diff --git "a/Week6_\353\263\265\354\212\265\352\263\274\354\240\234_\352\266\214\354\247\200\354\233\220.ipynb" "b/Week6_\353\263\265\354\212\265\352\263\274\354\240\234_\352\266\214\354\247\200\354\233\220.ipynb"
new file mode 100644
index 0000000..1771fa7
--- /dev/null
+++ "b/Week6_\353\263\265\354\212\265\352\263\274\354\240\234_\352\266\214\354\247\200\354\233\220.ipynb"
@@ -0,0 +1,1997 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w9hsexjp0YAQ",
+        "jp-MarkdownHeadingCollapsed": true
+      },
+      "source": [
+        "# **1. 단순 선형 회귀(Linear Regression)**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jOTpYj_zzTfL"
+      },
+      "source": [
+        "## **1-1. 단순 선형 회귀 식에서 각각 w0과 w1가 의미하는 바를 적으세요.**\n",
+        "$$f(x) = w_0 + w_1 *x$$\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FiuRw6mtzTfM"
+      },
+      "source": [
+        "- **w0**: 절편(Intercept) 또는 편향(Bias). $x$가 0일 때의 예측값(기본값)을 의미함.\n",
+        "- **w1**: 회귀 계수(Coefficient) 또는 가중치(Weight). 독립 변수 $x$가 1만큼 변할 때 종속 변수 $y$가 얼마만큼 변하는지를 나타내는 기울기를 의미."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Hk0op954zTfM"
+      },
+      "source": [
+        "## **1-2. RSS(Residual Sum of Square)의 회귀 식을 작성할 때, 중심 변수가 무엇인가요?**\n",
+        "\n",
+        "$$RSS(w_0, w_1) = \\frac{1}{N}\\sum_{i=1}^{N}(y_i - (w_0 + w_1 * x_i))^2$$"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IadaonumzTfP"
+      },
+      "source": [
+        "**답안**  \n",
+        ": $w_0, w_1$ (회귀 계수). 데이터($x_i, y_i$)는 상수로 간주하고, 오차를 최소화하는 최적의 회귀 계수를 찾는 것이 목적이기 때문."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SCvW0kF_zTfP"
+      },
+      "source": [
+        "## **1-3. 아래 코드의 빈칸을 채워 코드를 완성해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "OIX_R_w73c7j"
+      },
+      "outputs": [],
+      "source": [
+        "# 필요한 라이브러리 설치\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "\n",
+        "from scipy import stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "38gzqKiq3d-y",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 318
+        },
+        "outputId": "ddbdd430-8574-4ed5-e164-51a4cdad964b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<>:3: SyntaxWarning: invalid escape sequence '\\s'\n",
+            "<>:3: SyntaxWarning: invalid escape sequence '\\s'\n",
+            "/tmp/ipykernel_13987/755017604.py:3: SyntaxWarning: invalid escape sequence '\\s'\n",
+            "  raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Boston 데이터 세트 크기:  (506, 14)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
+              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
+              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
+              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
+              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
+              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
+              "\n",
+              "   PTRATIO       B  LSTAT  PRICE  \n",
+              "0     15.3  396.90   4.98   24.0  \n",
+              "1     17.8  396.90   9.14   21.6  \n",
+              "2     17.8  392.83   4.03   34.7  \n",
+              "3     18.7  394.63   2.94   33.4  \n",
+              "4     18.7  396.90   5.33   36.2  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-29615180-187a-4b62-986b-d6dbdb75c8aa\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>CRIM</th>\n",
+              "      <th>ZN</th>\n",
+              "      <th>INDUS</th>\n",
+              "      <th>CHAS</th>\n",
+              "      <th>NOX</th>\n",
+              "      <th>RM</th>\n",
+              "      <th>AGE</th>\n",
+              "      <th>DIS</th>\n",
+              "      <th>RAD</th>\n",
+              "      <th>TAX</th>\n",
+              "      <th>PTRATIO</th>\n",
+              "      <th>B</th>\n",
+              "      <th>LSTAT</th>\n",
+              "      <th>PRICE</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.00632</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>2.31</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.538</td>\n",
+              "      <td>6.575</td>\n",
+              "      <td>65.2</td>\n",
+              "      <td>4.0900</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>15.3</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>4.98</td>\n",
+              "      <td>24.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.02731</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.07</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.469</td>\n",
+              "      <td>6.421</td>\n",
+              "      <td>78.9</td>\n",
+              "      <td>4.9671</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>242.0</td>\n",
+              "      <td>17.8</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>9.14</td>\n",
+              "      <td>21.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.02729</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7.07</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.469</td>\n",
+              "      <td>7.185</td>\n",
+              "      <td>61.1</td>\n",
+              "      <td>4.9671</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>242.0</td>\n",
+              "      <td>17.8</td>\n",
+              "      <td>392.83</td>\n",
+              "      <td>4.03</td>\n",
+              "      <td>34.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.03237</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.18</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.458</td>\n",
+              "      <td>6.998</td>\n",
+              "      <td>45.8</td>\n",
+              "      <td>6.0622</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>222.0</td>\n",
+              "      <td>18.7</td>\n",
+              "      <td>394.63</td>\n",
+              "      <td>2.94</td>\n",
+              "      <td>33.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.06905</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.18</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.458</td>\n",
+              "      <td>7.147</td>\n",
+              "      <td>54.2</td>\n",
+              "      <td>6.0622</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>222.0</td>\n",
+              "      <td>18.7</td>\n",
+              "      <td>396.90</td>\n",
+              "      <td>5.33</td>\n",
+              "      <td>36.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29615180-187a-4b62-986b-d6dbdb75c8aa')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-29615180-187a-4b62-986b-d6dbdb75c8aa button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-29615180-187a-4b62-986b-d6dbdb75c8aa');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "bostonDF",
+              "summary": "{\n  \"name\": \"bostonDF\",\n  \"rows\": 506,\n  \"fields\": [\n    {\n      \"column\": \"CRIM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.601545105332487,\n        \"min\": 0.00632,\n        \"max\": 88.9762,\n        \"num_unique_values\": 504,\n        \"samples\": [\n          0.09178,\n          0.05644,\n          0.10574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ZN\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.322452994515036,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          25.0,\n          30.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"INDUS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.8603529408975845,\n        \"min\": 0.46,\n        \"max\": 27.74,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          8.14,\n          1.47,\n          1.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CHAS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2539940413404118,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NOX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11587767566755611,\n        \"min\": 0.385,\n        \"max\": 0.871,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          0.401,\n          0.538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RM\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7026171434153237,\n        \"min\": 3.561,\n        \"max\": 8.78,\n        \"num_unique_values\": 446,\n        \"samples\": [\n          6.849,\n          4.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28.148861406903638,\n        \"min\": 2.9,\n        \"max\": 100.0,\n        \"num_unique_values\": 356,\n        \"samples\": [\n          51.8,\n          33.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DIS\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1057101266276104,\n        \"min\": 1.1296,\n        \"max\": 12.1265,\n        \"num_unique_values\": 412,\n        \"samples\": [\n          2.2955,\n          4.2515\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RAD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.707259384239377,\n        \"min\": 1.0,\n        \"max\": 24.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168.53711605495926,\n        \"min\": 187.0,\n        \"max\": 711.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          370.0,\n          666.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PTRATIO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.164945523714446,\n        \"min\": 12.6,\n        \"max\": 22.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          19.6,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 91.29486438415779,\n        \"min\": 0.32,\n        \"max\": 396.9,\n        \"num_unique_values\": 357,\n        \"samples\": [\n          396.24,\n          395.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LSTAT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.141061511348571,\n        \"min\": 1.73,\n        \"max\": 37.97,\n        \"num_unique_values\": 455,\n        \"samples\": [\n          6.15,\n          4.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PRICE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.19710408737982,\n        \"min\": 5.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 229,\n        \"samples\": [\n          14.1,\n          22.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "# boston 데이터셋 준비\n",
+        "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
+        "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
+        "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
+        "target = raw_df.values[1::2, 2]\n",
+        "\n",
+        "# 데이터 세트 DataFrame 변환\n",
+        "bostonDF = pd.DataFrame(data, columns=['CRIM','ZN','INDUS','CHAS', 'NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT'])\n",
+        "\n",
+        "# 데이터 세트의 target 배열은 주택 가격임.\n",
+        "# 이를 PRICE 칼럼으로 DataFrame에 추가함.\n",
+        "\n",
+        "bostonDF['PRICE'] = target\n",
+        "print('Boston 데이터 세트 크기: ' , bostonDF.shape)\n",
+        "bostonDF.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "Ak9KyS4rzTfQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 276
+        },
+        "outputId": "198e1a68-60c4-4bee-c104-24c1c64fe4ad"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# 각 칼럼이 회귀 결과에 미치는 영향이 어느 정도인지 시각화해보기.\n",
+        "\n",
+        "# 2개의 행과 4개의 열을 가진 Subplots를 이용.\n",
+        "# axs는 4x2개의 ax를 가짐.\n",
+        "\n",
+        "fig, axs=plt.subplots(figsize=(16, 8), ncols=4, nrows=2)\n",
+        "lm_features =['RM', 'ZN', 'INDUS', 'NOX', 'AGE', 'PTRATIO', 'LSTAT', 'RAD']\n",
+        "for i, feature in enumerate(lm_features):\n",
+        "    row = int(i/4)\n",
+        "    col = i%4\n",
+        "\n",
+        "    # 시본의 regplot을 이용해 산점도와 선형 회귀 직선을 함께 표현\n",
+        "    sns.regplot(x=feature, y='PRICE', data=bostonDF, ax=axs[row][col])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iWPxvYnCzTfQ"
+      },
+      "source": [
+        "## **1-4. RM과 LSTAT의 PRICE와의 관계에 대해 `선형성`을 바탕으로 서술하세요.**\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "pYALOcXozTfR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0aed03e5-7ffa-44a0-b471-7a190c4bb901"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MSE : 17.297, RMES : 4.159\n",
+            "Variance score : 0.757\n"
+          ]
+        }
+      ],
+      "source": [
+        "# LinearRegression 클래스를 이용하여 보스턴 주택 가격의 회귀 모델 만들기\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "y_target = bostonDF['PRICE']\n",
+        "X_data = bostonDF.drop(['PRICE'], axis=1, inplace=False)\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X_data, y_target, test_size=0.3, random_state=156)\n",
+        "\n",
+        "# 선형 회귀 0LS로 학습/예측/평가 수행.\n",
+        "lr = LinearRegression()\n",
+        "lr.fit(X_train, y_train)\n",
+        "\n",
+        "y_preds = lr.predict(X_test)\n",
+        "\n",
+        "mse = mean_squared_error(y_test, y_preds)\n",
+        "rmse = np.sqrt(mse)\n",
+        "\n",
+        "print('MSE : {0:.3f}, RMES : {1:.3F}'.format(mse, rmse))\n",
+        "print('Variance score : {0:.3f}'.format(r2_score(y_test, y_preds)))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "T2makRHUzTfR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "117f7716-d9c4-4616-c839-781b4e8aa8c4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "절편 값:  40.995595172164826\n",
+            "회귀 계수값:  [ -0.1   0.1   0.    3.  -19.8   3.4   0.   -1.7   0.4  -0.   -0.9   0.\n",
+            "  -0.6]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# intercept값(절편)과 coefficients값(회귀 계수) 확인\n",
+        "\n",
+        "print('절편 값: ' , lr.intercept_)\n",
+        "print('회귀 계수값: ', np.round(lr.coef_, 1))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-gS_qP2_zTfQ"
+      },
+      "source": [
+        "**[RM과 PRICE]**\n",
+        "\n",
+        ": RM(방의 개수)은 PRICE와 양의 선형성을 가짐.\n",
+        "\n",
+        "**[LSTAT과 PRICE]**\n",
+        "\n",
+        ": LSTAT(하위 계층의 비율)은 PRICE와 음의 선형성을 가짐."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IiVDLU0n4SFn",
+        "jp-MarkdownHeadingCollapsed": true
+      },
+      "source": [
+        "# **2. 경사 하강법(Gradient Descent)**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gnVXtbYDzTfR"
+      },
+      "source": [
+        "간단한 회귀 식인 $y = 3x + 8$을 근사하기 위한 150개의 데이터 세트를 만들고, 여기에 `경사 하강법`을 이용해 회귀 계수 $w_1$, $w_0$을 도출하는 파이썬 코드를 작성해 봅시다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "8ULdePjjzTfR"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "89gzJ7JJzTfR"
+      },
+      "source": [
+        "## **2-1. 노이즈를 위한 난수를 생성해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "np.random.seed(0)"
+      ],
+      "metadata": {
+        "id": "P9q65fIxpLsv"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K6XidWoJzTfR"
+      },
+      "source": [
+        "## **2-2. $y = 3x + 8$ 식을 근사하는 코드를 작성해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "PSUJyIUWzTfS"
+      },
+      "outputs": [],
+      "source": [
+        "X = 2 * np.random.rand(150, 1)\n",
+        "y = 3 * X + 8 + np.random.randn(150, 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "evKlA84KzTfS"
+      },
+      "source": [
+        "## **2-3. X, y 데이터 세트를 산점도로 시각화 해주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "YsWJOwGRzTfS",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 399
+        },
+        "outputId": "e23eff46-3970-4daa-fd6c-963017a2b9ca"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.scatter(X, y)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QUZpaCdTzTfS"
+      },
+      "source": [
+        "## **2-4.**\n",
+        "실제 y값과 예측된 y값을 인자로 받아 $$\\frac{1}{N} \\displaystyle\\sum_{i=1}^{N}{(실제값_i-예측값_i)^2}$$ 식을 계산하는 비용함수 `get_cost()`를 정의해 주세요."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "PMvJTyBAzTfS"
+      },
+      "outputs": [],
+      "source": [
+        "def get_cost(y, y_pred):\n",
+        "    N = len(y)\n",
+        "    cost = np.sum(np.square(y - y_pred)) / N\n",
+        "    return cost"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m-GzAAWLzTfS"
+      },
+      "source": [
+        "## **2-5.**\n",
+        "`w1_update`로 $$-u\\frac{2}{N} \\displaystyle\\sum_{i=1}^{N}x_1*{(예측오류_i)}$$  \n",
+        "`w0_update`로 $$-u\\frac{2}{N} \\displaystyle\\sum_{i=1}^{N}{(예측오류_i)}$$ 값을 넘파이의 dot 행렬 연산으로 계산한 뒤 이를 반환하는 `get_weight_update` 함수를 정의해 주세요."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "FRuLr25WzTfS"
+      },
+      "outputs": [],
+      "source": [
+        "def get_weight_updates(w1, w0, X, y, learning_rate=0.01):\n",
+        "    N = len(y)\n",
+        "\n",
+        "    # 먼저 w1_update, w0_update를 각각 w1, w0의 shape와 동일한 크기를 가진 0 값으로 초기화\n",
+        "    w1_update = np.zeros_like(w1)\n",
+        "    w0_update = np.zeros_like(w0)\n",
+        "\n",
+        "    # 예측 배열 계산하고 예측과 실제 값의 차이 계산\n",
+        "    y_pred = np.dot(X, w1.T) + w0\n",
+        "    diff = y - y_pred\n",
+        "\n",
+        "    # w0_update를 dot 행렬 연산으로 구하기 위해 모두 1값을 가진 행렬 생성\n",
+        "    w0_factors = np.ones((N, 1))\n",
+        "\n",
+        "    # w1과 w0을 업데이트할 w1_update와 w0_update 계산\n",
+        "    w1_update = -(2/N) * learning_rate * (np.dot(X.T, diff))\n",
+        "    w0_update = -(2/N) * learning_rate * (np.dot(w0_factors.T, diff))\n",
+        "\n",
+        "    return w1_update, w0_update"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GiHhtdDWzTfS"
+      },
+      "source": [
+        "## **2-6. `get_weight_update()`을 경사 하강 방식으로 반복적으로 수행하여 w1과 w0를 업데이트 하는 함수인 `gradient_descent_steps()` 함수를 생성해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "NRyTAso4zTfT"
+      },
+      "outputs": [],
+      "source": [
+        "# 입력 인자 iters로 주어진 횟수만큼 반복적으로 w1과 w0를 업데이트 적용함.\n",
+        "def gradient_descent_steps(X, y, iters=10000):\n",
+        "\n",
+        "    # w0와 w1을 모두 0으로 초기화.\n",
+        "    w0 = np.zeros((1,1))\n",
+        "    w1 = np.zeros((1,1))\n",
+        "\n",
+        "    # 인자로 주어진 iters 만큼 반복적으로 get_weight_updates() 호출하여 w1, w0 업데이트 수행.\n",
+        "    for ind in range(iters):\n",
+        "        w1_update, w0_update = get_weight_updates(w1, w0, X, y, learning_rate=0.01)\n",
+        "        w1 = w1 - w1_update\n",
+        "        w0 = w0 - w0_update\n",
+        "\n",
+        "    return w1, w0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YOjBoti8zTfT"
+      },
+      "source": [
+        "## **2-7. `gradient_descent_steps()`를 호출해 w1과 w0를 구해보세요. 그리고 앞서 정의한 `get_cost()` 함수를 이용해 경사 하강법의 예측 오류를 계산해 보세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "I1tgvGMpzTfT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7a6f9e75-0121-43cb-a45b-b0038659ffd9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "w1:3.082 w0:7.799\n",
+            "Gradient Descent Total Cost:0.8541\n"
+          ]
+        }
+      ],
+      "source": [
+        "w1, w0 = gradient_descent_steps(X, y, iters=1000)\n",
+        "print(\"w1:{0:.3f} w0:{1:.3f}\".format(w1[0,0], w0[0,0]))\n",
+        "y_pred = w1[0,0] * X + w0\n",
+        "print('Gradient Descent Total Cost:{0:.4f}'.format(get_cost(y, y_pred)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vP4iFWF6zTfT"
+      },
+      "source": [
+        "## **2-8. `y_pred`에 기반해 회귀선을 그려보세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "IU-NUqnqzTfT",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 399
+        },
+        "outputId": "3cafb73a-25f0-4ee3-88a6-820539c14185"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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kwOCDiEgDIxZvU1sjw73tg++UocrhdYPPSMeD12i/weudqxHXabRlZUDnzoHt/+//AQsWxL5/iisGH0REKkU7C0RLwKIl72J1WRVuWxg4k6PKcRy3LSzBcxqTTeORq6H7NNqqKiA78JyOnNsNX76zFr07tASrdciPwQcRkQpaeiP8n6clYFGbd7G5/BAmv70z7DHf9/ZOTWu6mJKroVaIAmF1DRri3LvegislFXhxi6kr+ZJ6nGpLRBRBpN4IQOmNcLp8t4hm2qravItPyn/GkWP1Ybf55Vg9Nn97SNX+3PSctqsLp1OpPBok8Mi76y10unuZEnicZPZKvqQOez6IiCKIZhZItNNW1eZT7D/yq6rtissP4eIzW6na1k2KRdSEAHr0AL74IuAh5/5KXDJ/F44FeU9kWMmXImPPBxFRBNHMAtESsHhz512EumXaoAzb5LRorOqYEDT8icydqzG0x2meEuiGueEGpUCYf+Dx5ZeAENh6rGFU15bkweCDiCiCaGaBRDtt1Z13ASAgAPHOu7iog7rejL4qt5NCYaGyouwbb/i2b9ig9ISccw4Aicq3U9QYfBARRaC2N8J7Fkgs01bV5F1c2LElmjdpGHbfzZs0xIVWWAL+1VeVoOPBB33bX39dCTr69fNplqJ8O8WEOR9EZElG1Ntwi2YWSKzTViPlXaSm2DDz2q5Bp9q6zby2q9w5D+vWAVdeGdj+8MPAAw+EfJqp5dtJFzYhRHQDgnHicDhgt9tRXV2NjIwMsw+HiCQU66qrRr2ue7YLEDxg0WP2iFJkbBeqHHWetqyMNDx4TWd5p5vu2gV06RLYPmYMMH++ql2oubamJ80ayMhgPBQt928GH0RJSIY/VNEKVW9Dzxt6OFqvnRGBkmXez8pKICcnsP2CC4CtWzXvLty1BWBKgGoGs4Jxfww+iCgkWf5QRcPpErhk1rqQMx3c3e2bJl0h1c3XMsFBvBw9CjRrFtjeuDFQU6PU8YhSsGu7uqzK1AA1WtF8TswOxr1puX8z54MoiURbpVMWeq26ajTdS4xbxYkTQMMQSbG1tUCTJjG/hP+1jba+itmi+VJg1XMFONuFKGlEW6VTJpxiaRFCAN26BQ88DhxQHtch8Agm2voqZoqmEi5gzXN1Y/BBlCSs/IfKjVMsLWDkSKVA2E6/dWe++koJOlq3juvLWy1AjeVLgdXO1RuDD6IkYeU/VG7R1NtIVk6XQHH5IazYsQ/F5Yfi36M1fbpSq2PxYt/2Dz9Ugo5OneL7+idZLUCN5UuB1c7VG3M+iJKElf9QuUm96qpEDE0qnj8fuPHGwPY33lB6QQxmtRogsXwpsNq5emPPB1GSSJReA+lWXZVMtPkDmq1dq/R0+Acejz6q9HSYEHgA6svTyxKgxvKlINy5usl0rt7Y80GUJBKp10DrqqvJMtXVkNkPpaVA166B7TfeCLz8cnT71Jk7QPXv/cmScEq5HpVw547qhclv78SRY/U+j9kjlN83E4MPoiRipT/KkaidvmrFuibRBktxnYq8fz9w2mmB7X36AJs3a9uXAbQGqGbR60uBf+ABANXH6qWdQs/ggyjJWOWPsh6sWNcklmApLknFoQqENW0KVFfHVCBMb8GCNivUV4nlS4G7tysYmWt9MPggSkLJUPTKigWYYg2WdE0qNqBAmJ6s2MPlLdovBVYtvMeEUyJKSNFMYdQ6PVXP6ax6FIHTJalYCKBzZ1MKhEXLsCTbOHN/KRja4zT07dhSVVBs1Sn07PkgooSk9Y9yNCvW6vlNW49vsDHnDwwfDixZEtj+1VeG1enQyoo9XHqy6hR69nwQUULS8kdZ6zfneHzT1usbbFRTkadOVabN+gceH31kaIGwaJhRudfwAm5hWHUKPXs+iCghqZ3CeF67Fuj3+HrV35zj9U1bz2+wqvMHXnkFGDs2cAeLFyu9IBZg9LCDbLklVp1Cz54PIkpIaotNbdv7i6ZvzvH6pq33N9iw+QOrVys9Hf6Bx8yZSk+HRQIPQJ+gTW1Phqy5JVYsvMeeDyJKWO4/yg++swtVjjpPe5uMNDx4TWcM7JKNFTv2qdqX+5tzvL5pG/INdudOZbVZfzfdBLz4YvT7NVGsRbrU9mTInltitSn07PkgoiQQqu9D+zfneCb4xe0b7L59Sk+Hf+Bx0UVKT4dFAw8gtnLqWnoyrLAqdDSzZczCng8iSlih6mYccPxeN6MgL0vTN+d4L+al6zfYmhogIyOwPSMD+OUXICUxvn9GU6RLa0+GVae0yorBBxElJC03Fy3DHUYMj8RcBC5cgbBjx4DGjaPft6S0Bm1apzZbdUqrrBIj7CUi8qPl5qJ1uEPaBD8hgHPPDR54HDyoPJ6AgYeblmEHrT0ZVp3SKivNPR8bN27E448/jm3btqGyshLLli3DsGHDAAD19fWYMmUKVq1ahW+//RZ2ux35+fmYOXMmcnJy9D52IqKQ1pRVqdrOfXPR+s1ZugS/v/wFeOutwPavvwbOPhuAXKv7mn0sWnsyrDqlVVaag4/a2lp0794dY8eOxbXXXuvz2LFjx1BSUoKpU6eie/fu+OWXX3DnnXfimmuuwWeffabbQRMRheN0CSxTOYvF+yakdbhDijVypkwBHnkksH3TJuDiiz0/apnVEe+gQIZaGdHk7iTSqtBmswkhoi7NZrPZfHo+gvn000/Ru3dv7N27F23bto24T4fDAbvdjurqamQES5Qi0oHZ37oovorLD2HkvMjLvLds2ghbH8i35nv/0kvAzTcHtv/3v8B11/k0hUq8dZ+1e6jIiKBA7bEYwX0sQPCejFDHwr8fwWm5f8c94bS6uho2mw3NmzcP+nhdXR3q6n6ff+9wOOJ9SJTkZPjWRfGldjx/aI+ciDcN6W40H3wADBgQ2D5rFnDvvQHNahNvXS6B8W9sj3pFXTVkq5URbU+GFD1eFhfX4OP48eOYNGkSRo4cGTIKmjFjBgoLC+N5GEQesS5ZTtagdjy/IC8r7ONSBapffAF07x7YfsstwAsvhHya2sTbKStK4x4UyLj8u3S5O0kibrNd6uvrcd1110EIgblz54bc7r777kN1dbXn3w8//BCvQ6Ikp8eS5WQNkWYmAJFnJkhTSttdIMw/8LjkEmX2SpjAA1DfC3S4tj7kY3oV0JK1VoaVinMlirgEH+7AY+/evVi9enXYsZ+0tDRkZGT4/COKBytUKCR9RKp6aUP4mQlSBKoOhxJ0nH66b3vz5oDTqaw4q4KedSdiDQpirZUh02qyFBvdh13cgcfu3buxfv16tGzJcTGSg6zfuig+YpmZYOrwQH090KhR8MeiKBAWaVYHAJyS1gBH605E3FesgUws1WGlGgKjmGkOPo4ePYo9e/Z4fq6oqMCOHTuQmZmJ7Oxs/PnPf0ZJSQneffddOJ1OVFUpc+0zMzPRKNQvFJEBWKEw+UQ7nm9KoCoEcM45wDffBD72009Aq1ZR7dbdC3TbyVkdwRytO4HmTRqi+lh9XErG+x+L1loZzNVKPJqHXT777DP07NkTPXv2BABMnDgRPXv2xLRp07Bv3z688847+PHHH9GjRw9kZ2d7/n3yySe6HzyRFqxQmJyiGc83PFD905+UdVb8A49vvlGCkigDD7eCvCw0bxKi3Dp8h6a0Ls6mldbqsFIMgZHuNPd8XH755QhXGiSGsiFEccUKhQSomzobz8XjvF///Ocex2nPPxW40ccfKyvO6mRrxWEcORY+ofTIsXrclX82Fn/6fdwLaGnpkdpcfki6GTIUOy4sR0mFFQqtRe8aG5HyBrxfb8QFuZi9Zreugar79S/buAKziv4TuMGbbwJ//rPm84pE7fBQvdOJD+/pj217f4n7tFM1tTKKSisxeelOVftjrpa1MPigpMN5/dagdznwVV/sx+1vbA9od+cN3HpZe7zzeaXP67mHKrx7DaINVItKK7HoX/NQ/Ob0gMce6T8W5z39cNyCX7XDQ8+sL8fSkn2YPiQPQ3ucFpdjUStUnkcozNWylpjKq8cDy6sTkd7lwFd9UYk7FpVAa1qA+/Um5J+NM1o1iTpQdZZsR+p5vQLaX+8xEA8MuMMzjLNp0hUxV1wN9jgAXDJrXdgZL25mlDn353QJXDJrXdjhFjct147iS6ry6kREWuhdDryotBK3vxF6pkc47tdb/On30d3cfvwRyM1Fql/zltwuGH79TJ/XUZO3ECnYCvd4qHwnf2aUOfcXaaqzP+ZqWU/cKpwSEUVDj3LggHLz/O2EC4Ury2I6nqiKz7kLhOXm+jT/kt4M7e99xyfw8BYubyFSxdUZq8rCPg4g6CyTYMwuuKc2f6N544acZmtR7PkgIqnoWQ78teLvNH2Djvm4whQI63T326hrEL7WUbjKnpGmm877qCJib9GmSVegIC8Ls1d/g2fW7wmytS+zkjjV5m/MuaEXLj4ztmnIZA72fBCRVPRMHNx7+Jhu+wp7XEIAZ54ZPPD4+Wc4nS40zmga8umRasyoGYYIl8/i3ZORmmJTfcM2K4lTbU2eCztwaq1VMfggIqmoufG0bKquWnK7zCYxH0/E4nN//KNSIKy83Ld9zx4lKGnZEqvLqiLW2QiXt6BXD4R7P7IX3Iu0Ng/APA+rY/BBRFJRc+N5aGgXVTfP0X3PiLi6LQBcec6pIfcDhLjRTZ6s5HUsX+7b/sknStDRsSOA34dMwmnRpCEK8rJCPq5XD4R7P1a4uWuthErWwpwPIpKOmmJwKSmIWK22UYMUzyyPcNZ+9VPQ9qA1PV54Afjb3wI3fustpUy6HzVDJr8cqw8700XN4nApNiXmUVuR1QoF91iTJ3GxzgcRSStSTYt41/l49vqeuLpbjvLD//4HXH114Eb/93/AxIkh97Fixz7cuXhHxNd6akSPsIW93LNdgODB1q2XtccLGytCPh6qt0DvKrKUvFjng4gSQqQS3Gq/Gbdo2iiqAmMPvfclBpw4ELRAGMaNA559NuJ+9FqkTk1PRc+2LTT3ZKgpc06kNwYfRGRpam6e0SRsZjl+QvGsG4H7/R64/HJg/XrV+9FzkbpIwRaHKcgqGHwQUcLTkrDZrK4WO58cHvhAq1bAgQPKzBYN9F5NOVKwxZ4MsgLOdiGihBdpaikANHCewHez/hA88Pj1V+CnnzQHHm6cuUHkiwmnRJQUQiVsQghsfP5mtK0+EPCcAVPexqrCYboNWzC5kxIZE06JiPwES9ict/RfKNizNWDbfrfOw/ctsjF3eC9dg4NgQyIMSCgZMfggoqThTsg88Le/I+fFOQGPDxv9f9iR0wnZ9nTMNaDWhdqpwkSJhsMuRJQ8nn8euO22gGbnW0uxtUc/Q3sf3MNA/n+AI9XlIJIVh12IiLytWgUMHhzYPns2MGECUgH0NfBwIq1S616FtiAvi0MwlJAYfBAliaTMLSgpAc47L7B9/HjgmWeMP56TIpVc916FVs9ps0n5GSApMfggspBobx5Jl1vw/fdAu3aB7VdeCaxZY/zx+FFb9Eyv1WyBJPwMkNQYfBBZRLQ3j1C5BVXVxzFuYUli5RZUVwPNmwe2t2kD7N8fdZ0OvelVcl2tpPoMkCXI8ZtIRGG5bx7+XfXum0dRaWXQ50XKLQCU3AKn1oVP4sjpEiguP4QVO/ahuPyQumP77Tdleftggcfx40BVlTSBB6Cu6FmKDfil9jfPz1FdF1jzM0CJjz0fFsRxW2PIcp1jSU7UO7cg3tdEc++OEMC55wJffx342KFDQGbk9VLMkJpiwzXds/H8yVVog3EJYPwbJZiboixqF+2QiVn5JUThMPiwGI7bGkOm6xzLzUPP3IJ4XxPNQwOzZgGTJwfuqLwc6NAh5uOJp6LSSrwQJvDwdt/bO/HLsfqAdrVDJmbklxBFIk8/JEUUbdc7aSPbdY7l5qFXbkG8r4mmoYHXXlOGWPwDjy1blJ4QAwOPaIZCwp2rPwEEDTzcjwGRh0yMzi8hUoM9HxbBugDGkPE6x3Lz0GM5dyOuiZrenTN3fILU1PwgT94KXHBBVK8bi2h7giKdqxZqhkz0+AwQ6Y09HxahpeudoifjdY6UnGiDctMLdvNwL+fu3s7/eUDk5dyNuCbhenc6V+3Bd7P+gNeWTPN9YNUqpafDpMAj2p6geAxvhNunHp8BIr0x+LAIjtsaQ8brHOvNI9xy7hPyz0bdCVfYIQMjrkmwXpvcI1X4btYf8N6CCb4PvPyyEnQMGhT168Ui1tkj8RjeiLTPcJ8BTrMlM3DYxSI4bmsMWa9zsBVZAeXmoSbh072gmnumync/12LR1u8xe803nm1CDRm0OiVN1THGck28hwZaHKvGhudvQcZvx3y2eSH/r7jp/ZdN/4Ye6+yRSMMgbu6ztDdpiOpj9TEPmfh/BjhTjszE4MMiOG5rDJmvc6w3D/dy7kWllXhyzW5Vs0qKSivx4Du7wu5Xj2uSmmLDv648A7mDr8Q5P+/1eeyN7gPxwIDxmDv6PClulLH2BLl7ssYtLIENCBmAuANLAEG3jWbIxP0ZkJksU9wpvhh8WES4P1gct42N/x+7qYPPxfg3tkt5nWO9eWhJHl1dVhV06qs3Xa7JiRPA0KEoWLXKp3lD+/Nw85+m4tTMUwxZ3l4tPXrHQvVkZTZtiD/2OA35eVk+N91Yer2sRKYp7hRfNiGEVGXttCzJm4z4y6mvUNfzmu7ZeOfzyoS7zsXlhzBy3uaI271+cx/8883PI87KiOmaCAHcfjvw3HO+zXl52LpoFapcDaT85ut0CVwya13E3rFNk66IeNxavuUneo9AqDov7jNkbor8tNy/2fNhMRy31U+4olYvbKzAnOt7wd64IYq//RmA0uNwYQe5u6wjUTtkUFx+SNV00Cf+3B0Xn9VK+4HMmAHcf79v2ymnAOXlsLVujT7a92gYPXshtfRkWWHIJFoyTnGn+GLwYUGJ/EfIKGr+2D2wfCfSGqSgylEHAHhm/R7L936oTwpV1yH6c22dtgNYsAD4618D2/fsATp21LYvE8WaAEy+WAI++TD4oKSk5o9dLCWtZaU2obZvh1Z4Zn15xP2pDmbefx8YODCw/dNPgfPPV7cPycSzFzLRh1j8yTjFneKLwQclpWj/iFm9C1jtkMGFHVvqM+vns8+CFwH73/+CByMWE49eyGTM65J1ijvFD4uMUVKK5Y+Y1avJqik4FXNVzG+/VdZf8Q88XnlFSTQ1MPDwXn/l490/4+M9P2telt4osq0rZJRYqviSNbHng5KS2kJP4cSzCzje3e5qhgyiymv46Scld6Omxrf94YeBBx7Q7fjVCtaL4E2mHoVkTrpkKYHkw6m2lLTc3zIBtemVvhbdcmFckt9k63ZXFQjV1gK9ewNlZb7tt90GPPus0gtisFCzmbzJNI1T7TToeH3uZCDbZ5+04VRbIhVCfbPPtqfj13qnLiWttQo3/desRNeweQ0nTgBDhgBFRb7tgwYB77wDNDDnT4zaZetl6lFg0iVLCSQTBh+U1EL9sXNX9zSyC9hS3e5CAOPGAc8/79veuTOwZQvQtGnAU4ycwaFl2fpYpnHqeU5MulSwlEByYPBBSS/YHzsz6jhYptbBI48AU6b4tmVkKLU6Tj016FOM7k6PpndA63P0PieZ1xUi0huDD6IQjO4Clr7bff584MYbA9sjFAgzYygpmt4BLc+Jxzkx6ZKSCafaEoXh7hUZ2uM09O3YMq5/+KXtdi8qUhJG/QOPTz9Vhl/CBB6RhpIAZShJ7ymvkaZuetM6jVPPc/KeBlxcfggFeVkRp0ETJQL2fBBJQrpu91AFwoqKgAEDVO3CrKEkdy/CbSdnM0WipUdBr3MKN2yzadIVTLqkhMaeDyKD+X/bdX9Djrmwl17Ky4MXCFuwQOnpUBl4AOYOJRXkZaF5k4Zht0mxAXOu19ajoMc5RSomtrqsyrAeNyIzsOeDEppsa2RESlI0dcGygweBDh2Umh3eHn0UuO++qHZp5lDS1orDOBJkfR5vLgG0aNoo4r68P0c/16hbTC/UOVlqVhNRnDD4oIQlW8EitUmKhtc6OHpUKRD25Ze+7ePGAXPmxFQgzMyhJL16XYJ9jlJsSuASTKRzssysJqI44rALJSTZ1sjQmqRoSKJrfb2yxkqzZr6Bx+DBymM6VCY1cyhJj16XUJ+jcIEHEP6cpJ/VRGQABh+UcMyaYRGOlm+7cScEcOutQKNGylL3bl27Kr0g776ra2VSNQvZxUOsi5WpqZLqH1+oOSdpZzURGYjDLpRwZOzWlubb7sMPA1On+rbZ7cDu3SELhOnBjLLZsdbNUFMl1SWAqYPPRatmaarPSbpZTUQmYPBBCUeaG70X07/tvvwycNNNge3l5UqSqQqxJu+aUTY7lgRetZ+PVs3SMLTHaaqPyTsoCoXFxCjRMfighGP6jT4I077trlql5HD427YN6NVL9W5kS97VItpel3h+jgZ2ycatl7XHvI8qfPJHUmzALZe2l/6aEsWKOR+UcGId648HwxMvP/1USRb1Dzzef1/J+dAYeMiUvBuNaBJ44/k5KiqtxAsbKwISV4UAXthYYYlrShQLBh+UcKQp1uXHkMTLPXuUoKN3b9/2V19V7mxXXaVpdzIm7xolXp+jZL6mRG4cdqGEZGqxrgjHFZfEywMHlNyNY8d8mk88OgOfXneL8lrlhzS/lozJu0aKx+co2a8pEcDggxJYpBu9WdVPdU28PHoUOP984OuvfZoX9BqM6fm3IcVhg2veZk+71jwNGZN3jaZ3wMhrSsTggxJcqBu9lRMoAShFwAYPBlav9mlefWZv3PbHB+BMSQUQWAzLnacx5/peaNG0UcSbqYzJu2bQM2DkNSVi8EFJSG2Zcym5C4S9+KJP8zfZHXHNyFk43jD8Dct9zncsKvEJTEIFXqxJoT9eUyImnFKSsXSy37/+BaSk+AYeLVrg061f46r/91TEwMNbqB4R/1kWsibvWhmvKRGDD6mEWmqd9CNVmXO1XnpJmcEyfbpv+7ffAocPY3/DpjG/RLjAy6zy6ImM15SSHYddJGH5HASLsFSy33vvAX/4Q2B7SQnQs6fnR71yA8LNsjCjPHqi4zWlZMbgQwLR5CCYNVPD6iyR7LdlC3DhhYHtH3wAFBQENEfKIdAqVOBlRnn0RMdrSsmKwYfJIuUg2KB0hRfkZXmCC/aSRE/qZL/du4Gzzw5sf+01YNSokE8Lt4BaNDjLgojijTkfJtOag5AIpa7NJGWy34EDQOPGgYHHrFnK7JYwgYebe60QW4TDDvewGWXniSg5aQ4+Nm7ciCFDhiAnJwc2mw3Lly/3eVwIgWnTpiE7OxuNGzdGfn4+du/erdfxJhwtOQiWnqkhEWmS/WpqgE6dgKws4LjX5+COOwCXC7j3XtW7CrVWiFvTRkrdj0ifDO/AiwnQRBQvmoddamtr0b17d4wdOxbXXnttwOOPPfYYnn76aSxYsADt27fH1KlTMWDAAJSVlSE9nd25/rTkILAss35MTfarrweuvhpYs8a3/ZprgKVLgQbafi3DBaVutb85I+7n1st+X02VQ3uBmGdFpB/NwcegQYMwaNCgoI8JIfDkk09iypQpGDp0KADg1VdfRZs2bbB8+XKMGDEitqNNQFpyEN79Yr+qfUoxU8MCDE/2EwK4+Wbg5Zd923v2BDZtApo0iWq3kYJSNWwA3vm8EvcOPBery6qsW4QtThiMEelL15yPiooKVFVVIT8/39Nmt9vRp08fFBcXB31OXV0dHA6Hz79koiUHwRIzNSi4wkKlQJh34NGyJfDzz8rU2SgDD0CfYNPda7b520Mc2vPDPCsi/ekafFRVVQEA2rRp49Pepk0bz2P+ZsyYAbvd7vmXm5ur5yFZgtocBHcvSaiOXiYMSujFF5UCYQ8+6NteUaEEHi1j73nRM9gsLj9kvSJsccQ8K6L4MH2q7X333YeJEyd6fnY4HEkbgETKQQg3pZJlmSXz7rvAkCGB7du3Az166PpS+tb5ULeHZBnaY54VUXzo2vORlZUFADhw4IBP+4EDBzyP+UtLS0NGRobPv2TlzkEY2uM09O3YMmgQIc1MDQpuyxalp8M/8Fi9Wsn50DnwAMIP3anl7jXr26GVqu3jNbQn2wwbS1XEJbIQXXs+2rdvj6ysLKxduxY9Tv6RdTgc2LJlC8aNG6fnSyU12coycxYAgG++UabN+lu4ELjhhri/vDso9U+KVMO71+zCji1NK8ImY1In86yI4kNz8HH06FHs2bPH83NFRQV27NiBzMxMtG3bFhMmTMDDDz+Ms846yzPVNicnB8OGDdPzuJOeLGWZZbxhGKqqCmjXDvjtN9/2xx8H/vlPQw/FHZRu/vYQxr9egiO/1qt6Xpbf+2XG0F40SwwYQeqKuEQWpnnY5bPPPkPPnj3R8+TCVhMnTkTPnj0xbdo0AMC9996Lv//977j11ltxwQUX4OjRoygqKmKNjwSU1LMAamqAs84CsrN9A49//EMpEGZw4OGWmmJDis2mKvC4o/+ZWHTLhdg06QqfG7vRQ3syJ3VKWRGXKAHYhBBSpWk7HA7Y7XZUV1cndf6H7JwugUtmrQvZxe/+Rrhp0hWJ9Ye5vh4YNAhYu9a3fdgw4K23gNRUUw7L24od+3Dn4h0Rt3tqRA8M7XFayMeNGk4rLj+EkfM2R9xu0S0Xmtbbl/Q9fEQqaLl/mz7bhawp6WYBCAGMHQvMn+/b3quXUiCscWNNu4vnjV2vPAWjhvaskNQpW54VkdUx+KCoWOGGoZvp04F//cu3rVUr4KuvoqrTEe9v0VbLU7BKUqcseVZEiYCr2lJU0xutcsOIybx5yrRZ/8CjogL46SfNgYfTJfDUmt24Lc55MlbLU2DxPKLkw56PJBftt3CrfbvWZOVKZZE3fzEUCCsqrcSD7+xClaMu6OMCyjUrXFmGgrysmAODUFNv/We2yIDF84iSDxNOk1io6Y3uP/GRZja4nw8Ev2FYrujZ5s1A376B7WvWAFdeGfVuQ13nUPRMrLRSDRYmdRJZGxNOk5jam02k6Y1qvoVb6dt1WF9/DZxzTmD7668D118f067VLHfvT888GSvlKTCpkyh5MPiwqGBBxuqyKtXfHPWarWLpG0ZVFdC2rTJ91tsTTwB33w0g9p6DaJa7t3SeTIysFCwRUfQYfFhQsO7p5k0a4sixwMJSoSpE6jlbxcwbRlTBQU0N0LMnUF7u237nncDs2UqSKfQZBtDSi2HpPBkiIg0YfMRBPMfZQ+UPBAs8gNBDKFaYrRLpOmoODn77DRg4EFi/3rf92muBJUt8CoTpVe5b6/VjYiURJQMGHzqLZ9JcNPkDQPAhFNlnq0S6jpqCA5dLKRC2YIHPtuL887F1/jJU1dvQ+rsjnuBGj3wYN7XL3TOxkoiSCet86Cjea51Ekz/gzXsIQOZaEJGu46ovKtWvBTJtmtKj4R14tG6NtZt24aJrZ2L4a5/jzsU7MHLeZlwyax2KSis15cNEoma5+7vyzwpYX4WIKJEx+NCJEYtjxToLwn8IwOgFxNRQcx2nriiNGBz0/3AZUlNTgIce8n3wu+9QtHYHbl5ZETK4WV1WpepY1b4foa5ztj0dz43qhTvzz+ZQiwmiKa5HRPrgsItOjFjrJNr8i3BDKLLNVlFzHQ/V/hby8fzdW/Di2w8FPvD550C3bkpwM2td2CGVFTv2qzpWLe+HbNc52bGmCJG5GHzoxIi1TtTmD3hTM4Qi0/TGaK9Pr31f4u2F9wQ+sHYtcMUVnh/VBjeZTRvil9p6XfNhZLrOyUyvZGIiih6HXXRixOwRNXkazZs09Gk3cwglGmqvT2bThrAB6HjoB3w36w8BgcfU4Q/A6XT5BB6A+uDmjyeXmpctH4ZiY8TwKBFFxp4PnRg1eyRSVVGrd+2rvY4P985Ev4Lz0UC4fB5/uP9NeKn3HzF3VK+g5602uMnPy8IF7TOtX72VfBgxPEpEkTH40ImRi2NFyh+w8h/NSNfxlLpjWDvndjS5/3uf5714/lA8fMXNyG7eGHPDBAdagsTUFJvlgznyZcTwKBFFlvTBh54FwYxc6ySR8weCXceGznosXvogzqv43Gdbce2fsHnGs2h59DdMrf0Nmaekwd64EZwuEfR91BokJvJ19melReiiZYXiekTJIKlXtY1Xxnsy/BE3gtMlsLX8Z5x+1zjkvrfU98ELLgA+/BBo3Diq95GzHXwly/VwugQumbUuYs/XpklX8HeWSCMt9++kDT5iXU5eDwxSIpgyBXjkEd+2Nm2AsjIgU8mdieV95PVXyPC7YCT3+QLBe74S7XyJjMLgIwL3t59QiWdGfPtJlm+aUXnuOWDcuMD2vXuVVWhPkuF9tLpkvYb8/SPSn5b7d1LmfJid8c46AyEsXw788Y+B7ScLhPkz+31MBMl6DVn0jchcSRl8mJnxrmbRsgff2YVm6Q3x89G65Pij+MknwMUXB7avWwf07x/yaZy5ELtkvobJlExMJJukDD7MzHhX802zylGHG17c4mlL2O7gr74Czj03sH3xYmD48IhP58yF2PEaEpEZkrLCqbvWQ6i+BBuUG348lpOP5hukXqviSmP/fmWlWf/AY/ZsQAhVgQdg7vuYKHgNicgMSRl8mLmcfDTfIBOm7HN1NdC+PXDaaYDLqzLpxInKzxMmaNqdme9jouA1JCIzJGXwAZi3nHykb5qheCf+WU5dHdCvH9C8OfDdd7+3/+UvwIkTwP/9H2CLrbCb0e9jIuE1JCKjJeVUW29m1HoIVWdAjadG9MDQk4ueSc/lAsaMARYu9G3v0wfYsAFI1y+PgDU7YsdrSESx4FRbDczIeA9Vhl0NyyT+PfAA8Oijvm3Z2cCuXUCLFrq/HGcuxI7XkIiMkvTBh1n86wy0apqGu9/8HAccsa+Ka+o32LlzgdtvD2z//nsgN9eYYyAiIqkx+DCR/zfNB6+JfVVc0yo3hioQ9sUXQNeu8XtdIiKynKRNOJVRrIl/7lwS/6GcuE7V/fhjJVnUP/DYsEGZNsvAg4iI/LDnQzLRln1WUzm1cGUZCvKy9BmCKSsDOncObF+yRJnFQkREFAKDDwlFk/hn2Bod+/cDp5+u9Gp4e/JJ4M47o98vERElDQYfCSLua3RUVwPduysry3r75z+Bxx6Luk6HFpwKSkSUGBh8JIi4rdFRVwfk5wObNvm2Dx8OvPEGkGJM2pAeibQMXoiI5MDgI0G4K6dWVcc+VReAUiBs9GglwPB24YXA+vW6FgiLxJ1I639e7kRatcm4pswCIiKiAJztkiB0XaPj/vuVhd+8A4+cHODwYaC4OObAw+kSKC4/hBU79qG4/FDY9WoiJdICkde8MWUWEBERhcSejwQSqnJqltpv+M8+C4wfH9j+ww9KkqkOtPZAxJpIa/gsICIiiojBBxIrFyCqqbpvvw386U+B7Tt3Al26eH6M9TpFM3wSayKtYbOAiIhItaQPPhIxF0D1VN1Nm4BLLw1s37BBWYXWS6zXKdoeiFgTaeM+C4iIiDRL6pwPGXMBtORDRK2sTJka6x94vPmmUr8jSOAR63XS0gPhzZ1IG6p/xQYlCAqVSBu3WUBERBS1pO35kDEXIO69MPv2Bc/dePpp4O9/D/oUva5TtD0Q7kTaaNe80X0WkI4SabiPiEiLpO35iPabeLzEtRemuhpo2zYw8Lj3XjhPOFF89fUhe1r0uk6x9EDEsuaNrrOAdFRUWolLZq3DyHmbcefiHRg5bzMumbWOM2+IKCkkbc+HTLkAceuFqasDrrxSWfzN24gRwOuvo6jsAAofWx+2p0Wv6xRrD0S0a964nxvTLCCd6VG3hIjIypI2+JApF0D3GRkuFzBqFLBokW/7RRcBa9cC6emqb4B6XadYh0/c+4h2RkoswYueZBzuIyIyWtIOu8SayKgnXXthJk9WCoR5Bx6nnw788ovSA5Kerqlwl57XKZbhEz24g5ehPU5D344tTbm5yzbcR0RkhqTt+fD+Jh6KUbkAuvQuPPNM8KTRIAXCtPa0xNpj4U2WHgizyDTcR0RklqTt+QCUG+Gtl7WH/30vxQbcell7w8bdY+pdWLpUmTbrH3iUlirTZoPMbtF6A9S7x0KGHgizyDTcR0RklqTt+QCUxL8XNlYEDD8IAbywsQI927bQdGONdupkVPkQH30EXHZZ4M42bgxeOMxLNDfAZO+x0IvMU3+JiIyStMGH3ol/sdboUD0jY9cun5LnHm+9FbxEehDR3gBjSfgkhR6Jt0REVmcTQsShhGb0HA4H7HY7qqurkZGREbfXKS4/hJHzNkfcbtEtF0a84YaaOeK+fWgZmgjZe/Ljj0BubuAT/vMf4I47VO072DEDwW+AnO4ZX4lY1p+IkpuW+3fS9nzolfindw9KQO/CkSNA165K8OFt8mRgxoyI+wtFttoXyYbDWESUzJI2+NAr8S9uq6YePw5ccQVQXOzbfv31wGuvASmx5wrzBmguDmMRUbJK2uBDr8Q/3adOulxKgPHf//q2X3IJsGYNkJambj8q8QZIRERGS9qptnqt+aHb1EkhgEmTlAJh3oFH27ZKgbCPPtI98CAiIjJD0gYfgD71K3SpAPr008owymOP+bb/+COwdy/QvHnE45CR0yVQXH4o5KJ1yX48RETJKmmHXdxizXuIaerkm28C110X2L5rF5CXp/VUpCLbbA7ZjoeIKJkl7VRbvWm6uW3cCPTrF7iTjz5ScjssLtTUY0AJyIyexqvnVGgiIgpOy/2bwYeOIlY4LS1Vps36W7oUuPZa4w40jpwugUtmrQs7Ayjbno5Nk64wZFZNpONxJxYbdTxERImKdT5MEnLmyA8/KImj/p55Bhg/Pv4HZqBIU4+BKKcex+l4op4KTUREUUvqhNO4O3IEOO20wMBj8mRldkuCBR4AUFX9q67bxYqryBIRyYc9H/Fw/DjQvz+w2a98+w03AK++qkuBMFkdrv1N1+1ixVVkiYjkk7h3QTM4ncDw4UDjxr6Bx6WXKgHJwoUJHXgAQOYp6mqRqN0uVrpMhSYiIl0l9p1QR2FrRAgB3HMP0KABsGTJ7+3t2ilDLxs3Jk2BsKwMdT0IareLlV7F5IiISD8cdlEh7DTadW8Cd97p+wTbyVVoc3IMPlLzuXsaIs12MbKngYvoERHJJemDj0jTY0PViOi1ZQ0G3p8fuMOyMuDcc+N70BLzLroGaCy6FkdcRI+ISB66Bx9OpxMPPvggFi5ciKqqKuTk5OCvf/0rpkyZApvNvD/0wYKM1WVVYQuDOV0ChSvLfG6gF37/BRYvuj/wBTZtAi6+OP4nYgFG9jRErK3ihYvoERHJQffgY9asWZg7dy4WLFiAzp0747PPPsONN94Iu92Of/zjH3q/nCrBhk2aN2mII8fqA7atqj6OcQtLMHdUL9gbN/I85+yfvsMHL98RsP3f/ng//vr4RN1valpuqkbuSy09expCHT9LphMRWZPuwccnn3yCoUOHYvDgwQCAM844A4sWLcLWrVv1filVQg2bBAs8AGWYwAagcGUZ7h14DrIdP6F47o0B200pGIeFvZRzvFrnGhF63lTNvEHr0dMQ6viv6Z6NFzZWBLyv3sEjAxAiIjnpPtvloosuwtq1a/HNN98AAD7//HNs2rQJgwYNCrp9XV0dHA6Hzz+9BBs2UUMAqD3wMwbk9wwIPJ7pex3OmPSuJ/AA9K0R4Q6W/BM23TfVotJKU/ZlhnDH/3yQwAP4PcekcGUZV60lIpKU7j0fkydPhsPhwDnnnIPU1FQ4nU488sgjuOGGG4JuP2PGDBQWFup9GADUlfr2l3biN/z3jcnoUfmNT/vSzv3xz8F3Qdh+j9fc64LoNXMjXLDk3SNTkJcVcfhCz32ZIdLxh8OS6UREctM9+FiyZAlef/11vPHGG+jcuTN27NiBCRMmICcnB2PGjAnY/r777sPEiRM9PzscDuTm5upyLFpKZqe4nHj6ncfxh683+bQfPr8vLrz8XtSnNoz7zA091yGx+pom0QSO/lgynYhITroHH/fccw8mT56MESNGAAC6du2KvXv3YsaMGUGDj7S0NKTFqQCXquEQIfDA+pdwy6fLfZr3tchC1rdfIbO5HU8HyTvQe+aG0yXw8Z6fVG2r5qZq9TVN9DgulkwnIpKT7sHHsWPHkOJXQjw1NRUul0vvl4rIXfCqqvp40K76sZ+uwLR183zaTthScPG4V1A4rgCnNbcDiG3mhpqZJsGSKsNRc1O1+pomsRyX3sNhRESkL92DjyFDhuCRRx5B27Zt0blzZ2zfvh3//ve/MXbsWL1fKiLvglc2/J4rMPjLjzDnnVkB219581wc63AWCoP0aEQzc0PNTJNQs3GC0XJTjRR4yX6DjnT8obBkOhGR/GxCCF2nBNTU1GDq1KlYtmwZDh48iJycHIwcORLTpk1Do0aNIj7f4XDAbrejuroaGRkZuhyTOwhou/NT/HfRfQGP71yyCt+e1U3XGhihggr3nueO6oWCvCxcMmudqh4P7+epHepxHwMQvNKo7NNRIx3/rZe1xzufV7LOBxGRBLTcv3UPPmIVj+ADAJxVB5CaneXbuHw5MHSobq/heS2XCBtUuHsdnvhLd9zw4hZV+7RinQ89RDp+MwqoERFRIC3376RZ2yXV7nUh5s4Fbrstbq+ldqZJcfkhVfu7o/+ZuKvg7KhuqlZf0yTS8bNkOhGR9SRN8IHGjQGDOnnUz9RQdzwXn9kqpmDB6jdoqx8/ERH50r3CKamfqdG3Qytk29MRKqywwfjl54mIiOKNwUccuGdqRAoqLuzYEtOH5Hna/LcBOGuDiIgSD4OPOHBP8QUiBxXu5eez7L69JVn2dOlnoxAREUUjaWa7mEHLTBPO2iAiIivjVFuJMKggIqJkwKm2EuFMDSIiIl/M+SAiIiJDMfggIiIiQzH4ICIiIkMx+CAiIiJDMfggIiIiQzH4ICIiIkMx+CAiIiJDMfggIiIiQ7HImEWwUioRESUKBh8WoGWNGCIiItlx2EVyRaWVGLewxCfwAICq6uMYt7AERaWVAJSekeLyQ1ixYx+Kyw/B6ZJqyR4iIiIP9nxIzOkSKFxZhmBhhABgA1C4sgwul8BD733JnhEiIrIE9nxIbGvF4YAeD28CQGX1cdz+xvaIPSNERESyYPAhsYM1oQOPSNy9JYUryzgEQ0REUmHwIbHWzdJjer67Z2RrxWF9DoiIiEgHDD4k1rt9JrLt6Yh1Qm0sPShERER6Y/AhsdQUG6YPyQOAgABES0ASaw8KERGRnhh8SG5gl2zMHdULWXbfACLLno5nr+8VtmfEBmXWS+/2mXE/TiIiIrU41dYCBnbJRkFeVtAKpykpwLiFJbABPlNy3QHJ9CF5rIRKRERSsQkhpJoK4XA4YLfbUV1djYyMDLMPxxJYAZWIiMym5f7Nno8EEK5nhIiISDYMPhJEaooNfTu2NPswiIiIImLCKRERERmKwQcREREZisEHERERGYrBBxERERmKwQcREREZisEHERERGYrBBxERERmKwQcREREZisEHERERGYrBBxERERmKwQcREREZisEHERERGYoLyyUop0twlVsiIpISg48EVFRaicKVZaisPu5py7anY/qQPAzskm3ikREREXHYJeEUlVZi3MISn8ADAKqqj2PcwhIUlVaadGREREQKBh8JxOkSKFxZBhHkMXdb4coyOF3BtiAiIjIGg48EsrXicECPhzcBoLL6OLZWHDbuoIiIiPww+EggB2tCBx7RbEdERBQPDD4SSOtm6bpuR0REFA8MPhJI7/aZyLanI9SEWhuUWS+922caeVhEREQ+GHwkkNQUG6YPyQOAgADE/fP0IXms90FERKZi8JFgBnbJxtxRvZBl9x1aybKnY+6oXqzzQUREpmORsQQ0sEs2CvKyWOGUiIikxOAjQaWm2NC3Y0uzD4OIiCgAh12IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQDD6IiIjIUAw+iIiIyFAMPoiIiMhQ0lU4FUIAABwOh8lHQkRERGq579vu+3g40gUfNTU1AIDc3FyTj4SIiIi0qqmpgd1uD7uNTagJUQzkcrmwf/9+NGvWDDabPguhORwO5Obm4ocffkBGRoYu+5RJop8fwHNMFIl+jol+fgDPMRHE6/yEEKipqUFOTg5SUsJndUjX85GSkoLTTz89LvvOyMhIyA+SW6KfH8BzTBSJfo6Jfn4AzzERxOP8IvV4uDHhlIiIiAzF4IOIiIgMlRTBR1paGqZPn460tDSzDyUuEv38AJ5jokj0c0z08wN4jolAhvOTLuGUiIiIEltS9HwQERGRPBh8EBERkaEYfBAREZGhGHwQERGRoSwZfMyZMwdnnHEG0tPT0adPH2zdujXs9m+++SbOOeccpKeno2vXrli1apXP40IITJs2DdnZ2WjcuDHy8/Oxe/fueJ5CRFrOcd68ebj00kvRokULtGjRAvn5+QHb//Wvf4XNZvP5N3DgwHifRlhaznH+/PkBx5+enu6zjWzvo5bzu/zyywPOz2azYfDgwZ5tZHsPN27ciCFDhiAnJwc2mw3Lly+P+JwNGzagV69eSEtLw5lnnon58+cHbKP19ztetJ7f22+/jYKCApx66qnIyMhA37598f777/ts8+CDDwa8h+ecc04czyI8ree4YcOGoJ/Tqqoqn+1keQ8B7ecY7PfMZrOhc+fOnm1keh9nzJiBCy64AM2aNUPr1q0xbNgwfP311xGfZ/Z90XLBx3//+19MnDgR06dPR0lJCbp3744BAwbg4MGDQbf/5JNPMHLkSNx0003Yvn07hg0bhmHDhqG0tNSzzWOPPYann34azz33HLZs2YKmTZtiwIABOH78uFGn5UPrOW7YsAEjR47E+vXrUVxcjNzcXFx11VXYt2+fz3YDBw5EZWWl59+iRYuMOJ2gtJ4joFTj8z7+vXv3+jwu0/uo9fzefvttn3MrLS1Famoq/vKXv/hsJ9N7WFtbi+7du2POnDmqtq+oqMDgwYPRv39/7NixAxMmTMDNN9/sc4OO5nMRL1rPb+PGjSgoKMCqVauwbds29O/fH0OGDMH27dt9tuvcubPPe7hp06Z4HL4qWs/R7euvv/Y5h9atW3sek+k9BLSf41NPPeVzbj/88AMyMzMDfhdleR8//PBDjB8/Hps3b8bq1atRX1+Pq666CrW1tSGfI8V9UVhM7969xfjx4z0/O51OkZOTI2bMmBF0++uuu04MHjzYp61Pnz7ib3/7mxBCCJfLJbKyssTjjz/uefzIkSMiLS1NLFq0KA5nEJnWc/R34sQJ0axZM7FgwQJP25gxY8TQoUP1PtSoaT3HV155Rdjt9pD7k+19jPU9nD17tmjWrJk4evSop02299AbALFs2bKw29x7772ic+fOPm3Dhw8XAwYM8Pwc63WLFzXnF0xeXp4oLCz0/Dx9+nTRvXt3/Q5MR2rOcf369QKA+OWXX0JuI+t7KER07+OyZcuEzWYT3333nadN5vfx4MGDAoD48MMPQ24jw33RUj0fv/32G7Zt24b8/HxPW0pKCvLz81FcXBz0OcXFxT7bA8CAAQM821dUVKCqqspnG7vdjj59+oTcZzxFc47+jh07hvr6emRmZvq0b9iwAa1bt0anTp0wbtw4HDp0SNdjVyvaczx69CjatWuH3NxcDB06FLt27fI8JtP7qMd7+NJLL2HEiBFo2rSpT7ss72E0Iv0u6nHdZOJyuVBTUxPwe7h7927k5OSgQ4cOuOGGG/D999+bdITR69GjB7Kzs1FQUICPP/7Y055o7yGg/C7m5+ejXbt2Pu2yvo/V1dUAEPC58ybDfdFSwcfPP/8Mp9OJNm3a+LS3adMmYMzRraqqKuz27v9q2Wc8RXOO/iZNmoScnByfD87AgQPx6quvYu3atZg1axY+/PBDDBo0CE6nU9fjVyOac+zUqRNefvllrFixAgsXLoTL5cJFF12EH3/8EYBc72Os7+HWrVtRWlqKm2++2addpvcwGqF+Fx0OB3799VddPvsyeeKJJ3D06FFcd911nrY+ffpg/vz5KCoqwty5c1FRUYFLL70UNTU1Jh6petnZ2XjuueewdOlSLF26FLm5ubj88stRUlICQJ+/XzLZv38//ve//wX8Lsr6PrpcLkyYMAEXX3wxunTpEnI7Ge6L0q1qS7GZOXMmFi9ejA0bNvgkZI4YMcLz/127dkW3bt3QsWNHbNiwAVdeeaUZh6pJ37590bdvX8/PF110Ec4991w8//zzeOihh0w8Mv299NJL6Nq1K3r37u3TbvX3MJm88cYbKCwsxIoVK3zyIQYNGuT5/27duqFPnz5o164dlixZgptuusmMQ9WkU6dO6NSpk+fniy66COXl5Zg9ezZee+01E48sPhYsWIDmzZtj2LBhPu2yvo/jx49HaWmpqXlEalmq56NVq1ZITU3FgQMHfNoPHDiArKysoM/JysoKu737v1r2GU/RnKPbE088gZkzZ+KDDz5At27dwm7boUMHtGrVCnv27In5mLWK5RzdGjZsiJ49e3qOX6b3MZbzq62txeLFi1X9ATPzPYxGqN/FjIwMNG7cWJfPhQwWL16Mm2++GUuWLAno2vbXvHlznH322ZZ5D4Pp3bu35/gT5T0ElNkeL7/8MkaPHo1GjRqF3VaG9/GOO+7Au+++i/Xr1+P0008Pu60M90VLBR+NGjXCeeedh7Vr13raXC4X1q5d6/Ot2Fvfvn19tgeA1atXe7Zv3749srKyfLZxOBzYsmVLyH3GUzTnCCiZyQ899BCKiopw/vnnR3ydH3/8EYcOHUJ2drYux61FtOfozel0YufOnZ7jl+l9jOX83nzzTdTV1WHUqFERX8fM9zAakX4X9fhcmG3RokW48cYbsWjRIp9p0qEcPXoU5eXllnkPg9mxY4fn+BPhPXT78MMPsWfPHlVfBMx8H4UQuOOOO7Bs2TKsW7cO7du3j/gcKe6LuqStGmjx4sUiLS1NzJ8/X5SVlYlbb71VNG/eXFRVVQkhhBg9erSYPHmyZ/uPP/5YNGjQQDzxxBPiyy+/FNOnTxcNGzYUO3fu9Gwzc+ZM0bx5c7FixQrxxRdfiKFDh4r27duLX3/91fDzE0L7Oc6cOVM0atRIvPXWW6KystLzr6amRgghRE1NjfjnP/8piouLRUVFhVizZo3o1auXOOuss8Tx48ctcY6FhYXi/fffF+Xl5WLbtm1ixIgRIj09XezatcuzjUzvo9bzc7vkkkvE8OHDA9plfA9ramrE9u3bxfbt2wUA8e9//1ts375d7N27VwghxOTJk8Xo0aM923/77beiSZMm4p577hFffvmlmDNnjkhNTRVFRUWebSJdN5nP7/XXXxcNGjQQc+bM8fk9PHLkiGebu+++W2zYsEFUVFSIjz/+WOTn54tWrVqJgwcPGn5+Qmg/x9mzZ4vly5eL3bt3i507d4o777xTpKSkiDVr1ni2kek9FEL7ObqNGjVK9OnTJ+g+ZXofx40bJ+x2u9iwYYPP5+7YsWOebWS8L1ou+BBCiP/85z+ibdu2olGjRqJ3795i8+bNnsf69esnxowZ47P9kiVLxNlnny0aNWokOnfuLN577z2fx10ul5g6dapo06aNSEtLE1deeaX4+uuvjTiVkLScY7t27QSAgH/Tp08XQghx7NgxcdVVV4lTTz1VNGzYULRr107ccsstpv0xcNNyjhMmTPBs26ZNG3H11VeLkpISn/3J9j5q/Zx+9dVXAoD44IMPAvYl43vonnbp/899XmPGjBH9+vULeE6PHj1Eo0aNRIcOHcQrr7wSsN9w181IWs+vX79+YbcXQplanJ2dLRo1aiROO+00MXz4cLFnzx5jT8yL1nOcNWuW6Nixo0hPTxeZmZni8ssvF+vWrQvYryzvoRDRfU6PHDkiGjduLF544YWg+5TpfQx2bgB8frdkvC/aTh48ERERkSEslfNBRERE1sfgg4iIiAzF4IOIiIgMxeCDiIiIDMXgg4iIiAzF4IOIiIgMxeCDiIiIDMXgg4iIiAzF4IOIiIgMxeCDiIiIDMXgg4iIiAzF4IOIiIgM9f8ByDt45ZS0uekAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.scatter(X, y)\n",
+        "plt.plot(X, y_pred, color='red')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q-DPL7OI6x0J",
+        "jp-MarkdownHeadingCollapsed": true
+      },
+      "source": [
+        "# **3. 다항 회귀**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ta785wP1zTfT"
+      },
+      "source": [
+        "## **3-1. `PolynomialFeatures`를 이용해 단항값 $[x_1, x_2]$ 를 2차 다항값 $[1, x_1, x_2, x_1^2, x_1x_2, x_2^2]$ 로 변환하는 코드를 작성해 봅시다.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "CbH6y89bzTfT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "864dc559-e5e6-4bfa-9ae5-a45cb1aa7fc5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "일차 단항식 계수 피처:\n",
+            " [[0 1]\n",
+            " [2 3]]\n",
+            "변환된 2차 다항식 계수 피처:\n",
+            " [[1. 0. 1. 0. 0. 1.]\n",
+            " [1. 2. 3. 4. 6. 9.]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.preprocessing import PolynomialFeatures\n",
+        "import numpy as np\n",
+        "\n",
+        "# 다항식으로 변환한 단항식 생성, [[0, 1], [2, 3]]의 2X2 행렬 생성\n",
+        "X = np.array([[0, 1], [2, 3]])\n",
+        "print('일차 단항식 계수 피처:\\n', X)\n",
+        "\n",
+        "# degree = 2인 2차 다항식으로 변환하기 위해 PolynomialFeatures를 이용해 변환\n",
+        "poly = PolynomialFeatures(degree=2)\n",
+        "poly.fit(X)\n",
+        "poly_ftr = poly.transform(X)\n",
+        "print ('변환된 2차 다항식 계수 피처:\\n', poly_ftr)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "21cvjk3dzTfU"
+      },
+      "source": [
+        "## **3-2. `PolynomialFeatures`와 `LinearRegression` 클래스를 이용해 3차 다항 회귀 코드를 작성해 봅시다.**\n",
+        "\n",
+        "**이때 회귀의 결정 함수식은 $ y=1+3x_1+x_2^2+2x_2^3$ 입니다.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Le-pSVUizTfU"
+      },
+      "source": [
+        "### **step 1.**\n",
+        "해당 결정 함수식을 위한 `polynomial_func()` 함수를 작성해주세요. 해당 함수는 3차 다항 계수 피처 값이 입력되면 결정 값을 반환합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "2hMHewzezTfU"
+      },
+      "outputs": [],
+      "source": [
+        "def polynomial_func(X):\n",
+        "    y = 1 + 3 * X[:, 0] + X[:, 1]**2 + 2 * X[:, 1]**3\n",
+        "    return y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "XsBBVd8a7dc_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "53302a08-7141-4507-d75d-9f02121d89d5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "일차 단항식 계수: \n",
+            " [[0 1]\n",
+            " [2 3]]\n",
+            "삼차 다항식 결정값: \n",
+            " [ 4 70]\n"
+          ]
+        }
+      ],
+      "source": [
+        "X = np.array([[0, 1], [2, 3]])\n",
+        "print('일차 단항식 계수: \\n', X)\n",
+        "\n",
+        "y = polynomial_func(X)\n",
+        "print('삼차 다항식 결정값: \\n', y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oWK2PS0XzTfU"
+      },
+      "source": [
+        "### **step 2.**\n",
+        "일차 단항식 계수를 삼차 다항식 계수로 변환하고, 이를 선형 회귀에 적용하여 다항 회귀로 구현해 주세요."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "Y_2jpoSWzTfY",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cbcf9aeb-6535-4208-c74c-4f181ef05c5f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "3차 다항식 계수 feature: \n",
+            " [[ 1.  0.  1.  0.  0.  1.  0.  0.  0.  1.]\n",
+            " [ 1.  2.  3.  4.  6.  9.  8. 12. 18. 27.]]\n",
+            "Polynomial 회귀 계수\n",
+            " [0.   0.1  0.1  0.2  0.3  0.4  0.4  0.59 0.89 1.29]\n",
+            "Polynomial 회귀 Shape : (10,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "# 3차 다항식 변환\n",
+        "poly_ftr = PolynomialFeatures(degree=3).fit_transform(X)\n",
+        "print('3차 다항식 계수 feature: \\n', poly_ftr)\n",
+        "\n",
+        "# Linear Regression에 3차 다항식 계수 feature와 3차 다항식 결정값으로 학습 후 회귀 확인\n",
+        "model = LinearRegression()\n",
+        "model.fit(poly_ftr, y)\n",
+        "print('Polynomial 회귀 계수\\n', np.round(model.coef_, 2))\n",
+        "print('Polynomial 회귀 Shape :', model.coef_.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "jp-MarkdownHeadingCollapsed": true,
+        "id": "7jYCqswY-zGd"
+      },
+      "source": [
+        "# **4. 규제 선형 모델**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cO5tidY--zGd"
+      },
+      "source": [
+        "## **4-0. 데이터 세트 준비**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5sOLPury-zGd"
+      },
+      "source": [
+        "- 사이킷런에서 제공하는 당뇨병 데이터 세트로 실습을 진행합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "cyzNGKTx-zGd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 245
+        },
+        "outputId": "a3dd1742-bb65-4d59-ac13-7d93a02594bd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Diabetes 데이타셋 크기: (442, 11)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        age       sex       bmi        bp        s1        s2        s3  \\\n",
+              "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
+              "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
+              "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
+              "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+              "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+              "\n",
+              "         s4        s5        s6  target  \n",
+              "0 -0.002592  0.019907 -0.017646   151.0  \n",
+              "1 -0.039493 -0.068332 -0.092204    75.0  \n",
+              "2 -0.002592  0.002861 -0.025930   141.0  \n",
+              "3  0.034309  0.022688 -0.009362   206.0  \n",
+              "4 -0.002592 -0.031988 -0.046641   135.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-695d3108-8d8c-4cfe-abdc-3c8728d4d619\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>bmi</th>\n",
+              "      <th>bp</th>\n",
+              "      <th>s1</th>\n",
+              "      <th>s2</th>\n",
+              "      <th>s3</th>\n",
+              "      <th>s4</th>\n",
+              "      <th>s5</th>\n",
+              "      <th>s6</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.038076</td>\n",
+              "      <td>0.050680</td>\n",
+              "      <td>0.061696</td>\n",
+              "      <td>0.021872</td>\n",
+              "      <td>-0.044223</td>\n",
+              "      <td>-0.034821</td>\n",
+              "      <td>-0.043401</td>\n",
+              "      <td>-0.002592</td>\n",
+              "      <td>0.019907</td>\n",
+              "      <td>-0.017646</td>\n",
+              "      <td>151.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>-0.001882</td>\n",
+              "      <td>-0.044642</td>\n",
+              "      <td>-0.051474</td>\n",
+              "      <td>-0.026328</td>\n",
+              "      <td>-0.008449</td>\n",
+              "      <td>-0.019163</td>\n",
+              "      <td>0.074412</td>\n",
+              "      <td>-0.039493</td>\n",
+              "      <td>-0.068332</td>\n",
+              "      <td>-0.092204</td>\n",
+              "      <td>75.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.085299</td>\n",
+              "      <td>0.050680</td>\n",
+              "      <td>0.044451</td>\n",
+              "      <td>-0.005670</td>\n",
+              "      <td>-0.045599</td>\n",
+              "      <td>-0.034194</td>\n",
+              "      <td>-0.032356</td>\n",
+              "      <td>-0.002592</td>\n",
+              "      <td>0.002861</td>\n",
+              "      <td>-0.025930</td>\n",
+              "      <td>141.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>-0.089063</td>\n",
+              "      <td>-0.044642</td>\n",
+              "      <td>-0.011595</td>\n",
+              "      <td>-0.036656</td>\n",
+              "      <td>0.012191</td>\n",
+              "      <td>0.024991</td>\n",
+              "      <td>-0.036038</td>\n",
+              "      <td>0.034309</td>\n",
+              "      <td>0.022688</td>\n",
+              "      <td>-0.009362</td>\n",
+              "      <td>206.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.005383</td>\n",
+              "      <td>-0.044642</td>\n",
+              "      <td>-0.036385</td>\n",
+              "      <td>0.021872</td>\n",
+              "      <td>0.003935</td>\n",
+              "      <td>0.015596</td>\n",
+              "      <td>0.008142</td>\n",
+              "      <td>-0.002592</td>\n",
+              "      <td>-0.031988</td>\n",
+              "      <td>-0.046641</td>\n",
+              "      <td>135.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-695d3108-8d8c-4cfe-abdc-3c8728d4d619')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-695d3108-8d8c-4cfe-abdc-3c8728d4d619 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-695d3108-8d8c-4cfe-abdc-3c8728d4d619');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "diabetesDF",
+              "summary": "{\n  \"name\": \"diabetesDF\",\n  \"rows\": 442,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0476190476190476,\n        \"min\": -0.1072256316073538,\n        \"max\": 0.11072667545381144,\n        \"num_unique_values\": 58,\n        \"samples\": [\n          0.038075906433423026,\n          -0.09269547780327612,\n          0.01991321417832592\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0476190476190478,\n        \"min\": -0.044641636506989144,\n        \"max\": 0.05068011873981862,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -0.044641636506989144,\n          0.05068011873981862\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bmi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904761,\n        \"min\": -0.09027529589850945,\n        \"max\": 0.17055522598064407,\n        \"num_unique_values\": 163,\n        \"samples\": [\n          0.008883414898524095,\n          -0.05470749746044306\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904762,\n        \"min\": -0.11239880254408448,\n        \"max\": 0.13204361674121307,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          0.03333707926361473,\n          0.02531523648988596\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047619047619047554,\n        \"min\": -0.12678066991651324,\n        \"max\": 0.15391371315651542,\n        \"num_unique_values\": 141,\n        \"samples\": [\n          0.041085578784023497,\n          0.08374011738825825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047619047619047644,\n        \"min\": -0.11561306597939897,\n        \"max\": 0.19878798965729408,\n        \"num_unique_values\": 302,\n        \"samples\": [\n          0.07415490186505921,\n          0.07008397186179521\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904758,\n        \"min\": -0.10230705051741597,\n        \"max\": 0.18117906039727852,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          0.09281975309919192,\n          0.15908923357275687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.047619047619047554,\n        \"min\": -0.0763945037500033,\n        \"max\": 0.18523444326019867,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          0.08670845052151895,\n          -0.024732934523729287\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904763,\n        \"min\": -0.12609712083330468,\n        \"max\": 0.13359728192191356,\n        \"num_unique_values\": 184,\n        \"samples\": [\n          -0.07213275338232743,\n          -0.021395309255276825\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"s6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04761904761904766,\n        \"min\": -0.13776722569000302,\n        \"max\": 0.13561183068907107,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          -0.01764612515980379,\n          -0.09634615654165846\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77.09300453299109,\n        \"min\": 25.0,\n        \"max\": 346.0,\n        \"num_unique_values\": 214,\n        \"samples\": [\n          310.0,\n          140.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ],
+      "source": [
+        "# 코드를 실행해 주세요.\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "%matplotlib inline\n",
+        "\n",
+        "# 당뇨병 데이터셋 로드\n",
+        "diabetes = load_diabetes()\n",
+        "\n",
+        "# diabetes 데이터셋 DataFrame 변환\n",
+        "diabetesDF = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)\n",
+        "\n",
+        "# diabetes 데이터셋의 target array는 당뇨병 진행 정도를 나타냄. 이를 target 컬럼으로 DataFrame에 추가함.\n",
+        "diabetesDF['target'] = diabetes.target\n",
+        "\n",
+        "print('Diabetes 데이타셋 크기:', diabetesDF.shape)\n",
+        "diabetesDF.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "hl-0mwhp-zGe"
+      },
+      "outputs": [],
+      "source": [
+        "# 코드를 실행해 주세요.\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_squared_error, r2_score\n",
+        "\n",
+        "# 타겟 변수 설정\n",
+        "y_target = diabetesDF['target']\n",
+        "\n",
+        "# 특성 변수 설정\n",
+        "X_data = diabetesDF.drop(['target'], axis=1, inplace=False)\n",
+        "\n",
+        "# 데이터를 훈련 세트와 테스트 세트로 분할\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X_data, y_target, test_size=0.3, random_state=156)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yBY-64kB-zGe"
+      },
+      "source": [
+        "## **4-1. `get_linear_reg_eval()` 함수를 완성하세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "ldk-QTP6-zGe"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import Ridge, Lasso, ElasticNet\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "\n",
+        "def get_linear_reg_eval(model_name, params=None, X_data_n=None, y_target_n=None,\n",
+        "                        verbose=True, return_coeff=True):\n",
+        "    coeff_df = pd.DataFrame()\n",
+        "    if verbose : print('####### ', model_name , '#######')\n",
+        "    for param in params:\n",
+        "        if model_name =='Ridge': model = Ridge(alpha=param)\n",
+        "        elif model_name =='Lasso': model = Lasso(alpha=param)\n",
+        "        elif model_name =='ElasticNet': model = ElasticNet(alpha=param, l1_ratio=0.7)\n",
+        "\n",
+        "        neg_mse_scores = cross_val_score(model, X_data_n,\n",
+        "                                         y_target_n, scoring=\"neg_mean_squared_error\", cv = 5)\n",
+        "        avg_rmse = np.mean(np.sqrt(-1 * neg_mse_scores))\n",
+        "        print('alpha {0}일 때 5 폴드 세트의 평균 RMSE: {1:.3f} '.format(param, avg_rmse))\n",
+        "\n",
+        "        model.fit(X_data_n , y_target_n)\n",
+        "        if return_coeff:\n",
+        "            coeff = pd.Series(data=model.coef_ , index=X_data_n.columns )\n",
+        "            colname='alpha:'+str(param)\n",
+        "            coeff_df[colname] = coeff\n",
+        "\n",
+        "    return coeff_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N0oKy2wb-zGe"
+      },
+      "source": [
+        "## **4-2. 엘라스틱넷 회귀 모델에서 다음의 alpha 값 후보 중에서 alpha 의 값이 얼마일 때 가장 좋은 예측 성능을 보이는 지 RMSE 값과 함께 답하세요.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0dEjcWuk-zGe"
+      },
+      "source": [
+        "- 조건: `alphas = [0, 0.1, 1, 10]`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "HlqRFRY4-zGf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "300de571-639e-4530-fb42-d614f6e67587"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "#######  ElasticNet #######\n",
+            "alpha 0일 때 5 폴드 세트의 평균 RMSE: 54.692 \n",
+            "alpha 0.1일 때 5 폴드 세트의 평균 RMSE: 71.268 \n",
+            "alpha 1일 때 5 폴드 세트의 평균 RMSE: 76.805 \n",
+            "alpha 10일 때 5 폴드 세트의 평균 RMSE: 77.264 \n"
+          ]
+        }
+      ],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "# 엘라스틱넷에 사용될 alpha 파라미터의 값들을 정의하고 get_linear_reg_eval() 함수 호출\n",
+        "# l1_ratio는 0.7로 고정\n",
+        "\n",
+        "elastic_alphas = [0, 0.1, 1, 10]\n",
+        "coeff_elastic_df = get_linear_reg_eval('ElasticNet', params=elastic_alphas,\n",
+        "                                       X_data_n=X_data, y_target_n=y_target)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d4Gi502L-zGf"
+      },
+      "source": [
+        "**답안**  \n",
+        ": alpha 0일 때 가장 좋은 성능"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pE9qpUg6-zGf"
+      },
+      "source": [
+        "## **4-3. `get_scaled_data()` 함수를 완성하세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "WvamdBfE-zGf"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures\n",
+        "\n",
+        "def get_scaled_data(method='None', p_degree=None, input_data=None):\n",
+        "    if method == 'Standard':\n",
+        "        scaled_data = StandardScaler().fit_transform(input_data)\n",
+        "    elif method == 'MinMax':\n",
+        "        scaled_data = MinMaxScaler().fit_transform(input_data)\n",
+        "    elif method == 'Log':\n",
+        "        scaled_data = np.log1p(input_data)\n",
+        "    else:\n",
+        "        scaled_data = input_data\n",
+        "\n",
+        "    if p_degree != None:\n",
+        "        scaled_data = PolynomialFeatures(degree=p_degree,\n",
+        "                                         include_bias=False).fit_transform(scaled_data)\n",
+        "\n",
+        "    return scaled_data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "jp-MarkdownHeadingCollapsed": true,
+        "id": "d6PlWEkY-zGf"
+      },
+      "source": [
+        "# **5. 로지스틱 회귀**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Wp52rhBk-zGg"
+      },
+      "source": [
+        "## **5-0. 데이터 세트 준비**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_8kg_kHN-zGg"
+      },
+      "source": [
+        "- 사이킷런에서 제공하는 와인 등급 데이터 세트로 로지스틱 회귀 분석 실습을 진행합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "_c-Pjt6T-zGg"
+      },
+      "outputs": [],
+      "source": [
+        "# 코드를 실행해 주세요.\n",
+        "\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline\n",
+        "\n",
+        "from sklearn.datasets import load_wine\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "df = load_wine()\n",
+        "X, y = df.data, df.target"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S3yJwZ_L-zGh"
+      },
+      "source": [
+        "## **5-1. 정규 분포 형태의 표준 스케일링을 적용한 후 학습/테스트 데이터 세트로 분리해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {
+        "id": "ayCTgh1R-zGh"
+      },
+      "outputs": [],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "\n",
+        "#아래에 코드를 입력해주세요\n",
+        "data_scaled = scaler.fit_transform(X)\n",
+        "\n",
+        "# test_size=0.3.\n",
+        "X_train, X_test, y_train, y_test = train_test_split(data_scaled, y, test_size=0.3, random_state=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_oHoUZnA-zGi"
+      },
+      "source": [
+        "## **5-2. 로지스틱 회귀를 이용해 학습 및 예측을 수행해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "iuqxC7bl-zGi"
+      },
+      "outputs": [],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "lr_clf = LogisticRegression()\n",
+        "lr_clf.fit(X_train, y_train)\n",
+        "lr_preds = lr_clf.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r4MHSb75-zGi"
+      },
+      "source": [
+        "## **5-3. 모델의 정확도를 확인해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "EvqvwZWk-zGi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "933d1087-7fc8-45df-eae2-cf488a702946"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy: 1.000\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "print('accuracy: {0:.3f}'.format(accuracy_score(y_test, lr_preds)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C1pA8pXI-zGj"
+      },
+      "source": [
+        "## **5-4. GridSearchCV를 이용해 와인 등급 데이터 하이퍼 파라미터 최적화를 진행해 주세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "VvtuIAPQ-zGj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e8deb1f1-436d-4f4f-f156-14758f969833"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "최적 하이퍼 파라미터:{'C': 10, 'penalty': 'l1', 'solver': 'liblinear'}, 최적 평균 정확도:0.983\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "from sklearn.linear_model import LogisticRegression # LogisticRegression import 추가\n",
+        "\n",
+        "params = [\n",
+        "    {'solver':['liblinear'], 'penalty':['l1', 'l2'], 'C':[0.01,0.1,1,5,10]},\n",
+        "    {'solver':['lbfgs'], 'penalty':['l2'], 'C':[0.01,0.1,1,5,10]}\n",
+        "]\n",
+        "lr_clf = LogisticRegression() # 클래스 대신 인스턴스 생성\n",
+        "\n",
+        "grid_clf = GridSearchCV(lr_clf, param_grid=params, scoring='accuracy', cv=3 )\n",
+        "grid_clf.fit(data_scaled, y)\n",
+        "print('최적 하이퍼 파라미터:{0}, 최적 평균 정확도:{1:.3f}'.format(grid_clf.best_params_, grid_clf.best_score_))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y_c1Z-mI-zGj"
+      },
+      "source": [
+        "# **6. 회귀 트리**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZaKb5gDJ-zGj"
+      },
+      "source": [
+        "## **6-0. 데이터 세트 준비**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x3McEyst-zGj"
+      },
+      "source": [
+        "- 사이킷런에서 제공하는 캘리포니아 데이터 세트로 로지스틱 회귀 분석 실습을 진행합니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "L4Buc3O0-zGk"
+      },
+      "outputs": [],
+      "source": [
+        "# 코드를 실행해 주세요.\n",
+        "\n",
+        "from sklearn.datasets import fetch_california_housing\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# 캘리포니아 데이터 세트 로드\n",
+        "housing = fetch_california_housing()\n",
+        "housingDF = pd.DataFrame(housing.data, columns=housing.feature_names)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9BOK-KzM-zGk"
+      },
+      "source": [
+        "## **6-1. 회귀 트리 모델을 생성한 후 3번의 교차 검증을 수행하세요.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XSa-_HrG-zGl"
+      },
+      "source": [
+        "- 시간이 좀 걸릴 수 있습니다. 출제자는  정도 걸렸습니다."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "y4V9QuOQ-zGl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "be91e641-c04c-49eb-c275-edd24289f174"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "3 교차 검증의 개별 Negative MSE scores: [-0.51 -0.33 -0.54]\n",
+            "3 교차 검증의 개별 RMSE scores: [0.71 0.57 0.73]\n",
+            "3 교차 검증의 평균 RMSE:, 0.674\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "housingDF['PRICE'] = housing.target\n",
+        "y_target = housingDF['PRICE']\n",
+        "X_data = housingDF.drop(['PRICE'],axis=1, inplace=False)\n",
+        "\n",
+        "rf = RandomForestRegressor(random_state=0, n_estimators=100)\n",
+        "neg_mse_scores = cross_val_score(rf, X_data, y_target, scoring=\"neg_mean_squared_error\", cv=3)\n",
+        "rmse_scores = np.sqrt(-1 * neg_mse_scores)\n",
+        "avg_rmse = np.mean(rmse_scores)\n",
+        "\n",
+        "print('3 교차 검증의 개별 Negative MSE scores:', np.round(neg_mse_scores, 2))\n",
+        "print('3 교차 검증의 개별 RMSE scores:', np.round(rmse_scores, 2))\n",
+        "print('3 교차 검증의 평균 RMSE:, {0:.3f}'.format(avg_rmse))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5x4Kondx-zGl"
+      },
+      "source": [
+        "## **6-2. 랜덤 포레스트, 결정 트리, GBM, XGBoost, ightGBM을 모두 이용해서 캘리포니아 주택 가격 예측을 수행하세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "UQGiorpa-zGm"
+      },
+      "outputs": [],
+      "source": [
+        "# 코드를 실행해 주세요.\n",
+        "\n",
+        "def get_model_cv_prediction(model, X_data, y_target):\n",
+        "    y = np.array(y_target, dtype=np.float32)\n",
+        "    neg_mse_scores=cross_val_score(model, X_data, y, scoring=\"neg_mean_squared_error\", cv=2)\n",
+        "    rmse_scores = np.sqrt(-1*neg_mse_scores)\n",
+        "    avg_rmse = np.mean(rmse_scores)\n",
+        "    print('###', model.__class__.__name__, '####')\n",
+        "    print('2 교차 검증의 평균 RMSE: {0:.3f}'.format(avg_rmse))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "KKWwC4ws-zGm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7559c021-6850-4a31-dda3-1deca949d2d8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "### DecisionTreeRegressor ####\n",
+            "2 교차 검증의 평균 RMSE: 0.874\n",
+            "### RandomForestRegressor ####\n",
+            "2 교차 검증의 평균 RMSE: 0.722\n",
+            "### GradientBoostingRegressor ####\n",
+            "2 교차 검증의 평균 RMSE: 0.699\n",
+            "### XGBRegressor ####\n",
+            "2 교차 검증의 평균 RMSE: 0.736\n",
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001203 seconds.\n",
+            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Total Bins 1838\n",
+            "[LightGBM] [Info] Number of data points in the train set: 10320, number of used features: 8\n",
+            "[LightGBM] [Info] Start training from score 2.084387\n",
+            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001175 seconds.\n",
+            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Total Bins 1837\n",
+            "[LightGBM] [Info] Number of data points in the train set: 10320, number of used features: 8\n",
+            "[LightGBM] [Info] Start training from score 2.052729\n",
+            "### LGBMRegressor ####\n",
+            "2 교차 검증의 평균 RMSE: 0.717\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "from sklearn.ensemble import GradientBoostingRegressor\n",
+        "from xgboost import XGBRegressor\n",
+        "from lightgbm import LGBMRegressor\n",
+        "\n",
+        "dt_reg = DecisionTreeRegressor(random_state=0, max_depth=4)\n",
+        "rf_reg = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)\n",
+        "gb_reg = GradientBoostingRegressor(random_state=0, n_estimators=100)\n",
+        "xgb_reg = XGBRegressor(n_estimators=100, n_jobs=-1)\n",
+        "lgb_reg = LGBMRegressor(n_estimators=100, n_jobs=-1)\n",
+        "\n",
+        "# 트리 기반의 회귀 모델을 반복하면서 평가 수행\n",
+        "models = [dt_reg, rf_reg, gb_reg, xgb_reg, lgb_reg]\n",
+        "for model in models:\n",
+        "    get_model_cv_prediction(model, X_data, y_target)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2EDr8xkQ-zGn"
+      },
+      "source": [
+        "## **6-3. 피처 중요도를 시각화하세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "id": "84o7qvwi-zGn",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 383
+        },
+        "outputId": "4105aa5c-074f-4da4-9533-15a00f9cf951"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='None', ylabel='None'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "import seaborn as sns\n",
+        "%matplotlib inline\n",
+        "\n",
+        "rf_reg = RandomForestRegressor(n_estimators=100)\n",
+        "\n",
+        "rf_reg.fit(X_data, y_target)\n",
+        "\n",
+        "feature_series = pd.Series(data=rf_reg.feature_importances_, index=X_data.columns)\n",
+        "feature_series = feature_series.sort_values(ascending=False)\n",
+        "sns.barplot(x=feature_series, y=feature_series.index)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SQv5f5C8-zGn"
+      },
+      "source": [
+        "## **6-4. 선형회귀, max_depth=2인 회귀트리, max_depth=7인 트리의 예측 회귀선을 비교하세요.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wBVw54AY-zGn"
+      },
+      "source": [
+        "### **1) 각 모델을 생성, 학습, 예측하세요.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G5zn_TGj-zGo"
+      },
+      "source": [
+        "- 캘리포니아 데이터 개수 100개만 샘플링\n",
+        "- 2차원 평면상으로 가장 중요한 변수 1개만 추출 (피처 중요도 시각화 참고)\n",
+        "- 회귀 트리 1은 max_depth=2\n",
+        "- 회귀 트리 2는 max_depth=7\n",
+        "- 테스트용 데이터 세트를 4.5~8.5까지의 100개 데이터 세트로 생성"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "EoHPhHFQ-zGo"
+      },
+      "outputs": [],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "import numpy as np\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n",
+        "housingDF_sample = housingDF[['MedInc', 'PRICE']]\n",
+        "housingDF_sample = housingDF_sample.sample(n=100, random_state=0)\n",
+        "\n",
+        "lr_reg = LinearRegression()\n",
+        "rf_reg2 = DecisionTreeRegressor(max_depth=2)\n",
+        "rf_reg7 = DecisionTreeRegressor(max_depth=7)\n",
+        "\n",
+        "X_test = np.arange(4.5, 8.5, 0.04).reshape(-1,1)\n",
+        "\n",
+        "X_feature = housingDF_sample['MedInc'].values.reshape(-1,1)\n",
+        "y_target = housingDF_sample['PRICE'].values.reshape(-1,1)\n",
+        "\n",
+        "lr_reg.fit(X_feature, y_target)\n",
+        "rf_reg2.fit(X_feature, y_target)\n",
+        "rf_reg7.fit(X_feature, y_target)\n",
+        "\n",
+        "pred_lr = lr_reg.predict(X_test)\n",
+        "pred_rf2 = rf_reg2.predict(X_test)\n",
+        "pred_rf7 = rf_reg7.predict(X_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4RL8Fav_-zGp"
+      },
+      "source": [
+        "### **2) 산점도에 각각의 학습된 Regression의 PRICE 회귀선을 그리세요.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "ggMcuv6e-zGp",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 211
+        },
+        "outputId": "64492944-0752-4a3d-e4f5-f410672b59d2"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x799788ac8e60>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x400 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# 코드를 완성해 주세요.\n",
+        "\n",
+        "fig , (ax1, ax2, ax3) = plt.subplots(figsize=(14,4), ncols=3)\n",
+        "\n",
+        "## X축 값을 4.5 ~ 8.5로 변환하며 입력했을 때, 선형 회귀와 결정 트리 회귀 예측 선 시각화\n",
+        "# 선형 회귀로 학습된 모델 회귀 예측선\n",
+        "ax1.set_title('Linear Regression')\n",
+        "ax1.scatter(housingDF_sample.MedInc, housingDF_sample.PRICE, c=\"fuchsia\")\n",
+        "ax1.plot(X_test, pred_lr, label=\"linear\", linewidth=2 )\n",
+        "\n",
+        "## DecisionTreeRegressor의 max_depth를 2로 했을 때 회귀 예측선\n",
+        "ax2.set_title('Decision Tree Regression: \\n max_depth=2')\n",
+        "ax2.scatter(housingDF_sample.MedInc, housingDF_sample.PRICE, c=\"fuchsia\")\n",
+        "ax2.plot(X_test, pred_rf2, label=\"max_depth:2\", linewidth=2 )\n",
+        "\n",
+        "## DecisionTreeRegressor의 max_depth를 7로 했을 때 회귀 예측선\n",
+        "ax3.set_title('Decision Tree Regression: \\n max_depth=7')\n",
+        "ax3.scatter(housingDF_sample.MedInc, housingDF_sample.PRICE, c=\"fuchsia\")\n",
+        "ax3.plot(X_test, pred_rf7, label=\"max_depth:7\", linewidth=2)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "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": "3.9.25"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file