{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Лабораторная работа 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Определить две бизнес-цели
\n",
"Во-первых, у нас есть в датасете есть столбец NumWebPurchases — количество покупок через интернет. Ставим первую бизнес цель: Увеличение продаж через интернет-магазин. А еще у нас имеется столбец Response — отклик на текущую кампанию. Ставим вторую бизнес цель: Анализ отклика на предыдущие кампании для повышения их эффективности."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Определить цели технического проекта
\n",
"\n",
"Для увеличения интернет-продаж:\n",
"\n",
"Разработать модели сегментации клиентов на основе их характеристик (доход, покупки).\n",
"Создать прогнозные модели для определения вероятности веб-покупок.\n",
"\n",
"Для оптимизации кампаний:\n",
"\n",
"Провести анализ данных об откликах клиентов на прошлые кампании.\n",
"Сформировать рекомендации по улучшению таргетирования на основе анализа успешных кампаний."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. Выполнить разбиение каждого набора данных \n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1329, 16), (443, 16), (444, 16))"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"import pandas as pd\n",
"dataset = pd.read_csv(\".//datasetlab1//marketing_campaign.csv\", sep=\"\\t\")\n",
"\n",
"# Удаление неинформативных столбцов и выбор целевых данных для бизнес-целей\n",
"columns_to_use = [\n",
" \"Income\", \"Kidhome\", \"Teenhome\", \"NumWebPurchases\", \"MntWines\", \n",
" \"MntFruits\", \"MntMeatProducts\", \"MntFishProducts\", \"MntSweetProducts\",\n",
" \"MntGoldProds\", \"AcceptedCmp1\", \"AcceptedCmp2\", \"AcceptedCmp3\", \n",
" \"AcceptedCmp4\", \"AcceptedCmp5\", \"Response\", \"Recency\"\n",
"]\n",
"\n",
"# Очистка данных от пропусков и выбор только необходимых столбцов\n",
"filtered_data = dataset[columns_to_use].dropna()\n",
"\n",
"# Разделение данных на признаки (X) и целевую переменную (y) для оптимизации кампаний\n",
"X = filtered_data.drop(columns=[\"Response\"])\n",
"y = filtered_data[\"Response\"]\n",
"\n",
"# Разбиение на обучающую (60%), контрольную (20%) и тестовую (20%) выборки\n",
"X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)\n",
"X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
"\n",
"# Проверка размера выборок\n",
"X_train.shape, X_val.shape, X_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5. Оценить сбалансированность выборок
\n",
"\n",
"За 0 берем не отклик, за 1 - отклик клиента на рекламу."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Распределение классов в обучающей выборке (в процентах):\n",
"Response\n",
"0 84.951091\n",
"1 15.048909\n",
"Name: proportion, dtype: float64\n"
]
}
],
"source": [
"# Импорт необходимых библиотек\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Функция для визуализации распределения классов\n",
"def plot_class_distribution(y, title):\n",
" sns.countplot(x=y_train, color='orange')\n",
" plt.title(title)\n",
" plt.xlabel(\"Response (Целевой признак)\")\n",
" plt.ylabel(\"Количество записей\")\n",
" plt.show()\n",
"\n",
"# Оценка сбалансированности классов в выборках\n",
"plot_class_distribution(y_train, \"Распределение классов в обучающей выборке\")\n",
"plot_class_distribution(y_val, \"Распределение классов в контрольной выборке\")\n",
"plot_class_distribution(y_test, \"Распределение классов в тестовой выборке\")\n",
"\n",
"# Проверка пропорций классов в обучающей выборке\n",
"class_distribution_train = y_train.value_counts(normalize=True) * 100\n",
"print(\"Распределение классов в обучающей выборке (в процентах):\")\n",
"print(class_distribution_train)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Для второй бизнес цели\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размеры выборок для второй цели:\n",
"Обучающая: (1329, 15), Контрольная: (443, 15), Тестовая: (444, 15)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"# Целевой признак для второй бизнес-цели\n",
"target_col_2 = 'NumWebPurchases'\n",
"\n",
"# Разделение данных на обучающую, контрольную и тестовую выборки\n",
"X_train_2, X_temp_2, y_train_2, y_temp_2 = train_test_split(\n",
" X.drop(columns=[target_col_2]), # Все признаки, кроме целевого\n",
" X[target_col_2], # Целевой признак\n",
" test_size=0.4, # 40% на контрольную и тестовую выборки\n",
" random_state=42\n",
")\n",
"\n",
"X_val_2, X_test_2, y_val_2, y_test_2 = train_test_split(\n",
" X_temp_2,\n",
" y_temp_2,\n",
" test_size=0.5, # 50% от оставшихся данных для тестовой выборки\n",
" random_state=42\n",
")\n",
"\n",
"# Проверим размеры выборок\n",
"print(\"Размеры выборок для второй цели:\")\n",
"print(f\"Обучающая: {X_train_2.shape}, Контрольная: {X_val_2.shape}, Тестовая: {X_test_2.shape}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Оценка:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Bli1bpOGWLVvixx9/VJouNDQUoaGhAABDQ0N4e3tj8eLFKvdHxZ+Dm5ubQruuri5cXFykfaW6338dHR3Url1boe35z/h533//Pc6cOQMAKs95UXffp01YyFQCrVq1kq5aUmXOnDmYPn06Ro4ciVmzZsHS0hI6OjoICgpS6unQhOIMCxYsQNOmTVVO8/zOsaCgAHfv3pWO479suTKZDHv37lV5VcKLO9zi/0JtbW1fukxra2ts3LhR5fgXd7qtW7fGN998o9C2fPly7N69W+X8V69exbp16/Dzzz+rPN4sl8vRqFEjLFy4UOX8Dg4OJWYvdv36dTRv3hyLFi3Chx9+iPXr16ssIkuzPU6cOIE+ffqgQ4cOWLFiBezs7KCnp4fIyEilE3Q1wcbGBj///DOAZ8Xw2rVr0a1bN5w8eRKNGjWqsHnT09NhbGyM33//Hf369UN4eLj0R62stm/fDjMzM2n42rVrCAwMfK1llrfU1FQ4Ojq+cjpDQ0P8/vvvAJ6dWLp27VoEBQXBzs4OgwYNkqZT5/dHnV7j9PR0LFiwACEhIQo9YqXRtWtXfPrppwCenTs1b948dO7cGefOnVPonRw7diwGDhyIoqIiXL16FTNmzEC/fv3w999/Ky3z+fk05cyZM5g9ezZiYmIwZcoUdOvWDdWrV5fGq7vv0yYsZLTAtm3b0LlzZ6xZs0ahPSMjQ+GLWqdOHURHR6OwsLBcT9oq7nEpJoTA9evXpfs4FJ9EbGZmpnCyYkkuXryIwsLClxZvxcsVQsDZ2Vn6L+Rlrly5AplM9tL/fOvUqYNDhw7By8urVDuf6tWrK72nl52QGxISgqZNm+L9998vcf0XL16Ej49PmQ/3FR/Ws7Gxwe7duzF16lT06NFDaUdUmu2xfft2GBoaYv/+/QqHFCIjI5Vyy+VyXLlypcRitfh7EB8fr/TfdbHiP5IJCQnS4bRiCQkJSn9EDQ0NFbZ/nz59YGlpieXLl+P7778v8X297rxVq1bFvn374ObmhilTpmDOnDkYNGiQ1JPx/Pt40T///IPq1asr3QStQ4cOCr+v6pxwrMr169chhFD4Hl27dg0ApBONHR0dS8z4/PsAnh0Gun79Orp16/bKdevq6ips2549e8LS0hL79u1TKGRK8/vj6OgIuVyOxMREhcN+aWlpyMjIUFlYffPNNzA1NVXqASoNOzs7hUyurq5o27Ytdu3apXC7CxcXF2k6Pz8/PH78GF999ZXCFU7FnJ2dATz7Pjzfg1L8vpo1aya91+LpSvP9l8vl+PfffxX2fy9+xsVGjhyJL7/8Enfu3EH9+vUxZcoUbNiwQRqv7r5Pm/AcGS2gq6urdIx269atSpfrDRgwAOnp6Vi+fLnSMkp7jFeVn376CdnZ2dLwtm3bcPfuXXTv3h0A4OHhgTp16uDbb79FTk6O0vz3799Xyq6rq6vy0szn9e/fH7q6upg5c6ZSfiEEHjx4IA0/ffoU27dvR6tWrV7aNT5o0CAUFRVh1qxZSuOePn0qHY4pi9OnT2P37t2YO3duiUXKoEGDcPv2bfzwww9K4548eYLc3NxXrqdevXqwsbEBACxbtgxyuVxph17a7aGrqwuZTKZwuPLmzZtKf2z69esHHR0dhIWFKfUCFn82Xbt2hampKcLDw5XOgyiepkWLFrC2tsaqVasUDmPt3bsXV69eVbhKSpWCggI8ffq0VJeqv868NWrUkA4ThIWFoWbNmhgzZoz0Puzs7NC0aVOsX79e4TsTHx+PAwcOKF0RWBHu3LmjcEVPVlYWfvrpJzRt2lTqhevRowfOnj2L06dPS9Pl5uZi9erVcHJyUjhUuHv3bjx58kTpD2xpFG+XstzPpXhbLV68WKG9uNfyxe/EzZs3sXLlSsyYMaNc/iA/efIEAF75vSj+3qt6jz4+PjAwMMDSpUsVfj82btyItLQ0aV9Xlu//8/tzIQSWL18OPT09pfN6iq9MtLe3x7x58/Dzzz8r3BqhIvd9msYeGS3Qq1cvhIWFYcSIEWjbti0uX76MjRs3Kh07HTZsGH766ScEBwfj7NmzaN++PXJzc3Ho0CF8/PHH6Nu3b5nWb2lpiXbt2mHEiBFIS0vD4sWLUbduXYwZMwbAs+O4P/74I7p3744GDRpgxIgReOedd3D79m0cOXIEZmZm+P3335Gbm4uIiAgsXboU9erVU3heSnEBdOnSJZw+fRqenp6oU6cOvvnmG4SEhODmzZvo168fTE1NkZSUhJ07d2Ls2LH45JNPcOjQIUyfPh2XLl2SurtL0rFjR4wbNw7h4eGIi4tD165doaenh8TERGzduhVLlizBe++9V6btdODAAXTp0uWlvVIffvghfv31V4wfPx5HjhyBl5cXioqK8M8//+DXX3/F/v37X9lT9TxbW1ssWLAAo0ePxgcffIAePXqotT169uyJhQsXolu3bhg6dCju3buHiIgI1K1bF5cuXZKmq1u3Lr766ivMmjUL7du3R//+/WFgYICYmBjY29sjPDwcZmZmWLRoEUaPHo2WLVti6NChqFatGi5evIjHjx9j/fr10NPTw7x58zBixAh07NgRQ4YMkS4/dXJywpQpUxTy5ebmKhwe2rBhA/Ly8lReNvyi15n3eUZGRli9ejV8fX2xcuVKfPzxxwCeHUrt3r07PD09MWrUKOnya3Nzc5X3Qypv9erVw6hRoxATEwMbGxusXbsWaWlpCr1pX3zxBTZv3ozu3btj0qRJsLS0xPr165GUlITt27dDR0cHjx8/RmhoKFasWIG2bdtKl9K/TFFREfbt2wfg2aGlyMhI5Obmol+/fmq/jyZNmiAgIACrV69GRkYGOnbsiLNnz2L9+vXo168fOnfurDD9sWPH4O7ujhEjRqi9LuDZSdzF34vbt29j+fLlMDMzUyoMEhISsG/fPqkncsGCBWjZsqXKG95ZWlpi2rRpmD59Ovz8/NC3b1/8+++/WL58OZo0aYLRo0cDgNrff0NDQ+zbtw8BAQFo3bo19u7diz/++ANffvnlSw8FjR07Fps2bcL48eOlO8RX5L5P4zRxqRQ9U3xpXkxMzEuny8vLE1OnThV2dnbCyMhIeHl5idOnTytcilvs8ePH4quvvhLOzs5CT09P2Nraivfee0/cuHFDCFG2y683b94sQkJChLW1tTAyMhI9e/YUt27dUpr/woULon///sLKykoYGBgIR0dHMWjQIBEVFaWw7le9Xrw8cvv27aJdu3bC2NhYGBsbCzc3NxEYGCgSEhKEEEJMnDhRdOjQQezbt08pk6rLjYV4dimuh4eHMDIyEqampqJRo0bis88+E3fu3JGmUffya5lMJmJjYxXaVX1GBQUFYt68eaJBgwbCwMBAVKtWTXh4eIiZM2eKzMxMpfW9anlCCOHt7S1q1aolsrOz1d4ea9asES4uLsLAwEC4ubmJyMjIErfb2rVrRbNmzaTcHTt2FAcPHlSY5rfffhNt27YVRkZGwszMTLRq1Ups3rxZYZpffvlFWo6lpaXw9/eXbjdQrPjy0+KXiYmJaN68udiwYcNLt1F5zKvqUt4RI0YIMzMzhZyHDh0SXl5e0nvt3bu3uHLlisJ8FXX5dc+ePcX+/ftF48aNpc9u69atStPeuHFDvPfee8LCwkIYGhqKVq1aiT179kjj//vvP+Hg4CCCgoJUfv9ezFHabavO709hYaGYOXOmtN9ycHAQISEhIi8vT2mZAMTOnTsV2kv6zF5UPH/xq3r16qJr167i9OnT0jQv7qd0dHREzZo1RUBAgPTZl/T7ERERIdzc3ISenp6wsbER48aNU7g8v1hpv//Gxsbixo0bomvXrqJq1arCxsZGhIaGKlw2//zl189LSEgQhoaGYsqUKQrtpdn3aRuZEK9xzIHeakePHkXnzp2xdevWcqnUb968CWdnZyQlJZV4s7AZM2bg5s2bCnepJCJFTk5OaNiwIfbs2aPpKFRBhg8fjm3btqk8XE+KeI4MERERaS2eI0NvjImJCfz9/V968mnjxo2lRy4QERG9CgsZemOqV68unWRXkuefK0NERPQqPEeGiIiItBbPkSEiIiKtxUKGiIiItNZbf46MXC7HnTt3YGpq+lpPgCYiIqI3RwiB7Oxs2NvbKzxI+UVvfSFz586dUj2Ij4iIiCqflJQU1KxZs8Txb30hY2pqCuDZhnj+ybNERERUeWVlZcHBwUH6O16St76QKT6cZGZmxkKGiIhIy7zqtBCe7EtERERai4UMERERaS0WMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMERERaS0WMkRERKS1qmg6AFV+N9c4a2zdTqOSNLZuIiKq/NgjQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHW4sm+pLV4EjIREbFHhoiIiLQWCxkiIiLSWixkiIiISGuxkCEiIiKtpdFCxsnJCTKZTOkVGBgIAMjLy0NgYCCsrKxgYmKCAQMGIC0tTZORiYiIqBLRaCETExODu3fvSq+DBw8CAAYOHAgAmDJlCn7//Xds3boVx44dw507d9C/f39NRiYiIqJKRKOXX9eoUUNheO7cuahTpw46duyIzMxMrFmzBps2bYK3tzcAIDIyEu7u7jhz5gzatGmjichERERUiVSac2QKCgrw888/Y+TIkZDJZIiNjUVhYSF8fX2ladzc3FCrVi2cPn26xOXk5+cjKytL4UVERERvp0pTyOzatQsZGRkYPnw4ACA1NRX6+vqwsLBQmM7GxgapqaklLic8PBzm5ubSy8HBoQJTExERkSZVmkJmzZo16N69O+zt7V9rOSEhIcjMzJReKSkp5ZSQiIiIKptK8YiCW7du4dChQ9ixY4fUZmtri4KCAmRkZCj0yqSlpcHW1rbEZRkYGMDAwKAi4xIREVElUSl6ZCIjI2FtbY2ePXtKbR4eHtDT00NUVJTUlpCQgOTkZHh6emoiJhEREVUyGu+RkcvliIyMREBAAKpU+b845ubmGDVqFIKDg2FpaQkzMzNMnDgRnp6evGKJiIiIAFSCQubQoUNITk7GyJEjlcYtWrQIOjo6GDBgAPLz8+Hn54cVK1ZoICURERFVRhovZLp27QohhMpxhoaGiIiIQERExBtORURERNqgUpwjQ0RERFQWLGSIiIhIa7GQISIiIq3FQoaIiIi0FgsZIiIi0losZIiIiEhrsZAhIiIircVChoiIiLQWCxkiIiLSWixkiIiISGuxkCEiIiKtxUKGiIiItBYLGSIiItJaLGSIiIhIa7GQISIiIq3FQoaIiIi0FgsZIiIi0losZIiIiEhrsZAhIiIircVChoiIiLQWCxkiIiLSWixkiIiISGuxkCEiIiKtxUKGiIiItBYLGSIiItJaLGSIiIhIa7GQISIiIq3FQoaIiIi0FgsZIiIi0losZIiIiEhrsZAhIiIircVChoiIiLRWFU0HoP9zc42zRtbrNCpJI+slIiJ6XeyRISIiIq3FQoaIiIi0FgsZIiIi0losZIiIiEhrabyQuX37Nj744ANYWVnByMgIjRo1wrlz56TxQgh8/fXXsLOzg5GREXx9fZGYmKjBxERERFRZaLSQefToEby8vKCnp4e9e/fiypUr+O6771CtWjVpmvnz52Pp0qVYtWoVoqOjYWxsDD8/P+Tl5WkwOREREVUGGr38et68eXBwcEBkZKTU5uz8f5cgCyGwePFiTJs2DX379gUA/PTTT7CxscGuXbswePDgN56ZiIiIKg+N9sj89ttvaNGiBQYOHAhra2s0a9YMP/zwgzQ+KSkJqamp8PX1ldrMzc3RunVrnD59WuUy8/PzkZWVpfAiIiKit5NGC5l///0XK1euhIuLC/bv34+PPvoIkyZNwvr16wEAqampAAAbGxuF+WxsbKRxLwoPD4e5ubn0cnBwqNg3QURERBqj0UJGLpejefPmmDNnDpo1a4axY8dizJgxWLVqVZmXGRISgszMTOmVkpJSjomJiIioMtFoIWNnZ4f69esrtLm7uyM5ORkAYGtrCwBIS0tTmCYtLU0a9yIDAwOYmZkpvIiIiOjtpNFCxsvLCwkJCQpt165dg6OjI4BnJ/7a2toiKipKGp+VlYXo6Gh4enq+0axERERU+Wj0qqUpU6agbdu2mDNnDgYNGoSzZ89i9erVWL16NQBAJpMhKCgI33zzDVxcXODs7Izp06fD3t4e/fr102R0IiIiqgQ0Wsi0bNkSO3fuREhICMLCwuDs7IzFixfD399fmuazzz5Dbm4uxo4di4yMDLRr1w779u2DoaGhBpMTlUxTTzEH+CRzIvrfo9FCBgB69eqFXr16lTheJpMhLCwMYWFhbzAVERERaQONP6KAiIiIqKxYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMERERaS0WMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMERERaS0WMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWqqLuDJaWli8d//DhwzKHISIiIlJHqQqZQYMGYdKkSWjXrh2EEJDL5ZgyZQqcnZ0rOh8RERFRiUpVyAQEBKB37964d+8ebty4gRkzZuC7777D+PHjMW3aNJibm1d0TiIiIiIlpTpHpkePHsjNzcXDhw9haWmJpUuXIjY2FtevX0fdunWxbNkyFBUVVXRWIiIiIgWlKmTGjRuHpk2bwsbGRmqrV68edu7cie3bt+Onn35C/fr1sWvXrorKSURERKSkVIeW+vTpg6VLlwIA+vfvrzT+nXfewT///IMBAwawZ4aIiIjemFIVMr169ZJ+Lul8mPfee698EhERERGVktqXX0dGRlZEDiIiIiK1lemGeE+fPsWhQ4fw/fffIzs7GwBw584d5OTklGs4IiIiopdRu0fm1q1b6NatG5KTk5Gfn48uXbrA1NQU8+bNQ35+PlatWlUROYmIiIiUqN0jM3nyZLRo0QKPHj2CkZGR1P7uu+8iKiqqXMMRERERvYzaPTInTpzAX3/9BX19fYV2Jycn3L59u9yCEREREb2K2j0ycrlc5SXW//33H0xNTdVa1owZMyCTyRRebm5u0vi8vDwEBgbCysoKJiYmGDBgANLS0tSNTERERG8ptQuZrl27YvHixdKwTCZDTk4OQkND0aNHD7UDNGjQAHfv3pVeJ0+elMZNmTIFv//+O7Zu3Ypjx47hzp07Ku9jQ0RERP+b1D609N1338HPzw/169dHXl4ehg4disTERFSvXh2bN29WP0CVKrC1tVVqz8zMxJo1a7Bp0yZ4e3sDeHbpt7u7O86cOYM2bdqovS4iIiJ6u6hdyNSsWRMXL17EL7/8gosXLyInJwejRo2Cv7+/wsm/pZWYmAh7e3sYGhrC09MT4eHhqFWrFmJjY1FYWAhfX19pWjc3N9SqVQunT58usZDJz89Hfn6+NJyVlaV2JiIiItIOahcywLNeFH9/f/j7+7/Wylu3bo1169bB1dUVd+/excyZM9G+fXvEx8cjNTUV+vr6sLCwUJjHxsYGqampJS4zPDwcM2fOfK1cREREpB3ULmTCw8NhY2ODkSNHKrSvXbsW9+/fx+eff17qZXXv3l36uXHjxmjdujUcHR3x66+/lql3BwBCQkIQHBwsDWdlZcHBwaFMyyIiIqLKTe2Tfb///nuFK4uKNWjQ4LVvhmdhYYF69erh+vXrsLW1RUFBATIyMhSmSUtLU3lOTTEDAwOYmZkpvIiIiOjtpHYhk5qaCjs7O6X2GjVq4O7du68VJicnBzdu3ICdnR08PDygp6encJO9hIQEJCcnw9PT87XWQ0RERG8HtQ8tOTg44NSpU3B2dlZoP3XqFOzt7dVa1ieffILevXvD0dERd+7cQWhoKHR1dTFkyBCYm5tj1KhRCA4OhqWlJczMzDBx4kR4enryiiUiIiICUIZCZsyYMQgKCkJhYaF0WXRUVBQ+++wzTJ06Va1l/ffffxgyZAgePHiAGjVqoF27djhz5gxq1KgBAFi0aBF0dHQwYMAA5Ofnw8/PDytWrFA3MhEREb2l1C5kPv30Uzx48AAff/wxCgoKAACGhob4/PPPERISotaytmzZ8tLxhoaGiIiIQEREhLoxiYiI6H+A2oWMTCbDvHnzMH36dFy9ehVGRkZwcXGBgYFBReQjIiIiKlGZ7iMDACYmJmjZsmV5ZiEiIiJSi9qFzKuedbRjx44yhyEiIiJSh9qFzK5du2Bqaoq+fftCV1e3IjIRERERlYrahczBgwcxdepUxMbGYv78+ejZs2dF5CIiIiJ6JbVviOfj44MLFy7gk08+wbhx4+Dr64tLly5VRDYiIiKil1K7kAGeXbk0YsQIJCYmokOHDujQoQNGjhyJO3fulHc+IiIiohKpfWhp6dKlCsMWFhYYP348IiIisHXrVmRnZ5dbOCL633BzjfOrJ6oATqOSNLJeIio/ahcyixYtUtlevXr11w5DREREpA61C5mkJP4HQ0RERJVDmc6RISIiIqoM1O6RCQ4Ofun4hQsXljkMERERkTrULmQWL14MT09P6OvrK42TyWTlEoqIiIioNMr0rKWdO3fC2tq6vLMQERERqYXnyBAREZHWYiFDREREWqtMh5b2798Pc3NzleP69OnzWoGIiIiISqtMhUxAQIDKdplMhqKiotcKRERERFRaahcycrm8InIQERERqY3nyBAREZHWKtOhpdzcXBw7dgzJyckoKChQGDdp0qRyCUZERET0KmoXMhcuXECPHj3w+PFj5ObmwtLSEunp6ahatSqsra1ZyBAREdEbo/ahpSlTpqB379549OgRjIyMcObMGdy6dQseHh749ttvKyIjERERkUpqFzJxcXGYOnUqdHR0oKuri/z8fDg4OGD+/Pn48ssvKyIjERERkUpqFzJ6enrQ0Xk2m7W1NZKTkwEA5ubmSElJKd90RERERC+h9jkyzZo1Q0xMDFxcXNCxY0d8/fXXSE9Px4YNG9CwYcOKyEhERESkktqFzJw5c5CdnQ0AmD17NoYNG4aPPvoILi4uWLt2bbkHJKLycXONs8bW7TQqSWPrJqK3m9qFTIsWLaSfra2tsW/fvnINRERERFRaZbohXmFhIR4/fiz9fP78eamXhoiIiOhNUbuQ2bdvHywsLGBjY4MDBw6gRYsWaNGiBWrWrIlTp05VREYiIiIildQuZKZNm4ZJkyZh4cKFGDp0KLy8vJCRkYFBgwZh2rRpFZGRiIiISCW1C5krV65g3LhxGDNmDLKzszF+/HiYmZkhKCgIFy9erIiMRERERCqpXcjo6+ujqKgIAODi4oJq1aoBAKpWrYrCwsLyTUdERET0EmoXMq6urvj7778BAPHx8XBwcADwrKfGxcWlfNMRERERvYTal18fOHAA+vr6Su3vvPMOIiIiyiUUERERUWmoXciYm5urbG/atOnrZiEiIiJSS5nuI0NERERUGVSaQmbu3LmQyWQICgqS2vLy8hAYGAgrKyuYmJhgwIABSEtL01xIIiIiqlQqRSETExOD77//Ho0bN1ZonzJlCn7//Xds3boVx44dw507d9C/f38NpSQiIqLKRuOFTE5ODvz9/fHDDz9Il3IDQGZmJtasWYOFCxfC29sbHh4eiIyMxF9//YUzZ85oMDERERFVFmqf7AsARUVF2LVrF65evQoAaNCgAfr06QNdXV21lxUYGIiePXvC19cX33zzjdQeGxuLwsJC+Pr6Sm1ubm6oVasWTp8+jTZt2qhcXn5+PvLz86XhrKwstTMRERGRdlC7kLl+/Tp69uyJ//77D66urgCA8PBwODg44I8//kCdOnVKvawtW7bg/PnziImJURqXmpoKfX19WFhYKLTb2NggNTW1xGWGh4dj5syZpc5ARERE2kvtQ0uTJk1C7dq1kZKSgvPnz+P8+fNITk6Gs7MzJk2aVOrlpKSkYPLkydi4cSMMDQ3VjVGikJAQZGZmSq+UlJRyWzYRERFVLmr3yBw7dgxnzpyBpaWl1GZlZYW5c+fCy8ur1MuJjY3FvXv30Lx5c6mtqKgIx48fx/Lly7F//34UFBQgIyNDoVcmLS0Ntra2JS7XwMAABgYG6r0pIiIi0kpqFzIGBgbIzs5Was/JyVF5x9+S+Pj44PLlywptI0aMgJubGz7//HM4ODhAT08PUVFRGDBgAAAgISEBycnJ8PT0VDc2ERERvYXULmR69eqFsWPHYs2aNWjVqhUAIDo6GuPHj0efPn1KvRxTU1M0bNhQoc3Y2BhWVlZS+6hRoxAcHAxLS0uYmZlh4sSJ8PT0LPFEXyIiIvrfonYhs3TpUgQEBMDT0xN6enoAgKdPn6JPnz5YsmRJuYZbtGgRdHR0MGDAAOTn58PPzw8rVqwo13UQERGR9lK7kLGwsMDu3buRmJiIf/75BwDg7u6OunXrvnaYo0ePKgwbGhoiIiKCD6MkIiIilcp0HxkAcHFxgYuLC4BnJ+kSERERvWlqX36dlJSEIUOG4KOPPsKjR4/Qp08fGBgYwNXVFZcuXaqIjEREREQqqV3IjBs3DlevXkV8fDy8vb1RUFCA3bt3o379+goPfCQiIiKqaGofWoqOjsaJEyfg6OgIS0tLxMTEoHnz5qhbty5at25dERmJiIiIVFK7RyY7Oxt2dnYwNzdH1apVpZvVWVhYqLy/DBEREVFFKdPJvvv27YO5uTnkcjmioqIQHx+PjIyMco5GRERE9HJlKmQCAgKkn8eNGyf9LJPJXj8RERERUSmpXcjI5fKKyEFERESkNrXPkfnpp5+Qn59fEVmIiIiI1KJ2ITNixAhkZmZWRBYiIiIitahdyAghKiIHERERkdrKdLLvr7/+CjMzM5Xjhg0b9lqBiIiIiEqrTIXM/Pnzoaurq9Quk8lYyBAREdEbU6ZC5ty5c7C2ti7vLERERERqUfscGSIiIqLKQu1CxtHRUeVhJSIiIqI3Te1DS0lJSRWRg4iIiEhtavfITJo0CUuXLlVqX758OYKCgsojExEREVGpqF3IbN++HV5eXkrtbdu2xbZt28olFBEREVFpqF3IPHjwAObm5krtZmZmSE9PL5dQRERERKWhdiFTt25d7Nu3T6l97969qF27drmEIiIiIioNtU/2DQ4OxoQJE3D//n14e3sDAKKiovDdd99h8eLF5Z2PiIiIqERqFzIjR45Efn4+Zs+ejVmzZgEAnJycsHLlSt7Vl4iIiN6oMt3Z96OPPsJHH32E+/fvw8jICCYmJuWdi4iIiOiVynRn36dPn+LQoUPYsWOH9DTsO3fuICcnp1zDEREREb2M2j0yt27dQrdu3ZCcnIz8/Hx06dIFpqammDdvHvLz87Fq1aqKyElERESkRO0emcmTJ6NFixZ49OgRjIyMpPZ3330XUVFR5RqOiIiI6GXU7pE5ceIE/vrrL+jr6yu0Ozk54fbt2+UWjIhI026ucdbIep1G8VEwRKWldo+MXC5HUVGRUvt///0HU1PTcglFREREVBpqFzJdu3ZVuF+MTCZDTk4OQkND0aNHj/LMRkRERPRSah9a+u677+Dn54f69esjLy8PQ4cORWJiIqpXr47NmzdXREYiIiIildQuZGrWrImLFy9iy5YtuHTpEnJycjBq1Cj4+/srnPxLREREVNHKdEO8KlWq4IMPPijvLERERERqUbuQ+e233146vk+fPmUOQ0RERKQOtQuZfv36KQzLZDLp7r4ymUzlFU1EREREFaFMl18//6patSquX79e4mXZRERERBWlTM9aep5MJiuPHERERERqe61C5ubNm8jNzS3zjfBWrlyJxo0bw8zMDGZmZvD09MTevXul8Xl5eQgMDISVlRVMTEwwYMAApKWlvU5kIiIieouofY5M//79AQBPnjzBmTNn4OPjgxo1apRp5TVr1sTcuXPh4uICIQTWr1+Pvn374sKFC2jQoAGmTJmCP/74A1u3boW5uTkmTJiA/v3749SpU2VaHxEREb1d1C5kzM3NAQC2trbo3bs3Ro4cWeaV9+7dW2F49uzZWLlyJc6cOYOaNWtizZo12LRpE7y9vQEAkZGRcHd3x5kzZ9CmTZsyr5eIiIjeDmoXMpGRkRWRA0VFRdi6dStyc3Ph6emJ2NhYFBYWwtfXV5rGzc0NtWrVwunTp0ssZPLz85Gfny8NZ2VlVUheIiIi0jy1C5lXFQZmZmZqLe/y5cvw9PREXl4eTExMsHPnTtSvXx9xcXHQ19eHhYWFwvQ2NjZITU0tcXnh4eGYOXOmWhmIiIhIO6ldyFhYWKi8UkkIUab7yLi6uiIuLg6ZmZnYtm0bAgICcOzYMXVjSUJCQhAcHCwNZ2VlwcHBoczLIyIiospL7UKmdu3auHfvHr744gt4eXm9dgB9fX3UrVsXAODh4YGYmBgsWbIE77//PgoKCpCRkaHQK5OWlgZbW9sSl2dgYAADA4PXzkVERESVn9qFzNWrV7Fs2TLMnj0bFy5cwPz58+Hs7FxugeRyOfLz8+Hh4QE9PT1ERUVhwIABAICEhAQkJyfD09Oz3NZHRERE2kvt+8jo6ekhODgYiYmJeOedd9C4cWNMnToVGRkZaq88JCQEx48fx82bN3H58mWEhITg6NGj8Pf3h7m5OUaNGoXg4GAcOXIEsbGxGDFiBDw9PXnFEhEREQF4jRviWVpaYvHixbhw4QJu3ryJunXrYvHixWot4969exg2bBhcXV3h4+ODmJgY7N+/H126dAEALFq0CL169cKAAQPQoUMH2NraYseOHWWNTERERG8ZtQ8tNWvWTOlkXyEE8vPzMXXqVAQFBZV6WWvWrHnpeENDQ0RERCAiIkLdmERERPQ/4LWffk1ERESkKWoXMqGhoRWRg4iIiEhtGr8hHhEREVFZafyGeERERERlpXYhAwDbtm2DpaVleWchIiIiUkuZChkvLy9YW1uXdxYiIiIitZSpkLly5QoePHgAY2Nj2NraQl9fv7xzEREREb1SmW6I5+PjgwYNGsDZ2RnGxsZo1KgRFi1aVN7ZiIiIiF5K7R6ZpKQkCCFQWFiIrKws3LlzB2fPnsX06dPx9OlTfPrppxWRk4iIiEiJ2oWMo6OjwrCHhwd69+6NevXqISwsjIUMERERvTFlOkdGlcGDB6NBgwbltTgiIiKiVypzIRMbG4urV68CAOrXr4/mzZujefPm5RaMiIiI6FXULmTu3buHwYMH4+jRo7CwsAAAZGRkoHPnztiyZQtq1KhR3hmJiIiIVFL7qqWJEyciOzsbf//9Nx4+fIiHDx8iPj4eWVlZmDRpUkVkJCIiIlJJ7R6Zffv24dChQ3B3d5fa6tevj4iICHTt2rVcwxERERG9jNo9MnK5HHp6ekrtenp6kMvl5RKKiIiIqDTULmS8vb0xefJk3LlzR2q7ffs2pkyZAh8fn3INR0RERPQyahcyy5cvR1ZWFpycnFCnTh3UqVMHzs7OyMrKwrJlyyoiIxEREZFKap8j4+DggPPnz+PQoUP4559/AADu7u7w9fUt93BEREREL1PqQiY7OxumpqYAAJlMhi5duqBLly4K08TExKBly5blm5CIiIioBKU+tNS1a1fk5OSoHPf06VNMmzYNXl5e5RaMiIiI6FVKXchkZ2fD19cXWVlZCu3x8fFo2bIl1q1bh127dpV3PiIiIqISlbqQOXLkCHJzc9GlSxdkZWVBCIF58+ahRYsWcHd3x+XLl9GjR4+KzEpERESkoNTnyNSoUQOHDx+Gr68vvL29YWBggMTERPz888947733KjIjERERkUpqXbVUo0YNREVFwdfXF/Hx8YiLi4Obm1tFZSMiIiJ6KbXvI1O9enUcPnwY9evXx9ChQ/Ho0aOKyEVERET0SqXukenfv7/CsJmZGY4fP45WrVqhUaNGUvuOHTvKLx0RERHRS5S6kDE3N1cadnZ2LvdARERERKVV6kImMjKyInMQERERqU3tc2SIiIiIKgsWMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWkujhUx4eDhatmwJU1NTWFtbo1+/fkhISFCYJi8vD4GBgbCysoKJiQkGDBiAtLQ0DSUmIiKiykSjhcyxY8cQGBiIM2fO4ODBgygsLETXrl2Rm5srTTNlyhT8/vvv2Lp1K44dO4Y7d+4oPfeJiIiI/jeV+hEFFWHfvn0Kw+vWrYO1tTViY2PRoUMHZGZmYs2aNdi0aRO8vb0BPHtUgru7O86cOYM2bdpoIjYRERFVEpXqHJnMzEwAgKWlJQAgNjYWhYWF8PX1laZxc3NDrVq1cPr0aZXLyM/PR1ZWlsKLiIiI3k4a7ZF5nlwuR1BQELy8vNCwYUMAQGpqKvT19WFhYaEwrY2NDVJTU1UuJzw8HDNnzqzouEREGnNzjbNG1us0Kkkj6yV6mUrTIxMYGIj4+Hhs2bLltZYTEhKCzMxM6ZWSklJOCYmIiKiyqRQ9MhMmTMCePXtw/Phx1KxZU2q3tbVFQUEBMjIyFHpl0tLSYGtrq3JZBgYGMDAwqOjIREREVAlotEdGCIEJEyZg586dOHz4MJydFbtLPTw8oKenh6ioKKktISEBycnJ8PT0fNNxiYiIqJLRaI9MYGAgNm3ahN27d8PU1FQ678Xc3BxGRkYwNzfHqFGjEBwcDEtLS5iZmWHixInw9PTkFUtERESk2UJm5cqVAIBOnToptEdGRmL48OEAgEWLFkFHRwcDBgxAfn4+/Pz8sGLFijeclIiIiCojjRYyQohXTmNoaIiIiAhERES8gURERESkTSrNVUtERERE6mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMERERaS0WMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMERERaS0WMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktapoOgAREb0dbq5x1ti6nUYlaWzdpFnskSEiIiKtxUKGiIiItBYLGSIiItJaLGSIiIhIa2m0kDl+/Dh69+4Ne3t7yGQy7Nq1S2G8EAJff/017OzsYGRkBF9fXyQmJmomLBEREVU6Gi1kcnNz0aRJE0RERKgcP3/+fCxduhSrVq1CdHQ0jI2N4efnh7y8vDeclIiIiCojjV5+3b17d3Tv3l3lOCEEFi9ejGnTpqFv374AgJ9++gk2NjbYtWsXBg8e/CajEhERUSVUac+RSUpKQmpqKnx9faU2c3NztG7dGqdPn9ZgMiIiIqosKu0N8VJTUwEANjY2Cu02NjbSOFXy8/ORn58vDWdlZVVMQCIiItK4StsjU1bh4eEwNzeXXg4ODpqORERERBWk0hYytra2AIC0tDSF9rS0NGmcKiEhIcjMzJReKSkpFZqTiIiINKfSFjLOzs6wtbVFVFSU1JaVlYXo6Gh4enqWOJ+BgQHMzMwUXkRERPR20ug5Mjk5Obh+/bo0nJSUhLi4OFhaWqJWrVoICgrCN998AxcXFzg7O2P69Omwt7dHv379NBeaiIiIKg2NFjLnzp1D586dpeHg4GAAQEBAANatW4fPPvsMubm5GDt2LDIyMtCuXTvs27cPhoaGmopMRERahk/lfrtptJDp1KkThBAljpfJZAgLC0NYWNgbTEVERETaotKeI0NERET0KixkiIiISGuxkCEiIiKtxUKGiIiItBYLGSIiItJaLGSIiIhIa7GQISIiIq3FQoaIiIi0lkZviKcpmrrLI+/wSEREVL7YI0NERERai4UMERERaS0WMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWut/8s6+REREmqapu8wDb9ed5tkjQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMERERaS0WMkRERKS1WMgQERGR1mIhQ0RERFqLhQwRERFpLRYyREREpLVYyBAREZHWYiFDREREWouFDBEREWmtKpoOQERERJXLzTXOGlmv06gktefRih6ZiIgIODk5wdDQEK1bt8bZs2c1HYmIiIgqgUpfyPzyyy8IDg5GaGgozp8/jyZNmsDPzw/37t3TdDQiIiLSsEpfyCxcuBBjxozBiBEjUL9+faxatQpVq1bF2rVrNR2NiIiINKxSnyNTUFCA2NhYhISESG06Ojrw9fXF6dOnVc6Tn5+P/Px8aTgzMxMAkJWVJbVlP5FXUOKXez6DKsyl7GXZmEuZNuYC+N1/EXOpTxu/+5U1F1A5vmPFPwshXj6TqMRu374tAIi//vpLof3TTz8VrVq1UjlPaGioAMAXX3zxxRdffL0Fr5SUlJfWCpW6R6YsQkJCEBwcLA3L5XI8fPgQVlZWkMlkr7XsrKwsODg4ICUlBWZmZq8btdwwl3oqay6g8mZjLvUwl3oqay6g8mb7X8glhEB2djbs7e1fOl2lLmSqV68OXV1dpKWlKbSnpaXB1tZW5TwGBgYwMDBQaLOwsCjXXGZmZpXqi1OMudRTWXMBlTcbc6mHudRTWXMBlTfb257L3Nz8ldNU6pN99fX14eHhgaioKKlNLpcjKioKnp6eGkxGRERElUGl7pEBgODgYAQEBKBFixZo1aoVFi9ejNzcXIwYMULT0YiIiEjDKn0h8/777+P+/fv4+uuvkZqaiqZNm2Lfvn2wsbF541kMDAwQGhqqdOhK05hLPZU1F1B5szGXephLPZU1F1B5szHX/5EJ8arrmoiIiIgqp0p9jgwRERHRy7CQISIiIq3FQoaIiIi0FgsZIiIi0losZEopIiICTk5OMDQ0ROvWrXH27FlNR8Lx48fRu3dv2NvbQyaTYdeuXZqOBAAIDw9Hy5YtYWpqCmtra/Tr1w8JCQmajoWVK1eicePG0o2aPD09sXfvXk3HUjJ37lzIZDIEBQVpNMeMGTMgk8kUXm5ubhrNVOz27dv44IMPYGVlBSMjIzRq1Ajnzp3TdCw4OTkpbTOZTIbAwECN5ioqKsL06dPh7OwMIyMj1KlTB7NmzXr1M2zegOzsbAQFBcHR0RFGRkZo27YtYmJi3miGV+1LhRD4+uuvYWdnByMjI/j6+iIxMVHjuXbs2IGuXbtKd66Pi4ur8ExA6fbxnTp1Uvo9GD9+fIXkYSFTCr/88guCg4MRGhqK8+fPo0mTJvDz88O9e/c0mis3NxdNmjRBRESERnO86NixYwgMDMSZM2dw8OBBFBYWomvXrsjNzdVorpo1a2Lu3LmIjY3FuXPn4O3tjb59++Lvv//WaK7nxcTE4Pvvv0fjxo01HQUA0KBBA9y9e1d6nTx5UtOR8OjRI3h5eUFPTw979+7FlStX8N1336FatWqajoaYmBiF7XXw4EEAwMCBAzWaa968eVi5ciWWL1+Oq1evYt68eZg/fz6WLVum0VwAMHr0aBw8eBAbNmzA5cuX0bVrV/j6+uL27dtvLMOr9qXz58/H0qVLsWrVKkRHR8PY2Bh+fn7Iy8vTaK7c3Fy0a9cO8+bNq9AcLyrtPn7MmDEKvw/z58+vmEDl8XDHt12rVq1EYGCgNFxUVCTs7e1FeHi4BlMpAiB27typ6Rgq3bt3TwAQx44d03QUJdWqVRM//vijpmMIIYTIzs4WLi4u4uDBg6Jjx45i8uTJGs0TGhoqmjRpotEMqnz++eeiXbt2mo5RKpMnTxZ16tQRcrlcozl69uwpRo4cqdDWv39/4e/vr6FEzzx+/Fjo6uqKPXv2KLQ3b95cfPXVVxrJ9OK+VC6XC1tbW7FgwQKpLSMjQxgYGIjNmzdrLNfzkpKSBABx4cKFN5bnear28W9yH8YemVcoKChAbGwsfH19pTYdHR34+vri9OnTGkymPTIzMwEAlpaWGk7yf4qKirBlyxbk5uZWmsddBAYGomfPngrfNU1LTEyEvb09ateuDX9/fyQnJ2s6En777Te0aNECAwcOhLW1NZo1a4YffvhB07GUFBQU4Oeff8bIkSNf+4G1r6tt27aIiorCtWvXAAAXL17EyZMn0b17d43mevr0KYqKimBoaKjQbmRkVCl6/wAgKSkJqampCr+X5ubmaN26Nf8G/H8l7eM3btyI6tWro2HDhggJCcHjx48rZP2V/s6+mpaeno6ioiKlOwnb2Njgn3/+0VAq7SGXyxEUFAQvLy80bNhQ03Fw+fJleHp6Ii8vDyYmJti5cyfq16+v6VjYsmULzp8//8bPDXiZ1q1bY926dXB1dcXdu3cxc+ZMtG/fHvHx8TA1NdVYrn///RcrV65EcHAwvvzyS8TExGDSpEnQ19dHQECAxnK9aNeuXcjIyMDw4cM1HQVffPEFsrKy4ObmBl1dXRQVFWH27Nnw9/fXaC5TU1N4enpi1qxZcHd3h42NDTZv3ozTp0+jbt26Gs1WLDU1FQBU/g0oHve/rKR9/NChQ+Ho6Ah7e3tcunQJn3/+ORISErBjx45yz8BChipUYGAg4uPjK81/V66uroiLi0NmZia2bduGgIAAHDt2TKPFTEpKCiZPnoyDBw8q/WeqSc//t964cWO0bt0ajo6O+PXXXzFq1CiN5ZLL5WjRogXmzJkDAGjWrBni4+OxatWqSlXIrFmzBt27d4e9vb2mo+DXX3/Fxo0bsWnTJjRo0ABxcXEICgqCvb29xrfZhg0bMHLkSLzzzjvQ1dVF8+bNMWTIEMTGxmo0F5VOSfv4sWPHSj83atQIdnZ28PHxwY0bN1CnTp1yzcBDS69QvXp16OrqIi0tTaE9LS0Ntra2GkqlHSZMmIA9e/bgyJEjqFmzpqbjAHj2RPW6devCw8MD4eHhaNKkCZYsWaLRTLGxsbh37x6aN2+OKlWqoEqVKjh27BiWLl2KKlWqoKioSKP5illYWKBevXq4fv26RnPY2dkpFZ7u7u6V4rBXsVu3buHQoUMYPXq0pqMAAD799FN88cUXGDx4MBo1aoQPP/wQU6ZMQXh4uKajoU6dOjh27BhycnKQkpKCs2fPorCwELVr19Z0NACQ9vP8G6BMnX1869atAaBC9h8sZF5BX18fHh4eiIqKktrkcjmioqIqzbkVlY0QAhMmTMDOnTtx+PBhODs7azpSieRyOfLz8zWawcfHB5cvX0ZcXJz0atGiBfz9/REXFwddXV2N5iuWk5ODGzduwM7OTqM5vLy8lC71vHbtGhwdHTWUSFlkZCSsra3Rs2dPTUcBADx+/Bg6Ooq7e11dXcjlcg0lUmZsbAw7Ozs8evQI+/fvR9++fTUdCQDg7OwMW1tbhb8BWVlZiI6O/p/9G1CWfXzxpeEVsf/goaVSCA4ORkBAAFq0aIFWrVph8eLFyM3NxYgRIzSaKycnR6G6TUpKQlxcHCwtLVGrVi2N5QoMDMSmTZuwe/dumJqaSseRzc3NYWRkpLFcISEh6N69O2rVqoXs7Gxs2rQJR48exf79+zWWCXh2nsCL5w8ZGxvDyspKo+cVffLJJ+jduzccHR1x584dhIaGQldXF0OGDNFYJgCYMmUK2rZtizlz5mDQoEE4e/YsVq9ejdWrV2s0VzG5XI7IyEgEBASgSpXKsYvt3bs3Zs+ejVq1aqFBgwa4cOECFi5ciJEjR2o6Gvbv3w8hBFxdXXH9+nV8+umncHNze6P711ftS4OCgvDNN9/AxcUFzs7OmD59Ouzt7dGvXz+N5nr48CGSk5Nx584dAJAKfFtb2wrtLXrVPv7GjRvYtGkTevToASsrK1y6dAlTpkxBhw4dKubWEm/k2qi3wLJly0StWrWEvr6+aNWqlThz5oymI4kjR44IAEqvgIAAjeZSlQmAiIyM1GiukSNHCkdHR6Gvry9q1KghfHx8xIEDBzSaqSSV4fLr999/X9jZ2Ql9fX3xzjvviPfff19cv35do5mK/f7776Jhw4bCwMBAuLm5idWrV2s6kmT//v0CgEhISNB0FElWVpaYPHmyqFWrljA0NBS1a9cWX331lcjPz9d0NPHLL7+I2rVrC319fWFraysCAwNFRkbGG83wqn2pXC4X06dPFzY2NsLAwED4+Pi8kc/3VbkiIyNVjg8NDa3QXK/axycnJ4sOHToIS0tLYWBgIOrWrSs+/fRTkZmZWSF5ZP8/FBEREZHW4TkyREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMkQYMHz68wu8KWladOnVCUFCQpmOobfr06QoPqntbZWVloWnTpsjJycF///1XaZ4SXZmkp6fD2toa//33n6aj0BvAQoYqteHDh0Mmk2Hu3LkK7bt27YJMJqvw9bdp0wbjx49XaFu1ahVkMhnWrVun0D58+HC0b9++3NZd/N5lMpn0sMuwsDA8ffq03NbxtkhNTcWSJUvw1VdfSW0lFYtHjx6FTCZDRkbGmwtYjszMzNCuXTtYWFjAyckJH330kaYjVTrVq1fHsGHDEBoaquko9AawkKFKz9DQEPPmzcOjR4/e+Lo7d+6Mo0ePKrQdOXIEDg4OSu1Hjx6Ft7d3ua6/W7duuHv3LhITEzF16lTMmDEDCxYsKPPyCgoKyjFd5fHjjz+ibdu2lerBkRVp+fLluHfvHh4+fIipU6dqOk6lNGLECGzcuBEPHz7UdBSqYCxkqNLz9fWFra0twsPDS5xmxowZaNq0qULb4sWL4eTkJA0X/4c+Z84c2NjYwMLCQurh+PTTT2FpaYmaNWsiMjJSmqdz585ISEiQHooGAMeOHcMXX3yhUMgkJSXh1q1b6Ny5MwAgJSUFgwYNgoWFBSwtLdG3b1/cvHlTKffMmTNRo0YNmJmZYfz48UqFhoGBAWxtbeHo6IiPPvoIvr6++O233wCoPgTUr18/DB8+XBp2cnLCrFmzMGzYMJiZmUmHXk6dOoVOnTqhatWqqFatGvz8/BQKRblcjs8++wyWlpawtbXFjBkzFNazcOFCNGrUCMbGxnBwcMDHH3+MnJwcafytW7fQu3dvVKtWDcbGxmjQoAH+/PNPaXx8fDy6d+8OExMT2NjY4MMPP0R6ero0ftu2bWjUqBGMjIxgZWUFX19f5ObmKm2/Ylu2bEHv3r1LHP8qGRkZGD16tPRZeHt74+LFiwrT3Lx5U+ohe/71fM/O7t270bx5cxgaGqJ27dqYOXOmQg/a8/OZmZmhS5cuuHHjhjT+0aNHGDZsGKpVq4aqVauie/fuSExMlMavW7cOFhYWAABLS0uYmZmhQ4cOkMlk0tOFVXFyclKZ/fkeq06dOmHChAmYMGECzM3NUb16dUyfPh3PP8XGyckJixcvloajoqKUlvPhhx/C2toaBgYGqF27Nr799luV+V/crsX5i4qKMGrUKDg7O8PIyAiurq5YsmSJwjwv9rbt3bsXJiYm2Lt3r9TWoEED2NvbY+fOnSVuF3o7sJChSk9XVxdz5szBsmXLXvuY9+HDh3Hnzh0cP34cCxcuRGhoKHr16oVq1aohOjoa48ePx7hx46T1eHl5QU9PD0eOHAEAXLlyBU+ePMGoUaPw4MEDJCUlAXjWS2NoaAhPT08UFhbCz88PpqamOHHiBE6dOgUTExN069ZNoVCJiorC1atXcfToUWzevBk7duzAzJkzX5rfyMhI7V6Vb7/9Fk2aNMGFCxcwffp0xMXFwcfHB/Xr18fp06dx8uRJ9O7dG0VFRdI869evh7GxMaKjozF//nyEhYXh4MGD0ngdHR0sXboUf//9N9avX4/Dhw/js88+k8YHBgYiPz8fx48fx+XLlzFv3jyYmJgAeFY0eHt7o1mzZjh37hz27duHtLQ0DBo0CABw9+5dDBkyBCNHjpS2T//+/VHSY+EePnyIK1euoEWLFmptl+cNHDgQ9+7dw969exEbG4vmzZvDx8dH4b/54vUfOnQId+/exfbt2xWWceLECQwbNgyTJ0/GlStX8P3332PdunWYPXu2wnSRkZG4e/cujh8/jnv37uHLL7+Uxg0fPhznzp3Db7/9htOnT0MIgR49eqCwsFBl7h07duDChQuleo9hYWG4e/eu9Cre3s9bv349qlSpgrNnz2LJkiVYuHAhfvzxR5XLk8vlmDp1qvS5Fhs8eDAOHTqExMREzJ49GyEhITh+/HipMhYvt2bNmti6dSuuXLmCr7/+Gl9++SV+/fVXldOfOHECgwYNwpo1a9C9e3eFca1atcKJEydKvW7SUhXyKEqichIQECD69u0rhBCiTZs2YuTIkUIIIXbu3Cme//qGhoaKJk2aKMy7aNEi4ejoqLAsR0dHUVRUJLW5urqK9u3bS8NPnz4VxsbGYvPmzVKbl5eXGDt2rBBCiIiICNGjRw8hhBBdu3YVa9euFUII8eGHH4rOnTsLIYTYsGGDcHV1FXK5XFpGfn6+MDIyEvv375eyWFpaitzcXGmalStXChMTEynf8+9dLpeLgwcPCgMDA/HJJ58IIVQ/Ibtv374KTz93dHQU/fr1U5hmyJAhwsvLS5SkY8eOol27dgptLVu2FJ9//nmJ82zdulVYWVlJw40aNRIzZsxQOe2sWbNE165dFdpSUlKkJ0bHxsYKAOLmzZslru95Fy5cEABEcnKyQvvz2+95xU8UfvTokRBCiBMnTggzMzORl5enMF2dOnXE999/Lw0nJCQIACI+Pl7lcnx8fMScOXMUlrFhwwZhZ2cnDQMQO3fuFEIIkZGRIby8vMSYMWOEEEJcu3ZNABCnTp2Spk9PTxdGRkbi119/FUI8e9qxubm5EEKIgoICUbduXTFr1iwBQFy4cKHEbeTo6CgWLVr00u3TsWNH4e7urvC9/fzzz4W7u7vK5axdu1a4ubkJf39/ldtZiGefjaGhoTh8+LBS/mJJSUmvzB8YGCgGDBiglD02NlaYm5srfE7PmzJliujUqVOJy6W3A3tkSGvMmzcP69evx9WrV8u8jAYNGkBH5/++9jY2NmjUqJE0rKurCysrK9y7d09q69Spk3QY6ejRo+jUqRMAoGPHjgrtxYeVLl68iOvXr8PU1BQmJiYwMTGBpaUl8vLyFA4jNGnSBFWrVpWGPT09kZOTg5SUFKltz549MDExgaGhIbp37473339f6TDPq7zYU1HcI/MyjRs3Vhi2s7NT2CaHDh2Cj48P3nnnHZiamuLDDz/EgwcP8PjxYwDApEmT8M0338DLywuhoaG4dOmSNO/Fixdx5MgRaduYmJjAzc0NAHDjxg00adIEPj4+aNSoEQYOHIgffvjhpedHPXnyBMCzc6nK4uLFi8jJyYGVlZVCpqSkJIXPKysrCwBgbGxc4nLCwsIUljFmzBjcvXtX2i4AMGTIEJiYmKBatWrIzs6WDplevXoVVapUQevWraVprays4OrqqvI7HxERAXNzc/j7+5fpfavSpk0bhZPoPT09kZiYqNBbBwCPHz/GtGnTMH/+fFSpUkVpOePHj4eRkRFatGiB6dOnS78bAJCZmamwjRo0aKDyvXl4eKBGjRowMTHB6tWrkZycrDBNUlIS/Pz8kJeXJ/1OvsjIyEhh29PbiYUMaY0OHTrAz88PISEhSuN0dHSUDj2o6o7X09NTGJbJZCrb5HK5NNy5c2dcu3YNt2/fxtGjR9GxY0cA/1fI3LhxAykpKdKJvjk5OfDw8EBcXJzC69q1axg6dKha77lz586Ii4tDYmIinjx5Ih3yUec9v/iH18jI6JXrfdk2uXnzJnr16oXGjRtj+/btiI2NRUREBID/O5l49OjR+Pfff/Hhhx/i8uXLaNGiBZYtWwbg2fbp3bu30vZJTExEhw4doKuri4MHD2Lv3r2oX78+li1bBldXV+kw3ouqV68OAGU+GTwnJwd2dnZKeRISEvDpp59K0925cwc6OjqwtbUtcTkzZ85UWMbly5eRmJioUGQtWrQIcXFxOHv2LGxtbRXOaSqtR48eYdasWVi4cOEbuXrvRQsWLICrq2uJ5yWFhYUhNjYWixcvxsKFCxUKMVNTU4Vt9Py5U8Cz850++eQTjBo1CgcOHEBcXBxGjBihdEj10qVLGD16NPz9/TFy5EiF39liDx8+RI0aNcrhHVNlplxKE1Vic+fORdOmTeHq6qrQXqNGDaSmpkIIIe3YX3byozratm0LfX19rFixAnl5efDw8AAAtGzZEvfv38fatWthbGyMVq1aAQCaN2+OX375BdbW1jAzMytxuRcvXsSTJ0+kwuLMmTMwMTGBg4ODNI2xsXGJ9wmpUaMG7t69Kw0XFRUhPj5e4b9fVRo3boyoqKhXno9TktjYWMjlcnz33XdS75aq8xccHBwwfvx4jB8/HiEhIfjhhx8wceJENG/eHNu3b4eTk5PK/+aBZ4WTl5cXvLy88PXXX8PR0RE7d+5EcHCw0rR16tSBmZkZrly5gnr16qn9fpo3b47U1FRUqVJF4eTwF8XExMDNza3Enp/mzZsjISHhlfd1sbW1laaZOHEi+vTpg8LCQri7u+Pp06eIjo5G27ZtAQAPHjxAQkIC6tevr7CMWbNmoX379ujQoYPKk8jLKjo6WmH4zJkzcHFxga6urtR29+5drFy5EseOHStxOdbW1rC2tkb9+vWxZs0a/PHHH3B3dwfwrAB/fhu9+B04deoU2rZti48//lhqe75nrFiHDh0QHh6OzMxMNGzYEEuWLMGUKVMUpomPjy+xt4beHuyRIa3SqFEj+Pv7Y+nSpQrtnTp1wv379zF//nzcuHEDERERClcwvA4jIyO0adMGy5Ytg5eXl7RT19fXV2gv7sXw9/dH9erV0bdvX5w4cQJJSUk4evQoJk2apHCyckFBAUaNGoUrV67gzz//RGhoKCZMmKBw6OtlvL298ccff+CPP/7AP//8g48++qhU90YJCQlBTEwMPv74Y1y6dAn//PMPVq5cqXDV0MvUrVsXhYWFWLZsGf79919s2LABq1atUpgmKCgI+/fvR1JSEs6fP48jR45If8gCAwPx8OFDDBkyBDExMbhx4wb279+PESNGoKioCNHR0ZgzZw7OnTuH5ORk7NixA/fv35fmf5GOjg58fX1x8uRJpXGZmZlKPS3Xr18HAFy+fBk5OTnw9fWFp6cn+vXrhwMHDuDmzZv466+/8NVXX+HcuXMoKCjAhg0bsHDhQowYMaLE7fL111/jp59+wsyZM/H333/j6tWr2LJlC6ZNm6YwXUZGBlJTU5GQkIA1a9agdu3a0NPTg4uLC/r27YsxY8bg5MmTuHjxIj744AO888476Nu3rzT/48ePsXr1asyfP79Un5c6kpOTERwcjISEBGzevBnLli3D5MmTFaaJiIjAu+++i2bNminNn5GRgXXr1uGff/7Bv//+i6VLl+Ly5csqpy2Ji4sLzp07h/379+PatWuYPn06YmJilKarVq0aAMDc3ByrV6/GtGnTFK7wevz4MWJjY9G1a9dSr5u0EwsZ0jphYWFK3cju7u5YsWIFIiIi0KRJE5w9exaffPJJua2zc+fOyM7OVvrvrmPHjsjOzlboBalatSqOHz+OWrVqoX///nB3d8eoUaOQl5en0EPj4+MDFxcXdOjQAe+//z769Omj1vkvI0eOREBAAIYNG4aOHTuidu3ar+yNAYB69erhwIEDuHjxIlq1agVPT0/s3r27xN6RFzVp0gQLFy7EvHnz0LBhQ2zcuFHp0viioiIEBgbC3d0d3bp1Q7169bBixQoAgL29PU6dOoWioiJ07doVjRo1QlBQECwsLKCjowMzMzMcP34cPXr0QL169TBt2jR89913SlekPG/06NHYsmWL0vfi6NGjaNasmcJrzJgxAJ79R3/u3DnIZDL8+eef6NChA0aMGIF69eph8ODBuHXrFmxsbHD58mXMmDED06dPV9kjVMzPzw979uzBgQMH0LJlS7Rp0waLFi1SurfNiBEjYGdnh5YtW+LRo0fYtm2bNC4yMhIeHh7o1asXPD09IYTAn3/+qXCor7CwUMpZ3oYNG4YnT56gVatWCAwMxOTJk5XuliyXy5WuxComhMC6devg6emJhg0bYvXq1Vi5cuUrz8l63rhx49C/f3+8//77aN26NR48eKDQO6NK9+7dMXjwYIVDTLt370atWrXK9SaVVDnJxIsH2YmItIwQAq1bt8aUKVMwZMiQUs3j5OSEdevW8dDD/9epUyc0bdpU4T4x2qxNmzaYNGmS2uelkfZhjwwRaT2ZTIbVq1er9fiG+vXrK90Dhd4O6enp6N+/f6mLWtJu7JEhIqK3rkeG/newkCEiIiKtxUNLREREpLVYyBAREZHWYiFDREREWouFDBEREWktFjJERESktVjIEBERkdZiIUNERERai4UMERERaS0WMkRERKS1/h//q6Pr96K+BwAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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EJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwwyREREJFsMMkRERCRbDDJEREQkWwa6LoCoot1c56rrEuAyMknXJRARvZY4IkNERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESypfVPFNjY2BQ7/fHjx2UuhoiIiEgbpQoy/fv3x8SJE9GmTRsIIaBUKjFlyhS4uur+N2yIiIjov6tUQWbo0KHo1asXUlNTcePGDcyePRtLlizBuHHjMHPmTFhaWlZ0nURERERqSnWMTI8ePZCdnY3Hjx/DxsYGERERiImJwfXr11GvXj2sWLEC+fn5FV0rERERkUSpgszYsWPRpEkT2Nvbq9rq16+PnTt3YseOHdi0aRMaNGiAXbt2VVSdRERERGpKtWupd+/eiIiIAAD069dPbXqNGjVw9epVvP322xyZISIion9NqYLMm2++qfq7qONh3nnnnfKpiIiIiKiUtD79OioqqiLqICIiItJamS6I9+LFCxw8eBBfffUVMjMzAQB3795FVlZWuRZHREREVBytR2Ru3bqFbt264fbt28jNzUWXLl1gbm6OhQsXIjc3F2vWrKmIOomIiIjUaD0iM2nSJDRv3hxPnjyBiYmJqv2tt95CdHR0uRZHREREVBytR2SOHTuGP/74A4aGhpJ2FxcX3Llzp9wKIyIiIiqJ1iMySqVS4ynWf//9N8zNzculKCIiIqLS0DrIdO3aFcuWLVPdVygUyMrKQmhoKHr06KF1AXfu3MH7778PW1tbmJiYwNvbG+fOnVNNF0Jg1qxZcHR0hImJCfz9/ZGYmKj1eoiIiOj1o3WQWbJkCU6cOIEGDRogJycHgwYNUu1WWrhwoVZ9PXnyBH5+fqhSpQr27NmDy5cvY8mSJbC2tlbNs2jRIkRERGDNmjU4ffo0TE1NERAQgJycHG1LJyIioteM1sfI1KxZE3Fxcfj+++8RFxeHrKwsjBw5EoGBgZKDf0tj4cKFqFWrluTaNIV/UVsIgWXLlmHmzJno06cPAGDTpk2wt7fHrl27MGDAAG3LJyIioteI1kEGAAwMDBAYGIjAwMB/tPKffvoJAQEBePfdd3HkyBHUqFEDH3zwAUaPHg0ASEpKwv379+Hv769axtLSEq1atcLJkyc1Bpnc3Fzk5uaq7mdkZPyjGomIiKjy0nrXUlhYGNavX6/Wvn79eq13Lf31119YvXo13NzcsG/fPowfPx4TJ07Exo0bAQD3798HAMmPVRbcL5imqT5LS0vVrVatWlrVRERERPKhdZD56quv4OHhodbu5eWl9cXwlEolmjVrhgULFqBp06YYM2YMRo8e/Y8uqhccHIz09HTVLTk5ucx9ERERUeWmdZC5f/8+HB0d1dqrV6+Oe/fuadWXo6MjGjRoIGnz9PTE7du3AQAODg4AgJSUFMk8KSkpqmmvMjIygoWFheRGREREryetg0ytWrVw4sQJtfYTJ07AyclJq778/PyQkJAgabt27RqcnZ0BvDzw18HBQXLF4IyMDJw+fRq+vr7alk5ERESvGa0P9h09ejQmT56M58+fo1OnTgCA6OhozJgxA9OmTdOqrylTpqB169ZYsGAB+vfvjzNnzmDt2rVYu3YtgJfXqJk8eTI+//xzuLm5wdXVFSEhIXByckLfvn21LZ2IiIheM1oHmenTp+PRo0f44IMPkJeXBwAwNjbGxx9/jODgYK36atGiBXbu3Ing4GDMnTsXrq6uWLZsmeRsqBkzZiA7OxtjxoxBWloa2rRpg71798LY2Fjb0omIiOg1oxBCiLIsmJWVhStXrsDExARubm4wMjIq79rKRUZGBiwtLZGeni6r42VurnMteaYK5jIyqcR55FCnHGokIiKp0v7/LtN1ZADAzMwMLVq0KOviRERERP+Y1kGmX79+xU7/8ccfy1wMERERkTa0DjK7du2Cubk5+vTpA319/YqoiYiIiKhUtA4yBw4cwLRp0xATE4NFixahZ8+eFVEXERERUYm0vo5M586dceHCBXz00UcYO3Ys/P39cfHixYqojYiIiKhYWgcZ4OX1XYYPH47ExES0a9cO7dq1w4gRI3D37t3yro+IiIioSFrvWoqIiJDct7Kywrhx4xAZGYnt27cjMzOz3IojIiIiKo7WQWbp0qUa26tVq/aPiyEiIiLShtZBJimJF/YiIiKiyqFMx8gQERERVQZaj8hMnTq12Onh4eFlLoaIiIhIG1oHmWXLlsHX1xeGhoZq0xQKRbkURURERFQaZfqtpZ07d8LOzq68ayEiIiLSCo+RISIiItlikCEiIiLZKtOupX379sHS0lLjtN69e/+jgoiIiIhKq0xBZujQoRrbFQoF8vPz/1FBRERERKWldZBRKpUVUQcRERGR1niMDBEREclWmXYtZWdn48iRI7h9+zby8vIk0yZOnFguhRERERGVROsgc+HCBfTo0QNPnz5FdnY2bGxs8PDhQ1StWhV2dnYMMkRERPSv0XrX0pQpU9CrVy88efIEJiYmOHXqFG7dugUfHx98+eWXFVEjERERkUZaB5nY2FhMmzYNenp60NfXR25uLmrVqoVFixbh008/rYgaiYiIiDTSOshUqVIFenovF7Ozs8Pt27cBAJaWlkhOTi7f6oiIiIiKofUxMk2bNsXZs2fh5uaG9u3bY9asWXj48CE2b96Mhg0bVkSNRERERBppPSKzYMECODo6AgDmz58Pa2trjB8/Hg8ePMDatWvLvUAiIiKiomg9ItO8eXPV33Z2dti7d2+5FkRERERUWmW6IN7z58/x9OlT1d/nz59HZmZmuRZGREREVBKtg8zevXthZWUFe3t77N+/H82bN0fz5s1Rs2ZNnDhxoiJqJCIiItJI6yAzc+ZMTJw4EeHh4Rg0aBD8/PyQlpaG/v37Y+bMmRVRIxEREZFGWgeZy5cvY+zYsRg9ejQyMzMxbtw4WFhYYPLkyYiLi6uIGomIiIg00jrIGBoaIj8/HwDg5uYGa2trAEDVqlXx/Pnz8q2OiIiIqBhaBxl3d3f8+eefAID4+HjUqlULwMuRGjc3t/KtjoiIiKgYWp9+vX//fhgaGqq116hRA5GRkeVSFBEREVFpaB1kLC0tNbY3adLkn9ZCREREpJUyXUeGiIiIqDJgkCEiIiLZYpAhIiIi2WKQISIiItnS+mBfAMjPz8euXbtw5coVAICXlxd69+4NfX39ci2OiIiIqDhaB5nr16+jZ8+e+Pvvv+Hu7g4ACAsLQ61atfDrr7+ibt265V4kERERkSZa71qaOHEi6tSpg+TkZJw/fx7nz5/H7du34erqiokTJ1ZEjUREREQaaT0ic+TIEZw6dQo2NjaqNltbW3zxxRfw8/Mr1+KIiIiIiqP1iIyRkREyMzPV2rOysjRe8ZeIiIioomgdZN58802MGTMGp0+fhhACQgicOnUK48aNQ+/evSuiRiIiIiKNtA4yERERqFu3Lnx9fWFsbAxjY2P4+fmhXr16WL58eUXUSERERKSR1sfIWFlZYffu3UhMTMTVq1cBAJ6enqhXr165F0dERERUnDJdRwYA3Nzc4ObmBuDldWWIiIiI/m1a71pKSkrCwIEDMX78eDx58gS9e/eGkZER3N3dcfHixYqokYiIiEgjrYPM2LFjceXKFcTHx6NTp07Iy8vD7t270aBBA0yePLkCSiQiIiLSTOtdS6dPn8axY8fg7OwMGxsbnD17Fs2aNUO9evXQqlWriqiRiIiISCOtR2QyMzPh6OgIS0tLVK1aFVZWVgBeHgSs6foyRERERBWlTAf77t27F5aWllAqlYiOjkZ8fDzS0tLKuTQiIiKi4pUpyAwdOlT199ixY1V/KxSKf14RERERUSlpHWSUSmVF1EFERESkNa2Pkdm0aRNyc3MrohYiIiIirWgdZIYPH4709PSKqIWIiIhIK1oHGSFERdRBREREpLUyHey7bds2WFhYaJw2ZMiQf1QQERERUWmVKcgsWrQI+vr6au0KhYJBhoiIiP41ZQoy586dg52dXXnXQkRERKSVMv/6NRGVn5vrXHVdAlxGJum6BCIirWl9sK+zs7PG3UpERERE/zatR2SSkvitjYiIiCoHrUdkJk6ciIiICLX2lStXYvLkyeVRExEREVGpaB1kduzYAT8/P7X21q1b44cffiiXooiIiIhKQ+sg8+jRI1haWqq1W1hY4OHDh+VSFBEREVFpaB1k6tWrh71796q179mzB3Xq1CmXooiIiIhKQ+uDfadOnYoJEybgwYMH6NSpEwAgOjoaS5YswbJly8q7PiIiIqIiaT0iM2LECCxZsgTr1q1Dx44d0bFjR3zzzTdYvXo1Ro8eXeZCvvjiCygUCskBwzk5OQgKCoKtrS3MzMzw9ttvIyUlpczrICIioteL1kEGAMaPH4+///4bKSkpyMjIwF9//fWPfprg7Nmz+Oqrr9CoUSNJ+5QpU/Dzzz9j+/btOHLkCO7evYt+/fqVeT1ERET0eilTkHnx4gUOHjyIH3/8UfVr2Hfv3kVWVpbWfWVlZSEwMBBff/01rK2tVe3p6elYt24dwsPD0alTJ/j4+CAqKgp//PEHTp06VZayiYiI6DWjdZC5desWvL290adPHwQFBeHBgwcAgIULF+Kjjz7SuoCgoCD07NkT/v7+kvaYmBg8f/5c0u7h4YHatWvj5MmTRfaXm5uLjIwMyY2IiIheT1of7Dtp0iQ0b94ccXFxsLW1VbW/9dZbWh8js3XrVpw/fx5nz55Vm3b//n0YGhrCyspK0m5vb4/79+8X2WdYWBjmzJmjVR1EVDpy+E0oOdRIROVH6yBz7Ngx/PHHHzA0NJS0u7i44M6dO6XuJzk5GZMmTcKBAwdgbGysbRlFCg4OxtSpU1X3MzIyUKtWrXLrn4iIiCoPrXctKZVK5Ofnq7X//fffMDc3L3U/MTExSE1NRbNmzWBgYAADAwMcOXIEERERMDAwgL29PfLy8pCWliZZLiUlBQ4ODkX2a2RkBAsLC8mNiIiIXk9aB5muXbtKrhejUCiQlZWF0NBQ9OjRo9T9dO7cGZcuXUJsbKzq1rx5cwQGBqr+rlKlCqKjo1XLJCQk4Pbt2/D19dW2bCIiInoNab1racmSJQgICECDBg2Qk5ODQYMGITExEdWqVcOWLVtK3Y+5uTkaNmwoaTM1NYWtra2qfeTIkZg6dSpsbGxgYWGBDz/8EL6+vnjjjTe0LZuIiIheQ1oHmZo1ayIuLg5bt27FxYsXkZWVhZEjRyIwMBAmJiblWtzSpUuhp6eHt99+G7m5uQgICMCqVavKdR1EREQkX1oHGQAwMDDA+++/X9614PDhw5L7xsbGiIyMRGRkZLmvi4iIiORP6yDz008/FTu9d+/eZS6GiIiISBtaB5m+fftK7isUCtXVfRUKhcYzmoiIiIgqQplOvy58q1q1Kq5fv17kadlEREREFaVMv7VUmEKhKI86iIiIiLT2j4LMzZs3kZ2drdWF8IiIiIjKi9bHyPTr1w8A8OzZM5w6dQqdO3dG9erVy70wIiIiopJoHWQsLS0BAA4ODujVqxdGjBhR7kURERERlYbWQSYqKqoi6iAiIiLSmtZBJiMjo9jp/JFGIiIi+rdoHWSsrKw0nqkkhOB1ZIiIiOhfpXWQqVOnDlJTU/HJJ5/Az8+vImoiIiIiKhWtg8yVK1ewYsUKzJ8/HxcuXMCiRYvg6upaEbURERERFUvr68hUqVIFU6dORWJiImrUqIFGjRph2rRpSEtLq4DyiIiIiIpWpl+/BgAbGxssW7YMEyZMwMcff4x69eph5syZmDx5cjmWR0T0+rm5Tvej2C4jk3RdAlG50DrING3aVO1gXyEEcnNzMW3aNAYZIiIi+tf841+/JiIiItIVrYNMaGhoRdRBREREpDVeEI+IiIhkixfEIyIiItkq01lLP/zwA2xsbMq7FiIiIiKtlCnI+Pn5wc7OrrxrISIiItJKmYLM5cuX8ejRI5iamsLBwQGGhoblXRcRERFRibS+si8AdO7cGV5eXnB1dYWpqSm8vb2xdOnS8q6NiIiIqFhaj8gkJSVBCIHnz58jIyMDd+/exZkzZxASEoIXL15g+vTpFVEnERERkRqtg4yzs7Pkvo+PD3r16oX69etj7ty5DDJERET0rynzby29asCAAfDy8iqv7oiIiIhKVOYgExMTgytXrgAAGjRogGbNmqFZs2blVhgRERFRSbQOMqmpqRgwYAAOHz4MKysrAEBaWho6duyIrVu3onr16uVdIxEREZFGWp+19OGHHyIzMxN//vknHj9+jMePHyM+Ph4ZGRmYOHFiRdRIREREpJHWIzJ79+7FwYMH4enpqWpr0KABIiMj0bVr13ItjoiIiKg4Wo/IKJVKVKlSRa29SpUqUCqV5VIUERERUWloHWQ6deqESZMm4e7du6q2O3fuYMqUKejcuXO5FkdERERUHK2DzMqVK5GRkQEXFxfUrVsXdevWhaurKzIyMrBixYqKqJGIiIhII62PkalVqxbOnz+PgwcP4urVqwAAT09P+Pv7l3txRERERMUpdZDJzMyEubk5AEChUKBLly7o0qWLZJ6zZ8+iRYsW5VshERERURFKvWupa9euyMrK0jjtxYsXmDlzJvz8/MqtMCIiIqKSlDrIZGZmwt/fHxkZGZL2+Ph4tGjRAhs2bMCuXbvKuz4iIiKiIpU6yBw6dAjZ2dno0qULMjIyIITAwoUL0bx5c3h6euLSpUvo0aNHRdZKREREJFHqY2SqV6+O33//Hf7+/ujUqROMjIyQmJiIb775Bu+8805F1khERESkkVZnLVWvXh3R0dHw9/dHfHw8YmNj4eHhUVG1ERERERVL6+vIVKtWDb///jsaNGiAQYMG4cmTJxVRFxEREVGJSj0i069fP8l9CwsLHD16FC1btoS3t7eq/ccffyy/6oiIiIiKUeogY2lpqXbf1dW13AsiIiIiKq1SB5moqKiKrIOIiIhIa1ofI0NERERUWTDIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbDHIEBERkWwxyBAREZFsMcgQERGRbOk0yISFhaFFixYwNzeHnZ0d+vbti4SEBMk8OTk5CAoKgq2tLczMzPD2228jJSVFRxUTERFRZaLTIHPkyBEEBQXh1KlTOHDgAJ4/f46uXbsiOztbNc+UKVPw888/Y/v27Thy5Aju3r2Lfv366bBqIiIiqiwMdLnyvXv3Su5v2LABdnZ2iImJQbt27ZCeno5169bhu+++Q6dOnQAAUVFR8PT0xKlTp/DGG2/oomwiIiKqJCrVMTLp6ekAABsbGwBATEwMnj9/Dn9/f9U8Hh4eqF27Nk6ePKmxj9zcXGRkZEhuRERE9HqqNEFGqVRi8uTJ8PPzQ8OGDQEA9+/fh6GhIaysrCTz2tvb4/79+xr7CQsLg6WlpepWq1atii6diIiIdKTSBJmgoCDEx8dj69at/6if4OBgpKenq27JycnlVCERERFVNjo9RqbAhAkT8Msvv+Do0aOoWbOmqt3BwQF5eXlIS0uTjMqkpKTAwcFBY19GRkYwMjKq6JKJiIioEtDpiIwQAhMmTMDOnTvx+++/w9XVVTLdx8cHVapUQXR0tKotISEBt2/fhq+v779dLhEREVUyOh2RCQoKwnfffYfdu3fD3NxcddyLpaUlTExMYGlpiZEjR2Lq1KmwsbGBhYUFPvzwQ/j6+vKMJSIiItJtkFm9ejUAoEOHDpL2qKgoDBs2DACwdOlS6Onp4e2330Zubi4CAgKwatWqf7lSIiIiqox0GmSEECXOY2xsjMjISERGRv4LFREREZGcVJqzloiIiIi0xSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLJVKX79moiIKp+b61xLnqmCuYxM0nUJVMlxRIaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGSLQYaIiIhki0GGiIiIZItBhoiIiGTLQNcFEBERldXNda66LgEuI5OKnS6HGuWMIzJEREQkWwwyREREJFsMMkRERCRb/8ljZLi/koiI6PXAERkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0DXRdAREREundznauuS4DLyCStl+GIDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyRaDDBEREckWgwwRERHJFoMMERERyZYsgkxkZCRcXFxgbGyMVq1a4cyZM7ouiYiIiCqBSh9kvv/+e0ydOhWhoaE4f/48GjdujICAAKSmpuq6NCIiItKxSh9kwsPDMXr0aAwfPhwNGjTAmjVrULVqVaxfv17XpREREZGOGei6gOLk5eUhJiYGwcHBqjY9PT34+/vj5MmTGpfJzc1Fbm6u6n56ejoAICMjQ9WW+UxZQRWXXuF6NJFDjYA86mSNpcPnu/y8DjUC8qiTNZaOHJ/vgr+FEMUvJCqxO3fuCADijz/+kLRPnz5dtGzZUuMyoaGhAgBvvPHGG2+88fYa3JKTk4vNCpV6RKYsgoODMXXqVNV9pVKJx48fw9bWFgqFolzWkZGRgVq1aiE5ORkWFhbl0md5Y43lRw51ssbyI4c6WWP5kUOd/9UahRDIzMyEk5NTsfNV6iBTrVo16OvrIyUlRdKekpICBwcHjcsYGRnByMhI0mZlZVUh9VlYWFTaF1UB1lh+5FAnayw/cqiTNZYfOdT5X6zR0tKyxHkq9cG+hoaG8PHxQXR0tKpNqVQiOjoavr6+OqyMiIiIKoNKPSIDAFOnTsXQoUPRvHlztGzZEsuWLUN2djaGDx+u69KIiIhIxyp9kHnvvffw4MEDzJo1C/fv30eTJk2wd+9e2Nvb66wmIyMjhIaGqu3CqkxYY/mRQ52ssfzIoU7WWH7kUCdrLJ5CiJLOayIiIiKqnCr1MTJERERExWGQISIiItlikCEiIiLZYpAhIiIi2WKQ0VJkZCRcXFxgbGyMVq1a4cyZM7ouSeLo0aPo1asXnJycoFAosGvXLl2XpCYsLAwtWrSAubk57Ozs0LdvXyQkJOi6LInVq1ejUaNGqos7+fr6Ys+ePbouq1hffPEFFAoFJk+erOtSJGbPng2FQiG5eXh46LosNXfu3MH7778PW1tbmJiYwNvbG+fOndN1WRIuLi5q21KhUCAoKEjXpank5+cjJCQErq6uMDExQd26dTFv3rySfy/nX5aZmYnJkyfD2dkZJiYmaN26Nc6ePavTmkr6/BZCYNasWXB0dISJiQn8/f2RmJhYqWr88ccf0bVrV9XV9GNjYyu8JgYZLXz//feYOnUqQkNDcf78eTRu3BgBAQFITU3VdWkq2dnZaNy4MSIjI3VdSpGOHDmCoKAgnDp1CgcOHMDz58/RtWtXZGdn67o0lZo1a+KLL75ATEwMzp07h06dOqFPnz74888/dV2aRmfPnsVXX32FRo0a6boUjby8vHDv3j3V7fjx47ouSeLJkyfw8/NDlSpVsGfPHly+fBlLliyBtbW1rkuTOHv2rGQ7HjhwAADw7rvv6riy/7Nw4UKsXr0aK1euxJUrV7Bw4UIsWrQIK1as0HVpEqNGjcKBAwewefNmXLp0CV27doW/vz/u3Lmjs5pK+vxetGgRIiIisGbNGpw+fRqmpqYICAhATk5OpakxOzsbbdq0wcKFC/+1mir1j0ZWNi1bthRBQUGq+/n5+cLJyUmEhYXpsKqiARA7d+7UdRklSk1NFQDEkSNHdF1KsaytrcX//vc/XZehJjMzU7i5uYkDBw6I9u3bi0mTJum6JInQ0FDRuHFjXZdRrI8//li0adNG12VobdKkSaJu3bpCqVTquhSVnj17ihEjRkja+vXrJwIDA3VUkbqnT58KfX198csvv0jamzVrJj777DMdVSX16ue3UqkUDg4OYvHixaq2tLQ0YWRkJLZs2aKDCov/H5OUlCQAiAsXLlR4HRyRKaW8vDzExMTA399f1aanpwd/f3+cPHlSh5XJX3p6OgDAxsZGx5Volp+fj61btyI7O7tS/jRGUFAQevbsKXltVjaJiYlwcnJCnTp1EBgYiNu3b+u6JImffvoJzZs3x7vvvgs7Ozs0bdoUX3/9ta7LKlZeXh6++eYbjBgxotx+ELc8tG7dGtHR0bh27RoAIC4uDsePH0f37t11XNn/efHiBfLz82FsbCxpNzExqXSjhQWSkpJw//59yfvc0tISrVq1+s//D6r0V/atLB4+fIj8/Hy1Kwrb29vj6tWrOqpK/pRKJSZPngw/Pz80bNhQ1+VIXLp0Cb6+vsjJyYGZmRl27tyJBg0a6Losia1bt+L8+fM637dfnFatWmHDhg1wd3fHvXv3MGfOHLRt2xbx8fEwNzfXdXkAgL/++gurV6/G1KlT8emnn+Ls2bOYOHEiDA0NMXToUF2Xp9GuXbuQlpaGYcOG6boUiU8++QQZGRnw8PCAvr4+8vPzMX/+fAQGBuq6NBVzc3P4+vpi3rx58PT0hL29PbZs2YKTJ0+iXr16ui5Po/v37wOAxv9BBdP+qxhkSKeCgoIQHx9fKb8Fubu7IzY2Funp6fjhhx8wdOhQHDlypNKEmeTkZEyaNAkHDhxQ+2ZZmRT+Jt6oUSO0atUKzs7O2LZtG0aOHKnDyv6PUqlE8+bNsWDBAgBA06ZNER8fjzVr1lTaILNu3Tp0794dTk5Oui5FYtu2bfj222/x3XffwcvLC7GxsZg8eTKcnJwq1bbcvHkzRowYgRo1akBfXx/NmjXDwIEDERMTo+vSSEvctVRK1apVg76+PlJSUiTtKSkpcHBw0FFV8jZhwgT88ssvOHToEGrWrKnrctQYGhqiXr168PHxQVhYGBo3bozly5fruiyVmJgYpKamolmzZjAwMICBgQGOHDmCiIgIGBgYID8/X9clamRlZYX69evj+vXrui5FxdHRUS2genp6VrpdYAVu3bqFgwcPYtSoUbouRc306dPxySefYMCAAfD29sbgwYMxZcoUhIWF6bo0ibp16+LIkSPIyspCcnIyzpw5g+fPn6NOnTq6Lk2jgv8z/B+kjkGmlAwNDeHj44Po6GhVm1KpRHR0dKU8bqIyE0JgwoQJ2LlzJ37//Xe4urrquqRSUSqVyM3N1XUZKp07d8alS5cQGxurujVv3hyBgYGIjY2Fvr6+rkvUKCsrCzdu3ICjo6OuS1Hx8/NTuwTAtWvX4OzsrKOKihcVFQU7Ozv07NlT16Woefr0KfT0pP9a9PX1oVQqdVRR8UxNTeHo6IgnT55g37596NOnj65L0sjV1RUODg6S/0EZGRk4ffr0f/5/EHctaWHq1KkYOnQomjdvjpYtW2LZsmXIzs7G8OHDdV2aSlZWluSbblJSEmJjY2FjY4PatWvrsLL/ExQUhO+++w67d++Gubm5av+upaUlTExMdFzdS8HBwejevTtq166NzMxMfPfddzh8+DD27dun69JUzM3N1Y4rMjU1ha2tbaU63uijjz5Cr1694OzsjLt37yI0NBT6+voYOHCgrktTmTJlClq3bo0FCxagf//+OHPmDNauXYu1a9fqujQ1SqUSUVFRGDp0KAwMKt9HeK9evTB//nzUrl0bXl5euHDhAsLDwzFixAhdlyaxb98+CCHg7u6O69evY/r06fDw8NDp53lJn9+TJ0/G559/Djc3N7i6uiIkJAROTk7o27dvpanx8ePHuH37Nu7evQsAqi8IDg4OFTdyVOHnRb1mVqxYIWrXri0MDQ1Fy5YtxalTp3RdksShQ4cEALXb0KFDdV2aiqb6AIioqChdl6YyYsQI4ezsLAwNDUX16tVF586dxf79+3VdVokq4+nX7733nnB0dBSGhoaiRo0a4r333hPXr1/XdVlqfv75Z9GwYUNhZGQkPDw8xNq1a3Vdkkb79u0TAERCQoKuS9EoIyNDTJo0SdSuXVsYGxuLOnXqiM8++0zk5ubqujSJ77//XtSpU0cYGhoKBwcHERQUJNLS0nRaU0mf30qlUoSEhAh7e3thZGQkOnfu/K+/DkqqMSoqSuP00NDQCqtJIUQlu9wiERERUSnxGBkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIiIiki0GGSIiIpItBhkiIiKSLQYZIh0YNmzYv3o1Tm106NABkydP1nUZWgsJCcGYMWN0XUaFy8jIQJMmTZCVlYW///670v5asy49fPgQdnZ2+Pvvv3VdCv0LGGSoUhs2bBgUCgW++OILSfuuXbugUCgqfP1vvPEGxo0bJ2lbs2YNFAoFNmzYIGkfNmwY2rZtW27rLnjsCoVC9QOWc+fOxYsXL8ptHa+L+/fvY/ny5fjss89UbUWFxcOHD0OhUCAtLe3fK7AcWVhYoE2bNrCysoKLiwvGjx+v65IqnWrVqmHIkCEIDQ3VdSn0L2CQoUrP2NgYCxcuxJMnT/71dXfs2BGHDx+WtB06dAi1atVSaz98+DA6depUruvv1q0b7t27h8TEREybNg2zZ8/G4sWLy9xfXl5eOVZXefzvf/9D69atK+2PPJa3lStXIjU1FY8fP8a0adN0XU6lNHz4cHz77bd4/PixrkuhCsYgQ5Wev78/HBwcEBYWVuQ8s2fPRpMmTSRty5Ytg4uLi+p+wTf0BQsWwN7eHlZWVqoRjunTp8PGxgY1a9ZEVFSUapmOHTsiISFB9cOWAHDkyBF88sknkiCTlJSEW7duoWPHjgCA5ORk9O/fH1ZWVrCxsUGfPn1w8+ZNtbrnzJmD6tWrw8LCAuPGjVMLGkZGRnBwcICzszPGjx8Pf39//PTTTwA07wLq27cvhg0bprrv4uKCefPmYciQIbCwsFDtejlx4gQ6dOiAqlWrwtraGgEBAZKgqFQqMWPGDNjY2MDBwQGzZ8+WrCc8PBze3t4wNTVFrVq18MEHHyArK0s1/datW+jVqxesra1hamoKLy8v/Pbbb6rp8fHx6N69O8zMzGBvb4/Bgwfj4cOHquk//PADvL29YWJiAltbW/j7+yM7O1tt+xXYunUrevXqVeT0kqSlpWHUqFGq56JTp06Ii4uTzHPz5k3VCFnhW+GRnd27d6NZs2YwNjZGnTp1MGfOHMkIWuHlLCws0KVLF9y4cUM1/cmTJxgyZAisra1RtWpVdO/eHYmJiarpGzZsgJWVFQDAxsYGFhYWaNeuHRQKBWJjY4t8fC4uLhprLzxi1aFDB0yYMAETJkyApaUlqlWrhpCQEBT+FRsXFxcsW7ZMdT86Olqtn8GDB8POzg5GRkaoU6cOvvzyS431v7pdC+rPz8/HyJEj4erqChMTE7i7u2P58uWSZV4dbduzZw/MzMywZ88eVZuXlxecnJywc+fOIrcLvR4YZKjS09fXx4IFC7BixYp/vM/7999/x927d3H06FGEh4cjNDQUb775JqytrXH69GmMGzcOY8eOVa3Hz88PVapUwaFDhwAAly9fxrNnzzBy5Eg8evQISUlJAF6O0hgbG8PX1xfPnz9HQEAAzM3NcezYMZw4cQJmZmbo1q2bJKhER0fjypUrOHz4MLZs2YIff/wRc+bMKbZ+ExMTrUdVvvzySzRu3BgXLlxASEgIYmNj0blzZzRo0AAnT57E8ePH0atXL+Tn56uW2bhxI0xNTXH69GksWrQIc+fOxYEDB1TT9fT0EBERgT///BMbN27E77//jhkzZqimBwUFITc3F0ePHsWlS5ewcOFCmJmZAXgZGjp16oSmTZvi3Llz2Lt3L1JSUtC/f38AwL179zBw4ECMGDFCtX369euHon4W7vHjx7h8+TKaN2+u1XYp7N1330Vqair27NmDmJgYNGvWDJ07d5Z8my9Y/8GDB3Hv3j3s2LFD0sexY8cwZMgQTJo0CZcvX8ZXX32FDRs2YP78+ZL5oqKicO/ePRw9ehSpqan49NNPVdOGDRuGc+fO4aeffsLJkychhECPHj3w/PlzjXX/+OOPuHDhQqke49y5c3Hv3j3VrWB7F7Zx40YYGBjgzJkzWL58OcLDw/G///1PY39KpRLTpk1TPa8FBgwYgIMHDyIxMRHz589HcHAwjh49WqoaC/qtWbMmtm/fjsuXL2PWrFn49NNPsW3bNo3zHzt2DP3798e6devQvXt3ybSWLVvi2LFjpV43yVSF/RwlUTkYOnSo6NOnjxBCiDfeeEOMGDFCCCHEzp07ReGXb2hoqGjcuLFk2aVLlwpnZ2dJX87OziI/P1/V5u7uLtq2bau6/+LFC2Fqaiq2bNmiavPz8xNjxowRQggRGRkpevToIYQQomvXrmL9+vVCCCEGDx4sOnbsKIQQYvPmzcLd3V0olUpVH7m5ucLExETs27dPVYuNjY3Izs5WzbN69WphZmamqq/wY1cqleLAgQPCyMhIfPTRR0IIzb903adPH8kvnTs7O4u+fftK5hk4cKDw8/MTRWnfvr1o06aNpK1Fixbi448/LnKZ7du3C1tbW9V9b29vMXv2bI3zzps3T3Tt2lXSlpycrPpF55iYGAFA3Lx5s8j1FXbhwgUBQNy+fVvSXnj7FVbw671PnjwRQghx7NgxYWFhIXJyciTz1a1bV3z11Veq+wkJCQKAiI+P19hP586dxYIFCyR9bN68WTg6OqruAxA7d+4UQgiRlpYm/Pz8xOjRo4UQQly7dk0AECdOnFDN//DhQ2FiYiK2bdsmhHj5y8KWlpZCCCHy8vJEvXr1xLx58wQAceHChSK3kbOzs1i6dGmx26d9+/bC09NT8rr9+OOPhaenp8Z+1q9fLzw8PERgYKDG7SzEy+fG2NhY/P7772r1F0hKSiqx/qCgIPH222+r1R4TEyMsLS0lz1NhU6ZMER06dCiyX3o9cESGZGPhwoXYuHEjrly5UuY+vLy8oKf3fy97e3t7eHt7q+7r6+vD1tYWqampqrYOHTqodiMdPnwYHTp0AAC0b99e0l6wWykuLg7Xr1+Hubk5zMzMYGZmBhsbG+Tk5Eh2IzRu3BhVq1ZV3ff19UVWVhaSk5NVbb/88gvMzMxgbGyM7t2747333lPbzVOSV0cqCkZkitOoUSPJfUdHR8k2OXjwIDp37owaNWrA3NwcgwcPxqNHj/D06VMAwMSJE/H555/Dz88PoaGhuHjxomrZuLg4HDp0SLVtzMzM4OHhAQC4ceMGGjdujM6dO8Pb2xvvvvsuvv7662KPj3r27BmAl8dSlUVcXByysrJga2srqSkpKUnyfGVkZAAATE1Ni+xn7ty5kj5Gjx6Ne/fuqbYLAAwcOBBmZmawtrZGZmamapfplStXYGBggFatWqnmtbW1hbu7u8bXfGRkJCwtLREYGFimx63JG2+8ITmI3tfXF4mJiZLROgB4+vQpZs6ciUWLFsHAwECtn3HjxsHExATNmzdHSEiI6r0BAOnp6ZJt5OXlpfGx+fj4oHr16jAzM8PatWtx+/ZtyTxJSUkICAhATk6O6j35KhMTE8m2p9cTgwzJRrt27RAQEIDg4GC1aXp6emq7HjQNx1epUkVyX6FQaGxTKpWq+x07dsS1a9dw584dHD58GO3btwfwf0Hmxo0bSE5OVh3om5WVBR8fH8TGxkpu165dw6BBg7R6zB07dkRsbCwSExPx7Nkz1S4fbR7zq/94TUxMSlxvcdvk5s2bePPNN9GoUSPs2LEDMTExiIyMBPB/BxOPGjUKf/31FwYPHoxLly6hefPmWLFiBYCX26dXr15q2ycxMRHt2rWDvr4+Dhw4gD179qBBgwZYsWIF3N3dVbvxXlWtWjUAKPPB4FlZWXB0dFSrJyEhAdOnT1fNd/fuXejp6cHBwaHIfubMmSPp49KlS0hMTJSErKVLlyI2NhZnzpyBg4OD5Jim0nry5AnmzZuH8PDwf+XsvVctXrwY7u7uRR6XNHfuXMTExGDZsmUIDw+XBDFzc3PJNip87BTw8ninjz76CCNHjsT+/fsRGxuL4cOHq+1SvXjxIkaNGoXAwECMGDFC8p4t8PjxY1SvXr0cHjFVZupRmqgS++KLL9CkSRO4u7tL2qtXr4779+9DCKH6YC/u4EdttG7dGoaGhli1ahVycnLg4+MDAGjRogUePHiA9evXw9TUFC1btgQANGvWDN9//z3s7OxgYWFRZL9xcXF49uyZKlicOnUKZmZmqFWrlmoeU1PTIq8TUr16ddy7d091Pz8/H/Hx8ZJvv5o0atQI0dHRJR6PU5SYmBgolUosWbJENbql6fiFWrVqYdy4cRg3bhyCg4Px9ddf48MPP0SzZs2wY8cOuLi4aPw2D7wMTn5+fvDz88OsWbPg7OyMnTt3YurUqWrz1q1bFxYWFrh8+TLq16+v9eNp1qwZ7t+/DwMDA8nB4a86e/YsPDw8ihz5adasGRISEkq8rouDg4Nqng8//BC9e/fG8+fP4enpiRcvXuD06dNo3bo1AODRo0dISEhAgwYNJH3MmzcPbdu2Rbt27TQeRF5Wp0+fltw/deoU3NzcoK+vr2q7d+8eVq9ejSNHjhTZj52dHezs7NCgQQOsW7cOv/76Kzw9PQG8DOCFt9Grr4ETJ06gdevW+OCDD1RthUfGCrRr1w5hYWFIT09Hw4YNsXz5ckyZMkUyT3x8fJGjNfT64IgMyYq3tzcCAwMREREhae/QoQMePHiARYsW4caNG4iMjJScwfBPmJiY4I033sCKFSvg5+en+lA3NDSUtBeMYgQGBqJatWro06cPjh07hqSkJBw+fBgTJ06UHKycl5eHkSNH4vLly/jtt98QGhqKCRMmSHZ9FadTp0749ddf8euvv+Lq1asYP358qa6NEhwcjLNnz+KDDz7AxYsXcfXqVaxevVpy1lBx6tWrh+fPn2PFihX466+/sHnzZqxZs0Yyz+TJk7Fv3z4kJSXh/PnzOHTokOofWVBQEB4/foyBAwfi7NmzuHHjBvbt24fhw4cjPz8fp0+fxoIFC3Du3Dncvn0bP/74Ix48eKBa/lV6enrw9/fH8ePH1aalp6erjbRcv34dAHDp0iVkZWXB398fvr6+6Nu3L/bv34+bN2/ijz/+wGeffYZz584hLy8PmzdvRnh4OIYPH17kdpk1axY2bdqEOXPm4M8//8SVK1ewdetWzJw5UzJfWloa7t+/j4SEBKxbtw516tRBlSpV4Obmhj59+mD06NE4fvw44uLi8P7776NGjRro06ePavmnT59i7dq1WLRoUameL23cvn0bU6dORUJCArZs2YIVK1Zg0qRJknkiIyPx1ltvoWnTpmrLp6WlYcOGDbh69Sr++usvRERE4NKlSxrnLYqbmxvOnTuHffv24dq1awgJCcHZs2fV5rO2tgYAWFpaYu3atZg5c6bkDK+nT58iJiYGXbt2LfW6SZ4YZEh25s6dqzaM7OnpiVWrViEyMhKNGzfGmTNn8NFHH5XbOjt27IjMzEy1b3ft27dHZmamZBSkatWqOHr0KGrXro1+/frB09MTI0eORE5OjmSEpnPnznBzc0O7du3w3nvvoXfv3lod/zJixAgMHToUQ4YMQfv27VGnTp0SR2MAoH79+ti/fz/i4uLQsmVL+Pr6Yvfu3UWOjryqcePGCA8Px8KFC9GwYUN8++23aqfG5+fnIygoCJ6enujWrRvq16+PVatWAQCcnJxw4sQJ5Ofno2vXrvD29sbkyZNhZWUFPT09WFhY4OjRo+jRowfq16+PmTNnYsmSJWpnpBQ2atQobN26Ve11cfjwYTRt2lRyGz16NICX3+jPnTsHhUKB3377De3atcPw4cNRv359DBgwALdu3YK9vT0uXbqE2bNnIyQkROOIUIGAgAD88ssv2L9/P1q0aIE33ngDS5cuVbu2zfDhw+Ho6IgWLVrgyZMn+OGHH1TToqKi4OPjgzfffBO+vr4QQuC3336T7Op7/vy5qs7yNmTIEDx79gwtW7ZEUFAQJk2apHa1ZKVSqXYmVgEhBDZs2ABfX180bNgQa9euxerVq0s8JquwsWPHol+/fnjvvffQqlUrPHr0SDI6o0n37t0xYMAAyS6m3bt3o3bt2uV6kUqqnBTi1Z3sREQyI4RAq1atMGXKFAwcOLBUy7i4uGDDhg3c9fD/dejQAU2aNJFcJ0bO3njjDUycOFHr49JIfjgiQ0Syp1AosHbtWq1+vqFBgwZq10Ch18PDhw/Rr1+/UodakjeOyBAR0Ws3IkP/HQwyREREJFvctURERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLLFIENERESyxSBDREREssUgQ0RERLL1/wA/TF13d7G7mAAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Распределение классов в обучающей выборке (в процентах):\n",
"NumWebPurchases\n",
"1 15.876599\n",
"2 15.575621\n",
"3 14.672686\n",
"4 13.167795\n",
"5 10.233258\n",
"6 8.577878\n",
"7 6.471031\n",
"8 4.966140\n",
"9 3.837472\n",
"11 2.558315\n",
"0 2.257336\n",
"10 1.655380\n",
"23 0.075245\n",
"27 0.075245\n",
"Name: proportion, dtype: float64\n"
]
}
],
"source": [
"# Функция для визуализации распределения классов\n",
"def plot_class_distribution(y, title):\n",
" sns.countplot(x=y, color='orange')\n",
" plt.title(title)\n",
" plt.xlabel(\"NumWebPurchases (Целевой признак)\")\n",
" plt.ylabel(\"Количество записей\")\n",
" plt.show()\n",
"\n",
"# Оценка сбалансированности классов в выборках\n",
"plot_class_distribution(y_train_2, \"Распределение классов в обучающей выборке\")\n",
"plot_class_distribution(y_val_2, \"Распределение классов в контрольной выборке\")\n",
"plot_class_distribution(y_test_2, \"Распределение классов в тестовой выборке\")\n",
"\n",
"# Проверка пропорций классов в обучающей выборке\n",
"class_distribution_train_2 = y_train_2.value_counts(normalize=True) * 100\n",
"print(\"Распределение классов в обучающей выборке (в процентах):\")\n",
"print(class_distribution_train_2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6.
\n",
"\n",
"Наблюдаем несбалансированность, для Второй бизнес цели выполним Upsampling (увеличение выборки для редких классов). Делаем покупки больше."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Распределение классов после SMOTE (в процентах):\n",
"Response\n",
"0 50.0\n",
"1 50.0\n",
"Name: proportion, dtype: float64\n"
]
}
],
"source": [
"from imblearn.over_sampling import SMOTE\n",
"\n",
"# Применение SMOTE к обучающей выборке\n",
"smote = SMOTE(random_state=42)\n",
"X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
"\n",
"# Проверим распределение после аугментации\n",
"plot_class_distribution(y_train_balanced, \"Распределение классов после SMOTE (обучающая выборка)\")\n",
"\n",
"# Проверим процентное распределение\n",
"balanced_distribution = y_train_balanced.value_counts(normalize=True) * 100\n",
"print(\"Распределение классов после SMOTE (в процентах):\")\n",
"print(balanced_distribution)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Для первой бизнес цели:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Размерность выборки после RandomOverSampler:\n",
"X_train_res: (2954, 15)\n",
"y_train_res: (2954,)\n",
"\n",
"Распределение классов после балансировки (в процентах):\n",
"NumWebPurchases\n",
"2 7.142857\n",
"5 7.142857\n",
"1 7.142857\n",
"8 7.142857\n",
"9 7.142857\n",
"3 7.142857\n",
"11 7.142857\n",
"7 7.142857\n",
"6 7.142857\n",
"4 7.142857\n",
"0 7.142857\n",
"10 7.142857\n",
"23 7.142857\n",
"27 7.142857\n",
"Name: proportion, dtype: float64\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from imblearn.over_sampling import RandomOverSampler\n",
"import pandas as pd\n",
"\n",
"# Применение RandomOverSampler для балансировки классов\n",
"ros = RandomOverSampler(random_state=42)\n",
"X_train_res, y_train_res = ros.fit_resample(X_train_2, y_train_2)\n",
"\n",
"# Выводим новые размеры выборки\n",
"print(f\"Размерность выборки после RandomOverSampler:\")\n",
"print(f\"X_train_res: {X_train_res.shape}\")\n",
"print(f\"y_train_res: {y_train_res.shape}\")\n",
"\n",
"# Распределение классов в обучающей выборке после балансировки\n",
"class_distribution_res = pd.Series(y_train_res).value_counts(normalize=True) * 100\n",
"print(\"\\nРаспределение классов после балансировки (в процентах):\")\n",
"print(class_distribution_res)\n",
"\n",
"# Для визуализации можно использовать график\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Функция для визуализации распределения классов\n",
"def plot_class_distribution(y, title):\n",
" sns.countplot(x=y, color=\"orange\")\n",
" plt.title(title)\n",
" plt.xlabel(\"Response (Целевой признак)\")\n",
" plt.ylabel(\"Количество записей\")\n",
" plt.show()\n",
"\n",
"# Построение графика распределения классов\n",
"plot_class_distribution(y_train_res, \"Распределение классов после балансировки\")\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"7-8. Делаем конструирование признаков"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Income Kidhome Teenhome MntWines MntFruits \\\n",
"customer_id \n",
"0 -0.263557 1 1 68 0 \n",
"1 -1.102440 1 0 18 3 \n",
"2 0.633408 0 1 225 162 \n",
"3 1.135917 1 0 739 107 \n",
"4 1.299116 0 0 395 183 \n",
"\n",
" MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds \\\n",
"customer_id \n",
"0 16 0 0 8 \n",
"1 19 3 3 6 \n",
"2 387 106 36 29 \n",
"3 309 140 80 35 \n",
"4 565 166 141 28 \n",
"\n",
" AcceptedCmp1 AcceptedCmp2 AcceptedCmp3 AcceptedCmp4 \\\n",
"customer_id \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" AcceptedCmp5 Recency Income_binned \n",
"customer_id \n",
"0 0 6 1.0 \n",
"1 0 67 0.0 \n",
"2 0 77 1.0 \n",
"3 0 2 2.0 \n",
"4 0 19 2.0 \n",
"Размерность выборки после RandomOverSampler:\n",
"X_train_res: (2954, 17)\n",
"y_train_res: (2954,)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\study\\3_course\\aim\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index customer_id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"d:\\study\\3_course\\aim\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
" warnings.warn(\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.preprocessing import KBinsDiscretizer, MinMaxScaler, StandardScaler\n",
"import featuretools as ft\n",
"from imblearn.over_sampling import RandomOverSampler\n",
"\n",
"\n",
"# 1. One-hot encoding для категориальных признаков\n",
"X_train_2 = pd.get_dummies(X_train_2, drop_first=True)\n",
"X_test_2 = pd.get_dummies(X_test_2, drop_first=True)\n",
"\n",
"# 2. Дискретизация числовых признаков\n",
"discretizer = KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='uniform')\n",
"X_train_2['Income_binned'] = discretizer.fit_transform(X_train_2[['Income']])\n",
"X_test_2['Income_binned'] = discretizer.transform(X_test_2[['Income']])\n",
"\n",
"# 3. Масштабирование признаков\n",
"scaler_minmax = MinMaxScaler()\n",
"X_train_2[['Income']] = scaler_minmax.fit_transform(X_train_2[['Income']])\n",
"X_test_2[['Income']] = scaler_minmax.transform(X_test_2[['Income']])\n",
"\n",
"# Стандартизация признаков\n",
"scaler_standard = StandardScaler()\n",
"X_train_2[['Income']] = scaler_standard.fit_transform(X_train_2[['Income']])\n",
"X_test_2[['Income']] = scaler_standard.transform(X_test_2[['Income']])\n",
"\n",
"# 4. Применение Featuretools для создания признаков\n",
"es = ft.EntitySet(id=\"data\")\n",
"\n",
"# Мы добавляем данные в EntitySet с помощью метода add_dataframe\n",
"es = es.add_dataframe(\n",
" dataframe_name=\"customer_data\",\n",
" dataframe=X_train_2,\n",
" index=\"customer_id\" \n",
")\n",
"\n",
"# Применяем Featuretools для создания признаков\n",
"# Изменения: теперь указываем `target_dataframe_name` вместо `target_entity`\n",
"features, feature_names = ft.dfs(entityset=es, target_dataframe_name=\"customer_data\")\n",
"\n",
"print(features.head())\n",
"\n",
"# 5. Балансировка выборки с помощью RandomOverSampler\n",
"ros = RandomOverSampler(random_state=42)\n",
"X_train_res, y_train_res = ros.fit_resample(X_train_2, y_train_2)\n",
"\n",
"print(f\"Размерность выборки после RandomOverSampler:\")\n",
"print(f\"X_train_res: {X_train_res.shape}\")\n",
"print(f\"y_train_res: {y_train_res.shape}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Прилетает ошибка - UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
" warnings.warn. Она значит, что EntitySet состоит из одного DataFrame. Т.е. только одна сущность.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Следующая бизнес-цель"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Income Kidhome Teenhome NumWebPurchases MntWines \\\n",
"customer_id \n",
"0 0.739837 0 1 6 522 \n",
"1 -0.203068 1 1 1 22 \n",
"2 0.160233 0 1 7 479 \n",
"3 1.049812 0 0 4 594 \n",
"4 0.119182 1 2 6 416 \n",
"\n",
" MntFruits MntMeatProducts MntFishProducts MntSweetProducts \\\n",
"customer_id \n",
"0 0 522 227 120 \n",
"1 2 10 6 4 \n",
"2 5 82 7 17 \n",
"3 51 631 72 55 \n",
"4 0 26 0 0 \n",
"\n",
" MntGoldProds AcceptedCmp1 AcceptedCmp2 AcceptedCmp3 \\\n",
"customer_id \n",
"0 134 0 0 0 \n",
"1 34 0 0 0 \n",
"2 171 0 0 1 \n",
"3 32 0 0 0 \n",
"4 4 0 0 0 \n",
"\n",
" AcceptedCmp4 AcceptedCmp5 Recency Income_binned \n",
"customer_id \n",
"0 0 0 28 0.0 \n",
"1 0 0 84 0.0 \n",
"2 0 0 30 0.0 \n",
"3 0 0 42 0.0 \n",
"4 1 0 11 0.0 \n",
"Размерность выборки после RandomOverSampler для первой бизнес-цели:\n",
"X_train_res_1: (2258, 18)\n",
"y_train_res_1: (2258,)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\study\\3_course\\aim\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index customer_id not found in dataframe, creating new integer column\n",
" warnings.warn(\n",
"d:\\study\\3_course\\aim\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
" warnings.warn(\n"
]
}
],
"source": [
"# 1. One-hot encoding для категориальных признаков\n",
"X_train = pd.get_dummies(X_train, drop_first=True)\n",
"X_test = pd.get_dummies(X_test, drop_first=True)\n",
"\n",
"# 2. Дискретизация числовых признаков\n",
"discretizer = KBinsDiscretizer(n_bins=5, encode='ordinal', strategy='uniform')\n",
"X_train['Income_binned'] = discretizer.fit_transform(X_train[['Income']])\n",
"X_test['Income_binned'] = discretizer.transform(X_test[['Income']])\n",
"\n",
"# 3. Масштабирование признаков\n",
"scaler_minmax = MinMaxScaler()\n",
"X_train[['Income']] = scaler_minmax.fit_transform(X_train[['Income']])\n",
"X_test[['Income']] = scaler_minmax.transform(X_test[['Income']])\n",
"\n",
"# Стандартизация признаков\n",
"scaler_standard = StandardScaler()\n",
"X_train[['Income']] = scaler_standard.fit_transform(X_train[['Income']])\n",
"X_test[['Income']] = scaler_standard.transform(X_test[['Income']])\n",
"\n",
"# 4. Применение Featuretools для создания признаков\n",
"es = ft.EntitySet(id=\"data\")\n",
"es = es.add_dataframe(dataframe_name=\"customer_data\", dataframe=X_train, index=\"customer_id\")\n",
"\n",
"# Применяем deep feature synthesis для создания новых признаков\n",
"features, feature_names = ft.dfs(entityset=es, target_dataframe_name=\"customer_data\", max_depth=2)\n",
"\n",
"\n",
"print(features.head())\n",
"\n",
"# 5. Балансировка выборки с помощью RandomOverSampler\n",
"ros = RandomOverSampler(random_state=42)\n",
"X_train_res_1, y_train_res_1 = ros.fit_resample(X_train, y_train)\n",
"\n",
"print(f\"Размерность выборки после RandomOverSampler для первой бизнес-цели:\")\n",
"print(f\"X_train_res_1: {X_train_res_1.shape}\")\n",
"print(f\"y_train_res_1: {y_train_res_1.shape}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"9. Оценить качество каждого набора признаков