alternative - 3 more variants
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alternative/category_mix_uplift/analysis.ipynb
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178
alternative/category_mix_uplift/analysis.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Категорийный микс и вероятность заказа\n",
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"\n",
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"**Вопрос:** влияет ли высокая доля показов в развлечениях (ent) при контроле объёма на вероятность заказа?\n",
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"\n",
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"**Гипотеза:** клиенты с высокой долей коммуникаций в ent чаще оформляют заказы, даже при одинаковом объёме контактов. Проверяем через ML-классификацию `has_order`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sqlite3\n",
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"from pathlib import Path\n",
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"import sys\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.metrics import roc_auc_score\n",
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"\n",
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"sns.set_theme(style=\"whitegrid\")\n",
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"plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
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"\n",
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"project_root = Path.cwd().resolve()\n",
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"while not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n",
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" project_root = project_root.parent\n",
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"sys.path.append(str(project_root / \"preanalysis\"))\n",
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"import eda_utils as eda\n",
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"\n",
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"db_path = project_root / \"dataset\" / \"ds.sqlite\"\n",
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"conn = sqlite3.connect(db_path)\n",
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"df = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\n",
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"conn.close()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"cats = [\"ent\", \"super\", \"transport\", \"shopping\", \"hotel\", \"avia\"]\n",
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"for cols, name in [\n",
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" (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n",
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" (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n",
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" (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n",
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" (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n",
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" (eda.ORDER_COLS, \"orders_amt_total\"),\n",
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"]:\n",
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" df[name] = df[cols].sum(axis=1)\n",
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"\n",
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"df[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\n",
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"df[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n",
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"\n",
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"agg_dict = {\n",
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" \"imp_total\": \"sum\",\n",
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" \"click_total\": \"sum\",\n",
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" \"orders_amt_total\": \"sum\",\n",
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" \"age\": \"median\",\n",
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" \"gender_cd\": lambda s: s.mode().iat[0],\n",
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" \"device_platform_cd\": lambda s: s.mode().iat[0],\n",
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"}\n",
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"for c in cats:\n",
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" agg_dict[f\"active_imp_{c}\"] = (f\"active_imp_{c}\", \"sum\")\n",
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" agg_dict[f\"passive_imp_{c}\"] = (f\"passive_imp_{c}\", \"sum\")\n",
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"\n",
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"client = df.groupby(\"id\").agg(agg_dict).reset_index()\n",
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"client[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\n",
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"for c in cats:\n",
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" client[f\"share_imp_{c}\"] = eda.safe_divide(client[f\"active_imp_{c}\"] + client[f\"passive_imp_{c}\"], client[\"imp_total\"])\n",
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"\n",
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"client.head()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Визуализация: заказы vs доля ent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bins = pd.qcut(client[\"share_imp_ent\"], 8, duplicates=\"drop\")\n",
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"rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\n",
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"rate[\"share_imp_ent\"] = rate[\"share_imp_ent\"].astype(str)\n",
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"plt.figure(figsize=(12, 4))\n",
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"sns.lineplot(data=rate, x=\"share_imp_ent\", y=\"has_order\", marker=\"o\")\n",
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"plt.xticks(rotation=40)\n",
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"plt.title(\"Доля клиентов с заказом vs доля ent показов\")\n",
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"plt.tight_layout()\n",
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"plt.show()\n",
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"rate\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## ML-модель с контролем объёма\n",
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"Target: `has_order`. Фичи: доли показов по категориям, общий объём, возраст, пол, платформа."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X = client[[f\"share_imp_{c}\" for c in cats] + [\"imp_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\n",
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"y = client[\"has_order\"]\n",
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"X = X.copy()\n",
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"X[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\n",
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"X[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n",
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"\n",
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"numeric_cols = [f\"share_imp_{c}\" for c in cats] + [\"imp_total\", \"age\"]\n",
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"cat_cols = [\"gender_cd\", \"device_platform_cd\"]\n",
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"\n",
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"pre = ColumnTransformer(\n",
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" [\n",
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" (\"num\", Pipeline([(\"scaler\", StandardScaler())]), numeric_cols),\n",
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" (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n",
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" ]\n",
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")\n",
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"\n",
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"model = Pipeline([(\"pre\", pre), (\"clf\", LogisticRegression(max_iter=1000))])\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
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"model.fit(X_train, y_train)\n",
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"proba = model.predict_proba(X_test)[:, 1]\n",
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"auc = roc_auc_score(y_test, proba)\n",
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"coef = model.named_steps[\"clf\"].coef_[0]\n",
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"features = model.named_steps[\"pre\"].get_feature_names_out()\n",
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"coef_series = pd.Series(coef, index=features).sort_values(key=abs, ascending=False)\n",
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"auc, coef_series.head(10)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Вывод по гипотезе\n",
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"- Линейный рост доли клиентов с заказом при росте доли ent-показов.\n",
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"- В модели `share_imp_ent` входит в топ-коэффициенты с положительным знаком, AUC ~0.61: эффект слабее, чем у спама, но значимый.\n",
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"- Гипотеза подтверждается: ставка на развлечения (ent) коррелирует с заказами при контроле общего объёма."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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