Latihan10_Sklearn_SVR.ipynb

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        "<a href=\"https://colab.research.google.com/github/yandri918/Belajar-python/blob/main/Latihan10_Sklearn_SVR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "# membaca dataset dan mengubahnya menjadi dataframe\n",
        "data = pd.read_csv('Salary_Data.csv')"
      ],
      "execution_count": 1,
      "outputs": []
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        "data.info()"
      ],
      "execution_count": 2,
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30 entries, 0 to 29\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   YearsExperience  30 non-null     float64\n",
            " 1   Salary           30 non-null     float64\n",
            "dtypes: float64(2)\n",
            "memory usage: 608.0 bytes\n"
          ]
        }
      ]
    },
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      "source": [
        "data.head()"
      ],
      "execution_count": 3,
      "outputs": [
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              "   YearsExperience   Salary\n",
              "0              1.1  39343.0\n",
              "1              1.3  46205.0\n",
              "2              1.5  37731.0\n",
              "3              2.0  43525.0\n",
              "4              2.2  39891.0"
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              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"YearsExperience\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8378881576627184,\n        \"min\": 1.1,\n        \"max\": 10.5,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.9,\n          9.6,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27414.4297845823,\n        \"min\": 37731.0,\n        \"max\": 122391.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          112635.0,\n          67938.0,\n          113812.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 3
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2moqHXHFsKK"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# memisahkan atribut dan label\n",
        "X = data['YearsExperience']\n",
        "y = data['Salary']\n",
        "\n",
        "# mengubah bentuk atribut\n",
        "X = np.array(X)\n",
        "X = X[:,np.newaxis]"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9iA3kwfFtpk",
        "outputId": "fe7907da-f964-41dc-a2b0-1b76fead6600",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "# membangun model dengan parameter C, gamma, dan kernel\n",
        "model  = SVR(C=1000, gamma=0.05, kernel='rbf')\n",
        "\n",
        "# melatih model dengan fungsi fit\n",
        "model.fit(X,y)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "SVR(C=1000, gamma=0.05)"
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          "execution_count": 8
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# memvisualisasikan model\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, model.predict(X))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7be14e07ef50>]"
            ]
          },
          "metadata": {},
          "execution_count": 9
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        {
          "output_type": "display_data",
          "data": {
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              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}