diff --git a/lec3-1-regression.ipynb b/lec3-1-regression.ipynb
new file mode 100644
index 0000000..acf2dc1
--- /dev/null
+++ b/lec3-1-regression.ipynb
@@ -0,0 +1,388 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YVAZkjYZu7Tp"
+      },
+      "source": [
+        "#### Загрузка данных"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eqUQyj22pEUP",
+        "outputId": "38936c5b-aaca-4131-e8e0-51dc39fe2345"
+      },
+      "outputs": [
+        {
+          "data": {
+            "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>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>167.0812</td>\n",
+              "      <td>51.242630</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>181.6608</td>\n",
+              "      <td>61.900227</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>176.2760</td>\n",
+              "      <td>69.401361</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>173.2788</td>\n",
+              "      <td>64.553288</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>172.1866</td>\n",
+              "      <td>65.442177</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>195</th>\n",
+              "      <td>167.1320</td>\n",
+              "      <td>54.802721</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>196</th>\n",
+              "      <td>167.9194</td>\n",
+              "      <td>52.507937</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>197</th>\n",
+              "      <td>173.3296</td>\n",
+              "      <td>58.185941</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>198</th>\n",
+              "      <td>172.7708</td>\n",
+              "      <td>57.809524</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>199</th>\n",
+              "      <td>181.3306</td>\n",
+              "      <td>57.995465</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>200 rows × 2 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       Height     Weight\n",
+              "0    167.0812  51.242630\n",
+              "1    181.6608  61.900227\n",
+              "2    176.2760  69.401361\n",
+              "3    173.2788  64.553288\n",
+              "4    172.1866  65.442177\n",
+              "..        ...        ...\n",
+              "195  167.1320  54.802721\n",
+              "196  167.9194  52.507937\n",
+              "197  173.3296  58.185941\n",
+              "198  172.7708  57.809524\n",
+              "199  181.3306  57.995465\n",
+              "\n",
+              "[200 rows x 2 columns]"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "df = pd.read_csv(\"data/wh_data.csv\", index_col=0)\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HquD4-wzvz6-"
+      },
+      "source": [
+        "#### Обучение модели линейной регрессии"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4OYkFByZvHyD",
+        "outputId": "1a76004f-fe0c-4490-e2b0-616a05ee9462"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'weight = -48.08512764117069 + 0.6129012675686306 * height'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn import linear_model\n",
+        "\n",
+        "\n",
+        "num_records = df.shape[0]\n",
+        "X = df[\"Height\"].to_numpy().reshape(num_records, 1)\n",
+        "y = df[\"Weight\"].to_numpy().reshape(num_records, 1)\n",
+        "\n",
+        "model = linear_model.LinearRegression().fit(X, y)\n",
+        "display(f\"weight = {model.intercept_[0]} + {model.coef_.T[0][0]} * height\") # type: ignore"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1B2CgY0Ev9ZD"
+      },
+      "source": [
+        "В среднем на каждый килограмм приходится 0,6 см роста."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3eJTKdzLykXV"
+      },
+      "source": [
+        "#### Оценка ошибки работы модели"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3p-VQkfPyVsB",
+        "outputId": "4e6b99b2-fdfd-414b-d636-90d03554de8c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'MAE = 3.518701759813252'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from sklearn import metrics\n",
+        "\n",
+        "\n",
+        "y_pred = model.predict(X)\n",
+        "mae = metrics.mean_absolute_error(y, y_pred)\n",
+        "display(f\"MAE = {mae}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JrLR5nEmytq4"
+      },
+      "source": [
+        "В среднем предсказание отстает от фактического значения на 3,5 кг."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Визуализация результатов"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "r0TbhuiBy6KI",
+        "outputId": "343af02b-1447-4186-fc4a-05c83ede1fd2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Вес, кг')"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "plt.scatter(X, y, color='black')\n",
+        "plt.plot(X, y_pred, color='blue', linewidth=3)\n",
+        "plt.plot(X, y_pred + mae, color='lightgray')\n",
+        "plt.plot(X, y_pred - mae, color='lightgray')\n",
+        "plt.xlabel('Рост, см')\n",
+        "plt.ylabel('Вес, кг')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bXhC75uRzSMV"
+      },
+      "source": [
+        "#### Определение линейной зависимости (линейная корреляция)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uF45zDJkzR-0",
+        "outputId": "8a5a0ef5-e0bd-488c-ef45-e086b255bcd1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0.5568647346122992"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "1.1029015151266403e-17"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "'1.1029015151266403e-17 < 0.05'"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from scipy import stats\n",
+        "\n",
+        "\n",
+        "corr, pval = stats.pearsonr(X[:,0], y[:,0])\n",
+        "\n",
+        "display(corr)\n",
+        "display(pval)\n",
+        "display(f\"{pval} < 0.05\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tVUen-uczxXq"
+      },
+      "source": [
+        "corr > 0, значит обнаружена линейная зависимость между x и y\n",
+        "\n",
+        "corr -> 1, присутствует сильная корреляция\n",
+        "\n",
+        "pval < 5%, значит корреляция присутствует\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyM5PyQgrWDfgi5yGxN9RXFr",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv (3.11.12)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/lec3-2-data.ipynb b/lec3-2-data.ipynb
new file mode 100644
index 0000000..798789b
--- /dev/null
+++ b/lec3-2-data.ipynb
@@ -0,0 +1,1699 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a2ec8d9e",
+   "metadata": {},
+   "source": [
+    "#### Загрузка и подготовка данных для анализа\n",
+    "\n",
+    "Cardiovascular Disease dataset https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "e703ff4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 70000 entries, 0 to 99999\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   age          70000 non-null  int64  \n",
+      " 1   gender       70000 non-null  int64  \n",
+      " 2   height       70000 non-null  int64  \n",
+      " 3   weight       70000 non-null  float64\n",
+      " 4   ap_hi        70000 non-null  int64  \n",
+      " 5   ap_lo        70000 non-null  int64  \n",
+      " 6   cholesterol  70000 non-null  int64  \n",
+      " 7   gluc         70000 non-null  int64  \n",
+      " 8   smoke        70000 non-null  int64  \n",
+      " 9   alco         70000 non-null  int64  \n",
+      " 10  active       70000 non-null  int64  \n",
+      " 11  cardio       70000 non-null  int64  \n",
+      "dtypes: float64(1), int64(11)\n",
+      "memory usage: 6.9 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"data/cardio_train.csv\", sep=\";\", index_col=\"id\")\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3d8a4fc",
+   "metadata": {},
+   "source": [
+    "#### Описание признаков\n",
+    "\n",
+    "1. age -- возраст в днях;\n",
+    "2. gender -- пол (1 - ж, 2 - м);\n",
+    "3. height -- рост в см.;\n",
+    "4. weight -- вес в кг.;\n",
+    "5. ap-hi -- систолическое давление в мм рт. ст.;\n",
+    "6. ap_lo -- диастолическое давление в мм рт. ст.;\n",
+    "7. cholesterol -- холестерин в крови (1 - в норме, 2 - выше нормы, 3 - сильно выше нормы);\n",
+    "8. gluc -- сахар в крови (1 - в норме, 2 - выше нормы, 3 - сильно выше нормы);\n",
+    "9. smoke -- курит (0 - нет, 1 - да);\n",
+    "10. alco -- употребляет алкоголь (0 - нет, 1 - да);\n",
+    "11. active -- ведет активный образ жизни (0 - нет, 1 - да);\n",
+    "12. cardio -- риск ССЗ (целевой признак) (0 - нет, 1 - да).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e807122c",
+   "metadata": {},
+   "source": [
+    "#### Отображение набора данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7bcd2ac9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>ap_hi</th>\n",
+       "      <th>ap_lo</th>\n",
+       "      <th>cholesterol</th>\n",
+       "      <th>gluc</th>\n",
+       "      <th>smoke</th>\n",
+       "      <th>alco</th>\n",
+       "      <th>active</th>\n",
+       "      <th>cardio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18393</td>\n",
+       "      <td>2</td>\n",
+       "      <td>168</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20228</td>\n",
+       "      <td>1</td>\n",
+       "      <td>156</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>90</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18857</td>\n",
+       "      <td>1</td>\n",
+       "      <td>165</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>17623</td>\n",
+       "      <td>2</td>\n",
+       "      <td>169</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17474</td>\n",
+       "      <td>1</td>\n",
+       "      <td>156</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99993</th>\n",
+       "      <td>19240</td>\n",
+       "      <td>2</td>\n",
+       "      <td>168</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>22601</td>\n",
+       "      <td>1</td>\n",
+       "      <td>158</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>90</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>19066</td>\n",
+       "      <td>2</td>\n",
+       "      <td>183</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>180</td>\n",
+       "      <td>90</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>22431</td>\n",
+       "      <td>1</td>\n",
+       "      <td>163</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>135</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>20540</td>\n",
+       "      <td>1</td>\n",
+       "      <td>170</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>70000 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
+       "id                                                                             \n",
+       "0      18393       2     168    62.0    110     80            1     1      0   \n",
+       "1      20228       1     156    85.0    140     90            3     1      0   \n",
+       "2      18857       1     165    64.0    130     70            3     1      0   \n",
+       "3      17623       2     169    82.0    150    100            1     1      0   \n",
+       "4      17474       1     156    56.0    100     60            1     1      0   \n",
+       "...      ...     ...     ...     ...    ...    ...          ...   ...    ...   \n",
+       "99993  19240       2     168    76.0    120     80            1     1      1   \n",
+       "99995  22601       1     158   126.0    140     90            2     2      0   \n",
+       "99996  19066       2     183   105.0    180     90            3     1      0   \n",
+       "99998  22431       1     163    72.0    135     80            1     2      0   \n",
+       "99999  20540       1     170    72.0    120     80            2     1      0   \n",
+       "\n",
+       "       alco  active  cardio  \n",
+       "id                           \n",
+       "0         0       1       0  \n",
+       "1         0       1       1  \n",
+       "2         0       0       1  \n",
+       "3         0       1       1  \n",
+       "4         0       0       0  \n",
+       "...     ...     ...     ...  \n",
+       "99993     0       1       0  \n",
+       "99995     0       1       1  \n",
+       "99996     1       0       1  \n",
+       "99998     0       0       1  \n",
+       "99999     0       1       0  \n",
+       "\n",
+       "[70000 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "4196f618",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>19468.865814</td>\n",
+       "      <td>2467.251667</td>\n",
+       "      <td>10798.0</td>\n",
+       "      <td>17664.0</td>\n",
+       "      <td>19703.0</td>\n",
+       "      <td>21327.0</td>\n",
+       "      <td>23713.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.349571</td>\n",
+       "      <td>0.476838</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>164.359229</td>\n",
+       "      <td>8.210126</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weight</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>74.205690</td>\n",
+       "      <td>14.395757</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ap_hi</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>128.817286</td>\n",
+       "      <td>154.011419</td>\n",
+       "      <td>-150.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>16020.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ap_lo</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>96.630414</td>\n",
+       "      <td>188.472530</td>\n",
+       "      <td>-70.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>11000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cholesterol</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.366871</td>\n",
+       "      <td>0.680250</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gluc</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.226457</td>\n",
+       "      <td>0.572270</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>smoke</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.088129</td>\n",
+       "      <td>0.283484</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alco</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.053771</td>\n",
+       "      <td>0.225568</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>active</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.803729</td>\n",
+       "      <td>0.397179</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cardio</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.499700</td>\n",
+       "      <td>0.500003</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               count          mean          std      min      25%      50%  \\\n",
+       "age          70000.0  19468.865814  2467.251667  10798.0  17664.0  19703.0   \n",
+       "gender       70000.0      1.349571     0.476838      1.0      1.0      1.0   \n",
+       "height       70000.0    164.359229     8.210126     55.0    159.0    165.0   \n",
+       "weight       70000.0     74.205690    14.395757     10.0     65.0     72.0   \n",
+       "ap_hi        70000.0    128.817286   154.011419   -150.0    120.0    120.0   \n",
+       "ap_lo        70000.0     96.630414   188.472530    -70.0     80.0     80.0   \n",
+       "cholesterol  70000.0      1.366871     0.680250      1.0      1.0      1.0   \n",
+       "gluc         70000.0      1.226457     0.572270      1.0      1.0      1.0   \n",
+       "smoke        70000.0      0.088129     0.283484      0.0      0.0      0.0   \n",
+       "alco         70000.0      0.053771     0.225568      0.0      0.0      0.0   \n",
+       "active       70000.0      0.803729     0.397179      0.0      1.0      1.0   \n",
+       "cardio       70000.0      0.499700     0.500003      0.0      0.0      0.0   \n",
+       "\n",
+       "                 75%      max  \n",
+       "age          21327.0  23713.0  \n",
+       "gender           2.0      2.0  \n",
+       "height         170.0    250.0  \n",
+       "weight          82.0    200.0  \n",
+       "ap_hi          140.0  16020.0  \n",
+       "ap_lo           90.0  11000.0  \n",
+       "cholesterol      2.0      3.0  \n",
+       "gluc             1.0      3.0  \n",
+       "smoke            0.0      1.0  \n",
+       "alco             0.0      1.0  \n",
+       "active           1.0      1.0  \n",
+       "cardio           1.0      1.0  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eaf8554b",
+   "metadata": {},
+   "source": [
+    "Преобразование возраста из дней в годы"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "79535342",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    }\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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>53.304309</td>\n",
+       "      <td>6.755152</td>\n",
+       "      <td>29.564122</td>\n",
+       "      <td>48.36272</td>\n",
+       "      <td>53.945351</td>\n",
+       "      <td>58.391742</td>\n",
+       "      <td>64.924433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.349571</td>\n",
+       "      <td>0.476838</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>164.359229</td>\n",
+       "      <td>8.210126</td>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>159.00000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>170.000000</td>\n",
+       "      <td>250.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weight</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>74.205690</td>\n",
+       "      <td>14.395757</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>65.00000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>82.000000</td>\n",
+       "      <td>200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ap_hi</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>128.817286</td>\n",
+       "      <td>154.011419</td>\n",
+       "      <td>-150.000000</td>\n",
+       "      <td>120.00000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>16020.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ap_lo</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>96.630414</td>\n",
+       "      <td>188.472530</td>\n",
+       "      <td>-70.000000</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>11000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cholesterol</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.366871</td>\n",
+       "      <td>0.680250</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gluc</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.226457</td>\n",
+       "      <td>0.572270</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>smoke</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.088129</td>\n",
+       "      <td>0.283484</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alco</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.053771</td>\n",
+       "      <td>0.225568</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>active</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.803729</td>\n",
+       "      <td>0.397179</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cardio</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.499700</td>\n",
+       "      <td>0.500003</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               count        mean         std         min        25%  \\\n",
+       "age          70000.0   53.304309    6.755152   29.564122   48.36272   \n",
+       "gender       70000.0    1.349571    0.476838    1.000000    1.00000   \n",
+       "height       70000.0  164.359229    8.210126   55.000000  159.00000   \n",
+       "weight       70000.0   74.205690   14.395757   10.000000   65.00000   \n",
+       "ap_hi        70000.0  128.817286  154.011419 -150.000000  120.00000   \n",
+       "ap_lo        70000.0   96.630414  188.472530  -70.000000   80.00000   \n",
+       "cholesterol  70000.0    1.366871    0.680250    1.000000    1.00000   \n",
+       "gluc         70000.0    1.226457    0.572270    1.000000    1.00000   \n",
+       "smoke        70000.0    0.088129    0.283484    0.000000    0.00000   \n",
+       "alco         70000.0    0.053771    0.225568    0.000000    0.00000   \n",
+       "active       70000.0    0.803729    0.397179    0.000000    1.00000   \n",
+       "cardio       70000.0    0.499700    0.500003    0.000000    0.00000   \n",
+       "\n",
+       "                    50%         75%           max  \n",
+       "age           53.945351   58.391742     64.924433  \n",
+       "gender         1.000000    2.000000      2.000000  \n",
+       "height       165.000000  170.000000    250.000000  \n",
+       "weight        72.000000   82.000000    200.000000  \n",
+       "ap_hi        120.000000  140.000000  16020.000000  \n",
+       "ap_lo         80.000000   90.000000  11000.000000  \n",
+       "cholesterol    1.000000    2.000000      3.000000  \n",
+       "gluc           1.000000    1.000000      3.000000  \n",
+       "smoke          0.000000    0.000000      1.000000  \n",
+       "alco           0.000000    0.000000      1.000000  \n",
+       "active         1.000000    1.000000      1.000000  \n",
+       "cardio         0.000000    1.000000      1.000000  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"age\"] = df[\"age\"] / 365.24\n",
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c15cd077",
+   "metadata": {},
+   "source": [
+    "Минимальный возраст -- 29.5 лет, максимальный возраст -- 64.9 года.\n",
+    "\n",
+    "Минимальный рост -- 55 см, максимальный рост -- 250 см.\n",
+    "\n",
+    "Минимальный вес -- 10 кг, максимальный вес -- 200 кг.\n",
+    "\n",
+    "Минимальное систолическое давление -- -150 мм рт ст, максимальное систолическое давление -- 16020 мм рт ст.\n",
+    "\n",
+    "Минимальное диастолическое давление --  -70 мм рт ст, максимальное диастолическое давление -- 11000 мм рт ст.\n",
+    "\n",
+    "Необходима очистка данных от ошибочных значений."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0504bac",
+   "metadata": {},
+   "source": [
+    "#### Визуализация признаков попарно для определения корреляции между ними"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77ac76a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x16666c690>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 3000x3000 with 90 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df, corner=True, kind=\"hist\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cbcd1da",
+   "metadata": {},
+   "source": [
+    "#### Визуализация распределения значений признаков"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f72b1c26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x377270910>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x2000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(\n",
+    "    x=\"value\",\n",
+    "    data=df.melt(value_vars=df),\n",
+    "    col=\"variable\",\n",
+    "    kind=\"hist\",\n",
+    "    col_wrap=3,\n",
+    "    facet_kws={\"sharex\": False, \"sharey\": False},\n",
+    "    common_bins=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf5c2d55",
+   "metadata": {},
+   "source": [
+    "#### Визуализация выбросов в данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b798a159",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x4958e87d0>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x2000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(\n",
+    "    y=\"value\",\n",
+    "    data=df.melt(value_vars=df.columns),\n",
+    "    col=\"variable\",\n",
+    "    kind=\"box\",\n",
+    "    col_wrap=3,\n",
+    "    sharey=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "398ea8d1",
+   "metadata": {},
+   "source": [
+    "Большое количество выбросов в значениях давления, значения роста и веса также требуют некоторой корректировки"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa31c55b",
+   "metadata": {},
+   "source": [
+    "#### Визуализация зависимости значения целевого признака от значения каждого из входных признаков"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "619f34c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x499792e10>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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qk3W1UR8fI+nWs3bTsMIc9clMa/f3KfK72FBfp0+Wl2hjea0Kc9O149Bsvbm4VOtKq1WYk64JQ3IVSk51/cgXn5qlSz5rep0fZkmfFORoXVmN+uWk6fLvjdTQoUOUl5XWqq3v7v2I/QthQf8uBH39/NbVuuillTX6Zl25GhqkYX2zNLpftotDtLUtsbTN0cT6+veUpJC9U73stttu0/XXX69Vq1Zp4sSJuuWWWzR58mTf5R9++GFdeumlWrRokbbeemv98Y9/1MEHH9yuv1VSUqL8/HwVFxcrLy9PQVdcWavF68u1ZEOFVhZXqrK6XtnpqRqSn6kR/bI1ql+O8rPSWi2/oaxGdQ0hbaio1sqiSlXX2gEhWempSbLY0sbyGq0srlZxRY3Ka+ukkJSbmabUJAuCVbnnV9TWq7rWdiK5wLp9URrsS2M7WK++K0BwZCRLI/p5AbOhBVlKTZZWFFvQrkIV1fUuSJaclKScjBSNLMzW+GEFGm3LZ6Zo6YYKvb94o9aWVLsAcE1dnQveDcnL0uCCLG3RP1vfGz9Y2w3J19KNFXpv4Xot21CpmrqQ25eHF2Zply36aUTf7Gb7flFVrTaWVbu9tV9uutuPVxRXqqquQSlKbnzuNkPyVFcf0rxVpfpyZYk2lNcoPSVZfTJT3InB0IJsd4Eg8rXyMtPcyUffnHR3HGl5rJm7ulRfrihRWVWdkr4NyA/Ny9SuW/fTlv1ytd6C3XUNqquvtxh7s+NZy9e0Cx72t1oe5yK19b4EVctjdXu2NZZ0tF3vjjbavhszP12l9xet1/NfrOnQc287YVLUNrbl5/fEh8v0wucr3Yl1fYOUkizlpqdq6pj++sGuo7Xd0Dz3/E3tn+F9Kl4+ewCJ2+7Ee9sWND15Dt1d59H73/CyvlobmR7j79DtB+uIHYZp8hb92vw+RX4XZ3+zVv+bu871VU11XUg1NXVKtvPipCQ1NISUlZGiicMKNGveunatS16GdM73ttU2Q/Ib2/qu4rcfdaafjPgU9GNt0NfPb11XlFTqg4UbNG91iapqGmQR9ey0FBdE32mLQg3Lz/bdllja5mhiff0TKoj+4IMP6qSTTtLtt9+uKVOm6Oabb3YN/Lx58zRw4MBWy7/11lvaa6+9dO211+rQQw/Vf/7zH9cBmDNnjiZMmBBXQXT7In+2vFhrS6tdcMuCTplpqaqsrVdaSpK7aj6gT6YmDMtvPEm35cur69Q3O10bK2r02tdrta6kRoPy0zVmYB+tKqrUK/PWqKa2QcP6ZsuSXFcUVaq4olZJyVJKSCqurlVtXcgFBOpDXuAcgL/MFKlPVqq7Wp2dkaLiijrV1NUrJcm74OSu3yYluYDylv2y1SfbMqWlb9aWu2xtu39NSbVq6huUlBRSn4xUfWdYvuobklWQk6YDJgzSV6vKVFRR6wLOWekpqqyp18riKhVkp2nPsf1dZo3t+2kpyS54WFrpZfNYI+jt43UalJ+hMQNz3UotWl+uipp6F3BeWVSlqpp691p23/qyarf/98tN0/C+WUr5Nqu7T1aaRvbNVlFljVaXVGtQXqaGFWQ1Hmu+WFGsxesqFGpoUF52muobGlRZ26CGUJLyslK1x1b91TcnQ6uLK10m7hb9chVKkjueWeZPaVVd42umpyarpq7BvbZl5YePc5FsO5/9ZKXv+3Lw9kMCGUhveaxuz7bGko62693RRtt347/vLNacxUV6e+GGTr3GzcdNbNbGtvz8Zn6+Qk99tFIVNXVKS7Jd3P4jN8LC9sPdxvTT93ceqW2H5mnJ+oqo+2d4n7J9Px4+ewDB1BPtTry3bUHT0+fQ3XEe3ZEAetheW/fTUTuN0L7jBvp+nyK/i1+uLNaD7y1ReXW98jJTXWKJjarWt/3z7HSrb5vs2mEbCdoR+enSqXttrW2H5GnKlm0H9jdnP7IRdTayblBehoYVZLN/JbCgH2uDvn5+62qjwmd+vlIfLSt2mdiF2enKyUjW6tJad9zYsn+OjtllhIYXZLfallja5mhiff17Wq+Pd7/xxht12mmn6ZRTTtF2223nOgLZ2dm65557oi7/l7/8RQceeKDOP/98bbvttrrqqqu044476tZbb1U8ceVW1pe7cisNIQtoN2hAnyw3hG1gn0yXHW6XP+zxJRtsqEmDW96++EPyLQCV5DJLLTBmV8ctC31DWbUWrC1TSlKSMtJSVFxVrcrqOqUkJWtAnwxVVtVpQ4UF60Mug9QC6ADaZsO7MpIaVFXboPWl1aqtr3eB8aTkZGWkpbpyLjmZqa5kimWpF5dVauGacpepPiA3wwV+TV5WugpzMlVdG9KSjZXaZnCONpRW68F3l2hjeZXGDurjjgGpyZYtnuZ+Lyqv0azPV7thZ4PzM11jZw29ZZEPzsvU/DWlLtN926F9XOC6yDWGKSrIStPG8mp9tqxIVdV17iqzZZ/bELa6UMhdeLMhrvPXlLnXsdezK/Ibyqtdxo4FrkOyYKHcsaa8qsZdjLOTkMLcLOVmpCkjzV4vWQXZqSqpqNO7i9YrKVTvRr3YBcGqOm/77aKd/S3bFnvNjLRklxlkgXU7ntlxzY5zkdd87ZhnGei2HlHfl4pavb9og1suiMf28LHatrGtbY01HW3Xu5p95u8sWKe5K0o6HUA38+cvaGxjw59H+PPbWFahN75a5/Zd933OSFd2Rrrb3wuyMlzn+5NlRfpq5Qb3PbXXabl/hvcpu8/2sXj47AEET0+0O4nQtgVNrJ9DWwmXjgbQzWtfr9dHi9dr0brSqN+nyO9i/5xUvfj5alXUNGhkv1yXpGKlC8MJMPbs6hqpjwXXOxhAN8U1UkXJRpeYsnh92WZ/v/32I+sXWzzA9b1DUkZq2/1kxKegH2uDvn5+62oXp75aVayv15S5UqOj+uW6ciYVNSF3zmyB5VWlVXr/m3Xu3DdyW1rG4YK8zbH+mQVFrwbRa2pq9MEHH2jatGlNK5Sc7H6fPXt21OfY/ZHLmwMOOMB3+erqanfVPPIWC6wWkQ2lyExPUUlFrQsMRbKsTgtYZaWlan1pjasHbMvbDm42VtRqTXGV+uVmuN/tCtLyoiqXPWqBstSUJFVUN6ioqs41xDasLcWuODXIDd2o80avkIUOtNPa8gZlpiWpuk6u0a2tt2FQyS4TPO3b3zMyUlVcVafqBmldeY3L1i6prnelTzLTkt2+Z+VfLABtmeW2X9swqkXrKpSe2lQbPVLf3HQtWFeupKRkN0ol8nhRWlXrgnzp6ckuUG2lU8oq69z9lvVtw0KXbaxSVoY3PYYtY8eevllpKqmqcxnoVrfdlg0fd2xkzBqrCZ+fqeLyWq0sqXbHGstct/JQVpqiLlTvtruuoUGZ6cmqqg0pOyPZbdM36yrdetgxyba7us5G2CRrdXGVCnMy3GvadjTbxux0d5yzdWt8v8tqXAkXW49o7P6l6yvcckE8toeP1S1F29ZY0tF2vTvaaPvM560uC48B6bTbPyhrbGPDn0f481u0oVJry2vcPp8UspJN3nPcsHA1KMf2tap6zV1T7kacWFvecv+MbMvtsVj/7AEEU0+0O/HetgVNT5xDd/d59NlPzO/0c5cVVembdRVRv0+R30Wrgb7Gap5ne+1umc0xZue635YmtUCI9XCrrBPbSc/MLXEnzHaevbnfb7/9KNx/cH3vb/sMkdi/EkfQj7VBXz+/dbXY2QKbo6w+pD6Z3rpbmePKmjp3fm6jne282OY7W1VS3WxbWsbhgrzNsf6ZBUWvBtHXrVun+vp6DRo0qNn99rvVdovG7u/I8jZkzYadhW8jRoxQLLAsNgtCWda4ZYVaEC5SmgXnQl7GuC1n9ZTtpw29MFaupSYUcsEpYzt/dV2de11XF90mJpFNGhhScoq1/TapqNejsKtM7joTF5uAdrP+t5VzsG54crhzntxUzsV+WqfdguT19SG3n6W7C1Yh9xy7sOWWC9m/bSRISNV1NqlHkttv/eb0yLAAfZ3twd7rRh4vahrsXq+mmb2e+6mQKzdhGS3pyclulEvjJIe273/7fLuQZiWebIVqvz25sOOOPdcy7e3qtP0tG+1ixxp7bl1ISrfMnm/nULD/WAfEMtdt/e2nlYuxbQqvi414seORvUZaqne8s+2I5K2PN8lpmFdD3krhRL+4YOtnj9tyQTy2h4/VLUXb1ljS0Xa9O9po+8zt4pGVRtocdmIdbmPDn0f48yutqleDu1Bm+46375rGfTjZm0ukvMpGqFhpp6RW+2dkW27HhVj/7AEEU0+0O/HetgVNT5xDd/d59KpiLxDVGXaea/OERfs+RX4XSyprXT/X+oSm1vWLW19j35wMy9LqWtfxt/nHNvf77bcfhfsP1ucN9xkisX8ljqAfa4O+fn7rascUO6e1TnzjqaX15e1nkhdLszKttbV1jSPIw9vSMg4X5G2O9c8sKHq9nEt3u+iii1zdtvBt6dKligWWydoY+EpKagxihdU2NLj7vZN1b7iF/bTaRSY9LVnpSUmuvISxL35Gaqp7XQssWQDNTvstmNVQb18EC6p7BwkLBHo1nHthw4EYZXEx64QnR0zAa5VEwgH0pG+DchawTrGAuQWOv520w2XCWGA9HLSrl1d2KTXZ3W/7rV//vrq+wU3+aXutvW7k8cKC9Pa/xgty9lNJbpiaC1y7ALoF0r0Xt2WSvn2+taMhtyFJjUE/O+7Yc9NSUlyHwf6WZdjascaem+rqQXvbED6GWGc/Odk7VtnPbDsBsJOBb9fFOiQuoG9/t8473tl2RPLWx45XTU2WHfPsgmC4I9OSrZ89Hj5xCtqxPXysbinatsaz7mij7TO3E82QFdzfDC5T7ds2Nvx5hD8/m3w32S5wuX3n25Py8L7uAu/eaK6czGS3PtaWt9w/I9tyOy4k2mcPIH7aHdq2+NSd59GD873R0p1h57k2EWi071Pkd9FGX1s/N5xQkeb6xa0TxdycJp3UJyPNdfyt5Mrmfr/99qNw/8H6vOE+QyT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lZX6qU1u9Hg3ZjtreeeedvCdW7cfEHiCxR2U8BeuEE04I//rXv0Jbaui2vP322/n/pdhDpXYvrI76msT3yGGHHZb3AiuF12TNmjX5qaDxCHo8Zbyjvj86ss5anyM1urSo0a1HjVajW4Ma3fqa8nm+vjYvJy1RD1OfheWgqe133nnn5Z9J8WyIcteUNrznnnvyz9R49lK/fv3CzjvvHC688ML8M7fcNKX99tprr/wxhSFfXnrppfws1AMPPLDN1rsjm91GNUSI3kzxy1P8gCgWb1dVVYV33303LFu2LP/QqGuZ2mOAlpJLL700/xJ/6KGHVs+LXwrjmEIzZszIxxWMXx7jaU5xR75UxJ3yeErlL3/5y3yKOyBxXMZ4+kzUEV+Pf/7zn/mYjfFUlGLt+XrEACeeHrP33nvnxbGx749CWxd+ttfr0dDtqO2MM87Ig5HiD+l4SvL//M//hJkzZ4aLL744P/35c5/7XJt9aWjotsQd1RtuuCH86le/Cv/3//7f/HGxYP/jH//osK9J/KIRT/Wt/R5pj9fkmWeeCRtttFE+Dtvxxx8fpk+fHj72sY91yPdHR9dZ63OkRpcONbr1qNFqdEtTo9tOUz7P19fm5aQl6mFdn4XlointF4dciwf84vAjNK0NY+gbh3GJj4vh79lnnx1+8IMfhAsuuCCUm6a03xFHHJEfyPnEJz4RNthgg7D11lvn35PLdTiXlt73ayndWuyZ6DTiWEznnntu/gW+eJzS+MW2IF6YJ+4gxp4yt99+e8kcrY07H3EqiDseL774Yrj88svDz3/+89AR3XTTTaFPnz7rXNikPV+PeHQ57hC19ni7ra0p2xHH04y9l2LvqeKLfcUeVgXxQkjxNYmFLy63//77h1LZltg7oLjXVXyP7LjjjuEnP/lJOP/880NHfE3iF97Y5sOHD68xvz1ek/j5E8dgiz314pfIo48+Og8GUjvp0FhqdGlRo1uPGq1GtzQ1mnKR+iykbvEA91FHHZUH6H379m3v1emw4oHW+N302muvza+DMWzYsHxM/u9///v5ON/UL75fY8/9H//4x/n3xhdeeCGceuqpef2PByQoDXqiN1P//v3DkiVLasyLt+NFgeIVdeOHcPwAqWuZ+NhSE4tt7CkSd/LWd9Q67jTGqy3HN3cpi1/aC+vY0V6POPxT7JEUi3r37t1L4vU4+eSTw69//ev8ojXxIk1NeX8U2rrwsz1ej8ZsR3Hvz/il9IEHHsh39uoTT9+P/9/a4v3RlG0piEe5d9ttt+r17GivSTwVO35uNSSUaovXJL5P45XA45fGqVOn5hdDiRch7Gjvj86gs9XnSI0uLWp061GjP6BGtyw1uu005fN8fW1eTppTDxvzWdhZNbb94gH9eDHMeNH2bt265VM8UyYOTxJ/j/eXm6b8H4xnHMbvOvFxBfFAcOwhHIc3KSdNab8YlMfvlPG7fjy4/IUvfCEP1WO9igcoaN6+X0sRojdT7C0ST0MsFq9cX+hFEr+sxS9qxcvEN0C8nRqDr73ccsstYfz48fnPeFXb9YmnkseCEj8sS1nscVJYx470ekSxd0zcmWjIzkdrvx4xLIg7UPHU19/+9rfhox/9aLPfH/E54odd8TLxdJs//vGPrfZ6NGU7oksuuSQ/ChxPzd9jjz3Wu3w89TqO7dma74+mbkuxeJpZPL25sJ4d6TWJ7rjjjrBy5crwla98pSRek9ri50tcv47y/uhMOlN9jtTo0ntN1OiWp0bXpEa3LjW69TTl83x9bV5OmloPG/tZ2Fk1tv122GGH/LM2ficpTJ///OfDvvvum/8eh58rN035PxiH+orfi4oD3+effz7/XF9fZ4POpintF69jEMdNL1Y4IPHBtTWpT5vVkBa9TGkn8NZbb2VPP/10PsXmueyyy/LfX3nllfz+iRMnZkcddVT18i+99FK24YYbZt/+9rezefPmZdOmTcu6du2azZgxo3qZW2+9Nb9S+4033pj97W9/y4477risT58+2eLFi0tmO37xi19k3bp1y9f/tddeq57iVeYLvvnNb2azZs3KXn755eyxxx7LRo0alfXt2zdbunRpyWzH5Zdfnt19993Z3//+9+yZZ57JTj311KxLly7ZQw891KFej4KvfOUr+VXp69LWr8cJJ5yQVVZW5n+z+P/IO++8U71M3Ia4LQVxveL/q0svvTR/f0yZMiXbYIMN8tem4KKLLsrb/1e/+lX2l7/8Jb8K+kc/+tHs3XffLZntiOvYvXv37M4776zxmPi6RvHnt771rWz27Nn56xH/v+2+++7Ztttum7333nutsh1N3ZZzzz03u//++7MXX3wxmzNnTnbYYYdlPXv2zP761792qNek4BOf+ER+1e3a2uM1iev3yCOP5H8vtlu8XVFRkT3wwAMd5v1RyjpLfW7KtqjRavT6qNFqdCm9JgVqdHlb3+d5U9q8nDS2/db3WVhuGtt+tR199NH5e7qcNbYNFy5cmG288cbZySefnD333HPZr3/962zzzTfPLrjggqwcNbb94mdebL9bbrkl34+J9WnrrbfODj300KwcvdUK+34tQYhey8MPP5y/QLWn+CEaxZ+f+tSn1nnM0KFD86K11VZbZT/72c/Wed4rr7wy+8hHPpIvM3z48OwPf/hDSW1H/L2+5aP4JXjAgAH5NmyxxRb57RdeeKGktuPiiy/OP2jiDsemm26affrTn85++9vfdrjXI4rhSK9evbJrr722zuds69ejrvWPU/H/97gNxf9nottvvz3bbrvt8vXcaaedsnvvvbfG/WvXrs3OPvvsrF+/fnmR2X///fOiW0rbseWWW9b5mFjoorgjecABB2SbbbZZ/mU/Lv/1r3+91YO4pmzLaaedVv1/P7b5gQcemD311FMd7jWJ5s+fny9X2AEu1h6vyTHHHJP/ndi28e/Gditet47w/ihlnaU+N2Vb1Gg1en3UaDW6lLYjUqNZ3+d5U9q83DSm/db3WViOGvv/r5gQvWlt+Pjjj+cdDOJnYvzu/b3vfS97//33s3LVmPZbvXp1ds4551R/Vx40aFB24oknZm+88UZWjh5upX2/5qqI/7Rs33YAAAAAAOgcjIkOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAlYPDgweGKK65o79UAahGiAwAAAABAghAdAAAAAAAShOgAAAAA0EzXXnttGDhwYFi7dm2N+Yccckg45phjwosvvpj/3q9fv7DRRhuFPffcMzz00EPJ51uwYEGoqKgIc+fOrZ735ptv5vNmzZpVPe/ZZ58Nn/vc5/LnjM991FFHhWXLlrXSVkJ5EqIDAAAAQDN9+ctfDv/617/Cww8/XD3v3//+d5gxY0Y48sgjw9tvvx0OPPDAMHPmzPD000+Hz372s+Hggw8OCxcubPLfjKH6fvvtF3bbbbfw5JNP5n9ryZIl4dBDD22hrQKibpoBAAAAAJpnk002yXuE33zzzWH//ffP5915552hb9++Yd999w1dunQJQ4YMqV7+/PPPD9OnTw/33HNPOPnkk5v0N6+66qo8QL/wwgur591www1h0KBB4fnnnw/bbbddC2wZoCc6AAAAALSA2OP8l7/8ZVi5cmV++xe/+EU47LDD8gA99kT/1re+FXbcccfQp0+ffPiVefPmNasn+p///Oe853t8rsK0ww475PfF4WOAlqEnOgAAAAC0gDg8S5Zl4d57783HPP/9738fLr/88vy+GKA/+OCD4dJLLw3bbLNN6NWrV/jSl74UVq1aVedzxeA9is9XsHr16hrLxGA+/s2LL754nccPGDCghbcOypcQHQAAAABaQM+ePcMXv/jFvAf6Cy+8ELbffvuw++675/c99thj4atf/Wr4whe+UB2Ax4uHpmy22Wb5z9deey0fsiUqvshoFJ879nwfPHhw6NZNzAetxXAuUEbqurL3+sQCP2bMmHqX+fSnPx1OO+20UMpuvPHG/HQ5AGgv5VyHm2LWrFl5e8ULpgFARxvSJfZEj2OTx98Ltt1223DXXXfl3wXiMCxHHHFEWLt2bfJ5Yk/1j3/84+Giiy7Kh3155JFHwllnnVVjmZNOOim/eOnhhx8e/vSnP+VDuNx///1h/PjxYc2aNa26nVBOhOhQRuKFReIR7J133rm9VwUAyo463LgDAXvttVfeXpWVle22XgDQFPvtt1/YdNNNw3PPPZcH5QWXXXZZfvHRWOPiECyjR4+u7qWeEoP4999/PwwbNiyvlRdccEGN+wcOHJj3cI+B+QEHHBB22WWXfLnYiawwHAzQfM7zgDIRx1jr3r176N+/f3uvCgCUHXW48bQXAB1VDK//+c9/rjM/Drny29/+dp2e5MVqD+8SL0L6+OOP15hXPEZ6cQ93oPU4JAUl5tprr82PJNc+peuQQw4JxxxzTP57PD0r3u7Xr19+5e14sZKHHnponeJ8/vnnh3HjxoXevXuH4447bp3TyOOR6mOPPTZ89KMfzU8Ti2O1/fCHP6xzvc4999x8PLb4XMcff3zywidRvAp5vGDKFltsET70oQ+FESNG5Kdkt6Z4RD8ecY9/L/b0O/HEE/Px5erz//7f/8vbLo5Z17dv3+px6aI33ngjb7vYS2DDDTcMn/vc58Lf//73Vt0GANqfOtx6dTj2kos9zmNdjfU19r6L9TYOWRNPT4/bHtsnTrGtiodzqaqqytvoN7/5TY3nnD59eth4443DO++8k99etGhROPTQQ/Ped7EHYHyd6htrFgAAGkKIDiXmy1/+cvjXv/4VHn744ep5cXyzGTNmVI+lFndKDzzwwDBz5szw9NNPh89+9rP5qWALFy6s8Vzxit9DhgzJlzn77LPX+VsxIPjwhz8c7rjjjvC3v/0tTJ48OXz3u98Nt99+e43l4t+J46/FndlbbrklP8Idd+ZTTj755DB79uxw6623hr/85S/5NsV1rC+EjiF1DCJS00477bTeI/0/+tGPwl//+tdw00035Uf3v/Od7ySXj+PTxdA8tmNsn7iNw4cPr74/7tA/+eST4Z577sm3JR7pj8vWvhI6AJ2LOtw6dTgeONh///3Dxz72sXzdHn300bzN4oGEGJ6PHDkyfP3rX8+Hb4lTDOKLxYMH//3f/x1uvvnmGvPjRdvimPExmI81OgbzMVT//e9/n4f2cd3jttd30AEAANYrA0rOIYcckh1zzDHVt3/yk59kAwcOzNasWZN8zE477ZRdeeWV1be33HLLbMyYMTWWefnll+M5X9nTTz+dfJ6TTjop+z//5/9U3z766KOzTTfdNFuxYkX1vKuvvjrbaKONqtfnU5/6VHbqqafmv7/yyitZ165ds1dffbXG8+6///7ZpEmTkn/3H//4R/b3v/89OS1YsCBrjDvuuCP7r//6r+rbP/vZz7LKysrq2yNHjsyOPPLIOh/7/PPP5+302GOPVc9btmxZ1qtXr+z2229v1HoA0PGowy1fhw8//PBs7733Ti5fvA0FDz/8cN5eb7zxRn57+vTp+XYX2mL58uVZz549s9/85jf57Z///OfZ9ttvn61du7b6OVauXJnX7/vvv79R6w8AAMWMiQ4lKPZ0i72xfvzjH4cePXrkvawOO+yw6ouCxB5w55xzTt6bOvbWihcZeffdd9fpAbfHHnus929NmzYtv1BJfGx8jthTa+jQoTWWib3oYg+vgthbLK5DPGV6yy23rLHsM888k/cq22677dY5tfy//uu/kusRTzlvjnga/dSpU8P8+fPzU75jm7z33nv56d3F617cIy62cV1ib79u3brlp78XxHWPp9nH+wDo3NThlq/Dse7GHvHNEXv/b7DBBvlZYvH1+OUvf5n3UB81alR+/5///Ofwwgsv5D3Ri8X1iEPwAABAUwnRoQTF05vj8CFx5zyOsxpPSb788sur74/jnD744IP5aeLbbLNNPkbol770pXVOVY7jktYnnuYdn+sHP/hBvkMedzq///3vhz/+8Y9NXve4U9+1a9cwZ86c/GexeEp1faeRx+1MiSFBPEW8LnGs03iK9wknnBC+973v5WOgxtPE4zizsU3qCtFjmwFAXdThlq/DLVF344VGYzvHIV1iiB5/jh07Nj/wXdj2YcOG5Qc9aovjyQMAQFMJ0aEExQtdfvGLX8x3AmOPqtgDevfdd6++P47xGcfsLlwIM+40NuWiWfF59tprr/ziXwV19dSKPbti77jCDvAf/vCHfEe89nil0W677Zb3gFu6dGnYZ599GrwuP/3pT/O/kRJ7nqXEoCCOKxtDiEIvwdrjyda266675mPMjh8/fp374tXPYw+6GGLE9oni+LjPPfdcPpYrAJ2bOtzydbhQd1NjuceAPK53Q84S+MxnPpMH+nHc9QsuuKD6vvga3XbbbWHzzTfPe6gDAEBLEaJDiYo7ibFXV9xJ/MpXvlLjvm233Ta/qFjsKVdRUZFfrCzuvDZWfJ7/+Z//Cffff3/46Ec/Gn7+85+HP/3pT/nvxWIvstib7KyzzspDgilTpuQXLSvsKBeLp4/HdR83bly+Mx135l9//fV8xznuQB900EEtfhp57AUYLyZ25ZVX5m0SQ4lrrrmm3sfEbYgXONt6663z3mwxNL/vvvvCGWeckbfLIYcckp/K/5Of/CTvGThx4sR8HeN8ADo/dbhl6/CkSZPCLrvskh8wOP744/PQPF68NQ7x0rdv3zB48OD84HXcvniAIPZmr8snP/nJ0L9//3wbYzsVD70W58We/LFWn3feeflFW1955ZX8tYoXOY23AQCgKdb95g2UhP322y/fgYy9n4844oga91122WVhk002yXuvxZ3V0aNH1+gh11Df+MY38p528VTouBMae1sX94YriGFz3NGPO65x2c9//vP5WLApP/vZz/Kd929+85t5770xY8bkocBHPvKR0BriWLGxTS6++OKw88475z0H47is9fn0pz8d7rjjjnxc1Tj2bGzvJ554osY2xFPCY4AST7GPp/XHkL2+nngAdB7qcMvW4RjuP/DAA3mv+uHDh+e19Ve/+lX1UCxxWJs4/Ew84ysOvVJ7fPmCeNDi8MMPz58nhubF4rAxv/vd7/LtjO0azyyLBx/imOh6pgMA0BwV8eqizXoGAAAAAADopPREBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAASyi5Ez7IsVFVV5T8BgNKhRgMAAFCKyi5Ef+utt0JlZWX+EwAoHWo0AAAApajsQnQAAAAAAGgoIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASOiWuoP1Gzzx3nXmLbjooHZZFwA6FjWkdWlfAJpKDQEASqon+u9+97tw8MEHh4EDB4aKiopw9913r/cxs2bNCrvvvnvo0aNH2GabbcKNN94YSuWLVX3zAaAj1RA1GoBypIYAACUXoq9YsSIMGTIkTJs2rUHLv/zyy+Gggw4K++67b5g7d2447bTTwte+9rVw//33h7a0vi9QvmAB0NFriBoNQLlRQwCAlIosy7JQAmIvt+nTp4cxY8YklznjjDPCvffeG5599tnqeYcddlh48803w4wZMxr0d6qqqkJlZWVYvnx56N27d6PXszFfnJzyB0BnqCFqNACdnRoCAHSaC4vOnj07jBo1qsa80aNH5/NTVq5cme+UF08AQMtSowEAAOisOlSIvnjx4tCvX78a8+LtuNP97rvv1vmYqVOn5r3aCtOgQYPaaG0BoHyo0QAAAHRWHSpEb4pJkyblp4UXpkWLFrX3KgEAajQAAAAdRLfQgfTv3z8sWbKkxrx4O46b2qtXrzof06NHj3wCAFqPGg0AAEBn1aF6oo8cOTLMnDmzxrwHH3wwn99WGnoRGRebAaCcaogaDUBHpoYAACUbor/99tth7ty5+RS9/PLL+e8LFy6sPs173Lhx1csff/zx4aWXXgrf+c53wvz588OPf/zjcPvtt4fTTz+9Tdd7fV+cfLECoKPXEDUagHKjhgAAJRmiP/nkk2G33XbLp2jChAn575MnT85vv/baa9U769FHP/rRcO+99+Y924YMGRJ+8IMfhJ/+9Kdh9OjRbb7uqS9QvlgB0BlqiBoNQDlSQwCAulRkWZaFMlJVVRUqKyvzC5jFcVoBgNKgRgMAAFCKOtSY6AAAAAAA0JaE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAAJRqiD5t2rQwePDg0LNnzzBixIjwxBNP1Lv8FVdcEbbffvvQq1evMGjQoHD66aeH9957r83WFwDKhRoNAAAA7Ryi33bbbWHChAlhypQp4amnngpDhgwJo0ePDkuXLq1z+ZtvvjlMnDgxX37evHnh+uuvz5/ju9/9bpuvOwB0Zmo0AAAAfKAiy7IstJPYq23PPfcMV111VX577dq1ec+1U045Jd8Rr+3kk0/Od8xnzpxZPe+b3/xm+OMf/xgeffTRBv3NqqqqUFlZGZYvXx569+7dglsDAJ2HGg0AAADt3BN91apVYc6cOWHUqFHV87p06ZLfnj17dp2P2WuvvfLHFE4nf+mll8J9990XDjzwwOTfWblyZb5TXjwBAGlqNAAAAPxHt9BOli1bFtasWRP69etXY368PX/+/Dofc8QRR+SP+8QnPhFiB/r3338/HH/88fWeKj516tRw7rnntvj6A0BnpUYDAABACV1YtDFmzZoVLrzwwvDjH/84H5/1rrvuCvfee284//zzk4+ZNGlSflp4YVq0aFGbrjMAlAM1GgAAgM6q3Xqi9+3bN3Tt2jUsWbKkxvx4u3///nU+5uyzzw5HHXVU+NrXvpbf3mWXXcKKFSvCcccdF84888z8VPPaevTokU8AQMOo0QAAAFACPdG7d+8ehg0bVuMCZPGiZfH2yJEj63zMO++8s85OeNzJj9rx+qgA0Kmo0QAAAFACPdGjCRMmhKOPPjrsscceYfjw4eGKK67Ie62NHz8+v3/cuHFhiy22yMdMjQ4++OBw2WWXhd122y2MGDEivPDCC3nPtzi/sKMOADSfGg0AAAAlEKKPHTs2vP7662Hy5Mlh8eLFYejQoWHGjBnVFzJbuHAIIxJjAAAtAklEQVRhjV5tZ511VqioqMh/vvrqq2GzzTbLd86/973vteNWAEDno0YDAADAByqyMjvHuqqqKlRWVuYXMOvdu3d7rw4A8L/UaAAAAEpRu42JDgAAAAAApU6IDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAACjVEH3atGlh8ODBoWfPnmHEiBHhiSeeqHf5N998M5x00klhwIABoUePHmG77bYL9913X5utLwCUCzUaAAAAQujWnn/8tttuCxMmTAjXXHNNvnN+xRVXhNGjR4fnnnsubL755ussv2rVqvCZz3wmv+/OO+8MW2yxRXjllVdCnz592mX9AaCzUqMBAADgAxVZlmWhncSd8j333DNcddVV+e21a9eGQYMGhVNOOSVMnDhxneXjjvz3v//9MH/+/LDBBhs06W9WVVWFysrKsHz58tC7d+9mbwMAdEZqNAAAALTzcC6xx9qcOXPCqFGjqud16dIlvz179uw6H3PPPfeEkSNH5qeK9+vXL+y8887hwgsvDGvWrGnDNQeAzk2NBgAAgBIYzmXZsmX5jnXc0S4Wb8debHV56aWXwm9/+9tw5JFH5mOsvvDCC+HEE08Mq1evDlOmTKnzMStXrsyn4l5uAECaGg0AAAAldGHRxoinksexVq+99towbNiwMHbs2HDmmWfmp5CnTJ06NT81vDDFU9EBgJalRgMAANBZtVuI3rdv39C1a9ewZMmSGvPj7f79+9f5mAEDBoTtttsuf1zBjjvuGBYvXpyfel6XSZMm5WOrFqZFixa18JYAQOeiRgMAAEAJhOjdu3fPe6rNnDmzRi+2eDuOqVqXvffeOz89PC5X8Pzzz+c77vH56tKjR4/84mTFEwCQpkYDAABAiQznMmHChHDdddeFm266KcybNy+ccMIJYcWKFWH8+PH5/ePGjct7qRXE+//973+HU089Nd8xv/fee/OLlsWLmAEALUeNBgAAgHa+sGgUx0t9/fXXw+TJk/PTvYcOHRpmzJhRfSGzhQsXhi5d/pPzx7FS77///nD66aeHXXfdNWyxxRb5zvoZZ5zRjlsBAJ2PGg0AAAAfqMiyLAtlpKqqKr94WRx71WnjAFA61GgAAABKUbsO5wIAAAAAAKVMiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAADQWiH6P/7xj3wCAAAAAIDOpkkh+tq1a8N5550XKisrw5ZbbplPffr0Ceeff35+HwAAAAAAdAbdmvKgM888M1x//fXhoosuCnvvvXc+79FHHw3nnHNOeO+998L3vve9ll5PAIDGe/XVEN59N4QuXWpOXbuGUFHxwc/a96WmuDwAAABlpyLLsqyxDxo4cGC45pprwuc///ka83/1q1+FE088Mbwad1hLVFVVVd6Dfvny5aF3797tvToAQGvU6AULQrj66hDWrAlhgw1C6NXrg2nDDT+Yav9emGKoXp+6wvj6gvfC/TGAF8gDAACUT0/0f//732GHHXZYZ36cF+8DAGhXL70UwiWXNP5xPXqsG7TXF77X/pm6v2fPmsF5XaF6Q3vE1zUBAABQWiH6kCFDwlVXXRV+9KMf1Zgf58X7AADa1YoVTXvcypUfTG+80bLrE0Py9QXw9fWSr337Qx/6z+/du9ffC74xkx7yAAAALROiX3LJJeGggw4KDz30UBg5cmQ+b/bs2WHRokXhvvvua8pTAgC0nEGDQvjGN0JYujSEt976YFz0d975YIq/xykG7fFn40e2a7z4Nwp/v6XVN1zN+n7W/j2G88VTt27pIWn0kAcAAMpEk8ZEj/75z3+GadOmhfnz5+e3d9xxx3w89DheeikzJjoAlFGNjtdpqa9XevwaFHuepwL22uF77eXqu/3ee6HDi8Pb1O75Xvt2qpd8cTC/0Ub/+RmnOL+xPeX1kgcAADpaiN5RCdEBoIxqdLywaJzi1521a9f9Wdfvxcukfi/8XN/fLoTrhYC9vnC+eNnUcoXbq1eHDi2G4nGc+PrC+LrC+eKpEMwXpo03/mBefN71hfFCeQAAoDWGc/nLX/4Sdt5559ClS5f89/rsuuuujVkHAIDWEXs7x6m1pML5hvxMzUvNLxZD9Pp6yRd+1g7j1/czTm3RvyJuV2sNbxOHoEmNH197CJt4X+1hbAq95QshfSGcj7/HoXPquyAsAABQ3iH60KFDw+LFi8Pmm2+e/15RURHq6sQe56+JPa8AADq7GKK2ZkhfLNWDvq5gvr7Avr4gP061h7epqyd8fUF9ew9v8/778bSGD6a2GN6mOJgvhPJ1jS9fu8d8nOLvhfl1DW+jtzwAAHSsEP3ll18Om222WfXvAAC0oUJP59YO7Zvai359P2Pv+cKQNoVwva7e8/UF8amfbTW8TTzAEKc332zZ541hefFFX2uPLV9Xj/naw9rU1YM+TrWHtxHMAwBA64XoW265ZZ2/AwDQibRV7/r6etQ3tgf9qlUhvP32BwF97al2GL++cehr3x+fv7XF7WjL4W0KveXr6zVfHMoXes0X/4xTYXgbAADo5Bocot9zzz0NftLPf/7zTV0fAADKQVuNI96ccesLF4eN4XYM6QtBfaFHfX1hfbkMb1M7kC/uOR+D9uKQvrinfO1wPl5MuPB7DPv1mAcAoCOG6GPGjKlxu/aY6PF2gTHRAQAou571jRnqJn5fLoTydf0sHvqmeAichlxQtq2Ht3njjZZ/zYovAFscxBfC+fqGs6ndc754ue7dW3ZdAQAoCw0O0dcWncr60EMPhTPOOCNceOGFYeTIkfm82bNnh7POOiufBwAAZaUpver/93pDDba+YL7we+zlXughXzzMTe2QvrhHfe1x6lM96NtqeJvCerXW8DZ1hfPrG2++eCib2HO+9vJtdZFhAADaXEVW3J28gXbeeedwzTXXhE984hM15v/+978Pxx13XJg3b14oVVVVVaGysjIsX7489I5ffgGAkqBGQwmpa8z62Hu+ELy/9VbNIW5qB/aFIXBq96Qv/N5ew9u0puLhbRp7Mdi6gvrC8oa3AQDoOD3Ri7344ouhT58+68yPO74LFiwIZeO550I499wPdiziF9t4caXYAyX2cKk9xfsKP+MyhdvF96WWr+tn4ffi+cW3i6fYM8oXbwAAmjtmfQx1G9uDPqU4oI9D0BTC+eKfhaC+roC+rnC+rqC+swxvU98FYYuD+eJ5tS8IW3tZw9sAALReT/RPfvKToWfPnuHnP/956NevXz5vyZIlYdy4ceG9994LjzzySCiLXm6/+10In/pU6BBimF4I+Ov7WXwgYH0/C1Pt+bUPJqQeV9/9xfPqelzhYEHtx9T1txujoQcbGrpcY95e9T1nXfc1dF5T/l5bLdsSj2vsczb27zR221vqNW/K3+cDtV+DVBsWzy9+TOPLYvPEIIScnuhAq4gheiGIL/Sgrx3WF37W7lVf15A3hQvJxt/bYnib1lQ8vE1x7/naF4St/XtxKB8/r+taxvA2AEC590S//vrrwxe/+MXwkY98JAwaNCift2jRorDtttuGu+++O5SNturV0hLef/+DKfaOKSexB1VdBwFSwX1Dlq3vMfU9PnVAorl/s3he4SBCnISv0DFsu633K0Brit+PNtnkg6klFY8/Xwjm6+pBXxza155qh/TFF4htC3H/oKrqg6k1h7ep7+KwxeF77ZC+riFuDG8DAHSUnuhRfNiDDz4Y5s+fn9/ecccdw6hRo0JFiX+hadFebi+/HMItt3zwJXfVqv8E1XVNMXCP40gW/177dvHyhdvxZ7y/9rzaP6G2hgb8xcF7Sx0MqOushoaecdCQgwj1LWf4IjoaIXo1PdEB/lfs4V57/PnikL4wrziwTwX0tS8Y25E6AtUl1swYpBcC9doBfe2Qvnj4mtrD2sTbxReJjcPbqMkAQEuE6KtXrw69evUKc+fOzS8w2tF0yh30woWeCoF87VC+doAfA/9CMF+YV/wzNS9OhcfWfv665tV1EKH2/OLH1HVwoPj+uDNRfACio+8A0HqaehZAfcs1JdBvylkO6xsKqb7H2+nrmITonbtGA5SawvjzxcF8YUoF88WBfF0XjC1MnWF4m0IwH6fKyhDGjg1h4sT2XjMAoKMN57LBBhvkw7is0fu5dMTwpRCw9ewZOsVBgdRUuPhUYSqE6YWDA8VBf+0Qv/jAQO0DBrUPNNQ+WyAV9teeXxzwx3WtfcCgrgMDqftTf7twm7RCe5Wb+oYvasgZB8W/x+da3zUKGnsQob51acoZC8XzhNAA0DCxDvfp88HUkuJ38zh0ZO1QvvbFYVM95+sae74wtE1bDm9TWPeCT36ybf42AND5xkQ/88wzw3e/+938wqKbbrppy68V5S2GYaUUiNUX6rfUVDg4UNffq33goPgAQiqob0gIX1fw39ADA/UdYGjo42svU7h/fWcndPQeTq0ptk3hjI1y09iAvznXG2hu8F/7/qVLQxg4MISttmrvVgSApovf32OHnjhttlnrDG8Tw+04fnvt8eZrB/O1Q/raPeeLLw67vu9NcZgXAKDsNSlEv+qqq8ILL7wQBg4cGLbccsvwoVpfLJ566qmWWj9of6UW6kepYL126F74vb7lU2F9XY8phQC7ELSvL8Rv6BkDLRH8N/bAQF1/f32PL8dgvDEKbRbPROmIjjsuhJ/8pL3XAgBKUzxDrnBx0QEDWva5i3vP1w7m4/ew7bZr2b8HAJRPiD5mzJiWXxOgcTsS7aUp4XtD7mvocvGARtz+2DO43NQ+SFA7pF9fL/7UQYLi4YyacvbB+g4sNGWdaw971NmV4/9nACgFPXp8MPXt295rAgB0thB9ypQpLb8mQMdQKj3z6+st39BgvjGPr31feygMDdK9eygrheGLGnK9gcYG/6nQPjVMUvH9DT07ofjxda1f1NLj0gIAAADtG6IDtLsY5MdAub20ZHDfmB79xePnl+PFk2NPsc5m221L48AUAAAA0HIh+po1a8Lll18ebr/99rBw4cKwqtYYtP/+97+b8rQAHbNHfnuE+U0J7Fuylz4AAABAmWhSiH7uueeGn/70p+Gb3/xmOOuss8KZZ54ZFixYEO6+++4wefLkll9LAEprWJ2mjmvfkr32AQAAANpARZY1PonYeuutw49+9KNw0EEHhY033jjMnTu3et4f/vCHcPPNN4dSVVVVFSorK8Py5ctD796923t1AGiqxvS8b+h9DQ3zW5LhXKqp0QAAAHSanuiLFy8Ou+yyS/77RhttlO/sRv/93/8dzj777JZdQwAotd74DR0upyHzBOgAAADQ+UL0D3/4w+G1114LH/nIR/Ie6A888EDYfffdw5/+9KfQozNe9A0ASunCtgAAAECb6dKUB33hC18IM2fOzH8/5ZRT8t7n2267bRg3blw45phjWnodAQAAAACg44yJXlscB/3xxx/Pg/SDDz44lDLjrQJAaVKjAQAA6DQ90adOnRpuuOGG6tsf//jHw4QJE8Lrr78eLr744pZcPwAAAAAA6Fgh+k9+8pOwww47rDN/p512Ctdcc01LrBcAAAAAAHTMEH3x4sVhwIAB68zfbLPN8guOAgAAAABA2YbogwYNCo899tg68+O8gQMHtsR6AQAAAABAu+vWlAd9/etfD6eddlpYvXp12G+//fJ5M2fODN/5znfCN7/5zZZeRwAAAAAA6Dgh+re//e3wr3/9K5x44olh1apV+byePXuGM844I0yaNKml1xEAAAAAANpFRZZlWVMf/Pbbb4d58+aFXr16hW233Tb06NEjlLqqqqpQWVkZli9fHnr37t3eqwMA/C81GgAAgE7TE71go402CnvuuWfLrQ0AAAAAAHT0C4sCAAAAAEA5EKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAABKOUSfNm1aGDx4cOjZs2cYMWJEeOKJJxr0uFtvvTVUVFSEMWPGtPo6AkA5UqMBAAAod+0eot92221hwoQJYcqUKeGpp54KQ4YMCaNHjw5Lly6t93ELFiwI3/rWt8I+++zTZusKAOVEjQYAAIASCNEvu+yy8PWvfz2MHz8+fOxjHwvXXHNN2HDDDcMNN9yQfMyaNWvCkUceGc4999yw1VZbten6AkC5UKMBAACgnUP0VatWhTlz5oRRo0b9Z4W6dMlvz549O/m48847L2y++ebh2GOPbaM1BYDyokYDAADAB7qFdrRs2bK8x1q/fv1qzI+358+fX+djHn300XD99deHuXPnNuhvrFy5Mp8KqqqqmrnWAND5qdEAAABQIsO5NMZbb70VjjrqqHDdddeFvn37NugxU6dODZWVldXToEGDWn09AaDcqNEAAAB0Vu3aEz3uZHft2jUsWbKkxvx4u3///uss/+KLL+YXKzv44IOr561duzb/2a1bt/Dcc8+FrbfeusZjJk2alF8UrbiXm510AKifGg0AAAAlEKJ37949DBs2LMycOTOMGTOmeoc73j755JPXWX6HHXYIzzzzTI15Z511Vt777Yc//GGdO949evTIJwCg4dRoAAAAKIEQPYo90I4++uiwxx57hOHDh4crrrgirFixIowfPz6/f9y4cWGLLbbIT/nu2bNn2HnnnWs8vk+fPvnP2vMBgOZRowEAAKAEQvSxY8eG119/PUyePDksXrw4DB06NMyYMaP6QmYLFy4MXbp0qKHbAaBTUKMBAAAghIosy7JQRuJ4q/HiZcuXLw+9e/du79UBAP6XGg0AAEAp0n0MAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAACUcog+bdq0MHjw4NCzZ88wYsSI8MQTTySXve6668I+++wTNtlkk3waNWpUvcsDAE2nRgMAAFDu2j1Ev+2228KECRPClClTwlNPPRWGDBkSRo8eHZYuXVrn8rNmzQqHH354ePjhh8Ps2bPDoEGDwgEHHBBeffXVNl93AOjM1GgAAAAIoSLLsqw9VyD2attzzz3DVVddld9eu3ZtvtN9yimnhIkTJ6738WvWrMl7u8XHjxs3br3LV1VVhcrKyrB8+fLQu3fvFtkGAOiM1GgAAABo557oq1atCnPmzMlP965eoS5d8tuxB1tDvPPOO2H16tVh0003rfP+lStX5jvlxRMAUD81GgAAAEogRF+2bFneS61fv3415sfbixcvbtBznHHGGWHgwIE1dvKLTZ06Ne/VVphiDzoAoH5qNAAAAJTImOjNcdFFF4Vbb701TJ8+Pb/gWV0mTZqUnxZemBYtWtTm6wkA5UaNBgAAoLPo1p5/vG/fvqFr165hyZIlNebH2/3796/3sZdeemm+g/7QQw+FXXfdNblcjx498gkAaDg1GgAAAEqgJ3r37t3DsGHDwsyZM6vnxYuWxdsjR45MPu6SSy4J559/fpgxY0bYY4892mhtAaB8qNEAAABQAj3RowkTJoSjjz4639EePnx4uOKKK8KKFSvC+PHj8/vHjRsXtthii3zc1Ojiiy8OkydPDjfffHMYPHhw9bisG220UT4BAC1DjQYAAIASCNHHjh0bXn/99XynO+5sDx06NO+9VriQ2cKFC0OXLv/pMH/11VeHVatWhS996Us1nmfKlCnhnHPOafP1B4DOSo0GAACAECqyLMtCGamqqgqVlZX5Bcx69+7d3qsDAPwvNRoAAIBS1K5jogMAAAAAQCkTogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAEoToAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0QEAAAAAIEGIDgAAAAAACUJ0AAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAASuqXuYP0GT7x3nXkLLjqoXdYFgI5FDWld2heAplJDAICS7Ik+bdq0MHjw4NCzZ88wYsSI8MQTT9S7/B133BF22GGHfPlddtkl3HfffaEUvljVNx8AOmINUaMBKCdqCABQkiH6bbfdFiZMmBCmTJkSnnrqqTBkyJAwevTosHTp0jqXf/zxx8Phhx8ejj322PD000+HMWPG5NOzzz7bZuu8vi9QvmAB0BlqiBoNQDlRQwCAlIosy7LQjmKvtj333DNcddVV+e21a9eGQYMGhVNOOSVMnDhxneXHjh0bVqxYEX79619Xz/v4xz8ehg4dGq655pr1/r2qqqpQWVkZli9fHnr37t3o9W3MFyen/AHQkWuIGg1AuVBDAICS7Ym+atWqMGfOnDBq1Kj/rFCXLvnt2bNn1/mYOL94+Sj2ikstv3LlynynvHgCAOqnRgMAAEAJhOjLli0La9asCf369asxP95evHhxnY+J8xuz/NSpU/NebYUp9qADAOqnRgMAAECJjIne2iZNmpSfFl6YFi1a1N6rBACo0QAAAHQQ3drzj/ft2zd07do1LFmypMb8eLt///51PibOb8zyPXr0yCcAoOHUaAAAACiBnujdu3cPw4YNCzNnzqyeFy9aFm+PHDmyzsfE+cXLRw8++GBy+ZbW0IvIuNgMAB25hqjRAJQTNQQAKOnhXCZMmBCuu+66cNNNN4V58+aFE044IaxYsSKMHz8+v3/cuHH56d4Fp556apgxY0b4wQ9+EObPnx/OOeec8OSTT4aTTz65zdZ5fV+cfLECoDPUEDUagHKihgAAJRuijx07Nlx66aVh8uTJYejQoWHu3Ln5DnjhwmQLFy4Mr732WvXye+21V7j55pvDtddeG4YMGRLuvPPOcPfdd4edd965Tdc79QXKFysAOksNUaMBKDdqCABQl4osy7JQRqqqqkJlZWV+AbPevXu39+oAAP9LjQYAAKAUtXtPdAAAAAAAKFVCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHQAAAAAAErqFMpNlWf6zqqqqvVcFAFrFxhtvHCoqKkJHo0YD0Nl11BoNAOWu7EL0t956K/85aNCg9l4VAGgVy5cvD7179w4djRoNQGfXUWs0AJS7iqzQ7atMrF27Nvzzn/9ssR4Asbdc3NlftGiRL0NNoP2aTxs2j/ZrHu1Xmu3XUXu5qdGlRfs1nzZsHu3XPNqvedRoAKCse6J36dIlfPjDH27x541frHw5bTrt13zasHm0X/Nov+bRfh9Qo0uT9ms+bdg82q95tF/zaD8AIHJhUQAAAAAASBCiAwAAAABAghC9mXr06BGmTJmS/6TxtF/zacPm0X7No/2aR/u1Lu3bPNqv+bRh82i/5tF+zaP9AICyvrAoAAAAAAA0lJ7oAAAAAACQIEQHAAAAAIAEIToAAAAAACQI0Rtg2rRpYfDgwaFnz55hxIgR4Yknnqh3+TvuuCPssMMO+fK77LJLuO+++0I5a0z7XXfddWGfffYJm2yyST6NGjVqve1dDhr7f7Dg1ltvDRUVFWHMmDGhnDW2/d58881w0kknhQEDBuQXk9puu+3K+n3c2Pa74oorwvbbbx969eoVBg0aFE4//fTw3nvvhXL0u9/9Lhx88MFh4MCB+Xvx7rvvXu9jZs2aFXbffff8/94222wTbrzxxjZZ145KjW4eNbr51OjmUaObR41uOjUaAGiUeGFR0m699dase/fu2Q033JD99a9/zb7+9a9nffr0yZYsWVLn8o899ljWtWvX7JJLLsn+9re/ZWeddVa2wQYbZM8880xWjhrbfkcccUQ2bdq07Omnn87mzZuXffWrX80qKyuzf/zjH1m5amwbFrz88svZFltske2zzz7ZIYcckpWrxrbfypUrsz322CM78MADs0cffTRvx1mzZmVz587NylFj2+8Xv/hF1qNHj/xnbLv7778/GzBgQHb66adn5ei+++7LzjzzzOyuu+6KF/HOpk+fXu/yL730UrbhhhtmEyZMyGvIlVdemdeUGTNmtNk6dyRqdPOo0c2nRjePGt08anTzqNEAQGMI0ddj+PDh2UknnVR9e82aNdnAgQOzqVOn1rn8oYcemh100EE15o0YMSL7xje+kZWjxrZfbe+//3628cYbZzfddFNWrprShrHd9tprr+ynP/1pdvTRR5f1Dnpj2+/qq6/Ottpqq2zVqlVtuJadp/3isvvtt1+NeXFnc++9987KXUN20L/zne9kO+20U415Y8eOzUaPHt3Ka9cxqdHNo0Y3nxrdPGp086jRLUeNBgDWx3Au9Vi1alWYM2dOfrpyQZcuXfLbs2fPrvMxcX7x8tHo0aOTy3dmTWm/2t55552wevXqsOmmm4Zy1NQ2PO+888Lmm28ejj322FDOmtJ+99xzTxg5cmR+qni/fv3CzjvvHC688MKwZs2aUG6a0n577bVX/pjC6eQvvfRSfpr9gQce2Gbr3ZGpIQ2nRjePGt18anTzqNHNo0a3PTUEAMpbt/ZegVK2bNmy/Et5/JJeLN6eP39+nY9ZvHhxncvH+eWmKe1X2xlnnJGPU1j7C2u5aEobPvroo+H6668Pc+fODeWuKe0Xdyh/+9vfhiOPPDLfsXzhhRfCiSeemAdFU6ZMCeWkKe13xBFH5I/7xCc+Ec90Cu+//344/vjjw3e/+902WuuOLVVDqqqqwrvvvpuPYcsH1OjmUaObT41uHjW6edTotqdGA0B50xOdknXRRRflF92aPn16frEk1u+tt94KRx11VH7xt759+7b36nRIa9euzXsIXnvttWHYsGFh7Nix4cwzzwzXXHNNe69ahxAvuBV7Bf74xz8OTz31VLjrrrvCvffeG84///z2XjWgBanRjadGN58a3TxqNABA0+mJXo+4g9O1a9ewZMmSGvPj7f79+9f5mDi/Mct3Zk1pv4JLL70030F/6KGHwq677hrKVWPb8MUXXwwLFiwIBx98cI0dzqhbt27hueeeC1tvvXUoF035PzhgwICwwQYb5I8r2HHHHfPeR/HU6e7du4dy0ZT2O/vss/OQ6Gtf+1p+e5dddgkrVqwIxx13XB50xFPNSUvVkN69e+vhVosa3TxqdPOp0c2jRjePGt321GgAKG++KdUjfhGPvVxmzpxZY2cn3o7jMdYlzi9ePnrwwQeTy3dmTWm/6JJLLsl7xMyYMSPsscceoZw1tg132GGH8Mwzz+SniRemz3/+82HffffNfx80aFAoJ035P7j33nvnp4cXgo3o+eefz3fcy2nnvKntF8dIrr0TXgg7PrhuF/VRQxpOjW4eNbr51OjmUaObR41ue2oIAJS59V56tMzdeuutWY8ePbIbb7wx+9vf/pYdd9xxWZ8+fbLFixfn9x911FHZxIkTq5d/7LHHsm7dumWXXnppNm/evGzKlCnZBhtskD3zzDNZOWps+1100UVZ9+7dszvvvDN77bXXqqe33norK1eNbcPajj766OyQQw7JylVj22/hwoXZxhtvnJ188snZc889l/3617/ONt988+yCCy7IylFj2y9+5sX2u+WWW7KXXnope+CBB7Ktt946O/TQQ7NyFD+7nn766XyKJfeyyy7Lf3/llVfy+2PbxTYsiG224YYbZt/+9rfzGjJt2rSsa9eu2YwZM9pxK0qXGt08anTzqdHNo0Y3jxrdPGo0ANAYQvQGuPLKK7OPfOQj+Y7j8OHDsz/84Q/V933qU5/Kd4CK3X777dl2222XL7/TTjtl9957b1bOGtN+W265Zf4ltvYUv/SXs8b+HyxW7jvoTWm/xx9/PBsxYkS+Y7rVVltl3/ve97L3338/K1eNab/Vq1dn55xzTr5T3rNnz2zQoEHZiSeemL3xxhtZOXr44Yfr/EwrtFn8Gduw9mOGDh2at3f8//ezn/2snda+Y1Cjm0eNbj41unnU6OZRo5tOjQYAGqMi/tPeveEBAAAAAKAUGRMdAAAAAAAShOgAAAAAAJAgRAcAAAAAgAQhOgAAAAAAJAjRAQAAAAAgQYgOAAAAAAAJQnQAAAAAAEgQogMAAAAAQIIQHWg1gwcPDldccUV7rwYAUIsaDQAADSdEBwAAAACABCE6AAAAAAAkCNGBOl177bVh4MCBYe3atTXmH3LIIeGYY44JL774Yv57v379wkYbbRT23HPP8NBDDyWfb8GCBaGioiLMnTu3et6bb76Zz5s1a1b1vGeffTZ87nOfy58zPvdRRx0Vli1b1kpbCQAdjxoNAABtS4gO1OnLX/5y+Ne//hUefvjh6nn//ve/w4wZM8KRRx4Z3n777XDggQeGmTNnhqeffjp89rOfDQcffHBYuHBhk/9m3GHfb7/9wm677RaefPLJ/G8tWbIkHHrooS20VQDQ8anRAADQtrq18d8DOohNNtkk72128803h/333z+fd+edd4a+ffuGfffdN3Tp0iUMGTKkevnzzz8/TJ8+Pdxzzz3h5JNPbtLfvOqqq/Kd8wsvvLB63g033BAGDRoUnn/++bDddtu1wJYBQMemRgMAQNvSEx1Iir3ZfvnLX4aVK1fmt3/xi1+Eww47LN85j73cvvWtb4Udd9wx9OnTJz+1e968ec3q5fbnP/8571UXn6sw7bDDDvl98dR0AOADajQAALQdPdGBpHjqd5Zl4d57783HU/39738fLr/88vy+uHP+4IMPhksvvTRss802oVevXuFLX/pSWLVqVZ3PFXfqo/h8BatXr66xTNzpj3/z4osvXufxAwYMaOGtA4COS40GAIC2I0QHknr27Bm++MUv5r3bXnjhhbD99tuH3XffPb/vscceC1/96lfDF77wheqd63hhspTNNtss//naa6/lp4NHxRcwi+Jzx151gwcPDt26+XgCgBQ1GgAA2o7hXID1ni4ee7nFcU/j7wXbbrttuOuuu/Kd7HiK9xFHHBHWrl2bfJ7YC+7jH/94uOiii/JTyh955JFw1lln1VjmpJNOyi+Mdvjhh4c//elP+enh999/fxg/fnxYs2ZNq24nAHQ0ajQAALQNITpQr/322y9suumm4bnnnst3wgsuu+yy/MJme+21V3569+jRo6t7wKXEnfz3338/DBs2LJx22mnhggsuqHH/wIED895zcWf8gAMOCLvssku+XBzPtXCqOQDwATUaAADaRkVWPPghAAAAAABQTbcRAAAAAABIEKIDAAAAAECCEB0AAAAAABKE6AAAAAAAkCBEBwAAAACABCE6AAAAAAAkCNEBAAAAACBBiA4AAAAAAAlCdAAAAAAASBCiAwAAAABAghAdAAAAAAAShOgAAAAAABDq9v8BMAh/s2aKc0YAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x2000 with 11 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.lmplot(\n",
+    "    x=\"value\",\n",
+    "    y=\"cardio\",\n",
+    "    data=df.melt(id_vars=\"cardio\", value_vars=df),\n",
+    "    col=\"variable\",\n",
+    "    col_wrap=3,\n",
+    "    facet_kws={\"sharex\": False, \"sharey\": False},\n",
+    "    line_kws={\"color\": \"red\"},\n",
+    "    scatter_kws={\"alpha\": 0.2},\n",
+    ")\n",
+    "ax.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b239e98c",
+   "metadata": {},
+   "source": [
+    "Из графиков видно, что риск ССЗ возрастает с увеличением возраста, веса, давления и холестерин."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbfe93b6",
+   "metadata": {},
+   "source": [
+    "#### Определение корреляции между признаками"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "faa82c28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(\n",
+    "    df.corr(),\n",
+    "    annot=True,\n",
+    "    cmap=\"Blues\",\n",
+    "    fmt=\".2f\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3b2cc96",
+   "metadata": {},
+   "source": [
+    "Существует небольшая прямая корреляция между целевым признаком, возрастом, весом и холестерином."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf47804c",
+   "metadata": {},
+   "source": [
+    "#### Определение выброс через расчет межквартильного размаха"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e6176eea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_iqr_boundaries(series):\n",
+    "    q25 = series.quantile(0.25)\n",
+    "    q75 = series.quantile(0.75)\n",
+    "    iqr = q75 - q25\n",
+    "    boundaries = (q25 - 1.5 * iqr, q75 + 1.5 * iqr)\n",
+    "    return boundaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "2ccc3d40",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>ap_hi</th>\n",
+       "      <th>ap_lo</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>142.5</td>\n",
+       "      <td>39.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>170.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>186.5</td>\n",
+       "      <td>107.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ap_hi  ap_lo  height  weight\n",
+       "0   90.0   65.0   142.5    39.5\n",
+       "1  170.0  105.0   186.5   107.5"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "boundaries_columns = [\"ap_hi\", \"ap_lo\", \"height\", \"weight\"]\n",
+    "boundaries = pd.DataFrame(calculate_iqr_boundaries(df[boundaries_columns]))\n",
+    "boundaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad4f3d21",
+   "metadata": {},
+   "source": [
+    "#### Удаление выбросов из данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "b4c65c35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>height</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>ap_hi</th>\n",
+       "      <th>ap_lo</th>\n",
+       "      <th>cholesterol</th>\n",
+       "      <th>gluc</th>\n",
+       "      <th>smoke</th>\n",
+       "      <th>alco</th>\n",
+       "      <th>active</th>\n",
+       "      <th>cardio</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>50.358668</td>\n",
+       "      <td>2</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>55.382762</td>\n",
+       "      <td>1</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>90</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>51.629066</td>\n",
+       "      <td>1</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48.250465</td>\n",
+       "      <td>2</td>\n",
+       "      <td>169.0</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47.842515</td>\n",
+       "      <td>1</td>\n",
+       "      <td>156.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>100</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99993</th>\n",
+       "      <td>52.677691</td>\n",
+       "      <td>2</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>61.879860</td>\n",
+       "      <td>1</td>\n",
+       "      <td>158.0</td>\n",
+       "      <td>107.5</td>\n",
+       "      <td>140</td>\n",
+       "      <td>90</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>52.201292</td>\n",
+       "      <td>2</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>170</td>\n",
+       "      <td>90</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>61.414412</td>\n",
+       "      <td>1</td>\n",
+       "      <td>163.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>135</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>56.236995</td>\n",
+       "      <td>1</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>70000 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  \\\n",
+       "id                                                                          \n",
+       "0      50.358668       2   168.0    62.0    110     80            1     1   \n",
+       "1      55.382762       1   156.0    85.0    140     90            3     1   \n",
+       "2      51.629066       1   165.0    64.0    130     70            3     1   \n",
+       "3      48.250465       2   169.0    82.0    150    100            1     1   \n",
+       "4      47.842515       1   156.0    56.0    100     65            1     1   \n",
+       "...          ...     ...     ...     ...    ...    ...          ...   ...   \n",
+       "99993  52.677691       2   168.0    76.0    120     80            1     1   \n",
+       "99995  61.879860       1   158.0   107.5    140     90            2     2   \n",
+       "99996  52.201292       2   183.0   105.0    170     90            3     1   \n",
+       "99998  61.414412       1   163.0    72.0    135     80            1     2   \n",
+       "99999  56.236995       1   170.0    72.0    120     80            2     1   \n",
+       "\n",
+       "       smoke  alco  active  cardio  \n",
+       "id                                  \n",
+       "0          0     0       1       0  \n",
+       "1          0     0       1       1  \n",
+       "2          0     0       0       1  \n",
+       "3          0     0       1       1  \n",
+       "4          0     0       0       0  \n",
+       "...      ...   ...     ...     ...  \n",
+       "99993      1     0       1       0  \n",
+       "99995      0     0       1       1  \n",
+       "99996      0     1       0       1  \n",
+       "99998      0     0       0       1  \n",
+       "99999      0     0       1       0  \n",
+       "\n",
+       "[70000 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for column in boundaries_columns:\n",
+    "    df.loc[df[column] > boundaries[column][1], column] = boundaries[column][1]\n",
+    "    df.loc[df[column] < boundaries[column][0], column] = boundaries[column][0]\n",
+    "df    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "86b8832e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x31fa4fb50>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x2000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.catplot(\n",
+    "    y=\"value\",\n",
+    "    data=df.melt(value_vars=df.columns),\n",
+    "    col=\"variable\",\n",
+    "    kind=\"box\",\n",
+    "    col_wrap=3,\n",
+    "    sharey=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "4dd8b2a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>53.304309</td>\n",
+       "      <td>6.755152</td>\n",
+       "      <td>29.564122</td>\n",
+       "      <td>48.36272</td>\n",
+       "      <td>53.945351</td>\n",
+       "      <td>58.391742</td>\n",
+       "      <td>64.924433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.349571</td>\n",
+       "      <td>0.476838</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>164.407179</td>\n",
+       "      <td>7.825729</td>\n",
+       "      <td>142.500000</td>\n",
+       "      <td>159.00000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>170.000000</td>\n",
+       "      <td>186.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>weight</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>73.925678</td>\n",
+       "      <td>13.442403</td>\n",
+       "      <td>39.500000</td>\n",
+       "      <td>65.00000</td>\n",
+       "      <td>72.000000</td>\n",
+       "      <td>82.000000</td>\n",
+       "      <td>107.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ap_hi</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>126.712329</td>\n",
+       "      <td>16.385978</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>120.00000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>170.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ap_lo</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>81.779500</td>\n",
+       "      <td>9.062702</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>80.00000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cholesterol</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.366871</td>\n",
+       "      <td>0.680250</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gluc</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>1.226457</td>\n",
+       "      <td>0.572270</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>smoke</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.088129</td>\n",
+       "      <td>0.283484</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alco</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.053771</td>\n",
+       "      <td>0.225568</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>active</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.803729</td>\n",
+       "      <td>0.397179</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cardio</th>\n",
+       "      <td>70000.0</td>\n",
+       "      <td>0.499700</td>\n",
+       "      <td>0.500003</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               count        mean        std         min        25%  \\\n",
+       "age          70000.0   53.304309   6.755152   29.564122   48.36272   \n",
+       "gender       70000.0    1.349571   0.476838    1.000000    1.00000   \n",
+       "height       70000.0  164.407179   7.825729  142.500000  159.00000   \n",
+       "weight       70000.0   73.925678  13.442403   39.500000   65.00000   \n",
+       "ap_hi        70000.0  126.712329  16.385978   90.000000  120.00000   \n",
+       "ap_lo        70000.0   81.779500   9.062702   65.000000   80.00000   \n",
+       "cholesterol  70000.0    1.366871   0.680250    1.000000    1.00000   \n",
+       "gluc         70000.0    1.226457   0.572270    1.000000    1.00000   \n",
+       "smoke        70000.0    0.088129   0.283484    0.000000    0.00000   \n",
+       "alco         70000.0    0.053771   0.225568    0.000000    0.00000   \n",
+       "active       70000.0    0.803729   0.397179    0.000000    1.00000   \n",
+       "cardio       70000.0    0.499700   0.500003    0.000000    0.00000   \n",
+       "\n",
+       "                    50%         75%         max  \n",
+       "age           53.945351   58.391742   64.924433  \n",
+       "gender         1.000000    2.000000    2.000000  \n",
+       "height       165.000000  170.000000  186.500000  \n",
+       "weight        72.000000   82.000000  107.500000  \n",
+       "ap_hi        120.000000  140.000000  170.000000  \n",
+       "ap_lo         80.000000   90.000000  105.000000  \n",
+       "cholesterol    1.000000    2.000000    3.000000  \n",
+       "gluc           1.000000    1.000000    3.000000  \n",
+       "smoke          0.000000    0.000000    1.000000  \n",
+       "alco           0.000000    0.000000    1.000000  \n",
+       "active         1.000000    1.000000    1.000000  \n",
+       "cardio         0.000000    1.000000    1.000000  "
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.11.12)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}