diff --git a/full_analysis/full_analysis.ipynb b/full_analysis/full_analysis.ipynb
deleted file mode 100644
index 4492476..0000000
--- a/full_analysis/full_analysis.ipynb
+++ /dev/null
@@ -1,1308 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "724ac88b",
-   "metadata": {},
-   "source": [
-    "# Возраст и эффективность коммуникаций\n",
-    "\n",
-    "**Вопрос:** как возраст влияет на CTR и конверсию в заказ после активных и пассивных коммуникаций внутри экосистемы «Город Т-Банка».\n",
-    "\n",
-    "**Гипотеза:** 15–29 лет реагируют кликами и заказами на пассивные коммуникации сильнее, чем остальные; 40–54 года показывают более высокий CTR и CR после активных коммуникаций. Механизм: молодые чаще скроллят и замечают баннеры; старшие реже заходят, но осмысленно кликают на активные уведомления.\n",
-    "\n",
-    "**Подход к регламенту:** структура покрывает EDA, гипотезу, визуализации, тесты значимости, выводы и обсуждение ограничений.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6dc707dd",
-   "metadata": {},
-   "source": [
-    "## Импорты и подключение данных\n",
-    "Работаем с SQLite `dataset/ds.sqlite` (получена миграцией из CSV)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0bb1276d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:06.474846Z",
-     "iopub.status.busy": "2025-12-12T18:38:06.474405Z",
-     "iopub.status.idle": "2025-12-12T18:38:12.978558Z",
-     "shell.execute_reply": "2025-12-12T18:38:12.976466Z"
-    }
-   },
-   "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 scipy import stats\n",
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
-    "pd.set_option(\"display.max_columns\", 80)\n",
-    "pd.set_option(\"display.width\", 160)\n",
-    "\n",
-    "project_root = Path.cwd().resolve()\n",
-    "if not (project_root / \"preanalysis\").exists():\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_raw = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
-    "conn.close()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca4a480d",
-   "metadata": {},
-   "source": [
-    "## Структура и качество данных"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9dc3de67",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:12.986058Z",
-     "iopub.status.busy": "2025-12-12T18:38:12.985545Z",
-     "iopub.status.idle": "2025-12-12T18:38:13.070384Z",
-     "shell.execute_reply": "2025-12-12T18:38:13.068628Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(rows                  118189\n",
-       " cols                      35\n",
-       " unique_clients          8339\n",
-       " unique_dates             284\n",
-       " start_dt          2025-01-09\n",
-       " end_dt            2025-11-04\n",
-       " duplicates_key             0\n",
-       " dtype: object,\n",
-       " Series([], dtype: float64),\n",
-       " active_imp_ent          0\n",
-       " active_imp_super        0\n",
-       " active_imp_transport    0\n",
-       " active_imp_shopping     0\n",
-       " active_imp_hotel        0\n",
-       " dtype: int64)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary = {\n",
-    "    \"rows\": len(df_raw),\n",
-    "    \"cols\": df_raw.shape[1],\n",
-    "    \"unique_clients\": df_raw[\"id\"].nunique(),\n",
-    "    \"unique_dates\": df_raw[\"business_dt\"].nunique(),\n",
-    "    \"start_dt\": str(df_raw[\"business_dt\"].min().date()),\n",
-    "    \"end_dt\": str(df_raw[\"business_dt\"].max().date()),\n",
-    "    \"duplicates_key\": int(df_raw.duplicated(subset=[\"id\", \"business_dt\"]).sum()),\n",
-    "}\n",
-    "\n",
-    "missing = df_raw.isna().mean().sort_values(ascending=False)\n",
-    "negatives = (df_raw[eda.NUMERIC_COLS] < 0).sum().sort_values(ascending=False)\n",
-    "\n",
-    "summary_df = pd.Series(summary)\n",
-    "missing_nonzero = missing[missing > 0]\n",
-    "summary_df, missing_nonzero.head(10), negatives.head(5)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f55ed2c1",
-   "metadata": {},
-   "source": [
-    "## Инженерия признаков\n",
-    "Биннинги возраста, сегменты гипотезы, суммы метрик по каналам и расчёт CTR/CR."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "4ca45464",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:13.075714Z",
-     "iopub.status.busy": "2025-12-12T18:38:13.075195Z",
-     "iopub.status.idle": "2025-12-12T18:38:13.927698Z",
-     "shell.execute_reply": "2025-12-12T18:38:13.926168Z"
-    }
-   },
-   "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>business_dt</th>\n",
-       "      <th>active_imp_ent</th>\n",
-       "      <th>active_click_ent</th>\n",
-       "      <th>active_imp_super</th>\n",
-       "      <th>active_click_super</th>\n",
-       "      <th>active_imp_transport</th>\n",
-       "      <th>active_click_transport</th>\n",
-       "      <th>active_imp_shopping</th>\n",
-       "      <th>active_click_shopping</th>\n",
-       "      <th>active_imp_hotel</th>\n",
-       "      <th>active_click_hotel</th>\n",
-       "      <th>active_imp_avia</th>\n",
-       "      <th>active_click_avia</th>\n",
-       "      <th>passive_imp_ent</th>\n",
-       "      <th>passive_click_ent</th>\n",
-       "      <th>passive_imp_super</th>\n",
-       "      <th>passive_click_super</th>\n",
-       "      <th>passive_imp_transport</th>\n",
-       "      <th>passive_click_transport</th>\n",
-       "      <th>passive_imp_shopping</th>\n",
-       "      <th>passive_click_shopping</th>\n",
-       "      <th>passive_imp_hotel</th>\n",
-       "      <th>passive_click_hotel</th>\n",
-       "      <th>passive_imp_avia</th>\n",
-       "      <th>passive_click_avia</th>\n",
-       "      <th>orders_amt_ent</th>\n",
-       "      <th>orders_amt_super</th>\n",
-       "      <th>orders_amt_transport</th>\n",
-       "      <th>orders_amt_shopping</th>\n",
-       "      <th>orders_amt_hotel</th>\n",
-       "      <th>orders_amt_avia</th>\n",
-       "      <th>gender_cd</th>\n",
-       "      <th>age</th>\n",
-       "      <th>device_platform_cd</th>\n",
-       "      <th>age_fine</th>\n",
-       "      <th>age_segment</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>ctr_active</th>\n",
-       "      <th>ctr_passive</th>\n",
-       "      <th>ctr_all</th>\n",
-       "      <th>cr_click2order</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7119</td>\n",
-       "      <td>2025-04-02</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>40</td>\n",
-       "      <td>iOS</td>\n",
-       "      <td>40-44</td>\n",
-       "      <td>40-54</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.142857</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1797</td>\n",
-       "      <td>2025-08-27</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>38</td>\n",
-       "      <td>iOS</td>\n",
-       "      <td>35-39</td>\n",
-       "      <td>other</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.066667</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>8010</td>\n",
-       "      <td>2025-07-10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>51</td>\n",
-       "      <td>Android</td>\n",
-       "      <td>50-54</td>\n",
-       "      <td>40-54</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2360</td>\n",
-       "      <td>2025-08-10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>M</td>\n",
-       "      <td>37</td>\n",
-       "      <td>iOS</td>\n",
-       "      <td>35-39</td>\n",
-       "      <td>other</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3457</td>\n",
-       "      <td>2025-05-23</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>F</td>\n",
-       "      <td>27</td>\n",
-       "      <td>iOS</td>\n",
-       "      <td>25-29</td>\n",
-       "      <td>15-29</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     id business_dt  active_imp_ent  active_click_ent  active_imp_super  active_click_super  active_imp_transport  active_click_transport  \\\n",
-       "0  7119  2025-04-02             0.0               0.0               3.0                 1.0                   1.0                     0.0   \n",
-       "1  1797  2025-08-27             1.0               1.0               0.0                 0.0                   0.0                     0.0   \n",
-       "2  8010  2025-07-10             0.0               0.0               1.0                 1.0                   0.0                     0.0   \n",
-       "3  2360  2025-08-10             0.0               0.0               0.0                 0.0                   0.0                     1.0   \n",
-       "4  3457  2025-05-23             0.0               0.0               1.0                 0.0                   0.0                     0.0   \n",
-       "\n",
-       "   active_imp_shopping  active_click_shopping  active_imp_hotel  active_click_hotel  active_imp_avia  active_click_avia  passive_imp_ent  passive_click_ent  \\\n",
-       "0                  1.0                    0.0                 0                   0                0                  0              0.0                0.0   \n",
-       "1                  0.0                    0.0                 0                   0                3                  0              2.0                0.0   \n",
-       "2                  0.0                    0.0                 0                   0                0                  0              1.0                0.0   \n",
-       "3                  0.0                    0.0                 0                   0                0                  0              0.0                0.0   \n",
-       "4                  3.0                    1.0                 0                   0                0                  0              0.0                0.0   \n",
-       "\n",
-       "   passive_imp_super  passive_click_super  passive_imp_transport  passive_click_transport  passive_imp_shopping  passive_click_shopping  passive_imp_hotel  \\\n",
-       "0                0.0                  0.0                    0.0                      0.0                   0.0                     0.0                  2   \n",
-       "1                1.0                  0.0                    2.0                      0.0                   1.0                     0.0                  0   \n",
-       "2                1.0                  0.0                    1.0                      0.0                   1.0                     0.0                  0   \n",
-       "3                0.0                  0.0                    1.0                      0.0                   0.0                     0.0                  1   \n",
-       "4                0.0                  0.0                    0.0                      0.0                   0.0                     0.0                  2   \n",
-       "\n",
-       "   passive_click_hotel  passive_imp_avia  passive_click_avia  orders_amt_ent  orders_amt_super  orders_amt_transport  orders_amt_shopping  orders_amt_hotel  \\\n",
-       "0                    0                 0                   0               0                 0                     0                    0                 0   \n",
-       "1                    0                 5                   0               0                 0                     0                    0                 0   \n",
-       "2                    0                 1                   0               0                 0                     0                    0                 0   \n",
-       "3                    0                 1                   0               0                 0                     0                    0                 0   \n",
-       "4                    0                 0                   0               0                 0                     0                    0                 0   \n",
-       "\n",
-       "   orders_amt_avia gender_cd  age device_platform_cd age_fine age_segment  active_imp_total  passive_imp_total  active_click_total  passive_click_total  \\\n",
-       "0                0         F   40                iOS    40-44       40-54               5.0                2.0                 1.0                  0.0   \n",
-       "1                0         M   38                iOS    35-39       other               4.0               11.0                 1.0                  0.0   \n",
-       "2                0         M   51            Android    50-54       40-54               1.0                5.0                 1.0                  0.0   \n",
-       "3                0         M   37                iOS    35-39       other               0.0                3.0                 1.0                  0.0   \n",
-       "4                0         F   27                iOS    25-29       15-29               4.0                2.0                 1.0                  0.0   \n",
-       "\n",
-       "   orders_amt_total  imp_total  click_total  ctr_active  ctr_passive   ctr_all  cr_click2order  \n",
-       "0                 0        7.0          1.0        0.20          0.0  0.142857             0.0  \n",
-       "1                 0       15.0          1.0        0.25          0.0  0.066667             0.0  \n",
-       "2                 0        6.0          1.0        1.00          0.0  0.166667             0.0  \n",
-       "3                 0        3.0          1.0         NaN          0.0  0.333333             0.0  \n",
-       "4                 0        6.0          1.0        0.25          0.0  0.166667             0.0  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = df_raw.copy()\n",
-    "df[\"gender_cd\"] = eda.normalize_gender(df[\"gender_cd\"])\n",
-    "df[\"device_platform_cd\"] = eda.normalize_device(df[\"device_platform_cd\"])\n",
-    "\n",
-    "fine_bins = [15, 20, 25, 30, 35, 40, 45, 50, 55, 65, 100]\n",
-    "fine_labels = [\"15-19\", \"20-24\", \"25-29\", \"30-34\", \"35-39\", \"40-44\", \"45-49\", \"50-54\", \"55-64\", \"65+\"]\n",
-    "df[\"age_fine\"] = pd.cut(df[\"age\"], bins=fine_bins, labels=fine_labels, right=False, include_lowest=True)\n",
-    "\n",
-    "conditions = [df[\"age\"].between(15, 30, inclusive=\"left\"), df[\"age\"].between(40, 55, inclusive=\"left\")]\n",
-    "choices = [\"15-29\", \"40-54\"]\n",
-    "df[\"age_segment\"] = np.select(conditions, choices, default=\"other\")\n",
-    "\n",
-    "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",
-    "df[\"ctr_active\"] = eda.safe_divide(df[\"active_click_total\"], df[\"active_imp_total\"])\n",
-    "df[\"ctr_passive\"] = eda.safe_divide(df[\"passive_click_total\"], df[\"passive_imp_total\"])\n",
-    "df[\"ctr_all\"] = eda.safe_divide(df[\"click_total\"], df[\"imp_total\"])\n",
-    "df[\"cr_click2order\"] = eda.safe_divide(df[\"orders_amt_total\"], df[\"click_total\"])\n",
-    "\n",
-    "df.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17555ac0",
-   "metadata": {},
-   "source": [
-    "## Одномерные распределения и объёмы"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "27f8f09a",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:13.933747Z",
-     "iopub.status.busy": "2025-12-12T18:38:13.933194Z",
-     "iopub.status.idle": "2025-12-12T18:38:16.711142Z",
-     "shell.execute_reply": "2025-12-12T18:38:16.709384Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x400 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
-    "\n",
-    "sns.histplot(df[\"age\"], bins=30, kde=True, ax=axes[0])\n",
-    "axes[0].set_title(\"Возраст\")\n",
-    "\n",
-    "sns.countplot(y=\"age_fine\", data=df, order=df[\"age_fine\"].value_counts().index, ax=axes[1])\n",
-    "axes[1].set_title(\"Частота по age_fine\")\n",
-    "\n",
-    "sns.boxplot(x=df[\"orders_amt_total\"], ax=axes[2])\n",
-    "axes[2].set_title(\"Заказы на запись\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "469bd3e4",
-   "metadata": {},
-   "source": [
-    "## Базовые метрики по каналам\n",
-    "CTR/CR на уровне датасета."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2c23be49",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:16.716857Z",
-     "iopub.status.busy": "2025-12-12T18:38:16.716340Z",
-     "iopub.status.idle": "2025-12-12T18:38:16.735625Z",
-     "shell.execute_reply": "2025-12-12T18:38:16.733857Z"
-    }
-   },
-   "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>ctr</th>\n",
-       "      <th>cr_click2order</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>active</th>\n",
-       "      <td>0.671264</td>\n",
-       "      <td>0.084424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>passive</th>\n",
-       "      <td>0.038249</td>\n",
-       "      <td>0.687351</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>all</th>\n",
-       "      <td>0.238850</td>\n",
-       "      <td>0.075189</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              ctr  cr_click2order\n",
-       "active   0.671264        0.084424\n",
-       "passive  0.038249        0.687351\n",
-       "all      0.238850        0.075189"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agg = df[[\"active_imp_total\", \"passive_imp_total\", \"active_click_total\", \"passive_click_total\", \"orders_amt_total\", \"imp_total\", \"click_total\"]].sum()\n",
-    "overall = pd.DataFrame(\n",
-    "    {\n",
-    "        \"ctr\": [eda.safe_divide(agg[\"active_click_total\"], agg[\"active_imp_total\"]), eda.safe_divide(agg[\"passive_click_total\"], agg[\"passive_imp_total\"]), eda.safe_divide(agg[\"click_total\"], agg[\"imp_total\"])],\n",
-    "        \"cr_click2order\": [eda.safe_divide(agg[\"orders_amt_total\"], agg[\"active_click_total\"]), eda.safe_divide(agg[\"orders_amt_total\"], agg[\"passive_click_total\"]), eda.safe_divide(agg[\"orders_amt_total\"], agg[\"click_total\"])],\n",
-    "    },\n",
-    "    index=[\"active\", \"passive\", \"all\"],\n",
-    ")\n",
-    "overall\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb57e4c3",
-   "metadata": {},
-   "source": [
-    "## Аггрегация по клиентам\n",
-    "Используем медианные CTR/CR по клиенту, чтобы снизить влияние экстремальных дней."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5c1634c1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:16.740643Z",
-     "iopub.status.busy": "2025-12-12T18:38:16.740326Z",
-     "iopub.status.idle": "2025-12-12T18:38:20.791422Z",
-     "shell.execute_reply": "2025-12-12T18:38:20.789442Z"
-    }
-   },
-   "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>age_segment</th>\n",
-       "      <th>ctr_active</th>\n",
-       "      <th>ctr_passive</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",
-       "      <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>other</td>\n",
-       "      <td>0.424242</td>\n",
-       "      <td>0.085714</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>40-54</td>\n",
-       "      <td>0.703704</td>\n",
-       "      <td>0.044944</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>other</td>\n",
-       "      <td>0.649123</td>\n",
-       "      <td>0.000000</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>40-54</td>\n",
-       "      <td>0.700000</td>\n",
-       "      <td>0.027027</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>40-54</td>\n",
-       "      <td>0.565217</td>\n",
-       "      <td>0.150000</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  passive_click_total  orders_amt_total  imp_total  click_total   age gender_cd age_segment  \\\n",
-       "id                                                                                                                                                       \n",
-       "1               33.0               35.0                14.0                  3.0                 0       68.0         17.0  58.0         M       other   \n",
-       "2               27.0               89.0                19.0                  4.0                 3      116.0         23.0  54.0         M       40-54   \n",
-       "3               57.0              236.0                37.0                  0.0                 2      293.0         37.0  70.0         F       other   \n",
-       "4               20.0               37.0                14.0                  1.0                 0       57.0         15.0  43.0         F       40-54   \n",
-       "5               23.0               20.0                13.0                  3.0                 1       43.0         16.0  46.0         M       40-54   \n",
-       "\n",
-       "    ctr_active  ctr_passive   ctr_all  cr_click2order  \n",
-       "id                                                     \n",
-       "1     0.424242     0.085714  0.250000        0.000000  \n",
-       "2     0.703704     0.044944  0.198276        0.130435  \n",
-       "3     0.649123     0.000000  0.126280        0.054054  \n",
-       "4     0.700000     0.027027  0.263158        0.000000  \n",
-       "5     0.565217     0.150000  0.372093        0.062500  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "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] if not s.mode().empty else \"UNKNOWN\",\n",
-    "        \"age_segment\": lambda s: s.mode().iat[0] if not s.mode().empty else \"other\",\n",
-    "    }\n",
-    ")\n",
-    "\n",
-    "client[\"ctr_active\"] = eda.safe_divide(client[\"active_click_total\"], client[\"active_imp_total\"])\n",
-    "client[\"ctr_passive\"] = eda.safe_divide(client[\"passive_click_total\"], client[\"passive_imp_total\"])\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",
-    "\n",
-    "client.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d534f26e",
-   "metadata": {},
-   "source": [
-    "## Сравнение сегментов возраста\n",
-    "Агрегаты CTR/CR по сегментам гипотезы."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "615316a1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:20.796679Z",
-     "iopub.status.busy": "2025-12-12T18:38:20.796220Z",
-     "iopub.status.idle": "2025-12-12T18:38:20.822143Z",
-     "shell.execute_reply": "2025-12-12T18:38:20.820601Z"
-    }
-   },
-   "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>ctr_active</th>\n",
-       "      <th>ctr_passive</th>\n",
-       "      <th>cr_active_click2order</th>\n",
-       "      <th>cr_passive_click2order</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>age_segment</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>15-29</th>\n",
-       "      <td>16977.0</td>\n",
-       "      <td>40372.0</td>\n",
-       "      <td>11505.0</td>\n",
-       "      <td>1259.0</td>\n",
-       "      <td>845</td>\n",
-       "      <td>0.677682</td>\n",
-       "      <td>0.031185</td>\n",
-       "      <td>0.073446</td>\n",
-       "      <td>0.671168</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40-54</th>\n",
-       "      <td>100748.0</td>\n",
-       "      <td>210027.0</td>\n",
-       "      <td>67640.0</td>\n",
-       "      <td>8268.0</td>\n",
-       "      <td>5497</td>\n",
-       "      <td>0.671378</td>\n",
-       "      <td>0.039366</td>\n",
-       "      <td>0.081268</td>\n",
-       "      <td>0.664852</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>other</th>\n",
-       "      <td>101770.0</td>\n",
-       "      <td>222741.0</td>\n",
-       "      <td>68194.0</td>\n",
-       "      <td>8570.0</td>\n",
-       "      <td>6097</td>\n",
-       "      <td>0.670080</td>\n",
-       "      <td>0.038475</td>\n",
-       "      <td>0.089407</td>\n",
-       "      <td>0.711435</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             active_imp_total  passive_imp_total  active_click_total  passive_click_total  orders_amt_total  ctr_active  ctr_passive  cr_active_click2order  \\\n",
-       "age_segment                                                                                                                                                   \n",
-       "15-29                 16977.0            40372.0             11505.0               1259.0               845    0.677682     0.031185               0.073446   \n",
-       "40-54                100748.0           210027.0             67640.0               8268.0              5497    0.671378     0.039366               0.081268   \n",
-       "other                101770.0           222741.0             68194.0               8570.0              6097    0.670080     0.038475               0.089407   \n",
-       "\n",
-       "             cr_passive_click2order  \n",
-       "age_segment                          \n",
-       "15-29                      0.671168  \n",
-       "40-54                      0.664852  \n",
-       "other                      0.711435  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "seg = client.groupby(\"age_segment\").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",
-    "    }\n",
-    ")\n",
-    "\n",
-    "seg[\"ctr_active\"] = eda.safe_divide(seg[\"active_click_total\"], seg[\"active_imp_total\"])\n",
-    "seg[\"ctr_passive\"] = eda.safe_divide(seg[\"passive_click_total\"], seg[\"passive_imp_total\"])\n",
-    "seg[\"cr_active_click2order\"] = eda.safe_divide(seg[\"orders_amt_total\"], seg[\"active_click_total\"])\n",
-    "seg[\"cr_passive_click2order\"] = eda.safe_divide(seg[\"orders_amt_total\"], seg[\"passive_click_total\"])\n",
-    "seg\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "65d393ba",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:20.827667Z",
-     "iopub.status.busy": "2025-12-12T18:38:20.827405Z",
-     "iopub.status.idle": "2025-12-12T18:38:21.416356Z",
-     "shell.execute_reply": "2025-12-12T18:38:21.414719Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_df = seg.reset_index()\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "\n",
-    "sns.barplot(data=plot_df, x=\"age_segment\", y=\"ctr_active\", ax=axes[0], color=\"#4c72b0\", label=\"active\")\n",
-    "sns.barplot(data=plot_df, x=\"age_segment\", y=\"ctr_passive\", ax=axes[0], color=\"#dd8452\", label=\"passive\", alpha=0.7)\n",
-    "axes[0].set_title(\"CTR по сегментам\")\n",
-    "axes[0].legend()\n",
-    "\n",
-    "sns.barplot(data=plot_df, x=\"age_segment\", y=\"cr_active_click2order\", ax=axes[1], color=\"#55a868\", label=\"active\")\n",
-    "sns.barplot(data=plot_df, x=\"age_segment\", y=\"cr_passive_click2order\", ax=axes[1], color=\"#c44e52\", label=\"passive\", alpha=0.7)\n",
-    "axes[1].set_title(\"CR click→order по сегментам\")\n",
-    "axes[1].legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e132301f",
-   "metadata": {},
-   "source": [
-    "## Детализация по возрастным бинам"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "1306d3dd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:21.422467Z",
-     "iopub.status.busy": "2025-12-12T18:38:21.421980Z",
-     "iopub.status.idle": "2025-12-12T18:38:22.039839Z",
-     "shell.execute_reply": "2025-12-12T18:38:22.038103Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_1027130/1265874255.py:3: 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",
-      "  fine_stats = client_bins.groupby(\"age_fine\").agg(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "client_bins = client.copy()\n",
-    "client_bins[\"age_fine\"] = pd.cut(client_bins[\"age\"], bins=[15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 100], right=False)\n",
-    "fine_stats = client_bins.groupby(\"age_fine\").agg(\n",
-    "    {\n",
-    "        \"ctr_active\": \"median\",\n",
-    "        \"ctr_passive\": \"median\",\n",
-    "        \"cr_click2order\": \"median\",\n",
-    "        \"click_total\": \"sum\",\n",
-    "    }\n",
-    ").reset_index()\n",
-    "fine_stats[\"age_fine_str\"] = fine_stats[\"age_fine\"].astype(str)\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "sns.lineplot(data=fine_stats, x=\"age_fine_str\", y=\"ctr_active\", marker=\"o\", label=\"active CTR\", ax=axes[0])\n",
-    "sns.lineplot(data=fine_stats, x=\"age_fine_str\", y=\"ctr_passive\", marker=\"o\", label=\"passive CTR\", ax=axes[0])\n",
-    "axes[0].set_title(\"Медианный CTR по возрасту\")\n",
-    "axes[0].tick_params(axis=\"x\", rotation=35)\n",
-    "\n",
-    "sns.lineplot(data=fine_stats, x=\"age_fine_str\", y=\"cr_click2order\", marker=\"o\", color=\"#55a868\", ax=axes[1])\n",
-    "axes[1].set_title(\"Медианный CR click→order\")\n",
-    "axes[1].tick_params(axis=\"x\", rotation=35)\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3b6f46c",
-   "metadata": {},
-   "source": [
-    "## Проверка гипотезы (тесты значимости)\n",
-    "Сравниваем сегменты против остальных с альтернативой в пользу гипотезы."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "59754e13",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:38:22.046740Z",
-     "iopub.status.busy": "2025-12-12T18:38:22.046390Z",
-     "iopub.status.idle": "2025-12-12T18:38:22.083592Z",
-     "shell.execute_reply": "2025-12-12T18:38:22.081844Z"
-    }
-   },
-   "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>test</th>\n",
-       "      <th>U_stat</th>\n",
-       "      <th>p_value</th>\n",
-       "      <th>median_segment</th>\n",
-       "      <th>median_rest</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>CTR_passive 15-29 &gt; rest</td>\n",
-       "      <td>2286842.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.022347</td>\n",
-       "      <td>0.030303</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>CTR_active 40-54 &gt; rest</td>\n",
-       "      <td>8587516.0</td>\n",
-       "      <td>0.590551</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.666667</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       test     U_stat   p_value  median_segment  median_rest\n",
-       "0  CTR_passive 15-29 > rest  2286842.0  1.000000        0.022347     0.030303\n",
-       "1   CTR_active 40-54 > rest  8587516.0  0.590551        0.666667     0.666667"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "young_passive = client.loc[client[\"age_segment\"] == \"15-29\", \"ctr_passive\"].dropna()\n",
-    "others_passive = client.loc[client[\"age_segment\"] != \"15-29\", \"ctr_passive\"].dropna()\n",
-    "old_active = client.loc[client[\"age_segment\"] == \"40-54\", \"ctr_active\"].dropna()\n",
-    "others_active = client.loc[client[\"age_segment\"] != \"40-54\", \"ctr_active\"].dropna()\n",
-    "\n",
-    "young_vs_rest = stats.mannwhitneyu(young_passive, others_passive, alternative=\"greater\")\n",
-    "old_vs_rest = stats.mannwhitneyu(old_active, others_active, alternative=\"greater\")\n",
-    "\n",
-    "results = pd.DataFrame(\n",
-    "    {\n",
-    "        \"test\": [\"CTR_passive 15-29 > rest\", \"CTR_active 40-54 > rest\"],\n",
-    "        \"U_stat\": [young_vs_rest.statistic, old_vs_rest.statistic],\n",
-    "        \"p_value\": [young_vs_rest.pvalue, old_vs_rest.pvalue],\n",
-    "        \"median_segment\": [young_passive.median(), old_active.median()],\n",
-    "        \"median_rest\": [others_passive.median(), others_active.median()],\n",
-    "    }\n",
-    ")\n",
-    "results\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24f020f3",
-   "metadata": {},
-   "source": [
-    "## Выводы и ограничения\n",
-    "- CTR_active значительно выше CTR_passive для всех возрастов. Агрегатно: CTR_active ~0.67, CTR_passive ~0.038.\n",
-    "- По сегментам гипотеза не подтверждена статистически: p-value для преимущества 15–29 в пассивном CTR ≈ 1.0, для преимущества 40–54 в активном CTR ≈ 0.59; медианы близки.\n",
-    "- CR click→order схож между сегментами; молодые не дают лучшего CR на пассивных кликах, старшие не превосходят по активным.\n",
-    "- Возможные объяснения: офферы и категории более релевантны старшим в пассивных каналах; молодые уже перекрыты активными коммуникациями; нужен контроль по категориям и насыщенности контактов.\n",
-    "- Ограничения: не учтены категории сервисов, время суток и лаги заказа; CR по клиентам медианой нулевой из-за редкости заказов.\n"
-   ]
-  }
- ],
- "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/full_analysis/segment_matrix.ipynb b/full_analysis/segment_matrix.ipynb
deleted file mode 100644
index 6a7ed57..0000000
--- a/full_analysis/segment_matrix.ipynb
+++ /dev/null
@@ -1,286 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "91de22bd",
-   "metadata": {},
-   "source": [
-    "# Матрица выводов по сегментам\n",
-    "\n",
-    "Ответ на фидбек организаторов: делаем матрицу по возрастным сегментам и типам каналов, фиксируем закономерности и идеи изменений."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "16660d45",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:42:58.838813Z",
-     "iopub.status.busy": "2025-12-12T18:42:58.838553Z",
-     "iopub.status.idle": "2025-12-12T18:43:05.571711Z",
-     "shell.execute_reply": "2025-12-12T18:43:05.569612Z"
-    }
-   },
-   "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",
-    "\n",
-    "sns.set_theme(style=\"whitegrid\")\n",
-    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
-    "\n",
-    "project_root = Path.cwd().resolve()\n",
-    "if not (project_root / \"preanalysis\").exists():\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",
-    "\n",
-    "age_bins = [15, 25, 35, 45, 55, 120]\n",
-    "age_labels = [\"15-24\", \"25-34\", \"35-44\", \"45-54\", \"55+\"]\n",
-    "df[\"age_segment\"] = pd.cut(df[\"age\"], bins=age_bins, labels=age_labels, right=False, include_lowest=True)\n",
-    "\n",
-    "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",
-    "df[\"ctr_active\"] = eda.safe_divide(df[\"active_click_total\"], df[\"active_imp_total\"])\n",
-    "df[\"ctr_passive\"] = eda.safe_divide(df[\"passive_click_total\"], df[\"passive_imp_total\"])\n",
-    "df[\"ctr_all\"] = eda.safe_divide(df[\"click_total\"], df[\"imp_total\"])\n",
-    "df[\"cr_click2order\"] = eda.safe_divide(df[\"orders_amt_total\"], df[\"click_total\"])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "317a8a38",
-   "metadata": {},
-   "source": [
-    "## Матрица CTR/CR по 5 возрастным сегментам и каналам"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6743acba",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:43:05.578992Z",
-     "iopub.status.busy": "2025-12-12T18:43:05.578499Z",
-     "iopub.status.idle": "2025-12-12T18:43:05.614188Z",
-     "shell.execute_reply": "2025-12-12T18:43:05.612157Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_1031533/2377539060.py:1: 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",
-      "  segment_perf = df.groupby(\"age_segment\").agg(\n"
-     ]
-    },
-    {
-     "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>ctr_active</th>\n",
-       "      <th>ctr_passive</th>\n",
-       "      <th>cr_active</th>\n",
-       "      <th>cr_passive</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>age_segment</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>15-24</th>\n",
-       "      <td>0.669682</td>\n",
-       "      <td>0.026672</td>\n",
-       "      <td>0.057846</td>\n",
-       "      <td>0.627820</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25-34</th>\n",
-       "      <td>0.675315</td>\n",
-       "      <td>0.035316</td>\n",
-       "      <td>0.087974</td>\n",
-       "      <td>0.707119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35-44</th>\n",
-       "      <td>0.672207</td>\n",
-       "      <td>0.037188</td>\n",
-       "      <td>0.090557</td>\n",
-       "      <td>0.741876</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45-54</th>\n",
-       "      <td>0.668323</td>\n",
-       "      <td>0.040513</td>\n",
-       "      <td>0.077198</td>\n",
-       "      <td>0.631981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55+</th>\n",
-       "      <td>0.668371</td>\n",
-       "      <td>0.045245</td>\n",
-       "      <td>0.077028</td>\n",
-       "      <td>0.594488</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             ctr_active  ctr_passive  cr_active  cr_passive\n",
-       "age_segment                                                \n",
-       "15-24          0.669682     0.026672   0.057846    0.627820\n",
-       "25-34          0.675315     0.035316   0.087974    0.707119\n",
-       "35-44          0.672207     0.037188   0.090557    0.741876\n",
-       "45-54          0.668323     0.040513   0.077198    0.631981\n",
-       "55+            0.668371     0.045245   0.077028    0.594488"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "segment_perf = df.groupby(\"age_segment\").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",
-    "    }\n",
-    ")\n",
-    "\n",
-    "segment_perf[\"ctr_active\"] = eda.safe_divide(segment_perf[\"active_click_total\"], segment_perf[\"active_imp_total\"])\n",
-    "segment_perf[\"ctr_passive\"] = eda.safe_divide(segment_perf[\"passive_click_total\"], segment_perf[\"passive_imp_total\"])\n",
-    "segment_perf[\"cr_active\"] = eda.safe_divide(segment_perf[\"orders_amt_total\"], segment_perf[\"active_click_total\"])\n",
-    "segment_perf[\"cr_passive\"] = eda.safe_divide(segment_perf[\"orders_amt_total\"], segment_perf[\"passive_click_total\"])\n",
-    "\n",
-    "matrix = segment_perf[[\"ctr_active\", \"ctr_passive\", \"cr_active\", \"cr_passive\"]]\n",
-    "matrix\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cef644cd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-12-12T18:43:05.620380Z",
-     "iopub.status.busy": "2025-12-12T18:43:05.620110Z",
-     "iopub.status.idle": "2025-12-12T18:43:06.282656Z",
-     "shell.execute_reply": "2025-12-12T18:43:06.280874Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x500 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
-    "ctr_mat = matrix[[\"ctr_active\", \"ctr_passive\"]]\n",
-    "cr_mat = matrix[[\"cr_active\", \"cr_passive\"]]\n",
-    "\n",
-    "sns.heatmap(ctr_mat, annot=True, fmt=\".3f\", cmap=\"Blues\", ax=axes[0])\n",
-    "axes[0].set_title(\"CTR active/passive по возрастам\")\n",
-    "\n",
-    "sns.heatmap(cr_mat, annot=True, fmt=\".3f\", cmap=\"Greens\", ax=axes[1])\n",
-    "axes[1].set_title(\"CR click→order по возрастам\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b108afe9",
-   "metadata": {},
-   "source": [
-    "## Интерпретация и идеи\n",
-    "- Наблюдение: CTR_active ≈ одинаков для 15–29, 40–54 и других; CTR_passive чуть выше у 40–54 и other, молодые слабее.\n",
-    "- Наблюдение: CR click→order высок и близок между сегментами; пассивные клики конвертируются сопоставимо с активными.\n",
-    "- Почему так: пассивные офферы могут быть более релевантны старшим; молодые получают больше активных контактов и пассивные баннеры теряются на фоне «шумного» интерфейса.\n",
-    "- Что изменить:\n",
-    "  1) Тестировать более громкие или персонализированные пассивные форматы для 15–29 (категории ent/shopping).\n",
-    "  2) Для 40–54 усилить активные коммуникации с чётким CTA и минимизировать частоту пассивных баннеров, которые уже работают на конверсию.\n",
-    "  3) Добавить контроль насыщенности: ограничить показы при падении CTR в бинах частоты, особенно у 15–29.\n",
-    "  4) Проверить категории: где сегменты реально различаются, и фокусировать креативы там.\n"
-   ]
-  }
- ],
- "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/spam_hypot/best_bins.png b/main_hypot/best_bins.png
similarity index 100%
rename from spam_hypot/best_bins.png
rename to main_hypot/best_bins.png
diff --git a/spam_hypot/best_model_and_plots.py b/main_hypot/best_model_and_plots.py
similarity index 97%
rename from spam_hypot/best_model_and_plots.py
rename to main_hypot/best_model_and_plots.py
index 5f5ac00..2d52b1d 100644
--- a/spam_hypot/best_model_and_plots.py
+++ b/main_hypot/best_model_and_plots.py
@@ -10,7 +10,7 @@ sns.set_theme(style="whitegrid")
 plt.rcParams["figure.figsize"] = (10, 5)
 
 project_root = Path(__file__).resolve().parent.parent
-sys.path.append(str(project_root / "preanalysis"))
+sys.path.append(str(project_root / "preanalysis_old_bad"))
 import eda_utils as eda  # noqa: E402
 
 db_path = project_root / "dataset" / "ds.sqlite"
@@ -137,7 +137,7 @@ plt.grid(alpha=0.3)
 plt.tight_layout()
 
 plt.savefig(
-    project_root / "spam_hypot" / "orders_vs_avg_imp_with_costs.png",
+    project_root / "main_hypot" / "orders_vs_avg_imp_with_costs.png",
     dpi=150
 )
 
diff --git a/spam_hypot/best_model_prob.png b/main_hypot/best_model_prob.png
similarity index 100%
rename from spam_hypot/best_model_prob.png
rename to main_hypot/best_model_prob.png
diff --git a/spam_hypot/model_compare.py b/main_hypot/model_compare.py
similarity index 98%
rename from spam_hypot/model_compare.py
rename to main_hypot/model_compare.py
index c45f307..3e76bec 100644
--- a/spam_hypot/model_compare.py
+++ b/main_hypot/model_compare.py
@@ -13,7 +13,7 @@ from sklearn.ensemble import GradientBoostingClassifier
 from sklearn.metrics import roc_auc_score
 
 project_root = Path(__file__).resolve().parent.parent
-sys.path.append(str(project_root / "preanalysis"))
+sys.path.append(str(project_root / "preanalysis_old_bad"))
 import eda_utils as eda  # noqa: E402
 
 db_path = project_root / "dataset" / "ds.sqlite"
diff --git a/spam_hypot/orders_vs_avg_imp_per_day.png b/main_hypot/orders_vs_avg_imp_per_day.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_per_day.png
rename to main_hypot/orders_vs_avg_imp_per_day.png
diff --git a/spam_hypot/orders_vs_avg_imp_per_day_filtered_smoothed.png b/main_hypot/orders_vs_avg_imp_per_day_filtered_smoothed.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_per_day_filtered_smoothed.png
rename to main_hypot/orders_vs_avg_imp_per_day_filtered_smoothed.png
diff --git a/spam_hypot/orders_vs_avg_imp_per_day_smoothed.png b/main_hypot/orders_vs_avg_imp_per_day_smoothed.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_per_day_smoothed.png
rename to main_hypot/orders_vs_avg_imp_per_day_smoothed.png
diff --git a/spam_hypot/orders_vs_avg_imp_per_day_smoothed_clean.png b/main_hypot/orders_vs_avg_imp_per_day_smoothed_clean.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_per_day_smoothed_clean.png
rename to main_hypot/orders_vs_avg_imp_per_day_smoothed_clean.png
diff --git a/spam_hypot/orders_vs_avg_imp_with_costs.png b/main_hypot/orders_vs_avg_imp_with_costs.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_with_costs.png
rename to main_hypot/orders_vs_avg_imp_with_costs.png
diff --git a/spam_hypot/orders_vs_avg_imp_without_costs.png b/main_hypot/orders_vs_avg_imp_without_costs.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_without_costs.png
rename to main_hypot/orders_vs_avg_imp_without_costs.png
diff --git a/spam_hypot/orders_vs_avg_imp_without_costs_no_filter.png b/main_hypot/orders_vs_avg_imp_without_costs_no_filter.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_without_costs_no_filter.png
rename to main_hypot/orders_vs_avg_imp_without_costs_no_filter.png
diff --git a/spam_hypot/orders_vs_avg_imp_without_costs_no_filter_no_dropouts.png b/main_hypot/orders_vs_avg_imp_without_costs_no_filter_no_dropouts.png
similarity index 100%
rename from spam_hypot/orders_vs_avg_imp_without_costs_no_filter_no_dropouts.png
rename to main_hypot/orders_vs_avg_imp_without_costs_no_filter_no_dropouts.png
diff --git a/spam_hypot/quad_regression_with_costs.png b/main_hypot/quad_regression_with_costs.png
similarity index 100%
rename from spam_hypot/quad_regression_with_costs.png
rename to main_hypot/quad_regression_with_costs.png
diff --git a/spam_hypot/quadreg.py b/main_hypot/quadreg.py
similarity index 98%
rename from spam_hypot/quadreg.py
rename to main_hypot/quadreg.py
index ce2ce12..7863537 100644
--- a/spam_hypot/quadreg.py
+++ b/main_hypot/quadreg.py
@@ -15,7 +15,7 @@ plt.rcParams["figure.figsize"] = (10, 6)
 # Load + feature engineering (как у тебя)
 # -----------------------------
 project_root = Path(__file__).resolve().parent.parent
-sys.path.append(str(project_root / "preanalysis"))
+sys.path.append(str(project_root / "preanalysis_old_bad"))
 import eda_utils as eda  # noqa: E402
 
 db_path = project_root / "dataset" / "ds.sqlite"
@@ -233,7 +233,7 @@ plt.legend()
 plt.grid(alpha=0.3)
 plt.tight_layout()
 
-out_dir = project_root / "spam_hypot"
+out_dir = project_root / "main_hypot"
 out_dir.mkdir(parents=True, exist_ok=True)
 out_path = out_dir / "quad_regression_with_costs.png"
 plt.savefig(out_path, dpi=150)
diff --git a/spam_hypot/stat_analysis.py b/main_hypot/stat_analysis.py
similarity index 96%
rename from spam_hypot/stat_analysis.py
rename to main_hypot/stat_analysis.py
index 69a94e1..cc938f6 100644
--- a/spam_hypot/stat_analysis.py
+++ b/main_hypot/stat_analysis.py
@@ -11,7 +11,7 @@ sns.set_theme(style="whitegrid")
 plt.rcParams["figure.figsize"] = (10, 5)
 
 project_root = Path(__file__).resolve().parent.parent
-sys.path.append(str(project_root / "preanalysis"))
+sys.path.append(str(project_root / "preanalysis_old_bad"))
 import eda_utils as eda  # noqa: E402
 
 db_path = project_root / "dataset" / "ds.sqlite"
@@ -83,5 +83,5 @@ ax1.set_xlabel("avg_imp_per_day bins")
 plt.xticks(rotation=35)
 ax1.set_title("CTR и CR по децилям avg_imp_per_day")
 fig.tight_layout()
-plt.savefig(project_root / "spam_hypot" / "stat_bins.png", dpi=150)
+plt.savefig(project_root / "main_hypot" / "stat_bins.png", dpi=150)
 print("Saved plot stat_bins.png")
diff --git a/spam_hypot/stat_bins.png b/main_hypot/stat_bins.png
similarity index 100%
rename from spam_hypot/stat_bins.png
rename to main_hypot/stat_bins.png
diff --git a/preanalysis/eda_utils.py b/preanalysis/eda_utils.py
deleted file mode 100644
index 802a6d8..0000000
--- a/preanalysis/eda_utils.py
+++ /dev/null
@@ -1,154 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-from typing import Dict, Iterable, List
-
-import numpy as np
-import pandas as pd
-
-# Paths and column groups
-DATA_PATH = Path("dataset/ds.csv")
-CATEGORIES: List[str] = ["ent", "super", "transport", "shopping", "hotel", "avia"]
-
-ACTIVE_IMP_COLS = [f"active_imp_{c}" for c in CATEGORIES]
-PASSIVE_IMP_COLS = [f"passive_imp_{c}" for c in CATEGORIES]
-ACTIVE_CLICK_COLS = [f"active_click_{c}" for c in CATEGORIES]
-PASSIVE_CLICK_COLS = [f"passive_click_{c}" for c in CATEGORIES]
-ORDER_COLS = [f"orders_amt_{c}" for c in CATEGORIES]
-
-NUMERIC_COLS = (
-    ACTIVE_IMP_COLS
-    + PASSIVE_IMP_COLS
-    + ACTIVE_CLICK_COLS
-    + PASSIVE_CLICK_COLS
-    + ORDER_COLS
-    + ["age"]
-)
-CAT_COLS = ["gender_cd", "device_platform_cd"]
-
-
-def safe_divide(numerator: pd.Series | float, denominator: pd.Series | float) -> pd.Series:
-    """Divide with protection against zero (works for Series and scalars)."""
-    if isinstance(denominator, pd.Series):
-        denom = denominator.replace(0, np.nan)
-    else:
-        denom = np.nan if float(denominator) == 0 else denominator
-    return numerator / denom
-
-
-def normalize_gender(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("UNKNOWN").astype(str).str.strip().str.upper()
-    mapping = {"M": "M", "MALE": "M", "F": "F", "FEMALE": "F"}
-    return cleaned.map(mapping).fillna("UNKNOWN")
-
-
-def normalize_device(series: pd.Series) -> pd.Series:
-    cleaned = series.fillna("unknown").astype(str).str.strip()
-    lowered = cleaned.str.lower().str.replace(" ", "").str.replace("_", "")
-    mapping = {"android": "Android", "ios": "iOS", "ipados": "iPadOS", "ipad": "iPadOS"}
-    mapped = lowered.map(mapping)
-    fallback = cleaned.str.title()
-    return mapped.fillna(fallback)
-
-
-def add_age_group(df: pd.DataFrame) -> pd.DataFrame:
-    bins = [0, 25, 35, 45, 55, np.inf]
-    labels = ["<25", "25-34", "35-44", "45-54", "55+"]
-    df["age_group"] = pd.cut(df["age"], bins=bins, labels=labels, right=False)
-    return df
-
-
-def add_totals(df: pd.DataFrame) -> pd.DataFrame:
-    df["active_imp_total"] = df[ACTIVE_IMP_COLS].sum(axis=1)
-    df["passive_imp_total"] = df[PASSIVE_IMP_COLS].sum(axis=1)
-    df["active_click_total"] = df[ACTIVE_CLICK_COLS].sum(axis=1)
-    df["passive_click_total"] = df[PASSIVE_CLICK_COLS].sum(axis=1)
-    df["orders_amt_total"] = df[ORDER_COLS].sum(axis=1)
-    df["click_total"] = df["active_click_total"] + df["passive_click_total"]
-    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]
-    df["active_ctr"] = safe_divide(df["active_click_total"], df["active_imp_total"])
-    df["passive_ctr"] = safe_divide(df["passive_click_total"], df["passive_imp_total"])
-    df["ctr_all"] = safe_divide(df["click_total"], df["imp_total"])
-    df["cr_click2order"] = safe_divide(df["orders_amt_total"], df["click_total"])
-    df["cr_imp2order"] = safe_divide(df["orders_amt_total"], df["imp_total"])
-    return df
-
-
-def add_flags(df: pd.DataFrame) -> pd.DataFrame:
-    df["has_active_comm"] = (df[ACTIVE_IMP_COLS + ACTIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_passive_comm"] = (df[PASSIVE_IMP_COLS + PASSIVE_CLICK_COLS].sum(axis=1) > 0).astype(int)
-    df["has_any_order"] = (df[ORDER_COLS].sum(axis=1) > 0).astype(int)
-    df["order_categories_count"] = (df[ORDER_COLS] > 0).sum(axis=1)
-    return df
-
-
-def load_data(path: Path | str = DATA_PATH) -> pd.DataFrame:
-    df = pd.read_csv(path)
-    df["business_dt"] = pd.to_datetime(df["business_dt"])
-    df["gender_cd"] = normalize_gender(df["gender_cd"])
-    df["device_platform_cd"] = normalize_device(df["device_platform_cd"])
-    df = add_age_group(df)
-    df = add_totals(df)
-    df = add_flags(df)
-    return df
-
-
-def describe_zero_share(df: pd.DataFrame, cols: Iterable[str]) -> pd.DataFrame:
-    stats = []
-    for col in cols:
-        series = df[col]
-        stats.append(
-            {
-                "col": col,
-                "count": series.count(),
-                "mean": series.mean(),
-                "median": series.median(),
-                "std": series.std(),
-                "min": series.min(),
-                "q25": series.quantile(0.25),
-                "q75": series.quantile(0.75),
-                "max": series.max(),
-                "share_zero": (series == 0).mean(),
-                "p95": series.quantile(0.95),
-                "p99": series.quantile(0.99),
-            }
-        )
-    return pd.DataFrame(stats)
-
-
-def build_daily(df: pd.DataFrame) -> pd.DataFrame:
-    agg_cols = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS
-    daily = df.groupby("business_dt")[agg_cols].sum().reset_index()
-    daily = add_totals(daily)
-    daily["day_of_week"] = daily["business_dt"].dt.day_name()
-    return daily
-
-
-def build_client(df: pd.DataFrame) -> pd.DataFrame:
-    agg_spec: Dict[str, str] = {col: "sum" for col in ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ORDER_COLS}
-    meta_spec: Dict[str, str | callable] = {
-        "age": "median",
-        "gender_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "UNKNOWN",
-        "age_group": lambda s: s.mode().iat[0] if not s.mode().empty else np.nan,
-        "device_platform_cd": lambda s: s.mode().iat[0] if not s.mode().empty else "Other",
-    }
-    agg_spec.update(meta_spec)
-    client = df.groupby("id").agg(agg_spec).reset_index()
-    contact_days = df.groupby("id")["business_dt"].nunique().rename("contact_days")
-    imp_day = df.copy()
-    imp_day["imp_day_total"] = imp_day[ACTIVE_IMP_COLS + PASSIVE_IMP_COLS].sum(axis=1)
-    max_imp_day = imp_day.groupby("id")["imp_day_total"].max().rename("max_impressions_per_day")
-    client = add_totals(client)
-    client = add_flags(client)
-    client = client.merge(contact_days, on="id", how="left")
-    client = client.merge(max_imp_day, on="id", how="left")
-    client = add_contact_density(client)
-    return client
-
-
-def add_contact_density(df: pd.DataFrame) -> pd.DataFrame:
-    # contact_days must already be present
-    if "contact_days" in df.columns:
-        df["avg_impressions_per_contact_day"] = safe_divide(df["imp_total"], df["contact_days"])
-    return df
-    return df
diff --git a/preanalysis/01_load_and_clean.ipynb b/preanalysis_old_bad/01_load_and_clean.ipynb
similarity index 100%
rename from preanalysis/01_load_and_clean.ipynb
rename to preanalysis_old_bad/01_load_and_clean.ipynb
diff --git a/preanalysis/02_univariate_bivariate.ipynb b/preanalysis_old_bad/02_univariate_bivariate.ipynb
similarity index 100%
rename from preanalysis/02_univariate_bivariate.ipynb
rename to preanalysis_old_bad/02_univariate_bivariate.ipynb
diff --git a/preanalysis/03_time_and_lags.ipynb b/preanalysis_old_bad/03_time_and_lags.ipynb
similarity index 100%
rename from preanalysis/03_time_and_lags.ipynb
rename to preanalysis_old_bad/03_time_and_lags.ipynb
diff --git a/preanalysis/04_clients_segmentation.ipynb b/preanalysis_old_bad/04_clients_segmentation.ipynb
similarity index 100%
rename from preanalysis/04_clients_segmentation.ipynb
rename to preanalysis_old_bad/04_clients_segmentation.ipynb
diff --git a/preanalysis/05_exploratory_models.ipynb b/preanalysis_old_bad/05_exploratory_models.ipynb
similarity index 100%
rename from preanalysis/05_exploratory_models.ipynb
rename to preanalysis_old_bad/05_exploratory_models.ipynb
diff --git a/preanalysis/eda_report.ipynb b/preanalysis_old_bad/eda_report.ipynb
similarity index 100%
rename from preanalysis/eda_report.ipynb
rename to preanalysis_old_bad/eda_report.ipynb
diff --git a/preanalysis/eda_report.md b/preanalysis_old_bad/eda_report.md
similarity index 100%
rename from preanalysis/eda_report.md
rename to preanalysis_old_bad/eda_report.md
diff --git a/full_analysis/eda_utils.py b/preanalysis_old_bad/eda_utils.py
similarity index 100%
rename from full_analysis/eda_utils.py
rename to preanalysis_old_bad/eda_utils.py
diff --git a/preanalysis/task.md b/preanalysis_old_bad/task.md
similarity index 100%
rename from preanalysis/task.md
rename to preanalysis_old_bad/task.md