07-2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZJpCOr4Mo0lP"
      },
      "source": [
        "# 07-2 머신러닝으로 예측하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJCDtGI0pZsl"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://nbviewer.jupyter.org/github/rickiepark/hg-da/blob/main/07-2.ipynb\"><img src=\"https://jupyter.org/assets/share.png\" width=\"61\" />주피터 노트북 뷰어로 보기</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-da/blob/main/07-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩(Colab)에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mup10UcJEnA2"
      },
      "source": [
        "## 모델 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "u_VrtVrroycg",
        "outputId": "ef1eddcc-612d-490f-beb0-0a3ff56b5802"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://bit.ly/3pK7iuu\n",
            "To: /content/ns_book7.csv\n",
            "100%|██████████| 53.8M/53.8M [00:00<00:00, 73.6MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ns_book7.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import gdown\n",
        "\n",
        "gdown.download('https://bit.ly/3pK7iuu', 'ns_book7.csv', quiet=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "oUwKvOu5prmZ",
        "outputId": "7c633a7d-9b96-45e2-b606-1ba16c24c1d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   번호                    도서명                저자    출판사  발행년도           ISBN  \\\n",
              "0   1                인공지능과 흙            김동훈 지음    민음사  2021  9788937444319   \n",
              "1   2           가짜 행복 권하는 사회            김태형 지음   갈매나무  2021  9791190123969   \n",
              "2   3  나도 한 문장 잘 쓰면 바랄 게 없겠네            김선영 지음   블랙피쉬  2021  9788968332982   \n",
              "3   4                예루살렘 해변  이도 게펜 지음, 임재희 옮김  문학세계사  2021  9788970759906   \n",
              "4   5  김성곤의 중국한시기행 : 장강·황하 편            김성곤 지음    김영사  2021  9788934990833   \n",
              "\n",
              "  세트 ISBN 부가기호    권 주제분류번호  도서권수  대출건수        등록일자  \n",
              "0     NaN  NaN  NaN    NaN     1     0  2021-03-19  \n",
              "1     NaN  NaN  NaN    NaN     1     0  2021-03-19  \n",
              "2     NaN  NaN  NaN    NaN     1     0  2021-03-19  \n",
              "3     NaN  NaN  NaN    NaN     1     0  2021-03-19  \n",
              "4     NaN  NaN  NaN    NaN     1     0  2021-03-19  "
            ],
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              "\n",
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              "      <th>번호</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>인공지능과 흙</td>\n",
              "      <td>김동훈 지음</td>\n",
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              "      <td>2021</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>1</th>\n",
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              "      <td>가짜 행복 권하는 사회</td>\n",
              "      <td>김태형 지음</td>\n",
              "      <td>갈매나무</td>\n",
              "      <td>2021</td>\n",
              "      <td>9791190123969</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2021-03-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>나도 한 문장 잘 쓰면 바랄 게 없겠네</td>\n",
              "      <td>김선영 지음</td>\n",
              "      <td>블랙피쉬</td>\n",
              "      <td>2021</td>\n",
              "      <td>9788968332982</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2021-03-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>예루살렘 해변</td>\n",
              "      <td>이도 게펜 지음, 임재희 옮김</td>\n",
              "      <td>문학세계사</td>\n",
              "      <td>2021</td>\n",
              "      <td>9788970759906</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2021-03-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>김성곤의 중국한시기행 : 장강·황하 편</td>\n",
              "      <td>김성곤 지음</td>\n",
              "      <td>김영사</td>\n",
              "      <td>2021</td>\n",
              "      <td>9788934990833</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
              "      <td>2021-03-19</td>\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "ns_book7 = pd.read_csv('ns_book7.csv', low_memory=False)\n",
        "ns_book7.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CY8rU6IQ7uxO"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_set, test_set = train_test_split(ns_book7, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xz5Wa_Xj8MeM",
        "outputId": "62a80a09-6c99-461e-e565-5889459bd8c2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "282577 94193\n"
          ]
        }
      ],
      "source": [
        "print(len(train_set), len(test_set))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgny8P-U8MX9",
        "outputId": "144b2302-b454-4e76-cea4-8fc2f8ecfcff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(282577, 1) (282577,)\n"
          ]
        }
      ],
      "source": [
        "X_train = train_set[['도서권수']]\n",
        "y_train = train_set['대출건수']\n",
        "\n",
        "print(X_train.shape, y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "nZcdOc9Z8MSD",
        "outputId": "4a36c34a-8ba0-4899-dab1-fb6154ae0276"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ws7WflrbDQyf"
      },
      "source": [
        "## 훈련된 모델을 평가하기: 결정계수"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBAw34iO8MLr",
        "outputId": "2d5e526f-b9a2-4b5f-f228-d5cf12217d5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10025676249337112"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "X_test = test_set[['도서권수']]\n",
        "y_test = test_set['대출건수']\n",
        "\n",
        "lr.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXcT8BB1Hp_R",
        "outputId": "3332c0b4-8256-49cb-f52f-1fef502db300"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "lr.fit(y_train.to_frame(), y_train)\n",
        "lr.score(y_test.to_frame(), y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QGNVpvMaHp48"
      },
      "source": [
        "## 연속적인 값 예측하기: 선형 회귀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTLRUj_XHp17",
        "outputId": "7881bdab-07e2-483a-bde1-c451820cd600"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.] -3.907985046680551e-14\n"
          ]
        }
      ],
      "source": [
        "print(lr.coef_, lr.intercept_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yt1e5LaoxuNe"
      },
      "source": [
        "## 카테고리 예측하기: 로지스틱 회귀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "zXhjjEhCsH3g"
      },
      "outputs": [],
      "source": [
        "borrow_mean = ns_book7['대출건수'].mean()\n",
        "y_train_c = y_train > borrow_mean\n",
        "y_test_c = y_test > borrow_mean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MvKm8XGsHvp",
        "outputId": "b18e2caa-acd6-4e5d-bc49-f51dc1e0abbf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7106154385145393"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "logr = LogisticRegression()\n",
        "logr.fit(X_train, y_train_c)\n",
        "logr.score(X_test, y_test_c)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lr6N08h7sHa4",
        "outputId": "6f39a010-32fd-4bfb-aeaa-d0c6129cbdfb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    65337\n",
              "True     28856\n",
              "Name: 대출건수, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "y_test_c.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYbeHgpwsHiP",
        "outputId": "2497f2f0-ce12-4faa-91ae-6022a76a61bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6936502712515792"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "dc = DummyClassifier()\n",
        "dc.fit(X_train, y_train_c)\n",
        "dc.score(X_test, y_test_c)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0FGwNY0DQyh"
      },
      "source": [
        "## 평균제곱오차와 평균절댓값오차로 모델 평가하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ud-vomMYHpzJ",
        "outputId": "ea21fc59-d561-4878-db96-b604c903278b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "lr.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "buKGBnSFPWYM"
      },
      "outputs": [],
      "source": [
        "y_pred = lr.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fnZmsLjHptV",
        "outputId": "143dc52b-4723-493d-d8e5-8a7adbb0fa04"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.35809175285386"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mean_absolute_error(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KXZ_YqWusH6s",
        "outputId": "9f0be0e4-f1f3-4abb-e6e0-3609535173a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11.577558841952163"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "y_test.mean()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}