diff --git a/lab_3/lab3.ipynb b/lab_3/lab3.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "df = pd.read_csv(\"C://Users//annal//aim//static//csv//Forbes_Billionaires.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Определим бизнес цели:\n",
+    "## 1- Прогнозирование места в рейтинге\n",
+    "## 2- Оценка факторов, влияющих на место в рейтинге"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Определим цели технического проекта:\n",
+    "## Построить модель, которая будет прогнозировать место в рейтинге на основе представленных данных об участнике\n",
+    "## Провести анализ данных для выявления важнейших характеристик для прогнозирования"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Проверим выбросы и усредним"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Колонка Networth:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 226\n",
+      "  Минимальное значение: 1.0\n",
+      "  Максимальное значение: 9.0\n",
+      "  1-й квантиль (Q1): 1.5\n",
+      "  3-й квантиль (Q3): 4.5\n",
+      "\n",
+      "Колонка Age:\n",
+      "  Есть выбросы: Да\n",
+      "  Количество выбросов: 6\n",
+      "  Минимальное значение: 26.5\n",
+      "  Максимальное значение: 100.0\n",
+      "  1-й квантиль (Q1): 55.0\n",
+      "  3-й квантиль (Q3): 74.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_columns = ['Networth', 'Age']\n",
+    "for column in numeric_columns:\n",
+    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
+    "        q1 = df[column].quantile(0.25)  # Находим 1-й квантиль (Q1)\n",
+    "        q3 = df[column].quantile(0.75)  # Находим 3-й квантиль (Q3)\n",
+    "        iqr = q3 - q1  # Вычисляем межквантильный размах (IQR)\n",
+    "\n",
+    "        # Определяем границы для выбросов\n",
+    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
+    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
+    "\n",
+    "        # Подсчитываем количество выбросов\n",
+    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
+    "        outlier_count = outliers.shape[0]\n",
+    "\n",
+    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
+    "        df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
+    "\n",
+    "        print(f\"Колонка {column}:\")\n",
+    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
+    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
+    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
+    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
+    "        print(f\"  1-й квантиль (Q1): {q1}\")\n",
+    "        print(f\"  3-й квантиль (Q3): {q3}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Превратим номинальные столбцы в числовые"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Rank   Networth   Age  Country  Source  Industry  \\\n",
+      "0      1       9.0  50.0       70     123         0   \n",
+      "1      2       9.0  58.0       70       5        15   \n",
+      "2      3       9.0  73.0       20      73         3   \n",
+      "3      4       9.0  66.0       70      81        15   \n",
+      "4      5       9.0  91.0       70      11         4   \n",
+      "\n",
+      "   Name_Abdulla Al Futtaim & family   \\\n",
+      "0                                0.0   \n",
+      "1                                0.0   \n",
+      "2                                0.0   \n",
+      "3                                0.0   \n",
+      "4                                0.0   \n",
+      "\n",
+      "   Name_Abdulla bin Ahmad Al Ghurair & family   Name_Abdulsamad Rabiu   \\\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",
+      "   Name_Abhay Firodia   ...  Name_Zhu Yan & family   Name_Zhu Yiming   \\\n",
+      "0                  0.0  ...                     0.0               0.0   \n",
+      "1                  0.0  ...                     0.0               0.0   \n",
+      "2                  0.0  ...                     0.0               0.0   \n",
+      "3                  0.0  ...                     0.0               0.0   \n",
+      "4                  0.0  ...                     0.0               0.0   \n",
+      "\n",
+      "   Name_Zhu Yiwen & family   Name_Zhuo Jun   Name_Ziv Aviram   \\\n",
+      "0                       0.0             0.0               0.0   \n",
+      "1                       0.0             0.0               0.0   \n",
+      "2                       0.0             0.0               0.0   \n",
+      "3                       0.0             0.0               0.0   \n",
+      "4                       0.0             0.0               0.0   \n",
+      "\n",
+      "   Name_Zong Qinghou   Name_Zong Yanmin   Name_Zugen Ni   Name_Zuowen Song   \\\n",
+      "0                 0.0                0.0             0.0                0.0   \n",
+      "1                 0.0                0.0             0.0                0.0   \n",
+      "2                 0.0                0.0             0.0                0.0   \n",
+      "3                 0.0                0.0             0.0                0.0   \n",
+      "4                 0.0                0.0             0.0                0.0   \n",
+      "\n",
+      "   Name_Zygmunt Solorz-Zak   \n",
+      "0                       0.0  \n",
+      "1                       0.0  \n",
+      "2                       0.0  \n",
+      "3                       0.0  \n",
+      "4                       0.0  \n",
+      "\n",
+      "[5 rows x 2603 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
+    "\n",
+    "# Определение категориальных признаков для преобразования\n",
+    "categorical_columns = ['Name']\n",
+    "\n",
+    "# Инициализация OneHotEncoder\n",
+    "encoder = OneHotEncoder(sparse_output=False, drop=\"first\")\n",
+    "\n",
+    "# Применение OneHotEncoder к выбранным категориальным признакам\n",
+    "encoded_values = encoder.fit_transform(df[categorical_columns])\n",
+    "\n",
+    "# Получение имен новых закодированных столбцов\n",
+    "encoded_columns = encoder.get_feature_names_out(categorical_columns)\n",
+    "\n",
+    "# Преобразование в DataFrame\n",
+    "encoded_values_df = pd.DataFrame(encoded_values, columns=encoded_columns)\n",
+    "\n",
+    "# Объединение закодированных значений с оригинальным DataFrame, исключив исходные категориальные столбцы\n",
+    "df = df.drop(columns=categorical_columns)\n",
+    "df = pd.concat([df.reset_index(drop=True), encoded_values_df.reset_index(drop=True)], axis=1)\n",
+    "\n",
+    "# Применение Label Encoding для столбца 'Country', 'Source', 'Industry'\n",
+    "label_encoder = LabelEncoder()\n",
+    "df['Country'] = label_encoder.fit_transform(df['Country'])\n",
+    "df['Source'] = label_encoder.fit_transform(df['Source'])\n",
+    "df['Industry'] = label_encoder.fit_transform(df['Industry'])\n",
+    "\n",
+    "\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Создадим выборки данных по параметру места в рейтинге"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Размер обучающей выборки: (1560, 2602)\n",
+      "Размер контрольной выборки: (520, 2602)\n",
+      "Размер тестовой выборки: (520, 2602)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Выделение признаков (X) и целевой переменной (y)\n",
+    "X = df.drop(columns=['Rank '])  # Признаки\n",
+    "y = df['Rank ']                 # Целевая переменная\n",
+    "\n",
+    "# Разделение данных на обучающую и временную выборки\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# Разделение временной выборки на контрольную и тестовую выборки\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
+    "\n",
+    "# Проверка размеров выборок\n",
+    "print(f\"Размер обучающей выборки: {X_train.shape}\")\n",
+    "print(f\"Размер контрольной выборки: {X_val.shape}\")\n",
+    "print(f\"Размер тестовой выборки: {X_test.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "# Функция для оценки распределения цены\n",
+    "def plot_distribution(y_data, title):\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    sns.histplot(y_data, kde=True, bins=50)\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Rank ')\n",
+    "    plt.ylabel('Frequency')\n",
+    "    plt.grid(True)\n",
+    "    plt.show()\n",
+    "\n",
+    "# Оценка распределения цены в каждой выборке\n",
+    "plot_distribution(y_train, \"Распределение места в обучающей выборке\")\n",
+    "plot_distribution(y_val, \"Распределение места в контрольной выборке\")\n",
+    "plot_distribution(y_test, \"Распределение места в тестовой выборке\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Применим min-max нормировку для улучшения качества работы модели"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Нормированные данные:\n",
+      "   Networth       Age   Country    Source  Industry  \\\n",
+      "0       1.0  0.319728  0.945946  0.137584  0.000000   \n",
+      "1       1.0  0.428571  0.945946  0.005593  0.882353   \n",
+      "2       1.0  0.632653  0.270270  0.081655  0.176471   \n",
+      "3       1.0  0.537415  0.945946  0.090604  0.882353   \n",
+      "4       1.0  0.877551  0.945946  0.012304  0.235294   \n",
+      "\n",
+      "   Name_Abdulla Al Futtaim & family   \\\n",
+      "0                                0.0   \n",
+      "1                                0.0   \n",
+      "2                                0.0   \n",
+      "3                                0.0   \n",
+      "4                                0.0   \n",
+      "\n",
+      "   Name_Abdulla bin Ahmad Al Ghurair & family   Name_Abdulsamad Rabiu   \\\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",
+      "   Name_Abhay Firodia   Name_Abigail Johnson   ...  Name_Zhu Yan & family   \\\n",
+      "0                  0.0                    0.0  ...                     0.0   \n",
+      "1                  0.0                    0.0  ...                     0.0   \n",
+      "2                  0.0                    0.0  ...                     0.0   \n",
+      "3                  0.0                    0.0  ...                     0.0   \n",
+      "4                  0.0                    0.0  ...                     0.0   \n",
+      "\n",
+      "   Name_Zhu Yiming   Name_Zhu Yiwen & family   Name_Zhuo Jun   \\\n",
+      "0               0.0                       0.0             0.0   \n",
+      "1               0.0                       0.0             0.0   \n",
+      "2               0.0                       0.0             0.0   \n",
+      "3               0.0                       0.0             0.0   \n",
+      "4               0.0                       0.0             0.0   \n",
+      "\n",
+      "   Name_Ziv Aviram   Name_Zong Qinghou   Name_Zong Yanmin   Name_Zugen Ni   \\\n",
+      "0               0.0                 0.0                0.0             0.0   \n",
+      "1               0.0                 0.0                0.0             0.0   \n",
+      "2               0.0                 0.0                0.0             0.0   \n",
+      "3               0.0                 0.0                0.0             0.0   \n",
+      "4               0.0                 0.0                0.0             0.0   \n",
+      "\n",
+      "   Name_Zuowen Song   Name_Zygmunt Solorz-Zak   \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",
+      "[5 rows x 2602 columns]\n",
+      "\n",
+      "Стандартизированные данные:\n",
+      "   Networth       Age   Country    Source  Industry  \\\n",
+      "0  2.266803 -1.081352  1.173910 -1.505003 -1.701719   \n",
+      "1  2.266803 -0.475422  1.173910 -2.004526  1.339990   \n",
+      "2  2.266803  0.660697 -0.805574 -1.716665 -1.093377   \n",
+      "3  2.266803  0.130508  1.173910 -1.682800  1.339990   \n",
+      "4  2.266803  2.024040  1.173910 -1.979126 -0.890597   \n",
+      "\n",
+      "   Name_Abdulla Al Futtaim & family   \\\n",
+      "0                          -0.019615   \n",
+      "1                          -0.019615   \n",
+      "2                          -0.019615   \n",
+      "3                          -0.019615   \n",
+      "4                          -0.019615   \n",
+      "\n",
+      "   Name_Abdulla bin Ahmad Al Ghurair & family   Name_Abdulsamad Rabiu   \\\n",
+      "0                                    -0.019615               -0.019615   \n",
+      "1                                    -0.019615               -0.019615   \n",
+      "2                                    -0.019615               -0.019615   \n",
+      "3                                    -0.019615               -0.019615   \n",
+      "4                                    -0.019615               -0.019615   \n",
+      "\n",
+      "   Name_Abhay Firodia   Name_Abigail Johnson   ...  Name_Zhu Yan & family   \\\n",
+      "0            -0.019615              -0.019615  ...               -0.019615   \n",
+      "1            -0.019615              -0.019615  ...               -0.019615   \n",
+      "2            -0.019615              -0.019615  ...               -0.019615   \n",
+      "3            -0.019615              -0.019615  ...               -0.019615   \n",
+      "4            -0.019615              -0.019615  ...               -0.019615   \n",
+      "\n",
+      "   Name_Zhu Yiming   Name_Zhu Yiwen & family   Name_Zhuo Jun   \\\n",
+      "0         -0.019615                 -0.019615       -0.019615   \n",
+      "1         -0.019615                 -0.019615       -0.019615   \n",
+      "2         -0.019615                 -0.019615       -0.019615   \n",
+      "3         -0.019615                 -0.019615       -0.019615   \n",
+      "4         -0.019615                 -0.019615       -0.019615   \n",
+      "\n",
+      "   Name_Ziv Aviram   Name_Zong Qinghou   Name_Zong Yanmin   Name_Zugen Ni   \\\n",
+      "0         -0.019615           -0.019615          -0.019615       -0.019615   \n",
+      "1         -0.019615           -0.019615          -0.019615       -0.019615   \n",
+      "2         -0.019615           -0.019615          -0.019615       -0.019615   \n",
+      "3         -0.019615           -0.019615          -0.019615       -0.019615   \n",
+      "4         -0.019615           -0.019615          -0.019615       -0.019615   \n",
+      "\n",
+      "   Name_Zuowen Song   Name_Zygmunt Solorz-Zak   \n",
+      "0          -0.019615                 -0.019615  \n",
+      "1          -0.019615                 -0.019615  \n",
+      "2          -0.019615                 -0.019615  \n",
+      "3          -0.019615                 -0.019615  \n",
+      "4          -0.019615                 -0.019615  \n",
+      "\n",
+      "[5 rows x 2602 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+    "\n",
+    "# Предполагаем, что вы уже выделили ваши признаки X\n",
+    "# Применение нормировки Min-Max к всем числовым признакам\n",
+    "min_max_scaler = MinMaxScaler()\n",
+    "X_normalized = pd.DataFrame(min_max_scaler.fit_transform(X), columns=X.columns)\n",
+    "\n",
+    "# Применение стандартизации к всем числовым признакам\n",
+    "standard_scaler = StandardScaler()\n",
+    "X_standardized = pd.DataFrame(standard_scaler.fit_transform(X), columns=X.columns)\n",
+    "\n",
+    "# Проверка первых 5 строк после нормировки\n",
+    "print(\"Нормированные данные:\")\n",
+    "print(X_normalized.head())\n",
+    "\n",
+    "# Проверка первых 5 строк после стандартизации\n",
+    "print(\"\\nСтандартизированные данные:\")\n",
+    "print(X_standardized.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Приведём пример использования future tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting featuretoolsNote: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  Downloading featuretools-1.31.0-py3-none-any.whl.metadata (15 kB)\n",
+      "Collecting cloudpickle>=1.5.0 (from featuretools)\n",
+      "  Downloading cloudpickle-3.1.0-py3-none-any.whl.metadata (7.0 kB)\n",
+      "Collecting holidays>=0.17 (from featuretools)\n",
+      "  Downloading holidays-0.59-py3-none-any.whl.metadata (25 kB)\n",
+      "Requirement already satisfied: numpy>=1.25.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from featuretools) (2.1.1)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from featuretools) (24.1)\n",
+      "Requirement already satisfied: pandas>=2.0.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from featuretools) (2.2.2)\n",
+      "Requirement already satisfied: psutil>=5.7.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from featuretools) (6.0.0)\n",
+      "Requirement already satisfied: scipy>=1.10.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from featuretools) (1.14.1)\n",
+      "Collecting tqdm>=4.66.3 (from featuretools)\n",
+      "  Downloading tqdm-4.66.6-py3-none-any.whl.metadata (57 kB)\n",
+      "Collecting woodwork>=0.28.0 (from featuretools)\n",
+      "  Downloading woodwork-0.31.0-py3-none-any.whl.metadata (10 kB)\n",
+      "Requirement already satisfied: python-dateutil in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from holidays>=0.17->featuretools) (2.9.0.post0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from pandas>=2.0.0->featuretools) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from pandas>=2.0.0->featuretools) (2024.1)\n",
+      "Requirement already satisfied: colorama in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from tqdm>=4.66.3->featuretools) (0.4.6)\n",
+      "Requirement already satisfied: scikit-learn>=1.1.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from woodwork>=0.28.0->featuretools) (1.5.2)\n",
+      "Collecting importlib-resources>=5.10.0 (from woodwork>=0.28.0->featuretools)\n",
+      "  Downloading importlib_resources-6.4.5-py3-none-any.whl.metadata (4.0 kB)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from python-dateutil->holidays>=0.17->featuretools) (1.16.0)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from scikit-learn>=1.1.0->woodwork>=0.28.0->featuretools) (1.4.2)\n",
+      "Requirement already satisfied: threadpoolctl>=3.1.0 in c:\\users\\annal\\aim\\.venv\\lib\\site-packages (from scikit-learn>=1.1.0->woodwork>=0.28.0->featuretools) (3.5.0)\n",
+      "Downloading featuretools-1.31.0-py3-none-any.whl (587 kB)\n",
+      "   ---------------------------------------- 0.0/587.9 kB ? eta -:--:--\n",
+      "   ----------------- ---------------------- 262.1/587.9 kB ? eta -:--:--\n",
+      "   ---------------------------------------- 587.9/587.9 kB 1.5 MB/s eta 0:00:00\n",
+      "Downloading cloudpickle-3.1.0-py3-none-any.whl (22 kB)\n",
+      "Downloading holidays-0.59-py3-none-any.whl (1.1 MB)\n",
+      "   ---------------------------------------- 0.0/1.1 MB ? eta -:--:--\n",
+      "   --------- ------------------------------ 0.3/1.1 MB ? eta -:--:--\n",
+      "   ---------------------------- ----------- 0.8/1.1 MB 1.9 MB/s eta 0:00:01\n",
+      "   ---------------------------------------- 1.1/1.1 MB 2.2 MB/s eta 0:00:00\n",
+      "Downloading tqdm-4.66.6-py3-none-any.whl (78 kB)\n",
+      "Downloading woodwork-0.31.0-py3-none-any.whl (215 kB)\n",
+      "Downloading importlib_resources-6.4.5-py3-none-any.whl (36 kB)\n",
+      "Installing collected packages: tqdm, importlib-resources, cloudpickle, holidays, woodwork, featuretools\n",
+      "Successfully installed cloudpickle-3.1.0 featuretools-1.31.0 holidays-0.59 importlib-resources-6.4.5 tqdm-4.66.6 woodwork-0.31.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install --upgrade featuretools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting setuptools\n",
+      "  Downloading setuptools-75.3.0-py3-none-any.whl.metadata (6.9 kB)\n",
+      "Downloading setuptools-75.3.0-py3-none-any.whl (1.3 MB)\n",
+      "   ---------------------------------------- 0.0/1.3 MB ? eta -:--:--\n",
+      "   ---------------- ----------------------- 0.5/1.3 MB 3.4 MB/s eta 0:00:01\n",
+      "   ---------------------------------------- 1.3/1.3 MB 3.7 MB/s eta 0:00:00\n",
+      "Installing collected packages: setuptools\n",
+      "Successfully installed setuptools-75.3.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install --upgrade setuptools"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}