596 lines
829 KiB
Plaintext
596 lines
829 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 2 лабораторная"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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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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"Index(['ID', 'Price', 'Levy', 'Manufacturer', 'Model', 'Prod. year',\n",
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" 'Category', 'Leather interior', 'Fuel type', 'Engine volume', 'Mileage',\n",
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" 'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel', 'Color',\n",
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" 'Airbags'],\n",
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" dtype='object')\n",
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"Index(['Rank ', 'Name', 'Networth', 'Age', 'Country', 'Source', 'Industry'], dtype='object')\n",
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"Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"import matplotlib.ticker as ticker\n",
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"import seaborn as sns\n",
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"\n",
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"CarPrices = pd.read_csv(\".//static//csv//car_price_prediction.csv\")\n",
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"Forbes = pd.read_csv(\".//static//csv//ForbesBillionaires.csv\")\n",
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"GoogleStockPriceChart = pd.read_csv(\".//static//csv//GOOGLEstockPrice.csv\")\n",
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"\n",
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"print(CarPrices.columns)\n",
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"\n",
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"print(Forbes.columns)\n",
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"print(GoogleStockPriceChart.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 1. Рынок автомобилей\n",
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"### Проблемная область\n",
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"Данные могут быть использованы для анализа рынка автомобилей, прогнозирования цен, определения факторов, влияющих на стоимость автомобилей, и т.д.\n",
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"Прогнозирование цен на автомобили для оптимизации ценовой политики. \n",
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"\n",
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" - **Эффект для бизнеса:** Повышение конкурентоспособности за счет более точного ценообразования, увеличение продаж.\n",
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" \n",
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" **Техническая цель:** Разработка модели машинного обучения для прогнозирования цен на автомобили.\n",
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" - **Вход:** Атрибуты автомобиля (производитель, модель, год выпуска, пробег и т.д.).\n",
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" - **Целевой признак:** Цена автомобиля."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1400x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"CarPrices['Prod. year'] = pd.to_datetime(CarPrices['Prod. year'], format='%Y')\n",
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"\n",
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"plt.figure(figsize=(14, 6))\n",
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"plt.scatter(CarPrices['Prod. year'], CarPrices['Price'])\n",
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"plt.xlabel('Production Year')\n",
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"plt.ylabel('Price')\n",
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"plt.title('Scatter Plot of Price vs Production Year')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Мы видим 1 выброс, который очень выделяется среди других результатов в данном датасете\n",
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"Зашумленность невысокая. Выброс был удален с помощью пороги квантилями."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1400x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"CarPrices['Prod. year'] = pd.to_datetime(CarPrices['Prod. year'], format='%Y')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Определяем границы для цены с использованием квантилей\n",
|
|||
|
|
"lower_bound = CarPrices['Price'].quantile(0.05)\n",
|
|||
|
|
"upper_bound = CarPrices['Price'].quantile(0.95)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Фильтруем данные, оставляя только те, которые находятся в пределах границ\n",
|
|||
|
|
"df_filtered = CarPrices[(CarPrices['Price'] >= lower_bound) & (CarPrices['Price'] <= upper_bound)]\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(14, 6))\n",
|
|||
|
|
"plt.scatter(df_filtered['Prod. year'], df_filtered['Price'])\n",
|
|||
|
|
"plt.xlabel('Production Year')\n",
|
|||
|
|
"plt.ylabel('Price')\n",
|
|||
|
|
"plt.title('Scatter Plot of Price vs Production Year (Filtered)')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAJzCAYAAAB+heDsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gU5f7+8c+mEwKBQEhCh9B770UgNCkCKtLbkSaoCIqCQESOImIBBcGGqMejIKB+FUQBQRFRaihSpYPSSyCUQHL//uC3c7IktBhklffruvaCzE55ZnZmdu55nnnWJUkGAAAAAPAaPre7AAAAAAAATwQ1AAAAAPAyBDUAAAAA8DIENQAAAADwMgQ1AAAAAPAyBDUAAAAA8DIENQAAAADwMgQ1AAAAAPAyBDUAAAAA8DIENQAA/mb2799v06dPd/7evXu3ffTRR7evQACADEdQA/4i06dPN5fL5byCgoKsWLFiNnDgQDt06NDtLh6AvxGXy2UDBgywb775xnbv3m1Dhw61pUuX3u5iAQAykN/tLgBwp3n22WetUKFCdv78efvxxx9typQpNm/ePNu4caMFBwff7uIB+BvIkyeP9e7d25o1a2ZmZlFRUbZkyZLbWygAQIZySdLtLgRwJ5g+fbr17NnTVq5caVWqVHGGDxkyxF555RX773//ax07dryNJQTwd7Njxw47evSolSlTxjJnzny7iwMAyEA0fQRus4YNG5qZ2a5du8zM7Pjx4/b4449b2bJlLSQkxLJmzWrNmze3devWpZr2/Pnz9swzz1ixYsUsKCjIoqKirF27drZjxw4zu/zcSsrmlle+7rrrLmdeS5YsMZfLZTNmzLDhw4dbZGSkZc6c2Vq3bm379u1LtexffvnFmjVrZqGhoRYcHGz169e3ZcuWpbmOd911V5rLf+aZZ1KN+5///McqV65smTJlsrCwMOvQoUOay7/WuqWUnJxsEyZMsNKlS1tQUJBFRERY37597cSJEx7jFSxY0Fq2bJlqOQMHDkw1z7TKPn78+FTb1MzswoULFhsba0WKFLHAwEDLly+fDR061C5cuJDmtkrpatvN/dq9e3eq8n/77bdWoUIFCwoKslKlStmcOXNSzXfnzp12//33W1hYmAUHB1uNGjVs7ty5HuO494cra2latGiRav2nTJli5cuXt9DQUMucObOVL1/e3n33XY/pevTo4VH27Nmz21133ZVmc7033njDSpcubYGBgZY7d24bMGCAnTx5Ms1tdLVtk7LcN7ouzzzzzDW395XzuJljoGDBgtedn5nZ119/bXXr1rXMmTNblixZrEWLFvbrr7+m2pYFCxY0M7Po6GirXr26HT9+3DJlypRqv0jLlZ/F9cr06aefOsdkzpw5rUuXLnbgwIFU892yZYu1b9/ewsPDLVOmTFa8eHF7+umnb+m2SIu7mfkPP/xgffv2tRw5cljWrFmtW7duqY77m1nOtdbv9OnT9uCDD1qBAgUsMDDQ8ubNa/369UvVrN1dtoCAADty5IjHe8uXL3e2xapVq5zhN3MeMPvf/n7ly73PpBznRo5v93Fx9OhRj3FXrVplLpfL41nJq+1bRYoUccb54osvrEWLFpY7d24LDAy06OhoGzNmjCUlJaXa5sCdjqaPwG3mDlU5cuQws8sX0Z9//rndf//9VqhQITt06JC9+eabVr9+fdu0aZPlzp3bzMySkpKsZcuWtmjRIuvQoYM9+uijdvr0aVuwYIFt3LjRoqOjnWV07NjR7r77bo/lDhs2LM3yPPfcc+ZyuezJJ5+0w4cP24QJEywmJsbi4uIsU6ZMZmb23XffWfPmza1y5coWGxtrPj4+9t5771nDhg1t6dKlVq1atVTzzZs3r40dO9bMzM6cOWP9+/dPc9kjR4609u3b24MPPmhHjhyx119/3erVq2dr1661bNmypZqmT58+VrduXTMzmzNnjn322Wce7/ft29epzXzkkUds165dNmnSJFu7dq0tW7bM/P3909wON+PkyZPOuqWUnJxsrVu3th9//NH69OljJUuWtA0bNtirr75q27Zts88///y680653dzmzZtnH3/8capxt2/fbg888ID169fPunfvbu+9957df//9Nn/+fGvcuLGZmR06dMhq1aplZ8+etUceecRy5Mhh77//vrVu3dpmzZplbdu2vWpZfvjhB5s3b16q4adPn7YmTZpYdHS0SbKZM2fagw8+aNmyZbN7773XGS9nzpz26quvmtnlzjAmTpxod999t+3bt8/5bJ955hkbPXq0xcTEWP/+/W3r1q02ZcoUW7ly5VU/r8aNG1u3bt3MzGzlypX22muvXWerpr0u7dq187igfOyxx6xkyZLWp08fZ1jJkiXNLH3HQN26dZ15bd682Z5//nmP9z/88EPr3r27NW3a1MaNG2dnz561KVOmWJ06dWzt2rUeF9pXGjVqlJ0/f/666+0WGBho77zzjsewtLad+9ipWrWqjR071g4dOmQTJ060ZcuWeRyT69evt7p165q/v7/16dPHChYsaDt27LAvv/zSnnvuub90W7gNHDjQsmXLZs8884yzH+3Zs8cJKTeznOut3/Hjx239+vX24IMPWmRkpP322282depUmz9/vq1YscJy5crlUTZfX1/7z3/+Y4899pgz7L333rOgoKA0P8ebOQ+4DR8+3Nlf33rrLdu7d+81t9fVju+blda+lSVLFuf/06dPt5CQEBs8eLCFhITYd999Z6NGjbL4+HgbP378n14+8I8iAH+J9957T2amhQsX6siRI9q3b58++eQT5ciRQ5kyZdL+/fslSefPn1dSUpLHtLt27VJgYKCeffZZZ9i0adNkZnrllVdSLSs5OdmZzsw0fvz4VOOULl1a9evXd/5evHixzEx58uRRfHy8M3zmzJkyM02cONGZd9GiRdW0aVNnOZJ09uxZFSpUSI0bN061rFq1aqlMmTLO30eOHJGZKTY21hm2e/du+fr66rnnnvOYdsOGDfLz80s1fPv27TIzvf/++86w2NhYpTytLV26VGamjz76yGPa+fPnpxpeoEABtWjRIlXZBwwYoCtPlVeWfejQocqVK5cqV67ssU0//PBD+fj4aOnSpR7TT506VWamZcuWpVpeSvXr11fp0qVTDR8/frzMTLt27fIov5lp9uzZzrBTp04pKipKFStWdIYNGjRIZuZRptOnT6tQoUIqWLCgs++594fFixc741WvXl3NmzdPtf5XunTpkrJmzaqBAwc6w7p3764CBQp4jPfWW2/JzLRixQpJ0uHDhxUQEKAmTZp4HAOTJk2SmWnatGke0ycmJsrMPJbz6aefpip3etelQIEC6t69e6rh6TkG8uTJo549e161TKdPn1a2bNnUu3dvj+kOHjyo0NBQj+FXbsuNGzfKx8fHWZ+U+0VaunfvrsyZM6cafuW2S0xMVK5cuVSmTBmdO3fOGe+rr76SmWnUqFHOsHr16ilLlizas2ePxzxTbp9bsS3S4j7XVq5cWYmJic7wF198UWamL7744qaXczPr57Zx40YFBgaqV69eqcrWsWNHlS1b1hmekJCgrFmzqlOnTjIzrVy50nnvZs4DkrRgwQKZmb7//ntn2JX7zM0cE+7z6pEjRzyWs3LlSpmZ3nvvPY/lpLVvpXT27NlUw/r27avg4GCdP3/+mtMCdxqaPgJ/sZiYGAsPD7d8+fJZhw4dLCQkxD777DPLkyePmV2+G+njc/nQTEpKsmPHjllISIgVL17c1qxZ48xn9uzZljNnTnv44YdTLePKpno3o1u3bh53P++77z6Liopy7rTGxcXZ9u3brVOnTnbs2DE7evSoHT161BISEqxRo0b2ww8/WHJyssc8z58/b0FBQddc7pw5cyw5Odnat2/vzPPo0aMWGRlpRYsWtcWLF3uMn5iYaGaXt9fVfPrppxYaGmqNGzf2mGflypUtJCQk1TwvXrzoMd7Ro0evW0tx4MABe/31123kyJEWEhKSavklS5a0EiVKeMzT3dz1yuX/Wblz5/aoEXM391q7dq0dPHjQzC7fha9WrZrVqVPHGS8kJMT
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"CarPrices['Manufacturer'].value_counts().plot(kind='bar', color='blue')\n",
|
|||
|
|
"plt.title('Распределение производителей до ресэмплинга')\n",
|
|||
|
|
"plt.xlabel('Производитель')\n",
|
|||
|
|
"plt.ylabel('Количество')\n",
|
|||
|
|
"plt.xticks(rotation=45)\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# resampling\n",
|
|||
|
|
"manufacturer_counts = CarPrices['Manufacturer'].value_counts()\n",
|
|||
|
|
"min_count = manufacturer_counts.min()\n",
|
|||
|
|
"balanced_data1 = pd.concat([CarPrices[CarPrices['Manufacturer'] == manufacturer].sample(min_count) for manufacturer in manufacturer_counts.index])\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"balanced_data1['Manufacturer'].value_counts().plot(kind='bar', color='green')\n",
|
|||
|
|
"plt.title('Распределение производителей после ресэмплинга')\n",
|
|||
|
|
"plt.xlabel('Производитель')\n",
|
|||
|
|
"plt.ylabel('Количество')\n",
|
|||
|
|
"plt.xticks(rotation=45)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# 2. Список Forbes. \n",
|
|||
|
|
"https://www.kaggle.com/datasets/surajjha101/forbes-billionaires-data-preprocessed\n",
|
|||
|
|
"\n",
|
|||
|
|
"### Проблемная область\n",
|
|||
|
|
"Данные о богатейших людях мира могут быть использованы для анализа тенденций в бизнесе, инвестициях, а также для исследования факторов, влияющих на успешность бизнеса.\n",
|
|||
|
|
"Анализ успешных бизнес-моделей для разработки собственной стратегии.\n",
|
|||
|
|
" - **Эффект для бизнеса:** Повышение эффективности бизнеса, увеличение прибыли за счет внедрения успешных практик.\n",
|
|||
|
|
"**Техническая цель:** Анализ данных о богатейших людях для выявления факторов успеха.\n",
|
|||
|
|
" - **Вход:** Атрибуты богатейших людей (возраст, страна, источник богатства, отрасль и т.д.).\n",
|
|||
|
|
" - **Целевой признак:** Чистая стоимость.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 4,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"plt.scatter(Forbes['Age'], Forbes['Networth'])\n",
|
|||
|
|
"plt.xlabel('Age')\n",
|
|||
|
|
"plt.ylabel('Networth (in billions)')\n",
|
|||
|
|
"plt.title('Scatter Plot of Networth vs Age')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
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|
|
]
|
|||
|
|
},
|
|||
|
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"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
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}
|
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|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Вычисление IQR\n",
|
|||
|
|
"Q1 = Forbes['Networth'].quantile(0.25)\n",
|
|||
|
|
"Q3 = Forbes['Networth'].quantile(0.75)\n",
|
|||
|
|
"IQR = Q3 - Q1\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Определение границ для выбросов\n",
|
|||
|
|
"lower_bound = Q1 - 1.5 * IQR\n",
|
|||
|
|
"upper_bound = Q3 + 1.5 * IQR\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Удаление выбросов\n",
|
|||
|
|
"filtered_Forbes = Forbes[(Forbes['Networth'] >= lower_bound) & (Forbes['Networth'] <= upper_bound)]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Построение диаграммы рассеяния без выбросов\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"plt.scatter(filtered_Forbes['Age'], filtered_Forbes['Networth'])\n",
|
|||
|
|
"plt.xlabel('Age')\n",
|
|||
|
|
"plt.ylabel('Networth (in billions)')\n",
|
|||
|
|
"plt.title('Scatter Plot of Networth vs Age (без выбросов)')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Диаграмма выборосов получилась вполне удобной для анализа.\n",
|
|||
|
|
"Выбросов немного, они не мешают анализировать данные. \n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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|
|
" vertical-align: middle;\n",
|
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|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Rank</th>\n",
|
|||
|
|
" <th>Name</th>\n",
|
|||
|
|
" <th>Networth</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Country</th>\n",
|
|||
|
|
" <th>Source</th>\n",
|
|||
|
|
" <th>Industry</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>Elon Musk</td>\n",
|
|||
|
|
" <td>219.0</td>\n",
|
|||
|
|
" <td>50</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>Tesla, SpaceX</td>\n",
|
|||
|
|
" <td>Automotive</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>2</td>\n",
|
|||
|
|
" <td>Jeff Bezos</td>\n",
|
|||
|
|
" <td>171.0</td>\n",
|
|||
|
|
" <td>58</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>Amazon</td>\n",
|
|||
|
|
" <td>Technology</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>3</td>\n",
|
|||
|
|
" <td>Bernard Arnault & family</td>\n",
|
|||
|
|
" <td>158.0</td>\n",
|
|||
|
|
" <td>73</td>\n",
|
|||
|
|
" <td>France</td>\n",
|
|||
|
|
" <td>LVMH</td>\n",
|
|||
|
|
" <td>Fashion & Retail</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>4</td>\n",
|
|||
|
|
" <td>Bill Gates</td>\n",
|
|||
|
|
" <td>129.0</td>\n",
|
|||
|
|
" <td>66</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>Microsoft</td>\n",
|
|||
|
|
" <td>Technology</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>5</td>\n",
|
|||
|
|
" <td>Warren Buffett</td>\n",
|
|||
|
|
" <td>118.0</td>\n",
|
|||
|
|
" <td>91</td>\n",
|
|||
|
|
" <td>United States</td>\n",
|
|||
|
|
" <td>Berkshire Hathaway</td>\n",
|
|||
|
|
" <td>Finance & Investments</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Rank Name Networth Age Country \\\n",
|
|||
|
|
"0 1 Elon Musk 219.0 50 United States \n",
|
|||
|
|
"1 2 Jeff Bezos 171.0 58 United States \n",
|
|||
|
|
"2 3 Bernard Arnault & family 158.0 73 France \n",
|
|||
|
|
"3 4 Bill Gates 129.0 66 United States \n",
|
|||
|
|
"4 5 Warren Buffett 118.0 91 United States \n",
|
|||
|
|
"\n",
|
|||
|
|
" Source Industry \n",
|
|||
|
|
"0 Tesla, SpaceX Automotive \n",
|
|||
|
|
"1 Amazon Technology \n",
|
|||
|
|
"2 LVMH Fashion & Retail \n",
|
|||
|
|
"3 Microsoft Technology \n",
|
|||
|
|
"4 Berkshire Hathaway Finance & Investments "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"Forbes.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_16592\\3179761046.py:6: FutureWarning: \n",
|
|||
|
|
"\n",
|
|||
|
|
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
|
|||
|
|
"\n",
|
|||
|
|
" sns.barplot(x=country_networth.values, y=country_networth.index, palette='viridis')\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Группировка по стране и суммирование состояния\n",
|
|||
|
|
"country_networth = Forbes.groupby('Country')['Networth'].sum().sort_values(ascending=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация данных\n",
|
|||
|
|
"plt.figure(figsize=(12, 8))\n",
|
|||
|
|
"sns.barplot(x=country_networth.values, y=country_networth.index, palette='viridis')\n",
|
|||
|
|
"plt.title('Суммарное состояние миллиардеров по странам')\n",
|
|||
|
|
"plt.xlabel('Состояние (в миллиардах долларов)')\n",
|
|||
|
|
"plt.ylabel('Страна')\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"# "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Мы видим, что лидируют США и Китай, остальные страны же сильно отстают. Удалив последние 50 стран, заметим, что диаграмма стала выглядеть сбалансировано. Нам необходимо удалить данные страны."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 57,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1200x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"# Сортировка состояний людей от минимума до максимума\n",
|
|||
|
|
"sorted_networth = Forbes.sort_values(by='Networth')\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация данных\n",
|
|||
|
|
"plt.figure(figsize=(12, 8))\n",
|
|||
|
|
"plt.plot(sorted_networth.index, sorted_networth['Networth'], marker='o', linestyle='-', color='b')\n",
|
|||
|
|
"plt.title('Состояние миллиардеров от минимума до максимума')\n",
|
|||
|
|
"plt.xlabel('Порядковые числа')\n",
|
|||
|
|
"plt.ylabel('Состояние (в миллиардах долларов)')\n",
|
|||
|
|
"plt.xlim(0, 250) # Установка максимального значения оси X\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"\n",
|
|||
|
|
"# Группировка по стране и суммирование состояния\n",
|
|||
|
|
"country_networth = df.groupby('Country')['Networth'].sum().sort_values(ascending=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Удаление США и 20-30 стран с самыми бедными людьми\n",
|
|||
|
|
"num_countries_to_remove = 30\n",
|
|||
|
|
"countries_to_remove = country_networth.index[-num_countries_to_remove:].tolist() + ['United States']\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Удаление стран из датасета\n",
|
|||
|
|
"filtered_df = df[~df['Country'].isin(countries_to_remove)]\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Группировка по стране и суммирование состояния для отфильтрованного датасета\n",
|
|||
|
|
"filtered_country_networth = filtered_df.groupby('Country')['Networth'].sum().sort_values(ascending=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация данных\n",
|
|||
|
|
"plt.figure(figsize=(12, 8))\n",
|
|||
|
|
"sns.barplot(x=filtered_country_networth.values, y=filtered_country_networth.index, palette='viridis')\n",
|
|||
|
|
"plt.title('Суммарное состояние миллиардеров по странам (без США и 20-30 самых бедных стран)')\n",
|
|||
|
|
"plt.xlabel('Состояние (в миллиардах долларов)')\n",
|
|||
|
|
"plt.ylabel('Страна')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# 3. Финансовый рынок.\n",
|
|||
|
|
"https://www.kaggle.com/datasets/varpit94/google-stock-data\n",
|
|||
|
|
"### Проблемная область\n",
|
|||
|
|
"Данные о ценах на акции могут быть использованы для анализа рынка, прогнозирования цен на акции, определения трендов и т.д.\n",
|
|||
|
|
"\n",
|
|||
|
|
"## Бизнес-цели, для достижения которых могут подойти выбранные наборы данных.\n",
|
|||
|
|
"Прогнозирование цен на акции для принятия решений по инвестициям.\n",
|
|||
|
|
" - **Эффект для бизнеса:** Увеличение доходности инвестиций, снижение рисков за счет более точного прогнозирования рынка.\n",
|
|||
|
|
"\n",
|
|||
|
|
"**Техническая цель:** Разработка модели прогнозирования цен на акции.\n",
|
|||
|
|
" - **Вход:** Исторические данные о ценах на акции (открытие, закрытие, максимум, минимум, объем торгов).\n",
|
|||
|
|
" - **Целевой признак:** Цена закрытия на следующий день.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1400x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df_Gstocks = pd.DataFrame(GoogleStockPriceChart)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Line Plot для цен на акции по датам\n",
|
|||
|
|
"plt.figure(figsize=(14, 6))\n",
|
|||
|
|
"plt.plot(df_Gstocks['Date'], df_Gstocks['Close'], marker='o')\n",
|
|||
|
|
"plt.title('Изменение цен на акции по датам')\n",
|
|||
|
|
"plt.xlabel('Дата')\n",
|
|||
|
|
"plt.ylabel('Цена закрытия')\n",
|
|||
|
|
"plt.grid(True)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Данные сбалансированы. Выбросов нет.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA1sAAAIjCAYAAAD1OgEdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADJoklEQVR4nOzdeVyU1f4H8M+wzAwDzAzMiIgCokPuC2qZDbhlkUtmWd3Qe0PJWze3ll+mbW7dNK1upbbe3Cq1uplZZotpJpCVyxTiFiiJhYiDzAwwzP78/sCZGGZAMAjQz/v14nWb5znPM+cZqMuHc873iARBEEBERERERERNKqClO0BERERERHQ5YtgiIiIiIiJqBgxbREREREREzYBhi4iIiIiIqBkwbBERERERETUDhi0iIiIiIqJmwLBFRERERETUDBi2iIiIiIiImgHDFhERERERUTNg2CIiIiKPX3/9FSKRCOvWrWvprhARtXkMW0TUJn344YcQiUR+v3r37t3S3SNqlXbv3o3bbrsN0dHREIvFiIqKws0334yPPvqopbvWqhQWFuJf//oXOnfuDIlEgqioKEyYMAHZ2dkt3TUiamOCWroDRER/xuOPP44ePXp4Xj/zzDMt2Bui1mvBggVYvHgxEhMTcd999yE+Ph6lpaXYvn07Jk6ciA0bNmDSpEkt3c0Wl52djTFjxgAApk2bhp49e6K4uBjr1q1DSkoKXn75ZcyaNauFe0lEbQXDFhG1aTfccAOGDx/uef3WW29Br9e3XIeIWqEPP/wQixcvxu23346NGzciODjYc27OnDn48ssvYbfbW7CHrUNZWRluv/12hISEIDs7G127dvWce/jhh5GamooHH3wQAwcOxHXXXdeCPSWitoLTCImoTbLZbACAgICL/2ds3bp1EIlE+PXXXz3HXC4X+vbt67M2JScnB1OmTEGXLl0glUoRHR2NjIwMlJaWet1z4cKFfqcwBgX98Tes4cOHo3fv3jhw4ACuu+46hISEICEhAa+//rrPs8yfPx8DBw6EQqFAaGgoUlJS8M0333i1c6+lEYlE+Pjjj73OWSwWREREQCQS4fnnn/fpZ1RUlM8v05s2bfLcr2ZA3bp1K8aOHYuYmBhIJBJ07doVTz/9NJxO50U/a/f7HTt2DHfeeSfkcjlUKhUeeOABWCwWr7Zr167FyJEjERUVBYlEgp49e+K1117ze9/PP/8cw4YNQ3h4OORyOa6++mps3LjRq80PP/yAMWPGICIiAqGhoejbty9efvllrzbHjh3D7bffjsjISEilUgwaNAiffPKJz/vpdDrcdNNNaNeundf3d9y4cZ427p+r/fv3e12r1+shEomwcOFCn8+lpoqKCkRHR0MkEmH37t1e51577TX07t0bMpnM6/0//PBDTxuz2Yxjx4416I8LTz31FCIjI7FmzRqvoOWWmprq9Wz+7Nq1CykpKQgNDYVSqcQtt9yCo0ePerUpLy/Hgw8+6DX97oYbbsDBgwe92v3www+46aaboFAoIJPJMGzYsItO0Tt79iyCgoKwaNEin3PHjx+HSCTCqlWrAAB2ux2LFi1CYmIipFIpVCoVkpOTsWPHjnrf44033kBxcTGee+45r6AFACEhIVi/fj1EIhEWL17sOe7+OdizZw/uu+8+qFQqyOVy3H333SgrK/N5j88//9zzOYaHh2Ps2LE4fPiwV5spU6YgLCwMv//+OyZMmICwsDC0a9cOjzzySIP+PSSi1oNhi4jaJHfYkkgkl3T9O++8g0OHDvkc37FjB06ePImpU6di5cqVuOuuu/Dee+9hzJgxEATBp/1rr72Gd955x/P19ttve50vKyvDmDFjMHDgQCxfvhydOnXC/fffjzVr1njamEwmvPXWWxg+fDiWLVuGhQsX4ty5c0hNTcVPP/3k855SqRRr1671OvbRRx/5hJmaysvLsW3bNq9ja9euhVQq9Wm7bt06hIWF4eGHH8bLL7+MgQMHYv78+Zg3b16d96/tzjvvhMViwdKlSzFmzBisWLEC9957r1eb1157DfHx8Xj88cfxwgsvIDY2FtOnT8crr7zi05+xY8fi/PnzeOyxx/Dss8+if//++OKLLzxtduzYgaFDh+LIkSN44IEH8MILL2DEiBFez3z48GFce+21OHr0KObNm4cXXngBoaGhmDBhArZs2eJpZzQaMXr0aOh0Ojz88MOe761arW7w8zfECy+8gLNnz/ocf//99zF9+nS0a9cOL774It555x08/vjjPu1+/PFH9OjRwxMw6pKXl4djx45hwoQJCA8Pv6S+fv3110hNTUVJSQkWLlyIhx9+GN999x20Wq3XHzH+9a9/4bXXXsPEiRPx6quv4pFHHkFISIhXKNu1axeGDh0Kk8mEBQsWYMmSJTAYDBg5ciR+/PHHOvvQvn17DBs2DB988IHPuffffx+BgYG44447AFSH20WLFmHEiBFYtWoVnnjiCcTFxfmEvto+/fRTSKVS3HnnnX7PJyQkIDk5Gbt27UJVVZXXuZkzZ+Lo0aNYuHAh7r77bmzYsAETJkzw+u/GO++8g7FjxyIsLAzLli3DU089hSNHjiA5OdnrcwQAp9OJ1NRUqFQqPP/88xg2bBheeOEFvPnmm/U+AxG1MgIRURv00ksvCQCEn3/+2ev4sGHDhF69enkdW7t2rQBAKCgoEARBECwWixAXFyeMHj1aACCsXbvW09ZsNvu816ZNmwQAwp49ezzHFixYIAAQzp07V2cfhw0bJgAQXnjhBc8xq9Uq9O/fX4iKihJsNpsgCILgcDgEq9XqdW1ZWZnQvn17ISMjw3OsoKBAACCkpaUJQUFBQnFxsefc9ddfL0yaNEkAIDz33HM+/UxLSxPGjRvnOX7q1CkhICBASEtL83kOf5/BfffdJ8hkMsFisdT5vDXfb/z48V7Hp0+f7vP98vc+qampQpcuXTyvDQaDEB4eLgwePFioqqryautyuQRBqP78EhIShPj4eKGsrMxvG0Go/oz69Onj9Qwul0u47rrrhMTERM+xL7/8UgAgbNq0yete8fHxwtixYz2v3T9X+/bt82p37tw5AYCwYMECn8/FraSkRAgPD/f8DH7zzTeec2lpaYJSqfR63m+++UYAIPzvf//zOVbzffzZunWrAEB48cUX623n5v45q/nvhftntrS01HPs559/FgICAoS7777bc0yhUAgzZsyo894ul0tITEwUUlNTvb43ZrNZSEhIEG644YZ6+/bGG28IAIRDhw55He/Zs6cwcuRIz+t+/fp5fa8aSqlUCv369au3zezZswUAQk5OjiAIf/wcDBw40PPvtCAIwvLlywUAwtatWwVBEITy8nJBqVQK//znP73uV1xcLCgUCq/j6enpAgBh8eLFXm2TkpKEgQMHNvq5iKjlcGSLiNok97S+du3aNfraV155BaWlpViwYIHPuZCQEM8/WywW6PV6XHvttQBw0b+K+xMUFIT77rvP81osFuO+++5DSUkJDhw4AAAIDAyEWCwGUD298fz583A4HBg0aJDf9xwwYAB69eqFd955BwBw6tQpfPPNN5gyZUqd/cjIyMAXX3yB4uJiAMD69esxZMgQXHXVVT5ta34G5eXl0Ov1SElJ8Uxba4gZM2Z4vXYXFNi+fbvf9zEajdDr9Rg2bBhOnjwJo9EIoHrEqry8HPPmzfMZhXNPy9PpdCgoKMCDDz4IpVLpt8358+exa9cu3HnnnZ5n0uv1KC0tRWpqKvLy8vD77797nhkAVCpVg57V3Xf31/nz5y96zdNPPw2FQoHZs2f7nCsvL4dMJvM76ljT8OHDIQiC13RFf0wmEwBc8qjWmTNn8NNPP2HKlCmIjIz0HO/bty9uuOEGr++pUqnEDz/8gKKiIr/3+umnn5CXl4dJkyahtLTU85lVVlbi+uuvx549e+Byuersy2233YagoCC8//77nmO5ubk4cuQI/va3v3n14/Dhw8jLy2vUs5aXl1/0c3Kfd3+ubvfee6/XFM37778fQUFBns9nx44dMBgMSEtL8/p5CQwMxODBg32mDQPVI4U1paSk4OTJk416JiJ
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Построение диаграммы рассеяния\n",
|
|||
|
|
"plt.figure(figsize=(10, 6))\n",
|
|||
|
|
"sns.scatterplot(x='Open', y='Close', data=df_Gstocks)\n",
|
|||
|
|
"plt.title('Диаграмма рассеяния: Close vs Open')\n",
|
|||
|
|
"plt.xlabel('Цена открытия')\n",
|
|||
|
|
"plt.ylabel('Цена закрытия')\n",
|
|||
|
|
"plt.show()\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "aimenv",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.12.0"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|