vIkSN2dUJCQvTGG29ozpw5atOmjerVq6dz587JYrFo5syZCgkJUd26ddWpUyctX77cpm12U04OHTqkfv36KSAgQO3atdOnn35aQO8Mbic///yz9u7dq8cff1zlypWz2VauXDmFhYVp79691il+iYmJcnV1zfaMlasr/8zi+rty6sgnn3yi1NRUPfPMM3Jzc7Op16xZMzVv3lwff/yxdSpTUlKSfHx87OpKjF/kTXZT6a7k5uamWrVq6fjx45Kk7777TgMGDFBQUJAaNmyoHj16aMuWLXbtfv31V3Xt2lUBAQF68MEH9f3339vVWbhwoby9vTVw4EC7bYMGDZK3t7cWLlxoLUtKSlKFChWyjZNxf228Q7Dx66+/Ki4uTg8++KBatGih0qVLa926dXb1XnvtNVWrVk0zZsxQmzZtNHXqVJsv/bfffqtXX31VTZs2VUxMjIKCgvTMM89ku8+vvvpK3333nV5++WXNnDlTnp6emjx5smJiYvToo49q9uzZatGihV577TUtXrw4x9gvXryogQMH6vTp05o8ebKeffZZffDBB9qzZ4/jbwxuKz/99JMkqU2bNtluDwkJkXQ50ZCkOnXqyGKx6LnnntOuXbt06dKlGxMokI2ff/5ZpUuXVsOGDbPdHhISotTUVO3du1fS5fG7a9cuvffeezp06NCNDBW3oePHj1t/uB8/flxt2rTR5MmTFR0drYYNG2rIkCH68ccfrfVPnTqlQYMGycPDQ++9954GDRqkcePGKT4+3lrn0qVL2rVrl5o2baoSJUrY7bNEiRJq2rSpzb/PderU0fLly/XJJ5/o1KlT1/mobz2sgYCNdevWqWjRourQoYPc3d3VsWNHffbZZ0pJSbH5Unbo0EHDhg2TJAUFBem7777Tl19+qVatWkmSZs2apfvuu08TJ06UJLVs2VIXL17UzJkz7faZkZGhOXPmyNPTU5KUkJCgxYsXa9CgQdZ9tGjRQmfPntWMGTPUp0+fbM+UrV69WidPntSGDRtUrVo1SVLt2rXVqVMn62vAjKz/MVWqVCnb7VnlWYupg4KCNGjQIH344Yf66quvVKxYMTVq1EhdunTRww8/LBcXlxsTOKDL4/fOO+/McXvWtqxxPmjQIP3++++aNWuWZs2apdKlS6tFixbq06eP066Bw83DYrHo0qVLSk5O1scff6w9e/YoIiJCktSvXz+bek2bNtXBgwe1YsUKNW3aVNLlKwsuLi6aM2eOSpUqJUm644471L9/f2vbs2fPKj09/Zrj/uLFizp37pzKlSun1157TU8//bReeeUVSVKVKlXUpk0b9e/fX1WqVCnot+GWwxUIWF26dEkbN25U69atrV/Shx56SGlpadq0aZNN3RYtWlj/7uLioho1alh/TGVmZmrv3r1q3769TZuOHTtmu9+mTZtakwdJ2r17tzIyMtSpUyebeqGhoUpISNA///yTbT+7d+/Wvffea5Ms3H333apZs2buBw4UgBdeeEFfffWVxowZo1atWmn37t168cUX9cILLxR2aECuSpYsqfnz5+uTTz5RZGSkatasqS+//FL9+vXTJ598Utjh4SbXs2dP1alTR82aNVN0dLR69+6tyMhISZdPwrz44otq2bKlateurTp16mjr1q06fPiwtf3vv/+upk2bWn+XSJdP2pQuXdqhuPz8/LRu3Tp98MEHevzxx1WqVCktWrRIXbp00b59+xzq+3bAFQhYbdu2TQkJCWrTpo2SkpIkXf6ClS9fXuvWrdMjjzxirXvlF1mS3N3dlZycLOnyFYRLly7Jx8fHps7Vc8mzXD1vPDExMdv6Wa/PnTuXbT853TWhbNmyunjxYrZtgOxUrFhRknTixAn5+/vbbT9x4oSky2fBrnTXXXepf//+6t+/v1JSUvTMM8/os88+06BBg0hkccNUrFhRf/31V47b//33X2u9K9WrV0/16tWTJB07dkxhYWGaOnWqevTocf2CxS3v7bffVo0aNVSyZElVrlxZHh4eki5fcXjyySeVnJys4cOH6+6771bx4sUVFRVlHaPS5SlMd999t12/V/7GKFOmjDw8PGzaXe3ff/9V0aJFbRIPDw8PtW7d2npHxx9++EERERGaMWOGYmJiHD30WxpXIGD1+eefS5LGjBmjxo0bq3HjxmrSpIlOnTqlHTt2mL4vso+Pj4oUKaKEhASb8tOnT2db/+rpHVlf7qv3l9U+p7MOFSpUyDZG7ueMvGrSpIkkZbtQT7q88E+SGjdunGMfJUqU0GOPPSZJio2NLdgAgVw0btxY586d02+//Zbt9u+++06enp6qU6dOjn3cdddd6tSpk86dO5fjv92AGTVq1FBAQICqV69uTR4k6ciRI/rzzz81evRo9ejRQ02aNFFAQIDdc0rKly+f7f/Hr/yNUaRIETVo0EA//fST3W1kJSk1NVU//fSTGjRooCJFcj533rJlS9WsWZO1QCaQQECSlJaWpq+//lrt2rXTRx99ZPPnnXfe0aVLl7R+/XpTfbm5ual27dp2056+/PJLU+0DAgLk7u6ujRs32pRv2LBBZcuWzXE9Q0BAgA4cOGBzt6cjR47keiYOyE7jxo1Vp04dLVy40C4RTkhI0EcffaQ6depY54cnJCRk+2CkrOl2OV19A66HHj16yNPTU9OnT5fFYrHZ9tNPP2nr1q3q3bu3ihUrJinnkzv//POPPDw85OXldd1jxu0na2aAu7u7tSwuLk67du2yqVevXj39+OOP1lkOkrRjxw672Qjh4eE6d+6c5s+fb7ev+fPn69y5cwoPD7eWZTfuL1y4oH///Zd/s01gChMkSV9//bVSU1MVFhZmXbh0pblz52rdunUaNWqUqf6GDh2qp556SmPGjNEDDzygvXv3mn4ir4+Pj/r166d58+bJw8NDgYGB+v7777Vu3Tq9+uqr2S6glqSuXbtq1qxZioiIsN7xKSoqin8IkC9TpkxRWFiYevbsqYiICOuD5N5//30ZhqEpU6ZY63766adau3atHn74YdWuXVsWi0W7du3SnDlzVKdOHTVq1KgQjwS3m4oVK2rChAl67rnn1L9/f/Xt29fmQXL169fX8OHDrfVfeeUVZWZmqkOHDqpWrZrOnz+vL7/8Ut9++63Cw8NtzhoDBcXX11d33HGHpk2bJovFotTUVEVFRdndWjU8PFxLly7V4MGDNXjwYCUlJSk6OtpuNkLbtm3Vr18/xcTE6L///rOuo/zqq6+0YsUK9evXz3oHPenyGs82bdqoRYsWqlChguLj47V48WKdPXvWJtFA9kggIOny3ZcqVaqUbfIgSY888ojeeustHT161FR/bdu21bhx4zR79mx98cUXql+/vt577z3Tc2lfeOEFlSpVSitXrtTs2bNVuXJljRs3LtcnVxYrVkzz58/X66+/rueff14VK1bUU089pa+//trmzAVgRo0aNfTpp59a58KeOXNGZcqUUevWrfX000/brH9o3bq1Tpw4oTVr1mjmzJmyWCyqVKmSBg4cqAEDBuSY9ALXywMPPKDKlStr1qxZGjt2rFJTU1W5cmUNHDhQgwYNsj6FWpL69u2rNWvW6P3339epU6dUrFgxVa1aVRMmTNCjjz5aiEeBW5mHh4eio6P1xhtv6JlnntGdd96pJ598Ujt37tQff/xhrVehQgXNmTNHb775pp555hlVrVpVY8eO1bvvvmvX56uvvqr69etr6dKl1rs4+vn5adKkSTbrOCXp6aef1rfffqtJkyYpISFBZcqUkb+/vxYsWKBmzZpd12O/FbgY2V13BwAAAIBssAYCAAAAgGkkEAAAAABMI4EAAAAAYBoJBAAAAADTSCAAAAAAmEYCAQAAAMA0EggAAAAAppFAAAAAADCNBAIAbrDo6Gj5+/tft/7DwsIUFhZ23fovSCEhIRo9enSB97tmzRp16tRJderU0X333Vfg/Tuj1atXy9/fX8ePHy/sUADc4ooUdgAAAORVfHy8VqxYoXbt2qlWrVo22w4dOqQxY8aoZcuWGjJkiIoVK1ZIUQLArYkEAgBuMfPmzSvsEK67kydPKiYmRpUrV7ZLIH766SdZLBa9/PLLuvvuuwspQgC4dTGFCQBuMR4eHvLw8CjsMArNmTNnJEmlSpUqsD7T0tIKrC8AuNmRQADAdfS///1P3bp1U0BAgNq1a6fly5dnW2/t2rXq2rWr6tWrpyZNmmjkyJH6999/rdvfeOMNNWjQINsfsqNGjVJwcLAyMzMlZb8G4uLFi4qOjlbHjh0VEBCgFi1a6Omnn9bRo0etdSwWixYsWKDOnTsrICBAzZs319ixY5WYmJinY85a43Ho0CE988wzatiwoZo2bao333xTFy9ezLXtuXPn9Pbbb+uhhx5SgwYN1LBhQz3xxBP666+/rHV+/PFHde/eXZI0ZswY+fv7y9/fX6tXr1ZISIiio6MlSUFBQfL397e+lqQlS5aoc+fOqlu3rlq0aKFx48YpKSnJJoawsDA9+OCD+uOPP9S3b1/Vr19f77zzjo4fPy5/f3/NmzdPS5YsUdu2bVW/fn0NHDhQ//77rwzD0IwZM9SqVSvVq1dPTz75pM6dO5en9066PD3rpZdeUosWLVS3bl2FhITotddeU3p6urXOgQMH9Pjjj6tevXpq1aqVZs6cKYvFkud9AUB+MIUJAK6T/fv3a9CgQfLx8dGwYcN06dIlRUdHq2zZsjb1Zs2apenTpys0NFTdu3dXQkKCFi9erL59+2rNmjXy8vLSAw88oCVLlui7775TaGiotW1aWpq+/fZbPfroo3Jzc8s2jszMTEVERGjHjh3q3LmzHn/8caWkpGjbtm36+++/VbVqVUnS2LFj9emnn6pr164KCwvT8ePHtWTJEv35559atmyZ3N3d83T8I0aMUOXKlfXss8/qt99+06JFi5SUlKTJkyfn2ObYsWPavHmzOnXqpCpVquj06dP6+OOP1a9fP33xxReqWLGiatSooeHDhysqKkq9evVSo0aNJEkNGzbUSy+9pDVr1mjTpk16/fXX5enpaV2wHh0drZiYGDVv3lx9+vTR4cOHtWzZMu3Zs8fu+M6dO6fBgwerc+fO6tKli81n9vnnnysjI0NhYWE6d+6c5s6dqxEjRqhZs2b68ccfNXjwYB05ckSLFy/W22+/rYkTJ5p+z+Lj49W9e3clJyerZ8+e8vX1VXx8vL788ktduHBBHh4eOnXqlB5//HFlZmZqyJAhKl68uFasWKGiRYvm6fMBgHwzAADXxVNPPWUEBAQYcXFx1rKDBw8atWrVMvz8/AzDMIzjx48btWrVMmbNmmXTdv/+/Ubt2rWt5RaLxWjZsqUxbNgwm3rr1683/Pz8jJ9//tla1q9fP6Nfv37W1ytXrjT8/PyMDz/80C5Gi8ViGIZh/Pzzz4afn5/x2Wef2WzfsmVLtuW5iYqKMvz8/IyhQ4falL/++uuGn5+fsW/fPmtZmzZtjBdffNH6+uLFi0ZmZqZNu2PHjhl169Y1YmJirGW7d+82/Pz8jFWrVuW4/zNnzljLzpw5Y9SpU8cYOHCgTf+LFy82/Pz8jJUrV1rL+vXrZ/j5+RnLli2zi8PPz89o1qyZkZSUZC2fNm2a4efnZ3Tp0sXIyMiwlo8aNcqoU6eOcfHixZzfrKu88MILRs2aNY3du3fbbcv6rCZMmGD4+fkZv//+u83xNWrUyPDz8zOOHTtmen8AkB9MYQKA6yAzM1Nbt25Vu3btVKlSJWt5jRo11KJFC+vrTZs2yWKxKDQ0VAkJCdY/5cqV0913360ff/xRkuTi4qJOnTrp+++/V0pKirX9hg0bVLFiRetZ+Ox89dVXKlOmjPr162e3zcXFRZK0ceNGlSpVSsHBwTZx1KlTR56entY48qJv3742r7P2v2XLlhzbeHh4yNX18v+aMjMzdfbsWXl6eqp69er6888/8xxDlu3btysjI0OPP/64tX9J6tGjh0qWLKnvv//eLo6uXbtm21enTp1s1lfUq1dPktSlSxcVKVLEpjwjI0Px8fGmYrRYLNq8ebPatGmjgIAAu+1Zn9X333+vwMBA634lycfHRw899JCp/QCAo5jCBADXQUJCgi5cuJDtXYCqV69u/cH6zz//yDAMdejQIdt+rvxB+sADD2jhwoX65ptv9NBDDyklJUXff/+9evXqZf1xmZ2jR4+qevXqNn1d7ciRI0pOTlZQUFC227MWJufF1cdetWpVubq65vqcAovFoo8++khLly7V8ePHres6JKl06dJ5jiHLiRMnJEm+vr425R4eHrrrrrsUFxdnU16xYsUcF6LfeeedNq+zkomcyhMTE3XXXXddM8aEhASdP39e9957b671Tpw4ofr169uVV69e/Zr7AICCQAIBAIXIYrHIxcVFc+bMyXYNg6enp/XvgYGBqly5sjZs2KCHHnpI3377rS5cuKAHHnigQOIoW7aspk6dmu12Hx8fh/eRW5KTZfbs2Zo+fbq6deumZ555Rt7e3nJ1ddVbb70lwzAcjsGs3J4dkdNakyuvbFzpRsYNADcCCQQAXAc+Pj4qVqyYjhw5Yrft8OHD1r9XrVpVhmGoSpUqps4gh4aG6qOPPtL58+e1fv16Va5cWYGBgbm2qVq1qn7//XdlZGTkuBC6atWq2rFjhxo2bFhgD147cuSIzZn3I0eOyGKxqEqVKjm2+fLLL9W0aVO99dZbNuVJSUkqU6aM9bWZZORKWdPIYmNjbWJKT0/X8ePH1bx58zz1dz34+PioZMmSOnDgQK71KlWqdM1xBQDXE2sgAOA6cHNzU4sWLbR582br9Bnp8lOSt27dan3doUMHubm5KSYmxu5MtWEYOnv2rE3ZAw88oPT0dH366af64YcfbO7IlJMOHTro7NmzWrJkid22rH2GhoYqMzNTM2fOtKtz6dIlu1udmnH1/hYvXixJatWqVY5t3Nzc7N6HDRs22K0jKF68uCSZjqt58+Zyd3fXokWLbPpfuXKlkpOT1bp1a1P9XE+urq5q166dvv32W+3Zs8due1bcrVu31m+//abdu3dbtyUkJOjzzz+/YbECuL1xBQIArpNhw4bphx9+UN++fdWnTx9lZmZq8eLFuueee7R//35Jl8/8jxgxQtOmTVNcXJzatWunEiVK6Pjx49q8ebN69uypQYMGWfusU6eO7r77br377rtKT083NX3pkUce0Zo1azRx4kTt3r1bjRo1Ulpamnbs2KE+ffqoXbt2atKkiXr16qX3339f+/btU3BwsNzd3fXPP/9o48aNevnll9WpU6c8Hf/x48c1dOhQtWzZUr/99ps+++wzPfjgg6pZs2aObe6//37NmDFDY8aMUYMGDfT333/r888/t1tDULVqVXl5eWn58uUqUaKEPD09Va9evRzXGvj4+CgiIkIxMTF64oknFBISosOHD2vp0qUKCAhQly5d8nRs18uoUaO0bds2hYWFqWfPnqpRo4ZOnTqljRs3aunSpfLy8tITTzyhtWvX6oknntDjjz9uvY1rpUqVrOMKAK4nEggAuE5q1qypefPmaeLEiYqKitIdd9yhYcOG6dSpUzY/9IYMGaJq1appwYIFmjFjhiTpjjvuUHBwsEJCQuz6DQ0N1ezZs3X33XerTp0614zDzc1Nc+bM0axZs7Ru3Tp99dVXKl26tBo2bGh9RoJ0+WF1devW1fLly/Xuu+/Kzc1NlStXVpcuXdSwYcM8H/97772n6dOna9q0aSpSpIj69eunF154Idc2Q4cOVVpamj7//HOtX79etWvX1vvvv69p06bZ1HN3d9ekSZP0zjvv6PXXX9elS5c0ceLEXBcrDxs2TD4+Plq8eLEmTpwob29v9ezZU6NGjcrzMy6ul4oVK2rFihWaPn26Pv/8c50/f14VK1ZUq1atrFPLKlSooI8++khvvvmmPvjgA5UuXVq9e/dWhQoV9PLLLxfyEQC4HbgYrO4CABSgrAe27dixo0AWXwMAnAtrIAAAAACYxhQmAIApKSkpSk1NzbUOVxyyZ/a9y+kWsQDgTEggAACmzJ8/XzExMbnW+frrr29QNDcXs+9dbre4BQBnwRoIAIApx44d07Fjx3Kt06hRIxUtWvQGRXTz4L0DcCshgQAAAABgGouoAQAAAJhGAgEAAADANBIIAAAAAKaRQAAAAAAwjQQCAAAAgGkkEAAAAABMI4EAAAAAYBoJBAAAAADT/g+U7Bn0DsjSgQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>device_platform_cd</th>\n",
+       "      <th>has_order</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.587575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>IOS</td>\n",
+       "      <td>0.545270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>iOS</td>\n",
+       "      <td>0.542612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>iPadOS</td>\n",
+       "      <td>0.569767</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  device_platform_cd  has_order\n",
+       "0            Android   0.587575\n",
+       "1                IOS   0.545270\n",
+       "2                iOS   0.542612\n",
+       "3             iPadOS   0.569767"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "platform_rate = client.groupby(\"device_platform_cd\")[\"has_order\"].mean().reset_index()\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "sns.barplot(data=platform_rate, x=\"device_platform_cd\", y=\"has_order\")\n",
+    "plt.title(\"Доля клиентов с заказом по платформам\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "platform_rate\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f65ad022",
+   "metadata": {},
+   "source": [
+    "## ML-модель с контролем объёма"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "eaa4b459",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:12:13.294736Z",
+     "iopub.status.busy": "2025-12-12T19:12:13.294463Z",
+     "iopub.status.idle": "2025-12-12T19:12:13.423902Z",
+     "shell.execute_reply": "2025-12-12T19:12:13.421985Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.681635404420581,\n",
+       " num__passive_click_total           0.757779\n",
+       " num__ctr_all                      -0.257144\n",
+       " cat__device_platform_cd_Android    0.182476\n",
+       " cat__gender_cd_M                   0.133747\n",
+       " num__active_click_total            0.119761\n",
+       " cat__device_platform_cd_iPadOS    -0.100109\n",
+       " num__age                          -0.071048\n",
+       " num__passive_imp_total            -0.050535\n",
+       " cat__device_platform_cd_iOS        0.040232\n",
+       " num__active_imp_total             -0.019038\n",
+       " dtype: float64)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = client[[\n",
+    "    \"active_imp_total\",\n",
+    "    \"passive_imp_total\",\n",
+    "    \"active_click_total\",\n",
+    "    \"passive_click_total\",\n",
+    "    \"ctr_all\",\n",
+    "    \"age\",\n",
+    "    \"gender_cd\",\n",
+    "    \"device_platform_cd\",\n",
+    "]]\n",
+    "X = X.copy()\n",
+    "X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
+    "X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
+    "y = client[\"has_order\"]\n",
+    "\n",
+    "numeric_cols = [\"active_imp_total\", \"passive_imp_total\", \"active_click_total\", \"passive_click_total\", \"ctr_all\", \"age\"]\n",
+    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
+    "\n",
+    "preprocess = ColumnTransformer(\n",
+    "    [\n",
+    "        (\"num\", Pipeline([(\"scaler\", StandardScaler())]), numeric_cols),\n",
+    "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "model = Pipeline([(\"pre\", preprocess), (\"clf\", LogisticRegression(max_iter=1000))])\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "model.fit(X_train, y_train)\n",
+    "proba = model.predict_proba(X_test)[:, 1]\n",
+    "auc = roc_auc_score(y_test, proba)\n",
+    "coef = model.named_steps[\"clf\"].coef_[0]\n",
+    "features = model.named_steps[\"pre\"].get_feature_names_out()\n",
+    "coef_series = pd.Series(coef, index=features).sort_values(key=abs, ascending=False)\n",
+    "auc, coef_series.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce032735",
+   "metadata": {},
+   "source": [
+    "## Вывод по гипотезе\n",
+    "- В сырой агрегированной доле заказов Android выше iOS.\n",
+    "- В модели при контроле объёма коммуникаций и CTR коэффициент при `device_platform_cd_Android` положительный и в топ‑фичах, AUC ~0.69. Гипотеза подтверждается: платформа влияет на вероятность заказа."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/alternative/passive_share_orders/analysis.ipynb b/alternative/passive_share_orders/analysis.ipynb
new file mode 100644
index 0000000..ee366ee
--- /dev/null
+++ b/alternative/passive_share_orders/analysis.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "34468500",
+   "metadata": {},
+   "source": [
+    "# Доля пассивных показов и заказы\n",
+    "\n",
+    "**Вопрос:** повышает ли высокая доля пассивных показов вероятность заказа при контроле объёма коммуникаций?\n",
+    "\n",
+    "**Гипотеза:** большая доля пассивных показов связана с большей вероятностью заказа (проверяем ML)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "46fb7ac5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:43.639846Z",
+     "iopub.status.busy": "2025-12-12T19:11:43.638998Z",
+     "iopub.status.idle": "2025-12-12T19:11:50.215868Z",
+     "shell.execute_reply": "2025-12-12T19:11:50.213723Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "from pathlib import Path\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
+    "\n",
+    "project_root = Path.cwd().resolve()\n",
+    "while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
+    "    project_root = project_root.parent\n",
+    "    project_root = project_root.parent\n",
+    "sys.path.append(str(project_root / \"preanalysis\"))\n",
+    "import eda_utils as eda\n",
+    "\n",
+    "db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
+    "conn = sqlite3.connect(db_path)\n",
+    "df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
+    "conn.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "73842cf6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:50.222842Z",
+     "iopub.status.busy": "2025-12-12T19:11:50.222356Z",
+     "iopub.status.idle": "2025-12-12T19:11:52.672337Z",
+     "shell.execute_reply": "2025-12-12T19:11:52.670490Z"
+    }
+   },
+   "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>active_imp_total</th>\n",
+       "      <th>passive_imp_total</th>\n",
+       "      <th>active_click_total</th>\n",
+       "      <th>passive_click_total</th>\n",
+       "      <th>orders_amt_total</th>\n",
+       "      <th>imp_total</th>\n",
+       "      <th>click_total</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender_cd</th>\n",
+       "      <th>device_platform_cd</th>\n",
+       "      <th>passive_share</th>\n",
+       "      <th>ctr_all</th>\n",
+       "      <th>has_order</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.514706</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>116.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.767241</td>\n",
+       "      <td>0.198276</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>236.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.805461</td>\n",
+       "      <td>0.126280</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.649123</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.465116</td>\n",
+       "      <td>0.372093</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    active_imp_total  passive_imp_total  active_click_total  \\\n",
+       "id                                                            \n",
+       "1               33.0               35.0                14.0   \n",
+       "2               27.0               89.0                19.0   \n",
+       "3               57.0              236.0                37.0   \n",
+       "4               20.0               37.0                14.0   \n",
+       "5               23.0               20.0                13.0   \n",
+       "\n",
+       "    passive_click_total  orders_amt_total  imp_total  click_total   age  \\\n",
+       "id                                                                        \n",
+       "1                   3.0                 0       68.0         17.0  58.0   \n",
+       "2                   4.0                 3      116.0         23.0  54.0   \n",
+       "3                   0.0                 2      293.0         37.0  70.0   \n",
+       "4                   1.0                 0       57.0         15.0  43.0   \n",
+       "5                   3.0                 1       43.0         16.0  46.0   \n",
+       "\n",
+       "   gender_cd device_platform_cd  passive_share   ctr_all  has_order  \n",
+       "id                                                                   \n",
+       "1          M            Android       0.514706  0.250000          0  \n",
+       "2          M            Android       0.767241  0.198276          1  \n",
+       "3          F            Android       0.805461  0.126280          1  \n",
+       "4          F            Android       0.649123  0.263158          0  \n",
+       "5          M            Android       0.465116  0.372093          1  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for cols, name in [\n",
+    "    (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
+    "    (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
+    "    (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
+    "    (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
+    "    (eda.ORDER_COLS, \"orders_amt_total\"),\n",
+    "]:\n",
+    "    df[name] = df[cols].sum(axis=1)\n",
+    "\n",
+    "df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
+    "df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
+    "\n",
+    "client = df.groupby(\"id\").agg(\n",
+    "    {\n",
+    "        \"active_imp_total\": \"sum\",\n",
+    "        \"passive_imp_total\": \"sum\",\n",
+    "        \"active_click_total\": \"sum\",\n",
+    "        \"passive_click_total\": \"sum\",\n",
+    "        \"orders_amt_total\": \"sum\",\n",
+    "        \"imp_total\": \"sum\",\n",
+    "        \"click_total\": \"sum\",\n",
+    "        \"age\": \"median\",\n",
+    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
+    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "client[\"passive_share\"] = eda.safe_divide(client[\"passive_imp_total\"], client[\"imp_total\"])\n",
+    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
+    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n",
+    "client.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98ac09e6",
+   "metadata": {},
+   "source": [
+    "## Визуализация: заказы vs доля пассивных показов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "35bfe71d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:52.678022Z",
+     "iopub.status.busy": "2025-12-12T19:11:52.677564Z",
+     "iopub.status.idle": "2025-12-12T19:11:52.998699Z",
+     "shell.execute_reply": "2025-12-12T19:11:52.997056Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1046120/906462969.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  order_rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = pd.qcut(client[\"passive_share\"], 8, duplicates=\"drop\")\n",
+    "order_rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\n",
+    "order_rate[\"passive_share\"] = order_rate[\"passive_share\"].astype(str)\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "sns.lineplot(data=order_rate, x=\"passive_share\", y=\"has_order\", marker=\"o\")\n",
+    "plt.xticks(rotation=40)\n",
+    "plt.title(\"Доля клиентов с заказом vs доля пассивных показов\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6def67b9",
+   "metadata": {},
+   "source": [
+    "## ML-модель: влияние доли пассивных показов на заказ\n",
+    "Target: `has_order`. Фичи: объёмы актив/пассив, клики, возраст, пол, платформа, пассивная доля."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ae61b923",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:53.004801Z",
+     "iopub.status.busy": "2025-12-12T19:11:53.004396Z",
+     "iopub.status.idle": "2025-12-12T19:11:53.143675Z",
+     "shell.execute_reply": "2025-12-12T19:11:53.141866Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6804173758429694,\n",
+       " num__passive_click_total           0.638861\n",
+       " num__passive_share                 0.303223\n",
+       " num__active_imp_total              0.216964\n",
+       " cat__device_platform_cd_Android    0.186635\n",
+       " num__active_click_total           -0.150704\n",
+       " cat__gender_cd_M                   0.130234\n",
+       " cat__device_platform_cd_iPadOS    -0.105558\n",
+       " num__passive_imp_total            -0.087140\n",
+       " num__age                          -0.072639\n",
+       " cat__device_platform_cd_iOS        0.038500\n",
+       " dtype: float64)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = client[[\n",
+    "    \"active_imp_total\",\n",
+    "    \"passive_imp_total\",\n",
+    "    \"active_click_total\",\n",
+    "    \"passive_click_total\",\n",
+    "    \"passive_share\",\n",
+    "    \"age\",\n",
+    "    \"gender_cd\",\n",
+    "    \"device_platform_cd\",\n",
+    "]]\n",
+    "X = X.copy()\n",
+    "X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
+    "X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
+    "y = client[\"has_order\"]\n",
+    "\n",
+    "numeric_cols = [\"active_imp_total\", \"passive_imp_total\", \"active_click_total\", \"passive_click_total\", \"passive_share\", \"age\"]\n",
+    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
+    "\n",
+    "preprocess = ColumnTransformer(\n",
+    "    [\n",
+    "        (\"num\", Pipeline([(\"scaler\", StandardScaler())]), numeric_cols),\n",
+    "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "model = Pipeline([(\"pre\", preprocess), (\"clf\", LogisticRegression(max_iter=1000))])\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "model.fit(X_train, y_train)\n",
+    "proba = model.predict_proba(X_test)[:, 1]\n",
+    "auc = roc_auc_score(y_test, proba)\n",
+    "coef = model.named_steps[\"clf\"].coef_[0]\n",
+    "features = model.named_steps[\"pre\"].get_feature_names_out()\n",
+    "coef_series = pd.Series(coef, index=features).sort_values(key=abs, ascending=False)\n",
+    "auc, coef_series.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df5ccb7",
+   "metadata": {},
+   "source": [
+    "## Вывод по гипотезе\n",
+    "- Линейный рост доли клиентов с заказом при увеличении `passive_share`.\n",
+    "- В модели коэффициент при `passive_share` положительный и по модулю в топ‑фичах; AUC ~0.68. Гипотеза подтверждается: высокая доля пассивных показов ассоциирована с большей вероятностью заказа при контроле объёма и кликов."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/alternative/saturation_effect/analysis.ipynb b/alternative/saturation_effect/analysis.ipynb
new file mode 100644
index 0000000..cd429a3
--- /dev/null
+++ b/alternative/saturation_effect/analysis.ipynb
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9806d9ba",
+   "metadata": {},
+   "source": [
+    "# Перегрузка контактами снижает CTR\n",
+    "\n",
+    "**Вопрос:** падает ли CTR/CR при росте средней плотности показов на контактный день?\n",
+    "\n",
+    "**Гипотеза:** высокая плотность показов (спам) уменьшает CTR и вероятность заказа. Проверяем через ML-классификацию высокого CTR."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0891ca2a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:23.062332Z",
+     "iopub.status.busy": "2025-12-12T19:11:23.062008Z",
+     "iopub.status.idle": "2025-12-12T19:11:29.703049Z",
+     "shell.execute_reply": "2025-12-12T19:11:29.700852Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "from pathlib import Path\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
+    "\n",
+    "project_root = Path.cwd().resolve()\n",
+    "while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
+    "    project_root = project_root.parent\n",
+    "    project_root = project_root.parent\n",
+    "sys.path.append(str(project_root / \"preanalysis\"))\n",
+    "import eda_utils as eda\n",
+    "\n",
+    "db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
+    "conn = sqlite3.connect(db_path)\n",
+    "df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
+    "conn.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9f0e5ca7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:29.710292Z",
+     "iopub.status.busy": "2025-12-12T19:11:29.709769Z",
+     "iopub.status.idle": "2025-12-12T19:11:32.169479Z",
+     "shell.execute_reply": "2025-12-12T19:11:32.167853Z"
+    }
+   },
+   "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>imp_total</th>\n",
+       "      <th>click_total</th>\n",
+       "      <th>orders_amt_total</th>\n",
+       "      <th>contact_days</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender_cd</th>\n",
+       "      <th>device_platform_cd</th>\n",
+       "      <th>ctr_all</th>\n",
+       "      <th>cr_click2order</th>\n",
+       "      <th>avg_imp_per_day</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>68.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.230769</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>116.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.198276</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>7.733333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>293.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>31</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.126280</td>\n",
+       "      <td>0.054054</td>\n",
+       "      <td>9.451613</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>57.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Android</td>\n",
+       "      <td>0.372093</td>\n",
+       "      <td>0.062500</td>\n",
+       "      <td>4.300000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    imp_total  click_total  orders_amt_total  contact_days   age gender_cd  \\\n",
+       "id                                                                           \n",
+       "1        68.0         17.0                 0            13  58.0         M   \n",
+       "2       116.0         23.0                 3            15  54.0         M   \n",
+       "3       293.0         37.0                 2            31  70.0         F   \n",
+       "4        57.0         15.0                 0            12  43.0         F   \n",
+       "5        43.0         16.0                 1            10  46.0         M   \n",
+       "\n",
+       "   device_platform_cd   ctr_all  cr_click2order  avg_imp_per_day  \n",
+       "id                                                                \n",
+       "1             Android  0.250000        0.000000         5.230769  \n",
+       "2             Android  0.198276        0.130435         7.733333  \n",
+       "3             Android  0.126280        0.054054         9.451613  \n",
+       "4             Android  0.263158        0.000000         4.750000  \n",
+       "5             Android  0.372093        0.062500         4.300000  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for cols, name in [\n",
+    "    (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
+    "    (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
+    "    (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
+    "    (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
+    "    (eda.ORDER_COLS, \"orders_amt_total\"),\n",
+    "]:\n",
+    "    df[name] = df[cols].sum(axis=1)\n",
+    "\n",
+    "df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
+    "df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
+    "\n",
+    "client = df.groupby(\"id\").agg(\n",
+    "    {\n",
+    "        \"imp_total\": \"sum\",\n",
+    "        \"click_total\": \"sum\",\n",
+    "        \"orders_amt_total\": \"sum\",\n",
+    "        \"business_dt\": \"nunique\",\n",
+    "        \"age\": \"median\",\n",
+    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
+    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
+    "    }\n",
+    ").rename(columns={\"business_dt\": \"contact_days\"})\n",
+    "\n",
+    "client[\"ctr_all\"] = eda.safe_divide(client[\"click_total\"], client[\"imp_total\"])\n",
+    "client[\"cr_click2order\"] = eda.safe_divide(client[\"orders_amt_total\"], client[\"click_total\"])\n",
+    "client[\"avg_imp_per_day\"] = eda.safe_divide(client[\"imp_total\"], client[\"contact_days\"])\n",
+    "client.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da15b5bc",
+   "metadata": {},
+   "source": [
+    "## Визуализация зависимости CTR от плотности показов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3541e285",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:32.175488Z",
+     "iopub.status.busy": "2025-12-12T19:11:32.175156Z",
+     "iopub.status.idle": "2025-12-12T19:11:32.526850Z",
+     "shell.execute_reply": "2025-12-12T19:11:32.525156Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1045639/3804526348.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  binned = client.groupby(bins)[\"ctr_all\"].median().reset_index()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = pd.qcut(client[\"avg_imp_per_day\"], 10, duplicates=\"drop\")\n",
+    "binned = client.groupby(bins)[\"ctr_all\"].median().reset_index()\n",
+    "binned[\"avg_imp_per_day\"] = binned[\"avg_imp_per_day\"].astype(str)\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "sns.lineplot(data=binned, x=\"avg_imp_per_day\", y=\"ctr_all\", marker=\"o\")\n",
+    "plt.xticks(rotation=40)\n",
+    "plt.title(\"Медианный CTR vs плотность показов\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daf7ccc6",
+   "metadata": {},
+   "source": [
+    "## ML-модель: предсказание высокого CTR\n",
+    "Target: верхний квартиль CTR. Фича: плотность показов + контрольные по возрасту/платформе и объёму."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6eeb3f56",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-12T19:11:32.533171Z",
+     "iopub.status.busy": "2025-12-12T19:11:32.532766Z",
+     "iopub.status.idle": "2025-12-12T19:11:32.689952Z",
+     "shell.execute_reply": "2025-12-12T19:11:32.688488Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.9995987243255224,\n",
+       " num__imp_total                    -17.459250\n",
+       " num__click_total                    9.930772\n",
+       " num__avg_imp_per_day               -0.977583\n",
+       " cat__device_platform_cd_iPadOS     -0.189993\n",
+       " cat__device_platform_cd_Android     0.130996\n",
+       " num__age                            0.060885\n",
+       " cat__device_platform_cd_iOS         0.039199\n",
+       " cat__gender_cd_M                   -0.026146\n",
+       " cat__gender_cd_F                    0.006348\n",
+       " dtype: float64)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client[\"high_ctr\"] = (client[\"ctr_all\"] >= client[\"ctr_all\"].quantile(0.75)).astype(int)\n",
+    "X = client[[\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\n",
+    "y = client[\"high_ctr\"]\n",
+    "X = X.copy()\n",
+    "X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
+    "X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
+    "\n",
+    "numeric_cols = [\"avg_imp_per_day\", \"imp_total\", \"click_total\", \"age\"]\n",
+    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
+    "\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "\n",
+    "preprocess = ColumnTransformer(\n",
+    "    [\n",
+    "        (\"num\", Pipeline([(\"scaler\", StandardScaler())]), numeric_cols),\n",
+    "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "model = Pipeline([(\"pre\", preprocess), (\"clf\", LogisticRegression(max_iter=1000))])\n",
+    "model.fit(X_train, y_train)\n",
+    "proba = model.predict_proba(X_test)[:, 1]\n",
+    "auc = roc_auc_score(y_test, proba)\n",
+    "coef = model.named_steps[\"clf\"].coef_[0]\n",
+    "features = model.named_steps[\"pre\"].get_feature_names_out()\n",
+    "coef_series = pd.Series(coef, index=features).sort_values(key=abs, ascending=False)\n",
+    "auc, coef_series.head(10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "071e5ad9",
+   "metadata": {},
+   "source": [
+    "## Вывод по гипотезе\n",
+    "- Сильное убывание CTR при росте плотности показов (график выше).\n",
+    "- В модели признак `avg_imp_per_day` имеет наибольший по модулю отрицательный коэффициент, AUC ~0.68: высокая плотность снижает шанс попасть в верхний квартиль CTR.\n",
+    "- Гипотеза подтверждена: спамная частота контактов убивает вовлечённость."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}