diff --git a/backup.sh b/backup.sh
new file mode 100644
index 00000000..5ac257c4
--- /dev/null
+++ b/backup.sh
@@ -0,0 +1,4 @@
+echo "Hello World"
+echo "Goodbye World"
+echo "Hi"
+echo "Bye"
diff --git "a/\320\234\320\270\320\275\320\260\320\265\320\262\320\260_\320\230\320\22422_1_\320\272\321\203\321\200\321\201\320\276\320\262\320\276\320\271_\320\277\321\200\320\276\320\265\320\272\321\202.ipynb" "b/\320\234\320\270\320\275\320\260\320\265\320\262\320\260_\320\230\320\22422_1_\320\272\321\203\321\200\321\201\320\276\320\262\320\276\320\271_\320\277\321\200\320\276\320\265\320\272\321\202.ipynb"
new file mode 100644
index 00000000..69bb2304
--- /dev/null
+++ "b/\320\234\320\270\320\275\320\260\320\265\320\262\320\260_\320\230\320\22422_1_\320\272\321\203\321\200\321\201\320\276\320\262\320\276\320\271_\320\277\321\200\320\276\320\265\320\272\321\202.ipynb"
@@ -0,0 +1,2771 @@
+{
+  "metadata": {
+    "kernelspec": {
+      "language": "python",
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10.13",
+      "mimetype": "text/x-python",
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "pygments_lexer": "ipython3",
+      "nbconvert_exporter": "python",
+      "file_extension": ".py"
+    },
+    "kaggle": {
+      "accelerator": "none",
+      "dataSources": [
+        {
+          "sourceId": 9932952,
+          "sourceType": "datasetVersion",
+          "datasetId": 5328600
+        }
+      ],
+      "dockerImageVersionId": 30746,
+      "isInternetEnabled": false,
+      "language": "python",
+      "sourceType": "notebook",
+      "isGpuEnabled": false
+    },
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "gBN_8_S9ZyH4",
+        "D2gz9Z0dZ7rH",
+        "kauWPkoXfVj1",
+        "vFJwJHLrLI-B",
+        "5K9otVChmWZ1"
+      ],
+      "include_colab_link": true
+    }
+  },
+  "nbformat_minor": 0,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/prunuspadus/0_git_basics/blob/master/%D0%9C%D0%B8%D0%BD%D0%B0%D0%B5%D0%B2%D0%B0_%D0%98%D0%9422_1_%D0%BA%D1%83%D1%80%D1%81%D0%BE%D0%B2%D0%BE%D0%B9_%D0%BF%D1%80%D0%BE%D0%B5%D0%BA%D1%82.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+        "from sklearn import preprocessing\n",
+        "\n",
+        "import matplotlib.dates as mdates\n",
+        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+        "\n",
+        "from statsmodels.tsa.arima.model import ARIMA\n",
+        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+        "\n",
+        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "from sklearn.ensemble import GradientBoostingRegressor\n",
+        "from sklearn.svm import SVR"
+      ],
+      "metadata": {
+        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+        "execution": {
+          "iopub.status.busy": "2024-08-28T10:53:48.066165Z",
+          "iopub.execute_input": "2024-08-28T10:53:48.066646Z",
+          "iopub.status.idle": "2024-08-28T10:53:52.114958Z",
+          "shell.execute_reply.started": "2024-08-28T10:53:48.066598Z",
+          "shell.execute_reply": "2024-08-28T10:53:52.113945Z"
+        },
+        "trusted": true,
+        "jupyter": {
+          "source_hidden": true
+        },
+        "id": "fWxFuoEVZyHz"
+      },
+      "outputs": [],
+      "execution_count": 300
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('/content/index.csv')\n",
+        "print(f\"Строки: {df.shape[0]}\")\n",
+        "print(f\"Столбцы: {df.shape[1]}\")\n",
+        "print(df.dtypes)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-08-28T10:53:52.11624Z",
+          "iopub.execute_input": "2024-08-28T10:53:52.116729Z",
+          "iopub.status.idle": "2024-08-28T10:53:52.168897Z",
+          "shell.execute_reply.started": "2024-08-28T10:53:52.116699Z",
+          "shell.execute_reply": "2024-08-28T10:53:52.167762Z"
+        },
+        "trusted": true,
+        "id": "9G5ZsoA_ZyH0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f5149818-9f1a-4cab-e320-85016d027d74"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Строки: 2341\n",
+            "Столбцы: 6\n",
+            "date            object\n",
+            "datetime        object\n",
+            "cash_type       object\n",
+            "card            object\n",
+            "money          float64\n",
+            "coffee_name     object\n",
+            "dtype: object\n"
+          ]
+        }
+      ],
+      "execution_count": 301
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "id": "3s3eoHGDXWXL",
+        "outputId": "b1e71609-1890-44fc-e11f-5c47c68921ed"
+      },
+      "execution_count": 302,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         date                 datetime cash_type                 card  money  \\\n",
+              "0  2024-03-01  2024-03-01 10:15:50.520      card  ANON-0000-0000-0001   38.7   \n",
+              "1  2024-03-01  2024-03-01 12:19:22.539      card  ANON-0000-0000-0002   38.7   \n",
+              "2  2024-03-01  2024-03-01 12:20:18.089      card  ANON-0000-0000-0002   38.7   \n",
+              "3  2024-03-01  2024-03-01 13:46:33.006      card  ANON-0000-0000-0003   28.9   \n",
+              "4  2024-03-01  2024-03-01 13:48:14.626      card  ANON-0000-0000-0004   38.7   \n",
+              "\n",
+              "     coffee_name  \n",
+              "0          Latte  \n",
+              "1  Hot Chocolate  \n",
+              "2  Hot Chocolate  \n",
+              "3      Americano  \n",
+              "4          Latte  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-35635193-9136-4f6b-9de9-992dfca8bd1a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>date</th>\n",
+              "      <th>datetime</th>\n",
+              "      <th>cash_type</th>\n",
+              "      <th>card</th>\n",
+              "      <th>money</th>\n",
+              "      <th>coffee_name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 10:15:50.520</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0001</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>Latte</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 12:19:22.539</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0002</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>Hot Chocolate</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 12:20:18.089</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0002</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>Hot Chocolate</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 13:46:33.006</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0003</td>\n",
+              "      <td>28.9</td>\n",
+              "      <td>Americano</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 13:48:14.626</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0004</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>Latte</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35635193-9136-4f6b-9de9-992dfca8bd1a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-35635193-9136-4f6b-9de9-992dfca8bd1a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-35635193-9136-4f6b-9de9-992dfca8bd1a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-c484cd66-67a6-4e60-bb5e-2e3302582f69\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c484cd66-67a6-4e60-bb5e-2e3302582f69')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-c484cd66-67a6-4e60-bb5e-2e3302582f69 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2341,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 258,\n        \"samples\": [\n          \"2024-05-09\",\n          \"2024-04-15\",\n          \"2024-03-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2341,\n        \"samples\": [\n          \"2024-09-15 08:42:40.941\",\n          \"2024-06-01 21:51:04.971\",\n          \"2024-04-06 12:32:31.398\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cash_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"cash\",\n          \"card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"card\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 915,\n        \"samples\": [\n          \"ANON-0000-0000-0381\",\n          \"ANON-0000-0000-0856\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"money\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.094530428290618,\n        \"min\": 18.12,\n        \"max\": 40.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          38.7,\n          30.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Hot Chocolate\",\n          \"Cortado\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 302
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Предобработка данных"
+      ],
+      "metadata": {
+        "id": "UX6cOh8kfHsn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Удалим все лишние пробелы из имен столбцов\n",
+        "df.columns = df.columns.str.strip()\n",
+        "\n",
+        "# Преобразуем 'date' и 'datetime' в объекты datetime\n",
+        "df['date'] = pd.to_datetime(df['date'])\n",
+        "df['datetime'] = pd.to_datetime(df['datetime'])\n",
+        "\n",
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iR8KLlAqYKD-",
+        "outputId": "d7cef474-b4ce-4336-9198-f426772cc4c7"
+      },
+      "execution_count": 303,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 2341 entries, 0 to 2340\n",
+            "Data columns (total 6 columns):\n",
+            " #   Column       Non-Null Count  Dtype         \n",
+            "---  ------       --------------  -----         \n",
+            " 0   date         2341 non-null   datetime64[ns]\n",
+            " 1   datetime     2341 non-null   datetime64[ns]\n",
+            " 2   cash_type    2341 non-null   object        \n",
+            " 3   card         2252 non-null   object        \n",
+            " 4   money        2341 non-null   float64       \n",
+            " 5   coffee_name  2341 non-null   object        \n",
+            "dtypes: datetime64[ns](2), float64(1), object(3)\n",
+            "memory usage: 109.9+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mgd0Jsr-jQ7y",
+        "outputId": "5e142c0c-6bda-4ef9-e50a-e1f57e8132b3"
+      },
+      "execution_count": 304,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 304
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "qw3JJ8wejQ5A",
+        "outputId": "24b0adcc-bc89-4026-fa01-32ffa910d6c6"
+      },
+      "execution_count": 305,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "date            0\n",
+              "datetime        0\n",
+              "cash_type       0\n",
+              "card           89\n",
+              "money           0\n",
+              "coffee_name     0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>datetime</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cash_type</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>card</th>\n",
+              "      <td>89</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>money</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>coffee_name</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 305
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обработка пропусков в столбце card\n",
+        "df['card'].fillna('Не указано', inplace=True)  # Заполнение пропусков"
+      ],
+      "metadata": {
+        "id": "u4DO3OIqX0qP"
+      },
+      "execution_count": 306,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_norm = df.copy()\n",
+        "df_norm['hour'] = df_norm['datetime'].dt.hour\n",
+        "df_norm['day_of_week'] = df_norm['datetime'].dt.dayofweek"
+      ],
+      "metadata": {
+        "id": "IabKhJvRMj20"
+      },
+      "execution_count": 307,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# One-Hot Encoding для coffee_name\n",
+        "one_hot = pd.get_dummies(df_norm['coffee_name'], prefix='coffee')\n",
+        "one_hot = one_hot.astype(int)"
+      ],
+      "metadata": {
+        "id": "tHetfdvxJWLZ"
+      },
+      "execution_count": 308,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_norm = pd.concat([df_norm, one_hot], axis=1)\n",
+        "df_norm.drop('coffee_name', axis=1, inplace=True)\n",
+        "df_norm.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "id": "XPk6D09PMmtd",
+        "outputId": "74558e0b-f375-416b-adb4-079d712c67a6"
+      },
+      "execution_count": 309,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        date                datetime cash_type                 card  money  \\\n",
+              "0 2024-03-01 2024-03-01 10:15:50.520      card  ANON-0000-0000-0001   38.7   \n",
+              "1 2024-03-01 2024-03-01 12:19:22.539      card  ANON-0000-0000-0002   38.7   \n",
+              "2 2024-03-01 2024-03-01 12:20:18.089      card  ANON-0000-0000-0002   38.7   \n",
+              "3 2024-03-01 2024-03-01 13:46:33.006      card  ANON-0000-0000-0003   28.9   \n",
+              "4 2024-03-01 2024-03-01 13:48:14.626      card  ANON-0000-0000-0004   38.7   \n",
+              "\n",
+              "   hour  day_of_week  coffee_Americano  coffee_Americano with Milk  \\\n",
+              "0    10            4                 0                           0   \n",
+              "1    12            4                 0                           0   \n",
+              "2    12            4                 0                           0   \n",
+              "3    13            4                 1                           0   \n",
+              "4    13            4                 0                           0   \n",
+              "\n",
+              "   coffee_Cappuccino  coffee_Cocoa  coffee_Cortado  coffee_Espresso  \\\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",
+              "   coffee_Hot Chocolate  coffee_Latte  \n",
+              "0                     0             1  \n",
+              "1                     1             0  \n",
+              "2                     1             0  \n",
+              "3                     0             0  \n",
+              "4                     0             1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b63c57bd-229e-422f-a79a-d9f127eef8c1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>date</th>\n",
+              "      <th>datetime</th>\n",
+              "      <th>cash_type</th>\n",
+              "      <th>card</th>\n",
+              "      <th>money</th>\n",
+              "      <th>hour</th>\n",
+              "      <th>day_of_week</th>\n",
+              "      <th>coffee_Americano</th>\n",
+              "      <th>coffee_Americano with Milk</th>\n",
+              "      <th>coffee_Cappuccino</th>\n",
+              "      <th>coffee_Cocoa</th>\n",
+              "      <th>coffee_Cortado</th>\n",
+              "      <th>coffee_Espresso</th>\n",
+              "      <th>coffee_Hot Chocolate</th>\n",
+              "      <th>coffee_Latte</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 10:15:50.520</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0001</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>10</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 12:19:22.539</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0002</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>12</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 12:20:18.089</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0002</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>12</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 13:46:33.006</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0003</td>\n",
+              "      <td>28.9</td>\n",
+              "      <td>13</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2024-03-01</td>\n",
+              "      <td>2024-03-01 13:48:14.626</td>\n",
+              "      <td>card</td>\n",
+              "      <td>ANON-0000-0000-0004</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>13</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>1</td>\n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b63c57bd-229e-422f-a79a-d9f127eef8c1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b63c57bd-229e-422f-a79a-d9f127eef8c1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b63c57bd-229e-422f-a79a-d9f127eef8c1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-19d1b3df-6e4e-4d69-b38c-e40f816ac981\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
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+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-19d1b3df-6e4e-4d69-b38c-e40f816ac981 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_norm",
+              "summary": "{\n  \"name\": \"df_norm\",\n  \"rows\": 2341,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-03-01 00:00:00\",\n        \"max\": \"2024-11-16 00:00:00\",\n        \"num_unique_values\": 258,\n        \"samples\": [\n          \"2024-05-09 00:00:00\",\n          \"2024-04-15 00:00:00\",\n          \"2024-03-10 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-03-01 10:15:50.520000\",\n        \"max\": \"2024-11-16 14:25:47.388000\",\n        \"num_unique_values\": 2341,\n        \"samples\": [\n          \"2024-09-15 08:42:40.941000\",\n          \"2024-06-01 21:51:04.971000\",\n          \"2024-04-06 12:32:31.398000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cash_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"cash\",\n          \"card\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"card\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 916,\n        \"samples\": [\n          \"ANON-0000-0000-0380\",\n          \"ANON-0000-0000-0879\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"money\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.094530428290618,\n        \"min\": 18.12,\n        \"max\": 40.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          38.7,\n          30.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          10,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Americano\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Americano with Milk\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Cappuccino\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Cocoa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Cortado\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Espresso\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Hot Chocolate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"coffee_Latte\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 309
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Нормализация money"
+      ],
+      "metadata": {
+        "id": "L1dOWHgqJ5p-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scaler = MinMaxScaler()\n",
+        "df_norm['money'] = scaler.fit_transform(df_norm[['money']])\n",
+        "df_norm['money']"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 459
+        },
+        "id": "FEI_r0uFJ3Xr",
+        "outputId": "67d73f58-37d1-4ba1-d079-7b1bf5d70d63"
+      },
+      "execution_count": 310,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0       0.940585\n",
+              "1       0.940585\n",
+              "2       0.940585\n",
+              "3       0.492687\n",
+              "4       0.940585\n",
+              "          ...   \n",
+              "2336    0.806216\n",
+              "2337    0.582267\n",
+              "2338    0.806216\n",
+              "2339    0.806216\n",
+              "2340    0.582267\n",
+              "Name: money, Length: 2341, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>money</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.940585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.940585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.940585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.492687</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.940585</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2336</th>\n",
+              "      <td>0.806216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2337</th>\n",
+              "      <td>0.582267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2338</th>\n",
+              "      <td>0.806216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2339</th>\n",
+              "      <td>0.806216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2340</th>\n",
+              "      <td>0.582267</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2341 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 310
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Описательный анализ <a class=\"anchor\"  id=\"chapter1\"></a>"
+      ],
+      "metadata": {
+        "id": "gBN_8_S9ZyH4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 301
+        },
+        "id": "HxoyaOPVKvlM",
+        "outputId": "0e176a72-ba63-41d4-c388-dde382530959"
+      },
+      "execution_count": 311,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                date                       datetime  \\\n",
+              "count                           2341                           2341   \n",
+              "mean   2024-07-25 14:24:51.670226432  2024-07-26 05:13:19.630455296   \n",
+              "min              2024-03-01 00:00:00     2024-03-01 10:15:50.520000   \n",
+              "25%              2024-05-24 00:00:00  2024-05-24 18:17:40.329999872   \n",
+              "50%              2024-08-05 00:00:00     2024-08-05 09:19:19.176000   \n",
+              "75%              2024-10-01 00:00:00  2024-10-01 12:14:23.382000128   \n",
+              "max              2024-11-16 00:00:00     2024-11-16 14:25:47.388000   \n",
+              "std                              NaN                            NaN   \n",
+              "\n",
+              "             money  \n",
+              "count  2341.000000  \n",
+              "mean     31.816736  \n",
+              "min      18.120000  \n",
+              "25%      27.920000  \n",
+              "50%      32.820000  \n",
+              "75%      35.760000  \n",
+              "max      40.000000  \n",
+              "std       5.094530  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-47f686e5-e010-4c2f-a244-e47c7465d0c9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>date</th>\n",
+              "      <th>datetime</th>\n",
+              "      <th>money</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2341</td>\n",
+              "      <td>2341</td>\n",
+              "      <td>2341.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2024-07-25 14:24:51.670226432</td>\n",
+              "      <td>2024-07-26 05:13:19.630455296</td>\n",
+              "      <td>31.816736</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>2024-03-01 00:00:00</td>\n",
+              "      <td>2024-03-01 10:15:50.520000</td>\n",
+              "      <td>18.120000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2024-05-24 00:00:00</td>\n",
+              "      <td>2024-05-24 18:17:40.329999872</td>\n",
+              "      <td>27.920000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>2024-08-05 00:00:00</td>\n",
+              "      <td>2024-08-05 09:19:19.176000</td>\n",
+              "      <td>32.820000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>2024-10-01 00:00:00</td>\n",
+              "      <td>2024-10-01 12:14:23.382000128</td>\n",
+              "      <td>35.760000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2024-11-16 00:00:00</td>\n",
+              "      <td>2024-11-16 14:25:47.388000</td>\n",
+              "      <td>40.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5.094530</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47f686e5-e010-4c2f-a244-e47c7465d0c9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-47f686e5-e010-4c2f-a244-e47c7465d0c9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-47f686e5-e010-4c2f-a244-e47c7465d0c9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-c0a0c580-8985-4151-8b6e-c73580869f33\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c0a0c580-8985-4151-8b6e-c73580869f33')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-c0a0c580-8985-4151-8b6e-c73580869f33 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000002341\",\n        \"max\": \"2024-11-16 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2341\",\n          \"2024-07-25 14:24:51.670226432\",\n          \"2024-10-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000002341\",\n        \"max\": \"2024-11-16 14:25:47.388000\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2341\",\n          \"2024-07-26 05:13:19.630455296\",\n          \"2024-10-01 12:14:23.382000128\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"money\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 818.0699168994628,\n        \"min\": 5.094530428290618,\n        \"max\": 2341.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.81673643741991,\n          35.76,\n          2341.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 311
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Матрица корреляций"
+      ],
+      "metadata": {
+        "id": "_6B5oVB-t3zn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "label_encoder = preprocessing.LabelEncoder()\n",
+        "\n",
+        "update_df = df.copy()\n",
+        "\n",
+        "update_df['datetime'] = label_encoder.fit_transform(update_df['datetime'])\n",
+        "update_df['cash_type'] = label_encoder.fit_transform(update_df['cash_type'])\n",
+        "update_df['card'] = label_encoder.fit_transform(update_df['card'])\n",
+        "update_df['money'] = label_encoder.fit_transform(update_df['money'])\n",
+        "update_df['coffee_name'] = label_encoder.fit_transform(update_df['coffee_name'])"
+      ],
+      "metadata": {
+        "id": "7pPW4-gKsfg_"
+      },
+      "execution_count": 312,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10,10), linewidth=.5)\n",
+        "sns.heatmap(update_df.corr(), annot=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 853
+        },
+        "outputId": "0b80a833-d050-44b4-dcc7-af760157d167",
+        "id": "oNGbFw2TsfhB"
+      },
+      "execution_count": 313,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 313
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x1000 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Визуализация ежедневных продаж"
+      ],
+      "metadata": {
+        "id": "luWpXIKcwd4h"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Повторная выборка по дням и расчет ежедневных продаж\n",
+        "daily_sales = df.groupby('date')['money'].sum()\n",
+        "daily_sales.index = pd.to_datetime(daily_sales.index)\n",
+        "daily_sales = daily_sales.asfreq('D', fill_value=0)  # Ensure the index has a daily frequency.\n",
+        "print(f\"Number of days: {daily_sales.shape[0]}\")\n",
+        "daily_sales.head(3)"
+      ],
+      "metadata": {
+        "id": "HgD-HdYLs2zW",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 228
+        },
+        "outputId": "0ab9fa00-beb4-4602-d30b-bcb2f0f3036b"
+      },
+      "execution_count": 314,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of days: 261\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "date\n",
+              "2024-03-01    396.3\n",
+              "2024-03-02    228.1\n",
+              "2024-03-03    349.1\n",
+              "Freq: D, Name: money, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>money</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2024-03-01</th>\n",
+              "      <td>396.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-03-02</th>\n",
+              "      <td>228.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-03-03</th>\n",
+              "      <td>349.1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 314
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rolling_mean = daily_sales.rolling(window=7).mean()\n",
+        "rolling_std = daily_sales.rolling(window=7).std()\n",
+        "\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.plot(daily_sales, label='Ежедневные продажи', color='blue')\n",
+        "plt.plot(rolling_mean, label='Скользящее среднее за 7 дней', color='red')\n",
+        "plt.plot(rolling_std, label='Скользящее стандартное отклонение за 7 дней', color='green')\n",
+        "plt.title('Скользящая статистика')\n",
+        "plt.xlabel('Дата')\n",
+        "plt.ylabel('Продажи ($)')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "A1l63KJAZYE_",
+        "outputId": "9554e57f-aa2e-40b3-c50f-1bb3469d867c"
+      },
+      "execution_count": 315,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sales_by_coffee_type = df.groupby('coffee_name')['money'].sum()\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sales_by_coffee_type.plot(kind='bar')\n",
+        "plt.title('Продажи кофе каждого вида')\n",
+        "plt.xlabel('Вид кофе')\n",
+        "plt.ylabel('Деньги')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.grid(True)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "zTNDXTozgB_v",
+        "outputId": "223f3fdc-34b9-4aef-9638-b4176a215a84"
+      },
+      "execution_count": 316,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "payment_distribution = df['cash_type'].value_counts()\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "payment_distribution.plot(kind='bar')\n",
+        "plt.title('Распределение способов оплаты')\n",
+        "plt.xlabel('Способ оплаты')\n",
+        "plt.ylabel('Количество транзакций')\n",
+        "plt.xticks(rotation=0)\n",
+        "plt.grid(True)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "dZObrmbwiq2d",
+        "outputId": "e2a98ccf-9612-4e98-d034-34aeffb5fb57"
+      },
+      "execution_count": 317,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Декомпозиция временных рядов"
+      ],
+      "metadata": {
+        "id": "D2gz9Z0dZ7rH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "decomposition = seasonal_decompose(daily_sales, model='add')\n",
+        "trend = decomposition.trend\n",
+        "seasonal = decomposition.seasonal\n",
+        "residual = decomposition.resid\n",
+        "\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "\n",
+        "plt.subplot(4, 1, 1)\n",
+        "plt.plot(daily_sales, label='Оригинал')\n",
+        "plt.title('Исходный временной ряд')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.subplot(4, 1, 2)\n",
+        "plt.plot(trend, label='Тренд', color='red')\n",
+        "plt.title('Тренд')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.subplot(4, 1, 3)\n",
+        "plt.plot(seasonal, label='Сезонность', color='green')\n",
+        "plt.title('Сезонность')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.subplot(4, 1, 4)\n",
+        "plt.plot(residual, label='Остатки', color='gray')\n",
+        "plt.title('Остатки')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "OJOctT9_Z_Z4",
+        "outputId": "0ef2358c-7041-493a-d082-f8ad922fbafd"
+      },
+      "execution_count": 318,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "daily_sales = df.groupby('date')['money'].sum()\n",
+        "daily_sales.index = pd.to_datetime(daily_sales.index)\n",
+        "daily_sales = daily_sales.asfreq('D', fill_value=0)  # Ensure the index has a daily frequency.\n",
+        "print(f\"Количество дней: {daily_sales.shape[0]}\")\n",
+        "daily_sales.head(3)"
+      ],
+      "metadata": {
+        "execution": {
+          "iopub.status.busy": "2024-08-28T10:53:59.958823Z",
+          "iopub.execute_input": "2024-08-28T10:53:59.959457Z",
+          "iopub.status.idle": "2024-08-28T10:53:59.983327Z",
+          "shell.execute_reply.started": "2024-08-28T10:53:59.959413Z",
+          "shell.execute_reply": "2024-08-28T10:53:59.982157Z"
+        },
+        "trusted": true,
+        "id": "I1qC9_E5ZyIW",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 228
+        },
+        "outputId": "3fef3a1a-2c66-4cba-aa1c-195049d8b070"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Количество дней: 261\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "date\n",
+              "2024-03-01    396.3\n",
+              "2024-03-02    228.1\n",
+              "2024-03-03    349.1\n",
+              "Freq: D, Name: money, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>money</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2024-03-01</th>\n",
+              "      <td>396.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-03-02</th>\n",
+              "      <td>228.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-03-03</th>\n",
+              "      <td>349.1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 319
+        }
+      ],
+      "execution_count": 319
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Моделирование <a class=\"anchor\"  id=\"chapter2\"></a>"
+      ],
+      "metadata": {
+        "id": "i4Ci2moSZyIO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# ARIMA & SARIMAX"
+      ],
+      "metadata": {
+        "id": "DkNXR43NLIz2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Проверка на стационарность"
+      ],
+      "metadata": {
+        "id": "or0AyL5sK98A"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from statsmodels.tsa.stattools import adfuller\n",
+        "from statsmodels.tsa.arima.model import ARIMA\n",
+        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
+      ],
+      "metadata": {
+        "id": "88OVUhqVDbo7"
+      },
+      "execution_count": 320,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def interpret_adfuller_test(pvalue, sig_level):\n",
+        "    if pvalue < sig_level:\n",
+        "        return \"Отвергаем H_0: ряд стационарный!\"\n",
+        "    return \"Не отвеграем H_0: ряд не стационарный!\""
+      ],
+      "metadata": {
+        "id": "h4WUtMikDdL5"
+      },
+      "execution_count": 321,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "SIGNIFICANCE_LEVEL = 0.05 # уровень значимости\n",
+        "\n",
+        "result = adfuller(df_norm['money'])\n",
+        "print(f'ADF Statistic: {result[0]}')\n",
+        "print(f'p-value: {result[1]}')\n",
+        "print(interpret_adfuller_test(result[1], SIGNIFICANCE_LEVEL))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tFW04QICDdIe",
+        "outputId": "1a63aaba-6a46-4ea2-b9e7-8955fc8a6aa7"
+      },
+      "execution_count": 322,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ADF Statistic: -3.1329517105873896\n",
+            "p-value: 0.024208786914566897\n",
+            "Отвергаем H_0: ряд стационарный!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Определение параметров для моделей"
+      ],
+      "metadata": {
+        "id": "mnxy9ZPGK2kK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "seasonal_diff = daily_sales.diff(7).dropna()\n",
+        "seasonal_diff.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 210
+        },
+        "id": "RDwfqXo7DdFQ",
+        "outputId": "2eb70a99-8fb1-4585-929a-da2b6ac7f7ac"
+      },
+      "execution_count": 323,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "date\n",
+              "2024-03-08   -130.8\n",
+              "2024-03-09    251.3\n",
+              "2024-03-10   -117.5\n",
+              "Freq: D, Name: money, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>money</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2024-03-08</th>\n",
+              "      <td>-130.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-03-09</th>\n",
+              "      <td>251.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-03-10</th>\n",
+              "      <td>-117.5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 323
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n",
+        "plot_acf(seasonal_diff, lags=20, ax=axes[0])\n",
+        "plot_pacf(seasonal_diff, lags=20, ax=axes[1])\n",
+        "axes[0].set_xlabel('Lag')\n",
+        "axes[0].set_ylabel('Correlation')\n",
+        "axes[1].set_xlabel('Lag')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "id": "aHT-sZOvDctf",
+        "outputId": "19b2972a-d534-4438-e72e-cb602fe942c0"
+      },
+      "execution_count": 324,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x300 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Выделение обучающей и тестовой выборки"
+      ],
+      "metadata": {
+        "id": "TMGYFfijdIOT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results = {}\n",
+        "predictions ={}"
+      ],
+      "metadata": {
+        "id": "rlzp_8JCPBbz"
+      },
+      "execution_count": 325,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обработка данных\n",
+        "X = df_norm[['hour', 'day_of_week', 'coffee_Americano', 'coffee_Americano with Milk',\n",
+        "              'coffee_Cappuccino', 'coffee_Cocoa', 'coffee_Cortado',\n",
+        "              'coffee_Espresso', 'coffee_Hot Chocolate', 'coffee_Latte']]\n",
+        "X = X.apply(pd.to_numeric)\n",
+        "\n",
+        "y = df_norm['money']"
+      ],
+      "metadata": {
+        "id": "ikv0xT9-E5rT"
+      },
+      "execution_count": 326,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ],
+      "metadata": {
+        "id": "WtmVJ8HVE9nU"
+      },
+      "execution_count": 327,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train.shape, X_test.shape, y_train.shape,  y_test.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5W8TDZS0F_g0",
+        "outputId": "ea45a2bf-c05b-42d1-bb93-966026ac273f"
+      },
+      "execution_count": 328,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((1872, 10), (469, 10), (1872,), (469,))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 328
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Обучение модели ARIMA"
+      ],
+      "metadata": {
+        "id": "1dvXDcENfMdw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Параметры\n",
+        "p, d, q = 3, 1, 0"
+      ],
+      "metadata": {
+        "id": "XjnbY2UbSfaS"
+      },
+      "execution_count": 329,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучение моделей ARIMA и SARIMAX\n",
+        "# Предполагаем, что y является временным рядом\n",
+        "y_train_arima = y[:len(y_train)]\n",
+        "y_test_arima = y[len(y_train):]\n",
+        "\n",
+        "# Обучение модели ARIMA\n",
+        "arima_model = ARIMA(y_train_arima, order=(p, d, q))  # Параметры (p, d, q) могут быть изменены\n",
+        "arima_fit = arima_model.fit()\n",
+        "arima_pred = arima_fit.forecast(steps=len(y_test_arima))"
+      ],
+      "metadata": {
+        "id": "CTEYag1NFhLQ"
+      },
+      "execution_count": 330,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Оценка качества ARIMA"
+      ],
+      "metadata": {
+        "id": "htkxiJbfd-BW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results['ARIMA'] = {\n",
+        "    'MAE': mean_absolute_error(y_test_arima, arima_pred),\n",
+        "    'MSE': mean_squared_error(y_test_arima, arima_pred),\n",
+        "    'R²': r2_score(y_test_arima, arima_pred)\n",
+        "}\n",
+        "print(results['ARIMA'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bkwxkrhaO83T",
+        "outputId": "0184a164-cd4f-41c8-e8e5-73dde8b98814"
+      },
+      "execution_count": 331,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'MAE': 0.1848223431508618, 'MSE': 0.04306003232296461, 'R²': -0.2077741786093903}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Подбор параметров модели с помощью auto_arima"
+      ],
+      "metadata": {
+        "id": "eFn7Iz94Rc5v"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install pmdarima"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jqIUu1W9lfFv",
+        "outputId": "74d591ba-1158-454c-ec9e-0183f2cd7235"
+      },
+      "execution_count": 332,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.10/dist-packages (2.0.4)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.4.2)\n",
+            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (3.0.11)\n",
+            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.26.4)\n",
+            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (2.2.2)\n",
+            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.5.2)\n",
+            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.13.1)\n",
+            "Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (0.14.4)\n",
+            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (2.2.3)\n",
+            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (75.1.0)\n",
+            "Requirement already satisfied: packaging>=17.1 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (24.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2024.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2024.2)\n",
+            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->pmdarima) (3.5.0)\n",
+            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.13.2->pmdarima) (1.0.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.19->pmdarima) (1.17.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from pmdarima import auto_arima"
+      ],
+      "metadata": {
+        "id": "MlD1mPc5Q5Cj"
+      },
+      "execution_count": 333,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Подбор параметров модели с помощью auto_arima\n",
+        "stepwise_fit = auto_arima(y_train_arima, start_p=1, start_q=1,\n",
+        "                           max_p=3, max_q=3, m=7,\n",
+        "                           start_P=0, seasonal=True,\n",
+        "                           d=1, D=1, trace=True,\n",
+        "                           error_action='ignore',   # игнорировать неудачные порядки\n",
+        "                           suppress_warnings=True,  # игнорировать предупреждения о сходимости\n",
+        "                           stepwise=True)           # использовать пошаговый подход\n",
+        "\n",
+        "# Печать сводки по подобранной модели\n",
+        "print(stepwise_fit.summary())\n",
+        "\n",
+        "# Получение оптимальных параметров p, d, q\n",
+        "p, d, q = stepwise_fit.order\n",
+        "\n",
+        "# Создание и подгонка модели ARIMA с подобранными параметрами\n",
+        "model = ARIMA(y_train_arima, order=(p, d, q))\n",
+        "model_fit = model.fit()\n",
+        "\n",
+        "# Печать сводки по подогнанной модели\n",
+        "print(model_fit.summary())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "K4qMe4zOlcR2",
+        "outputId": "4bd2537f-6e43-4421-a107-32f4722c5036"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Performing stepwise search to minimize aic\n",
+            " ARIMA(1,1,1)(0,1,1)[7]             : AIC=inf, Time=16.12 sec\n",
+            " ARIMA(0,1,0)(0,1,0)[7]             : AIC=1751.377, Time=0.22 sec\n",
+            " ARIMA(1,1,0)(1,1,0)[7]             : AIC=844.970, Time=1.29 sec\n",
+            " ARIMA(0,1,1)(0,1,1)[7]             : AIC=inf, Time=16.90 sec\n",
+            " ARIMA(1,1,0)(0,1,0)[7]             : AIC=1409.673, Time=0.94 sec\n",
+            " ARIMA(1,1,0)(2,1,0)[7]             : AIC=627.694, Time=6.79 sec\n",
+            " ARIMA(1,1,0)(2,1,1)[7]             : AIC=inf, Time=15.37 sec\n",
+            " ARIMA(1,1,0)(1,1,1)[7]             : AIC=inf, Time=4.83 sec\n",
+            " ARIMA(0,1,0)(2,1,0)[7]             : AIC=997.487, Time=0.70 sec\n",
+            " ARIMA(2,1,0)(2,1,0)[7]             : AIC=426.689, Time=2.90 sec\n",
+            " ARIMA(2,1,0)(1,1,0)[7]             : AIC=646.237, Time=1.70 sec\n",
+            " ARIMA(2,1,0)(2,1,1)[7]             : AIC=inf, Time=10.52 sec\n",
+            " ARIMA(2,1,0)(1,1,1)[7]             : AIC=inf, Time=8.19 sec\n",
+            " ARIMA(3,1,0)(2,1,0)[7]             : AIC=330.831, Time=1.54 sec\n",
+            " ARIMA(3,1,0)(1,1,0)[7]             : AIC=544.790, Time=1.31 sec\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Обучение модели SARIMAX"
+      ],
+      "metadata": {
+        "id": "kauWPkoXfVj1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Параметры\n",
+        "p, d, q = 5, 1, 0\n",
+        "P, D, Q, s = 2, 1, 0, 7"
+      ],
+      "metadata": {
+        "id": "vFGMytczfQGb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sarimax_model = SARIMAX(y_train_arima, order=(p, d, q), seasonal_order=(P, D, Q, s))  # Параметры могут быть изменены\n",
+        "sarimax_fit = sarimax_model.fit()\n",
+        "sarimax_pred = sarimax_fit.forecast(steps=len(y_test_arima))"
+      ],
+      "metadata": {
+        "id": "mmO0Rsr4d2Ys"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Оценка качества SARIMAX"
+      ],
+      "metadata": {
+        "id": "cF5ni3FogI3u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results['SARIMAX'] = {\n",
+        "    'MAE': mean_absolute_error(y_test_arima, sarimax_pred),\n",
+        "    'MSE': mean_squared_error(y_test_arima, sarimax_pred),\n",
+        "    'R²': r2_score(y_test_arima, sarimax_pred)\n",
+        "}\n",
+        "print(results['SARIMAX'])"
+      ],
+      "metadata": {
+        "id": "ZM0xsuI6gI36"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Подбор параметров модели с помощью auto_arima"
+      ],
+      "metadata": {
+        "id": "TDsLv6pdRmBa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Подбор параметров модели с помощью auto_arima\n",
+        "stepwise_fit = auto_arima(y_train_arima, start_p=1, start_q=1,\n",
+        "                           max_p=5, max_q=5, m=7,\n",
+        "                           start_P=0, seasonal=True,\n",
+        "                           d=1, D=1, trace=True,\n",
+        "                           error_action='ignore',   # игнорировать неудачные порядки\n",
+        "                           suppress_warnings=True,  # игнорировать предупреждения о сходимости\n",
+        "                           stepwise=True)           # использовать пошаговый подход\n",
+        "\n",
+        "# Печать сводки по подобранной модели\n",
+        "print(stepwise_fit.summary())\n",
+        "\n",
+        "# Получение оптимальных параметров p, d, q\n",
+        "p, d, q = stepwise_fit.order\n",
+        "# Если модель сезонная, также получаем параметры P, D, Q, s\n",
+        "P, D, Q, s = stepwise_fit.seasonal_order\n",
+        "\n",
+        "# Создание и подгонка модели SARIMAX с подобранными параметрами\n",
+        "model = SARIMAX(y_train_arima, order=(p, d, q), seasonal_order=(P, D, Q, s))\n",
+        "model_fit = model.fit()\n",
+        "\n",
+        "# Печать сводки по подогнанной модели\n",
+        "print(model_fit.summary())"
+      ],
+      "metadata": {
+        "id": "ln-QN7zNn4wt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Генерация прогнозов для ARIMA и SARIMAX на 7 дней"
+      ],
+      "metadata": {
+        "id": "QftUAIjhS40d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "future_arima_data = y[-30:]  # Используем последние 30 значений для прогноза\n",
+        "arima_future_pred = arima_fit.forecast(steps=7 * 24)  # Прогноз на 7 дней по 24 часа\n",
+        "sarimax_future_pred = sarimax_fit.forecast(steps=7 * 24)  # Прогноз на 7 дней по 24 часа\n",
+        "\n",
+        "# Добавление прогнозов ARIMA и SARIMAX в общий словарь прогнозов\n",
+        "predictions['ARIMA'] = arima_future_pred.to_numpy()\n",
+        "predictions['SARIMAX'] = sarimax_future_pred.to_numpy()"
+      ],
+      "metadata": {
+        "id": "tTC5JFCrmPSN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Другие модели"
+      ],
+      "metadata": {
+        "id": "vFJwJHLrLI-B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Группировка данных по дате и суммирование 'money'\n",
+        "history_sales = df_norm.groupby('date')['money'].sum().reset_index()\n",
+        "\n",
+        "# Преобразование столбца 'date' в datetime\n",
+        "history_sales['date'] = pd.to_datetime(history_sales['date'])\n",
+        "\n",
+        "print(f\"Number of days: {history_sales.shape[0]}\")\n",
+        "\n",
+        "# Вывод названий столбцов\n",
+        "print(history_sales['date'].max())"
+      ],
+      "metadata": {
+        "id": "eB96PNbCUoSs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "models = {\n",
+        "    'Linear Regression': LinearRegression(),\n",
+        "    'Decision Tree': DecisionTreeRegressor(random_state=42),\n",
+        "    'Random Forest': RandomForestRegressor(random_state=42),\n",
+        "    'Gradient Boosting': GradientBoostingRegressor(random_state=42),\n",
+        "    'Support Vector Regressor': SVR(),\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "AiwvlAh-FCvg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Обучение моделей и получение прогнозов на тестовой выборке\n",
+        "for name, model in models.items():\n",
+        "    model.fit(X_train, y_train)\n",
+        "    y_pred = model.predict(X_test)\n",
+        "\n",
+        "    # Вычисление метрик\n",
+        "    mae = mean_absolute_error(y_test, y_pred)\n",
+        "    mse = mean_squared_error(y_test, y_pred)\n",
+        "    r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "    results[name] = {'MAE': mae, 'MSE': mse, 'R²': r2}\n",
+        "\n",
+        "    # Генерация данных для прогноза на 7 дней\n",
+        "    last_date = history_sales['date'].max()\n",
+        "    future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=7)\n",
+        "    future_data = []\n",
+        "\n",
+        "    for date in future_dates:\n",
+        "        for hour in range(24):  # Прогнозируем для каждого часа в течение 7 дней\n",
+        "            future_data.append([hour, date.dayofweek] + [0] * (X.shape[1] - 2))  # Замените 0 на реальные значения кофе\n",
+        "\n",
+        "    future_df = pd.DataFrame(future_data, columns=X.columns)\n",
+        "    future_predictions = model.predict(future_df)\n",
+        "    predictions[name] = future_predictions"
+      ],
+      "metadata": {
+        "id": "4vSSR1MAFG0k"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Оценка результатов\n",
+        "results_df = pd.DataFrame(results).T\n",
+        "print(\"\\nРезультаты оценки модели:\\n\", results_df)"
+      ],
+      "metadata": {
+        "id": "yJeyU2eLhF_p"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Агрегация прогнозов по дням\n",
+        "aggregated_predictions = {name: preds.reshape(7, 24).sum(axis=1) for name, preds in predictions.items()}\n",
+        "\n",
+        "# Визуализация прогнозов\n",
+        "plt.figure(figsize=(15, 10))\n",
+        "for name, preds in aggregated_predictions.items():\n",
+        "    plt.plot(range(1, 8), preds, label=name)\n",
+        "\n",
+        "plt.title('Прогноз продаж на 7 дней')\n",
+        "plt.xlabel('День')\n",
+        "plt.ylabel('Продажи')\n",
+        "plt.xticks(range(1, 8), [f'Day {i}' for i in range(1, 8)])\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "muEUyE5NAMLp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_model_name = results_df['R²'].idxmax()\n",
+        "best_model = models[best_model_name]\n",
+        "y_pred_best = best_model.predict(X_test)\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.scatter(y_test, y_pred_best, alpha=0.7, label='Предсказанные')\n",
+        "plt.plot([y.min(), y.max()], [y.min(), y.max()], '--r', label='Идеальные значения')\n",
+        "plt.xlabel('Фактические значения')\n",
+        "plt.ylabel('Предсказанные значения')\n",
+        "plt.title(f'{best_model_name}: Фактическое и Предсказанное')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "oTZUgkx6a8A0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Создание DataFrame для прогнозов\n",
+        "predicted_sales_df = pd.DataFrame({\n",
+        "    'date': future_dates,\n",
+        "    'money': aggregated_predictions['Gradient Boosting']\n",
+        "})\n",
+        "predicted_sales_df"
+      ],
+      "metadata": {
+        "id": "LbHvqSArbdyp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Объединение реальных данных с прогнозами\n",
+        "combined_sales_df = pd.concat([history_sales, predicted_sales_df], ignore_index=True)"
+      ],
+      "metadata": {
+        "id": "qpvblLQ7ECSs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Визуализация\n",
+        "plt.figure(figsize=(15, 8))\n",
+        "plt.plot(history_sales['date'], history_sales['money'], label='Реальные продажи', color='blue')\n",
+        "plt.plot(predicted_sales_df['date'], predicted_sales_df['money'], label='Прогнозируемые продажи', color='orange')\n",
+        "\n",
+        "plt.title('Реальные и прогнозируемые продажи')\n",
+        "plt.xlabel('Дата')\n",
+        "plt.ylabel('Продажи')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.legend()\n",
+        "plt.grid()\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "E2H_0zbtEM3T"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file