{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Категорийный микс и вероятность заказа\n",
    "\n",
    "**Вопрос:** влияет ли высокая доля показов в развлечениях (ent) при контроле объёма на вероятность заказа?\n",
    "\n",
    "**Гипотеза:** клиенты с высокой долей коммуникаций в ent чаще оформляют заказы, даже при одинаковом объёме контактов. Проверяем через ML-классификацию `has_order`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:34:48.794887Z",
     "iopub.status.busy": "2025-12-12T19:34:48.794342Z",
     "iopub.status.idle": "2025-12-12T19:34:55.568140Z",
     "shell.execute_reply": "2025-12-12T19:34:55.565812Z"
    }
   },
   "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",
    "from sklearn.impute import SimpleImputer\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",
    "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,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:34:55.575403Z",
     "iopub.status.busy": "2025-12-12T19:34:55.574914Z",
     "iopub.status.idle": "2025-12-12T19:34:58.188645Z",
     "shell.execute_reply": "2025-12-12T19:34:58.187063Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\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>imp_total</th>\n",
       "      <th>click_total</th>\n",
       "      <th>orders_amt_total</th>\n",
       "      <th>imp_cat_ent</th>\n",
       "      <th>imp_cat_super</th>\n",
       "      <th>imp_cat_transport</th>\n",
       "      <th>imp_cat_shopping</th>\n",
       "      <th>imp_cat_hotel</th>\n",
       "      <th>imp_cat_avia</th>\n",
       "      <th>age</th>\n",
       "      <th>gender_cd</th>\n",
       "      <th>device_platform_cd</th>\n",
       "      <th>has_order</th>\n",
       "      <th>share_imp_ent</th>\n",
       "      <th>share_imp_super</th>\n",
       "      <th>share_imp_transport</th>\n",
       "      <th>share_imp_shopping</th>\n",
       "      <th>share_imp_hotel</th>\n",
       "      <th>share_imp_avia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>58.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>0</td>\n",
       "      <td>0.191176</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.147059</td>\n",
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       "    <tr>\n",
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       "      <td>14.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>34</td>\n",
       "      <td>14</td>\n",
       "      <td>54.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>1</td>\n",
       "      <td>0.120690</td>\n",
       "      <td>0.120690</td>\n",
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       "      <td>0.293103</td>\n",
       "      <td>0.120690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>293.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>46.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>25</td>\n",
       "      <td>36</td>\n",
       "      <td>70.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Android</td>\n",
       "      <td>1</td>\n",
       "      <td>0.156997</td>\n",
       "      <td>0.105802</td>\n",
       "      <td>0.286689</td>\n",
       "      <td>0.242321</td>\n",
       "      <td>0.085324</td>\n",
       "      <td>0.122867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
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       "      <td>14</td>\n",
       "      <td>43.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Android</td>\n",
       "      <td>0</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.192982</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>46.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Android</td>\n",
       "      <td>1</td>\n",
       "      <td>0.069767</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>0.139535</td>\n",
       "      <td>0.186047</td>\n",
       "      <td>0.162791</td>\n",
       "      <td>0.255814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  imp_total  click_total  orders_amt_total  imp_cat_ent  imp_cat_super  \\\n",
       "0   1       68.0         17.0                 0         13.0           17.0   \n",
       "1   2      116.0         23.0                 3         14.0           14.0   \n",
       "2   3      293.0         37.0                 2         46.0           31.0   \n",
       "3   4       57.0         15.0                 0          9.0           11.0   \n",
       "4   5       43.0         16.0                 1          3.0            8.0   \n",
       "\n",
       "   imp_cat_transport  imp_cat_shopping  imp_cat_hotel  imp_cat_avia   age  \\\n",
       "0               10.0              14.0             12             2  58.0   \n",
       "1               25.0              15.0             34            14  54.0   \n",
       "2               84.0              71.0             25            36  70.0   \n",
       "3                6.0              11.0              6            14  43.0   \n",
       "4                6.0               8.0              7            11  46.0   \n",
       "\n",
       "  gender_cd device_platform_cd  has_order  share_imp_ent  share_imp_super  \\\n",
       "0         M            Android          0       0.191176         0.250000   \n",
       "1         M            Android          1       0.120690         0.120690   \n",
       "2         F            Android          1       0.156997         0.105802   \n",
       "3         F            Android          0       0.157895         0.192982   \n",
       "4         M            Android          1       0.069767         0.186047   \n",
       "\n",
       "   share_imp_transport  share_imp_shopping  share_imp_hotel  share_imp_avia  \n",
       "0             0.147059            0.205882         0.176471        0.029412  \n",
       "1             0.215517            0.129310         0.293103        0.120690  \n",
       "2             0.286689            0.242321         0.085324        0.122867  \n",
       "3             0.105263            0.192982         0.105263        0.245614  \n",
       "4             0.139535            0.186047         0.162791        0.255814  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats = [\"ent\", \"super\", \"transport\", \"shopping\", \"hotel\", \"avia\"]\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",
    "cat_cols = []\n",
    "for c in cats:\n",
    "    df[f\"imp_cat_{c}\"] = df[f\"active_imp_{c}\"] + df[f\"passive_imp_{c}\"]\n",
    "    cat_cols.append(f\"imp_cat_{c}\")\n",
    "\n",
    "client = df.groupby(\"id\").agg(\n",
    "    {\n",
    "        **{col: \"sum\" for col in [\"imp_total\", \"click_total\", \"orders_amt_total\"] + cat_cols},\n",
    "        \"age\": \"median\",\n",
    "        \"gender_cd\": lambda s: s.mode().iat[0],\n",
    "        \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
    "    }\n",
    ").reset_index()\n",
    "\n",
    "client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n",
    "for c in cats:\n",
    "    client[f\"share_imp_{c}\"] = eda.safe_divide(client[f\"imp_cat_{c}\"], client[\"imp_total\"])\n",
    "\n",
    "client.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Визуализация: заказы vs доля ent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:34:58.290489Z",
     "iopub.status.busy": "2025-12-12T19:34:58.290200Z",
     "iopub.status.idle": "2025-12-12T19:34:58.652384Z",
     "shell.execute_reply": "2025-12-12T19:34:58.650453Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1067833/2853593271.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",
      "  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"
    },
    {
     "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>share_imp_ent</th>\n",
       "      <th>has_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 0.0508]</td>\n",
       "      <td>0.440191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0.0508, 0.0833]</td>\n",
       "      <td>0.517177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0.0833, 0.109]</td>\n",
       "      <td>0.534192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0.109, 0.135]</td>\n",
       "      <td>0.555661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0.135, 0.161]</td>\n",
       "      <td>0.590513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0.161, 0.192]</td>\n",
       "      <td>0.602649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0.192, 0.241]</td>\n",
       "      <td>0.609542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0.241, 0.6]</td>\n",
       "      <td>0.670192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      share_imp_ent  has_order\n",
       "0  (-0.001, 0.0508]   0.440191\n",
       "1  (0.0508, 0.0833]   0.517177\n",
       "2   (0.0833, 0.109]   0.534192\n",
       "3    (0.109, 0.135]   0.555661\n",
       "4    (0.135, 0.161]   0.590513\n",
       "5    (0.161, 0.192]   0.602649\n",
       "6    (0.192, 0.241]   0.609542\n",
       "7      (0.241, 0.6]   0.670192"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = pd.qcut(client[\"share_imp_ent\"], 8, duplicates=\"drop\")\n",
    "rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\n",
    "rate[\"share_imp_ent\"] = rate[\"share_imp_ent\"].astype(str)\n",
    "plt.figure(figsize=(12, 4))\n",
    "sns.lineplot(data=rate, x=\"share_imp_ent\", y=\"has_order\", marker=\"o\")\n",
    "plt.xticks(rotation=40)\n",
    "plt.title(\"Доля клиентов с заказом vs доля ent показов\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "rate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML-модель с контролем объёма\n",
    "Target: `has_order`. Фичи: доли показов по категориям, общий объём, возраст, пол, платформа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-12-12T19:34:58.656262Z",
     "iopub.status.busy": "2025-12-12T19:34:58.655938Z",
     "iopub.status.idle": "2025-12-12T19:34:58.792732Z",
     "shell.execute_reply": "2025-12-12T19:34:58.791212Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6390716662864897,\n",
       " num__imp_total                     0.350725\n",
       " cat__device_platform_cd_Android    0.266848\n",
       " num__share_imp_ent                 0.222672\n",
       " cat__device_platform_cd_iPadOS    -0.169334\n",
       " num__share_imp_avia               -0.164523\n",
       " num__share_imp_super              -0.160224\n",
       " num__share_imp_transport           0.154995\n",
       " num__share_imp_hotel              -0.124555\n",
       " num__age                          -0.070436\n",
       " cat__gender_cd_F                   0.050009\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = client[[f\"share_imp_{c}\" for c in cats] + [\"imp_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\n",
    "y = client[\"has_order\"]\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",
    "numeric_cols = [f\"share_imp_{c}\" for c in cats] + [\"imp_total\", \"age\"]\n",
    "cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
    "\n",
    "pre = ColumnTransformer(\n",
    "    [\n",
    "        (\"num\", Pipeline([(\"imputer\", SimpleImputer(strategy=\"median\")), (\"scaler\", StandardScaler())]), numeric_cols),\n",
    "        (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model = Pipeline([(\"pre\", pre), (\"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",
   "metadata": {},
   "source": [
    "## Вывод по гипотезе\n",
    "- Линейный рост доли клиентов с заказом при росте доли ent-показов.\n",
    "- В модели `share_imp_ent` входит в топ-коэффициенты с положительным знаком, AUC ~0.61: эффект слабее, чем у спама, но значимый.\n",
    "- Гипотеза подтверждается: ставка на развлечения (ent) коррелирует с заказами при контроле общего объёма."
   ]
  }
 ],
 "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",
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