450 lines
79 KiB
Plaintext
450 lines
79 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ef83309f",
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"metadata": {},
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"source": [
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"# 05. Эксплориторные модели и гипотезы\n",
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"\n",
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"Цели: построить простые модели прогнозирования наличия заказа, оценить важность признаков, собрать таблицу статистических гипотез."
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]
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},
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{
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"cell_type": "code",
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"id": "55cfab4e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-05T18:35:44.710487Z",
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"start_time": "2025-12-05T18:35:33.975533Z"
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}
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},
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"source": [
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"\n",
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"import pandas as pd\n",
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"import numpy as np\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 pathlib import Path\n",
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"from eda_utils import (\n",
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" load_data, DATA_PATH, CATEGORIES, ACTIVE_IMP_COLS, PASSIVE_IMP_COLS,\n",
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" ACTIVE_CLICK_COLS, PASSIVE_CLICK_COLS, ORDER_COLS, NUMERIC_COLS, CAT_COLS,\n",
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" describe_zero_share, safe_divide, build_daily, build_client, add_contact_density\n",
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")\n",
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"pd.set_option(\"display.max_columns\", None)\n",
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"pd.options.display.float_format = '{:,.3f}'.format\n",
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"sns.set_theme(style=\"ticks\", palette=\"deep\")\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import OneHotEncoder, StandardScaler\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.ensemble import RandomForestClassifier\n",
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"from sklearn.metrics import classification_report, roc_auc_score\n",
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"from sklearn.impute import SimpleImputer\n",
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"from scipy import stats\n",
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"\n",
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"df = load_data()\n",
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"client = build_client(df)\n"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"cell_type": "code",
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"id": "d4f620fe",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-05T18:35:44.721273Z",
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"start_time": "2025-12-05T18:35:44.714476Z"
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}
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},
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"source": [
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"# Подготовка данных для модели\n",
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"target = client['has_any_order']\n",
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"num_features = ACTIVE_IMP_COLS + PASSIVE_IMP_COLS + ACTIVE_CLICK_COLS + PASSIVE_CLICK_COLS + ['age', 'contact_days', 'avg_impressions_per_contact_day', 'active_ctr', 'passive_ctr', 'ctr_all']\n",
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"cat_features = ['gender_cd', 'device_platform_cd', 'age_group']\n",
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"X = client[num_features + cat_features]\n",
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"\n",
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"preprocess = ColumnTransformer([\n",
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" ('num', Pipeline([('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]), num_features),\n",
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" ('cat', OneHotEncoder(handle_unknown='ignore'), cat_features),\n",
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"])\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, target, test_size=0.2, random_state=42, stratify=target)\n"
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],
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"outputs": [],
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"execution_count": 2
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},
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{
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"cell_type": "code",
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"id": "31ad015b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-05T18:35:46.234607Z",
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"start_time": "2025-12-05T18:35:44.727554Z"
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}
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},
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"source": [
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"log_reg = Pipeline([\n",
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" ('preprocess', preprocess),\n",
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" ('model', LogisticRegression(max_iter=500, n_jobs=-1)),\n",
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"])\n",
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"log_reg.fit(X_train, y_train)\n",
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"preds = log_reg.predict(X_test)\n",
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"proba = log_reg.predict_proba(X_test)[:, 1]\n",
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"print(classification_report(y_test, preds))\n",
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"print('ROC-AUC', roc_auc_score(y_test, proba))\n"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 0.63 0.60 0.61 726\n",
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" 1 0.70 0.73 0.71 942\n",
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"\n",
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" accuracy 0.67 1668\n",
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" macro avg 0.66 0.66 0.66 1668\n",
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"weighted avg 0.67 0.67 0.67 1668\n",
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"\n",
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"ROC-AUC 0.7244009288016237\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"cell_type": "code",
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"id": "6815f1d0",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-05T18:35:46.309611Z",
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"start_time": "2025-12-05T18:35:46.302180Z"
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}
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},
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"source": [
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"model = log_reg.named_steps['model']\n",
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"num_names = list(log_reg.named_steps['preprocess'].transformers_[0][2])\n",
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"cat_encoder = log_reg.named_steps['preprocess'].named_transformers_['cat']\n",
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"cat_names = list(cat_encoder.get_feature_names_out(cat_features))\n",
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"feature_names = num_names + cat_names\n",
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"coef = pd.DataFrame({'feature': feature_names, 'coef': model.coef_.flatten()})\n",
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"coef['odds_ratio'] = np.exp(coef['coef'])\n",
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"coef.sort_values('odds_ratio', ascending=False).head(20)\n"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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" feature coef odds_ratio\n",
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"20 passive_click_transport 0.433 1.542\n",
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"18 passive_click_ent 0.426 1.531\n",
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"0 active_imp_ent 0.424 1.528\n",
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"27 active_ctr 0.399 1.491\n",
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"38 age_group_55+ 0.355 1.426\n",
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"14 active_click_transport 0.336 1.399\n",
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"2 active_imp_transport 0.322 1.380\n",
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"22 passive_click_hotel 0.239 1.270\n",
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"6 passive_imp_ent 0.230 1.258\n",
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"3 active_imp_shopping 0.224 1.251\n",
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"32 device_platform_cd_Android 0.213 1.237\n",
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"23 passive_click_avia 0.203 1.225\n",
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"4 active_imp_hotel 0.189 1.208\n",
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"36 age_group_35-44 0.176 1.192\n",
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"1 active_imp_super 0.172 1.188\n",
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"19 passive_click_super 0.144 1.155\n",
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"5 active_imp_avia 0.120 1.128\n",
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"28 passive_ctr 0.116 1.123\n",
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"33 device_platform_cd_iOS 0.084 1.087\n",
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"9 passive_imp_shopping 0.079 1.082"
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],
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>feature</th>\n",
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" <th>coef</th>\n",
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" <th>odds_ratio</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>20</th>\n",
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" <td>passive_click_transport</td>\n",
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" <td>0.433</td>\n",
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" <td>1.542</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>18</th>\n",
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" <td>passive_click_ent</td>\n",
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" <td>0.426</td>\n",
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" <td>1.531</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>active_imp_ent</td>\n",
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" <td>0.424</td>\n",
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" <td>1.528</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>27</th>\n",
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" <td>active_ctr</td>\n",
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" <td>0.399</td>\n",
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" <td>1.491</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>38</th>\n",
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" <td>age_group_55+</td>\n",
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" <td>0.355</td>\n",
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" <td>1.426</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>active_click_transport</td>\n",
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" <td>0.336</td>\n",
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" <td>1.399</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>active_imp_transport</td>\n",
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" <td>0.322</td>\n",
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" <td>1.380</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>22</th>\n",
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" <td>passive_click_hotel</td>\n",
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" <td>0.239</td>\n",
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" <td>1.270</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>passive_imp_ent</td>\n",
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" <td>0.230</td>\n",
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" <td>1.258</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>active_imp_shopping</td>\n",
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" <td>0.224</td>\n",
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" <td>1.251</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>32</th>\n",
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" <td>device_platform_cd_Android</td>\n",
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" <td>0.213</td>\n",
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" <td>1.237</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>23</th>\n",
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" <td>passive_click_avia</td>\n",
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" <td>0.203</td>\n",
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" <td>1.225</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>active_imp_hotel</td>\n",
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" <td>0.189</td>\n",
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" <td>1.208</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>36</th>\n",
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" <td>age_group_35-44</td>\n",
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" <td>0.176</td>\n",
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" <td>1.192</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>active_imp_super</td>\n",
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" <td>0.172</td>\n",
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" <td>1.188</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>19</th>\n",
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" <td>passive_click_super</td>\n",
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" <td>0.144</td>\n",
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" <td>1.155</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>active_imp_avia</td>\n",
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" <td>0.120</td>\n",
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" <td>1.128</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>28</th>\n",
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" <td>passive_ctr</td>\n",
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" <td>0.116</td>\n",
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" <td>1.123</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>33</th>\n",
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" <td>device_platform_cd_iOS</td>\n",
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" <td>0.084</td>\n",
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" <td>1.087</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>passive_imp_shopping</td>\n",
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" <td>0.079</td>\n",
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" <td>1.082</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 4
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},
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{
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"cell_type": "code",
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"id": "1da69077",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-05T18:35:46.851634Z",
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"start_time": "2025-12-05T18:35:46.333923Z"
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}
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},
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"source": [
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"rf = Pipeline([\n",
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" ('preprocess', preprocess),\n",
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" ('model', RandomForestClassifier(n_estimators=200, random_state=42, n_jobs=-1)),\n",
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"])\n",
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"rf.fit(X_train, y_train)\n",
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"rf_model = rf.named_steps['model']\n",
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"rf_features = feature_names\n",
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"importances = pd.DataFrame({'feature': rf_features, 'importance': rf_model.feature_importances_}).sort_values('importance', ascending=False)\n",
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"plt.figure(figsize=(10, 6))\n",
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"sns.barplot(data=importances.head(20), x='importance', y='feature')\n",
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"plt.title('Feature importance (RandomForest)')\n",
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"plt.tight_layout()\n"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 1000x600 with 1 Axes>"
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],
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"jetTransient": {
|
|
"display_id": null
|
|
}
|
|
}
|
|
],
|
|
"execution_count": 5
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "4481d690",
|
|
"metadata": {
|
|
"ExecuteTime": {
|
|
"end_time": "2025-12-05T18:35:46.893437Z",
|
|
"start_time": "2025-12-05T18:35:46.875542Z"
|
|
}
|
|
},
|
|
"source": [
|
|
"hypotheses = []\n",
|
|
"stat, p = stats.mannwhitneyu(df['active_ctr'].dropna(), df['passive_ctr'].dropna(), alternative='greater')\n",
|
|
"hypotheses.append({'hypothesis': 'CTR_active > CTR_passive', 'test': 'Mann-Whitney', 'pvalue': p})\n",
|
|
"m_ctr = client.loc[client['gender_cd'] == 'M', 'ctr_all'].dropna()\n",
|
|
"f_ctr = client.loc[client['gender_cd'] == 'F', 'ctr_all'].dropna()\n",
|
|
"stat, p = stats.mannwhitneyu(m_ctr, f_ctr, alternative='two-sided')\n",
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"hypotheses.append({'hypothesis': 'CTR отличается по полу', 'test': 'Mann-Whitney', 'pvalue': p})\n",
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"hypo_df = pd.DataFrame(hypotheses)\n",
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"hypo_df\n"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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" hypothesis test pvalue\n",
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"0 CTR_active > CTR_passive Mann-Whitney 0.000\n",
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"1 CTR отличается по полу Mann-Whitney 0.277"
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],
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>hypothesis</th>\n",
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" <th>test</th>\n",
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" <th>pvalue</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>CTR_active > CTR_passive</td>\n",
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" <td>Mann-Whitney</td>\n",
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" <td>0.000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>CTR отличается по полу</td>\n",
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" <td>Mann-Whitney</td>\n",
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" <td>0.277</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 6
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}
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"kernelspec": {
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