연습문제_회귀1.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wlsisl/ESAA_22-2/blob/main/%EC%97%B0%EC%8A%B5%EB%AC%B8%EC%A0%9C_%ED%9A%8C%EA%B7%801.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbedHKJOR3Ba"
      },
      "source": [
        "# **1. 단순 선형 회귀 분석**\n",
        "- 전복의 나이를 예측하는 선형회귀모델을 생성하세요.\n",
        "- 전복의 ‘성별’, ‘키’, ‘지름’, ‘높이’, ‘전체무게’, ‘몸통무게’, ‘내장무게’, ‘껍질무게’를 이용해 ‘껍질의 고리 수’를 예측한 뒤, **예측된 ‘껍질의 고리 수’에 1.5를 더하면 전복의 나이**가 됩니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1vwSrjeS4SR"
      },
      "source": [
        "# 기본 모듈 불러오기\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wX69fxAlS4pW"
      },
      "source": [
        "**1) 데이터 load 및 변형** "
      ]
    },
    {
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      "source": [
        "# 데이터 로드\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "data = pd.read_csv('/content/drive/MyDrive/ESAA_YB/abalone.csv')\n",
        "data.head()\n",
        "print(data.shape)\n",
        "\n",
        "# 성별 M은 Male, F는 Female, I는 Infant 이므로 따로 열 만들기\n",
        "for label in \"MFI\":\n",
        "    data[label] = data[\"Sex\"] == label\n",
        "data.drop('Sex', axis=1, inplace=True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "(4177, 9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fpAzVpf3kJku"
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      "source": [
        "**2) X, y 선택**\n",
        ": y는 Rings열, X는 Rings열을 제외한 나머지를 선택하되 전부 실수가 되도록 한다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xlz6IKQAkQ0R"
      },
      "source": [
        "# X,y 데이터 선택\n",
        "y=data.Rings.values\n",
        "data.drop('Rings',axis=1,inplace=True)\n",
        "\n",
        "X = data.values.astype(float)"
      ],
      "execution_count": 29,
      "outputs": []
    },
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              "      Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
              "0      0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
              "1      0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
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              "3      0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
              "4      0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
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              "4172   0.565     0.450   0.165        0.8870          0.3700          0.2390   \n",
              "4173   0.590     0.440   0.135        0.9660          0.4390          0.2145   \n",
              "4174   0.600     0.475   0.205        1.1760          0.5255          0.2875   \n",
              "4175   0.625     0.485   0.150        1.0945          0.5310          0.2610   \n",
              "4176   0.710     0.555   0.195        1.9485          0.9455          0.3765   \n",
              "\n",
              "      Shell weight      M      F      I  \n",
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              "4172        0.2490  False   True  False  \n",
              "4173        0.2605   True  False  False  \n",
              "4174        0.3080   True  False  False  \n",
              "4175        0.2960  False   True  False  \n",
              "4176        0.4950   True  False  False  \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSHNJy43TBRt"
      },
      "source": [
        " **3) train/test set 분리**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thhAcD2STD9B"
      },
      "source": [
        "# 필요한 모듈 불러오기\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnulnpP0Zbnw"
      },
      "source": [
        "# train과 test set 분리 (train:test = 7:3 비율로)\n",
        "\n",
        "X_train, X_test, y_train,y_test = train_test_split(X,y, test_size=0.3, random_state=100)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elvVID1GTEoj"
      },
      "source": [
        "**4) 선형회귀모델 생성, 모델 예측치 구하기**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IchCQXzTHSj"
      },
      "source": [
        "#필요한 모듈 불러오기\n",
        "from sklearn.linear_model import LinearRegression\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN-aDnzfakUR"
      },
      "source": [
        "#선형회귀모델 생성 및 훈련\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)\n",
        "y_preds = lr.predict(X_test)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jxxOtz2ObIq4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d385365b-6e38-4634-b1e5-580d481e1bd5"
      },
      "source": [
        "# 모델 예측치 구하기\n",
        "y_preds\n",
        "# 모델 예측치를 활용해 최종적으로 전복의 나이를 예측\n",
        "y_preds+1.5"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12.984375 , 14.703125 , 12.4375   , ..., 17.8125   , 12.9609375,\n",
              "        9.8359375])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "70GURxffTH5e"
      },
      "source": [
        "**5) 모델 평가: MSE, RMSE, R2 score, corr 구하기**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZGoQGRNTJkB"
      },
      "source": [
        "#필요한 모듈 불러오기\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lp7yxDDilhTW"
      },
      "source": [
        "- MSE, RMSE "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQ3urJwYbcad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cdd6878-b0a9-4fad-f28c-15b96c68ba8c"
      },
      "source": [
        "#mse, rmse\n",
        "mse= mean_squared_error(y_test,y_preds)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print('Mse:{0:.3f}, RMSEE:{1:.3F}'.format(mse, rmse))\n"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mse:4.931, RMSEE:2.221\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlcAHJ7olnRi"
      },
      "source": [
        "- R2 score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Srj9IlLzlpOH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27445459-3d87-4736-e5d7-ce022491c56e"
      },
      "source": [
        "#R2 score 측정\n",
        "R2= r2_score(y_test, y_preds)\n",
        "R2"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5336269894594383"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PYnBM6gblwks"
      },
      "source": [
        "- 회귀 절편값"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zs5NRnEmbyCY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e8bd6db-0ed6-4467-8599-3909602f87b4"
      },
      "source": [
        "#회귀 절편 값\n",
        "lr.intercept_"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48785761923294.74"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE4Fbx51lyxo"
      },
      "source": [
        "- 회귀 계수 값"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ooGY_KnDcAsk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d15f22b4-a665-4e51-f3be-7672d3ae05f7"
      },
      "source": [
        "#회귀 계수 값\n",
        "np.round(lr.coef_,1)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.60000000e+00,  9.20000000e+00,  7.10000000e+00,  1.00000000e+01,\n",
              "       -2.15000000e+01, -1.04000000e+01,  7.90000000e+00, -4.87857619e+13,\n",
              "       -4.87857619e+13, -4.87857619e+13])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNzYzbJfl5I2"
      },
      "source": [
        "- 상관계수\n",
        "\n",
        "Hint: corr 함수 이용."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kx21T36vcSYC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "480319ec-510a-4cbc-ab4e-887d7d4a3892"
      },
      "source": [
        "df = pd.DataFrame({'y_pred' : y_preds, 'y_true' : y_test})\n",
        "cor = df['y_pred'].corr(df['y_true'])\n",
        "cor"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7305459432088361"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Polynomial features**"
      ],
      "metadata": {
        "id": "gS8CtaO8duLT"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1O8EJnNP3F6"
      },
      "source": [
        "# PolynomialFeatures 라이브러리 호출\n",
        "from sklearn.preprocessing import PolynomialFeatures"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h34vo_eyQVUR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "5a0bea1f-faa3-4c17-99c8-4a1f82167d90"
      },
      "source": [
        "# 임의 데이터 생성\n",
        "\n",
        "X = np.arange(6).reshape(3, 2)\n",
        "\n",
        "df =  pd.DataFrame(X)\n",
        "df.columns = ['x_1','x_2']\n",
        "df"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   x_1  x_2\n",
              "0    0    1\n",
              "1    2    3\n",
              "2    4    5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a03781e3-3e3e-4f4a-b834-1b6e5e644d2b\">\n",
              "    <div 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>x_1</th>\n",
              "      <th>x_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a03781e3-3e3e-4f4a-b834-1b6e5e644d2b')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a03781e3-3e3e-4f4a-b834-1b6e5e644d2b 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-a03781e3-3e3e-4f4a-b834-1b6e5e644d2b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPfmeTkZ-0gm"
      },
      "source": [
        "# 차원은 2로 설정\n",
        "poly_features = PolynomialFeatures(degree=2) \n",
        "# fit_transform 메소드를 통해 데이터 변환\n",
        "df_poly = poly_features.fit_transform(df) \n",
        "# PolynomialFeatures로 변환 된 데이터를 데이터 프레임 형태로 변환\n",
        "df_poly = pd.DataFrame(df_poly)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i11NN6gg-0Vt"
      },
      "source": [
        "# df_poly의 컬럼을 1,x1,x2,x1^2,x1*x2,x2^2 로 변경\n",
        "df_poly.columns = ['1','x1','x2','x1^2','x1*x2','x2^2'] "
      ],
      "execution_count": 28,
      "outputs": []
    }
  ]
}